diff --git a/.DS_Store b/.DS_Store index 3a1ce30..8724e38 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/Data engineering and science/.DS_Store b/Data engineering and science/.DS_Store index 961cd9e..2bbffe4 100644 Binary files a/Data engineering and science/.DS_Store and b/Data engineering and science/.DS_Store differ diff --git a/Data engineering and science/More complex analysis/global-emisssions.ipynb b/Data engineering and science/More complex analysis/global-emisssions.ipynb new file mode 100644 index 0000000..bdfd430 --- /dev/null +++ b/Data engineering and science/More complex analysis/global-emisssions.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3426904,"sourceType":"datasetVersion","datasetId":2065357},{"sourceId":4360255,"sourceType":"datasetVersion","datasetId":2432740},{"sourceId":4537501,"sourceType":"datasetVersion","datasetId":2651128},{"sourceId":4560499,"sourceType":"datasetVersion","datasetId":2661487},{"sourceId":5081220,"sourceType":"datasetVersion","datasetId":2950449},{"sourceId":5472882,"sourceType":"datasetVersion","datasetId":3106323},{"sourceId":5822810,"sourceType":"datasetVersion","datasetId":3346108},{"sourceId":6101670,"sourceType":"datasetVersion","datasetId":3495122},{"sourceId":7478877,"sourceType":"datasetVersion","datasetId":4353311}],"dockerImageVersionId":30746,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Global-co-emissions and other factors\n","metadata":{}},{"cell_type":"markdown","source":"Global emissions has risen - it is impacting the environment [1]. Demography and GDP may be factors to increase carbon dioxide emissions [2,3]. The latter has been discussed in the last two decades and various analytical model created. We use various data made available on Kaggle to explore those factors. We aim to answer the following questions: \n\n\n- Is there a trend of emissions throug time?\n- Has the GDP changed through time?\n\n\n\n__References:__ \n1. Töbelmann, Daniel, and Tobias Wendler. \"The impact of environmental innovation on carbon dioxide emissions.\" Journal of Cleaner Production 244 (2020): 118787\n2. O'Neill, Brian C., et al. \"Demographic change and carbon dioxide emissions.\" The Lancet 380.9837 (2012): 157-164.\n3. Tucker, Michael. \"Carbon dioxide emissions and global GDP.\" Ecological Economics 15.3 (1995): 215-223.","metadata":{}},{"cell_type":"markdown","source":"## Upload libraries and data","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom sklearn.mixture import GaussianMixture as GMM\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2025-02-20T19:38:14.978901Z","iopub.execute_input":"2025-02-20T19:38:14.979248Z","iopub.status.idle":"2025-02-20T19:38:17.403656Z","shell.execute_reply.started":"2025-02-20T19:38:14.979214Z","shell.execute_reply":"2025-02-20T19:38:17.402645Z"},"trusted":true},"outputs":[{"name":"stdout","text":"/kaggle/input/air-quality-per-country-with-gdp-and-population/air_gdp_density.csv\n/kaggle/input/2023-world-population-by-country/countries-table.csv\n/kaggle/input/2023-world-population-by-country/countries-table.json\n/kaggle/input/gdp-growth-around-the-globe/API_NY.GDP.MKTP.KD.ZG_DS2_en_csv_v2_4701072.csv\n/kaggle/input/gdp-growth-around-the-globe/Metadata_Indicator_API_NY.GDP.MKTP.KD.ZG_DS2_en_csv_v2_4701072.csv\n/kaggle/input/gdp-growth-around-the-globe/Metadata_Country_API_NY.GDP.MKTP.KD.ZG_DS2_en_csv_v2_4701072.csv\n/kaggle/input/global-co-emissions/GlobalCO2Emissions.csv\n/kaggle/input/countries-gdp-19602020/Countries GDP 1960-2020.csv\n/kaggle/input/world-population-dataset/world_population.csv\n/kaggle/input/countries-of-the-world-2023/world-data-2023.csv\n/kaggle/input/co2-emissions-by-country/co2_emissions_kt_by_country.csv\n/kaggle/input/countries-gdp-2012-to-2021/GDP.csv\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"d = pd.read_csv('/kaggle/input/global-co-emissions/GlobalCO2Emissions.csv')\n\nd.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:17.405370Z","iopub.execute_input":"2025-02-20T19:38:17.405820Z","iopub.status.idle":"2025-02-20T19:38:17.430383Z","shell.execute_reply.started":"2025-02-20T19:38:17.405794Z","shell.execute_reply":"2025-02-20T19:38:17.429390Z"},"trusted":true},"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"Year int64\nEmissions float64\ndtype: object"},"metadata":{}}],"execution_count":2},{"cell_type":"code","source":"d.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:17.431816Z","iopub.execute_input":"2025-02-20T19:38:17.432186Z","iopub.status.idle":"2025-02-20T19:38:17.447189Z","shell.execute_reply.started":"2025-02-20T19:38:17.432156Z","shell.execute_reply":"2025-02-20T19:38:17.446121Z"},"trusted":true},"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":" Year Emissions\n0 1750 0.03\n1 1760 0.03\n2 1770 0.03\n3 1780 0.03\n4 1790 0.04","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearEmissions
017500.03
117600.03
217700.03
317800.03
417900.04
\n
"},"metadata":{}}],"execution_count":3},{"cell_type":"markdown","source":"## Data exploration\n\nThe dataset has two statistical variables - year and emissions. The observations appears to have occurred between 1750 and 2023. The emissions appears range from extremely low values to high ones. However, most occurences appear to be low. ","metadata":{}},{"cell_type":"code","source":"d.describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:17.449138Z","iopub.execute_input":"2025-02-20T19:38:17.449431Z","iopub.status.idle":"2025-02-20T19:38:17.468910Z","shell.execute_reply.started":"2025-02-20T19:38:17.449408Z","shell.execute_reply":"2025-02-20T19:38:17.467826Z"},"trusted":true},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":" Year Emissions\ncount 29.000000 29.000000\nmean 1889.827586 5.937586\nstd 84.855368 11.443084\nmin 1750.000000 0.030000\n25% 1820.000000 0.060000\n50% 1890.000000 0.430000\n75% 1960.000000 3.740000\nmax 2023.000000 40.900000","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearEmissions
count29.00000029.000000
mean1889.8275865.937586
std84.85536811.443084
min1750.0000000.030000
25%1820.0000000.060000
50%1890.0000000.430000
75%1960.0000003.740000
max2023.00000040.900000
\n
"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"d.hist(grid = False)","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:17.471286Z","iopub.execute_input":"2025-02-20T19:38:17.471609Z","iopub.status.idle":"2025-02-20T19:38:17.930761Z","shell.execute_reply.started":"2025-02-20T19:38:17.471576Z","shell.execute_reply":"2025-02-20T19:38:17.929655Z"},"trusted":true},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"array([[,\n ]], dtype=object)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":5},{"cell_type":"markdown","source":"## Is there a trends of emissions produced through time?","metadata":{}},{"cell_type":"code","source":"import seaborn as sns\nsns.set_style(\"whitegrid\", {'axes.grid' : False})\nsns.scatterplot(x='Year', y='Emissions', data=d)","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:17.931940Z","iopub.execute_input":"2025-02-20T19:38:17.932222Z","iopub.status.idle":"2025-02-20T19:38:18.432932Z","shell.execute_reply.started":"2025-02-20T19:38:17.932200Z","shell.execute_reply":"2025-02-20T19:38:18.431860Z"},"trusted":true},"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":6},{"cell_type":"markdown","source":"We model the relationship using the exponential equation and predict the observations to check accuracy.\n\nFor fitting an exponential equation (equation 1.0), we take the logarithm of both side to fit log of y against x. (see equation 2.0). Fitting _log(y)_ - as if it is linear - emphasizes small values of _y_, causing large deviation for large value of _y_. A linear regression seeks finding a gradient ($\\delta y$) that minimises the distance between the predicted and known values of _y_ (see equation 3.0). We assume $Y_i = \\log y_i$. We also assume an approximation of the $\\Delta Y_i$ is the difference betweeh _y_ and its absolute values, causing favouring small values (see equation 4.0). For that reason, the reduce large values with a square root. \n\n$y = Ae^{Bx}$ (1.0)\n\n$log y = log (A + Bx)$ (2.0) \n\n$\\sum_{i} \\Delta y{2} = \\sum{i} \\left(y_{i} -\\hat{y_{i}} \\right)^2$ (3.0)\n\n$\\Delta Y_i = \\Delta (\\log y_i) ≈ \\Delta y_i / |y_i|$ (4.0)\n\n\nWe use two models one with the reduction of values of _y_ and without.\n","metadata":{}},{"cell_type":"code","source":"fit = np.polyfit(d['Year'], np.log(d['Emissions']), 1)\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:18.434136Z","iopub.execute_input":"2025-02-20T19:38:18.434396Z","iopub.status.idle":"2025-02-20T19:38:18.442355Z","shell.execute_reply.started":"2025-02-20T19:38:18.434375Z","shell.execute_reply":"2025-02-20T19:38:18.441411Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.02869030176345192\nCoefficient B = -54.781444681281656\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"d['model_A'] = np.exp(fit[1]) * np.exp(fit[0] * d['Year']) \nd['errors_model_A'] = np.abs(d['Emissions'] - d['model_A'])\nd.errors_model_A.hist(grid = False)\nd.errors_model_A.describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:18.443384Z","iopub.execute_input":"2025-02-20T19:38:18.443706Z","iopub.status.idle":"2025-02-20T19:38:18.756035Z","shell.execute_reply.started":"2025-02-20T19:38:18.443681Z","shell.execute_reply":"2025-02-20T19:38:18.755051Z"},"trusted":true},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"count 29.000000\nmean 1.958857\nstd 3.997135\nmin 0.003330\n25% 0.019677\n50% 0.150126\n75% 0.864781\nmax 14.875568\nName: errors_model_A, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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"},"metadata":{}}],"execution_count":8},{"cell_type":"markdown","source":"This could be alleviated by giving each entry a \"weight\" proportional to y. polyfit supports weighted-least-squares via the w keyword argument.","metadata":{}},{"cell_type":"code","source":"np.sqrt(d.Emissions).describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:18.757082Z","iopub.execute_input":"2025-02-20T19:38:18.757354Z","iopub.status.idle":"2025-02-20T19:38:18.766763Z","shell.execute_reply.started":"2025-02-20T19:38:18.757332Z","shell.execute_reply":"2025-02-20T19:38:18.765769Z"},"trusted":true},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"count 29.000000\nmean 1.555659\nstd 1.908700\nmin 0.173205\n25% 0.244949\n50% 0.655744\n75% 1.933908\nmax 6.395311\nName: Emissions, dtype: float64"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"fit = np.polyfit(d['Year'], np.log(d['Emissions']), 1, w = np.sqrt(d.Emissions))\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:18.767923Z","iopub.execute_input":"2025-02-20T19:38:18.768191Z","iopub.status.idle":"2025-02-20T19:38:18.777682Z","shell.execute_reply.started":"2025-02-20T19:38:18.768170Z","shell.execute_reply":"2025-02-20T19:38:18.776674Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.03430748369375951\nCoefficient B = -65.67787308498968\n","output_type":"stream"}],"execution_count":10},{"cell_type":"code","source":"d['model_B'] = np.exp(fit[1]) * np.exp(fit[0] * d['Year']) \nd['errors_model_B'] = np.abs(d['Emissions'] - d['model_B'])\nd.errors_model_B.hist(grid=False)\nd.errors_model_B.describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:18.778902Z","iopub.execute_input":"2025-02-20T19:38:18.779172Z","iopub.status.idle":"2025-02-20T19:38:19.052751Z","shell.execute_reply.started":"2025-02-20T19:38:18.779150Z","shell.execute_reply":"2025-02-20T19:38:19.051622Z"},"trusted":true},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"count 29.000000\nmean 0.449098\nstd 0.745753\nmin 0.000193\n25% 0.020054\n50% 0.026446\n75% 0.697636\nmax 2.969357\nName: errors_model_B, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.scatter(x=d['Year'], y= d['Emissions'])\nplt.scatter(x=d['Year'], y= d['model_A'], marker = \"+\")\nplt.scatter(x=d['Year'], y= d['model_B'], marker = \"x\")\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.054091Z","iopub.execute_input":"2025-02-20T19:38:19.054457Z","iopub.status.idle":"2025-02-20T19:38:19.333516Z","shell.execute_reply.started":"2025-02-20T19:38:19.054423Z","shell.execute_reply":"2025-02-20T19:38:19.332455Z"},"trusted":true},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":12},{"cell_type":"markdown","source":"Accuracy of Model A:","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import mean_absolute_error\nprint(\"MAE\",mean_absolute_error(d['Emissions'],d['model_A']))\n\n\nfrom sklearn.metrics import r2_score\nr2 = r2_score(d['Emissions'], d['model_A'])\nprint('R2 ' , r2)\nk=2\nn = d.shape[0]\nadj_r2_score = 1 - ((1-r2)*(n-1)/(n-k-1))\nprint('Adj R2 score ' ,adj_r2_score)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.335151Z","iopub.execute_input":"2025-02-20T19:38:19.335538Z","iopub.status.idle":"2025-02-20T19:38:19.343838Z","shell.execute_reply.started":"2025-02-20T19:38:19.335505Z","shell.execute_reply":"2025-02-20T19:38:19.342620Z"},"trusted":true},"outputs":[{"name":"stdout","text":"MAE 1.9588565646273792\nR2 0.8476354892671631\nAdj R2 score 0.8359151422877141\n","output_type":"stream"}],"execution_count":13},{"cell_type":"markdown","source":"Accuracy of model B","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import mean_absolute_error\nprint(\"MAE\",mean_absolute_error(d['Emissions'],d['model_B']))\n\n\nfrom sklearn.metrics import r2_score\nr2 = r2_score(d['Emissions'], d['model_B'])\nprint('R2 ' , r2)\nk=2\nn = d.shape[0]\nadj_r2_score = 1 - ((1-r2)*(n-1)/(n-k-1))\nprint('Adj R2 score ' ,adj_r2_score)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.348309Z","iopub.execute_input":"2025-02-20T19:38:19.348636Z","iopub.status.idle":"2025-02-20T19:38:19.358624Z","shell.execute_reply.started":"2025-02-20T19:38:19.348610Z","shell.execute_reply":"2025-02-20T19:38:19.357629Z"},"trusted":true},"outputs":[{"name":"stdout","text":"MAE 0.44909784325335167\nR2 0.9941575105083272\nAdj R2 score 0.993708088239737\n","output_type":"stream"}],"execution_count":14},{"cell_type":"markdown","source":"We have demonstrated that exponential relationship may exists in yearly carbon dioxide measurements through time. The relationship shows it is likely the carbon dioxide emissions should increase even more rapidly.","metadata":{}},{"cell_type":"markdown","source":"# Has the GDP changed through time?\n\nWe model the relationship using the exponential equation and predict the observations to check accuracy.\n\nFor fitting an exponential equation (equation 1.0), we take the logarithm of both side to fit log of y against x. (see equation 2.0). Fitting _log(y)_ - as if it is linear - emphasizes small values of _y_, causing large deviation for large value of _y_. A linear regression seeks finding a gradient ($\\delta y$) that minimises the distance between the predicted and known values of _y_ (see equation 3.0). We assume $Y_i = \\log y_i$. We also assume an approximation of the $\\Delta Y_i$ is the difference betweeh _y_ and its absolute values, causing favouring small values (see equation 4.0). For that reason, the reduce large values with a square root. \n\n$y = Ae^{Bx}$ (1.0)\n\n$log y = log (A + Bx)$ (2.0) \n\n$\\sum_{i} \\Delta y{2} = \\sum{i} \\left(y_{i} -\\hat{y_{i}} \\right)^2$ (3.0)\n\n$\\Delta Y_i = \\Delta (\\log y_i) ≈ \\Delta y_i / |y_i|$ (4.0)\n\n","metadata":{}},{"cell_type":"code","source":"for dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.359801Z","iopub.execute_input":"2025-02-20T19:38:19.360113Z","iopub.status.idle":"2025-02-20T19:38:19.374346Z","shell.execute_reply.started":"2025-02-20T19:38:19.360090Z","shell.execute_reply":"2025-02-20T19:38:19.373390Z"},"trusted":true},"outputs":[{"name":"stdout","text":"/kaggle/input/air-quality-per-country-with-gdp-and-population/air_gdp_density.csv\n/kaggle/input/2023-world-population-by-country/countries-table.csv\n/kaggle/input/2023-world-population-by-country/countries-table.json\n/kaggle/input/gdp-growth-around-the-globe/API_NY.GDP.MKTP.KD.ZG_DS2_en_csv_v2_4701072.csv\n/kaggle/input/gdp-growth-around-the-globe/Metadata_Indicator_API_NY.GDP.MKTP.KD.ZG_DS2_en_csv_v2_4701072.csv\n/kaggle/input/gdp-growth-around-the-globe/Metadata_Country_API_NY.GDP.MKTP.KD.ZG_DS2_en_csv_v2_4701072.csv\n/kaggle/input/global-co-emissions/GlobalCO2Emissions.csv\n/kaggle/input/countries-gdp-19602020/Countries GDP 1960-2020.csv\n/kaggle/input/world-population-dataset/world_population.csv\n/kaggle/input/countries-of-the-world-2023/world-data-2023.csv\n/kaggle/input/co2-emissions-by-country/co2_emissions_kt_by_country.csv\n/kaggle/input/countries-gdp-2012-to-2021/GDP.csv\n","output_type":"stream"}],"execution_count":15},{"cell_type":"code","source":"gdp = pd.read_csv('/kaggle/input/countries-gdp-19602020/Countries GDP 1960-2020.csv')\ngdp.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.375668Z","iopub.execute_input":"2025-02-20T19:38:19.375973Z","iopub.status.idle":"2025-02-20T19:38:19.395242Z","shell.execute_reply.started":"2025-02-20T19:38:19.375950Z","shell.execute_reply":"2025-02-20T19:38:19.394121Z"},"trusted":true},"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\n1960 float64\n1961 float64\n1962 float64\n ... \n2016 float64\n2017 float64\n2018 float64\n2019 float64\n2020 float64\nLength: 63, dtype: object"},"metadata":{}}],"execution_count":16},{"cell_type":"code","source":"value_vars = [ str(year) for year in range(1960, 2021)]\nvalue_vars","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.396837Z","iopub.execute_input":"2025-02-20T19:38:19.397166Z","iopub.status.idle":"2025-02-20T19:38:19.404770Z","shell.execute_reply.started":"2025-02-20T19:38:19.397133Z","shell.execute_reply":"2025-02-20T19:38:19.403581Z"},"trusted":true},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"['1960',\n '1961',\n '1962',\n '1963',\n '1964',\n '1965',\n '1966',\n '1967',\n '1968',\n '1969',\n '1970',\n '1971',\n '1972',\n '1973',\n '1974',\n '1975',\n '1976',\n '1977',\n '1978',\n '1979',\n '1980',\n '1981',\n '1982',\n '1983',\n '1984',\n '1985',\n '1986',\n '1987',\n '1988',\n '1989',\n '1990',\n '1991',\n '1992',\n '1993',\n '1994',\n '1995',\n '1996',\n '1997',\n '1998',\n '1999',\n '2000',\n '2001',\n '2002',\n '2003',\n '2004',\n '2005',\n '2006',\n '2007',\n '2008',\n '2009',\n '2010',\n '2011',\n '2012',\n '2013',\n '2014',\n '2015',\n '2016',\n '2017',\n '2018',\n '2019',\n '2020']"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"\ngdp_long = gdp.melt(id_vars='Country Code', value_vars = value_vars)\ngdp_long['log_10_value'] = np.log10(gdp_long['value'])\ngdp_long['variable'] = gdp_long['variable'].astype(int)\ngdp_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.406141Z","iopub.execute_input":"2025-02-20T19:38:19.406788Z","iopub.status.idle":"2025-02-20T19:38:19.431025Z","shell.execute_reply.started":"2025-02-20T19:38:19.406761Z","shell.execute_reply":"2025-02-20T19:38:19.429930Z"},"trusted":true},"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"Country Code object\nvariable int64\nvalue float64\nlog_10_value float64\ndtype: object"},"metadata":{}}],"execution_count":18},{"cell_type":"code","source":"gdp_long.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.432315Z","iopub.execute_input":"2025-02-20T19:38:19.432963Z","iopub.status.idle":"2025-02-20T19:38:19.443344Z","shell.execute_reply.started":"2025-02-20T19:38:19.432927Z","shell.execute_reply":"2025-02-20T19:38:19.442361Z"},"trusted":true},"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":" Country Code variable value log_10_value\n0 AFE 1960 1.931311e+10 10.285852\n1 AFW 1960 1.040428e+10 10.017212\n2 AUS 1960 1.860679e+10 10.269671\n3 AUT 1960 6.592694e+09 9.819063\n4 BDI 1960 1.960000e+08 8.292256","text/html":"
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Country Codevariablevaluelog_10_value
0AFE19601.931311e+1010.285852
1AFW19601.040428e+1010.017212
2AUS19601.860679e+1010.269671
3AUT19606.592694e+099.819063
4BDI19601.960000e+088.292256
\n
"},"metadata":{}}],"execution_count":19},{"cell_type":"markdown","source":"The yearly distributions appears to vary greatly, but the point of centrality appears to increase through time. The quartile of the log values appears to grow linear, which suggest an exponential relationship. The errors distributions is skewed to the left and near 0, suggesting the model fitting process was quite accurate. ","metadata":{}},{"cell_type":"code","source":"temp = np.log10(gdp.loc[:, value_vars])\ntemp.boxplot(rot=45, fontsize = 12, grid = False, figsize=[20,20])\ngdp.describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.444640Z","iopub.execute_input":"2025-02-20T19:38:19.445029Z","iopub.status.idle":"2025-02-20T19:38:20.971626Z","shell.execute_reply.started":"2025-02-20T19:38:19.444996Z","shell.execute_reply":"2025-02-20T19:38:20.970498Z"},"trusted":true},"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":" 1960 1961 1962 1963 1964 \\\ncount 1.190000e+02 1.200000e+02 1.200000e+02 1.200000e+02 1.200000e+02 \nmean 7.737603e+10 7.918769e+10 8.417576e+10 9.092311e+10 9.976049e+10 \nstd 2.252438e+11 2.335094e+11 2.507523e+11 2.696523e+11 2.944479e+11 \nmin 1.201201e+07 1.159201e+07 1.254156e+07 1.283323e+07 1.341655e+07 \n25% 4.362676e+08 4.747200e+08 4.716388e+08 5.081636e+08 5.418066e+08 \n50% 2.723593e+09 2.667191e+09 3.050546e+09 3.570681e+09 3.184116e+09 \n75% 2.926200e+10 3.041073e+10 3.294838e+10 3.774822e+10 3.686680e+10 \nmax 1.390000e+12 1.440000e+12 1.550000e+12 1.670000e+12 1.820000e+12 \n\n 1965 1966 1967 1968 1969 \\\ncount 1.200000e+02 1.200000e+02 1.200000e+02 1.200000e+02 1.200000e+02 \nmean 1.091809e+11 1.177544e+11 1.244980e+11 1.338719e+11 1.480550e+11 \nstd 3.211343e+11 3.498230e+11 3.731073e+11 4.036768e+11 4.443198e+11 \nmin 1.359393e+07 1.446908e+07 1.583518e+07 1.460000e+07 1.585000e+07 \n25% 6.561320e+08 6.940201e+08 7.417202e+08 7.688965e+08 7.862815e+08 \n50% 3.590080e+09 4.230784e+09 4.194304e+09 4.571298e+09 5.726672e+09 \n75% 4.106156e+10 4.430473e+10 4.379965e+10 4.683330e+10 5.355294e+10 \nmax 1.990000e+12 2.160000e+12 2.290000e+12 2.480000e+12 2.730000e+12 \n\n ... 2011 2012 2013 2014 \\\ncount ... 1.200000e+02 1.200000e+02 1.200000e+02 1.200000e+02 \nmean ... 4.266303e+12 4.403837e+12 4.564958e+12 4.711580e+12 \nstd ... 1.111174e+13 1.135331e+13 1.167128e+13 1.199261e+13 \nmin ... 6.761296e+08 6.929333e+08 7.212074e+08 7.277148e+08 \n25% ... 1.775774e+10 1.768566e+10 1.878204e+10 1.958781e+10 \n50% ... 2.365000e+11 2.250000e+11 2.345000e+11 2.395000e+11 \n75% ... 1.610000e+12 1.680000e+12 1.790000e+12 1.850000e+12 \nmax ... 7.370000e+13 7.530000e+13 7.740000e+13 7.960000e+13 \n\n 2015 2016 2017 2018 2019 \\\ncount 1.200000e+02 1.200000e+02 1.200000e+02 1.200000e+02 1.200000e+02 \nmean 4.499309e+12 4.564773e+12 4.886253e+12 5.207231e+12 5.303437e+12 \nstd 1.139102e+13 1.158322e+13 1.231733e+13 1.311345e+13 1.332070e+13 \nmin 7.554000e+08 7.744296e+08 7.921778e+08 8.113000e+08 8.250407e+08 \n25% 1.941603e+10 2.070647e+10 2.203821e+10 2.353911e+10 2.328174e+10 \n50% 2.165000e+11 2.310000e+11 2.525000e+11 2.750000e+11 2.740000e+11 \n75% 1.680000e+12 1.560000e+12 1.670000e+12 1.750000e+12 1.800000e+12 \nmax 7.510000e+13 7.630000e+13 8.120000e+13 8.630000e+13 8.760000e+13 \n\n 2020 \ncount 1.200000e+02 \nmean 5.146832e+12 \nstd 1.292930e+13 \nmin 8.074741e+08 \n25% 2.052449e+10 \n50% 2.580000e+11 \n75% 1.702500e+12 \nmax 8.470000e+13 \n\n[8 rows x 61 columns]","text/html":"
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1960196119621963196419651966196719681969...2011201220132014201520162017201820192020
count1.190000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+02...1.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+02
mean7.737603e+107.918769e+108.417576e+109.092311e+109.976049e+101.091809e+111.177544e+111.244980e+111.338719e+111.480550e+11...4.266303e+124.403837e+124.564958e+124.711580e+124.499309e+124.564773e+124.886253e+125.207231e+125.303437e+125.146832e+12
std2.252438e+112.335094e+112.507523e+112.696523e+112.944479e+113.211343e+113.498230e+113.731073e+114.036768e+114.443198e+11...1.111174e+131.135331e+131.167128e+131.199261e+131.139102e+131.158322e+131.231733e+131.311345e+131.332070e+131.292930e+13
min1.201201e+071.159201e+071.254156e+071.283323e+071.341655e+071.359393e+071.446908e+071.583518e+071.460000e+071.585000e+07...6.761296e+086.929333e+087.212074e+087.277148e+087.554000e+087.744296e+087.921778e+088.113000e+088.250407e+088.074741e+08
25%4.362676e+084.747200e+084.716388e+085.081636e+085.418066e+086.561320e+086.940201e+087.417202e+087.688965e+087.862815e+08...1.775774e+101.768566e+101.878204e+101.958781e+101.941603e+102.070647e+102.203821e+102.353911e+102.328174e+102.052449e+10
50%2.723593e+092.667191e+093.050546e+093.570681e+093.184116e+093.590080e+094.230784e+094.194304e+094.571298e+095.726672e+09...2.365000e+112.250000e+112.345000e+112.395000e+112.165000e+112.310000e+112.525000e+112.750000e+112.740000e+112.580000e+11
75%2.926200e+103.041073e+103.294838e+103.774822e+103.686680e+104.106156e+104.430473e+104.379965e+104.683330e+105.355294e+10...1.610000e+121.680000e+121.790000e+121.850000e+121.680000e+121.560000e+121.670000e+121.750000e+121.800000e+121.702500e+12
max1.390000e+121.440000e+121.550000e+121.670000e+121.820000e+121.990000e+122.160000e+122.290000e+122.480000e+122.730000e+12...7.370000e+137.530000e+137.740000e+137.960000e+137.510000e+137.630000e+138.120000e+138.630000e+138.760000e+138.470000e+13
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8 rows × 61 columns

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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":20},{"cell_type":"code","source":"col = ['variable', 'value']\ntemp = gdp_long.loc[:,col] \nstats = temp.groupby(['variable']).describe().droplevel(axis =1 , level= 0).reset_index()\nstats","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:20.972985Z","iopub.execute_input":"2025-02-20T19:38:20.973353Z","iopub.status.idle":"2025-02-20T19:38:21.119163Z","shell.execute_reply.started":"2025-02-20T19:38:20.973319Z","shell.execute_reply":"2025-02-20T19:38:21.118137Z"},"trusted":true},"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":" variable count mean std min 25% \\\n0 1960 119.0 7.737603e+10 2.252438e+11 1.201201e+07 4.362676e+08 \n1 1961 120.0 7.918769e+10 2.335094e+11 1.159201e+07 4.747200e+08 \n2 1962 120.0 8.417576e+10 2.507523e+11 1.254156e+07 4.716388e+08 \n3 1963 120.0 9.092311e+10 2.696523e+11 1.283323e+07 5.081636e+08 \n4 1964 120.0 9.976049e+10 2.944479e+11 1.341655e+07 5.418066e+08 \n.. ... ... ... ... ... ... \n56 2016 120.0 4.564773e+12 1.158322e+13 7.744296e+08 2.070647e+10 \n57 2017 120.0 4.886253e+12 1.231733e+13 7.921778e+08 2.203821e+10 \n58 2018 120.0 5.207231e+12 1.311345e+13 8.113000e+08 2.353911e+10 \n59 2019 120.0 5.303437e+12 1.332070e+13 8.250407e+08 2.328174e+10 \n60 2020 120.0 5.146832e+12 1.292930e+13 8.074741e+08 2.052449e+10 \n\n 50% 75% max \n0 2.723593e+09 2.926200e+10 1.390000e+12 \n1 2.667191e+09 3.041073e+10 1.440000e+12 \n2 3.050546e+09 3.294838e+10 1.550000e+12 \n3 3.570681e+09 3.774822e+10 1.670000e+12 \n4 3.184116e+09 3.686680e+10 1.820000e+12 \n.. ... ... ... \n56 2.310000e+11 1.560000e+12 7.630000e+13 \n57 2.525000e+11 1.670000e+12 8.120000e+13 \n58 2.750000e+11 1.750000e+12 8.630000e+13 \n59 2.740000e+11 1.800000e+12 8.760000e+13 \n60 2.580000e+11 1.702500e+12 8.470000e+13 \n\n[61 rows x 9 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
variablecountmeanstdmin25%50%75%max
01960119.07.737603e+102.252438e+111.201201e+074.362676e+082.723593e+092.926200e+101.390000e+12
11961120.07.918769e+102.335094e+111.159201e+074.747200e+082.667191e+093.041073e+101.440000e+12
21962120.08.417576e+102.507523e+111.254156e+074.716388e+083.050546e+093.294838e+101.550000e+12
31963120.09.092311e+102.696523e+111.283323e+075.081636e+083.570681e+093.774822e+101.670000e+12
41964120.09.976049e+102.944479e+111.341655e+075.418066e+083.184116e+093.686680e+101.820000e+12
..............................
562016120.04.564773e+121.158322e+137.744296e+082.070647e+102.310000e+111.560000e+127.630000e+13
572017120.04.886253e+121.231733e+137.921778e+082.203821e+102.525000e+111.670000e+128.120000e+13
582018120.05.207231e+121.311345e+138.113000e+082.353911e+102.750000e+111.750000e+128.630000e+13
592019120.05.303437e+121.332070e+138.250407e+082.328174e+102.740000e+111.800000e+128.760000e+13
602020120.05.146832e+121.292930e+138.074741e+082.052449e+102.580000e+111.702500e+128.470000e+13
\n

61 rows × 9 columns

\n
"},"metadata":{}}],"execution_count":21},{"cell_type":"code","source":"plt.scatter(stats['variable'],stats['50%'], s= 1, label='Median GDP - USD')\nplt.scatter(stats['variable'],stats['25%'], s= 1, label='Q1 GDP - USD')\nplt.scatter(stats['variable'],stats['75%'], s= 1, label='Q3 GDP - USD')\nplt.legend()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:21.120515Z","iopub.execute_input":"2025-02-20T19:38:21.120933Z","iopub.status.idle":"2025-02-20T19:38:21.496241Z","shell.execute_reply.started":"2025-02-20T19:38:21.120899Z","shell.execute_reply":"2025-02-20T19:38:21.495216Z"},"trusted":true},"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":22},{"cell_type":"code","source":"fit = np.polyfit(stats['variable'], np.log(stats['50%']), 1,\n w =np.sqrt(stats['50%']))\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:21.497684Z","iopub.execute_input":"2025-02-20T19:38:21.498323Z","iopub.status.idle":"2025-02-20T19:38:21.505081Z","shell.execute_reply.started":"2025-02-20T19:38:21.498288Z","shell.execute_reply":"2025-02-20T19:38:21.504125Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.07111806201145401\nCoefficient B = -117.09232591432259\n","output_type":"stream"}],"execution_count":23},{"cell_type":"code","source":"stats['model_50%'] = np.exp(fit[1]) * np.exp(fit[0] * stats['variable']) \nstats['errors_model_50%'] = np.abs(stats['50%'] - stats['model_50%'])\nstats['errors_model_50%'].hist(grid=False)\nstats['errors_model_50%'].describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:21.506338Z","iopub.execute_input":"2025-02-20T19:38:21.506713Z","iopub.status.idle":"2025-02-20T19:38:21.796238Z","shell.execute_reply.started":"2025-02-20T19:38:21.506688Z","shell.execute_reply":"2025-02-20T19:38:21.795274Z"},"trusted":true},"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"count 6.100000e+01\nmean 1.109316e+10\nstd 1.591221e+10\nmin 2.165587e+08\n25% 2.494373e+09\n50% 3.968645e+09\n75% 1.374232e+10\nmax 8.677551e+10\nName: errors_model_50%, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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"},"metadata":{}}],"execution_count":24},{"cell_type":"code","source":"fit = np.polyfit(stats['variable'], np.log(stats['75%']), 1,\n w = np.sqrt(stats['75%']))\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:21.797356Z","iopub.execute_input":"2025-02-20T19:38:21.797673Z","iopub.status.idle":"2025-02-20T19:38:21.804498Z","shell.execute_reply.started":"2025-02-20T19:38:21.797649Z","shell.execute_reply":"2025-02-20T19:38:21.803621Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.0646318667262896\nCoefficient B = -102.0929023995858\n","output_type":"stream"}],"execution_count":25},{"cell_type":"code","source":"stats['model_75%'] = np.exp(fit[1]) * np.exp(fit[0] * stats['variable']) \nstats['errors_model_75%'] = np.abs(stats['75%'] - stats['model_75%'])\nstats['errors_model_75%'].hist(grid=False)\nstats['errors_model_75%'].describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:21.805630Z","iopub.execute_input":"2025-02-20T19:38:21.806455Z","iopub.status.idle":"2025-02-20T19:38:22.124313Z","shell.execute_reply.started":"2025-02-20T19:38:21.806429Z","shell.execute_reply":"2025-02-20T19:38:22.123301Z"},"trusted":true},"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"count 6.100000e+01\nmean 8.975987e+10\nstd 1.171396e+11\nmin 3.714321e+08\n25% 1.831220e+10\n50% 3.356206e+10\n75% 1.093801e+11\nmax 5.964380e+11\nName: errors_model_75%, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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"},"metadata":{}}],"execution_count":26},{"cell_type":"code","source":"fit = np.polyfit(stats['variable'], np.log(stats['25%']), 1,\n w = np.sqrt(stats['25%']))\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:22.125636Z","iopub.execute_input":"2025-02-20T19:38:22.125915Z","iopub.status.idle":"2025-02-20T19:38:22.132406Z","shell.execute_reply.started":"2025-02-20T19:38:22.125893Z","shell.execute_reply":"2025-02-20T19:38:22.131350Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.06031305728310042\nCoefficient B = -97.86712787332907\n","output_type":"stream"}],"execution_count":27},{"cell_type":"code","source":"stats['model_25%'] = np.exp(fit[1]) * np.exp(fit[0] * stats['variable']) \nstats['errors_model_25%'] = np.abs(stats['25%'] - stats['model_25%'])\nstats['errors_model_25%'].hist(grid=False)\nstats['errors_model_25%'].describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:22.133630Z","iopub.execute_input":"2025-02-20T19:38:22.133979Z","iopub.status.idle":"2025-02-20T19:38:22.457668Z","shell.execute_reply.started":"2025-02-20T19:38:22.133947Z","shell.execute_reply":"2025-02-20T19:38:22.456698Z"},"trusted":true},"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"count 6.100000e+01\nmean 7.988075e+08\nstd 8.209953e+08\nmin 3.983350e+07\n25% 3.445744e+08\n50% 5.018512e+08\n75% 8.281231e+08\nmax 5.059890e+09\nName: errors_model_25%, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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"},"metadata":{}}],"execution_count":28},{"cell_type":"code","source":"plt.scatter(stats['variable'],stats['50%'], s= 2, c = 'b', label='Median GDP - USD')\nplt.scatter(stats['variable'],stats['model_50%'], s= 1, c ='b', marker = \"+\", label='Modelled Median GDP - USD')\n\nplt.scatter(stats['variable'],stats['25%'], s= 2, c='r', label='Q1 GDP - USD')\nplt.scatter(stats['variable'],stats['model_25%'], s= 1, c ='r', marker = \"+\", label='Modelled Q1 GDP - USD')\n\nplt.scatter(stats['variable'],stats['75%'], s= 1, c= 'magenta', label='Q3 GDP - USD')\nplt.scatter(stats['variable'],stats['model_75%'], s= 1, c ='magenta', marker = \"+\", label='Modelled Median GDP - USD')\n\nplt.legend()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:22.458816Z","iopub.execute_input":"2025-02-20T19:38:22.459078Z","iopub.status.idle":"2025-02-20T19:38:22.907583Z","shell.execute_reply.started":"2025-02-20T19:38:22.459056Z","shell.execute_reply":"2025-02-20T19:38:22.906613Z"},"trusted":true},"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":29},{"cell_type":"code","source":"plt.scatter(stats['variable'],stats['50%'], s= 1)\nplt.scatter(stats['variable'],stats['model_50%'], c='black', s = 1, marker = '+')","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:22.908996Z","iopub.execute_input":"2025-02-20T19:38:22.909471Z","iopub.status.idle":"2025-02-20T19:38:23.180297Z","shell.execute_reply.started":"2025-02-20T19:38:22.909437Z","shell.execute_reply":"2025-02-20T19:38:23.179272Z"},"trusted":true},"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":30},{"cell_type":"code","source":"plt.scatter(stats['variable'],stats['25%'], s= 1)\nplt.scatter(stats['variable'],stats['model_25%'], c='black', s = 1, marker = '+')","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:23.181794Z","iopub.execute_input":"2025-02-20T19:38:23.182165Z","iopub.status.idle":"2025-02-20T19:38:23.492379Z","shell.execute_reply.started":"2025-02-20T19:38:23.182132Z","shell.execute_reply":"2025-02-20T19:38:23.491383Z"},"trusted":true},"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":31},{"cell_type":"code","source":"plt.scatter(stats['variable'],stats['75%'], s= 1)\nplt.scatter(stats['variable'],stats['model_75%'], c='black', s = 1, marker = '+')","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:23.493428Z","iopub.execute_input":"2025-02-20T19:38:23.493740Z","iopub.status.idle":"2025-02-20T19:38:23.761167Z","shell.execute_reply.started":"2025-02-20T19:38:23.493716Z","shell.execute_reply":"2025-02-20T19:38:23.760191Z"},"trusted":true},"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":32},{"cell_type":"markdown","source":"# World population\n\nWe discover again the population per country has some great variation. The median population per country appears to exponentially.","metadata":{}},{"cell_type":"code","source":"file = '/kaggle/input/world-population-dataset/world_population.csv'\npop = pd.read_csv(file)\nprint(pop.shape)\nprint(pop.dtypes)","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:23.762397Z","iopub.execute_input":"2025-02-20T19:38:23.762741Z","iopub.status.idle":"2025-02-20T19:38:23.776399Z","shell.execute_reply.started":"2025-02-20T19:38:23.762716Z","shell.execute_reply":"2025-02-20T19:38:23.775399Z"},"trusted":true},"outputs":[{"name":"stdout","text":"(234, 17)\nRank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\n2022 Population int64\n2020 Population int64\n2015 Population int64\n2010 Population int64\n2000 Population int64\n1990 Population int64\n1980 Population int64\n1970 Population int64\nArea (km²) int64\nDensity (per km²) float64\nGrowth Rate float64\nWorld Population Percentage float64\ndtype: object\n","output_type":"stream"}],"execution_count":33},{"cell_type":"code","source":"pop.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:23.777758Z","iopub.execute_input":"2025-02-20T19:38:23.778141Z","iopub.status.idle":"2025-02-20T19:38:23.793903Z","shell.execute_reply.started":"2025-02-20T19:38:23.778107Z","shell.execute_reply":"2025-02-20T19:38:23.792699Z"},"trusted":true},"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":" Rank CCA3 Country/Territory Capital Continent 2022 Population \\\n0 36 AFG Afghanistan Kabul Asia 41128771 \n1 138 ALB Albania Tirana Europe 2842321 \n2 34 DZA Algeria Algiers Africa 44903225 \n3 213 ASM American Samoa Pago Pago Oceania 44273 \n4 203 AND Andorra Andorra la Vella Europe 79824 \n\n 2020 Population 2015 Population 2010 Population 2000 Population \\\n0 38972230 33753499 28189672 19542982 \n1 2866849 2882481 2913399 3182021 \n2 43451666 39543154 35856344 30774621 \n3 46189 51368 54849 58230 \n4 77700 71746 71519 66097 \n\n 1990 Population 1980 Population 1970 Population Area (km²) \\\n0 10694796 12486631 10752971 652230 \n1 3295066 2941651 2324731 28748 \n2 25518074 18739378 13795915 2381741 \n3 47818 32886 27075 199 \n4 53569 35611 19860 468 \n\n Density (per km²) Growth Rate World Population Percentage \n0 63.0587 1.0257 0.52 \n1 98.8702 0.9957 0.04 \n2 18.8531 1.0164 0.56 \n3 222.4774 0.9831 0.00 \n4 170.5641 1.0100 0.00 ","text/html":"
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RankCCA3Country/TerritoryCapitalContinent2022 Population2020 Population2015 Population2010 Population2000 Population1990 Population1980 Population1970 PopulationArea (km²)Density (per km²)Growth RateWorld Population Percentage
036AFGAfghanistanKabulAsia411287713897223033753499281896721954298210694796124866311075297165223063.05871.02570.52
1138ALBAlbaniaTiranaEurope284232128668492882481291339931820213295066294165123247312874898.87020.99570.04
234DZAAlgeriaAlgiersAfrica4490322543451666395431543585634430774621255180741873937813795915238174118.85311.01640.56
3213ASMAmerican SamoaPago PagoOceania4427346189513685484958230478183288627075199222.47740.98310.00
4203ANDAndorraAndorra la VellaEurope7982477700717467151966097535693561119860468170.56411.01000.00
\n
"},"metadata":{}}],"execution_count":34},{"cell_type":"code","source":"pop.columns","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:23.795254Z","iopub.execute_input":"2025-02-20T19:38:23.795539Z","iopub.status.idle":"2025-02-20T19:38:23.806311Z","shell.execute_reply.started":"2025-02-20T19:38:23.795516Z","shell.execute_reply":"2025-02-20T19:38:23.805391Z"},"trusted":true},"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"Index(['Rank', 'CCA3', 'Country/Territory', 'Capital', 'Continent',\n '2022 Population', '2020 Population', '2015 Population',\n '2010 Population', '2000 Population', '1990 Population',\n '1980 Population', '1970 Population', 'Area (km²)', 'Density (per km²)',\n 'Growth Rate', 'World Population Percentage'],\n dtype='object')"},"metadata":{}}],"execution_count":35},{"cell_type":"code","source":"cols = ['1970 Population', '1980 Population', \n '1990 Population','2000 Population',\n '2010 Population','2015 Population',\n '2020 Population','2022 Population']\ntemp = pop.loc[:, cols].copy(deep = True)\ntemp = np.log10(temp)\ntemp.boxplot(grid = False, rot =45)","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:23.807446Z","iopub.execute_input":"2025-02-20T19:38:23.807862Z","iopub.status.idle":"2025-02-20T19:38:24.094768Z","shell.execute_reply.started":"2025-02-20T19:38:23.807826Z","shell.execute_reply":"2025-02-20T19:38:24.093699Z"},"trusted":true},"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":36},{"cell_type":"code","source":"years_label =['2022 Population', \n '2020 Population', \n '2015 Population',\n '2010 Population', \n '2000 Population', \n '1990 Population',\n '1980 Population', \n '1970 Population']\n\nid_vars = ['Rank', 'CCA3', 'Country/Territory', \n 'Capital', 'Continent', 'Area (km²)']\npop_long = pop.melt(id_vars = id_vars, value_vars = years_label)\npop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.096154Z","iopub.execute_input":"2025-02-20T19:38:24.096528Z","iopub.status.idle":"2025-02-20T19:38:24.110981Z","shell.execute_reply.started":"2025-02-20T19:38:24.096494Z","shell.execute_reply":"2025-02-20T19:38:24.110022Z"},"trusted":true},"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"Rank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\nArea (km²) int64\nvariable object\nvalue int64\ndtype: object"},"metadata":{}}],"execution_count":37},{"cell_type":"code","source":"pop_long.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.112243Z","iopub.execute_input":"2025-02-20T19:38:24.113035Z","iopub.status.idle":"2025-02-20T19:38:24.128686Z","shell.execute_reply.started":"2025-02-20T19:38:24.113001Z","shell.execute_reply":"2025-02-20T19:38:24.127639Z"},"trusted":true},"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":" Rank CCA3 Country/Territory Capital Continent Area (km²) \\\n0 36 AFG Afghanistan Kabul Asia 652230 \n1 138 ALB Albania Tirana Europe 28748 \n2 34 DZA Algeria Algiers Africa 2381741 \n3 213 ASM American Samoa Pago Pago Oceania 199 \n4 203 AND Andorra Andorra la Vella Europe 468 \n\n variable value \n0 2022 Population 41128771 \n1 2022 Population 2842321 \n2 2022 Population 44903225 \n3 2022 Population 44273 \n4 2022 Population 79824 ","text/html":"
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RankCCA3Country/TerritoryCapitalContinentArea (km²)variablevalue
036AFGAfghanistanKabulAsia6522302022 Population41128771
1138ALBAlbaniaTiranaEurope287482022 Population2842321
234DZAAlgeriaAlgiersAfrica23817412022 Population44903225
3213ASMAmerican SamoaPago PagoOceania1992022 Population44273
4203ANDAndorraAndorra la VellaEurope4682022 Population79824
\n
"},"metadata":{}}],"execution_count":38},{"cell_type":"code","source":"len(pop_long['Country/Territory'].unique())","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.130003Z","iopub.execute_input":"2025-02-20T19:38:24.130680Z","iopub.status.idle":"2025-02-20T19:38:24.142614Z","shell.execute_reply.started":"2025-02-20T19:38:24.130646Z","shell.execute_reply":"2025-02-20T19:38:24.141526Z"},"trusted":true},"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"234"},"metadata":{}}],"execution_count":39},{"cell_type":"code","source":"current = ['2022 Population', \n '2020 Population', \n '2015 Population',\n '2010 Population', \n '2000 Population', \n '1990 Population',\n '1980 Population', \n '1970 Population']\n\nnew = [2022, 2020,2015,2010,2000,1990,1980,1970]\n\npop_long = pop_long.replace(current, new)\npop_long.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.143979Z","iopub.execute_input":"2025-02-20T19:38:24.144361Z","iopub.status.idle":"2025-02-20T19:38:24.168699Z","shell.execute_reply.started":"2025-02-20T19:38:24.144329Z","shell.execute_reply":"2025-02-20T19:38:24.167798Z"},"trusted":true},"outputs":[{"name":"stderr","text":"/tmp/ipykernel_33/2926351034.py:12: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n pop_long = pop_long.replace(current, new)\n","output_type":"stream"},{"execution_count":40,"output_type":"execute_result","data":{"text/plain":" Rank CCA3 Country/Territory Capital Continent Area (km²) \\\n0 36 AFG Afghanistan Kabul Asia 652230 \n1 138 ALB Albania Tirana Europe 28748 \n2 34 DZA Algeria Algiers Africa 2381741 \n3 213 ASM American Samoa Pago Pago Oceania 199 \n4 203 AND Andorra Andorra la Vella Europe 468 \n\n variable value \n0 2022 41128771 \n1 2022 2842321 \n2 2022 44903225 \n3 2022 44273 \n4 2022 79824 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
RankCCA3Country/TerritoryCapitalContinentArea (km²)variablevalue
036AFGAfghanistanKabulAsia652230202241128771
1138ALBAlbaniaTiranaEurope2874820222842321
234DZAAlgeriaAlgiersAfrica2381741202244903225
3213ASMAmerican SamoaPago PagoOceania199202244273
4203ANDAndorraAndorra la VellaEurope468202279824
\n
"},"metadata":{}}],"execution_count":40},{"cell_type":"code","source":"pop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.174725Z","iopub.execute_input":"2025-02-20T19:38:24.175041Z","iopub.status.idle":"2025-02-20T19:38:24.181989Z","shell.execute_reply.started":"2025-02-20T19:38:24.175017Z","shell.execute_reply":"2025-02-20T19:38:24.181001Z"},"trusted":true},"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"Rank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\nArea (km²) int64\nvariable int64\nvalue int64\ndtype: object"},"metadata":{}}],"execution_count":41},{"cell_type":"code","source":"pop_long['Area_log_10'] = np.log10(pop_long['Area (km²)'])\npop_long['value_log_10'] = np.log10(pop_long['value'])\npop_long.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.183132Z","iopub.execute_input":"2025-02-20T19:38:24.183411Z","iopub.status.idle":"2025-02-20T19:38:24.204824Z","shell.execute_reply.started":"2025-02-20T19:38:24.183389Z","shell.execute_reply":"2025-02-20T19:38:24.203842Z"},"trusted":true},"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":" Rank CCA3 Country/Territory Capital Continent Area (km²) \\\n0 36 AFG Afghanistan Kabul Asia 652230 \n1 138 ALB Albania Tirana Europe 28748 \n2 34 DZA Algeria Algiers Africa 2381741 \n3 213 ASM American Samoa Pago Pago Oceania 199 \n4 203 AND Andorra Andorra la Vella Europe 468 \n\n variable value Area_log_10 value_log_10 \n0 2022 41128771 5.814401 7.614146 \n1 2022 2842321 4.458608 6.453673 \n2 2022 44903225 6.376895 7.652278 \n3 2022 44273 2.298853 4.646139 \n4 2022 79824 2.670246 4.902133 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
RankCCA3Country/TerritoryCapitalContinentArea (km²)variablevalueArea_log_10value_log_10
036AFGAfghanistanKabulAsia6522302022411287715.8144017.614146
1138ALBAlbaniaTiranaEurope28748202228423214.4586086.453673
234DZAAlgeriaAlgiersAfrica23817412022449032256.3768957.652278
3213ASMAmerican SamoaPago PagoOceania1992022442732.2988534.646139
4203ANDAndorraAndorra la VellaEurope4682022798242.6702464.902133
\n
"},"metadata":{}}],"execution_count":42},{"cell_type":"code","source":"pop.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.205830Z","iopub.execute_input":"2025-02-20T19:38:24.206088Z","iopub.status.idle":"2025-02-20T19:38:24.221006Z","shell.execute_reply.started":"2025-02-20T19:38:24.206067Z","shell.execute_reply":"2025-02-20T19:38:24.220021Z"},"trusted":true},"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":" Rank CCA3 Country/Territory Capital Continent 2022 Population \\\n0 36 AFG Afghanistan Kabul Asia 41128771 \n1 138 ALB Albania Tirana Europe 2842321 \n2 34 DZA Algeria Algiers Africa 44903225 \n3 213 ASM American Samoa Pago Pago Oceania 44273 \n4 203 AND Andorra Andorra la Vella Europe 79824 \n\n 2020 Population 2015 Population 2010 Population 2000 Population \\\n0 38972230 33753499 28189672 19542982 \n1 2866849 2882481 2913399 3182021 \n2 43451666 39543154 35856344 30774621 \n3 46189 51368 54849 58230 \n4 77700 71746 71519 66097 \n\n 1990 Population 1980 Population 1970 Population Area (km²) \\\n0 10694796 12486631 10752971 652230 \n1 3295066 2941651 2324731 28748 \n2 25518074 18739378 13795915 2381741 \n3 47818 32886 27075 199 \n4 53569 35611 19860 468 \n\n Density (per km²) Growth Rate World Population Percentage \n0 63.0587 1.0257 0.52 \n1 98.8702 0.9957 0.04 \n2 18.8531 1.0164 0.56 \n3 222.4774 0.9831 0.00 \n4 170.5641 1.0100 0.00 ","text/html":"
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RankCCA3Country/TerritoryCapitalContinent2022 Population2020 Population2015 Population2010 Population2000 Population1990 Population1980 Population1970 PopulationArea (km²)Density (per km²)Growth RateWorld Population Percentage
036AFGAfghanistanKabulAsia411287713897223033753499281896721954298210694796124866311075297165223063.05871.02570.52
1138ALBAlbaniaTiranaEurope284232128668492882481291339931820213295066294165123247312874898.87020.99570.04
234DZAAlgeriaAlgiersAfrica4490322543451666395431543585634430774621255180741873937813795915238174118.85311.01640.56
3213ASMAmerican SamoaPago PagoOceania4427346189513685484958230478183288627075199222.47740.98310.00
4203ANDAndorraAndorra la VellaEurope7982477700717467151966097535693561119860468170.56411.01000.00
\n
"},"metadata":{}}],"execution_count":43},{"cell_type":"code","source":"plt.scatter(pop_long['Area (km²)'],pop_long['value'], s=1)\nplt.xlabel('Area in square KM')\nplt.ylabel('Population')\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.222343Z","iopub.execute_input":"2025-02-20T19:38:24.222762Z","iopub.status.idle":"2025-02-20T19:38:24.553824Z","shell.execute_reply.started":"2025-02-20T19:38:24.222728Z","shell.execute_reply":"2025-02-20T19:38:24.552790Z"},"trusted":true},"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Population')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":44},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"plt.scatter(pop_long['Area_log_10'],\n pop_long['value_log_10'], s=1)\nplt.xlabel('Area in square KM (log scale)')\nplt.ylabel('Population (log scale)')\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.554938Z","iopub.execute_input":"2025-02-20T19:38:24.555196Z","iopub.status.idle":"2025-02-20T19:38:24.847877Z","shell.execute_reply.started":"2025-02-20T19:38:24.555175Z","shell.execute_reply":"2025-02-20T19:38:24.846850Z"},"trusted":true},"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Population (log scale)')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":45},{"cell_type":"code","source":"pop_long.groupby(['CCA3']).describe()['value']","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.849285Z","iopub.execute_input":"2025-02-20T19:38:24.849731Z","iopub.status.idle":"2025-02-20T19:38:26.890032Z","shell.execute_reply.started":"2025-02-20T19:38:24.849695Z","shell.execute_reply":"2025-02-20T19:38:26.888984Z"},"trusted":true},"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% \\\nCCA3 \nABW 8.0 8.672675e+04 2.099067e+04 59106.0 64850.75 94721.0 \nAFG 8.0 2.444019e+07 1.272585e+07 10694796.0 12053216.00 23866327.0 \nAGO 8.0 2.038648e+07 1.140585e+07 6029700.0 10953990.25 19879123.5 \nAIA 8.0 1.141812e+04 3.955593e+03 6283.0 7877.00 12109.5 \nALB 8.0 2.906065e+06 2.860820e+05 2324731.0 2860717.00 2897940.0 \n... ... ... ... ... ... ... \nWSM 8.0 1.869280e+05 2.718362e+04 142771.0 167365.75 189340.0 \nYEM 8.0 2.091169e+07 1.043535e+07 6843607.0 12332575.25 21686323.0 \nZAF 8.0 4.560999e+07 1.392213e+07 22368306.0 37274064.75 49299093.5 \nZMB 8.0 1.207067e+07 6.042178e+06 4281671.0 7194910.25 11841611.0 \nZWE 8.0 1.164829e+07 3.978175e+06 5202918.0 9347901.25 12337223.5 \n\n 75% max \nCCA3 \nABW 104804.00 106585.0 \nAFG 35058181.75 41128771.0 \nAGO 29452912.00 35588987.0 \nAIA 14790.00 15857.0 \nALB 3001743.50 3295066.0 \n... ... ... \nWSM 206410.50 222382.0 \nYEM 29458420.25 33696614.0 \nZAF 56607859.75 59893885.0 \nZMB 16918101.25 20017675.0 \nZWE 14533619.25 16320537.0 \n\n[234 rows x 8 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
CCA3
ABW8.08.672675e+042.099067e+0459106.064850.7594721.0104804.00106585.0
AFG8.02.444019e+071.272585e+0710694796.012053216.0023866327.035058181.7541128771.0
AGO8.02.038648e+071.140585e+076029700.010953990.2519879123.529452912.0035588987.0
AIA8.01.141812e+043.955593e+036283.07877.0012109.514790.0015857.0
ALB8.02.906065e+062.860820e+052324731.02860717.002897940.03001743.503295066.0
...........................
WSM8.01.869280e+052.718362e+04142771.0167365.75189340.0206410.50222382.0
YEM8.02.091169e+071.043535e+076843607.012332575.2521686323.029458420.2533696614.0
ZAF8.04.560999e+071.392213e+0722368306.037274064.7549299093.556607859.7559893885.0
ZMB8.01.207067e+076.042178e+064281671.07194910.2511841611.016918101.2520017675.0
ZWE8.01.164829e+073.978175e+065202918.09347901.2512337223.514533619.2516320537.0
\n

234 rows × 8 columns

\n
"},"metadata":{}}],"execution_count":46},{"cell_type":"code","source":"fit = np.polyfit(pop_long['Area_log_10'], np.log(pop_long.value_log_10), 1,\n w = np.sqrt(pop_long['Area_log_10']))\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:26.891291Z","iopub.execute_input":"2025-02-20T19:38:26.891548Z","iopub.status.idle":"2025-02-20T19:38:26.898459Z","shell.execute_reply.started":"2025-02-20T19:38:26.891525Z","shell.execute_reply":"2025-02-20T19:38:26.897413Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.11403525470531985\nCoefficient B = 1.3153462719013784\n","output_type":"stream"}],"execution_count":47},{"cell_type":"code","source":"pop_long['model_a'] = np.exp(fit[1]) * np.exp(fit[0] * pop_long['Area_log_10']) \nstats['errors_model_a'] = np.abs(pop_long['Area_log_10'] - pop_long['model_a'])\nstats['errors_model_a'].hist(grid=False)\nstats['errors_model_a'].describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:26.899638Z","iopub.execute_input":"2025-02-20T19:38:26.899947Z","iopub.status.idle":"2025-02-20T19:38:27.234065Z","shell.execute_reply.started":"2025-02-20T19:38:26.899925Z","shell.execute_reply":"2025-02-20T19:38:27.233032Z"},"trusted":true},"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"count 61.000000\nmean 1.771376\nstd 0.424304\nmin 1.277968\n25% 1.425505\n50% 1.665613\n75% 1.960580\nmax 2.807485\nName: errors_model_a, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":48},{"cell_type":"code","source":"# gaussian mixture clustering\nfrom sklearn.mixture import GaussianMixture as GMM\nfrom matplotlib import pyplot\nfrom sklearn import metrics\n# define dataset\nX = np.array(pop_long['Area_log_10']).reshape(-1,1)\ny = np.array(pop_long['value_log_10']).reshape(-1,1)\n\nprint('X \\n' , X)\nprint('y \\n', y)","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:27.235240Z","iopub.execute_input":"2025-02-20T19:38:27.235537Z","iopub.status.idle":"2025-02-20T19:38:27.242905Z","shell.execute_reply.started":"2025-02-20T19:38:27.235504Z","shell.execute_reply":"2025-02-20T19:38:27.241894Z"},"trusted":true},"outputs":[{"name":"stdout","text":"X \n [[5.81440077]\n [4.45860764]\n [6.37689453]\n ...\n [5.7226076 ]\n [5.87657114]\n [5.59190677]]\ny \n [[7.61414573]\n [6.45367312]\n [7.65227753]\n ...\n [6.83528506]\n [6.63161329]\n [6.71624698]]\n","output_type":"stream"}],"execution_count":49},{"cell_type":"code","source":"def SelBest(arr:list, X:int)->list:\n '''\n returns the set of X configurations with shorter distance\n '''\n dx=np.argsort(arr)[:X]\n return arr[dx]","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:27.244032Z","iopub.execute_input":"2025-02-20T19:38:27.244296Z","iopub.status.idle":"2025-02-20T19:38:27.254440Z","shell.execute_reply.started":"2025-02-20T19:38:27.244275Z","shell.execute_reply":"2025-02-20T19:38:27.253387Z"},"trusted":true},"outputs":[],"execution_count":50},{"cell_type":"markdown","source":"An early investigation suggested a high level of clusterisation may be suitable. Therefore, we start from 100 to 230, to lower time and computations resources.","metadata":{}},{"cell_type":"code","source":"n_clusters=np.arange(100,230 , 10)\nbic=[]\nbics_err=[]\niterations=20\nfor n in n_clusters:\n tmp_bic=[]\n for _ in range(iterations):\n gmm=GMM(n, n_init=2).fit(X,y) \n labels=gmm.predict(X)\n tmp_bic.append(gmm.bic(X)) \n val=np.mean(SelBest(np.array(tmp_bic), int(iterations)))\n err=np.std(tmp_bic)\n bic.append(val)\n bics_err.append(err)\n print('n_cluster : ' , n , 'mean_bic : ', val, 'std_bic : ', err)\n \nplt.errorbar(n_clusters, bic, yerr=bics_err)\nplt.title(\"Silhouette Scores\", fontsize=20)\nplt.xticks(n_clusters)\nplt.xlabel(\"N. of clusters\")\nplt.ylabel(\"Score\")","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:27.255849Z","iopub.execute_input":"2025-02-20T19:38:27.256218Z","iopub.status.idle":"2025-02-20T19:40:37.806662Z","shell.execute_reply.started":"2025-02-20T19:38:27.256184Z","shell.execute_reply":"2025-02-20T19:40:37.805687Z"},"trusted":true},"outputs":[{"name":"stdout","text":"n_cluster : 100 mean_bic : 3856.636008099107 std_bic : 55.47173557029389\nn_cluster : 110 mean_bic : 3565.0920937256656 std_bic : 51.752097688005044\nn_cluster : 120 mean_bic : 3238.5537606329126 std_bic : 50.77634947393244\nn_cluster : 130 mean_bic : 2970.963428753665 std_bic : 29.957044359627815\nn_cluster : 140 mean_bic : 2718.7738015616546 std_bic : 36.56849723532222\nn_cluster : 150 mean_bic : 2461.7434436986937 std_bic : 28.967210977215455\nn_cluster : 160 mean_bic : 2270.44758783618 std_bic : 20.198361504024515\nn_cluster : 170 mean_bic : 2145.6805203386057 std_bic : 12.24340901736794\nn_cluster : 180 mean_bic : 2077.753389432272 std_bic : 7.143630339477631\nn_cluster : 190 mean_bic : 2103.315153452777 std_bic : 5.4441102760476765\nn_cluster : 200 mean_bic : 2201.952212797958 std_bic : 1.697177350126278\nn_cluster : 210 mean_bic : 2420.0825722867294 std_bic : 3.6913212538211284e-05\nn_cluster : 220 mean_bic : 2646.1275722457517 std_bic : 0.0003102267595525465\n","output_type":"stream"},{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Score')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":51},{"cell_type":"code","source":"n_clusters=np.arange(180,200)\nbic=[]\nbics_err=[]\niterations=20\nfor n in n_clusters:\n tmp_bic=[]\n for _ in range(iterations):\n gmm=GMM(n, n_init=2).fit(X,y) \n labels=gmm.predict(X)\n tmp_bic.append(gmm.bic(X)) \n val=np.mean(SelBest(np.array(tmp_bic), int(iterations)))\n err=np.std(tmp_bic)\n bic.append(val)\n bics_err.append(err)\n print('n_cluster : ' , n , 'mean_bic : ', val, 'std_bic : ', err)\n \nplt.errorbar(n_clusters, bic, yerr=bics_err)\nplt.title(\"Silhouette Scores\", fontsize=20)\nplt.xticks(n_clusters)\nplt.xlabel(\"N. of clusters\")\nplt.ylabel(\"Score\")","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:40:37.807799Z","iopub.execute_input":"2025-02-20T19:40:37.808080Z","iopub.status.idle":"2025-02-20T19:46:21.845896Z","shell.execute_reply.started":"2025-02-20T19:40:37.808058Z","shell.execute_reply":"2025-02-20T19:46:21.844812Z"},"trusted":true},"outputs":[{"name":"stdout","text":"n_cluster : 180 mean_bic : 2077.3715072718583 std_bic : 6.009613137236369\nn_cluster : 181 mean_bic : 2074.610155198689 std_bic : 6.132013300665143\nn_cluster : 182 mean_bic : 2076.9811505087523 std_bic : 7.742041551849214\nn_cluster : 183 mean_bic : 2072.006716491217 std_bic : 4.6829381188961\nn_cluster : 184 mean_bic : 2075.581410104163 std_bic : 4.8345628408635255\nn_cluster : 185 mean_bic : 2078.718600132686 std_bic : 5.410150134557857\nn_cluster : 186 mean_bic : 2082.679191338651 std_bic : 4.369518481868247\nn_cluster : 187 mean_bic : 2086.374873318467 std_bic : 4.443574187828375\nn_cluster : 188 mean_bic : 2091.777437198426 std_bic : 5.27966678892769\nn_cluster : 189 mean_bic : 2097.306969274856 std_bic : 4.407326580151155\nn_cluster : 190 mean_bic : 2101.285361999117 std_bic : 4.428161881020638\nn_cluster : 191 mean_bic : 2109.4111420235167 std_bic : 3.964093365119614\nn_cluster : 192 mean_bic : 2114.2438137323797 std_bic : 3.0248979834840086\nn_cluster : 193 mean_bic : 2124.2408702586276 std_bic : 3.666845164893888\nn_cluster : 194 mean_bic : 2133.906395659718 std_bic : 3.72052880851393\nn_cluster : 195 mean_bic : 2141.462880793213 std_bic : 1.796548480564042\nn_cluster : 196 mean_bic : 2151.2642721469065 std_bic : 3.2553922108512405\nn_cluster : 197 mean_bic : 2163.631598992382 std_bic : 2.8430415838810754\nn_cluster : 198 mean_bic : 2175.5214552718066 std_bic : 1.9922646827976598\nn_cluster : 199 mean_bic : 2189.2082085183433 std_bic : 2.2941454065542075\nn_cluster : 200 mean_bic : 2203.244380306086 std_bic : 3.0120442417340234\nn_cluster : 201 mean_bic : 2218.485116993871 std_bic : 4.0680801004301905\nn_cluster : 202 mean_bic : 2239.044799942589 std_bic : 2.5570123980141233\nn_cluster : 203 mean_bic : 2261.244248494023 std_bic : 1.8265364708540335\nn_cluster : 204 mean_bic : 2283.1917096295947 std_bic : 0.18798540760169105\nn_cluster : 205 mean_bic : 2305.855992595264 std_bic : 0.33339569586501683\nn_cluster : 206 mean_bic : 2329.0116064023596 std_bic : 0.6541869234109525\nn_cluster : 207 mean_bic : 2352.116475599726 std_bic : 0.39658898224762856\nn_cluster : 208 mean_bic : 2374.874062142659 std_bic : 1.2101533414852998e-05\nn_cluster : 209 mean_bic : 2397.4783528092335 std_bic : 0.00019030039784818077\n","output_type":"stream"},{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Score')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":52},{"cell_type":"markdown","source":"The minimum and optimum number of clusters may vary each run. It is unknown whether it is a global or local optimum. However, it has been approximately around 180 more than 31 times.","metadata":{}},{"cell_type":"code","source":"from sklearn.mixture import GaussianMixture \ngmm = GaussianMixture(n_components=180)\ngmm.fit(X)\nlabels = gmm.predict(X)\nprobs = gmm.predict_proba(X)\n\nplt.scatter(X, y, c=labels, s=1, cmap='viridis',)\nplt.xlabel('Area in square KM (log scale)')\nplt.ylabel('Population (log scale)')\nlabels\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:21.847119Z","iopub.execute_input":"2025-02-20T19:46:21.847414Z","iopub.status.idle":"2025-02-20T19:46:22.410766Z","shell.execute_reply.started":"2025-02-20T19:46:21.847392Z","shell.execute_reply":"2025-02-20T19:46:22.409466Z"},"trusted":true},"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"array([ 54, 122, 76, ..., 120, 110, 135])"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":53},{"cell_type":"markdown","source":"Earlier we have discovered there are 234 countries. The number of components is quite close from the number of unique territories. The yearly observations made for each country may lead to a cluster for each country. However, some countries may have similar population and areas and form a cluster. \n\nWe propose to use instead the expected population through time to try to overcome this observation. We use the median point.","metadata":{}},{"cell_type":"code","source":"pop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:22.412261Z","iopub.execute_input":"2025-02-20T19:46:22.412675Z","iopub.status.idle":"2025-02-20T19:46:22.421820Z","shell.execute_reply.started":"2025-02-20T19:46:22.412638Z","shell.execute_reply":"2025-02-20T19:46:22.420627Z"},"trusted":true},"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"Rank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\nArea (km²) int64\nvariable int64\nvalue int64\nArea_log_10 float64\nvalue_log_10 float64\nmodel_a float64\ndtype: object"},"metadata":{}}],"execution_count":54},{"cell_type":"code","source":"means = pop_long.loc[: , ['CCA3','Area (km²)','value']].copy(deep = True)\nmeans.describe()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:22.423183Z","iopub.execute_input":"2025-02-20T19:46:22.423552Z","iopub.status.idle":"2025-02-20T19:46:22.446528Z","shell.execute_reply.started":"2025-02-20T19:46:22.423520Z","shell.execute_reply":"2025-02-20T19:46:22.445452Z"},"trusted":true},"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":" Area (km²) value\ncount 1.872000e+03 1.872000e+03\nmean 5.814494e+05 2.661274e+07\nstd 1.758542e+06 1.133600e+08\nmin 1.000000e+00 5.100000e+02\n25% 2.586000e+03 3.180410e+05\n50% 8.119950e+04 4.225097e+06\n75% 4.383170e+05 1.546753e+07\nmax 1.709824e+07 1.425887e+09","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Area (km²)value
count1.872000e+031.872000e+03
mean5.814494e+052.661274e+07
std1.758542e+061.133600e+08
min1.000000e+005.100000e+02
25%2.586000e+033.180410e+05
50%8.119950e+044.225097e+06
75%4.383170e+051.546753e+07
max1.709824e+071.425887e+09
\n
"},"metadata":{}}],"execution_count":55},{"cell_type":"code","source":"tmp = means.groupby(['CCA3','Area (km²)']).median().reset_index()\ntmp","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:22.447660Z","iopub.execute_input":"2025-02-20T19:46:22.448037Z","iopub.status.idle":"2025-02-20T19:46:22.465989Z","shell.execute_reply.started":"2025-02-20T19:46:22.448001Z","shell.execute_reply":"2025-02-20T19:46:22.465046Z"},"trusted":true},"outputs":[{"execution_count":56,"output_type":"execute_result","data":{"text/plain":" CCA3 Area (km²) value\n0 ABW 180 94721.0\n1 AFG 652230 23866327.0\n2 AGO 1246700 19879123.5\n3 AIA 91 12109.5\n4 ALB 28748 2897940.0\n.. ... ... ...\n229 WSM 2842 189340.0\n230 YEM 527968 21686323.0\n231 ZAF 1221037 49299093.5\n232 ZMB 752612 11841611.0\n233 ZWE 390757 12337223.5\n\n[234 rows x 3 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
CCA3Area (km²)value
0ABW18094721.0
1AFG65223023866327.0
2AGO124670019879123.5
3AIA9112109.5
4ALB287482897940.0
............
229WSM2842189340.0
230YEM52796821686323.0
231ZAF122103749299093.5
232ZMB75261211841611.0
233ZWE39075712337223.5
\n

234 rows × 3 columns

\n
"},"metadata":{}}],"execution_count":56},{"cell_type":"code","source":"# gaussian mixture clustering\nfrom sklearn.mixture import GaussianMixture as GMM\nfrom matplotlib import pyplot\nfrom sklearn import metrics\n# define dataset\nX = np.array(np.log10(tmp['Area (km²)'])).reshape(-1,1)\ny = np.array(np.log10(tmp['value'])).reshape(-1,1)\n\nprint('X \\n' , len(X))\nprint('y \\n', len(y))\nplt.scatter(X,\n y, s=1)\nplt.xlabel('Area in square KM (log scale)')\nplt.ylabel('Population (log scale)')","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:22.467073Z","iopub.execute_input":"2025-02-20T19:46:22.467358Z","iopub.status.idle":"2025-02-20T19:46:22.755772Z","shell.execute_reply.started":"2025-02-20T19:46:22.467335Z","shell.execute_reply":"2025-02-20T19:46:22.754618Z"},"trusted":true},"outputs":[{"name":"stdout","text":"X \n 234\ny \n 234\n","output_type":"stream"},{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Population (log scale)')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":57},{"cell_type":"markdown","source":"We reduced the number of dimension from three to two - we removed the temporal dimension. We now discover with the log scale that an exponential tend may exist. \n\nA Gaussian mixture model appears not to fit properly. A regression has demonstrated that a exponential equation is likely to model the relationship between the size of a country and an expected population. The exponential equation suggests the size of the territories are likely to impact on the population residing in the country. Other factors such as geographical features, for example, should be considered. This suggestion is not exhaustive. \n","metadata":{}},{"cell_type":"code","source":"n_clusters=np.arange(2,10,1)\nbic=[]\nbics_err=[]\niterations=20\nfor n in n_clusters:\n tmp_bic=[]\n for _ in range(iterations):\n gmm=GMM(n, n_init=2).fit(X,y) \n labels=gmm.predict(X)\n tmp_bic.append(gmm.bic(X)) \n val=np.mean(SelBest(np.array(tmp_bic), int(iterations)))\n err=np.std(tmp_bic)\n bic.append(val)\n bics_err.append(err)\n print('n_cluster : ' , n , 'mean_bic : ', val, 'std_bic : ', err)\n \nplt.errorbar(n_clusters, bic, yerr=bics_err)\nplt.title(\"Silhouette Scores\", fontsize=20)\nplt.xticks(n_clusters)\nplt.xlabel(\"N. of clusters\")\nplt.ylabel(\"Score\")","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:22.757496Z","iopub.execute_input":"2025-02-20T19:46:22.757817Z","iopub.status.idle":"2025-02-20T19:46:25.513013Z","shell.execute_reply.started":"2025-02-20T19:46:22.757795Z","shell.execute_reply":"2025-02-20T19:46:25.511905Z"},"trusted":true},"outputs":[{"name":"stdout","text":"n_cluster : 2 mean_bic : 810.7546783544826 std_bic : 1.1368683772161603e-13\nn_cluster : 3 mean_bic : 826.0248752749292 std_bic : 0.04386275255791023\nn_cluster : 4 mean_bic : 836.9482665520421 std_bic : 2.4987458886256007\nn_cluster : 5 mean_bic : 851.1583710650709 std_bic : 0.3326180440302201\nn_cluster : 6 mean_bic : 864.5621764585967 std_bic : 2.986333140880444\nn_cluster : 7 mean_bic : 877.8200894841821 std_bic : 3.4934227400776683\nn_cluster : 8 mean_bic : 889.3653062190849 std_bic : 3.624720032953729\nn_cluster : 9 mean_bic : 900.9320916437897 std_bic : 1.271264802422914\n","output_type":"stream"},{"execution_count":58,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Score')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":58},{"cell_type":"code","source":"from sklearn.mixture import GaussianMixture \ngmm = GaussianMixture(n_components=7)\ngmm.fit(X)\nlabels = gmm.predict(X)\nprobs = gmm.predict_proba(X)\n\nplt.scatter(X, y, c=labels, s=1, cmap='viridis',)\nplt.xlabel('Area in square KM (log scale)')\nplt.ylabel('Population (log scale)')\n#labels\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:25.514418Z","iopub.execute_input":"2025-02-20T19:46:25.515061Z","iopub.status.idle":"2025-02-20T19:46:25.822648Z","shell.execute_reply.started":"2025-02-20T19:46:25.515023Z","shell.execute_reply":"2025-02-20T19:46:25.821481Z"},"trusted":true},"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Population (log scale)')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":59},{"cell_type":"code","source":"\nfit = np.polyfit(np.log10(tmp['Area (km²)']), \n np.log10(tmp['value']), 1,\n w = np.sqrt(np.log10(tmp['Area (km²)'])))\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:25.824137Z","iopub.execute_input":"2025-02-20T19:46:25.824522Z","iopub.status.idle":"2025-02-20T19:46:25.832211Z","shell.execute_reply.started":"2025-02-20T19:46:25.824486Z","shell.execute_reply":"2025-02-20T19:46:25.830989Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.6973094427440153\nCoefficient B = 3.233127897973355\n","output_type":"stream"}],"execution_count":60},{"cell_type":"code","source":"import matplotlib.pyplot as plt\ntmp['model'] = fit[1] + fit[0] * np.log10(tmp['Area (km²)'])\n\n\nplt.scatter(x=np.log10(tmp['Area (km²)']), y= np.log10(tmp['value']), label = 'Area in square km')\nplt.scatter(x=np.log10(tmp['Area (km²)']), y= tmp['model'], marker = \"+\")\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:25.833821Z","iopub.execute_input":"2025-02-20T19:46:25.834459Z","iopub.status.idle":"2025-02-20T19:46:26.178116Z","shell.execute_reply.started":"2025-02-20T19:46:25.834426Z","shell.execute_reply":"2025-02-20T19:46:26.177071Z"},"trusted":true},"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":61},{"cell_type":"code","source":"import matplotlib.pyplot as plt\ntmp['model'] = np.exp(fit[1]) * np.exp(fit[0] * np.log10(tmp['Area (km²)'])) \n\n\nplt.scatter(x=tmp['Area (km²)'], y= tmp['value'], label = 'Area in square km')\nplt.scatter(x=tmp['Area (km²)'], y= tmp['model'], marker = \"+\")\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:26.179183Z","iopub.execute_input":"2025-02-20T19:46:26.179445Z","iopub.status.idle":"2025-02-20T19:46:26.501953Z","shell.execute_reply.started":"2025-02-20T19:46:26.179425Z","shell.execute_reply":"2025-02-20T19:46:26.500943Z"},"trusted":true},"outputs":[{"execution_count":62,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":62},{"cell_type":"code","source":"tmp['errors_model'] = np.abs(tmp['Area (km²)'] - tmp['model'])\ntmp['errors_model'].hist(grid=False)\ntmp['errors_model'].describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:26.503445Z","iopub.execute_input":"2025-02-20T19:46:26.503856Z","iopub.status.idle":"2025-02-20T19:46:26.776192Z","shell.execute_reply.started":"2025-02-20T19:46:26.503811Z","shell.execute_reply":"2025-02-20T19:46:26.775226Z"},"trusted":true},"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"count 2.340000e+02\nmean 5.805673e+05\nstd 1.761308e+06\nmin 8.985719e-01\n25% 2.374108e+03\n50% 8.042166e+04\n75% 4.291368e+05\nmax 1.709431e+07\nName: errors_model, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":63},{"cell_type":"markdown","source":"We explore next the population concentration in countries. We surmise the more individuals living closely, the global emissions may occur. We will explore hot the population concentration has evolved between 1970 and 2022.\n\nWe are going find the population per km square - $pop_per_km_square = value/ area in km square$.","metadata":{}},{"cell_type":"code","source":"pop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:26.777544Z","iopub.execute_input":"2025-02-20T19:46:26.777964Z","iopub.status.idle":"2025-02-20T19:46:26.785706Z","shell.execute_reply.started":"2025-02-20T19:46:26.777929Z","shell.execute_reply":"2025-02-20T19:46:26.784617Z"},"trusted":true},"outputs":[{"execution_count":64,"output_type":"execute_result","data":{"text/plain":"Rank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\nArea (km²) int64\nvariable int64\nvalue int64\nArea_log_10 float64\nvalue_log_10 float64\nmodel_a float64\ndtype: object"},"metadata":{}}],"execution_count":64},{"cell_type":"markdown","source":"We population per country","metadata":{}},{"cell_type":"code","source":"pop_long['pop_per_km_square'] = pop_long.value/pop_long['Area (km²)']\npop_long.groupby(['CCA3']).describe()['pop_per_km_square']","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:26.786929Z","iopub.execute_input":"2025-02-20T19:46:26.787214Z","iopub.status.idle":"2025-02-20T19:46:29.468258Z","shell.execute_reply.started":"2025-02-20T19:46:26.787192Z","shell.execute_reply":"2025-02-20T19:46:29.467189Z"},"trusted":true},"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% \\\nCCA3 \nABW 8.0 481.815278 116.614859 328.366667 360.281944 526.227778 \nAFG 8.0 37.471742 19.511296 16.397277 18.480009 36.591888 \nAGO 8.0 16.352353 9.148836 4.836528 8.786388 15.945395 \nAIA 8.0 125.473901 43.468054 69.043956 86.560440 133.071429 \nALB 8.0 101.087550 9.951370 80.865834 99.510122 100.804926 \n... ... ... ... ... ... ... \nWSM 8.0 65.773399 9.564960 50.236101 58.890130 66.622097 \nYEM 8.0 39.607873 19.765118 12.962162 23.358566 41.075071 \nZAF 8.0 37.353488 11.401890 18.319106 30.526565 40.374774 \nZMB 8.0 16.038369 8.028277 5.689081 9.559920 15.734018 \nZWE 8.0 29.809550 10.180688 13.314971 23.922543 31.572623 \n\n 75% max \nCCA3 \nABW 582.244444 592.138889 \nAFG 53.751256 63.058692 \nAGO 23.624699 28.546552 \nAIA 162.527473 174.252747 \nALB 104.415733 114.618965 \n... ... ... \nWSM 72.628607 78.248417 \nYEM 55.795844 63.823213 \nZAF 46.360479 49.051654 \nZMB 22.479181 26.597603 \nZWE 37.193497 41.766461 \n\n[234 rows x 8 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
CCA3
ABW8.0481.815278116.614859328.366667360.281944526.227778582.244444592.138889
AFG8.037.47174219.51129616.39727718.48000936.59188853.75125663.058692
AGO8.016.3523539.1488364.8365288.78638815.94539523.62469928.546552
AIA8.0125.47390143.46805469.04395686.560440133.071429162.527473174.252747
ALB8.0101.0875509.95137080.86583499.510122100.804926104.415733114.618965
...........................
WSM8.065.7733999.56496050.23610158.89013066.62209772.62860778.248417
YEM8.039.60787319.76511812.96216223.35856641.07507155.79584463.823213
ZAF8.037.35348811.40189018.31910630.52656540.37477446.36047949.051654
ZMB8.016.0383698.0282775.6890819.55992015.73401822.47918126.597603
ZWE8.029.80955010.18068813.31497123.92254331.57262337.19349741.766461
\n

234 rows × 8 columns

\n
"},"metadata":{}}],"execution_count":65},{"cell_type":"code","source":"pop_long.dtypes","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-20T19:48:54.001825Z","iopub.execute_input":"2025-02-20T19:48:54.002228Z","iopub.status.idle":"2025-02-20T19:48:54.010270Z","shell.execute_reply.started":"2025-02-20T19:48:54.002199Z","shell.execute_reply":"2025-02-20T19:48:54.009300Z"}},"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"Rank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\nArea (km²) int64\nvariable int64\nvalue int64\nArea_log_10 float64\nvalue_log_10 float64\nmodel_a float64\npop_per_km_square float64\ndtype: object"},"metadata":{}}],"execution_count":67},{"cell_type":"code","source":"pop_long.loc[pop_long['Area (km²)'] > pop_long['value'],:]","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:49:15.633976Z","iopub.execute_input":"2025-02-20T19:49:15.634349Z","iopub.status.idle":"2025-02-20T19:49:15.654116Z","shell.execute_reply.started":"2025-02-20T19:49:15.634324Z","shell.execute_reply":"2025-02-20T19:49:15.653078Z"},"trusted":true},"outputs":[{"execution_count":68,"output_type":"execute_result","data":{"text/plain":" Rank CCA3 Country/Territory Capital Continent Area (km²) \\\n64 231 FLK Falkland Islands Stanley South America 12173 \n78 208 GRL Greenland Nuuk North America 2166086 \n298 231 FLK Falkland Islands Stanley South America 12173 \n312 208 GRL Greenland Nuuk North America 2166086 \n532 231 FLK Falkland Islands Stanley South America 12173 \n546 208 GRL Greenland Nuuk North America 2166086 \n766 231 FLK Falkland Islands Stanley South America 12173 \n780 208 GRL Greenland Nuuk North America 2166086 \n1000 231 FLK Falkland Islands Stanley South America 12173 \n1014 208 GRL Greenland Nuuk North America 2166086 \n1234 231 FLK Falkland Islands Stanley South America 12173 \n1248 208 GRL Greenland Nuuk North America 2166086 \n1400 172 ESH Western Sahara El Aaiún Africa 266000 \n1468 231 FLK Falkland Islands Stanley South America 12173 \n1473 184 GUF French Guiana Cayenne South America 83534 \n1482 208 GRL Greenland Nuuk North America 2166086 \n1634 172 ESH Western Sahara El Aaiún Africa 266000 \n1702 231 FLK Falkland Islands Stanley South America 12173 \n1707 184 GUF French Guiana Cayenne South America 83534 \n1716 208 GRL Greenland Nuuk North America 2166086 \n1773 134 MNG Mongolia Ulaanbaatar Asia 1564110 \n1779 145 NAM Namibia Windhoek Africa 825615 \n1868 172 ESH Western Sahara El Aaiún Africa 266000 \n\n variable value Area_log_10 value_log_10 model_a \\\n64 2022 3780 4.085398 3.577492 5.937129 \n78 2022 56466 6.335676 4.751787 7.673990 \n298 2020 3747 4.085398 3.573684 5.937129 \n312 2020 56026 6.335676 4.748390 7.673990 \n532 2015 3408 4.085398 3.532500 5.937129 \n546 2015 55895 6.335676 4.747373 7.673990 \n766 2010 3187 4.085398 3.503382 5.937129 \n780 2010 56351 6.335676 4.750902 7.673990 \n1000 2000 3080 4.085398 3.488551 5.937129 \n1014 2000 56184 6.335676 4.749613 7.673990 \n1234 1990 2332 4.085398 3.367729 5.937129 \n1248 1990 55599 6.335676 4.745067 7.673990 \n1400 1990 178529 5.424882 5.251709 6.916944 \n1468 1980 2240 4.085398 3.350248 5.937129 \n1473 1980 66825 4.921863 4.824939 6.531340 \n1482 1980 50106 6.335676 4.699890 7.673990 \n1634 1980 116775 5.424882 5.067350 6.916944 \n1702 1970 2274 4.085398 3.356790 5.937129 \n1707 1970 46484 4.921863 4.667303 6.531340 \n1716 1970 45434 6.335676 4.657381 7.673990 \n1773 1970 1293880 6.194267 6.111894 7.551235 \n1779 1970 754467 5.916778 5.877640 7.316028 \n1868 1970 76371 5.424882 4.882928 6.916944 \n\n pop_per_km_square \n64 0.310523 \n78 0.026068 \n298 0.307812 \n312 0.025865 \n532 0.279964 \n546 0.025805 \n766 0.261809 \n780 0.026015 \n1000 0.253019 \n1014 0.025938 \n1234 0.191572 \n1248 0.025668 \n1400 0.671162 \n1468 0.184014 \n1473 0.799974 \n1482 0.023132 \n1634 0.439004 \n1702 0.186807 \n1707 0.556468 \n1716 0.020975 \n1773 0.827231 \n1779 0.913824 \n1868 0.287109 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
RankCCA3Country/TerritoryCapitalContinentArea (km²)variablevalueArea_log_10value_log_10model_apop_per_km_square
64231FLKFalkland IslandsStanleySouth America12173202237804.0853983.5774925.9371290.310523
78208GRLGreenlandNuukNorth America21660862022564666.3356764.7517877.6739900.026068
298231FLKFalkland IslandsStanleySouth America12173202037474.0853983.5736845.9371290.307812
312208GRLGreenlandNuukNorth America21660862020560266.3356764.7483907.6739900.025865
532231FLKFalkland IslandsStanleySouth America12173201534084.0853983.5325005.9371290.279964
546208GRLGreenlandNuukNorth America21660862015558956.3356764.7473737.6739900.025805
766231FLKFalkland IslandsStanleySouth America12173201031874.0853983.5033825.9371290.261809
780208GRLGreenlandNuukNorth America21660862010563516.3356764.7509027.6739900.026015
1000231FLKFalkland IslandsStanleySouth America12173200030804.0853983.4885515.9371290.253019
1014208GRLGreenlandNuukNorth America21660862000561846.3356764.7496137.6739900.025938
1234231FLKFalkland IslandsStanleySouth America12173199023324.0853983.3677295.9371290.191572
1248208GRLGreenlandNuukNorth America21660861990555996.3356764.7450677.6739900.025668
1400172ESHWestern SaharaEl AaiúnAfrica26600019901785295.4248825.2517096.9169440.671162
1468231FLKFalkland IslandsStanleySouth America12173198022404.0853983.3502485.9371290.184014
1473184GUFFrench GuianaCayenneSouth America835341980668254.9218634.8249396.5313400.799974
1482208GRLGreenlandNuukNorth America21660861980501066.3356764.6998907.6739900.023132
1634172ESHWestern SaharaEl AaiúnAfrica26600019801167755.4248825.0673506.9169440.439004
1702231FLKFalkland IslandsStanleySouth America12173197022744.0853983.3567905.9371290.186807
1707184GUFFrench GuianaCayenneSouth America835341970464844.9218634.6673036.5313400.556468
1716208GRLGreenlandNuukNorth America21660861970454346.3356764.6573817.6739900.020975
1773134MNGMongoliaUlaanbaatarAsia1564110197012938806.1942676.1118947.5512350.827231
1779145NAMNamibiaWindhoekAfrica82561519707544675.9167785.8776407.3160280.913824
1868172ESHWestern SaharaEl AaiúnAfrica2660001970763715.4248824.8829286.9169440.287109
\n
"},"metadata":{}}],"execution_count":68},{"cell_type":"code","source":"rows = pop_long['Area (km²)'] > pop_long['value']\ntemp = pop_long.loc[rows,['CCA3','Country/Territory','Continent','variable']]\ntemp = temp.groupby(['CCA3','Country/Territory','Continent']).count().reset_index()\ntemp","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:49:19.803960Z","iopub.execute_input":"2025-02-20T19:49:19.804300Z","iopub.status.idle":"2025-02-20T19:49:19.820536Z","shell.execute_reply.started":"2025-02-20T19:49:19.804276Z","shell.execute_reply":"2025-02-20T19:49:19.819466Z"},"trusted":true},"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":" CCA3 Country/Territory Continent variable\n0 ESH Western Sahara Africa 3\n1 FLK Falkland Islands South America 8\n2 GRL Greenland North America 8\n3 GUF French Guiana South America 2\n4 MNG Mongolia Asia 1\n5 NAM Namibia Africa 1","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
CCA3Country/TerritoryContinentvariable
0ESHWestern SaharaAfrica3
1FLKFalkland IslandsSouth America8
2GRLGreenlandNorth America8
3GUFFrench GuianaSouth America2
4MNGMongoliaAsia1
5NAMNamibiaAfrica1
\n
"},"metadata":{}}],"execution_count":69},{"cell_type":"code","source":"pop.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:49:23.538044Z","iopub.execute_input":"2025-02-20T19:49:23.538435Z","iopub.status.idle":"2025-02-20T19:49:23.546279Z","shell.execute_reply.started":"2025-02-20T19:49:23.538403Z","shell.execute_reply":"2025-02-20T19:49:23.545239Z"},"trusted":true},"outputs":[{"execution_count":70,"output_type":"execute_result","data":{"text/plain":"Rank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\n2022 Population int64\n2020 Population int64\n2015 Population int64\n2010 Population int64\n2000 Population int64\n1990 Population int64\n1980 Population int64\n1970 Population int64\nArea (km²) int64\nDensity (per km²) float64\nGrowth Rate float64\nWorld Population Percentage float64\ndtype: object"},"metadata":{}}],"execution_count":70},{"cell_type":"code","source":"tmp = pd.DataFrame()\ntmp['1970'] = np.log10(pop['1970 Population']/pop['Area (km²)'])\ntmp['1980'] = np.log10(pop['1980 Population']/pop['Area (km²)'])\ntmp['1990'] = np.log10(pop['1990 Population']/pop['Area (km²)'])\ntmp['2000'] = np.log10(pop['2000 Population']/pop['Area (km²)'])\ntmp['2015'] = np.log10(pop['2015 Population']/pop['Area (km²)'])\ntmp['2020'] = np.log10(pop['2020 Population']/pop['Area (km²)'])\ntmp['2022'] = np.log10(pop['2022 Population']/pop['Area (km²)'])\nax = tmp.boxplot(grid = False, rot =45)\nplt.title('Population concentration (log scale)')\ntmp.describe()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:49:27.142435Z","iopub.execute_input":"2025-02-20T19:49:27.143534Z","iopub.status.idle":"2025-02-20T19:49:27.502297Z","shell.execute_reply.started":"2025-02-20T19:49:27.143496Z","shell.execute_reply":"2025-02-20T19:49:27.501278Z"},"trusted":true},"outputs":[{"execution_count":71,"output_type":"execute_result","data":{"text/plain":" 1970 1980 1990 2000 2015 2020 \\\ncount 234.000000 234.000000 234.000000 234.000000 234.000000 234.000000 \nmean 1.594789 1.682442 1.769069 1.834934 1.922655 1.946820 \nstd 0.784700 0.765499 0.754869 0.742757 0.729774 0.724947 \nmin -1.678295 -1.635786 -1.590609 -1.586063 -1.588303 -1.587286 \n25% 1.120070 1.224847 1.313055 1.417633 1.529090 1.579103 \n50% 1.629144 1.736050 1.850794 1.895206 1.965093 1.972973 \n75% 2.098137 2.164787 2.230460 2.269495 2.356745 2.381238 \nmax 4.084040 4.131555 4.180828 4.210385 4.311923 4.353007 \n\n 2022 \ncount 234.000000 \nmean 1.954727 \nstd 0.723207 \nmin -1.583889 \n25% 1.584492 \n50% 1.979262 \n75% 2.378272 \nmax 4.364969 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
1970198019902000201520202022
count234.000000234.000000234.000000234.000000234.000000234.000000234.000000
mean1.5947891.6824421.7690691.8349341.9226551.9468201.954727
std0.7847000.7654990.7548690.7427570.7297740.7249470.723207
min-1.678295-1.635786-1.590609-1.586063-1.588303-1.587286-1.583889
25%1.1200701.2248471.3130551.4176331.5290901.5791031.584492
50%1.6291441.7360501.8507941.8952061.9650931.9729731.979262
75%2.0981372.1647872.2304602.2694952.3567452.3812382.378272
max4.0840404.1315554.1808284.2103854.3119234.3530074.364969
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":71},{"cell_type":"markdown","source":"The median population concentration appears to increase through time. The number of outliers appears to both become smaller or larger. It may indicates some countries may be becoming less populated while other increase. \n\nAll the values are shown in log scale 10. A negative value suggests a very low density - the area in square km is greater than the population. We found five countries above. ","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]} \ No newline at end of file diff --git a/Data engineering and science/More complex analysis/uk-house-prices-and-population.ipynb b/Data engineering and science/More complex analysis/uk-house-prices-and-population.ipynb new file mode 100644 index 0000000..25e83b9 --- /dev/null +++ b/Data engineering and science/More complex analysis/uk-house-prices-and-population.ipynb @@ -0,0 +1,4964 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d97a33e8", + "metadata": { + "papermill": { + "duration": 0.013511, + "end_time": "2025-02-16T13:26:20.262900", + "exception": false, + "start_time": "2025-02-16T13:26:20.249389", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# population across the UK" + ] + }, + { + "cell_type": "markdown", + "id": "155623cd", + "metadata": { + "papermill": { + "duration": 0.014118, + "end_time": "2025-02-16T13:26:20.290484", + "exception": false, + "start_time": "2025-02-16T13:26:20.276366", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Introduction \n", + "This notebook aims to explore the Housing prices and population across the UK. We use a wide range of analytical techniques that includes visualisation as well as application of advanced statistical techniques. It is aimed for teaching those techniques, rather being produced as a reference. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f96d3ac1", + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", + "execution": { + "iopub.execute_input": "2025-02-16T13:26:20.318684Z", + "iopub.status.busy": "2025-02-16T13:26:20.318243Z", + "iopub.status.idle": "2025-02-16T13:26:24.868432Z", + "shell.execute_reply": "2025-02-16T13:26:24.867392Z" + }, + "papermill": { + "duration": 4.566687, + "end_time": "2025-02-16T13:26:24.870614", + "exception": false, + "start_time": "2025-02-16T13:26:20.303927", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/kaggle/input/united-kingdom-real-estate-and-population-data/data.csv\n" + ] + } + ], + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n", + "# For example, here's several helpful packages to load\n", + "\n", + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.cluster import KMeans\n", + "\n", + "# Input data files are available in the read-only \"../input/\" directory\n", + "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", + "\n", + "import os\n", + "for dirname, _, filenames in os.walk('/kaggle/input'):\n", + " for filename in filenames:\n", + " print(os.path.join(dirname, filename))\n", + "\n", + "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n", + "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session" + ] + }, + { + "cell_type": "markdown", + "id": "d57ecf9b", + "metadata": { + "papermill": { + "duration": 0.013082, + "end_time": "2025-02-16T13:26:24.897236", + "exception": false, + "start_time": "2025-02-16T13:26:24.884154", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Exploration of the data and its features" + ] + }, + { + "cell_type": "markdown", + "id": "fa8e521b", + "metadata": { + "papermill": { + "duration": 0.012808, + "end_time": "2025-02-16T13:26:24.923229", + "exception": false, + "start_time": "2025-02-16T13:26:24.910421", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The data is substantially large - more than 7 millions observations. Numerical data brings a high level of variability. Analysis may suffer from some large dispersion within the numerical values. The datasets appears to have some categorical values as well as time series data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "38f8860b", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:26:24.951862Z", + "iopub.status.busy": "2025-02-16T13:26:24.950786Z", + "iopub.status.idle": "2025-02-16T13:26:46.528615Z", + "shell.execute_reply": "2025-02-16T13:26:46.527388Z" + }, + "papermill": { + "duration": 21.594396, + "end_time": "2025-02-16T13:26:46.530723", + "exception": false, + "start_time": "2025-02-16T13:26:24.936327", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(7853837, 13)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "source = '/kaggle/input/united-kingdom-real-estate-and-population-data/data.csv'\n", + "data = pd.read_csv(source)\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5ed0eef6", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:26:46.560899Z", + "iopub.status.busy": "2025-02-16T13:26:46.560533Z", + "iopub.status.idle": "2025-02-16T13:26:46.569831Z", + "shell.execute_reply": "2025-02-16T13:26:46.568888Z" + }, + "papermill": { + "duration": 0.027658, + "end_time": "2025-02-16T13:26:46.571814", + "exception": false, + "start_time": "2025-02-16T13:26:46.544156", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Unnamed: 0 int64\n", + "Code object\n", + "Name object\n", + "Geography object\n", + "Area (sq km) float64\n", + "Price int64\n", + "Date of Transfer object\n", + "Property Type object\n", + "Old/New object\n", + "Duration object\n", + "PPDCategory Type object\n", + "pp_sq_m float64\n", + "est_pop float64\n", + "dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dtypes\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a368cca1", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:26:46.600309Z", + "iopub.status.busy": "2025-02-16T13:26:46.599916Z", + "iopub.status.idle": "2025-02-16T13:27:54.061022Z", + "shell.execute_reply": "2025-02-16T13:27:54.059829Z" + }, + "papermill": { + "duration": 67.479754, + "end_time": "2025-02-16T13:27:54.064966", + "exception": false, + "start_time": "2025-02-16T13:26:46.585212", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "data['Date of Transfer'] = pd.to_datetime(data['Date of Transfer'])\n", + "data['Year'] = data['Date of Transfer'].dt.year\n", + "data['Month'] = data['Date of Transfer'].dt.month\n", + "data['Day'] = data['Date of Transfer'].dt.day\n", + "data.dtypes\n", + "data.to_csv('data_with_year.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "651686fd", + "metadata": { + "papermill": { + "duration": 0.043762, + "end_time": "2025-02-16T13:27:54.138750", + "exception": false, + "start_time": "2025-02-16T13:27:54.094988", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Numerical values and their features\n", + "It appears some large disparity in the data occurs in the data. A large standard deviation exists. The difference between arithmetical mean and median may indicate some large prices and area skewness to the right. However, the kustosis is only large for the prices. We will need to investigate further the presence of data in the tails for the Prices.\n", + "\n", + "The people per square metres and populations appears to be skewed to the left. A low kurtosis indicates they may not many observations in the tails. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d32fd570", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:54.288716Z", + "iopub.status.busy": "2025-02-16T13:27:54.288124Z", + "iopub.status.idle": "2025-02-16T13:27:56.720363Z", + "shell.execute_reply": "2025-02-16T13:27:56.719207Z" + }, + "papermill": { + "duration": 2.512935, + "end_time": "2025-02-16T13:27:56.722558", + "exception": false, + "start_time": "2025-02-16T13:27:54.209623", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0Area (sq km)PriceDate of Transferpp_sq_mest_popYearMonthDay
count7.853837e+067.853837e+067.853837e+0678538377.853837e+065.208853e+067.853837e+067.853837e+067.853837e+06
mean5.431394e+064.194698e+022.209744e+052008-06-24 11:25:39.1786214402.599774e+031.372225e+062.007962e+036.711091e+001.691465e+01
min1.282500e+041.631770e+011.000000e+002001-01-01 00:00:009.704739e+012.140000e+032.001000e+031.000000e+001.000000e+00
25%2.674884e+067.461320e+011.025000e+052004-03-05 00:00:009.843264e+021.340490e+052.004000e+034.000000e+009.000000e+00
50%5.370908e+061.581281e+021.550000e+052007-06-15 00:00:002.608139e+032.380160e+052.007000e+037.000000e+001.700000e+01
75%8.102541e+064.144144e+022.407510e+052013-03-08 00:00:004.011644e+034.418580e+052.013000e+031.000000e+012.500000e+01
max1.083096e+071.572031e+039.890000e+072017-06-29 00:00:005.218986e+037.322403e+062.017000e+031.200000e+013.100000e+01
std3.154738e+065.338312e+025.708082e+05NaN1.613246e+032.584126e+064.943425e+003.349538e+009.011419e+00
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Area (sq km) Price \\\n", + "count 7.853837e+06 7.853837e+06 7.853837e+06 \n", + "mean 5.431394e+06 4.194698e+02 2.209744e+05 \n", + "min 1.282500e+04 1.631770e+01 1.000000e+00 \n", + "25% 2.674884e+06 7.461320e+01 1.025000e+05 \n", + "50% 5.370908e+06 1.581281e+02 1.550000e+05 \n", + "75% 8.102541e+06 4.144144e+02 2.407510e+05 \n", + "max 1.083096e+07 1.572031e+03 9.890000e+07 \n", + "std 3.154738e+06 5.338312e+02 5.708082e+05 \n", + "\n", + " Date of Transfer pp_sq_m est_pop \\\n", + "count 7853837 7.853837e+06 5.208853e+06 \n", + "mean 2008-06-24 11:25:39.178621440 2.599774e+03 1.372225e+06 \n", + "min 2001-01-01 00:00:00 9.704739e+01 2.140000e+03 \n", + "25% 2004-03-05 00:00:00 9.843264e+02 1.340490e+05 \n", + "50% 2007-06-15 00:00:00 2.608139e+03 2.380160e+05 \n", + "75% 2013-03-08 00:00:00 4.011644e+03 4.418580e+05 \n", + "max 2017-06-29 00:00:00 5.218986e+03 7.322403e+06 \n", + "std NaN 1.613246e+03 2.584126e+06 \n", + "\n", + " Year Month Day \n", + "count 7.853837e+06 7.853837e+06 7.853837e+06 \n", + "mean 2.007962e+03 6.711091e+00 1.691465e+01 \n", + "min 2.001000e+03 1.000000e+00 1.000000e+00 \n", + "25% 2.004000e+03 4.000000e+00 9.000000e+00 \n", + "50% 2.007000e+03 7.000000e+00 1.700000e+01 \n", + "75% 2.013000e+03 1.000000e+01 2.500000e+01 \n", + "max 2.017000e+03 1.200000e+01 3.100000e+01 \n", + "std 4.943425e+00 3.349538e+00 9.011419e+00 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4611f670", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:56.754619Z", + "iopub.status.busy": "2025-02-16T13:27:56.754201Z", + "iopub.status.idle": "2025-02-16T13:27:57.357834Z", + "shell.execute_reply": "2025-02-16T13:27:57.356742Z" + }, + "papermill": { + "duration": 0.62429, + "end_time": "2025-02-16T13:27:57.360605", + "exception": false, + "start_time": "2025-02-16T13:27:56.736315", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Area (sq km) 0.622937\n", + "Price 7994.700840\n", + "pp_sq_m -1.376422\n", + "est_pop 1.475516\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols= ['Area (sq km)', 'Price', 'pp_sq_m','est_pop']\n", + "data_num = data.loc[:, cols]\n", + "data_num.kurt()" + ] + }, + { + "cell_type": "markdown", + "id": "346c6381", + "metadata": { + "papermill": { + "duration": 0.014271, + "end_time": "2025-02-16T13:27:57.388762", + "exception": false, + "start_time": "2025-02-16T13:27:57.374491", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Non-numerical values\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "511c40ad", + "metadata": { + "papermill": { + "duration": 0.013573, + "end_time": "2025-02-16T13:27:57.416395", + "exception": false, + "start_time": "2025-02-16T13:27:57.402822", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Code object\n", + "Name object\n", + "Geography object\n", + "Date of Transfer object\n", + "Property Type object\n", + "Old/New object\n", + "Duration object\n", + "PPDCategory Type object" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3ed5962d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:57.445236Z", + "iopub.status.busy": "2025-02-16T13:27:57.444854Z", + "iopub.status.idle": "2025-02-16T13:27:57.883365Z", + "shell.execute_reply": "2025-02-16T13:27:57.882187Z" + }, + "papermill": { + "duration": 0.455462, + "end_time": "2025-02-16T13:27:57.885499", + "exception": false, + "start_time": "2025-02-16T13:27:57.430037", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "135" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data.Code.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fb1dec5e", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:57.914913Z", + "iopub.status.busy": "2025-02-16T13:27:57.914563Z", + "iopub.status.idle": "2025-02-16T13:27:58.432270Z", + "shell.execute_reply": "2025-02-16T13:27:58.430474Z" + }, + "papermill": { + "duration": 0.534925, + "end_time": "2025-02-16T13:27:58.434425", + "exception": false, + "start_time": "2025-02-16T13:27:57.899500", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Unitary Authority', 'Metropolitan District',\n", + " 'Non-metropolitan District', 'Region', 'London Borough'],\n", + " dtype=object)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.Geography.unique()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "11fb7d23", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:58.464166Z", + "iopub.status.busy": "2025-02-16T13:27:58.463773Z", + "iopub.status.idle": "2025-02-16T13:27:58.822498Z", + "shell.execute_reply": "2025-02-16T13:27:58.821328Z" + }, + "papermill": { + "duration": 0.376451, + "end_time": "2025-02-16T13:27:58.824968", + "exception": false, + "start_time": "2025-02-16T13:27:58.448517", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['T', 'D', 'S', 'F', 'O'], dtype=object)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Property Type'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b3356c20", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:58.916577Z", + "iopub.status.busy": "2025-02-16T13:27:58.916135Z", + "iopub.status.idle": "2025-02-16T13:27:59.272355Z", + "shell.execute_reply": "2025-02-16T13:27:59.271400Z" + }, + "papermill": { + "duration": 0.435033, + "end_time": "2025-02-16T13:27:59.274519", + "exception": false, + "start_time": "2025-02-16T13:27:58.839486", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['N', 'Y'], dtype=object)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Old/New'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "954ab8d8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:59.305692Z", + "iopub.status.busy": "2025-02-16T13:27:59.305286Z", + "iopub.status.idle": "2025-02-16T13:27:59.663731Z", + "shell.execute_reply": "2025-02-16T13:27:59.662728Z" + }, + "papermill": { + "duration": 0.376514, + "end_time": "2025-02-16T13:27:59.665930", + "exception": false, + "start_time": "2025-02-16T13:27:59.289416", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['A', 'B'], dtype=object)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['PPDCategory Type'].unique()" + ] + }, + { + "cell_type": "markdown", + "id": "1facb4b7", + "metadata": { + "papermill": { + "duration": 0.013865, + "end_time": "2025-02-16T13:27:59.694468", + "exception": false, + "start_time": "2025-02-16T13:27:59.680603", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Is there a relationship between an estimated population and an area?\n" + ] + }, + { + "cell_type": "markdown", + "id": "90b81d05", + "metadata": { + "papermill": { + "duration": 0.014052, + "end_time": "2025-02-16T13:27:59.722789", + "exception": false, + "start_time": "2025-02-16T13:27:59.708737", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We explore the area and estimated population by Geography. Some large differences in centrality and dispersion exist. Consequently, we explore the relationship for each type of geography and across all types of geography." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "257ffd8d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:59.754561Z", + "iopub.status.busy": "2025-02-16T13:27:59.754154Z", + "iopub.status.idle": "2025-02-16T13:28:02.839270Z", + "shell.execute_reply": "2025-02-16T13:28:02.838137Z" + }, + "papermill": { + "duration": 3.104522, + "end_time": "2025-02-16T13:28:02.841491", + "exception": false, + "start_time": "2025-02-16T13:27:59.736969", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Geography\n", + "London Borough 152901\n", + "Metropolitan District 1490010\n", + "Non-metropolitan District 1384878\n", + "Region 823056\n", + "Unitary Authority 1358008\n", + "Name: est_pop, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby(['Geography']).count()['est_pop']" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a49eda45", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:28:02.872660Z", + "iopub.status.busy": "2025-02-16T13:28:02.872274Z", + "iopub.status.idle": "2025-02-16T13:28:07.238873Z", + "shell.execute_reply": "2025-02-16T13:28:07.237895Z" + }, + "papermill": { + "duration": 4.384846, + "end_time": "2025-02-16T13:28:07.241107", + "exception": false, + "start_time": "2025-02-16T13:28:02.856261", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanmin25%50%75%maxstd
Geography
London Borough227540.077.39757237.259350.464580.821186.4887150.13263.455596e+01
Metropolitan District2191246.0232.88426669.4365115.6485142.3450338.6198568.00641.518014e+02
Non-metropolitan District2135570.0219.22949421.430540.553076.6971331.32881307.93772.619611e+02
Region1261082.01572.0308001572.03081572.03081572.03081572.03081572.03081.591616e-12
Unitary Authority2038399.0154.96948416.317773.342179.8497230.09331125.82261.300433e+02
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" + ], + "text/plain": [ + " count mean min 25% \\\n", + "Geography \n", + "London Borough 227540.0 77.397572 37.2593 50.4645 \n", + "Metropolitan District 2191246.0 232.884266 69.4365 115.6485 \n", + "Non-metropolitan District 2135570.0 219.229494 21.4305 40.5530 \n", + "Region 1261082.0 1572.030800 1572.0308 1572.0308 \n", + "Unitary Authority 2038399.0 154.969484 16.3177 73.3421 \n", + "\n", + " 50% 75% max std \n", + "Geography \n", + "London Borough 80.8211 86.4887 150.1326 3.455596e+01 \n", + "Metropolitan District 142.3450 338.6198 568.0064 1.518014e+02 \n", + "Non-metropolitan District 76.6971 331.3288 1307.9377 2.619611e+02 \n", + "Region 1572.0308 1572.0308 1572.0308 1.591616e-12 \n", + "Unitary Authority 79.8497 230.0933 1125.8226 1.300433e+02 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby(['Geography']).describe()['Area (sq km)']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bc0c017d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:28:07.272382Z", + "iopub.status.busy": "2025-02-16T13:28:07.271256Z", + "iopub.status.idle": "2025-02-16T13:28:11.530741Z", + "shell.execute_reply": "2025-02-16T13:28:11.529546Z" + }, + "papermill": { + "duration": 4.277242, + "end_time": "2025-02-16T13:28:11.532919", + "exception": false, + "start_time": "2025-02-16T13:28:07.255677", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanmin25%50%75%maxstd
Geography
London Borough152901.02.548180e+05149045.0210044.0277266.0296218.0335112.060986.888813
Metropolitan District1490010.04.403575e+05176826.0266241.0422915.0513102.0984642.0237751.740921
Non-metropolitan District1384878.01.083403e+0555802.089836.0107264.0122366.0165895.023795.293115
Region823056.07.322403e+067322403.07322403.07322403.07322403.07322403.00.000000
Unitary Authority1358008.02.031151e+052140.0150334.0191202.0240954.0310088.057311.131448
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" + ], + "text/plain": [ + " count mean min 25% \\\n", + "Geography \n", + "London Borough 152901.0 2.548180e+05 149045.0 210044.0 \n", + "Metropolitan District 1490010.0 4.403575e+05 176826.0 266241.0 \n", + "Non-metropolitan District 1384878.0 1.083403e+05 55802.0 89836.0 \n", + "Region 823056.0 7.322403e+06 7322403.0 7322403.0 \n", + "Unitary Authority 1358008.0 2.031151e+05 2140.0 150334.0 \n", + "\n", + " 50% 75% max std \n", + "Geography \n", + "London Borough 277266.0 296218.0 335112.0 60986.888813 \n", + "Metropolitan District 422915.0 513102.0 984642.0 237751.740921 \n", + "Non-metropolitan District 107264.0 122366.0 165895.0 23795.293115 \n", + "Region 7322403.0 7322403.0 7322403.0 0.000000 \n", + "Unitary Authority 191202.0 240954.0 310088.0 57311.131448 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "group_by_geo = data.groupby(['Geography'])\n", + "group_by_geo.describe()['est_pop']" + ] + }, + { + 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Area (sq km)Pricepp_sq_mest_pop
Area (sq km)1.0000000.1699820.4098600.945912
Price0.1699821.0000000.1174330.335731
pp_sq_m0.4098600.1174331.0000000.618912
est_pop0.9459120.3357310.6189121.000000
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" + ], + "text/plain": [ + " Area (sq km) Price pp_sq_m est_pop\n", + "Area (sq km) 1.000000 0.169982 0.409860 0.945912\n", + "Price 0.169982 1.000000 0.117433 0.335731\n", + "pp_sq_m 0.409860 0.117433 1.000000 0.618912\n", + "est_pop 0.945912 0.335731 0.618912 1.000000" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_num.corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "568c670a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:15.429628Z", + "iopub.status.busy": "2025-02-16T13:29:15.428850Z", + "iopub.status.idle": "2025-02-16T13:29:39.447836Z", + "shell.execute_reply": "2025-02-16T13:29:39.446262Z" + }, + "papermill": { + "duration": 24.047255, + "end_time": "2025-02-16T13:29:39.453531", + "exception": false, + "start_time": "2025-02-16T13:29:15.406276", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Area in (sq km) (log 10)')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(np.log10(data_num.est_pop), np.log10(data_num['Area (sq km)']))\n", + "plt.xlabel('estimated population (log10)')\n", + "plt.ylabel('Area in (sq km) (log 10)')" + ] + }, + { + "cell_type": "markdown", + "id": "2773476a", + "metadata": { + "papermill": { + "duration": 0.017758, + "end_time": "2025-02-16T13:29:39.492841", + "exception": false, + "start_time": "2025-02-16T13:29:39.475083", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Methods\n", + "\n", + "We have applied an analytical methodology based on linear regression. We used the estimated popuplation, area in squared km, and people per square km. We remove any unknown values. \n", + "\n", + "We explored linear, polynomial, and cluster relationship between the estimated population and the areas in square meter. We mostly used a linear regression, a polynomial regression, and K-means to analyse possible clusters.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "91cb1d21", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:39.531522Z", + "iopub.status.busy": "2025-02-16T13:29:39.530415Z", + "iopub.status.idle": "2025-02-16T13:29:39.537863Z", + "shell.execute_reply": "2025-02-16T13:29:39.536632Z" + }, + "papermill": { + "duration": 0.029383, + "end_time": "2025-02-16T13:29:39.540013", + "exception": false, + "start_time": "2025-02-16T13:29:39.510630", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def extract(geography : str)-> pd.DataFrame:\n", + " rows = data.Geography.str.contains(geography)\n", + " cols = ['Area (sq km)', 'est_pop', 'pp_sq_m']\n", + " geo = data.loc[rows, cols]\n", + " geo = geo.dropna()\n", + " geo = geo.drop_duplicates()\n", + " return geo" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "cfa107e8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:39.577722Z", + "iopub.status.busy": "2025-02-16T13:29:39.577296Z", + "iopub.status.idle": "2025-02-16T13:29:39.582481Z", + "shell.execute_reply": "2025-02-16T13:29:39.581474Z" + }, + "papermill": { + "duration": 0.026602, + "end_time": "2025-02-16T13:29:39.584666", + "exception": false, + "start_time": "2025-02-16T13:29:39.558064", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def print_summary(df : pd.DataFrame) -> None:\n", + " print(df.shape) \n", + " print(df.dtypes)\n", + " print(df.describe())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "13e60de0", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:39.622916Z", + "iopub.status.busy": "2025-02-16T13:29:39.622541Z", + "iopub.status.idle": "2025-02-16T13:29:39.628626Z", + "shell.execute_reply": "2025-02-16T13:29:39.627567Z" + }, + "papermill": { + "duration": 0.027706, + "end_time": "2025-02-16T13:29:39.630594", + "exception": false, + "start_time": "2025-02-16T13:29:39.602888", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def analyse_linear(df : pd.DataFrame) -> None:\n", + " x = df[['Area (sq km)']] #numpy.linalg.solve(f.A, b)\n", + " y = df[['est_pop']]\n", + " x = sm.add_constant(x)\n", + " model = sm.OLS(y, x).fit()\n", + " print(model.summary())\n", + " print(\"---- params / coeficient -------\")\n", + " print(model.params)\n", + " print('------------p values----------')\n", + " print(model.pvalues)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "d9241b52", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:39.669101Z", + "iopub.status.busy": "2025-02-16T13:29:39.668723Z", + "iopub.status.idle": "2025-02-16T13:29:39.674014Z", + "shell.execute_reply": "2025-02-16T13:29:39.672929Z" + }, + "papermill": { + "duration": 0.027634, + "end_time": "2025-02-16T13:29:39.676177", + "exception": false, + "start_time": "2025-02-16T13:29:39.648543", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def visualise(df : pd.DataFrame)-> None:\n", + " plt.scatter(df['Area (sq km)'], df.est_pop)\n", + " plt.xlabel(\"Area (sq kn)\")\n", + " plt.ylabel(\"Estimated population\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "77dce3fe", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:39.715165Z", + "iopub.status.busy": "2025-02-16T13:29:39.714412Z", + "iopub.status.idle": "2025-02-16T13:29:39.720066Z", + "shell.execute_reply": "2025-02-16T13:29:39.718914Z" + }, + "papermill": { + "duration": 0.028122, + "end_time": "2025-02-16T13:29:39.722540", + "exception": false, + "start_time": "2025-02-16T13:29:39.694418", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def complete_analysis(geography : str) -> None:\n", + " df = extract(geography)\n", + " print_summary(df)\n", + " visualise(df)\n", + " analyse_linear(df)" + ] + }, + { + "cell_type": "markdown", + "id": "1bda7e50", + "metadata": { + "papermill": { + "duration": 0.017562, + "end_time": "2025-02-16T13:29:39.758306", + "exception": false, + "start_time": "2025-02-16T13:29:39.740744", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Analysis and interpretation" + ] + }, + { + "cell_type": "markdown", + "id": "289a5feb", + "metadata": { + "papermill": { + "duration": 0.017422, + "end_time": "2025-02-16T13:29:39.793752", + "exception": false, + "start_time": "2025-02-16T13:29:39.776330", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### London Borough" + ] + }, + { + "cell_type": "markdown", + "id": "b08834ee", + "metadata": { + "papermill": { + "duration": 0.017523, + "end_time": "2025-02-16T13:29:39.829101", + "exception": false, + "start_time": "2025-02-16T13:29:39.811578", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The null hypothesis $H_0$ suggests there is no correlation between the area and the estimated population. The alternative hypothesis $H_1$ suggests the contrary. The p-value appears to be in range $ 0.01 \\leq 0.02 \\leq 0.05$. It appears some moderate evidence to reject the null hypothesis in favour of the alternative hypothesis. It is, therefore inconclusive a linear relationship exists between an area and an estimated population in London Borough.\n", + "\n", + "\n", + "Other types of geography has no clear pattern in their data. Some strong collinearity suggests the area and estimated population are dependent variables. Therefore, no regression analysis was successfully completed." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "522d94b1", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:39.867919Z", + "iopub.status.busy": "2025-02-16T13:29:39.867551Z", + "iopub.status.idle": "2025-02-16T13:29:42.377428Z", + "shell.execute_reply": "2025-02-16T13:29:42.376219Z" + }, + "papermill": { + "duration": 2.532556, + "end_time": "2025-02-16T13:29:42.379648", + "exception": false, + "start_time": "2025-02-16T13:29:39.847092", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameArea (sq km)est_pop
3930985BARNET86.7667319481.0
3942717BEXLEY60.5784218757.0
3946217BROMLEY150.1326296218.0
3967398CROYDON86.4887335112.0
3992967ENFIELD80.8211277266.0
4018729HARROW50.4645210044.0
4041706HOUNSLOW55.9628215976.0
4052419KINGSTON UPON THAMES37.2593149045.0
4064371SUTTON43.8477181461.0
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" + ], + "text/plain": [ + " Name Area (sq km) est_pop\n", + "3930985 BARNET 86.7667 319481.0\n", + "3942717 BEXLEY 60.5784 218757.0\n", + "3946217 BROMLEY 150.1326 296218.0\n", + "3967398 CROYDON 86.4887 335112.0\n", + "3992967 ENFIELD 80.8211 277266.0\n", + "4018729 HARROW 50.4645 210044.0\n", + "4041706 HOUNSLOW 55.9628 215976.0\n", + "4052419 KINGSTON UPON THAMES 37.2593 149045.0\n", + "4064371 SUTTON 43.8477 181461.0" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = data.Geography.str.contains('London Borough')\n", + "cols = ['Name', 'Area (sq km)', 'est_pop']\n", + "data.loc[rows, cols].drop_duplicates().dropna()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "36d98203", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:42.420063Z", + "iopub.status.busy": "2025-02-16T13:29:42.419309Z", + "iopub.status.idle": "2025-02-16T13:29:45.101289Z", + "shell.execute_reply": "2025-02-16T13:29:45.100078Z" + }, + "papermill": { + "duration": 2.703926, + "end_time": "2025-02-16T13:29:45.103642", + "exception": false, + "start_time": "2025-02-16T13:29:42.399716", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(9, 3)\n", + "Area (sq km) float64\n", + "est_pop float64\n", + "pp_sq_m float64\n", + "dtype: object\n", + " Area (sq km) est_pop pp_sq_m\n", + "count 9.00000 9.000000 9.000000\n", + "mean 72.48020 244817.777778 3636.848486\n", + "std 34.38624 64545.766174 668.873753\n", + "min 37.25930 149045.000000 1973.042497\n", + "25% 50.46450 210044.000000 3611.138624\n", + "50% 60.57840 218757.000000 3859.277949\n", + "75% 86.48870 296218.000000 4000.209344\n", + "max 150.13260 335112.000000 4162.213041\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: est_pop R-squared: 0.564\n", + "Model: OLS Adj. R-squared: 0.502\n", + "Method: Least Squares F-statistic: 9.050\n", + "Date: Sun, 16 Feb 2025 Prob (F-statistic): 0.0197\n", + "Time: 13:29:44 Log-Likelihood: -108.18\n", + "No. Observations: 9 AIC: 220.4\n", + "Df Residuals: 7 BIC: 220.8\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------\n", + "const 1.427e+05 3.72e+04 3.835 0.006 5.47e+04 2.31e+05\n", + "Area (sq km) 1409.5066 468.543 3.008 0.020 301.579 2517.434\n", + "==============================================================================\n", + "Omnibus: 0.461 Durbin-Watson: 1.944\n", + "Prob(Omnibus): 0.794 Jarque-Bera (JB): 0.488\n", + "Skew: 0.368 Prob(JB): 0.783\n", + "Kurtosis: 2.128 Cond. No. 194.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "---- params / coeficient -------\n", + "const 142656.456677\n", + "Area (sq km) 1409.506611\n", + "dtype: float64\n", + "------------p values----------\n", + "const 0.006419\n", + "Area (sq km) 0.019710\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n", + " warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "complete_analysis('London Borough')\n" + ] + }, + { + "cell_type": "markdown", + "id": "13a565d7", + "metadata": { + "papermill": { + "duration": 0.018618, + "end_time": "2025-02-16T13:29:45.141616", + "exception": false, + "start_time": "2025-02-16T13:29:45.122998", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Our analysis has shown the London Borough may have a polynomial relationship of degree 3 relationship between an area and the estimated population. Our results are not statiscally significant - there is not enough observations. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "adcc95df", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:45.181160Z", + "iopub.status.busy": "2025-02-16T13:29:45.180797Z", + "iopub.status.idle": "2025-02-16T13:29:47.730968Z", + "shell.execute_reply": "2025-02-16T13:29:47.729901Z" + }, + "papermill": { + "duration": 2.572416, + "end_time": "2025-02-16T13:29:47.732929", + "exception": false, + "start_time": "2025-02-16T13:29:45.160513", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(8, 3)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "\n", + "geo = extract('London Borough')\n", + "\n", + "geo = geo.loc[geo['Area (sq km)'] < 140, :]\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "print(geo.shape)\n", + "visualise(geo)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3e2ee0ce", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:47.773276Z", + "iopub.status.busy": "2025-02-16T13:29:47.772907Z", + "iopub.status.idle": "2025-02-16T13:29:47.857266Z", + "shell.execute_reply": "2025-02-16T13:29:47.856152Z" + }, + "papermill": { + "duration": 0.107331, + "end_time": "2025-02-16T13:29:47.859514", + "exception": false, + "start_time": "2025-02-16T13:29:47.752183", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error: 26185868.997357935\n", + "Coefficient : [-766835.3079051] [[ 4.74722740e+04 -7.76448590e+02 4.31938558e+00]]\n" + ] + } + ], + "source": [ + "x = geo['Area (sq km)']\n", + "y = geo['est_pop']\n", + "poly = PolynomialFeatures(degree=3, include_bias=False)\n", + "\n", + "#reshape data to work properly with sklearn\n", + "poly_features = poly.fit_transform(x.values.reshape(-1, 1))\n", + "poly_reg_model = LinearRegression()\n", + "poly_reg_model.fit(poly_features, y.values.reshape(-1,1))\n", + "\n", + "\n", + "y_pred = poly_reg_model.predict(poly_features)\n", + "\n", + "print(\"Mean Squared Error: \" ,mean_squared_error(y.values.reshape(-1, 1),y_pred, multioutput = 'uniform_average'))\n", + "print(\"Coefficient : \", poly_reg_model.intercept_, poly_reg_model.coef_)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "363ed8a0", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:47.899487Z", + "iopub.status.busy": "2025-02-16T13:29:47.899056Z", + "iopub.status.idle": "2025-02-16T13:29:48.157672Z", + "shell.execute_reply": "2025-02-16T13:29:48.156551Z" + }, + "papermill": { + "duration": 0.281209, + "end_time": "2025-02-16T13:29:48.159905", + "exception": false, + "start_time": "2025-02-16T13:29:47.878696", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#use model to make predictions on response variable\n", + "y_predicted = poly_reg_model.predict(poly_features)\n", + "\n", + "#create scatterplot of x vs. y\n", + "plt.scatter(x, y)\n", + "\n", + "#add line to show fitted polynomial regression model\n", + "plt.plot(x, y_predicted, color='purple')" + ] + }, + { + "cell_type": "markdown", + "id": "ac8269a2", + "metadata": { + "papermill": { + "duration": 0.019655, + "end_time": "2025-02-16T13:29:48.199823", + "exception": false, + "start_time": "2025-02-16T13:29:48.180168", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Metropolitain district" + ] + }, + { + "cell_type": "markdown", + "id": "30da8335", + "metadata": { + "papermill": { + "duration": 0.019483, + "end_time": "2025-02-16T13:29:48.239288", + "exception": false, + "start_time": "2025-02-16T13:29:48.219805", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The null hypothesis $H_0$ suggests there is no correlation between the area and the estimated population. The alternative hypothesis $H_1$ suggests the contrary. The p-value appears to be in range $ 0.01 \\leq 0.02 \\leq 0.05$. It appears some moderate evidence to reject the null hypothesis in favour of the alternative hypothesis. It is, therefore inconclusive a linear relationship exists between an area and an estimated population across Metropolitain districts. The number of observations is lower than 31, and therefore more observations is required to bring some statistical significance." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "43708752", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:48.280303Z", + "iopub.status.busy": "2025-02-16T13:29:48.279882Z", + "iopub.status.idle": "2025-02-16T13:29:51.060951Z", + "shell.execute_reply": "2025-02-16T13:29:51.060031Z" + }, + "papermill": { + "duration": 2.804369, + "end_time": "2025-02-16T13:29:51.063167", + "exception": false, + "start_time": "2025-02-16T13:29:48.258798", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameArea (sq km)est_pop
110237GATESHEAD142.3391191178.0
132800NEWCASTLE UPON TYNE113.4515266241.0
201862SUNDERLAND137.4365284601.0
340360BOLTON139.7923261302.0
390312BURY99.4604180655.0
410640MANCHESTER115.6485422915.0
602278OLDHAM142.3450218537.0
637390ROCHDALE158.1281206440.0
657571SALFORD97.1974216978.0
677149STOCKPORT126.0404284557.0
722923WIGAN188.1711301453.0
885067LIVERPOOL111.8357441858.0
995898ST. HELENS136.3587176826.0
1014514WIRRAL160.9220315004.0
1146767BARNSLEY329.0776218124.0
1182952DONCASTER568.0064286900.0
1238766ROTHERHAM286.5345248349.0
1272975SHEFFIELD367.9302513102.0
1378950BRADFORD366.4197470753.0
1447613LEEDS551.7067715609.0
1572830WAKEFIELD338.6198315380.0
2168458BIRMINGHAM267.7912984642.0
2334015COVENTRY98.6390302804.0
2396909DUDLEY97.9584305052.0
2409882SOLIHULL178.2821199574.0
2438332WALSALL103.9734253333.0
2468145WOLVERHAMPTON69.4365238016.0
\n", + "
" + ], + "text/plain": [ + " Name Area (sq km) est_pop\n", + "110237 GATESHEAD 142.3391 191178.0\n", + "132800 NEWCASTLE UPON TYNE 113.4515 266241.0\n", + "201862 SUNDERLAND 137.4365 284601.0\n", + "340360 BOLTON 139.7923 261302.0\n", + "390312 BURY 99.4604 180655.0\n", + "410640 MANCHESTER 115.6485 422915.0\n", + "602278 OLDHAM 142.3450 218537.0\n", + "637390 ROCHDALE 158.1281 206440.0\n", + "657571 SALFORD 97.1974 216978.0\n", + "677149 STOCKPORT 126.0404 284557.0\n", + "722923 WIGAN 188.1711 301453.0\n", + "885067 LIVERPOOL 111.8357 441858.0\n", + "995898 ST. HELENS 136.3587 176826.0\n", + "1014514 WIRRAL 160.9220 315004.0\n", + "1146767 BARNSLEY 329.0776 218124.0\n", + "1182952 DONCASTER 568.0064 286900.0\n", + "1238766 ROTHERHAM 286.5345 248349.0\n", + "1272975 SHEFFIELD 367.9302 513102.0\n", + "1378950 BRADFORD 366.4197 470753.0\n", + "1447613 LEEDS 551.7067 715609.0\n", + "1572830 WAKEFIELD 338.6198 315380.0\n", + "2168458 BIRMINGHAM 267.7912 984642.0\n", + "2334015 COVENTRY 98.6390 302804.0\n", + "2396909 DUDLEY 97.9584 305052.0\n", + "2409882 SOLIHULL 178.2821 199574.0\n", + "2438332 WALSALL 103.9734 253333.0\n", + "2468145 WOLVERHAMPTON 69.4365 238016.0" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = data.Geography.str.contains('Metropolitan District')\n", + "cols = ['Name', 'Area (sq km)', 'est_pop']\n", + "data.loc[rows, cols].drop_duplicates().dropna()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4c68c7f4", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:51.106718Z", + "iopub.status.busy": "2025-02-16T13:29:51.106303Z", + "iopub.status.idle": "2025-02-16T13:29:54.059627Z", + "shell.execute_reply": "2025-02-16T13:29:54.058585Z" + }, + "papermill": { + "duration": 2.976993, + "end_time": "2025-02-16T13:29:54.061800", + "exception": false, + "start_time": "2025-02-16T13:29:51.084807", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(27, 3)\n", + "Area (sq km) float64\n", + "est_pop float64\n", + "pp_sq_m float64\n", + "dtype: object\n", + " Area (sq km) est_pop pp_sq_m\n", + "count 27.000000 27.000000 27.000000\n", + "mean 203.463044 326673.444444 1964.005356\n", + "std 135.911305 178115.620112 973.121974\n", + "min 69.436500 176826.000000 505.099943\n", + "25% 112.643600 218330.500000 1296.926626\n", + "50% 142.339100 284557.000000 1816.351030\n", + "75% 277.162850 315192.000000 2391.627946\n", + "max 568.006400 984642.000000 3950.956627\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: est_pop R-squared: 0.195\n", + "Model: OLS Adj. R-squared: 0.162\n", + "Method: Least Squares F-statistic: 6.041\n", + "Date: Sun, 16 Feb 2025 Prob (F-statistic): 0.0213\n", + "Time: 13:29:53 Log-Likelihood: -361.32\n", + "No. Observations: 27 AIC: 726.6\n", + "Df Residuals: 25 BIC: 729.2\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------\n", + "const 2.09e+05 5.72e+04 3.653 0.001 9.12e+04 3.27e+05\n", + "Area (sq km) 578.1413 235.222 2.458 0.021 93.692 1062.591\n", + "==============================================================================\n", + "Omnibus: 29.735 Durbin-Watson: 2.000\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 69.985\n", + "Skew: 2.155 Prob(JB): 6.35e-16\n", + "Kurtosis: 9.606 Cond. No. 444.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "---- params / coeficient -------\n", + "const 209043.053986\n", + "Area (sq km) 578.141307\n", + "dtype: float64\n", + "------------p values----------\n", + "const 0.001201\n", + "Area (sq km) 0.021256\n", + "dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geo = extract('Metropolitan District')\n", + "\n", + "geo = geo.loc[geo['Area (sq km)'] < 400, :]\n", + "geo = geo.loc[geo['est_pop'] < 9e5, :]\n", + "\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "print(geo.shape)\n", + "visualise(geo)\n" + ] + }, + { + "cell_type": "markdown", + "id": "84479113", + "metadata": { + "papermill": { + "duration": 0.021624, + "end_time": "2025-02-16T13:29:57.002764", + "exception": false, + "start_time": "2025-02-16T13:29:56.981140", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A polynomial relationship may exists between the area and the estimated population. However, it has been been established clearly in our analysis below. Some cluster of data may suggest some cluster may exist for the metropolitain district." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f81a5eeb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:57.047876Z", + "iopub.status.busy": "2025-02-16T13:29:57.047524Z", + "iopub.status.idle": "2025-02-16T13:29:57.058368Z", + "shell.execute_reply": "2025-02-16T13:29:57.057377Z" + }, + "papermill": { + "duration": 0.03662, + "end_time": "2025-02-16T13:29:57.060908", + "exception": false, + "start_time": "2025-02-16T13:29:57.024288", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error: 3820156977.9389973\n", + "Coefficient : [-1379587.1745336] [[ 5.11222610e+04 -5.86372387e+02 3.13474961e+00 -7.90235273e-03\n", + " 7.58399631e-06]]\n" + ] + } + ], + "source": [ + "x = geo['Area (sq km)']\n", + "y = geo['est_pop']\n", + "poly = PolynomialFeatures(degree=5, include_bias=False)\n", + "\n", + "#reshape data to work properly with sklearn\n", + "poly_features = poly.fit_transform(x.values.reshape(-1, 1))\n", + "poly_reg_model = LinearRegression()\n", + "poly_reg_model.fit(poly_features, y.values.reshape(-1,1))\n", + "\n", + "\n", + "y_pred = poly_reg_model.predict(poly_features)\n", + "\n", + "print(\"Mean Squared Error: \" ,mean_squared_error(y.values.reshape(-1, 1),y_pred, multioutput = 'uniform_average'))\n", + "print(\"Coefficient : \", poly_reg_model.intercept_, poly_reg_model.coef_)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "4320fd13", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:57.107100Z", + "iopub.status.busy": "2025-02-16T13:29:57.106747Z", + "iopub.status.idle": "2025-02-16T13:29:57.296671Z", + "shell.execute_reply": "2025-02-16T13:29:57.295527Z" + }, + "papermill": { + "duration": 0.216865, + "end_time": "2025-02-16T13:29:57.299152", + "exception": false, + "start_time": "2025-02-16T13:29:57.082287", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#use model to make predictions on response variable\n", + "y_predicted = poly_reg_model.predict(poly_features)\n", + "\n", + "#create scatterplot of x vs. y\n", + "plt.scatter(x, y)\n", + "\n", + "#add line to show fitted polynomial regression model\n", + "plt.plot(x, y_predicted, color='purple')" + ] + }, + { + "cell_type": "markdown", + "id": "d2c832bb", + "metadata": { + "papermill": { + "duration": 0.021727, + "end_time": "2025-02-16T13:29:57.343293", + "exception": false, + "start_time": "2025-02-16T13:29:57.321566", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A cluster analysis - KMeans - suggest 5 centroids may be suitable. The population appears to impact on the centroid over the area. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "94f19d82", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:57.389309Z", + "iopub.status.busy": "2025-02-16T13:29:57.388943Z", + "iopub.status.idle": "2025-02-16T13:29:57.897313Z", + "shell.execute_reply": "2025-02-16T13:29:57.896240Z" + }, + "papermill": { + "duration": 0.534004, + "end_time": "2025-02-16T13:29:57.899458", + "exception": false, + "start_time": "2025-02-16T13:29:57.365454", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import make_blobs\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "geo = geo.loc[data['Area (sq km)'] < 350, :]\n", + "x = geo['Area (sq km)'].values\n", + "y = geo['est_pop'].values\n", + "data_to_fit = list(zip(x, y))\n", + "inertias = []\n", + "\n", + "for i in range(2,7):\n", + " kmeans = KMeans(n_clusters=i)\n", + " kmeans.fit(data_to_fit)\n", + " inertias.append(kmeans.inertia_)\n", + "\n", + "plt.plot(range(2,7), inertias, marker='o')\n", + "plt.title('Elbow method')\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('Inertia')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "14de0ed9", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:57.945991Z", + "iopub.status.busy": "2025-02-16T13:29:57.945579Z", + "iopub.status.idle": "2025-02-16T13:29:58.186458Z", + "shell.execute_reply": "2025-02-16T13:29:58.185283Z" + }, + "papermill": { + "duration": 0.26699, + "end_time": "2025-02-16T13:29:58.188768", + "exception": false, + "start_time": "2025-02-16T13:29:57.921778", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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NameArea (sq km)est_pop
306108BARROW-IN-FURNESS77.897171960.0
319086CARLISLE1038.2969100764.0
759658BURNLEY110.684089521.0
787134CHORLEY202.7622100559.0
806848LANCASTER566.9315134049.0
............
4875912EXETER47.0330111180.0
4906479CHELTENHAM46.5961110024.0
4942597GLOUCESTER40.5530109947.0
4979207STROUD460.5422108060.0
4990633TEWKESBURY414.414476524.0
\n", + "

66 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " Name Area (sq km) est_pop\n", + "306108 BARROW-IN-FURNESS 77.8971 71960.0\n", + "319086 CARLISLE 1038.2969 100764.0\n", + "759658 BURNLEY 110.6840 89521.0\n", + "787134 CHORLEY 202.7622 100559.0\n", + "806848 LANCASTER 566.9315 134049.0\n", + "... ... ... ...\n", + "4875912 EXETER 47.0330 111180.0\n", + "4906479 CHELTENHAM 46.5961 110024.0\n", + "4942597 GLOUCESTER 40.5530 109947.0\n", + "4979207 STROUD 460.5422 108060.0\n", + "4990633 TEWKESBURY 414.4144 76524.0\n", + "\n", + "[66 rows x 3 columns]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = data.Geography.str.contains('Non-metropolitan District')\n", + "cols = ['Name', 'Area (sq km)', 'est_pop']\n", + "data.loc[rows, cols].drop_duplicates().dropna()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "ff29a701", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:01.220154Z", + "iopub.status.busy": "2025-02-16T13:30:01.219705Z", + "iopub.status.idle": "2025-02-16T13:30:04.281157Z", + "shell.execute_reply": "2025-02-16T13:30:04.280018Z" + }, + "papermill": { + "duration": 3.092493, + "end_time": "2025-02-16T13:30:04.283555", + "exception": false, + "start_time": "2025-02-16T13:30:01.191062", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(66, 3)\n", + "Area (sq km) float64\n", + "est_pop float64\n", + "pp_sq_m float64\n", + "dtype: object\n", + " Area (sq km) est_pop pp_sq_m\n", + "count 66.000000 66.000000 66.000000\n", + "mean 258.307398 103076.636364 1180.008029\n", + "std 268.691958 24246.021386 1082.819562\n", + "min 21.430500 55802.000000 97.047386\n", + "25% 46.705325 86267.750000 257.300281\n", + "50% 157.164950 100661.500000 508.837899\n", + "75% 362.460375 117088.500000 2199.312867\n", + "max 1307.937700 165895.000000 3751.569025\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: est_pop R-squared: 0.069\n", + "Model: OLS Adj. R-squared: 0.055\n", + "Method: Least Squares F-statistic: 4.767\n", + "Date: Sun, 16 Feb 2025 Prob (F-statistic): 0.0327\n", + "Time: 13:30:03 Log-Likelihood: -757.11\n", + "No. Observations: 66 AIC: 1518.\n", + "Df Residuals: 64 BIC: 1523.\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------\n", + "const 9.694e+04 4039.801 23.996 0.000 8.89e+04 1.05e+05\n", + "Area (sq km) 23.7574 10.882 2.183 0.033 2.019 45.496\n", + "==============================================================================\n", + "Omnibus: 2.722 Durbin-Watson: 1.696\n", + "Prob(Omnibus): 0.256 Jarque-Bera (JB): 2.193\n", + "Skew: 0.444 Prob(JB): 0.334\n", + "Kurtosis: 3.099 Cond. No. 517.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "---- params / coeficient -------\n", + "const 96939.931907\n", + "Area (sq km) 23.757370\n", + "dtype: float64\n", + "------------p values----------\n", + "const 1.055017e-33\n", + "Area (sq km) 3.269236e-02\n", + "dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geo = extract('Non-metropolitan District')\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "print(geo.shape)\n", + "visualise(geo)\n" + ] + }, + { + "cell_type": "markdown", + "id": "48f67d0f", + "metadata": { + "papermill": { + "duration": 0.025854, + "end_time": "2025-02-16T13:30:07.216404", + "exception": false, + "start_time": "2025-02-16T13:30:07.190550", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A cluster analysis - KMeans - suggest 5 centroids may be suitable. The population appears to impact on the centroid over the area. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "55290cfb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:07.268572Z", + "iopub.status.busy": "2025-02-16T13:30:07.268166Z", + "iopub.status.idle": "2025-02-16T13:30:07.276152Z", + "shell.execute_reply": "2025-02-16T13:30:07.274574Z" + }, + "papermill": { + "duration": 0.036654, + "end_time": "2025-02-16T13:30:07.278545", + "exception": false, + "start_time": "2025-02-16T13:30:07.241891", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "from sklearn.datasets import make_blobs\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "if False: \n", + " x = geo['Area (sq km)'].values\n", + " y = geo['est_pop'].values\n", + " data_to_fit = list(zip(x, y))\n", + " inertias = []\n", + "\n", + " for i in range(1,11):\n", + " kmeans = KMeans(n_clusters=i)\n", + " kmeans.fit(data_to_fit)\n", + " inertias.append(kmeans.inertia_)\n", + "\n", + " plt.plot(range(1,11), inertias, marker='o')\n", + " plt.title('Elbow method')\n", + " plt.xlabel('Number of clusters')\n", + " plt.ylabel('Inertia')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "455cd9ee", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:07.331454Z", + "iopub.status.busy": "2025-02-16T13:30:07.330576Z", + "iopub.status.idle": "2025-02-16T13:30:07.578241Z", + "shell.execute_reply": "2025-02-16T13:30:07.577171Z" + }, + "papermill": { + "duration": 0.275936, + "end_time": "2025-02-16T13:30:07.580171", + "exception": false, + "start_time": "2025-02-16T13:30:07.304235", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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aNFFUVCT27t0rwsLCxMqVK5War7/+Wvj7+4tly5aJ8+fPi/Xr1wutViv27t2r1GzevFno9XqRk5Mjzp07J1588UURFBTkdJVdR710hFfDERER9Tyu/v7u8gDvjrz11lvQaDSYOXMmmpqakJ6ejj/84Q/Kcq1Wi507d+Kll16CyWRCv379MHfuXLzxxhtKzdChQ7Fr1y4sXboUa9euxaBBg/D+++8jPT1dqZk9ezauXLmCVatWwWw2IykpCXv37nUa9N1RL0REREQdkYQQwtNNeCur1YrAwEBYLBaekiMiIuohXP39zXvDEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRii6FpQ0bNmDMmDEwGo0wGo0wmUzYs2ePsvyhhx6CJElOjwULFjhto7y8HBkZGfD390d4eDiWLVuGlpYWp5qDBw9i7Nix0Ov1iIuLQ05Ozm29rF+/HkOGDIGfnx9SU1Nx9OhRp+WNjY3IzMxESEgIAgICMHPmTFRWVnbl7RIRERF1LSwNGjQIb775JgoLC3H8+HE8/PDDePzxx3H27Fml5oUXXsDly5eVx5o1a5RldrsdGRkZsNlsOHz4MDZu3IicnBysWrVKqSkrK0NGRgamTJmCoqIiLFmyBPPnz8e+ffuUmi1btiArKwurV6/GiRMnkJiYiPT0dFRVVSk1S5cuxY4dO7Bt2zbk5+ejoqICM2bMuKOdRERERH2YuEsDBgwQ77//vhBCiMmTJ4vFixe3W7t7926h0WiE2WxWXtuwYYMwGo2iqalJCCHEq6++Ku677z6n9WbPni3S09OV5ykpKSIzM1N5brfbRVRUlMjOzhZCCFFTUyN8fX3Ftm3blJrz588LAKKgoKDT781isQgAwmKxdHodIiIi8ixXf3/f8Zglu92OzZs3o66uDiaTSXn9o48+QmhoKBISErBy5UrU19crywoKCjB69GhEREQor6Wnp8NqtSpHpwoKCpCWlub0s9LT01FQUAAAsNlsKCwsdKrRaDRIS0tTagoLC9Hc3OxUM3LkSMTExCg1bWlqaoLVanV6EBERUd/m09UVTp8+DZPJhMbGRgQEBGD79u2Ij48HADz99NMYPHgwoqKicOrUKSxfvhzFxcX45JNPAABms9kpKAFQnpvNZtUaq9WKhoYGXL9+HXa7vc2aCxcuKNvQ6XQICgq6rab157QlOzsbr7/+ehf3CBEREfVmXQ5LI0aMQFFRESwWC/70pz9h7ty5yM/PR3x8PF588UWlbvTo0Rg4cCCmTp2K0tJSxMbGurTx7rBy5UpkZWUpz61WK6Kjoz3YEREREXlal0/D6XQ6xMXFITk5GdnZ2UhMTMTatWvbrE1NTQUAlJSUAAAiIyNvuyKt9XlkZKRqjdFohMFgQGhoKLRabZs1N2/DZrOhpqam3Zq26PV65Uq/1gcRERH1bXc9z5LD4UBTU1Oby4qKigAAAwcOBACYTCacPn3a6aq13NxcGI1G5VSeyWRCXl6e03Zyc3OVcVE6nQ7JyclONQ6HA3l5eUpNcnIyfH19nWqKi4tRXl7uNL6KiIiIqENdGQ2+YsUKkZ+fL8rKysSpU6fEihUrhCRJ4i9/+YsoKSkRb7zxhjh+/LgoKysTn376qRg2bJiYNGmSsn5LS4tISEgQ06ZNE0VFRWLv3r0iLCxMrFy5Uqn5+uuvhb+/v1i2bJk4f/68WL9+vdBqtWLv3r1KzebNm4Verxc5OTni3Llz4sUXXxRBQUFOV9ktWLBAxMTEiP3794vjx48Lk8kkTCZTl0a/82o4IiKinsfV399dCkvPP/+8GDx4sNDpdCIsLExMnTpV/OUvfxFCCFFeXi4mTZokgoODhV6vF3FxcWLZsmW3NfrNN9+I6dOnC4PBIEJDQ8Urr7wimpubnWoOHDggkpKShE6nE8OGDRMffvjhbb288847IiYmRuh0OpGSkiKOHDnitLyhoUG8/PLLYsCAAcLf3188+eST4vLly115uwxLREREPZCrv78lIYTw7LEt72W1WhEYGAiLxcLxS0RERD2Eq7+/eW84IiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERF5hM1uhxDC0210yMfTDRCRazmEwBfffoMvv/8OAgLjowZh8uAh0Gr4uxEReV6dzYackyfwn6eKUFVXB51Wi4zhI/BPyeNxb0iop9trkyR6QqTzEKvVisDAQFgsFhiNRk+3Q9Shr69fwws7/oyymuvw+Xs4anE4EG0MxPs/fhLDQ0I83CER9WW1TU146pMtuHD1Khw3xQ+tJMFHo8HGJ2Yh5Z5Bd/1zXP39zV81iXoJS2Mjnv7vrSi31ACQQ1KLwwEAqKi14qlPtuBaQ70HOySivu6tLw+j+JagBAB2IdDscCBz9w402+0e6q59DEtEvcS2c2dwpb4O9jYOFtuFQE1jIzafOe2BzoiIgIbmZmw5c7rNf6MAeQhBdUM9cr8udXNnHWNYIuoldvztAtTOqTuEwI6/XXBbP0REN/vOakVDS7NqjY9Gg/NXq9zUUecxLBH1ErW2po5rmjquISLqDjqttsMaIQT0Wu+79oxhiaiXGB4cAq0ktbtcK0mIC+YAbyLyjJjAQAwJCkL7/0rJQwYeHjrMbT11FsMSUS/xdEJiu2MBAPkfoZ+MSXRjR0REP5AkCQvHT2h3uIBWkjAxOgbxYeFu7aszGJaIeolJg4dgxsj4dpc/NnwEHh4a68aOiIiczRh1H7ImTIQEORxJkJQj4mMiIrFu+o8922A7OM+SCs6zRD2NQwjkFJ3A+18dh/nGDQBAeL9+mHd/Mp5PSubElETkFb6tqcHWc6fxTU0NAnQ6PDZ8BCbGDIZGZShBV7j6+5thSQXDEvVUdocDFbW1EBC4p7+RIYmI+hRXf39735BzIrprWo0G0YGBnm6DiKhX4K+bRERERCp4ZIk6rc5mw+azp7H5zCmYb9xAiMGA/3VfAp4ZnYggP4On2yMiIuoWHLOkgmOWfnC9oQFz/nsLSq5VA4By6adGkhAZEICts+Ygqn/f3kdEROQdeCNd8ojVB/Pw9fVrEIDTHBkOIVB54wZe+cseT7VGnSUEUF8P1NTIf/L3JCKiTuFpOOrQlbo67C752213iW5lFwJffv8dLlZXY3gIZ4j2Oo2NQFERcOgQUFICtLQAPj5AXBzwwANAUhLg5+fpLomIvBbDEnXodFVlu0HpZh+fOYlVkx92Q0fUaSUlwAcfAKWlgCQBISFAv35AczNw7Bhw9CgQGwvMmyeHJw8SQuCEuQLf1tTAqNdjYvRgGHx9PdoTERHAsESdoHa/sZttOXsayydOgt6HHyuvUFICrF0LmM3A8OGATue8PDwcsNmAixflusWLPRaYjlV8hxWf/QVlNdeV1wJ0OixKmYD594+D5KKJ6oiI7gTHLFGHxg6Mgq4Tkxo2tLRgT8lFN3REHWpslI8omc1AfPztQamVTicvN5vl+sZG9/YJ4GSlGT/5ZBu+tdQ4vX7DZkP2oc+x7tgRt/dERHSzLoWlDRs2YMyYMTAajTAajTCZTNiz54eBvY2NjcjMzERISAgCAgIwc+ZMVFZWOm2jvLwcGRkZ8Pf3R3h4OJYtW4aWlhanmoMHD2Ls2LHQ6/WIi4tDTk7Obb2sX78eQ4YMgZ+fH1JTU3H06FGn5Z3phTqnv16Px+4d2WGdj0aDi9euuqEj6lBRkXzqbfhw+fSbGkmSjyiVlgInT7qlvZv99q+fwy5Eu6d63zl6BNca6t3cFRHRD7oUlgYNGoQ333wThYWFOH78OB5++GE8/vjjOHv2LABg6dKl2LFjB7Zt24b8/HxUVFRgxowZyvp2ux0ZGRmw2Ww4fPgwNm7ciJycHKxatUqpKSsrQ0ZGBqZMmYKioiIsWbIE8+fPx759+5SaLVu2ICsrC6tXr8aJEyeQmJiI9PR0VFVVKTUd9UJd8/L41A5rhBAw+HCMiccJIQ/mlqT2jyjdSq+X67/4wq1XyVXeuIHD311SHRNndziw6+Lf3NYTEdGt7nqepeDgYPz2t7/FrFmzEBYWhk2bNmHWrFkAgAsXLmDUqFEoKCjAhAkTsGfPHjz22GOoqKhAREQEAODdd9/F8uXLceXKFeh0Oixfvhy7du3CmTNnlJ8xZ84c1NTUYO/evQCA1NRUjB8/HuvWrQMAOBwOREdHY9GiRVixYgUsFkuHvXQG51n6gRACj276I/5WfRVqH5g9z8zFiJBQt/VFbaivB7KyAI1GHpfUWVVVgMMB/O53gL9/9/V3k3NXqvDYx/+pWuOj0eClcSlYOmGiW3oiop7Pa+ZZstvt2Lx5M+rq6mAymVBYWIjm5makpaUpNSNHjkRMTAwKCgoAAAUFBRg9erQSlAAgPT0dVqtVOTpVUFDgtI3WmtZt2Gw2FBYWOtVoNBqkpaUpNZ3ppS1NTU2wWq1OD5JJkoSfp5raDUoaScKUIUMZlLyBzSZPD9DVK8l8fOT1bLbu6asNYf790NHQbbvDgYh+AW7ph4ioLV0OS6dPn0ZAQAD0ej0WLFiA7du3Iz4+HmazGTqdDkFBQU71ERERMJvNAACz2ewUlFqXty5Tq7FarWhoaMDVq1dht9vbrLl5Gx310pbs7GwEBgYqj+jo6M7tlD5iety9+JfJD0MrSdBIEnwkDXz+PvD7HwbFYO0jj3m4QwIgn3rz8ZGnB+iK1vmXOnvqzgXC+vXDAzGDVa+49NVqkTF8hNt6IiK6VZev8R4xYgSKiopgsVjwpz/9CXPnzkV+fn539OZ2K1euRFZWlvLcarUyMN3ip4n3Y3rcvfjv82fxTc11BOj0yBh+L5IiB/Lybm9hMMgDto8d69ppuOpqYPx4eX03WjFxEmZs/RjCYW9z7FLWhIkI5KSZRORBXQ5LOp0OcX+fiyU5ORnHjh3D2rVrMXv2bNhsNtTU1Dgd0amsrERkZCQAIDIy8rar1lqvULu55tar1iorK2E0GmEwGKDVaqHVatusuXkbHfXSFr1eD71e34W90TeF9euHBeNSPN0GtUeS5Jm5jx6VT6l15khRU5M8sPvBBzu+es7FRoWFY/Os2Xhtfy7OXvnhIo0BfgYsnfAP+MmYJLf2Q0R0q7ueZ8nhcKCpqQnJycnw9fVFXl6esqy4uBjl5eUwmUwAAJPJhNOnTztdtZabmwuj0Yj4+Hil5uZttNa0bkOn0yE5OdmpxuFwIC8vT6npTC9EvVpSkjwz98WLHV/dJoQ8gWVsLJCY6Jb2bpUYEYkdTz2LnU89i/WP/hh/fGIWCub9E4MSEXmFLh1ZWrlyJaZPn46YmBjU1tZi06ZNOHjwIPbt24fAwEDMmzcPWVlZCA4OhtFoxKJFi2AymZSrz6ZNm4b4+Hg8++yzWLNmDcxmM1577TVkZmYqR3QWLFiAdevW4dVXX8Xzzz+P/fv3Y+vWrdi1a5fSR1ZWFubOnYtx48YhJSUFb7/9Nurq6vDcc88BQKd6IerV/PzkW5isXQucOyeflmvrqGlTkxyUIiOB+fM9fo+4+LBwxId14dQhEZEbdCksVVVV4ac//SkuX76MwMBAjBkzBvv27cOPfvQjAMBbb70FjUaDmTNnoqmpCenp6fjDH/6grK/VarFz50689NJLMJlM6NevH+bOnYs33nhDqRk6dCh27dqFpUuXYu3atRg0aBDef/99pKenKzWzZ8/GlStXsGrVKpjNZiQlJWHv3r1Og7476oWo14uLk29hcuu94Vqvequulo8qxcXJQSk21tMdExF5pbueZ6k34zxL1Cs0Nsozc3/xhXwUqfWqt7g4eYxSYqLHjygREbmSq7+/ecdTot7Ozw9ITQVSUoCGhh8GfRsMbh/MTUTUEzEsEfUVkiTPzO2m2bmJiHqLu74ajoiIiKg3Y1giIiIiUsGwRERERKSCYYmIiIhIBcMSERERkQqGJaK70NaNX4mIqHfh1AHUJ9Q3N2PT6ZP4+MwpXL5RiyA/P8wcdR9+mng/wvz7dWlbDc3N2HjyK3x0+iS+r7XC4OODH987Ei8mj8ewAcHd9A6IiMhTOIO3Cs7g3TtYmxox57+3ovjqFQBA6wdeI0kINhiwddYcDAka0Klt1dlsePqTrThTVYmb/+JoJQm+Wi3++MQsjIu6x7VvgIiIusTV3988DUe93q8/P4iL1VchAKeA4xAC1xsa8PM9Ozu9rbeOHMbZK1W49TcMuxCw2e3I3L0DzXa7K9omIiIvwbBEvdr1hgb8ufg87O0cQLULgTNXqnCy0tzhthpbmrH57Kl2xyk5hMCV+jrklX19Vz0TEZF3YViiXq24+ipaHA7VGgnASfPlDrd1yWJFfXOzao2PRoOzVyq70iIREXk5hiXq1bSazt0oVqvp+K+Cr7bjGiEEdFptp34mERH1DAxL1KuNDo9AgE7XYd0D0YM7rBkcGISYwECoxS+7EJgyZFgXOiQiIm/HsES9mp+PL55LGttuwNFKEtKGxWJwUFCH25IkCZnjJ9w2uPvmbU24JxoJ4RF32i4REXkhhiXq9RalmPDje0cCkAMNIE8bAABjIiLx2x9N7/S2Zo26D4tSJjhtq/XP+8LCsf7RH7usbyIi8g6cZ0kF51nqPYQQOFbxPbaePY1LVgtC/P3x5Ih4TBk6DD6dGK90q9Jr1dhy9jS+tdSgv06PjHtHYFLMkE6NfSIiou7l6u9vhiUVDEtEREQ9DyelJCIiInIjhiUiIiIiFQxLRERERCoYloiIiIhUMCwRERERqWBYIiIiIlLBsERERESkgmGJiIiISAXDEhEREZEKhiUiIiIiFQxLRERERCoYloiIiIhU+Hi6AaJbCSFQdOAMcv+Yj6vfVyP0nhD86KeTkTQlAZIkebo9IiLqYxiWyKvYGm1443//Dl/uLIRGq4HD7oBGq0HuH/Mx4cfJ+NXWV6DT+3q6TSIi6kN4Go68yruvbMTR3ScAAA67w+nPL3edwL//4o8e642IiPomhiXyGtbqWux5Pw/CIdpcLhwCu/7jM9Rev+HmzoiIqC9jWCKvcTL/HFqa7ao1LbYWnMo/56aOiIiIGJbIi7TYWlxaR0RE5Aoc4E1e495xwzpZF9vNnXSdrdGGgv85jstlVTAGB2DikykIDDV6ui0iInIBhiXyGvfEDUTyj8ag6MAZ2Fscty3X+GgwdupoDBwW4YHu2pe/9TDeXvDvuFFTB62PBna7A+8sfB+zXvlHPPfrOdBoeACXiKgn47/i5FVe+eBlhEQFQ6N1/mhqtBqE3ROCVz542UOdte3LXYX49VNv4YalDgDkkCeAlmY7Nr+5HR++ttnDHRIR0d1iWCKvEjYoBBsK1+Anr81C6KBg+PhqETYoBD/51Sz84fj/h9CoYE+3qBBC4P2VH0GCBLR9AR/+9G//A8tVq3sbIyIil5KEEO38M09WqxWBgYGwWCwwGjn+hJyVX/ge8+KXqBdJwJINLyLjxR+5pSciInL99zePLBHdodprHc/3pNFqOlVHRETei2GJ6A5FDA4FOrhVnaPFgcih4e5piIiIugXDEtEdCr0nBON+lHjbYHSFBAQE+eMfJt8L1NcDPONNRNQjMSwR3YUFb/0Mfv30twUmSQIggJ+P1UD32kogKwv4t38DjhwBGhs90ywREd0RhiWiuzB41CD8/vBvMDZtjNMpucH9gV8/IGHKvQagXz9AowGOHQN+9zvg9deBkhLPNU1ERF3Cq+FU8Gq4OyeEwNm/XlBmtL5/6mjo/HSebqtbXTlUiMq3/x39a68hZmwcJL3+9iKbDbh4EYiMBBYvBuLi3N8oEVEv5+rvb87gTS5XdOAM3nrxXVSUViqvBQT1w8/+zxz848vpkKQORkX3RI2NCNv1J4RJ14AJ8X8/D9cGnQ6IjwfOnQM++ABYvRrw83Nvr0RE1CU8DUcudebQeaxI/zUul1U5vX6jpg7rFn2A/35rp4c662ZFRUBpKTB8ePtBqZUkyUeUSkuBkyfd0h4REd05hiVyqX9/9T8hHA4IR9tnd3N+tRl11no3d9XNhAAOHZJDkK6Tpxr1ern+iy94lRwRkZdjWCKXufx1Jc4fuQhHO0EJAJoabTj0yZdu7MoNGhrkAdshIV1bLyREXq+hoXv6IiIil2BYIpe5Zq7psEaj1eB6J+p6FJsNaGkBfH27tp6Pj7yezdY9fRERkUt0KSxlZ2dj/Pjx6N+/P8LDw/HEE0+guLjYqeahhx6CJElOjwULFjjVlJeXIyMjA/7+/ggPD8eyZcvQ0tLiVHPw4EGMHTsWer0ecXFxyMnJua2f9evXY8iQIfDz80NqaiqOHj3qtLyxsRGZmZkICQlBQEAAZs6cicrKytu2Q64RPDCowxpHiwMhXnQzXJfQ6eTg09zctfVaWuT1OnvqjojczuFwoKGuEbxwvG/rUljKz89HZmYmjhw5gtzcXDQ3N2PatGmoq6tzqnvhhRdw+fJl5bFmzRplmd1uR0ZGBmw2Gw4fPoyNGzciJycHq1atUmrKysqQkZGBKVOmoKioCEuWLMH8+fOxb98+pWbLli3IysrC6tWrceLECSQmJiI9PR1VVT8MLF66dCl27NiBbdu2IT8/HxUVFZgxY0aXdxJ1zsChEUh4YCQ0mvYHOPv10+OBGSlu7MoNDAZ5wHZ1ddfWq66W1zMYuqcvIrpj35dcxu9e2IAfB/wE/9j/WTwZ/DO894s/4nqVxdOtkQfc1TxLV65cQXh4OPLz8zFp0iQA8pGlpKQkvP32222us2fPHjz22GOoqKhAREQEAODdd9/F8uXLceXKFeh0Oixfvhy7du3CmTNnlPXmzJmDmpoa7N27FwCQmpqK8ePHY926dQDk9B8dHY1FixZhxYoVsFgsCAsLw6ZNmzBr1iwAwIULFzBq1CgUFBRgwoQJHb4/zrPUdee/vIisyavgaLG3OXZp4Tvz8HjmIx7orJsdOSJPOBkX17kjRU1N8tVwr7wCpKZ2f39E1GmlJ79B1uRVaKpvgr3Fobyu0WoQEjUAvz/8G4Te08UxiuRWrv7+vqsxSxaLnLCDg51Pq3z00UcIDQ1FQkICVq5cifr6H65+KigowOjRo5WgBADp6emwWq04e/asUpOWlua0zfT0dBQUFAAAbDYbCgsLnWo0Gg3S0tKUmsLCQjQ3NzvVjBw5EjExMUrNrZqammC1Wp0e1DWjUofjt3mrET1qkNPrQeGByPqPBb0zKAFAUhIQGytPONnR7x9CyAO7Y2OBxES3tEdEnSOEwL8+/TYa65yDEgA47A5UX76OdYv+r4e6I0+540kpHQ4HlixZgokTJyIhIUF5/emnn8bgwYMRFRWFU6dOYfny5SguLsYnn3wCADCbzU5BCYDy3Gw2q9ZYrVY0NDTg+vXrsNvtbdZcuHBB2YZOp0NQUNBtNa0/51bZ2dl4/fXXu7gn6FYJE0fiP079G/5W+DXMZVXoHxyAMZNGwce3F8+B6ucHzJsHrF0rTzgZFydPD3CrpiY5KEVGAvPnc0JKIi9z9nAxys9/3+5yR4sDh//nGK5+X82jS33IHX97ZWZm4syZMzh06JDT6y+++KLy36NHj8bAgQMxdepUlJaWIjY29s47dYOVK1ciKytLeW61WhEdHe3BjnouSZIwYlwsRozz7v/nLhUXJ9/C5IMP5FNskiRPD9B61Vt1tXxUKS5ODkpe/veBqC/6+uS3kCT1A8TCIfDtue8YlvqQOwpLCxcuxM6dO/H5559j0KBBqrWpfx+PUVJSgtjYWERGRt521VrrFWqRkZHKn7detVZZWQmj0QiDwQCtVgutVttmzc3bsNlsqKmpcTq6dHPNrfR6PfRtHQ0g6qy4OPkWJidPyhNOlpQA9fVyYBo/HnjwQfnUG48oEXklnUHXqXlie/u9LslZl8KSEAKLFi3C9u3bcfDgQQwdOrTDdYqKigAAAwcOBACYTCb85je/QVVVFcLDwwEAubm5MBqNiI+PV2p2797ttJ3c3FyYTCYAgE6nQ3JyMvLy8vDEE08AkE8L5uXlYeHChQCA5ORk+Pr6Ii8vDzNnzgQAFBcXo7y8XNkOUbfw85MHbaekyBNO2mzyoG+DoeNboRCRR41/JAkarQYOu6PdGmNIf4xM5U2w+5IuhaXMzExs2rQJn376Kfr376+M/QkMDITBYEBpaSk2bdqERx99FCEhITh16hSWLl2KSZMmYcyYMQCAadOmIT4+Hs8++yzWrFkDs9mM1157DZmZmcpRnQULFmDdunV49dVX8fzzz2P//v3YunUrdu3apfSSlZWFuXPnYty4cUhJScHbb7+Nuro6PPfcc0pP8+bNQ1ZWFoKDg2E0GrFo0SKYTKZOXQlHdNckCfD3lx9E1COEDByA9J89hL0fHmj3tk2zX30cvrouTkJLPZvoAgBtPj788EMhhBDl5eVi0qRJIjg4WOj1ehEXFyeWLVsmLBaL03a++eYbMX36dGEwGERoaKh45ZVXRHNzs1PNgQMHRFJSktDpdGLYsGHKz7jZO++8I2JiYoROpxMpKSniyJEjTssbGhrEyy+/LAYMGCD8/f3Fk08+KS5fvtzp92uxWASA2/onIqLeq6mhSayesUakSbNEuu9sMc3nf4t03/8t0qRZ4g9LPhQOh8PTLVIHXP39fVfzLPV2nGeJiKjvKj5Wgs/+83PUXLUiIiYU6c9NQfSIezzdFnWCq7+/e/G13ERERHduxPg4jBjPsUnEG+kSERERqWJYIiIiIlLB03BuZLfbceDjv+LTdXvwzdlL0Bl0mDzLhBlLMjDo3ihPt0dERERt4ABvFa4cIGa32/GbOW/hi//+EhqNpNxkVuujgcZHi9/sXIn7Hx7tiraJiIj6NK+6kS513v+s34dDn8gzlztumrvD3uJAi60Fr8/8/9FQ1+ip9oiIiKgdDEtuIITAJ2t3QaDtg3jCIVBnqcfBzX91c2dERETUEYYlN6i9fgPmsiq0k5UAAFofLc4fuei+poiIiKhTGJbcQOuj7WQd/3cQERF5G347u0E/oz/uHRcLjab9m6jaW+wY/8j9buyKiIiIOoNhyU3mLH/CaWD3zTRaDQYOi0Bqxlg3d0VEREQdYVhykwdnTsDP/s8cAIDm76fbJEk+0hQSNQDZe/+506friIiIyH04z5KK7riRbtmZcux6Lxdfn/4Whn5+eGBGKh6aMxGGfn4u2T4REVFf5+rvb4YlFd0RloiIiKh7cVJKIiIiIjdiWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCh9PN0DUW9W11OG7hu+hhQYx/QZDp/H1dEtERHQHGJaIXKy+pR4fl2/F4erDaBF2AIC/1h/pkT/CP0Y9Bo3EA7pE1LNdqv8OB6vycbnRDIPWgJTg8UgecD98NL0zVvTOd0Xdwi7sKLtRhkZHEwb6RSJEH+LplrxOk70J2RfW4Lv67+GAQ3m93l6P7d9/iqrGKrwwbB4kSfJgl0REd0YIgT999wl2Xt4NDTRwwAEJEo5fL0SUXxSWj/wFgnSBnm7T5RiWqENCCOyvOog/f/8/sLZYlddHBybgp4N/gnC/MA92510OXMnHpfrvICDaXP7X6gI8FD4Z9/Yf7ubOiIju3hdXD2Hn5d0AoPxC2PrvnbnRjN9fXIdfxf+y1/1CyPMB1KEdl3fhj9/+l1NQAoCzlnN449yvcbXpqoc68z4Hqg62G5QAQAMN8q984caOiIhcQwiBHRW7213ugAOldV+j9EapG7tyD4YlUlVjq8H27z5tc5kDDtS11OPP3+9wc1fe65rtmupyBxyoaqxyUzdERK5z1VaNqib1f7800OCU5YybOnIfhiVS9dfqAtUjJQ44UFB9BE32Jjd25b38tf1Ul2uggdHX6KZuvIAQQH09UFMj/yna/ywRkXezO1o6rJEkCS2i47qehmOWSFV10zVoJA3sf7+qqy0togU3Wm5Ar9W7sTPv9GDoROy6vMdpcPfNHHBgYqjJzV15QGMjUFQEHDoElJQALS2Ajw8QFwc88ACQlAT4+Xm6SyLqglB9KPy1/qi317dbYxd2DO03xH1NuQnDEqnq7xMA0cHRAAkS/H383dSRd0uLmIqDVz5HXUvdbYFJAw2G9BuCxKAxHurOTUpKgA8+AEpLAUkCQkKAfv2A5mbg2DHg6FEgNhaYN08OT0TUI/hofDA1fAp2Xt7d5hkHCRKMvkbcH5Tk/ua6GU/DkSpT6IR2j5IAcgC4PygJBq3BjV15ryBdIF4btQL3GKIAyP94SJCvChkTNBq/GLEUWknryRa7V0kJsHat/GdcHDBqFBAeDgwYIP85apT8+s11RNRj/OM9j7V5Na8GGug0Ovx8eGavnGtJEh0dNujDrFYrAgMDYbFYYDT2oXEmt/i/ZRvx+ZUvbvtNQoIEH8kHq+77Z8T4R3uoO+8khEDpjVJ8XVcGjaRFQuB9iPSL8HRb3auxEXj9dTkAxcfLR5XaIwRw7pwcnFav5ik5oh6k2dGM/CtfIK9yP6qarkCv0WNCSCrSI3+ECL9wT7cHwPXf370v/pHLzR3yE/hp9Pisar/T2KVQfQheHDafQakNkiQhrn8c4vr3odNMRUXyqbfhw9WDEiAvj4uT60+eBFJT3dIiEd09X40v0iIeRlrEw55uxW0YlqhDWkmLpwfPwY+jMnCy5hQaHU2IMgzEyP4jeOsOkgkhD+aWJECn69w6er1c/8UXQEpKxwGLiMhDGJao0/r79scDYRM93QZ5o4YG+fRbSBdvgRMSIq/X0AD48yIBIvJOPCxARHfPZpOnB/D17dp6Pj7yejZb9/RFROQCDEtEdPd0Ojn4NDd3bb3W+Zc6e+qOiMgDeBqOqAersdUgr+oADl89gnp7PSL9IvBw+EMwhUxw7+W7BoM8YPvYMXmKgM6qrgbGj5fXJyLyUjyyRNRDfVf/PX55ehV2VuzGVdtV1NvrUVb3Dd4v+xC/+9taNDu6eJTnbkiSPDO3EJ0/pdbUJNc/+CAHdxORV2NYIuqBHMKBtRffQYO9wWnS0Na5sM5Zz+PTCjff4DgpSZ6Z++LFju8BJ4Q8sDs2FkhMdEt7RER3imGJqAc6azmHqqYr7c6uLiCQV7nfvUeX/PzkW5hERsoTTja1c3PlpiZ5eWQkMH8+J6QkIq/HMUtEPVDJjVJooFG9FU29vQGVjZUY5D/IfY3FxQGLF99+b7jWq96qq+WjSnFxclCKjXVfb0REd4hhiagH6uxkoB6ZNLT1FiYnT8oTTpaUAPX1cmAaP14eo5SYyCNKRNRjMCwR9UD3GUfhk+//rFoT5BuESL9I9zR0Kz8/+RYmKSnyhJM2mzw9gMHAwdxE1ONwzBJRDxQbEIuh/YZAo/JXePrAdM/fjkaS5Jm5g4LkPxmUiKgHYlgi6oEkScLPhy9EmD5Mfg45hLSGp8lhD2JaRJpbe7ILO8rrL6Gs7hs02dsZ3E1E1AN1KSxlZ2dj/Pjx6N+/P8LDw/HEE0+guLjYqaaxsRGZmZkICQlBQEAAZs6cicrKSqea8vJyZGRkwN/fH+Hh4Vi2bBlaWlqcag4ePIixY8dCr9cjLi4OOTk5t/Wzfv16DBkyBH5+fkhNTcXRo0e73AtRTxWsG4Bfj/4XzBv6HOKNozDEfzBSQ1KwcuSreG7IXLcdVXIIB3Zf3oslX/0CvzrzL/iXs/8Hi75aio++3czQRES9Qpf+Nc3Pz0dmZiaOHDmC3NxcNDc3Y9q0aairq1Nqli5dih07dmDbtm3Iz89HRUUFZsyYoSy32+3IyMiAzWbD4cOHsXHjRuTk5GDVqlVKTVlZGTIyMjBlyhQUFRVhyZIlmD9/Pvbt26fUbNmyBVlZWVi9ejVOnDiBxMREpKeno6qqqtO9EPV0Oo0Ok8IewKsjX8HrCauwIPYFjDSOgOSm011CCOR880dsubQN1har8nqTowm5lZ9hTfG/webO6QuIiLqBJERHs8e178qVKwgPD0d+fj4mTZoEi8WCsLAwbNq0CbNmzQIAXLhwAaNGjUJBQQEmTJiAPXv24LHHHkNFRQUiIiIAAO+++y6WL1+OK1euQKfTYfny5di1axfOnDmj/Kw5c+agpqYGe/fuBQCkpqZi/PjxWLduHQDA4XAgOjoaixYtwooVKzrVS0esVisCAwNhsVhgNBrvdDcR9VoXa0vw6/PZqjU/HfwMpkY87KaOiIhc//19V8fpLRYLACA4OBgAUFhYiObmZqSl/TBWYuTIkYiJiUFBQQEAoKCgAKNHj1aCEgCkp6fDarXi7NmzSs3N22itad2GzWZDYWGhU41Go0FaWppS05lebtXU1ASr1er0IKL25V/5XHWQOQDsrzronmaIiLrJHYclh8OBJUuWYOLEiUhISAAAmM1m6HQ6BAUFOdVGRETAbDYrNTcHpdblrcvUaqxWKxoaGnD16lXY7fY2a27eRke93Co7OxuBgYHKIzo6upN7g6hvMjdWqk6MCQBXmq66qRsiou5xx2EpMzMTZ86cwebNm13Zj0etXLkSFotFeVy6dMnTLRF5tQCfAOVKvPYYtAY3dUNE1D3uKCwtXLgQO3fuxIEDBzBo0A+3UoiMjITNZkNNTY1TfWVlJSIjI5WaW69Ia33eUY3RaITBYEBoaCi0Wm2bNTdvo6NebqXX62E0Gp0eRNQ+U0iqcvPetmigwcTQf3BjR0RErtelsCSEwMKFC7F9+3bs378fQ4cOdVqenJwMX19f5OXlKa8VFxejvLwcJpMJAGAymXD69Gmnq9Zyc3NhNBoRHx+v1Ny8jdaa1m3odDokJyc71TgcDuTl5Sk1nemFiO5O8oCxiDYManPckgYaGLQG/IiDu4moh+vS1XAvv/wyNm3ahE8//RQjRoxQXg8MDITBIB9qf+mll7B7927k5OTAaDRi0aJFAIDDhw8DkKcOSEpKQlRUFNasWQOz2Yxnn30W8+fPx7/+678CkKcOSEhIQGZmJp5//nns378fP//5z7Fr1y6kp6cDkKcOmDt3Lt577z2kpKTg7bffxtatW3HhwgVlLFNHvXSEV8MRdczaXIs/lLyL87UXoIEEQIIDDkT6RWBRXCYG+d/j6RaJqI9x+fe36AIAbT4+/PBDpaahoUG8/PLLYsCAAcLf3188+eST4vLly07b+eabb8T06dOFwWAQoaGh4pVXXhHNzc1ONQcOHBBJSUlCp9OJYcOGOf2MVu+8846IiYkROp1OpKSkiCNHjjgt70wvaiwWiwAgLBZLp9ch6qu+rSsXuyr2iP/5fqc4azknHA6Hp1sioj7K1d/fdzXPUm/HI0t0p4SwAY1/gWg+BUg+kHSTAF2q2yaLJCLqy1z9/e3jgp6I6CbCVghx/WVAXEfrXzFR9z7gMxIY8O+QtG1fYEBERN6JN9IlciHR8i3EtecBYfn7Ky1/fwBouQhx7afyUSciIuoxGJaIXEjUfwjABrQ5UaMdsH8DNO5rYxkREXkrhiUiV2rYBcCuUqCBaNzrrm6IiMgFGJaIXEk0dFDgAEStW1ohIiLXYFgiciWfoYDq7T+0gE+cu7ohIiIXYFgiciHJ/xlA5fYfgB2SYba72iEiIhdgWKK+RQigvh6oqZH/dPU0Y4aZgG4ibj+69Pfn/TIh+Y64dS0iIvJinGeJ+obGRqCoCDh0CCgpAVpaAB8fIC4OeOABICkJ8PO76x8jSb7AgPeAuv+AqP8vwFEtL9DGQgp4EZLhibv+GURE5F6cwVsFZ/DuJUpKgA8+AEpLAUkCQkIAX1+guRmorpaPLsXGAvPmyeHJRYRoARxVAHwATRhn7yYichPO4E3UFSUlwNq1gNkMDB8O6HTOy8PDAZsNuHhRrlu82GWBSZJ8AG2US7ZFRESewzFL1Hs1NspHlMxmID7+9qDUSqeTl5vNcn1jo3v7JCIir8awRHdMCBtE8xkI20kIR72n27ldUZF86m34cPn0mxpJko8olZYCJ0+6pT0iIuoZeBqOukyIFqDuXYi6jT/cA00yQBhmQ+q/FJJk8GyDgDwO6dAhOQS1d0TpVnq9XP/FF0BKSscBi4iI+gQeWaIuEUJAWJZB3HjnppvFQp65uv6PENfmeceNYhsa5PFKISFdWy8kRF6voaOZuImIqK9gWKKusR0GGneh7YkXHUDzcaDhU3d3dTubTZ4ewNe3a+v5+Mjr2bwg8BERkVdgWKIuEfVbAWhVKiSI+o/d1U77dDo5+DQ3d2291vmXOnvqjoiIej2GJeoa+zcA7CoFArBfclMzKgwGecB2dXXX1quultcztD/uSrSUQDT8GaJhF4Tj2l02SkRE3o4DvKlrNMGQM7ZDpSbITc2okCR5Zu6jR+VTap05UtTUJA8Mf/DBNgd3i5bvICwrgOajN73qA2GYCcn4GiRJ77r+iYjIa/DIEnWJZPhHqAYlaCAZnnRXO+qSkuSZuS9e7PgecELIA7tjY4HExNsX26shrs0BmgtvWdICNGyDuL4QnAyfiKh3YliirvF7FPCJQ9vjlrTykSf/Oe7uqm1+fvItTCIjgXPn5CNHbWlqkpdHRgLz57d5jzhR/8e/3+etrVOQDsCWD9iOtrGMiIh6OoYl6hJJ0kMa8EfAN/nvr2igfIx84iAFb4KkCfZUe7eLi/vhFialpcD580BVFXDtmvzn+fPy63FxwJIl8pGltjRsg/pYLS1E4/ZueANERORpHLNEXSZpQyGF/BdE83l5KgHhAHTJgO/93nmz2Lg4YPVqeWbuL76QT7fV18tXvY0fL49RSkxs84iSwnG9gx9iB+xVLm2biIi8A8MS3THJdxTgO8rTbXSOnx+QmirPzN3Q8MOgb4OhczN1a0IBR6VKgRbQRrqsXSIi8h48DUd9iyQB/v5AUJD8Z2ePhBn+F9T/utghGWa4oEEiIvI2DEtEnSD1+ymgHYi2B7ZLgD79pnFcRETUmzAsEXWCpAmCFLwF0E8CcPPRKD3g/xykoN9553gtIiK6axyzRNRJkjYc0oD3IOzfA81nAfgCuvGQNAGebo2IiLoRwxJRF0naewDtPZ5ug4iI3ISn4YiIiIhUMCwRERERqWBYIiIiIlLBsERERESkgmGJiIiISAXDEhEREZEKhiUiIiIiFQxLRERERCoYloiIiIhUcAZvFUIIAIDVavVwJ0RERNRZrd/brd/jd4thSUVtbS0AIDo62sOdEBERUVfV1tYiMDDwrrcjCVfFrl7I4XCgoqIC/fv35x3lVVitVkRHR+PSpUswGo2ebqfH4/50He5L1+L+dC3uT9e6eX/2798ftbW1iIqKgkZz9yOOeGRJhUajwaBBgzzdRo9hNBr5F96FuD9dh/vStbg/XYv707Va96crjii14gBvIiIiIhUMS0REREQqGJborun1eqxevRp6vd7TrfQK3J+uw33pWtyfrsX96VrduT85wJuIiIhIBY8sEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxK16fPPP8ePf/xjREVFQZIk/PnPf3ZaLoTAqlWrMHDgQBgMBqSlpeHixYtONdeuXcMzzzwDo9GIoKAgzJs3Dzdu3HDju/AeHe3Pn/3sZ5AkyenxyCOPONVwf8qys7Mxfvx49O/fH+Hh4XjiiSdQXFzsVNPY2IjMzEyEhIQgICAAM2fORGVlpVNNeXk5MjIy4O/vj/DwcCxbtgwtLS3ufCteoTP786GHHrrt87lgwQKnGu5P2YYNGzBmzBhlYkSTyYQ9e/Yoy/nZ7JqO9qe7PpsMS9Smuro6JCYmYv369W0uX7NmDX7/+9/j3XffxZdffol+/fohPT0djY2NSs0zzzyDs2fPIjc3Fzt37sTnn3+OF1980V1vwat0tD8B4JFHHsHly5eVx8cff+y0nPtTlp+fj8zMTBw5cgS5ublobm7GtGnTUFdXp9QsXboUO3bswLZt25Cfn4+KigrMmDFDWW6325GRkQGbzYbDhw9j48aNyMnJwapVqzzxljyqM/sTAF544QWnz+eaNWuUZdyfPxg0aBDefPNNFBYW4vjx43j44Yfx+OOP4+zZswD42eyqjvYn4KbPpiDqAACxfft25bnD4RCRkZHit7/9rfJaTU2N0Ov14uOPPxZCCHHu3DkBQBw7dkyp2bNnj5AkSXz//fdu690b3bo/hRBi7ty54vHHH293He7P9lVVVQkAIj8/XwghfxZ9fX3Ftm3blJrz588LAKKgoEAIIcTu3buFRqMRZrNZqdmwYYMwGo2iqanJvW/Ay9y6P4UQYvLkyWLx4sXtrsP9qW7AgAHi/fff52fTRVr3pxDu+2zyyBJ1WVlZGcxmM9LS0pTXAgMDkZqaioKCAgBAQUEBgoKCMG7cOKUmLS0NGo0GX375pdt77gkOHjyI8PBwjBgxAi+99BKqq6uVZdyf7bNYLACA4OBgAEBhYSGam5udPp8jR45ETEyM0+dz9OjRiIiIUGrS09NhtVqdfmPti27dn60++ugjhIaGIiEhAStXrkR9fb2yjPuzbXa7HZs3b0ZdXR1MJhM/m3fp1v3Zyh2fTd5Il7rMbDYDgNOHr/V56zKz2Yzw8HCn5T4+PggODlZq6AePPPIIZsyYgaFDh6K0tBS//OUvMX36dBQUFECr1XJ/tsPhcGDJkiWYOHEiEhISAMifPZ1Oh6CgIKfaWz+fbX1+W5f1VW3tTwB4+umnMXjwYERFReHUqVNYvnw5iouL8cknnwDg/rzV6dOnYTKZ0NjYiICAAGzfvh3x8fEoKiriZ/MOtLc/Afd9NhmWiLzAnDlzlP8ePXo0xowZg9jYWBw8eBBTp071YGfeLTMzE2fOnMGhQ4c83Uqv0N7+vHls3OjRozFw4EBMnToVpaWliI2NdXebXm/EiBEoKiqCxWLBn/70J8ydOxf5+fmebqvHam9/xsfHu+2zydNw1GWRkZEAcNsVHJWVlcqyyMhIVFVVOS1vaWnBtWvXlBpq37BhwxAaGoqSkhIA3J9tWbhwIXbu3IkDBw5g0KBByuuRkZGw2Wyoqalxqr/189nW57d1WV/U3v5sS2pqKgA4fT65P3+g0+kQFxeH5ORkZGdnIzExEWvXruVn8w61tz/b0l2fTYYl6rKhQ4ciMjISeXl5ymtWqxVffvmlch7ZZDKhpqYGhYWFSs3+/fvhcDiUDzO177vvvkN1dTUGDhwIgPvzZkIILFy4ENu3b8f+/fsxdOhQp+XJycnw9fV1+nwWFxejvLzc6fN5+vRppwCam5sLo9GoHN7vKzran20pKioCAKfPJ/dn+xwOB5qamvjZdJHW/dmWbvts3uFgdOrlamtrxVd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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------ Centers --------\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[1.42637640e+02, 2.53448200e+05],\n", + " [1.13742100e+02, 4.32386500e+05],\n", + " [1.63969600e+02, 3.01264429e+05],\n", + " [1.81006040e+02, 2.11930600e+05],\n", + " [1.26052733e+02, 1.82886333e+05]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans = KMeans(n_clusters=5)\n", + "kmeans.fit(data_to_fit)\n", + "centers = kmeans.cluster_centers_\n", + "plt.scatter(centers[:, 0], centers[:, 1], c='red', s=100, alpha=0.5);\n", + "plt.scatter(x, y, c=kmeans.labels_)\n", + "plt.show()\n", + "print(\"------ Centers --------\")\n", + "centers" + ] + }, + { + "cell_type": "markdown", + "id": "5d3507ce", + "metadata": { + "papermill": { + "duration": 0.025334, + "end_time": "2025-02-16T13:30:07.631027", + "exception": false, + "start_time": "2025-02-16T13:30:07.605693", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Unitary Authority" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "df01d38d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:07.687718Z", + "iopub.status.busy": "2025-02-16T13:30:07.686337Z", + "iopub.status.idle": "2025-02-16T13:30:10.573565Z", + "shell.execute_reply": "2025-02-16T13:30:10.572429Z" + }, + "papermill": { + "duration": 2.918096, + "end_time": "2025-02-16T13:30:10.576039", + "exception": false, + "start_time": "2025-02-16T13:30:07.657943", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameArea (sq km)est_pop
0DARLINGTON197.477997894.0
24298HARTLEPOOL93.717090152.0
45279MIDDLESBROUGH53.8816141233.0
79564STOCKTON-ON-TEES204.9331183795.0
232679BLACKPOOL34.8709142270.0
265981WARRINGTON180.6279191202.0
1043317YORK271.9314181291.0
1600231DERBY78.0311230726.0
1661744LEICESTER73.3421282757.0
1755801NOTTINGHAM74.6132268939.0
2020112STOKE-ON-TRENT93.4485240422.0
2559930BEDFORD476.4082148113.0
2601891LUTON43.3525185889.0
2642153PETERBOROUGH343.3782157439.0
2695524SOUTHEND-ON-SEA41.6742160362.0
4083886MILTON KEYNES308.6267212707.0
4140194PORTSMOUTH40.3816188043.0
4165294READING40.3888144684.0
4231578SLOUGH32.5419120577.0
4262177SOUTHAMPTON49.8808219539.0
4347818WOKINGHAM178.9690150334.0
4766901ISLES OF SCILLY16.31772140.0
4767083PLYMOUTH79.8497240954.0
4821786SWINDON230.0933180129.0
4997110CONWY1125.8226109674.0
4999239WREXHAM503.7739128540.0
5019603SWANSEA377.6145223463.0
5065066BRIDGEND250.7852128735.0
5084286CARDIFF140.9144310088.0
5153665MERTHYR TYDFIL111.446356207.0
5160445CAERPHILLY277.3879169546.0
5169995NEWPORT190.4311137642.0
\n", + "
" + ], + "text/plain": [ + " Name Area (sq km) est_pop\n", + "0 DARLINGTON 197.4779 97894.0\n", + "24298 HARTLEPOOL 93.7170 90152.0\n", + "45279 MIDDLESBROUGH 53.8816 141233.0\n", + "79564 STOCKTON-ON-TEES 204.9331 183795.0\n", + "232679 BLACKPOOL 34.8709 142270.0\n", + "265981 WARRINGTON 180.6279 191202.0\n", + "1043317 YORK 271.9314 181291.0\n", + "1600231 DERBY 78.0311 230726.0\n", + "1661744 LEICESTER 73.3421 282757.0\n", + "1755801 NOTTINGHAM 74.6132 268939.0\n", + "2020112 STOKE-ON-TRENT 93.4485 240422.0\n", + "2559930 BEDFORD 476.4082 148113.0\n", + "2601891 LUTON 43.3525 185889.0\n", + "2642153 PETERBOROUGH 343.3782 157439.0\n", + "2695524 SOUTHEND-ON-SEA 41.6742 160362.0\n", + "4083886 MILTON KEYNES 308.6267 212707.0\n", + "4140194 PORTSMOUTH 40.3816 188043.0\n", + "4165294 READING 40.3888 144684.0\n", + "4231578 SLOUGH 32.5419 120577.0\n", + "4262177 SOUTHAMPTON 49.8808 219539.0\n", + "4347818 WOKINGHAM 178.9690 150334.0\n", + "4766901 ISLES OF SCILLY 16.3177 2140.0\n", + "4767083 PLYMOUTH 79.8497 240954.0\n", + "4821786 SWINDON 230.0933 180129.0\n", + "4997110 CONWY 1125.8226 109674.0\n", + "4999239 WREXHAM 503.7739 128540.0\n", + "5019603 SWANSEA 377.6145 223463.0\n", + "5065066 BRIDGEND 250.7852 128735.0\n", + "5084286 CARDIFF 140.9144 310088.0\n", + "5153665 MERTHYR TYDFIL 111.4463 56207.0\n", + "5160445 CAERPHILLY 277.3879 169546.0\n", + "5169995 NEWPORT 190.4311 137642.0" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = data.Geography.str.contains('Unitary Authority')\n", + "cols = ['Name', 'Area (sq km)', 'est_pop']\n", + "data.loc[rows, cols].drop_duplicates().dropna()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "86541075", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:10.630127Z", + "iopub.status.busy": "2025-02-16T13:30:10.629427Z", + "iopub.status.idle": "2025-02-16T13:30:13.460318Z", + "shell.execute_reply": "2025-02-16T13:30:13.458783Z" + }, + "papermill": { + "duration": 2.861789, + "end_time": "2025-02-16T13:30:13.464277", + "exception": false, + "start_time": "2025-02-16T13:30:10.602488", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(32, 3)\n", + "Area (sq km) float64\n", + "est_pop float64\n", + "pp_sq_m float64\n", + "dtype: object\n", + " Area (sq km) est_pop pp_sq_m\n", + "count 32.000000 32.000000 32.000000\n", + "mean 194.278537 169546.437500 1874.322193\n", + "std 214.988748 65004.272087 1571.624909\n", + "min 16.317700 2140.000000 97.416769\n", + "25% 52.881400 135415.250000 572.163541\n", + "50% 126.180350 164954.000000 929.406800\n", + "75% 256.071750 214415.000000 3587.820838\n", + "max 1125.822600 310088.000000 4656.650554\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: est_pop R-squared: 0.020\n", + "Model: OLS Adj. R-squared: -0.012\n", + "Method: Least Squares F-statistic: 0.6248\n", + "Date: Sun, 16 Feb 2025 Prob (F-statistic): 0.435\n", + "Time: 13:30:13 Log-Likelihood: -399.20\n", + "No. Observations: 32 AIC: 802.4\n", + "Df Residuals: 30 BIC: 805.3\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------\n", + "const 1.779e+05 1.57e+04 11.337 0.000 1.46e+05 2.1e+05\n", + "Area (sq km) -43.1873 54.637 -0.790 0.435 -154.772 68.397\n", + "==============================================================================\n", + "Omnibus: 2.149 Durbin-Watson: 2.032\n", + "Prob(Omnibus): 0.341 Jarque-Bera (JB): 1.004\n", + "Skew: -0.293 Prob(JB): 0.605\n", + "Kurtosis: 3.640 Cond. No. 390.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "---- params / coeficient -------\n", + "const 177936.800432\n", + "Area (sq km) -43.187287\n", + "dtype: float64\n", + "------------p values----------\n", + "const 2.277620e-12\n", + "Area (sq km) 4.354778e-01\n", + "dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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", 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ERETUIhz9/XZpUQWgzsfmzZuFEEIUFRWJBx54QPj6+gqtViv69Okjli5dWutD/frrr2L8+PHCy8tL+Pn5iSVLlojq6mpZzL59+8SgQYOEh4eH6NWrl/Qet3v//fdFSEiI8PDwENHR0eLIkSOy/Tdv3hTz5s0TXbp0Ed7e3uKxxx4TxcXFDn/e1lpUfXf6ohj25vci9KVd0mPYm9+L705fZLFFRERtnqO/3yohhHDVVbL2xmKxQK/Xw2w2Q6fTuTodh6TlFmPupydw5z8kKtyqgH28O6L8RrW0PVDvicQJERgXGdiSaRIRETUbR3+/3WKiOrknq01g1c68WgUVAGnb7QUVAJjMFZj76Qmk5RY3e35ERETuhEUV1etoYRmKzRVOvcZebK3amQerjRdBiYio/WBRRfUqvepcQWUnABSbK3C0sEzZhIiIiNwYiyqql39nzya9vrFFGRERUWvEoorqFR3mi0C9J1SNfH1TizIiIqLWhEUV1UujViFxwq0Gqs4UVircugswOsy3wVgiIqK2gkUV3dW4yECkzBgMg15+1cnHuyOA2sWW/XnihAho1I29xkVERNT6uM2CyuS+xkUG4uEIA44WlqH0agX8O9+6CpWeZ8KqnXmyOwQN7FNFRETtFJt/tqDW2PyzIVabqFVs8QoVERG1JY7+fvNKFTmkvuJJo1bB2Lurq9MjIiJyORZV1KC03OJaw3xcjoaIiEiOE9Xpruxr/93ZWZ3L0RAREcmxqKJ6ObL2H5ejISIiuoVFFdWrobX/uBwNERHRf7Coono5uswMl6MhIiJiUUV34egyM1yOhoiIiHf/0V3Y1/4zmSvqnFelwq1mn1yOpm1gzzEioqZhUUX1sq/9N/fTE1ABssKKy9G0LWybQUTUdBz+o7uqb+0/g94TKTMG8we3DWDbDCIiZfBKFTWovrX/eIWq9WuobYYKt9pmPBxh4PdNRNQAFlXkEC5H0zY50zaD3z8R0d2xqGqnOCmZALbNICJSEouqdoiTksmObTOIiJTTqKIqIyMDGRkZKC0thc1mk+376KOPFEmMmod9UvKdc2jsk5I5+bx9YdsMIiLlOH3336pVqzB27FhkZGTg0qVLuHLliuxB7otr+dGd7G0zgP+0ybBj2wwiIuc4faVqw4YNSE1NxRNPPNEc+VAz4qRkqou9bcadQ8IGDgkTETnF6aKqqqoKw4cPb45cqJlxUjLVh20ziIiazunhv2effRZ/+9vfmiMXamaclEx3Y2+b8eig7jD27sqCiojISU5fqaqoqMCHH36I77//HgMHDkTHjh1l+9955x3FkiNlcVJy68K2F0RErYvTRdWpU6cwaNAgAEBubq5sn0rF/+C7M67l13qw7QURUeujEkLwVq8WYrFYoNfrYTabodPpXJYHf7DdW31tL+ylLtteEBG1LEd/v5vU/PO3334DAPTo0aMph6EWxknJ7otr8RERtV5OT1S32WxYvXo19Ho9QkNDERoaCh8fH7z22mu1GoGS++KkZPfkTNsLIiJyL05fqVqxYgU2bdqENWvWYMSIEQCAQ4cO4dVXX0VFRQXeeOMNxZMkai/Y9oKIqPVyuqj6+OOPsXHjRvz+97+Xtg0cOBDdu3fHvHnzWFQRNQHbXhARtV5OD/+VlZUhPDy81vbw8HCUlXFIgqgp7G0v6huMVeHWTQVse0FE5H6cLqqioqLwwQcf1Nr+wQcfICoqSpGkiNorrsVHRNR6OT38t3btWsTHx+P777+H0WgEAGRmZuLChQvYvXu34gkStTdci4+IqHVqVJ+qixcvIjk5GWfPngUA3HfffZg3bx6CgoIUT7AtcZc+VdQ6sKM6EZF7cPT3m80/WxCLKiIiotZH0eafp06dQmRkJNRqNU6dOnXX2IEDBzqXKREREVEb4FBRNWjQIJhMJvj7+2PQoEFQqVSo6wKXSqWC1WpVPEkiIiIid+dQUVVYWIhu3bpJfxMRERGRnEMtFUJDQ6FS3Zog+89//hPdu3eXlqixP7p3745//vOfTr15UlISfve736Fz587w9/fHxIkTUVBQIIupqKhAQkICunbtik6dOmHy5MkoKSmRxRQVFSE+Ph7e3t7w9/fH0qVLUVNTI4vZv38/Bg8eDK1Wiz59+iA1NbVWPsnJyejZsyc8PT0RExODo0ePOp0LERERtU9O96kaPXp0nU0+zWYzRo8e7dSxDhw4gISEBBw5cgTp6emorq7G2LFjcf36dSlm0aJF2LlzJ7Zt24YDBw7g4sWLmDRpkrTfarUiPj4eVVVVOHz4MD7++GOkpqZi5cqVUkxhYSHi4+MxevRo5OTkYOHChXj22WexZ88eKebzzz/H4sWLkZiYiBMnTiAqKgpxcXEoLS11OBciIiJqx4STVCqVKC0trbW9oKBAdO7c2dnDyZSWlgoA4sCBA0IIIcrLy0XHjh3Ftm3bpJj8/HwBQGRmZgohhNi9e7dQq9XCZDJJMSkpKUKn04nKykohhBAvvvii6N+/v+y9pkyZIuLi4qTn0dHRIiEhQXputVpFUFCQSEpKcjiXhpjNZgFAmM1mh+KJiIjI9Rz9/Xa4+af9ioxKpcJTTz0FrVYr7bNarTh16hSGDx/epALPbDYDAHx9by3BkZ2djerqasTGxkox4eHhCAkJQWZmJoYNG4bMzEwMGDAAAQEBUkxcXBzmzp2LM2fO4P7770dmZqbsGPaYhQsXAgCqqqqQnZ2N5cuXS/vVajViY2ORmZnpcC53qqysRGVlpfTcYrE09tQQERGRm3O4qNLr9QAAIQQ6d+4MLy8vaZ+HhweGDRuG5557rtGJ2Gw2LFy4ECNGjEBkZCQAwGQywcPDAz4+PrLYgIAAmEwmKeb2gsq+377vbjEWiwU3b97ElStXYLVa64yxNzh1JJc7JSUlYdWqVQ6eASIiImrNHC6qNm/eDADo2bMnXnjhBdxzzz2KJpKQkIDc3FwcOnRI0eO60vLly7F48WLpucViQXBwsAszIiIioubi9Np/iYmJiicxf/587Nq1CwcPHkSPHj2k7QaDAVVVVSgvL5ddISopKYHBYJBi7rxLz35H3u0xd96lV1JSAp1OBy8vL2g0Gmg0mjpjbj9GQ7ncSavVyoZJiYiIqO1y+u4/APjyyy/xhz/8AcOGDcPgwYNlD2cIITB//nxs374de/fuRVhYmGz/kCFD0LFjR2RkZEjbCgoKUFRUJC3mbDQacfr0adldeunp6dDpdIiIiJBibj+GPcZ+DA8PDwwZMkQWY7PZkJGRIcU4kgsRERG1Y87OgF+3bp3o1KmTmD9/vvDw8BB//OMfRWxsrNDr9eLPf/6zU8eaO3eu0Ov1Yv/+/aK4uFh63LhxQ4qZM2eOCAkJEXv37hXHjx8XRqNRGI1GaX9NTY2IjIwUY8eOFTk5OSItLU1069ZNLF++XIr55ZdfhLe3t1i6dKnIz88XycnJQqPRiLS0NClm69atQqvVitTUVJGXlydmz54tfHx8ZHcVNpRLQ3j3HxERUevj6O+300VVv379xN/+9jchhBCdOnUS58+fF0II8corr8haEjgCQJ2PzZs3SzE3b94U8+bNE126dBHe3t7iscceE8XFxbLj/Prrr2L8+PHCy8tL+Pn5iSVLlojq6mpZzL59+8SgQYOEh4eH6NWrl+w97N5//30REhIiPDw8RHR0tDhy5IhsvyO53A2LquZVY7WJw+cuiR0nfxOHz10SNVabq1MiIqI2wNHfb5UQdSzidxfe3t7Iz89HaGgo/P39kZ6ejqioKPz8888YNmwYLl++rOyltDbE0VWuyXlpucVYtTMPxeYKaVug3hOJEyIwLjLQhZkREVFr5+jvt9NzqgwGg9RRPSQkBEeOHAFwq2u5k/UZkSLScosx99MTsoIKAEzmCsz99ATScotdlBkREbUnThdVY8aMwTfffAMAePrpp7Fo0SI8/PDDmDJlCh577DHFEyRlWG0Cmecv4+ucfyHz/GVYbW2jALbaBFbtzENdn8a+bdXOvDbzeYmIyH053VLhww8/hM1mAwBpceHDhw/j97//Pf74xz8qniA1XVseGjtaWFbrCtXtBIBicwWOFpbB2LtryyVGRETtjtNFlVqthlr9nwtcU6dOxdSpUxVNipRjHxq78zpNsbkCcz49gQ0zBrfqwqr0av0FVWPiiIiIGsuhourUqVMOH3DgwIGNToaUdbehMbtlX53GwxEGaNSqFstLSf6dPRWNIyIiaiyHiqpBgwZBpVI1OBFdpVLBarUqkhg1XUNDYwBQfqMaH+w9hwWxfVsoK2VFh/kiUO8Jk7mizuJRBcCg90R0mG9Lp0ZERO2MQ0VVYWFhc+dBzcDRIa/Nhwsxf0yfVnm1SqNWIXFCBOZ+egIqQFZY2T9N4oQI2Wez2gSOFpah9GoF/DvfKrha42cnIiL34lBRFRoa2tx5UDNwdMir/EZ1q57IPS4yECkzBteajG+oYzJ+W560T0REruX0RPVPPvnkrvuffPLJRidDyooO84WPV0eU36xuMLa1T+QeFxmIhyMMd70CVd+kfXs/q5RmmLTPq2JERO2H00XVggULZM+rq6tx48YNeHh4wNvbm0WVG9GoVXh6RE+8+/3PDca2hYncGrWq3qttDfWzUuFWPyslJ+3zqhgRUfvidPPPK1euyB7Xrl1DQUEBRo4ciS1btjRHjtQE88f0hY93x3r3q3Drh76tT+R2pp+VXVMaprLLOxFR++P0laq69O3bF2vWrMGMGTNw9uxZJQ5JCtGoVVgzaUCdw171TeRui5ztZ9WUq0yuuCpGRESu5/SVqvp06NABFy9eVOpwpCD7RO5AvXyIz6D3bJZ5RO7ImX5WTb3K1JirYkRE1Po5faXKvu6fnRACxcXF+OCDDzBixAjFEiNlOTKRuy1ztJ/VkNAuePCtfU26ysQu70RE7ZPTRdXEiRNlz1UqFbp164YxY8bg7bffViovagZ3m8jd1jnazyr7n1eavJYgu7wTEbVPThdV9sWUiVobR/pZfZ3zL4eOdberTOzyTkTUPjVporp92RqVqn0MIVHr19AwqBJXmRrT5Z2IiFq/Rk1U37RpEyIjI+Hp6QlPT09ERkZi48aNSudG1Czsw6CPDuoOY++usuLGfpWpvnLH0RYU9qtihnZ8cwARUXvj9JWqlStX4p133sGf/vQnGI1GAEBmZiYWLVqEoqIirF69WvEkiVqKkleZ2vvNAURE7Y1K2MfwHNStWzesX78e06ZNk23fsmUL/vSnP+HSpUuKJtiWWCwW6PV6mM1m6HQ6V6dDd8Fu6EREZOfo77fTV6qqq6sxdOjQWtuHDBmCmpoaZw9H5JZ4lYmIiJzldFH1xBNPICUlBe+8845s+4cffojp06crlhiRqyndgoKLKxMRtW2Nuvtv06ZN+Pvf/45hw4YBALKyslBUVIQnn3wSixcvluLuLLyI2isOJxIRtX1Oz6kaPXq0YwdWqbB3795GJdVWcU5V+2Rf9qa+tRd5NyARkXtrtjlV+/bta1JiRO0JF1cmImo/mrSg8m+//YbffvtNqVyI2hwurkxE1H44XVTZbDasXr0aer0eoaGhCA0NhY+PD1577TUuYUN0By6uTETUfjg9/LdixQps2rQJa9aswYgRIwAAhw4dwquvvoqKigq88cYbiidJ1FpxcWUiovbD6aLq448/xsaNG/H73/9e2jZw4EB0794d8+bNY1FFdBsurkxE1H44PfxXVlaG8PDwWtvDw8NRVsZ5IUS3sy97A6DWeoJcXJmIqG1xuqiKiorCBx98UGv7Bx98gKioKEWSImpLuLgyEVH74HSfqgMHDiA+Ph4hISGyBZUvXLiA3bt3Y9SoUc2SaFvg6j5V7OjtWjz/REStk6O/304XVQBw8eJFJCcn4+zZswCA++67D/PmzUNQUFDjM24HXFlUsaM3ERFR4zRrUUWN46qiih29iYiIGq/ZOqoDwJUrV7Bp0ybk5+cDACIiIvD000/D15d3MLkbdvRufzjMSETkGk4XVQcPHsSECROg1+sxdOhQAMD69euxevVq7Ny5Ew888IDiSVLjOdPR29i7a8slRs2Cw7xERK7j9N1/CQkJmDJlCgoLC/HVV1/hq6++wi+//IKpU6ciISGhOXKkJmBH7/bDPsx7ZxFtMldg7qcnkJZb7KLMiIjaB6eLqnPnzmHJkiXQaDTSNo1Gg8WLF+PcuXOKJkdNx47e7UNDw7zArWFeq41TKImImovTRdXgwYOluVS3y8/PZ58qN2Tv6F3fjBoVbg0PsaN368aFm4mIXM/pOVXPP/88FixYgHPnzmHYsGEAgCNHjiA5ORlr1qzBqVOnpNiBAwcqlyk1ir2j99xPT0AFyK5ksKN328FhXiIi13O6pYJaffeLWyqVCkIIqFQqWK3WJiXX1rBPFTWXzPOXMe2vRxqM2/LcMN6QQETkpGZrqVBYWNikxMg1xkUG4uEIA2+1b6O4cDMRkes5XVSFhoY2Rx7UAjRqVZu+StGe+zNxmJeIyPUa1fyTyN1wePM/CzffeR4M7ew8EBG5itN3/ynJ3kg0KCgIKpUKO3bskO1/6qmnoFKpZI9x48bJYsrKyjB9+nTodDr4+Phg1qxZuHbtmizm1KlTGDVqFDw9PREcHIy1a9fWymXbtm0IDw+Hp6cnBgwYgN27d8v2CyGwcuVKBAYGwsvLC7Gxsfj555+VORHUJOzP9B/jIgNx6KUx2PLcMKybOghbnhuGQy+NYUFFRNQCXFpUXb9+HVFRUUhOTq43Zty4cSguLpYeW7Zske2fPn06zpw5g/T0dOzatQsHDx7E7Nmzpf0WiwVjx45FaGgosrOz8dZbb+HVV1/Fhx9+KMUcPnwY06ZNw6xZs3Dy5ElMnDgREydORG5urhSzdu1arF+/Hhs2bEBWVhbuuecexMXFoaKCd1O5Evsz1WYf5n10UHcYe3dtM0N+VptA5vnL+DrnX8g8f7ldfadE1Dq4zYLKKpUK27dvx8SJE6VtTz31FMrLy2tdwbLLz89HREQEjh07Ji2Zk5aWhkceeQS//fYbgoKCkJKSghUrVsBkMsHDwwMAsGzZMuzYsQNnz54FAEyZMgXXr1/Hrl27pGMPGzYMgwYNwoYNGyCEQFBQEJYsWYIXXngBAGA2mxEQEIDU1FRMnTrVoc/oyrv/2ire9dY+cHiXiFzJ0d9vl16pcsT+/fvh7++Pfv36Ye7cubh8+bK0LzMzEz4+PlJBBQCxsbFQq9XIysqSYh544AGpoAKAuLg4FBQU4MqVK1JMbGys7H3j4uKQmZkJ4NYdjyaTSRaj1+sRExMjxdSlsrISFotF9iBlsT9T28fhXSJqLRyaqN6lSxeoVI4NIZSVKdexedy4cZg0aRLCwsJw/vx5/PnPf8b48eORmZkJjUYDk8kEf39/2Ws6dOgAX19fmEwmAIDJZEJYWJgsJiAgQNrXpUsXmEwmadvtMbcf4/bX1RVTl6SkJKxataoRn1x5bfXOOC7D07Y1NLyrwq3h3YcjDG3in2ciat0cKqree+896e/Lly/j9ddfR1xcHIxGI4BbV3r27NmDV155RdHkbh9WGzBgAAYOHIjevXtj//79eOihhxR9r+awfPlyLF68WHpusVgQHBzc4nm05aET9mdq25xZfofDu0Tkag4VVTNnzpT+njx5MlavXo358+dL255//nl88MEH+P7777Fo0SLls/z/9erVC35+fjh37hweeughGAwGlJaWymJqampQVlYGg8EAADAYDCgpKZHF2J83FHP7fvu2wMBAWcygQYPqzVer1UKr1TbikyrHPnRyZ8FhHzpJmTG4VRdW7M/UtnF4l4haE6fnVO3Zs6dWWwPg1lDd999/r0hS9fntt99w+fJlqbAxGo0oLy9Hdna2FLN3717YbDbExMRIMQcPHkR1dbUUk56ejn79+qFLly5STEZGhuy90tPTpStxYWFhMBgMshiLxYKsrCwpxh1ZbQKvftP274yz92cy6OVDfAa9Z6svGts7Du8SUWvidPPPrl274uuvv8aSJUtk27/++mt07erc5fdr167h3Llz0vPCwkLk5OTA19cXvr6+WLVqFSZPngyDwYDz58/jxRdfRJ8+fRAXFwcAuO+++zBu3Dg899xz2LBhA6qrqzF//nxMnToVQUFBAIDHH38cq1atwqxZs/DSSy8hNzcX69atw7vvviu974IFC/Dggw/i7bffRnx8PLZu3Yrjx49LbRdUKhUWLlyI119/HX379kVYWBheeeUVBAUFye5WdDcf7P0ZJkvDQyepPxTCr7O2Vc+14jI8bROHd4moNXG6pUJqaiqeffZZjB8/XroalJWVhbS0NPz1r3/FU0895fCx9u/fj9GjR9faPnPmTKSkpGDixIk4efIkysvLERQUhLFjx+K1116TTRgvKyvD/PnzsXPnTqjVakyePBnr169Hp06dpJhTp04hISEBx44dg5+fH/70pz/hpZdekr3ntm3b8PLLL+PXX39F3759sXbtWjzyyCPSfiEEEhMT8eGHH6K8vBwjR47EX/7yF9x7770Of96WbKmQlluMOZ+ecPp1bWWuFbUd9iFsoO7hXV6NJKLm5ujvd6P6VGVlZWH9+vXIz88HcOuK0fPPPy8VWVS3liqqrDaBkf+z964TfOvDHypyR235Zgsicn/NWlRR47RUUeVoQ8z62IdUDr00hsNn5DbaalsQInJ/jv5+N2pB5fPnz2Pz5s345Zdf8N5778Hf3x/fffcdQkJC0L9//0YnTcpo6p1QbeU2df4Ity325XeIiNyV00XVgQMHMH78eIwYMQIHDx7E66+/Dn9/f/z444/YtGkTvvzyy+bIk5yg1J1Qrfk2dQ4XERFRS3O6pcKyZcvw+uuvIz09Xbb0y5gxY3DkSOOHnEg59jummnpNprXeps5lTYiIyBWcLqpOnz6Nxx57rNZ2f39/XLp0SZGkqGnsDTEB1Cqs7M99vDvWW3SpcOuqTmu8Tb2hZU2AttGbi4iI3I/TRZWPjw+Ki2v/n/7JkyfRvXt3RZKiprtbQ8wNMwZjzaQBAOovulprF3JnljUhIiJSktNzqqZOnYqXXnoJ27Ztg0qlgs1mww8//IAXXngBTz75ZHPkSI3UUEPMlBmDa807MrTyeUdc1oSIiFzF6aLqzTffREJCAoKDg2G1WhEREQGr1YrHH38cL7/8cnPkSE1wtzum2mIXci5rQkRErtLoPlUXLlzA6dOnce3aNdx///3o27ev0rm1OS3ZUb29sjc+bWhZE/bgIiIiRzn6++30nKrVq1fjxo0bCA4OxiOPPII//OEP6Nu3L27evInVq1c3KWmipnJkkn5rnS9GRETuzekrVRqNBsXFxfD395dtv3z5Mvz9/WG1WhVNsC3hlaqWwz5VRESklGbrqC6EgEpV+//yf/zxR/j6tr5b8Kltaqn5Yuza7r743RBRS3O4qOrSpQtUKhVUKhXuvfdeWWFltVpx7do1zJkzp1mSJGqM5l7WhFfD3Be/GyJyBYeH/z7++GMIIfDMM8/gvffeg16vl/Z5eHigZ8+eMBqNzZZoW8Dhv7bD3rX9zn957P+rkTJjMH+8XYTfDREpTfHhv5kzZwIAwsLCMHz4cHTs2LHpWRK1Qg11bVfhVtf2hyMMHG5qYfxuiMiVnL7778EHH5QKqoqKClgsFtmDqK1j13b3xe+GiFzJ6aLqxo0bmD9/Pvz9/XHPPfegS5cusgdRW8eu7e6L3w0RuZLTRdXSpUuxd+9epKSkQKvVYuPGjVi1ahWCgoLwySefNEeORG6FXdvdF78bInIlp4uqnTt34i9/+QsmT56MDh06YNSoUXj55Zfx5ptv4rPPPmuOHIncSnSYLwL1nrWai9qpcOtOs+gwthhpafxuiMiVnC6qysrK0KtXLwCATqdDWdmtuQkjR47EwYMHlc2OyA2xa7v74ndDRK7kdFHVq1cvFBYWAgDCw8PxxRdfALh1BcvHx0fR5KhprDaBzPOX8XXOv5B5/jKstkYt80h1GBcZiJQZg2HQy4eRDHpP3rLvYvxuiMhVnF6m5t1334VGo8Hzzz+P77//HhMmTIAQAtXV1XjnnXewYMGC5sq11WvJPlVsftgy2LXbffG7ISKlOPr77XRRdad//vOfyM7ORp8+fTBw4MCmHKrNa6miis0PiYiIlNNsa//dKTQ0FKGhoU09DCmEzQ+JiIhco1FF1bFjx7Bv3z6UlpbCZrPJ9r3zzjuKJEaN40zzw+ZcF4+IiKi9cbqoevPNN/Hyyy+jX79+CAgIkC2sfPvf5BpsfkhEROQaThdV69atw0cffYSnnnqqGdKhpmLzQyIiItdwuqWCWq3GiBEjmiMXUgCbHxIREbmG00XVokWLkJyc3By5kALY/JCIiMg1nG6pYLPZEB8fj59++gkRERHo2LGjbP9XX32laIJtCftUERERtT7N1lLh+eefx759+zB69Gh07dqVk9Pd1LjIQDwcYWDzQyIiohbidFH18ccf4//9v/+H+Pj45siHFKRRq9g2gYiIqIU4XVT5+vqid+/ezZELERERkdPcZVkqp4uqV199FYmJidi8eTO8vb2bIyciIiIih7jTHGKnJ6rff//9OH/+PIQQ6NmzZ62J6idOnFA0wbakJSeqExERtXUttdZts01UnzhxYlPyIiIiImoyd1zr1umiKjExsTnyICIiInKYO65163TzTyIiIiJXc8e1bh26UuXr64uffvoJfn5+6NKly117U5WVlSmWHBEREVFd3HGtW4eKqnfffRedO3eW/mbDTyIiInIl+1q3JnNFnfOqVAAMLbzWrdN3/1Hj8e4/IiIi5djv/gMgK6xcdfef03OqNBoNSktLa22/fPkyNBqNs4cjIiIiapRxkYFImTEYBr18iM+g91SsoHKG03f/1Xdhq7KyEh4eHk1OiIiIiMhR7rTWrcNF1fr16wEAKpUKGzduRKdOnaR9VqsVBw8eRHh4uPIZEhEREd2Fu6x16/Dw37vvvot3330XQghs2LBBev7uu+9iw4YNuHHjBjZs2ODUmx88eBATJkxAUFAQVCoVduzYIdsvhMDKlSsRGBgILy8vxMbG4ueff5bFlJWVYfr06dDpdPDx8cGsWbNw7do1WcypU6cwatQoeHp6Ijg4GGvXrq2Vy7Zt2xAeHg5PT08MGDAAu3fvdjoXcl9Wm0Dm+cv4OudfyDx/GVYbpxISEZGyHC6qCgsLUVhYiAcffBA//vij9LywsBAFBQXYs2cPYmJinHrz69evIyoqCsnJyXXuX7t2LdavX48NGzYgKysL99xzD+Li4lBR8Z+eE9OnT8eZM2eQnp6OXbt24eDBg5g9e7a032KxYOzYsQgNDUV2djbeeustvPrqq/jwww+lmMOHD2PatGmYNWsWTp48iYkTJ2LixInIzc11KhdyT2m5xRj5P3sx7a9HsGBrDqb99QhG/s9epOUWuzo1IiJqQ5p895/VasXp06cRGhqKLl26ND4RlQrbt2+XlsERQiAoKAhLlizBCy+8AAAwm80ICAhAamoqpk6divz8fERERODYsWMYOnQoACAtLQ2PPPIIfvvtNwQFBSElJQUrVqyAyWSS5nwtW7YMO3bswNmzZwEAU6ZMwfXr17Fr1y4pn2HDhmHQoEHYsGGDQ7nUpbKyEpWVldJzi8WC4OBg3v3XglpqXSgiImq7mu3uv4ULF2LTpk0AbhVUDzzwAAYPHozg4GDs37+/0QnfqbCwECaTCbGxsdI2vV6PmJgYZGZmAgAyMzPh4+MjFVQAEBsbC7VajaysLCnmgQcekE2ij4uLQ0FBAa5cuSLF3P4+9hj7+ziSS12SkpKg1+ulR3BwcGNPBzVCQ+tCAbfWheJQIBERKcHpomrbtm2IiooCAOzcuRO//vorzp49i0WLFmHFihWKJWYymQAAAQEBsu0BAQHSPpPJBH9/f9n+Dh06wNfXVxZT1zFuf4/6Ym7f31AudVm+fDnMZrP0uHDhQgOfmpTkzLpQRERETeV0UXX58mUYDAYAwO7du/Hf//3fuPfee/HMM8/g9OnTiifYmmm1Wuh0OtmDWo47rgtFRERtl9NFVUBAAPLy8mC1WpGWloaHH34YAHDjxg1Fm3/aC7eSkhLZ9pKSEmmfwWCo1Yi0pqYGZWVlspi6jnH7e9QXc/v+hnIh9+OO60IREVHb5XRR9fTTT+MPf/gDIiMjoVKppHlGWVlZivapCgsLg8FgQEZGhrTNYrEgKysLRqMRAGA0GlFeXo7s7GwpZu/evbDZbNKdiEajEQcPHkR1dbUUk56ejn79+kkT641Go+x97DH293EkF3I/9nWh6mv/pgIQ2MLrQhERUdvldFH16quvYuPGjZg9ezZ++OEHaLVaALeWr1m2bJlTx7p27RpycnKQk5MD4NaE8JycHBQVFUGlUmHhwoV4/fXX8c033+D06dN48sknERQUJN0heN9992HcuHF47rnncPToUfzwww+YP38+pk6diqCgIADA448/Dg8PD8yaNQtnzpzB559/jnXr1mHx4sVSHgsWLEBaWhrefvttnD17Fq+++iqOHz+O+fPnA4BDuZD70ahVSJwQAQC1Civ788QJES7puktERG2QcKF9+/YJ3JovLHvMnDlTCCGEzWYTr7zyiggICBBarVY89NBDoqCgQHaMy5cvi2nTpolOnToJnU4nnn76aXH16lVZzI8//ihGjhwptFqt6N69u1izZk2tXL744gtx7733Cg8PD9G/f3/x7bffyvY7kktDzGazACDMZrNTr3NnNVabOHzukthx8jdx+NwlUWO1uTqlWr47fVEMe/N7EfrSLukx7M3vxXenL7o6NSIiagUc/f12uE/VI488gi1btkCv1wMA1qxZgzlz5sDHxwfArQnso0aNQl5envKVXxvhaJ+L1iIttxirdubJ7rAL1HsicUKE2/V+stqEW6wLRURErY+jv98OF1UajQbFxcVSCwOdToecnBz06tULwK1J20FBQbBarQqk3za1paKKTTWJiKi9ULz55521l4O1GLVBbKpJRERUm9MT1YnYVJOIiKg2h4sqlUoFlUpVaxu1P2yqSUREVFsHRwOFEHjqqaekFgoVFRWYM2cO7rnnHgCQLRxMbRubahIREdXmcFE1c+ZM2fMZM2bUinnyySebnhG5PXtTTZO5os55VSoABjbVJCKidsbhomrz5s3NmQe1IvammnM/PQEVICus2FSTiIjaK05Up0YZFxmIlBmDYdDLh/gMek+2UyAionbJ4StVRHcaFxmIhyMMbKpJREQEFlXURBq1CsbeXV2dBhERkctx+I+IiIhIASyqiIiIiBTAooqIiIhIASyqiIiIiBTAooqIiIhIASyqiIiIiBTAooqIiIhIASyqiIiIiBTAooqIiIhIASyqiIiIiBTAooqIiIhIASyqiIiIiBTAooqIiIhIASyqiIiIiBTAooqIiIhIASyqiIiIiBTAooqIiIhIASyqiIiIiBTAooqIiIhIASyqiIiIiBTAooqIiIhIASyqiIiIiBTAooqIiIhIASyqiIiIiBTAooqIiIhIAR1cnQC1XVabwNHCMpRerYB/Z09Eh/lCo1a5Oi0iIqJmwaKKmkVabjFW7cxDsblC2hao90TihAiMiwx0YWZERETNg8N/pLi03GLM/fSErKACAJO5AnM/PYG03GIXZUZERNR8WFSRoqw2gVU78yDq2GfftmpnHqy2uiKIiIhaLxZVpKijhWW1rlDdTgAoNlfgaGFZyyVFRETUAlhUkaJKr9ZfUDUmjoiIqLVgUUWK8u/sqWgcERFRa8GiihQVHeaLQL0n6mucoMKtuwCjw3xbMi0iIqJm59ZF1auvvgqVSiV7hIeHS/srKiqQkJCArl27olOnTpg8eTJKSkpkxygqKkJ8fDy8vb3h7++PpUuXoqamRhazf/9+DB48GFqtFn369EFqamqtXJKTk9GzZ094enoiJiYGR48ebZbP3Npp1CokTogAgFqFlf154oQI9qsiIqI2x62LKgDo378/iouLpcehQ4ekfYsWLcLOnTuxbds2HDhwABcvXsSkSZOk/VarFfHx8aiqqsLhw4fx8ccfIzU1FStXrpRiCgsLER8fj9GjRyMnJwcLFy7Es88+iz179kgxn3/+ORYvXozExEScOHECUVFRiIuLQ2lpacuchFZmXGQgUmYMhkEvH+Iz6D2RMmMw+1QREVHbJNxYYmKiiIqKqnNfeXm56Nixo9i2bZu0LT8/XwAQmZmZQgghdu/eLdRqtTCZTFJMSkqK0Ol0orKyUgghxIsvvij69+8vO/aUKVNEXFyc9Dw6OlokJCRIz61WqwgKChJJSUlOfR6z2SwACLPZ7NTrWqsaq00cPndJ7Dj5mzh87pKosdpcnRIREZHTHP39dvsrVT///DOCgoLQq1cvTJ8+HUVFRQCA7OxsVFdXIzY2VooNDw9HSEgIMjMzAQCZmZkYMGAAAgICpJi4uDhYLBacOXNGirn9GPYY+zGqqqqQnZ0ti1Gr1YiNjZVi6lNZWQmLxSJ7tCcatQrG3l3x6KDuMPbuyiE/IiJq09y6qIqJiUFqairS0tKQkpKCwsJCjBo1ClevXoXJZIKHhwd8fHxkrwkICIDJZAIAmEwmWUFl32/fd7cYi8WCmzdv4tKlS7BarXXG2I9Rn6SkJOj1eukRHBzs9DkgIiKi1sGt1/4bP3689PfAgQMRExOD0NBQfPHFF/Dy8nJhZo5Zvnw5Fi9eLD23WCwsrIiIiNoot75SdScfHx/ce++9OHfuHAwGA6qqqlBeXi6LKSkpgcFgAAAYDIZadwPanzcUo9Pp4OXlBT8/P2g0mjpj7Meoj1arhU6nkz2IiIiobWpVRdW1a9dw/vx5BAYGYsiQIejYsSMyMjKk/QUFBSgqKoLRaAQAGI1GnD59WnaXXnp6OnQ6HSIiIqSY249hj7Efw8PDA0OGDJHF2Gw2ZGRkSDFEREREbn3335IlS8T+/ftFYWGh+OGHH0RsbKzw8/MTpaWlQggh5syZI0JCQsTevXvF8ePHhdFoFEajUXp9TU2NiIyMFGPHjhU5OTkiLS1NdOvWTSxfvlyK+eWXX4S3t7dYunSpyM/PF8nJyUKj0Yi0tDQpZuvWrUKr1YrU1FSRl5cnZs+eLXx8fGR3FTqivd39R0RE1BY4+vvt1nOqfvvtN0ybNg2XL19Gt27dMHLkSBw5cgTdunUDALz77rtQq9WYPHkyKisrERcXh7/85S/S6zUaDXbt2oW5c+fCaDTinnvuwcyZM7F69WopJiwsDN9++y0WLVqEdevWoUePHti4cSPi4uKkmClTpuDf//43Vq5cCZPJhEGDBiEtLa3W5HUiIiJqv1RCCOHqJNoLi8UCvV4Ps9nM+VVERESthKO/361qThURERGRu2JRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKQAFlVERERECmBRRURERKSADq5OgJrGahM4WliG0qsV8O/siegwX2jUKlenRURE1O6wqGrF0nKLsWpnHorNFdK2QL0nEidEYFxkoAszIyIian84/NdKpeUWY+6nJ2QFFQCYzBWY++kJpOUWuygzIiKi9olFVStktQms2pkHUcc++7ZVO/NgtdUVQURERM2BRZWTkpOT0bNnT3h6eiImJgZHjx5t8RyOFpbVukJ1OwGg2FyBo4VlLZcUERFRO8eiygmff/45Fi9ejMTERJw4cQJRUVGIi4tDaWlpi+ZRerX+gqoxcURERNR0LKqc8M477+C5557D008/jYiICGzYsAHe3t746KOP6oyvrKyExWKRPZTg39lT0TgiIiJqOhZVDqqqqkJ2djZiY2OlbWq1GrGxscjMzKzzNUlJSdDr9dIjODhYkVyiw3wRqPdEfY0TVLh1F2B0mK8i70dEREQNY1HloEuXLsFqtSIgIEC2PSAgACaTqc7XLF++HGazWXpcuHBBkVw0ahUSJ0QAQK3Cyv48cUIE+1URERG1IBZVzUir1UKn08keShkXGYiUGYNh0MuH+Ax6T6TMGMw+VURERC2MzT8d5OfnB41Gg5KSEtn2kpISGAwGl+Q0LjIQD0cY2FGdiIjIDfBKlYM8PDwwZMgQZGRkSNtsNhsyMjJgNBpdlpdGrYKxd1c8Oqg7jL27sqAiIiJyEV6pcsLixYsxc+ZMDB06FNHR0Xjvvfdw/fp1PP30065OjYiIiFyMRZUTpkyZgn//+99YuXIlTCYTBg0ahLS0tFqT14mIiKj9UQkhuJZJC7FYLNDr9TCbzYpOWiciIqLm4+jvN+dUERERESmARRURERGRAlhUERERESmARRURERGRAlhUERERESmARRURERGRAtinqgXZu1dYLBYXZ0JERESOsv9uN9SFikVVC7p69SoAIDg42MWZEBERkbOuXr0KvV5f7342/2xBNpsNFy9eROfOnaFSOb9Gn8ViQXBwMC5cuMDmoY3Ec9h0PIfK4HlsOp7DpuM5dIwQAlevXkVQUBDU6vpnTvFKVQtSq9Xo0aNHk4+j0+n4D38T8Rw2Hc+hMngem47nsOl4Dht2tytUdpyoTkRERKQAFlVERERECmBR1YpotVokJiZCq9W6OpVWi+ew6XgOlcHz2HQ8h03Hc6gsTlQnIiIiUgCvVBEREREpgEUVERERkQJYVBEREREpgEUVERERkQJYVLUSycnJ6NmzJzw9PRETE4OjR4+6OiW3kZSUhN/97nfo3Lkz/P39MXHiRBQUFMhiKioqkJCQgK5du6JTp06YPHkySkpKZDFFRUWIj4+Ht7c3/P39sXTpUtTU1LTkR3Eba9asgUqlwsKFC6VtPIcN+9e//oUZM2aga9eu8PLywoABA3D8+HFpvxACK1euRGBgILy8vBAbG4uff/5ZdoyysjJMnz4dOp0OPj4+mDVrFq5du9bSH8VlrFYrXnnlFYSFhcHLywu9e/fGa6+9JltzjedR7uDBg5gwYQKCgoKgUqmwY8cO2X6lztepU6cwatQoeHp6Ijg4GGvXrm3uj9b6CHJ7W7duFR4eHuKjjz4SZ86cEc8995zw8fERJSUlrk7NLcTFxYnNmzeL3NxckZOTIx555BEREhIirl27JsXMmTNHBAcHi4yMDHH8+HExbNgwMXz4cGl/TU2NiIyMFLGxseLkyZNi9+7dws/PTyxfvtwVH8mljh49Knr27CkGDhwoFixYIG3nOby7srIyERoaKp566imRlZUlfvnlF7Fnzx5x7tw5KWbNmjVCr9eLHTt2iB9//FH8/ve/F2FhYeLmzZtSzLhx40RUVJQ4cuSI+Mc//iH69Okjpk2b5oqP5BJvvPGG6Nq1q9i1a5coLCwU27ZtE506dRLr1q2TYnge5Xbv3i1WrFghvvrqKwFAbN++XbZfifNlNptFQECAmD59usjNzRVbtmwRXl5e4v/+3//bUh+zVWBR1QpER0eLhIQE6bnVahVBQUEiKSnJhVm5r9LSUgFAHDhwQAghRHl5uejYsaPYtm2bFJOfny8AiMzMTCHErf8oqdVqYTKZpJiUlBSh0+lEZWVly34AF7p69aro27evSE9PFw8++KBUVPEcNuyll14SI0eOrHe/zWYTBoNBvPXWW9K28vJyodVqxZYtW4QQQuTl5QkA4tixY1LMd999J1QqlfjXv/7VfMm7kfj4ePHMM8/Itk2aNElMnz5dCMHz2JA7iyqlztdf/vIX0aVLF9m/yy+99JLo169fM3+i1oXDf26uqqoK2dnZiI2Nlbap1WrExsYiMzPThZm5L7PZDADw9fUFAGRnZ6O6ulp2DsPDwxESEiKdw8zMTAwYMAABAQFSTFxcHCwWC86cOdOC2btWQkIC4uPjZecK4Dl0xDfffIOhQ4fiv//7v+Hv74/7778ff/3rX6X9hYWFMJlMsnOo1+sRExMjO4c+Pj4YOnSoFBMbGwu1Wo2srKyW+zAuNHz4cGRkZOCnn34CAPz44484dOgQxo8fD4Dn0VlKna/MzEw88MAD8PDwkGLi4uJQUFCAK1eutNCncX9cUNnNXbp0CVarVfZDBQABAQE4e/asi7JyXzabDQsXLsSIESMQGRkJADCZTPDw8ICPj48sNiAgACaTSYqp6xzb97UHW7duxYkTJ3Ds2LFa+3gOG/bLL78gJSUFixcvxp///GccO3YMzz//PDw8PDBz5kzpHNR1jm4/h/7+/rL9HTp0gK+vb7s4hwCwbNkyWCwWhIeHQ6PRwGq14o033sD06dMBgOfRSUqdL5PJhLCwsFrHsO/r0qVLs+Tf2rCoojYlISEBubm5OHTokKtTaVUuXLiABQsWID09HZ6enq5Op1Wy2WwYOnQo3nzzTQDA/fffj9zcXGzYsAEzZ850cXatxxdffIHPPvsMf/vb39C/f3/k5ORg4cKFCAoK4nkkt8fhPzfn5+cHjUZT6y6rkpISGAwGF2XlnubPn49du3Zh37596NGjh7TdYDCgqqoK5eXlsvjbz6HBYKjzHNv3tXXZ2dkoLS3F4MGD0aFDB3To0AEHDhzA+vXr0aFDBwQEBPAcNiAwMBARERGybffddx+KiooA/Occ3O3fZYPBgNLSUtn+mpoalJWVtYtzCABLly7FsmXLMHXqVAwYMABPPPEEFi1ahKSkJAA8j85S6ny193+/HcWiys15eHhgyJAhyMjIkLbZbDZkZGTAaDS6MDP3IYTA/PnzsX37duzdu7fWJeohQ4agY8eOsnNYUFCAoqIi6RwajUacPn1a9h+W9PR06HS6Wj+UbdFDDz2E06dPIycnR3oMHToU06dPl/7mOby7ESNG1Grl8dNPPyE0NBQAEBYWBoPBIDuHFosFWVlZsnNYXl6O7OxsKWbv3r2w2WyIiYlpgU/hejdu3IBaLf9p0mg0sNlsAHgenaXU+TIajTh48CCqq6ulmPT0dPTr149Df7dz9Ux5atjWrVuFVqsVqampIi8vT8yePVv4+PjI7rJqz+bOnSv0er3Yv3+/KC4ulh43btyQYubMmSNCQkLE3r17xfHjx4XRaBRGo1Hab28HMHbsWJGTkyPS0tJEt27d2k07gLrcfvefEDyHDTl69Kjo0KGDeOONN8TPP/8sPvvsM+Ht7S0+/fRTKWbNmjXCx8dHfP311+LUqVPi0UcfrfPW9vvvv19kZWWJQ4cOib59+7bZVgB1mTlzpujevbvUUuGrr74Sfn5+4sUXX5RieB7lrl69Kk6ePClOnjwpAIh33nlHnDx5Uvzzn/8UQihzvsrLy0VAQIB44oknRG5urti6davw9vZmS4U7sKhqJd5//30REhIiPDw8RHR0tDhy5IirU3IbAOp8bN68WYq5efOmmDdvnujSpYvw9vYWjz32mCguLpYd59dffxXjx48XXl5ews/PTyxZskRUV1e38KdxH3cWVTyHDdu5c6eIjIwUWq1WhIeHiw8//FC232aziVdeeUUEBAQIrVYrHnroIVFQUCCLuXz5spg2bZro1KmT0Ol04umnnxZXr15tyY/hUhaLRSxYsECEhIQIT09P0atXL7FixQrZrfw8j3L79u2r87+BM2fOFEIod75+/PFHMXLkSKHVakX37t3FmjVrWuojthoqIW5rU0tEREREjcI5VUREREQKYFFFREREpAAWVUREREQKYFFFREREpAAWVUREREQKYFFFREREpAAWVUREREQKYFFFREREpAAWVURETfDKK69g9uzZzXb81NRU+Pj4NOkYGzZswIQJE5RJiIjqxaKKiFqVzMxMaDQaxMfHuzoVmEwmrFu3DitWrHB1Knf1zDPP4MSJE/jHP/7h6lSI2jQWVUTUqmzatAl/+tOfcPDgQVy8ePGusUII1NTUNFsuGzduxPDhwxEaGtps76EEDw8PPP7441i/fr2rUyFq01hUEVGrce3aNXz++eeYO3cu4uPjkZqaKtu/f/9+qFQqfPfddxgyZAi0Wi0OHToEm82GpKQkhIWFwcvLC1FRUfjyyy+l11mtVsyaNUva369fP6xbt67BfLZu3VprWO3LL7/EgAED4OXlha5duyI2NhbXr1+X3mfx4sXw8fFB165d8eKLL2LmzJmYOHGiw+fg3//+N4YOHYrHHnsMlZWV0mfOyMjA0KFD4e3tjeHDh6OgoED2ugkTJuCbb77BzZs3HX4vInIOiyoiajW++OILhIeHo1+/fpgxYwY++ugj1LUm/LJly7BmzRrk5+dj4MCBSEpKwieffIINGzbgzJkzWLRoEWbMmIEDBw4AAGw2G3r06IFt27YhLy8PK1euxJ///Gd88cUX9eZSVlaGvLw8DB06VNpWXFyMadOm4ZlnnkF+fj7279+PSZMmSTm+/fbbSE1NxUcffYRDhw6hrKwM27dvd/jzX7hwAaNGjUJkZCS+/PJLaLVaad+KFSvw9ttv4/jx4+jQoQOeeeYZ2WuHDh2KmpoaZGVlOfx+ROQkQUTUSgwfPly89957QgghqqurhZ+fn9i3b5+0f9++fQKA2LFjh7StoqJCeHt7i8OHD8uONWvWLDFt2rR63yshIUFMnjy53v0nT54UAERRUZG0LTs7WwAQv/76a52vCQwMFGvXrpWeV1dXix49eohHH3203vfZvHmz0Ov14uzZsyI4OFg8//zzwmaz1frM33//vbTt22+/FQDEzZs3Zcfq0qWLSE1Nrfe9iKhpOri0oiMiclBBQQGOHj0qXdnp0KEDpkyZgk2bNuG//uu/ZLG3Xz06d+4cbty4gYcfflgWU1VVhfvvv196npycjI8++ghFRUW4efMmqqqqMGjQoHrzsQ+jeXp6StuioqLw0EMPYcCAAYiLi8PYsWPxf/7P/0GXLl1gNptRXFyMmJgYKb5Dhw4YOnRonVfb7nyvUaNG4fHHH8d7771XZ8zAgQOlvwMDAwEApaWlCAkJkbZ7eXnhxo0bd30vImo8FlVE1Cps2rQJNTU1CAoKkrYJIaDVavHBBx9Ar9dL2++55x7p72vXrgEAvv32W3Tv3l12TPvw2datW/HCCy/g7bffhtFoROfOnfHWW2/ddajMz88PAHDlyhV069YNAKDRaJCeno7Dhw/j73//O95//32sWLECWVlZ8PX1bfRn12q1iI2Nxa5du7B06dJanwMAOnbsKP2tUqkA3BrWvF1ZWZmUKxEpj3OqiMjt1dTU4JNPPsHbb7+NnJwc6fHjjz8iKCgIW7Zsqfe1ERER0Gq1KCoqQp8+fWSP4OBgAMAPP/yA4cOHY968ebj//vvRp08fnD9//q459e7dGzqdDnl5ebLtKpUKI0aMwKpVq3Dy5El4eHhg+/bt0Ov1CAwMlBVqNTU1yM7ObvDzq9Vq/O///i+GDBmC0aNHN3jXY13Onz+PiooK2dU5IlIWr1QRkdvbtWsXrly5glmzZsmuSAHA5MmTsWnTJsyZM6fO13bu3BkvvPACFi1aBJvNhpEjR8JsNuOHH36ATqfDzJkz0bdvX3zyySfYs2cPwsLC8L//+784duwYwsLC6s1JrVYjNjYWhw4dku7ey8rKQkZGBsaOHQt/f39kZWXh3//+N+677z4AwIIFC7BmzRr07dsX4eHheOedd1BeXu7QOdBoNPjss88wbdo0jBkzBvv374fBYHDotQDwj3/8A7169ULv3r0dfg0ROYdXqojI7W3atAmxsbG1CirgVlF1/PhxnDp1qt7Xv/baa3jllVeQlJSE++67D+PGjcO3334rFU1//OMfMWnSJEyZMgUxMTG4fPky5s2b12Bezz77LLZu3SoNs+l0Ohw8eBCPPPII7r33Xrz88st4++23MX78eADAkiVL8MQTT2DmzJnSMONjjz3m8Hno0KEDtmzZgv79+2PMmDEoLS11+LVbtmzBc88953A8ETlPJRqaIUlERHUSQiAmJgaLFi3CtGnTGnWMp556CuXl5dixY4eyyd3mzJkzGDNmDH766ac6C1MiUgavVBERNZJKpcKHH37YrF3blVBcXIxPPvmEBRVRM+OcKiKiJhg0aNBdWy+4g9jYWFenQNQucPiPiIiISAEc/iMiIiJSAIsqIiIiIgWwqCIiIiJSAIsqIiIiIgWwqCIiIiJSAIsqIiIiIgWwqCIiIiJSAIsqIiIiIgX8f4wJk0rQviKJAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geo = extract('Unitary Authority')\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "print(geo.shape)\n", + "visualise(geo)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cf1505a4", + "metadata": { + "papermill": { + "duration": 0.026583, + "end_time": "2025-02-16T13:30:16.428036", + "exception": false, + "start_time": "2025-02-16T13:30:16.401453", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A cluster analysis - KMeans - suggest 5 centroids may be suitable. The population appears to impact on the centroid over the area. " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "3218ecd6", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:16.483867Z", + "iopub.status.busy": "2025-02-16T13:30:16.482786Z", + "iopub.status.idle": "2025-02-16T13:30:16.866963Z", + "shell.execute_reply": "2025-02-16T13:30:16.865883Z" + }, + "papermill": { + "duration": 0.414679, + "end_time": "2025-02-16T13:30:16.869336", + "exception": false, + "start_time": "2025-02-16T13:30:16.454657", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = geo['Area (sq km)'].values\n", + "y = geo['est_pop'].values\n", + "data_to_fit = list(zip(x, y))\n", + "inertias = []\n", + "\n", + "for i in range(1,11):\n", + " kmeans = KMeans(n_clusters=i)\n", + " kmeans.fit(data_to_fit)\n", + " inertias.append(kmeans.inertia_)\n", + "\n", + "plt.plot(range(1,11), inertias, marker='o')\n", + "plt.title('Elbow method')\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('Inertia')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "045b53fb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:16.926883Z", + "iopub.status.busy": "2025-02-16T13:30:16.925924Z", + "iopub.status.idle": "2025-02-16T13:30:17.172277Z", + "shell.execute_reply": "2025-02-16T13:30:17.171293Z" + }, + "papermill": { + "duration": 0.277234, + "end_time": "2025-02-16T13:30:17.174719", + "exception": false, + "start_time": "2025-02-16T13:30:16.897485", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------ Centers --------\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[2.64922342e+02, 1.28320667e+05],\n", + " [1.64575217e+02, 2.27968500e+05],\n", + " [6.38820000e+01, 2.91735000e+04],\n", + " [1.81528900e+02, 1.77521778e+05],\n", + " [9.62899000e+01, 2.87261333e+05]])" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans = KMeans(n_clusters=5)\n", + "kmeans.fit(data_to_fit)\n", + "centers = kmeans.cluster_centers_\n", + "plt.scatter(centers[:, 0], centers[:, 1], c='red', s=100, alpha=0.5);\n", + "plt.scatter(x, y, c=kmeans.labels_)\n", + "plt.show()\n", + "print(\"------ Centers --------\")\n", + "centers" + ] + }, + { + "cell_type": "markdown", + "id": "aa621d2a", + "metadata": { + "papermill": { + "duration": 0.027809, + "end_time": "2025-02-16T13:30:17.230548", + "exception": false, + "start_time": "2025-02-16T13:30:17.202739", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### All data" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "f37c0bdd", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:17.288901Z", + "iopub.status.busy": "2025-02-16T13:30:17.288514Z", + "iopub.status.idle": "2025-02-16T13:30:20.348950Z", + "shell.execute_reply": "2025-02-16T13:30:20.347844Z" + }, + "papermill": { + "duration": 3.092874, + "end_time": "2025-02-16T13:30:20.351303", + "exception": false, + "start_time": "2025-02-16T13:30:17.258429", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(315669, 4)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geo = data_num\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "geo = geo.drop_duplicates().dropna()\n", + "geo = geo.loc[geo.est_pop < 9e5]\n", + "print(geo.shape)\n", + "visualise(geo)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "f9b66fcb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:20.411142Z", + "iopub.status.busy": "2025-02-16T13:30:20.410173Z", + "iopub.status.idle": "2025-02-16T13:30:39.848527Z", + "shell.execute_reply": "2025-02-16T13:30:39.847436Z" + }, + "papermill": { + "duration": 19.470328, + "end_time": "2025-02-16T13:30:39.850622", + "exception": false, + "start_time": "2025-02-16T13:30:20.380294", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = geo['Area (sq km)'].values\n", + "y = geo['est_pop'].values\n", + "data_to_fit = list(zip(x, y))\n", + "inertias = []\n", + "\n", + "for i in range(1,11):\n", + " kmeans = KMeans(n_clusters=i)\n", + " kmeans.fit(data_to_fit)\n", + " inertias.append(kmeans.inertia_)\n", + "\n", + "plt.plot(range(1,11), inertias, marker='o')\n", + "plt.title('Elbow method')\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('Inertia')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "84bb47c6", + "metadata": { + "papermill": { + "duration": 0.028807, + "end_time": "2025-02-16T13:30:39.909567", + "exception": false, + "start_time": "2025-02-16T13:30:39.880760", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A cluster analysis - KMeans - suggest 5 centroids may be suitable. The population appears to impact on the centroid over the area. " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "bb080ce0", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:39.969741Z", + "iopub.status.busy": "2025-02-16T13:30:39.968884Z", + "iopub.status.idle": "2025-02-16T13:30:45.145799Z", + "shell.execute_reply": "2025-02-16T13:30:45.144645Z" + }, + "papermill": { + "duration": 5.209848, + "end_time": "2025-02-16T13:30:45.148537", + "exception": false, + "start_time": "2025-02-16T13:30:39.938689", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------ Centers --------\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[2.16001231e+02, 1.07181088e+05],\n", + " [1.46898757e+02, 2.79987631e+05],\n", + " [5.51706700e+02, 7.15609000e+05],\n", + " [2.10517597e+02, 4.55918908e+05],\n", + " [2.20252450e+02, 1.91956420e+05]])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans = KMeans(n_clusters=5)\n", + "kmeans.fit(data_to_fit)\n", + "centers = kmeans.cluster_centers_\n", + "plt.scatter(centers[:, 0], centers[:, 1], c='red', s=100, alpha=0.5);\n", + "plt.scatter(x, y, c=kmeans.labels_, s= 0.5)\n", + "plt.xlabel('Area square km')\n", + "plt.ylabel('Estimated population')\n", + "plt.show()\n", + "print(\"------ Centers --------\")\n", + "centers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6f7bf4a", + "metadata": { + "papermill": { + "duration": 0.031206, + "end_time": "2025-02-16T13:30:45.210579", + "exception": false, + "start_time": "2025-02-16T13:30:45.179373", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "392c6f54", + "metadata": { + "papermill": { + "duration": 0.031454, + "end_time": "2025-02-16T13:30:45.272196", + "exception": false, + "start_time": "2025-02-16T13:30:45.240742", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Density of population" + ] + }, + { + "cell_type": "markdown", + "id": "2b3c8d80", + "metadata": { + "papermill": { + "duration": 0.029676, + "end_time": "2025-02-16T13:30:45.332229", + "exception": false, + "start_time": "2025-02-16T13:30:45.302553", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The metric _people per sq_km_ is skewed to the right. The difference between the arithmetical mean and median is rather large. The dispersion is also quite large. A 3D representation at a logarithm scale suggest the size of an area may limit its population density. \n", + "\n", + "We surmise the density of population may be restricted to its size, but is can vary immensely for a certain area. Certain factors such as use of the land and the geography itself may impact on the relationship between the population and its area. None of those factors were clearly captured in the data. " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "81a4b55c", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:45.394669Z", + "iopub.status.busy": "2025-02-16T13:30:45.394290Z", + "iopub.status.idle": "2025-02-16T13:30:45.970724Z", + "shell.execute_reply": "2025-02-16T13:30:45.969707Z" + }, + "papermill": { + "duration": 0.610783, + "end_time": "2025-02-16T13:30:45.972974", + "exception": false, + "start_time": "2025-02-16T13:30:45.362191", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Area (sq km)est_poppp_sq_m
count135.0000001.350000e+02135.000000
mean229.5041212.264777e+051690.937385
std255.5694676.274943e+051349.913246
min16.3177002.140000e+0397.047386
25%58.2706009.844850e+04473.015434
50%139.7923001.353810e+051343.116544
75%328.6507502.183305e+052835.794533
max1572.0308007.322403e+064657.925913
\n", + "
" + ], + "text/plain": [ + " Area (sq km) est_pop pp_sq_m\n", + "count 135.000000 1.350000e+02 135.000000\n", + "mean 229.504121 2.264777e+05 1690.937385\n", + "std 255.569467 6.274943e+05 1349.913246\n", + "min 16.317700 2.140000e+03 97.047386\n", + "25% 58.270600 9.844850e+04 473.015434\n", + "50% 139.792300 1.353810e+05 1343.116544\n", + "75% 328.650750 2.183305e+05 2835.794533\n", + "max 1572.030800 7.322403e+06 4657.925913" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols = ['Area (sq km)', 'est_pop', 'pp_sq_m']\n", + "geo = data.loc[:, cols]\n", + "geo = geo.dropna()\n", + "geo = geo.drop_duplicates()\n", + "geo.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "841ba07a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:46.035020Z", + "iopub.status.busy": "2025-02-16T13:30:46.034609Z", + "iopub.status.idle": "2025-02-16T13:30:46.307950Z", + "shell.execute_reply": "2025-02-16T13:30:46.306901Z" + }, + "papermill": { + "duration": 0.307108, + "end_time": "2025-02-16T13:30:46.310114", + "exception": false, + "start_time": "2025-02-16T13:30:46.003006", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(6,6))\n", + "\n", + "#ax = Axes3D(fig) # Method 1\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "\n", + "x = np.log10(geo['Area (sq km)'])\n", + "y = np.log10(geo.est_pop)\n", + "z = np.log10(geo.pp_sq_m)\n", + "\n", + "plt.figure(figsize = [20,16])\n", + "ax.scatter(x, y, z, c=x, marker='o')\n", + "ax.set_xlabel('Area - sq km')\n", + "ax.set_ylabel('Estimated pop')\n", + "ax.set_zlabel('Person per km')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "68611191", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:46.376292Z", + "iopub.status.busy": "2025-02-16T13:30:46.375896Z", + "iopub.status.idle": "2025-02-16T13:31:23.361371Z", + "shell.execute_reply": "2025-02-16T13:31:23.360155Z" + }, + "papermill": { + "duration": 37.055479, + "end_time": "2025-02-16T13:31:23.397784", + "exception": false, + "start_time": "2025-02-16T13:30:46.342305", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Area (sq km) Price pp_sq_m est_pop\n", + "count 7.853837e+06 7.853837e+06 7.853837e+06 5.208853e+06\n", + "mean 4.194698e+02 2.209744e+05 2.599774e+03 1.372225e+06\n", + "std 5.338312e+02 5.708082e+05 1.613246e+03 2.584126e+06\n", + "min 1.631770e+01 1.000000e+00 9.704739e+01 2.140000e+03\n", + "25% 7.461320e+01 1.025000e+05 9.843264e+02 1.340490e+05\n", + "50% 1.581281e+02 1.550000e+05 2.608139e+03 2.380160e+05\n", + "75% 4.144144e+02 2.407510e+05 4.011644e+03 4.418580e+05\n", + "max 1.572031e+03 9.890000e+07 5.218986e+03 7.322403e+06\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: pp_sq_m R-squared: 0.168\n", + "Model: OLS Adj. R-squared: 0.168\n", + "Method: Least Squares F-statistic: 1.586e+06\n", + "Date: Sun, 16 Feb 2025 Prob (F-statistic): 0.00\n", + "Time: 13:30:50 Log-Likelihood: -6.8430e+07\n", + "No. Observations: 7853837 AIC: 1.369e+08\n", + "Df Residuals: 7853835 BIC: 1.369e+08\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------\n", + "const 2080.2172 0.668 3115.083 0.000 2078.908 2081.526\n", + "Area (sq km) 1.2386 0.001 1259.249 0.000 1.237 1.241\n", + "==============================================================================\n", + "Omnibus: 2898113.039 Durbin-Watson: 0.000\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 500464.485\n", + "Skew: -0.305 Prob(JB): 0.00\n", + "Kurtosis: 1.925 Cond. No. 863.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "---- params / coeficient -------\n", + "const 2080.217194\n", + "Area (sq km) 1.238605\n", + "dtype: float64\n", + "------------p values----------\n", + "const 0.0\n", + "Area (sq km) 0.0\n", + "dtype: float64\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Population density (pp/km^2)')" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geo = data_num\n", + "print(geo.describe())\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "\n", + "x = geo[['Area (sq km)']] \n", + "y = geo[['pp_sq_m']]\n", + "x = sm.add_constant(x)\n", + "model = sm.OLS(y, x).fit()\n", + "print(model.summary())\n", + "print(\"---- params / coeficient -------\")\n", + "print(model.params)\n", + "print('------------p values----------')\n", + "print(model.pvalues)\n", + "\n", + "\n", + "plt.scatter(geo[['Area (sq km)']] , geo[['pp_sq_m']])\n", + "plt.xlabel(\"Area (sq kn)\")\n", + "plt.ylabel(\"Population density (pp/km^2)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "f05bd10e", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:31:23.470230Z", + "iopub.status.busy": "2025-02-16T13:31:23.469849Z", + "iopub.status.idle": "2025-02-16T13:31:46.283598Z", + "shell.execute_reply": "2025-02-16T13:31:46.282549Z" + }, + "papermill": { + "duration": 22.855241, + "end_time": "2025-02-16T13:31:46.285501", + "exception": false, + "start_time": "2025-02-16T13:31:23.430260", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(7853837, 4)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geo = data_num\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "print(geo.shape)\n", + "visualise(geo)\n" + ] + }, + { + "cell_type": "markdown", + "id": "02891f7f", + "metadata": { + "papermill": { + "duration": 0.032246, + "end_time": "2025-02-16T13:31:46.351048", + "exception": false, + "start_time": "2025-02-16T13:31:46.318802", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A cluster analysis - KMeans - suggest 5 centroids may be suitable. The population appears to impact on the centroid over the area. " + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "df194312", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:31:46.419615Z", + "iopub.status.busy": "2025-02-16T13:31:46.419213Z", + "iopub.status.idle": "2025-02-16T13:36:26.344167Z", + "shell.execute_reply": "2025-02-16T13:36:26.342953Z" + }, + "papermill": { + "duration": 279.996234, + "end_time": "2025-02-16T13:36:26.380813", + "exception": false, + "start_time": "2025-02-16T13:31:46.384579", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = geo['Area (sq km)'].values\n", + "y = geo['pp_sq_m'].values\n", + "data_to_fit = list(zip(x, y))\n", + "inertias = []\n", + "\n", + "for i in range(1,11):\n", + " kmeans = KMeans(n_clusters=i)\n", + " kmeans.fit(data_to_fit)\n", + " inertias.append(kmeans.inertia_)\n", + "\n", + "plt.plot(range(1,11), inertias, marker='o')\n", + "plt.title('Elbow method')\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('Inertia')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "99bc0f0c", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:36:26.453507Z", + "iopub.status.busy": "2025-02-16T13:36:26.453066Z", + "iopub.status.idle": "2025-02-16T13:39:04.629263Z", + "shell.execute_reply": "2025-02-16T13:39:04.628146Z" + }, + "papermill": { + "duration": 158.254367, + "end_time": "2025-02-16T13:39:04.669109", + "exception": false, + "start_time": "2025-02-16T13:36:26.414742", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------ Centers --------\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[ 236.8852271 , 1496.3982078 ],\n", + " [ 98.17824303, 3939.25590807],\n", + " [ 367.61878674, 529.88931171],\n", + " [1572.03080001, 4852.80537318],\n", + " [ 71.12833405, 2706.46815779]])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans = KMeans(n_clusters=5)\n", + "kmeans.fit(data_to_fit)\n", + "centers = kmeans.cluster_centers_\n", + "plt.scatter(centers[:, 0], centers[:, 1], c='red', s=100, alpha=0.5);\n", + "plt.scatter(x, y, c=kmeans.labels_)\n", + "plt.xlabel(\"Area (km^2)\")\n", + "plt.ylabel(\"density of population (pp/km^2)\")\n", + "plt.show()\n", + "print(\"------ Centers --------\")\n", + "centers" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "9b1cd3e2", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:39:04.742910Z", + "iopub.status.busy": "2025-02-16T13:39:04.742509Z", + "iopub.status.idle": "2025-02-16T13:39:05.162040Z", + "shell.execute_reply": "2025-02-16T13:39:05.160979Z" + }, + "papermill": { + "duration": 0.459915, + "end_time": "2025-02-16T13:39:05.164430", + "exception": false, + "start_time": "2025-02-16T13:39:04.704515", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "76211.43788470532\n", + "Chi2 value= \n", + "p-value= 0.0\n", + "Degrees of freedom= 134\n", + "\n" + ] + } + ], + "source": [ + "from scipy.stats import chi2_contingency\n", + "from scipy.stats import chi2\n", + "geo = geo.loc[:,['Area (sq km)','est_pop']]\n", + "geo = geo.drop_duplicates()\n", + "geo = geo.dropna()\n", + "\n", + "nl = \"\\n\"\n", + "stat, p, dof, expected = chi2_contingency(geo)\n", + "print(stat)\n", + "\n", + "print(f\"Chi2 value= {chi2}{nl}p-value= {p}{nl}Degrees of freedom= {dof}{nl}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "d280b31a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:39:05.238236Z", + "iopub.status.busy": "2025-02-16T13:39:05.237181Z", + "iopub.status.idle": "2025-02-16T13:40:13.869905Z", + "shell.execute_reply": "2025-02-16T13:40:13.868975Z" + }, + "papermill": { + "duration": 68.672649, + "end_time": "2025-02-16T13:40:13.872543", + "exception": false, + "start_time": "2025-02-16T13:39:05.199894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "data['label'] = kmeans.labels_\n", + "data.to_csv('data_with_labels.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "1c9d65fe", + "metadata": { + "papermill": { + "duration": 0.035585, + "end_time": "2025-02-16T13:40:13.943468", + "exception": false, + "start_time": "2025-02-16T13:40:13.907883", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Is there a relationship between houses prices and clusters of density and areas?" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "2a8ba5fe", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:40:14.015891Z", + "iopub.status.busy": "2025-02-16T13:40:14.015515Z", + "iopub.status.idle": "2025-02-16T13:40:46.775130Z", + "shell.execute_reply": "2025-02-16T13:40:46.774042Z" + }, + "papermill": { + "duration": 32.832975, + "end_time": "2025-02-16T13:40:46.811890", + "exception": false, + "start_time": "2025-02-16T13:40:13.978915", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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HELENS',\n", + " 'STAFFORD',\n", + " 'STOCKTON-ON-TEES',\n", + " 'TAMWORTH',\n", + " 'WALSALL',\n", + " 'WARRINGTON',\n", + " 'WARWICK',\n", + " 'WIGAN',\n", + " 'WIRRAL',\n", + " 'WOLVERHAMPTON',\n", + " 'WORCESTER',\n", + " 'YORK'}" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grouping = data.groupby(['label'])['Name'].apply(set).reset_index()\n", + "g_4 = grouping.loc[4,'Name']\n", + "g_4\n" + ] + }, + { + "cell_type": "markdown", + "id": "7b21e246", + "metadata": { + "papermill": { + "duration": 0.043893, + "end_time": "2025-02-16T13:40:48.704639", + "exception": false, + "start_time": "2025-02-16T13:40:48.660746", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Findings and conclusion\n", + "\n", + "We applied a chi-square test. We use the H0 (Null Hypothesis): There is no relationship between the area and estimated population. We also used the the H1 (Alternative Hypothesis): There is a relationship between an area and an estimated population. $p-value = 0$ and $p < 0.01$ suggest we can reject the Null hupothesis. Therefore, we surmise there is a relationship between an area and an estimated population. \n", + "\n", + "We have shown a strong Pearson correlation between the area (square km) and estimated population may exist - i.e., 0.94. A weaker Pearson correlation between the area and the people per square km was approximated to 0.40. Some KMeans analysis suggests the 5 centroids across each type of geography and across the whole dataset. Each cluster appears to be mostly guided by the estimated population or population density. Therefore, we surmise and confirm that an estimated population may vary across an area. The latter may impact on the population density. Other factors may impact on the population density. Further research based on employment, geography features, and other aspects should be further conducted.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "deee784c", + "metadata": { + "papermill": { + "duration": 0.037409, + "end_time": "2025-02-16T13:40:48.777667", + "exception": false, + "start_time": "2025-02-16T13:40:48.740258", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kaggle": { + "accelerator": "none", + "dataSources": [ + { + "datasetId": 4404514, + "sourceId": 8376714, + "sourceType": "datasetVersion" + } + ], + "dockerImageVersionId": 30664, + "isGpuEnabled": false, + "isInternetEnabled": true, + "language": "python", + "sourceType": "notebook" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "papermill": { + "default_parameters": {}, + "duration": 873.639834, + "end_time": "2025-02-16T13:40:50.743126", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2025-02-16T13:26:17.103292", + "version": "2.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Data engineering and science/Simple analysis/UK real estate.ipynb b/Data engineering and science/Simple analysis/UK real estate.ipynb new file mode 100644 index 0000000..4a9b260 --- /dev/null +++ b/Data engineering and science/Simple analysis/UK real estate.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":10378395,"sourceType":"datasetVersion","datasetId":6047000}],"dockerImageVersionId":30822,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Exploring United Kingdom Real Estate\n\n","metadata":{}},{"cell_type":"markdown","source":"We use the [real-estate in United Kingdom](https://www.kaggle.com/datasets/patriciaryserwelch/geonames-gb) to explore further the data and properties that could positively or negatively impact analysis or any model fitting for prediction purposes. ","metadata":{}},{"cell_type":"markdown","source":"# Upload libraries and data","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as mcolors\nimport seaborn as sns\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:10.793747Z","iopub.execute_input":"2025-01-11T12:05:10.794119Z","iopub.status.idle":"2025-01-11T12:05:13.023228Z","shell.execute_reply.started":"2025-01-11T12:05:10.794089Z","shell.execute_reply":"2025-01-11T12:05:13.022058Z"}},"outputs":[{"name":"stdout","text":"/kaggle/input/geonames-gb/geo_locations.csv\n/kaggle/input/geonames-gb/summarised_property_price_data.csv\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"def addlabels(x,y):\n for i in range(len(x)):\n plt.text(i,y[i],y[i],fontsize=20,horizontalalignment='center')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:13.025347Z","iopub.execute_input":"2025-01-11T12:05:13.025914Z","iopub.status.idle":"2025-01-11T12:05:13.031163Z","shell.execute_reply.started":"2025-01-11T12:05:13.025881Z","shell.execute_reply":"2025-01-11T12:05:13.029895Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"font = {'family' : 'normal',\n 'weight' : 'bold',\n 'size' : 22}\n\nplt.rc('font', **font)\nplt.rc","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:13.033261Z","iopub.execute_input":"2025-01-11T12:05:13.033683Z","iopub.status.idle":"2025-01-11T12:05:13.066067Z","shell.execute_reply.started":"2025-01-11T12:05:13.033649Z","shell.execute_reply":"2025-01-11T12:05:13.064891Z"}},"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}}],"execution_count":3},{"cell_type":"markdown","source":"## Property prices overtime\n\nThe distribution of the summarised prices is shown and described in the [metadata description](https://www.kaggle.com/datasets/patriciaryserwelch/geonames-gb). We find out the data is reasonably clean and few cleaning pre-processing operations may be required, which enhances usability.\n\nThe size of the dataset has been reduced by 90% - the statistical summarisation has positively simplified the dataset. It may demand some computing resources to process. \n\n","metadata":{}},{"cell_type":"code","source":"source = '/kaggle/input/geonames-gb/summarised_property_price_data.csv'\ndata = pd.read_csv(source)\ndata.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:13.067052Z","iopub.execute_input":"2025-01-11T12:05:13.067318Z","iopub.status.idle":"2025-01-11T12:05:15.871401Z","shell.execute_reply.started":"2025-01-11T12:05:13.067292Z","shell.execute_reply":"2025-01-11T12:05:15.870263Z"}},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"(807245, 16)"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"data.dtypes","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:15.872466Z","iopub.execute_input":"2025-01-11T12:05:15.872914Z","iopub.status.idle":"2025-01-11T12:05:15.885173Z","shell.execute_reply.started":"2025-01-11T12:05:15.872878Z","shell.execute_reply":"2025-01-11T12:05:15.884155Z"}},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"Month int64\nYear int64\nLocation object\nProperty Type object\nOld/New object\nTenure object\nPrice:Count int64\nPrice:Min int64\nPrice:Mean float64\nPrice:Std float64\nPrice:Q1 float64\nPrice: Q2 float64\nPrice:Q3 float64\nPrice:Max int64\nPrice:IQR float64\nPrice:Range int64\ndtype: object"},"metadata":{}}],"execution_count":5},{"cell_type":"code","source":"data.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:15.887846Z","iopub.execute_input":"2025-01-11T12:05:15.888182Z","iopub.status.idle":"2025-01-11T12:05:15.926062Z","shell.execute_reply.started":"2025-01-11T12:05:15.888150Z","shell.execute_reply":"2025-01-11T12:05:15.925106Z"}},"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":" Month Year Location Property Type Old/New Tenure Price:Count \\\n0 1 2023 ABBOTS LANGLEY D N F 2 \n1 1 2023 ABBOTS LANGLEY S N F 1 \n2 1 2023 ABBOTS LANGLEY T N F 3 \n3 1 2023 ABERAERON D N F 4 \n4 1 2023 ABERAERON S N F 1 \n\n Price:Min Price:Mean Price:Std Price:Q1 Price: Q2 Price:Q3 \\\n0 674500 887250.000000 300873.935395 780875.0 887250.0 993625.0 \n1 438250 438250.000000 NaN 438250.0 438250.0 438250.0 \n2 210000 344166.666667 116198.465280 310000.0 410000.0 411250.0 \n3 171000 285250.000000 81385.400001 260250.0 305000.0 330000.0 \n4 157500 157500.000000 NaN 157500.0 157500.0 157500.0 \n\n Price:Max Price:IQR Price:Range \n0 1100000 212750.0 425500 \n1 438250 0.0 0 \n2 412500 101250.0 202500 \n3 360000 69750.0 189000 \n4 157500 0.0 0 ","text/html":"
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MonthYearLocationProperty TypeOld/NewTenurePrice:CountPrice:MinPrice:MeanPrice:StdPrice:Q1Price: Q2Price:Q3Price:MaxPrice:IQRPrice:Range
012023ABBOTS LANGLEYDNF2674500887250.000000300873.935395780875.0887250.0993625.01100000212750.0425500
112023ABBOTS LANGLEYSNF1438250438250.000000NaN438250.0438250.0438250.04382500.00
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312023ABERAERONDNF4171000285250.00000081385.400001260250.0305000.0330000.036000069750.0189000
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\n
"},"metadata":{}}],"execution_count":6},{"cell_type":"markdown","source":"Some standard deviation were set to NaN. There is only one observation. Therefore, we set it to -1, to prevent errors and miscomputations.","metadata":{}},{"cell_type":"code","source":"data.isnull().sum(axis = 0)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:15.927865Z","iopub.execute_input":"2025-01-11T12:05:15.928156Z","iopub.status.idle":"2025-01-11T12:05:16.113282Z","shell.execute_reply.started":"2025-01-11T12:05:15.928129Z","shell.execute_reply":"2025-01-11T12:05:16.111802Z"}},"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"Month 0\nYear 0\nLocation 0\nProperty Type 0\nOld/New 0\nTenure 0\nPrice:Count 0\nPrice:Min 0\nPrice:Mean 0\nPrice:Std 183259\nPrice:Q1 0\nPrice: Q2 0\nPrice:Q3 0\nPrice:Max 0\nPrice:IQR 0\nPrice:Range 0\ndtype: int64"},"metadata":{}}],"execution_count":7},{"cell_type":"code","source":"data['Price:Std'] = data['Price:Std'].fillna(-1)\ndata.isnull().sum(axis = 0)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:16.114682Z","iopub.execute_input":"2025-01-11T12:05:16.115151Z","iopub.status.idle":"2025-01-11T12:05:16.300848Z","shell.execute_reply.started":"2025-01-11T12:05:16.115108Z","shell.execute_reply":"2025-01-11T12:05:16.299838Z"}},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"Month 0\nYear 0\nLocation 0\nProperty Type 0\nOld/New 0\nTenure 0\nPrice:Count 0\nPrice:Min 0\nPrice:Mean 0\nPrice:Std 0\nPrice:Q1 0\nPrice: Q2 0\nPrice:Q3 0\nPrice:Max 0\nPrice:IQR 0\nPrice:Range 0\ndtype: int64"},"metadata":{}}],"execution_count":8},{"cell_type":"markdown","source":"### Monthly and Yearly distribution\nWe discover the number of observations has increased between 2017 and 2024. It may not reflect the actual activity of properties sales or exchanged titles. \n\nThe location may have been more refined between 2017 and 2024. Therefore, the monthly distribution appears to be negatively impacted. It appears no data for 2011 may have yet to be included within the dataset. It may cause some inaccuracies in predictions and analysis. The number of entries through time should be used for any data consumption.","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(20, 12))\nsns.histplot(data=data, x='Year', bins=24, kde=True)\nplt.title('Distribution of number of observation per year')\nplt.xlabel('Year')\nplt.ylabel('Frequency')\nplt.grid(False)\nplt.show()\ndata.Year.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:16.301894Z","iopub.execute_input":"2025-01-11T12:05:16.302280Z","iopub.status.idle":"2025-01-11T12:05:20.428999Z","shell.execute_reply.started":"2025-01-11T12:05:16.302240Z","shell.execute_reply":"2025-01-11T12:05:20.427899Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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807245.000000\nmean 2016.660658\nstd 6.304367\nmin 2001.000000\n25% 2013.000000\n50% 2019.000000\n75% 2021.000000\nmax 2023.000000\nName: Year, dtype: float64"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"plt.figure(figsize=(20, 12))\nsns.histplot(data=data, x='Month', bins=12, kde=True)\nplt.title('Distribution of number of observation per year')\nplt.xlabel('Year')\nplt.ylabel('Frequency')\nplt.grid(False)\nplt.show()\ndata.Year.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:20.430196Z","iopub.execute_input":"2025-01-11T12:05:20.430474Z","iopub.status.idle":"2025-01-11T12:05:23.822089Z","shell.execute_reply.started":"2025-01-11T12:05:20.430449Z","shell.execute_reply":"2025-01-11T12:05:23.820995Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"
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0/HSSedhEMOOQQlJSVQVRWtra3YuHGjVs52ODLtgxdccAF+9atfAQD+8Y9/4L777kuqFOAbb7yBpqYm7fEFF1ww5Ouzs7Nx1FFH4eCDD0Z5eTkyMzPR3NyM1atX4+WXX0Zvby8A4JlnnkFFRQX++Mc/DlhGbm6utg/s2rVL24alpaWDlrEcS9lEM/bVJ554Av/7v/8LAJg9ezZOPfVUTJs2DX6/H6tXr8bTTz8Nn88HALjvvvtwwgkn4JRTThn13zSUZcuW4cUXXwQAzJ07F2eddRYmTpyIlpYWvPnmm3j77behhies49prr0V5efmw2/3aa6/FH/7wB+1xWloaTjzxRBxxxBEoLCxEc3Mz/v3vf2vfibt378bChQvxxRdfoLi4eNh1VlUVF198MT755BMA4euFxYsXo7y8HK2trfjggw9GVdrw9NNPR1FREZqbm6GqKpYvX45bbrklqfVZvny59vicc85J2B9SVVWce+65+Pvf/649l52djVNOOQWHHHIIcnNzUVdXh1deeQWrVq0CED4XHX/88Vi9enVSx6gex9xgzLgPICIinVgWriMiIqKUoEemmaqGSzjFLucHP/jBsO83WPbI22+/HTfTNVFJtFitra3qXXfdpT733HMJfz5YKb5kIMFM0muvvTap2amjyTQTs5Jnz56tbt26dcBr29ra1NNPPz1ufW644YZBl21Eplms0e47ya7X66+/HvceM2fOVDdu3Djgdd3d3QMyRy644IJBl9t/n3A4HGpGRsagZcveeuutuCyk008/Pem/dTg1NTVqQUGBtuysrCz1pZdeSvjae++9N26G87777qt2d3cPuuzRZmslK3b5Yt8tLi4edF947LHH4tb/pz/9qe7rPppMM7FOV155ZcLynitWrFDT09O1159xxhnqxIkTVSCcsZOofNTbb78dl1Hzk5/8ZNB16X/Mic/yRz/6UcJzzerVq+Oye91ud8LjQvj000/jshhnz549aGbIZ599pk6YMEF77YIFCwaUn4v9XPqfH9PT09Wnnnpq0HUZrbVr18b9DWVlZeqHH3444HWBQEC9/vrr49bpm9/85pCluEZTuk6GdTdyv7nwwgu1182YMWPI7wFVDWd6XHbZZWp7e3vCn8u2D27atCnud/7xj38M+XrhvPPO035n3rx5CV+zbt06de7cueoTTzwxaLaNqobP/YsWLYo7D61fv37I90/2/NZfsvu4mfuq0+lU77777oTb9osvvogrs/eNb3wj6b91OIm+/wGov/71rxOu/+uvv65mZ2drry8oKBgy8+nFF1+MW/5RRx2l7ty5M+Fr33rrrbjKAuecc86gy02UcVhQUKD++9//HvmHMIRrrrlGW/6kSZMGPfZivfPOO3HrtWrVqoSvu+uuu+Jed8YZZ6iNjY0JX/vEE0/E7Yu/+MUvBn1/I4+5/p85kPx9ABERWYdBMyIiIhoTvYJmvb29cQPEhx566LDvN9hA+P/+7//qGpzQM2j2ve99L+nfHU3QDICal5c36ACLqqpqX19fXA+ltLS0QQcd7B40mz9/ftKfSzAYVJcsWRK3Tp9//nnC1yYaaB2ulFHsfpmWljbo4PBIXXXVVSNaj9tuuy3u9UOVvDMzaAaES7OuXr16yN+JHYyvqKhIatlGB82GG6xU1fiBRPHv/PPPH/J3fvKTn2ivLS8vH/R1/Y85ce4bKtDzySefxJWGPOOMMwZ97WGHHaa9bsqUKWpTU9OQ671u3bq48/krr7yS8HWJjqPHHntsyGWP1sknnxy3n33yySdDvr5/udWhjiujg2ZGrbuR+40ICgNQX3zxxaT/1sHIuA/G9sgc6vgROjs71czMTO13fvOb3yR8XV9fX1KBBrHMWbNmacv88Y9/POTrjQ6ambmvDleu9S9/+Uvc6zdv3jzMX5mcRPvMNddcM+TvvPDCC3Gv/9nPfpbwdX6/P66X3sEHHzxsT6433ngjbtmDBXH6f98qiqKuXLkyuT96BNavXx/3Pm+99dawvxP7vT5nzpyEr2lqaoo7fk455ZRh+4o9+OCD2us9Hs+g5w0jj7n++8pI7gOIiMg6DhARERFJID09HRUVFdrj5ubmUS+rrq5O+78oeyIDj8eD22+/3fD3+eUvf5mwjJ6QlpaGO++8U3vs8/nwxBNPGL5eZvvwww+xbt067fENN9ww5OficDhw7733xpWluv/++5N6ryVLluC0004b8jWXXXaZ9n+fz4cvvvgiqWUPpbu7G4899pj2eOnSpcOux3XXXYcZM2Zojx944IExr4devv/97+OQQw4Z8jWxn2NtbS327Nlj9GoNy+1244477hjyNd/5zncG/E5s+a3hfqeuri7pv1Uc44qiDPqagw8+GN/73ve0xy+//DIaGxsHvO6jjz7Cxx9/rD2+7777UFRUNOT7z5s3Dz/4wQ+0x6J06XAOOeQQXHjhhUm9diT27NmjldwDgMsvvxwHH3zwkL/z29/+Nq48a7LnAr2Zue567jd6fg/Lug/GHp+vvvoq2trahnz9Cy+8oJVfUxQF559/fsLXpaWlweFIbqgmKysL119/vfb4jTfeSOr3jGDmvjpnzhz85Cc/GfI15513HrKysrTHq1evTmrZI1VcXDxsCcJTTz0VJ5xwgvb48ccfh9/vH/C6v//971qpaEVR8MgjjwxbUvCEE07AySefrD1Odl8/66yzcPTRRyf12pGYO3cuDj/8cO3xI488MuTrOzo68I9//EN7fMkllyR83UMPPaQdP5mZmXj44YeHPFcB4euF/fbbDwDQ29sbd70Uy6xjzqz7ACIiGjsGzYiIiEga+fn52v9bWlpGvZzYPggfffTRWFZJV0uXLh12oG+snE5n3IDmYBYsWIC5c+dqj19++WUjV8sSr776qvZ/l8uV1Ocyffr0uIGt2AHAoVx88cXDvmbixImorKzUHm/ZsiWpZQ/lgw8+iBuovfzyy4f9HZfLFRd42rJlS1xvOSsl8zkecsghcYNbenyOY3X88cfHBf0TmT9/PpxOp/ZY9I8Zyn777Rf3O8n+rSeccEJS/c9i9wO/349//etfA17zzDPPaP/fZ599sHTp0qTWITbw8O6772q9rZL9HT3961//0vrDAckdJ/n5+XF9NVesWKH1sjGTmeuu536j5/ewrPvgueeeqx2ffX19cX2WEnnyySe1/3/zm99MukfhcI477jjt/19//TXa29t1We5Imbmvfve73x02YJKRkYH9999fe2zUd8W55547aG+4WLHHTWNjY8LjInZfP+644zBv3ryk1iF2v3377bdH/Dt6+/73v6/9//nnnx8yoPzMM89owTCXyzXoesV+Nueffz5KSkqSWpfY4Hayn81wRnvMmXEfQERE+mDQjIiIiKSRnZ2t/b+jo2PUyznggAO0/7/33nu4+uqr4fV6x7Bm+liwYIHh77H//vujtLQ0qdfGBoc+++wzqKpq1GpZInZW+cEHH5z0QEXsgOzevXvjMiYGc9hhhyW17NigmR77ZOzf6HA4sHjx4qR+r/+gs1Ez8EfC7XbjwAMPHPZ1Ho8nLjNBhmP70EMPHfY1aWlpKCgoGNPvJPu3xh7bQzn44IPjlv/pp58OeM0HH3yg/X/JkiVJLRcIn4dF1mZ7e3tSA9ZGnSNj9+/y8vK4gfShxB4nfr8fn3/+ue7rNhwz113P/Sb2e/inP/0pnn/++VF/x8i6D5aXl8cNnscGxfprbGzEW2+9pT2+4IILkn6f4ZSVlWn/V1UVNTU1ui17JMzcV636zk0k2eOm//dzouPmww8/1P4/kn099vtk06ZN6OzsHPZ3jLwmPeecc7Rr+p6enriAV3+xmWgnnnhi3P4sdHR0xFUOGO1n88knnyT9e0MZ7TFnxn0AERHpg0EzIiIikkZsoCw3N3fUyznzzDPjAiT33HMPKisrccYZZ+BPf/oTNmzYYEmAaPr06Ya/R2z22HBiZzB7vV40NDQYsUqW+eqrr7T/z58/P+nfE6V8hK1btw77O8NlDAmxpaK6urqSXqfBxP6N06dPj8vuGMrMmTORlpaWcDlWKSwsjCuNORS9P8exGs32TzQwONzvJPu3jvY8kGhfjx2onD17dtLLdbvdccFNUXJsKEadI/U6F1hxnJi57nruN7GlEdva2nDGGWdgypQpuOqqq/DPf/4zYUnHwci8D8YGv1auXInq6uqEr3v22WcRCAQAhIPh3/72t5Na/urVq3Hdddfh2GOPRVVVFXJycuBwOKAoivav/3lzuDKRRjFzX7XqOzeRZI+bvLw8TJw4UXvc/7ipra2NOy5Gsq/Hfp+EQiHU1tYO+frc3FwUFxcnvfyRys7Oxrnnnqs9HqxE45YtW7Bq1Srt8WAVAdavXx+XKTraz6ahoSFhWcxYRh5zZtwHEBGRPhg0IyIiImnEzgKOHegaqZycHDz99NNxAYSenh48//zzuPLKKzFv3jyUlZXhggsuwCuvvBJXTshIyZTvGatks8wADCht09raqvfqWCr270m2jE+i1ybzuXg8nuRXLEKPwO1o/0an0xl3jMmw7UfzGQL6fI5jNZp1N3KfGe15oP9+0NnZib6+Pu3xlVdeGTdwONy/+vp67XeTyfIw6hxp5rlAb2auu177DRDuJxVbjg4Adu/ejXvvvRdnnnkmysrKsN9+++H666/H5s2bB30f2ffBM844Q7vWCIVCePrppxO+LjYLbenSpXGZeols3boVixYtwmGHHYbf//73WLFiBaqrq9HZ2TnsecCKMqLA+PjOTUSv46Z/L98TTzwx6f08IyMj7neH29fNuB6NLdH48ccfY9OmTQNe89e//lX7f3l5+aClV/t/NnPnzk36s+kfYBvsszHjmDPjcyciIn0waEZERERS6OvriyuDN9YZsIsXL8aaNWtwyimnJGzu3djYiKeeegonn3wy5s+fj/fee29M75cMl8tl+Hv0HzgZSv+spGTK+dhJ7KzyZDOwgPiZ6YDcn8to/0Yg/u+U+W+kkRvteaD/fqBnxoroWTMUo86Roz1O0tPT43rKWXGcmLnueu03woMPPojHH38c++yzz4CfqaqKdevW4f/+7/8wZ84cXHDBBQmDJbLvg9nZ2Tj11FO1x4lKNG7fvj2uf1Vsj6VENm7ciCOPPBIrV64c8LPMzExUVFRg6tSpmD59uvYvllUTCex8nI2FHc+3ZlyPHnbYYXEZh/2zzYLBIB5//HHt8YUXXjjoehn92Zh1zJnxuRMRkT54xiYiIiIprFmzBj6fT3ucTG+j4cyaNQsvvvgi9u7di1deeQUrVqzA+++/P6BszaZNm3DcccfhhRdewIknnjjm97VST09P0q/tP3AQ21MuFWRnZ2sDLckMlgr9SzjJ/LnErttI/kYg/u+U+W+kkRvteaD/ftB/4LusrGzU+4qVM+xHe5z09fXFZSJbcZyYue567TexvvOd7+CCCy7AqlWr8MYbb+C9997DRx99FJeZoaoqnnrqKXz22WdYtWoV8vPztZ/ZYR+84IILtAyzL774Ahs3bsScOXO0nz/11FPa//Py8nDSSScNuixVVXHJJZegqakJAKAoCi688EKcf/75g/bmVFU14eQgs9n5OBuLnp6epNd5JOfbCRMmjDoDe7S/p7dLL70U11xzDQDg8ccfx29+8xstQPqvf/0r7np8sNKMwMDPZsqUKXGB1pHoH7iy8zFHRETGYdCMiIiIpPDmm2/GPV64cKFuy66qqsIVV1yBK664AkA4SPbiiy/i4YcfxrZt2wAAgUAAl112GbZv3y7NYMNojKQvWf+eMonKRSmKMuJ1GMnAq5EKCgq0oNlI+uck87nIInbdRvI3BoPBuKwOmf/G0RrNvgvIs/+OxWjPA/33g/z8fDidTm1A+9e//jUuvfRSfVbSRKM9TmQ4F5i57nrtN/0pioIjjjgCRxxxBIBwkOS9997Dc889hyeffFI75jZv3oybb74Zd9xxh/a7dtgHv/Wtb6GkpET7TJ588kn8+te/1n4em3121llnIT09fdBlffTRR1i9erX2+C9/+QsuueSSId8/mbKTZrDzcTYWDQ0NSQfNhjpu+gdnHn30URx33HFjX0ELXXjhhfjFL36hVZN4/fXXtaBxbObZggULMGvWrEGX0/+z+fe//61bfzA7H3NERGQcTo0gIiIiy/l8Pvz5z3/WHrvdbixatMiw95s9ezZ++ctfYtOmTTj//PO152tra7FixQrD3tcMGzZsSPq169ev1/6fn5+fsC9H7OzeZGeOx/aQsVJsSbAvv/wy6d/r/9p9991Xt3XSW+zfuG3btqS30ZYtW+L6BMn8N47WaPZdv98vRX+3sRrJeSD2tTNmzIj7maIocftGdXX12FfOArHHybp165L+PRnOBWauu177zXDS09OxePFiPPTQQ9iwYQPKysq0n/XvCWaHfdDlcuGcc87RHj/11FNaubbPPvssrmfbBRdcMOSy3nnnHe3/s2bNGnbwHgB27Ngx0lU2hJ2Ps7FI9rhpa2vDnj17tMf9j5vKysq44JuM+/pIFRYW4vTTT9cei0BZU1MTXn75Ze35obLMAGDmzJlxj/X8bOx8zBERkXEYNCMiIiLL/fa3v43rZ/bd7353RI3VR8vtduO+++6Ly0hJ1Kjc7XbHPQ6FQoav22itXbs26WyB2Oy+gw46KGFmTmyZrJqamqSWG9u7ZTixZXL0/lwPO+ww7f9r1qwZ0Eh+MK+//rr2/4kTJ6KiokLX9dJT7N8YCoXw1ltvJfV7sX9j/+Wkith9t6WlJS5IOJg1a9bA7/cbuFbm6J+5O5hPP/0ULS0t2uNDDjlkwGtis34T9Xuxg9j9u66uDmvXrk3q92KPE7fbjW984xu6r9twzFx3PfebZE2dOhXXX3+99ri+vj5u2YA99sHYYNjOnTvx4YcfAogvzVhVVTVsFn3s9+z++++f1HuPZLJP7PWMkd+5djvOxiLZ46b/93P/48blcuHII4/UHsu6r4/U97//fe3/L7/8MpqamvDkk09qJdmzsrLigs6JlJWVxQXO9PxsjD7miIjInhg0IyIiIku9+OKLWLZsmfbY5XLFDaAZLT8/HyUlJdrjQCAw4DX9y+60t7cbvl6jFQwGsXz58mFf9/HHH8fNjh6sx0rsTOi1a9cOG3hQVRWPPfZYciuL+M9W7881tj9dIBDAX/7yl2F/Z8eOHXjjjTcSLkNGRx55ZFxwKDZjczCBQAAPP/yw9njWrFmYNm2aEatnqdh9NxgM4tNPPx32dx599FEjV8k0b775ZlxGw2Bijwm3241vfetbA17z7W9/W/v/ihUrRpSNJIslS5bE9b9J5jhpa2uLy3o67rjjLCnda+a667nfjET/jJv+38N22AcPP/zwuHJxTz75JEKhUNx2OO+884btgyQy1ADE9X0bTCAQSGqfEIz8zrXzcTYWzz77LDo7O4d9XexxU1JSknCySuy+/uyzz2p9tuzs2GOPxdSpUwGEs7mffPLJuNKMZ511VlL9BmM/m4ceeiiuD/JYGH3MERGRPTFoRkRERJbo7OzEDTfcgNNPPz1utvM999wz5j4Fu3btSvq1tbW1cRlIkydPHvCa/s/FljWU0W9+85shBz79fr/WmB0Il8r6zne+k/C1sYM6nZ2deO6554Z877vuuithtt5gYj9bvT/XBQsWYL/99tMe33bbbUPuG6FQCFdddVVcppHogyerjIwMXHjhhdrj119/HS+++OKQv/PHP/4xrlyY7H/jaE2ePBnl5eXa47/+9a9Dvv6TTz6JCybamc/nw89+9rMhX/P555/H/b0nn3xy3AQC4bjjjsPBBx8MIDy4+N3vfhddXV0jWp9kBiKNVFVVhaVLl2qPH3rooWGDqNdff33cd4NVx4mZ667XftPd3T2inlaxpfwyMzNRXFwc93O77IOx2WbPPfcc3nrrrbgsluFKMwLh7GbhvffeGzYYc9NNN+Grr75Keh2N/M6183E2Fo2NjXGTvxJ5+eWX8a9//Ut7fOGFFw6oYiCenzBhAgCgq6sLF110kdbPL1lWn2/7UxQlrvzib37zm7gsxOFKMwpXX301MjIyAAB79uzB1VdfPeJ1SfTZGH3MERGRPTFoRkRERKapr6/HSy+9hGuuuQZVVVW47bbb4mZ4XnfddboMmFx88cU49thj8c9//nPIzCiv14sLL7xQG5BIT0/HCSecMOB1+fn5cTPhly1bNqIBQTM5HA54vV7813/9F7Zt2zbg5x0dHTj//PPjSihee+21AwYphVmzZsWVq/n5z3+ecJZ/KBTCPffcg2uvvTZhmcfBxAblHnjggRH1QUnGb3/7W+3/bW1tWLJkCbZs2TLgdb29vbjsssvw6quvas995zvfSbpUj5Wuv/56FBQUaI8vuOCCuL8j1gMPPBCXybnvvvvi8ssvN3wdrRJb8mn58uV49tlnE77u3XffxdKlSxEIBEa0/8rK4XDg73//O66++uqE5SbXrFmDk046ScvocbvduPXWWwdd3gMPPID09HQA4R5NCxYswCeffDLkOgQCAfz73//Gt7/9bfz4xz8ew1+jj1/96lfaIHUgEMDJJ5+csJRsMBjEjTfeiPvvv1977uijj8Ypp5xi2rr2Z9a667XfNDQ0YMqUKfjJT36CL774Ysj3/OCDD3Dbbbdpj0866aSE2Vh22AdjJ580Nzfjyiuv1B7Pmzcvqe+TxYsXa/9vbW3FJZdcknCgv6+vD//93/+N2267bdjstVix37nPPvss3nvvvaR/Nxl2Ps5Gy+Fw4A9/+AN++9vfxl3TCm+++WZcwLSgoAC//OUvEy4rLS0N999/v/Y99Nprr+G4447D1q1bh1yH3t5evPjii/jWt74Vd90ji0suuUTLQozte7vPPvvg6KOPTmoZJSUluP3227XHf/7zn3HWWWcN29+svb0dTz75JBYsWIBnnnlmwM+NPuaIiMieXMO/hIiIiCh5F1xwgTYTFAjPCu/o6EBbW9ugpVRyc3Pxu9/9TrfBe1VV8e6772LFihXIzc3FkUceiYMOOggVFRXIysqC1+vFl19+ieeffx6tra3a791www1xwYdYF198Mf77v/8bAPD222+jvLwcU6ZMiSspc/DBB1ueqXLZZZfh+eefx4YNGzB//nycddZZOPTQQ5GWlobNmzfj6aefjusfN3/+fNxwww1DLvPGG2/EmWeeCSA8o/qggw7C+eefj0MOOQQulws7d+7ECy+8gI0bNwIAbr75Ztx0001Jre9FF12EBx98EEC4sft+++2HyspKFBUVxQ1KDDfwOpglS5bgyiuvxH333QcA2Lx5Mw444ACcfvrpOPzww5GRkYGvvvoKzzzzTFx23tSpU3H33XeP6j3NVlFRgfvvvx/nnXceVFVFV1cXTjrpJBxzzDFYsmQJiouLUVNTgxdeeAFr1qzRfs/j8eDRRx+NO15Tzc9+9jM8/PDD6OrqQigUwrnnnovly5fjW9/6FvLy8lBXV4e33npL609yySWX4J133hlRtqqMbrjhBtx6662455578Oqrr+Kcc87BtGnT0NnZiffffx8vvfRSXAm8m266CXPmzBl0eQcddBAeeOABXHrppQiFQli3bh0OPfRQHHrooVi0aBEmT56MzMxMdHR0oLa2FmvXrsWqVau08+tFF11k+N88nP322w+33XYbrrvuOgDhnktHHXUUli5dimOOOQa5ubnYtWsXnnvuubjAemFhIf76179aGkw1a9313G+6u7tx99134+6778b06dNxxBFHYO7cuSguLoaiKKipqcF7772Hf//731qgwePxDBq8tcM+uO++++LQQw/F6tWrASBu4koyWWZA+Dri2GOPxTvvvAMA+Pvf/47Vq1fj3HPPxYwZM+D3+7F582b885//1L6zli1bhhtvvDGp5Z9//vlYtmwZfD4fOjs7sXDhQpSUlKCsrCyutOJrr72GysrKpJYZy87H2WiJ4+aXv/wlnnzySZx55pmoqqpCa2sr3nrrLbz11ltxwbS77747YVavcPLJJ2PZsmXaddTKlSsxa9YsLFy4EEcddRQmTJgAj8eDtrY27N27F1988QVWrVqlZV8uWLDA2D94FCZMmIAlS5YMmNBzySWXjGg5P/7xj/H5559rmeP/+Mc/8NJLL+H444/HggULUF5eDpfLBa/Xi507d+KLL77ARx99pN1//OAHPxiwTKOPOSIisimViIiIaAweeeQRFcCo/pWVlanXX3+9Wl9fP6r3mzx5csLXLFy4cMTr8qMf/UgNhUKDvm9fX5967LHHDrmMhQsXDvi92J+vWLEi6b+z/99x0003Jf26d955R83NzR32b953333VmpqapNbliiuuGHZ5iqKoN998s7pjx46453fs2DHksm+44YZhl93fRRddpP3soosuGnL5oVBI/eEPf5j0vjBr1ix1z549Qy5zxYoVQ67fYJLdpqPx6KOPqm63O6m/MScnR33nnXeGXWYyx9tYjHb5kydP1n7vkUceGfK1Tz75pOpwOIb9TJYuXar29vYmveyRrIPRv5PomLvqqquS2hd+/OMfJ7UeqqqqL730UlLnlv7/vve97yVc3miPo7G47bbbVEVRklrviooK9csvvxx2mTfddNOQ3wOyrrtR+03/5SZ7TnrzzTeH/Qxk3wfvuuuuAe+tKIq6a9eupJexd+9edeLEiUn9XRdddJEaCoXinhvuWuOBBx5QnU7nkMvt/7090n3cjH01GSO5VkhW/30mEAiop59++rB/p6Io6u9+97uk3+fBBx9U09PTR7yv33LLLQmXZ/T3+XCef/75uPV0Op3q3r17R7Wsm266Kanv9f7/HnvssYTLM/KYG8mxSURE8mBOMRERERnK4XAgIyMDFRUVOPDAA3H22Wfj17/+Nf7zn/+gpqYGt912G0pLS3V9z1tvvRU//OEPMW3atGFfe8QRR+DVV1/FfffdN+QM57S0NLz55pt48skncdppp2HKlCnIysqSclb0Mcccg08++QSLFy9OuH4ejwc/+tGP8Nlnn6GioiKpZf7pT3/CHXfcgfz8/IQ/nzNnDl5++eVRzbz91a9+hffeew+XXHIJ5syZg9zcXF1L3yiKgj/96U94/fXXceCBBw76uuLiYtxyyy34/PPPUVVVpdv7m+W73/0uvvzyS5xyyilwuRIXlPB4PLjooouwefNmHHPMMSavoTXOP/98vPbaa3ElVmOVlpbi9ttvxyuvvKKVf0sFd999Nx555BFMmTIl4c8nTZqEp59+Gvfcc0/Syzz55JOxbds2XHfddUNmSgDhDOJTTz0VTz31lJbpKYPrr78eq1atwqJFiwY9f+fm5uKaa67Bxo0bMX/+fJPXcHBmrLse+01FRQUefPBBnHTSScjLyxvy/bKysnDJJZdg48aNcWXSBiP7PnjuuecOOP9+85vfxKRJk5JexoQJE/Dpp5/i7LPPHvS7cJ999sHy5cuxfPnyEV+H/OAHP8CaNWvwox/9CAcccADy8/Pjssz0YOfjbKScTif+8Y9/4Pbbb0dZWVnC18yZMwdvvfUWrr322qSXe9lll2Hr1q24/PLLhz2OiouLcd555+Gll16KK8Esk5NOOimumsMJJ5yg9W8bqWXLlmHdunU455xzhs2Wr6qqwqWXXop33nln0P69Rh9zRERkP4qqJii6TERERJQi6urqsG7dOuzYsQOtra0IBALIycnB5MmTcfDBB4/6ht0udu7ciY8++gjV1dVwOByYNGkSjj/++GEHYAbT19eHlStXYsuWLejs7ERFRQXmzp2LQw45ROc1N87OnTuxatUq1NXVoa+vD6WlpZg1axYOP/zwlOlT4fV68e6776K6uhptbW0oLCzE5MmTsXDhQmRmZlq9epZQVRWffvopPvvsM7S0tKCgoADTp0/HokWLtB48qUhVVXzwwQfYunUr6uvrUVhYiLlz5+LII48c08Cfqqr44osvsH79ejQ1NaG7uxvZ2dmoqKjA7NmzMXv27EGDt7Kor6/He++9h9raWnR1daG4uBjTp0/HUUcdhbS0NKtXb0hGr7te+00oFMKmTZuwZcsW7N27Fx0dHXC5XCgoKMDs2bNx0EEHjfqclAr74HBqamqwcuVK7N27FwC0v+2ggw6yeM2SZ+fjLJF33303btJJ7JCa3+/HypUrsX37djQ3N6O0tBQHHHDAmLdXIBDAJ598gi1btqCpqQk+nw85OTmoqqrCnDlzMGPGDOkDObt27cLUqVO1z+vvf/+7Vvp7LPr6+vDhhx9ix44daGpqQjAYRG5uLiZPnoy5c+di6tSpI1peKhxzREQ0dgyaERERERERERERDWOooBkN7uabb8ayZcsAQOu1msoTVoiIyN5SYyotERERERERERERSSUYDOKvf/2r9viiiy5iwIyIiKTGoBkRERERERERERHp7vHHH8fu3bsBhPvMXn755RavERER0dAYNCMiIiIiIiIiIiJdvfvuu7j22mu1x2eeeSZmzJhh4RoRERENz95dcYmIiIiIiIiIiMhyNTU1WLp0KQCgrq4O9fX12s88Hg9uu+02q1aNiIgoaQyaERERERERERER0Zj4fD6sXbt2wPMOhwP3338/9t13XwvWioiIaGQYNKOUEwqFUFNTg5ycHCiKYvXqEBEREREREVEK6Orqinvc3t5u0ZrIqaOjQ/u/oigoKirCYYcdhquvvhqHH344Py8iIrKUqqro6OhAZWUlHI7BO5cpqqqqJq4XkeH27t2LiRMnWr0aREREREREREREREQkkT179qCqqmrQnzPTjFJOTk4OgPDOn5uba/HaEBERERERERERERGRldrb2zFx4kQtfjAYBs0o5YiSjLm5uQyaERERERERERERERERAAzb0mnwwo1ERERERERERERERERE4wSDZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERERERERERERERDTuMWhGRERERERERERERERE4x6DZkRERCmssaMP2xo7rV4NIiIiIiIiIiIi6TFoRkRElMK++9fVWHLne9hc1271qhAREREREREREUmNQTMiIqIU1dTZh0217fAHVTzyn51Wrw4REREREREREZHUGDQjIiJKUeuq27T/v/BFNVq6fBauDRERERERERERkdwYNCMiIkpR6/ZGg2Z9gRCeXr3bwrUhIiIiIiIiIiKSG4NmREREKerLSNBs/6o8AMDjq3bBHwxZuUpERERERERERETSYtCMiIgoRa2r9gIAfrFkFoqz01HX3ot/ra+zdqWIiIiIiIiIiIgkxaAZERFRCqpv70V9ex8cCnDApHxccNgkAMDyD3dau2JERERERERERESSYtCMiIgoBYnSjDPKcpCZ5sIFh0+C26lgza5WfLnXa+3KERERERERERERSYhBMyIiohS0LhIYmz8h3M+sNMeDk/arBAAs/2CnRWtFREREREREREQkLwbNiIiIUtCX1eFMs/2q8rTnLj5iCgDg5S9r0NDRa8VqERERERERERERSYtBMyIiohSjqirWRcozzq/K157ff2I+DpyUD39QxVMf77Zo7YiIiIiIiIiIiOTEoBkREVGKqWnrRXOXDy6HglnlOXE/u+TIqQCAJz7ajb5A0IrVIyIiIiIiIiIikhKDZkRERClG9DObWZ4Dj9sZ97Ml88pRnutBU2cfXltXa8HaERERERERERERyYlBMyIiohTz5d6B/cwEt9OBCxdMBgA88sFOqKpq6roREZGxmjr78Ic3t+DltTXo6gtYvTpERERERES24rJ6BYiIiEhf66oj/cwm5Cf8+bmHTMRdb3+FL/e24bPdXhw0ucDEtSMiIiM99P52/HnldgBAusuBRTNLsHR+BY6dVYocj9vitSMiIiIiIpIbg2ZEREQpRFXVITPNAKAoOx2nHVCJ5z7di0c+2MGgGRFRCvmqvhMAkJ3uQmdfAG9sqMcbG+qR5nTg6BnFWDq/AiftV4k0F4uOEBERERER9cc7JSIiohSyu6UbbT1+pLkcmFGWM+jrLj5iKgDg9fV1qG/vNWv1iIjIYDuaugAAD154EF67+pu46th9MK0kC75gCP/e1ICfPbcWv39zi8VrSUREREREJCcGzYiIiFKIyDKbXZE7ZBbBnMpczKnIRTCk4os9XpPWjoiIjOQPhrC7pRsAMK0kG3Mqc/HzE2bi7Z8txJs/PRpnHlgFAFgX+a4gIiIiIqLUcs/bX+F7yz/B+181Wr0qtsWgGRERUQoR/cz2m5C4NGOsSYWZAMBMMyKiFLGnpRvBkIoMtxNluena84qiYEZZDs4/bCIAaIE1IiIiIiJKLa+vr8M7mxvQ1Nln9arYFoNmREREKeTLvV4AwPxB+pnFKs/zAABq2xg0IyJKBdsbw6UZpxZnQVGUAT+fVJgFAKhp60FfIGjquhERERERkbEa2nuxsbYdigIcvW+J1atjWwyaERERpYhQSMX66nYAwH5JBM0qIkGzOgbNiIhSguhnNrUkK+HPi7PTkJnmhKoCe1t7zFw1IiIiIiIy2HtfNQEA5k/IQ1F2+jCvpsEwaEZERJQidjR3obMvAI/bgX1Ksod9fTTTjAOnRESpYHskaDatOHHQTFEUrTTv7maWaCQiIiIiSiXvbmkAACycwSyzsWDQjIiIKEWs2xvuZzavMg8u5/Bf8RV5GQCYaUZElCp2NHUCAKYNkmkGAJOLwkGzXc1dpqwTEREREREZLxhS8X4k02zRTAbNxoJBMyIiohSxdgT9zIBoecbatl6oqmrUahERkUm08ozFg2cbTy4KB9R2tTDTjIiIiIgoVazd60Vbjx+5Hhf2r8q3enVsjUEzIiKiFCEyzZLpZwYApbnh+tZ9gRC83X7D1ouIiIzX1RdAfXsfAGBq0eCZZizPSERERESUet7d0ggA+Oa+JUlVH6LB8dMjIiJKAYFgCBtq2gEA8yfkJ/U76S4nirPTAISzzYiIyL5ElllRVhryMt2Dvm4KM82IiIiIiFLOyq3hoBn7mY0dg2ZEREQpYFtjF3r8QWSlOTGtePAMg/7KIyUa69p7jFo1IiIywXatNOPQ3wGip9nulm6EQizNS0RERERkdy1dPnwZadmxkP3MxoxBMyIiohQgLo7mTciDw6Ek/XvluRkAmGlGRGR3OxqTC5pV5HngcijwBUKo7+C5n4iIiIjI7t7/qhGqCswqz0FZrsfq1bE9Bs2IiIhSwLrqkfUzEypEphmDZkREtrajqRMAMLVk6KCZy+lAVUF4wsQu9jUjIiIiIrK9lZF+Zswy0weDZkRERCngy73hoNn8qvwR/Z4oz1jjZdCMiMjORE+zacXZw752UqSv2W4GzYiIiIiIbC0UUvHeV+xnpicGzYiIiGzOHwxhY207AGD/0WaasacZEZFtqaqq9TSbNkymGQBMLgz3NdvV0mXoehERERERkbE21rajqdOHrDQnDp5caPXqpAQGzYiIiGxua30HfIEQcj0uTIoMhCarIo89zYiI7K65y4eO3gAUBUl9D0wuigTNmGlGRERERGRr725pAAAcsU8x0lwM9+iBnyIREZHNidKM+1XlQ1GUEf1ubE8zVVV1XzciIjKeKM04IT8DHrdz2NeLwNruFgbNiIiIiFJRMKTyHn+cWLmVpRn1xqAZERGRzUX7mY2sNCMQ7WnW7QuivTeg63oREZE5tjd2AgCmFg9fmhEAJkd6mjHTjIiIiCj1NHX24Yj/exuXPvqp1atCBmvr8eOz3V4ADJrpiUEzIiIim1tX7QUA7Ddh5EEzj9uJgkw3gHC2GRER2Y/WzyzJoJnINGvr8aOt22/YehERERGR+V78ogb17X14Z3MDmjv7rF4dMtAHXzchGFIxvSQLE0fYroMGx6AZERGRjYVCKrbUdQAA5o0iaAYA5Vpfsx7d1ouIiMyzozESNCvJTur1GWlOlOakAwB2tXQZtl5ERGStvkAQP3pyDf73hfVWrwoRmeiltTXa/z/Z2WLhmpDRVm4RpRlLLV6T1MKgGRERkY15e/zwB8N1ystyPaNaRmxfMyIish/R0yzZ8owAMLkoPBOVJRqJiFLX/e9uw2vr6vD4R7uYbUI0Tuxs6sLaPV7t8cc7GDRLVaqqRvuZzWRpRj0xaEZERGRjTZGb37wMN9Jco/taF33Nahk0IyKynWBI1QJfIwmaTSoMv3Z3C4NmRESp6OuGTvxpxTbt8cbadgvXhojMIrLMMtxOAMBqBs1S1tb6TtS198LjduCwqYVWr05KYdCMiIjIxkTQrDg7bdTLqMhlphkRkV3VeHvgC4aQ5nKgMj8j6d+LZpqxPCMRUapRVRU3PL8OvmBIe25jDYNm483Gmnb84u9fMstwHFFVFS9+UQ0AuOq4fQCEA+ZtPexhm4re3dIAADh8WhE8kSAp6YNBMyIiIhtr6vQBAIqz00e9DC3TrJ1BMyIiu9keKc04pSgTToeS9O+xPCMRUer625q9+HhHCzxuB847dCIAZpqNN6qq4rq/r8Wzn+7Box/utHp1yCQba9uxrbELaS4HLjx8MqYWZ0FVgTW7mG2WirTSjDNYmlFvDJoRERHZWFOHyDQbfdCsIi+cmVDX1qPLOhERkXm2N3YCGFlpRgCYVBgOmrE8IxFRamnu7MNtr20CAPz0+Bk4YW45AGaajTertjVjQ2Sbfx7T34pSmyjNeNysUuR43Dh0SrhkH/uapZ6uvgA+2Rnergya6Y9BMyIiIhtr7hp7eUb2NCMisq8dkUyzqcXZI/q9yUXhIFtdey96/UHd14uIiKzxq1c3wdvtx+yKXHzvqKmYW5ELANjW2Mnz/Tjy4Pvbtf+v3eNFKKRauDZkhlBIxctfhINmpx5QCQA4NNLn6uPtDJqlmg+3NcMfVDGpMHPEk+doeAyaERER2VhTh37lGTt6A+jsC+iyXkREZA4RNJtWMrKb5YJMN3LSXVBVYG8rs82IiFLB+1814vnPq6EowG/OmA+304GSnHQUZ6chpAJb6jqsXkUywdb6Dry7pRGKAridCtp7A9jJHqYpb83uVtS09SIn3YVFM0sBAIdNCwfN1le3oYv3+ill5dZwP7OFM0qgKMmXaKfkMGhGRERkY02Rps7FOaMPmmWnu5DjcQEA6phtRkRkK9sbI0GzEc4wVRQFk9jXjIgoZfT6g/ifF9YDAC5aMAUHTMwHED7fz45km7Gv2fjwcCTL7FtzyrFfVT4A4AuWaEx5L35RDQD41rxyeNxOAEBVQSYm5GcgEFLx2e5WK1ePdPbZLi8A4Mh9iq1dkRTFoBkREZGNNXWFM82KskZfnhEAKiLZZgyaERHZR68/iJpIP8rRlGWZzKAZEVHKuPvtr7CruRvluR78/IQZcT+bUxkJmrGvWcpr6OjFC5+HS/RddvQ0LXi6lkGzlOYPhvDaujoAwCn7V8b97LBIicbV7GuWUurbw2M34nqe9MWgGRERkY01dYw90wwAyvMyAAC1kcFXIiKS367mbqgqkOtxoXAUkycmFYYDbbtbGDQjIrKzLXUdePC9cHbRslPmIsfjjvv5HGaajRuPfbgLvmAIB00uwEGTC7B/JGjGTLPU9p+vm9DS5UNxdhqOmF4U9zOtrxmDZinDFwihOTKBunSMY0GUGINmRERENqWqqlaesWQMPc0AoCKXmWZERHazo6kTADC1JHtUvQyimWbsc0KUyjbVtuOG59dhN7NKU1IopOK/n1+HQEjF4jllWDKvfMBr5kYyzTbVtiMUUs1eRTJJty+Axz/aBQC47JtTAQDfiATNNta2oy8QtGrVyGAvfxHOLjxxfgVczvjhfhE0+2KPF71+7gOpQIwDuZ0KCjLHVnWIEmPQjIiIyKa6fEH0BUIAgKLssV0olUfKM9a2M2hGRGQX20bZz0yYXBgJmjHTjChlfbqzBWf/eRWe/Hg3/vKf7VavDhngq4ZOrNnVCo/bgZtPmZvwNVOLs+FxO9DtC/Kcn8L+vmYv2nr8mFyUicVzwsHTqoIMFGalwR9UWZ4zRfX4gnhjQ6Q04wETBvx8anEWSnLS4QuEWKYzRYjSjKU5HjgcI584R8Nj0IyIiMimRGnGzDQnMtNcY1oWe5oREdnPjqaxBc0mRTLN9rR0I8jMA6KU8/5XjbjwL6vR0RsAAGyu67B4jcgIosTuvqU5qMzPSPgap0PBzHL2NUtlwZCKh9/fAQC49KipcEYG0hVFYV+zFPf25np0+YKoKsjAgZPyB/xcURQt24x9zVJDQ2QsqISlGQ3DoBkREZFNiZT84jGWZgRiMs0YNCMisg0RNJtaMrqgWUVeBtxOBf6gyp6WRCnmjQ11uHT5p+jxBzGrPAcAsLW+A6rKAHmqqW4NB82qChIHzATR12xDTZvh60Tme3NDHXa3dCM/042zDqqK+9n+VfkA2NcsVb0UKc14yv6Vg5brPox9zVJKQyTTrCyXQTOjMGhGRERkUyJoNtbSjEB44BQA6jhoSkRkG1rQbJSZZk6HgokF4Wwz9joiSh3Pf74XP3ryM/iCIfzXvHI8d8UCKArQ2u1HU6fP6tUjnVV7w9fvEwbJMhPmRPqabaxlplkqeuj9cPnVCw+fPKAKyQGR7CMGzVJPW48f725pBACcmqA0o3DY1CIAwJpdrfAHQ6asGxlHZJqV5ngsXpPUxaAZERGRTYlBDz0zzVq7/WwOTERkA95uH1q6wt8DU4pGFzQDoiUa2eOGKDU88dEu/Oy5tQiGVJx5YBXuOe8byPW4tfPE1nqWaEw1e1sjQbMkM81YnjH1rNnVgs92e5HmdODCBZMH/Hz/qjwAwM7mbni7GThPJW+sr4MvGMLMshzMjGQVJ7JvaTbyM93o8QexrprZpnZXz0wzwzFoRkREZFN6lmfM9biQmeYEwL5mRER2ILLMynM9yEoffV/LyYWRoBkzzYhs74GV2/A/L6yHqgIXLZiM3521H1zO8LDPvqXZAIAt7GuWcpLNNJtVngNFCWcoNEayFCg1PPReuJfZ6d+YkDDzJD8zTctKZ7ZZanlxbTUA4JQDKod8ncOh4JAp7GuWKphpZjwGzYiIiGwqGjQbe3lGRVHY14yIyEbGWppRmBTJPtnd0jXmdSIi6zz36R783+ubAQA/WjQdy06ZC4cj2ttmZkxfM0ot1UlmmmWluzA1cs7fxBKNKWNnUxfe2FgHAPj+N6cO+jqRbbZ2D7OMUkVDRy9WbWsGEO5nNhzR14xBM/urb48EzZhpZhgGzYiIUpy324crn/wMb2+qt3pVSGfNOpZnBICKSNCsrp19zYiIZLe9MRI0Kxlb0IyZZkSp4c0N4Wv97x05Ff9vySwoihL38xllDJqloh5fEM2RUr1V+ZnDvn42+5qlnOUf7oSqAsfMLMG+ZYOX5ztgYj4A4Is9rSatGRntyz1tCKnhLNKJhcMf/6Kv2Sc7WhAMqUavHhmosSM80ZmZZsZh0IyIKMX97dO9eHVdLW58cQNCvDBKKXqWZwSA8tzw7FRmmhERyU9kmk0bY6bZ5EhPs93N3VBVXicQ2dWeSF/Co2cUJ/x5NNOsk8d6ChGlGbPTXcjNGL5UL/uapZ4v93oBAGccWDXk6w6YVAAAWLu3jeeAFFEX6WtVVTB8wAwA5lTmIjvdhY6+ALNNbcwfDGn97dnTzDgMmhERpbiPtofT9au9Pfh0F2eVpRJxoVSkQ3lGICbTjEEzIiLpbRdBszFmmomZyR19AbR2+8e8XkRkPlVVsTsSNJs0SLbBlKIsuBwKOvsCqOG1XsqI7WfWP7swkbnMNEs5Nd7w8TxcptHsihykOR1o6fJhTwsri6QCcd8u7uOH43QoOHhKOHj6MUs02pboSelyKCjI1GcsiAZi0IyIKIUFQ2pcvernP6+2cG1Ib7pnmrGnGVHKUFWVs4hTWCikYqfW0yx7TMvyuJ0ozw2f/3c1s68ZkR01dfrQ4w9CUQbva5XmcmhB9q11LNGYKpLtZybMiQTNtjd2oscXNGy9yByBYAgNkTJtlflDB07SXU6tPOfnLNGYEkSmWXmSQTMAOFTra9ZsyDqR8RoiQbPSnPS43qWkLwbNiIhS2MaadnT0BSAmHb76ZQ36Arw5SgW9/iA6egMAgBK9e5oxaEZka6qq4rt/XY3j/rASXzd0Wr06ZID6jl70+INwORRUJTlQOpRJokRjC/uapSpVVbG1vkPLSqHUsqc1fOxW5HqQ7nIO+jr2NUs91d7wtp+Qn9x3QWmOB8XZ6QipwBbuB7ZX39GHkAq4nQqKs4a/JzygKg8AsHZPm9GrRiYQ9+1i8lMyRF+z1TtaOMHOpuojwdLSEWx3GjkGzYiIUpgozbhoRgkq8jxo7w1gxeZGi9eK9CAafrudSlL9C5LBTDOi1NDU6cP7XzVhe1MXzv7zKqyv5sBIqtnRGM4Im1SYCbdz7Ld0kyMlnXY1M2iWSgLBEFZta8YtL2/EN29fgRPueA9L73ofnX0Bq1eNdCb6mVUNU55NBM0YLEkdeyOZZiOZQCGyzdjXzP5qIhMhyvM8SWWcHDApHwDwBTPNUsJoMs3mT8iDx+1Aa7cfX3FynS3FZpqRcRg0IyJKYSJodsT0YpxyQCUA4AWWaEwJzZHSjEVZ6Un1L0hGRV74Zrupsw++QEiXZRKR+bbElN1q6fLh3Ac/wsfbWYIllWzXSjOOrZ+ZMLmIQbNU0eML4l/r6/Cz577Awb/+N8576CP89YMd2sB6W4+fWUYpaHfz0P3MBGaapZ6RlmcEgDkV4aDZhhpOqrE7ETSrzEtu++9flQ8AWF/TDn+Q93t2p2WajSBoluZy4MBJ7GtmZw2RYGkZM80MxaAZEVGKiu1ndvi0Ipz+jQkAgHc2N6Ct22/lqpEOtH5mOfo1fi3IdCPNFb40ECn/RGQ/IoPgqH2KsWBaETr7AvjuX1fjnc31Fq8Z6WVHJGgm+hON1aSi8HJ2t7CnmZ35AiEsvmMlrnhiDf75WTW83X4UZLpx1kFV+POFB+GQKeFBsm2cWZ5yRGnV4YJmM8vDQbOvGzoRDLEsVyoQJVeTLc8IxGSa1TLTzO5EhZDKJLf/1OIs5Hpc8AVC2FzL4LmddfT6tczxkZRnBOJLNJL9NLQz08wMDJoREaUo0c8sJ92FOZW5mFWei1nlOfAFQ3htfa3Vq0dj1NQRLs9YrFM/MwBQFCXa14xBs5QQ4oDYuLQ1kml24KR8PHLJITh+dhn6AiFc/tgavPgFs41TgThHVyQ5s3w4LM+YGnY0dWFvaw/SXA5cetRUPHv54fjkhuPx+2/vj2/NLcfsSHbJtkYGR1ON6Gk2XNBsUmEm0l0O9PpDWklHsi9/MKRNdBtNptnm2g4GT21OZJpVJJlppCgK9p+YDwD4Yq/XoLUiM4hjP8fjQlb6yNo1HDq1EABYicKm6juYaWYGBs2IiFKUKM146NRCOCP1zU+LZJs9zxKNttcYU55RT2KWGvua2d/qHS2YfeO/8Kd3v7Z6VchkmyOZZjPLc+FxO3H/dw7E6d+YgEBIxTXPfoHHV+20dgVpzFojfS2LsvXJNhblGRs6+tDjC+qyTDKfyECcVZ6D/z1pDg6bVgRXTM+76SXZAIBtjcw0SzV7WsID5xMLhw6cOB0K9ikN7wfsa2Z/dW29CKnhcmvFI7gnmFqcBY/bgR5/EDubGUS3sxrvyDLNAOAbImi222vAGpFZ6trC4wEjzTIDgHkTwoHzho4+dPvY59RuRKZZSS4zzYzEoBkRUYoSQbPDpxVpz52yfyUUJTyYvreVs0vtrLkzkmmmY3lGIDpLsa6tR9flkvleW1eLvkAIf3hzK9btZc+K8SIUUvGVFjQLD4y6nQ784dv74+IjpkBVgf99cQPuW8Fgqp21RIJmhVn6fAfkZ6Yh1xOepbyb2Se2JQa/pxQlLtspgiUsz5hafIEQatpE0GzoTDMAmCn6mtUxaGZ3olfhhPwMOBzJ9zh2OhTMKo+UaKxhiUY7q40c+5X5yQdODpiUDwBYy0wzWxNVB0bSz0zITnchw+0EADR29Om6XmS8BpFplsNMMyMxaEZElIL69zMTKvMzcHikfvWLX9RYsm6kD9HTrETH8owAUB4p9SVmLZJ9iebuwZCK//ePL9nse5zY29qDbl8QaU4HJscMnDscCm46eQ6uPm5fAMDv3tiCXZxdblsiaFaQqd/ECbG/cL+wr52RTLMpxYmDZiLTbFdLN3wBfiekimpvD1QV8LgdSV0Xzoj0NdvK4KntjaafmcC+ZqkhWp4x+X1g/6p8AOGs4/Ze9jq3KzHJdTSZZoqioDSSpdTAoJmt+IMhNEfuA0qZaWYoBs2IiFJQ/35msU6PKdGoqqxhb1ciaKZXaS4hmmnGoJmdhUIqNkRmDqe7HNhU244H39tu8VqRGUS5reml2XA74y/1FUXBzxbPwNzI98LWeg6Y2pGqqmjt1jfTDAAmRUo0MtPMvkR5xqnFibONynLTkZXmRDCkYncLg6OpQvQmm1SYCUUZPttoRlk4eMpMM/urbh1D0KyCmWZ21+MLorU7HPSqHEHQrCg7HRMLM6CqwJd7WI3CrqL9bUeXbSQmWYhSf2QPTZ19UFXA5VBQqOPkORqIQTMiohSUqJ+ZsGR+OdJcDnzd0KkNqpP9aOUZdc80i/Q0a2fQzM52NHeh2xeEx+3Ar0+fDwC4699f4esGDpCluq2iNGNkUDQRUbqNwRF76ugLwB8MT3rRM2g2OVLWbVcz9wu7Gq48o6IomB4p0fh1A4NmqWJ3TNAsGTMi5Rm3NXYy49Dmqr3hbT+hgJlm45EozZiV5kRuhmtEvyuyzVii0b7EJNeyUQbNoplmvO+3E62fWU76iMry0sgxaEZElIIS9TMTcj1uLJ5dBgB44fNqU9eL9CMyzfQOmrGnWWoQAfHZFbk488AJOGZmCXzBEP7f379EMMQM01S2OZI5IMpvJSJ63uxmGT5bao2UZMlMc8IT6UehBzHgvoc9T22p2xdAfWQgZeog5RkBYJ9IicZtjcw0TRUi06yqILmg2YT8DGSlOREIqVqglexJ9DSrGkXQbFZ5DhQl3M+Ig+b2VBsJmlTkZySVZRrrgIn5AIDPd3t1Xisyi9bTbBTlGQGgNNIPiz3N7KU+st1LR7ndKXkMmhERpZjB+pnFOi1SovHFtTUcQLehQDCElkhpLr3LM4pMs4aOPvbAsrEN1eFSK3Mrc6EoCn59+nxkpTnx2W4vHl+109qVI0OJcluzhgiaTWYZPlszop8ZEJ2EIYJyZC87m8LHc36mG/lD7Bsi02wb+1mljJFmmimKEu1rVs8MdDsbS0+zzDSXFmDfVMv9wI6qtX5mIx88F0GzL/Z42bLBpkSmWfloyzPmsKeZHdVHtldZDvuZGY1BMyKiFDNUPzNh4YwS5Ge60djRhw+3NZm8hjRWrd1+qCqgKNC9jnVxVjpcDgWqyllndra+Jhw0m1eZBwCozM/AL5fOBgDc/sYWbVY6pRZfIKRlj4jyW4mIgVUGzexJBM30njRRECn1KCZlkL0MV5pRmF4S/jkzzVKHyA5NNmgGADNKI0Ez9jWzrVBIRa03PGg+mvKMAPua2Z22/UcRNJ03IQ9Oh4Kmzj7UsJe17fgCITRF2jWMNtOMQTN7atQyzRg0MxqDZkREKWaofmZCmsuBk/arAAA8zxKNtiNKMxZmpsHl1Per3OFQUBa58K7lDZQtqaqK9dXhwY95E/K05y84dBIOnVqIbl8Q//38Os4qTUE7mroQCKnITncNOYASLcPXgxCzjW3HqEwz0R/N2+XXdblkjh1N4aDZUKUZAWC6Vp6xi98DKWJ3pA/hpKIRBM0imWZbmGlmW42dffAFQ3A6lFEPmrOvmb2JnmYVeSMPmnncTq0qwdo9Xj1Xi0wgSvSlOR2j7m9bGgmacaKsvYhS3GU5LM9oNAbNiIhSzMc7Bu9nFuv0SInGN9bXodsXMHy9SD8iaKZ3loEQ7WvGoJkdVXt70Nbjh8uhYN+ybO15h0PB/50xH+kuB97/qgl/X7PXwrUkI4jBzxll2UP2tqjI88DlUOALhFDPPia20xrJBBvtIMlgCjLdAICOvgB8AZbntZudTcllmk0uyoLToaCzL9oDjeyrrduP9t7wdfxI+lrNjGQjf1XPjEO7Ev3MynM9o55EJzLNNkQqFJC9aOUZ80c3eL5/pETj+mpuf7sRQbOyvPQR97MToj3NeC9gJ6IHJTPNjMegGRFRCgmGVHwc6Wd22LTCIV974KQCTCzMQJcviLc21puxeqST5kgpBtF/Rm+iLrqYvUj2IrLMZpTlIN3ljPvZtJJs/HTxDADAra9sREM7b5JSiSizNXOIfmYA4HI6tFJOIkOB7KO5y5igWa7HDZGg7mWJRtvRyjMWD51tlOZyYHIk25QlGu1PlNktzk5HZpor6d+bEZlUs7O5C73+oCHrRsYaSz8zQWSa7WjqQo+P+4HdiKogo90HRKCdEyjsR2z7itzRH/8i6NLc5UOAvcxtQxyvpaPMMKbkMWhGRJRCNtW2o6M30s+sInE/M0FRFJx2QDjb7KUvasxYPdKJyDQzKmjGTDN7E7OF501IfA74/lFTMX9CHtp7A/jjW1vNXDUy2OY6kWk2dNAMYF8zO2s1KGjmcCjIj5R8bO1miUa72dEUPpaHK88IhCdQAAyapYJoP7ORDZyW5KQjP9ONkAp83cD9wI6qI5lmo+1nBoQzTTLTnFDVaPYC2YOqqqgVmWZ5oxs8F/eSjZ0MmtlNNNNs9IGTwsw0OCO9zEV/NJKf6EEnymuScRg0IyJKIaKf2SFTC5Mq07FwRgmA6EAr2UOjwUGz8khd/FpmIdnShpqB/cxiuZwOXHXsPgCAL/eyHEsq2VqfXKYZAExk0My2WiI9x/TuaRZepjvyHhw8sZOOXr82oWZKEkGzfUojQTMGS2xPnMPFRIhkKYqiTbDYyr5mtlTtDW/7sWSaAdFy700MnNhKe08AXZHswNH0NAPCwXMAaGJPK9vRMs3GEDRzOBQUR45/9jWzh0AwhOauSE8zZpoZjkEzIqIUIoJmhw9TmlGozBclGXoRCrEZvF2I8ozsaUaJiL4EcysHzzYVg6p7WxkwSRXdvoA2eDqTmWYprSVys6x3plnsMlme0V52RcqsFmWlIdfjHvb100vC3wFfM9PM9kYbNAOi3xVb2dfMlvTINANiso06eN63k5pIGf2CTDcy0pzDvDqxEmaa2VadyDQbY+BE9DVjpqk9NHX6oKqAy6Gg0IDJcxSPQTMiohQR28/s8GlFSf1OSU46HAoQCKlo6uLFsl2ImaAlBvc0Y9DMfho6etHQ0QdFAWYPUaJVzEpu7w2grYdl2FKBGPQszk5DURLnhskMmtmWKJ1oRNBMlGdsYdDMVnY0iX5mw2eZAcB0LdOsy7B1InPsiZzDq0YRNBN9zZhpZk97I0GzqjEGzYqyRF8j3gvaieg9XTmGTENR3q25sw9BTqC1lXodMs2AaLZhAzPNbEGU5SzJSYdDNCImwzBoRjROVHt78MRHu9jgM4WNpJ+Z4HY6tAslBkjsQ+tplmNspll9ey9voGxGlGacXpKNzDTXoK/LSndpA+5ipjLZ29a65EszAtHyjHsYNLOdFq2n2fAZRSMlZq22sjyjrewUQbOiJINmkZ5mde296OwLGLZeZLw9Y8g0E+UZt7BMu+2oqorqSD+rsZZnLIncTzQx08xWarwiaDL67V+YlQZFAUIqyzLbjSjPOPZMs0jQrJ1BMztgPzNzMWhGNA4EQypOuvt9/M8L67FmV6vVq0MGGWk/M0HrX8WgmW2Im1oxM1RvJdnRDMRmluuwlQ1JlGYUxMxklmhMDVsimQIzkijNCACTisIDrE2dPg6a24g/GNKyQwsN+A4oiATTRTYb2cOO5nDQbGpxcoGTvAy3NmlqO0s02lYwpGrZRmMJmlV7e/g9YDPebj+6I/2sxpJpBETLM7Knmb3UeEWm2eiDJi6nQ5ssw+1vH6GQqpVTHGummQi+NHZyLMgORKZZKfuZmYJBM6JxwOlQcMzMUgDAvzfVW7w2ZJSR9jMTKiMXWrVeZpvYgaqqWvmUYoNmGLmcDq2+OYOp9rK+OpxpNq8yb9jXRoNmPPZTgcgUSKafGQDketzIzwxnKjHbzD68kWCWooQDH3oriOwTzDSzF9HTLNnyjEBMX7MGBs3sqratB4GQijSnY1TZBgVZaVrw9CuWaLQVkWVWnJ0Oj3t0/ayEoshkCZZntBdxjzbWoKk4BzSyPJ9tNHf54A+qUJTo9hutksh3BzPN7KFBBM2YaWYKBs2Ixonj55QBAN7e1GDxmpARRtPPTBD9q2rbGRyxg/aeAPzBcMnEIgP62QjafsGgma2sr4lkmk0YPtNsYkGkPB8zzVKCyDRLtjwjwL5mdtQa6TWWn+GG04BeBiLTjD3N7GWk5RmBaInGbcw0sy1x7p5QkDHq84GYaMG+ZvYiJjxNGGM/MyA6CY/lGe1FZJrp1dOKQTP7ENlGxdnpcI+gwlAiokc6e5rZg9hOYy3LSclh0IxonDh6RgnSnA5sb+rizXEKGk0/M0FcaLOnmT00Rkpn5KS7xjyzdCjF2aJEF2+g7aKt268Nosxlptm40tLl0wY79k0y0wxgXzM7au4U/cyMmTSh9TRjeUbbaO/1ozmSGTiyTLNI0Kyhy5D1IuPtbQl/f08cRWlGIdrXjPeHdiIyzarGmGUERMu9NzHTzFZq2kR5Rn3KczayPKNtiEmtYw2YAkBpLoOmdiICpmW5zDQzA4NmRONEdroLh08PZyD9eyNLNKaaz3aHe9UdNKVgRP3MAPY0sxvRY8yo0oxCbqTsl5cDp7axIZJlNrEwI6mybVWRTDMGzexPlGasKshAdror6d8TPXBEaTeSn5jIYFTQrCCL5RntRmSZFWenj+j436eUmWZ2JzLNJhWOftB8Znl4P/iqgZlmdlKtY6ZZSU6kpxUHzW0jFFK1Ca96lWfk9rePOi1wokPQLCbTUFXVMS+PjCUyzUQrDTIWg2ZE48ji2exrlqpqvOELp6kjmGEsMNPMXpoiWQYiE8wo+Rnh5Xt7OHBqF6I0YzL9zIDYTDMGTOxOlNWaNYLSjEA0aMbyjPbREglmFWQaFDQTmWYMmtnGjkjQbGrxyLKNpkeCZjubuxAIhnRfLzJeNGg2+kyzfbVMMwbN7KTaGynNqUOmmcg0au8NoC8QHPPyyHhNXX3wB1U4FKBsrD2tmGlmO3Vt+pTmBKJBU18whLYeTpaVXX2k91wpM81MwaAZ0Thy7OxwX7M1u1q1QRdKDdGGoCO/cIoNmoVCnF0ku6bIDY0opWKU/MxwtkEbM81sY0NNOwBg3oTkgmZidnJHb4A3STYn+pnNGEFpRgCYVMTyjHYjrt+KDJo4IYJmHX0B+BlIsYWdTeHjdyT9zACgIteDDLcT/qDKwLlN6RI0iwRPGzr6GCy3EVGeUY+gWa7HDVekJx7HCOxBTJgtzfGMuMpMf+xpZj91bfr1tUp3ObUKJexrJrdAMITmLmaamYlBM6JxZEJ+BuZU5CKkAis2N1i9OqSjaJr2yAMppTkeKEp4dlEL+1dJL1qe0eBMMxE0YzDFNtZXhzPN5lQm19cwM82FokiJN2ab2ZvIEJg5ykyzva09CHLShC0YnWmWm+FGZOyUPS1tYmdzONNsJP3MAMDhUDC9NPw72xrZ18yOxHe3KLc8GjketxZ4EVnLJD9RWrtqDKU5BYdD0SZiNHXwvG8HtV7Rz2zsA+daTzMGTGyjrl2/TDMgvkQjyaup0wdVBZwORbuHJ2MxaEY0zhw/J5xtxhKNqaWhY/R1rdNcDu1imSUa5deolWc0NtMsjz3NbKWrL4DtkRJdyZZnBKIlGve0sK+ZXamqiq2jDJpV5GXA5VDgC4a0/ggkN6N7mjkdCvK1Eo08/9tBtDzjyEt0Ty9hXzO76uoLaCW7RdbwaM0oC+8HWxu4H9hBV19Auz7XI9MMiFawaOrioLkd1ETu2St02P5aTzOWZ7QNMWZTrkOmGRAt9SfGlEhOYvuUZKfDIWa4kaEYNCMaZxZHSjSu3NqIXj9rlqeKsdY2FrOUahk0k55WntGsoBkzzWxhU207VBUoy03Xbn6TUaVlGjHTzK5q23rR0ReAy6FgWnH2iH7X6VC0wOnuZu4DdiAyzYwKmgHRTGNmmtmDlmk2wvKMQDRo9jWDJbazJ/K9nZ/pRq7HPaZlzYhMuNjKvma2IEoz5npcyBnjtheKReCEmSa2oGWa6djTqrXbD1+AZZntQAua6ZZpFl5OQzuPf5mJMb8y9jMzDYNmROPMvAm5KMtNR7cviI+2N1u9OqSDXn9QK6E3mvKMQHSWkmgqS/IS5RlLDOpnI4hMgzYOmtqC1s9sBFlmQDTTTJT5IfsRpRmnFmchzTXyS/tJkYF29jWzB608o4FBs0It04znf9l5u31axsmU4pFnGzHTzL7ERIex9DMTRHnHemYc20J15JptwhjKcvZXLMozdvK8bwc1baI839gzzfIzoj3tmplpKL2OXj+6fOHJ73oFzUTglD3N5KZlmrGfmWkYNCMaZxRFwXGzWaIxlYja02kuh5YdNFLMNLOPJpPKM+ZnsKeZnYh+ZnOT7GcmiIEyBs3sa0v96EozCpMi/VB2M2hmCyKQVWhQTzMgGpBrZXle6YnSjGW56chMc4349/cpjQTNGjqhquxraCd7It/bE3UImhVHjvlmBsptYW8ky0iv0oxA9L6imSX6bKHGG75nr9RhH4jtaceeVvITkxtyPa5Rfe8nwp5m9sBMM/MxaEY0DokSjW9vauANcgoQM05Kc9KhKKOrbSzqoTNoJj+zyjOK8lxdviBLddjA+kim2dwJo800Y8DErrR+ZmWjDZqFB1x3MWgmPVVVtUFtI8szFrA8o22MpTQjAEwuyoRDAdp7A8wwsRmRHTxRh2yjIgZMbEVkmolrOD1EM824D9hBbSTTrDJf30wjbn/51epcmhGIzTTjWJDMGrVxP2aamYVBM6JxaMH0ImS4naht69VKepF9NWgzTkb/5RnNNGO2icy6fQF0R8oxFBtcnjG2RwKzzeTWFwjiq0i20bwRBs0mRgZcqlt7OInCpjZHgmYzRp1pFh5wZaaZ/Hr8QfRFJjEYGjSLLLuFWSfS29EUPm6nFo8uaOZxO7VMJfY1sxdxztajPKPIMmlm4NQWqg3MNGPwXH6+QEgro6dHeUYAKMlmppFdRPuZ6Xf8szyjPTDTzHwMmhGNQx63E0fPKAbAEo2pQKToj7afGRDb04yzi2QmBjPSXQ5kp+tTjmEwToeCXE/4Pdp6eAMts611nQiEVORnukfcEHxCfniwraMvgPaegBGrRwYKBEP4OtKLaNaog2bsaWYXIoiV5nIgM81p2PtoPc2YaSa9nZHyjFNGGTQD2NfMrvQMmhVnhe8hOvoC6PUHx7w8MlZ1pDrABB0zzYqymWlkF/XtvVDV8LVAkU4TaIoZNLMNLWimY+BEZC5x+8tNjPuNZbI8jQyDZkTj1PHsa5YyxIygsQTNxCy12rZeZptIrDFyI1ucPfpSnCORHxk4ZaaZ3NbXhPuZzavMG/F+kZHm1G6U97BEo+3saumGLxCCx+0YdYmuiZGeZi1dPnT08liXWUtMPzMjvwMKRNCMmWbSG2t5RiCmrxmDZrahqqo20UGPoFluhgsuR/icwgxT+Yk+tPpmmonyjNz+shPl+SryPHA49LkWiJZn5PaXXV27/plmpZEAXEcvJ07ITIz7lYxh3I9GJiWCZl9//TWeeeYZXHvttVi0aBFyc3OhKMqAfzt37hzRcltbW3H33XdjyZIlmDJlCjIzM5Gbm4t9990X5513Hp5++mkEAiOblR0IBPD000/j/PPPx7777ou8vDxkZGRg8uTJ+Na3voW77roLLS0tI1omAKxcuRJXXXUV5s+fj+LiYqSnp6OyshKHH344brrpJmzZsmXEy9y6dSuWLVuGBQsWoLKyEunp6SguLsb8+fPx4x//GO++++6Il2nEZ0qjc8ysUigKsL66nSX5bE4Lmo1hxklZXviLty8Qgrebg6ayauoQQTNjSzMKoq8Z9wm5ra8OB83mTsgd1e+zr5l9bRGlGctyRj1wkuNxa6X+WKJRbi0m9DMDYsoz8twvNVVVsSOSaTba8owAML0k/Lssz2gfjR196AuE4FCACh16GimKwhKNNtEXCGr3fnpmmokJVC1dfQiFOIFSZjWR8pwVBvS0YqaR/KKZZvpt/5x0FzzucHhAtP4guQSCIS0TmJlm5jG2tpMJbr31Vtx44426L/cvf/kL/t//+38JA1gdHR1aoO7WW2/Fww8/jCOOOGLYZX744Yf4/ve/j02bNg342e7du7F79268+eabuOWWW3D77bfj0ksvHXaZ1dXVuOyyy/D6668P+FltbS1qa2vx8ccf47bbbsOVV16J22+/HWlpQ99o+3w+/OIXv8C99947IIDV3NyM5uZmrF+/Hvfddx/+67/+Cw899BAmTJgw7Loa8ZnS6BVnp+PASQVYs6sV/97UgAsPn2z1KtEo6VGeMd3lRHF2Gpo6faht69UGzEguzZEBU3Fja7S8DAbN7ED0ppxXObJ+ZkJVQQa+2OPVZi6TfYig2cyy0ZVmFCYWZqKly4c9Ld2YO8r9iIwnyiUaHjTTJkxw8Fxm4ezQ8L3a5KLRZxuJ8ozbG7t0WS8ynpjgUJmfAbdTn3nQRVnpqG/vQ1MXB0xlVusN3/d53PqV5gOi3yshNfxdU2TSvQaNXE1kwnOljpmGDJrZRzTTTL9jVFEUlOSkY09LDxo6ejFpDNcUZIzmLh9UNdxCQ89zPw3N9plmwaD+qaO33HILvv/97yeV8bVp0yYcf/zxeOONN4Z83RtvvIHjjz8+YcCsv5aWFnz/+9/HLbfcMuTrdu7ciaOPPjphwKy/QCCAu+66C6eddhr6+gb/Iuzt7cVpp52GO++8M6mMr9dffx1HH330sFl8RnymNHaiROPbLNFoa406ZJoBQHlkthozD+UVzTQzOWjG8ozSCgRD2FQbDprNrRxtpln4xohBM/vZWh8Jmo2yn5kwOVLei5lmchMZIEZPbNEyzVimTWqiNGNlngce9+h73ImgWbW3B90+VvywAz37mQnMNLOHam+0NKOeZXrdToc2YYIl+uQmAqeVOpbn03qasaed9KKZZvptf4B9zWQnJsqXZKfrVpaVhmf7oFki6emjH0x89dVXcdNNNw143uFwYObMmZg0adKAn/X09OCcc85BTU1NwmVWV1fjnHPOQU/PwMGoiRMnYtasWXA4Bm6Km266Ca+++mrCZQaDQZx77rnYvn37gJ8VFhZi7ty5yMgYeBJ9/fXX8T//8z8JlwkA//M//5MwCJeRkYG5c+eisLBwwM+2b9+Oc889F6FQKOEyjfhMSR+L55QCAD78uhldfbxJtitRoqNsjM1gxYWXqJNO8hEp+UUml2dkTzN5bWvsQl8ghKw056h72rA8o33FlmccCzHwuquZ+4DMRKaZ0TNMCyM9zTp6A/AHE1/fk/V2NIWP1yljKM0IhIOkYp9itpk97GkJjyvoGTQTg+bNHDSXWrXoZzbKPqZDKeI+YAtigqsepVkFracZAyZS6wsEtcoz5TqW5wSiVYsauA9ISZTNLB3jmB+NjO2DZjk5OTj66KPxs5/9DE899RS2bt2aVOZVIn6/H1dcccWA56dNm4YNGzZg8+bN2LVrF15++WV4PPEnqLa2Nvz0pz9NuNyf/exnaGtri3vO4/HgxRdfxO7du7Fp0yZs3LgR06ZNG/C7V1xxBfz+gYOVDz74ID7++OMBz994442or6/H+vXrUVtbixNPPHHAa+644w6sX79+wPPr16/HnXfeOeD5pUuXoqamBuvXr0d9fX3CANjHH3+MP//5zwOeN+ozJX1ML8nGlKJM+IIhvP9Vo9WrQ6PgC4S0meBidtBoibrodQyaSavJ5PKM+RnhQbQ2luiS1oaaSD+zyrxRzzqLBs2YaWYnvf6glmkya4yZZpOYaWYLLV3he4KCTGODZrkZbogEBpbnldfOSD+zsQbNgGi22bZG9jWzA3GunqhnplkkcNrMDFOp7Y3JNNOb6JnMbCO5VRuQaSaCZh19AfT49K/mRfoQgZM0VzQzVC/RoBnHgmRU3yFasrCfmZlsHzT7+c9/jpUrV+IPf/gDzjvvPOy7776jTlN/4YUXsHfv3gHP33///Zg1a5b2+KSTTsI111wz4HX//Oc/UVdXF/dcbW0t/vnPfw547U9/+lOccsop2uOZM2fiT3/604DX7d27Fy+++OKA5++9994Bzy1YsAA333wzXK5wq7q8vDw89thjyMyMv5gOBoN44IEHBvz+/fffP6DcZVZWFh5//HHk5+cDAFwuF5YtW4bDDz98wO/fd999A54z4jMl/SiKopVofGtjg8VrQ6MhbmrcTmXMF07R8oy8UJKVVp5xDP3rRkJkmrE8o7y21ocHOGdXjD5oIgbd9rR0Q1XZ/N0uGjv6EFLDN84lYzwnxO4DJK+WSK+hwix9B0r6czoU5EfK87Zy0oS0dkSC5lNHmWUca3ppeBnbGhg0s4M9RgTNIhOymhgwkZrINBMTnvQUzTTjeV9mtQb0NMtJdyHdFR4e5jlAXlo/s1yPruVZgWjgVATmSC7MNLOG7YNmenr66acHPFdSUoLFixcPeP78888f8FwgEMBzzz0X99zf/va3hL3BEv3+4sWLUVxcPOD5p556Ku7xl19+iY0bNya1zMLCQixZsmTA808//XTcwJiqqnjmmWcGvG7JkiUJSzImeq8NGzbgyy+/HPA+/Y31MyV9HRcJmq3Y0oBgiIOldiNqG5fmjP3CqTJS4qGundkmshI3McUmNX/NFT3NmGkgLTEbsHwMs03FbOUuX5Db2kbE+aAkO33M5//JRdG+drwWkFdrJNOsMMv4G2b2NZOfMZlmLM9oB+xpNn5Ve8Pb3ohMsxIGTqXX7Qto1+p6lmdUFEWrZMLyfPISk5v1Ls0IxPQ04/EvJXHPX8ZMM1MxaBbjo48+GvDcN77xjYQDEXPmzEnYM6x/ycREy8zIyMCcOXMGPO9wOHDggQeOapkAcNBBByV8/uCDDx7wXEtLC77++mvt8VdffYWWlpYxLTPZdR3rZ0r6OnhKAfIy3Gjp8uHz3a1Wrw6NkJhxMtYsAyCmp5mXmWayEiVzTMs0y2CmmexEs/biMfS587id2jmEJRrtQ2x7PXocluV6kOZ0IBBSUePlPiCrlkjWV4HBmWZAtASkl5lmUlJVVQuaTS0ee+BkeinLM9pFrz+olWnSt6eZKM/IAVOZVYvyjAZkmol9gEEzedVE7tOz013I9eh7LaD1NeP2l1Z9WzTTTG8lucw0kxkzzazBoFlEY2MjamtrBzw/adKkhK93Op2oqKgY8PzatWuHfAwAEyZMgMOR+KOvqqoa8FxNTQ2ampqGXOZQ65pomf2XY8QyjfpMSV9upwOHTCkAAGyu67B4bWikGsWMEx2+PCtiyjOyRJt8/MGQNrPQtJ5mkUHTdgbNpNXYoU/gPNrXjOX57KJZZJ7qcD5wOhRtH2CJRnmJrK9CE7KNRdBM9FEjuTR29qHLF4RD0adE3z6RTLPtTV3MNpVctbcHqhoeNNezp01RFkvzyS4YUrXJjUZkmrE8o/yipRkNCJpE7iUamWkmLVGescKQTDNmGsqsXsdxP0oeg2YRsUGpWIlKEwpFRUXDLifRcke6zP7LGem62mWZgy13sOUIfX19aG9vj/tHI1MRKetVx15WtlMvZpzokKYt0vx7/EG09wwsK0vWEjewsb1mjKb1NGOmgbSadAqcVBVEy/ORPYjM0yKdAihi4H03g2ZSCoZU7VxsRtBM9E1jTzM57WwKH6eV+RlIdznHvLzwchzwBUJazySSkzhHVxVk6NrTJrY8IyfPyam+vReBkAqXQ0GZAZkmxSzPKD0RNK0YQ1n2wTBoJj8xXmfE8S+2f3NXHwLBkO7Lp7HRc9yPksegWURbW1vC59PTBx+ESvQzr9c77HJHusz+yx3putplmYP9rP9n2t9vfvMb5OXlaf8mTpw45OtpIBEsETNXyD5EbeNSHcr1edxObcZqLfuaSUfcwBZmpcHh0Lfx72DyIsG5th4/Qpx5Lp1gSNWyjZhpNv6IQY0inTJPRV8zBs3k1N7jhzgNiywwI4n3aGVPMylFSzOOvZ8ZEJ6QI+4HxLUlyWmPAf3MgGimmS8YQkcfJ8/JSJRPLs/zwGnAvUC0PCPP+7IS5TmNyDQTQVP2tJKXkZlmRVnpcCiAqrKfrWwCwZB2z8/yjOZi0GwYQ82yGu0MLCOWOdTv2mWZo13u9ddfj7a2Nu3fnj17Rr1u45WoicxMM/sR6fN6zTYqj8xaq+W+IB29MopGQgTNQio4gCKh1m4fQiqgKGPPPNFK8zHDwDa0Hoc69DQDogOwuxg0k5LY3jkeF9xO42/hCiLnlBZmmklpR3M4aDalSJ+gGRDNWuWAudx2NxsTNMtIcyIrLZy1yPJ8cop+7xtzLxCbacZsQzlp5RkNzDRrYqaZtLRMMwOCZk6Hop0DWKJRLs1d4Xt+p0PRJriQORg0i8jLy0v4vM83+AVjX9/AE0l+fv6wyx3pMvsvd6TrapdlDrbc/p9pf+np6cjNzY37RyNTwUwz2xJp2iU6zTipzGMAVVZiEEuvAfJkeNxOeNzhSwX2NZOPCKQWZKaNeRB9olaekQETu9CzpxkQLc/InmZyEmUS9SrHOZwCrTwvz/0yEplmU3TKNAOAwshADGeYy02UUdajl11/0Z5WHDCVkSjRq2cvu1iiRGdfIIROTpaTkpjYWmFAT7sSZppJLRRSUW9gphkQzWJixrlcGtrFPV+aIVnGNDgGzSKKi4sTPt/c3Dzo7yT6Wf/lJFruSJfZfzkjXVe7LHOwnw22HNJPGQMlttUoGoLqVNtYlOap9TLbRDZ6D5AnKz8jfAPNgVP5iPJ8JTrsE9HyjD2cXWwTIhOgSOdMM5ZnlJMIZBSYFjRLi3tfksvOSLbR1GL9AifFWk8rDpjKTEyY0aM0e39FLM8ntdbItbhRJXoz01zIZLah1LTyjAYETUpywvsVe5rJqamrD4GQCoeiz71fIlrglPuAVESw1IhedjQ0Bs0iSkpKUFlZOeD53bt3J3x9MBhEbW3tgOcPOOCAIR8DQHV1NYLBYMLlJiotOGHChLjAUaJlDrWug5UrjF2OEcs06jMl/YnyjJ19AXT0cmDcLvzBkFamQ6/axmLWEsszyidantG8TDMAyBfZBj28eZaNtk/kjH2fqIzMWO32BbVBGZKb3iVbRdDM2+1HGzNLpSOCV4Um9DMDoiVfW1meUTqqqmKXAeUZxTZvZqBUaqJkqhEBdFH2qbmLA6YyEufjfAO/B2JLNJJcVFVFrTd8j15pSKZZeByA5TnlVN8W7WPtMqhMd2lkIrbIbCI5iHKZRkyWoaExaBbjsMMOG/Dc559/nvALY8OGDejtHTio3H8ZiZbZ29uLjRs3Dng+FArh888/H9UyAWDNmjVJP19UVIR99tlHe7zPPvugsLBwTMtMdl3H+pmS/rLSXcjxuABEZzGQ/MIXtIDLoeg2iCZ6mrFUp3yi5RnNvVjKzWCJLlmJWYB67BMet1O7EGeJRvkFQ6o2cKpXpllWuksLyrNEo3y0oJlJmWZiULaVARTpNHT0odsXhNOh6FqiT+xbzC6Um7geM+JcEM025D4gI2+XyDQzpjwjEN0HGDSTT1uPHz3+8OT7cgMyzcQkvF4/y3PKSPSzKzcw2yhanpHHv0zEGG0pM81Mx6BZjPPPP3/Ac01NTXjzzTcHPP/0008PeM7lcuHb3/523HNnn302XC7XgNc+9dRTA5576623EpYn7L9e++23H+bOnZvUOrW0tOCNN94Y8Px5550X91hRlAHPAcAbb7yBlpaWpN5r3rx5mD9//pDrDoz9MyVjaH3N2vgFaRdiBlBJTjocOtU2ZqaZvMTNa5Hp5RnDN+bMPJGPCKTqVaIjtkQjya212wcx/0jPzKOJLNEorVaTg2bifdp7A/AHQ6a8JyVHlFMvzUkfcz/LWGICBrOM5BUMqTF9rQzINGOJTqlpmWYGfg8UaZlmDJzKpiaSZVaUlQaP26n78jPTXMhOD49dsjyffMwo0VeSw55mMmKmmXUYNItx6qmnoqqqasDzP/rRj7B582bt8csvv4w77rhjwOvOPPNMlJeXxz1XXl6OM844Y8Br77zzTrz00kva482bN+OHP/zhgNdVVVXhlFNOGfD8lVdeOeC5VatW4aabbkIgEJ4V4vV6ceGFF6K7O37gw+l04oorrhjw+1dccQWczvgv366uLlx44YXwer0AgEAggJtuugkfffRRUutkxGdKxhBfvmIGC8lPm3Gi45dnBfvbSSuaaWZNeUYGzeTTJDLNdDoHVBWEAybMNJOfyAIoyHTrWqKFfc3kZXZPs7wMN5TIfBxmGsvFqKxDrTwjB8ul1d7jRygyYSLfgGwjUZ6xidmGUvJ2m5FpxvKMsqqJ9DOryDcuaCLuMxk0k4+Y1FxhQJahIMaVuP3l0sCeZpYZmAJlM6+88gqWLVsW91xHR0fC155yyilIS4u/uXj55ZdRUVEBAHC73bj//vtx8sknx71m+/btmDt3Lvbdd1/09PQk7MmVl5eHP/7xjwnf949//CPeeOMNtLW1ac/19vbi1FNPxcSJE5GVlYWtW7ciFBo4i/OBBx6A2z3woujyyy/H8uXLsXr16rjnb7nlFtx7772orKzEtm3b0NMzMADy05/+NGGm2rx583DNNdfgD3/4Q9zzr732GiorKzFt2jTU1tYmzDw77LDDcPnllw943qjPlPQnvnxZntE+xIyTkhz9vjxFqQfR3y7HY9xNGY2M3v2LkiVKdHnZ10Y6jTrvExMLw5lme1o4eUJ2Rp0PJjNoJi1RjtOsTDOnQ0Fehhvebj+83T5t9jFZz6igmZZlxICJtMR5IMfj0jXLUGCmmdxaDcwyFFiiU15icnNlnv79zISSnHTsbO5mpqGERPuMMgODZmJcieUZ5cJMM+vYPmjW1NQ0aH+t/tatWzfgub6++JPBSSedhGXLlg0IxIVCIWzZsiXhcjMyMvDss8+isrIy4c8nTJiAZ555BmecccaAINaePXsGXd+bb74ZJ554YsKfOZ1OPPvsszj22GOxY8eOuJ+1tLQkDGwBwH/913/h1ltvHfQ9f/WrX2Hjxo14/fXX457v6enBhg0bEv7O1KlT8cwzz8DhSHzhbsRnSvorz2VZPrsRX55lufp9eWamuZCX4UZbjx+1bb0MmkkiFFK1QTKzg2Z57GkmrcaOaIlWPTDTzD6i5Vr1HTjTyjM2cx+QjVae0cDB0v4KM9Pg7fazx5VkxPYo0jtoFskyau3yIRRSdSv9TfoxukyrVqKTA+ZSao1cixuRZSgw00xeNZFxmsp8Y4NmANDI8nzSEZPbzcg0a+jog6qqUBReB8hA73t+Sh7LMyZw00034cEHH0RBQcGwr501axbeeustfOtb3xrydUuWLMFbb72FWbNmDbvMgoICPPTQQ7jxxhuHfN2UKVPw/vvvY8mSJcMu0+Vy4eqrr8YLL7wAj2fwk6zH48ELL7yAq6++OmEvtv6WLFmC999/H1OmTBnydUZ8pqSv8siMJWaa2UeDVp5R3wsn9jWTT1uPH8FIPR6zsgyEPPY0k1Y020iffYI9zexDDGjq3eOQ5Rnl1WxyecbY92plprFUjNoXxPVFIKSivZff+TLSyrQaFDxntqG8VNXYfnYCg2by0sozGplpFNn+jdz+0hFjM2b0NPMFQmjvCRj2PjQy3h7jz/2UmO0zzYxy2WWX4cwzz8QTTzyBV199FZs2bUJjYyNcLhfKyspw0EEH4dRTT8XZZ5+dVHAJAI488kisW7cOf/vb3/Diiy/i008/RUNDA3w+H0pKSjB79mwsXboU3/3ud1FYWJjUMidMmIDXX38d7777Lv72t79h5cqVqK2tRUdHBwoLCzF58mSccMIJ+M53voOZM2cmtcy0tDTcdddduPLKK/HEE0/gzTffxM6dO9HS0oLs7GxUVFRg4cKFOPvss7Fo0aKklgkY85mSfsrzwl+QDJTYhxGZZkC4ROPmug7Usb+dNMTgVWaaE2kuc+e7iNmsXgbNpBKMyT7UP9Osh7MLJScGs0r0DpoVhfeBam8PAsGQrv3SaGxaDcouGorom9PKTGOptHRFMk113hfSXA7keFzo6A2gucunlWcmeYisf6MmUGnZht0+fgdIprMvgEBkAp2RA6dFLM8orVpvJNPIwEwzETRlTyu5qKqq9ZyvMLA8p8ftRK7HhfbeABo7e5FnYFYrJacvEESvP9zKKTeD28Nsto9MXHzxxbj44osNWXZhYSGuvvpqXH311bot0+Vy4bzzzsN5552n2zIBYNGiRSMKYCVjxowZuOWWW3DLLbfotkwjPlPSR3kuM83spiFSNqFU56AZM83kI2Z65VpQLjM/I3zz3MZBU6m0dPkQUgFF0a9cW2WksXiPP4iWLp/uWUykHy3TTOeB07IcD9JcDvgCIdS29WrlGslavf4gunxBACZnmkXOLSzPKJeWLhE40f8cXZydHg6adfowvUT3xdMYtRicaVSQ6YaiAKoaDpazFJQ8RMA03eVARprTsPcpZqaRtJq7jJkwFUsc8+xpJpeOvgC6I9eB5QZmmgFAaa4H7b2daGjvwz6lOYa+Fw1PVPtRFCAn3fYhHNvh1CEiAhDOLgLCF0h9gaDFa0PJqG8XDUH1Ls8YDqDWMWgmDZFplmfB7KJophlvnmQiZoAWZqbpNhM83eXUMldZolFuYuBE78Cmw6FoZTr3sESjNER5RJdDQa7HvBtmEaDzsjyjVESmWWGW/tcEIoNJvAfJJdrTzJjrQZfToQXkmrkPSKXVhNKMQLTkd0dvAL1+jgnIRAROCww6/oHYnmY8/mVSHxmXyctwGxo0B+L7mpH1xMTlvAw3e81agEEzIgIQnlkoyr41tPMLUnbBkIrmyAxAvTPNRAC1hkEzaYgZRrkZ5s8uEoE6LzPNpKKV59N5FvjESInGPa0MmMhMzADWq59dLDHLvIWBEmm0xPSwMrNsajTTjOd/mbRogRP9sw1E0IxZBnIS297I0pkig5nl+eQiyuTmG1wuLS/DDbcz/D3DLGN5qKqqlcoXVUCMwKCZnEQFIKOzzADuA7IR40BWTJ4mBs2IKEJRFO1LuI4lGqXX3NmHkAo4lGj/Ab2I8ozsaSaPdhE0s6A8o6hl3hcIccapRMSNTLHOmUYiy4iZZnITQVMjSmgWsiSfdFpFOT6Te0yJbJZWBlClEg2aGRE05/EvM3EsGtXTDIj2tGpieT6peE3KNFMURbu35D4gj46+AIKRnnZGBk7FfUVTZx9Ckfcj6zUZNFk6kWimGccEZcCgmbUYNCMiTbkWLOEXpOxEacbi7HQ4dU7TZk8z+YjyjFY0f81Jd2n7mLhoI+sZlWlWFck028tMM6k1G5hpVpDFQXPZiDJpRpZkSkRkszBoJg9/MIT23nCfU717GgKx5Rm5zWWkZZ0amWkWGTRnpplcWrWMY+O/B0TglPuAPLyRyTMZbic8buPK84ltHwipvO+TiFfLNDV+8pRo/cHyjHJg0MxaDJoRkUbLNGOwRHpi5k+ZASn65ZGeZh29AXT2BXRfPo1ce094O1hxsaQoCks0SkgEzfQOmjDTTH7dvgB6IlmfhmSaiewiDppLQ2wLvTPLhyMCKNwX5CG2hUMx5pqAGSZyE9dhRmaaFWexp5mMWk0cNBfZRo08D0hD9JY2ujxnusupvQe3vzyipTmNHwsQ2Wxs2SIHbzeDZlZi0IyINFpZPpZnlJ7INCvVOcsEALLTXcjxhHtnMYAqB62nmcf8nmZAbF8zDpzKwrjyjCLTjEEzWTV1hI9Dj9uBLAOagWt9rBgkl4bYFmZnmhWwVKd0mmMyjYxoCF/E8oxSa9HKMxp3LmCmmZyi5RmN/x6ILdFHcjAzaFqSzZ5WsmnrNidoCsRsfx7/UmCmmbUYNCMiTRkzzWxDZJoZVde6gqU6pWJleUYgepHGMh3yaIoMZulfnlFkmnVDVdnLQEZNkdn/RVnpUBT9B82ZXSSflsg2N7unmRicbe8NIBAMmfrelFirgf3MgGimGQMm8gkEQ9p1mJED59GeZtwHZCKCJkb3NAOiVQx4HpCHCJqakWnEoKl8vCYGTqKZZhwHkgGDZtZi0IyINNFeVswwkJ2oMS1qTutNlGis4b4ghXYt08yaiyUxq83LoJk0jMo0q8j3QFGAXn9Iy2gguRjZzwxgTyMZtXYZX5ItkbwMN0Rclud/OWiZZgbtC4VaaT4e/7Jp6/FDzGUxcuBcC5yyPKNUWrVME/PKMzJoIg+viRnnYkIeM83kYWZPs5LI+FJ7bwC9kXLwZJ3oZBkGzazAoBkRacoiQbN61i+Wnpj5Y1imGbMOpdLeG+5plpthTXlGMTjTxnJt0hADGXpnmqW7nFp/yz0t3boum/TR3GlMwFTQMs1YjlUaLQYHSgbjcjq0ma3MPJRDi9bfzph9QQTjW7t9CIWYbSwTcU7Oy3DD5TRuGIdZRnLSgiYmDJxGsw05JiALM4OmDJrJx8yeZrkeF9Jc4e8Y7gPWY6aZtRg0IyJNhRY06+WNsuREplmZYZlmIuuQQTMZaJlmFl0siRs00YSarBUIhrS+JkYETqIlGplpKiMxiFVkUKZZbB8rluiUQ4vBJfmGIvaHVk6akEKzwfuCCMwGQypLMktGHINGnweiPc04WCoTKzLNGDiVh5ZpZGJ5RgZM5GFmTzNFUVAaCZw2cB+wHINm1mLQjIg0JdnpcChAIKRqPVNITg2RbECjMs0q80WmGQfNZdBmcXnGXPY0k0o4mAE4FGMGz6oKMgEwaCYr0WemyOBMs75ACD0syyIFESS3JmgWPv+zXKccWg3ONHM7Hcj1hLPaWZ5PLlrGqcGDpmJCRpcviB4fvwNkYWamGcszykf0NDOjp52WacbtLw2vySX6SrVsQ06gtlo0aGb+PQAxaEZEMVxOh3aRxLJ88gqGVO0i1uieZsw0k0N7r7UzjMSsRi8zDaQgjv/CrHQ4HYruy49mmrE8o4xEpolR5Rkz05xaWRYGSqynqqoWKLEiaCbe08tynVIwo1Qns0zkZNZ5ICfdhbRI+UcGTuXgC4TQ2Rcu1W5G0ESU6Gzp8iHI6jNSEJmmeSYETVieUS6hmMxvswInYoyJmWbWE+MvzDSzBoNmRBSnnL2spCduYBQlelOjN1Gqs66d+4HV+gJB9PpDAKzLNBOz2phpJgeRaWTU8c/yjHJr6hA9zYzZ/oqioDCmRCNZq703gEBk0NKMwdL+RCmwFgbNpCCCGEYGTsSym3n8S6XFpPJ8iqJo2WYMnMpBlEdXFHNKtYtzQEhlf1NZiEwjUzLNmGkolY7eAES1dLMCJwycykFVVa1NhxkBcxqIQTMiilPOYIn0GiJp8kVZ6YY1Ahf7gbfbz9IsFuvoDWj/z46UTDKbCJox00wO4gZG3NDorULLNGXQTEZi0Lwoy5jtD0SzWBg0s57ILslKc8Ljdpr+/mLwtJX7ghRau8Lfw0Ye/1rAhNtcKmZmnEb3AQ6YyiC2n5URFQb6czkdWhlIBk7k4DWxp1VxTvQ7IBAMGf5+NDQRNM+KqQRhNK2nWTuPfyv1+kPwRY5BZppZg0EzIorDTDP5af3MDBowB8KlWbLSwoNzHDi3lsjuyvG4TLlRTiSPPc2kIgYwSgzuadXKIKmUmrWeZkZmmoSPec4wt57ILjGyHN9QxAAdzwdyaDYhcFIYCci1MMtIKi1d5mWaiKBsE/cBKbR2mdfPSmCZVrmY2dOuKCvc515VmWUuAy1obuLxX5obCZqxp5mlRMDU5VC0sTkyF4NmRBRH9LJi0Exe4uKlLNe4oJmiKNGsQ+4LlhIp+VaVZgSi9dPZ00YOItOs2KDAeWwPI1VlLwuZBEOqNoBhVE8zIDowJwZpyToicGFFPzMAWqlOZppZLxRStUC2kftDMbOMpOTVtr0Jg+YszyiVVm3Q3Lx7gWKW6JNGMKRq/a3NCJw4HYo2eYLl+azn7TG/pxV7msmhLWbbK4o1k6fHOwbNiChOeV74AonlGeUVzTTzGPo+lfmiRBv3BSu1R8ozmtHDYDDiJr29N8CG4BIwOtNMBEz8QVVrPE9yaOnyQVXDfU2MnG1cxJJ80mgxIUgyFK1UJydNWK691699BxcYGDhhTzM5aVmnJgyaR7OMOGAqA6+J214QgVNmG1qvrcfPnlbjmJmlOQVufzm0dZsfMKV4DJoRUZzyXGaaya7ehEwzIKZUJwOolopmmlnTzwyIv1BrZ4lGy4mgmeg5oLeMNCc87vAlYiszjaQiMj8KMtMM62kJMFAiE62PkYmDpbHEIC17WlpP9BjMSXch3WVcmZ4iBkykpJXoM6OnGQOnUmm1oDwbM83kIYImOekuuA289oslMo4ZNLGeyDYyM2gm2oA0dfZxwqyFxLa3cvL0eMegGRHF0UrytfeyLJekRKZZSa6xmWYVkX2hxsueZlYS5TisnGHkdjq0OtpeBs0sp5VnNLA8n1aSjUETqYhSWcUG9jMDYvraccDUci0mDpQnIkrBtXBfsJxZ+4IImHCby6XFxL5WRQyYSCWaaWZ+pkkTgyaWE0HTPCu2PzMNLefVso3MzDQNb/+QyntBK3ktCJhSPAbNiCiOyC7q9gW1snAkl/oOUZ7R4Ewz9reTgiwzjMTs1jYGzSwnbmBLDDwHiO3NTCO5iAHMoixjz//Rnmbc/lYT28Cy8owx5/5AMGTJOlBYs0n7glaekYOl0vAHQ9p9mRnnAvY0k4sYtDZz8gSzDeXR1mN+eU6W55OH14Kehk6HgpxIlRve+1un3YJ+dhSPQTMiipOR5tROyvUsyyelxnZRntGcTDP2NLNWe0+kp5nH2oslcV7wMohiKX8wpA2iG5ppliVKsnF7y0QETIvMyjTj9rdcq8U9zWJv1DlwYi1x7i8yOtMsO3r8syyTHMSgqaKYM3hWHJmYIUoCk7VaLRg0Z3lGeYhS6ab2tIps/0Zuf8t5I0HTfJMDJ+K7htd+1mlj0MxyDJoR0QAMlsgrFFK1i1ejM81EUK6hg/uBlUR5xtwM63qaAdEbNV44W0sMmjodiqEzTrWeVuxpJhXRY8jIgCkQm2nG7W+1ZhNLsiXicjq0m3UGUa1lVtah2NdCKidOyEJsh/wMN5wOxfD3E4HTli4fy/VLIFqe0czybOH3YnlG64nvXjN72rE8pzzaLAiax74f7/2to2UZMmhmGQbNiGgAESypZ9BMOq3dPviD4ZtXwwdNs0RmkZ83zBYSaflWZ5qJC2dx8UbWEGVSCrPSDB04E30z2NNKLqb3NOv2IcRME0uJY9Do7MKhFDKILgWzgmZup0P7zmeJVjmY2c8MiO5j/qDKcv0SsDTTjIFTy4mghak97ZhpJg1vj/k9zcLvFwma8d7fMrK06RjPGDQjogGYaSavhg7RzyYNaS5jT+H5kQuzQEhFly9o6HvR4GRJy2eJBjk0mpxpxMwSuWg9zQze/mJgLhhS0cEBU0uZPVieiNgfeD6wlpn97cR7NLGnlRTM7mnlcTuRkx6ucNDMQXPLWZFpJq4zfYEQOvp4HWCl1m7zy/Oxp5k8tExjkzPNeO9vPVnGgcYzBs2IaACRaVbHnmbSEUGzEoNLMwLh/nbpkcAcs02sI2b4Wj3DSMxuY6aZtZpMOgewp5WcmkzqaeRxO5GV5gQAtHAfsIw/GNK+A6zqaQYAhSKIzmsBS5kZNBM9rZhpJgeR5WlFeb5m7gOWUlVVu/Y2c/tnpEWvA5oZPLeUVqLNgvKMbT1+9AU4edZKInDCoNn442XQzHIMmhHRACLTrK6tx+I1of7qI4FMEdg0GmtZW69DK88oR08z0YyYrBHNNDP2xpmlueSk9TQzYeJEtK8d9wGriKC1Q7H2hjlfyzzltYCVrMg0a+5iloEMxLmgMMu884DIaGammbU6+gIIRMokmz1oLq41mrgPWEoLmpp4/OdluOF2hsvAM2hqndigeb7p5RnD78dxIOu095gfMKd4DJoR0QBlImjWzgtk2YgSCaUmDJgCLNEmg/ZeOWpZ57OuuRSaOsLHolmZZswslIeqqtrAlcgCMZLIZmN2kXVau6I3y0b2MByOGKjntYC1zAyaaVlGHCyVgjgPm1WeEYh+B7BEp7W8ke+BDLcTHrfT1PfW9gGW6LNUtDyjece/oihaiU6WaLROly9oWdBcTNbivaB1WJ7RegyaEdEAzDSTV0Mk06w015ygGS+WrKWqqjQXSyzRIAcRNCkxqacZs4zk0e0LotcfAhAd0DaSlmnGQIllov3MrD3/M+tQDiLrq8jEoDm3uRzEebjQ1PKMItOM+4CVtH52FnwPiKBJE88DloqWZzR3H2BfM+uJfmbpLofpQXPe+1tLpnGg8YxBMyIaoDxS+q+1249eP2tYy6Q+kv1nVnlGMXDu5aCpJXr9IfiD4dllVmea5WnlGXnhbCVx41psdNAspqeZqqqGvhclRwxcZridyEo3vlwr+1hZr0XrYWfORJnB8FrAej0xQfNCE4LmLM8oFy3TzMSgWXE29wEZaFlGFpTn0sozMmhiKa9F+4CWacbynJaxKmAKRAM17bz3t0RnXwBBi7IMKYpBMyIaIC/DDY87fHoQPbRIDg0dkUwzk8ozan2smGlmCVGa0aFAa8ZtFVEShPuCtbRMM6PLM0ZuzP1BFV0+Tp6QQZPIMjFhwBxgppkMxGdvZh+TRJh5aj0RuEhzOky5HmCWkVxatJ5G5pdn5D5gLSv6WQnFDJ5bzhcIadfhZmcbiqoWDJpax6p+ZgAzzawmPvc0C7IMKYpBMyIaQFEULdusro1BM5mITLOSHHMyzfK1nma8WLKCmNmVm+GGoljXzwaIBlDbeph5ZCUx29PoTLOMNKc2eYKZRnIQgxZFBm97oZA9zSzXamIPq6Fo+wKvBSwT28/MjOsBLWDC418K0XOBeYPm4rumiVkmlpIj04znAat4e8KfvaIAOR5ryjM2MGhmGbH98yzINIre+/PazwoszSgHBs2IKKFy0deMmWbSUFVVK81WZlJPMy3TrIc3S1YQmWa5Jt8kJSL2BX9QRQ/LtlrCHwxpMw6NzjQDmF0iGzF4XWJWppm2/XmzbJUWSYJmYnZ7K7MOLWP2viACJjz/y8GK8oxF2QycykBMVrCip5koDczAqXXEdX9ehhtOh7kTKGNLtZM1oplm1pVn5DiQNdq6GTSTAYNmRJSQyDSrZaaZNNp6/PAFw/0szBgwB6I3aCzJZw2ZZhhluJ1wO8M3a9wfrCFKJDkdiik3TwWZvFmWSXNk0Mqs/lYio4Hb3zotFgyUJyIGztp6/AhErkPIXFp/O5OC5oUxg6WipwZZwx8MoaMvAMDcAHqxVqKTARMriX5WVnwPFDNwajmtPKcF25/l+awnPnsrelqJfuq9/hD6ApwwazZt20swDjSeMWhGRAmV52UAYHlGmYjSjAWZbqS7zKlrnKf1seLNkhXae8KDJLkZLovXJFy2NY99zSwlMk2LstLgMGG2aSFnmEqlqdPcQXNmGlrPqw2WWBs0EzfsqsrBM6uYHUAtyHRDUcLbnN8B1hKfv0Mxt/JAUUxZVgbLrSMyzawtz8jAqVXE8W/FBErxnu383reM18LyrDnpLohq0Lz2M59Mk6fHMwbNiCih8kj5PwbN5NHQEd4WpSb1MwOYaWY1mcozAizXaTVRHsesTFOxvVmeTw5i+5vd04xBM+tofS091k6ccDkd2jqwr5k1mk0uz+hyOrRgqchyJmu0dkWDJmZMmBHyM9Mg3q6FgVPLRDPNzL8XKI5ktnf0BdDL0uyWaLOwPCczzazntbBEn8OhMHBqIQbN5MCgGRElpGWasaeZNBoimWalJvUzA6Kzmry8ULJEdMBUjoslMYDWxkFTS4hMs2KTgybMNJWDGLguNivTjCX5LKdNnJDghpmZp9YSPa2KTCzPJwL0zV3MMrFSNMvQ3POA06Foxz0Dp9axskxvboYLac7wkCH7mlmj1cJMIwbNrOe1sDwjENPXjPf+phPbPs+ibU9hDJoRUULleeFsJmaayaPe0kwzH0LsaWG6NskulnjzZK1GkzPNWJ5PLmLg2qygaX6GWyvLwokT1ujojZTolWDiRD7PB5YSmWYFJgbNGDCRgxg0N7OfmSB6aHIfsI6327pBc0VRolUmOGhuCSuDJlqWUW8AqspxACuIiar5GdaU6ea9v3WYaSYHBs2IKKGKSNCssbOPM8wl0WJylgEQDdaEVGhNyMk8Wk8zi0tzCXlaeUZeOFtBzPI1K2gigubMLJGD2T3NXE6HdqPWykCJJUS2cY4E3wHMPLVWixWZZizRKoXWbusyjcT3DbMNrWPl9gfY18pqXgu3v9j2wZCKTo4DWEK0RLA604xBM/MxaCYHBs2IKKHi7HQ4HQqCIVUbqCNrWZGine5yIjPNGX5/DpSZTqbSXEB0lhtnm1ojWp7R3PJ8rexpZrlAMKQNnJkVNAWAQmYXWaYvEERfIDxpSYbvgGjmKc8HVmgxuacZEBsw4fFvpVYLtr0gSnTyXtAafYEgun3hXmJWBc1ytWwjnvutEO1paP51gMft0MpzMmhiDSt7mgHR45/b33xtFm97CmPQjIgScjoUlEZKgLGvmRzExYrZ6fn5rGVtGS1oJkFpLiB6w9bWw8ETKzSZXJ6RPYzk0drth6oCimLuwFkB9wHLiNKMAJCdbn2mWWy5ZjKflmlmYrWBQq00H7OMrNSiDZpbUZ5RlOjkPmAFce/lUKzLOGamibWimUbmH/+KojBoYiFVVS3vaZbP7W+ZNou3PYUxaEZEgyrLFX3NeixeEwKsm20iLtI5aGo+rTxjhvUDpkBs0IwXzlYQmWYlppVnZJaRLERprMLMNDgdimnvy+wi62ilGdNdpm7zwRSwVJ9l/MGQ9r1rZtBcZDVzm1sr2tPM/IEzsQ+wp5k1xLbPz0yDw6LvAVEintf+1tB62lmUbZKXwe1vlV5/CL5IxQErgqZAdNyJk6fNx/KMcmDQjIgGJfqa1bUx00wGVtW0ZqDEOrJdLPHC2VqiPJJZmWYFWdFynGwAbq2mDvNLMwLRQVpOmjCfyDSToTQjwMxTK4nPXFHMHTgrzGLARAYiaGlNT7NItiF7mlnCytJ8QrSnGXtaWUHcc7Gn3fgjxn5cDgVZkXYZZuP2t45s40DjFYNmRDQokWlWy/KMUrDqi1NcpLdyprHpZCvPyKCZdfoCQe0cYFbgRPSz8gVD6Ir01CBriAFLM0uzAcwuspI4/1tVkqs/UZ6R+4L5xMB5gcmZpkVZDJjIIJppZl15RvY0s4Yoh2tVwARgeUarRbMNrco04/a3ipZlmOmGoliTacrtb41QSJWut/14xaAZEQ1KZJrVM9NMClY1gs0TfUx4sWQqVVW1WV2yXCyJGe68cDafmOnvciimnQMy0pxId4UvFRk0t5YYsCwyO9OMJToto2WaSTJpQnwPtfcy28BsImhVYPKgqQjSN/P4t5QYNC+wImjGTDNLtWpZRtZ9D0TP/bz2N1uPL4g+rTwfg2bjjVVjP7G4/a3R0RuAKPLCTDNrMWhGRIMqjwTNahk0s1yv37qLZnGjxuwic3X5gghFLpZkGTRlM2DrNHWGB6yKs9NN7WvBkmxyENu/yORBU2aaWUfraSZJppn4HurgwKnpxPEnMr/MUhRTojcQDJn63hQVm2loNvY0s1ZsTzOr5PLa3zKx5fmy0625FmDQxDptPdYf/5w8bQ1xvGW4nUh3WVOak8IYNCOiQZVHyjPWszyj5cQXp9OCi+b8DDFowhtmM4ltnuZ0wOOW4+ta3Dh19gXg5wCaqRo7IkGzHJODJsw0kkJzJGhmVj87QWSaMWhqPtnKsoigGfvamE+cf80uz5efmQZREaqF5wBL9AWC6OwLH3OFFvY06/YF0e3jsW+2aHlGCzPNPAyaWCW2px3L840/WnlGZpqNOyJgziwz68kxCkdEUorNNFNFfjBZIjY93+yLZpHZ1spMM1NFSzO6LLtR6i928JYXz+aKzTQzU0GWOP45YGolMcvf7EyzwmwGTa0iyjPKkmkm1qPHH+SkCZNpQTOTexo6HQpLtFpMXP87HYol54KsmDLNzDYzX6vW08j6nmbtvO43nVeCTKNopiGD5mYT2V15FgbNGTSzhvi8rSrLSlEMmhHRoMoimWZ9gRC/KC0mZhpaMdNIXKgzLd9cWtBMktKMQHjQJjcyaMNyneYSPa1KzA6aiUyjLm5vKzV1WdvTjD3tzCfbd0DsgH0H+5qZSguaWTBwKrLbGDCxhtj2BZluU0szC4qiaJN12NvOfNFMM+uDZgyamE+mTCMGTc0X3f7WH/++QAi9/qBl6zHetEnW1348Y9CMiAblcTu1chDsa2atNgtnGkV7mvFm2UztIstAsoslEURlIN1c0fKMJgdN2NNMCk1i+5ucaSJ6mnX5grxZNpkITOVmyJFp5nI6kJkW7qvAvmbmaraoPGPsezJgYo3WLuuDJkVaX7M+y9ZhvBKZZpaWZ4x8B7XzvG86GXraMdPIOtGeZtYd/9npLjgjEza4D5hHG/uTbBxoPGLQjIiGVJ6XAQCoY18zS3kt/OLM14JmvFAyUzTLQI4BUyF688QBNDM1ip5WJmca5bM0l+VUVUVzlzXlOXM90ZtlfgeYSwxQ5kiSaQZEs96YaWauFlGe1eSgORA957QwYGIJLWhiQcBUKGK2oWVkCpow08R8XgmCpsw0s46WaWbh9lcURdsHeB9gntjWLGQtBs2IaEjlueGb5TpmmlmqzcLyDOJGrb3Xj2CIve3MIusMIwZRrdFkVaYZt7flun1B9PrDPaTMHjRXFEXLcGDg1FztkVJYspRnBKIlGjl4Zi4xcM5Ms/GnpTtantEqoixwUxcDp2bTgiZZ1m3/rDQXRGVQnvvNpbVnsDJolslMM6vIEjhhtqH5xLnWytKsFMagGRENqSQyQMuSHNaKNgM1f8BEXCipKm+WzCSyDGSrZc3ZZtYQmWZWledjwMQ6TZFtn5nmRGaa+ZmnhZHBOpboNFc000yebGMtaMZMM1M1W1iiTyvNx+8AS7RaWJpTiJZn5D5gplBIlaKnmcOhaPciHDQ3VzTTyPpMw7YeP1SVk2fN5LVw/CcWj3/zyTp5ejxi0IyIhiS+pDlAbi1vpBSeFQEUt9OB7HRXZD24H5hFxiwDICbTjPuCqUSmWanJmWZioIYBE+s0WViaDQAzzSwS7Wkmz3eAWBf2NDOPqqpa4MSKc0C0NB8nz1mhRYKeZsVZnEBphY7eAESBDyszjYDovQgHzc3VKkF5PjFoHwip6PaxPKeZ2iTINAaYaWYFLcvQ4m1PDJoR0TDERVorg2aWaosEUKxK0Y7uBxw0NUs000yeLAMAyM+IlOvkhbNpev1BLbPD7J5WYnY7j33riIHKoixzt71QyGxDS2jfAVJlmkV6mzDTzDTtvQEEIiPn1mSaRXqa8fi3hJWlOYUClui0hNj2mWlOpLuclq6L1teKEyZMJUOmYYbbCbczXJ+TQRNzaZlmGdZmmuVrVWb4HWAWZprJg0EzIhqS+JLml6S1rK5pLt63jcFT08h6sZTHC2fTiYEqt1MxfX8Qg2WtXSzLYhWx/c0uzSmwRKf5QiEVnX3hwFSORNnGojwjM83MI467rDQnPG7zB861nmYszWcJMWnRykHzaMCEwXIztUoQMBGYaWKNaHk+664DFEXh9rdAXyCoZfZZnW2kfQdw+5tG1nGg8YhBMyIaUgFLsUnB6i9Olmgzn7gwla08Yx7PCaYTpRmLs9OhKIqp7y2+A3zBELpYlsUSsdvfCkXMNjRdR18AIkYtU08z8X0kygeT8Vq6wsd/oUVB82L2NLOUKM1ZkGV9eTYOmJrLK0FpPkFUveC531zapFmLM43Y08p84rN2KEBOurXXgQyams/qsT+KYtCMiIaUz2CJFNosnmkWzS7ixZJZ2iXsZwPElmjgvmCWRguDJhluJ9Jd4cvFVg6aWqLZwn5GAHuaWUFkcqW5HJZkFw2GmWbmExlehZaVZw2/b1uPH/5gyJJ1GM9k6GkWDZjwuDcTM83GN1VVtXstK4PmALe/FUR1n7wMNxwOcydM9sftbz4GzeTBoBkRDYll+eSgNQO1aKaZuGFjST7zRDPN5MkyAKKBdA6emKcp0tOqJMf8QVNFUdjXzGJNkvQ04/Y3j5jNL1umsZjE0cEybaYRx12RRT2t8jPcEON1nDhhPhl6msUOmLJMs3laZco083DQ3GydfdF+llZnmjFoYr5oaU4JguaZ3P5mCgRDWol2Gbb/eMegGRENSQuW8EbJMqGQqjVetmq2ST5L8plObHPZMs20rEPuC6aJZppZNGjKTCNLiaBZsQVBUyC2pxmPebN0aOd/uSZNiEkc7cw0M02zxZlGDkd04kQT+5qZqtcf7WlTYGHQTARMAiEVPX6WaTaLV6JMs1yW6DSdmDCb7nIgI83ajHOWaDWft1ueTCPe+5srtn+obJOnxyMGzYhoSCJYEgypbABtkY7eaG8T64JmItOAF0tmCIZUbSa/DBfLsbQAarcPoRAD6WbQgiYW9bQqzGJJTiuJ8mzFFg2aForzP4OmphHXWzmSZZpFyzPyetAsLZ3WlmcFollOnDhhLpFl5nIolva0yUxzwhVJN2SmgXmi5Rmt/x5gppH5ZOppx+1vPq2fHbf/uCO2fXa6Cy4nQzZW4xYgoiF53E543OFTBUs0WsPbE/7izEpzIs1lzWk72seKAyZm6IwZkMyRbIaRuHAOqUCnjwOnZhDBaqvKMzHTzFrRnmZWZZqFj/mWbh8zzk0ia3lekXHCnmbmaZGgPJ8oDdvc1WfZOoxHrV3R8lyKYl1PG0VRYjKNeN1nlmh5RokyzXjuNw172o1vWj97CSbPMtPQXOxnJhcGzYhoWAWZ7GdiJRnS8wuYaWIqcVPqcTuQ7rK2JEd/DKSbz2vxxXMhexpaJhAMad+9VpXnFIP1vkBIKxVGxtLKM0qXaSYGTjlwbhYxWaHQwoHTwsi5p5nlGU0V7Wdm/XmAg+bm08ozSrX9ee43S7SnlUzbn8e/WbwSBc3Z19JcDJrJhUEzIhoW6xhbS/vitPCiKS9D9LbjgIkZ2nrkHDAVRENq3jyZw+qLZ62nFYNmpgtndwEOxbob5wy3E+mRLGdmG5pDBKVk62kWLc/IgROzaEEzCzPNilme0RItFvezi6X1M+R1n2liMw2txu1vPq08X4YE259BM9OJMRcZAicicOsPqpw8ZwKr7/spHoNmRDSsAmYZWMorQXq+qKfv7eLFshnaJb9Y0gLpzDQzhdX7gzj+W3n8my520MzpsKY8l6Io7GlkMpFpJltPMzFw5g+q6AuELF6b8UFkdxVa2tOM5Rmt0CpBaU6Bg+bm80pYno9BM/OI6z+5Mg25/c0iU0+7DLcTbif7WpqFQTO5MGhGRMMSF2utHCyzRFu39TONxCzHjr4A/EEOlBlNlGfMlfRiKU8EUZl5aIpotqlF5RmzWKLXKrLcOIlBO2YbmkP0DZKtp1lWmhMidsvBU3OI826RlT3NWJ7RElqmmURBM/a0Mo/oaVYgwaC5uAbp6AsgGGKWsRmimUYSHP8eBs3M1iZReU5FURg4NVGbRAFTYtCMiJIQLc3HL0kryHDRFDtgy4sl48k6YCrkM9PMNKqqxjSDtubGWQuYcOKE6bRSrRYHzcSgOSfPmKOjT47t3p+iKOxrZqJef1ArhWRl4EQE7Jp5/JtKXGNZ2c9O4ICpuXr9QfT4w8e+FOUZY76LOhg4NYVXwqApJ8uYR8s0kyBoCjDb2EyyTJikMAbNiGhYWmk+DpBbQnzuVmWZAIDToWgBHO4HxpM900ysVwcHTQ3X5Qtqs3qtK8/ITDOrWF2aU2Dg1Fxi4kSOhBMnxDox48R4IkjldirISbduX2B5VmuIz1uG2eYi00Scm8hY4l4r9v7LSm6nA5lpTgAcNDeLVOU5M6MBE/YzNYeWaSjB+R+ITpjl8W88ryQTJimMQTMiGhYHTK3llWXQNIu97cwiBspzJetnI2RHBu8429R44uYkzemAx23NZZtWorebN8tmk2W2IUt0mkubOCHhd4DINOOkCeO1dEZ7WimKNT0NAaAoO9zTrKmTPc3MJFNPM2aamUts+/wMt6XHfiwGTs3VKsGkWSEvpp+pyIAkY0Uzzazf/kDMdwAnTxtOlns/CmPQjIiGlcdMM0tZXZpNYEk+88h+sSRmvXLQ1HgiSJ1r4cCJGLDzBUJaqTAyh1ae0eKZ5tFMM57/zSDOrTkSBs2i53/uC0ZrkSTTQJRn7OgNwBdgX1uzyNXTjBmmZtKCZhIETAQGTs0lU6ZZVpoTzkhDU25/4wWCIe06UIbyrACPfzPJ0JqFohg0I6JhiYs1ZhhZQ5ZmoPnMODSN6BXz/9k78zBJqirtvxG5VXUtvdDQ0N00iOwNLoDiwoe4ICAIDA4CIowDiCAOiMs4OirayjijMwqfMCCoo4OyiMOACgzgQssnroBK093MsPQU0E1D0UtWdVVuEfH9kXUjIiuzqnKJyIx7z/t7Hh7trKyoyLxx7z33LO9RToqkoRy540UGzeImCKD27lnoz6SQTVdNRspzdZekBNAXqWpDjn9X8KuNE7gHDLHaoGts2VGt7FI9BXvF/P6M7zDlHtA91HrLnmbyCPpZ9X7sFXwGusu2BDnOLcvi+HeRcM/YXifNKTj+3UP5/np99iNVGDQjhMyJMta2cZPsCb6mdY83zgXzaCx1i6TLMw6x0qBrJKGnlWVZvtOOlabdRWX193r9V5UOW5g0ETue5/kZxkncA1hp1j1e8uUZcz29D9u2fOf9Szso0dgttiRInjGQ5uO87wZBpVnvx17BasPu4bhe4qpNKM/XPVSi+lBfGulUMlz2DJp1j6QkTJIqyZiBhJBEs3AeM8x7SVI2Tva26x5+P5uEGkuDfergzEqDuAkOzb11nDBo0huSEDQFgkoH2gHxU6y4KDlVCbyhhGQYh1H7EuV540fZWzslIGgSVJvSYdYNJksOCuXqOpAEp7nagxg06w5BpVnvx14xTKd51xgrlKFaCPe6PYOC4989klRlqJivkic5/rGTlNYspAqDZoSQOVHO2nyhgorDXgbdZltC5Bnns6dZ19ie+EozOk27RXKC5kye6AX+WtDr8R9g0kS3UE5p2wIGsskLmg31sdqgW/g9rRJQbeIHTTjuXUGttZmUhcFc79eBYX/8afd1g60J6menYLVh99g6ddYeyAby6L2GlUbdw2/NkaCgCce/OxQrDibL1f7hvT77kyrJWIEJIYkmvGDzsNRdCmUHxamm673eOJXTnEGz+FG9Yno95jOhnKbjRT4LcZOYoBmDJj0hKWvBIn/8y3Bdr6f3YjrKzhrMpWFP9ZFKEsNMmugavjxjj3uaAXSYdZtwwNSyer8OqPEfLzKBshtsTUjCZBiuAd0jifKcHP/uoVpzcP7LQ32/lpVMtQmJMGhGCJmTTMrG0FSWIx2m3UVtnCm795mmC/yyfD4DcRPIMybTWFLrAZ2m8aOC1L2uNKI8X29IStBUHdwd12OlScwkXZ7XrzSj4yR2VOAkCfKMrDLpLlsT1M8MqHXe0faLH9XTKAlVpgrK83WP7YkMmnLv7xbq7Ndr2z8MJXq7g/p+hxKaOCcRBs0IIU2xYIBVRr3Al2bsz/Q803SBL8/GZyBOyo6LiVK1LF8HeUbPY9VJnCQlaKIqTdnTrLskRao1l075wfItDJzGinJI93rMZ4LyvN1jS4ICJ3SYd5ckSXMC1QTKgWwKAJ+BbrDVD5olZx+YT4nOrrE1gUFTVhp1j6S05gjD8e8OSellTgIYNCOENIXSVN5Gh2lXUd93rx3mQLB501iKl7AjMqll+eq+HNfzdbdJPCQmaBaS5yPdoVRxE6VrT4nO7uBnmSZ0/VcV0Kw4jB8VOElS0Izj3h22JmjsFXwGukfgNE/O+NNp3j38SiMGTUTiB04S1NNMBfC2T5aZMBsjSawylA6DZoSQpvCrjOgw7Sq+wzwBRvNC/xmgwzRO1JgP5tJIp5K5Tc/LpqAUA8aZcRor+cmg2rSX+D2tWGXUNZRjMim69ipotoXVxrGSfHlGVpp1g4rj+s6TJAROAocpx70bsKeVbJJYaTQ8ZYeMcfxjZ1uCKw05/+Nn20Rye5o5rofxIu2AuEhKsiwJSKY3jhCSOPx+VgyYdJVtCdo4VbbTRMlBscLqorjI+3JsvXeSz4RlBT32KNMSL0kJnKs9gNJ83WN7wnTtF/kSvXwG4kQFo5IQKG2E39OM1Saxouw/y+p90gQQ2CTsZ9IdktbTDAj3taPdFyeu6/n7f6KCJvMYNOkWKmiepKApg2bdI0n+H0VfJoVsuho+4DMQH0k595MABs0IIU2hjHb2NOsu2yeSUWUCVB1lym+7nc9BbCS9ykARVBvwWYiTpGScLfITJzje3cLvZ5aQtcCvNGPyTKwEiRPJGPfpqPsaL1bgupToiQuVoDC/P5OIqnM6TLtL0nqaAexr1y3yhTLU0pokeUY/aFqgPFvcJDFowvnfPZIozwrQDugGlGdMHr23wAkhWuBXmk3SWdZNktQM1LYtfwPfRmMpNlQGb1IdpgpVbUCJhvgIZxv32nheOFD9+1smSnSWdImkjL1CBU5ZaRYvqtIsqdXGau33PGC8xPU/Lvx+Zgmw/wD2s+o2iaw0Yz/DrqCqjAayQWVHElC2SNlhP+O42ZZAeU5K9HaPwP+TDPtfoZK4mTwdH0k7+xEGzQghTaI2SfY06y4qSJmYSgM6TWMnqC5JpsNUoRyn7GsTH+Olip9t3GvjWc39UsXFRInOkm6QT9jBKehpxvU/TpJebRyW6OH6Hx/5hEn0qHWI8ozdwc82T8j4A0EyF6sM4mWr388oOQEToNrPOD0lOcJnIF62JbinYX6SlYZx4/c0S5gdyEqz+Ena2Y8waEYIaRJVZcCeZt1lW4LkGYHg8M5Ks/hIusNUQXnG+FGZfNm0jb5Mqqf3Mi+U8byV+0BXSNrBSVU8cPzjJenyjAD7W3WD7Ql7DvxKs8kKHaZdQNmCSVn/AQZOu4VfZTSQnLEHqv2Mw+sAiY8kBk7V/C85Lgplt8d3Yy41KiMJCpoCDJp1A7/KMEF7v3QYNCOENMWCfvaz6QVJK89f6Pc1otM0LnRwmAKsNOsGSZJosCwrJM/HfaAbJM1prtb/l1hpFitqTR1KqDwjEE6a4PofF3kl05mA9R+odZgWK3SYxk0g1Z2cdYA9jbpD4DRNTsBEQad5d1D+loUJOf8DwGAujRQrDWNnrJgclZHpcP7HTxL7GUqHQTNCSFOooA2DZt0lSU5zIMh64XMQH/pUmjFoFjf5hGWbqX1gC4PmXSFpmaZ+pRmDZrGiwx4w7K//tAXiIkigSUbQZCCbwpS/lA6zmHFdz59bSUmaAEKVZrT7YsXva5lAmXZWGcdP2XH9ftFJqjSzLMsff+4B8aFURuZlU8ile6syMh0mTsRP0nx/hEEzQkiT+L2s6CztKknTNF/gPwc0luJiewKzixsxmGOlQdwkzXBWQRNWmnYHVWmQtPFnT7N40anSLM+gWWwkTZ6vVpqN4x4nO0L9TJMUPKfDvDuo+TWUS87YK+g0j59wYmpS1n8FK43iR/WzT0rCZJgFbNMRO0lLmCQMmhFCmkRtkhMlB8WK0+O7kUPgNE9Gppl6DrZP0mkaF0nrYzQTQ6w0iJ2kSTQsZNCkq2xPWKWJCprlCxWUHcqzxYUOEr2sNI4fX54vIes/QIdpt1DzKpuykUsnx12jxn+M4x8rSa404xoQP+qMPdwXyCEmBY5//KjE5PkJqjJUcPzjxfM8v9IwKWd/wqAZIaRJhvsygSwLq4y6guN6ics0Vtrq7GkUHzpIcwFheS46TeMiaZVmwfxn0Kwb+EGzhIz//P4MrCk7gBK98VBxXOwoVROTkjLujRhmT7PYSVpPQyC4F1YYxktgB6ZhWclxmrPKqDuo8R9K0NxX+NWmXANiQwVNVKJakuAaED9KzSOJlWbzWW0eK4Wyi9JUUmJSzv6EQTNCSJPYtuUv3izJ7g5jhTK8hDWCVVlP21hpFhs6VBkAwOBU0Ezp7pPoSVrQZBHlWbtK0pImUrblH+Ip1RwP4fU02fKM7GsTN+HASVJglnl38KsME2YHzg8FTDx1QCGRo3rGJaXKPAzXgPhRiWlJ6mem4PjHj/puk9KaIwzHP17U95qyLQzmkrf+S4VBM0JI0/j9rFhl0BXUxjmQTSGbEHkWVWnCKoP4yCdYliXMkN/TjM9CXCSu0kzJMzJg0hWSNv4AJTrjRjnL+zMpZFLJ2PcbEVQbMGkiLpIo1azsEvWcknjwe1olLGii5n3Z8TBZplR/XATjn5y5r/CrTbkGxIZKTmalkUyS1s8+zAL6gWIlfO5LUpW5dJJ7GiOEJA61UbLKoDtsS6Cm8YKp3mo0luIjiZJMjWBPm/hJWrbhQiZOdJWkVRoCQbUhg2bxEMhyJctZPh2/0oxJE7Hh9zVKkC3ALPPukFSZ7oFsyu+xxKBJfAQ9zZI1/gDXgG6g5PkWJsT2D8Pxj5/A/8NKQ2mouZ8k3x9h0IwQ0gIL/I2SzrJuoDLNktQINgic8hmIg0LZQakypWWdwMNSGJUBO0Z5xthIWqWBqjJi4kT8OK7nO86SMv4As4zjJqnO8ukMsadZ7CQxaB5UmXD+x0lSZboty/IlA+k0jY8kJ0/41aZMmIiNoNIoOed/BW3A+FEtMJKSMBkm3NPQdSnRGzVJtPsIg2aEkBZYyH42XWV7AuUZlAFXrLgoUJolctQh1LKAwWzyDsthgkozrgdxkTR5vkWsNOsa46FgRFLGH6g9MJPoCXoZJXv9H+b6HysVx/X72yXpWRhmlnlXGEuwTPd87gGxk8QqUwWDJvGzNcHyfKw0ip/tE8nz/yjU+Hsek2bjIGnnflKFQTNCSNOojCdK83WH7Qks0R7MpZGekmZhtVn0KIfpUC4N2062lrUKmhXKLsqO2+O7MZOkSbSqA/yWiRI8jxmGcaIOTvOyyeptpRz4lOaKhzG/wiAZc34mhlhxFCvjIWdUkjKOGTTvDn7FaQLXAT9wyrNgbCS1px3AoEk3UBJtSQ6acPzjY1vCpPnD5NIp9GWqZxLaf9GTxIR5wqAZIaQFguafDJZ0gyQ2grUsi01gY0SnsvzBXHCYp0RXPCQt42zRlDxjqeJikpWmsZLU3oZ0msdLPsG9bMKwp2W8qKB00oLmdJh2B7/iNIHrACvN4qVYcVCckmlP2v4PUKK1G6jztZJETxLcA+In6GuVvPEH2N8+TpJ27idVkmOFE0ISz0L2s+oq/saZoKAZEGzkNJaiRzkhdDCW0ikb/ZkUgFopORINrusl7nmYl00hm66ajlso0RgrST04UZopXsYS3MsmjHoOGDSLh8QGzVlp2hUS3dOqj07zOAmvqYMJHH+19u8oOVSZiAnlZ0liTzNK9MbP9gRXmgEMnMZJUs9+0mHQjBDSNJRn7C7bErpxLvSfAzrNoyapzd9nQjl0mHEcPWPFCpQCYlKyzS3LCpIndnDM4yRpAVOFn2XOYEksBD3NkjXu01Fr/2SZjtM48OX5EtbTis6y7qCDPCMDp/GggmaDuTRSCZRpDwdymTQRD0l2nHMPiBfP8xKpNBSGz0B8JD1gKhUGzQghTUNZvu7iG00JK8/3nwMaS5GTT3Dz90ZQois+VAC1L2Ojb6qiLwmooDkrjuMlqVKtam1ipVk86FJpRnneeMkn1GlKedbuEMgzJm8dUPdEh2k8BMlzyRt7oKoyodZ/PgPxoPbUJD4DSv2mWHFRoEx75OwoOai41YzJpPl/FKw2jA/l+0va2U86DJoRQprGrzCapLO0G+QTmm2ygE7z2NCv0qx6n+NFOk2jJqmZpqqvGed/vARBs2Q5TYJKMx6W4yCoMErWvJ9OOmVjIFsN5jOAGj1JrTRS+9F4sQJ3yrFHomcsoeMPsKdZ3KiAyVACx17BSpP4cFzPP1Ml0Q4YzKahCiC590ePUvHJpm30ZZLpqg+Sp3kOjJqknv2lk8yZSAhJJGoB3zpRhufxsBw3yhhJ2sa5QB2WWHEYOfmEVpfMRFBpxmchalS2WdLmv19pxp5msZLUgxOlueIlkGdMVrC0Ecqpy0qz6Elqpana8z2P4x4ngepAssYfYE+zuEmqNGsYX5qdz0DkhHtEJ7Hi3LYtVhrFSKAylIFlJU+eFWDQPE6SqjIgHQbNCCFNs3CqwqBUcTHJkvzYSazTnJUmsZHUPkYzQXnG+Ehq0GThQPV+tjBoHitJPTix0ixexorJrTCZjnLqMmkiepIaPM2lU372O9eAePA8z1//k+g09yvN6DCNhUCiN7l7AJ3m8aHW1VzaRi6dHGn2MBz/+NChpxX3gPjYpsH4S4RBM0JI0wxkU8ikqlkv7GsWP0k1nJSxxGcgepLqKJuJoZyqNOCzEDVJDZotYqVZV9ieUKlWFSiZKDkoO26P78Y8ktzLaDpDDKDGRpITaOgwjZfJctDTJmnrP8B+NnGT5H5WCvY2jI88g6aiSWo/+zAc/3gIJ8wkce+XDINmhJCmsSwL8/tZZdQNCmUHxUrVIZk0p4nf245Bs8jRpZ+NYlBVmrGnWeQEQbNkHZzY07A7JDVoOpgLHHmsMI0eHaoMFL5EF5+DyEmyVLNfbUqHWSyowHnKtjAvm7xKE7Uncf2Ph6DKMHlzX0GneXz4QdMEJ85w/ONDteZI4t6v4PjHQ6Hs+gkzSawylwyDZoSQllg4j1VG3UB9vynbqnFSJgE2gI2PpFaXzATlGeMjqUGTRZRn7QoqEJG08U+nbH9P4oE5WjzPC3oZabAHDLOnWWwk2Ragwyxe/OSpvnQie9qoCiiOfzzkNQqasLdp9OgQNGW1aXyoOZU02z/MfPoCY0ElzVkWMJBN7vovEQbNCCEtwSqj7uBLMyawEawKmm3lMxA5fh+jhElyzsQQnaaxkdSgmeppuGUH53+cJHktUE5TVppEy0TJgaNk2RLsMFUM8TmIjSQ7zinNFi9jCVccUDbJeLGCCiV6I0cHeT4VzGfQJHp0kOdk4kR8BGoDHH9pKLtvMJeGbSfL9ycdBs0IIS0x3w+YsMogTrZNfb9Jc5gDgTzb9okyPM/r8d2YhU5VBkC40oyGc9Rsn1RrQLIOTkG1MfeAOElypQmd5vGgnGUp20J/JnmybNMZpkxbbCRZnpEOs3gJetsmb+yB2meScz96xjQ4Byi7lAkT0eMHzRM9/twD4oJBU7mMF5O/9kuFQTNCSEsohyk3ynjZluAqA/UMlBwXEyWnx3djDjUNYBMWKJmJoSmZtnE6TiJne0LXAPaziR/P8xJbaQiEnwHO+yhJuizbdIKeZlwLoiafYMdpUGnK+R8H+YRXGmRStt9rjXM/egJ5vmSOP8DEmThRyZNJHn8GTeJDh7624b6WSh2BdI4OVYZSYdCMENISqspo6w5WGcRJWJ4xafRnUsimqtvHNhrMkTFZdvwGsEl0lDWC8ozxkdSgiXKW7Cg5lGaKibBMX9LGHwiC+nSYRYsOzpIwwfrP5yBqkrr+A3SYxk0+wVXGCsrzxYdfaZLAua/gGhAfSZdnBcI97Tj+UTOmUdAU4DMQJWMheUaSLBg0I4S0BPtZdYftE8l1mFiWFTwHDJ5Ghjp8pmzLz+JNOpRnjI/AaZrt8Z3UEj7IMVgaD2rssykbfZnkmeqsNowHX5ZNk0rjYX/95zoQJcWKg0K5mpCQxMAJq0ziJcn97BSB05xzP2qSXmkIhNYA2gCRo+bUUIId5wyaxkcQNEve3q/IpGwMTPkp+AxEByvNkkvyTuKEkESzUPWzmmSwJE62TX2/qrIvaSygTGfkhLPLdJDmAoBB5TQt0nESNUkNnFOaKX62h2Rak7gW0GkeD0mW5GuEHzzlcxApyhawrGQ6T4bpMI0VLSrNpgJ6fAaiR4+eZlwD4mKsmHzHOcc/PnQImgN8BuJAh4CpVBg0I4S0hJILZKVZvCRZmgcIyXROMHgaFTpIMkxH3et4sQKXuuaR4bqeH4hM4hrAnlbxEgTNkjf2AHsaxYUOvUzCDLHSLBZU0GQwl4ZtJzBozkrTWMlrJM/HgHm0eJ4XyPMleB8Ixr8Cz6PtHyWU55SNLr4AJs9Ejy5jLxEGzQghLaGCJdsYLImVbQmtMlGo4Ok2Bk8jwy/LzyVzzBuhnGeeB+wo0XEaFWOFCpQfIolrAB1m8ZJPeNIEK83iQYcKkzDqOWDQLFq2J/w5CDvMSfTkNQiasKdZPOwoOVD5Z0kOmqjxd1wPO0pOj+/GLJQdkORqEwbN4iOvQU87gM9AHPg9zRK890uFQTNCSEssHGCwpBsoI0TJICaNhQyeRs54UT9jKZe2kUlVM+HpOI0OJc/an0khm06eqaakmVhpEA9Jd5qzn0k86CbNMtQXrAOsNoiOpFcaUZovXnRwmnMPiAf1fWZSFnIJtP0UfRkb2VT1/rgOREsgz5ncs6Ca/4Wyi2KFQdOocF3P9wUkvdpI+ae2cf5HxphmEu2SSO5uTAhJJAv6p4IldJLEStKDZr6xxOBpZIwXkt/8eTqWZWEwF0g0kmhIujwrexnFiz7jzzkfJUGGsR57gHoOKq6HQtnt8d2YQ1BpmsznYD4DJrGS9KApQGmuuAj3M0tiP1OFZVlMnoqJoKdVcuf/UC4N9XhyDYiOHaVAZSTpgRPaAdFDecbkwqAZIaQlVLDECfXcIdGTeHlGv6cZjaWo0NVYUge7MQZQIiPpQfMgy5x7QBwkX56RzrI40KHCJMy8bAqpqZ5bDKBHRz7h2cZq/S9WXBTKrDKImjG/0ji5tiAlOuMhCJgkd+wVDJzGgw69TW3bYm/LGFB+gKRXmgKUZ4wDXaoMJZLs2UgISRx9mRT6MtWlY9sObpRxoWQP509V9iUN5czfPkl5xqgY01CeEQhJdNF5Ehm+PF9Sgyb+mHMPiAM1lxIbNGOlYSzoIMsUJlxpzKSJ6Ej6+j+YDaoMuAZEjw49bdQaRYdptIxpMPYKVppET6HsoFSpVm0n/Rlg0CR6whLdSa40BULjz+TpyNCxt70UGDQjhLTMQr/KiAGTOAhX8SW10mTh1H2x0iw6lDzjoGbGkgqasadZdCReno/OklgJnObJDJ7MZ6VhLOjgLJ+OX3XI9T8y1LxKaqUZqwziRQd5RgZM4kHNfR0qDdQawKBJdITPUYMJl+pn0Cx6xjSqNFXjv43J05Gh5r9uydMSYNCMENIySpqPzT/jYaxQ9jWtk+o0VxVw2xg4jQydjOUwKsg3TqdpZCQ+aMaeVrGiy/hPhrKiSefoKNGrMmLpPI8OFTxN6vwHgmDpdgbOI6Wm0iTB6wATZ+JhLOHSrGEYNImeoNIk7UsfJxWOf/ToZAPOn/IFcvyjQwdpVqkwaEYIaZkFKruEAZNYUAbIQDaFTCqZy/TCAfUM0FiKCl21rIf7KM8VNdsT3tOQPa3iJek9zcJZkJRni478pD4OUwUrjaMnn/BKU4CVRnGh1lPLAgayGow/1/9I0clpyirj6NFp/CnPFz15jeT5gqAp539U6JQ0IY1kemMJIYmGAZN4Ud+rquhLIgv6g2pDT5XFkY5QQbOkS3JMh07T6NGl0ogOs3hIek+jlG1hKMfAadTkNTwwq2eU6390bNcgeMo9IB58eb5cGnaCK02GQ1UmPANEh057AAPn0aNTTztWG0ePVpVmnP+RUqq4KE5Vmesw/tJg0IwQ0jJKmo89zeJhW8IdpkCwoTuuh8my0+O7MYN8qAGwTqiqExX0I52jnKZJ7Wk4zJ5WsaKF09yvNOAzEAWliotCeUqWLcEVRtNRtgCDJ9GhU08rSjNFiy59DdX4lx3PX7dI5wQ9zZI9/gCd5nGgU9BkmHtA5Ixp5AegDRAtYR+KbsnTEmDQjBDSMgvnsdIsTnyHeYIPzfOyKV9vnRnm0TA+5SzRzVgaYsZ55LDSTDZJH38gFCzhgTkSwvK2Ou0Bai2gPG90jGkw//09gPM/UnSRaB0InQHoNI0OnXobq2eU4x8dav4zaCITnea/8lGNFysoO0yc6BQ19v2ZFNIJbc0iGY4IIaRlFk7JBrKnWTxsn/pek1plAgCWZdFpGjG69jSjPGP0JF2ejz3N4qNQdnyJjvkJ3gOG2dMmUtT6OZDV68A8zPU/coJqo+TaAmptosM0Wsb8KsPkjj1QPQMMs8o0cnSoMlUwaBI9/vzX4BzI8Y8encY/vEbxLNg5OlWZSkSfUxkhJDGow/JWVprFgqrgS3KWMRCuNqGzLAp0NZiGWGkQOUmvNFJzf0fJQYUZhpGiHJCWBQxmk7sWBJUmXP+jQBdZtukMseIoUjzP00Oe1U+a4vyPknxBn0oTyrNFj06VJkyciR6d5j/lOaNHp/EP9zbmHtA5eY3WfokwaEYIaRm/0oybZCz4DvMEVxkA7GUSJY7rYaJU7Q2nkzQXAN9oZk+z6Nie8MB52KhnhUm0hOW57Cn5qyTiVxty/Y+EoJeNXuu/eg64DkRDoeyi7HgAkh1AnU+HeSyodSDJAVMFnebRo4s8J8BKozjQpdIU4PjHgU7jD1BpJkrGNepnJxEGzQghLRP0NKM8YxxsS3iViSLoZUJjqVPGQ9/hoGZOUxrN0eK4HsamApBJXQPSKRsD2RQAOk2jJulVhgr2NIoWVWGgg7M0DHtaRov6HlO25a+xSYRVRvGggzSngj2tokcnxQlWm0ePTpVGDJpFz5hG4w+ElWa4BnSKTmu/RBg0I4S0jOq1tXUHg2ZxoOQZF/Rne3wns8OeZtExVqx+h9m0jVw6uY6yRtBojpbwfEpy4MSX5qHDJFLU95nksQcozRQ1+sozMmkiSoJKkzQsK8mVppz/caBjpRHPANGh1tGk7/9AcI+TZQelCmW6o0CninMGzaJHt8BJYP/xGegUnaR5JcKgGSGkZRZMyTPmCxU4rtfjuzEPdQBdkHB5RuU0obOsc5S04ZBm0oxAUBk3zucgEtQBdCCbQiaVXDNtmBUmseD3M0p4pQF7GkWLbs4SBSvOoyWY/wm3/1hlFAt5X54r2eMPBHvUdu4BkVB2XEyWqzLtOuwDQ31pqLg+7cBo0KninEHT6BnTTKKPfqDo8Mc+p8fYSyO53hhCSGIJZ8DxwBw92yarFXxJzzRkT7Po0NVhCgT3XHJcFKYO/KR9tJHn62elaRzoMv7saRQtOlWYhGHFebTkNXGazmelcSwETvPk24KsNoyWsONZh97Gtm3590lfQDTkNToLhgM7HP9oyGtWbUQ/UHSotgy6jL00GDQjhLRMJmX7FTHsaxY9Sp4x6U7TIMOcxlKnqCot3fqZAcBgNsg2ZbZZ5+hWacDDUrToEjQbpjRXpOjkLAujnoPxUgUulQc6Rh951sBZxnGPDrWe6lBpwGrDaMmHVAbSCVYZCEOJvmgZ00imOWVbvr3C8e8c1/UC1RlN7MAgaMazf6foVmUoDT12ZEJI4lgwMNXXbIKGUtRs10SecYjyXJGhMox0yC6djm1bGMxS1zwq9AuacP5HSV67oCnHPwp072nmedXAGemM4DlIti2g5j/HPVoCecZkjz/AnmZRM6aRNKfCtwP4DESC/wxoEjRh0DQ6dpQq8KbyT5Jeaa4YYvJ0ZLCnWbJh0IwQ0hYL+qt9zVhpFi2FsoPilDa4Lk5zGkudExhLyR7zmfD7mhXpPOuUbboEzSjLEQvbNZHpozxntKjgs24H5lw6hWy6epzks9A5ush09mVSyE2N+3Ymz0WGLuMPBGcAOsyjQTdpNoBBkyjxPE+rnmYAZbqjRAVMMynL31uTjlqrqDLTOWMaKw5JQI8Z2UOeeuop/OM//iOOO+447LXXXhgeHkY6ncbAwAB22203vPGNb8TFF1+Mn//8501fc+vWrfi///f/4thjj8Wee+6JefPmYXh4GPvssw/OOOMM3HTTTahUWlt8KpUKbrrpJrznPe/BPvvsg/nz56O/vx977LEHjjnmGFx55ZXYsmVLqx8fq1evxt/8zd/g4IMPxuLFi5HL5bB06VK87nWvw2WXXYbHH3+85Wv+93//Nz73uc/h9a9/PZYuXYpcLofFixfj4IMPxoc+9CHcf//9LV+TdB9VBbWNh+VIUd9nKqQVn1SGWZYfGeN+A9hkj/lM0HCOjrwmlaaU54sHbSoNKc8ZKbo5y8IEUs1c/ztFF3legD2t4kB9l0lf/4Gww5zzPgp03AP4DETHjpIDpXSrSwIlq02jIyzPZ6meBwmHlWbRoVM/U4lwVGagXC7jox/9KK655pqGAayJiQlMTEzg+eefx4MPPoivf/3reM1rXoMbb7wRe++994zX/da3voW//du/bRjAGhsbwxNPPIGbb74ZX/jCF/DNb34Tb3jDG+a81wcffBDnnXce1q1bV/ezkZERjIyM4N5778WqVavw5S9/Geeee+6c13zuuefw/ve/H3fffXfdzzZt2oRNmzbht7/9Lf7hH/4BF110Eb785S8jm83Oes1SqYRPfOITuOqqq+q+05deegkvvfQS1qxZg6uvvhrHHXccrr/+eixbtmzOeyW9YeG86nhvZaVZpPjSjP3JN5rY0yw6VIWWrhlGNJyjQ7+gCZ0lUaLN+E/dX6HsolhxkEunenxHepPXUJpLMdyXxuh4kY6zCFAVhzo4Tub3Z/DiWJFVJhFRqrgolKtKEzoETvzEOY5/JOhYbcyK8+hQ32HattCX0aOugZWG0aGjPN8wE2Yjgz3Nko0eK3IPOPvss/H1r3+9pYqv3//+9zjiiCPw/PPPN/z5qlWrcN555zVV8bVu3Tq87W1vwz333DPr++655x687W1vaxgwm86WLVtw3nnnYdWqVbO+b8OGDTjyyCMbBsymU6lUcOWVV+Lkk09GsVic8X2FQgEnn3wyrrjiiqa+07vvvhtHHnkkNmzYMOd7SW9gpVk8KLnLpDtMgWBjZ0+jzgmMJX2M5TCqKpIBlM5RUldJXwPoLIkHXYInQ7k0VF4H94DO0dFhomClcXToVGk0zL62kRJOOtIhgYpVJtGiY19LBk2iI9zTLulJswp//OkL6hgd/QC0/aJDJU/rNP6SYNCsAb/61a9w8803N/zZ/PnzcdBBB2Hp0qUNf75582b88z//c93rd955Jy677LK6123bxn777YcVK1bU/WxychKnnXYaNm7c2PBvPffcczjttNMwOTlZ97Pdd98d+++/P2y7fogvu+wy3HnnnQ2v6TgOTj/9dDz11FN1P1u0aBFWrlyJ/v7+up/dfffd+PSnP93wmgDw6U9/umEQrr+/HytXrsSiRYvqfvbUU0/h9NNPh+u6M16X9I4FU5Vm2yZZaRYlfj+jhEuzAYHTnNVFneNrWeeSP+6NUEbeOA3njtGm0ojyfLGQ12T87ZCEMJ+BztGpl9F0/P6mRT4HnaKT45xBk2jJ+3ZgGik7+U7zYN5X4ChdOdI2eQ2d5r4dyDWgY3RMnGHQNDr8noYa+QGoMhMdrDRLNgyaNWCmCqsrrrgCo6OjePTRR/Hcc8/hF7/4BYaHh+vet3r16pp/l8tlXHDBBXXv22uvvfDYY49h/fr1+N///V/8+Mc/Rl9fX817tm/fjksvvbTh/XzkIx/B9u3ba17r6+vDHXfcgZGREaxbtw5r167FXnvtVfe7F1xwAcrl+gXuuuuuw29/+9u61z/72c9i8+bNWLNmDTZt2oTjjz++7j1f+9rXsGbNmrrX16xZgyuuuKLu9Xe84x3YuHEj1qxZg82bNzcMKv72t7/FN77xjbrXSe9ZMGUobWV2UaSE5RmTjtrYd5QcVBwGtzthfMrZqEN2cSOG2NMmMnTpaRP0NOOYR4kuQVOADrOocF0PY0V9ZPmmM8SKo8jYrlHwlD3NoiUInOuxBoSfUTpNO0fLnmbzGDSJiryG4z/MoFlk6Bg0Z6VZNDiuF7Tp0LS3vekwaNaAF154oe61Aw88EJdccgnS6eBBPuqoo/De97637r1jY2M1/7799tvx7LPP1r3vmmuuwf777+//+4QTTsCHP/zhuvfddtttdZKPmzZtwm233Vb33ksvvRQnnnii/+/99tsP//qv/1r3vmeffRZ33HFH3etXXXVV3Wuvf/3r8fnPf97/7PPnz8e///u/Y968eTXvcxwH1157bd3vX3PNNXAcp+a1gYEB3HDDDViwYAEAIJ1O43Of+xxe97rX1f3+1VdfXfca6T0LB1iSHwe6SLMBtYad2uxJe/iyHBoZy2ECXXOuB52yTZOgCSvNoqfiuP5amvTxB8JOc67/nbCjVIE3VaiR9GB5I9jfNDr8nmb9ybcFWGkWLTpVGQJANm2jP1PtZUmneecEPc30GH8gtAZw7e8YHeX5WGkWHWOarf8AE2ajIuxD02n+S4JBswbssssuda+Nj4/D8+qlB6ZXegGoCYQBwE033VT3np133hlHH3103evvec976l6rVCr4wQ9+UPParbfe2rA3WKPfP/roo7F48eK612+88caaf//5z3/G2rVrm7rmokWLcOyxx9a9ftNNN9V8T57nNZS6PPbYYxtKMjb6W4899hj+/Oc/171OeouSZ9w6QXnGKFFyl+r7TTKZVHBgZoZ5Z+ieYaTum8HTzlEOyKSvAexpFj3hg6cOB6egpxGfgU5QQcdsykYurd/RjNnG0aFXTzM6TKPED5jqGDThGaBjAqd58vd+BdeA6FB2lA62n4LVxtGhY9BU3WvJcVEoO3O8m8yE8p1kUzb6pvxqJFnodzLrAieddFLdayMjI3jf+96Hxx9/HMViEZs2bcI///M/1wXELMvCJZdcUvPab37zm7rrvfrVr27Y5PPAAw9s2DNsumRio2v29/fjwAMPrHvdtm0ccsghbV0TAA499NCGrx922GF1r23ZsgVPPPGE/+//+Z//wZYtWzq6ZqN7Jb1HyQduY6VZpOgizabwHec0mDtivKB30IxO0+jQRZ5vmPKskaPGfiCbQiaVfBOdDpNoCPcyaXQ2SDpDrDqNBM/ztOptN5+VppGS1zFoMnWvDJp0jt/TSIO5r6BMd3TkC/oFzXn2i44xDef/YDYNZbLyGWgfHfsZSiP5J/Ie8JrXvAYf+9jH6l7/93//d+y///7o6+vD0qVL8fGPfxyuGziK+vv78d3vfhdvfvOb/ddefPFFbNq0qe5aK1asaPi3U6kUdtttt7rX//SnP836bwBYtmwZbLvxkC5fvrzutY0bN2J0dHTWa852r42uOf06cVyTJIOFU1UQ21hpFinq4JF0h7mCzrJoyGveAJbPQTToJM8XNu55WIoGXQKmiqCnGce/EwJJPj3GfTp+xSHXgY4YL1bgaiTTyYBJtGjZ04qJE5Gho0z7fK4BkTGm4TlwmEGzyNBx/tu2hcEs2zN0io5VhtJg0GwGvvKVr+D666+fMYgTxrZtXHLJJVi7di3OOuusmp+Fg1JhGkkTKnbaaae616Zfp9F1W73m9Ou0eq+9uuZ0isUi8vl8zX8kflTQbEfJQanCKoOoyGuWbUKDORrGi3qN+3QG+RxEQtjpnPSDUzplYyA7Jc/Kw1Ik6NbThpXG0aCjLFMYP2mCjtOO0E2mkz3NoiXoaaXPOkB5vujQutKsUIbr1rcxIc2jY6XpEPuZRoaugRP2Nu4cNX8GNRt7SSTfIu8h733ve/GZz3xmxkCOwnVd/Nu//Ru+8Y1vYGJiouZnjXqeAUAul5vxeo1+tm3btjmv2+o1p1+31Xvt1TWn86UvfQnz58/3/9t9991nfC+JjqG+NOypkmzVh4t0zphm8gx0lnVO2XFRKFcDz7rLM7KnWWcox9NgLo20TvJ8rDSKBO3kebn+R8JYUb8KkzCUaIqGfGj+6yDTyYBJtOiWNAEwcBolav2cr1HQRK0BngeMl7j+d4KOlWbhvd/zGDTtBB3lGYHwM8A9oF38uZ/Ta+wlkXyPTI944IEHsO++++IDH/gAXnrpJf/1efPm4YADDqgLpOXzefzjP/4j3vCGNzTs4TWd2TaWdjedOK452+8m5Zqf/OQnsX37dv+/Z555pu37Is1j25Z/WGJfs+gI+lnocWhSh3s6y9pnPPTd6ZplNMxsw0hQcrfayfNx3CNBO3lGZphGQiDPqOn638/1PwqCoJkezwF7GkaLTv3sFOoZYOC0MzzP0zJo0pdJ+VWx2+kL6AgdK87Vs1pxPT/5k7SHrpVmTJrqHF3HXhIMmjXgz3/+M4455pia4Itt27j66quRz+exdu1ajI6O4uc//zl23XXXmt/905/+hA996EP+v+fPn9/wb5RKM1fmFIvFutcWLFhQ8+9G1231mtOv2+q99uqa08nlchgeHq75j3SHBVMSjVt3sNIsKnQ7NA31UZ6rU1R1Vl/GRkaD6qJG0GiOBu0qjZQ8Hx1mkaBb0IxVBtHgZxhrmmU6xJ5mkbBds6DJfAZMIkXNH12CpgADp1ExUXLgTMkb6jL/FexrFw069jQcyKZ81SEmzXSGbv4fBSU6O0fXsZeEnt65mPnEJz6BycnJmtc+8IEP4IMf/CBSqZT/2pvf/GZceeWVdb9/yy23YNOmTQCAxYsXN/wb4eq1Zn42/TqNrtvqNadfp9V77dU1SXJYMG+q0owH5sgY00zTPKgworOsXdR3N6ipwxQIZCXDB3/SOsr5uECToAkrzaLFrzjS5OA0zKSJSNDRWR6GlcbREDwHusz/6n0Wyi6KFafHd6M/WlaaTe0B2ynR3BFqD03bFvoyernnWG0YDUF7Bn3sAMuy/PMfk2Y6Q7ee9gomzXbOmKZjLwm9duUuUCwW8bOf/azu9be85S0N3//mN7+57jXXdfGHP/wBALDzzjtj6dKlde8ZGRlpeD3HcfyAW5hXvepVs/4bAJ577jk4TuNDSyPJwmXLltUEoxpdc7Z7nUkGMXydOK5JksPCqUozJSlGOqPiuNhRqs5hXbJN/AxzHpbaJsgu1NdYCj+v4zSc2yavWaURe5pFi26VZsOsNIuEQJZJj3GfTjh4UqpQoqlddFv/h/rSUK3XuAd0zphmQVOA1cZRER57HfoZhqHTPBp07GkIsNIoClzX81VndAucUGmgcyjPmHwYNJvG6OgoyuX6RX8mA2am18fHx/3/f/jhh9f9/JFHHmnYv+uxxx5DoVCoe336NRpds1AoYO3atXWvu66LRx55pK1rAsBDDz3U9Os77bQT9t57b//fe++9NxYtWtTRNWe7N9JbFrCnWaQogwnQZ+Mc5mGpY9S469rPDACyadvva8Cqk/bRLmjCSqNICZzmeqwFQaUh1/9O0DHDPEx476LjrH3ymiXQ2Ha4yoDj3ik6VhqwyigadOxnpVBBEybMdYaujnMGTTtnR6kC5RbWqdIYYNA0ClhplnwYNJvGTD24fvGLXzR8/f7772/4+s477+z///e85z11Px8dHcW9995b9/pNN91U91o6ncapp55a89q73/1upNP1E+vGG2+se+2+++5rKHs4/b5e8YpXYOXKlU3d05YtW3DPPffUvX7GGWfU/NuyrLrXAOCee+7Bli1bmvpbBx10EA4++OC610nv8XuaMWgWCSpTtz+T0qa3FfsZdI4fNMvpbSzx4NQ5ftBsnh6HJlYaRYt+46+kuTj+nRA4y/UY9+mkQsETrv/to1tPS4B9zaJER3lG9rOKBl0DJkDY9ucz0C5lx8XElNKMTvMfYJuGKFCJZ5mU5Seg6gLP/p0TVBnqNfclodes7AKDg4M1lVKKa6+9Ft/4xjfguoHsyOrVq3HJJZfUvTedTuOwww7z/33SSSdh+fLlde/74Ac/iPXr1/v//vGPf4yvfe1rde9717vehV133bXmtV133RWnnHJK3XuvuOIK/OhHP/L/vX79elx44YV171u+fDlOPPHEutcvuuiiutd+/etf47LLLkOlUp3Q27Ztw1lnnYWJiYma96VSKVxwwQV1v3/BBRfU9IIDgB07duCss87Ctm3bAACVSgWXXXYZfvOb3zR1TyQZKIcZDeVoyGvWzwygsRQFeY0Py2H8bNMin4V2UVW7+lSasdIoSvKaNYJXzv1SxUWhzJ5G7WLCHjDEqtOO0a2nIRDaAxg064iwPLtOQVOOfzTotveHGWLCRMeEq/R0Ux1h0LRzxkLzXz95VlaadYoJZwDTYdCsAX/9139d95rjOLjgggswPDyMlStXYpdddsFRRx2FjRs31r331FNPxYIFC/x/ZzIZXHPNNXXve+qpp7By5Ursv//+2GOPPXDiiSeiWCzWvGf+/Pn46le/2vA+v/rVr9ZVxhUKBZx00klYsWIFDjjgAKxcuRJPP/103e9ee+21yGTqDbPzzz8fr33ta+teX7VqFZYsWYKDDz4YS5cuxV133VX3nksvvbRhpdpBBx2ED3/4w3Wv33XXXVi6dCkOOuggLFmyBKtWrap7z+GHH47zzz+/7nWSDOgwjRYds82DZ4DGUruow9JgTp9xbwQPTp2jW6WBCvDTYRYNuslzDmZDPY0479tmTMO9fzpMoOkcNYd0mf9AuNKI494J4Xmjk+NMVUXnJysN206Q5tDZaeqv/UyYaxs1/3VSmlFw7+8cnStN2aajc8YKZigOmYxeq3KX+OhHPzpjD60dO3Zg7dq1ePHFFxv+fM8998S//Mu/1L1+wgkn4HOf+1zd667r4vHHH8fIyEjdz/r7+3HLLbdg6dKlDf/WsmXLcPPNN6O/v7/uZ8888wzWr19fUxmn+PznP4/jjz++4TVTqRRuueUWvOxlL6v72ZYtW7BmzRpMTk7W/ey4447DF77whYbXBIAvfvGLOO644+pen5ycxGOPPdZQqvFlL3sZbr75Ztg2H9OkQmmuaNGxr8kQZRk6ZrxohpY1D06do4ImCzRxmjJoHi26Bc1s2/KzzFWVDGmdcY0dJophZht3jC/Pp5HaACVao0HtofOyejnN1Xml5LgolOt9DqQ5xnSuNOPa3zE6Ks0oOP6do3PiFM/+naPz+EtBH6usi+RyOfz0pz/F+973vpZKZI877jg88MAD2G233Rr+/LLLLsN1112HhQsXznmt/fffH/fddx+OOeaYWd937LHH4r777sP+++8/5zUXLlyI66+/Hp/97Gdnfd+ee+6JBx54AMcee+yc10yn07j44otx++23o6+vb8b39fX14fbbb8fFF1/csBfbdI499lg88MAD2HPPPed8L+kdw5TjiZSgEbQ+m2a40oRZpu2hc4ZZmKCnDdeDdtEtaBIkTvCw1Cme5/l7gC7jD7CvZRQE/Qz03QN8eUauBW2zXeeeVgyadYSuduBgLg2b1cYdo9ZNnc5/ikCal2t/u+ioNKPg+HeOrus/wKBpFOiYNC8NjswMDA4O4t/+7d/wqU99Cj/4wQ/wq1/9CuvWrcNLL72EiYkJ5HI5zJ8/H/vuuy9e+9rX4t3vfndNH7OZeP/73493vetd+N73voc777wT69atw4svvoh0Oo0lS5bg0EMPxUknnYR3v/vdTQWXAOCNb3wjHn30Udx6662444478Ic//AEvvPACSqUSdt55ZxxwwAF4xzvegbPPPhuLFi1q6prLli3D3Xffjfvvvx+33norVq9ejU2bNmFsbAyLFi3CHnvsgbe//e1473vfi/3226+pa2azWVx55ZW46KKL8L3vfQ/33nsvNmzYgC1btmBwcBC77bYb3vSmN+Hd7343jjrqqKauSXoLK82iRUejSRlLFddDoeyiP5ua4zfIdMYNKcv3DWdKtLSNbkETVppFx3ixAncq70AXeU5APQOTtAPapOK4mJjqZaSjw0zB4Gnn+I4T7eY/zwGdktcwYAoAlmVhuD+DbRNlbJ8sY8nwzAm0ZGbGjKg0ou3fLkE/S46/RIyQZ+X4t4XneaHEOb32f0noNzO7zD777IO///u/j/SaixYtwsUXX4yLL744smum02mcccYZOOOMMyK7JgAcddRRkQew9t13X6xataphDzOiF+xpFi2BPIM+m+ZANgXbAlyvev8MmrXOmCHGEg3nztGv0ow9zaJCjX02baMvo8866j8DnPdtsaPo+P9/IKfPuE+H63/n6JY0AYR7mnEP6AQd7X/F/KmgGe2A9gmc5vqNP/sZd47O8mwc/87RefyHQ0FTz/NaUmkjwGTZgTOVMalj0FQKlGckhLRNIMdDQykKdKw0syyLpfkdor63QY3GvRFDlGfsiLLjYsdUxYkuTlN1WNpRclBx2M+kE3QLmCpYadIZylmeTdvIpXUOmjF40gmO6/kJNDpVG6ggD3uadYbOlSZqD+Az0D5BTzP9xp8JE52j4/lfwfHvHBPGv+S4KFZ4DmwVNfa2Ve1pSpIJg2aEkLZRh+VixUWh7MzxbjIXujaCDhrB02BuB78s3xB5xnEenNoi7HDSJds8fMDjgbkzlNNUu6AZK006YlzDQEkjhinR1BHhZBOdss3ns69lJOheaQZwD+gEHXtaK4Zp+3eMzvPf3/uLnP/tonOl2UA2DYt9LdvGT5zOpVmll2AYNCOEtM1QLtgo6SjpHF0zTYdyrDTrhHGNM8zCMNuwM7b7TpM0UrYehnM6ZWNgKjOOh6XO2D6pZ6Z5UGnGed8OKmimf09LKg90gpo//ZkUsml9judB0hTHvRN07WkGhJ6BCT4D7RL0M9RvH6A8X+eYUGnEs1/7+PNfw/G3bcu3X/kMtM6YxtK8ktDHKieEJI7wRkmHaeeoLC3dMs3Y06YzlMGkvTwjKw06Qlt5PlYaRIKO/YyA8PpPG6AddM4wDkPHWWeo+aPd/KcsZyTkNXaaB5VmnPvtktdUaQQIEj52lILePKQ1dA6a8+zXOToHTQEqDXSC7mMvBQbNCCEdwX4m0aGczrptnOxp1hljhlQaqKAfnWftoW3QjE7TSNB+/GkDtIWfNKH5+q+C55Roag/faapZpcl89jSLBBPk2fgMtE9QaaLf+IcTPijR2B46VxqFKw09j0HTdtA9eYrVpu2j89ovCQbNCCEd4TtKaCh3jLY9zSjP1TbFioPSVONcXY1lhTKaldwYaQ3tK43oMOsIXZ2mrDLoDFMqjYd9eUY+B+2wXdNKg6DSmA7TTgjk2fUaf6D2GSCtU3ZcTJSqfcF1S5oEgGzaRm5KUpbJU+2hkk10PAeqZ7bseChOnWdJa+iaNK2g0kD7+D3NNB17KTBoRgjpiGFWl0RGXlNdY2YYtU84K1P7SgMazR2hfaUR539HaDv+dJh2hEoy0NVZolB2C5Mm2kP3oLnrcew7IRh//daBYVYbdkT4HKDrPkCJvs7wg+Yazv+BbNDfnueA9tC/0oyKQ+1CeUY9YNCMENIR7GcTDZ7nhYwmvTZO/xmgsdQyysk0L5tCyrZ6fDedEXaaMuO8dbZN6B404R7QCdoGzZg40xHKYTqkedKESvqgPFd7qPVTt/mfS9vIplSVCce+XXTuaTSfZ4COUN/bvGwK6ZSerrlhJk92hM5Bk3B/ewZN20NneU6AlWadMGZI4pzp6LkzE0ISA6sMoqFQdlF2qoEG3TKNWWHUPiZlGKlDk+N6vtQMaR4/aDJPz/nPPaAzfHk23dZ/Vpp1hM7OsjBqDys5Lgplrv+t4lcaaWYLWJbFNSACfKepZus/EDyz25k40xYm9LSh07wz8po/A8OsNGwb1/UwXtJTaUgx5J8DOf6tYsoZwHQYNCOEdAT72USD2jRtCxjIpnp8N60R9DTjM9Aqfj8bzasMgNpqOco0tY62lUZ0mEaCrpUG4UpDVpi2jsoy1b2fwUA2uH+u/62ja9AcCM4BlOdrn7ymShMApbk6Re39Oo69wn8GinwGWkVnpRkF2zS0z3ipAmU66zv+3APaxaTkaZNh0IwQ0hGsNIuGfCjTxLL0kuljhmH7jPsOU/0cZdOxrLBEB9eDVtE2aObvAZz/naDv+AcVRmwC3zrjhiROUKKpM3QNmgMheT4GzdrCdT3fFtRx/Kk20Rl5jasMFeocSHne1tFZaUZBP0D7qO8sm7LRl9EraVrB8W8fP2Cu+RnAdBg0I4R0BPvZRENe40wT9jRrn/GinpJMM6GcpgygtM5YQU+nKauNo2G7pj2NBrJpqHaMfAZax6QsUzpO20ftmbrNfyDYs1hp1h5jRb0rDdjPtjN0rjJUUJ6tfXRWmlGw0qh9dK8yBDj+nRCcAfSz/STBoBkhpCPYzyYadNa0Z4ZR+5gkzwjQadoJKvFAt4MTq42jQX1/uvW0s23LP+zxGWidcYOagPuVZpToahm/0qxfv+fArzTjvt8WauxzaT0rDdTaxX627aHz+U8xmGNPq3bJh86BuinNKOgHaB8TEqdYbdw+Jp0BTIZBM0JIR7CfTTTorGnPnmbtY1rQjM2g20c5mnXLNmO1cecUyg5KU9KGOladBj2N+Ay0iklNwOk4a5/tGsszsqdZZ6hkA12l2cL9bDn3W0fn85+CPa3aR/f5D7DSsBNMsAGZPNk+rDTTAwbNCCEdwX420TCmsaa9MpZ3lBw4LqVZWiHIMNJv3BvBg3P75H15Pr0cJzwsdY5yNqdCfaF0gs9A+/h9LTUc9+mo3pysNG4dnR2n7GnWGWrv1zFhAmA/207R+fynYMJE+5jgNKc8X/uYUGnG+d8+JshzSoBBM0JIR7CfTTTorGkfNvTpLGsNZSwNajjujRik4dwWnudpG0DlHtA5QZWJnvI8rDZuH5OqjYfoOG+bvKY9DQHO/04xodKAlSbto/P5TzHMoEnb5EP2n64waNI+Ove0VwxRZaZt8gadAUyGQTNCSEcwwzwa1EFDR2mebNpGX6a6nfA5aA0VZBwyxFjyD05FGs6tMBGq0tRtDVD3u6PkoOK4Pb4bPQn6Gek19go/cMoDc0uUKi6KviynnmMfxu9pyfW/JUoVF5Plai8oHZ+DoKcZ7b92yBtRacSgSbvofP5TMGjSPqw0k41JSRNjhTI8j4pDzVKshKX59R1/CTBoRgjpCLXIF8ouihU2gG4X3eVZGDxtD9MawPLg1B5q3qRtyw9A60L42aXDpD3ymjvNWGnSHuHg0kAu1cM7iQZfoo1Bs5YI75c6Vp2rYA97mrUHK01ko85/Op8DWGnSPkHQVN/xH+b8bxsz5n/13suO5yeCkbkJqzPpaPtJQi/PDCEkcQzSYRoJumca+dIsk3wGWsEvyzfEWAr6WvA5aIVwTwvd5PnSKRsD2arDn0Hz9tC9p8EwK03aQu37/ZkU0in9j2R0nLaHCjYN5dJI2Xqt/0A4aM5xbwed+9kp6DRvn7Gi/uPPfsbtY8L8Z9C8fXT3/wDAQDYNdXTlOaB51HwZyKa0tP0kof8JjRDSU1K25UvLMcu8fQJ5Fr2dpjwwtca4YVrWynHC3natodZO7YMmdJq2he49Deg0bw/dg6XTGeT63xa6y/PNZ6VZRwRKE3qOP0CVgU7wk6Y03gcozd4+JtgBnP/tY8L8t22LSbNtMGZY4rTJMGhGCOmYIMucG2W76J5pNNTHZ6AdAnlGPcd9Ov7BqciDUyvofmimPGtn6N7TJOhpxvFvBbX+m3JgHsqx2qAdzOlpyHFvh3xB76QZgJUmnRAkTek5/4Hg3seLFbguexq1QiDPqvP4c/63i+72v2KYSgMto7vvTxIMmhFCOoayDJ0TZBrpuXEO8xloizEDnCVheHBqD+17WvWz2rgTgp4Gmo4/e5q1RRAs13Pcp6PW/3FWG7REXvOeNqrSbKLkoOywn0mrjBklz8Y9oBU8z9P+/AcE4+95wI4S1/9W0D1pDqA0cyeYMP4A94B2GDOsr73JMGhGCOkYSnN1ju7ybEOU52oZz/OCSgND5Bkpz9Aexsjz8bDUFroHz+ez2rwtxotBLysTGGTSRFts17zSLBz0ZeC8dQJ5Rn3XATrN22Oy7KAyVZml6/4PALm0jUyq2pOHz0BrmJA8o57dkuOiUHZ6fDd6YcL4A0yabQdTxl4CDJoRQjqGDtPOGdO8p4WqNGGGUfMUKy7Kjv6H5TB0nLSH7vIcTJzoDN0zTYPx5/rfCn4/A0OCZlz/20Otm/M1tf9qehtz7Fsmb1ClGce/NdRambItzMumenw37WNZFtf/Ngnmv752wGA2DasaM+X4t4juSXMK9rVrHVPGXgIMmhFCOobSXJ3huJ72JdoMnLaOOlhYFjCQ1XPcp0N5hvbQX56PPW06Qf+gKW2AdtA9WDodFfyjPGNr6C7PCwQBn+1cA1rGhPGnw7Q9wiojloo6aArt//YwodrEti0MZjn+7WCKHchKs9bxx96QxDmTYdCMENIxDJh0RtjBpKvRNExjqWV8acZsGrat92FZoZ7fYsVFqcLeJs2ie7YZHaadkfcrjTUd/5AN4Hlej+9GH/w9QNN5P51wTzM+B82Tn9S/0oDVpu0TVBrqO/50mLaHv/drHDBR8BloD3/919wO4Pi3jut6GC/pHzQFwucAjn+z6H72lwSDZoSQjqE0V2cogzmXtpFL6ynPMcTAacuMF8xymAK1MmPMNmyevO7yrH10mHZCcHDSdPynntuy46FQZrC8WXQf9+mog7/jephkX5Om8XuaafwcKIcvEydaw/M8I9YBP3GuyPFvhbxBTtOhHM+BrWJS0ITynK0zXqpA5Rfpvgb4Er20AZpmvGjG3JcAg2aEkI6hNFdnmCDNwAyz1lGOElP62QBAOmX7fRko0dU8umeb+fJ8nP9tobs8y0A2BVUsSzugecYNk2bpz6SQmnoQxrkWNI1aN3XtaQbQYdouO0oO3Cmnqc5BU45/e4yx0kw0JgZNmDDZPGquZFM2+jJ6Jk0ruAe0Tl7zs58kGDQjhHQMZVk6w4QmwHwGWkf3PnYzwYNz6+juOGGlWWfonjhhWRb3gDYYM6za2LIsPwmEAfTmCeQZ9Zz/QFiim/O/FdTYZ1IW+jL6umXCdh+lWZsn3NNMd5T9woS55lHjn02bEDTh2a9VdE+YDMOgaevofvaThL7WGSEkMVDHuDNM2DRpLLdOIM+o77g3InCa0nBuFt17GvgBE455yziu5zuZdB1/gL1N28HExAm1/tNx2jx+4pTGz4HaA2gDtkYw9hlYlr69bdX5hdKsrTGmuTR3GDrNWydImNN37VewTUPrqLYmJtiA9AO1jomKQ6bCoBkhpGN8aS5mmLeF7g5zoNZhyizT5vAzzAwzlvxsUxrOTaN74DyoNOOYt0p4nug6/kDYDuAz0Cx+4oRBe4BynHD9bx4TKs2GKNPeFmq91HnsgVqJXjpNm8eonmZ0mrdM3oB+lgqOf+uY0M9SoZ5h9rVsHpOC5qbDoBkhpGOYYd4ZYwX9jWZlLJcdD8WK2+O70YNxA6sMAB6c2kF3idagpxn3gFZR31kubSOb1tcspx3QOsq5YILDRMFqg9bwPM8PnOjd04z7fjuYkDQH1Eqzcu43jwnnPwXXgNbRvZ9tGPa0ah2zxp/zv1XGNU+YlYS+p3NCSGJglUFnBPIc+hpNA9m0n2XKisPmUNJcJlUZAHSatkrFcTFRqsoZ6Wo4qz1gouSg7DBo3gq6Vxkq2NeudcYNcpgofMc55RmbolhxUZpaM3WuNgocppz/rZA3qNJgiFL9LWOWPBvXgFZRiTM6r/0Knv1ax6yeZgyatopJ4286DJoRQjpGBXsmyw5KrDJqGRMOzbZthXpZ0WBqhjG/p5lZxtJQjoZzK4R7/+hqOIfvm+PeGmOaVxkqgmpDjn8zeJ4X7AEGJU4M0nHSEirIbFtViTtdCeQZOe6tYELSnIKVBq0T7P/6nv8UXANax6Sg6TAlelsm78vzmTP/x9imoykc18MOP2FW//lvOgyaEUI6JuzwYYZR65iiaawOfTSYm8PUsny/pw0rDZpCHZr7MylkUnqaZemU7Tt8WWnUGnlD1gFWmrVGseKi4lYdCyYdmNnTrDW2h/qZWZbV47tpn2EGS9vCpJ5Gw6w0apm8Iec/gJUm7RD0ttZ//nP8W8cUpQmAbTpaJWwjm5Y8bSJ6emcIIYkinbJZZdQBJlSaATSYW8XvaWZQlQEQGH+U52oOUxrBM2jeHkFPE46/JNQ+aVlVeWNTUPvZOJvBN4WaLzr3MwNCVSYMmrdE3sBKI54BmsfMnmZcA5olz0pT0ZgkzzeQTUPl/fAcMDfqO8qmbeTS+qoMSIFBM0JIJAzzwNw2pjSC5TPQGspYNi3DSAVPWWnQHKY4zdjbsj3MW/85/s3gr//ZNGxb3wqj6dBx1hpqvujuNGc/o/YIxl/v9R9g0KQdAnk+vec/EDzDXPubZ8yQpFmAe0A7mGL/A7VtOrgGzI1KnDZh75cAg2aEkEhQDl9ulK1jijwLK81awyRjOcxQjo6TVjDlOQh6WnHcW8GUTHNWmrWGOjCbljThO01YadwUxlQah2SZ2c+keUxJmgF4BmgHU3qaAqGEOa4BTWOWPCcDJq1iiv2voExz85gkzSkBBs0IIZHgVxnQYdYydJrLxHeaGirPyJ5mzRH0NNTbcFbyYqw0bY28Ieu/Gv/tHP+mUJW45q3/dJq0Qt6Q9V85flwPfnN7MjemBE0BOs1bpeK4/lwxwXGqxt9xPUyWuQY0g7KXTRp/zv/mMcX/o2C1cfOYJM0pAQbNCCGR4AdM6DBrGVMyTdkEvDVMM5YVlGdojeDQrPdzwMSJ9jBFnmeYQdOWMCVYOh31eca5DjSFKet/X8ZGJlWVGaUN2DymJM0ArDZulXBime7zHwD6MymkbLUG0P5vBn/+a37+BwIbtuS4KDBo2hSmVRtRpr95xgxNnDMVBs0IIZFAh2n7mOI8Y0+b5vE8zz8wm2IsK5ht2BqmHJqCoAnHvRXMWf+VDcDxb4ZAnlHveT8dJc/LSuPmMGX9tyzL/wzcA5qHlSZyUfOkP5NCJqW/S86ywj2N6AtoBpMqTcPOf64BzWFatRErzZpnrGjG2U8K+u/QhJBEQIdpexTKDkoVF4D+mWZsAtw8hbILx61q/puWZTREecaWyBvS08IPmnP+t4QplQbhanP2M5kb05wlCvY1ag2TngM6zFrHl+fUfP8HeAZoFVNsvzBDvh3I9b8ZTFIcSdkMmraKSeMPMHGiFUxRGZECg2aEkEigw7Q9lGFhWcBgVm+jKehpRmNpLpSxZFvAvGyqx3cTLYM5NgNvBVMaQVOerz1MkWdTz2+F/UyaQvU0GzIsacLvaUk7oClMcprRYdYanucZ5Tjj+LeGKVWmYZg00Rqm2P8KrgHN47heqNrIlPFn4kSzmGT7SYBBM0JIJNBh2h7KsBjMpWFPacHrCo2l5lGG8mAuDcvSe9ynw2bgraGqc4c1N5wpz9cepjhN52WDfiasOJ+b8dAeYBLq84yXKnBdJk3MxZghPW0BYChHmfZWKFZclJ3qHDHBcTZMh3lL+JVmBoy9gtWmzVOquCiUp5RmNLf/FAyaNY9pPQ0BVpq2gilnPykwaEYIiQQ6TNsjb4g0F8AGsK0wbmCGqWJeNgUVB2S1wdyMFc0wnMPyfKR5TMk2tCyLFectkDd0D1DPsecBO0pc/+cisAH1nv9AsAfQYdocap00QWkCYOJcq5hYacbAafOE58mgAes/wDWgFdR3lE3Z6MuYoTjDStPmGTNUbcJUGDQjhEQCHabtwX4WMlHGkmlVBsC0ZuDsazYnpgRNgsQJzv9WMKWnGcCK81bwK800n/fTyaVtZFLVrAn2tZwbk7KNh7gHtIRvB2b1V5oAaqtMKM09N6ZIM4dh0KR58qFzYMqA+Q+w0qwVTDn7haEfqHnGDRx/k2HQjBASCXSYtkcgzaa/w8R3mNJYnpPxonmH5TDDzDZrGuU40V2eKwiYcMybpey4voSpSjzRGdoBzWNSwkwYy7KYbdwCJjnO6DBtDZPGHggCJhXX82XnyMyYWGmmnmWqTMyNiTYAEyeax0+Y0/zsF4Y2QPOYuP6bDINmhJBIoMO0PUwymv3DUrECh71MZsXPMDZg3Bvh97Wh4TwnpjjOGDBpnfDB0oSq06DinPN+LsYNlmbxK425/s+JSZWmrDJpDZOqDAFgIJuCKpjhMzA3Ywb3NGPy5NyYtPYrGDRpHpP8Pwo/YbbI9X8u8gaOv8kwaEYIiQQ6TNsjb1IT+NDGz2DJ7JgszwgEwcBxGs6z4nleqBm83muACphMlByUHWaZN4M6NM/LppBO6W+Sq2d4O+UZ58RUeUYglDRBecZZcVzP/45McJywn1FrmJIwowhLczNoMjcmzX3FYI5Vxs1ipjwn94BmMW39Bzj+rWDi+JuM/id0QkgiUIs+HaatYdKmmUunkEtXtxUGT2cnOCzrHSiZCRrOzVGsuCg71apM3deAcACY494cJq3/APsZtILJ0ix8DpojnFxkwnPAfb81TKw0YLVh85i4B3Dtbx4T5fmGOf+bxl//c+aMP6W5m8fE/d9kGDQjhERCeNHnZtk8fj8jQw5NQV8zGsyzYWKGaRjKczWHmieWBQxk9X4W0ik7yDJnpVFTmFJlqOCBuXnUgdnEamP2tWkONf9zaRvZtP5Hcl9xgut/U5gdNOHcnwsT5bk4/s3D8ZdN3rCkOaA2aO55bNMxE57nGZ88bRr6W+iEkESQTtkYyKYA8MDcCuZWGtBgno0gw8yMcZ9OuL8dmRnV+2kol4atmoFojJLnYtC8OfzxN2T9D2SaOe9no/bAbMbYh2HwtDlMC5pw3FvDRKfpMJ+BpjFRpp09jZrHxPnv+wA4/nNi2v4PBONfdjwUK1SdmomJkgN3KqZo0vw3GQbNCCGRwSqj1skbJs/ATOPm8A/LhhpL6hDAoNnsBPIMhsx/tQdMctybwbTxH2LQtClMPzD7lcZc/2dlzO9pa8YzQGm21jBt/Qf4DLSC2ePPtX8uxgxTGgACqUGO/9yYKM83kE1D5X/yHDAzan6kbAv9mVSP74Y0A4NmhJDIYIZh65gmz8ADU3OoYJJJGaZhAnlGGs2zYWzQnOPeFKw0lola/009MA9SnrEp8oZlmnP+t4Zp6z/AZ6AVzBx/+gGaJVAaMGP9Bzj/W8HE+W/bVkimn8/ATITl2S1Lf5UZCTBoRgiJDJUtyyqj5jGtPJ/Vhs1h2rhPhz3NmsO0TEPuAa1hWiP4ITaBbwrTD8ysNmmOoNLAjPXfrzAvVeC67GcyF6aNP8A9oBX8/d+gcwD7WTaPaZXGAOd/K5jW01jBZ2BuTJRmNR0GzQghkcEqg9ZRzmVTDs3DzDJrinHDDSb2NGuOwGlixnPAPaA1TKs0Vs4frv+zY2IvmzCqVyfX/9kxLdNcfQ7PozRnM5iYPBVI9HL8Z6PsuJgsOwDMmf9A8FlKjovC1OcjjckbLM/J+T83pu3/ClYbzk3Q09icuW86DJoRQiKD/WxaxzRNe/Y0aw7T5RlpNDdHEDQ3ZP5zD2gJ03pacP1vDlOdJYpBrv9N4dt/OTPmf18mhWy66lpglvncmLgODDFxpil2hILKJvU2HsimoYqnuf7PjmlJc0BgA5YqLooVBk1nwzT/j4KtWubGNJUZCTBoRgiJjGE/w4iHpWZwXc/PxjVFnoHBkuYwrcJkOoNTTkBKtMyOaU4z7gGtYdr4c/1vjiDL1Ixxn44KArHaaHYCeVZzngOqDTSPiU5T7gHNob6f/kwKmZQ57jjbtjCYpTxvM5hYaRoOAHMNmB3T7H8F5bnnxsSAuemYs0sTQnpOUGXAjbIZdpQq8KbaPhhTacCeZnPieV5QaWaowaQ+F+W5ZsfXtDekpxX3gNYwraeJcv5Mlh2UHbfHd5Ncxg2XZ/TXf9oBs2KmPBezzJvFRKcpHabNoea+iWcABk6bI29gT8OUbWEgmwLA8Z8LE9d/gPO/GcJ9jYkeMGhG3UwY/wABAABJREFUCImMoJ8NN8pmUN9TNmUjlzZjOaaxNDcTJccPlpoiyzSdIVYcNYVphybuAa1hWsVp+HOwynRmTAyWhKEd0BwmNoNn0KR5TNv/AUpzNYuJY69g4HxuPM8zstIMCI8/94CZcNwgedaUpEkFx39uTJ37JmPeTk0I6RlKYoZVBs0R1jS2lAi85gyzn8GcKGMpbVvoyzQOlo6MjGB0dLSbtxUpWyarWvbjhQoeeughY57vTlm8eDFWrFjh/1utlaYYztwDWsO0g1MmZaM/k8Jk2UG+UMbCgWyvbymRmF5prJJBWGk8O6bNf4A2YLMUyg5KU9W4Jo0/A+bNYeLcVzBwPjeTZQeOW82eNC1wOtSXxvN5rgGzEbaNTBx/gMmTs2Fy0oSpcKQIIZHBw3JrBP0szDk0McNwbsaLgSxLo2DSyMgI9j/gAExOTHT71iLDyuSw4iP/AQ/Aa15/BLxyode3lAj6583D+nXr/MCZafJ83ANaw8Rm0EN9aUyWHe4Bs+AfmA2VZlHBwImSg4rjIm1Qz54oGTNQnotBk+YIfz8mSTSxyqA5TJz7Cq4Bc6O+m5RtYd6UnKEpMGg6N+q7yaZt5NKmjT/9QHNhctKEqZi3UxNCekbQz4YbZTMEVSbmLMV+hhErTWZEGUszOUpGR0cxOTGBMz/xFSxZ8fJu3lpkeB7wn8948GDhgn++Ef3mPOJts3nkSXz/nz6O0dFRP2hmmjwf94DWyBuZOJHGC2NFBk5nYdzwLNPw3raj6GD+PAbNGmGi44QO8+YI9zRJ2eZU4ofH3/M8qgzMgMmVBkNMnpqT8Pw3bY4MUaZ9ToKESRPnP4Omc2FiwqTpcKQIIZHBKoPWMPHQpJy/dJjMTLOOsiUrXo7l+6zsxi3FQnbTkyhWXOy0Yh8sokxbQ0yrNlV7wHYGzeekUHZQqih5Lu4BkvDlGQ2qMAlTzZ62Uay4GCuWMX+eGetb1JiZOMVzQDOYaP8DweepuB4KZRf9hlXRRIXvNDWwr7F6BijPOzMm9rNUMHFibkxMmFFw/OfG1P3fZJj6RwiJDPazaY28L89hjtGkDICS46JQdnp8N8lEHSRNleZSZNNVE0MFBkg9plWaqc8xWXZQdjjus6EOTZYFDGbNGH8g5DSnHTAjwbw3Z++fDiV65sY0eV4grDbAcZ8NU51mA9k0VOEMKw1mZqxo5vgDXPubweygCSVa58LkSiNlz4wVOf4zMW7w+m8qDJoRQiJDGUo7pvpYkNkx8dA8GDowM9O4Mb4sh0Hj3ojcVNCsWGHwtBGu6/mGsylO0/AzPU6Hyaz460A2DdtQeS7SGL/SzOA9gNUGs1N2XExOJRaZZAMO02HaFGOGBs5t2/IraCnPNjO+TLtBc19Beba5MTtoQhtwLkz0/yhUAj3Hf2ZM3f9NxryZSgjpGeHNf6xQwUJKss2KysQ3xWEOBAfmsUIFY4UKdhnq9R0lD5ON5TDZFCvNZmO8VIHnVf+/Kc9CJmVjXjaFiZLDPWAOTJPmVJjgMBkZGcHo6Ghs139x2zgA4PmRp/FwcWNsf6eX2E4JAPDImvVIbenr8d1UWbx4sd9PsteEkwpMWf8BBs2bxWQ7cLgvM3UGYNBkJsyuNOIaMBfsaSWbvNHyrFSbmAuT939T4UgRQiIj7DDNF8p0mM5B3tBDkzow02BqjOn9bBS+PCOrThuijOZsykZfxpy+H0N9aX8PIDNjmjSnQvdKk5GREex/wAGYnJiI7W8s++B3kB5ajPP/+iyUNj8Z29/pJbucdjn693wlLv3bT2Ji3S97fTsAgP5587B+3bpEBM7U+j8vm0I6ZY7wC6W5msNkiVYGTebG5Eojjv/cmFxpwr62c2Ny0CQ8/z3Pg2WZo6QRFSYnTZiKeTOVENJThvsyVYcp+xnMid/TrN+spZgHptkxWZYlDHuazY5faWrY/B/uy2Bzvsig2RyYemjWff0fHR3F5MQEzvzEV7Bkxctj+Rt3PJNBxQP+6pNfwZChZ+Zfv5jGxkng7X/1Eew19OFe3w42jzyJ7//TxzE6OpqIoJm5QXO953+3MHX9B/TfA7qB0ZVGOQbO54LzXzZmJ01UP1PF9VAou+jPmpMUGgWFsuMnE5uePG0SHClCSKQM96fxfJ7GcjOYmmmisszoNG/MuH9YNmvcp5P1e5oxaNYIU+e/OjAzcWJ2TM009qVZNF//l6x4OZbvszLy67qeh8rIEwCAPfbeD/OyZh7F5pefx8bJMczbaQmW77Go17eTOEx1mpky/+PGbKc5gyZzYer+DzBo0gxGz38GTefE5PEfyKZgW4DrVZ8BBs1qCff5ZdBMH8zRgyCEJIJhHpibJuhpZtamyUzj2ZEiz5hLVQ1lyjM2xtT5T4dZc5h6aKbDbHbKofUwa5As33TY03J2TJ3/qnKa83921P5oYvIU94C5MXX+A+HAOcd/JkxNmgA4/5vB5PlvWZbv3+AaUI+vNpRLI2VTulIXzJuphJCe4lcZscpgTkzNNBxmE9hZUYcl04NmlGecnbGiofOf/QyaIm9oxanuPc3iRq2HtgWjD8xc/2dnzND5r/aziZKDiuMa1a8tSkx2mgZOc+4BM2Gq0gDA8W8Gs+c/g6ZzkfSkiZGREYyOjrb9+znbAwD84U9rkN8pG9VtGcETW0oAgJzt4uGHH+7a3128eHEipMl1JbKV+rrrrsOZZ56JgYGBqC5JCNEQVTXBSrO5MdVoZpbZ7KhKM9PGfTp0ms6O6fOfe8DsqKQCU8ef639jlFxtLp0yukF6Lj1Vacz1vyGmz3+gaussmEeHWSOCpBmzxh8IK45wD2iE63pGK06o8S9WXJQqrn8WIAGmJs0CDJo2g580k8Ce1iMjI9j/gAMwOTHR9jV2++uvI7vLy/DX538QhQ2PRHh3+tO3xyux5PTLsWnkaRx66Du69nf7583D+nXrGDhrk8hm6gUXXICPf/zjeM973oPzzz8fr371q6O6NCFEI4JKMxpLc6GcyvP7zTKa2dNsdlRPs0EDnSVhGDSbnUCe0az5z6BJc5iaac6eRrOj1kPTHYm+PCPleRti6vzPpGz0ZWwUyi7GCgyazYQ//jmzxh8ISzTTBmjEeCn4XkwMmg5OC5wvSnMNmI6pSXMAg6bNkOSg6ejoKCYnJnDmJ76CJSte3tY1Vm9OY7QInHjRZ7F8nhfxHerNcxMWfjMK7Lpsd5xx9W1d+ZubR57E9//p4xgdHWXQrE0iXanHx8dx3XXX4brrrsOhhx6KCy64AKeffjrmzZsX5Z8hhCQYZhg2R6niolCuOpNMM5rpNJ8dk50lYeg0nR1TD82U52uO4NBs1vgHlYZc/xshJmg29fmKTJpoSCDPZNb8B6qOwEK5iO2TZeze65tJKKbu/wArTeZCjX02ZaMvk+rx3URPyrYwL5vCRMnBWKGMRQMMmk0nkOc1b/6Hg6ZjhTJ2Gsz18G6SiQ7r/5IVL8fyfVa29btDExsxWtyBwcXLsHzZ/IjvTG/yG/PA6GYMDQ1i+T7Len07pEkiO7FddtllWLZsGTzPg+d5eOihh/D+978fS5cuxYc+9CH8+c9/jupPEUISjCo1Z6XZ7IQPk6bJc7Cn2exIkWfMsdJsVlQlzrBplaYqaMK+lrMSyLMYNv5Tn6dUcVGsOD2+m+Shkgiyhvd6YqXx7OjgNGuXYSZOzUmSKw06hYlzs2NqwkwYPgOzY/L8T9kWBrLVYDDHvzGmVporaP/NjDoX5Qw/A5hGpEGzDRs24Ec/+hFOOOEE2LYNz/OQz+dxzTXX4NWvfjVe//rX47vf/S4KhUJUf5YQkjCGKc3UFMpgGsimjGuUTmmWmanpZWDwgRlgpcFc5A11mqqgierZQhqTN9RxFk4C4R5Qj7RKMzpNGqPWR9OC5kDYBuQeMBOm7v9AqNqcNkBDTA6YKyjTPDOe5xn/DNAPMDNOyA9g6virgBDP//VIOQOYRqSjZds2TjjhBPzoRz/C008/jc9+9rNYvny5X332u9/9Dueccw6WLl2KSy65BI899liUf54QkgCCnmY0lGbD1CoTIFRtyMNSHab3MghDp+nsqEpM0zINh1hp1hSmyvOkbMsPnLHauB7lRMgZfmDOMWliVtT6aKIdwCqT2SlWHN8uMq2nKcDxnwuTq4wUfAZmplB2UXGrfZ5MfQYo0TozKmAGmLn/Azz/z0bRUWcA86R5TSa2E9vy5cvxuc99Dhs2bMAdd9yB448/3q8+27ZtG6666iq84hWvwBFHHIHvfe97KBaLcd0KIaSLsNKsOUzOMmOG2cyMh3oZmG4whXuaeR4bAU/H1KAJqwyaw2THGR1mM+NnmRpWYT4d9rScHX/+G9jblH0tZye8LpqoOMAzwOyYfP5T8BmYGbUu2hZ8GUPTYG/bmVHjn02b6wfIZaaSphxKtE+HlWZ6Evto2baNd77znfjxj3+Mp59+Gp/5zGdqqs9+/etf46/+6q+wdOlSfOQjH8Hjjz8e9y0RQmKEPc2aQ30/JmaZBj2N+AxMR4o0I1BbSUHHaT2mBk2G6SyZk7A8j5l7AJ+BmZByYFafz3E9OC6TJqZjsuOcDtPZCcuzp2yrx3cTPeEqEyZM1WOyNKeClUYzo8Z/MJeGZZk3/wEmz82GqjI3LWEyTC5VDQYWyzz7T0fKGcA0ujpay5cvx+c//3k89dRT+NCHPuS/7nketm7diiuvvBIHHnggjj/+eDz00EPdvDVCSEQElWY8LM+G2Q6T6jMwXqrApbOsBnWACPf9MZWUbUH5gyjRUI9aI1WigSkEDlM6zGZisuyE5HnMGn+ADrPZUJm3ph+Yw5+P6389wfpvYNC8nw7T2TA1YUah1v+y41GetQHjftDEzPEHgoAAE2fqMX3+A1QbmA0J4095xpmRItFuGl09qb/44ov49re/jW9+85t46qmnYFmW71Dp7+/H5OQkAOC//uu/cM899+ATn/gELr/88m7eIiGkQ5ShNF6soOK4SBsuQdQueYONJvUMeB4wVqxgvoFOoXYxOVg6HcuykE3bKJRdGs4NUAcn0yqNlMNUOcz6MmbKj3SCWgdStoV5BsrzhAOnpBYpWaa2ZSGTslB2PJQcF/0w7znvhMBxZp4tMJSjw3Q2TLcDB7JpWFb1DJAvlNuyAUZGRjA6OhrD3fWe/9mQBwAUxrbg4Ycf7vHdxMPE9upnfHLkOTz88BgAYPHixVixYkUvbysRmDT/Z5qnxfHtAID/fnoED8/b2u3bSjR/3FgAANhOKZHzf926dR1fw+9pS5WZOqScAUyjK6v1T3/6U1x33XX40Y9+hHI5yDzOZDL4i7/4C1x44YU44ogj8OMf/xjXXnst7r33Xnieh3/8x3/EQQcdhDPOOKMbt0kIiYBwEGi8WMGCedke3k1yMbXKBAD6Milk0zZKFRdjhTKDZiFMOiw1QzY1FTSj4VxDqeKiMCVbYdqzMJBNwbYA16tKtDJoVk+44tREeR72M5mZkqAs02zaRtlxUKw4AGgHKIoVx882NjlxivO/MX7CjKG2sW1bGMylMVaoYKxQwS5Drf3+yMgI9j/gAExOTMRzgz1m0dEXYOiQE/D973wL/3re93t9O7Ew//WnYcGRZ+F7t/wH/u85VwEA+ufNw/p168QHzkyR5p5tni446q8x//B34bp/uwH/9Itv9eDuksvAgUdh8Ts/hjWP/B6H/t2ne307MzI+Pt7277LSbGaq9rCMM4BJxOapeeGFF/yqsqeffhoA/GDZ7rvvjvPPPx/nnXcelixZ4v/OySefjJNPPhm//OUv8a53vQsvvfQSrrzySgbNCNGIbNpGfyaFybKD/CSDZjNhenn+cF8Go+PFqnb3wl7fTXIYEyDLEoaGc2PCslWmSXVaVtVhli9UkC9UsMtwr+8oeZicNAGEepvSaV6HSiDICqjCz6Zs7IDD9X8a4WCSaes/ENi1rDRtjISeVsN9GT9o1iqjo6OYnJjAmZ/4CpaseHkMd9dbfjeawjMTwP85/lTse8a7en07sfDEmI0/bQUOeMPROPzEt2DzyJP4/j99HKOjowyaGVJlPNs8Xb/dxmPbgVe8+UQc+pfH9+gOk8mTYzb+uBV4+QGvwHuvvq3Xt1PHut+txt3fvRKFQqHta/iVZlMBIhIg6QxgEpGv1vfdd59fVVapVA0lz/NgWRaOOeYYfPCDH8Txxx8P2575QTnyyCPx8Y9/HH/3d3+Hxx9/POpbJITEzHB/uho044F5RoJGsGYGT4b70hgdL7KnxTTGi0qST+/DUrPk0tUqIzpNa1GOpIFsykgJ2+H+DPKFCuf/DOQnp5wmhgbP2QR+ZoqCpFmYNNGYIHkmjZRtYqUpg+azESgOmLn+A9H0tVyy4uVYvs/KqG4pMTw8sRGY2IFddl2K5cvm9/p2YiG/KQ9s3YxU3xCW77PMfz0K6TfdWfdktYKnPJFPpDxfs6ixbDRPX3pmG7D9RaQH5mP5Prv14O6Sy8YNW4CtL2HBggVYvs+SuX+hy2weebLja4RtPxUHIFUoz6gnkXntvvSlL+Gb3/wmNmzYACCoKttpp51wzjnn4AMf+AD22muvpq+3cmV18c3n81HdIiGkSwz3ZbA5X2TQbBZMyTSbiaF+lWlMp0kYcfKMfrYZnaZh8obLM1WdgZOc/zNg+jrgO80nOf7TCeQZzZctZdJEY4J+lmbOf7WvMWjeGNPtf4ASnbMhQZ4rN832z295EQDw3ve+t2f3lBTmH3EmFrzxDNx5+w9xw0XX9vp2OqaRjB8TZmZGgkS3sv1cD6i4HjIpBs0AwHU9lJ1qjETCGcAkIrPW/v7v/x6WZfnBste//vW48MILceqppyKXy7V+Y2lzDUlCTEcdmOkwmxnTnebDEWSZmoifYW6wsySMkh9gT7NapARNOP8bY3qlASvNZkZSlqla/9kMvhZlG5s7/xkwmQ3T93+Ae8BsSNgDfNt/6rNOjleT4I//wN9jv1cc2rP7SgJ/3JLCk+PAYUcdh4NOfnuvb6dtZpPxozzfzKjvxOT5n0lZsAB4qK4BGQMVVdoh7AsxefxNJFJrbd68eTjzzDNx4YUX4pWvfGVH13rTm97k90IjhOjFsC/NwsPSTJh+aFayk0qGjFQx3Vk+HWYbNsaX5zP0ORj2HWZ0mjbCrzQxtacZneYNcVwPFbeaXCjhwMz1vzGmVxoNdzFgMjIygtHR0dj/TpRseG4bAGDspRfx8MOTvb2ZmChPVIMk657YgIdTrY2P6RJ+EoJmftDEqQ2a7LR0DyMlN1th7drngfExLN55Fyzfc1Gvb6dtZpPx494/MxIqzSzLQjZto1hxUay4GGi9fsZIVOVtyraMlOY2mcis9auuugpnnXUWhoaGIrleX18f9thjj0iuRQjpLkGlGQMmM+FXmhnqNGemcWOUE2kwZ6azbDo8ODVGzQtj5bl8eT7uAY0wff33neZFjn+YcjjLVEDmLdf/xpieNKU+V6HsolRxYwsOjIyMYP8DDsDkxEQs14+LxSd/EgP7vRFf/+qX8aVH7uz17cTCoqMvxNAhx+OKf/0GPv//bmzrGo1k30ygKECil2v/zMgImlafbUrz1yOlr60KmnENCJAQMDWVyKz1D37wg1FdihCiOX6VEQMmM2K809zvaUanaZjxotnOsumwp1lj8gXDK836WWk2G1Kc5pRorkValikdp40xff0PJwWNFcrYaTCeNPPR0VFMTkzgzE98BUtWvDyWvxEHD7yQxgsF4Ngz3o8V553b69uJhTXbUng8Dxx27Gl45Rl/2dLvzib7pjue5/kSXSY7zVXQpOx4cKeqq0kVBk1lI6WvbS5tYwyU6AwjIWBuKmae1gkhPUVJTrHKoDGe5wVBM0N7mg3lWGnWiCBYaua4TyfHnmYNyfvz30wzbIgSvbNiftCM/WwaIS3LNJDo4vofZszw9T+dsjGQTWFHycFYoRJb0EyxZMXLtZJ8s7aNAIUidl22O5bvPNjr24mFTRu2APmXkBlcgOX77NrS784m+6Y7ZceDNxVDMnkfCDuFaf/XIsFxrp7tiuvBcT0RSULNImH8AQZOG6ECiCav/aYSmbU+MTGBz3zmM/A8D3/5l3+JN7zhDXP+zoMPPogf/vCHSKVSuPzyy5HNZqO6HUJIDwkqzegwa8REyYEzlXlnqtOUlWaN8eUZDR336dBobsyY4ZUGlGedHdPHXwUDxgoVeJ4Hy6LDBAg5SwRIMwLB5+T6X4vplWZA9bOpoBmpRUKlSY62X0PU92FZQNrgQELKtpC2LVRcj8/ANIoCkmfCNk6p4qI/a+5a1yoSxh+gRGcjpARMTSQyr90Pf/hDfO1rX0M2m8UnP/nJpn5n3333xTXXXINSqYRDDz0Up59+elS3QwjpIUFPMx6WG6GcCCnbQn/GTEOSTvPGSJVn5KG5FrU2mvocDLPSaFbyhlecqmBAxfVQKNNhoig61SxTKQdmOs4bY3qlKVD9bM/nuQc0QoLjjNLcjfErDVK28ckk2bSNSsnhMzANCfPfti1kUhbKjodixaENGEKtASaPPxBSGuD89ykKS5wzichG7O677wYAvPWtb8XOO+/c1O8sXrwYb3vb2+B5Hu6808xGuIRIhJVms6O+l+G+tLGHJv8ZoERnDXnfWWams3w6rDRozJi/Bpj5HAz1MXFiNtS6aKrTfCCbgkqipx0QIMFZFoZJE40xvdIUCEv0cg+YjoRKA1VlwLlfi4R+Zgqu//V4nidGoo2B83pcz0PZqSoNSRl/zv8Atf6bXGVuKpGd1h9++GFYloUjjjiipd874ogjcOedd+Khhx6K6lYIIT2GPc1mJ2qHycjICEZHRyO5VlRserEIAHhx+zgefvjhHt9NMig7gUzJU+sfw+ZsY4N53bp13bytWPErDdjToIZAnsvMoIm/BzBg0hDTK00sy8JgLo18oYKxQhlLhvt6fUuJQFpPs2wC1/8k7K8bX9wKANjy/LN4+OGXenw38eCVJgEAjz7+BHYpbYzlbyRhLFvFmerxA5gdOKHDvDFFQYkTQaWJ0+M7SQ6O62Fq+hv/DORSKeyAw6BJiPB3Yfz4c/7XIWn9N43ITuvPPfccAGDPPfds6fdWrFgBAHj22WejuhVCSI8JpLmYYdoIVX0RRRP4kZER7H/AAZicmOj4WlGS2XlPLD3nKjzz/CgOPfS4Xt9OIrD7h7H7xTcCAI58w+GAN/tBYnx8vBu3FStZGs0NUWujkrI1jSFBe0A7SQvbdhQAAM889T/wRs0MnPWlPOQB/P5PjyG/kz49i+N0xIurNEslZ/3Pb3kRAPDe9763x3cC7Hr215DbbR/83Uc/jMJTf+j17cTC4nd+HAMHvglf+Mev4G//8KNY/5ZOtlJ4Lpi8DlCatTFB4oT5lQasNKknHEQ2XaKNgfN61Fyo9vyTMf6c/wHSEudMIrKTeqUy1aMn1ZoRYE8tGMViMapbIYT0mKCnGasMGuFXmeQ6d5iPjo5icmICZ37iK1iy4uUdXy8qdlSA/9oIZAYW4CNX39br20kE42Xgnk1A2vLwkat+OOP71v1uNe7+7pUoFApdvLt4UEZz2fHgeh5sQ+VIW8UPmhlaaRRIc5m9B7SXtGBhxcdvh2Wn8BcnHAN3x7a4bq+n7Pa+/4vskr1wzgcuQuFp/aqN43DE+9JchjvLFGGnied5PZWjnhzPAwCO/8DfY79XHNqz+wCA/9qYwY4K8O4P/T0W57ye3ktcPLwlhafHgTeecg4O/Ov3xfI3dLSVlNMsk7KMtofoMG2MpMSJ3JRPkEGTgPD4m9qeQcHAeT2Selpx/tcjpZ+diUTmrdlpp53w/PPPY2RkpKXfe+aZZwAACxYsiOpWCCE9RjlMx4oVOK6HlG22Ydgq+UJ0lWaKJStejuX7rIzsep1SLDvAxqfgwsKuex2AtAADcS425wvApmfQl81g+T77zvy+kSe7eFfxEjYMy44rIru2GfKG97RR1cbjxQpc14Nt6B7QTtJC2QV+9Gx1HvzNP34bpi6NqzenMVoE3nnhZ7H7gD6H5jgd8UVBVQZAsP67XlWWKp3q/Tqw09I9em4ruZueAuBg+Z4vx+LBXE/vJS42PDGKp8e3om94MZbv01yv81bR0VaSsgYoh7njeag4Ls8AU0joZ6fwK40cF2Y/7c0jcvwTUGmeFPxKo4yc8WfQNEBS0oRpROax3W+//bBp0ybceeed+NjHPtb07915550AgH322SeqWyGE9Jhwn5bxQgXz55npGG4XCU3gwwZBscIDMyDTWErbNlKWBcfzUKwwaAZUG4EHlWZmrgFqD/A8YLxUMfZzKlpJWhgrlIFnN8C2gBX7HmhstvHQxEaMFndgcOelWL5sfq9vp2nidMRL2wPC2dS0A6p4nuc7EU12nFKaqzFS1gDO/cZIGX+gttKov8f3khQkVZqw0qweCXu/IhcKmpMqftCU+6F2RBY0O/roo3H//ffjl7/8Je666y684x3vmPN3fvKTn2D16tWwLAtvf/vbo7oVQkiPyaVT6MvYKJRd5AtlBs2m4fc0M9iRbFkWsikbJcdFqeJiwMxk6paQJs2lyKZtTJbZDFrxp8fWw3EXAACeXL8GGw3NOEzbQMUFfv37P2LnATODpe30vwpXGpgaMAPoMGlESZA0DzDNDnBcDPT6hhKA43pwpxQZTU4iySWon12SkFJpwrnfGElO8yyDZnVIcppnGTSpQ1LQnJVm9ai5IGH8TSOyoNl5552Hyy+/HJOTkzjjjDPwb//2bzjllFNmfP9//Md/4JxzzgEA9PX14fzzz4/qVgghCWC4L4NCuYjtk2Xs3uubSRhBpZmZ/YwUuUz1wMxM4ypSG8AyaFYlv+VFAMD5F12C5Rd9F57r4IjXvabHdxUfyz90A1IDC/HOU05FefR/e307sdJK/ysph+YcpXnqkDL2YbLpIHmGBEETC9W+VqbCoHljJK0Bau7zDBAgafxpA9QjyWmukkK4BwT4SRMpcxNmFJz/9ZRCSZNELyLz2O688864/PLLcemll2J8fBynnnoqDjnkEJx00kk48MADMTg4iPHxcaxduxZ33HEHHn74Yb8p9KpVq7DrrrtGdSuEkAQw3J/BC2NFX4aMBKieZqYHzahnXktR0GE5jJ9tJjzbcHI8DwA46qxL8QSqB4qPXH1bb28qRu7ZmMF4BTjjk1dgcZ/X69uJhXb6X0mpNKA8Wz0lQQ4zRS5tY7xIx5kibAeYXGmazXD+N8KvNBJQacK5X48kpzkrTeqR5DSnDViPpKBpeP4rn790pJz/TCRSj+0ll1yCZ555Bl/96lcBAA8//DAefvjhhu/1vKoD5aMf/Sg++tGPRnkbhJAEMDwVEMpPVVWRAFVpNtxvrjwjwEzj6UjKMA2jpMj4HFQZ3HkZUAT6shks32ffXt9ObAxsG8F4vojh3VZg+eLBXt9OLLTT/0pKTwtmGdcj8cBMx1ktUuwAFRTg/K9FyvgDoblfZuKcQuT4cw3wkZQ8SR9APZIUZ9QZwPWAiusZXVnfDJ7niVr/TSPyEfvnf/5n/PCHP8SBBx4Iz/Nm/G/lypX4z//8T3z5y1+O+hYIIQlABYTykwyaTUdV3w0bXmmmDCYemKpIMpbD8OBUS2XK9MplzM409bMMyxz3MFLWgRwdZnVIPDCz0rgWKT2N6DBvjKo0kFBp4u8BnPs+kqqNmThTjxT7D6DaTCNUAoGE+Z9JWVBhMq4BQNkJFFckjL9pxOKxPeWUU3DKKafgoYcewgMPPIBnn30W+Xwew8PDWL58OY488kgccsghcfxpQkhCGO6bCppRnrEOFUhU35Gp0Glai5QKk+lQoqUWx5sKmhkuz6QqDTj/aykKkefhvK/Hd5gaPvfDsNK4FinyXEyWaYykwDn3gHokVRszcF6PpHMg94B6JAVNLctCNm2jWKn2tRzI9fqOeoua+7YFpG3ZVXc6EmuZw6GHHopDDz00zj9BCEkow/1T8oysNKtjzO9pZnbQjFlmtfjOEkEOUyD4vDw4V6l4VWM5lzH7OVCfj1nmtUiR52ET8FoqrgvHrWaaSnCYKOg4q0Xa/Hc8DxXHRVqY3TMTkoImVJuoR1LQlGt/PZKCJpz/9UjqaQZUn/Nq0IzngPDaz/5u+iFjxhJCuk5Qacag2XTyfk8z0+UZeWAKI0mWJwzluWpR8oymH5ooz9gYKU4TZpnXEt4HM4aPfRhWm9QiJWgS3t+4BgQwaCIXz/PEyLMCoSpjx4U3x3ulICVpAqAN2AgpleYK2n8BUlRGTMX8FZsQ0hOCnmaUZwxTcVxMlKqHJtMrzZhlVoskZ0kYGs21+JVmhhvOrDRqjBR5HvYzqUV9D5mUBVtQlmlQacx1AJBjB1iWVeM0J1VEBU1oA9TguB6mio2Nn/9A7TPu0OUIQFbQxK82dj2/yl46koKmAP1AYSTKs5tErGUOpVIJ27ZtQ6FQaOr9K1asiPN2CCFdhJVmjRkL9Xgb6pNRaUZjqYoUZ9l06DipxRFSacbDUmOkVJqFqww8zxMvRyJ9/WfgpIq0oEnJcVFktbGPpHWAlWa1hG0hCY7TdMpGyrLgeB6DZlNICpqEn/FixcG8rNk+j2aQYv8rmDQbIGnvN5HIV6+nn34aV155Je6++248+eST8LzmMgssy0KlwooUQuJkZGQEo6OjXflbLz0/CQDY+OJWPPzww135mzrw/Hh1nculLDz6pz92fL1169Z1fI24YLCkFimyTNPJ0WlaQ1BpZvZzwMNSY6RIdKjx91Cd+6Z/3rmQmmXKdaAWaZUG40XagGGkrP8A5dmmE+5rLCWJJJu2MVl2UIH5z3szSAqa2LaFTMpC2fFQrLiYl+31HfUeKUoTCiZPB0j1AZlCpEGzW265Beeeey4mJ6vO8mYDZoSQ+BkZGcH+BxyAyYmJrvy9vj1fjSWnfQGPrFmPQz92cVf+pg5kl7wcu73vSuzYNopDDz0+suuOj49Hdq2oYJZpLVKzjHyJJj4HAICKV/0+TDeceVhqjJR1IG1bsC3A9aqfWYKTeDYkOcvDsOK0FlGVBrQB65Cy/gOU6J2OpLmvUEEzVppVkTT/geoaUHYqXAMAuJ6HslP1jZt+/lPQBgiQNvdNI7Kg2aOPPoqzzjoLjuPA8zz09fXhsMMOw/Lly5HL5aL6M4SQNhkdHcXkxATO/MRXsGTFy2P/e1uKFn6xGViw9GU44+rbYv97uvBCwcIDLwALFy7CaRF8L+t+txp3f/fKpmVwuwmdZQGe57HSgM8BAKAiRp6RlaaNkJJtaFkWsmkbhbKLYsXFUK9vqMdIPTBTnrEWKfMfCO0BHHsA1d4+FVeO05SVZrWoNVDC2CvUZ60waIaK48Lx5Mx/IFxtzDUgfAaWkjzFc2CAJGluE4ksaPaVr3wFlUoFlmXhvPPOw1e+8hXMnz8/qssTQiJiyYqXY/k+K2P/OwMTJWDz/6JipbB8n31j/3u6UHxxHHhhEwbm9UfyvWweeTKCu4oHVpoEhB2G0gwmBs1qCeQZzT40cdwbIyl4kkunUCi7fAYga9zDsNK4FknPASuNagl/DxKSp6g2UYs0aTYg+KwO5RnF9bQD2KYhjFoHU7aFlC1HnhXgHgDIsv1MJLKg2erVq2FZFo466ihcd911UV2WEKIpYUeJ53li9NvnQjVEz2XM3zTDxpL0Z0AZS7YFMcaywnecMdscAHyZGtODp6w0rccNVZyaPv4AEyfCFIVWGtNxXoukbGPfYVrm2APB2GdSFmwBdiAdprVI6melYKVZQFhtRMp5mGtAgKQqc0UuxXOggkEzvYls1F544QUAwGmnnRbVJQkhGhM2CmgsBfgOEwGOs5pnQHjAJDgsp8QclhTKSVx2PLjsdepXmpluOPvOEteD43LcAXnyLMwyDpB6YJ6ePCMdSb3tGDCtRdoaoMbf8TxUhJ8BALk9zQCgwkozkePPxKkAaes/wKBpGN/2S3Et1JHIZq2SYly0aFFUlySEaEw6ZfsVNTSWAnyjWUClWTplI2XxGQBkHpYUWQbQAyxbTKVZuKJG/LhPIU2ehU7zAIlVBkCw/nuoJk5Ipqa3qYDnwA+aOwyaA/KcZmEbQPoZAKhNnpOC+qwOK81E2gBUnAiQVGWuYF/TAIlBU5OIbNQOPPBAAMAzzzwT1SUJIZrDDKN6pB2aKM9TRbKxFA4QSHee27l5/v83fQ2wbQuZlAqa02kKyJNnydIG8JG6B6RtC6q4WnrFednxoIrtJKwBDJrXIilgCgCWZQVS/cLnPiAzeS7HSjMfyT3tuAfInP8c/4CiIGl+E4ls1M4++2x4nofbbrstqksSQjQnCJrRYaqQ5jSl06SK38dIgCxnI+g4qWLnBgBUHckyKo2YZRpGWqYxxz/Ad5gJ2wNqHOfCnwP1+S2rugeYDud/LdLsf4CJE2EkJk6oz8pKM3n2H0A/UJiSsEpjgOMfRuL6bxKRjdpf/dVf4cgjj8SDDz6Ir3/961FdlhCiMTww1yMt0yyXocEEyMwwC0PHSRVrqtJMynPAoHkt0tZ/ZpkGlARnmXIdqBKWZ5LQ25T7fi0SnWac+wGSelorWGkWIPEcyPkfIHP8q/OePW1l7v8mEdmoWZaF2267DUceeSQ+/OEP45xzzsGjjz4a1eUJIRoSBExoLCmkZZrSaVpFurHEg1MVOzcIQN785x5QJVj/ZTiQmGUaIHkPyPI5ACB3/kvf9xUSe9pw7gdITJxQa12FlWbi2jMAPAOEkVhpqMbf9YCKKzdo5nmeyP3fJNJRXWivvfYCAFQqFXieh+9+97v47ne/i4GBASxatAi2PfsDYlkWnnzyyahuhxCSAHyHWZmHJYU0o5nVhlUkGsthGDytonqayZn/dJiFkRY4odM8QNrYh6E8YxVpzwADJrVIG3+Ae0AYyeNPeUZWGklHmtIEAGRS1Z62nld9/jOCqmzDOK4HFTOUNP4mEVnQbMOGDb7UhGVZfgnm+Pg4xsfH5/x9CTIVhEiDAZN6pBnNOWaZAZBpLIeh07SK3VftaSblOWCWaS1SK405/vKqjML4SRPCe1r6z4AQx1E4YOJ5nvizvsQ1gHtAgOTxpzyjzORJ+gACJI6/6mlbrLjVz5/r9R31hvDzL62vsSlEFjRbsWKFeGOYEFILjaV6pBlNrDSpIjHDNEyOTlMAgJ2rBs3kzH9mmYaRJs/B8a/ieZ7oPYCVxlWkPQNq/itppkxKtp9A2vgD3APCyBx/VpopJCZPsto4oOjIm/9AdQ0oVlzRz4DyfWRTMvrZmkiklWaEEBKGAZNaJGoa01lWxe9lkJKZbcnnoIq8oBkTJ8JIc5qxyqBK2fGgujlImfthqDpQpehM2X8ZGc9AJmXBAuBBtjSTQlqlMcA9IIw0pREgWPsdpABLzuduhLSkWYDVxmGktedQ8Pwvc+03DY4cISQ2mGFYSyWkaSzFaKKzrIp0g4nZhlVU0EzKc8Bxr0WaPBP72VRRz79lAWlbntOI8oxVimVZyTOWZdFhFkJa0gTAPUDhuB6cqQOgpKBJ+FlXPX2lIvEcOL3aWDISkyYA+oEAmQFz0+DIEUJiQ2XTKkeBdNSmaQFiZGpYaVJForMkDJ2mVawppwGDJjKRdnAKy7K6nlyHSXjcJWZas6dlFYl2ABMnAqQpTQAcf0X480vqaZOyLT9RRCWNScW3A4QkTQBBtTFAP4DE/R9gpRkgU5rVNDhyhJDYYMCklnCWmRTHGSU6q0jNMFPQaVrF7hsEIOc5YIZhLdIyjcOfU/Lcl1ZhOB0Gz6uoniZS1n+AYx9GotOU419Fff5MyoItrNpYPQMWK80AyJr/rDYOkJg0AdAXCMjc+00jsp5m0/n973+Pe+65B2vXrsWWLVtQLpfxs5/9rOY9o6OjKJVK6Ovrw6JFi+K6FUJIjwgcprIDJgqJBhON5SrSDSY+B1Vsv9JMxnPAca9F2h6Qtm2kbAuO66FUcdGXkRk0kugsC8N1oIpvBwjpaQYwcSKMxOA5e5pVkXwGyKZt7Cg5sHODvb6VnlISmDQBVD9vseKK9gW5roeyU1VbkLYG0P6TpzJiIpEHzZ544gmcc845+NWvfuW/NlPjxy996Uu44oorsPPOO+O5555DSlC5MiESYHZJLRIdZ3SYVFGHJUljH4bPQRVpPc24B9Qi0XGWS9uYKDmin4FAlknOuIeh47xKsTwVNBf0HHDsqziu5/f0EbX+p9jbGggFTAX6uqr2f1l0T7OK64rsaQdwDwBqWxNISpoAqDgEyPT/mUakI/fwww/jsMMOw69+9St4nuf/NxMXXnghPM/Diy++iHvvvTfKWyGEJICwoTTbWiCFksAsUzrNZR+WFL48o/CeZipoJmUN4GGpFtmVBnKfAen9DLj+VykKTJ6hPF+V8LMvqaeV39ua4w9A1txXqDXA7pPb0yy8/mWEPQPK3pW8B6j1L2VbSAmTZ2WlmUz/n2lEtmpPTk7i5JNPRj6fRyqVwqc+9Sk8/vjj+MEPfjDj7+y999541ateBQC47777oroVQkhCCAcIpDtLAKBYlleerT5rNctW5jNQc1gS5CwJQ6O5ih80EyLPFR536YkT4UoDiXuA5LnvH5iFzPvpcP2vItFxwqB5FTX2aWFO0xxtAACy+xqrNUDZvxLxK01SNmwhPc0VTJ6VLc+nqmsljz8rzfQnspG7/vrr8eyzz8KyLNxyyy344he/iH322QeZTGbW3/s//+f/wPM8/OEPf4jqVgghCSGdsv3DoQoYSaboyOpnA9Rm1El1mJUEH5YUdJoCHoJMWylrgHIOux78gJFUws++pEoDSvPIluYCWHGqkOg4Z9C8irR+lgq1/jue5ysuSESiNLMix6CZ6PFn4oRstQGVLCbZBvD7GQo6+5lGZCN3xx13wLIsHHfccfiLv/iLpn/vgAMOAFDthUYIMQ9mGAWowKEko8m2LN9BLPUZYIZRsA5UXLmOExcWrFQ1kUhKpUEmZUHFiaXOf4U6NGdSFmxRlQaU5pG+B6jPXXY8uEKrTTzPE+k4ZT/TKhLHHqgmiKjdTvIzINppPrUGSA6aSUyYUDBxQnalGRPn5O7/JpGO6kKPPfYYAOD4449v6fcWLVoEANi2bVtUtxILW7ZswQ9/+EPcc889WLNmDV544QVMTExgp512wuLFi7FixQq89rWvxbHHHovXvOY1sGapJti6dStuuOEG3HXXXVi/fj1eeOEFpNNpLFmyBIcddhhOPPFEnHrqqUinmx+eSqWCW2+9FT/+8Y/x+9//Hi+88AJKpRJ22WUX7L///njHO96Bs846y/++m2X16tX44Q9/iPvvvx+bNm3C2NgYdtppJ6xYsQLHHHMM3vOe92C//fZr6Zr//d//jRtvvBH33HMP/vd//xcvvfQShoaGsNtuu+FNb3oT/vIv/xJHHXVUS9ckySWXtjFRckRnGCkkSvMAVSOh5LhiDSYaS7WylGXHRcqWNQcAoAL1mT1kUjKCJpZlIZeyUai41XmQ6/Ud9Q6p6z8TZ2Q7TIDaZ75UcdGXkTUHgFqJcknPAR1mVST2swSqNkA2baNYqZ4BBoTaAJL3ALUGWLl5Pb6T3iH5HMjECdmJU1QakFtpbhKRBc22bt0KANhll11a+r2k61s7joMvf/nL+Kd/+ids37697uebNm3Cpk2b8Oijj+LOO+/EZZddhk2bNmHXXXdteL1vfetb+Nu//Vts2bKl7mdjY2N44okncPPNN+MLX/gCvvnNb+INb3jDnPf44IMP4rzzzsO6devqfjYyMoKRkRHce++9WLVqFb785S/j3HPPnfOazz33HN7//vfj7rvvnvEz//a3v8U//MM/4KKLLsKXv/xlZLPZWa9ZKpXwiU98AldddRUqlUrNz1566SW89NJLWLNmDa6++mocd9xxuP7667Fs2bI575Ukm6qxVBadYaRQTeClbZq5tI3xIlAsyzSYSkLHPUzKtpC2LVRcD0WhTlNnqrg/DW/WxBrTyKarQTPJByZA7qGZ0jyyqwyA2vVfatBMzf+UZSEtSKKHVQZVZDvNVdBM7h4gffwB6ZVmcm0AJk6EguYCJbqV2pBkG0Dy+m8KkY3c/PnzAQD5fL6l33v22WcBADvttFNUtxIZY2NjeOc734lPfepTDQNmrbJq1Sqcd955DQNm01m3bh3e9ra34Z577pn1fffccw/e9ra3NQyYTWfLli0477zzsGrVqlnft2HDBhx55JENA2bTqVQquPLKK3HyySejWCzO+L5CoYCTTz4ZV1xxRV3ArBF33303jjzySGzYsGHO95JkwyzzABU0ymZkbZrSnSbhBtCSkd7XTFWapS1Zn59ZplWkyvPkeGAWXWWgkO44k+o0YdC8itT1H6ANAMitNARCQbM+uUEzyTaAdB8AIDdpDgit/46b+GKZuJC8/ptCZDN3zz33BAA89NBDLf3ez372MwDAgQceGNWtRILneTj99NNnDBwtWrQIBx10EA466CAsWbJkzuupKrTp2LaN/fbbDytWrKj72eTkJE477TRs3Lix4TWfe+45nHbaaZicnKz72e677479998ftl0/xJdddhnuvPPOhtd0HAenn346nnrqqbqfLVq0CCtXrkR/f3/dz+6++258+tOfbnhNAPj0pz/d8Lvs7+/HypUrG8pGPvXUUzj99NPhunI3WRNg0CwgyDSSZTTRWSbXWA7DoJmqNJP1+XlgriJ1Hchl6DBVVebSxj6MdIkeqU4zrv9VJFea8BmQu/8DwWe2s3KDZlLXf4B7PxDy/whLmgaCZ97zqn3NJSJ5/TeFyEburW99KzzPwy233NJ0tdkf//hH3HPPPbAsC29729uiupVIuPLKK3HXXXfVvX766afjj3/8I1566SU8+uijePTRR/H8889jdHQUt99+O84999w6mcJyuYwLLrig7lp77bUXHnvsMaxfvx7/+7//ix//+Mfo6+urec/27dtx6aWXNrzHj3zkI3UVcH19fbjjjjswMjKCdevWYe3atdhrr73qfveCCy5AuVyue/26667Db3/727rXP/vZz2Lz5s1Ys2YNNm3a1LB33de+9jWsWbOm7vU1a9bgiiuuqHv9He94BzZu3Ig1a9Zg8+bNDYOKv/3tb/GNb3yj7nWiD6qqSqo0Xxg/00SYNJH6vFIPzJIzDMOoSruiI3MtCCrNZB0a/KB5Web8V0jVtJeeNAEEz77kLFP12WkHyJr/rDKqInX8gcBRLNkGYKUhK80AmTYAbcCQ/S8saRoAMikLqiOBxGfAcT0/WMigmb5ENnLvf//7kU6nsWXLFvzVX/3VnBJ8Tz31FP7yL/8Snudh3rx5OOecc6K6lY4pFAr4h3/4h7rXP/OZz+Cmm27CK1/5yrqf7bTTTjjppJPwzW9+s65q6vbbb/dlKMNcc8012H///f1/n3DCCfjwhz9c977bbrsNzz//fM1rmzZtwm233Vb33ksvvRQnnnii/+/99tsP//qv/1r3vmeffRZ33HFH3etXXXVV3Wuvf/3r8fnPfx7pdLUF3vz58/Hv//7vmDevtqGr4zi49tpr637/mmuugTPNSTowMIAbbrgBCxYsAACk02l87nOfw+te97q637/66qvrXiP6wANzgNRMM2UkSn0GJGcYh5GebeyIlWeUHSxVSJXnkD7vAdkOc4V0x5n0oHmpIleaCZBr/wOU6ARClQYCneZ+pVlu3hzvNBfJ8196wgwgu9LIsizRfc1UX3tA5vpvCpGN3F577YWPfexj8DwPP/rRj/CqV70K3/zmN2tk/tauXYv/+q//wiWXXIJXvvKVeOqpp2BZFi677LJE9TS76aab8OKLL9a8tu+++zashGr2etPZeeedcfTRR9e9/p73vKfutUqlgh/84Ac1r916660NA5ONfv/oo4/G4sWL616/8cYba/795z//GWvXrm3qmosWLcKxxx5b9/pNN91UcyjyPA8333xz3fuOPfbYhpKMjf7WY489hj//+c91rxM9oDxjgFTHmZ9lKvTALNlYDkN5RqnyjDwwA3Izzf0KU6Hrv+d5/qFZ8h4g3RaU6jRV4+4BKDtyg2aSK02YPBkkDUmb/0BwBrRzrDSTWGkkfe8HAoluies/IFuiU839tG0hZVs9vhvSLpGu3JdffjnOPPNMeJ6HdevW4QMf+AA+9rGPwZqqyTz44INx/PHH46qrrsKOHTsAAOeccw4+9rGPRXkbHXPvvffWvXbqqafioYcewrnnnot99tkH/f39GBgYwMtf/nK8973vxX/913/NeL3f/OY3da+9+tWv9r+XMAceeGDDnmHTJRMbXbO/v79hbzjbtnHIIYe0dU0AOPTQQxu+fthhh9W9tmXLFjzxxBP+v//nf/4HW7Zs6eiaje6V6IPkjTKMG3KciXOaCjeYJTtLwvhBM0fmc0B5RpnjrvArTYT1NFCfV2rQNLzeSdv7w0ivOJRqB6TtsDST3HOA1KApQKc5IDdpEgg+s5XOQmhLIz9oKnH8WW0cnH8krv+A7KRZyWu/SUQ6epZl4YYbbsA111yDXXfdFZ7nzfjfzjvvjKuvvhrXX399lLcQCb/61a/qXrv99ttx+OGH49vf/jaeeOIJFAoFTExM4KmnnsL3v/99HHfccXjrW9+KzZs31/zeiy++iE2bNtVdb8WKFQ3/diqVwm677Vb3+p/+9KdZ/w0Ay5Ytg203HtLly5fXvbZx40aMjo7Oes3Z7rXRNadfJ45rTqdYLCKfz9f8R5IDMwyrhA0FaUaT9EoTlWEmvSw/l5L9HPiVZtLkGVWlqdBgqUJqXyvpNoD63CnLQlrwHiBdok1qpallWeIDpoBsx5n05EnX9fwqS2nnP0CdfaqfvxKt61EbJCuOhNc8qUmTktd/QPY5gC06zCAdx0U/8IEP4K//+q9x77334pe//CU2bNiAbdu2YXBwEMuXL8eb3vQmHHfccXU9sZKA67oN+4899thjc/7uz3/+cxx11FF48MEHsXDhQgCoCUqFaSRNqNhpp51qZC0bXafRdee6ZiNGR0d96cZW73W2a852n51eczpf+tKX8PnPf37Gn5PeEkjzydsowyiDKWVbSM8Q3DYV6Vmmkg9LYSRnmgGhnmaQlWkZVJrJdJgppDrN1fhXXA+O64mTJ+H6X0Wy0wSQ/Rzk0ikUyq7YsQdCjjOBgXPxahM11caykmaAauA8BRcOUqh4svZ/hdSetgCQTtlIWRYcz0Ox4or8DiTLswKyz/+SbT+TiCVoBgDZbBYnnHACTjjhhLj+RCxs3bp1ztLh3XffHQDwzDPP1P1s/fr1uPTSS/Gd73wHALB9+/aG18jlcjNev9HPtm3bVvPvRtdt9ZrTr9vqvfbqmtP55Cc/iY985CP+v/P5vD9GpPfk6DAFINdhCjDLlAZTFclGMyC40kxwA+gwvjyjsHUg7CQuVVz0Z2U5TKTL8iikVxtJnf8AgyaA7EoD6WoT4aRJaUkjirQfNJP3/AM8B2bTNibLTtUe6uv13XQfyes/IDt5WnLA3CRkztxZKBQKM/5s+fLleOihhzAyMoKRkRE89NBDDeUEb7jhBjz33HOz/p3ZAnPt6v3Gcc3Zfjcp18zlchgeHq75jyQH6dnFCjpM5D4DkgOmYaQ/B6qnWQqyPn8uM7UHCJVlUUg9OKVsC5lU1VEoMXFCci+TMNLXf9E9rZg4Eaz/GVnrPyDbYQrwDAAAKVT3QanyjNKfAclJM9LlWQHZ4y89YG4KHL1pzBZw+eIXv4hDDjnE//chhxyCL37xi3Xvc10X9913HwBg/vz5Da9VKpVm/DvFYrHutQULFtT8u9F1W73m9Ou2eq+9uibRi/BGKbUBLCB702SWqdyxD6MqTqRq2jt+pZmsdTCQZ5Q57orAaSpvHZAcMJGeYaxgxbnMoDkQlmmXOfaO66HiynWacu6zr3F6KlnMESjP6ExJUwMy5z8QsgEdeWuAdHlWQHZPW5UwKv0MoDscvWkMDQ0hk8k0/Nlb3vKWutfe/OY3N3zvhg0bAMDvFzadl156acZ7aPSz6ddpdN1Wrzn9Oq3ea6+uSfRCbRIe5DrLAblVBkBtlqm0wKnnef5zL/nADFCeUVWaiZNnFD7uwNQ6INhxlkvJTZyQXGEURrrqgOTgqeSgOVB79hG5/mfkrv9AqJ+dwLmvkFxpFg4USH0GVOJESWDynNr3JMuzSrb/JJ/9TCKynmZ77bVXR79vWRaefPLJiO6mM1auXIk//vGPda/vsssuda8tWbKk4TVUxdTOO++MpUuXYuPGjTU/HxkZafh7juNg06ZNda+/6lWvqvv32rVra1577rnn4DgOUql6p3yj/mvLli2rCUZN/xvhe20kQ9nomtOvM9s1G9HMNYlepGxLfANYQLY0Q/gzlxxZz0Bthpm8sQ8jWaLH87ygpxlkBY79oJnjwvU82Ja8Q2PYWShxHZDsNJdcYRRGetKE5OCpZIcZEDzzaaFOU7XnlZ1qxY2070BywFyhKs0qAivN1PhnUpZI+xcIEqck7gGc/7LtPyZNmEFkQbMNGzbAsqw5qwisqc1i+vusBG0ihx9+eMOg2QsvvIDdd9+95rXNmzc3vEY4wHb44YfjP//zP2t+/sgjj8DzvLrP/dhjjzXsq3b44YfX/fvGG2+sea1QKGDt2rU4+OCDa153XRePPPJIU9dsxEMPPYQ3vOENDV+fzk477YS9997b//fee++NRYsWYcuWLXP+7myvz3RvJPlYliW+ASwge9NM2RZsC3A9iAuc+g3ALQtp4VlGko1mx/Xg+fKMsj5/eM0rV1yRPV3CmaYS1wE/y1jg3JccLAkjOWkCkN3XVro8n+SxB2oz7EsVF/1ZWTYA9wAgrSrNPHnfgWSlGUUgzyhv/5fs/1FItv8YNDWDyEZvxYoVWLFiBfbYY48Z/1u+fDn6+/v9gJllWVi2bBn22GMPrFixIqpb6Zh3v/vdDV//+c9/XvfaL37xi4bvDQd53vOe99T9fHR0FPfee2/d6zfddFPda+l0GqeeemrdPabT9THP6YE0ALjvvvsayh5Ov69XvOIVWLlyZVP3tGXLFtxzzz11r59xxhk1/7Ysq+41ALjnnnvqAmkz/a2DDjqoLhBI9EKyw0whedO0LEtsXzMelgMk9zRTz4HnOkgJqzRL27afWS7xwATIrjQGgFxKrtNcusNcoT5/tb+TrHXA9TyUHbk9bfxqY4HSXAD72tq2hUxK2QAC9wCHQZOU6mmG5CTJdwvp8x+QLc8o2f+jkJw0Sz+QGUQ2ehs2bMDTTz89638jIyMYHx/Hww8/jDPPPBOe52GfffbBI488gqeffjqqW+mYN7/5zTjwwAPrXv/MZz5TU7H1yCOP4NOf/nTd+/bcc8+aoNlJJ53UUN7wgx/8INavX+//+8c//jG+9rWv1b3vXe96F3bdddea13bddVeccsopde+94oor8KMf/cj/9/r163HhhRfWvW/58uU48cQT616/6KKL6l779a9/jcsuuwyVSgUAsG3bNpx11lmYmJioeV8qlcIFF1xQ9/sXXHBBnWTkjh07cNZZZ2Hbtm0AgEqlgssuuwy/+c1vmronohfSs0wBZpr5WWbCDGYelgKyIaepaootBTX/3eIEElRY3zUkZxkCDJxQnlHu2CvCe6A0x0mtPKs8G1BVF0uc/wDtf0C2RCfPAaw0A2TbALITp7j+S/YDUqLdDHqyer/qVa/CDTfcgK9+9au4//77ccopp8wp69hNLMvCN77xDdh27dfzzDPP4JBDDvGr6g455BA8++yzdb//T//0TzVBokwmg2uuuabufU899RRWrlyJ/fffH3vssQdOPPFEvxeaYv78+fjqV7/a8D6/+tWvYv78+TWvFQoFnHTSSVixYgUOOOAArFy5smFA8tprr0Umk6l7/fzzz8drX/vautdXrVqFJUuW4OCDD8bSpUtx11131b3n0ksvbVipdtBBB+HDH/5w3et33XUXli5dioMOOghLlizBqlWr6t5z+OGH4/zzz697neiF5MOSQrrR7BtMjiyDiQ7TAMlOU3VQcIs7enwnvUFyliHAQ7PUSmOAWaYKy7L8amNptiB7WskcdwWDJrKfAZ4DQj3NWGkmEslnAI5/SGmG4080paej9+EPfxive93rsHr1anz3u9/t5a3UccQRR+BrX/taw15rzzzzDJ555pmGv/d3f/d3DeUdTzjhBHzuc5+re911XTz++OMYGRmp+1l/fz9uueUWLF26tOHfWrZsGW6++Wb09/c3vMf169fDbSCB8vnPfx7HH398w2umUinccssteNnLXlb3sy1btmDNmjWYnJys+9lxxx2HL3zhCw2vCQBf/OIXcdxxx9W9Pjk5iccee6yhVOPLXvYy3HzzzXXBS6Ifkg9LCuma1lLleXyHqcA+RtOxrUCiR5pEo6qwdAvjPb6T3iA5yxAIBc0yMteBbEauDcAs0wCpFYfSA6dc/xk0yQp+BqSf/wAgJbrSbEppQPA5UHK1Mdf/UPK84yaqUKYbSLf/TKHno3fqqafC87zEBc0A4OKLL8aNN96IJUuWzPne4eFhXHfddfjSl74043suu+wyXHfddVi4cOGc19t///1x33334Zhjjpn1fcceeyzuu+8+7L///nNec+HChbj++uvx2c9+dtb37bnnnnjggQdw7LHHznnNdDqNiy++GLfffjv6+vpmfF9fXx9uv/12XHzxxQ17sU3n2GOPxQMPPIA999xzzveS5CP9wAww01BqtaEKDtFYqiI128zvaSa00kzq/FcUy7LlGSnNwz0AkNvfVro8q/T1n5nmocQ5gc8Axz9UaSYwaMbxl508zaB58Nk9D6gIa88g3f9nCnNHL2Jm9913BwCsXbu2x3fSmNNPPx0nnHACbrjhBvzkJz/BmjVr8OKLLwIAdtppJ7zyla/E0Ucfjfe97311UomNeP/73493vetd+N73voc777wT69atw4svvoh0Oo0lS5bg0EMPxUknnYR3v/vdTQWXAOCNb3wjHn30Udx6662444478Ic//AEvvPACSqUSdt55ZxxwwAF4xzvegbPPPhuLFi1q6prLli3D3Xffjfvvvx+33norVq9ejU2bNmFsbAyLFi3CHnvsgbe//e1473vfi/3226+pa2azWVx55ZW46KKL8L3vfQ/33nsvNmzYgC1btmBwcBC77bYb3vSmN+Hd7343jjrqqKauSfRAcoaRQro8l9QMcx6WasmmbewoOeKc5748Y2EHgJ17ezM9QLLDDOD6L3n8eWAO8IOnZVnrv3Q7QLLDFGDQFJB9DvT3f8GVRqrSzBEoz1h0ZNt/gNyESSBkAwqe/5mUBcuqBs2KFRcZId+F53lB8rSQz2wqPQ+abd26FQCQz+d7fCczMzg4iAsvvBAXXnhhJNdbtGgRLr74Ylx88cWRXA+oVnydccYZOOOMMyK7JgAcddRRkQew9t13X6xataphDzNiJv6BWZg0Xxj12cU6TZhh3uM7SQZSde2V00R6TzOJDjOA8iySx597QID/HEiT55UeNJ+y/xzXQ8VxkRbmPJIeNAVknwM5/qw0A2SPf86X6JaVMANQbQAIetoWK251PuR6fUfdIdyKgmcAven56N16660A0JQEIiFETyjPGGycUjdNqfJcPCzV4gfNhDpNpQbNpO8B0gMnKlggLVjueh7KTlWKhntAUG0i7TkQHzQPBclkBs5lB00B2TYAxz8UNIMltqeR1PUfkC3Ry762VSTuAWrsbQtI2fKqbE2iZ6v3+Pg4/uZv/gY//elPYVkW3vSmN/XqVgghMSPZWAKAiuPCmdJwlmo0S5VmYdCsllxK5nOg5MjcwniP76Q3SA2aKKQfmqVWmoWfd6ljHyaQZ5T1HEgPmluWJVqikXagbBuAEr2BPCNg+YkkUuD8Dz57xfV8f4gUWGlWReIeEE6YsCwGzXQmMnnGc845p6n3lUolPPfcc/jd736HQqEAAEilUvjYxz4W1a0QQhKG5H4mQK2TQKrRJNZpqioMU3SYApRnlFppJnX+K1SQQEnUSEOqDaCe95RtMcsUkmWa1fyXawfk0lVpJklZ5gpWmsjta1fT00bw+Nvw4DllWKkMihVH1HchPWkCqO3nVaq46M/K2QsZNK8i8RzIgLk5RBY0+853vtNSBFWVZvf19eH666/HwQcfHNWtEEISRqBlLWejDONvmilbbKaJdKcpDaYq4oNmBZlBM6kOM4V0p6n63I4nq6cRnSW1BD3NZAVO/KC54Ocgl0kBhYq4KkOg9gwgFYkOU4A9bRQWALc4gdS8+ShWXAz1+oa6CB3ngG1byKSqVYbFiiMqaKbsHcnjD8g8B0o/+5lEZEEzAC1pFO+111447rjjcMkll2DvvfeO8jYIIQnDl2csy3KUKIIsY7mbpkQta4CHpelIDZqV/EozqfKMMue/Qn1uqetA+HMXK3KCZswwr8WX5xUWOOFzINNhplBOU46/PBuAPW0C3OIOpObNF2f/s6ddlWzaRtlxxI0/k6eqSDz/M2HGHCILmj399NNNvS+Xy2HBggXo6+uL6k8TQhJO+LDseZ64aivpDlNAbl87ZhnV4q8FjrTnQPU0k1lpJvGwFEa608SyLGRTNkqOi1LFxUCu13fUHZg0UYt4eUah8x+QGzQBuA4APANk03KVRhTK/pX2DHD+V8mlU9hRdESNv+t6fg8/jr88G4Bz3xwiC5rtscceUV2KEGIYarPwAJQdD9m0rIMDs4zkOs1pMNXiG83Cqk6DnmYTPb6T3iDVYQZM9TThHoBsuho0k/QMMFhSi1SJNqoNyN0DnJDTVHRPO6Ey/SXuAT7K/pXkNHdcDxV3av4Ltv8AmdXGtfKsstcAiX4gVpmbA0eQEBI7aduCUqWQZCwr6DgLDIaK68Fxm5fy1R0GzWqR6jgLgmbS5RlljTtQPTSrFU/ywUlilil7WtYi8RkAgiQRzn95e0DYQZgTLNGk5KlKU4ojUuAeEKDsX0lrQHj+S5doExk0mfqsadsSL88q8fxPH5A5cAQJIbFjWZbIzVJBib7pPW1kOMwqrgvHY4ZhmJzAQ1PFcf1AsXR5RkdY0BwI1v+UbYnp5dUIiXOfFYa1KDtQ0jMAMHEKkFxlPiXPnrJhC3aahtfAkiB5bn8PELz3K7ypSjNJ67+a/5mUJXr+AzKTZhg0CZAYNGWlsTlwBhNCukJwYJazWSpoNAH2VE8bQE7gNGwYZnhgBiBTniv4rB680mRP76VXSAyaK9SeJz1wInPus8IojP8MOHKqTTzPY+IUAmlCSfMfYKWRIp2y/UoLSedA9rQOcIvyeprx/B9AG1A2EqvNuf+bQ2Q9zVatWhXVper47Gc/G9u1CSHdIZexgclA31cSNJqqqJ42UrKMfGMpZcMW3gBcITHT0K80ggtAhqN4Oipornpazcv2+o66B6uNqkisMqLDrBY1BzxPTn/b2p4mcp8DiQ4zACgoaU7B/ewUubSNiZIj6hlQ81/y3FcEQTN59n8uxUoTiTYggyYBkivNOP76E1nQ7HOf+xysmJyCDJoRoj++sSQow1BBaZ4qubSN8aIcpwmNpXpUtnnZ8eC6ngi5EuUgSEOOo6AR0oLmiiBpgus/IGf9B7j3T0f1t3W96v4oYW9UVTWUZ5WXMAMEdmAf1wA/aCbJBlDzX8JaNxcqaCZp/FXQlOMv0wakPF+ARBuAKgPmEOkIep7n/zf939P/m+vn4fcRQvRHorGkYKZRlawwg4lBs3rCfR2KQvpa+I2gIePzzoTUPYCHpiqq0kLK+g9w75+OZVni7ICiHzSR/QxI7WvM9T8geAZkzH0gNP4ZOs3dqZ5mktYAzv8AaXs/QBswjGrRISpozvE3hsgqzX7xi18AAL7+9a/jtttug23bePvb3463vvWt2HvvvTEwMIAdO3bgiSeewM9+9jPce++9cF0Xp5xyCj70oQ9FdRuEkIQi1WEKUJ5LIe0ZoCxLPbZtIZOyUHY8FMsO+gU4ElSmMSvN5B2YATpNFBKd5tz768mlUyiUXTHPAStNq/jrvzC1Ca7/AdLOAADl+cO4BYGVZnSa+6g5IHH8Of9DZ4CpnrZxKdQlCfqBzCGyoNmb3vQmXHrppfjP//xPHHDAAbj55ptx8MEHN3zvRz7yEaxZswannXYabrvtNqxYsQL/8i//EtWtEEISiMQMQwUPTVWk6Zkzw6wxuXQKZacixnGi5n+KlWYA5Mx/RXGqp01WeE8bNf6qx48EuPfXI81xXiirShPZz0BfJlj/pTjMgFBPM+FBUyCcOCNj7gMMmobx/J5mksafNoBCYuKUGn/6AQIbyPOAiushkzLfBvDHX7A0tylENoL33XcfrrzySixatAg///nPZwyYKQ466CD8/Oc/x8KFC3HFFVfgpz/9aVS3QghJINmMvMOSgn1NqkjLNPYzDGks1SAteBLIM8oJFjRC4oEZ4PqvyAm0AZhlXo+0ZvB0mlVR65/jeai4ctovBPJ8sscfENrTpsz9X+H6QTM5408bIEBi0NyvNKMfAGnbgsqVkeAH8jyP898gIhvBa6+9FpZl4dxzz8WSJUua+p0lS5bg3HPPhed5+MY3vhHVrRBCEkhOWMAkDCuOqkgLllCWoTHSDk4MmlWRVmGiYKZ5FYlBUwZM65HmOA96msl+BjIpCyqvXNYawEoTherrJXH8+xg09YNmUs6AAG2AMNJ8AAD9P2Esy/KDhxLsv4rrQeUHcf7rT2Qz+A9/+AMA4FWvelVLv/fqV78aAPC73/0uqlshhCQQaY4SRTjTRPqhWdozwAzzxkh9DtLC5Rn9ChNhiRN0mlbpEzbvHTeoqOEeECA1aUL6/LcsK5Q8J2MNAOg0DyNNbQLg+IdxixMAgLLjwRVSbcpKk4Dw2c/zZI0/538V/xzomL8HqLG3ABFSlKYT2Qr+wgsvAACKxWJLv6fer36fEGImErPMgVrDQLrTRNozwMNSY6RlG6sgUYqVZgCAoiPre6DTrIo/74U4TMPZ1JTmCRDX21T1tGKlibi9HwjJ83H8RVeaSD//AUGlGQAUBTjNAY5/GHUWdr1qUpEEWGlWiyQ/UNgHJKWHq8lENoMXLlwIAFi9enVLv6fev2DBgqhuhRCSQKRLc9kWkLJlb5rSMsyZYdYYaVKtgTyjjM87E9Kc5Qo6Taqoz19xPVRc858BVVGXSVmwhe/9YaTZggyaB0gbe4CVxmGkqQxUHNcPDnD8AbgO7Ck7WIodyOTJgHB/byl7AJWGapHU05YBU7OIbBRf97rXwfM8fO9738Ovf/3rpn7nN7/5Db73ve/Bsiy87nWvi+pWCCEJRNphSREOnEjPNFGZthKMJSDIpMyyyqAGadnG7GlWRaI0ExDaA4RXGoQPjhKeATrLGpMVZgsyaB4gbewBBk3DSKoyAGo/J/eBKmmrGkSUItHKoHlAjUSvlDXA4fiHkTT+bNFhFpGN4gc+8AEAgOM4OOaYY3DttdeiXC43fG+5XMY3vvENHHvssahUKgCACy+8MKpbIYQkkLAsixQtayBwDnLTlBc4pdO0MYHjRMZzwJ5mVQJ5Rlnfgy/PJtxpaluWyCzTXEr2uE9HXNJEmU4zhSSHmYJB0wBp4x8ee+lJkwplB0t5BngOrEWSDQjQBzQdSePvJ0wycdoI0lFd6JhjjsG5556Lb33rW9ixYwcuuugifOpTn8Ib3/hG7L333pg3bx4mJibwxBNP4Fe/+hW2b9/uO87PPfdcvP3tb4/qVgghCUQdljyv2gQ4m5ZxgGCWUYC0LFPKMjRGquNEfE8zYZWmAOB5Hp2mIXJpG6WKi4KAgDmlWRojzQ7w53+GwVNpY18jzye80hgQGDBnlVEdKcsDvNp+3ybDStNacmkbY5CRNOm4Hiq+PCvHH5CVPO2rDXH9N4LIgmYAcN1112HevHm46qqr4Hketm3bhrvuuqvufSpYZlkW/uZv/gZf+9rXorwNQkgCSdsWbKvaALZYccRsIqUyHaaKcIaR53nGZ17SadoYqUEz8fKMKSXPKOd7KDseVF019wCgL53CGCoi5j5lORsjKdMYYKVRGD9xQoA8KzBNno/Z5jUqAyLOAGUGTKbjV5oJWAPcUNCE58AqkhInwoFhjn8VSfYf+9qbRaQz2LIsXHnllfjlL3+Jk08+GdlsFp7n1f2Xy+XwF3/xF3jggQdwxRVXGG80EUKUlrUcY0nBLLMAv9oQVWey6VCWozGS+pqEM83FyzMqiV5HjkSvesZTloWUTVvXD5gLcJj5VQZ0ltcgL2mC1SYKSVnmAOX5pqNsP9eDH0wwmSITJ+rwe5oJWAPCexztgCoSgyZpm/a/QpIfkD4gs4i00kzxxje+EW984xtRKpXwpz/9CRs3bsT4+DgGBwexbNkyvOIVr0A2m43jTxNCEkw2bWOy7IjYLBWsNgqQVG3oeZ6fZUZnWS2SjObwZ0xJD5oJlOgNr/90mgbOQwkOMx6YGyMpcOK4np8gRHlGWXs/wIDpdDIpC5ZVtQGKFRcZwwMJHP960lZ17ksImqjxz6Zs2AyaAJCVNOOPP+e/j6SgKf1/ZhFL0EyRzWbxmte8Js4/QQjRCGl69gAlmsKoakMVOB3q9Q3FSI0sg+GOgVYJHOfmrwNhx7lV6fHN9BhJQXOFL8/E9R9A4DQvCJj7RT9pgsGSMGrelx0PrufBNjiYXGKlQQ2SHKYA+9lNp3oGsFEouyiWHQzmYnVD9RwqjdTjyzMKWAMKdJrXISlphn3N65FkA3D8zYKjSAjpGpKyzBWUaKolK8RgUsZSyrKQ5tjXEA6emy7Tx342ARIleplpXouknkYqYEqHWS1hB7LpCVSsNKhF2hmgyJ7GdUiyATj+9aR8eUYJ419d5/qYNOXDSiPZSBp/qk2YRawpPs8++yzWrl2LLVu2oFQq4eyzz47zzxFCEo5/WBLgMFPQaKpFSrUhx31mwt9JqeIanYXNoEktucyURK+QPYCZ5rWoeVAQ4DRnlmljUraFtG2h4nooVlz0Gb3+0w4I41eailn/uf9PR1KlgT/+DJr4BJVm5tsAtP/qkRQ0L3H865BUaVh0uP+bRCxBs29/+9v4l3/5F6xfv77m9elBs8svvxyrV6/G7rvvjm9961tx3AohJEFIOiwpSpRnqSErxGBihtHMpG27xmlq8tzgobmWvnQKQFlE0ARgpeF0+gQ5THhgnplc2kal5BifPFMo02keRkrSlIL7fz2SKg04/vWkpyrNRIw/Kw3rkOQHYtJMPRKDpmzRYQaRjuLk5CSOP/54vP/978f69evheZ7/XyMOO+ww/PSnP8V3vvMdrFu3LspbIYQkEEkZJgo6TWuRYjCzymB2pMh08tBciy/PxUoDkfjrf9l8G4CJEzMjLXmmj05zALVnANOlmYFwTzOuAQpJ58AC9/860pacnmasNKxHUtCcfoB6/PF3JLVnoP1nApHO4rPPPht33303PM/DHnvsgU9+8pO44IILZnz/0UcfjZ133hkA8JOf/CTKWyGEJBBJGSYKOk1rkfIMFJlhNCtSHCc8NNciSZ4P4KFpOqqq1PT1H+DYz4Y0O4D2XxU17q4HVFyzHWZAkBzA8Q+QMveBUNIU7T+fFOT0NCvQBqhDytkPCPU05frvo8bf84CyY7YNwMQ5s4hsFH/2s5/hP/7jP2BZFs444ww8/vjjuPzyy3HMMcfM/MdtG0cffTQ8z8P/+3//L6pbIYQkFClVRmFYnl+LFHkeGkuzI8VxQsd5LX3C+lqqz5ml0wyALBvAH3vuAXVIsQNYaVJLJmXBsqr/X8IeUGSlYR2i9gDaf3WoSjPT134gCJr0cf33kXL2A5g004i0HdgApq8BrDQ0i8hG8Tvf+Q4AYK+99sJ3vvMdZDKZpn7vla98JQBQnpEQAfjSXAIyjBRsBFtLVog8V8mhsTQbwVpgttHMQ1Mt0vYAVhrXIkqekXvAjMiT56X9BwCWZQmrNOAaMB1J40+naT1pv9LMfInWoNKQ679CkjyjGn8mTQRYloVcyvw9wHE9v5qeiXNmENko/upXv4JlWTj77LObDpgBwNKlSwEAzz//fFS3QghJKDlhVQbhTZOHpipSMswpyzA7UpznDJrUIinLFKDTbDpheUaTHWYV14XDvX9GpDjO2dOqHkl7gG8Hcvx91B5QMvwc6HkeK00boCrNXA/+HmkqDJrXE/YBmGwDApTnn4lwXzNTCfu42KbDDCIbxc2bNwMA9ttvv5Z+r6+vDwBQKBSiuhVCSEKRJMsB1DqEuGlWkeIwoTzj7IipNGClaQ3q8FgwPFiq4PjXomSKPMg5MGe4B9Sh5oOU5Bk6TQMknQMKrDSsQ4rtV3Y8qJhAHyuNfFRPM8D8Z4Drfz05ITYgQPt/JiT4gdTcz6Qs2LbV47shURDZKp5KTTX3dVubAFu2bAEALFiwIKpbIYQkFClVRgplEHDTDJAiy0dZztmR4jQtsdKghj4Bh6UwzDSuJWVbSE01NDC54tzvZZqyYVvc+6cjxXFOp1k9UqoMAVYaN0JK0FQ937ZV7eNDqliWnPW/QHnGOlK2BVtITyuVHMj1vxYJEp20/cwjslm8ZMkSAMATTzzR0u899NBDAIDdd989qlshhCSUILvEfC1zgIGTRmQFaFkDtU5TUo8Yx0mZTrMwgSyn2eOuYKZxLZZliUicKLLSeFbErf9MmvCRItPueZ6//rOnTYCUoGnYaWoxcaIGKQm06vP10Q7wqfa1lJE8pz4fK01rkWD/+QFT2n7GENlIvuENb4Dnebj99tub/p0dO3bg1ltvhWVZOOKII6K6FUJIQlEOJNeD3+vLZFhlUI8yIIw/LDl0ms6GHMeJCprw0AQEh8eC4eMOKKcpM42nI2Hus8JkdiQ8AwCD5o2QEDQHqmccdcyh4yxAjMO8zDPATEhY/2t72tH+CyOh0rDG/ucaUIOESjOeAcwjspE89dRTAQCPPPIIvv3tbzf1OxdeeCG2bt0KADjzzDOjuhVCSELJpCyohDvTs0yBUBNwbpo+Ug7MNJhmR8pzwENTLRIyDBXhniYc/wAJc597/+xIcJoAlOhphASHORBkmlOerxYpNoBfZciAaR0SgiY19h+fgRokVBqGPxvt/1ok2AAFv8qUtp8pRDaLTzjhBLzuda+D53m44IIL8KUvfQnj4+MN3/vII4/g+OOPx/e//31YloXjjjsOr33ta6O6FUJIQqmW5Zu/WSoYOKlHfRcV14NjcLUh5blmR4LjpOK6fkUt14AqynnguB4qxjcBp9O0EeoZUE5lE+HePzsSAqcAkyYaIW/sKc8XRs0Fx/VQcc19BhgwnxkJPY1p/81MVoAfSAVNUraFNNs01CAhaapIeUbjSEd5sVtuuQWHH344nn/+eXz605/GF77wBb/XGQC85jWvwbPPPosXXngBQLV0dcWKFfjOd74T5W0QQhJMLp1Coewaf2AGGDhpRLjHV7HiYF420m0oMZQ49rPiSzQJcJwDfA4U2ZQNC4CH6vpo8mGSTtPGSAiYc++fHd9pZrDiQMVx/cQgOk4CJMx/gAHTmQiviaWKi3TWzO+H4z8zEtaAQpn230xIGH91tmU/u3okJM4wacI8Ip3Ju+++O37729/6FWeFQgEjIyP+ZvHwww9j8+bN8DwPnufh8MMPx4MPPojFixdHeRuEkAQjwVhScNOsx7YtZFLVPcHkZ4BBs9mRkWk69QykbB6apwhXG5tcaQTQaTYT/oHZ4IAJ9/7Z8eWZDK42Vc+AhdpkIelISJgBQv3sGDCtwbIsfz6YfAbwKw24/9chQXFGnW0oz1mPpPMf+xnXI8EPWGA/W+OIfCR33313PPjgg7jjjjtwyimnYKeddvKDZJ7nYXBwEMcffzx+8IMf4Ne//jWWLl0a9S0QQhKMBGNZUSrTadoI052mFceF41GWbzbCRrPnmSnTqZ5vOs1qUYdIkw9MAJ2mM6GcSCbbAJRnnB0JMq3hakMmTQRIyDIHQvs/A+d1+IFTg58BOs1nRoI8W+A05/hPR0JPOwZNZkbC/Ff7fx/Xf2OITRfrne98J975zncCACYmJrBt2zYMDg5ieHg4rj9JCNEAKQdmACg6NJoa0ZexMV4012kazp5nhnlj1JzwUG2YnU2b51Qs8tDUEAlZhkCQNMFq01ok2ABq7nPsG1Mr02ymTCvX/8ZIWf9ZaTwzubSNMZhdbcjxnxkRNgCT5mZEQvJ0kUnTMyJi/Ln+G0dkQbO3vOUtAIAjjjgCq1atqvnZvHnzMG/evKj+FCFEYyRkGCqKdJo2RB2YCoZWmlGWb25StoWUZcHxPBQrjpFzhBJtjZEjz8Xxb4QEpzkrzWbHsixk0zZKFRclx8VAr28oBphp3BgJDjOAQdPZEFFpwPGfEQk2AMd/ZnIi5j/3/5ng+k90JLKRXL16NVavXo1ddtklqksSQgzEN5YMDZiEKdFp2hDT5bnYz2xulNMUMPfgzEyzxvSpoLmh467g+DdGQk+7IveAOfEdp4baggVWGzYkXGViqjQzQHm+2RBVacTzXx1ZAYHzAs//M2L62Q9g0GQ2JKz/hTL3f9OIbCYvXrwYALDrrrtGdUlCiIEEm6W5xrKCTtPGmF5pxiqD5jA925SHpsYEznKz9wCOf2PUIdLkLFMmzMyN6Y5T2n+NUZXGnleVZjYVynPNjOm2HxAOmnL8pyOh0qjkVxpz/Kej7CKTx7/AoPmM+JVmjrmJM8qu7eP+bwyRjeRee+0FANi8eXNUlySEGIiEw5KCfU0aY3qlGasMmiNn+nPAQ1NDVNBETqUZxz+MOkQWDJ33AAMmzWC645TyTI1J2xbsKdVqU/d+gEkTsyFBopPjPzMiKk388ef6Px0JfiB//jNoWoff09zQxBnP83j+M5DIZvLJJ58Mz/Pwk5/8JKpLEkIMRIKxpGDFUWNMrzTjYbk5/GxDY5+DqeApD0019AlwmAEMnMyECpqaKssHUKK3GUx3nJZYadQQy7KMH3uASROzwfGXjQx5Pq7/MyGip5WqNOT8ryNtW1Dt3k18BsqOB1VAx6CpOUQ2khdccAF233133Hvvvbj55pujuiwhxDCyhleXKDzPQ9HhoakRYiqMmGE+K6YH0Bk8bYyEoAnATNOZUPOh4npwXFOzTDn358J0xxkrDWZGlNOc638dpleZ1lYacPynEx5/Y+XZyrQBZkJGpSnX/5mwLAu5lLnPgLL9UpaFtCqrJ9oT2UyeP38+7rjjDixfvhxnn302PvrRj2LDhg1RXZ4QYgh+hqHhDlNmmsxMn+GVZgVqWTeF6Y4zVpo2xvRgqYLynI0JV1+ZeGB2XA8qFshKs5kxfR2g03xmRDhN6TSfkSB50sy5Hw4GcvzrCX8nJcfMZyAImtD+m46yicuOB9fAxCkgnDTD+d+IcF8z0wgSp21YFoNmppCO6kJvectbAFSDZ8888wyuuOIKXHHFFVi6dCmWL1+O/v7+WX/fsiz87Gc/i+p2CCEJxXRHiUI5AywLzDSZhvE9zegsbwrTHWeU52mMGneTe1oBdJrPhG1ZyKZtlCouimUX87K9vqNoCds22RTHfibMX/9ZaToTqs+bqdLMAPf/2fCrDMqmzv3q2KdsC2nuAXWkpvoaul71uzJxjvg9LWn/1ZGdFjTtsw0cf/oBZqX6vVSM9AVSacJMIgua3X///X40Vf2v53nYuHEjNm7cOOvvep7HSCwhQgg7Skye++EqE1M/Y7uY3tOsQGdZU5je14JBk8b0CZFnZKXhzORU0MzAuV/k3t8Ufk9LA58BgE6z2TA9eY7yfLPjSzQbOv4c+9lRfQ0ny46x639BVZqy0qyOlF2Vrau41XWyz7DvKCzR3Uc/QEN8G8DAcyATZswk0pnseV7Nf41ea/QfIUQOahNxvWpPE1Phpjkzxvc08zMMOfazYbrjjJUGjTG9wgSY3teK68B0fIleA58B5QSkNOPsmC7PS8f5zJi+94clpzj+9Zg+/ipgwjPAzGQNdpo7ruf7Nzj/G2PyOaASkuim/d8YdS428QwQlmck5hBZpZnrmrfpEUKiJ5OyYAHwUD0wZQyVrqDDZGZUVlnZ8eC4HlKGyVfSYGoO44OnrDRoiOnzH5h2aOY6UIfZWaaUZmkG0x3nfA5mJqgyN3vvpzxfY9ScMLXKKOhnxbGfCX/9d8xbA8LrGpNnGpNN29hRMrPSUAXNLavq8yL1mKw4wn52ZsLRJIR0laosg9l69kBgNNNgridsSJjoNFEGE7NMZ4eZpjLJGj7/geCZttnTsiEmB8xZadYcJjvOa+T5DJOeioKsyjI3cO8HmDQ3F2ptLDkuXAMVh3j+mxuT139l/2XTNmxKNDfEZHn+sNoMJbobY3KloZr/9AGZRVu7+Vve8ha89a1vxYMPPhj1/RBCBGC6nj3AfjazYVsWsilzAybhAxOZGZN72jDTdGZq5r+BYw+gRpqRh+Z6JDhMWGE6O1mDnSZlx4PnyzNx/Z+OyQ4zgFWGcxFeG820/3j+mwuTk+aCoAnHfyZMrjQPVEY4/jOhKs1MTJxhawYzaWs077//ftx///0YHR2d8T2HHHIIDj30UKxevbrtmyOEmInJGWaKIrPNZ8VUPWs2AG4eow9Nav6nmGnaCL/SyMADE8D1fy5MHn86TJvD7MBp1QZgpWljTN77gZDTnFWGDUnZlj8vTHwGWGkwN/7675g3/gX2s52TrMF+IAZN5sbkxBn2tTeTyHqaTeePf/wjLMvC9u3b4/oThBBNMb0BPMBs87noy6QwVqgYl2XEBsDNEzaaPc8zqiKHPS1mpy+dwhgqxgXNFQyczI7JB2bKMzZHOHnK2PWflaYNMbnKHKDaQDPk0jYqJWdqD8j0+nYihU7zuTE5eZZ9refG5DYdBfp/5sTkSjPV0y7L+W8UHE1CSNcx2WGmoDzj7Jj6DChjyWYD4DlRBwrXg9//yxTUQZBOs8aYXGkEUJ5rLlQGZsFEhxnHvinU2uihKmdoEnSazo7plWYFrgFz4lcaGWgDMGlybrIGB01oA8yNyZWG6pmmPOfMqLlhYuIkK83MhLOZENJ1TJblUbAR9Oz0GXpgZoZ582RSFtRXZNpawKD57Jh8YALCPQ14aGqEyVnG/5+9O4+X7Kzq/f/du8Yz9NydodMZSUJGhCQM4SrglSFwDbMQQHAgRK7x8lIUNUYJBBSv96roJQRJUFRkFH8ISAioF/DKDCGQkIEkJJ10ujP0dE73OTXu/fuj6tlVJ+f0OTXs4Xl2fd6vV14vuvp0ddFP7WmtZ61Fpdlgir4nPzr/5+t7QNB0dXk+/iWSJoMwCeVGHoPm3P+tKc+VZlQarS3f7RnNphnW/0hMpVneYkAS5/+8YjUBpC6qMsjhzZLBRXN1eZ1pxg7zwXmep0ohn8Ezgmarix6YcnoNMLtnOQ+srJLj9Y+O/QLH/mo8z8vtBip2Gq+u//gPw3xVGUpSw8y04vx/RL1Ko3wd+1LvfpbnvyPL67lf6h3/3P8dWSXXxz/xn7XktduQ1Hf+5/jPFVYTQOryvstU6q80IWiykrzuMjJJQIJlgzHBs7ztNiZpvrq8XwMaPDSvKs/t2ZhnOLi87jbn/L+6So5bc0r9lYbcBx5JroOmVJqsKc+zzXkOXFt0/Lfzd/zXmGm4JhMDarZDtXM2nqHGpqlc4mgGkLo87zAzzGwrdpquLK/t2ag0G05edxvSnmt1eb8GEDRdXZ6TprRnHFxek6cMgl9dnltzSv3t2Vj/I8nzPQBJ87XluT0jlUZry+uGGYlK80H0Hxu1HD0HtNpBlAQkDpQvrCaA1FVzmjDpV2OmzaryOtOMAfDDyetuU2ZarS5qz5qjh6V+BM1Wl+f2bCTMB1fOabUJ7XlXl+fWnBLngEHkNWEusf6DyHelIZVGa6nkNAYg0Z51EJ17gPxdA/r/v5QLrH+ejLWanufF8jMAJkslp635jDAMo5tmKs1WlveZZuwwG0xeH5xp0ba6ao4DphJJs7VU+9qz5a01a1RpxgPzmvJabRDd/3H8H1E02ziHzwG051tbXhPm7SCMWo6y/kc2EXNNeQ48ojzPtuf8PxjTojFPmyf7n/3IgeRLcZw//OIXv3jV3w/DcM2fMTzPU6vVGufjAHBENedVBs12KNOiucpN04py35aPZMlA8rrbnJ3Gq8vzA7PUW3/as62s4HsqeJ7aYah6M8hNcCkMQ9XbBMwGldfzPzNt15bXDTMS7dkGkdeEef//nwobJ47IJE1bQWemUcHPT4CZTVNrixImrbbCMMxVgoGOM4PJY6VZbzQL9355M/bRHIbhiv95nhedAI/0M4/9D8Bk6O0uyc+Fsp+5aBY8T8UcPQjEqf+GOU/YYTicPN40S3wP1tJrz5qv49+gPefqPM/LZeK02Q5lHmfYOLG2vLbnrTHbdE15TZhKbJoZRF7X36x9qeDJ5/nviPorsfOWOCVwvrao20CYv24DUccZ1n9VeWzTT8I8v0Ze0bUSXSTDAByJCZg2+gZm5kl/a7Y87Z6KUzWnLTrNzR/BssHkdbd5gxvnVZl/l1rOgiUGD05ry+Oxb457zxMbZgaQ12oTkiZry+uGmaC/PR+bJo4oj5smpN49DWu/uoLvqVToXCPzdA/QGc/A/d9aigU/ukfKUxygFQRqBeb8z/qvJo9t+rn3y6+R2jMGQX6+3ADS138xqbfami6P1SnWOuwyW1ulrzVHKwhU9PNxg2Fu/phpNhjT872Ro4cmiaTJWkzArNEKFISh/BxtLuifacn6H1knqNjM5wNzgQ0zg8hj4lSi0ngQvRbdOVv7vqoJzv9Hltv1Z+PcwMpFX812O1f3AA2O/4FVSr5a9bZqzbbWT5Wy/jix6E8Alln/VeWy0izqMsC9X95wNANIne97vbY8OQuWS33VRtwwHdGSxGmOvgPMshhOXneb92bbceO8kv5gct6qTFpBb6YlQfMjy+MDswmYESwZTF7bM9Zpz7gmc23M39rTnm8Q5tqYt+s/XQYGVynk7ztgzv0F31ORmXarMptL89RxwlzPykU/V5sBk9Br05+/9a9y/s8dVhRAJqpRe678BMwMcwNIpdmReZ7Xa9GWo6Cp+T6z9oPJY9C0vaQ9E7dZK+lvzZOn41/qfZc9T9H/RyyXx4Q5FUbDyWPgPAjDKHnK+f/I8nj8S5wDBtW//nka5cH6Dy6PLTrpMjG43piG/DwDmA2TJE3WlsfZ9rWoywjn/7zhiAaQiehimaMdJkY9as/IKXY11RzuNOaBaTh5bM/VHwAus9P0iCo57Gcv9bVnokXfqip53GVKpfFQ8pg46T//Ezg5sjxe+yXuAQdlNkyFUrTJKA9qBM0HVs7hOSAaz8C5f03VqNtAfq7/tOcbXB7v/+gykF+sKIBM9JJm+blZNqJKM26aV1XJWbVhqx2obQYAc8M0kDwmTkwAoFzwac+0ijzuMpb6gqY8NK+qWspfwKzR15oHazP/TnmqNDPHf9H3VOD8f0R5vPZLzLQaVNH3ZA6PPF0DehsnuP6vJZdB8xZB80FVinmuNGL915LHOCDzrPOLFQWQiWoO55kYNR6aBxIFzXOyy6zW15aNCqPB5PmhmcD56qK1z9k1gEqDweQxaF5v88A8jDxWG5E0GUwer/0S7fkG1WnRnsNrgLn/4/hfE0HzyZbHOBDdBgbXewbM0fm/yXiWvOKIBpCJPA6ANWjPMJjoO5CTG+YoWFakLdugTGCxHYRqtfNxLmCn6WDyeg2g2mgweQyas/bDMUHzZjtUEOSjRRtJk8H0Nk3l4/7PYNPE4PJ4DSBpMrjeM2CO1p/2fAOr5HBMhzmXkTRZW75nmnH+zxtWFEAm8jgA1uCmaTB5a89WI1g2tP6KvLx8D/qTpziy/AZNWf9BRO15c7T+JEyG059cbORt0wTH/6ryWGUkcf4fRh5nWkXPf1wD1lTN4T0g5//BVXN5/HP+H1R/t6EwZNMU7MYRDSATlRwOgDWiSjMqTVZlHirzUppvbpZZ98F5npe7uTbcNA+mktNKM9Z/MGaXcV6Oe4mA2bAKvqdid7BRXpIn7DQeTKXvup+XgJnETKthmOfAXF0Dokojjv+19CpN8rP+5vxP0nRt1RxWmtU4/w/MHCPtMFQrL50GOP/nFisKIBN5LMs2uGkaTJQ4zcl3gGDJaPLWoofA+WDyuMtUoj3noKJKsxytP+0Zh5e3uWYN2nMNxKx7qPxUGUqc/4dRydnGOYlKk2FU8jzTiuN/TXm8B4yOf9Z/TaWCJzPJIg/XgCAMo3sZzv/5w4oCyEQ1h62ZDCqOBlPNWXsekiWjyVvQlKDJYCql/AXMJNpzDqqaw/Xn2B+eCZznpdqk156N78BqigVfhZxVGUqcA4aRtw1TEpXmw8jbXGuJ438YubwHbNKedVCe5/XNtnb/HNB/HeP8nz+c0QFkIo9l+ZLUDkI1250yc2aarS5vuwzN/w92mA0nb7NNCJoMJo8BM4n1H5RZ/1YQqp2T1ixUmg2vnLPzQC9oyvG/lugakKPnAM7/g8vbhimJzXPDyGN7Ro7/wfXiQDk8/okDDKR/rpnrzIbJUsGLNgQhPziiAWSit8MoPzdL0tKbPwJnq8vfTDN2mI0ib8kTgiaDyVvS3GD9B9N/fcxL0JSA2fDydv6vcfwPrH+uWV702nSz/mvJW5Vpqx1EG0AImq+t2jfTLsjJxhmO/8FV+zZOtXLSopeZpsPJY6UZ9//5xBENIBO9XtY5GwLeFzDxPXaarCZ/M82oNBtF3nYbR9UmfA9Wlbf2rAbteQbje57KhfzsMpVImI6iV22Uj/O/+f/B+X9tlRwFzIxee3YCZ2vJW8K8//+HubbhyPqDy3n5DtQ4/gdWLvoyUZK8rD/tGYeTp82TdBvKN1YVQCb6byjzcrMk9S6a3DCvrT9onofEaY1Ks5HkbbcxM60Gk7dgucFuw8FFrVlycOwHYRidw3hoHlw5Z9VGVJwPLm9JE4nE+TDy15q1t/YemybXVPB7G2fykjjn+B+c53m5SpoEYahGm3vAYeRp82SDe79c44gGkImC76lU6DxU5OFmyaA0f3DmprIdhGrloDUHyZLRlHPU01wiaTKoqEVvTpLmhvke0553bb2Kc/fvAfrPXzw0D65izgM5ac9E0HRweds40QqC6F6W9V9b3roMUGU+vDwlTWjPObxeez73r//9G394/htMnmaa0Zo731hVAJnJ5RBgU5pPpdmaygVfZjNmHm6YegOAWfth5G23eVRtyo3zqsy6h1K0O9N1IdVGQ8nTXEuT+CsXfPkMAR9YVG2Sg++AROB8GOUcVZpKS7/DbJpYWyVHVQZS3zwrngEGFsUBcnD+N99jT7TnHFQvaeJ+0tQ8+xV9TwXuAQcSJU1zsP6M6Mg3VhVAZnoBM/cvlgYB88F5nperSoOolz1rP5Q87TYOw7DXppPAyaqKBT96sMxLwLwdhGqHVBoMqtee0f1jv9eamXUfRqWQs/aMBM4HlrekSYP2fEPJX8KcSoNhVXNUbWrWv8zxP7A8Jk159htcnmbb1+gyk2tc1QFkpteWwf2bJaNGwGQoeao0IFg2mjwFzlpBGLVn4cFpbXmrMmSn8XDydOzXqDIfSZQ4bbsfNOlvNU3gfG152jAjkTQZVrVv00QeWjRTZTq8PHWcYab58HrtGd2/BnD+H16eZprV2Tida6wqgMz0dhi5f7NkRBdNdpsPJC+7jPpnWXDDNBzzgJGHSoPF7rnM9xTNbMSR5ak1h8RO42HlaZ5BnYDZSPJUbdKf/KE939rydO2X+mcacw4YhDlXBqHUbLufNKPSYHh5mmlG0mR41TytP+35hpav9WfjdJ5xVAPITDVHrfkMdpsPJy+VZsyyGF2eqo36ZxqSNFlbJW8zbdhpPpQ8VZos0pp5JJVC5x4gD4mTKGle8OVz/l+TuU92/f7PiIJmnAMG0j/7Jw9B00YUNGX9B5WnjVPc/w2vkqNrAEnz4VVyEgOSSJrnHasKIDN56mVt1LhpHkpeKs3qzLIYWfTQ5Ph3QOoFzqdImg8kT0kTqe88wPoPpNeax/17gBprP5I8Jc7rBM2HkqcNM1Jf0Jz1H4jnefmqNOD5b2h5as9Ipcnw8rR5us7GqaHl6v6P63+usaoAMpPH9oz0NB9ObirNeFgemanMa7Z788BcxfE/HCoNJluekqa99oys/TDKOWrRx33AcHozDd0//qX+zVNc/wdlngEWc/AcaNa/yvoPLF9JU+7/hpWnzdNsmhteL2nu/lzLqNMU5/9c4qwOIDN56mVu1LloDiUvlWa05RxdpdC7FXE9cFojcD6USo52mUoEzYeVq9Y8XANGYo6Vdhiq1Xb7e0DSZDj5qzQjaD6saONMDr4DdBoZXp42Tpn15/l/cHmKA3H8D89U5YWh1HD+/o/1zzNWFUBmoiqjHDwsGdFNM0HzgVBpBt/3VCp0Wlq6vuOcwPlw8tTPXmL9h5WnoHnv2s/aD6Nc8GU6Gru+25xK0+GU+45/13eZS71qU9Z/cOZZKReVZt3jv8zz38ByNdOM9R9anuJAXP+HVyz40VxLl58DwzCk0jDnOKoBZCZPbRmk7kWTnuZDyUulGes+nkpOHpxozzicvBz/xiLrP5QoaZaDe4Aa8yxG4nle32w7t78HzLQYTn9w0fVd5hLtuUaRp0ojKk2Hl6d7wHqLe4Bh5WlMR9SelfP/UPIw167ZDmX2/XD85xOrCiAzeeplLXUe+s1eWS6ag8nLA3ONh6Wx5KXihMD5cPK0y1SiPeewKn2tuVyvNKHKcHR52UBVI2g+lLzsMjdozzg8c77MRaUZzwFD638GdP0eoFdpxPl/UP3PfoHr60/HmZHkoU27iQEVPC+6p0G+cFQDyEz/7mLXb5alXtCs6HsqFji9DiIvM42oNBtP78HJ7e8BlUbDMbuMXX5Y6kel4XCieQZyv9KEtR9dXgLnBM2Gl5cNMxLrP4qpnFSaLGnPRdJkYEvuARw/B/QqTTn+B9V/v+T6+teIA4wkD3GgXgzIl+eRNMsjzuoAMmN2F+dhAKjUe+jjhnlweZlpxADg8ZRzEjijPcdwepVm7j4s9TMPzVOs/0AKvqeCZ+YZunvsB0sCplwDhpWXivMG1YZDy8uGGYlKk1FUclJl2t+ei2fAwRULvordyoyaw/cAUn/HEY7/QRX6Zlq7fg5g08Ro8nD/x9rnHysLIDN5a83Sa83GDfOgTOK07ni1odkhx9qPxuzMazh+HjCVEiRNBmOSpXlp0cs1YDie5+Wi2rB/hzQJk+GZ+wD3K81InA4rL/NMJSpNRjGVkzb9Jmjqe4qSQBhMXuZacf4fTSXqOuTuOaC/0pT7/+HkYaYZVcb5x1kdQKbyMstCospkFOYGIwg7OzVdVWsSLBlHXlo0UW06nLwkzQ1mmg0vD5UmZt3LfRuBMLg87DSWqDgfRR6S5pIJmrL+w4ra9Dv+DNgfNKU913DyUG0YhGG0eYb7/+FEzwEOrz+VpqOr5GDjBM/++cfKAshUNQc7jAwCpsMrFTyZGKPLQVOCJePJQ+A8DMMo8Eel2WD6k+atwO2kWRCG0XWMjRODy0OlCZsmxpOfmWbsNh5WHq79Uuf6ZS5hrP/goo2Tjq9/rzUn14Bh9RKn7t4D9Febc/wPp5qHpEn3/FXwPCpNh2QqzVxOmlJlmH9c2QFkKi9tGaRe4o8b5sF5ntdrzeDyDTMDgMeSh8B5vRXIpH1ImgxmSdLc4eNfWvrdZf0Hl4dKk96GGdZ9FFN5qTYheTq0PLTmknpr73mKZvRgbf1Vpi5Xm0cb5zj2h5aHxKm5/yv6HtXmQ+ptnnZ4/bvn/3LRp9J0SCZu4vTzP/d+ucfKAshUrtozUmk2kv4Wba6qRwOgWftR5KE9ozmHlQo8NA9qSdLc4eNfokXfqCo5mGdQ4/w/ljwETWnPN5qpnGyc6197gqaDM0mzUG7f/1FlOro8tOdl48zo8rBxKooBEP8ZWjTTzOF7AO798o+VBZCpqNLM4Yclg2qj0bheadYOwmgeG2s/mjy0aDLfXx6ah5OHB2aJ9ryjykOVKcf+ePLQnon2fKOJEqYNd6/9EkmTURV8L6rMcztoSnvGUeVhrh3rP7o8dBzi/D+6PFSa1WjPmHuc2QFkylxgXO5lbFBtNBrXK836PzcPTKMpd//dGg7fNC+y03QkeUiYSqz/qPIwBJ5d5uPJU9CM9nzDiebZOX7+J2g+ujwkzc31i/UfXiUHlcas/+iqeeg2YNafTXNDy0PHqTrrn3usLIBMRTfLDj8sGew2H03F8QdmEywpF3z5tOUZSR52mtGedTTVnMy04fw/mnxUmnHsj6M/aOLqXKP+oCnt+QY35fj9n0HQbHR5SprTbWJ4eTgHsP6jy0XSnE0TI8vDXFMqDfOPIxtApvLwsGTU2Gk2kqrjlSYMgB1fVG3k8EOTqTSa4qZ5KJUcVBpJJE5GlYt5hrRmGYu5DwxCRa2OXUPQZDR5eQZg/UeXi0oDguYjq7D+E633/Ofw+je5BxyVOf83WoECZzdNcfznHSsLIFN5KMs3zE0zlQbDcX2mWS1qy8m6j8rcaDba7t40M9NwNHmoNJJ6AZ8p1n8oeQiY0JpnPEXfU8F3e64RQdPRmIDZYk5mmtGefXjm3nnR0WNf6m364/gfXm9Mg7v3gNGmKZ4Dh5aH2fbR8c894ND6N5q4OqIhGs/C819ucWQDyJS5wLh8s2ww12Q0zs80o9JsbOW+QIOrN80kTUbTm2nl5robtGccTT5as7L24/A8z/lqE4JmozHXy1YQqtV2+BwQJU04BwwrF0HzJpWGo+qtv5vnfqmvTT/n/6HlodqYbgOjK/heNAfW1e9AjU1TucfKAshUHm6WJKnVDtQKOhUy7DQdjvszzdhhOq6i76vYrTRwNmnWoj3fKHr97N2+BrBpYjS5aM/I2o/N9WoTguajKRd9mRFwrt4DSr3PTtB8eFM5eA7szbRi/YeVi5mWUccR1n9Ylb6Nc86uf/fcxfl/NC7PNWu1A7W78T/O//nFygLIVNTLvOXuzZLUu9B7Wlo1g7W5PtOsRlvOWLgePKfSaDSur7uxyEyzkeRh40yNgNnYqo5XHNbYPDMSz/OcT5hKbJ4aRz5mWtGeb1S5mGlJe/aRmWOmHYbR5mPX1Kk0G0vV4dnW9f74X4Hrf16xsgAyFd0sBe7eLEm9C32l5Msz22YxENdnmkVrT7BkLL3ZVu7dNEtUm4wqDwEzifUflTlvtoIw2q3pkjAMac8Yg2i2laPnAXP/Qnve4eWp0oig6fCmHO82IdGeaxxF31Oh+9zsascBZlqOrlTw5EfVxo6uf5P1H4fLcaD+Y5/4X35xZAPIVB5ulqS+KhMemIfm/EyzFm2Z4lB2vOKISqPRmHOmq205DSoNR9Nfme3iNaDeCmRSfbRmGZ3r820XmWk5smrZ/Y0TDYLmI3O92jgMw2j96TQyPM/zlrToc1GNSsOReZ7ndNJE6m/Pz/qPwuU4UC3aNM/a5xlXdgCZ6gyAd/tmSeprzUPQbGiVvmCZiy06a03WPg6uPzRTbTKaXote9x6W+tUImo/E97yopYmLCXOz7qWCp6LPNWBU5rzpbKVZg6DZqKYcX3uJ+8BxVB2vNu/f8MP6j8b1xKm5/ydpOhqXkyYSlYbjcnmmGWs/GVhdAJmrOt6WTaI11zjMHJhQUqPt7g0TOwzHU3F4tl07CKPvLueA4UTnf0eTpVJnFodpL0yl4fBcTpgz0zIergfOo0rjMsf/sPKwcY6OA6Nzff3N2hd9Nk6MyjwHunj+D8OwN9OO+7+RuHwOaLWDqLU4SfPRuD3TjA0zk4DVBZC53kwb926WDBInoysWfBW6PTpdDJr2BkBzSR1Hb6aZe9+B/gd9dpsNp3+mVStwb+0lqRl2/j/4HoOgR+Fywtw85HPtH4/rlQZUmo7O9SrD/vZ8XP+HZ9a/0Rd8dknUaYS1H1l0/nfw/r8VhDJfW5LmozHHjosdJ8wzqyfu/0dVcThp2ptnx7GfZxzZADLnerBE6q8047Q6iqrDN8z0so9HxeGZZtHxX/TlMwh4KEtmWjn4wCRJze7tdKVYYBD0CKoOz7NYpC1bLKYcDppIzDQbx5TjzwCN/rmGJE6GVnF9riVB07FVHK40NuvveZ02zRieyzNNo9a8RZ/7/xFVHd4414sBce3PM1YXQOaqOZhpU+OhaSwVh2+YqTSLh8vVJjXmmY2sMwTc3YSp1Ks0Y9PEaCoOz7NglmE8osSpg9+BVjtQs91Jm5A0G545b7paabbYP9eQSoOh+X33AC4mzaPWnFz/R+Z00qRv4yRJk9G4vHm6d/xz7R9VxeGNc6z/ZODqDiBzLu8yN+pUmo2l4mg/+yDszbJih/F4Kg7PtooemrlpHkkUMHEwYC5JrShpxvqPIg+tWdllOh6XZ5rV+ioNynwPhuZywFSiyjAOLrforNOecWwub5ow9y2c+0fn8ky7Oq15x1bNwcY51j/fWF0AmevtMHPvYmmYPuwETUfTC5q7FTRt9H1eqgzHUy27u9t8kaT5WFzeZS71Ks0Imo7G5UpDrv3x6K80CEO35hotNqk0GEdeWnNyDhhdFDR18P6vFzRn/Ufl8qYJNs2Or+JoDEDqrT+VpqNzeqYZmyYmAqsLIHO9XubuXSyNGjdNY3F1plmtry1PwSdYNg6X55rQom08Lrfnk3ozzVj/0bhcaVIjYB4L8+8Xyr3AmfkOTJX5DozC5SojSao1Ot9X1n90Ln8HqDQYX9XpoDlJ03G5nDStsf5jc3mmGc//k4GrO4DMudyWwTA3zVVumkbi6kwzHpbi4/JDMzvNx+Nya06pvz0jt9WjiGYaNdy7B6ixyzwWBd9TqdDZeOJa4Iz2fOMxx06jFSgI3KoylFj/OLg80yqqNOAaMLKKoxsnJdrzxaEXB3Lw+KdF99jM+b/ZDtV27B6gRqXZRGB1AWTO5R1GBrvNx+PqTDMqDOPTX2kWONaei8D5eFytNDWazDQbi6nQcLHKoMYu09i4unGC9rzj6d9s5uI1gE0z45squnsNYP3H53LSlOfA8bncbSBKmnL8j6x/HqBr1WZsnp4MnN0BZM7VQIkRhCE7zcbk6kwzKgzjk4f2XARNRuNqpanREjPNxuFya1YT5OcaMD5XA2e1BpVG4/B9L7p3drnalPUfncubJ03SbJr1H5mr536JoHkcohbtDj4DUGk0Pt/zosSZa7HAqD0vSfNcY3UBZM7lm2Wp01LGIGg+GlcrTZhlEJ+C33fT7FjgzNzkEzQbTSXqZ+/Ww5JBpdl4zHHjYsCcB+b4uBo4X2Sm2dhc3jxH0mx80fo7eA9gZtpVOf5HZs79rSBUq+3Wd6AebZzhHmBUZtNRox04156POEA8XJxrFgShGm02T08Cjm4AmTMXShdvlqXeA3Op4Kngexl/Gje5WmlSY5ZBrKLguWNB06g9Cw9NIzEPGy49LPVrhp3zPu3ZRmOSDTXHZhqFYUjAPEauzjWhPdv4ompTB68BJtnP+X90Lm+eZKbd+MoFX1738dm1838vacL6j6qSg/Z8XP/H4+LGmXpfzJLn/3xjdQFkrlz0ZVJNLlYa1GjNMDZXZ5rRnjFerifNCJqMplJysy2H0aLSbCyuzjRqtAOZFB8PzOOrOnv+p9J4XCbh5GK1KZWG43O1yjQMQ5JmMfA8r7dpwrHvQG+mFfcAo/L7Oo24tnm2TnvGWEQdRxw6/ut9m+Z9Ns3nGkc3gMx5ntcXNHXnYmnUm+wyHZezM80YAB0rFwMnnWoTdhqOo+JgW44eT02RNBuHqzONzHFf9D0VC1wDxlUtuRc0kZhpGQen5xpy/R+bi1UGktRsh1E7OdZ/PK7OtWKmVTxcHdNQi1p0c/yPw8UWvcwznByc3QFYwdUHJokH5jj0zzQKQ3fac9WoNIuVi5VmrSBUOyRoMo6Kw+d/rzItifaM44paNDr0HSBZEi9XK82oNBmfq88AAS1aY+Fqe0bzeQu+p1KBSoNx9NrzuvUdqJM0iYWr1wAqzeLhYqVZjU3zE4MVBmAFV2+WJeYZxaG/UsularM6M81iZQapm8HqLjABU98TQZMRuTgA2ihMrZPUWfuiz3lgVCbgvNBsZfxJBscDc7ymHA2amepI2vONzlz7XUuY1lu9Fq0kz0dnzqGtIFRb7txH9SfMPc+dz20jFztNSL371ioxgLFUHKw0bwehmu3upkk2z46lQqUZLMbZHYAVXL1ZlhgCG4ei76vY7Qft0neAAdDxcrHSrL/ahKDJaMzDUrMdKgjcqTSVJH9qvSTO/+OKEiYOJcypMo+Xi226m+1Arag9G4/Vo5pydJ5RrZswLRd9FZhpMrJywZf552vJnfMpVabxcbHSqD9pwnPgeHqbp91Z/0bfZ2Xj9HhcbM/dS5qx9nnHCg9h165d2rRpkzzPW/bfL/7iLw70Hvv379df/uVf6qKLLtJJJ52k6elprV+/Xqeddppe9apX6SMf+YhareF22bZaLX3kIx/Rq1/9ap122mnasGGDpqamdOKJJ+p5z3ue/uIv/kL79u0b+v/vl7/8Zf2P//E/dO6552rr1q2qVCravn27nva0p+mqq67SHXfcMfR73nnnnXrb296mCy+8UNu3b1elUtHWrVt17rnn6td+7df0pS99aej3RD5EM60culk2aNEUDxfnmvUSplxO4+Bm0qzzHSBoMrr+Bw6Xjn9J8ruVZpz/xzPlYKVJLdphztrHwcW5VrW+SuMyc+1GZu6hXDr+JZImcfE8L0o6OJU06yZNq2WO/XFVHUyckzSJj4stWs09YLngy2fTxFhcTJoy135yFLP+AC55wxveoAMHDoz85z/wgQ/ot3/7t1dMYM3Pz+uuu+7SRz/6Ub3jHe/Q9ddfr6c//elrvudXv/pVXXrppbrtttuW/d7OnTu1c+dOfeELX9DVV1+tP/mTP9HrX//6Nd9z165desMb3qAbbrhh2e/t3r1bu3fv1je+8Q390R/9kS6//HL9yZ/8icrl8qrv2Wg09Du/8zt6z3vesywpuHfvXu3du1e33HKLrrnmGj3/+c/Xddddp+OOO27Nz4r8cLo9I60ZYlEp+jpUd+yGOWrN6c5Dvs2cfGgiaT423/NULvpqtALVmm2n2pwVqt2kGef/sbg4z4r2jPHqBU3dCZr0VxtSaTy63kxDd45/iaRZnKolX4vNtpouJc1Y/9hElcYOxQHM+Yqkyfh6M63cuf6bz1rm/n9sZv1dugdgrv3k4Agf0PXXX79iEmlQV199tS699NKBKr5uu+02PfvZz9aNN9646s/deOONevazn71iwuyx9u3bp0svvVRXX331qj9377336hnPeMZA/19brZb+4i/+Qi9+8YtVr9eP+HO1Wk0vfvGL9e53v3ugKrobbrhBz3jGM3Tvvfeu+bPIj15bHndulowaQ4Bj4VqlWRiGlObHzM1KMwLncejNtHJn7SXaM8Zl2sljn2t/nMwx1GgHajvSppWgeTxcbM0m9daf6//4zHfApUqzGsd/bFzsOGPuV13a6GWr6BrgUNI0mmfH+X9srsWApL5KM2JAuccKD2Dnzp36zd/8zZH//L/8y7/oqquuWva67/t6/OMfrxNOOGHZ7y0uLuqVr3ylHnzwwRXfc9euXXrlK1+pxcXFZb93/PHH64wzzpC/wkD6q666Sv/yL/+y4nu2221dcskluueee5b93ubNm3X22Wdrampq2e/dcMMN+v3f//0V31OSfv/3f3/FJNzU1JTOPvtsbd68ednv3XPPPbrkkksUBO6cODEeFytMDHPRpNJgPK7tMlrSloMb5lhEsw0bbnwHJOYaxSVKmDq09hLtGeNSNZUmDq1/nYB5rPqvo3VHAmfmfEXQfDz9zwBh6EbCVOqdrwiaj2/KwaQZx398XJxtzvrHx8X1722cZf3H5eJM22j9Of5zj6e8NYRhqNe//vWam5uLXlurFWG/ZrOpN77xjcteP+WUU3Trrbfq9ttv13333afPfOYzqlarS37m4MGD+o3f+I0V3/fNb36zDh48uOS1arWqf/7nf9bOnTt122236Yc//KFOOeWUZX/2jW98o5rN5rLX3//+9+sb3/jGstff+ta36qGHHtItt9yi3bt367/9t/+27Gf+/M//XLfccsuy12+55Ra9+93vXvb6C17wAj344IO65ZZb9NBDD62YVPzGN76hv/qrv1r2OvKp6mBbBiMqz+aiORZzw+TKLiOz7kXfU3GFTQoYXtSiqRUocKzSgON/PC7OtJIkv9uekaDJeJysMuXaHyvf8/o2zzhyH2DO/yRNxmKeAUK5cw8oUWkYJ/MM4GJ7Ro7/8bk406hGpVlsXKw2ptNIfMzx79L1P5prz6b53GOF1/C+971P//qv/xr9+qKLLtKFF1448J//1Kc+pQceeGDZ69dee63OOOOM6Nc/+7M/q1//9V9f9nP/9E//pD179ix5bffu3fqnf/qnZT/7G7/xG3rhC18Y/frxj3+83vve9y77uQceeED//M//vOz197znPcteu/DCC/X2t79dxWJn/N2GDRv0d3/3d5qenl7yc+12W+973/uW/flrr71W7fbSAMjMzIz+/u//Xhs3bpQkFYtFve1tb9PTnva0ZX/+mmuuWfYa8im6WDp0s2Qw0ygern0HGAAbv/6+4K4k0Kk2iYeLSRNJKkSVZqz/OFxc/+jazy7j2LjWdYCkSTyKvq9SoTMTyKVzAJtm4uNkpRnHf2xcO/dLfe0ZWf+xRTPNHHn2k3pJE2aajc/EUtpBqFbbjThQNNOQ57/cY4VXcc899+gtb3lL9OuNGzfq+uuvH+o9PvKRjyx7bdu2bXrOc56z7PVXv/rVy15rtVr6+Mc/vuS1T3ziEyvOBlvpzz/nOc/R1q1bl73+4Q9/eMmvv//97+uHP/zhQO+5efNmXXTRRcte/8hHPrKkpUYYhvroRz+67OcuuuiiFVsyrvR33Xrrrfr+97+/7HXkj4s3y1J3rlWTuVZxcK2fOQNg4+f77lUaLBI4j0VUaeZQez6J9oxxcXH92WUcP9daNBE0j4+LzwHmPoX1H59pceVSpVmtwfrHJeo24si9v9TXnpFKs7G5WGlWJw4Qm3LBl9fZN+PMd4D1nxw85R1BGIb6pV/6JR0+fDh67f/8n/+j4447bqj3+frXv77stSc96UnyzFmhz1lnnbXizLDHtkxc6T2npqZ01llnLXvd932dd955I72nJJ1//vkrvn7BBRcse23fvn266667ol//6Ec/0r59+8Z6z5U+K/Kp18vYjQul0QpCtbvJYoKm44l2mTnyHWAAbDJcm23FTLN4TDtYaSRJ/tR6Saz/uEyypBWEajqzy5RjP26uBc5InMZnyrG1lwiax2mq6HClGes/NnPub7QDtR1rz07SdHy99pzuzLWk40x8PM9b8h1wgamKJA6Uf6zwEfzlX/6lvvKVr0S/fslLXqKf//mfH+o9HnnkEe3evXvZ6yeccMKKP18oFHTssccue/3mm29e9deSdNxxx8k/wkydHTt2LHvtwQcf1KOPPrrqe672WVd6z8e+TxLvifxy8WZZ6gVMfE9RaxmMpuLYXDsGwCbDvYpDHprj4GKlkdRrz8j6j6dc8FXw3GnPFoZhdOyzyzQ+rlUbETSNj2trL9GeMU7RxglHkmZhGPZmWrH+Y+sPPLvSoo+keXyiuZZhJxbkgmjjFPeAsXBp83QYhsSBJghJsxX86Ec/0hVXXBH9etu2bSvO61pLf1Kq30qtCY0tW7as+T4rve+w7/nY9xn2s2b1niup1+uam5tb8h/c4+LNstS7YaoUCytWkGJwzs00YwBsIlxLntTYaRgLF2daSZJf7STNWP/xeJ6narm7ccKBY7/RDmQ2Q1NlFB9zPXVm00S3PVuVoOnYzHHk0jWApEl8TOLRlaRZrRXIbPEkaTo+33O3PTvH//iKBV8FvxNHcSUOQKVpvFzaPN1sh71nAOJAuccKP0YQBPrFX/xFLS4uRq9de+21Ouqoo4Z+r4MHD674eqVSOeKfWen3Dhw4sOb7Dvuej33fYT9rVu+5kne9613asGFD9N/xxx+/6s/DTv03y67cLEm9BB9Bs/E5V2EUJUu4WY6TS4Gz/pmGPDSPx7VkqSQFkvzKtCTWPw4uJU7NcV/0PRULXP/jUnXoOyARNI3TlGOVZkHQ22nOM8D4zLHvykwzs7mjXOwF+zEe16pNqTSLl2ubZqL15/ofC3P8uxAHNN/RAs8AE4EVfow//dM/1Ve/+tXo16961av0spe9LNa/Y7U+vaP28E3iPVf7sza95xVXXKGDBw9G/91///0jfzZky7WkicRMkzi5NgQ6Kstnh1GsXAqc1dlpHJv+hIkr8wxaKnb/V8h5IAYuJc1qtGVLhEtzrcIwJGkWI9cSpv3PKrTnGp9r7Rk59uNXcShp0n/+n+Y7EAvXZpouNFuSSJrGxaXjv94kBjRJimv/yOS47bbb9Na3vjX69bHHHqv3vOc9I7/fhg0bVny90Wgc8c/U6/Vlr23cuHHZ+z788MNjvedj33e1zzo1NRX7e476OVdSqVRWrbSDO1xryyD1LuxcNMdn/g0b7UBBEMq3fOdmNACYtY+Vi4HzUsFjp/GYzENnOwjVbIcqF+3/92x2954VFdCeNwbRse9AteEibVkTEbXnceD83wrCaAYvydPxRQnThhvPAOY8VS361t+vusAcQ6E8eeXlsQfbkDSLn0uVJs127/xP0iQevc2z9l//gzCM4lWcA+Lh0pgOzv+ThSe9Pn/2Z3+mWq0W/fq6665bdVbYWrZu3bri63v37j3in1np9x77Piu977Dv+dj3GfazZvWeyDfX2jJIvQs7AZPxVfp26poqLpvVWqx9EqoOtemj0jQ+pYKvYjfw6ELCVOrtiC95blTG2S469h1Y/95MS479OLlUaWy+pwXPU6lA0mRcrj0DRNd/AuaxKPXNNDKzQm0WJU3ZOBGbqkObJsz5v+h7KtGeLRbmfsqFzdP931ESJ/FwqeMUrVknC2f4Ps1mc8mvf/Znf1ae5y3778tf/vKyP/u3f/u30e+fdNJJkqRt27Zp+/bty352586dK/797XZbu3fvXvb6E5/4xFV/LUm7du1Su73yCWaldoXHHXfckmTUSu+52mc9UgvE/vdJ4j2Rby7dLBtRiyYCZ2Mr+J7KBYdK86kyTIRL7bkWadEWK9fmmjW7DRtKnv3fVRe4WGVKwDReFYfO/2amUbXsU2kag2ieqQP3fxI7zZNgvgOFKQeSZk2CpnFzKWlC0Dx+UaW5A9eAhb6kOZXG8XBpTAfX/8nCk17CnvrUpy577aabblpxXsett966pNLtSO+x0nvWajX98Ic/XPZ6EAS66aabRnpPSfrOd74z8OtbtmzRqaeeGv361FNPXbFSb5j3XO2zIX+im2UHqoyMqD0jgbNYuHTDZB7qKtwwxcqluSYEzuNlHj7MnADbmUqzouw/X7nApfaMVJkmw8VKM4Im8ei1Z7R/7SXWPwnmfOo7kDSrsf6xc6nSJJpnxfrHxqX2nFHSlPWPjUszzRYaneN/mqT5RCDKk7BXv/rVy1579NFH9YUvfGHZ6x/5yEeWvVYsFvVzP/dzS157xSteoWJx+Ti6D3/4w8te++IXv7hi28PHfq4nPOEJOvvsswf6TPv27dONN9647PVXvepVS37ted6y1yTpxhtv1L59+wb6u8455xyde+65y15HPvVuluy/WBq0Z4yXS0FzU2lWpdIsVi4FTaOgCZWmsXCt0qzXntH+B3wXmPV3YZe5eajn2h8vswGhFYRqte3+HlBpHK9qX5XhSptLbcP6x8/cS/lT6zP+JGsjaRo/l2ZakjSJX2/ztAPr3/2OTpeXx2QxGieTpiTNJgKRvj4nn3yyzj///DX/m52dXfZnt2zZEv3+E57whOj1F73oRdqxY8eyn//VX/1V3X777dGvP/OZz+jP//zPl/3cy172Mh1zzDFLXjvmmGP00pe+dNnPvvvd79anP/3p6Ne33367/vt//+/Lfm7Hjh164QtfuOz1yy+/fNlrX/va13TVVVep1eoEsA8cOKDXvva1WlhYWPJzhUJBb3zjG5f9+Te+8Y0qFJaeTA4fPqzXvva1OnDggCSp1Wrpqquu0te//vWBPhPyq3ezbP/F0qDSJF7TjgTNwzCMZtpQaRYv8wBabwXRkG1bUW0Yr2mHqgwlqWkqzUiaxcLJ9oxsmohVueDLdDq0veuAOf8TNI2HCZi1w1DNtt3XfqlXEUfQLD7mOdCvLo+12Caaacb6x8apoDntOWNH0nSyuZg0Zf0nA6nxPldddZWuuuqqNX/uWc961rK5Zj/7sz+rD37wg8t+tlQq6dprr9XFF1+85PV77rlHZ599tk477TQtLi6uOOdrw4YN+rM/+7MVP8Of/dmf6cYbb9TBgwej12q1ml70ohfp+OOP18zMjO68804FwfKbjve9730qlUrLXr/sssv0wQ9+UN/85jeXvH711VfrPe95j7Zv3667775bi4uLy/7sb/zGb6xYqXbOOefo13/91/Wnf/qnS17/3Oc+p+3bt+uUU07R7t27V6w8e+pTn6rLLrtsxf//yCeX2jIYUeKESpNYmIePBcuTZo12ILMRmqBpvPpbndaabc1U7L1VoT1PvKg0m2xRa1YH1p/2jMnwPE/VYkGLzbZqzbZmLT7/EzSLV6ngqeB7agehas22ypbfWxE0i5/5tyxQaTaRqg61Z6s12DQRN5M0cSFpukDSNHYujegwcSoqDSeD3XejOfGzP/uzetvb3rbs9SAIdMcdd6yYMJuamtLHPvYxbd++fcX3PO644/TRj35UU1NTy37v/vvv1+23375iwuztb3+7/tt/+28rvmehUNDHPvYxnXzyyct+b9++fbrllltWTJg9//nP1zve8Y4V31OS3vnOd+r5z3/+stcXFxd16623rpgwO/nkk/XRj35Uvs9XdJJEN8sO7DAyqDSLl7n5sD1pZpKlBd9TscDax8n3vOh4sv1cwPEfL5cqjaRepVmJmWaxmI7aM7atb89mjn3mmcbPlfM/7fni5fVd+124Bixy/Y+dWzPNSJrErb9Fq+2imWYkTWITXfsdSJrSni9+/ZVmtj8DsGlqsnCXl5KrrrpK73//+7Vp06Y1f/aMM87QF7/4RT3vec9b9ecuuugiffGLX9QZZ5yx5ntu2rRJ1113nd761reu+nMnnXSS/uM//kMXXXTRmu9ZLBb1pje9SZ/61KdUrVaP+HPValWf+tSn9KY3vWnFWWyPddFFF+k//uM/dNJJJ635s8gXl26WDXabx2vakUoDswuqYvlOaFdVHUmemPZhHP/xoNJsspnjKFRvY4Kt6gRME+PKvWCNneax6629/dcAKs3jFyXNqvYnzQiax8+l45+gefxcufZLvfWfZv1jYzahhaGsb9G8EM20Y/0nAfWEKXrDG96gl73sZfrQhz6kf/mXf9Ftt92mRx55RMViUUcffbTOP/98vehFL9IrXvGKgZJLkvRf/st/0Q9+8AN94hOf0D//8z/r29/+th5++GE1Gg1t27ZNZ555pl7wghfoda97nTZv3jzQex533HG64YYb9KUvfUmf+MQn9OUvf1m7d+/W/Py8Nm/erBNPPFHPfe5z9fM///N6/OMfP9B7lstl/cVf/IUuv/xyfehDH9IXvvAF3Xvvvdq3b59mZ2d17LHH6pnPfKZe8YpX6FnPetZA74n86fUyt/9mWZKCIFSjTfIkTlF7xu4OPlvVW2aeDTdLSZgqFXRATesfnGpUGsTKtUqzFjPNYlXwPZULvhrtQIuNttXHldkJTWvm+LkSOKXSKH5TRfeCpiRN4mOOJdsrzdp9z38kTeJjnqXrrUBBGMo3Ay4txEyz+Lly7ZeoNExC0fdU8Dy1Q7tbNLeDUI3uxj7WfzKQNBvBl770pZH/7ObNm/WmN71Jb3rTm2L7PMViUa961av0qle9Krb3lDqz2+JOYJ1++um6+uqrdfXVV8f6vsiHStTL3P6bZWnpTniSJ/GYdqTSxAR0aM2VDFeSJwRN4+VapVmTSrPYTZULaiwGWmy2tXZvhmyE3Qd6iWM/Ca60Z6TSKH7VshvXfolOE0kw/5YFy5Nm5vvpeWyajFP/sdRoBVYfW1Saxc8cS60gVCsIVLR4TAvrHz/P81Qp+VpotK3uNmHW3uf8PzFYZQDWeOzNsu3MTvNywZfv253gc4UrM816VQZcRpPgTHtGKs1i5UqyVOokTlrMNIudC9+BZjtU0O0cw7EfP1daNC2SNIudKwnTVhBQaZQAswHR9vaMJmhaLRbkWb7B0yUF31Op0Pn3tP0cQKVZ/PqfqeuOXP9pzxevaK6Zxcd/VGVY4vw/KYj2AbCGSzfLkthpnoCpvl3GNg+BpS1Pssy/a83i5Gk7CKOe6wTO42HWvdkO1Wrb/cDcaAcK1bleFT17z1WuMddTm6sNzbW/4HsqsmEmdi5smgjDULVGt9KI+4DYTDnSnsskdKk0ipcr7RmZZ5gcFzZNtNpBdP/PTKv4eJ6natH+jRNBEEbfT84B8TIdfMzmZBsRA5o83OUBsIoLN8tGr0UfF824mIBJGNr9HTCVcKYyDvFyodqk/4GOoFk8ygVfJgexYPHaS73zU9Csq0DSLDZTDrRnq0UzLX12mSagGs21sfc70GyHanc39lBpFB8XEqYSlUZJMevvV2dl8b45qkwTFFWaWHz+N+vve7J27pKrTEylZnHHof7rE5sm42X+PW2uNCRpNnk4ywOwSpQ0s/hm2aj3Bc4Qj4LvRQmIhUYr409zZIsN2jIkKao2sThwFlWaFn3r5y+6wvM8Z+aamfUPavMZf5J8cSNhziyjJLnxHaDaMAlTjmyco9IoGeac6nm+WrL3uGKebXIqDrRo7Z9nRdI8XuaYqtu8/n1Jc57/4mXuAWwe02E2dU6X2Dg9KbjSA7CKC2X5BoGzZEw7UGlgEnokzZLhQosmjv9kuBAwl/qSZoskzeIUHfsWPzDXo4Apx34SKi7sNG4SNE1C1YFrv0R79qQUfE8Fdf5tm6G9/7b9SRPEy4lKE5LmielVGlq8/hz/iTFxFRc2TnP8Tw5770YATCQXbpYN89Bc4aE5VlNld3YZccOcDBfatEYt2vgOxMqdSrNue8bFuYw/Sb640J6RKoNkuZA4pz1bMlxYe4n1T1KxmzRrWZw0o9IwOS5sniVpkhwnKg05/hMz7UIMiKTZxLH3bgTARHLhZskwiT2zKwrxMHPCbA6aLzLTLFEuJE4InCfDlaCpuUa1qTSLlQvrb3ZAV7j2J6Ladx8YWjrYyFRCVsuc/+NUdeQZgKRZckzSrGlxmIr1T05vTIO9m+bYOJkcFzZPkzRJjomr2Jw0M+f/aY7/iWHv3QiAieRCWb5BpUkypi3vZx2EITPNEmYeRBvtQO3A0qApLdoSYXrE25wwlXoPTbRnjJcLCfM6CfNEmXNqEErNtp3nf4LmyTBr32yHagX2PgfUGrRnTkrJgUozjv/kuNCilfZsyYniQC6sP8d/7KYdeAbg+J889t6NAJhILtwsG7RnTEbUnrFpZz/rWrMtE8bjhjkZlaIfjYC39VzATLNkuNCeT+qfaUZ7xjg50ZqVYz9RRd9Twe9cAWw9/y+yaSIRS6/99p4DaM+VnKJDM82qrH/sXOg4Q9I0OdH6t+xffzbOxs+FmWbMtZ889t6NAJhIrrRmkaR6i/aMSbB9l9FC3w4z3/fW+GmMwvO8KBhpa/KkRrVJIqL2fJYe/4YJ6LZrhzL+JPnSX2Vqa6UJVabJ8jyvN9fG0sCZOf4JmsZrybXf4msAQfPklBxImnH8J8c8U9ct7jjDTLvkuNGesZM04fiPn2nPWGvZ22mG6//ksfduBMBEcmGXucFMo2RMWT4ElrL8dNieQCdwngwqzSZbpejL6+5FMC3QbBO1Zi5y7U+K7feCBE2SY/u1X+L6n6SiOsd8y9IwVRiGHP8JcuH4X6A9X2Js3zAjUWmcpGqp9wxg48aZVjuI2oaz/pPDzrsRABPLhbJsozfXqpjxJ8kXM9PI1qSZ+VwMgE2W7RVH7DROhvn3tPX4N8wDPTPN4uV5Xu/YtzRoRnvG5NneqrvWIGmSFNvXXiJpmqSiOs9/zdDOTg7NdhhVQBA0jZ/tGyYkkiZJqriw/sw1T0z/M4CNscCF7rFf8DyVC6RSJgUrDcAqJgF1uNFWGNpZli11dpqY1hHcNMXL/vaM9LJOg+0VR9FMQ6pNYmX7uhvmgT6oUWkWN/uTZiRMkmZ7tQFB0+TYfvxLdBxIkmnP2LK0PaP5XhZ8T0VatMfOtGesteyNAyxSaZaYqNLMhfM/65+IaYs7DvVf+z2P8/+ksPNuBMDEMhfKdhCq0bZ3l5HZaeJ7BM3jZr4DjXagloXfAYJl6bB9t2mNneaJMMdVw+J+9lLvPNCm0ix2NleZhmEYzdqgNXNybD//U2mUHNvXvtkO1OpemzgHxM+0Z2xaGqbqP/YJmsbPHFNhKCvjAO0gjDbN8hwYP7N5um7pM0AQhKqx/oky34EFCxOnC1QZTiQ770YATKxSwY/KnW3cYWIs9LVm5KEpXuWiL7N508adxgu05UyFzbvNwzCkRVtCqn0zrWxce6nz0NzoPjTTnjF+1bK97dlaQah2aALmHPtJqVp//membVKmLG/PWOvbNEd7pviZ9oy2VprVqDJMVLHgq9B9CKxbmDjvPy9xDxC/aqkXA7CxPd8i6584m0e1sGFqMtl5NwJgopkHkYW6nQ/MEi36kuR5Xm+XkYWJU3YZpcPmpFmzTeA8KZ7nRe15bKw0kpYOKA9qhzL8JPkUzTOw8Niv9c0zoDVXckwyqm7hd6DRDmQ2wHP+j1+13Fl7G6/9EpVGSTPtGZuWJs0ImibP5va8/evvc/zHzvYYAOufPFfaM2Jy2Hk3AmCizVi8w8QwCT0SJ8mYsviGyXwvuWFKVtXi3eYmaeJ7UqnAQ1Pcpi2fa2aqDAtqS6F9O6FdF1WaWHj+N2tfKfkEzBNkc6WZ+Q4UfU8lKo1iZ/O1X+oFzarcAybCtGdsy7eyPdsiVaaJ6801s+/+inlWybM5abJA0iRxVidNWf+JxNUegHVsvlgatOhL1rTFM20WqTRLRbTb3MLvQK81FzvNk2DzTCupFzQzO+IRrymLk6b9xz6SY4KmdZuDptwDJGLK8plm5nMRNE9GUW2F3c0oNiZOSZokz+bEOXOtk2eerw9buHma4z95NrdnXGh2O02x/hOFpBkA60xX7N1hZNCeMVlRpVnTwhsmEqapsHmuCUGzZNmcNJF6LeOKJM0SYXbw27j+zLJKh83teWnPlqyoPa+Fay+x/knz1Gt7bOf9H0mTpFndnpGkSeKizdMWjukw53/iP8mh0hC24YkPgHVs3mFkMNcqWVF7NstumBqtQK1uuxjWPlk2B02pNkkWlWaTzepjv1v5ZAL7SIbNQVPO/8kywSgb117qb8/H+iclWOwmzWysNCVpmriKac9oYbWpmbVqumEgfjZXGpE0TZ5Jmtr4DEingcnE2R6AdWjPCFu/A+ZhmVkmyTMPJM12qFbbrgdnqk2SZXulWTTTiKRZIqYs3TQhkTBJi/n3rTcDhaFdc40ImifLXFfrrUCBhTOtaqx/4oLanCQ7E6cc/8lzIWkyXeL5PykzFTtjAFKvAw5Jk+T0b5wOLL3/4/ifLER7AFhnxuKbZYP2jMmyNWjKuqenXPRlxoXZttvUfB4C58mwvdKsRqVZovpbs9qWMKlHxz6PUEmqdP99Q9k314xNE8nqr+Kstew7x9YarH/SokozG5NmDTZOJM0kTQ5beA/ITLPk2dxxiEqj5JlngFB2XQPCMGT9JxR3ewCsY2uVUT/z2cyNPeJlBqwuWHSzJFFhmCbP86ydbUJ7pmTZX2nGTLMkmQfmIJQallWZmu9khWM/UUXfV6nQ2TVhU9BEoj1T0nzfU6Vo2nPadfxLBM3T0F40lWb2rX8005b1T4zZPHu4bnHShPN/YqJKQxtnmkWVhqx/Unzfizal2BQLbLZDRnRMKJJmAKzTPwDUtl3mktRqB9HOZy6aybC10owdRumydbYJlQbJsr3SjJlmySoWegkT274D9W7lS7XIsZ+0alRxaFfgPNo0wX1AYqoWzzWkPV/ygtq8JPvu/YIwpD1nCqYtbs9XI2meuBmLN0+zaSIdNm6gZ0TH5GK1AVjHJKLaQaiGZW15pF71k+8p2g2LePX3s7cpcdqrNONmOQ1TlgbOoqRZke9BEuyvNGOmWdLsPfa7VQYETBNXLdm6aYLvQNLMhhTb1l6iPXMagkU7k2b1ViDzRML6J6e/0symZ0Cp9xzI+T8505XOv22jHahpWbcB1j8dNs41ZOP05CLaC8A6xYKvcsG+smyjv0WfZ4YuIVb97blsmmfCTLN0mcCZrYFzgibJ6E+Y2DYEWqI9YxpsrTSp0Z4xNaaaz7a5VlQaJc/W4z8MQ9Y/Be0oaWbP/b/Um2dXLvoq+Dz/JcVUmbSCUM22PfeAYRhG1yMC58kpF3rHl01xoHYQRjEJ1j9Z/V2nbEEMaHKRNANgJbPLyMYhsFw0k9efOLWpPRc7zNJl/p1rFn0HJNGeJ2H9yUjbdppLvSA+7RmTY2uLXrP2tGZNnrXtGRvMtEzalKVVhs12qHZ3pglB0+TY2p5xgXu/VJSLvRbNNsUBaq1AZh8X34HkeJ5nZaWROR954vqfNBvbM3L+n1w88QGwko07TAwzmJakWbJs/A4s9lUZInm2tunrVZtwG5WEgu9FrW9tS5qEYdjXntGeh/m8mbIwYdK/9rRmTZ6N7RmXVBoQOEmMrQlT810s+J6KVBolJmrPaFmVKRum0hMFzev2fAcWqTRMjY1zzRb6Nsz4dBpKlI1JU9ozTi6iPQCsZOPNkrFA4iQV5qZkoWnPDZPZZUTCNB02Bs5aQaBatz3HDOeAxNiaMG0FvUoDKs2SY+NMs/4qE3YZJ8/GuVaNvkoDqg2TEx3/lj0D9LdmpD17coLFOUl23ftJBE3TNGNhx5lFuo2kZrpvrp0tFokBpGaKjdOwCHf7AKxk4w4Tg/aM6Zi2sD0Xa58uGwPnZter7xE0TZLtQdOC58mXPbM28qZq4fnfBO/KBV/lIsd+0myca2U+S6ngqVjgO5AUGxOmEvPs0hLUDkmyd/2590uejZtnSZqkx+ZuM5z/k2djDIjr/+Tiig/ASjb2MjZ6lWZcNJNk2y6jIOi15mKXaTpsbM9lAuczlSI7zRM0bWmlWa0vaMbqJ2fawoSJ2fFsZq4iWeb8X7eo2oSgSTpsnWlm5qtWy4RQktTuVpq1glDNNsf/JLKy0oikSWqmK/bFgaLjnxhA4myMAxL/m1zc8QGwko03ywbtGdMxXbLrhmmRAcCps7HS7HC30ozWjMmytdIsmmnFOSBRVQvXn2M/Xaaaw6bzf6/ShOM/STa2ZpZImqQlbCzKV2ftbXoOrNGeMTUz3aSJVe0ZSZqkZsbCjkO0Z01Pf8epMLSjqwfrP7lImgGwko1l+QYt+tJhW2n+Qt/NEgOA0zFl2XdA6gVwZqg2SZStM81qBM1TYWXCvMGxnybzHai37Emc1BpUm6fBxtacEkmzNFU8kzSz5zvA+qfHxjiAeRbh/i95dlYade4BOf6TZ7pNBKE994Cc/ycXSTMAVrKxLN8wn8nsgkMyovaMTTt2mUU3ywTLUmMqDWxq0dPfnhHJsbfSjJkmaTDnWZvas/US5hz7aagW7UuckDRPR9SesdW2Zpe5RKVhmspe59/aykoj1j9xpqLbpkpD8zw6zfonzsaOQ8y0S0+xb3awDbHAMAzZND/BeOIHYKX+KiObHphbQRDteOGimSzbKs0W6WWdunLBl98t6rMleH6IwHkqeklzO9bdYKZFOvqrjNqBHfcAh82GGdozpsIkJhqtQIEl3wGC5ukwmxLCsLP+togqDVn/xJmk2SGLgua0Z07PjIWbZ2nPmJ7+9bclDrTA/X+qTHLahhadjXYgcxvK+k8ekmYArGQSE+0wtKYsW+rdMPmeVClyCk2Sba0ZTPDezFpD8jzPs262yUI014ib5iTZ2J5P6iVOpkmaJqpS8mWa4NqSMKc1a7r677FqLTu+AyTN0lEs+Cp2d8zYdA0gaJ6eXnvG7AOmBjNt0tPfntGaTRMkTVJj1r8VhGpY0mmE83+6bGrRaj5DueCrWCD+N2lYcQBWKvp+FDCx4WJpLETVRkV5zLVKlLkptaXSYIGH5UzYljw51N3xNkvSJFE2zrOTegG8WaqNEuX3JcxtOfajpBlrnwrf96KKI1vuA2nPmp5ei1Y7AqYS7TnT1GvPaMex3wqCKHhP0iR5U+VCtHHGlnsAcy7iOTB5pYKvUqHzDbDl+r/YFwNC8mzaPM2GicnGHT8Aa/V2mNizy5B+xumpFn2ZvKQND0ysfTZsm23FXKN0RDNtmva0ZpH623NyHkiaSUxYc+wzzzR1ZnOCLdUmVBqkx7akuUSlYZrKllWamYSJR6eRVPieFwWobZhrF4Yh5/+URUmTevbXgHbQ63zE+qfDpjEdXPsnG1d8ANaajoYAZ3+xNBaYa5Uaz/OimxMbEqesfTaq5V7yJGutIIgCJ1SbJMsES4JQVrXoNcEbEifJm7Lo2G+2g2i2EgnT9JjjzJa5RrRnSo9Jmttw/EudoDmVhumpmEozW4797jNAtVig00hKZiyKAzTagdrdDVyc/9MxY9HmaXPu98T5Py02bZ4nBjTZOOIBWMumi6VhdjtRmp8Oq3YZ0ZYhEza1ZzTHv+/x0JS0ou+r3O0bb8PaS1IQhtF3gPacybPp2DeB26LvRd9LJM+moKnUqzahPV/ybDr+pU7Q3HQKZ7d58qL2jJYc+yTM0zddsScOYJ4Bi76nEvcAqbCpPZ/5DNUSSfO02LT+tGecbJzxAVhrxqKLpUGLvnRNWTgElhumdEUt2iwInPVXGfHQlDzb5potNtoK1dlpynkgeTa1Zu1vzcixn55ZiyrN+iuNSJokr1qyp9JU6p2HSgVPRYLmiTPtGRt9Vb5Z4thPX7RpwoJ7AJKm6TNJUxvac5r1J/6THptiQLRmnWzc8QGw1lTFnoulQXl2uswuo6yDpv297Fn7dE1ZFDgzO55pzZgO2yoNTOB+ulKQT+IkcVHS1IL1XzCz7Dj/p8q0wrShRVu9FchMV6TSLHk2Jc0lqgzTVvRClQqd66wVQXOCpqmLOs5YcP5n/dNn0+ZpKo3SZ1PHqYUmm+YnGUkzANay6WJpLNCiL1XTZqZZxkHT/l720zwwpcqmxIkJ3DLTKB22VZpF68/5PxVVm479vkozpMdUmlkRNO9+D8sFXwWfpHnSbKoylPoqTbgHTE0UNLegRaNZ/2qZ8FlaZipUmk2yaYsqjUwsivN/evrXPwzDNX46WSRNJxtXfQDWsmmHkUF7xnRNWZI4Nd/BcsGnLU/Kqt3vQK2RfXue/vaMSN6UJUlz4zDzzFJlU8L8EAnTTMxYlDghaJIum9ZeImmWBZu+A6x/+kxltw2VxlG3EdY/NdM2rT9J09SZDeqtIFSznW3SbIHz/0Qj8gfAWjbtMDJoz5iuaUsqTZhnlh2bAudRe0aSJqmwrdLsEEnTVE1ZlDBfoMo0EyZBvVBvK8h4p7FpEWzmbCJZUZWhBVVGklRrmEojzgFpmbFoplGN9nypm67Ys3mWpEn6rFp/kqapKxd9FbtV/Vlvnl6k09RE464fgLX62zNmXZYtSa0gUL07jJqgaTpsGQJLhWF2+pNmWZ8HDjPXKFU2JUwlEidps2n9ac+YjalyQZ6kUNknz6P2bATNUmGSZovNtlpB9olzKo3SF7Xno9JkIs1Y0m1E6l1/OP+nx6Y4EMd/NmzYQB+GIes/4UiaAbCW2c0RhFKtZcEDc/eC7XtSpcjpMw3TJTt2mS1SYZgZ84DaDkK1gmwfmkylEe350hElTSzYZSr1tehj/VNhY8Kca0C6fM/TdDdJnXWLtlqzcx9K0iQd1VJvdpwN1WYkzdI3W7an2pD1T5+JAzTboRoZxwEWCJqnztxvBaGiTctZoeNMNqYtGNVSawUyjyCc/ycTUV8A1ir4XpScWrBgl+HhvtJsz2MIfBqi9owZB025Wc5OqeCp0D3esq44McPoac+QjqmyPZVGUu8aMMv6p8Ksf9uCeQYmaUbCPH2zllSbEDRPl+d5UaVJ1glTifacWbCp0swkzak0Sk+56KtU6CbObWnPxvqnpuj7URwo63NAb/25B0zTtAXVpmbtK8XeRh5MFu76AFhtxoIdJgYt+tLXHzRttLPbZbbAzXJmPM9Ttdy5XalleB5oB732DATO02FbpdlhKs1SVfS96AE1y8RpKwiianfWPn3mPjDrxAnVhumzJWEq9a5DJE3TY8tMszAMWf+MRJUmGVcb0p4tG7bEgVj/bNjQnnGRjdMTj6QZAKvZcLE0FmjRl7pSobfLMMvAOe0Zs2XDbCMTtPE9dpqnZcqSSlOpkzRdiOZacR5Ig+d5Vhz7JljXX/2O9PSqTbK9DzxU61YbVkmcpsUkzbJOmEoETbNgy7HfbIdqd+9BWP90mWrTrBOnJE2zYUMcqB2EUXtIjv90maR5ljEgs2meY39y8eQHwGrTluwylPqTZgRM0mRDP2uqDLNVtSBw3l9lRHvWdEz1zbPLuj3fQl/SlAen9JgARZZVpof7zv8c++mzJXEy3/3711VKmX6OSTJTtWPtJdrzZcFUmTTaQaYzrcy9Z9H3VCoQPkuTSZxm+QzYbAfRTGWSJumyIQ5kjn/Pk6psnEqVDUlTs/7EgCYXRz0Aq9mQMDEWaM2TCSsqDZokTLNkvgMmaJUFs9N5hu9AakoFO9rzSb31Z6Zlumw4/5u1py1rNmxo0RaGYZS4odIsPVF7xlq2SbMgDKOZZmyaSI8tM60Wo3l2rH3azDN3li1azfr7nlQmaZoqG+JA/VWG3P+ny4aZZsy1B2d9AFaz4WJp0J4xG1bsMuKGKVNVC2Zb9SrN+A6kZUl7vow3TpiAHYmTdNmRNGPDTJZsmGu12Gyr3a004ByQHluqDOutQKbWmcRJumZsOP55BshMtP5ZJk371p+kSbpsiAPRni87UzbEgMzGaebaTyySZgCsZkPCxKA9YzamMr5hbgVB1MucoGk2epVm2bdomyFgmqrpcvZJE6kXtCVpmi4bkqYc+9masSBxcqgvcWqqX5E8G9Ze6iVsqkWf9U/ZbDn7uWZUGWbHhjjAIuufGdPdYyHD4595ltmxqtKQ9Z9YJM0AWM2Gi6XBXKtsREHzjL4D5u/1PalCL/NMTFmQOKE9YzZsSJpIS2faIT3Vcueca8Wxz9pnwlQb1ZqBWkE2LXoP1ag0zUKvyrCtMMxurmW0/rTmTJ1NM41ImqRvpkyl4SSz4vg3m6Y5/lNnYkCNdqBWO5v7v4UG5/9JR/QPgNVmzA4zC4aA054xG1knThdoy5G5aqkbOKc948SxIWEqkTjJStbnf6mv0oxrfyYqfdU9We02n++e/9eRNEmVud62wzDTa4BZf5Km6bOiPStB08yYey4qzSaTVZWG3AOmrlL0VejGXhYyugeI2jOy/hOLpBkAq0UBs2a2u0z7W/QRNE1X1pUmi7TlzJwNc01o0ZaNrI9/wwTsZjkPpGpd93ibrzUz+wxUGWbL87zMrwFUmmWj6PvRNSDL9nxm/ddVS5l9hkllQ4tO057RVD4jPf3dRoKM4gC9SiPO/2kzlYY2rD9Js/R5npf5XDPWH1z5AVjNXKDCMNtKA1r0ZSfaZZbR+i/QliFzJlA1X29lljynPWM2ooelZrbVxocaVBpmwVT2zNcyTJhz7GfOVPllVW1iAva050tf1glTSZqvd5L2rH/6ZiyYaXaYpElmpsoFeZJCZbh5kkqjzJhNE1asP3GATExnONs+6KtyZ/0nF5FfAFYr+F7Umi3b9ky9aiNa9KVrKuOZZiZYz8NSdqIWTUE2LZr6/16SJumyrdKMaqN0mSB1vRWo0Up/ngHHvh2yrjaJKo0qVBqlzRx3mSbNovXn/J+2GQtmGplK53VTrH/a/L5Kk6y+A+b+08QjkB7f96LnABvGNCB9WVaa1fpiDiTNJhdnfgDWs2GmidndQj/j9E33zTQKgvSrjBaZZZe5ou9HlQZZVJyYB3Xf46Y5bTbMNGsFgWrNTsKG9mzpqhQLUXV3Fi0aFzj2rdCba5TtTDOO//TZUGnWa8/I+qdtxoKZZiRNs2WqDbOaaUmlWbamK9lVGkm058xalnPtFvoS5r7PpvlJRdIMgPWyLMs2FkicZKbaF6jMInC+wEwzK8xm2KbNPKjPVKg0TZsNlWZm/Qu+R3veDETHfgZBU5Okoco8W1GlWQb3gWEY0p4xQ7MZJ02WrD9Jk9SZhEmzHWZSbVxvtaOZ1sy0y8Z0xtWGCyRNMjWT8ebpBZKmmTLxl8UMkuYkTCGRNAPggCx3mBgkTrLje9m2ZiBhagcTrMhit7n5O5lplD4bKs1MoGamXCBxkgGzu/9QhlWmtGbMVtSiLYPz/2KzrXa3yp2kSfpmqtlWmtVagVpm/Umapq5c9FUudEJWWSRNzHWnUvRVZtNMJqK5dlnPtOI5MBNZxoHaQS9ZTxwgG9MZzrbm2IdE0gyAA7Juy9D5u2nPmKXpDAPnpsKRG6ZsmcB5Fi3aevOs+A6kbbqbMG+2Q7Xa6e8yl/qSpgTMM2ES5pm0ZiVhboUsW/Qd6rv/K9CeJ3VZt2c0SZOpUkFFn9BJFrJMmpsKZ1pzZicKmmew/v1JE1o0Z2M6w5l2Ju7geaLTREZsaM9IDGiyceQDsB7tGTGV4XegV5rP2mdpXYbtGXuVRgRN0lYu+jJx6qyqzUyLPqpMstE79rNImPdasyI7Wc41MkkTjv9sRO0ZM7j2S73zDkmT7Jh7rywSp9E8M1ozZiY6/2cQNI+SJurMNUL6stw8bWIAUyU6TWRlOsP2nMSAIJE0A+CA6YzbMki0Z8xaVGmW8ncgDMPogYm1z1a21SYEzrPiZdyeVeqvNGT9s2BHwpwH5ixlOdeISpNsmaRZrRVkUm08zzyzzJlrbxZB8yhpyvpnZibDSjPz3FklaZIZM9Mui42zdJvJXpab501LSNZ/spE0A2C9LG+WDPN3U2mWDTOANe2gea0VqDvKghumjJlZIvP1DKpNmGuUqdmMZ9r0WvSx/llYV+kmzDNYfxKmduifa5T2eYBKs2xVin7UFjOT9pw1kqZZM/dehzJ4Dpxn/TM3nWGlmXnmYJ5hdjKtNGr2Ks2QDRN7qzWDaL5sWhZpzwiRNAPggCx7GRu0Z8zWVEYzzczNUn/QBtlYF7Xnaqd+00zgPFumynBuMf2EqdQL1LH+2TDBykO1lsIw3WPfXPtZ++zNZtSi0SRqCJpmw/O8vrXPImjO+mcty/astGfM3kyGlSbzi52/cz3Hf2YynWlG/Cdz1VJBJgKTVRyI9oyTjaQZAOuZtjyLjbaClANmktQKAtW77YCmCZxlIqvEKRWG9pguF1TotkZJO3AStWekRWcm1mfYnk+iPWfWZipFeZLaYZj6NeAQVYbWMNUmqSfNTNC8QtA8KyZplmmlGeufGXPvlUnSlJl2mZvOsD3vXLT+HP9ZMcd/JpVGVJplzve83ubptONATSrNQNIMgAPMjUooqZbyDhOpd4H2Pala5LSZhamMdhkuUJZvDc/zei0aU0yetIPeXDvaM2YjqjSrZVNpdpiZNpkq+F6UsEzz2A/CMLr+kzDN3kxGiRNmWmUvas+XSaUR7dmyllWVaRCG0XeOpFl2ykVfpUJ301zKz4FzNSrNslYt+TLNXlJPmhAHsEJWc816lYYc/5OM6C8A6/m+FyXOsthluNB3wWQIcDamM9phxM2SXdZlMNfMPKD7HjsNs5JlpVmz3as0JmmanejYTzFxuthoK5TkiYCJDbJo0RcSNLdCVpVmYRhG37d1JE0zY2Zbp50wWWi0FYSS59FpIGvRXKuU4wCmLfj6KSrNsuL1VRqlfQ7otefj+M9SFnPt2kEYPf/xDDDZSJoBcEJWO0yk3g0aLfqyk9UQ4AV6WVtlXQbVJuYBnaR5dkylWRZJM7Ozveh7Khe4bc5KL2Ge3nfABOinywX5HPuZiyrNUrwPXGz2ZmhSbZidrCqNFhpttUPWP2szGbXnM605Z8pF+cw1zlRWc83mo0ozkmZZmskoDhDFgNg0l6mpDMZ0mO5WHp2mJh6rD8AJWc206v872WWSHVPh0wpCNdvpPTAz08wuWSRPDtGaK3Om0myx2U71+JeWzjMjaZodM08ozWPfBEsIltshi5lm/YnTAkHzzGRVadY/05D1z0656EebVtI8/plnZg9zHT6cYhyg2Q6i9uy0Z8xWVpun5xZJmtogi/WP4n+lAs9/E46kGQAnTFey2WHU/3fSmiM7pYKnYjdgkeZ3gISpXWYzaNHWC5zzHchKpVSIAmZpV5uZ9Sdpmq0s2jMu1JlnZpMsEiem0oTjP1uZzbMz60/APHNZzLUz60/SLHsmaJ5m0tS0ZiwXfVXoOJIp03EmzaRpo9WXNJ3iHJClLDbPmwQdoxlA0gyAE7Jsz7gYtWfjopmV/n7mac41MzfLzDSzgwlcpBk0OVzvtedBdtZ1H1hNECMtUaUBSdNMrctgrt3hvioTZG+mr0Vf2G2Zl7R55plZYTaDtZd6539T6Yrs9CqNskiasf5Zy3L9qTLLXhQHSvH572D3eaNa9FUpch+YpSzGdLBpAgZJMwBOyKqXtcRMM1uYf/80EybRTDPW3gpZzDQ7TLWJFUxrlLkUK42kvsQJ65+pTFqzRrMsWHsbmPvAIJRqzXTatFJpZgdz/g3C3mamNJjKVirNstdLmqe4/nXaM9oii0oTc79Ja77szWTQcSha/ynWP2tZbJ43SdMNrP/EI2kGwAk2zDSj2ihbG0zQPMVKk6g0n6SZFUzgvN4KUhsGT3tGO2RRaST1WsHMcv7P1Lq+uXatlObaRe0ZOf9boeB7UZuctDbPRDMtCZpnKou1l3pJU5Im2ZvJoD0flQb2iDbPppg0nYsqzQiaZy2TpOkiSTNbZLH+B1l/dJE0A+CEqJd5Bu0ZF6g0s8KG6c5Ny4GUkmbNdqBmu9MGiLW3Q7noq1I0s63S+R5QaWQHKs0mW6XoR3Mt0wqaM8/OPv1t+tIQBc1pz5e52QzaM0ftOTkHZC7T9owc/5nLZP27z5vrmGeVuSziQHOLnb9rA0nTzE2Xehvn0mrRTKUZDJJmAJwwncEOM4MWfXYwNy1pVZqZ2WkF31O5wOXSFiZwNp9W4DyqNuGhOUuZVZox08wKnuel/h0wxz7tGe1hjsNDKQXOokozvgOZixKmtfRnmlBpmL3ZlNszNttB1AqUmVbZm+6bax2kFDSn0sweWYzpOBi1Z+T4z5rp+BOm2KJ5jqQZuogCAnBCdLPcbCsI0hsC3goC1btt4AicZcvctKRVadafLPU8L5W/E2szO74PpRA4bwdhdHNO0iRb6zOYaSUx084mac41C8NelTntGe3RS5yk8R0Io6QZ7dmyFyVMU9owE4RhVNVApVH2TNA8tdas3XNMueCrXCRklrWpckGepFC9TY1J68004/yftenu+b/RClJr0U17RnsUfE/V7nk4jeO/3myr1o3/kTQDdwAAnGBulqV0h4CbC7PvKbpYIxvmpmW+1kwlcRrNMysRMLVJmoFz8x3wPb4HWVvX15qrndLGiUYrUKP7cE6lYfZ6lWbJb5xoypf5mjHP1B4meZ1Gpdlisx2da0iaZ88kTNNKmizU2wpDyfN6AVtkxyRNFxqtVNpzzfclzNk4lz3f86JqkzRa9LXaQbR5ch1B88yVC74K3RbdaVSbhWEYJU1pz2iH6RSrDU1rxulyQSW6DU08vgEAnOB7nqql9IeA9qqNeGjK2mylqILvKQjTac230KQtp43SDJxH7dk4/jM3XS5ED8xpz7Rip7kd1qXYmrURdtZ7qtT73iF7Mym2aDPnmf5zD7KTdtJsvt65x5gpF+Vz/c+cOfab7TDazJIkc49Ja057zKQ4qsFszCsVPDbNWsDzvOh5PI040GKzHc01p9LcDmmuP/PM0I8rAABn9O8yTIsJmpI4yZ7neVGLjIMptGg8HAXMuFm2SZqBc3P805oxe/0zrdKaa8g8M7ukWWVaDztrztrbpTfXKPnvgGnPxjwzO6S59lJv/QmY2qFU8KP5wmkkzc11Zh3HvzVMxWcalWa91owlNs1ZwiRNU1n/xd71v0ilkRV6SbPk1783z46kGUiaAXBImmXZhvm7pkiaWcHs+EkjaXZwgV1GNjKzRdIInJsd7QRN7ZD2XLNDUdKM9beBCV6mUWVqKs1Ye7ukOddqnnlmVkm/0oykiW3M8Z9G4jRKmtGazRq9pEnycYA5kubWiZImKSTNmWdnH1P1O5fCM2BUacb5HyJpBsAh0yn2MjdM0ox5NnZIM2l2oPt3bJzmhskm/bOtkp5rYR7MOP7tEFWapZA0kXq72Umc2KHXmjX5Y79hKs049q0y07d5KunZplSa2cWsQ70VqJlKe77u+hM0tUbUnjWF50CzOYOkiT16SZM0Ko2oNLFNmnGgg6y/dTZOlyVJ+xcaif9dptKQjdOQSJoBcEiavYyNxTpzrWwSJc0WUkiaLZA0s5EJmrSDMPFzAZVGdkm70szsZp8lcWIFEzRvBaFqrWSD5g3aM1ppulyQGS+WdOAsqjQmaG6FctFXMcW5loeoNLJOmjMN56k0sk4vaZpmpRHHvy2mK+l1HCJpap+N3bU4kEIMiJlm6EfSDIAzsmjPyEwzu0RJs4QrTerNthabne/Zxqlyon8XhlPwvSiQnfRcM2aa2SX9SjPW3ybFgh9di5Nu0Ri1ZyRhahXP86J7waQD572ZRgRNbOB5XqpzzebrnXMMlYb2MBtYkk6ahmHY156V498WMylWmpnzP+357JHmTCvTApD2fPbY1K00m6s11U6w00AQhNEzBkkzSCTNADgkzZtlwyTopgmcWaG/0izJ9lz7uzuMpssFlYtcKm1jgpiHEq44ipImHP9WSH2mWYNKQ9tE7VkT/g7Uo0oz1t42sym1aGOmpX3SnGt2iEoj65gNLEk/By4221FQluPfHtOpzjTrtuckaG6NmRQ3T/faM3L822KmUlDR9xSGyW6ePFRvKQiXbtLFZCMSCMAZU1Ev6/QqzRaoNLOKSZo12oFqzeTacx2kNaPVerONkq02YaaVXfoTJknPtJJYfxuZAGbSidOo0owHZuuYNUkycRqGYZSYIWlij5lqOkmzdhBGzxokTexhrsWHEk6Ym+vLTKWggukHi8xFSdOE178VBNH9H5Vm9khrTEcQ9iqNaM9oD8/zorhMki0azUz79dWiPI/zP0iaAXBIVGW0mGyVkRGGIUFTyxQLfhTAMLvAknCgO2SW1ox2ipJmCQbO2kEYtegkcG6H2UpRnie1wzDxzROd8z+VJrZZl1K1YTTTjCpT66RRadZfacL9nz2ita8le/43537fY9OcTWZozTrRTKVZsx2qkeBcU7Mho+h7mipx/Ntipq89b5JxoMPdSiPf4/7fNhu7LRpNnCYJc8wzw2OQNAPgjPXVknyvE8xOozXLYrOtRjvo/t3cNNnCtEpINGm2SKWZzdIInJudrL4nHpot4fteX6VRwjOtWoFaJmhO0NQaaVSZ+tV1CtXZXTpNwtw6Mym06DPvPV2m0sQmabVnnO/bMMFOc3uYDUxJB83N9YUqU7uUi75Khc7xmGS12Vw0z6zE8W8Rcy/eCsJEu83MLfbmGfqsv1U2dhNZ+xOsNDtI0gyPQdIMgDN834vK5JO8WBqm9HtdtahigdOlLforDpNi1n4jN0xWSiNxYnYyT5cJmtnEBLHMQ21STCVbpehz/rdIGlWmhdnNkqRqyVfRZ+1t09ttnly1iak0YJe5XaLWnAknzXrzzLgHtIk59ltBGG1qTMI8rVmtNZ1CtaGpNFnHPCurFAt+dEzuT7DS6GCNeWa2itozLia4/ou05sRSPAkCcIpJYiRZlm2YGzKqjexiWiYmecMUtWecpj2jjfpnWyXFtP6iNaNd1neDmEkOgZZ6QVmC5nZJo8rUJM1ozWin2b4WTUkhaG6n9CrNOteXWdbfKqWCr3KxE75KMmkyT9LUWjPl5OeazfdVmsEum7vP5fuSTJqZSiPW3zqbovaMVJohPSTNADgljYulYf6OTcy1skrSlWa1Zlu1bq98EqZ2MoHMw43e3Jm4Mc/KTr32fAlXmtVN0pT1t8m6voRJUsd+YXaTJNbeViZommh7RirNrGTWY6HRUpKTjaNKM9bfOrPlFJLmtGe0VlRpnOBcW7Mpi9EM9jFxoP2Hk59pRaWRfUxcZr7WUiuhamNmmuGxSJoBcEqvLDu9pBmJE7uYm5ik2rOZdZ+pFFSiLZuVpkq9OTNJBU772zPCHmlVmvWSZlQa2mS6XFDB8xQquaBpYcZUmrH2NjKJk3orSCxoElWaEjS1irkeB6HUVHLHp9mUwfrbZ7pvrllS5kmaWmu6nPz6kzSx16aZ5Md0ROtPpZl1pkqFqNo4ic3T9b6N0yTNYBANBOCUjVGlWQrtGRdp0WcjcxNzqJ7MLqOoNSMVhtbyPC8KZiQ114z2fHZKr9KskzSlRZ9dPM+LAtlJfQei9owc+1YqF30VE9400QuaEzSxScH3oqB5Q8mtjflekTSxT9SiM6H2fK0g0EK3ion2jPZZn8Jc67ka7XltlUqlWXf9SZrYx/O8aFRLEolTM89uuszGafTwTQDgFFP1dXCxqSCh1kySFIahDpr2jFSaWaVa8lUuJLfLaP8iFYYuSDpwbmaaTVNpZJX10UyrpsIwuWuACciRNLVPlDitJxM0I2lmN8/zei26EpprxKYJe0WVhkpubZhpZa/Ej/3u2hd9T9USoTLbbJ7pJE32JpQ0aQdhVMVGpZF9zPofrDXVCuLfONsKguj6v36K67+Nel2n4j8HmNgfCVP0404AgFPWVYoq+J6CMNn2XIfqLbWCUL7HTbNtPM/ThunkdhoepC2nE3qB86TaM3aDplQaWcWse7MdRi00kmDWn6SpfZKuNoxmmtGe0Vqz0Vyb+L8DYRj2Ko2oNLCOWftGQkmzVjvQYrO95O+CPWYSbs/Xf+x7npfI34HRmaTJgYVGInNND9U78xL7q1phj+lyQeWCrzDsPa/Hab4vaT5VYv1ttCnqOpVcpRmtWdGPpBkAp/SXZSdxsTTMe6+fKsn3eWiyzYZqckkzs3OJ9ox2M22zkmrPGLXnI2hmlWLBjwIZ8wm25zlMpYm1esd+sjPNpll7a5lZg0m0Z1xstqNgLOd/+8xESbNkglrmO1Wg0shKvUqzhFuzsmHSSusqRZUKnc2zSTwDmnlWJE3t5HleonPNzPpvmCqx/paKKs2SSJqZ9ef8jz7cCQJwTq8sO7mA6f5orhUXTRslVWkWhmF0E0almd1MBcChBALn/TvNZ6g0so5Z+7mEkiZhGDLTzGK9SrP47wFC9dozkjC112yCgXOTNJkuF1Rg05R1km7P2D/PjKCpfaKkWSOZ9ozzzLOymud5fS0a67G/v+liQ5cZe5lKo30JzLc3cQUqjexlNjUfSGD95xaZZ4flSJoBcM7G6eQuloZJyJkbM9hlQ0KDoGvNQPVuyzdumOyWZIu2fd1ZCdWiT3sOC/XPNUtCrRWo3Z2XRntG+yTZmrUtX36pIon2jDYzgfMkKs3MRgySpnZKuj2juaeYJWlipf72jEnMNTX3FRz/9jJJs30JzDUzm7HWc/xba5Np0cn6TySzqflwo61GzG36Dy4y0wzLkTQD4BxzsUyiLN+g2shuSSXNTGvG2UpRpQKXSJutqybXou3R7oPY1tkKO80tlHSlmalemSoVVPQ5D9gmyWN/UZ1gTEltFbkGWKtXaRZ/tck888ysZqq/k2rPyPrbzSTMW0EYbXKLE5Vm9tsy09nYkkTSzCRN1xE0t9bmBCvN5qg0s161VIg2tJq4TRyCIIyOf5Jm6MfTIADnbEqwLNuI2jNSaWYlczMztxjvTlOSpe6Idpu3A9Vb8QZOHz3UafmyZZbj30ZJV5qZpBmtOe0UHfut+I/9w6p2/g4/uU05GJ9pm0ql2eRJvD2jSZpUuA+0UalvrmkSM22YaWa/RCvNFqk0st0ms3n6cDP2alMqjdyQxFyzQ/WWgrAzz5TnP/QjaQbAOeZCOV9rqRXEv8swCMJop9EmkidWWlcpyvekdhjGGjSLkmbcLFuvXPRVKXZuY+KuONl7qFdpBvsk2ZpT6gXiZwiaW6lc9FXtHvtxzzQ8rG5rRj+Z7xbiYQIaSbRoi2ZaETS3kmmb2FZBXqka+/tH7fkImlvL3JuZDU5xCcNQ8/VupRHrby2TNNu/0FQQxHv+Z6aZ/TZMl+Sps2lyIebZhqy/G5JImkXz7KrMM8VSJM0AOGe6XFCp4ClUb0dYnOZqzWinCTuN7eT7XhTQirNF4wEqDJ1ighpxB85NIIakmZ3MsT+XWKVZ5yHcVLPAPkm1aDRJMyrN7Dbb16It7pkW81SaWa1c8FUqdAJahXVbYn//KGnK+ltra7cLQNxJs3orULPdScKw/vZaXy2q6HtqB6EOxngfGAS9jZgkTexV9P2ofWKc1YaNVqBas3M/sX6K499mGxPoOkWVIY6EpBkA53iep03TZpdZ/K0Z+lv0sdPEXuam5kCcSbNF2jO6JInA+UKjFe1cNLtZYRfzMFtrBmq24682nqPSwHqzCVQbhmEYtWecIWlmtWKhr9I45haNJmjK8W8nz+ttaCvOxp80i5KmrL+1epVm8T4DmrWfKhWYaWkxz/MSadF4qN5SGEq+R3tu2/WqDeNPmlSLvipF1t9mphNUnDEgkmY4Eu4GADjJtM9Lop99NM+Mi6bVenPN4vkOhGFIe0bHmJ3App1OHEwQZsNUSeUit0k2qhQL0drEdfz3iyoNSZpaK2rRGeOxP19vqa2CwnZL0x7tGW1nNrfEGTQN+1o+U2liL9M6N+5Ks2Y7UL1bucj626u/PWOc7VlNa05aM9rPJE32xnj+n4vWn02ztovmmsUYB4paMxIDsN7GBDbPR+0ZWX88BtEgAE4yF8s4y7KNA9E8MwKmNjOJrYMx3TAvNttqdKtW2GXkhiRmW+2NWjNy/NtsfUJzzYIw7M20W0d7TlslceybZGlz3wPyiZdZ76h1narAh+fja9G22Gyr3Z2Rw0xDe5lKs0LMlWam1XOp4LFpxmKbZkryvU47xTjnGpvrCUkz+yVRaWbWfz3rb71NptIszqQplUbOMGtUawaqNeOZa0elGY6Eu0EATkpiAKjR354R9toQc2m+Wfd11SJtWRyRRIs2U2m2hXlmVktqrtnBhaZaQaii7/HgZLF1lfhbs5pjv/Hwj2N7TyTnqG5S++H5WmzvaQLw0+WCCmROrTWbUKXZfFRlSKWJzYq+H21sfCTGuWbR+jPPynpbEkiazVFp4gxz/O+LcfP03GI3acr6W69c9KMWqnHFAkma4kiICgJw0sYEehkbUXtGKs2sZoY0H4w5aUZrRneYwEacO40fpdLMCUlVmvXWvyKfoKm1epVm8d0D7O1WLDUfvje290RytnWTZo/MxdeizVQazVJlZjVz/Jc2Hhvr+84zz9IZScw1oz2jO/orzYKYzv9zUaUZz4G229yN0czXWrHNNj5o2jNy/Dth41R8XafqzbZqLboNYWUkzQA4yewwOlSP72ZJklrtIArCbqLSzGrmpqbeiqc0/8AiyVLX9AfO4wiaBmEYzUfYSqWZ1ZKqNHuEpKkTzLF/qN6KLWAWVZo9QqWZC7bMluV7Uq0VxJY871WaEDSzmUmYlo95nGIcaRUlTVl/+21d151pFWN71qg9I0lz662fKqnge2oHYWzn/zmSps6YKhdULXVC2VQaTSazgX5/DJunTcJ0ulxQiW5DeAy+EQCcVC0VVC3Ge7Mk9aqWygVfU6VCbO+L+JWLvqbLnTWKo9qMtpzumS0X5UkKQmmhMX7i9OBiU21a8zkhuUozkqYumCkX5XnxHfutdhBVmTcfuXfs90Pyir6vLTOmRWM8gXMqzdzQOT+HKsxsUiOML5xhkqasv/2SqTSjPaMrfM+LNrfujalF5zyVZk4xG6j3x1BpFIZhFEugPaMbzPrHUWnGPDOshqQZAGdtjPFiaZh2jxunmWfgAnNzE2vSjBsmZ/i+p5lKfMmTR7uB180zZVrzWS6pSrP+9oywl+97UWD7UAzH/r7DDYWSimqpfWjf2O+HdEQtGmNKmpmd5gTN7VYq+JpWZ83ng/jWikozd5hr9P6FhloxdBxpB6EOU2nqlM0xzjULwjBqz7l+ivV3QZzrv9hsqxV0ypY5/t0QjWqJYfM88+ywGpJmAJwVZ1m20ZtnxkXTBXElzcIwpD2jo+KcbfQorRmdYYIah+tttYN4+nPVm+0o+Up7Rvutq8R47HerFWYUX6svJO+odabSrBbL++2Z67yPScbBXrPqrNWhIL5zNZVm7pjptmcLpait9jgO11sKJRU8L+piAbuZSuM4kiaH6y0FoeR7ijbjwW5xVpqZpMlspaiiT4jcBWaT84GF8Uc0mBjQBjZMYQWcEQA4K86ybMPsVtlE4sQJcSXNFhptNduhPFGa75oocF4fv9pkL/OsnDFVKqjod6oB40iaSL3EybpqURXa81rPVAPFUmXaPfZJmrnlqPXxtWc8XG9prvtdOno9STPbmaRZMpVm3AfazvO8vhaN4x//UcK0WqTTiCNMpVEcSdO5vta8dJpwg2nPuf9wDDOtTGtGqsycYeI1jXYwdpt2kzQlBoSVkDQD4Kw4y7IN5lq5Ja6kmVn3ddWiCj4PSy6ZjXG2FfOs3OF5Xl+VYTxzzWjN6JY419+s/bTiqVhCOrbOVuSps/Hl8JgbJ3Yf7Kz9ltmyKkWS5raLKs3CeO7X6622Gt02f1SauSHOuWZm8w2t2dyxZaZXaTRupck886ycsynG9Z+rsf6uKRb86Hx9YMw4EDPNsBqSZgCc1V+WHZdee0YqTVwQW9KM1ozO6lWbjPcdaLSC6Hu0hUozJ8Q91+xRKg2dYhLm465/GIa0Z3RUqeBHgbNxq832dJNmx66vjv25kLwZ1RQGbTXCwtgJU6m307xS9FUuEiJxgblWx1Jpxjw752yYKsn3pGY7HHvzzBzr75wN1c76t4Jw7G4jcyRNnRRH16kg6M0zJGmGlXBHCMBZJsGx2Gyr3hyvLFvqBM1NefcmLppOMDc387WWWsHog8CpMHRXXNUmZibCdLmg6TIPzS5YH3Ol2SNUmjll83QvWTLOLuOFRluLzbY8SdMkzZwT11yz3QcXJUnHbpga+zMheQWFau57QJL0UAwz7R7qzrPj/O+O/vaMY1camaRJhecAV/i+FwXNx51rFlUa0ZrVGb7vRbGg/WOu/0GTNGH9nRLHBvpD3XmGBd/TTIUuA1iOpBkAZ5WLfjSsef+YlUZSb5fKVKnAPBtHTJcLKhXMXKPRA+dR0oxkqXNMe5ZHD9XVbI+eOKU1n3virDQLwlB7u9VG2/gOOOGYDVX5XueBd26M87859jdOl1TQeIFXpG9bN2n2yBiVZu0g1EPdP3/sRirNXNHYc7ck6ZG58ZPdpj3nsRtYf1dsmSnLk1RrBjo87kwb2jM6Ka65ZrTnc1M012zMrkOm0nj9FMe/S+IY1dI/z455llgJSTMAToujLNsw/ZCpNnKH53nRA87BMW6YaM/org1TJc1WigpC6cEDiyO/D6353BNnpdnBxaZaQaii72kD1wAnlAq+ju620ntg/8LI78MsQ7f1Ks1GT5w8Ml9XOwhVLflsnnFI46G7JI3fmlOSHjSVhiRNnVEs+NFz4DgtGsMwjNqzch1wi0majV1pZpImJE2dEkelYRD22vORNHWLiduYOM4omGeGtZA0A+C0OHaYGL15Zlw0XbJxzLlmYRjSntFhnufp+E2ddloP7B89aWaqjLYQMHFGb57d+EmzR7tB1y2zZfnsNHTGju6xvyuWhDnHvotMpdl8raXFEatNTGvGY9ZX2WnsEFNpNm7SbKHRiu4Dt9Oe0ynRXLNxkuaH6qq3ApULfpSEhxu2xJA0C8NQh2omacZzoEtM0nT/GJunTXs+35NmKyRNXdIfBxy1RS9JM6yFpBkAp8XRy9gw77GJaiOnmF1hB0ZMmh2ut9UKQnkeD0uu2rFpWtLoSbMwDKk0c9C6KVNpNvrDkkG1kZuO2zh+wpxj322VYiEKdow612xP1JqPhIlLGg/dLSnUoXpLh+ujb54wrRk3T5dVpT27U7asM3PNRg+am+vH9o1V+T5Jc5f0V5qNeh94uNFWO+w8B5I0cYuJ2YyTNJtbNK1ZS2yac8z6akmeJ7WCzn3AKKL2jCTNcAQkzQA4bWMMN0sG1UZuMsGyuRGTZqakf321pAIPy04y1SYPzdfUaA0/1+xwo61aK5DndYJmcMNsuSjPk4Kwk/weB9VGbjp2w5R8r1NlNMo1oB2E0Q511t5dR40512z3HPOsXBQ2a5r2OoGycWba7T7QXX9aMzrHzCAdpz2jSZqZDVhwx8bpsnxParSDkYPm5t5htlIkaeqYTTOdGMDhelv11mjPAcwzc1fB97ShOt4GeirNsBaSZgCcFpVlL45faWDmom2cImjuknHbM5Isdd/6qZLWV4sKR5xrZoItm6bKKha4NXKF73vRrmAzxH1UVBu5qVz0ddS67lyzEY79/QsNBaFULvhaxywTZ40z1+xQraX5WkueFM3Igztm/c65f5wWjWaeGa0Z3WOu2fsXGmoFw2+aCsIwau9rNmDBHQXfi57bR23RaJ4bTKtHuKNSLGi63KkO3j9q0qT7/LCBbjNOGndUyxxJM6yByBAAp5mESaMVaLE5eqXBYrNTaSKRPHHNhr6k2SiJU9PWcSM3S04bp0Vjb54ZD8yuMa159syN1pZNkuqttua68yyoNnJPNNdshGO/P1nKLCt3bRsjaWbmmW2drahc5NHYNb2k2WjXgFYQ6OG5zveGSjP3zFaKqhR9BaG0//DwQdNH5utqdOeZbWOemZM2jznX7N69C5Kkk7bOxPaZkB7TIWT/iOs/R3s+p5muU6Zz0DDqffE/kmY4Ep4MADit2Lc7fJy5ZqbKbLZSVIlKE6esq5bkqdPP+nBj+MRpVGFIWz6nHd8NnN+/f2HoP0trPned0E2W3r9v+HU3zCyU2UqReTYOOs4kzUaqMqU1Yx6YasODi82hWzSZ1ozH0JrRSeu6SbOH5karNHt4rq52GGqqVGDzlIM8z4vO36O0aDSbLY7bNMU8I0eZpNneEZImtWY7qjQ9aQtJMxdtmhlvVEc004pKMyeZze6jVBqaKsPpcoH4H46IbwYA541blt3/Z6kyc0/B96LE6SgtGln7fDCVZo/M14cOmtKaz13Hb+6s+64Di2oHo7XoZf3dtn3DlDyvc/6fH7JNJwnzfJgqF6L7gGFnW+05yDwzl5lKs0P1lhYaw8802t23/lSbuslcu0dJmpmNVrRmdNc4lWY79y0oDDvVSlSauGmTSZqMUGlab7WjSmOeAdxkNrscGCFpamJAHPtYDUkzAM4zvcxH3WHU/2dJnLhpQ3fdHh0yWBaGIe0Zc2K2WtTGqZJCDdemrR2E0YM2gXP3bJ0ta6pUULMdRsHvYZnzBuvvps5cs87aDduiMUqarSNY4rpR5potac1H0sxJRS+M7t+GTZhKvXlG2zeSNHFVr9JsuOfAIAj14IHOfcMO1t9Z/UmzYdv037v3sCTppK3TsX8upGPzGJVm9z66oHYYauN0KXofuGVTt1PQwcWmgiGP/7sePiSJebZYHUkzAM6LKs1GqDIyzE6TTbToc5JpqXH7nvmh/tyhekvtIJTv0ZYhD8xO4WHmmh1YaCgIpXJfq1e4w/M8Hb+5s+47R2jNKfUCbcwzcdeOjd2ZhkO0aFxstHW43qlK3TLD2rvOHL/DJE4eme+15mOnsbuOWt9Z+4dG2Di1m0pD55mk2bBJ04cP1dVoB6oUfW3l+u+sTdOdNv31VqCFIdr0h2Goex/tzjOjNaOzTOzmwEJTwZAdJ0zS5NRts1QaO2pdtaiC7ykIpfna4NXm9VZb9zzaSZqfecy6pD4ecoCkGQDn9W6WRq80o0Wf2x5/9Dp5nrRnrjbUTjOz7uurJfk+N8uuMy0ah0mamYTJltkyD0yOMi0aR5lrFoah9h6m0sx1x42QMDdVZhumSioXeSRynZlrNkylGa358iFa+7nhqo0PLDa12Gyr4HtR4g3u2dJtq7bYbOtwffCgaTTPbCPzzFxWLPjRpodh5po9PF/XYrOtcsGn0tRhJmnSDkPNDdGiu9kOokrDU4+aTerjIWGe50XH/zCxwB89fEjtINTmmTKbJrEqnhABOK9/ptmwbRkk06Kvc5HdNEWlmYtmKkWd2A2c37Z7buA/R7I0X0yl2SOH6qo1B9ttagLnW+hl76wTusnSPXO1oefZHVxsqtkOVfA9WrQ6bPvGqjx11vPQgDtNmWWXL6Y94/7DDTXbwUB/xiTNjqHKyGmjtOaUpN3d1nxHrauo6BMWcVWp4Ef38cPMNWOeWX6MMtfs3m6VyfGbp1Rg46SzPM+L5prtGyJpct/eBbWCUOurxegaAjdt6osFDur23Z3uRGccs45NU1gVd4cAnLe+WpLnSa0gjFotDeNwo61mO5TnSesJmjrrzGPXS+q0aBw0ebp7rrPLdCNtOXNhplKMHpwHrTjpBc55YHLV+qmSNkyVFIajzLTqVhrOlKk2dVilWIh2ij5wYLCKw16VKcd+HsxUipouFxRq8MD5Hlrz5YIJeM7XWloccMOMJD14sDvPbANJE9eZe7i9A84168wz66y/6VIAd5mNb0MlzfZ2WzNupTWj6zabrkOHB0+amNaMjzuK1oyuM12n7h+wTf/cYlO7uuf/M2jNiDWQNAPgvILvRfOoRhkCa0q511dL7DRz2ClbZ1Qu+pqvtQZKmByqtXRHdwbaabRlyI3eXLPBbpxNK5etzDRy2glRi8bhkmaPkDTNDXPsD5o4pdIsf6KKo7m1k2bztaYO1VvyPIbAu67SN5NumBaNptLs2I2sv+vMefyRARPmD8/X1WyHnXlmXAOcZzbMmXbba1lotLSne65gnpn7NplKwwHjQK0g0I+7lYanbiMG4DqT+Lr7kcMDJc5vf6gT/9mxcUrrmGmPNZA0W8H8/Lw++9nP6q1vfate+MIX6uyzz9bmzZtVLpc1NTWlY445Rj/1Uz+lt7zlLfre97431Hvv2rVL//N//k8961nP0vHHH69qtapNmzbpjDPO0C//8i/rM5/5zNDt5RYWFnT99dfrJS95iU455RTNzs5qdnZWp5xyil70ohfp+uuv18LCcHM+wjDUZz/7Wb3+9a/XmWeeqc2bN6tarWrHjh165jOfqT/+4z/WAw88MNR7StJ3v/td/fZv/7bOP/98HX300SqXyzr66KN13nnn6S1veYu+853vDP2egNTXonFx8B1GBi368qFY8HV6N/l12561WzTedP9+BWGnrRe97PNjxxCzjerNdjQ0mKCJ247f3Fn3nQMmS429JE5y47juefyBA2sf+0EY9hLmJExzY5i5ZqY147bZikoFHoldN2yLxlqzHQVYqTR037bueXzQKtMH+lozUmXivqg946HGQLG0+7pVZttmK5qtFBP9bEieqTTaP2Cl4f37FtVoB5qpFDj/58CW2Yoet62T/P72fftW/dkwDHV7d5THGcdSZYa1cYV4jP/1v/6XrrzySjWbKwfem82marWaHnroIf2///f/9L//9//Wy172Ml177bXatm3bEd83DEO9613v0h/+4R8uS2DV63UdOHBAd9xxh/7mb/5GT3va0/TXf/3XOvPMM9f8vJ/+9Kd1+eWXr5jA+vGPf6wf//jH+vSnP623v/3tuuaaa/TCF75wzfe87bbb9Mu//Mv6+te/vuz3du3apV27dukrX/mK3vGOd+jKK6/UFVdcsebN5tzcnC6//HL9wz/8w7IbmYcfflgPP/ywbrrpJv3pn/6pXvOa1+iaa67R+vXr1/ysgLFpuqz79i4MNQDUMNVpzDNz35nHrtctD87procP6VmnByoXVw6E1Zpt/WDXQUnSBSduTvMjImE7NnYqjvYebmih0dJ0+ci3Oo92H65mK0VVSoVUPh+ScXy3vdK+ww0dqrcGDoKYFn0kTtxnkmYHFpo6XG9pZpXvwMGFptpBqCKz7HLFtOh8ZIikGfPM8uGo9RX96OFDAyfNTGvGjdOlVe8T4AZzDd93uKF2EK7ZOcRsrKI1Yz6YpEmtFWix2V7zmL53b6fK6KStrH8ebJoxHYcG2zwdtWbcRmvGvLjgxM26+5HDumPPvJ528pYjjlx5eL6u/QtNFXxPp9JpCANgW91j7Nq164gJsyP55Cc/qWc+85nau3fvir8fhqHe8IY36Morrxyo4uvrX/+6fuqnfko33XTTqj/3wQ9+UC95yUsGqvh64IEH9JKXvEQf/OAHV/25m266ST/1Uz+1YsLssRYWFnTllVfqDW94w6o7evbt26ef+Zmf0Yc+9KE1d/6EYagPfehD+pmf+Rnt27f6LgGgnwl6DTMA1KDSLD+O3VDVhqmSmu1Qdz9y6Ig/9/0HDqrZDrVltqyTtvDAlCdT5UJUNbRWtRnt2fKjWipElQb37xus2qzeautgtzqZpJn7KqW+uWYDH/sVAiY5Ys4Bew/X1QqCVX92dzdpwi7zfDBVhoMkTKVea0bmmeXDumpR5YKvIFy7VX87CKOk6XF0msiFUsGPWrSu1Z4tCMKo0ozWjPlgkqaLzfaa1WbtINQ93RgBrRnz45gNVR2/aUpBKH135/4j/tztuzutGR+3dUaVIhtmsTaSZgPYsGGDzjrrLB199NFH/JnbbrtNv/Vbv7Xi711zzTX6wAc+sOz1UqmkM888U8ccc8yy39u7d69e+tKX6vDhwyu+5/e+9z294Q1vULDCA+Epp5yiU089ddnrQRDosssu080337ziex46dEgvfelLV0z+HX300TrrrLNUKi1PKnzgAx/Qe9/73hXfU5Iuu+wyffvb3172+rp163TOOeesWFH27W9/W7/yK79yxPcEHsskvEabaUbSLC88z9OZ3VL723av3KKx1Q70vfsPSJIuOHETAdMcMjuH1wqc76XKKFd6c80GnGfXXf+ZSkFTZR6c8sAEQHet0aKxV2FIwjxP1lWLqhY7gXNzfK+k1Q6i5MqxJE1ywSRMDy42VWu21/x5U2nIPLN88DwvOp+v1aLx4fmamu1Q1RLzzPKkN9ds9VjAnrma6q1A1aJPpXFOlAp+tAn2P+56dNWf3XVgUbVWoKlSgaR5zjz5pE73oFsenNPhemvZ77eDUHd055mdcSxdzTAYkmZHsG3bNv3e7/2evvvd72r//v269dZbtWfPHt1xxx169rOfveKf+Yd/+AcdPHhwyWuPPPKIrrjiimU/e8EFF+jHP/6xfvjDH+rBBx/Uddddtyxwe++99+rqq69e8e/6lV/5FbVaS08EW7Zs0X/+53/q7rvv1o9+9CN97Wtf05YtW5b8TLPZ1GWXXbbie77jHe/Qvffeu+Q1z/P0V3/1V9q9e7duvfVW3XfffXryk5+87M/+7u/+rh555JFlr99www365Cc/uez117/+9dqzZ49+8IMfaM+ePbr00kuX/cw//uM/6oYbbljxswKPtXW2Ik+dsvy9A/azl5ZWGphdSnDbmcd0boLu37+o+dryysNbd89psdnW+mpRpx9FL+s86s01Wz15YgIrWwia5MLx3aTZzv0LA8206K82Qj4Meuw/wtrnkud52rZ+7RaND83XFYTSdLmg9VVa8+VBtdRby7VaNLaDUHvmqDTLm63RXLPVkyZmQ9VxG5lnlicmaWYS4kdiWjOesGVaPuufG884bZt8T/rxo4d176MrFx5IvdaMp2ybkb9GG1e4ZcemKR2zvqp2EEYbpPvt3LegxWZbU6VCtNESWAtJs8fYsGGD3vWud2nnzp36wz/8Qz3pSU9acjN1+umn63Of+5zOOeecZX+22WzqW9/61pLXPvjBD+rQoaUtwjzP04c+9CEdd9xx0a8vvfRSvfKVr1z2nh/4wAdUry+98f/Wt76lb37zm8t+9o/+6I/09Kc/Pfr10572NP3hH/7hsp/75je/uazyq16v6/rrr1/2s5dccokuu+yy6N/g2GOP1d/93d8t+7lDhw7pb//2b5e9/p73vGfZayeeeKLe+973anq6c6KamprSNddcoxNOOGHZz15zzTXLXgNWMlMp6pTuANCbHzi4xk/33PrgnNphqM3TZa0jcJIL66dK2tHdOXbbnvklvxcEob57X6dk/7wTNnGznFNm5+D+haYOrbDTTJKCMKTSLGe2b6iq4Hs6XG8PNNeAeWb503/sr7TL1CBhml+mTd9qiZM9pspoQ5WgeY4ctd6s/epB80fm62oHoapFX5voMpEbvaTZ6klTkzQ7nnlmuWIqje7YMx+131vJvY92NtWcTGvGXNk0U9YTj98oSfryjx5RO1i+eS4Ie+MbmGeVP57n6cknbZLUGcVRf0zV+e3dLkSPP3rdmnMvAYOk2WO8/e1v1+/+7u+qWj1yqXapVNJrXvOaFX/v0UeXlgN/5CMfWfYz5513nh7/+Mcve/3Vr371stf27t2rG2+8cc339H1fl1xyybLXL7nkkhUfBj/84Q8v+fWNN9644gyxlT7TGWecofPOO2/N99y/f78+//nPL/u5l7/85SqXl+7qL5fLevnLX77sZz//+c9r//4j96QF+pkbpdt2zy27SK4k6NuF8sQTNhI4yZEz+lo09lec3PnwvOZqLU2VCjprO2X5edU/32qlipNWO9ANt+xRox2oVPCoMs2JYsHX9m6rrZ0DtGg0gbVtJE5yo1rqzTR88AgtGg/VWpqvdRJqVJnmjzn3r5Y4MfPMaM2VL2btH5lbPWli5lkdS6VRrmxdt3Z7xnYQRteG4zZRZZgnOzZNR7GAL/zwIc2t0G3kUK0VVZqfwEzr3HnKyZs1VSrowEJTN69QabT7QE0LjbbKRZ+keU6dvHVGW2bKarQD3byrt5G+3mrr7m4FookTAYMgaTaileZwSZ1KLGNxcVHf//73l/3M+eefv+KfveCCC1Z8/Rvf+MaSX3/9619f9jOnnnrqip9pw4YNOu2000Z6z2E/6w9+8AMtLvYCFN/61rdWnLk2zHu22+0V56EBKzlu45S2zJTVCkLdeoR5Vv3ufvSQ5mstVUu+zjyGi2eenHbUOhV9TwcWmnqoGzwJw1Df6VaZPfH4jSoVuATmWa9N29LAeb3Z1qe+96DueviQCp6n55x5NLvNcuSETYPNNQvDsK/aiMRJnuzYeOSZhrv2L+qj394pSdoyU1a1xCy7vNm2rteibaWd5mEY9uZZ0ZovV0zS7KE12jPuPtCrNER+bJnprH+n2nzlFo0PzdXUCkJNlQraMsO1P29+8tStOnp9RfVWoM/fsmfZNeDefZ2g+THrq5ou02EmbyrFgv7LqZ3xNN/48b5lHQfu6laZnbJ1hme/nPI8Txd0q82+t/OAmu1OPPquhw+pHYTaNF2K7hWAQRAxHNFK7RErlYrOPffc6Ne33HKL2u3l1S4rtSGUpGOOOUbF4vKL980337zqr1d7T0nasWPHSO9ZKpV0zDHHDPyerVZLt95666rvudpnXek9V3sf4LE8z9NP7NgoqVOSvdZMm5t2HpAknXvcBhVJoORKuehHbRd+2E2g3rd3QY8eaqhU8PSEHRuy/HhIwY5NywPnh2otfeK7D2jXgUWVC75e9MTtOu1oEuZ5YuaaPbB/UcEKAXNjrtZSsx2q4HnaSKVhrpjqgV19lWZhGOpb9+7TJ7/7gA7X29o8XdYLzj32SG8Bh22cKqlc8NUOQv3fOx7WfXsPq38L33ytpYVGW74nHU3gJFdMe8aDi03VWyt3nAjDMKo0Y55ZvpSLvo7uzjT8xLcfWLHTQDTPbBNVhnlU8D09/5xjVS762n2wpq/ds3fJ75tZVydRZZZbZx27Xkevr6jRDvTVu3vrH4ZhNM+M1oz5dvpR67S+WtRis61bH+zEgW7f3RnZccax6zn3YyhEiUdw++2362Mf+9iy11/96ldr8+bN0a8f26rR6P+Zfp7nadOmTcte73+fxcVFLSwsvwE80ntK0pYtW5a9dvjwYdVqvbYlK33WTZs2HfGEstJ7PvZ9hv3/P8h7rqRer2tubm7Jf5hcZxy7TpWir4OLTd2798iVBnvmatp9sCbfU5RoQ76c0a0evPOhebWCQN/uVpmde9wGqgsmwPaNVXleJ3g2X2tq3+GGPv6d+7X3UEPT5YJefv6OKMGC/Ni2rqJq0VejHeihVdqzPdKtRNg8W2a3ac6YuWZ7Dze00GhpsdHWP9/8oL56916Fks48Zp0uecrx2kyVQS55nhe13br1wTl96nsP6hs6XVsvfosebk3p3r2doOm2dRU2TOXMVKkQzSd++AgtGuf6k6brSZrmzQvOOVbb1lW02Gzr/7tpl26+/8CSTZQmkbaD1oy5tWGqpOecebQk6Tv37dePu4mydhBGrbtP2so8s7zyPE/PPH2bpM7G2T1znWeBh+bqOlRvqVTwdCLPf7nm+54uOLETc/7Offt1YKGhB7ob6c5gsyyGxJPCkB566CG95CUvWZJwkqStW7fqne9855LXDh48qJVUKke+QV/p9w4cOBD7ew7yvkm852p/dpD3XMm73vUubdiwIfrv+OOPX/XnkW+lgh/Nqrr5gQNH/LmbdnYSKKcfvU4zFdoz5NHxm6c1Wymq3gr0tbv3ateBRfme9KTjl29OQP5Uir25Zt++b78+/u37NV9radN0Sa+84PiohRfyxfc87eg+DK8218wkzWjNmD9T5V7bre/ed0Af/uZO3bd3QQXf07PPPErPOeto2vPm3EVnH6OLf+JYnb19vabLBbVV0MxZz9RtjU36v3c8Ikk6dj1B8zyK5podoUWjmWd11LoqSdMcWj9V0s+dv0OnHz2rIJS+dOcj+tfbHlarHagVBFFr1h0bOf7z7NSjZvUT3a4iX/jhHs3XmnrwwKKa7VDT5QLt2XLu2A1T0eiNL9/xSKfKrNua8aQtM5z7J8CZx67TdLmgQ/WWPnfLHkmdTXXrp0oZfzK4hrPFEO655x494xnP0O23377k9enpaX3605/W9u3bB3qf1VrGrdVOzvX3XO3PjvqeV1xxhQ4ePBj9d//994/82ZAPpnLsvr0LK/a0n681o/L8J3UHBiN/fM+Lqs2+223FecYx6zVbJUk6KUyLxu8/cFD1VqBj1lf1c+cfzw1zzvXmmi2faSVJP3poPppveMx6ZtrkkWnR+J2d+3Wo3kmWX/Lk43X29g20ZZkABd/TKVtn9ewzj9alP3mynqAf6+DXPqFprxn9zIm058qlo9Z1zunf32G+NkcAAC+4SURBVHVQN99/QAuNpTNtT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807245.000000\nmean 2016.660658\nstd 6.304367\nmin 2001.000000\n25% 2013.000000\n50% 2019.000000\n75% 2021.000000\nmax 2023.000000\nName: Year, dtype: float64"},"metadata":{}}],"execution_count":10},{"cell_type":"markdown","source":"### Location\n\nWe discover and confirm the concept of location varies through the dataset. It appears more granularity in the locations exists after 2017. However the majority of the locations are present in both time periods. Only St-Helens is not listed after 2018. ","metadata":{}},{"cell_type":"code","source":"len(data.Location.unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:23.822920Z","iopub.execute_input":"2025-01-11T12:05:23.823263Z","iopub.status.idle":"2025-01-11T12:05:23.890809Z","shell.execute_reply.started":"2025-01-11T12:05:23.823232Z","shell.execute_reply":"2025-01-11T12:05:23.888618Z"}},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"1152"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"rows = data.Year < 2018\ncol = 'Location'\nbefore_2018 = data.loc[rows, col]\nlen(before_2018.unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:23.893366Z","iopub.execute_input":"2025-01-11T12:05:23.893977Z","iopub.status.idle":"2025-01-11T12:05:23.927100Z","shell.execute_reply.started":"2025-01-11T12:05:23.893931Z","shell.execute_reply":"2025-01-11T12:05:23.924466Z"}},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"135"},"metadata":{}}],"execution_count":12},{"cell_type":"code","source":"rows = data.Year >= 2018\ncol = 'Location'\nafter_2018 = data.loc[rows, col]\nlen(after_2018.unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:23.928877Z","iopub.execute_input":"2025-01-11T12:05:23.929321Z","iopub.status.idle":"2025-01-11T12:05:23.988633Z","shell.execute_reply.started":"2025-01-11T12:05:23.929283Z","shell.execute_reply":"2025-01-11T12:05:23.986868Z"}},"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"1151"},"metadata":{}}],"execution_count":13},{"cell_type":"code","source":"before_2018 = set(before_2018)\nafter_2018 = set(after_2018)\nbefore_2018.difference(after_2018)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:23.989930Z","iopub.execute_input":"2025-01-11T12:05:23.990350Z","iopub.status.idle":"2025-01-11T12:05:24.077928Z","shell.execute_reply.started":"2025-01-11T12:05:23.990305Z","shell.execute_reply":"2025-01-11T12:05:24.076570Z"}},"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"{'ST. HELENS'}"},"metadata":{}}],"execution_count":14},{"cell_type":"markdown","source":"We discover again that London and metropolitain cities are likely to have the most property exchanges. Smaller localities are likely to have a lot less. T\n","metadata":{"execution":{"iopub.status.busy":"2025-01-05T11:40:17.821040Z","iopub.execute_input":"2025-01-05T11:40:17.821400Z","iopub.status.idle":"2025-01-05T11:40:17.827906Z","shell.execute_reply.started":"2025-01-05T11:40:17.821373Z","shell.execute_reply":"2025-01-05T11:40:17.826919Z"}}},{"cell_type":"code","source":"group = ['Location']\ncol = ['Price:Count']\ngrouped_by = data.groupby(group).sum()[col].reset_index()\ngrouped_by.sort_values('Price:Count')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:24.079178Z","iopub.execute_input":"2025-01-11T12:05:24.079602Z","iopub.status.idle":"2025-01-11T12:05:24.584736Z","shell.execute_reply.started":"2025-01-11T12:05:24.079563Z","shell.execute_reply":"2025-01-11T12:05:24.583852Z"}},"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":" Location Price:Count\n522 KELSO 1\n785 PORT DINORWIC 1\n606 LLANSANFFRAID 2\n423 GRETNA 7\n403 GATWICK 7\n.. ... ...\n556 LEEDS 254153\n736 NOTTINGHAM 295664\n112 BIRMINGHAM 330038\n647 MANCHESTER 381448\n614 LONDON 1665495\n\n[1152 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
LocationPrice:Count
522KELSO1
785PORT DINORWIC1
606LLANSANFFRAID2
423GRETNA7
403GATWICK7
.........
556LEEDS254153
736NOTTINGHAM295664
112BIRMINGHAM330038
647MANCHESTER381448
614LONDON1665495
\n

1152 rows × 2 columns

\n
"},"metadata":{}}],"execution_count":15},{"cell_type":"code","source":"locations = pd.read_csv(\"/kaggle/input/geonames-gb/geo_locations.csv\")\nlocations.dtypes","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:24.585864Z","iopub.execute_input":"2025-01-11T12:05:24.586208Z","iopub.status.idle":"2025-01-11T12:05:24.831587Z","shell.execute_reply.started":"2025-01-11T12:05:24.586178Z","shell.execute_reply":"2025-01-11T12:05:24.830166Z"}},"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"Area object\nName object\nRegion object\nLatitude float64\nLongitude float64\ndtype: object"},"metadata":{}}],"execution_count":16},{"cell_type":"markdown","source":"### Type of Property\n\nThe property type appears to be the same across the whole dataset.","metadata":{}},{"cell_type":"code","source":"len(data['Property Type'].unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:24.832917Z","iopub.execute_input":"2025-01-11T12:05:24.833342Z","iopub.status.idle":"2025-01-11T12:05:24.877448Z","shell.execute_reply.started":"2025-01-11T12:05:24.833307Z","shell.execute_reply":"2025-01-11T12:05:24.876510Z"}},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"5"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"rows = data.Year < 2018\ncol = 'Property Type'\nbefore_2018 = data.loc[rows, col]\nlen(before_2018.unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:24.881186Z","iopub.execute_input":"2025-01-11T12:05:24.881479Z","iopub.status.idle":"2025-01-11T12:05:24.905596Z","shell.execute_reply.started":"2025-01-11T12:05:24.881454Z","shell.execute_reply":"2025-01-11T12:05:24.904655Z"}},"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"5"},"metadata":{}}],"execution_count":18},{"cell_type":"code","source":"rows = data.Year >= 2018\ncol = 'Property Type'\nafter_2018 = data.loc[rows, col]\nlen(after_2018.unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:24.907194Z","iopub.execute_input":"2025-01-11T12:05:24.907462Z","iopub.status.idle":"2025-01-11T12:05:24.948746Z","shell.execute_reply.started":"2025-01-11T12:05:24.907438Z","shell.execute_reply":"2025-01-11T12:05:24.947382Z"}},"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"5"},"metadata":{}}],"execution_count":19},{"cell_type":"code","source":"before_2018 = set(before_2018)\nafter_2018 = set(after_2018)\nbefore_2018.difference(after_2018)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:24.950021Z","iopub.execute_input":"2025-01-11T12:05:24.950330Z","iopub.status.idle":"2025-01-11T12:05:25.020354Z","shell.execute_reply.started":"2025-01-11T12:05:24.950300Z","shell.execute_reply":"2025-01-11T12:05:25.019262Z"}},"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"set()"},"metadata":{}}],"execution_count":20},{"cell_type":"markdown","source":"We discover terraced houses (30%) are likely to be properties with the most exchanged titles, after semi-detached houses (27%) and flat. Others (3%) appears to be the least, with detached houses (20%) and flat (21%). \n\nThis interpretations may suggest real-estates in the UK could be mainly composed of terraced houses, flats and semi-detached properties. \n","metadata":{"execution":{"iopub.status.busy":"2025-01-05T11:40:17.821040Z","iopub.execute_input":"2025-01-05T11:40:17.821400Z","iopub.status.idle":"2025-01-05T11:40:17.827906Z","shell.execute_reply.started":"2025-01-05T11:40:17.821373Z","shell.execute_reply":"2025-01-05T11:40:17.826919Z"}}},{"cell_type":"code","source":"group = ['Property Type']\ncol = ['Price:Count']\ngrouped_by = data.groupby(group).sum()[col].reset_index()\ngrouped_by.sort_values('Price:Count')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:05:25.021432Z","iopub.execute_input":"2025-01-11T12:05:25.021841Z","iopub.status.idle":"2025-01-11T12:06:25.672060Z","shell.execute_reply.started":"2025-01-11T12:05:25.021795Z","shell.execute_reply":"2025-01-11T12:06:25.670893Z"}},"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":" Property Type Price:Count\n2 O 413560\n0 D 2790506\n1 F 2909812\n3 S 3693681\n4 T 4117586","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Property TypePrice:Count
2O413560
0D2790506
1F2909812
3S3693681
4T4117586
\n
"},"metadata":{}}],"execution_count":21},{"cell_type":"code","source":"grouped_by['Percentage'] = grouped_by['Price:Count'] / np.sum(grouped_by['Price:Count'])\ngrouped_by","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:06:25.673104Z","iopub.execute_input":"2025-01-11T12:06:25.673462Z","iopub.status.idle":"2025-01-11T12:06:25.685048Z","shell.execute_reply.started":"2025-01-11T12:06:25.673431Z","shell.execute_reply":"2025-01-11T12:06:25.683845Z"}},"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":" Property Type Price:Count Percentage\n0 D 2790506 0.200393\n1 F 2909812 0.208961\n2 O 413560 0.029699\n3 S 3693681 0.265253\n4 T 4117586 0.295694","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Property TypePrice:CountPercentage
0D27905060.200393
1F29098120.208961
2O4135600.029699
3S36936810.265253
4T41175860.295694
\n
"},"metadata":{}}],"execution_count":22},{"cell_type":"code","source":"plt.figure(figsize=(20, 12))\ncolors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple']\nplt.bar(grouped_by['Property Type'],grouped_by['Price:Count'], color=colors )\naddlabels(grouped_by['Property Type'],grouped_by['Price:Count'])\nplt.title('Comparison: Number of properties exchanged overtime per type')\nplt.xlabel('Type of Property',fontsize=20)\nplt.ylabel('Number of exchanges')\nplt.grid(False)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:06:25.686120Z","iopub.execute_input":"2025-01-11T12:06:25.686441Z","iopub.status.idle":"2025-01-11T12:06:26.015330Z","shell.execute_reply.started":"2025-01-11T12:06:25.686411Z","shell.execute_reply":"2025-01-11T12:06:26.014168Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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Old/New\n\nThe old and new properties appears to be the same across the whole dataset.","metadata":{}},{"cell_type":"code","source":"len(data['Old/New'].unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:06:26.016880Z","iopub.execute_input":"2025-01-11T12:06:26.017371Z","iopub.status.idle":"2025-01-11T12:06:26.059478Z","shell.execute_reply.started":"2025-01-11T12:06:26.017320Z","shell.execute_reply":"2025-01-11T12:06:26.058375Z"}},"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"2"},"metadata":{}}],"execution_count":24},{"cell_type":"code","source":"rows = data.Year < 2018\ncol = 'Old/New'\nbefore_2018 = data.loc[rows, col]\nlen(before_2018.unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:06:26.060516Z","iopub.execute_input":"2025-01-11T12:06:26.060907Z","iopub.status.idle":"2025-01-11T12:06:26.085145Z","shell.execute_reply.started":"2025-01-11T12:06:26.060875Z","shell.execute_reply":"2025-01-11T12:06:26.084093Z"}},"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"2"},"metadata":{}}],"execution_count":25},{"cell_type":"code","source":"rows = data.Year >= 2018\ncol = 'Old/New'\nafter_2018 = data.loc[rows, col]\nlen(after_2018.unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:06:26.086181Z","iopub.execute_input":"2025-01-11T12:06:26.086456Z","iopub.status.idle":"2025-01-11T12:06:26.129200Z","shell.execute_reply.started":"2025-01-11T12:06:26.086430Z","shell.execute_reply":"2025-01-11T12:06:26.128123Z"}},"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"2"},"metadata":{}}],"execution_count":26},{"cell_type":"code","source":"before_2018 = set(before_2018)\nafter_2018 = set(after_2018)\nbefore_2018.difference(after_2018)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:06:26.130177Z","iopub.execute_input":"2025-01-11T12:06:26.130431Z","iopub.status.idle":"2025-01-11T12:06:26.201179Z","shell.execute_reply.started":"2025-01-11T12:06:26.130408Z","shell.execute_reply":"2025-01-11T12:06:26.200009Z"}},"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"set()"},"metadata":{}}],"execution_count":27},{"cell_type":"markdown","source":"We discover 10% of new properties have been purchased.\n","metadata":{"execution":{"iopub.status.busy":"2025-01-05T11:40:17.821040Z","iopub.execute_input":"2025-01-05T11:40:17.821400Z","iopub.status.idle":"2025-01-05T11:40:17.827906Z","shell.execute_reply.started":"2025-01-05T11:40:17.821373Z","shell.execute_reply":"2025-01-05T11:40:17.826919Z"}}},{"cell_type":"code","source":"group = ['Old/New']\ncol = ['Price:Count']\ngrouped_by = data.groupby(group).sum()[col].reset_index()\ngrouped_by.sort_values('Price:Count')","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"grouped_by['Percentage'] = grouped_by['Price:Count'] / np.sum(grouped_by['Price:Count'])\ngrouped_by","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:09:52.256068Z","iopub.execute_input":"2025-01-11T12:09:52.256397Z","iopub.status.idle":"2025-01-11T12:09:52.266339Z","shell.execute_reply.started":"2025-01-11T12:09:52.256370Z","shell.execute_reply":"2025-01-11T12:09:52.265302Z"}},"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":" Old/New Price:Count Percentage\n0 N 12400707 0.890526\n1 Y 1524438 0.109474","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Old/NewPrice:CountPercentage
0N124007070.890526
1Y15244380.109474
\n
"},"metadata":{}}],"execution_count":29},{"cell_type":"code","source":"plt.figure(figsize=(20, 12))\ncolors = ['tab:blue', 'tab:orange']\nplt.bar(grouped_by['Old/New'],grouped_by['Price:Count'], color=colors )\naddlabels(grouped_by['Old/New'],grouped_by['Price:Count'])\nplt.title('Comparison: Number of properties exchanged overtime per type')\nplt.xlabel('Old/New',fontsize=20)\nplt.ylabel('Number of exchanges')\nplt.grid(False)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:09:52.267663Z","iopub.execute_input":"2025-01-11T12:09:52.268192Z","iopub.status.idle":"2025-01-11T12:09:52.589004Z","shell.execute_reply.started":"2025-01-11T12:09:52.268157Z","shell.execute_reply":"2025-01-11T12:09:52.587868Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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Tenure \n\nThe tenure of the property appears to improve through time. Some unspecified tenure was present in the data prior 2018, but none after 2018. This observation may suggest some analysis including the tenure may not be as accurate until 2018.","metadata":{}},{"cell_type":"code","source":"len(data['Tenure'].unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:09:52.589998Z","iopub.execute_input":"2025-01-11T12:09:52.590325Z","iopub.status.idle":"2025-01-11T12:09:52.638016Z","shell.execute_reply.started":"2025-01-11T12:09:52.590296Z","shell.execute_reply":"2025-01-11T12:09:52.636711Z"}},"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"3"},"metadata":{}}],"execution_count":31},{"cell_type":"code","source":"rows = data.Year < 2018\ncol = 'Tenure'\nbefore_2018 = data.loc[rows, col]\nlen(before_2018.unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:09:52.639333Z","iopub.execute_input":"2025-01-11T12:09:52.639719Z","iopub.status.idle":"2025-01-11T12:09:52.664927Z","shell.execute_reply.started":"2025-01-11T12:09:52.639669Z","shell.execute_reply":"2025-01-11T12:09:52.663913Z"}},"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"3"},"metadata":{}}],"execution_count":32},{"cell_type":"code","source":"rows = data.Year >= 2018\ncol = 'Tenure'\nafter_2018 = data.loc[rows, col]\nlen(after_2018.unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:09:52.665975Z","iopub.execute_input":"2025-01-11T12:09:52.666270Z","iopub.status.idle":"2025-01-11T12:09:52.708522Z","shell.execute_reply.started":"2025-01-11T12:09:52.666232Z","shell.execute_reply":"2025-01-11T12:09:52.707604Z"}},"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"2"},"metadata":{}}],"execution_count":33},{"cell_type":"code","source":"before_2018 = set(before_2018)\nafter_2018 = set(after_2018)\nbefore_2018.difference(after_2018)\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:09:52.709443Z","iopub.execute_input":"2025-01-11T12:09:52.709712Z","iopub.status.idle":"2025-01-11T12:09:52.789361Z","shell.execute_reply.started":"2025-01-11T12:09:52.709689Z","shell.execute_reply":"2025-01-11T12:09:52.787927Z"}},"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"{'U'}"},"metadata":{}}],"execution_count":34},{"cell_type":"markdown","source":"We discover free-hold (73%) are likely to be properties with the most exchanged titles, after leasehold (27%). Less than 1% of tenure is unknow, which is very small.\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-01-05T11:40:17.821040Z","iopub.execute_input":"2025-01-05T11:40:17.821400Z","iopub.status.idle":"2025-01-05T11:40:17.827906Z","shell.execute_reply.started":"2025-01-05T11:40:17.821373Z","shell.execute_reply":"2025-01-05T11:40:17.826919Z"}}},{"cell_type":"code","source":"group = ['Tenure']\ncol = ['Price:Count']\ngrouped_by = data.groupby(group).sum()[col].reset_index()\ngrouped_by.sort_values('Price:Count')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:09:52.790458Z","iopub.execute_input":"2025-01-11T12:09:52.790810Z","iopub.status.idle":"2025-01-11T12:13:08.251015Z","shell.execute_reply.started":"2025-01-11T12:09:52.790738Z","shell.execute_reply":"2025-01-11T12:13:08.249837Z"}},"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":" Tenure Price:Count\n2 U 296\n1 L 3818965\n0 F 10105884","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
TenurePrice:Count
2U296
1L3818965
0F10105884
\n
"},"metadata":{}}],"execution_count":35},{"cell_type":"code","source":"grouped_by['Percentage'] = grouped_by['Price:Count'] / np.sum(grouped_by['Price:Count'])\ngrouped_by","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:13:08.252248Z","iopub.execute_input":"2025-01-11T12:13:08.252778Z","iopub.status.idle":"2025-01-11T12:13:08.263206Z","shell.execute_reply.started":"2025-01-11T12:13:08.252718Z","shell.execute_reply":"2025-01-11T12:13:08.261916Z"}},"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":" Tenure Price:Count Percentage\n0 F 10105884 0.725729\n1 L 3818965 0.274250\n2 U 296 0.000021","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
TenurePrice:CountPercentage
0F101058840.725729
1L38189650.274250
2U2960.000021
\n
"},"metadata":{}}],"execution_count":36},{"cell_type":"code","source":"plt.figure(figsize=(20, 12))\ncolors = ['tab:blue', 'tab:orange', 'tab:green']\nplt.bar(grouped_by['Tenure'],grouped_by['Price:Count'], color=colors )\naddlabels(grouped_by['Tenure'],grouped_by['Price:Count'])\nplt.title('Comparison: Number of properties exchanged overtime per type')\nplt.xlabel('Tenure',fontsize=20)\nplt.ylabel('Number of exchanges')\nplt.grid(False)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-11T12:13:08.264473Z","iopub.execute_input":"2025-01-11T12:13:08.264890Z","iopub.status.idle":"2025-01-11T12:13:08.610393Z","shell.execute_reply.started":"2025-01-11T12:13:08.264849Z","shell.execute_reply":"2025-01-11T12:13:08.609116Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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What can we explore using this data?","metadata":{}},{"cell_type":"markdown","source":"We have discovered the dataset is far from being perfect. For example, there is no monthly total value of properties titles exchange. It can be computed using the number of observation _Price:Count_ and _Price:Mean_. \n\nWe should be able to overcome issues with the location using the geo_location datasets. Some better groupings and statistical analysis should be possible to create. The geographical data should help merging with known datasets that hold more population, economical and other socio-economical data. \n\nThe number of titles exchanges per tenure, type of property, old/new, month and year can be used to create some classes to help build some predictive models, using some random-forrest or other type of classifiers. Some possible time series analysis may be possible too.","metadata":{}}]} \ No newline at end of file diff --git a/Data engineering and science/Structuring data/database-normalisation.ipynb b/Data engineering and science/Structuring data/database-normalisation.ipynb new file mode 100644 index 0000000..d68caf7 --- /dev/null +++ b/Data engineering and science/Structuring data/database-normalisation.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":4930282,"sourceType":"datasetVersion","datasetId":2859052}],"dockerImageVersionId":30380,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Database normalisation of olympic data. \n\nDatasets are often denormalised relational databases into large _flat tables_. It is worth considering reversing this data engineering task, so that we understand better the structure of the data, prepare better data for model fitting for advanced statistical methodologies and machine learning algorithms.\n\nIt is worth eliciting the data semantics from the existing data. The dataset description can help us understanding how real-life objects have been represented in the dataset. Categorical data may be identified, ranges of values, and other logical grouping of values. It could help identifying dependent statistical variables. The outcome could also let us transforming the data into other type data, such knowledge graphs with a semantic layer. \n\nSome reading about [Database normalisation](https://en.wikipedia.org/wiki/Database_normalization) can be useful.\n\n## Which dataset are we using?\n\nWe use a dataset based on the Olympics game. We aim to explore whether Decision trees or random forrest may be able to predict a medal winner or not from the data. We use the \n[Olympics 124 years dataset](https://www.kaggle.com/datasets/nitishsharma01/olympics-124-years-datasettill-2020).\n\nWe are assuming all the athletes stated in this dataset have all agreed to have their name added to the data. The name of those athletes are most likely to have been in the public domain at the time of the Olympic games occured.","metadata":{}},{"cell_type":"markdown","source":"# Import libraries and data","metadata":{}},{"cell_type":"code","source":"\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nimport matplotlib.pyplot as plt\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:17:50.352161Z","iopub.execute_input":"2024-01-12T14:17:50.352846Z","iopub.status.idle":"2024-01-12T14:17:50.367584Z","shell.execute_reply.started":"2024-01-12T14:17:50.352799Z","shell.execute_reply":"2024-01-12T14:17:50.366535Z"},"trusted":true},"outputs":[{"name":"stdout","text":"/kaggle/input/olympics-124-years-datasettill-2020/Athletes_summer_games.csv\n/kaggle/input/olympics-124-years-datasettill-2020/Athletes_winter_games.csv\n/kaggle/input/olympics-124-years-datasettill-2020/regions.csv\n","output_type":"stream"}],"execution_count":99},{"cell_type":"code","source":"path_summer = \"/kaggle/input/olympics-124-years-datasettill-2020/Athletes_summer_games.csv\"\npath_winter = \"/kaggle/input/olympics-124-years-datasettill-2020/Athletes_winter_games.csv\"\npath_regions = \"/kaggle/input/olympics-124-years-datasettill-2020/regions.csv\"\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:05.248242Z","iopub.execute_input":"2024-01-12T13:55:05.248872Z","iopub.status.idle":"2024-01-12T13:55:05.254197Z","shell.execute_reply.started":"2024-01-12T13:55:05.248823Z","shell.execute_reply":"2024-01-12T13:55:05.253153Z"},"trusted":true},"outputs":[],"execution_count":3},{"cell_type":"markdown","source":"We upload the data stored in the three csv files. ","metadata":{}},{"cell_type":"markdown","source":"## Summer data","metadata":{}},{"cell_type":"code","source":"summer_df = pd.read_csv(path_summer)\nsummer_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:05.255607Z","iopub.execute_input":"2024-01-12T13:55:05.256624Z","iopub.status.idle":"2024-01-12T13:55:06.236846Z","shell.execute_reply.started":"2024-01-12T13:55:05.256588Z","shell.execute_reply":"2024-01-12T13:55:06.233775Z"},"trusted":true},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"Unnamed: 0 int64\nName object\nSex object\nAge float64\nTeam object\nNOC object\nGames object\nYear int64\nSeason object\nCity object\nSport object\nEvent object\nMedal object\ndtype: object"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"summer_df.shape","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.241170Z","iopub.execute_input":"2024-01-12T13:55:06.242400Z","iopub.status.idle":"2024-01-12T13:55:06.252596Z","shell.execute_reply.started":"2024-01-12T13:55:06.242345Z","shell.execute_reply":"2024-01-12T13:55:06.250129Z"},"trusted":true},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"(237673, 13)"},"metadata":{}}],"execution_count":5},{"cell_type":"code","source":"summer_df.Season.unique()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.254388Z","iopub.execute_input":"2024-01-12T13:55:06.258451Z","iopub.status.idle":"2024-01-12T13:55:06.290075Z","shell.execute_reply.started":"2024-01-12T13:55:06.258390Z","shell.execute_reply":"2024-01-12T13:55:06.288815Z"},"trusted":true},"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"array(['Summer'], dtype=object)"},"metadata":{}}],"execution_count":6},{"cell_type":"code","source":"len(summer_df.NOC.unique())","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.291609Z","iopub.execute_input":"2024-01-12T13:55:06.292348Z","iopub.status.idle":"2024-01-12T13:55:06.322120Z","shell.execute_reply.started":"2024-01-12T13:55:06.292299Z","shell.execute_reply":"2024-01-12T13:55:06.320802Z"},"trusted":true},"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"233"},"metadata":{}}],"execution_count":7},{"cell_type":"markdown","source":"## Winter Olympic Games data","metadata":{}},{"cell_type":"code","source":"winter_df = pd.read_csv(path_winter)\nwinter_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.324718Z","iopub.execute_input":"2024-01-12T13:55:06.325147Z","iopub.status.idle":"2024-01-12T13:55:06.726074Z","shell.execute_reply.started":"2024-01-12T13:55:06.325112Z","shell.execute_reply":"2024-01-12T13:55:06.724853Z"},"trusted":true},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"Unnamed: 0 int64\nName object\nSex object\nAge float64\nTeam object\nNOC object\nGames object\nYear int64\nSeason object\nCity object\nSport object\nEvent object\nMedal object\ndtype: object"},"metadata":{}}],"execution_count":8},{"cell_type":"code","source":"winter_df.shape","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.727859Z","iopub.execute_input":"2024-01-12T13:55:06.728291Z","iopub.status.idle":"2024-01-12T13:55:06.737599Z","shell.execute_reply.started":"2024-01-12T13:55:06.728254Z","shell.execute_reply":"2024-01-12T13:55:06.736193Z"},"trusted":true},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"(48564, 13)"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"winter_df.Season.unique()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.739709Z","iopub.execute_input":"2024-01-12T13:55:06.740211Z","iopub.status.idle":"2024-01-12T13:55:06.752217Z","shell.execute_reply.started":"2024-01-12T13:55:06.740146Z","shell.execute_reply":"2024-01-12T13:55:06.750987Z"},"trusted":true},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"array(['Winter'], dtype=object)"},"metadata":{}}],"execution_count":10},{"cell_type":"markdown","source":"## Regions ","metadata":{}},{"cell_type":"code","source":"regions_df = pd.read_csv(path_regions)\nregions_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.758031Z","iopub.execute_input":"2024-01-12T13:55:06.758440Z","iopub.status.idle":"2024-01-12T13:55:06.774525Z","shell.execute_reply.started":"2024-01-12T13:55:06.758405Z","shell.execute_reply":"2024-01-12T13:55:06.773403Z"},"trusted":true},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"Unnamed: 0 int64\nNOC object\nregion object\nnotes object\ndtype: object"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"regions_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.775846Z","iopub.execute_input":"2024-01-12T13:55:06.776212Z","iopub.status.idle":"2024-01-12T13:55:06.797201Z","shell.execute_reply.started":"2024-01-12T13:55:06.776178Z","shell.execute_reply":"2024-01-12T13:55:06.795828Z"},"trusted":true},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":" Unnamed: 0 NOC region notes\n0 0 EOR Refugee NaN\n1 1 LBN Lebanon NaN\n2 2 SGP Singapore NaN\n3 3 ROC Russia NaN\n4 4 AFG Afghanistan NaN","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Unnamed: 0NOCregionnotes
00EORRefugeeNaN
11LBNLebanonNaN
22SGPSingaporeNaN
33ROCRussiaNaN
44AFGAfghanistanNaN
\n
"},"metadata":{}}],"execution_count":12},{"cell_type":"code","source":"len(regions_df.NOC.unique())","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.799004Z","iopub.execute_input":"2024-01-12T13:55:06.799720Z","iopub.status.idle":"2024-01-12T13:55:06.807683Z","shell.execute_reply.started":"2024-01-12T13:55:06.799674Z","shell.execute_reply":"2024-01-12T13:55:06.806610Z"},"trusted":true},"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"234"},"metadata":{}}],"execution_count":13},{"cell_type":"markdown","source":"## Comparisons of datasets","metadata":{}},{"cell_type":"markdown","source":"The number of columns is the same for both summer and winter datasets. The columns appears to have been harmonised, which is quite helpful. ","metadata":{}},{"cell_type":"code","source":"all(winter_df.columns.isin(summer_df.columns))","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.809849Z","iopub.execute_input":"2024-01-12T13:55:06.810670Z","iopub.status.idle":"2024-01-12T13:55:06.822388Z","shell.execute_reply.started":"2024-01-12T13:55:06.810618Z","shell.execute_reply":"2024-01-12T13:55:06.820722Z"},"trusted":true},"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":14},{"cell_type":"code","source":"winter_df.shape[1] == summer_df.shape[1]","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.824176Z","iopub.execute_input":"2024-01-12T13:55:06.825398Z","iopub.status.idle":"2024-01-12T13:55:06.834026Z","shell.execute_reply.started":"2024-01-12T13:55:06.825348Z","shell.execute_reply":"2024-01-12T13:55:06.832939Z"},"trusted":true},"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":15},{"cell_type":"code","source":"winter_df.columns","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.835713Z","iopub.execute_input":"2024-01-12T13:55:06.836488Z","iopub.status.idle":"2024-01-12T13:55:06.850321Z","shell.execute_reply.started":"2024-01-12T13:55:06.836425Z","shell.execute_reply":"2024-01-12T13:55:06.848792Z"},"trusted":true},"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"Index(['Unnamed: 0', 'Name', 'Sex', 'Age', 'Team', 'NOC', 'Games', 'Year',\n 'Season', 'City', 'Sport', 'Event', 'Medal'],\n dtype='object')"},"metadata":{}}],"execution_count":16},{"cell_type":"markdown","source":"# Merging all the data in one large dataset\n\nThe data is merge in one dataset to support analysis of the datasets, its structure and values. ","metadata":{}},{"cell_type":"code","source":"data_df = pd.concat([summer_df, winter_df])\ndata_df.shape","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.852269Z","iopub.execute_input":"2024-01-12T13:55:06.852903Z","iopub.status.idle":"2024-01-12T13:55:06.977612Z","shell.execute_reply.started":"2024-01-12T13:55:06.852813Z","shell.execute_reply":"2024-01-12T13:55:06.976362Z"},"trusted":true},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"(286237, 13)"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"data_df = pd.merge(data_df, regions_df, left_on = 'NOC', right_on='NOC')\ndata_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:06.979650Z","iopub.execute_input":"2024-01-12T13:55:06.980437Z","iopub.status.idle":"2024-01-12T13:55:07.172302Z","shell.execute_reply.started":"2024-01-12T13:55:06.980395Z","shell.execute_reply":"2024-01-12T13:55:07.171029Z"},"trusted":true},"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"Unnamed: 0_x int64\nName object\nSex object\nAge float64\nTeam object\nNOC object\nGames object\nYear int64\nSeason object\nCity object\nSport object\nEvent object\nMedal object\nUnnamed: 0_y int64\nregion object\nnotes object\ndtype: object"},"metadata":{}}],"execution_count":18},{"cell_type":"markdown","source":"\n\n","metadata":{}},{"cell_type":"markdown","source":"# Do all cell contain a single value?\n\nMost of the cell contain a single value, for the exception of _Games_ column.","metadata":{}},{"cell_type":"code","source":"data_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:07.173939Z","iopub.execute_input":"2024-01-12T13:55:07.175005Z","iopub.status.idle":"2024-01-12T13:55:07.195424Z","shell.execute_reply.started":"2024-01-12T13:55:07.174957Z","shell.execute_reply":"2024-01-12T13:55:07.194371Z"},"trusted":true},"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":" Unnamed: 0_x Name Sex Age Team NOC Games Year \\\n0 0 A Dijiang M 24.0 China CHN 1992 Summer 1992 \n1 1 A Lamusi M 23.0 China CHN 2012 Summer 2012 \n2 1072 Abudoureheman M 22.0 China CHN 2000 Summer 2000 \n3 2611 Ai Linuer M 25.0 China CHN 2004 Summer 2004 \n4 2612 Ai Yanhan F 14.0 China CHN 2016 Summer 2016 \n\n Season City Sport \\\n0 Summer Barcelona Basketball \n1 Summer London Judo \n2 Summer Sydney Boxing \n3 Summer Athina Wrestling \n4 Summer Rio de Janeiro Swimming \n\n Event Medal Unnamed: 0_y region notes \n0 Basketball Men's Basketball NaN 45 China NaN \n1 Judo Men's Extra-Lightweight NaN 45 China NaN \n2 Boxing Men's Middleweight NaN 45 China NaN \n3 Wrestling Men's Lightweight, Greco-Roman NaN 45 China NaN \n4 Swimming Women's 200 metres Freestyle NaN 45 China NaN ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Unnamed: 0_xNameSexAgeTeamNOCGamesYearSeasonCitySportEventMedalUnnamed: 0_yregionnotes
00A DijiangM24.0ChinaCHN1992 Summer1992SummerBarcelonaBasketballBasketball Men's BasketballNaN45ChinaNaN
11A LamusiM23.0ChinaCHN2012 Summer2012SummerLondonJudoJudo Men's Extra-LightweightNaN45ChinaNaN
21072AbudourehemanM22.0ChinaCHN2000 Summer2000SummerSydneyBoxingBoxing Men's MiddleweightNaN45ChinaNaN
32611Ai LinuerM25.0ChinaCHN2004 Summer2004SummerAthinaWrestlingWrestling Men's Lightweight, Greco-RomanNaN45ChinaNaN
42612Ai YanhanF14.0ChinaCHN2016 Summer2016SummerRio de JaneiroSwimmingSwimming Women's 200 metres FreestyleNaN45ChinaNaN
\n
"},"metadata":{}}],"execution_count":19},{"cell_type":"markdown","source":"### Games","metadata":{}},{"cell_type":"markdown","source":"A brief inspection of the data shows the column _Games_ contains several values - the year and the season. Both values are available in the dataset in the columns _City and Season_. Counting the number of event per Olympic games (see below) suggests the values in the column _Games_ have the same values. We have the same number of rows. ","metadata":{}},{"cell_type":"code","source":"columns = ['Games']\none_col = data_df.groupby(columns).count()[ 'Event'].reset_index()\n\nprint(\"-- columns -- \\n\", one_col.dtypes, sep = '')\nprint(\"--rows and columns--\\n \", one_col.shape, sep = '')","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:07.197013Z","iopub.execute_input":"2024-01-12T13:55:07.197430Z","iopub.status.idle":"2024-01-12T13:55:07.596562Z","shell.execute_reply.started":"2024-01-12T13:55:07.197396Z","shell.execute_reply":"2024-01-12T13:55:07.595337Z"},"trusted":true},"outputs":[{"name":"stdout","text":"-- columns -- \nGames object\nEvent int64\ndtype: object\n--rows and columns--\n (52, 2)\n","output_type":"stream"}],"execution_count":20},{"cell_type":"code","source":"columns = ['Games', 'Year', 'Season']\nthree_cols = data_df.groupby(columns).count()[ 'Event'].reset_index() \nthree_cols.dtypes\nprint(\"--columns--\\n\", three_cols.dtypes, sep = '')\nprint(\"--rows and columns--\\n\", three_cols.shape, sep = '')","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:07.598061Z","iopub.execute_input":"2024-01-12T13:55:07.598420Z","iopub.status.idle":"2024-01-12T13:55:07.873139Z","shell.execute_reply.started":"2024-01-12T13:55:07.598388Z","shell.execute_reply":"2024-01-12T13:55:07.871824Z"},"trusted":true},"outputs":[{"name":"stdout","text":"--columns--\nGames object\nYear int64\nSeason object\nEvent int64\ndtype: object\n--rows and columns--\n(52, 4)\n","output_type":"stream"}],"execution_count":21},{"cell_type":"code","source":"sum(three_cols.Event == one_col.Event) == three_cols.shape[0]","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:07.874626Z","iopub.execute_input":"2024-01-12T13:55:07.874976Z","iopub.status.idle":"2024-01-12T13:55:07.883344Z","shell.execute_reply.started":"2024-01-12T13:55:07.874945Z","shell.execute_reply":"2024-01-12T13:55:07.881777Z"},"trusted":true},"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":22},{"cell_type":"code","source":"sum(three_cols.Event == one_col.Event) == one_col.shape[0]","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:07.885270Z","iopub.execute_input":"2024-01-12T13:55:07.886017Z","iopub.status.idle":"2024-01-12T13:55:07.895069Z","shell.execute_reply.started":"2024-01-12T13:55:07.885972Z","shell.execute_reply":"2024-01-12T13:55:07.893781Z"},"trusted":true},"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":23},{"cell_type":"markdown","source":"We remove the _Games_ column from the dataset, it contains some redundant content. Other columns provide those values.","metadata":{}},{"cell_type":"code","source":"col_to_drop = ['Games']\ndata_df = data_df.drop(columns = col_to_drop)\ndata_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:07.897097Z","iopub.execute_input":"2024-01-12T13:55:07.897648Z","iopub.status.idle":"2024-01-12T13:55:07.957183Z","shell.execute_reply.started":"2024-01-12T13:55:07.897599Z","shell.execute_reply":"2024-01-12T13:55:07.955953Z"},"trusted":true},"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"Unnamed: 0_x int64\nName object\nSex object\nAge float64\nTeam object\nNOC object\nYear int64\nSeason object\nCity object\nSport object\nEvent object\nMedal object\nUnnamed: 0_y int64\nregion object\nnotes object\ndtype: object"},"metadata":{}}],"execution_count":24},{"cell_type":"markdown","source":"## Sport and events\nThe event columns appears to describe a specific event within a competition. Some gender appears to in the description but not in all. So no further transformation may be required. ","metadata":{}},{"cell_type":"code","source":"data_df.Sport.unique()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:07.959924Z","iopub.execute_input":"2024-01-12T13:55:07.961208Z","iopub.status.idle":"2024-01-12T13:55:07.997109Z","shell.execute_reply.started":"2024-01-12T13:55:07.961165Z","shell.execute_reply":"2024-01-12T13:55:07.995975Z"},"trusted":true},"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"array(['Basketball', 'Judo', 'Boxing', 'Wrestling', 'Swimming',\n 'Softball', 'Hockey', 'Archery', 'Triathlon', 'Football',\n 'Rhythmic Gymnastics', 'Athletics', 'Badminton', 'Fencing',\n 'Gymnastics', 'Volleyball', 'Baseball', 'Water Polo', 'Shooting',\n 'Weightlifting', 'Cycling', 'Rowing', 'Sailing', 'Diving',\n 'Modern Pentathlon', 'Art Competitions', 'Synchronized Swimming',\n 'Handball', 'Canoeing', 'Table Tennis', 'Tennis', 'Taekwondo',\n 'Beach Volleyball', 'Trampolining', 'Golf', 'Equestrianism',\n 'Cycling Track', 'Equestrian', 'Canoe Sprint', 'Rugby Sevens',\n 'Canoe Slalom', 'Trampoline Gymnastics', 'Artistic Gymnastics',\n 'Artistic Swimming', '3x3 Basketball', 'Karate', 'Sport Climbing',\n 'Cycling Road', 'Marathon Swimming', 'Cycling Mountain Bike',\n 'Skateboarding', 'Speed Skating', 'Short Track Speed Skating',\n 'Curling', 'Figure Skating', 'Snowboarding',\n 'Cross Country Skiing', 'Ice Hockey', 'Freestyle Skiing',\n 'Alpine Skiing', 'Biathlon', 'Ski Jumping', 'Tug-Of-War',\n 'Cycling BMX Racing', 'Bobsleigh', 'Military Ski Patrol',\n 'Nordic Combined', 'Luge', 'Skeleton', 'Rugby', 'Cricket',\n 'Croquet', 'Polo', 'Motorboating', 'Surfing',\n 'Cycling BMX Freestyle', 'Basque Pelota', 'Baseball/Softball',\n 'Roque', 'Lacrosse', 'Jeu De Paume', 'Alpinism', 'Aeronautics',\n 'Racquets'], dtype=object)"},"metadata":{}}],"execution_count":25},{"cell_type":"code","source":"columns = ['Sport']\ndata_df.groupby(columns).count()['Event'].reset_index()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:07.998379Z","iopub.execute_input":"2024-01-12T13:55:07.998761Z","iopub.status.idle":"2024-01-12T13:55:08.255909Z","shell.execute_reply.started":"2024-01-12T13:55:07.998699Z","shell.execute_reply":"2024-01-12T13:55:08.254667Z"},"trusted":true},"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":" Sport Event\n0 3x3 Basketball 64\n1 Aeronautics 1\n2 Alpine Skiing 8829\n3 Alpinism 25\n4 Archery 2592\n.. ... ...\n79 Tug-Of-War 170\n80 Volleyball 3692\n81 Water Polo 4132\n82 Weightlifting 4134\n83 Wrestling 7443\n\n[84 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
SportEvent
03x3 Basketball64
1Aeronautics1
2Alpine Skiing8829
3Alpinism25
4Archery2592
.........
79Tug-Of-War170
80Volleyball3692
81Water Polo4132
82Weightlifting4134
83Wrestling7443
\n

84 rows × 2 columns

\n
"},"metadata":{}}],"execution_count":26},{"cell_type":"code","source":"columns = ['Sport','Event']\ndata_df.groupby(columns).count()['Year'].reset_index()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:08.257266Z","iopub.execute_input":"2024-01-12T13:55:08.257614Z","iopub.status.idle":"2024-01-12T13:55:08.527341Z","shell.execute_reply.started":"2024-01-12T13:55:08.257583Z","shell.execute_reply":"2024-01-12T13:55:08.526253Z"},"trusted":true},"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":" Sport Event Year\n0 3x3 Basketball Men Team 32\n1 3x3 Basketball Women Team 32\n2 Aeronautics Aeronautics Mixed Aeronautics 1\n3 Alpine Skiing Alpine Skiing Men's Combined 569\n4 Alpine Skiing Alpine Skiing Men's Downhill 1164\n... ... ... ...\n1105 Wrestling Wrestling Women's Flyweight, Freestyle 68\n1106 Wrestling Wrestling Women's Heavyweight, Freestyle 64\n1107 Wrestling Wrestling Women's Light-Heavyweight, Freestyle 18\n1108 Wrestling Wrestling Women's Lightweight, Freestyle 67\n1109 Wrestling Wrestling Women's Middleweight, Freestyle 68\n\n[1110 rows x 3 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
SportEventYear
03x3 BasketballMen Team32
13x3 BasketballWomen Team32
2AeronauticsAeronautics Mixed Aeronautics1
3Alpine SkiingAlpine Skiing Men's Combined569
4Alpine SkiingAlpine Skiing Men's Downhill1164
............
1105WrestlingWrestling Women's Flyweight, Freestyle68
1106WrestlingWrestling Women's Heavyweight, Freestyle64
1107WrestlingWrestling Women's Light-Heavyweight, Freestyle18
1108WrestlingWrestling Women's Lightweight, Freestyle67
1109WrestlingWrestling Women's Middleweight, Freestyle68
\n

1110 rows × 3 columns

\n
"},"metadata":{}}],"execution_count":27},{"cell_type":"code","source":"len(data_df.Event.unique())","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:08.528480Z","iopub.execute_input":"2024-01-12T13:55:08.528824Z","iopub.status.idle":"2024-01-12T13:55:08.567115Z","shell.execute_reply.started":"2024-01-12T13:55:08.528795Z","shell.execute_reply":"2024-01-12T13:55:08.565774Z"},"trusted":true},"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"1071"},"metadata":{}}],"execution_count":28},{"cell_type":"code","source":"data_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:08.574717Z","iopub.execute_input":"2024-01-12T13:55:08.575319Z","iopub.status.idle":"2024-01-12T13:55:08.583808Z","shell.execute_reply.started":"2024-01-12T13:55:08.575274Z","shell.execute_reply":"2024-01-12T13:55:08.582830Z"},"trusted":true},"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"Unnamed: 0_x int64\nName object\nSex object\nAge float64\nTeam object\nNOC object\nYear int64\nSeason object\nCity object\nSport object\nEvent object\nMedal object\nUnnamed: 0_y int64\nregion object\nnotes object\ndtype: object"},"metadata":{}}],"execution_count":29},{"cell_type":"markdown","source":"# Do each row has a unique identifier?\n\nThe current data has yet some unique keys. Any row cannot be represented uniquely. The dataframe has an index that is unique. However, no logical grouping of the data has yet to be completed. So we explore how logical grouping of data can be made. ","metadata":{}},{"cell_type":"markdown","source":"## Games\n\nFrom our previous discussions and evidences, we can conclude the real-life concept of a game can be uniquely identified by the composite key year and season. We explore whether other fields may be needed. We identify the entity games as follow: \n\n__game__(year, season, city)","metadata":{}},{"cell_type":"code","source":"columns = ['Year','Season','City']\ndata_df.groupby(columns).count().reset_index()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:08.585703Z","iopub.execute_input":"2024-01-12T13:55:08.586517Z","iopub.status.idle":"2024-01-12T13:55:08.860322Z","shell.execute_reply.started":"2024-01-12T13:55:08.586446Z","shell.execute_reply":"2024-01-12T13:55:08.859186Z"},"trusted":true},"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":" Year Season City Unnamed: 0_x Name Sex Age \\\n0 1896 Summer Athina 380 380 380 217 \n1 1900 Summer Paris 1936 1936 1936 1146 \n2 1904 Summer St. Louis 1301 1301 1301 1027 \n3 1906 Summer Athina 1733 1733 1733 990 \n4 1908 Summer London 3101 3101 3101 2452 \n5 1912 Summer Stockholm 4040 4040 4040 3884 \n6 1920 Summer Antwerpen 4292 4292 4292 3447 \n7 1924 Summer Paris 5233 5233 5233 4148 \n8 1924 Winter Chamonix 460 460 460 403 \n9 1928 Summer Amsterdam 4992 4992 4992 4119 \n10 1928 Winter Sankt Moritz 582 582 582 492 \n11 1932 Summer Los Angeles 2969 2969 2969 2662 \n12 1932 Winter Lake Placid 352 352 352 329 \n13 1936 Summer Berlin 6506 6506 6506 6304 \n14 1936 Winter Garmisch-Partenkirchen 895 895 895 884 \n15 1948 Summer London 6405 6405 6405 5233 \n16 1948 Winter Sankt Moritz 1075 1075 1075 1071 \n17 1952 Summer Helsinki 8270 8270 8270 7993 \n18 1952 Winter Oslo 1088 1088 1088 1088 \n19 1956 Summer Melbourne 4829 4829 4829 4256 \n20 1956 Summer Stockholm 298 298 298 258 \n21 1956 Winter Cortina d'Ampezzo 1307 1307 1307 1282 \n22 1960 Summer Roma 8119 8119 8119 7906 \n23 1960 Winter Squaw Valley 1116 1116 1116 1108 \n24 1964 Summer Tokyo 7702 7702 7702 7659 \n25 1964 Winter Innsbruck 1778 1778 1778 1765 \n26 1968 Summer Mexico City 8588 8588 8588 8489 \n27 1968 Winter Grenoble 1891 1891 1891 1872 \n28 1972 Summer Munich 10304 10304 10304 10211 \n29 1972 Winter Sapporo 1655 1655 1655 1652 \n30 1976 Summer Montreal 8641 8641 8641 8600 \n31 1976 Winter Innsbruck 1861 1861 1861 1850 \n32 1980 Summer Moskva 7191 7191 7191 7005 \n33 1980 Winter Lake Placid 1746 1746 1746 1745 \n34 1984 Summer Los Angeles 9454 9454 9454 9249 \n35 1984 Winter Sarajevo 2134 2134 2134 2123 \n36 1988 Summer Seoul 12037 12037 12037 11931 \n37 1988 Winter Calgary 2639 2639 2639 2635 \n38 1992 Summer Barcelona 12977 12977 12977 12934 \n39 1992 Winter Albertville 3436 3436 3436 3435 \n40 1994 Winter Lillehammer 3160 3160 3160 3158 \n41 1996 Summer Atlanta 13780 13780 13780 13772 \n42 1998 Winter Nagano 3605 3605 3605 3603 \n43 2000 Summer Sydney 13821 13821 13821 13820 \n44 2002 Winter Salt Lake City 4109 4109 4109 4109 \n45 2004 Summer Athina 13443 13443 13443 13443 \n46 2006 Winter Torino 4382 4382 4382 4382 \n47 2008 Summer Beijing 13602 13602 13602 13600 \n48 2010 Winter Vancouver 4402 4402 4402 4402 \n49 2012 Summer London 12920 12920 12920 12920 \n50 2014 Winter Sochi 4891 4891 4891 4891 \n51 2016 Summer Rio de Janeiro 13688 13688 13688 13688 \n52 2020 Summer Tokyo 15121 15121 15121 15121 \n\n Team NOC Sport Event Medal Unnamed: 0_y region notes \n0 380 380 380 380 143 380 380 0 \n1 1936 1936 1936 1936 604 1936 1936 9 \n2 1301 1301 1301 1301 486 1301 1301 1 \n3 1733 1733 1733 1733 458 1733 1733 40 \n4 3101 3101 3101 3101 831 3101 3101 89 \n5 4040 4040 4040 4040 941 4040 4038 114 \n6 4292 4292 4292 4292 1308 4292 4292 15 \n7 5233 5233 5233 5233 832 5233 5233 116 \n8 460 460 460 460 130 460 460 7 \n9 4992 4992 4992 4992 734 4992 4992 86 \n10 582 582 582 582 89 582 582 8 \n11 2969 2969 2969 2969 647 2969 2969 1 \n12 352 352 352 352 92 352 352 0 \n13 6506 6506 6506 6506 917 6506 6506 173 \n14 895 895 895 895 108 895 895 24 \n15 6405 6405 6405 6405 852 6405 6405 171 \n16 1075 1075 1075 1075 135 1075 1075 30 \n17 8270 8270 8270 8270 897 8270 8270 216 \n18 1088 1088 1088 1088 136 1088 1088 9 \n19 4829 4829 4829 4829 857 4829 4829 53 \n20 298 298 298 298 36 298 298 0 \n21 1307 1307 1307 1307 150 1307 1307 36 \n22 8119 8119 8119 8119 911 8119 8119 337 \n23 1116 1116 1116 1116 147 1116 1116 0 \n24 7702 7702 7702 7702 1029 7702 7702 195 \n25 1778 1778 1778 1778 186 1778 1778 54 \n26 8588 8588 8588 8588 1057 8588 8588 191 \n27 1891 1891 1891 1891 199 1891 1891 46 \n28 10304 10304 10304 10304 1215 10304 10304 284 \n29 1655 1655 1655 1655 199 1655 1655 31 \n30 8641 8641 8641 8641 1320 8641 8641 212 \n31 1861 1861 1861 1861 211 1861 1861 41 \n32 7191 7191 7191 7191 1384 7191 7191 195 \n33 1746 1746 1746 1746 218 1746 1746 27 \n34 9454 9454 9454 9454 1476 9454 9454 359 \n35 2134 2134 2134 2134 222 2134 2134 115 \n36 12037 12037 12037 12037 1582 12037 12037 372 \n37 2639 2639 2639 2639 263 2639 2639 59 \n38 12977 12977 12977 12977 1712 12977 12977 211 \n39 3436 3436 3436 3436 318 3436 3436 81 \n40 3160 3160 3160 3160 331 3160 3160 12 \n41 13780 13780 13780 13780 1842 13780 13780 181 \n42 3605 3605 3605 3605 440 3605 3605 13 \n43 13821 13821 13821 13821 2004 13821 13821 232 \n44 4109 4109 4109 4109 478 4109 4109 24 \n45 13443 13443 13443 13443 2001 13443 13443 183 \n46 4382 4382 4382 4382 526 4382 4382 14 \n47 13602 13602 13602 13602 2048 13602 13599 108 \n48 4402 4402 4402 4402 520 4402 4402 3 \n49 12920 12920 12920 12920 1941 12920 12917 120 \n50 4891 4891 4891 4891 597 4891 4891 4 \n51 13688 13688 13688 13688 2023 13688 13675 137 \n52 15121 15121 15121 15121 2449 15121 15119 124 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearSeasonCityUnnamed: 0_xNameSexAgeTeamNOCSportEventMedalUnnamed: 0_yregionnotes
01896SummerAthina3803803802173803803803801433803800
11900SummerParis19361936193611461936193619361936604193619369
21904SummerSt. Louis13011301130110271301130113011301486130113011
31906SummerAthina17331733173399017331733173317334581733173340
41908SummerLondon310131013101245231013101310131018313101310189
51912SummerStockholm4040404040403884404040404040404094140404038114
61920SummerAntwerpen4292429242923447429242924292429213084292429215
71924SummerParis5233523352334148523352335233523383252335233116
81924WinterChamonix4604604604034604604604601304604607
91928SummerAmsterdam499249924992411949924992499249927344992499286
101928WinterSankt Moritz582582582492582582582582895825828
111932SummerLos Angeles29692969296926622969296929692969647296929691
121932WinterLake Placid352352352329352352352352923523520
131936SummerBerlin6506650665066304650665066506650691765066506173
141936WinterGarmisch-Partenkirchen89589589588489589589589510889589524
151948SummerLondon6405640564055233640564056405640585264056405171
161948WinterSankt Moritz107510751075107110751075107510751351075107530
171952SummerHelsinki8270827082707993827082708270827089782708270216
181952WinterOslo10881088108810881088108810881088136108810889
191956SummerMelbourne482948294829425648294829482948298574829482953
201956SummerStockholm298298298258298298298298362982980
211956WinterCortina d'Ampezzo130713071307128213071307130713071501307130736
221960SummerRoma8119811981197906811981198119811991181198119337
231960WinterSquaw Valley11161116111611081116111611161116147111611160
241964SummerTokyo77027702770276597702770277027702102977027702195
251964WinterInnsbruck177817781778176517781778177817781861778177854
261968SummerMexico City85888588858884898588858885888588105785888588191
271968WinterGrenoble189118911891187218911891189118911991891189146
281972SummerMunich103041030410304102111030410304103041030412151030410304284
291972WinterSapporo165516551655165216551655165516551991655165531
301976SummerMontreal86418641864186008641864186418641132086418641212
311976WinterInnsbruck186118611861185018611861186118612111861186141
321980SummerMoskva71917191719170057191719171917191138471917191195
331980WinterLake Placid174617461746174517461746174617462181746174627
341984SummerLos Angeles94549454945492499454945494549454147694549454359
351984WinterSarajevo2134213421342123213421342134213422221342134115
361988SummerSeoul120371203712037119311203712037120371203715821203712037372
371988WinterCalgary263926392639263526392639263926392632639263959
381992SummerBarcelona129771297712977129341297712977129771297717121297712977211
391992WinterAlbertville343634363436343534363436343634363183436343681
401994WinterLillehammer316031603160315831603160316031603313160316012
411996SummerAtlanta137801378013780137721378013780137801378018421378013780181
421998WinterNagano360536053605360336053605360536054403605360513
432000SummerSydney138211382113821138201382113821138211382120041382113821232
442002WinterSalt Lake City410941094109410941094109410941094784109410924
452004SummerAthina134431344313443134431344313443134431344320011344313443183
462006WinterTorino438243824382438243824382438243825264382438214
472008SummerBeijing136021360213602136001360213602136021360220481360213599108
482010WinterVancouver44024402440244024402440244024402520440244023
492012SummerLondon129201292012920129201292012920129201292019411292012917120
502014WinterSochi48914891489148914891489148914891597489148914
512016SummerRio de Janeiro136881368813688136881368813688136881368820231368813675137
522020SummerTokyo151211512115121151211512115121151211512124491512115119124
\n
"},"metadata":{}}],"execution_count":30},{"cell_type":"markdown","source":"We extract the columns year, season and city as one logical grouping and store them in a new dataframe. ","metadata":{}},{"cell_type":"code","source":"columns = ['Year', 'Season', 'City']\ngames = data_df.loc[:, columns].copy(deep = True)\ngames = games.drop_duplicates(subset=columns)\nprint(\"--columns--\\n\", games.dtypes, sep = '')\nprint(\"--rows and columns--\\n\", games.shape, sep = '')\ngames.sort_values('Year')","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:08.861821Z","iopub.execute_input":"2024-01-12T13:55:08.862883Z","iopub.status.idle":"2024-01-12T13:55:08.948264Z","shell.execute_reply.started":"2024-01-12T13:55:08.862843Z","shell.execute_reply":"2024-01-12T13:55:08.947301Z"},"trusted":true},"outputs":[{"name":"stdout","text":"--columns--\nYear int64\nSeason object\nCity object\ndtype: object\n--rows and columns--\n(53, 3)\n","output_type":"stream"},{"execution_count":31,"output_type":"execute_result","data":{"text/plain":" Year Season City\n7074 1896 Summer Athina\n5716 1900 Summer Paris\n21672 1904 Summer St. Louis\n5733 1906 Summer Athina\n5722 1908 Summer London\n5730 1912 Summer Stockholm\n5715 1920 Summer Antwerpen\n5765 1924 Summer Paris\n19335 1924 Winter Chamonix\n5724 1928 Summer Amsterdam\n15112 1928 Winter Sankt Moritz\n19324 1932 Winter Lake Placid\n1700 1932 Summer Los Angeles\n23 1936 Summer Berlin\n14996 1936 Winter Garmisch-Partenkirchen\n98 1948 Summer London\n9337 1948 Winter Sankt Moritz\n9338 1952 Winter Oslo\n3083 1952 Summer Helsinki\n5794 1956 Summer Melbourne\n5731 1956 Summer Stockholm\n15048 1956 Winter Cortina d'Ampezzo\n9410 1960 Winter Squaw Valley\n5753 1960 Summer Roma\n5795 1964 Summer Tokyo\n9333 1964 Winter Innsbruck\n5798 1968 Summer Mexico City\n9331 1968 Winter Grenoble\n5723 1972 Summer Munich\n14988 1972 Winter Sapporo\n14990 1976 Winter Innsbruck\n5797 1976 Summer Montreal\n5872 1980 Summer Moskva\n4905 1980 Winter Lake Placid\n10 1984 Summer Los Angeles\n4906 1984 Winter Sarajevo\n44 1988 Summer Seoul\n5035 1988 Winter Calgary\n0 1992 Summer Barcelona\n4921 1992 Winter Albertville\n4922 1994 Winter Lillehammer\n6 1996 Summer Atlanta\n4899 1998 Winter Nagano\n2 2000 Summer Sydney\n4902 2002 Winter Salt Lake City\n3 2004 Summer Athina\n4897 2006 Winter Torino\n8 2008 Summer Beijing\n4907 2010 Winter Vancouver\n1 2012 Summer London\n4903 2014 Winter Sochi\n4 2016 Summer Rio de Janeiro\n4323 2020 Summer Tokyo","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearSeasonCity
70741896SummerAthina
57161900SummerParis
216721904SummerSt. Louis
57331906SummerAthina
57221908SummerLondon
57301912SummerStockholm
57151920SummerAntwerpen
57651924SummerParis
193351924WinterChamonix
57241928SummerAmsterdam
151121928WinterSankt Moritz
193241932WinterLake Placid
17001932SummerLos Angeles
231936SummerBerlin
149961936WinterGarmisch-Partenkirchen
981948SummerLondon
93371948WinterSankt Moritz
93381952WinterOslo
30831952SummerHelsinki
57941956SummerMelbourne
57311956SummerStockholm
150481956WinterCortina d'Ampezzo
94101960WinterSquaw Valley
57531960SummerRoma
57951964SummerTokyo
93331964WinterInnsbruck
57981968SummerMexico City
93311968WinterGrenoble
57231972SummerMunich
149881972WinterSapporo
149901976WinterInnsbruck
57971976SummerMontreal
58721980SummerMoskva
49051980WinterLake Placid
101984SummerLos Angeles
49061984WinterSarajevo
441988SummerSeoul
50351988WinterCalgary
01992SummerBarcelona
49211992WinterAlbertville
49221994WinterLillehammer
61996SummerAtlanta
48991998WinterNagano
22000SummerSydney
49022002WinterSalt Lake City
32004SummerAthina
48972006WinterTorino
82008SummerBeijing
49072010WinterVancouver
12012SummerLondon
49032014WinterSochi
42016SummerRio de Janeiro
43232020SummerTokyo
\n
"},"metadata":{}}],"execution_count":31},{"cell_type":"markdown","source":"## Sports and events\nThe previous discussion suggests that events and sports are unique. They could be a logical grouping too. However, at this stage, we keep both as unique entity. \n\n__sports__(sport)\n\n__event__(event)\n\n","metadata":{}},{"cell_type":"code","source":"sport_values = data_df.Sport.unique()\nsports = pd.DataFrame({'sport': sport_values})\nprint(\"--columns--\\n\", sports.dtypes, sep = '')\nprint(\"--rows and columns--\\n\", sports.shape, sep = '')\nsports","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:08.949500Z","iopub.execute_input":"2024-01-12T13:55:08.950481Z","iopub.status.idle":"2024-01-12T13:55:08.988707Z","shell.execute_reply.started":"2024-01-12T13:55:08.950441Z","shell.execute_reply":"2024-01-12T13:55:08.987556Z"},"trusted":true},"outputs":[{"name":"stdout","text":"--columns--\nsport object\ndtype: object\n--rows and columns--\n(84, 1)\n","output_type":"stream"},{"execution_count":32,"output_type":"execute_result","data":{"text/plain":" sport\n0 Basketball\n1 Judo\n2 Boxing\n3 Wrestling\n4 Swimming\n.. ...\n79 Lacrosse\n80 Jeu De Paume\n81 Alpinism\n82 Aeronautics\n83 Racquets\n\n[84 rows x 1 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
sport
0Basketball
1Judo
2Boxing
3Wrestling
4Swimming
......
79Lacrosse
80Jeu De Paume
81Alpinism
82Aeronautics
83Racquets
\n

84 rows × 1 columns

\n
"},"metadata":{}}],"execution_count":32},{"cell_type":"code","source":"event_values = data_df.Event.unique()\nevents = pd.DataFrame({'sport': event_values})\n\nprint(\"--columns--\\n\", events.dtypes, sep = '')\nprint(\"--rows and columns--\\n\", events.shape, sep = '')\nevents","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:08.990423Z","iopub.execute_input":"2024-01-12T13:55:08.991320Z","iopub.status.idle":"2024-01-12T13:55:09.039471Z","shell.execute_reply.started":"2024-01-12T13:55:08.991275Z","shell.execute_reply":"2024-01-12T13:55:09.038349Z"},"trusted":true},"outputs":[{"name":"stdout","text":"--columns--\nsport object\ndtype: object\n--rows and columns--\n(1071, 1)\n","output_type":"stream"},{"execution_count":33,"output_type":"execute_result","data":{"text/plain":" sport\n0 Basketball Men's Basketball\n1 Judo Men's Extra-Lightweight\n2 Boxing Men's Middleweight\n3 Wrestling Men's Lightweight, Greco-Roman\n4 Swimming Women's 200 metres Freestyle\n... ...\n1066 Racquets Men's Singles\n1067 Racquets Men's Doubles\n1068 Motorboating Mixed B-Class (Under 60 Feet)\n1069 Motorboating Mixed C-Class\n1070 Sailing Mixed 18 foot\n\n[1071 rows x 1 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
sport
0Basketball Men's Basketball
1Judo Men's Extra-Lightweight
2Boxing Men's Middleweight
3Wrestling Men's Lightweight, Greco-Roman
4Swimming Women's 200 metres Freestyle
......
1066Racquets Men's Singles
1067Racquets Men's Doubles
1068Motorboating Mixed B-Class (Under 60 Feet)
1069Motorboating Mixed C-Class
1070Sailing Mixed 18 foot
\n

1071 rows × 1 columns

\n
"},"metadata":{}}],"execution_count":33},{"cell_type":"markdown","source":"## Athletes and Teams \n\nWe group logically the name, gender, age and team name. We discover that repetitions of teams appears in our grouping. So we keep separate the athlete and the team.","metadata":{}},{"cell_type":"code","source":"columns = ['Name', 'Sex', 'Age', 'Team']\nathlete_df = data_df.groupby(columns).count()['Sport'].reset_index()\nprint(\"--columns--\\n\", athlete_df.dtypes, sep = '')\nprint(\"--rows and columns--\\n\", athlete_df.shape, sep = '')","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:09.041228Z","iopub.execute_input":"2024-01-12T13:55:09.041694Z","iopub.status.idle":"2024-01-12T13:55:09.670677Z","shell.execute_reply.started":"2024-01-12T13:55:09.041636Z","shell.execute_reply":"2024-01-12T13:55:09.669502Z"},"trusted":true},"outputs":[{"name":"stdout","text":"--columns--\nName object\nSex object\nAge float64\nTeam object\nSport int64\ndtype: object\n--rows and columns--\n(193812, 5)\n","output_type":"stream"}],"execution_count":34},{"cell_type":"code","source":"athlete_df.sort_values(['Team'])","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:09.673758Z","iopub.execute_input":"2024-01-12T13:55:09.674410Z","iopub.status.idle":"2024-01-12T13:55:09.868013Z","shell.execute_reply.started":"2024-01-12T13:55:09.674374Z","shell.execute_reply":"2024-01-12T13:55:09.866584Z"},"trusted":true},"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":" Name Sex Age \\\n65729 Harald von Musil M 44.0 \n65636 Harald Fereberger M 23.0 \n88807 Jos Pablo Eustaquio Manuel Francisco Escandn y... M 44.0 \n88277 Jos Eustaquio Luis Francisco Escandn y Barrn M 38.0 \n88614 Jos Manuel Mara del Corazn de Jess Escandn y B... M 42.0 \n... ... .. ... \n18181 Axel Gustaf Estlander M 35.0 \n26185 Carl-Oscar Girsn M 23.0 \n33651 Curt Magnus Wilhelm Andstn M 30.0 \n21487 Bertel Jusln M 31.0 \n79572 Jarl Oskar Wilhelm Andstn M 27.0 \n\n Team Sport \n65729 30. Februar 1 \n65636 30. Februar 1 \n88807 A North American Team 1 \n88277 A North American Team 1 \n88614 A North American Team 1 \n... ... ... \n18181 rn-2 1 \n26185 rn-2 1 \n33651 rn-2 1 \n21487 rn-2 1 \n79572 rn-2 1 \n\n[193812 rows x 5 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
NameSexAgeTeamSport
65729Harald von MusilM44.030. Februar1
65636Harald FerebergerM23.030. Februar1
88807Jos Pablo Eustaquio Manuel Francisco Escandn y...M44.0A North American Team1
88277Jos Eustaquio Luis Francisco Escandn y BarrnM38.0A North American Team1
88614Jos Manuel Mara del Corazn de Jess Escandn y B...M42.0A North American Team1
..................
18181Axel Gustaf EstlanderM35.0rn-21
26185Carl-Oscar GirsnM23.0rn-21
33651Curt Magnus Wilhelm AndstnM30.0rn-21
21487Bertel JuslnM31.0rn-21
79572Jarl Oskar Wilhelm AndstnM27.0rn-21
\n

193812 rows × 5 columns

\n
"},"metadata":{}}],"execution_count":35},{"cell_type":"code","source":"columns = ['Name', 'Sex', 'Age']\nathlete_df = data_df.groupby(columns).count()['Team'].reset_index()\nprint(\"--columns--\\n\", athlete_df.dtypes, sep = '')\nprint(\"--rows and columns--\\n\", athlete_df.shape, sep = '')","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:09.869273Z","iopub.execute_input":"2024-01-12T13:55:09.869777Z","iopub.status.idle":"2024-01-12T13:55:10.508882Z","shell.execute_reply.started":"2024-01-12T13:55:09.869745Z","shell.execute_reply":"2024-01-12T13:55:10.507761Z"},"trusted":true},"outputs":[{"name":"stdout","text":"--columns--\nName object\nSex object\nAge float64\nTeam int64\ndtype: object\n--rows and columns--\n(192254, 4)\n","output_type":"stream"}],"execution_count":36},{"cell_type":"code","source":"athlete_df","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:10.510351Z","iopub.execute_input":"2024-01-12T13:55:10.510811Z","iopub.status.idle":"2024-01-12T13:55:10.529622Z","shell.execute_reply.started":"2024-01-12T13:55:10.510776Z","shell.execute_reply":"2024-01-12T13:55:10.528088Z"},"trusted":true},"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":" Name Sex Age Team\n0 Gabrielle Marie \"Gabby\" Adcock (White-) F 25.0 1\n1 Eleonora Margarida Josephina Scmitt F 16.0 2\n2 Jean Hauptmanns M 26.0 1\n3 Luis ngel Fernando de los Santos Grossi M 23.0 1\n4 Luis ngel Fernando de los Santos Grossi M 27.0 4\n... ... .. ... ...\n192249 zlem Kaya F 26.0 1\n192250 zman Graud M 33.0 1\n192251 zzet Safer M 26.0 1\n192252 zzet nce M 23.0 1\n192253 zzet nce M 27.0 1\n\n[192254 rows x 4 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
NameSexAgeTeam
0Gabrielle Marie \"Gabby\" Adcock (White-)F25.01
1Eleonora Margarida Josephina ScmittF16.02
2Jean HauptmannsM26.01
3Luis ngel Fernando de los Santos GrossiM23.01
4Luis ngel Fernando de los Santos GrossiM27.04
...............
192249zlem KayaF26.01
192250zman GraudM33.01
192251zzet SaferM26.01
192252zzet nceM23.01
192253zzet nceM27.01
\n

192254 rows × 4 columns

\n
"},"metadata":{}}],"execution_count":37},{"cell_type":"markdown","source":"Two entities representing the real-life concepts of _athlete_ and _team_ should be created. We propopose the following data dictionary:\n\n__team__(team)\n\n__athlete__(name, age, gender)\n\nThe composition of those fields appears to be composite keys. However, we will create a numerical primary key\n","metadata":{}},{"cell_type":"code","source":"columns = ['Name', 'Sex', 'Age']\nathletes = data_df.loc[:, columns].copy(deep = True)\nathletes = athletes.drop_duplicates(subset = columns)\n#athletes['id'] = range(1, athletes.shape[0] + 1) \nprint(\"--columns--\\n\", athletes.dtypes, sep = '')\nprint(\"--rows and columns--\\n\", athletes.shape, sep = '')\nathletes","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:10.531142Z","iopub.execute_input":"2024-01-12T13:55:10.531556Z","iopub.status.idle":"2024-01-12T13:55:10.712895Z","shell.execute_reply.started":"2024-01-12T13:55:10.531519Z","shell.execute_reply":"2024-01-12T13:55:10.711492Z"},"trusted":true},"outputs":[{"name":"stdout","text":"--columns--\nName object\nSex object\nAge float64\ndtype: object\n--rows and columns--\n(198610, 3)\n","output_type":"stream"},{"execution_count":38,"output_type":"execute_result","data":{"text/plain":" Name Sex Age\n0 A Dijiang M 24.0\n1 A Lamusi M 23.0\n2 Abudoureheman M 22.0\n3 Ai Linuer M 25.0\n4 Ai Yanhan F 14.0\n... ... .. ...\n286232 DOUEIHY Gabriella F 22.0\n286233 ELIAS Nacif M 32.0\n286234 FATTOUH Mahassen Hala F 31.0\n286235 HADID Noureddine M 28.0\n286236 KABBARA Munzer Mark M 18.0\n\n[198610 rows x 3 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
NameSexAge
0A DijiangM24.0
1A LamusiM23.0
2AbudourehemanM22.0
3Ai LinuerM25.0
4Ai YanhanF14.0
............
286232DOUEIHY GabriellaF22.0
286233ELIAS NacifM32.0
286234FATTOUH Mahassen HalaF31.0
286235HADID NoureddineM28.0
286236KABBARA Munzer MarkM18.0
\n

198610 rows × 3 columns

\n
"},"metadata":{}}],"execution_count":38},{"cell_type":"code","source":"team_values = data_df.Team.unique()\nteams = pd.DataFrame({'team': team_values})\n\nprint(\"--columns--\\n\", teams.dtypes, sep = '')\nprint(\"--rows and columns--\\n\", teams.shape, sep = '')\nteams","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:10.714689Z","iopub.execute_input":"2024-01-12T13:55:10.715149Z","iopub.status.idle":"2024-01-12T13:55:10.755924Z","shell.execute_reply.started":"2024-01-12T13:55:10.715104Z","shell.execute_reply":"2024-01-12T13:55:10.754813Z"},"trusted":true},"outputs":[{"name":"stdout","text":"--columns--\nteam object\ndtype: object\n--rows and columns--\n(1196, 1)\n","output_type":"stream"},{"execution_count":39,"output_type":"execute_result","data":{"text/plain":" team\n0 China\n1 China-2\n2 China-1\n3 China-3\n4 Denmark\n... ...\n1191 Newfoundland\n1192 Kosovo\n1193 South Sudan\n1194 Lesotho\n1195 Refugee Olympic Team\n\n[1196 rows x 1 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
team
0China
1China-2
2China-1
3China-3
4Denmark
......
1191Newfoundland
1192Kosovo
1193South Sudan
1194Lesotho
1195Refugee Olympic Team
\n

1196 rows × 1 columns

\n
"},"metadata":{}}],"execution_count":39},{"cell_type":"markdown","source":"## Medals and National Organisational Committe\nThe medals and National Organisational committe are explored as two separates logical groupings.","metadata":{}},{"cell_type":"markdown","source":"It appears we have four types of medals with a unique value. So we could consider as categorical data. ","metadata":{}},{"cell_type":"code","source":"data_df['Medal'].unique()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:10.757105Z","iopub.execute_input":"2024-01-12T13:55:10.757416Z","iopub.status.idle":"2024-01-12T13:55:10.779246Z","shell.execute_reply.started":"2024-01-12T13:55:10.757388Z","shell.execute_reply":"2024-01-12T13:55:10.777583Z"},"trusted":true},"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"array([nan, 'Silver', 'Bronze', 'Gold'], dtype=object)"},"metadata":{}}],"execution_count":40},{"cell_type":"code","source":"data_df['Medal'] = data_df['Medal'].fillna('None')\ndata_df['Medal'].unique()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:10.780799Z","iopub.execute_input":"2024-01-12T13:55:10.781173Z","iopub.status.idle":"2024-01-12T13:55:10.830864Z","shell.execute_reply.started":"2024-01-12T13:55:10.781140Z","shell.execute_reply":"2024-01-12T13:55:10.829519Z"},"trusted":true},"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"array(['None', 'Silver', 'Bronze', 'Gold'], dtype=object)"},"metadata":{}}],"execution_count":41},{"cell_type":"code","source":"medals = pd.DataFrame({'medal': data_df['Medal'].unique()})\nprint(\"--columns--\\n\", medals.dtypes, sep = '')\nprint(\"--rows and columns--\\n\", medals.shape, sep = '')\nmedals\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:10.832184Z","iopub.execute_input":"2024-01-12T13:55:10.832528Z","iopub.status.idle":"2024-01-12T13:55:10.866323Z","shell.execute_reply.started":"2024-01-12T13:55:10.832497Z","shell.execute_reply":"2024-01-12T13:55:10.865036Z"},"trusted":true},"outputs":[{"name":"stdout","text":"--columns--\nmedal object\ndtype: object\n--rows and columns--\n(4, 1)\n","output_type":"stream"},{"execution_count":42,"output_type":"execute_result","data":{"text/plain":" medal\n0 None\n1 Silver\n2 Bronze\n3 Gold","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
medal
0None
1Silver
2Bronze
3Gold
\n
"},"metadata":{}}],"execution_count":42},{"cell_type":"markdown","source":"The National Organisation Committees can be represented with three columns - NOC, region and notes. The key appear to be composite, that is NOC and region. The NOC was the column we merge the olympic results with the regions. It is a logical grouping.\n\n__noc__(noc, region, notes)","metadata":{}},{"cell_type":"code","source":"columns = ['NOC','region']\ndata_df.groupby(columns).count().reset_index()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:10.867925Z","iopub.execute_input":"2024-01-12T13:55:10.868508Z","iopub.status.idle":"2024-01-12T13:55:11.138374Z","shell.execute_reply.started":"2024-01-12T13:55:10.868449Z","shell.execute_reply":"2024-01-12T13:55:11.137469Z"},"trusted":true},"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":" NOC region Unnamed: 0_x Name Sex Age Team Year Season \\\n0 AFG Afghanistan 131 131 131 83 131 131 131 \n1 AHO Curacao 79 79 79 78 79 79 79 \n2 ALB Albania 80 80 80 80 80 80 80 \n3 ALG Algeria 596 596 596 590 596 596 596 \n4 AND Andorra 172 172 172 172 172 172 172 \n.. ... ... ... ... ... ... ... ... ... \n225 YEM Yemen 38 38 38 38 38 38 38 \n226 YMD Yemen 5 5 5 5 5 5 5 \n227 YUG Serbia 2583 2583 2583 2454 2583 2583 2583 \n228 ZAM Zambia 214 214 214 185 214 214 214 \n229 ZIM Zimbabwe 316 316 316 314 316 316 316 \n\n City Sport Event Medal Unnamed: 0_y notes \n0 131 131 131 131 131 0 \n1 79 79 79 79 79 79 \n2 80 80 80 80 80 0 \n3 596 596 596 596 596 0 \n4 172 172 172 172 172 0 \n.. ... ... ... ... ... ... \n225 38 38 38 38 38 0 \n226 5 5 5 5 5 5 \n227 2583 2583 2583 2583 2583 2583 \n228 214 214 214 214 214 0 \n229 316 316 316 316 316 0 \n\n[230 rows x 15 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
NOCregionUnnamed: 0_xNameSexAgeTeamYearSeasonCitySportEventMedalUnnamed: 0_ynotes
0AFGAfghanistan131131131831311311311311311311311310
1AHOCuracao79797978797979797979797979
2ALBAlbania8080808080808080808080800
3ALGAlgeria5965965965905965965965965965965965960
4ANDAndorra1721721721721721721721721721721721720
................................................
225YEMYemen3838383838383838383838380
226YMDYemen5555555555555
227YUGSerbia2583258325832454258325832583258325832583258325832583
228ZAMZambia2142142141852142142142142142142142140
229ZIMZimbabwe3163163163143163163163163163163163160
\n

230 rows × 15 columns

\n
"},"metadata":{}}],"execution_count":43},{"cell_type":"code","source":"columns = ['NOC','region','notes']\nnoc = data_df.loc[:, columns].copy(deep = True)\nnoc = noc.drop_duplicates(subset = columns)\nprint(\"--columns--\\n\", noc.dtypes, sep = '')\nprint(\"--rows and columns--\\n\", noc.shape, sep = '')\nnoc\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:11.139994Z","iopub.execute_input":"2024-01-12T13:55:11.140340Z","iopub.status.idle":"2024-01-12T13:55:11.238518Z","shell.execute_reply.started":"2024-01-12T13:55:11.140309Z","shell.execute_reply":"2024-01-12T13:55:11.237365Z"},"trusted":true},"outputs":[{"name":"stdout","text":"--columns--\nNOC object\nregion object\nnotes object\ndtype: object\n--rows and columns--\n(233, 3)\n","output_type":"stream"},{"execution_count":44,"output_type":"execute_result","data":{"text/plain":" NOC region notes\n0 CHN China NaN\n5715 DEN Denmark NaN\n9423 NED Netherlands NaN\n15653 FIN Finland NaN\n21169 NOR Norway NaN\n... ... ... ...\n285562 SSD South Sudan NaN\n285567 LES Lesotho NaN\n285635 ROC Russia NaN\n286196 EOR Refugee NaN\n286231 LBN Lebanon NaN\n\n[233 rows x 3 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
NOCregionnotes
0CHNChinaNaN
5715DENDenmarkNaN
9423NEDNetherlandsNaN
15653FINFinlandNaN
21169NORNorwayNaN
............
285562SSDSouth SudanNaN
285567LESLesothoNaN
285635ROCRussiaNaN
286196EORRefugeeNaN
286231LBNLebanonNaN
\n

233 rows × 3 columns

\n
"},"metadata":{}}],"execution_count":44},{"cell_type":"code","source":"noc.loc[~noc.notes.isna(), :]","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:11.240272Z","iopub.execute_input":"2024-01-12T13:55:11.240722Z","iopub.status.idle":"2024-01-12T13:55:11.255430Z","shell.execute_reply.started":"2024-01-12T13:55:11.240679Z","shell.execute_reply":"2024-01-12T13:55:11.254326Z"},"trusted":true},"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":" NOC region notes\n126933 UAR Syria United Arab Republic\n169843 YAR Yemen North Yemen\n248002 SKN Saint Kitts Turks and Caicos Islands\n248046 TTO Trinidad Trinidad and Tobago\n256789 AHO Curacao Netherlands Antilles\n262079 YMD Yemen South Yemen\n263093 ANZ Australia Australasia\n263179 SCG Serbia Serbia and Montenegro\n263772 IOA Individual Olympic Athletes Individual Olympic Athletes\n265344 YUG Serbia Yugoslavia\n269554 ISV Virgin Islands, US Virgin Islands\n272340 ROT NaN Refugee Olympic Team\n280508 CRT Greece Crete\n280812 ANT Antigua Antigua and Barbuda\n281499 HKG China Hong Kong\n284068 BOH Czech Republic Bohemia\n284918 WIF Trinidad West Indies Federation\n285061 NBO Malaysia North Borneo\n285479 UNK NaN Unknown\n285481 TUV NaN Tuvalu\n285542 NFL Canada Newfoundland","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
NOCregionnotes
126933UARSyriaUnited Arab Republic
169843YARYemenNorth Yemen
248002SKNSaint KittsTurks and Caicos Islands
248046TTOTrinidadTrinidad and Tobago
256789AHOCuracaoNetherlands Antilles
262079YMDYemenSouth Yemen
263093ANZAustraliaAustralasia
263179SCGSerbiaSerbia and Montenegro
263772IOAIndividual Olympic AthletesIndividual Olympic Athletes
265344YUGSerbiaYugoslavia
269554ISVVirgin Islands, USVirgin Islands
272340ROTNaNRefugee Olympic Team
280508CRTGreeceCrete
280812ANTAntiguaAntigua and Barbuda
281499HKGChinaHong Kong
284068BOHCzech RepublicBohemia
284918WIFTrinidadWest Indies Federation
285061NBOMalaysiaNorth Borneo
285479UNKNaNUnknown
285481TUVNaNTuvalu
285542NFLCanadaNewfoundland
\n
"},"metadata":{}}],"execution_count":45},{"cell_type":"markdown","source":"## Some logical grouping\n\nSome columns have not been use - they are artefacts produced by data transformation.\n\n\n","metadata":{}},{"cell_type":"code","source":"data_df.dtypes","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:11.257018Z","iopub.execute_input":"2024-01-12T13:55:11.257362Z","iopub.status.idle":"2024-01-12T13:55:11.270993Z","shell.execute_reply.started":"2024-01-12T13:55:11.257333Z","shell.execute_reply":"2024-01-12T13:55:11.269658Z"},"trusted":true},"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"Unnamed: 0_x int64\nName object\nSex object\nAge float64\nTeam object\nNOC object\nYear int64\nSeason object\nCity object\nSport object\nEvent object\nMedal object\nUnnamed: 0_y int64\nregion object\nnotes object\ndtype: object"},"metadata":{}}],"execution_count":46},{"cell_type":"markdown","source":"The logical groupings into some entities are listed below:\n\n__game__(year, season, city)\n\n__sports__(sport)\n\n__event__(event)\n\n__team__(team)\n\n__athlete__(name, age, gender)\n\n__noc__(noc, region, notes)\n\n__medal__(medal)\n","metadata":{}},{"cell_type":"markdown","source":"# Do we have any primary keys?\n\nWe had some unique identifiers, to support the data being stored in a relational database. The data can become a reference dataset to be used for groupings or certain classifiers - i.e., tree-base ML.\n\n__game__(id(PK), year, season, city)\n\n__sports__(id (PK), sport)\n\n__event__(id (PK), event)\n\n__team__(id (PK), team)\n\n__athlete__(id (PK), name, age, gender)\n\n__noc__(noc (PK), region, notes)\n\n__medal__(id(PK),medal)\n\n","metadata":{}},{"cell_type":"markdown","source":"# What are the relationship between each logical grouping? ","metadata":{}},{"cell_type":"markdown","source":"## Athlete and games\nWe assume athletes may have participated to at least one Olympic games. Olympic games have more than one athlete... A lot more. So we can positively argue that the relationship between a games and atheletes is many to many. ","metadata":{}},{"cell_type":"code","source":"columns = ['Name','Age','Sex']\ngrouped_data = data_df.groupby(columns).count()['Year']\nprint(\"minimum attendance :\" , grouped_data.min())\nprint(\"maximum attendance :\" , grouped_data.max())","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:11.272334Z","iopub.execute_input":"2024-01-12T13:55:11.272699Z","iopub.status.idle":"2024-01-12T13:55:11.861964Z","shell.execute_reply.started":"2024-01-12T13:55:11.272669Z","shell.execute_reply":"2024-01-12T13:55:11.860750Z"},"trusted":true},"outputs":[{"name":"stdout","text":"minimum attendance : 1\nmaximum attendance : 44\n","output_type":"stream"}],"execution_count":47},{"cell_type":"code","source":"grouped_data.sort_values(ascending = False)","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:11.864041Z","iopub.execute_input":"2024-01-12T13:55:11.864523Z","iopub.status.idle":"2024-01-12T13:55:11.931292Z","shell.execute_reply.started":"2024-01-12T13:55:11.864478Z","shell.execute_reply":"2024-01-12T13:55:11.929974Z"},"trusted":true},"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"Name Age Sex\nRobert Tait McKenzie 65.0 M 44\nAlfrd (Arnold-) Hajs (Guttmann-) 50.0 M 28\nMiltiades Manno 53.0 M 27\nAlfred James Munnings 69.0 M 25\nWilhelm (William) Hunt Diederich 48.0 M 19\n ..\nIrena Paweczyk (-Kowalska) 29.0 F 1\nIrena Soukupov 23.0 F 1\n 27.0 F 1\nIrena Svobodov (-Pettinari) 19.0 F 1\nzzet nce 27.0 M 1\nName: Year, Length: 192254, dtype: int64"},"metadata":{}}],"execution_count":48},{"cell_type":"markdown","source":"## Athletes and teams\nWe surmise an athlete is part of one team and a team has many athletes. Some teams have quite a lot of athletes. So we assume a one-to-many relationship would be sufficient for this dataset.\n","metadata":{}},{"cell_type":"code","source":"data_df.sort_values(by='Name')","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:11.933141Z","iopub.execute_input":"2024-01-12T13:55:11.933597Z","iopub.status.idle":"2024-01-12T13:55:12.475063Z","shell.execute_reply.started":"2024-01-12T13:55:11.933553Z","shell.execute_reply":"2024-01-12T13:55:12.473912Z"},"trusted":true},"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":" Unnamed: 0_x Name Sex Age \\\n207460 1560 Gabrielle Marie \"Gabby\" Adcock (White-) F 25.0 \n192156 215054 Eleonora Margarida Josephina Scmitt F 16.0 \n192157 215055 Eleonora Margarida Josephina Scmitt F 16.0 \n130443 92099 Jean Hauptmanns M 26.0 \n196895 51334 Luis ngel Fernando de los Santos Grossi M 23.0 \n... ... ... .. ... \n86812 114965 zlem Kaya F 26.0 \n86667 79870 zman Graud M 33.0 \n87068 207676 zzet Safer M 26.0 \n86756 102956 zzet nce M 27.0 \n86755 102955 zzet nce M 23.0 \n\n Team NOC Year Season City Sport \\\n207460 Great Britain GBR 2016 Summer Rio de Janeiro Badminton \n192156 Brazil BRA 1948 Summer London Swimming \n192157 Brazil BRA 1948 Summer London Swimming \n130443 Germany GER 1912 Summer Stockholm Wrestling \n196895 Uruguay URU 1948 Summer London Cycling \n... ... ... ... ... ... ... \n86812 Turkey TUR 2016 Summer Rio de Janeiro Athletics \n86667 Turkey TUR 1972 Summer Munich Shooting \n87068 Turkey TUR 2016 Summer Rio de Janeiro Athletics \n86756 Turkey TUR 2008 Summer Beijing Weightlifting \n86755 Turkey TUR 2004 Summer Athina Weightlifting \n\n Event Medal Unnamed: 0_y \\\n207460 Badminton Mixed Doubles None 78 \n192156 Swimming Women's 100 metres Freestyle None 33 \n192157 Swimming Women's 4 x 100 metres Freestyle Relay None 33 \n130443 Wrestling Men's Heavyweight, Greco-Roman None 83 \n196895 Cycling Men's Team Pursuit, 4,000 metres None 219 \n... ... ... ... \n86812 Athletics Women's 3,000 metres Steeplechase None 211 \n86667 Shooting Mixed Skeet None 211 \n87068 Athletics Men's 4 x 100 metres Relay None 211 \n86756 Weightlifting Men's Light-Heavyweight None 211 \n86755 Weightlifting Men's Light-Heavyweight None 211 \n\n region notes \n207460 UK NaN \n192156 Brazil NaN \n192157 Brazil NaN \n130443 Germany NaN \n196895 Uruguay NaN \n... ... ... \n86812 Turkey NaN \n86667 Turkey NaN \n87068 Turkey NaN \n86756 Turkey NaN \n86755 Turkey NaN \n\n[286237 rows x 15 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Unnamed: 0_xNameSexAgeTeamNOCYearSeasonCitySportEventMedalUnnamed: 0_yregionnotes
2074601560Gabrielle Marie \"Gabby\" Adcock (White-)F25.0Great BritainGBR2016SummerRio de JaneiroBadmintonBadminton Mixed DoublesNone78UKNaN
192156215054Eleonora Margarida Josephina ScmittF16.0BrazilBRA1948SummerLondonSwimmingSwimming Women's 100 metres FreestyleNone33BrazilNaN
192157215055Eleonora Margarida Josephina ScmittF16.0BrazilBRA1948SummerLondonSwimmingSwimming Women's 4 x 100 metres Freestyle RelayNone33BrazilNaN
13044392099Jean HauptmannsM26.0GermanyGER1912SummerStockholmWrestlingWrestling Men's Heavyweight, Greco-RomanNone83GermanyNaN
19689551334Luis ngel Fernando de los Santos GrossiM23.0UruguayURU1948SummerLondonCyclingCycling Men's Team Pursuit, 4,000 metresNone219UruguayNaN
................................................
86812114965zlem KayaF26.0TurkeyTUR2016SummerRio de JaneiroAthleticsAthletics Women's 3,000 metres SteeplechaseNone211TurkeyNaN
8666779870zman GraudM33.0TurkeyTUR1972SummerMunichShootingShooting Mixed SkeetNone211TurkeyNaN
87068207676zzet SaferM26.0TurkeyTUR2016SummerRio de JaneiroAthleticsAthletics Men's 4 x 100 metres RelayNone211TurkeyNaN
86756102956zzet nceM27.0TurkeyTUR2008SummerBeijingWeightliftingWeightlifting Men's Light-HeavyweightNone211TurkeyNaN
86755102955zzet nceM23.0TurkeyTUR2004SummerAthinaWeightliftingWeightlifting Men's Light-HeavyweightNone211TurkeyNaN
\n

286237 rows × 15 columns

\n
"},"metadata":{}}],"execution_count":49},{"cell_type":"code","source":"columns = ['Team', 'Year']\ngrouped_data = data_df.groupby(columns).count()['Name']\nprint(\"minimum attendance :\" , grouped_data.min())\nprint(\"maximum attendance :\" , grouped_data.max())","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:12.476295Z","iopub.execute_input":"2024-01-12T13:55:12.476641Z","iopub.status.idle":"2024-01-12T13:55:12.737693Z","shell.execute_reply.started":"2024-01-12T13:55:12.476610Z","shell.execute_reply":"2024-01-12T13:55:12.736211Z"},"trusted":true},"outputs":[{"name":"stdout","text":"minimum attendance : 1\nmaximum attendance : 936\n","output_type":"stream"}],"execution_count":50},{"cell_type":"code","source":"grouped_data.sort_values(ascending = False)","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:12.739250Z","iopub.execute_input":"2024-01-12T13:55:12.740044Z","iopub.status.idle":"2024-01-12T13:55:12.752215Z","shell.execute_reply.started":"2024-01-12T13:55:12.740004Z","shell.execute_reply":"2024-01-12T13:55:12.751114Z"},"trusted":true},"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"Team Year\nUnited States 1992 936\n 1988 886\n 2020 856\nUnified Team 1992 832\nUnited States 1996 827\n ... \nBrandenburg 1936 1\nMainz 1936 1\nBrazil 1994 1\n 1998 1\nKonstanz 1936 1\nName: Name, Length: 5376, dtype: int64"},"metadata":{}}],"execution_count":51},{"cell_type":"markdown","source":"## Sports, events and medals\n\nWe can see sports may have several events. So the relationship is one to many between sports and events. Modelling the relationship between the medal is a bit more complex. It appears to link a game, an event, some athtletes and a medal. We discover that a many to many between all those logical grouping would be suitable. \n","metadata":{}},{"cell_type":"code","source":"data_df.sort_values(by=['Sport','Event','Medal','Year'])","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:12.753868Z","iopub.execute_input":"2024-01-12T13:55:12.754199Z","iopub.status.idle":"2024-01-12T13:55:13.032962Z","shell.execute_reply.started":"2024-01-12T13:55:12.754169Z","shell.execute_reply":"2024-01-12T13:55:13.031451Z"},"trusted":true},"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":" Unnamed: 0_x Name Sex Age \\\n269307 3278 DOMOVIC BULUT Dusan M 35.0 \n269325 7999 MAJSTOROVIC Dejan M 33.0 \n269358 10955 RATKOV Aleksandar M 29.0 \n269377 13817 VASIC Mihailo M 28.0 \n268385 2124 CAVARS Agnis M 35.0 \n... ... ... .. ... \n278701 90558 Adla Hanzlkov F 22.0 \n100658 155091 Sara McMann F 23.0 \n70585 114017 Alyona Vladimirovna Kartashova F 26.0 \n1121 108583 Jing Ruixue F 24.0 \n81230 147979 Mariya Ruslanovna Mamoshuk F 23.0 \n\n Team NOC Year Season City Sport \\\n269307 Serbia SRB 2020 Summer Tokyo 3x3 Basketball \n269325 Serbia SRB 2020 Summer Tokyo 3x3 Basketball \n269358 Serbia SRB 2020 Summer Tokyo 3x3 Basketball \n269377 Serbia SRB 2020 Summer Tokyo 3x3 Basketball \n268385 Latvia LAT 2020 Summer Tokyo 3x3 Basketball \n... ... ... ... ... ... ... \n278701 Czech Republic CZE 2016 Summer Rio de Janeiro Wrestling \n100658 United States USA 2004 Summer Athina Wrestling \n70585 Russia RUS 2008 Summer Beijing Wrestling \n1121 China CHN 2012 Summer London Wrestling \n81230 Belarus BLR 2016 Summer Rio de Janeiro Wrestling \n\n Event Medal Unnamed: 0_y \\\n269307 Men Team Bronze 189 \n269325 Men Team Bronze 189 \n269358 Men Team Bronze 189 \n269377 Men Team Bronze 189 \n268385 Men Team Gold 118 \n... ... ... ... \n278701 Wrestling Women's Middleweight, Freestyle None 58 \n100658 Wrestling Women's Middleweight, Freestyle Silver 220 \n70585 Wrestling Women's Middleweight, Freestyle Silver 175 \n1121 Wrestling Women's Middleweight, Freestyle Silver 45 \n81230 Wrestling Women's Middleweight, Freestyle Silver 29 \n\n region notes \n269307 Serbia NaN \n269325 Serbia NaN \n269358 Serbia NaN \n269377 Serbia NaN \n268385 Latvia NaN \n... ... ... \n278701 Czech Republic NaN \n100658 USA NaN \n70585 Russia NaN \n1121 China NaN \n81230 Belarus NaN \n\n[286237 rows x 15 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Unnamed: 0_xNameSexAgeTeamNOCYearSeasonCitySportEventMedalUnnamed: 0_yregionnotes
2693073278DOMOVIC BULUT DusanM35.0SerbiaSRB2020SummerTokyo3x3 BasketballMen TeamBronze189SerbiaNaN
2693257999MAJSTOROVIC DejanM33.0SerbiaSRB2020SummerTokyo3x3 BasketballMen TeamBronze189SerbiaNaN
26935810955RATKOV AleksandarM29.0SerbiaSRB2020SummerTokyo3x3 BasketballMen TeamBronze189SerbiaNaN
26937713817VASIC MihailoM28.0SerbiaSRB2020SummerTokyo3x3 BasketballMen TeamBronze189SerbiaNaN
2683852124CAVARS AgnisM35.0LatviaLAT2020SummerTokyo3x3 BasketballMen TeamGold118LatviaNaN
................................................
27870190558Adla HanzlkovF22.0Czech RepublicCZE2016SummerRio de JaneiroWrestlingWrestling Women's Middleweight, FreestyleNone58Czech RepublicNaN
100658155091Sara McMannF23.0United StatesUSA2004SummerAthinaWrestlingWrestling Women's Middleweight, FreestyleSilver220USANaN
70585114017Alyona Vladimirovna KartashovaF26.0RussiaRUS2008SummerBeijingWrestlingWrestling Women's Middleweight, FreestyleSilver175RussiaNaN
1121108583Jing RuixueF24.0ChinaCHN2012SummerLondonWrestlingWrestling Women's Middleweight, FreestyleSilver45ChinaNaN
81230147979Mariya Ruslanovna MamoshukF23.0BelarusBLR2016SummerRio de JaneiroWrestlingWrestling Women's Middleweight, FreestyleSilver29BelarusNaN
\n

286237 rows × 15 columns

\n
"},"metadata":{}}],"execution_count":52},{"cell_type":"code","source":"columns = ['Event','Year', 'Season', 'Sport','Name']\ngrouped_data = data_df.groupby(columns).count()['Medal']\nprint(\"minimum attendance :\" , grouped_data.min())\nprint(\"maximum attendance :\" , grouped_data.max())","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:13.034746Z","iopub.execute_input":"2024-01-12T13:55:13.035869Z","iopub.status.idle":"2024-01-12T13:55:13.727840Z","shell.execute_reply.started":"2024-01-12T13:55:13.035820Z","shell.execute_reply":"2024-01-12T13:55:13.726118Z"},"trusted":true},"outputs":[{"name":"stdout","text":"minimum attendance : 1\nmaximum attendance : 43\n","output_type":"stream"}],"execution_count":53},{"cell_type":"code","source":"grouped_data.sort_values(ascending = False)","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:13.729339Z","iopub.execute_input":"2024-01-12T13:55:13.729682Z","iopub.status.idle":"2024-01-12T13:55:13.757052Z","shell.execute_reply.started":"2024-01-12T13:55:13.729650Z","shell.execute_reply":"2024-01-12T13:55:13.755787Z"},"trusted":true},"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"Event Year Season Sport Name \nArt Competitions Mixed Sculpturing, Unknown Event 1932 Summer Art Competitions Robert Tait McKenzie 43\nArt Competitions Mixed Painting, Unknown Event 1948 Summer Art Competitions Alfred James Munnings 25\nArt Competitions Mixed Painting, Drawings And Water Colors 1928 Summer Art Competitions Stanisaw Noakowski 17\nArt Competitions Mixed Painting, Unknown Event 1932 Summer Art Competitions Acee Blue Eagle 17\n Miltiades Manno 17\n ..\nCycling Men's Road Race, Individual 1996 Summer Cycling Damian John McDonald 1\n Daniel Valter Rogelin 1\n Daniel Wilhelmus Maria \"Danny\" Nelissen 1\n Dariusz Baranowski 1\nWrestling Women's Middleweight, Freestyle 2016 Summer Wrestling Yuliya Anatolivna Ostapchuk-Tkach 1\nName: Medal, Length: 284744, dtype: int64"},"metadata":{}}],"execution_count":54},{"cell_type":"markdown","source":"## Events and NOC\nSo we assume an event as one NOC. But a NOC can organise several events. However, each game has different organiser. So, we need to relate the event the game and the organisers. ","metadata":{}},{"cell_type":"code","source":"data_df.loc[:, ['Sport', 'Event', 'Year', 'NOC','City']].sort_values('Event')","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:13.758719Z","iopub.execute_input":"2024-01-12T13:55:13.759210Z","iopub.status.idle":"2024-01-12T13:55:14.108968Z","shell.execute_reply.started":"2024-01-12T13:55:13.759164Z","shell.execute_reply":"2024-01-12T13:55:14.107758Z"},"trusted":true},"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":" Sport Event Year NOC \\\n136184 Shooting 10m Air Pistol Men 2020 GER \n282422 Shooting 10m Air Pistol Men 2020 MYA \n240152 Shooting 10m Air Pistol Men 2020 PER \n260082 Shooting 10m Air Pistol Men 2020 ISL \n258964 Shooting 10m Air Pistol Men 2020 UKR \n... ... ... ... ... \n278701 Wrestling Wrestling Women's Middleweight, Freestyle 2016 CZE \n223537 Wrestling Wrestling Women's Middleweight, Freestyle 2008 POL \n124681 Wrestling Wrestling Women's Middleweight, Freestyle 2016 NGR \n270262 Wrestling Wrestling Women's Middleweight, Freestyle 2008 GUM \n258000 Wrestling Wrestling Women's Middleweight, Freestyle 2008 UKR \n\n City \n136184 Tokyo \n282422 Tokyo \n240152 Tokyo \n260082 Tokyo \n258964 Tokyo \n... ... \n278701 Rio de Janeiro \n223537 Beijing \n124681 Rio de Janeiro \n270262 Beijing \n258000 Beijing \n\n[286237 rows x 5 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
SportEventYearNOCCity
136184Shooting10m Air Pistol Men2020GERTokyo
282422Shooting10m Air Pistol Men2020MYATokyo
240152Shooting10m Air Pistol Men2020PERTokyo
260082Shooting10m Air Pistol Men2020ISLTokyo
258964Shooting10m Air Pistol Men2020UKRTokyo
..................
278701WrestlingWrestling Women's Middleweight, Freestyle2016CZERio de Janeiro
223537WrestlingWrestling Women's Middleweight, Freestyle2008POLBeijing
124681WrestlingWrestling Women's Middleweight, Freestyle2016NGRRio de Janeiro
270262WrestlingWrestling Women's Middleweight, Freestyle2008GUMBeijing
258000WrestlingWrestling Women's Middleweight, Freestyle2008UKRBeijing
\n

286237 rows × 5 columns

\n
"},"metadata":{}}],"execution_count":55},{"cell_type":"code","source":"columns = ['Sport','Event','Year']\ngrouped_data = data_df.groupby(columns).count()['NOC']\nprint(\"minimum attendance :\" , grouped_data.min())\nprint(\"maximum attendance :\" , grouped_data.max())","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:14.110710Z","iopub.execute_input":"2024-01-12T13:55:14.111223Z","iopub.status.idle":"2024-01-12T13:55:14.374339Z","shell.execute_reply.started":"2024-01-12T13:55:14.111175Z","shell.execute_reply":"2024-01-12T13:55:14.372912Z"},"trusted":true},"outputs":[{"name":"stdout","text":"minimum attendance : 1\nmaximum attendance : 648\n","output_type":"stream"}],"execution_count":56},{"cell_type":"code","source":"grouped_data.sort_values(ascending = False)","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:55:14.375912Z","iopub.execute_input":"2024-01-12T13:55:14.376336Z","iopub.status.idle":"2024-01-12T13:55:14.390522Z","shell.execute_reply.started":"2024-01-12T13:55:14.376301Z","shell.execute_reply":"2024-01-12T13:55:14.389297Z"},"trusted":true},"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"Sport Event Year\nArt Competitions Art Competitions Mixed Painting, Unknown Event 1932 648\nFootball Men Team 2020 344\nIce Hockey Ice Hockey Men's Ice Hockey 2002 312\n 1998 306\nArt Competitions Art Competitions Mixed Painting, Unknown Event 1936 300\n ... \nSailing Sailing Mixed 18 foot 1920 2\nAeronautics Aeronautics Mixed Aeronautics 1936 1\nArt Competitions Art Competitions Mixed Architecture 1920 1\n Art Competitions Mixed Music, Instrumental And Chamber 1936 1\n Art Competitions Mixed Sculpturing, Reliefs 1948 1\nName: NOC, Length: 6537, dtype: int64"},"metadata":{}}],"execution_count":57},{"cell_type":"markdown","source":"## The model\n\n__game__(id(PK), year, season, city)\n\n__game_athlete__(game_id(PK,FK), athlete_id(PK,FK))\n\n__achievement__(game_id(FK, PK), athlete_id(FK, PK), sport_id (FK,PK), event_id(FK,PK), medal_id(FK,PK), medal_ref(PK))\n\n__sports__(id (PK), sport)\n\n__event__(id (PK), event, sport_id (FK))\n\n__team__(id (PK), team)\n\n__game_event_noc__(game_id(PK,FK), event(PK, FK), NOC(PK, FK))\n\n\n__athlete__(id (PK), name, age, gender, team_id(FK))\n\n\n__noc__(noc (PK), region, notes)\n\n\n__medal__(id(PK),medal)\n","metadata":{}},{"cell_type":"markdown","source":"# Why was this exercise useful?\n\nThe data is even more documented - we can potentially analyse each real-life objects and concepts.","metadata":{}},{"cell_type":"markdown","source":"We can discover Winter Olympic games started in 1924, but the summer ones 28 years earlier. We discover some cities have hosted more than once some Olympic games - the maximum is 3 times.","metadata":{}},{"cell_type":"code","source":"games.groupby(['Season']).count()['Year']","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:56:03.157600Z","iopub.execute_input":"2024-01-12T13:56:03.158040Z","iopub.status.idle":"2024-01-12T13:56:03.170054Z","shell.execute_reply.started":"2024-01-12T13:56:03.158006Z","shell.execute_reply":"2024-01-12T13:56:03.168793Z"},"trusted":true},"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"Season\nSummer 31\nWinter 22\nName: Year, dtype: int64"},"metadata":{}}],"execution_count":59},{"cell_type":"code","source":"rows = games.Season.str.contains(\"Winter\")\ngames.loc[rows, :].describe()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:00:41.037450Z","iopub.execute_input":"2024-01-12T14:00:41.037906Z","iopub.status.idle":"2024-01-12T14:00:41.055416Z","shell.execute_reply.started":"2024-01-12T14:00:41.037867Z","shell.execute_reply":"2024-01-12T14:00:41.054273Z"},"trusted":true},"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":" Year\ncount 22.000000\nmean 1972.000000\nstd 27.512768\nmin 1924.000000\n25% 1953.000000\n50% 1974.000000\n75% 1993.500000\nmax 2014.000000","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Year
count22.000000
mean1972.000000
std27.512768
min1924.000000
25%1953.000000
50%1974.000000
75%1993.500000
max2014.000000
\n
"},"metadata":{}}],"execution_count":65},{"cell_type":"code","source":"rows = games.Season.str.contains(\"Summer\")\ngames.loc[rows, :].describe()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:00:58.980178Z","iopub.execute_input":"2024-01-12T14:00:58.980580Z","iopub.status.idle":"2024-01-12T14:00:58.997201Z","shell.execute_reply.started":"2024-01-12T14:00:58.980548Z","shell.execute_reply":"2024-01-12T14:00:58.996051Z"},"trusted":true},"outputs":[{"execution_count":66,"output_type":"execute_result","data":{"text/plain":" Year\ncount 31.000000\nmean 1958.645161\nstd 38.322794\nmin 1896.000000\n25% 1926.000000\n50% 1960.000000\n75% 1990.000000\nmax 2020.000000","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Year
count31.000000
mean1958.645161
std38.322794
min1896.000000
25%1926.000000
50%1960.000000
75%1990.000000
max2020.000000
\n
"},"metadata":{}}],"execution_count":66},{"cell_type":"code","source":"no_games_per_year = games.groupby(['Year']).count()['Season'].reset_index()\nno_games_per_year.describe()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T13:57:59.184451Z","iopub.execute_input":"2024-01-12T13:57:59.184920Z","iopub.status.idle":"2024-01-12T13:57:59.207682Z","shell.execute_reply.started":"2024-01-12T13:57:59.184883Z","shell.execute_reply":"2024-01-12T13:57:59.206574Z"},"trusted":true},"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":" Year Season\ncount 36.000000 36.000000\nmean 1966.277778 1.472222\nstd 39.488596 0.559904\nmin 1896.000000 1.000000\n25% 1931.000000 1.000000\n50% 1974.000000 1.000000\n75% 2000.500000 2.000000\nmax 2020.000000 3.000000","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearSeason
count36.00000036.000000
mean1966.2777781.472222
std39.4885960.559904
min1896.0000001.000000
25%1931.0000001.000000
50%1974.0000001.000000
75%2000.5000002.000000
max2020.0000003.000000
\n
"},"metadata":{}}],"execution_count":63},{"cell_type":"code","source":"no_games_per_city = games.groupby(['City']).count()['Year'].reset_index()\nno_games_per_city.describe()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:02:38.231684Z","iopub.execute_input":"2024-01-12T14:02:38.232175Z","iopub.status.idle":"2024-01-12T14:02:38.253597Z","shell.execute_reply.started":"2024-01-12T14:02:38.232140Z","shell.execute_reply":"2024-01-12T14:02:38.252057Z"},"trusted":true},"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":" Year\ncount 42.000000\nmean 1.261905\nstd 0.543679\nmin 1.000000\n25% 1.000000\n50% 1.000000\n75% 1.000000\nmax 3.000000","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Year
count42.000000
mean1.261905
std0.543679
min1.000000
25%1.000000
50%1.000000
75%1.000000
max3.000000
\n
"},"metadata":{}}],"execution_count":67},{"cell_type":"markdown","source":"We can describe statistically accurately. For example, it appears repeating athletes may slightly skew to the left the age of the athletes. The arithmetical mean is slightly higher for our athletes data frame than the whole dataset. Therefore, we have correct the error. ","metadata":{}},{"cell_type":"code","source":"plt.hist(athletes.Age, bins = 40)\nathletes.Age.describe()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:20:33.396027Z","iopub.execute_input":"2024-01-12T14:20:33.396443Z","iopub.status.idle":"2024-01-12T14:20:33.648653Z","shell.execute_reply.started":"2024-01-12T14:20:33.396407Z","shell.execute_reply":"2024-01-12T14:20:33.647568Z"},"trusted":true},"outputs":[{"execution_count":111,"output_type":"execute_result","data":{"text/plain":"count 192254.000000\nmean 25.855160\nstd 6.065129\nmin 10.000000\n25% 22.000000\n50% 25.000000\n75% 29.000000\nmax 97.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}],"execution_count":111},{"cell_type":"code","source":"plt.hist(data_df.Age, bins=40)\ndata_df.Age.describe()\n","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:20:03.528473Z","iopub.execute_input":"2024-01-12T14:20:03.528919Z","iopub.status.idle":"2024-01-12T14:20:03.798278Z","shell.execute_reply.started":"2024-01-12T14:20:03.528884Z","shell.execute_reply":"2024-01-12T14:20:03.797056Z"},"trusted":true},"outputs":[{"execution_count":107,"output_type":"execute_result","data":{"text/plain":"count 276763.000000\nmean 25.622916\nstd 6.359116\nmin 10.000000\n25% 22.000000\n50% 25.000000\n75% 28.000000\nmax 97.000000\nName: Age, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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MtLWn0D8MrQfkdbbUNbn18/qU1VnQDeAi7q/aFJdiaZSTJz/Pjx0+y6JGkUK32TuveTfy1QX6jN+4tV91XVVFVNTUxMnGYXJUmjON2AeL1dNqItj7X6UeDyof02Aq+2+sZO/aQ2SdYD5/P+S1qSpDPsdAPiILC9rW8HHhmqT7cnk64ANgFPtctQbyfZ3O4v3DqvzdyxbgaeaPcpJEljtOhTTEm+BnwWuDjJUeA3gT3A/iQ7gJeBWwCq6kiS/cCzwAng9vYEE8BtDJ6IOpfB00uPtfr9wENJZhnMHKZX5MwkScuyaEBU1T87xVtbTrH/bmB3pz4DXNOpv0MLGEnS6uEnqSVJXQaEJKnLgJAkdRkQkqQuA0KS1GVASJK6/JWjp2Gxby+VpA8DZxCSpC4DQpLUZUBIkroMCElSlwEhSeoyICRJXT7mqg/cQo8Fv7TnxjPYE0lL4QxCktRlQEiSugwISVKX9yA0Vot9bYn3KKTxcQYhSeoyICRJXV5i0qrmI7LS+BgQOmt5/0L6YBkQHf6+hw+H5fx3NFykVXQPIsnWJM8nmU2ya9z9kaS1blXMIJKsA/4z8E+Ao8C3kxysqmfH2zOtVc4+pFUSEMD1wGxV/QAgyT5gG2BA6KwzzkuUi4WTN/21FKslIDYArwxtHwX+wfydkuwEdrbNv07y/Bno2wftYuAvx92JVchx6VtwXHLn6R94OW1XAf++9PXG5e+P2ni1BEQ6tXpfoeo+4L4PvjtnTpKZqpoadz9WG8elz3Hpc1z6ljsuq+Um9VHg8qHtjcCrY+qLJInVExDfBjYluSLJR4Fp4OCY+yRJa9qquMRUVSeS/CvgvwHrgK9U1ZExd+tM+VBdMltBjkuf49LnuPQta1xS9b5L/ZIkrZpLTJKkVcaAkCR1GRBnUJLLk/xpkueSHEnyxVa/MMnjSV5oywvG3dczLcm6JN9J8o22vebHBCDJJ5IcSPL99vfmF9b62CT5t+3fz/eSfC3Jj6/VMUnylSTHknxvqHbKsUhyR/s6o+eT3LDY8Q2IM+sE8GtV9XPAZuD2JFcBu4BDVbUJONS215ovAs8NbTsmA78LfLOqfhb4FIMxWrNjk2QD8G+Aqaq6hsFDLdOs3TF5ANg6r9Ydi/b/mmng6tbmnvY1R6dkQJxBVfVaVf15W3+bwT/2DQy+VmRv220vcNNYOjgmSTYCNwJfHiqv6TEBSHIe8IvA/QBV9bdV9SMcm/XAuUnWAz/B4DNTa3JMqurPgDfmlU81FtuAfVX1blW9CMwy+JqjUzIgxiTJJHAtcBi4tKpeg0GIAJeMsWvj8DvArwN/N1Rb62MC8FPAceD32uW3Lyf5GGt4bKrq/wD/EXgZeA14q6r+mDU8Jh2nGoveVxptWOhABsQYJPk48IfAl6rqr8bdn3FK8nngWFU9Pe6+rELrgZ8H7q2qa4G/Ye1cOulq19O3AVcAPwl8LMkXxturs8ZIX2k0zIA4w5J8hEE4fLWqvt7Krye5rL1/GXBsXP0bg88Av5zkJWAf8Lkkv8/aHpM5R4GjVXW4bR9gEBhreWx+CXixqo5X1f8Fvg78Q9b2mMx3qrFY8lcaGRBnUJIwuJ78XFX99tBbB4HtbX078MiZ7tu4VNUdVbWxqiYZ3EB7oqq+wBoekzlV9RfAK0l+ppW2MPgK/LU8Ni8Dm5P8RPv3tIXBvby1PCbznWosDgLTSc5JcgWwCXhqoQP5SeozKMk/Av478Az//3r7bzC4D7Ef+CSDfwC3VNX8G08fekk+C/y7qvp8kotwTEjyaQY37z8K/AD4VQY/2K3ZsUnyW8CvMHgq8DvAvwQ+zhockyRfAz7L4Gu9Xwd+E/gvnGIskvwH4F8wGLsvVdVjCx7fgJAk9XiJSZLUZUBIkroMCElSlwEhSeoyICRJXQaEJKnLgJAkdf0/vIAA3/1djkwAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}],"execution_count":107},{"cell_type":"markdown","source":"The percentage of male to female athletes appears to affected by 1% error approximately. The normalised version appears to have correct quite a large error for the amount of data. ","metadata":{}},{"cell_type":"code","source":"athletes.groupby(['Sex']).count()['Age']/athletes.shape[0]","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:32:27.886632Z","iopub.execute_input":"2024-01-12T14:32:27.887370Z","iopub.status.idle":"2024-01-12T14:32:27.940401Z","shell.execute_reply.started":"2024-01-12T14:32:27.887313Z","shell.execute_reply":"2024-01-12T14:32:27.939455Z"},"trusted":true},"outputs":[{"execution_count":114,"output_type":"execute_result","data":{"text/plain":"Sex\nF 0.272277\nM 0.695720\nName: Age, dtype: float64"},"metadata":{}}],"execution_count":114},{"cell_type":"code","source":"data_df.groupby(['Sex']).count()['Age']/data_df.shape[0]","metadata":{"execution":{"iopub.status.busy":"2024-01-12T14:33:01.140574Z","iopub.execute_input":"2024-01-12T14:33:01.141006Z","iopub.status.idle":"2024-01-12T14:33:01.373450Z","shell.execute_reply.started":"2024-01-12T14:33:01.140970Z","shell.execute_reply":"2024-01-12T14:33:01.372152Z"},"trusted":true},"outputs":[{"execution_count":117,"output_type":"execute_result","data":{"text/plain":"Sex\nF 0.284254\nM 0.682648\nName: Age, dtype: float64"},"metadata":{}}],"execution_count":117},{"cell_type":"markdown","source":"The normalisation of the data exploring medal per team for each game. The relationship helps grouping our data.\n\n__achievement__(game_id(FK, PK), athlete_id(FK, PK), sport_id (FK,PK), event_id(FK,PK), medal_id(FK,PK), medal_ref(PK))\n\nWe discover that potentially older athletes may be achieving better. The expected age appears higher. ","metadata":{}},{"cell_type":"code","source":"columns = ['Year','City','Season','Event','Sport','Medal','Team','Age']\ngrouped_data = data_df.groupby(columns).count()['Name'].reset_index()\nrows = grouped_data.Medal.isin(['Bronze','Silver','Bronze'])\nmedals = grouped_data[rows]\nteams_medals = medals.groupby(['Team','Year','Age']).count()['Event'].reset_index()\nplt.hist(teams_medals.Event, bins = 50)\nteams_medals.describe()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T15:24:53.748157Z","iopub.execute_input":"2024-01-12T15:24:53.748612Z","iopub.status.idle":"2024-01-12T15:24:54.559008Z","shell.execute_reply.started":"2024-01-12T15:24:53.748573Z","shell.execute_reply":"2024-01-12T15:24:54.557568Z"},"trusted":true},"outputs":[{"execution_count":171,"output_type":"execute_result","data":{"text/plain":" Year Age Event\ncount 11837.00000 11837.000000 11837.000000\nmean 1975.82918 27.180113 1.963082\nstd 35.43089 7.144196 1.873691\nmin 1896.00000 10.000000 1.000000\n25% 1952.00000 22.000000 1.000000\n50% 1988.00000 26.000000 1.000000\n75% 2004.00000 31.000000 2.000000\nmax 2020.00000 73.000000 25.000000","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearAgeEvent
count11837.0000011837.00000011837.000000
mean1975.8291827.1801131.963082
std35.430897.1441961.873691
min1896.0000010.0000001.000000
25%1952.0000022.0000001.000000
50%1988.0000026.0000001.000000
75%2004.0000031.0000002.000000
max2020.0000073.00000025.000000
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}],"execution_count":171},{"cell_type":"code","source":"# create data\nx = teams_medals.Year\ny = teams_medals.Age\nz = teams_medals.Event\ncolours = teams_medals.Event\n \n# use the scatter function\n#plt.scatter(x, y, s=z*1000, alpha=0.5)\nplt.figure(figsize=(20,20))\n\nplt.scatter(x,y, s = z *100, alpha = 0.2, c = colours)\nplt.colorbar()\n\n# show the graph\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-01-12T15:06:47.165294Z","iopub.execute_input":"2024-01-12T15:06:47.165781Z","iopub.status.idle":"2024-01-12T15:06:47.985666Z","shell.execute_reply.started":"2024-01-12T15:06:47.165739Z","shell.execute_reply":"2024-01-12T15:06:47.984319Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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= teams_medals.groupby(['Year','Team']).agg({'Age': ['min','median', 'max'], 'Event' :['count']}).reset_index()\nteams_medals_more.sort_values(by = ['Year','Team'], ascending = False)","metadata":{"execution":{"iopub.status.busy":"2024-01-12T15:27:32.732158Z","iopub.execute_input":"2024-01-12T15:27:32.732623Z","iopub.status.idle":"2024-01-12T15:27:32.776450Z","shell.execute_reply.started":"2024-01-12T15:27:32.732587Z","shell.execute_reply":"2024-01-12T15:27:32.775103Z"},"trusted":true},"outputs":[{"execution_count":180,"output_type":"execute_result","data":{"text/plain":" Year Team Age Event\n min median max count\n1840 2020 Venezuela 21.0 25.0 36.0 3\n1839 2020 Uzbekistan 24.0 27.5 31.0 2\n1838 2020 United States 16.0 30.0 56.0 29\n1837 2020 Ukraine 19.0 26.0 38.0 13\n1836 2020 Uganda 20.0 22.0 24.0 2\n... ... ... ... ... ... ...\n4 1896 France 18.0 19.5 21.0 4\n3 1896 Ethnikos Gymnastikos Syllogos 10.0 10.0 10.0 1\n2 1896 Denmark 21.0 25.0 29.0 2\n1 1896 Austria 19.0 21.0 23.0 2\n0 1896 Australia/Great Britain 22.0 22.5 23.0 2\n\n[1841 rows x 6 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearTeamAgeEvent
minmedianmaxcount
18402020Venezuela21.025.036.03
18392020Uzbekistan24.027.531.02
18382020United States16.030.056.029
18372020Ukraine19.026.038.013
18362020Uganda20.022.024.02
.....................
41896France18.019.521.04
31896Ethnikos Gymnastikos Syllogos10.010.010.01
21896Denmark21.025.029.02
11896Austria19.021.023.02
01896Australia/Great Britain22.022.523.02
\n

1841 rows × 6 columns

\n
"},"metadata":{}}],"execution_count":180}]} \ No newline at end of file diff --git a/Data engineering and science/Structuring data/generation-of-synthetic-datasets.ipynb b/Data engineering and science/Structuring data/generation-of-synthetic-datasets.ipynb new file mode 100644 index 0000000..d56b03d --- /dev/null +++ b/Data engineering and science/Structuring data/generation-of-synthetic-datasets.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30839,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport random\nfrom random import randint, randrange\nimport string\nimport uuid\nfrom datetime import date, timedelta, datetime\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom random import choices\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n\nplt.rcParams['figure.figsize'] = [20, 14]\nplt.rcParams[\"font.size\"] = 16\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:55.021191Z","iopub.execute_input":"2025-02-13T11:42:55.021800Z","iopub.status.idle":"2025-02-13T11:42:59.126467Z","shell.execute_reply.started":"2025-02-13T11:42:55.021758Z","shell.execute_reply":"2025-02-13T11:42:59.124968Z"}},"outputs":[],"execution_count":1},{"cell_type":"markdown","source":"# Number of observations \n","metadata":{}},{"cell_type":"code","source":"n_skills_matching = 1000\nn_skills_matching_employees = 28\nn_organisations = 10\nn_individuals = 300\nn_skills = 60\nn_clusters = 20\nn_professions = 10\nn_functions = 5\nn_guk = 3\nn_courses = 150\nn_roles = 100\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.127705Z","iopub.execute_input":"2025-02-13T11:42:59.128285Z","iopub.status.idle":"2025-02-13T11:42:59.134707Z","shell.execute_reply.started":"2025-02-13T11:42:59.128251Z","shell.execute_reply":"2025-02-13T11:42:59.133075Z"}},"outputs":[],"execution_count":2},{"cell_type":"markdown","source":"# functions used to generates random data","metadata":{}},{"cell_type":"code","source":"def get_random_alphanum(pattern,length):\n # choose from all lowercase letter\n digits = string.digits\n result_str = \"\".join(random.choice(digits) for i in range(length))\n return pattern + result_str","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.135823Z","iopub.execute_input":"2025-02-13T11:42:59.136574Z","iopub.status.idle":"2025-02-13T11:42:59.174381Z","shell.execute_reply.started":"2025-02-13T11:42:59.136530Z","shell.execute_reply":"2025-02-13T11:42:59.173091Z"}},"outputs":[],"execution_count":3},{"cell_type":"code","source":"get_random_alphanum(\"test\",1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.176993Z","iopub.execute_input":"2025-02-13T11:42:59.177335Z","iopub.status.idle":"2025-02-13T11:42:59.201439Z","shell.execute_reply.started":"2025-02-13T11:42:59.177309Z","shell.execute_reply":"2025-02-13T11:42:59.200276Z"}},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"'test2'"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"def get_random_string(length):\n # choose from all lowercase letter\n letters = string.ascii_lowercase\n result_str = ''.join(random.choice(letters) for i in range(length))\n return result_str","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.203398Z","iopub.execute_input":"2025-02-13T11:42:59.203766Z","iopub.status.idle":"2025-02-13T11:42:59.223264Z","shell.execute_reply.started":"2025-02-13T11:42:59.203734Z","shell.execute_reply":"2025-02-13T11:42:59.222145Z"}},"outputs":[],"execution_count":5},{"cell_type":"code","source":"get_random_string(5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.224451Z","iopub.execute_input":"2025-02-13T11:42:59.224877Z","iopub.status.idle":"2025-02-13T11:42:59.250247Z","shell.execute_reply.started":"2025-02-13T11:42:59.224838Z","shell.execute_reply":"2025-02-13T11:42:59.249026Z"}},"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"'duaxj'"},"metadata":{}}],"execution_count":6},{"cell_type":"code","source":"def get_random_date(start, end):\n delta = end - start\n int_delta = (delta.days * 24 * 60 * 60) + delta.seconds\n random_second = randrange(int_delta)\n result = start + timedelta(seconds=random_second)\n return date(result.year, result.month, result.day)\n ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.251702Z","iopub.execute_input":"2025-02-13T11:42:59.252102Z","iopub.status.idle":"2025-02-13T11:42:59.286015Z","shell.execute_reply.started":"2025-02-13T11:42:59.252066Z","shell.execute_reply":"2025-02-13T11:42:59.284532Z"}},"outputs":[],"execution_count":7},{"cell_type":"code","source":"start = datetime.strptime('1-1-2008', '%d-%m-%Y')\nend = datetime.strptime('31-12-2009', '%d-%m-%Y')\nget_random_date(start, end)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.287522Z","iopub.execute_input":"2025-02-13T11:42:59.287960Z","iopub.status.idle":"2025-02-13T11:42:59.316312Z","shell.execute_reply.started":"2025-02-13T11:42:59.287926Z","shell.execute_reply":"2025-02-13T11:42:59.315022Z"}},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"datetime.date(2008, 8, 14)"},"metadata":{}}],"execution_count":8},{"cell_type":"markdown","source":"# Dimension: Taxonomy","metadata":{}},{"cell_type":"markdown","source":"## Skills\n\nWe use the definition of technical and soft skills as discussed on this page - not a reputable one - but informative. \n\nhttps://ca.indeed.com/career-advice/career-development/technical-vs-soft-skills","metadata":{}},{"cell_type":"code","source":"no_ids = range(0,n_skills)\nskill_ids = []\nfor i in no_ids:\n skill_ids.append(get_random_alphanum(\"1010101\",3))\n\nskill_ids = pd.Series(skill_ids)\nlen(skill_ids.unique()) == n_skills","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.317507Z","iopub.execute_input":"2025-02-13T11:42:59.317918Z","iopub.status.idle":"2025-02-13T11:42:59.352783Z","shell.execute_reply.started":"2025-02-13T11:42:59.317879Z","shell.execute_reply":"2025-02-13T11:42:59.351476Z"}},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"False"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"skill_ids[0:5]\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.354013Z","iopub.execute_input":"2025-02-13T11:42:59.354419Z","iopub.status.idle":"2025-02-13T11:42:59.381680Z","shell.execute_reply.started":"2025-02-13T11:42:59.354387Z","shell.execute_reply":"2025-02-13T11:42:59.380563Z"}},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"0 1010101263\n1 1010101987\n2 1010101349\n3 1010101080\n4 1010101952\ndtype: object"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"values = range(0,n_skills)\nskills = [get_random_string(6) for i in values]\nlen(skills) == n_skills","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.382828Z","iopub.execute_input":"2025-02-13T11:42:59.383195Z","iopub.status.idle":"2025-02-13T11:42:59.405273Z","shell.execute_reply.started":"2025-02-13T11:42:59.383158Z","shell.execute_reply":"2025-02-13T11:42:59.403758Z"}},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"skills[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.406656Z","iopub.execute_input":"2025-02-13T11:42:59.407141Z","iopub.status.idle":"2025-02-13T11:42:59.427954Z","shell.execute_reply.started":"2025-02-13T11:42:59.407098Z","shell.execute_reply":"2025-02-13T11:42:59.426597Z"}},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"['gqryuk', 'goaylk', 'bdbluo', 'bsncme', 'bxxnst']"},"metadata":{}}],"execution_count":12},{"cell_type":"code","source":"type_skills = random.choices([\"soft\", \"technical\",\"language\"], \n weights=[10, 10, 10], \n k=n_skills)\nlen(type_skills) == n_skills\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.433663Z","iopub.execute_input":"2025-02-13T11:42:59.434062Z","iopub.status.idle":"2025-02-13T11:42:59.452598Z","shell.execute_reply.started":"2025-02-13T11:42:59.434030Z","shell.execute_reply":"2025-02-13T11:42:59.451092Z"}},"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":13},{"cell_type":"code","source":"type_skills[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.455435Z","iopub.execute_input":"2025-02-13T11:42:59.455967Z","iopub.status.idle":"2025-02-13T11:42:59.477005Z","shell.execute_reply.started":"2025-02-13T11:42:59.455933Z","shell.execute_reply":"2025-02-13T11:42:59.475853Z"}},"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"['technical', 'language', 'language', 'technical', 'soft']"},"metadata":{}}],"execution_count":14},{"cell_type":"code","source":"data ={\"skills_uid\":skill_ids,\n \"skills\":skills,\n \"type\": type_skills}\nskills_pd = pd.DataFrame(data)\nskills_pd.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.478201Z","iopub.execute_input":"2025-02-13T11:42:59.478573Z","iopub.status.idle":"2025-02-13T11:42:59.517298Z","shell.execute_reply.started":"2025-02-13T11:42:59.478537Z","shell.execute_reply":"2025-02-13T11:42:59.515921Z"}},"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":" skills_uid skills type\n0 1010101263 gqryuk technical\n1 1010101987 goaylk language\n2 1010101349 bdbluo language\n3 1010101080 bsncme technical\n4 1010101952 bxxnst soft","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skills_uidskillstype
01010101263gqryuktechnical
11010101987goaylklanguage
21010101349bdbluolanguage
31010101080bsncmetechnical
41010101952bxxnstsoft
\n
"},"metadata":{}}],"execution_count":15},{"cell_type":"code","source":"df_encoded = pd.get_dummies(data[\"type\"])\nskills_pd['language'] = df_encoded['language']\nskills_pd['technical'] = df_encoded['technical']\nskills_pd['soft'] = df_encoded['soft']\nskills_pd = skills_pd.loc[:,['skills_uid','skills','language','technical','soft']]\nskills_pd.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.518702Z","iopub.execute_input":"2025-02-13T11:42:59.519184Z","iopub.status.idle":"2025-02-13T11:42:59.558790Z","shell.execute_reply.started":"2025-02-13T11:42:59.519144Z","shell.execute_reply":"2025-02-13T11:42:59.557478Z"}},"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":" skills_uid skills language technical soft\n0 1010101263 gqryuk False True False\n1 1010101987 goaylk True False False\n2 1010101349 bdbluo True False False\n3 1010101080 bsncme False True False\n4 1010101952 bxxnst False False True","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skills_uidskillslanguagetechnicalsoft
01010101263gqryukFalseTrueFalse
11010101987goaylkTrueFalseFalse
21010101349bdbluoTrueFalseFalse
31010101080bsncmeFalseTrueFalse
41010101952bxxnstFalseFalseTrue
\n
"},"metadata":{}}],"execution_count":16},{"cell_type":"markdown","source":"## Skill clusters","metadata":{}},{"cell_type":"code","source":"values = range(0,n_clusters)\nclusters = [get_random_string(4) for i in values]\nlen(clusters) == n_clusters","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.560503Z","iopub.execute_input":"2025-02-13T11:42:59.560987Z","iopub.status.idle":"2025-02-13T11:42:59.580022Z","shell.execute_reply.started":"2025-02-13T11:42:59.560922Z","shell.execute_reply":"2025-02-13T11:42:59.578700Z"}},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"clusters","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.580900Z","iopub.execute_input":"2025-02-13T11:42:59.581253Z","iopub.status.idle":"2025-02-13T11:42:59.604735Z","shell.execute_reply.started":"2025-02-13T11:42:59.581228Z","shell.execute_reply":"2025-02-13T11:42:59.603491Z"}},"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"['dkhk',\n 'nyzo',\n 'aywu',\n 'bomw',\n 'upnl',\n 'spom',\n 'nfun',\n 'plnm',\n 'dghm',\n 'veez',\n 'ydri',\n 'yacn',\n 'gaul',\n 'nurq',\n 'ceqx',\n 'whxk',\n 'gsck',\n 'bvwv',\n 'efit',\n 'pjuo']"},"metadata":{}}],"execution_count":18},{"cell_type":"code","source":"no_ids = range(0,n_clusters)\ncluster_ids = []\nfor i in no_ids:\n cluster_ids.append(get_random_alphanum(\"2020202\",3))\n\ncluster_ids = pd.Series(cluster_ids)\nlen(cluster_ids.unique()) == n_clusters","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.606538Z","iopub.execute_input":"2025-02-13T11:42:59.607040Z","iopub.status.idle":"2025-02-13T11:42:59.631897Z","shell.execute_reply.started":"2025-02-13T11:42:59.606963Z","shell.execute_reply":"2025-02-13T11:42:59.630764Z"}},"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"False"},"metadata":{}}],"execution_count":19},{"cell_type":"code","source":"cluster_ids[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.633125Z","iopub.execute_input":"2025-02-13T11:42:59.633536Z","iopub.status.idle":"2025-02-13T11:42:59.656544Z","shell.execute_reply.started":"2025-02-13T11:42:59.633498Z","shell.execute_reply":"2025-02-13T11:42:59.655452Z"}},"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"0 2020202215\n1 2020202404\n2 2020202703\n3 2020202999\n4 2020202888\ndtype: object"},"metadata":{}}],"execution_count":20},{"cell_type":"code","source":"data ={\"cluster_uid\":cluster_ids,\n \"cluster\":clusters}\nclusters_pd = pd.DataFrame(data)\nclusters_pd.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.658008Z","iopub.execute_input":"2025-02-13T11:42:59.658484Z","iopub.status.idle":"2025-02-13T11:42:59.686254Z","shell.execute_reply.started":"2025-02-13T11:42:59.658444Z","shell.execute_reply":"2025-02-13T11:42:59.685139Z"}},"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":" cluster_uid cluster\n0 2020202215 dkhk\n1 2020202404 nyzo\n2 2020202703 aywu\n3 2020202999 bomw\n4 2020202888 upnl","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
cluster_uidcluster
02020202215dkhk
12020202404nyzo
22020202703aywu
32020202999bomw
42020202888upnl
\n
"},"metadata":{}}],"execution_count":21},{"cell_type":"markdown","source":"Add the link between clusters and skills","metadata":{}},{"cell_type":"code","source":"values = range(0,n_skills) \nclusters_skills = [random.choice(cluster_ids) for i in values] \nlen(clusters_skills) == n_skills","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.687293Z","iopub.execute_input":"2025-02-13T11:42:59.687658Z","iopub.status.idle":"2025-02-13T11:42:59.716357Z","shell.execute_reply.started":"2025-02-13T11:42:59.687601Z","shell.execute_reply":"2025-02-13T11:42:59.715113Z"}},"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":22},{"cell_type":"code","source":"clusters_skills[0:10]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.717794Z","iopub.execute_input":"2025-02-13T11:42:59.718253Z","iopub.status.idle":"2025-02-13T11:42:59.740030Z","shell.execute_reply.started":"2025-02-13T11:42:59.718213Z","shell.execute_reply":"2025-02-13T11:42:59.738563Z"}},"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"['2020202265',\n '2020202454',\n '2020202005',\n '2020202005',\n '2020202703',\n '2020202213',\n '2020202703',\n '2020202867',\n '2020202379',\n '2020202404']"},"metadata":{}}],"execution_count":23},{"cell_type":"code","source":"skills_pd['cluster'] = clusters_skills\nskills_pd.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.741519Z","iopub.execute_input":"2025-02-13T11:42:59.741998Z","iopub.status.idle":"2025-02-13T11:42:59.768240Z","shell.execute_reply.started":"2025-02-13T11:42:59.741957Z","shell.execute_reply":"2025-02-13T11:42:59.767211Z"}},"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":" skills_uid skills language technical soft cluster\n0 1010101263 gqryuk False True False 2020202265\n1 1010101987 goaylk True False False 2020202454\n2 1010101349 bdbluo True False False 2020202005\n3 1010101080 bsncme False True False 2020202005\n4 1010101952 bxxnst False False True 2020202703","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skills_uidskillslanguagetechnicalsoftcluster
01010101263gqryukFalseTrueFalse2020202265
11010101987goaylkTrueFalseFalse2020202454
21010101349bdbluoTrueFalseFalse2020202005
31010101080bsncmeFalseTrueFalse2020202005
41010101952bxxnstFalseFalseTrue2020202703
\n
"},"metadata":{}}],"execution_count":24},{"cell_type":"markdown","source":"## Professions","metadata":{}},{"cell_type":"code","source":"values = range(0,n_professions)\nprofessions = [get_random_string(7) for i in values]\nlen(professions) == n_professions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.769677Z","iopub.execute_input":"2025-02-13T11:42:59.770096Z","iopub.status.idle":"2025-02-13T11:42:59.797918Z","shell.execute_reply.started":"2025-02-13T11:42:59.770055Z","shell.execute_reply":"2025-02-13T11:42:59.796812Z"}},"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":25},{"cell_type":"code","source":"professions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.798849Z","iopub.execute_input":"2025-02-13T11:42:59.799144Z","iopub.status.idle":"2025-02-13T11:42:59.829867Z","shell.execute_reply.started":"2025-02-13T11:42:59.799121Z","shell.execute_reply":"2025-02-13T11:42:59.828750Z"}},"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"['npotdsp',\n 'aushsmx',\n 'bnkimlk',\n 'esmgugd',\n 'usxhuhz',\n 'fcftnao',\n 'unsdcqf',\n 'nqsxppj',\n 'vmyvicq',\n 'wrtttwd']"},"metadata":{}}],"execution_count":26},{"cell_type":"code","source":"no_ids = range(0,n_professions)\nprof_ids = []\nfor i in no_ids:\n prof_ids.append(get_random_alphanum(\"3030303\",3))\n\nprof_ids = pd.Series(prof_ids)\nlen(prof_ids.unique()) == n_professions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.830960Z","iopub.execute_input":"2025-02-13T11:42:59.831344Z","iopub.status.idle":"2025-02-13T11:42:59.850941Z","shell.execute_reply.started":"2025-02-13T11:42:59.831307Z","shell.execute_reply":"2025-02-13T11:42:59.849770Z"}},"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":27},{"cell_type":"code","source":"prof_ids[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.852046Z","iopub.execute_input":"2025-02-13T11:42:59.852470Z","iopub.status.idle":"2025-02-13T11:42:59.876214Z","shell.execute_reply.started":"2025-02-13T11:42:59.852433Z","shell.execute_reply":"2025-02-13T11:42:59.875070Z"}},"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"0 3030303146\n1 3030303863\n2 3030303434\n3 3030303492\n4 3030303521\ndtype: object"},"metadata":{}}],"execution_count":28},{"cell_type":"code","source":"data ={\"prof_uid\":prof_ids,\n \"profession\":professions}\nprofessions_pd = pd.DataFrame(data)\nprofessions_pd.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.877539Z","iopub.execute_input":"2025-02-13T11:42:59.877968Z","iopub.status.idle":"2025-02-13T11:42:59.904501Z","shell.execute_reply.started":"2025-02-13T11:42:59.877928Z","shell.execute_reply":"2025-02-13T11:42:59.903171Z"}},"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":" prof_uid profession\n0 3030303146 npotdsp\n1 3030303863 aushsmx\n2 3030303434 bnkimlk\n3 3030303492 esmgugd\n4 3030303521 usxhuhz","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
prof_uidprofession
03030303146npotdsp
13030303863aushsmx
23030303434bnkimlk
33030303492esmgugd
43030303521usxhuhz
\n
"},"metadata":{}}],"execution_count":29},{"cell_type":"markdown","source":"Add the link between clusters and skills","metadata":{}},{"cell_type":"code","source":"values = range(0,n_clusters) \nclusters_profs = [random.choice(prof_ids) for i in values] \nlen(clusters_profs) == n_clusters","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.905793Z","iopub.execute_input":"2025-02-13T11:42:59.906518Z","iopub.status.idle":"2025-02-13T11:42:59.928560Z","shell.execute_reply.started":"2025-02-13T11:42:59.906475Z","shell.execute_reply":"2025-02-13T11:42:59.927220Z"}},"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":30},{"cell_type":"code","source":"clusters_profs","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.929735Z","iopub.execute_input":"2025-02-13T11:42:59.930069Z","iopub.status.idle":"2025-02-13T11:42:59.949041Z","shell.execute_reply.started":"2025-02-13T11:42:59.930038Z","shell.execute_reply":"2025-02-13T11:42:59.947856Z"}},"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"['3030303768',\n '3030303768',\n '3030303936',\n '3030303768',\n '3030303009',\n '3030303434',\n '3030303018',\n '3030303936',\n '3030303166',\n '3030303863',\n '3030303018',\n '3030303009',\n '3030303146',\n '3030303434',\n '3030303936',\n '3030303521',\n '3030303166',\n '3030303863',\n '3030303009',\n '3030303521']"},"metadata":{}}],"execution_count":31},{"cell_type":"code","source":"clusters_pd['profession'] = clusters_profs\nclusters_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.950224Z","iopub.execute_input":"2025-02-13T11:42:59.950827Z","iopub.status.idle":"2025-02-13T11:42:59.981520Z","shell.execute_reply.started":"2025-02-13T11:42:59.950786Z","shell.execute_reply":"2025-02-13T11:42:59.980198Z"}},"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":" cluster_uid cluster profession\n0 2020202215 dkhk 3030303768\n1 2020202404 nyzo 3030303768\n2 2020202703 aywu 3030303936\n3 2020202999 bomw 3030303768\n4 2020202888 upnl 3030303009\n5 2020202215 spom 3030303434\n6 2020202855 nfun 3030303018\n7 2020202043 plnm 3030303936\n8 2020202005 dghm 3030303166\n9 2020202213 veez 3030303863\n10 2020202870 ydri 3030303018\n11 2020202342 yacn 3030303009\n12 2020202265 gaul 3030303146\n13 2020202379 nurq 3030303434\n14 2020202867 ceqx 3030303936\n15 2020202373 whxk 3030303521\n16 2020202415 gsck 3030303166\n17 2020202454 bvwv 3030303863\n18 2020202378 efit 3030303009\n19 2020202601 pjuo 3030303521","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
cluster_uidclusterprofession
02020202215dkhk3030303768
12020202404nyzo3030303768
22020202703aywu3030303936
32020202999bomw3030303768
42020202888upnl3030303009
52020202215spom3030303434
62020202855nfun3030303018
72020202043plnm3030303936
82020202005dghm3030303166
92020202213veez3030303863
102020202870ydri3030303018
112020202342yacn3030303009
122020202265gaul3030303146
132020202379nurq3030303434
142020202867ceqx3030303936
152020202373whxk3030303521
162020202415gsck3030303166
172020202454bvwv3030303863
182020202378efit3030303009
192020202601pjuo3030303521
\n
"},"metadata":{}}],"execution_count":32},{"cell_type":"markdown","source":"## Functions","metadata":{}},{"cell_type":"code","source":"values = range(0,n_functions)\nfunctions = [get_random_string(8) for i in values]\nlen(functions) == n_functions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:42:59.982870Z","iopub.execute_input":"2025-02-13T11:42:59.983242Z","iopub.status.idle":"2025-02-13T11:43:00.006560Z","shell.execute_reply.started":"2025-02-13T11:42:59.983216Z","shell.execute_reply":"2025-02-13T11:43:00.005078Z"}},"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":33},{"cell_type":"code","source":"functions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.007727Z","iopub.execute_input":"2025-02-13T11:43:00.008045Z","iopub.status.idle":"2025-02-13T11:43:00.029856Z","shell.execute_reply.started":"2025-02-13T11:43:00.008019Z","shell.execute_reply":"2025-02-13T11:43:00.028464Z"}},"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"['ckzgyvfj', 'enydvwbk', 'hyhbuafh', 'easpvjiy', 'akvrfsup']"},"metadata":{}}],"execution_count":34},{"cell_type":"code","source":"no_ids = range(0,n_functions)\nfunction_ids = []\nfor i in no_ids:\n function_ids.append(get_random_alphanum(\"4040404\",3))\n\nfunction_ids = pd.Series(function_ids)\nlen(function_ids.unique()) == n_functions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.030985Z","iopub.execute_input":"2025-02-13T11:43:00.031393Z","iopub.status.idle":"2025-02-13T11:43:00.054329Z","shell.execute_reply.started":"2025-02-13T11:43:00.031340Z","shell.execute_reply":"2025-02-13T11:43:00.053181Z"}},"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":35},{"cell_type":"code","source":"function_ids[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.055562Z","iopub.execute_input":"2025-02-13T11:43:00.056000Z","iopub.status.idle":"2025-02-13T11:43:00.077104Z","shell.execute_reply.started":"2025-02-13T11:43:00.055964Z","shell.execute_reply":"2025-02-13T11:43:00.075776Z"}},"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"0 4040404718\n1 4040404329\n2 4040404206\n3 4040404061\n4 4040404835\ndtype: object"},"metadata":{}}],"execution_count":36},{"cell_type":"code","source":"data ={\"function uid\":function_ids,\n \"function\":functions}\nfunctions_pd = pd.DataFrame(data)\nfunctions_pd.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.078277Z","iopub.execute_input":"2025-02-13T11:43:00.078650Z","iopub.status.idle":"2025-02-13T11:43:00.106189Z","shell.execute_reply.started":"2025-02-13T11:43:00.078586Z","shell.execute_reply":"2025-02-13T11:43:00.104865Z"}},"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":" function uid function\n0 4040404718 ckzgyvfj\n1 4040404329 enydvwbk\n2 4040404206 hyhbuafh\n3 4040404061 easpvjiy\n4 4040404835 akvrfsup","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
function uidfunction
04040404718ckzgyvfj
14040404329enydvwbk
24040404206hyhbuafh
34040404061easpvjiy
44040404835akvrfsup
\n
"},"metadata":{}}],"execution_count":37},{"cell_type":"markdown","source":"Add the link between professions and functions","metadata":{}},{"cell_type":"code","source":"values = range(0,n_professions) \nfunctions_profs = [random.choice(function_ids) for i in values] \nlen(functions_profs) == n_professions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.107283Z","iopub.execute_input":"2025-02-13T11:43:00.107577Z","iopub.status.idle":"2025-02-13T11:43:00.130239Z","shell.execute_reply.started":"2025-02-13T11:43:00.107548Z","shell.execute_reply":"2025-02-13T11:43:00.129163Z"}},"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":38},{"cell_type":"code","source":"functions_profs","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.131320Z","iopub.execute_input":"2025-02-13T11:43:00.131686Z","iopub.status.idle":"2025-02-13T11:43:00.154524Z","shell.execute_reply.started":"2025-02-13T11:43:00.131650Z","shell.execute_reply":"2025-02-13T11:43:00.153197Z"}},"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"['4040404206',\n '4040404061',\n '4040404206',\n '4040404329',\n '4040404061',\n '4040404835',\n '4040404206',\n '4040404329',\n '4040404718',\n '4040404835']"},"metadata":{}}],"execution_count":39},{"cell_type":"code","source":"professions_pd['function'] = functions_profs\nprofessions_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.166707Z","iopub.execute_input":"2025-02-13T11:43:00.167052Z","iopub.status.idle":"2025-02-13T11:43:00.183281Z","shell.execute_reply.started":"2025-02-13T11:43:00.167027Z","shell.execute_reply":"2025-02-13T11:43:00.182130Z"}},"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":" prof_uid profession function\n0 3030303146 npotdsp 4040404206\n1 3030303863 aushsmx 4040404061\n2 3030303434 bnkimlk 4040404206\n3 3030303492 esmgugd 4040404329\n4 3030303521 usxhuhz 4040404061\n5 3030303768 fcftnao 4040404835\n6 3030303936 unsdcqf 4040404206\n7 3030303018 nqsxppj 4040404329\n8 3030303166 vmyvicq 4040404718\n9 3030303009 wrtttwd 4040404835","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
prof_uidprofessionfunction
03030303146npotdsp4040404206
13030303863aushsmx4040404061
23030303434bnkimlk4040404206
33030303492esmgugd4040404329
43030303521usxhuhz4040404061
53030303768fcftnao4040404835
63030303936unsdcqf4040404206
73030303018nqsxppj4040404329
83030303166vmyvicq4040404718
93030303009wrtttwd4040404835
\n
"},"metadata":{}}],"execution_count":40},{"cell_type":"markdown","source":"## GUK","metadata":{}},{"cell_type":"code","source":"values = range(0,n_guk)\nguks = [get_random_string(10) for i in values]\nlen(guks) == n_guk","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.187384Z","iopub.execute_input":"2025-02-13T11:43:00.187797Z","iopub.status.idle":"2025-02-13T11:43:00.207222Z","shell.execute_reply.started":"2025-02-13T11:43:00.187766Z","shell.execute_reply":"2025-02-13T11:43:00.206061Z"}},"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":41},{"cell_type":"code","source":"guks","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.208391Z","iopub.execute_input":"2025-02-13T11:43:00.208791Z","iopub.status.idle":"2025-02-13T11:43:00.228711Z","shell.execute_reply.started":"2025-02-13T11:43:00.208756Z","shell.execute_reply":"2025-02-13T11:43:00.227349Z"}},"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"['hpqmmbgtev', 'salskzxjyg', 'ldlmayhvvq']"},"metadata":{}}],"execution_count":42},{"cell_type":"code","source":"no_ids = range(0,n_guk)\nguk_ids = []\nfor i in no_ids:\n guk_ids.append(get_random_alphanum(\"5050505\",3))\n\nguk_ids = pd.Series(guk_ids)\nlen(guk_ids.unique()) == n_guk","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.229869Z","iopub.execute_input":"2025-02-13T11:43:00.230339Z","iopub.status.idle":"2025-02-13T11:43:00.250208Z","shell.execute_reply.started":"2025-02-13T11:43:00.230302Z","shell.execute_reply":"2025-02-13T11:43:00.248937Z"}},"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":43},{"cell_type":"code","source":"guk_ids","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.251537Z","iopub.execute_input":"2025-02-13T11:43:00.252087Z","iopub.status.idle":"2025-02-13T11:43:00.273271Z","shell.execute_reply.started":"2025-02-13T11:43:00.252048Z","shell.execute_reply":"2025-02-13T11:43:00.272052Z"}},"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"0 5050505452\n1 5050505195\n2 5050505591\ndtype: object"},"metadata":{}}],"execution_count":44},{"cell_type":"code","source":"data ={\"guk uid\":guk_ids,\n \"guk\":guks}\nguks_pd = pd.DataFrame(data)\nguks_pd.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.274552Z","iopub.execute_input":"2025-02-13T11:43:00.275069Z","iopub.status.idle":"2025-02-13T11:43:00.297880Z","shell.execute_reply.started":"2025-02-13T11:43:00.275027Z","shell.execute_reply":"2025-02-13T11:43:00.296707Z"}},"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":" guk uid guk\n0 5050505452 hpqmmbgtev\n1 5050505195 salskzxjyg\n2 5050505591 ldlmayhvvq","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
guk uidguk
05050505452hpqmmbgtev
15050505195salskzxjyg
25050505591ldlmayhvvq
\n
"},"metadata":{}}],"execution_count":45},{"cell_type":"markdown","source":"Add the link between guks and functions","metadata":{}},{"cell_type":"code","source":"values = range(0,n_functions) \nfunctions_guks = [random.choice(guk_ids) for i in values] \nlen(functions_guks) == n_functions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.298865Z","iopub.execute_input":"2025-02-13T11:43:00.299317Z","iopub.status.idle":"2025-02-13T11:43:00.318025Z","shell.execute_reply.started":"2025-02-13T11:43:00.299277Z","shell.execute_reply":"2025-02-13T11:43:00.316671Z"}},"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":46},{"cell_type":"code","source":"functions_guks","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.319113Z","iopub.execute_input":"2025-02-13T11:43:00.319427Z","iopub.status.idle":"2025-02-13T11:43:00.336454Z","shell.execute_reply.started":"2025-02-13T11:43:00.319402Z","shell.execute_reply":"2025-02-13T11:43:00.335061Z"}},"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"['5050505452', '5050505591', '5050505591', '5050505452', '5050505591']"},"metadata":{}}],"execution_count":47},{"cell_type":"code","source":"functions_pd['guk'] = functions_guks\nfunctions_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.338010Z","iopub.execute_input":"2025-02-13T11:43:00.338752Z","iopub.status.idle":"2025-02-13T11:43:00.361218Z","shell.execute_reply.started":"2025-02-13T11:43:00.338704Z","shell.execute_reply":"2025-02-13T11:43:00.359919Z"}},"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":" function uid function guk\n0 4040404718 ckzgyvfj 5050505452\n1 4040404329 enydvwbk 5050505591\n2 4040404206 hyhbuafh 5050505591\n3 4040404061 easpvjiy 5050505452\n4 4040404835 akvrfsup 5050505591","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
function uidfunctionguk
04040404718ckzgyvfj5050505452
14040404329enydvwbk5050505591
24040404206hyhbuafh5050505591
34040404061easpvjiy5050505452
44040404835akvrfsup5050505591
\n
"},"metadata":{}}],"execution_count":48},{"cell_type":"markdown","source":"## test ","metadata":{}},{"cell_type":"code","source":"professions_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.362551Z","iopub.execute_input":"2025-02-13T11:43:00.363007Z","iopub.status.idle":"2025-02-13T11:43:00.392978Z","shell.execute_reply.started":"2025-02-13T11:43:00.362967Z","shell.execute_reply":"2025-02-13T11:43:00.391753Z"}},"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":" prof_uid profession function\n0 3030303146 npotdsp 4040404206\n1 3030303863 aushsmx 4040404061\n2 3030303434 bnkimlk 4040404206\n3 3030303492 esmgugd 4040404329\n4 3030303521 usxhuhz 4040404061\n5 3030303768 fcftnao 4040404835\n6 3030303936 unsdcqf 4040404206\n7 3030303018 nqsxppj 4040404329\n8 3030303166 vmyvicq 4040404718\n9 3030303009 wrtttwd 4040404835","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
prof_uidprofessionfunction
03030303146npotdsp4040404206
13030303863aushsmx4040404061
23030303434bnkimlk4040404206
33030303492esmgugd4040404329
43030303521usxhuhz4040404061
53030303768fcftnao4040404835
63030303936unsdcqf4040404206
73030303018nqsxppj4040404329
83030303166vmyvicq4040404718
93030303009wrtttwd4040404835
\n
"},"metadata":{}}],"execution_count":49},{"cell_type":"code","source":"guks_functions = guks_pd.merge(functions_pd, left_on=\"guk uid\", right_on=\"guk\")\nguks_functions","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.394209Z","iopub.execute_input":"2025-02-13T11:43:00.394496Z","iopub.status.idle":"2025-02-13T11:43:00.435546Z","shell.execute_reply.started":"2025-02-13T11:43:00.394473Z","shell.execute_reply":"2025-02-13T11:43:00.434208Z"}},"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":" guk uid guk_x function uid function guk_y\n0 5050505452 hpqmmbgtev 4040404718 ckzgyvfj 5050505452\n1 5050505452 hpqmmbgtev 4040404061 easpvjiy 5050505452\n2 5050505591 ldlmayhvvq 4040404329 enydvwbk 5050505591\n3 5050505591 ldlmayhvvq 4040404206 hyhbuafh 5050505591\n4 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
guk uidguk_xfunction uidfunctionguk_y
05050505452hpqmmbgtev4040404718ckzgyvfj5050505452
15050505452hpqmmbgtev4040404061easpvjiy5050505452
25050505591ldlmayhvvq4040404329enydvwbk5050505591
35050505591ldlmayhvvq4040404206hyhbuafh5050505591
45050505591ldlmayhvvq4040404835akvrfsup5050505591
\n
"},"metadata":{}}],"execution_count":50},{"cell_type":"code","source":"gfp = guks_functions.merge(professions_pd, left_on=\"function uid\", right_on=\"function\")\ngfp","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.436928Z","iopub.execute_input":"2025-02-13T11:43:00.437237Z","iopub.status.idle":"2025-02-13T11:43:00.464279Z","shell.execute_reply.started":"2025-02-13T11:43:00.437205Z","shell.execute_reply":"2025-02-13T11:43:00.463177Z"}},"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":" guk uid guk_x function uid function_x guk_y prof_uid \\\n0 5050505452 hpqmmbgtev 4040404718 ckzgyvfj 5050505452 3030303166 \n1 5050505452 hpqmmbgtev 4040404061 easpvjiy 5050505452 3030303863 \n2 5050505452 hpqmmbgtev 4040404061 easpvjiy 5050505452 3030303521 \n3 5050505591 ldlmayhvvq 4040404329 enydvwbk 5050505591 3030303492 \n4 5050505591 ldlmayhvvq 4040404329 enydvwbk 5050505591 3030303018 \n5 5050505591 ldlmayhvvq 4040404206 hyhbuafh 5050505591 3030303146 \n6 5050505591 ldlmayhvvq 4040404206 hyhbuafh 5050505591 3030303434 \n7 5050505591 ldlmayhvvq 4040404206 hyhbuafh 5050505591 3030303936 \n8 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591 3030303768 \n9 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591 3030303009 \n\n profession function_y \n0 vmyvicq 4040404718 \n1 aushsmx 4040404061 \n2 usxhuhz 4040404061 \n3 esmgugd 4040404329 \n4 nqsxppj 4040404329 \n5 npotdsp 4040404206 \n6 bnkimlk 4040404206 \n7 unsdcqf 4040404206 \n8 fcftnao 4040404835 \n9 wrtttwd 4040404835 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
guk uidguk_xfunction uidfunction_xguk_yprof_uidprofessionfunction_y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\n
"},"metadata":{}}],"execution_count":51},{"cell_type":"code","source":"clusters_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.465495Z","iopub.execute_input":"2025-02-13T11:43:00.465907Z","iopub.status.idle":"2025-02-13T11:43:00.496859Z","shell.execute_reply.started":"2025-02-13T11:43:00.465870Z","shell.execute_reply":"2025-02-13T11:43:00.495427Z"}},"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":" cluster_uid cluster profession\n0 2020202215 dkhk 3030303768\n1 2020202404 nyzo 3030303768\n2 2020202703 aywu 3030303936\n3 2020202999 bomw 3030303768\n4 2020202888 upnl 3030303009\n5 2020202215 spom 3030303434\n6 2020202855 nfun 3030303018\n7 2020202043 plnm 3030303936\n8 2020202005 dghm 3030303166\n9 2020202213 veez 3030303863\n10 2020202870 ydri 3030303018\n11 2020202342 yacn 3030303009\n12 2020202265 gaul 3030303146\n13 2020202379 nurq 3030303434\n14 2020202867 ceqx 3030303936\n15 2020202373 whxk 3030303521\n16 2020202415 gsck 3030303166\n17 2020202454 bvwv 3030303863\n18 2020202378 efit 3030303009\n19 2020202601 pjuo 3030303521","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
cluster_uidclusterprofession
02020202215dkhk3030303768
12020202404nyzo3030303768
22020202703aywu3030303936
32020202999bomw3030303768
42020202888upnl3030303009
52020202215spom3030303434
62020202855nfun3030303018
72020202043plnm3030303936
82020202005dghm3030303166
92020202213veez3030303863
102020202870ydri3030303018
112020202342yacn3030303009
122020202265gaul3030303146
132020202379nurq3030303434
142020202867ceqx3030303936
152020202373whxk3030303521
162020202415gsck3030303166
172020202454bvwv3030303863
182020202378efit3030303009
192020202601pjuo3030303521
\n
"},"metadata":{}}],"execution_count":52},{"cell_type":"code","source":"gfpc = gfp.merge(clusters_pd, left_on=\"prof_uid\", right_on=\"profession\")\ngfpc","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.498094Z","iopub.execute_input":"2025-02-13T11:43:00.498472Z","iopub.status.idle":"2025-02-13T11:43:00.532659Z","shell.execute_reply.started":"2025-02-13T11:43:00.498429Z","shell.execute_reply":"2025-02-13T11:43:00.531464Z"}},"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":" guk uid guk_x function uid function_x guk_y prof_uid \\\n0 5050505452 hpqmmbgtev 4040404718 ckzgyvfj 5050505452 3030303166 \n1 5050505452 hpqmmbgtev 4040404718 ckzgyvfj 5050505452 3030303166 \n2 5050505452 hpqmmbgtev 4040404061 easpvjiy 5050505452 3030303863 \n3 5050505452 hpqmmbgtev 4040404061 easpvjiy 5050505452 3030303863 \n4 5050505452 hpqmmbgtev 4040404061 easpvjiy 5050505452 3030303521 \n5 5050505452 hpqmmbgtev 4040404061 easpvjiy 5050505452 3030303521 \n6 5050505591 ldlmayhvvq 4040404329 enydvwbk 5050505591 3030303018 \n7 5050505591 ldlmayhvvq 4040404329 enydvwbk 5050505591 3030303018 \n8 5050505591 ldlmayhvvq 4040404206 hyhbuafh 5050505591 3030303146 \n9 5050505591 ldlmayhvvq 4040404206 hyhbuafh 5050505591 3030303434 \n10 5050505591 ldlmayhvvq 4040404206 hyhbuafh 5050505591 3030303434 \n11 5050505591 ldlmayhvvq 4040404206 hyhbuafh 5050505591 3030303936 \n12 5050505591 ldlmayhvvq 4040404206 hyhbuafh 5050505591 3030303936 \n13 5050505591 ldlmayhvvq 4040404206 hyhbuafh 5050505591 3030303936 \n14 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591 3030303768 \n15 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591 3030303768 \n16 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591 3030303768 \n17 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591 3030303009 \n18 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591 3030303009 \n19 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591 3030303009 \n\n profession_x function_y cluster_uid cluster profession_y \n0 vmyvicq 4040404718 2020202005 dghm 3030303166 \n1 vmyvicq 4040404718 2020202415 gsck 3030303166 \n2 aushsmx 4040404061 2020202213 veez 3030303863 \n3 aushsmx 4040404061 2020202454 bvwv 3030303863 \n4 usxhuhz 4040404061 2020202373 whxk 3030303521 \n5 usxhuhz 4040404061 2020202601 pjuo 3030303521 \n6 nqsxppj 4040404329 2020202855 nfun 3030303018 \n7 nqsxppj 4040404329 2020202870 ydri 3030303018 \n8 npotdsp 4040404206 2020202265 gaul 3030303146 \n9 bnkimlk 4040404206 2020202215 spom 3030303434 \n10 bnkimlk 4040404206 2020202379 nurq 3030303434 \n11 unsdcqf 4040404206 2020202703 aywu 3030303936 \n12 unsdcqf 4040404206 2020202043 plnm 3030303936 \n13 unsdcqf 4040404206 2020202867 ceqx 3030303936 \n14 fcftnao 4040404835 2020202215 dkhk 3030303768 \n15 fcftnao 4040404835 2020202404 nyzo 3030303768 \n16 fcftnao 4040404835 2020202999 bomw 3030303768 \n17 wrtttwd 4040404835 2020202888 upnl 3030303009 \n18 wrtttwd 4040404835 2020202342 yacn 3030303009 \n19 wrtttwd 4040404835 2020202378 efit 3030303009 ","text/html":"
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guk uidguk_xfunction uidfunction_xguk_yprof_uidprofession_xfunction_ycluster_uidclusterprofession_y
05050505452hpqmmbgtev4040404718ckzgyvfj50505054523030303166vmyvicq40404047182020202005dghm3030303166
15050505452hpqmmbgtev4040404718ckzgyvfj50505054523030303166vmyvicq40404047182020202415gsck3030303166
25050505452hpqmmbgtev4040404061easpvjiy50505054523030303863aushsmx40404040612020202213veez3030303863
35050505452hpqmmbgtev4040404061easpvjiy50505054523030303863aushsmx40404040612020202454bvwv3030303863
45050505452hpqmmbgtev4040404061easpvjiy50505054523030303521usxhuhz40404040612020202373whxk3030303521
55050505452hpqmmbgtev4040404061easpvjiy50505054523030303521usxhuhz40404040612020202601pjuo3030303521
65050505591ldlmayhvvq4040404329enydvwbk50505055913030303018nqsxppj40404043292020202855nfun3030303018
75050505591ldlmayhvvq4040404329enydvwbk50505055913030303018nqsxppj40404043292020202870ydri3030303018
85050505591ldlmayhvvq4040404206hyhbuafh50505055913030303146npotdsp40404042062020202265gaul3030303146
95050505591ldlmayhvvq4040404206hyhbuafh50505055913030303434bnkimlk40404042062020202215spom3030303434
105050505591ldlmayhvvq4040404206hyhbuafh50505055913030303434bnkimlk40404042062020202379nurq3030303434
115050505591ldlmayhvvq4040404206hyhbuafh50505055913030303936unsdcqf40404042062020202703aywu3030303936
125050505591ldlmayhvvq4040404206hyhbuafh50505055913030303936unsdcqf40404042062020202043plnm3030303936
135050505591ldlmayhvvq4040404206hyhbuafh50505055913030303936unsdcqf40404042062020202867ceqx3030303936
145050505591ldlmayhvvq4040404835akvrfsup50505055913030303768fcftnao40404048352020202215dkhk3030303768
155050505591ldlmayhvvq4040404835akvrfsup50505055913030303768fcftnao40404048352020202404nyzo3030303768
165050505591ldlmayhvvq4040404835akvrfsup50505055913030303768fcftnao40404048352020202999bomw3030303768
175050505591ldlmayhvvq4040404835akvrfsup50505055913030303009wrtttwd40404048352020202888upnl3030303009
185050505591ldlmayhvvq4040404835akvrfsup50505055913030303009wrtttwd40404048352020202342yacn3030303009
195050505591ldlmayhvvq4040404835akvrfsup50505055913030303009wrtttwd40404048352020202378efit3030303009
\n
"},"metadata":{}}],"execution_count":53},{"cell_type":"code","source":"skills_pd[0:5]\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.533751Z","iopub.execute_input":"2025-02-13T11:43:00.534318Z","iopub.status.idle":"2025-02-13T11:43:00.555780Z","shell.execute_reply.started":"2025-02-13T11:43:00.534278Z","shell.execute_reply":"2025-02-13T11:43:00.554501Z"}},"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":" skills_uid skills language technical soft cluster\n0 1010101263 gqryuk False True False 2020202265\n1 1010101987 goaylk True False False 2020202454\n2 1010101349 bdbluo True False False 2020202005\n3 1010101080 bsncme False True False 2020202005\n4 1010101952 bxxnst False False True 2020202703","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skills_uidskillslanguagetechnicalsoftcluster
01010101263gqryukFalseTrueFalse2020202265
11010101987goaylkTrueFalseFalse2020202454
21010101349bdbluoTrueFalseFalse2020202005
31010101080bsncmeFalseTrueFalse2020202005
41010101952bxxnstFalseFalseTrue2020202703
\n
"},"metadata":{}}],"execution_count":54},{"cell_type":"code","source":"gfpcs = gfpc.merge(skills_pd, left_on=\"cluster_uid\", right_on=\"cluster\")\ngfpcs","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.557023Z","iopub.execute_input":"2025-02-13T11:43:00.557440Z","iopub.status.idle":"2025-02-13T11:43:00.597119Z","shell.execute_reply.started":"2025-02-13T11:43:00.557402Z","shell.execute_reply":"2025-02-13T11:43:00.595762Z"}},"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":" guk uid guk_x function uid function_x guk_y prof_uid \\\n0 5050505452 hpqmmbgtev 4040404718 ckzgyvfj 5050505452 3030303166 \n1 5050505452 hpqmmbgtev 4040404718 ckzgyvfj 5050505452 3030303166 \n2 5050505452 hpqmmbgtev 4040404718 ckzgyvfj 5050505452 3030303166 \n3 5050505452 hpqmmbgtev 4040404718 ckzgyvfj 5050505452 3030303166 \n4 5050505452 hpqmmbgtev 4040404718 ckzgyvfj 5050505452 3030303166 \n.. ... ... ... ... ... ... \n61 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591 3030303009 \n62 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591 3030303009 \n63 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591 3030303009 \n64 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591 3030303009 \n65 5050505591 ldlmayhvvq 4040404835 akvrfsup 5050505591 3030303009 \n\n profession_x function_y cluster_uid cluster_x profession_y skills_uid \\\n0 vmyvicq 4040404718 2020202005 dghm 3030303166 1010101349 \n1 vmyvicq 4040404718 2020202005 dghm 3030303166 1010101080 \n2 vmyvicq 4040404718 2020202005 dghm 3030303166 1010101767 \n3 vmyvicq 4040404718 2020202005 dghm 3030303166 1010101215 \n4 vmyvicq 4040404718 2020202005 dghm 3030303166 1010101070 \n.. ... ... ... ... ... ... \n61 wrtttwd 4040404835 2020202378 efit 3030303009 1010101043 \n62 wrtttwd 4040404835 2020202378 efit 3030303009 1010101621 \n63 wrtttwd 4040404835 2020202378 efit 3030303009 1010101691 \n64 wrtttwd 4040404835 2020202378 efit 3030303009 1010101525 \n65 wrtttwd 4040404835 2020202378 efit 3030303009 1010101457 \n\n skills language technical soft cluster_y \n0 bdbluo True False False 2020202005 \n1 bsncme False True False 2020202005 \n2 gbhazb False True False 2020202005 \n3 rolwgs False True False 2020202005 \n4 smkggd True False False 2020202005 \n.. ... ... ... ... ... \n61 ijzffe False True False 2020202378 \n62 dwxsuk False False True 2020202378 \n63 brxfmi False False True 2020202378 \n64 ilkukl False False True 2020202378 \n65 nthrwc False True False 2020202378 \n\n[66 rows x 17 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
guk uidguk_xfunction uidfunction_xguk_yprof_uidprofession_xfunction_ycluster_uidcluster_xprofession_yskills_uidskillslanguagetechnicalsoftcluster_y
05050505452hpqmmbgtev4040404718ckzgyvfj50505054523030303166vmyvicq40404047182020202005dghm30303031661010101349bdbluoTrueFalseFalse2020202005
15050505452hpqmmbgtev4040404718ckzgyvfj50505054523030303166vmyvicq40404047182020202005dghm30303031661010101080bsncmeFalseTrueFalse2020202005
25050505452hpqmmbgtev4040404718ckzgyvfj50505054523030303166vmyvicq40404047182020202005dghm30303031661010101767gbhazbFalseTrueFalse2020202005
35050505452hpqmmbgtev4040404718ckzgyvfj50505054523030303166vmyvicq40404047182020202005dghm30303031661010101215rolwgsFalseTrueFalse2020202005
45050505452hpqmmbgtev4040404718ckzgyvfj50505054523030303166vmyvicq40404047182020202005dghm30303031661010101070smkggdTrueFalseFalse2020202005
......................................................
615050505591ldlmayhvvq4040404835akvrfsup50505055913030303009wrtttwd40404048352020202378efit30303030091010101043ijzffeFalseTrueFalse2020202378
625050505591ldlmayhvvq4040404835akvrfsup50505055913030303009wrtttwd40404048352020202378efit30303030091010101621dwxsukFalseFalseTrue2020202378
635050505591ldlmayhvvq4040404835akvrfsup50505055913030303009wrtttwd40404048352020202378efit30303030091010101691brxfmiFalseFalseTrue2020202378
645050505591ldlmayhvvq4040404835akvrfsup50505055913030303009wrtttwd40404048352020202378efit30303030091010101525ilkuklFalseFalseTrue2020202378
655050505591ldlmayhvvq4040404835akvrfsup50505055913030303009wrtttwd40404048352020202378efit30303030091010101457nthrwcFalseTrueFalse2020202378
\n

66 rows × 17 columns

\n
"},"metadata":{}}],"execution_count":55},{"cell_type":"code","source":"gfpcs.columns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.598321Z","iopub.execute_input":"2025-02-13T11:43:00.598748Z","iopub.status.idle":"2025-02-13T11:43:00.628638Z","shell.execute_reply.started":"2025-02-13T11:43:00.598696Z","shell.execute_reply":"2025-02-13T11:43:00.627512Z"}},"outputs":[{"execution_count":56,"output_type":"execute_result","data":{"text/plain":"Index(['guk uid', 'guk_x', 'function uid', 'function_x', 'guk_y', 'prof_uid',\n 'profession_x', 'function_y', 'cluster_uid', 'cluster_x',\n 'profession_y', 'skills_uid', 'skills', 'language', 'technical', 'soft',\n 'cluster_y'],\n dtype='object')"},"metadata":{}}],"execution_count":56},{"cell_type":"code","source":"cols = ['skills_uid', 'skills', 'language', 'technical', 'soft',\n 'cluster_x','function_x','profession_x','guk_x']\ntaxonomy = gfpcs.loc[:,cols]\ntaxonomy.columns = ['skill_id', 'skill', 'language', 'technical', 'soft',\n 'cluster','function','profession','guk']\ntaxonomy","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.629696Z","iopub.execute_input":"2025-02-13T11:43:00.629996Z","iopub.status.idle":"2025-02-13T11:43:00.675422Z","shell.execute_reply.started":"2025-02-13T11:43:00.629973Z","shell.execute_reply":"2025-02-13T11:43:00.674282Z"}},"outputs":[{"execution_count":57,"output_type":"execute_result","data":{"text/plain":" skill_id skill language technical soft cluster function \\\n0 1010101349 bdbluo True False False dghm ckzgyvfj \n1 1010101080 bsncme False True False dghm ckzgyvfj \n2 1010101767 gbhazb False True False dghm ckzgyvfj \n3 1010101215 rolwgs False True False dghm ckzgyvfj \n4 1010101070 smkggd True False False dghm ckzgyvfj \n.. ... ... ... ... ... ... ... \n61 1010101043 ijzffe False True False efit akvrfsup \n62 1010101621 dwxsuk False False True efit akvrfsup \n63 1010101691 brxfmi False False True efit akvrfsup \n64 1010101525 ilkukl False False True efit akvrfsup \n65 1010101457 nthrwc False True False efit akvrfsup \n\n profession guk \n0 vmyvicq hpqmmbgtev \n1 vmyvicq hpqmmbgtev \n2 vmyvicq hpqmmbgtev \n3 vmyvicq hpqmmbgtev \n4 vmyvicq hpqmmbgtev \n.. ... ... \n61 wrtttwd ldlmayhvvq \n62 wrtttwd ldlmayhvvq \n63 wrtttwd ldlmayhvvq \n64 wrtttwd ldlmayhvvq \n65 wrtttwd ldlmayhvvq \n\n[66 rows x 9 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skill_idskilllanguagetechnicalsoftclusterfunctionprofessionguk
01010101349bdbluoTrueFalseFalsedghmckzgyvfjvmyvicqhpqmmbgtev
11010101080bsncmeFalseTrueFalsedghmckzgyvfjvmyvicqhpqmmbgtev
21010101767gbhazbFalseTrueFalsedghmckzgyvfjvmyvicqhpqmmbgtev
31010101215rolwgsFalseTrueFalsedghmckzgyvfjvmyvicqhpqmmbgtev
41010101070smkggdTrueFalseFalsedghmckzgyvfjvmyvicqhpqmmbgtev
..............................
611010101043ijzffeFalseTrueFalseefitakvrfsupwrtttwdldlmayhvvq
621010101621dwxsukFalseFalseTrueefitakvrfsupwrtttwdldlmayhvvq
631010101691brxfmiFalseFalseTrueefitakvrfsupwrtttwdldlmayhvvq
641010101525ilkuklFalseFalseTrueefitakvrfsupwrtttwdldlmayhvvq
651010101457nthrwcFalseTrueFalseefitakvrfsupwrtttwdldlmayhvvq
\n

66 rows × 9 columns

\n
"},"metadata":{}}],"execution_count":57},{"cell_type":"markdown","source":"# Organisation dimension","metadata":{}},{"cell_type":"code","source":"values = range(0,n_organisations)\norganisations = [get_random_string(20) for i in values]\nlen(organisations) == n_organisations","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.676721Z","iopub.execute_input":"2025-02-13T11:43:00.677128Z","iopub.status.idle":"2025-02-13T11:43:00.696927Z","shell.execute_reply.started":"2025-02-13T11:43:00.677091Z","shell.execute_reply":"2025-02-13T11:43:00.695730Z"}},"outputs":[{"execution_count":58,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":58},{"cell_type":"code","source":"organisations","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.698294Z","iopub.execute_input":"2025-02-13T11:43:00.698757Z","iopub.status.idle":"2025-02-13T11:43:00.719711Z","shell.execute_reply.started":"2025-02-13T11:43:00.698715Z","shell.execute_reply":"2025-02-13T11:43:00.718435Z"}},"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"['ogefzchrhslcymyobvml',\n 'mzyxsukirqawtcybjvll',\n 'wuemqowtlvvboneqjkpm',\n 'hbtrewuuerjmohvieezr',\n 'ctahjrijfpxqakqlneyk',\n 'ktfqqxpnwoznbyttjmlo',\n 'zcqpaublnbaiqlhcmvaw',\n 'bsgsxowefpwemmycgrgd',\n 'jcsurldsnmgzlowgykoi',\n 'nyfslxinrpbxjjmcvumo']"},"metadata":{}}],"execution_count":59},{"cell_type":"code","source":"values = range(0,n_organisations)\norg_ids = [get_random_alphanum(\"ORG_\",6) for i in values]\nlen(org_ids) == n_organisations","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.720689Z","iopub.execute_input":"2025-02-13T11:43:00.721183Z","iopub.status.idle":"2025-02-13T11:43:00.741722Z","shell.execute_reply.started":"2025-02-13T11:43:00.721142Z","shell.execute_reply":"2025-02-13T11:43:00.740514Z"}},"outputs":[{"execution_count":60,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":60},{"cell_type":"code","source":"org_ids","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.742794Z","iopub.execute_input":"2025-02-13T11:43:00.743177Z","iopub.status.idle":"2025-02-13T11:43:00.764483Z","shell.execute_reply.started":"2025-02-13T11:43:00.743139Z","shell.execute_reply":"2025-02-13T11:43:00.763353Z"}},"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"['ORG_371670',\n 'ORG_543768',\n 'ORG_412729',\n 'ORG_116955',\n 'ORG_033357',\n 'ORG_214219',\n 'ORG_156974',\n 'ORG_265929',\n 'ORG_677208',\n 'ORG_133045']"},"metadata":{}}],"execution_count":61},{"cell_type":"code","source":"values = range(0,n_organisations)\nstart = date(1980,1,23)\nend = date(2000,3,1)\nstart_dates = [get_random_date(start, end) for i in values]\nlen(start_dates) == n_organisations","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.765891Z","iopub.execute_input":"2025-02-13T11:43:00.766440Z","iopub.status.idle":"2025-02-13T11:43:00.786803Z","shell.execute_reply.started":"2025-02-13T11:43:00.766368Z","shell.execute_reply":"2025-02-13T11:43:00.785466Z"}},"outputs":[{"execution_count":62,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":62},{"cell_type":"code","source":"start_dates","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.787835Z","iopub.execute_input":"2025-02-13T11:43:00.788246Z","iopub.status.idle":"2025-02-13T11:43:00.809586Z","shell.execute_reply.started":"2025-02-13T11:43:00.788208Z","shell.execute_reply":"2025-02-13T11:43:00.808448Z"}},"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"[datetime.date(1990, 8, 2),\n datetime.date(1999, 6, 9),\n datetime.date(1990, 6, 6),\n datetime.date(1999, 1, 21),\n datetime.date(1983, 11, 16),\n datetime.date(1987, 6, 2),\n datetime.date(1982, 3, 6),\n datetime.date(1987, 8, 2),\n datetime.date(1990, 3, 11),\n datetime.date(1986, 12, 4)]"},"metadata":{}}],"execution_count":63},{"cell_type":"code","source":"values = range(0,n_organisations)\nstart = date(2000,3,2)\nend = date(2025,8,31)\nend_dates = [get_random_date(start, end) for i in values]\nlen(end_dates) == n_organisations","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.810654Z","iopub.execute_input":"2025-02-13T11:43:00.811053Z","iopub.status.idle":"2025-02-13T11:43:00.836151Z","shell.execute_reply.started":"2025-02-13T11:43:00.811015Z","shell.execute_reply":"2025-02-13T11:43:00.834971Z"}},"outputs":[{"execution_count":64,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":64},{"cell_type":"code","source":"end_dates","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.837290Z","iopub.execute_input":"2025-02-13T11:43:00.837727Z","iopub.status.idle":"2025-02-13T11:43:00.861897Z","shell.execute_reply.started":"2025-02-13T11:43:00.837689Z","shell.execute_reply":"2025-02-13T11:43:00.860709Z"}},"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":"[datetime.date(2023, 5, 24),\n datetime.date(2014, 10, 17),\n datetime.date(2016, 5, 6),\n datetime.date(2021, 12, 12),\n datetime.date(2000, 3, 28),\n datetime.date(2001, 2, 5),\n datetime.date(2010, 8, 14),\n datetime.date(2009, 12, 20),\n datetime.date(2015, 4, 14),\n datetime.date(2009, 9, 23)]"},"metadata":{}}],"execution_count":65},{"cell_type":"code","source":"data ={\"organisation uid\": org_ids,\n \"organisation\":organisations,\n \"start date\": start_dates,\n \"end date\": end_dates}\norganisations_pd = pd.DataFrame(data)\norganisations_pd.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.863031Z","iopub.execute_input":"2025-02-13T11:43:00.863508Z","iopub.status.idle":"2025-02-13T11:43:00.893698Z","shell.execute_reply.started":"2025-02-13T11:43:00.863468Z","shell.execute_reply":"2025-02-13T11:43:00.892443Z"}},"outputs":[{"execution_count":66,"output_type":"execute_result","data":{"text/plain":" organisation uid organisation start date end date\n0 ORG_371670 ogefzchrhslcymyobvml 1990-08-02 2023-05-24\n1 ORG_543768 mzyxsukirqawtcybjvll 1999-06-09 2014-10-17\n2 ORG_412729 wuemqowtlvvboneqjkpm 1990-06-06 2016-05-06\n3 ORG_116955 hbtrewuuerjmohvieezr 1999-01-21 2021-12-12\n4 ORG_033357 ctahjrijfpxqakqlneyk 1983-11-16 2000-03-28","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
organisation uidorganisationstart dateend date
0ORG_371670ogefzchrhslcymyobvml1990-08-022023-05-24
1ORG_543768mzyxsukirqawtcybjvll1999-06-092014-10-17
2ORG_412729wuemqowtlvvboneqjkpm1990-06-062016-05-06
3ORG_116955hbtrewuuerjmohvieezr1999-01-212021-12-12
4ORG_033357ctahjrijfpxqakqlneyk1983-11-162000-03-28
\n
"},"metadata":{}}],"execution_count":66},{"cell_type":"markdown","source":"# Job role dimension","metadata":{}},{"cell_type":"code","source":"values = range(0,n_roles)\nroles = [get_random_string(11) for i in values]\nlen(roles) == n_roles","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.894832Z","iopub.execute_input":"2025-02-13T11:43:00.895232Z","iopub.status.idle":"2025-02-13T11:43:00.914390Z","shell.execute_reply.started":"2025-02-13T11:43:00.895203Z","shell.execute_reply":"2025-02-13T11:43:00.913154Z"}},"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":67},{"cell_type":"code","source":"roles[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.915403Z","iopub.execute_input":"2025-02-13T11:43:00.915811Z","iopub.status.idle":"2025-02-13T11:43:00.938633Z","shell.execute_reply.started":"2025-02-13T11:43:00.915783Z","shell.execute_reply":"2025-02-13T11:43:00.937170Z"}},"outputs":[{"execution_count":68,"output_type":"execute_result","data":{"text/plain":"['naqdldozwyh', 'cqljjdixacw', 'sevplprmtbx', 'joyilptjvft', 'aedtathfnkb']"},"metadata":{}}],"execution_count":68},{"cell_type":"code","source":"values = range(0,n_roles)\nrole_ids = [get_random_alphanum(\"ROLE_\",6) for i in values]\nlen(role_ids) == n_roles","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.939883Z","iopub.execute_input":"2025-02-13T11:43:00.940290Z","iopub.status.idle":"2025-02-13T11:43:00.961762Z","shell.execute_reply.started":"2025-02-13T11:43:00.940256Z","shell.execute_reply":"2025-02-13T11:43:00.960535Z"}},"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":69},{"cell_type":"code","source":"role_ids[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.962926Z","iopub.execute_input":"2025-02-13T11:43:00.963359Z","iopub.status.idle":"2025-02-13T11:43:00.985433Z","shell.execute_reply.started":"2025-02-13T11:43:00.963320Z","shell.execute_reply":"2025-02-13T11:43:00.984215Z"}},"outputs":[{"execution_count":70,"output_type":"execute_result","data":{"text/plain":"['ROLE_989543', 'ROLE_329525', 'ROLE_025947', 'ROLE_743948', 'ROLE_482449']"},"metadata":{}}],"execution_count":70},{"cell_type":"code","source":"values = range(0,n_roles * 5)\nskills = [random.choice(skill_ids) for i in values]\nlen(skills) == (n_roles * 5)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:00.986802Z","iopub.execute_input":"2025-02-13T11:43:00.987100Z","iopub.status.idle":"2025-02-13T11:43:01.016222Z","shell.execute_reply.started":"2025-02-13T11:43:00.987076Z","shell.execute_reply":"2025-02-13T11:43:01.015049Z"}},"outputs":[{"execution_count":71,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":71},{"cell_type":"code","source":"values = range(0,n_roles * 5)\nlevels = [random.choice([3,4,5]) for i in values]\nlen(levels) == (n_roles * 5)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.017506Z","iopub.execute_input":"2025-02-13T11:43:01.017957Z","iopub.status.idle":"2025-02-13T11:43:01.050264Z","shell.execute_reply.started":"2025-02-13T11:43:01.017918Z","shell.execute_reply":"2025-02-13T11:43:01.048665Z"}},"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":72},{"cell_type":"code","source":"data = {\"role id\": role_ids,\n \"role\": roles\n }\nt = pd.DataFrame(data)\nt","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.051756Z","iopub.execute_input":"2025-02-13T11:43:01.052248Z","iopub.status.idle":"2025-02-13T11:43:01.077811Z","shell.execute_reply.started":"2025-02-13T11:43:01.052205Z","shell.execute_reply":"2025-02-13T11:43:01.076697Z"}},"outputs":[{"execution_count":73,"output_type":"execute_result","data":{"text/plain":" role id role\n0 ROLE_989543 naqdldozwyh\n1 ROLE_329525 cqljjdixacw\n2 ROLE_025947 sevplprmtbx\n3 ROLE_743948 joyilptjvft\n4 ROLE_482449 aedtathfnkb\n.. ... ...\n95 ROLE_281011 enmugvlbsfl\n96 ROLE_943766 kjqoddkwefj\n97 ROLE_185077 pfapomtdxcc\n98 ROLE_440020 zrztiwjkkiv\n99 ROLE_060023 rrsdulwsydt\n\n[100 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
role idrole
0ROLE_989543naqdldozwyh
1ROLE_329525cqljjdixacw
2ROLE_025947sevplprmtbx
3ROLE_743948joyilptjvft
4ROLE_482449aedtathfnkb
.........
95ROLE_281011enmugvlbsfl
96ROLE_943766kjqoddkwefj
97ROLE_185077pfapomtdxcc
98ROLE_440020zrztiwjkkiv
99ROLE_060023rrsdulwsydt
\n

100 rows × 2 columns

\n
"},"metadata":{}}],"execution_count":73},{"cell_type":"code","source":"roles_pd = pd.concat([t,t,t,t,t])\nroles_pd.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.078914Z","iopub.execute_input":"2025-02-13T11:43:01.079426Z","iopub.status.idle":"2025-02-13T11:43:01.101233Z","shell.execute_reply.started":"2025-02-13T11:43:01.079386Z","shell.execute_reply":"2025-02-13T11:43:01.099742Z"}},"outputs":[{"execution_count":74,"output_type":"execute_result","data":{"text/plain":"(500, 2)"},"metadata":{}}],"execution_count":74},{"cell_type":"code","source":"roles_pd['skill'] = skills\nroles_pd['level'] = levels\nroles_pd.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.102343Z","iopub.execute_input":"2025-02-13T11:43:01.102702Z","iopub.status.idle":"2025-02-13T11:43:01.128408Z","shell.execute_reply.started":"2025-02-13T11:43:01.102674Z","shell.execute_reply":"2025-02-13T11:43:01.127207Z"}},"outputs":[{"execution_count":75,"output_type":"execute_result","data":{"text/plain":"(500, 4)"},"metadata":{}}],"execution_count":75},{"cell_type":"code","source":"roles_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.129582Z","iopub.execute_input":"2025-02-13T11:43:01.130031Z","iopub.status.idle":"2025-02-13T11:43:01.154652Z","shell.execute_reply.started":"2025-02-13T11:43:01.129991Z","shell.execute_reply":"2025-02-13T11:43:01.153445Z"}},"outputs":[{"execution_count":76,"output_type":"execute_result","data":{"text/plain":" role id role skill level\n0 ROLE_989543 naqdldozwyh 1010101043 3\n1 ROLE_329525 cqljjdixacw 1010101772 3\n2 ROLE_025947 sevplprmtbx 1010101176 4\n3 ROLE_743948 joyilptjvft 1010101652 5\n4 ROLE_482449 aedtathfnkb 1010101910 4\n.. ... ... ... ...\n95 ROLE_281011 enmugvlbsfl 1010101952 5\n96 ROLE_943766 kjqoddkwefj 1010101822 3\n97 ROLE_185077 pfapomtdxcc 1010101762 3\n98 ROLE_440020 zrztiwjkkiv 1010101987 4\n99 ROLE_060023 rrsdulwsydt 1010101789 4\n\n[500 rows x 4 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
role idroleskilllevel
0ROLE_989543naqdldozwyh10101010433
1ROLE_329525cqljjdixacw10101017723
2ROLE_025947sevplprmtbx10101011764
3ROLE_743948joyilptjvft10101016525
4ROLE_482449aedtathfnkb10101019104
...............
95ROLE_281011enmugvlbsfl10101019525
96ROLE_943766kjqoddkwefj10101018223
97ROLE_185077pfapomtdxcc10101017623
98ROLE_440020zrztiwjkkiv10101019874
99ROLE_060023rrsdulwsydt10101017894
\n

500 rows × 4 columns

\n
"},"metadata":{}}],"execution_count":76},{"cell_type":"markdown","source":"# skills level description","metadata":{}},{"cell_type":"code","source":"descriptor =[\"Novice\",\"Aware\",\"Working\",\"Practitioner\",\"Expert\"]\nlevel_id =[1,2,3,4,5]\ndata ={\"level_id\":pd.Series(level_id),\n \"descriptor\":pd.Series(descriptor)}\nskills_level_pd = pd.DataFrame(data)\nskills_level_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.155729Z","iopub.execute_input":"2025-02-13T11:43:01.156113Z","iopub.status.idle":"2025-02-13T11:43:01.180932Z","shell.execute_reply.started":"2025-02-13T11:43:01.156077Z","shell.execute_reply":"2025-02-13T11:43:01.179779Z"}},"outputs":[{"execution_count":77,"output_type":"execute_result","data":{"text/plain":" level_id descriptor\n0 1 Novice\n1 2 Aware\n2 3 Working\n3 4 Practitioner\n4 5 Expert","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
level_iddescriptor
01Novice
12Aware
23Working
34Practitioner
45Expert
\n
"},"metadata":{}}],"execution_count":77},{"cell_type":"markdown","source":"# Employee Dimension","metadata":{}},{"cell_type":"markdown","source":"## CEI","metadata":{}},{"cell_type":"code","source":"values = range(0,n_individuals)\ncei = []\nfor i in values:\n cei.append(str(uuid.uuid4()))\n\ncei_ls = pd.Series(cei)\nlen(cei_ls.unique()) == n_individuals","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.182005Z","iopub.execute_input":"2025-02-13T11:43:01.182303Z","iopub.status.idle":"2025-02-13T11:43:01.208095Z","shell.execute_reply.started":"2025-02-13T11:43:01.182271Z","shell.execute_reply":"2025-02-13T11:43:01.206781Z"}},"outputs":[{"execution_count":78,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":78},{"cell_type":"code","source":"cei_ls[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.209579Z","iopub.execute_input":"2025-02-13T11:43:01.210199Z","iopub.status.idle":"2025-02-13T11:43:01.224765Z","shell.execute_reply.started":"2025-02-13T11:43:01.210149Z","shell.execute_reply":"2025-02-13T11:43:01.223741Z"}},"outputs":[{"execution_count":79,"output_type":"execute_result","data":{"text/plain":"0 a0e6fea9-df56-4198-aae1-0f898c665ab9\n1 5b5b6c68-5649-4a66-975b-eeb751d85522\n2 73c254c1-7467-4cb9-adef-ac2ecc86ebf6\n3 33f823ce-0389-4adf-a61a-fe89446bce25\n4 cbb459ae-d1ee-4d16-ae8c-03312a31a806\ndtype: object"},"metadata":{}}],"execution_count":79},{"cell_type":"markdown","source":"## Year of birth","metadata":{}},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"values = range(0, n_individuals)\nyears = [randint(1960, 2007) for i in values]\nlen(years) == n_individuals\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.225822Z","iopub.execute_input":"2025-02-13T11:43:01.226227Z","iopub.status.idle":"2025-02-13T11:43:01.248546Z","shell.execute_reply.started":"2025-02-13T11:43:01.226176Z","shell.execute_reply":"2025-02-13T11:43:01.247162Z"}},"outputs":[{"execution_count":80,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":80},{"cell_type":"code","source":"years[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.249740Z","iopub.execute_input":"2025-02-13T11:43:01.250129Z","iopub.status.idle":"2025-02-13T11:43:01.269285Z","shell.execute_reply.started":"2025-02-13T11:43:01.250100Z","shell.execute_reply":"2025-02-13T11:43:01.267835Z"}},"outputs":[{"execution_count":81,"output_type":"execute_result","data":{"text/plain":"[1967, 1988, 1972, 1968, 2003]"},"metadata":{}}],"execution_count":81},{"cell_type":"markdown","source":"## ceis","metadata":{}},{"cell_type":"code","source":"values = range(0,n_individuals)\nceis = [str(uuid.uuid4()) for i in values]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.270445Z","iopub.execute_input":"2025-02-13T11:43:01.270810Z","iopub.status.idle":"2025-02-13T11:43:01.292945Z","shell.execute_reply.started":"2025-02-13T11:43:01.270781Z","shell.execute_reply":"2025-02-13T11:43:01.291523Z"}},"outputs":[],"execution_count":82},{"cell_type":"code","source":"ceis[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.294280Z","iopub.execute_input":"2025-02-13T11:43:01.294672Z","iopub.status.idle":"2025-02-13T11:43:01.316959Z","shell.execute_reply.started":"2025-02-13T11:43:01.294600Z","shell.execute_reply":"2025-02-13T11:43:01.315782Z"}},"outputs":[{"execution_count":83,"output_type":"execute_result","data":{"text/plain":"['e829a4af-b90f-40f3-beae-b621267e6076',\n '071bbbd8-19ed-4773-a706-5b53fb9cbed4',\n 'faf58c99-b78a-43f4-8d68-2f5652291d41',\n '4a9eb9d8-7c7a-4480-b500-41f68701847f',\n 'bd55a8ee-b0e5-44a4-a481-20dc84739fe4']"},"metadata":{}}],"execution_count":83},{"cell_type":"markdown","source":"## genders","metadata":{}},{"cell_type":"code","source":"genders = [\"Gender 1\", \"Gender 2\"]\ngender_ids = [0,1]\ngenders_pd = pd.DataFrame({'gender':genders, \n 'gender id': gender_ids})\ngenders_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.317994Z","iopub.execute_input":"2025-02-13T11:43:01.318390Z","iopub.status.idle":"2025-02-13T11:43:01.343678Z","shell.execute_reply.started":"2025-02-13T11:43:01.318352Z","shell.execute_reply":"2025-02-13T11:43:01.342550Z"}},"outputs":[{"execution_count":84,"output_type":"execute_result","data":{"text/plain":" gender gender id\n0 Gender 1 0\n1 Gender 2 1","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
gendergender id
0Gender 10
1Gender 21
\n
"},"metadata":{}}],"execution_count":84},{"cell_type":"code","source":"genders = [random.choice(gender_ids) for i in values]\nlen(genders) == n_individuals","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.344495Z","iopub.execute_input":"2025-02-13T11:43:01.344911Z","iopub.status.idle":"2025-02-13T11:43:01.365739Z","shell.execute_reply.started":"2025-02-13T11:43:01.344872Z","shell.execute_reply":"2025-02-13T11:43:01.364563Z"}},"outputs":[{"execution_count":85,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":85},{"cell_type":"markdown","source":"## Carers","metadata":{}},{"cell_type":"code","source":"carers = ['carer type 0', \"Carer type 1\", \"Carer type 2\", \"Carer type 3\", 'Carer type 4']\ncarer_ids = [0,1,2,3,4]\ncarers_pd = pd.DataFrame({'carer':carers, \n 'carer id': carer_ids})\ncarers_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.366858Z","iopub.execute_input":"2025-02-13T11:43:01.367283Z","iopub.status.idle":"2025-02-13T11:43:01.400274Z","shell.execute_reply.started":"2025-02-13T11:43:01.367242Z","shell.execute_reply":"2025-02-13T11:43:01.398802Z"}},"outputs":[{"execution_count":86,"output_type":"execute_result","data":{"text/plain":" carer carer id\n0 carer type 0 0\n1 Carer type 1 1\n2 Carer type 2 2\n3 Carer type 3 3\n4 Carer type 4 4","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
carercarer id
0carer type 00
1Carer type 11
2Carer type 22
3Carer type 33
4Carer type 44
\n
"},"metadata":{}}],"execution_count":86},{"cell_type":"code","source":"carers = [random.choice(carer_ids) for i in values]\nlen(carers) == n_individuals","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.401518Z","iopub.execute_input":"2025-02-13T11:43:01.401864Z","iopub.status.idle":"2025-02-13T11:43:01.427229Z","shell.execute_reply.started":"2025-02-13T11:43:01.401838Z","shell.execute_reply":"2025-02-13T11:43:01.425890Z"}},"outputs":[{"execution_count":87,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":87},{"cell_type":"markdown","source":"## Background","metadata":{}},{"cell_type":"code","source":"background = [\"Background 1\", \"Background 2\", \"Background 3\", 'Background 4',\n 'Background 5', 'Background 6']\nbackground_ids = [0,1,2,3,4,5,]\nbackground_pd = pd.DataFrame({'background':background, \n 'background id': background_ids})\nbackground_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.428520Z","iopub.execute_input":"2025-02-13T11:43:01.428954Z","iopub.status.idle":"2025-02-13T11:43:01.454122Z","shell.execute_reply.started":"2025-02-13T11:43:01.428916Z","shell.execute_reply":"2025-02-13T11:43:01.452172Z"}},"outputs":[{"execution_count":88,"output_type":"execute_result","data":{"text/plain":" background background id\n0 Background 1 0\n1 Background 2 1\n2 Background 3 2\n3 Background 4 3\n4 Background 5 4\n5 Background 6 5","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
backgroundbackground id
0Background 10
1Background 21
2Background 32
3Background 43
4Background 54
5Background 65
\n
"},"metadata":{}}],"execution_count":88},{"cell_type":"code","source":"background = [random.choice(background_ids) for i in values]\nlen(background) == n_individuals","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.455429Z","iopub.execute_input":"2025-02-13T11:43:01.455804Z","iopub.status.idle":"2025-02-13T11:43:01.482370Z","shell.execute_reply.started":"2025-02-13T11:43:01.455774Z","shell.execute_reply":"2025-02-13T11:43:01.480816Z"}},"outputs":[{"execution_count":89,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":89},{"cell_type":"markdown","source":"## Marital status","metadata":{}},{"cell_type":"code","source":"status = [\"status 1\", \"status 2\", \"status 3\", 'status 4',\n 'status 5']\nstatus_ids = [0,1,2,3,4]\nstatus_pd = pd.DataFrame({'status': status, \n 'status id': status_ids})\nstatus_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.483401Z","iopub.execute_input":"2025-02-13T11:43:01.483739Z","iopub.status.idle":"2025-02-13T11:43:01.512654Z","shell.execute_reply.started":"2025-02-13T11:43:01.483713Z","shell.execute_reply":"2025-02-13T11:43:01.511383Z"}},"outputs":[{"execution_count":90,"output_type":"execute_result","data":{"text/plain":" status status id\n0 status 1 0\n1 status 2 1\n2 status 3 2\n3 status 4 3\n4 status 5 4","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
statusstatus id
0status 10
1status 21
2status 32
3status 43
4status 54
\n
"},"metadata":{}}],"execution_count":90},{"cell_type":"code","source":"status = [random.choice(status_ids) for i in values]\nlen(status) == n_individuals","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.513862Z","iopub.execute_input":"2025-02-13T11:43:01.514257Z","iopub.status.idle":"2025-02-13T11:43:01.535101Z","shell.execute_reply.started":"2025-02-13T11:43:01.514220Z","shell.execute_reply":"2025-02-13T11:43:01.533954Z"}},"outputs":[{"execution_count":91,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":91},{"cell_type":"code","source":"data ={\"ceis\": ceis,\n \"background\" : background, \n \"marital status\": status,\n \"gender\":genders,\n \"carers\":carers}\nemployees_pd = pd.DataFrame(data)\nemployees_pd.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.536176Z","iopub.execute_input":"2025-02-13T11:43:01.536491Z","iopub.status.idle":"2025-02-13T11:43:01.569968Z","shell.execute_reply.started":"2025-02-13T11:43:01.536468Z","shell.execute_reply":"2025-02-13T11:43:01.568926Z"}},"outputs":[{"execution_count":92,"output_type":"execute_result","data":{"text/plain":" ceis background marital status gender \\\n0 e829a4af-b90f-40f3-beae-b621267e6076 3 0 1 \n1 071bbbd8-19ed-4773-a706-5b53fb9cbed4 2 1 1 \n2 faf58c99-b78a-43f4-8d68-2f5652291d41 5 2 0 \n3 4a9eb9d8-7c7a-4480-b500-41f68701847f 2 0 1 \n4 bd55a8ee-b0e5-44a4-a481-20dc84739fe4 3 3 1 \n\n carers \n0 2 \n1 0 \n2 1 \n3 4 \n4 3 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ceisbackgroundmarital statusgendercarers
0e829a4af-b90f-40f3-beae-b621267e60763012
1071bbbd8-19ed-4773-a706-5b53fb9cbed42110
2faf58c99-b78a-43f4-8d68-2f5652291d415201
34a9eb9d8-7c7a-4480-b500-41f68701847f2014
4bd55a8ee-b0e5-44a4-a481-20dc84739fe43313
\n
"},"metadata":{}}],"execution_count":92},{"cell_type":"markdown","source":"## test","metadata":{}},{"cell_type":"code","source":"employees_pd['background'].plot.hist(bins =6)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:01.571022Z","iopub.execute_input":"2025-02-13T11:43:01.571411Z","iopub.status.idle":"2025-02-13T11:43:02.120260Z","shell.execute_reply.started":"2025-02-13T11:43:01.571373Z","shell.execute_reply":"2025-02-13T11:43:02.118796Z"}},"outputs":[{"execution_count":93,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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= 2)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:02.472569Z","iopub.execute_input":"2025-02-13T11:43:02.472877Z","iopub.status.idle":"2025-02-13T11:43:02.782960Z","shell.execute_reply.started":"2025-02-13T11:43:02.472854Z","shell.execute_reply":"2025-02-13T11:43:02.781529Z"}},"outputs":[{"execution_count":95,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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cKbJKtWrUqSHHrooRkxYsRu54wfP36nuQAAAAAAAG2hR3sXUAYvv/xykmTo0KF7nDNkyJCd5u5OU1NTmpqaWt+33GJt8+bNlSizU3m7aVt7lwAAAAAAQBtxXnz3Wv5eiqLY6zzhTZLGxsYkSU1NzR7n1NbWJtn7P7jZs2dn5syZu2xvCX4AAAAAAKAr6P/F9q6g3BobG9O/f/89jgtvKmjGjBm5/PLLW9+//fbbeeONN3LYYYelqqqqHSsrl82bN2fIkCFZvXp1+vXr197lAJ2QPgO0NX0GaGv6DNDW9BmgLekxe1YURRobG/Obv/mbe50nvEnSt2/fJMnWrVv3OGfLli1Jstd/aNXV1amurt5pW11d3YEX2En169fPf1ygTekzQFvTZ4C2ps8AbU2fAdqSHrN7e7vipkW3g1BH6Q0fPjxJsnr16j3OaRlrmQsAAAAAANAWhDdJxo4dmyTZsGFDXn755d3OWbFiRZJk3LhxB60uAAAAAACg6xHeJDnqqKNSX1+fJLnnnnt2GV+2bFlWr16d6urqTJo06WCX1+lUV1fn7//+73e5xRxApegzQFvTZ4C2ps8AbU2fAdqSHnPgqoqiKNq7iDJ46KGHcuaZZ6a2tjaPP/546xU2GzZsyAc+8IE888wzueKKKzJnzpx2rhQAAAAAAOjMhDe/Ytq0abn55pvTs2fPNDQ0pKamJkuWLMnGjRszYcKELFq0KL17927vMgEAAAAAgE5MePMO9913X2699dZ873vfy/bt23P00Udn6tSpueyyy9KrV6/2Lg8AAAAAAOjkhDcAAAAAAAAl0q29CwAAAAAAAOCXhDccsPvvvz8nnXRSBgwYkJqamhx77LG5/vrrs3379v1ab+XKlTnrrLMyaNCgHHLIIRkxYkT+9m//Nj/5yU8qXDnQUVSqz6xatSqzZ89OQ0NDBg0alJ49e2bAgAE54YQTcuutt+533wI6vkofz/yqBQsWpKqqKlVVVTnllFMqUC3QEbVFn/nWt76VM844I4MHD06vXr1y+OGH533ve1+uvvrqClYOdASV7DFbt27N7NmzM378+PTr1y89e/bM4MGDc/rpp+fhhx9ug+qBMnvuuedyyy235Nxzz82YMWPSo0ePVFVV5dprrz2gdRcvXpxJkyZl4MCB6d27d0aNGpXPfOYz2bJlS4Uq7/jcNo0Dcumll+amm25Kjx49cvLJJ6e2tjb//u//no0bN2bixIl57LHH0rt3731e74EHHsg555yTHTt2pL6+PiNGjMiKFSvy0ksvZdCgQVm2bFmOOeaYNtwjoGwq1Wd27NiRnj17Jklqa2tTX1+fQYMGZc2aNXnyySfT3Nyc448/PgsXLkxdXV0b7xVQJpU+nvlVP/3pTzN69OisXbs2RVGkoaEhixcvrvAeAGVX6T7z1ltvZerUqbn//vvTu3fv/P7v/34GDRqUdevW5Yc//GGam5uzfv36NtwjoEwq2WM2bNiQ97///fnf//3f1NbW5n3ve1/q6urywgsv5Omnn06SXHLJJbnpppvacpeAEmnpMe90zTXX5LOf/ex+rXnjjTfm8ssvT1VVVU444YQMGjQoS5cuzbp16zJy5MgsW7YsAwcOPNDSO74C9tO8efOKJEVtbW2xcuXK1u2vv/56MWbMmCJJccUVV+zzeq+99lrRp0+fIklx2223tW7fsWNHMXXq1CJJUV9fX7z99tsV3Q+gvCrZZ7Zv314cd9xxxX333Ve8+eabO439z//8T3HEEUcUSYrzzjuvovsAlFulj2fe6c///M+L7t27FxdddFGRpGhoaKhE2UAH0hZ95qMf/WiRpPiTP/mT4vXXX99prLm5uXjyyScrUjtQfpXuMZdcckmRpDjuuOOKDRs27DT2yCOPFD169CiS6DPQhdx+++3FJz/5yeIb3/hG8eyzzxZ/8Rd/USQprrnmmv1a7+mnny6qqqqK7t27FwsWLGjdvnXr1qKhoaFIUkyePLlS5Xdowhv2W319fZGkuPbaa3cZW7p0aZGkqK6uLjZu3LhP61155ZVFkuKUU07ZZayxsbHo379/kaR49NFHD7h2oGOodJ/Zm69//etFkqJ3797FW2+9dcDrAR1DW/aZBx98sEhSXHnllcWdd94pvIEuqtJ9ZvHixUWSYvTo0Y5ZgIr3mNGjRxdJivvuu2+346eeemqRpPjCF75wQHUDHdfHPvaxAwpvzjrrrCJJ8Zd/+Ze7jL3yyitFt27diiTFs88+e6CldnieecN+ee2117J8+fIkyZQpU3YZnzhxYoYMGZKmpqYsWLBgn9acN2/eHterra3NGWeckSR58MEH97dsoANpiz6zN2PHjk2S/OxnP3ObEegi2rLPrF+/PhdeeGFGjhzp2RPQhbVFn7nllluS/PwWJi23hAW6prboMYcccsg+zXM7I2B/vPXWW3nkkUeS7L5vDRs2LBMmTEjyy3PFXZnwhv2yatWqJMmhhx6aESNG7HbO+PHjd5q7N42NjXnhhRd2+tyBrAd0fJXuM7/O888/nyTp1atXDj300ANeDyi/tuwzF110UdavX5877rhjn0+CAJ1PpftMc3NzlixZkiR5//vfn3Xr1uWLX/xiLrroolx66aW56667POQXupC2OJb50Ic+lCT5/Oc/nzfeeGOnsQULFuQ//uM/Mnjw4NYv2AK8Gz/60Y+ybdu2JM4B74se7V0AHdPLL7+cJBk6dOge5wwZMmSnuXvzyiuvtL7e05rvZj2g46t0n9mboihy/fXXJ0lOP/30VFdXH9B6QMfQVn1m7ty5eeCBBzJt2rTWb40BXVOl+8xLL73UGs585zvfycUXX7xLWHPllVdm7ty5Ofnkk/e3bKCDaItjmU996lN56qmnsnDhwtZvwNfV1eWFF17IypUrM2HChNxxxx3p37//ge8A0OW09KK6urr07dt3t3OcA/4lV96wXxobG5MkNTU1e5xTW1ubJNm8efM+r7e3Nd/NekDHV+k+szczZ87Mk08+mdra2lx33XUHtBbQcbRFn1m3bl0+8YlP5Oijj86sWbMOvEigQ6t0n9mwYUPr6/PPPz/HHXdcli9fnsbGxnzve9/LpEmT8vrrr+eP//iPW68qBjqvtjiWqampyfz58/PJT34yW7duzcKFC3Pvvfdm5cqVOeyww3LKKafkyCOPPPDigS7pYJ7r6QyENwB0aXfffXeuvvrqdOvWLf/yL/+S3/qt32rvkoAO7K//+q/z05/+NP/8z/+cPn36tHc5QCdTFEXr6yOPPDILFy7M+PHjU1tbm2OPPTYPP/xwRo8enS1btvhCCrBf1q5dmwkTJuSWW27Jtdde23rF31NPPZXjjjsuM2fOzMSJE3f6Ei4AbUN4w35puaxt69ate5zTcvl+v3799nm9va35btYDOr5K95nduf/++/Pxj388SXL77bfnrLPO2q91gI6p0n3mrrvuyvz583PhhRfmpJNOqkiNQMfWlr83nXvuubvc6rV79+654IILkiSLFy9+1/UCHUtb/M70sY99LMuXL88111yTT3/60xkxYkRqampSX1+fb3/72xkzZky+//3vZ86cOQe+A0CXczDO9XQmnnnDfhk+fHiSZPXq1Xuc0zLWMndvhg0b1vr61VdfzZgxYw5oPaDjq3SfeacHH3wwU6ZMydtvv53bbrutNcQBuo5K95l58+YlSZYvX75LeLNu3bokycqVK1vH5s6dm8GDB7+7ooEOpdJ9Zvjw4amqqkpRFHnPe96z2zkt29euXfvuigU6nEr3mNdeey2LFi1Kkpxzzjm7jPfs2TMf+chH8swzz2Tx4sWZOXPmuy8a6NJaetHGjRvT2Ni42+feOAf8S668Yb+MHTs2yc/vubynh0etWLEiSTJu3Lhfu16/fv1yzDHH7PS5A1kP6Pgq3Wd+1UMPPZQ/+7M/S3Nzc77yla/kr/7qrw6sWKBDaqs+s2LFijz++OM7/Tz33HNJfv5LSsu2N9988wD3ACi7SveZ2trajBw5Mkmyfv363c5p2d5yv3ig86p0j3n11VdbX+/pG+/9+/dPkrzxxhvvqlaAJBk5cmTr7aWdA/71hDfsl6OOOir19fVJknvuuWeX8WXLlmX16tWprq7OpEmT9mnNM888c4/rbdmyJfPnz0+SfPjDH97fsoEOpC36TJLMnz8/Z599dnbs2JGvfOUrrbcWAbqeSveZhx56KEVR7PbnzjvvTJI0NDS0bvNNMuj82uJ4puU2r3u6LVrLt+aPP/74/SkZ6EAq3WOOPPLI1tff/e53dzvnO9/5TpJkxIgR+1My0MX16tUrp512WpLd960f//jHeeKJJ5L88lxxVya8Yb99+tOfTpJcd911efrpp1u3b9iwIRdffHGS5G/+5m9av5WR/Px2IqNGjUpDQ8Mu61166aXp06dPFi9enNtvv711e3Nzcy6++OJs3Lgx9fX1+YM/+IO22iWgZCrdZxYsWJCPfOQj2bFjR7761a8KboCK9xmAd6p0n7nkkksyYMCALFiwILfddttOY3Pnzs03vvGN1nlA51fJHjN06NDWMGjatGl55ZVXdhr/13/919x7771JkilTplR8X4DO40tf+lJGjRqVj370o7uMTZ8+PVVVVbnzzjvz6KOPtm7ftm1bzj///DQ3N2fy5MkZNWrUwSy5lKqKoijauwg6rmnTpuXmm29Oz54909DQkJqamixZsiQbN27MhAkTsmjRovTu3bt1/te+9rWcd955GTZs2C4HAcnPHx5+zjnnpLm5Ob/7u7+b4cOHZ/ny5XnppZcyaNCgLFu2rPX2akDXUKk+85Of/CRDhw5NU1NTjjrqqL2edJ0zZ04GDhzYlrsFlEilj2d2p+UzDQ0NHiIOXVCl+8yiRYtyxhln5M0338x73/ve/M7v/E5efPHFrFq1Kkly1VVX5eqrrz5Yuwe0s0r2mB/84Af5wAc+kPXr1+eQQw7J7/3e72XgwIF59tln88Mf/jBJMnXq1Nx9992pqqo6mLsJtJOnn366NQxOkhdffDHr16/PUUcdtdMVe/PmzcsRRxyRJPnc5z6XmTNn5sQTT8x//ud/7rLmjTfemMsvvzxVVVU58cQTc/jhh2fp0qVZu3ZtRo4cmWXLljkvk6RHexdAx3bTTTdlwoQJufXWW/PEE09k+/btOfroozN9+vRcdtll6dWr17ta76yzzsp73vOezJo1K0uXLs2qVatyxBFH5BOf+ESuuuqqDBo0qI32BCirSvWZbdu2pampKUmyZs2a3HXXXXuc+7nPfc5BAnQhlT6eAXinSveZU089Nd///vcza9asLF68ON/61rfSr1+/TJo0KdOmTXO3AuhiKtljRo8enR/84Ae58cYb82//9m9Zvnx5mpqaMmDAgPzhH/5hPv7xj+fss89uw70Bymbz5s27vZXimjVrsmbNmtb3Ledc9sVll12WMWPG5IYbbshTTz2VrVu3ZujQoZkxY0ZmzJiRvn37VqT2js6VNwAAAAAAACXimTcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASkR4AwAAAAAAUCLCGwAAAAAAgBIR3gAAAAAAAJSI8AYAAAAAAKBEhDcAAAAAAAAlIrwBAAAAAAAoEeENAAAAAABAiQhvAAAAAAAASuT/ASLJ63VTSSZPAAAAAElFTkSuQmCC\n"},"metadata":{}}],"execution_count":95},{"cell_type":"markdown","source":"# Course dimension\n","metadata":{}},{"cell_type":"code","source":"values = range(0,n_courses)\ncourses = [get_random_string(9) for i in values]\nlen(courses) ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:02.784130Z","iopub.execute_input":"2025-02-13T11:43:02.784549Z","iopub.status.idle":"2025-02-13T11:43:02.792666Z","shell.execute_reply.started":"2025-02-13T11:43:02.784520Z","shell.execute_reply":"2025-02-13T11:43:02.791576Z"}},"outputs":[{"execution_count":96,"output_type":"execute_result","data":{"text/plain":"150"},"metadata":{}}],"execution_count":96},{"cell_type":"code","source":"courses[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:02.793780Z","iopub.execute_input":"2025-02-13T11:43:02.794136Z","iopub.status.idle":"2025-02-13T11:43:02.816116Z","shell.execute_reply.started":"2025-02-13T11:43:02.794106Z","shell.execute_reply":"2025-02-13T11:43:02.814718Z"}},"outputs":[{"execution_count":97,"output_type":"execute_result","data":{"text/plain":"['fovvdfgve', 'wpnxftypj', 'ttbledoyq', 'ltcufoqua', 'cuwsgnebl']"},"metadata":{}}],"execution_count":97},{"cell_type":"code","source":"values = range(0,n_courses)\ncourse_ids = [get_random_alphanum(\"COURSE_\",4) for i in values]\nlen(course_ids)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:02.817372Z","iopub.execute_input":"2025-02-13T11:43:02.817785Z","iopub.status.idle":"2025-02-13T11:43:02.838830Z","shell.execute_reply.started":"2025-02-13T11:43:02.817755Z","shell.execute_reply":"2025-02-13T11:43:02.837736Z"}},"outputs":[{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"150"},"metadata":{}}],"execution_count":98},{"cell_type":"code","source":"course_ids[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:02.839900Z","iopub.execute_input":"2025-02-13T11:43:02.840302Z","iopub.status.idle":"2025-02-13T11:43:02.861939Z","shell.execute_reply.started":"2025-02-13T11:43:02.840262Z","shell.execute_reply":"2025-02-13T11:43:02.860925Z"}},"outputs":[{"execution_count":99,"output_type":"execute_result","data":{"text/plain":"['COURSE_4530', 'COURSE_7065', 'COURSE_5453', 'COURSE_5287', 'COURSE_9481']"},"metadata":{}}],"execution_count":99},{"cell_type":"code","source":"values = range(0,n_courses)\nlevels = [random.choice(level_id) for i in values]\nlen(levels)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:02.862973Z","iopub.execute_input":"2025-02-13T11:43:02.863283Z","iopub.status.idle":"2025-02-13T11:43:02.883412Z","shell.execute_reply.started":"2025-02-13T11:43:02.863258Z","shell.execute_reply":"2025-02-13T11:43:02.882405Z"}},"outputs":[{"execution_count":100,"output_type":"execute_result","data":{"text/plain":"150"},"metadata":{}}],"execution_count":100},{"cell_type":"code","source":"levels[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:02.884400Z","iopub.execute_input":"2025-02-13T11:43:02.884752Z","iopub.status.idle":"2025-02-13T11:43:02.905689Z","shell.execute_reply.started":"2025-02-13T11:43:02.884727Z","shell.execute_reply":"2025-02-13T11:43:02.904454Z"}},"outputs":[{"execution_count":101,"output_type":"execute_result","data":{"text/plain":"[2, 4, 2, 1, 2]"},"metadata":{}}],"execution_count":101},{"cell_type":"code","source":"values = range(0,n_courses)\nskills = [random.choice(roles_pd.skill.unique()) for i in values]\nlen(skills)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:02.906813Z","iopub.execute_input":"2025-02-13T11:43:02.907117Z","iopub.status.idle":"2025-02-13T11:43:02.939148Z","shell.execute_reply.started":"2025-02-13T11:43:02.907092Z","shell.execute_reply":"2025-02-13T11:43:02.937935Z"}},"outputs":[{"execution_count":102,"output_type":"execute_result","data":{"text/plain":"150"},"metadata":{}}],"execution_count":102},{"cell_type":"code","source":"skills[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:02.940064Z","iopub.execute_input":"2025-02-13T11:43:02.940350Z","iopub.status.idle":"2025-02-13T11:43:02.946565Z","shell.execute_reply.started":"2025-02-13T11:43:02.940328Z","shell.execute_reply":"2025-02-13T11:43:02.945504Z"}},"outputs":[{"execution_count":103,"output_type":"execute_result","data":{"text/plain":"['1010101294', '1010101691', '1010101693', '1010101040', '1010101954']"},"metadata":{}}],"execution_count":103},{"cell_type":"code","source":"data ={\"course_id\":course_ids,\n \"title\":courses,\n \"skills\": skills,\n \"skill level\": levels}\ncourses_pd = pd.DataFrame(data)\ncourses_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:02.947869Z","iopub.execute_input":"2025-02-13T11:43:02.948260Z","iopub.status.idle":"2025-02-13T11:43:02.974283Z","shell.execute_reply.started":"2025-02-13T11:43:02.948219Z","shell.execute_reply":"2025-02-13T11:43:02.973041Z"}},"outputs":[{"execution_count":104,"output_type":"execute_result","data":{"text/plain":" course_id title skills skill level\n0 COURSE_4530 fovvdfgve 1010101294 2\n1 COURSE_7065 wpnxftypj 1010101691 4\n2 COURSE_5453 ttbledoyq 1010101693 2\n3 COURSE_5287 ltcufoqua 1010101040 1\n4 COURSE_9481 cuwsgnebl 1010101954 2\n.. ... ... ... ...\n145 COURSE_5240 qcxnofnkd 1010101042 2\n146 COURSE_5014 qmqckstmz 1010101867 4\n147 COURSE_4964 ihyoyhxau 1010101679 3\n148 COURSE_3557 cijpyezmc 1010101349 5\n149 COURSE_4740 obucjdtuj 1010101263 5\n\n[150 rows x 4 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
course_idtitleskillsskill level
0COURSE_4530fovvdfgve10101012942
1COURSE_7065wpnxftypj10101016914
2COURSE_5453ttbledoyq10101016932
3COURSE_5287ltcufoqua10101010401
4COURSE_9481cuwsgnebl10101019542
...............
145COURSE_5240qcxnofnkd10101010422
146COURSE_5014qmqckstmz10101018674
147COURSE_4964ihyoyhxau10101016793
148COURSE_3557cijpyezmc10101013495
149COURSE_4740obucjdtuj10101012635
\n

150 rows × 4 columns

\n
"},"metadata":{}}],"execution_count":104},{"cell_type":"markdown","source":"## Test","metadata":{}},{"cell_type":"code","source":"courses_pd['skill level'].plot.hist(bins = 5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:02.975414Z","iopub.execute_input":"2025-02-13T11:43:02.975823Z","iopub.status.idle":"2025-02-13T11:43:03.318829Z","shell.execute_reply.started":"2025-02-13T11:43:02.975792Z","shell.execute_reply":"2025-02-13T11:43:03.317727Z"}},"outputs":[{"execution_count":105,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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How can we compare skills?\n\n**dimensions:** \n- skills id\n- role id\n- role level required\n- course-gained\n- self-endorsed\n- user-endorsed\n- soft\n- tech\n- language\n- month\n- year \n \n**Measure**\n- level","metadata":{}},{"cell_type":"code","source":"no_items = int(n_skills_matching_employees/4)\nno_items","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.319992Z","iopub.execute_input":"2025-02-13T11:43:03.320388Z","iopub.status.idle":"2025-02-13T11:43:03.327258Z","shell.execute_reply.started":"2025-02-13T11:43:03.320326Z","shell.execute_reply":"2025-02-13T11:43:03.326038Z"}},"outputs":[{"execution_count":106,"output_type":"execute_result","data":{"text/plain":"7"},"metadata":{}}],"execution_count":106},{"cell_type":"code","source":"roles_ids = np.repeat(random.choice(role_ids), no_items) \nlen(roles_ids) == no_items","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.328389Z","iopub.execute_input":"2025-02-13T11:43:03.328747Z","iopub.status.idle":"2025-02-13T11:43:03.350470Z","shell.execute_reply.started":"2025-02-13T11:43:03.328721Z","shell.execute_reply":"2025-02-13T11:43:03.349314Z"}},"outputs":[{"execution_count":107,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":107},{"cell_type":"code","source":"roles_ids[0:5]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.351634Z","iopub.execute_input":"2025-02-13T11:43:03.352040Z","iopub.status.idle":"2025-02-13T11:43:03.376811Z","shell.execute_reply.started":"2025-02-13T11:43:03.352002Z","shell.execute_reply":"2025-02-13T11:43:03.375516Z"}},"outputs":[{"execution_count":108,"output_type":"execute_result","data":{"text/plain":"array(['ROLE_713858', 'ROLE_713858', 'ROLE_713858', 'ROLE_713858',\n 'ROLE_713858'], dtype='\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
role idroleskilllevel
0ROLE_989543naqdldozwyh10101010433
1ROLE_329525cqljjdixacw10101017723
2ROLE_025947sevplprmtbx10101011764
3ROLE_743948joyilptjvft10101016525
4ROLE_482449aedtathfnkb10101019104
...............
95ROLE_281011enmugvlbsfl10101019525
96ROLE_943766kjqoddkwefj10101018223
97ROLE_185077pfapomtdxcc10101017623
98ROLE_440020zrztiwjkkiv10101019874
99ROLE_060023rrsdulwsydt10101017894
\n

500 rows × 4 columns

\n"},"metadata":{}}],"execution_count":109},{"cell_type":"code","source":"values = range(0,no_items)\nrows = roles_pd['role id'].isin(roles_ids)\nskills_uids = roles_pd.loc[rows,'skill'].to_list()\nskills_uids","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.405241Z","iopub.execute_input":"2025-02-13T11:43:03.405678Z","iopub.status.idle":"2025-02-13T11:43:03.429495Z","shell.execute_reply.started":"2025-02-13T11:43:03.405646Z","shell.execute_reply":"2025-02-13T11:43:03.428376Z"}},"outputs":[{"execution_count":110,"output_type":"execute_result","data":{"text/plain":"['1010101122', '1010101762', '1010101873', '1010101767', '1010101767']"},"metadata":{}}],"execution_count":110},{"cell_type":"code","source":"gap = no_items - len(skills_uids)\nothers = [random.choice(skill_ids) for i in range(0,gap)]\nskills_uids = skills_uids + others\nskills_uids\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.430752Z","iopub.execute_input":"2025-02-13T11:43:03.431096Z","iopub.status.idle":"2025-02-13T11:43:03.451309Z","shell.execute_reply.started":"2025-02-13T11:43:03.431068Z","shell.execute_reply":"2025-02-13T11:43:03.450290Z"}},"outputs":[{"execution_count":111,"output_type":"execute_result","data":{"text/plain":"['1010101122',\n '1010101762',\n '1010101873',\n '1010101767',\n '1010101767',\n '1010101767',\n '1010101772']"},"metadata":{}}],"execution_count":111},{"cell_type":"code","source":"type_skills_source = [\"self\",\"user\",\"course\"]\nn_cols = 0 \nvalues = range(0,no_items)\n\nwhile n_cols < 3: \n type_source = np.random.choice(type_skills_source,\n size=no_items, \n p=[0.35,0.35,0.30])\n df_encoded = pd.get_dummies(type_source)\n n_cols = df_encoded.shape[1]\n\ndf_encoded\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.452364Z","iopub.execute_input":"2025-02-13T11:43:03.452768Z","iopub.status.idle":"2025-02-13T11:43:03.482213Z","shell.execute_reply.started":"2025-02-13T11:43:03.452740Z","shell.execute_reply":"2025-02-13T11:43:03.481067Z"}},"outputs":[{"execution_count":112,"output_type":"execute_result","data":{"text/plain":" course self user\n0 False False True\n1 False True False\n2 False False True\n3 True False False\n4 False False True\n5 False False True\n6 False True False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
courseselfuser
0FalseFalseTrue
1FalseTrueFalse
2FalseFalseTrue
3TrueFalseFalse
4FalseFalseTrue
5FalseFalseTrue
6FalseTrueFalse
\n
"},"metadata":{}}],"execution_count":112},{"cell_type":"code","source":"\nlevels = np.random.choice([3,4], no_items)\nlen(levels) == no_items","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.483180Z","iopub.execute_input":"2025-02-13T11:43:03.483472Z","iopub.status.idle":"2025-02-13T11:43:03.506958Z","shell.execute_reply.started":"2025-02-13T11:43:03.483448Z","shell.execute_reply":"2025-02-13T11:43:03.505914Z"}},"outputs":[{"execution_count":113,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}],"execution_count":113},{"cell_type":"code","source":"levels","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.507974Z","iopub.execute_input":"2025-02-13T11:43:03.508385Z","iopub.status.idle":"2025-02-13T11:43:03.527001Z","shell.execute_reply.started":"2025-02-13T11:43:03.508334Z","shell.execute_reply":"2025-02-13T11:43:03.525647Z"}},"outputs":[{"execution_count":114,"output_type":"execute_result","data":{"text/plain":"array([3, 3, 3, 4, 4, 3, 3])"},"metadata":{}}],"execution_count":114},{"cell_type":"code","source":"months = np.random.choice([1,2,3,4,5,6,7,8,9,10,11,12], no_items)\nyear = np.repeat(2024,no_items)\nmonths","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.528215Z","iopub.execute_input":"2025-02-13T11:43:03.528546Z","iopub.status.idle":"2025-02-13T11:43:03.547690Z","shell.execute_reply.started":"2025-02-13T11:43:03.528511Z","shell.execute_reply":"2025-02-13T11:43:03.546513Z"}},"outputs":[{"execution_count":115,"output_type":"execute_result","data":{"text/plain":"array([ 3, 5, 6, 11, 12, 2, 2])"},"metadata":{}}],"execution_count":115},{"cell_type":"code","source":"data = {\"skill\": skills_uids,\n \"role\": roles_ids,\n \"month\":months,\n \"year\":year,\n \"level\": levels\n }\npd_2024 = pd.DataFrame(data)\npd_2024['self'] = df_encoded['self']\npd_2024['course'] = df_encoded['course']\npd_2024['user'] = df_encoded['user']\n\n\npd_2024.dtypes","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.548737Z","iopub.execute_input":"2025-02-13T11:43:03.549094Z","iopub.status.idle":"2025-02-13T11:43:03.575842Z","shell.execute_reply.started":"2025-02-13T11:43:03.549066Z","shell.execute_reply":"2025-02-13T11:43:03.574338Z"}},"outputs":[{"execution_count":116,"output_type":"execute_result","data":{"text/plain":"skill object\nrole object\nmonth int64\nyear int64\nlevel int64\nself bool\ncourse bool\nuser bool\ndtype: object"},"metadata":{}}],"execution_count":116},{"cell_type":"code","source":"pd_2024","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.576861Z","iopub.execute_input":"2025-02-13T11:43:03.577262Z","iopub.status.idle":"2025-02-13T11:43:03.603302Z","shell.execute_reply.started":"2025-02-13T11:43:03.577235Z","shell.execute_reply":"2025-02-13T11:43:03.602132Z"}},"outputs":[{"execution_count":117,"output_type":"execute_result","data":{"text/plain":" skill role month year level self course user\n0 1010101122 ROLE_713858 3 2024 3 False False True\n1 1010101762 ROLE_713858 5 2024 3 True False False\n2 1010101873 ROLE_713858 6 2024 3 False False True\n3 1010101767 ROLE_713858 11 2024 4 False True False\n4 1010101767 ROLE_713858 12 2024 4 False False True\n5 1010101767 ROLE_713858 2 2024 3 False False True\n6 1010101772 ROLE_713858 2 2024 3 True False False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skillrolemonthyearlevelselfcourseuser
01010101122ROLE_713858320243FalseFalseTrue
11010101762ROLE_713858520243TrueFalseFalse
21010101873ROLE_713858620243FalseFalseTrue
31010101767ROLE_7138581120244FalseTrueFalse
41010101767ROLE_7138581220244FalseFalseTrue
51010101767ROLE_713858220243FalseFalseTrue
61010101772ROLE_713858220243TrueFalseFalse
\n
"},"metadata":{}}],"execution_count":117},{"cell_type":"code","source":"pd_2023 = pd_2024.copy(deep = True) \npd_2023","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.604666Z","iopub.execute_input":"2025-02-13T11:43:03.604986Z","iopub.status.idle":"2025-02-13T11:43:03.630766Z","shell.execute_reply.started":"2025-02-13T11:43:03.604960Z","shell.execute_reply":"2025-02-13T11:43:03.629534Z"}},"outputs":[{"execution_count":118,"output_type":"execute_result","data":{"text/plain":" skill role month year level self course user\n0 1010101122 ROLE_713858 3 2024 3 False False True\n1 1010101762 ROLE_713858 5 2024 3 True False False\n2 1010101873 ROLE_713858 6 2024 3 False False True\n3 1010101767 ROLE_713858 11 2024 4 False True False\n4 1010101767 ROLE_713858 12 2024 4 False False True\n5 1010101767 ROLE_713858 2 2024 3 False False True\n6 1010101772 ROLE_713858 2 2024 3 True False False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skillrolemonthyearlevelselfcourseuser
01010101122ROLE_713858320243FalseFalseTrue
11010101762ROLE_713858520243TrueFalseFalse
21010101873ROLE_713858620243FalseFalseTrue
31010101767ROLE_7138581120244FalseTrueFalse
41010101767ROLE_7138581220244FalseFalseTrue
51010101767ROLE_713858220243FalseFalseTrue
61010101772ROLE_713858220243TrueFalseFalse
\n
"},"metadata":{}}],"execution_count":118},{"cell_type":"code","source":"pd_2023['role'] = np.repeat(random.choice(role_ids), no_items) \npd_2023\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.631753Z","iopub.execute_input":"2025-02-13T11:43:03.632048Z","iopub.status.idle":"2025-02-13T11:43:03.659753Z","shell.execute_reply.started":"2025-02-13T11:43:03.632016Z","shell.execute_reply":"2025-02-13T11:43:03.658601Z"}},"outputs":[{"execution_count":119,"output_type":"execute_result","data":{"text/plain":" skill role month year level self course user\n0 1010101122 ROLE_226242 3 2024 3 False False True\n1 1010101762 ROLE_226242 5 2024 3 True False False\n2 1010101873 ROLE_226242 6 2024 3 False False True\n3 1010101767 ROLE_226242 11 2024 4 False True False\n4 1010101767 ROLE_226242 12 2024 4 False False True\n5 1010101767 ROLE_226242 2 2024 3 False False True\n6 1010101772 ROLE_226242 2 2024 3 True False False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skillrolemonthyearlevelselfcourseuser
01010101122ROLE_226242320243FalseFalseTrue
11010101762ROLE_226242520243TrueFalseFalse
21010101873ROLE_226242620243FalseFalseTrue
31010101767ROLE_2262421120244FalseTrueFalse
41010101767ROLE_2262421220244FalseFalseTrue
51010101767ROLE_226242220243FalseFalseTrue
61010101772ROLE_226242220243TrueFalseFalse
\n
"},"metadata":{}}],"execution_count":119},{"cell_type":"code","source":"values = range(0,no_items)\nrows = roles_pd['role id'].isin(pd_2023['role'])\nskills_uids = roles_pd.loc[rows,'skill'].to_list()\nskills_uids","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.660817Z","iopub.execute_input":"2025-02-13T11:43:03.661241Z","iopub.status.idle":"2025-02-13T11:43:03.680261Z","shell.execute_reply.started":"2025-02-13T11:43:03.661203Z","shell.execute_reply":"2025-02-13T11:43:03.679111Z"}},"outputs":[{"execution_count":120,"output_type":"execute_result","data":{"text/plain":"['1010101525', '1010101263', '1010101122', '1010101043', '1010101954']"},"metadata":{}}],"execution_count":120},{"cell_type":"code","source":"gap = no_items - len(skills_uids)\n\nothers = [random.choice(skill_ids) for i in range(0,gap)]\nskills_uids = skills_uids + others\npd_2023['skill'] = skills_uids\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.696240Z","iopub.execute_input":"2025-02-13T11:43:03.696664Z","iopub.status.idle":"2025-02-13T11:43:03.702158Z","shell.execute_reply.started":"2025-02-13T11:43:03.696604Z","shell.execute_reply":"2025-02-13T11:43:03.701195Z"}},"outputs":[],"execution_count":121},{"cell_type":"code","source":"pd_2023['year'] = np.repeat(2023, no_items)\npd_2023['month'] = np.random.choice([1,2,3,4,5,6,7,8,9,10,11,12], pd_2023.shape[0])\npd_2023","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.705886Z","iopub.execute_input":"2025-02-13T11:43:03.706243Z","iopub.status.idle":"2025-02-13T11:43:03.732212Z","shell.execute_reply.started":"2025-02-13T11:43:03.706214Z","shell.execute_reply":"2025-02-13T11:43:03.731062Z"}},"outputs":[{"execution_count":122,"output_type":"execute_result","data":{"text/plain":" skill role month year level self course user\n0 1010101525 ROLE_226242 2 2023 3 False False True\n1 1010101263 ROLE_226242 7 2023 3 True False False\n2 1010101122 ROLE_226242 2 2023 3 False False True\n3 1010101043 ROLE_226242 12 2023 4 False True False\n4 1010101954 ROLE_226242 12 2023 4 False False True\n5 1010101553 ROLE_226242 10 2023 3 False False True\n6 1010101363 ROLE_226242 10 2023 3 True False False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skillrolemonthyearlevelselfcourseuser
01010101525ROLE_226242220233FalseFalseTrue
11010101263ROLE_226242720233TrueFalseFalse
21010101122ROLE_226242220233FalseFalseTrue
31010101043ROLE_2262421220234FalseTrueFalse
41010101954ROLE_2262421220234FalseFalseTrue
51010101553ROLE_2262421020233FalseFalseTrue
61010101363ROLE_2262421020233TrueFalseFalse
\n
"},"metadata":{}}],"execution_count":122},{"cell_type":"code","source":"pd_2023.loc[pd_2023.user == True,'self'] = True\npd_2023.loc[pd_2023.user == True,'user'] = False\n\npd_2023.loc[0:2,['self','course','user']] = [False, True, False]\npd_2023['level'] = np.random.choice([1,2,3], pd_2023.shape[0])\n\n\npd_2023\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.733319Z","iopub.execute_input":"2025-02-13T11:43:03.733714Z","iopub.status.idle":"2025-02-13T11:43:03.761885Z","shell.execute_reply.started":"2025-02-13T11:43:03.733680Z","shell.execute_reply":"2025-02-13T11:43:03.760740Z"}},"outputs":[{"execution_count":123,"output_type":"execute_result","data":{"text/plain":" skill role month year level self course user\n0 1010101525 ROLE_226242 2 2023 2 False True False\n1 1010101263 ROLE_226242 7 2023 3 False True False\n2 1010101122 ROLE_226242 2 2023 2 False True False\n3 1010101043 ROLE_226242 12 2023 1 False True False\n4 1010101954 ROLE_226242 12 2023 3 True False False\n5 1010101553 ROLE_226242 10 2023 2 True False False\n6 1010101363 ROLE_226242 10 2023 1 True False False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skillrolemonthyearlevelselfcourseuser
01010101525ROLE_226242220232FalseTrueFalse
11010101263ROLE_226242720233FalseTrueFalse
21010101122ROLE_226242220232FalseTrueFalse
31010101043ROLE_2262421220231FalseTrueFalse
41010101954ROLE_2262421220233TrueFalseFalse
51010101553ROLE_2262421020232TrueFalseFalse
61010101363ROLE_2262421020231TrueFalseFalse
\n
"},"metadata":{}}],"execution_count":123},{"cell_type":"code","source":"pd_2025 = pd_2024.copy(deep = True) \npd_2025","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.762944Z","iopub.execute_input":"2025-02-13T11:43:03.763364Z","iopub.status.idle":"2025-02-13T11:43:03.788305Z","shell.execute_reply.started":"2025-02-13T11:43:03.763297Z","shell.execute_reply":"2025-02-13T11:43:03.787136Z"}},"outputs":[{"execution_count":124,"output_type":"execute_result","data":{"text/plain":" skill role month year level self course user\n0 1010101122 ROLE_713858 3 2024 3 False False True\n1 1010101762 ROLE_713858 5 2024 3 True False False\n2 1010101873 ROLE_713858 6 2024 3 False False True\n3 1010101767 ROLE_713858 11 2024 4 False True False\n4 1010101767 ROLE_713858 12 2024 4 False False True\n5 1010101767 ROLE_713858 2 2024 3 False False True\n6 1010101772 ROLE_713858 2 2024 3 True False False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skillrolemonthyearlevelselfcourseuser
01010101122ROLE_713858320243FalseFalseTrue
11010101762ROLE_713858520243TrueFalseFalse
21010101873ROLE_713858620243FalseFalseTrue
31010101767ROLE_7138581120244FalseTrueFalse
41010101767ROLE_7138581220244FalseFalseTrue
51010101767ROLE_713858220243FalseFalseTrue
61010101772ROLE_713858220243TrueFalseFalse
\n
"},"metadata":{}}],"execution_count":124},{"cell_type":"code","source":"pd_2025['role'] = np.repeat(random.choice(role_ids), no_items) \npd_2025\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.789320Z","iopub.execute_input":"2025-02-13T11:43:03.789661Z","iopub.status.idle":"2025-02-13T11:43:03.814079Z","shell.execute_reply.started":"2025-02-13T11:43:03.789603Z","shell.execute_reply":"2025-02-13T11:43:03.813133Z"}},"outputs":[{"execution_count":125,"output_type":"execute_result","data":{"text/plain":" skill role month year level self course user\n0 1010101122 ROLE_860638 3 2024 3 False False True\n1 1010101762 ROLE_860638 5 2024 3 True False False\n2 1010101873 ROLE_860638 6 2024 3 False False True\n3 1010101767 ROLE_860638 11 2024 4 False True False\n4 1010101767 ROLE_860638 12 2024 4 False False True\n5 1010101767 ROLE_860638 2 2024 3 False False True\n6 1010101772 ROLE_860638 2 2024 3 True False False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skillrolemonthyearlevelselfcourseuser
01010101122ROLE_860638320243FalseFalseTrue
11010101762ROLE_860638520243TrueFalseFalse
21010101873ROLE_860638620243FalseFalseTrue
31010101767ROLE_8606381120244FalseTrueFalse
41010101767ROLE_8606381220244FalseFalseTrue
51010101767ROLE_860638220243FalseFalseTrue
61010101772ROLE_860638220243TrueFalseFalse
\n
"},"metadata":{}}],"execution_count":125},{"cell_type":"code","source":"values = range(0,no_items)\nrows = roles_pd['role id'].isin(pd_2025['role'])\nskills_uids = roles_pd.loc[rows,'skill'].to_list()\nskills_uids","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.815159Z","iopub.execute_input":"2025-02-13T11:43:03.815557Z","iopub.status.idle":"2025-02-13T11:43:03.840802Z","shell.execute_reply.started":"2025-02-13T11:43:03.815528Z","shell.execute_reply":"2025-02-13T11:43:03.839308Z"}},"outputs":[{"execution_count":126,"output_type":"execute_result","data":{"text/plain":"['1010101867', '1010101767', '1010101122', '1010101928', '1010101679']"},"metadata":{}}],"execution_count":126},{"cell_type":"code","source":"gap = no_items - len(skills_uids)\n\nothers = [random.choice(skill_ids) for i in range(0,gap)]\nskills_uids = skills_uids + others\npd_2025['skill'] = skills_uids\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.842152Z","iopub.execute_input":"2025-02-13T11:43:03.842489Z","iopub.status.idle":"2025-02-13T11:43:03.860101Z","shell.execute_reply.started":"2025-02-13T11:43:03.842459Z","shell.execute_reply":"2025-02-13T11:43:03.858924Z"}},"outputs":[],"execution_count":127},{"cell_type":"code","source":"pd_2025['skill']","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.861398Z","iopub.execute_input":"2025-02-13T11:43:03.861793Z","iopub.status.idle":"2025-02-13T11:43:03.889668Z","shell.execute_reply.started":"2025-02-13T11:43:03.861765Z","shell.execute_reply":"2025-02-13T11:43:03.888424Z"}},"outputs":[{"execution_count":128,"output_type":"execute_result","data":{"text/plain":"0 1010101867\n1 1010101767\n2 1010101122\n3 1010101928\n4 1010101679\n5 1010101867\n6 1010101721\nName: skill, dtype: object"},"metadata":{}}],"execution_count":128},{"cell_type":"code","source":"pd_2025['year'] = np.repeat(2025, no_items)\npd_2025['month'] = np.random.choice([1,2,3,4,5,6,7,8,9,10,11,12], no_items)\npd_2025","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.890867Z","iopub.execute_input":"2025-02-13T11:43:03.891216Z","iopub.status.idle":"2025-02-13T11:43:03.918965Z","shell.execute_reply.started":"2025-02-13T11:43:03.891173Z","shell.execute_reply":"2025-02-13T11:43:03.917928Z"}},"outputs":[{"execution_count":129,"output_type":"execute_result","data":{"text/plain":" skill role month year level self course user\n0 1010101867 ROLE_860638 9 2025 3 False False True\n1 1010101767 ROLE_860638 12 2025 3 True False False\n2 1010101122 ROLE_860638 4 2025 3 False False True\n3 1010101928 ROLE_860638 10 2025 4 False True False\n4 1010101679 ROLE_860638 4 2025 4 False False True\n5 1010101867 ROLE_860638 5 2025 3 False False True\n6 1010101721 ROLE_860638 3 2025 3 True False False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skillrolemonthyearlevelselfcourseuser
01010101867ROLE_860638920253FalseFalseTrue
11010101767ROLE_8606381220253TrueFalseFalse
21010101122ROLE_860638420253FalseFalseTrue
31010101928ROLE_8606381020254FalseTrueFalse
41010101679ROLE_860638420254FalseFalseTrue
51010101867ROLE_860638520253FalseFalseTrue
61010101721ROLE_860638320253TrueFalseFalse
\n
"},"metadata":{}}],"execution_count":129},{"cell_type":"code","source":"pd_2025.loc[pd_2025.course == True,'level'] = 5\npd_2025.loc[pd_2025.course == True,'course'] = True\npd_2025.loc[pd_2025.course == True,'user'] = False\n\n\n\npd_2025\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.920098Z","iopub.execute_input":"2025-02-13T11:43:03.920498Z","iopub.status.idle":"2025-02-13T11:43:03.946378Z","shell.execute_reply.started":"2025-02-13T11:43:03.920460Z","shell.execute_reply":"2025-02-13T11:43:03.945273Z"}},"outputs":[{"execution_count":130,"output_type":"execute_result","data":{"text/plain":" skill role month year level self course user\n0 1010101867 ROLE_860638 9 2025 3 False False True\n1 1010101767 ROLE_860638 12 2025 3 True False False\n2 1010101122 ROLE_860638 4 2025 3 False False True\n3 1010101928 ROLE_860638 10 2025 5 False True False\n4 1010101679 ROLE_860638 4 2025 4 False False True\n5 1010101867 ROLE_860638 5 2025 3 False False True\n6 1010101721 ROLE_860638 3 2025 3 True False False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skillrolemonthyearlevelselfcourseuser
01010101867ROLE_860638920253FalseFalseTrue
11010101767ROLE_8606381220253TrueFalseFalse
21010101122ROLE_860638420253FalseFalseTrue
31010101928ROLE_8606381020255FalseTrueFalse
41010101679ROLE_860638420254FalseFalseTrue
51010101867ROLE_860638520253FalseFalseTrue
61010101721ROLE_860638320253TrueFalseFalse
\n
"},"metadata":{}}],"execution_count":130},{"cell_type":"code","source":"skill = np.random.choice(skill_ids,3)\nmonth = [10, 9, 8]\nyear = [2025,2025,2025]\nlevel = [4, 4,4] \nself = [False, False, False]\ncourse = [True, True, True]\nuser = [False, False, False]\ndata = {\"skill\":skill,\n \"month\":month,\n \"year\": year,\n \"level\": level,\n \"self\": self,\n \"course\":course,\n \"user\": user}\npd_2025_new = pd.DataFrame(data)\npd_2025_new","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.947384Z","iopub.execute_input":"2025-02-13T11:43:03.947723Z","iopub.status.idle":"2025-02-13T11:43:03.975543Z","shell.execute_reply.started":"2025-02-13T11:43:03.947696Z","shell.execute_reply":"2025-02-13T11:43:03.974108Z"}},"outputs":[{"execution_count":131,"output_type":"execute_result","data":{"text/plain":" skill month year level self course user\n0 1010101677 10 2025 4 False True False\n1 1010101693 9 2025 4 False True False\n2 1010101144 8 2025 4 False True False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skillmonthyearlevelselfcourseuser
010101016771020254FalseTrueFalse
11010101693920254FalseTrueFalse
21010101144820254FalseTrueFalse
\n
"},"metadata":{}}],"execution_count":131},{"cell_type":"code","source":"skills_employee_pd = pd.concat([pd_2023, pd_2024, pd_2025, pd_2025_new])\nskills_employee_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:03.976742Z","iopub.execute_input":"2025-02-13T11:43:03.977035Z","iopub.status.idle":"2025-02-13T11:43:04.005667Z","shell.execute_reply.started":"2025-02-13T11:43:03.977005Z","shell.execute_reply":"2025-02-13T11:43:04.004540Z"}},"outputs":[{"execution_count":132,"output_type":"execute_result","data":{"text/plain":" skill role month year level self course user\n0 1010101525 ROLE_226242 2 2023 2 False True False\n1 1010101263 ROLE_226242 7 2023 3 False True False\n2 1010101122 ROLE_226242 2 2023 2 False True False\n3 1010101043 ROLE_226242 12 2023 1 False True False\n4 1010101954 ROLE_226242 12 2023 3 True False False\n5 1010101553 ROLE_226242 10 2023 2 True False False\n6 1010101363 ROLE_226242 10 2023 1 True False False\n0 1010101122 ROLE_713858 3 2024 3 False False True\n1 1010101762 ROLE_713858 5 2024 3 True False False\n2 1010101873 ROLE_713858 6 2024 3 False False True\n3 1010101767 ROLE_713858 11 2024 4 False True False\n4 1010101767 ROLE_713858 12 2024 4 False False True\n5 1010101767 ROLE_713858 2 2024 3 False False True\n6 1010101772 ROLE_713858 2 2024 3 True False False\n0 1010101867 ROLE_860638 9 2025 3 False False True\n1 1010101767 ROLE_860638 12 2025 3 True False False\n2 1010101122 ROLE_860638 4 2025 3 False False True\n3 1010101928 ROLE_860638 10 2025 5 False True False\n4 1010101679 ROLE_860638 4 2025 4 False False True\n5 1010101867 ROLE_860638 5 2025 3 False False True\n6 1010101721 ROLE_860638 3 2025 3 True False False\n0 1010101677 NaN 10 2025 4 False True False\n1 1010101693 NaN 9 2025 4 False True False\n2 1010101144 NaN 8 2025 4 False True False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skillrolemonthyearlevelselfcourseuser
01010101525ROLE_226242220232FalseTrueFalse
11010101263ROLE_226242720233FalseTrueFalse
21010101122ROLE_226242220232FalseTrueFalse
31010101043ROLE_2262421220231FalseTrueFalse
41010101954ROLE_2262421220233TrueFalseFalse
51010101553ROLE_2262421020232TrueFalseFalse
61010101363ROLE_2262421020231TrueFalseFalse
01010101122ROLE_713858320243FalseFalseTrue
11010101762ROLE_713858520243TrueFalseFalse
21010101873ROLE_713858620243FalseFalseTrue
31010101767ROLE_7138581120244FalseTrueFalse
41010101767ROLE_7138581220244FalseFalseTrue
51010101767ROLE_713858220243FalseFalseTrue
61010101772ROLE_713858220243TrueFalseFalse
01010101867ROLE_860638920253FalseFalseTrue
11010101767ROLE_8606381220253TrueFalseFalse
21010101122ROLE_860638420253FalseFalseTrue
31010101928ROLE_8606381020255FalseTrueFalse
41010101679ROLE_860638420254FalseFalseTrue
51010101867ROLE_860638520253FalseFalseTrue
61010101721ROLE_860638320253TrueFalseFalse
01010101677NaN1020254FalseTrueFalse
11010101693NaN920254FalseTrueFalse
21010101144NaN820254FalseTrueFalse
\n
"},"metadata":{}}],"execution_count":132},{"cell_type":"code","source":"rows = skills_employee_pd.year == 2025\nskills_employee_pd.loc[rows,'role'] = np.repeat(skills_employee_pd.loc[rows,'role'].unique()[0],\n skills_employee_pd.loc[rows,'role'].shape[0])\nskills_employee_pd.shape\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.006654Z","iopub.execute_input":"2025-02-13T11:43:04.007025Z","iopub.status.idle":"2025-02-13T11:43:04.036790Z","shell.execute_reply.started":"2025-02-13T11:43:04.006992Z","shell.execute_reply":"2025-02-13T11:43:04.035694Z"}},"outputs":[{"execution_count":133,"output_type":"execute_result","data":{"text/plain":"(24, 8)"},"metadata":{}}],"execution_count":133},{"cell_type":"code","source":"skills_employee_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.037848Z","iopub.execute_input":"2025-02-13T11:43:04.038238Z","iopub.status.idle":"2025-02-13T11:43:04.068554Z","shell.execute_reply.started":"2025-02-13T11:43:04.038201Z","shell.execute_reply":"2025-02-13T11:43:04.067336Z"}},"outputs":[{"execution_count":134,"output_type":"execute_result","data":{"text/plain":" skill role month year level self course user\n0 1010101525 ROLE_226242 2 2023 2 False True False\n1 1010101263 ROLE_226242 7 2023 3 False True False\n2 1010101122 ROLE_226242 2 2023 2 False True False\n3 1010101043 ROLE_226242 12 2023 1 False True False\n4 1010101954 ROLE_226242 12 2023 3 True False False\n5 1010101553 ROLE_226242 10 2023 2 True False False\n6 1010101363 ROLE_226242 10 2023 1 True False False\n0 1010101122 ROLE_713858 3 2024 3 False False True\n1 1010101762 ROLE_713858 5 2024 3 True False False\n2 1010101873 ROLE_713858 6 2024 3 False False True\n3 1010101767 ROLE_713858 11 2024 4 False True False\n4 1010101767 ROLE_713858 12 2024 4 False False True\n5 1010101767 ROLE_713858 2 2024 3 False False True\n6 1010101772 ROLE_713858 2 2024 3 True False False\n0 1010101867 ROLE_860638 9 2025 3 False False True\n1 1010101767 ROLE_860638 12 2025 3 True False False\n2 1010101122 ROLE_860638 4 2025 3 False False True\n3 1010101928 ROLE_860638 10 2025 5 False True False\n4 1010101679 ROLE_860638 4 2025 4 False False True\n5 1010101867 ROLE_860638 5 2025 3 False False True\n6 1010101721 ROLE_860638 3 2025 3 True False False\n0 1010101677 ROLE_860638 10 2025 4 False True False\n1 1010101693 ROLE_860638 9 2025 4 False True False\n2 1010101144 ROLE_860638 8 2025 4 False True False","text/html":"
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skillrolemonthyearlevelselfcourseuser
01010101525ROLE_226242220232FalseTrueFalse
11010101263ROLE_226242720233FalseTrueFalse
21010101122ROLE_226242220232FalseTrueFalse
31010101043ROLE_2262421220231FalseTrueFalse
41010101954ROLE_2262421220233TrueFalseFalse
51010101553ROLE_2262421020232TrueFalseFalse
61010101363ROLE_2262421020231TrueFalseFalse
01010101122ROLE_713858320243FalseFalseTrue
11010101762ROLE_713858520243TrueFalseFalse
21010101873ROLE_713858620243FalseFalseTrue
31010101767ROLE_7138581120244FalseTrueFalse
41010101767ROLE_7138581220244FalseFalseTrue
51010101767ROLE_713858220243FalseFalseTrue
61010101772ROLE_713858220243TrueFalseFalse
01010101867ROLE_860638920253FalseFalseTrue
11010101767ROLE_8606381220253TrueFalseFalse
21010101122ROLE_860638420253FalseFalseTrue
31010101928ROLE_8606381020255FalseTrueFalse
41010101679ROLE_860638420254FalseFalseTrue
51010101867ROLE_860638520253FalseFalseTrue
61010101721ROLE_860638320253TrueFalseFalse
01010101677ROLE_8606381020254FalseTrueFalse
11010101693ROLE_860638920254FalseTrueFalse
21010101144ROLE_860638820254FalseTrueFalse
\n
"},"metadata":{}}],"execution_count":134},{"cell_type":"code","source":"skills_pd.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.069605Z","iopub.execute_input":"2025-02-13T11:43:04.070017Z","iopub.status.idle":"2025-02-13T11:43:04.095167Z","shell.execute_reply.started":"2025-02-13T11:43:04.069989Z","shell.execute_reply":"2025-02-13T11:43:04.094038Z"}},"outputs":[{"execution_count":135,"output_type":"execute_result","data":{"text/plain":" skills_uid skills language technical soft cluster\n0 1010101263 gqryuk False True False 2020202265\n1 1010101987 goaylk True False False 2020202454\n2 1010101349 bdbluo True False False 2020202005\n3 1010101080 bsncme False True False 2020202005\n4 1010101952 bxxnst False False True 2020202703","text/html":"
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skills_uidskillslanguagetechnicalsoftcluster
01010101263gqryukFalseTrueFalse2020202265
11010101987goaylkTrueFalseFalse2020202454
21010101349bdbluoTrueFalseFalse2020202005
31010101080bsncmeFalseTrueFalse2020202005
41010101952bxxnstFalseFalseTrue2020202703
\n
"},"metadata":{}}],"execution_count":135},{"cell_type":"code","source":"emp = skills_employee_pd.merge(skills_pd, \n left_on=\"skill\", \n right_on=\"skills_uid\")\nemp.dtypes","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.096374Z","iopub.execute_input":"2025-02-13T11:43:04.096776Z","iopub.status.idle":"2025-02-13T11:43:04.120340Z","shell.execute_reply.started":"2025-02-13T11:43:04.096749Z","shell.execute_reply":"2025-02-13T11:43:04.119350Z"}},"outputs":[{"execution_count":136,"output_type":"execute_result","data":{"text/plain":"skill object\nrole object\nmonth int64\nyear int64\nlevel int64\nself bool\ncourse bool\nuser bool\nskills_uid object\nskills object\nlanguage bool\ntechnical bool\nsoft bool\ncluster object\ndtype: object"},"metadata":{}}],"execution_count":136},{"cell_type":"code","source":"emp.year.unique()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.121290Z","iopub.execute_input":"2025-02-13T11:43:04.121550Z","iopub.status.idle":"2025-02-13T11:43:04.139859Z","shell.execute_reply.started":"2025-02-13T11:43:04.121528Z","shell.execute_reply":"2025-02-13T11:43:04.138765Z"}},"outputs":[{"execution_count":137,"output_type":"execute_result","data":{"text/plain":"array([2023, 2024, 2025])"},"metadata":{}}],"execution_count":137},{"cell_type":"markdown","source":"## Skills development ","metadata":{}},{"cell_type":"code","source":"\nt = emp.loc[:, ['skills','level']].groupby(['skills']).describe().reset_index()\nt.columns = t.columns.map('_'.join)\nt.dtypes\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.140939Z","iopub.execute_input":"2025-02-13T11:43:04.141278Z","iopub.status.idle":"2025-02-13T11:43:04.233176Z","shell.execute_reply.started":"2025-02-13T11:43:04.141245Z","shell.execute_reply":"2025-02-13T11:43:04.232075Z"}},"outputs":[{"execution_count":138,"output_type":"execute_result","data":{"text/plain":"skills_ object\nlevel_count float64\nlevel_mean float64\nlevel_std float64\nlevel_min float64\nlevel_25% float64\nlevel_50% float64\nlevel_75% float64\nlevel_max float64\ndtype: object"},"metadata":{}}],"execution_count":138},{"cell_type":"code","source":"t","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.234245Z","iopub.execute_input":"2025-02-13T11:43:04.234685Z","iopub.status.idle":"2025-02-13T11:43:04.265314Z","shell.execute_reply.started":"2025-02-13T11:43:04.234648Z","shell.execute_reply":"2025-02-13T11:43:04.264109Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/pandas/io/formats/format.py:1458: RuntimeWarning: invalid value encountered in greater\n has_large_values = (abs_vals > 1e6).any()\n/usr/local/lib/python3.10/dist-packages/pandas/io/formats/format.py:1459: RuntimeWarning: invalid value encountered in less\n has_small_values = ((abs_vals < 10 ** (-self.digits)) & (abs_vals > 0)).any()\n/usr/local/lib/python3.10/dist-packages/pandas/io/formats/format.py:1459: RuntimeWarning: invalid value encountered in greater\n has_small_values = ((abs_vals < 10 ** (-self.digits)) & (abs_vals > 0)).any()\n","output_type":"stream"},{"execution_count":139,"output_type":"execute_result","data":{"text/plain":" skills_ level_count level_mean level_std level_min level_25% \\\n0 gbhazb 4.0 3.500000 0.57735 3.0 3.0 \n1 gqryuk 1.0 3.000000 NaN 3.0 3.0 \n2 idrtpm 1.0 4.000000 NaN 4.0 4.0 \n3 ieecmo 1.0 3.000000 NaN 3.0 3.0 \n4 ijzffe 1.0 1.000000 NaN 1.0 1.0 \n5 ilkukl 1.0 2.000000 NaN 2.0 2.0 \n6 iriayl 1.0 5.000000 NaN 5.0 5.0 \n7 jbkamg 1.0 2.000000 NaN 2.0 2.0 \n8 jisdwl 4.0 3.500000 0.57735 3.0 3.0 \n9 jnfqct 1.0 2.000000 NaN 2.0 2.0 \n10 lfgljg 1.0 3.000000 NaN 3.0 3.0 \n11 nyeeyx 1.0 4.000000 NaN 4.0 4.0 \n12 oducgf 1.0 4.000000 NaN 4.0 4.0 \n13 oksacc 1.0 3.000000 NaN 3.0 3.0 \n14 opswnk 1.0 3.000000 NaN 3.0 3.0 \n15 rqphnd 1.0 4.000000 NaN 4.0 4.0 \n16 seevwz 1.0 1.000000 NaN 1.0 1.0 \n17 tfncdy 2.0 3.000000 0.00000 3.0 3.0 \n18 wakpbw 1.0 3.000000 NaN 3.0 3.0 \n19 xfcycc 3.0 2.666667 0.57735 2.0 2.5 \n\n level_50% level_75% level_max \n0 3.5 4.0 4.0 \n1 3.0 3.0 3.0 \n2 4.0 4.0 4.0 \n3 3.0 3.0 3.0 \n4 1.0 1.0 1.0 \n5 2.0 2.0 2.0 \n6 5.0 5.0 5.0 \n7 2.0 2.0 2.0 \n8 3.5 4.0 4.0 \n9 2.0 2.0 2.0 \n10 3.0 3.0 3.0 \n11 4.0 4.0 4.0 \n12 4.0 4.0 4.0 \n13 3.0 3.0 3.0 \n14 3.0 3.0 3.0 \n15 4.0 4.0 4.0 \n16 1.0 1.0 1.0 \n17 3.0 3.0 3.0 \n18 3.0 3.0 3.0 \n19 3.0 3.0 3.0 ","text/html":"
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skills_level_countlevel_meanlevel_stdlevel_minlevel_25%level_50%level_75%level_max
0gbhazb4.03.5000000.577353.03.03.54.04.0
1gqryuk1.03.000000NaN3.03.03.03.03.0
2idrtpm1.04.000000NaN4.04.04.04.04.0
3ieecmo1.03.000000NaN3.03.03.03.03.0
4ijzffe1.01.000000NaN1.01.01.01.01.0
5ilkukl1.02.000000NaN2.02.02.02.02.0
6iriayl1.05.000000NaN5.05.05.05.05.0
7jbkamg1.02.000000NaN2.02.02.02.02.0
8jisdwl4.03.5000000.577353.03.03.54.04.0
9jnfqct1.02.000000NaN2.02.02.02.02.0
10lfgljg1.03.000000NaN3.03.03.03.03.0
11nyeeyx1.04.000000NaN4.04.04.04.04.0
12oducgf1.04.000000NaN4.04.04.04.04.0
13oksacc1.03.000000NaN3.03.03.03.03.0
14opswnk1.03.000000NaN3.03.03.03.03.0
15rqphnd1.04.000000NaN4.04.04.04.04.0
16seevwz1.01.000000NaN1.01.01.01.01.0
17tfncdy2.03.0000000.000003.03.03.03.03.0
18wakpbw1.03.000000NaN3.03.03.03.03.0
19xfcycc3.02.6666670.577352.02.53.03.03.0
\n
"},"metadata":{}}],"execution_count":139},{"cell_type":"markdown","source":"# Personal profile","metadata":{}},{"cell_type":"markdown","source":"## Skills level and development","metadata":{}},{"cell_type":"code","source":"X = t.loc[:,'skills_'].to_list()\nY_max = t.loc[:,'level_max'].to_list()\nY_min = t.loc[:,'level_min'].to_list()\nplt.bar(X, Y_max, color=\"green\", label=\"maximum level attained\")\nplt.bar(X, Y_min, color='#cbf5dd', label= \"starting level\")\nplt.xticks(X)\nplt.xlabel(\"Year\")\nplt.ylabel(\"Skill level\")\nplt.title(\"Skill level with skill development \")\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.266447Z","iopub.execute_input":"2025-02-13T11:43:04.266807Z","iopub.status.idle":"2025-02-13T11:43:04.798318Z","shell.execute_reply.started":"2025-02-13T11:43:04.266782Z","shell.execute_reply":"2025-02-13T11:43:04.797081Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":140},{"cell_type":"code","source":"emp.dtypes","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.799426Z","iopub.execute_input":"2025-02-13T11:43:04.799795Z","iopub.status.idle":"2025-02-13T11:43:04.807858Z","shell.execute_reply.started":"2025-02-13T11:43:04.799767Z","shell.execute_reply":"2025-02-13T11:43:04.806671Z"}},"outputs":[{"execution_count":141,"output_type":"execute_result","data":{"text/plain":"skill object\nrole object\nmonth int64\nyear int64\nlevel int64\nself bool\ncourse bool\nuser bool\nskills_uid object\nskills object\nlanguage bool\ntechnical bool\nsoft bool\ncluster object\ndtype: object"},"metadata":{}}],"execution_count":141},{"cell_type":"code","source":"emp","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.808858Z","iopub.execute_input":"2025-02-13T11:43:04.809251Z","iopub.status.idle":"2025-02-13T11:43:04.851044Z","shell.execute_reply.started":"2025-02-13T11:43:04.809213Z","shell.execute_reply":"2025-02-13T11:43:04.849867Z"}},"outputs":[{"execution_count":142,"output_type":"execute_result","data":{"text/plain":" skill role month year level self course user \\\n0 1010101525 ROLE_226242 2 2023 2 False True False \n1 1010101263 ROLE_226242 7 2023 3 False True False \n2 1010101122 ROLE_226242 2 2023 2 False True False \n3 1010101043 ROLE_226242 12 2023 1 False True False \n4 1010101954 ROLE_226242 12 2023 3 True False False \n5 1010101553 ROLE_226242 10 2023 2 True False False \n6 1010101553 ROLE_226242 10 2023 2 True False False \n7 1010101363 ROLE_226242 10 2023 1 True False False \n8 1010101122 ROLE_713858 3 2024 3 False False True \n9 1010101762 ROLE_713858 5 2024 3 True False False \n10 1010101873 ROLE_713858 6 2024 3 False False True \n11 1010101767 ROLE_713858 11 2024 4 False True False \n12 1010101767 ROLE_713858 11 2024 4 False True False \n13 1010101767 ROLE_713858 12 2024 4 False False True \n14 1010101767 ROLE_713858 12 2024 4 False False True \n15 1010101767 ROLE_713858 2 2024 3 False False True \n16 1010101767 ROLE_713858 2 2024 3 False False True \n17 1010101772 ROLE_713858 2 2024 3 True False False \n18 1010101867 ROLE_860638 9 2025 3 False False True \n19 1010101767 ROLE_860638 12 2025 3 True False False \n20 1010101767 ROLE_860638 12 2025 3 True False False \n21 1010101122 ROLE_860638 4 2025 3 False False True \n22 1010101928 ROLE_860638 10 2025 5 False True False \n23 1010101679 ROLE_860638 4 2025 4 False False True \n24 1010101867 ROLE_860638 5 2025 3 False False True \n25 1010101721 ROLE_860638 3 2025 3 True False False \n26 1010101677 ROLE_860638 10 2025 4 False True False \n27 1010101693 ROLE_860638 9 2025 4 False True False \n28 1010101144 ROLE_860638 8 2025 4 False True False \n\n skills_uid skills language technical soft cluster \n0 1010101525 ilkukl False False True 2020202378 \n1 1010101263 gqryuk False True False 2020202265 \n2 1010101122 xfcycc True False False 2020202999 \n3 1010101043 ijzffe False True False 2020202378 \n4 1010101954 ieecmo False False True 2020202215 \n5 1010101553 jnfqct True False False 2020202379 \n6 1010101553 jbkamg False True False 2020202999 \n7 1010101363 seevwz False False True 2020202342 \n8 1010101122 xfcycc True False False 2020202999 \n9 1010101762 oksacc False False True 2020202005 \n10 1010101873 opswnk False False True 2020202005 \n11 1010101767 gbhazb False True False 2020202005 \n12 1010101767 jisdwl False False True 2020202043 \n13 1010101767 gbhazb False True False 2020202005 \n14 1010101767 jisdwl False False True 2020202043 \n15 1010101767 gbhazb False True False 2020202005 \n16 1010101767 jisdwl False False True 2020202043 \n17 1010101772 wakpbw True False False 2020202373 \n18 1010101867 tfncdy False True False 2020202870 \n19 1010101767 gbhazb False True False 2020202005 \n20 1010101767 jisdwl False False True 2020202043 \n21 1010101122 xfcycc True False False 2020202999 \n22 1010101928 iriayl False False True 2020202888 \n23 1010101679 rqphnd False False True 2020202342 \n24 1010101867 tfncdy False True False 2020202870 \n25 1010101721 lfgljg True False False 2020202265 \n26 1010101677 idrtpm False True False 2020202415 \n27 1010101693 oducgf False True False 2020202404 \n28 1010101144 nyeeyx False True False 2020202342 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skillrolemonthyearlevelselfcourseuserskills_uidskillslanguagetechnicalsoftcluster
01010101525ROLE_226242220232FalseTrueFalse1010101525ilkuklFalseFalseTrue2020202378
11010101263ROLE_226242720233FalseTrueFalse1010101263gqryukFalseTrueFalse2020202265
21010101122ROLE_226242220232FalseTrueFalse1010101122xfcyccTrueFalseFalse2020202999
31010101043ROLE_2262421220231FalseTrueFalse1010101043ijzffeFalseTrueFalse2020202378
41010101954ROLE_2262421220233TrueFalseFalse1010101954ieecmoFalseFalseTrue2020202215
51010101553ROLE_2262421020232TrueFalseFalse1010101553jnfqctTrueFalseFalse2020202379
61010101553ROLE_2262421020232TrueFalseFalse1010101553jbkamgFalseTrueFalse2020202999
71010101363ROLE_2262421020231TrueFalseFalse1010101363seevwzFalseFalseTrue2020202342
81010101122ROLE_713858320243FalseFalseTrue1010101122xfcyccTrueFalseFalse2020202999
91010101762ROLE_713858520243TrueFalseFalse1010101762oksaccFalseFalseTrue2020202005
101010101873ROLE_713858620243FalseFalseTrue1010101873opswnkFalseFalseTrue2020202005
111010101767ROLE_7138581120244FalseTrueFalse1010101767gbhazbFalseTrueFalse2020202005
121010101767ROLE_7138581120244FalseTrueFalse1010101767jisdwlFalseFalseTrue2020202043
131010101767ROLE_7138581220244FalseFalseTrue1010101767gbhazbFalseTrueFalse2020202005
141010101767ROLE_7138581220244FalseFalseTrue1010101767jisdwlFalseFalseTrue2020202043
151010101767ROLE_713858220243FalseFalseTrue1010101767gbhazbFalseTrueFalse2020202005
161010101767ROLE_713858220243FalseFalseTrue1010101767jisdwlFalseFalseTrue2020202043
171010101772ROLE_713858220243TrueFalseFalse1010101772wakpbwTrueFalseFalse2020202373
181010101867ROLE_860638920253FalseFalseTrue1010101867tfncdyFalseTrueFalse2020202870
191010101767ROLE_8606381220253TrueFalseFalse1010101767gbhazbFalseTrueFalse2020202005
201010101767ROLE_8606381220253TrueFalseFalse1010101767jisdwlFalseFalseTrue2020202043
211010101122ROLE_860638420253FalseFalseTrue1010101122xfcyccTrueFalseFalse2020202999
221010101928ROLE_8606381020255FalseTrueFalse1010101928iriaylFalseFalseTrue2020202888
231010101679ROLE_860638420254FalseFalseTrue1010101679rqphndFalseFalseTrue2020202342
241010101867ROLE_860638520253FalseFalseTrue1010101867tfncdyFalseTrueFalse2020202870
251010101721ROLE_860638320253TrueFalseFalse1010101721lfgljgTrueFalseFalse2020202265
261010101677ROLE_8606381020254FalseTrueFalse1010101677idrtpmFalseTrueFalse2020202415
271010101693ROLE_860638920254FalseTrueFalse1010101693oducgfFalseTrueFalse2020202404
281010101144ROLE_860638820254FalseTrueFalse1010101144nyeeyxFalseTrueFalse2020202342
\n
"},"metadata":{}}],"execution_count":142},{"cell_type":"code","source":"cols =['month','year','self','course','user','language','technical','soft','skills','level']\nemp = emp[cols]\nemp\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.852233Z","iopub.execute_input":"2025-02-13T11:43:04.852676Z","iopub.status.idle":"2025-02-13T11:43:04.886475Z","shell.execute_reply.started":"2025-02-13T11:43:04.852637Z","shell.execute_reply":"2025-02-13T11:43:04.885257Z"}},"outputs":[{"execution_count":143,"output_type":"execute_result","data":{"text/plain":" month year self course user language technical soft skills \\\n0 2 2023 False True False False False True ilkukl \n1 7 2023 False True False False True False gqryuk \n2 2 2023 False True False True False False xfcycc \n3 12 2023 False True False False True False ijzffe \n4 12 2023 True False False False False True ieecmo \n5 10 2023 True False False True False False jnfqct \n6 10 2023 True False False False True False jbkamg \n7 10 2023 True False False False False True seevwz \n8 3 2024 False False True True False False xfcycc \n9 5 2024 True False False False False True oksacc \n10 6 2024 False False True False False True opswnk \n11 11 2024 False True False False True False gbhazb \n12 11 2024 False True False False False True jisdwl \n13 12 2024 False False True False True False gbhazb \n14 12 2024 False False True False False True jisdwl \n15 2 2024 False False True False True False gbhazb \n16 2 2024 False False True False False True jisdwl \n17 2 2024 True False False True False False wakpbw \n18 9 2025 False False True False True False tfncdy \n19 12 2025 True False False False True False gbhazb \n20 12 2025 True False False False False True jisdwl \n21 4 2025 False False True True False False xfcycc \n22 10 2025 False True False False False True iriayl \n23 4 2025 False False True False False True rqphnd \n24 5 2025 False False True False True False tfncdy \n25 3 2025 True False False True False False lfgljg \n26 10 2025 False True False False True False idrtpm \n27 9 2025 False True False False True False oducgf \n28 8 2025 False True False False True False nyeeyx \n\n level \n0 2 \n1 3 \n2 2 \n3 1 \n4 3 \n5 2 \n6 2 \n7 1 \n8 3 \n9 3 \n10 3 \n11 4 \n12 4 \n13 4 \n14 4 \n15 3 \n16 3 \n17 3 \n18 3 \n19 3 \n20 3 \n21 3 \n22 5 \n23 4 \n24 3 \n25 3 \n26 4 \n27 4 \n28 4 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
monthyearselfcourseuserlanguagetechnicalsoftskillslevel
022023FalseTrueFalseFalseFalseTrueilkukl2
172023FalseTrueFalseFalseTrueFalsegqryuk3
222023FalseTrueFalseTrueFalseFalsexfcycc2
3122023FalseTrueFalseFalseTrueFalseijzffe1
4122023TrueFalseFalseFalseFalseTrueieecmo3
5102023TrueFalseFalseTrueFalseFalsejnfqct2
6102023TrueFalseFalseFalseTrueFalsejbkamg2
7102023TrueFalseFalseFalseFalseTrueseevwz1
832024FalseFalseTrueTrueFalseFalsexfcycc3
952024TrueFalseFalseFalseFalseTrueoksacc3
1062024FalseFalseTrueFalseFalseTrueopswnk3
11112024FalseTrueFalseFalseTrueFalsegbhazb4
12112024FalseTrueFalseFalseFalseTruejisdwl4
13122024FalseFalseTrueFalseTrueFalsegbhazb4
14122024FalseFalseTrueFalseFalseTruejisdwl4
1522024FalseFalseTrueFalseTrueFalsegbhazb3
1622024FalseFalseTrueFalseFalseTruejisdwl3
1722024TrueFalseFalseTrueFalseFalsewakpbw3
1892025FalseFalseTrueFalseTrueFalsetfncdy3
19122025TrueFalseFalseFalseTrueFalsegbhazb3
20122025TrueFalseFalseFalseFalseTruejisdwl3
2142025FalseFalseTrueTrueFalseFalsexfcycc3
22102025FalseTrueFalseFalseFalseTrueiriayl5
2342025FalseFalseTrueFalseFalseTruerqphnd4
2452025FalseFalseTrueFalseTrueFalsetfncdy3
2532025TrueFalseFalseTrueFalseFalselfgljg3
26102025FalseTrueFalseFalseTrueFalseidrtpm4
2792025FalseTrueFalseFalseTrueFalseoducgf4
2882025FalseTrueFalseFalseTrueFalsenyeeyx4
\n
"},"metadata":{}}],"execution_count":143},{"cell_type":"code","source":"skills_level = emp.groupby(['year']).describe()['level'].reset_index()\nskills_level = pd.DataFrame(skills_level)\nskills_level\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.887650Z","iopub.execute_input":"2025-02-13T11:43:04.888033Z","iopub.status.idle":"2025-02-13T11:43:04.941167Z","shell.execute_reply.started":"2025-02-13T11:43:04.888002Z","shell.execute_reply":"2025-02-13T11:43:04.940127Z"}},"outputs":[{"execution_count":144,"output_type":"execute_result","data":{"text/plain":" year count mean std min 25% 50% 75% max\n0 2023 8.0 2.000000 0.755929 1.0 1.75 2.0 2.25 3.0\n1 2024 10.0 3.400000 0.516398 3.0 3.00 3.0 4.00 4.0\n2 2025 11.0 3.545455 0.687552 3.0 3.00 3.0 4.00 5.0","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
yearcountmeanstdmin25%50%75%max
020238.02.0000000.7559291.01.752.02.253.0
1202410.03.4000000.5163983.03.003.04.004.0
2202511.03.5454550.6875523.03.003.04.005.0
\n
"},"metadata":{}}],"execution_count":144},{"cell_type":"code","source":"skills_level.loc[:,['year','mean','std']]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.942465Z","iopub.execute_input":"2025-02-13T11:43:04.942810Z","iopub.status.idle":"2025-02-13T11:43:04.953821Z","shell.execute_reply.started":"2025-02-13T11:43:04.942783Z","shell.execute_reply":"2025-02-13T11:43:04.952785Z"}},"outputs":[{"execution_count":145,"output_type":"execute_result","data":{"text/plain":" year mean std\n0 2023 2.000000 0.755929\n1 2024 3.400000 0.516398\n2 2025 3.545455 0.687552","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
yearmeanstd
020232.0000000.755929
120243.4000000.516398
220253.5454550.687552
\n
"},"metadata":{}}],"execution_count":145},{"cell_type":"code","source":"X = skills_level.loc[:,'year'].to_list()\nY = skills_level.loc[:,'mean'].to_list()\nerr = skills_level.loc[:,'std'].to_list()\nplt.bar(X, Y, color=\"green\", label='mean skill level')\nplt.errorbar(X, Y, yerr=err, fmt=\"o\", color=\"black\", label='spread of skills level')\nplt.xticks(X)\nplt.xlabel(\"Year\")\nplt.ylabel(\"Expected skills levels\")\nplt.title(\"Expected skills level: average and range of \")\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:04.955759Z","iopub.execute_input":"2025-02-13T11:43:04.956060Z","iopub.status.idle":"2025-02-13T11:43:05.305395Z","shell.execute_reply.started":"2025-02-13T11:43:04.956036Z","shell.execute_reply":"2025-02-13T11:43:05.303993Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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Skill developments through time","metadata":{}},{"cell_type":"code","source":"emp_users = emp.loc[emp.user == True, :]\nemp_self = emp.loc[emp.self == True, :]\nemp_course = emp.loc[emp.course == True, :]\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:05.306472Z","iopub.execute_input":"2025-02-13T11:43:05.306891Z","iopub.status.idle":"2025-02-13T11:43:05.315439Z","shell.execute_reply.started":"2025-02-13T11:43:05.306861Z","shell.execute_reply":"2025-02-13T11:43:05.314015Z"}},"outputs":[],"execution_count":147},{"cell_type":"code","source":"ax = sns.barplot(data=emp_course,\n x=\"skills\",\n y=\"level\",\n hue=\"year\")\nplt.legend(loc=(0.05, 0.90))\nplt.title('Skills development through courses')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:05.316794Z","iopub.execute_input":"2025-02-13T11:43:05.317221Z","iopub.status.idle":"2025-02-13T11:43:05.811094Z","shell.execute_reply.started":"2025-02-13T11:43:05.317179Z","shell.execute_reply":"2025-02-13T11:43:05.809964Z"}},"outputs":[{"execution_count":148,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Skills development through courses')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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EghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEghTAEAAAAAAEhR6sOUMWPGRJcuXWKHHXaIKlWqxN577x1Dhw6N1atXl3TRAAAAAACA7UCpDlMuvPDCOOmkk2LKlCmx3377RY8ePeKLL76Iyy+/PA4++OBYsWJFSRcRAAAAAAD4mSu1Ycpjjz0Wt9xyS1StWjWmTp0azz77bDzyyCPxySefxF577RWTJ0+Oa6+9tqSLCQAAAAAA/MyV2jDlhhtuiIiIK664Itq2bZtdXqdOnbj99tsjImL48OGxePHiEikfAAAAAACwfSiVYcqXX34Zr732WkRE9O3bt8D6jh07RqNGjWLVqlXx9NNPF3fxAAAAAACA7UipDFPeeuutiIioVatWNGvWrNBt2rVrl29bAAAAAACAraFUhikzZsyIiIjGjRsXuU2jRo3ybQsAAAAAALA1lCvpAhRm6dKlERFRpUqVIrepWrVqREQsWbKkyG1WrVoVq1atyv47b36VtH3SrF21YpP2K06b+tkoXdQ1iou6RnFR1+D/LF25tqSLsEH8TWz71DXIb82KNSVdhPXy9/DzoK5RXNQ1isvPva7l7ZskSep2pTJM2VJuvPHGuP766wssz/tWy89RjVvPLekisJ1Q1ygu6hrFRV2Dn7ixRkmXgO2FugZZNS7390DxUNcoLuoaxWVL1LWlS5dGjRpFH6dUhinVqlWLiIhly5YVuc33338fERHVq1cvcpsrr7wyLrroouy/c3NzY8GCBVG7du3IZDJbqLQ/b0uWLIlGjRrF7NmzU3/WsLnUNYqLukZxUdcoLuoaxUVdo7ioaxQXdY3ioq5RXNS1TZMkSSxdujQaNmyYul2pDFOaNm0aERGzZ88ucpu8dXnbFiYnJydycnLyLatZs+bmFm+7VL16dX+AFAt1jeKirlFc1DWKi7pGcVHXKC7qGsVFXaO4qGsUF3Vt46V9IyVPqZyAfp999omIiPnz5xc5wfzrr78eERFt27YttnIBAAAAAADbn1IZpuy8887Rvn37iIgYNWpUgfWTJ0+O2bNnR05OThxxxBHFXTwAAAAAAGA7UirDlIiIq666KiIihgwZEm+++WZ2+fz582PAgAERETFw4MAN+voNmy4nJycGDx5cYLg02NLUNYqLukZxUdcoLuoaxUVdo7ioaxQXdY3ioq5RXNS1rSuTJElS0oUoygUXXBDDhg2L8uXLR7du3aJKlSrxwgsvxKJFi6JDhw7x3HPPRaVKlUq6mAAAAAAAwM9YqQ5TIiJGjx4dt912W7z99tuxevXqaNGiRZx66qnxm9/8JipUqFDSxQMAAAAAAH7mSn2YAgAAAAAAUJJK7ZwpAAAAAAAApYEwZRvVtGnTyGQyMXPmzOyy/v37RyaTiXvuuSfftr/97W8jk8nEb3/7261appkzZ0Ymk4mmTZtu1X1gc7344ouRyWSiS5cuJV0UNtDLL78chx56aNSqVSvKlClT6LUONtbGtKWb4v3334/jjjsu6tWrF2XLli3QFqvX24fC6tmG6NKlS2QymXjxxRe3Srk2h3a09Nja17E0W7uO3nPPPZHJZKJ///5b5fhsnk29tpWG32tpKAObVoeK6/oGRdnW74HW93wCrF+5ki4AAKT56quv4sgjj4zFixdHx44do2nTplGmTJnYZZddSrpoUKRly5bFkUceGTNnzox27drFYYcdFmXLlo02bdpEhHoNAFCcXnzxxejatWt07ty5VL6oAFvb+p5PgA0jTNlGvfDCC7F69erYaaedSrooAFvVv//971i0aFH07ds3HnjggZIuDj8jhbWlN954Y1xxxRXRoEGDzTr2a6+9FjNnzowDDzwwpkyZUmC9eg1sCVvzOgZpNvV59Pjjj4/9998/atSosZVKxrZCnwYUr/U9nwAbRpiyjWrRokVJFwGgWHzxxRcREbHrrruWcEn4uSmsLW3QoMEW6YBcX71Vr4EtYWtexyDNpj6P1qhRQ5BCROjTgOLm+QO2DHOmbKM2dYzan3r99dejQYMGUbZs2bjpppsiYv1jyG7KPCfLly+PY489NjKZTHTt2jUWLVq0WeVmy5g2bVqccMIJUadOnahcuXLstdde8be//S1yc3OLrGOzZ8+OM888Mxo0aBAVK1aMXXfdNa6++upYsWJFkWNnr7v8pZdeiqOPPjrq1q0bZcqUiXvuuSf69esXmUwmbrzxxiLLOnr06MhkMrHffvtll61vPqBNGc903rx5ceCBB0Ymk4nevXvHqlWrNnhf1m/QoEGRyWSiU6dOsWbNmgLrr7766shkMtG2bdsYMWJEZDKZGDx4cEREXH/99ZHJZAq9/ixfvjz+9re/RceOHWOHHXaInJycaNKkSRx99NExatSoiIjIzc2N5s2bRyaTif/+979FlnHAgAGRyWTisssuK7DuP//5T/Tq1St23nnnyMnJibp160b79u1j8ODBMX/+/ALbf/zxxzFgwIBo2bJlVK5cOapXrx6tWrWKAQMGxLRp0zbmR8dWsKFzDeRdS9b334svvpjdtl+/fhERce+99+bbJq+N3ZB6vWLFirjpppti//33j5o1a0bFihWjZcuWcdlllxVa3ygem9J25hk7dmx07NgxqlevHtWqVYsuXbrE008/vd5zvv3229GzZ8+oU6dO5OTkRKtWreKmm26KJEkKbDtv3rwYNmxYHHHEEdGsWbOoVKlSVK9ePdq1axd/+tOfYuXKlQX2yWun0/4rqi1dvnx5XHXVVbHLLrtExYoVo2HDhnHWWWfFl19+ud7PxebbmDlTcnNz4+9//3t06NAhatasGeXLl4969erF3nvvHYMGDSq03qbd9xWmZ8+ekclk4tFHH823fM2aNVGjRo3IZDJx0kknFdjvzDPPjEwmE3ffffdG/wwoGYXVvfXd80ekP2s++uijcfbZZ0fr1q1jhx12iIoVK0azZs3izDPPjI8++qjA9p07d45MJhP/+te/iizn0KFDi6x3lKyi2swFCxbEhRdeGE2aNImcnJxo3LhxDBw4MBYsWFDksda97k2bNi169+6d7ef47W9/G126dImuXbtGRMTEiRPztW/r3n+te5x33nknevbsGXXr1o1KlSrFL37xi7jlllti7dq1Bc6/7nPpV199FWeffXY0bNgwKlWqFK1bt4677roru+2HH34Yffv2jR133DEqVqwYe++9dzz00EOb98Mk1Zw5c2LQoEGx6667RsWKFaNGjRrRoUOHuPPOOwv9fUZE3HfffdG+ffuoXLly1KpVK3r06BEvvfRSkefY3H60hQsXxu9+97to165d1KhRIypVqhTNmzePk046KcaPH19g+1mzZkX//v2z9WjXXXeNwYMHx8qVKwv0y6zv+YTNs+7P8ZFHHsne61epUiU6dOhQ4F5/yZIlUb169ShXrlzMnj27yOMeccQRkclk4vbbby+w7uGHH44ePXpE3bp1o0KFCrHTTjvFqaeeGu+//36Rx1u4cGEMHjw42rRpE9WqVcs+x/zhD3+I5cuX59t2U/roFixYEE2aNIlMJhMjRowosP33338fu+++e2QymfjTn/5U5HG3Bb6Zsh0bN25c9OnTJ5IkiTFjxkTPnj23ynm+/fbbOOqoo+L111+PU089Ne66666oUKHCVjkXG27ixIlx+OGHx4oVK6JFixZxyCGHxPz58+Pyyy+PV155pdB9Pvzww+jcuXPMnTs3GjRoEMccc0wsW7Ys/vrXv8aECRPWe84xY8bEiBEjYvfdd4/u3bvHggULIicnJy644IK47777YsSIEXHZZZdF2bJlC+x72223RUTEwIEDN++Dp/j444/jiCOOiM8++ywuu+yyGDJkiJuLLeymm26KV155JSZPnhzXXHNNDBkyJLvumWeeiRtvvDGqV68eo0ePjm+++Sb69esXb7/9drzzzjux9957Z8dzrVOnTna/2bNnR48ePeL999+PypUrR4cOHaJ27drx5ZdfxksvvRTvvfde9O3bN8qUKRMDBw6Miy++OIYPHx4HHHBAgfItWbIk/vnPf0aZMmViwIAB+db9+te/jltvvTUiItq0aROdOnWKxYsXx0cffRS/+93vomvXrvk6G0eNGhVnnnlmrFq1Kho3bhxHHHFE5Obmxueffx4jRoyIevXqRevWrbfgT5etZccdd8w+fPzU4sWL47HHHouIiLJly0bdunWjX79+8emnn8aUKVOiRYsW0bFjx+z2u+yyywbV66+++ip69OgR7733XtSqVSvat28f1apVizfffDP+/Oc/x5gxY+LFF1+MJk2abLXPTUGb0nbmGTZsWPz1r3+Ndu3axVFHHRWfffZZTJw4MSZOnBjDhg2LQYMGFbrfs88+GzfffHP2fF9//XVMnjw5Lrnkkpg9e3b87W9/K7D9BRdcEDvttFPssssusf/++8e8efNi6tSpccUVV8Tjjz8eEyZMiJycnOw+PXr0KPLhfuLEiTFz5sxC2+YffvghunXrFu+++2506dIl2rZtG5MnT4677747nn766Zg0aZK3H0uRs88+O0aOHBkVK1aMjh07Rt26dWPBggXx+eefx/Dhw6Nbt2756sGm3Pd17949xo4dG88//3y+Z4tXX301lixZEhE/vpiQJEm+e6wXXnghuz/bvqLu+dfnpJNOygbGBx98cKxZsyamTZsWI0eOjNGjR8e///3vOPDAA7PbX3DBBTFp0qQYPnx49OnTp8DxcnNz44477oiIrfsMwZbz7bffRqdOneKTTz6JHXbYIY466qjIzc2NBx54IJ555pnYc889U/d/+eWX49xzz40GDRrEQQcdFCtWrIhq1apFjx49omLFivHss89G/fr1o0ePHtl91r3/yvPqq6/GeeedFzvuuGN069YtFi5cGC+++GJceOGFMXny5Gwn4k998cUXse+++0aFChWiU6dOMW/evJg0aVKcffbZsWjRoujQoUMceuih0bBhw+jatWvMmjUr/vvf/8bJJ58cERG9e/fezJ8gP/Xaa69Fjx49YsGCBdG4ceM47rjjYvHixfHiiy/Gyy+/HGPHjo1x48bl66O64IILYtiwYVGmTJno2LFjNGzYMHuvU9T92uZ455134sgjj4wvv/wyatSoER07doxq1arFF198EU8++WTMnTs3Dj/88Oz277//fnTu3Dm+++67aNiwYRx77LGxbNmyuOmmm+I///lP5Obm5jt+3rNMUc8nbBmDBw+O3//+93HggQfGEUccER9++GG8/PLLcdRRR8UjjzwSxx9/fEREVK9ePfr37x+33nprjBgxIv74xz8WONZnn30WzzzzTFSvXj1OP/307PI1a9bEKaecEqNHj46cnJzYd999Y6eddoqPP/44HnjggXj00Ufj0UcfzXeNi/ixzvTo0SNmz54dDRo0iI4dO0b58uXj1VdfjWuvvTYeeeSRePHFF7PfHN2UPrpatWrF6NGjo1OnTvGb3/wm9t9//3zz8fzqV7+Kjz76KI488shCX1zdpiRsk5o0aZJERDJjxozssn79+iURkYwcOTLftoMHD04iIhk8eHB22bBhw5IyZcokdevWTf773//m237kyJFJRCT9+vUr9NwzZsxIIiJp0qTJepdPnz49W9Zrrrlmg4/F1rV8+fJkp512SiIiufjii5O1a9dm102fPj2pX79+EhEF6lj79u2TiEhOOumkZMWKFdnls2bNSlq0aJHdZ8KECfnO17lz5+y62267rdAydejQIYmI5NFHHy2w7r333ksiIqlbt26ycuXK7PLC6va6JkyYkERE0rlz5/UunzRpUlKrVq2kbNmyyYgRIwo9HlvG559/ntSsWTPJZDLJ008/nSRJksyePTupU6dOEhHJ6NGj822f9nteu3Zt0q5duyQikkMPPTSZO3duvvUrVqxInnrqqey/Fy1alFSpUiWpUKFC8s033xQ43q233ppERHL00UfnWz5s2LAkIpLatWsn//nPfwrsN3Xq1OSLL77I/vv1119Pypcvn2QymWTYsGH5/saSJElmzpyZvP7660X8hCguG9OWFmbVqlVJ165ds9fF3Nzc7Lr1taVp9To3Nzd7TTzrrLOSJUuWZNetXr06ufjii5OISLp27bqhH5UtYFPbzrx6lslkkvvvvz/fMR988MEkk8kk5cqVS957771869ZtO3/aLr3wwgtJJpNJypYtm8yePTvfuvfff7/AvV2SJMmCBQuSQw89NImIZOjQoRv0mZ9++umkXLlySeXKlZOpU6dml+e1oxGR7LLLLsmsWbOy61asWJGccMIJSUQk+++//wadh023odexWbNmJRGR7LzzzsnXX39d4Djvv/9+vt9jkmzafd9HH32URESy66675jvW9ddfn0RE8otf/CKJiOSNN95Y7z7ru45Ssgqrextyz5/2e33wwQeT77//Pt+y3Nzc5LbbbksiItlzzz3ztbVr1qzJluPNN98scLwnnngiW+82tAwUn8Lq0IknnphERNKpU6dk0aJF2eXz589PfvnLX2br10/v0/KuexGRXHHFFQXuvZOk6GfDoo4zYMCAZPXq1dl106ZNS+rWrVtou5x3XxcRybnnnptvv3HjxiURkVSrVi1p0qRJ8oc//CFfPf7b3/6WbU/ZslauXJmtZ+eee27yww8/ZNd99tlnSdOmTZOISK666qrs8ieffDKJiKRKlSrJpEmT8h3vhhtuyP6ef1qPNrUf7fvvv08aNWqURERy+umnJ0uXLs23ftGiRclzzz2Xb1nbtm2TiEhOPvnkfP0jc+bMSVq2bFlkv4xr39aR9/OuWbNm8sorr+Rbl3dt2G233fIt//jjj5NMJpPUq1cv3+8wT97z3qBBg/Itv+qqq5KISH75y18mn3/+eb51Y8aMScqWLZvssMMOycKFC7PLly9fnr1vu+aaa5JVq1Zl1y1btizp06dPEhHJGWecke94m9JHlyRJ8te//jV7X5f3HHvHHXckEZE0btw4mT9/foHjbWuEKduoTQ1T1q5dm1x44YXZP+ZPP/20wLG3VJjywgsvJDVr1kzKly+f3H333Rt1LLau++67L/tzX/eGIs/w4cMLdAhNnjw5e1Px3XffFdhn7Nix6w1TDj744CLLNHr06CQikm7duhVY9z//8z9JRCRXXnllvuVbKkwZNWpUkpOTk1StWjXbuc/W9dhjj2XDic8//zzbUA8cOLDAtmm/57zjNGjQoMCNZ1EGDBiQRETy+9//vsC63XffPYmI5Nlnn80uW716dfbB6ZFHHtmgcxx33HGF3vxQumxOmJKbm5v07ds3+8D/05vIzQlTxo8fn0RE0qZNm3wP43nWrl2btG7dOomIAh3wbD2b0nYmyf/Vs+OOO67Q4+YFD+ecc06+5XltZ8+ePQvdr0ePHklEJPfdd98Gf4a8Tuv27duvd9s33ngjqVq1alK2bNlk3Lhx+datG6Y89thjBfb99ttvk8qVKycRkUyZMmWDy8fG29Dr2KuvvppERHLMMcds0HE3574vr1No3XCmU6dOScWKFZNHHnkkiYhkyJAh2XV5HeXnnXdevuPo9Cnd0sKUtHv+Tf29HnDAAUlEJNOnT8+3fOjQodmXD37qsMMOSyIiufPOO7dIGdiyflqHvvjii6RMmTJJJpMp8HtOkiR566231hum7LbbbsmaNWsKPd/GhCkNGjTIFyLnyXvx6qfhb959XePGjQvdLy9I3m+//fIFKUny47NGrVq1Clw32Xz//Oc/k4hIGjZsWGiH9cMPP5wNuvJ+b927d08iIrn88ssLPWabNm22aJiSF6a1adOmyLq7rkmTJiURkVStWrXQTum8MEiYUnzyft7Dhg0rsG7lypVJjRo1kojI9/JlkiTJEUcckURE8s9//jPf8uXLlyc77LBDkslkkg8//DC7fP78+UmlSpWSihUrJnPmzCm0LHl9Hbfeemt2WV6QcdRRRxW6z9KlS5N69eol5cqVSxYsWJBdvil9dHl69uyZRETSu3fv5M0330xycnKS8uXLF/rC17bInCnbkeXLl8cJJ5yQnVfgv//971ab9O3ee++NHj16RG5ubjz11FNxxhlnbJXzsGkmTpwYERG9evWK8uXLF1h/yimnFFiWN95mjx49onbt2gXWH3vsseudTPLEE08sct3xxx8fjRo1ihdeeCE+/PDD7PLFixfH/fffH2XLlo3zzjsv9fib4oYbbohTTjklateuHS+99FK+r8+y9Rx77LFx0UUXxfz582OfffaJKVOmRLt27bJzN22oZ555JiIi+vbtG1WrVt2gfX79619HJpOJO++8M9+8LXl1r2XLlnHIIYdkl7/xxhsxb968qFOnTvaruWnWrl0bzz33XET8+FVWfp6uuuqqGDVqVOy+++7x+OOPb9DwJRvqqaeeioiIE044IcqVKzgia5kyZeKggw6KiB+Hs6B4bErbua6ihorLW/7T+cbyHH300YUu32OPPSIiCp2bZO3atfHCCy/E73//+xgwYECcccYZ0b9//+wwAoXNPbCuWbNmxZFHHhnff/99DB8+vMgy1KxZM4455pgCy+vVq5cdXqCoz0Xx2n333aNatWrx9NNPxx//+MeYMWNG6vabc9+XN1RXXlu4bNmyeOWVV6Jjx45x2GGHRfny5eP555/Pbp/3/4b4+vlIu+dfn08//TSGDx8eF154YZx11lnRv3//6N+/f3z77bcRUfD6dfbZZ0flypVj1KhRsXDhwnzH+fe//x01a9aMU089dZPLQ/GZNGlS5ObmRtu2baNVq1YF1rdp0yZ+8YtfpB7juOOOK3Q4mo110kknRcWKFQssz2uzP/nkk/jqq68KrO/atWuh++UNeXn44YcXGB6sXLly2SEWCzsmmy6vLTv55JMLvVfv2bNn7LDDDrF06dJ44403Ys2aNTF58uSIiCKvG+sOubQl5D3PnnXWWRtUd/PuR3v06BG1atUqsP7II4+MmjVrbtEysmEKu1/OycmJ5s2bR0TBe/YLLrggIiKGDx+eb3lee9a9e/do2bJldvmECRNixYoV0aFDh9hpp50KLUPesOPrPiPmPVsWNYxg1apVo127drFmzZp47bXXsss3p4/u7rvvjubNm8dDDz0UXbt2jVWrVsWQIUNi//33L3T7bY0wZTvy17/+NR577LFo3bp1PP/884VeeLeEOXPmRP/+/WP16tXx1FNP5euUpHSYM2dORESR46PXrFmzwANy3j7NmjUrdJ+0ydTypK0vV65cdo6KdRuTe++9N5YtWxbHHHNMNGrUKPX4G2vKlClx9dVXR05OTkyaNCnfeI5sfX/605+iVatWsXjx4qhSpUqMHj16o+dTmjVrVkT82Em0oVq2bBmHHnpozJkzJzvXRcT/jfmZNwH9T8/RsmXLDZpDZ/78+bFs2bLsPvz8jBgxIoYMGRI77rhjPPPMM7HDDjts0eN//vnnERFx7bXXFjkZeN5EhPPmzdui56Zom9J2rquo9jNved7xf6px48aFLq9evXpERIEJ5T/55JPYe++9o3v37nHdddfFHXfcEffcc0/ce++9cd9990VEZOeuKMzChQvj8MMPj2+++SauuOKKOPfcc4vcNm/y4E35XBSvatWqxciRI6NSpUpxzTXXRPPmzaNhw4bRs2fP+Pvf/x7ff/99vu03574vLxTJC0kmTpwYq1evjkMOOSSqVKkS+++/f0yePDlWrlwZubm5MWHChChTpkwcfPDBW+jTUtLW90xQmLVr18Z5550Xu+22WwwaNChuueWWuPvuu+Pee++Ne++9N9s2/vT6tcMOO8Rpp50WK1asyDfJ9+233x5JksQZZ5wRlStX3qzPQ/FY33VnfesiNq3ubcx5qlWrlg2YC2vfimqz8176Kmp9tWrVIqJgm87myeu8TmvL8tZ9+eWXMX/+/OzvYH33bVvKxj7Pru9+NCLMqVhCNvae/ZBDDok99tgjpk6dGm+88UZ2eVHzBee1gy+88EKRz4gnnXRSROR/Rszb77TTTityv6effrrAfpvTR1ejRo345z//GRE/hi9HHHFEXHTRRYVuuy0yAf125Mgjj4zJkyfHtGnTYsiQITF48OBNOs5PJ7P6qXr16kWbNm1i/PjxceGFF8azzz5b6BttlLy0juGtMfF6pUqVUtefc8458bvf/S7uu+++uPHGG6Nq1arZDsNNmTRyfXV1zz33jPLly8frr78egwYNikceeWS9ZWTLmTp1anz88ccR8eNbq++9994WvzktygUXXBDPPvts3HbbbXHiiSfG7NmzY9y4cVG1atXo379/sZSBbdOTTz4ZAwcOjKpVq8ZTTz21VR5W8q5dHTt2XO83SNc3EStb3tZqO5MkKXR5mTIb9+7TiSeeGNOnT4+jjjoqLrvssmjVqlVUr149ypcvHz/88EPqt6hWrVoVxx13XHzwwQdxyimnxA033LBR5y5MUZ+L4nfCCSdE9+7dY9y4cfHSSy/FlClTYuzYsTF27Ni47rrr4rnnnou99tprs8/TrVu3yGQy8cILL0SSJNlQJe8Fq+7du8dLL70UkydPjurVq8eiRYuiffv23qT9GdmU++lbbrklRowYETvuuGPcfPPNceCBB0b9+vWzb/n37ds3/vWvfxV6Tfn1r38dd955Z9xxxx1x0UUXxcqVK2PkyJGRyWTi/PPP3+zPw7ajOJ/lCquL62uzN7ZN5+djfX0TG6u4+3JYv439+85kMjFo0KAYMGBADB8+PEaOHBn//e9/46233oqmTZvGUUcdlW/7vDq0yy67RIcOHVKPvW44l7dfjx49on79+qn7/fTZdnP66PLClIiIDz74IBYvXrze0Wy2FcKU7UibNm3ij3/8YxxyyCHx29/+NpYuXRp/+ctfCmyX92b40qVLCz1OXnJelAoVKsTjjz8effv2jYcffjg6d+4czz//fOy4446b/yHYIvK+Ejhz5sxC1y9evDgWLVq0UftErL9urE/t2rXjlFNOiX/84x9x3333xW677RYfffRRtGrVqtC3FTe3rtasWTPGjRsXRx11VIwfPz4OP/zwePLJJzd4uCg23XfffRcnn3xyrFmzJs4444y45557on///vHWW29tVOd03tsf637tdEP06NEjdtttt3jxxRdj+vTpMWrUqFi7dm2cdtpp2TdHfnqOjz/+OJIkWe/Nae3ataNy5cqxfPny+Oijj6J169YbVTZKr9deey169+4dmUwmxowZE23btt0q58l7w+fYY4+NSy65ZKucg423KW3numbMmBF77713geV5x9t55503t4jx4Ycfxrvvvhv16tWLsWPHFhgm7pNPPily3yRJol+/fjFp0qTo2rVr3H333eu93qXdE2zJz8WWU6NGjTjttNPitNNOi4iI2bNnx6BBg+Lxxx+PgQMHZocP2Zz7vvr160fr1q3jvffei3feeSeef/75qFOnTvYbwN27d4/BgwfH888/n21zDfHF6NGjIyLizjvvLHT4wLTrV6tWraJ79+7x/PPPx/jx4+Orr76KRYsWxeGHH77VhrVmy9uQ607aui2pqKEQly5dGvPnz48I7du2IK9O5b2ZX5i83/VOO+0UtWvXjpycnFi1alXMnDmz0JeWiqqDm9o30bhx4/jggw/iww8/3KC2sDj6ZSg+p59+elx11VXx4IMPxl/+8pfsN0DOO++8AuFM3jNiy5Yt45577tngczRq1Cg+/PDDOOusszZ6GM6N7aPL8+CDD8aIESOifv360a5du3jqqafizDPPjEceeWSjzl9aicW3M3vuuWe89NJL0bRp07jpppvi3HPPLZCQ512ci+qczBtvL0358uXjwQcfjP79+8f06dOjU6dOLuilSN5Y+2PGjMk3Z0SeUaNGFVjWuXPniPhxTM8FCxYUWD9u3LjUTqQN9etf/zoifvxqY15DUtQbZXl19YMPPih0/YbU1erVq8czzzwThx56aEycODG6d++eb7xltrwkSeK0006LOXPmxOmnnx533313XHzxxbFw4cLo3bt3rF69eoOPlTcm/7/+9a/s0FobIu8tkIiIm2++Of7xj39EROFvV7Rr1y7q1KkT8+bNyzcsWFHKli2bffv2f//3fze4TJRun3/+eRx11FGxfPnyGDFiRLbubQ15czeNGTPGW/2lyKa0neta9+2sdeUNvZU3xvHmyGufGzZsWOh8O/fff3+R+1522WXx0EMPRevWrWPs2LEbNOziokWL4oknniiwfN68edkxwLfE52LradSoUVx//fUREfH2229nl2/ufV9eh9ADDzwQ06ZNy35bJSJiv/32i+rVq8dzzz1nvhSy8upZYS/VTJ8+PV/9LMy6Y88XNUQKpdtBBx0UmUwm3nzzzUL7It5555149913N/n4ee1aYW34T40ZMyZWrVpVYHleW77LLrsUOWcBpUfePchDDz1U6BBqY8eOjYULF0a1atVi3333jXLlymXf+H/ggQcKPWZR93Ob2o+W90xx9913x9q1a4v+MP9f3v3oM888U2i/xfjx4/VnbEOqVKkSZ511VqxcuTJuuOGGePjhh6NixYpx1llnFdi2W7duUaFChXjxxRdj7ty5G3yOvGfLvJcWNtbG9NFF/PgS6q9+9asoU6ZMPPDAAzFq1Kho0aJFPProozFs2LBNKkNpI0zZDrVo0SJeeumlaNmyZdx5551x+umn57uhyHvAef/99ws0FGPGjNngyl+2bNm4++67Y+DAgfHpp59Gp06dskP6ULJ69eoVDRo0iJkzZ8bVV1+dL1D78MMP43e/+12BfTp16hRt27aN77//Ps4///x8N5ezZ8/eYm9P77XXXnHwwQfHBx98EOPGjYvq1asXOcnbwQcfHGXKlIlnn302+yZlxI+d9cOGDdvg1Lty5crxxBNPRM+ePWPq1KnRpUuX7CSXbHk33nhjPPPMM9GqVavsV0RvvPHGOOCAA2Lq1Klx2WWXbfCxjjnmmNhnn33iq6++il69emXfFMuzcuXKGD9+fKH79u/fP2rUqBF33313zJ07N7p27VroZJflypWLq6++OiJ+nFB+0qRJBbZ57bXX8o2bfPXVV0e5cuVi+PDh2TG71zVr1qx846JSus2fPz8OP/zwmDt3blx33XWF3txuSccee2y0b98+Xn311TjjjDMKnRdl4cKFMWLEiA3qEGDL2JS2c11jx46NBx98MN+yhx9+OB555JEoV65cNuDdHLvttluULVs23nvvvQITvz/xxBPx17/+tdD9hg8fHn/5y19ip512ivHjx2/UV/AvvvjifNe/VatWxfnnnx/Lli2L/fbbb73DEFA83nrrrXjooYdixYoVBdblBWLrdmJv7n1fXjgyfPjwSJIk3xyK5cqVi86dO8fbb78dU6ZMiUqVKqknxB577BERP3bWrHt9/frrrws8rxbmiCOOiF122SWeeeaZeOedd6JFixbZDiS2DY0bN47jjz8+cnNz47zzzss3P87ChQtjwIABm/WSSd43ST755JP1vrz11VdfxSWXXJKvc/uDDz7ItvW/+c1vNrkcFJ9evXpF48aN46uvvoqLLroo33VkxowZcfHFF0dExKBBg7JDCl544YUREXHrrbfmm8Q7ImLo0KHx5ptvFnquTe1HO/vss2PnnXeOt956K84555wCLwguWbIk++JBxI9hyt577x1Lly6NQYMGxQ8//JBd99VXX2U/E9uOgQMHRpkyZeLmm2+OH374Ifr06VPoVAn169ePQYMGxbJly+Loo4+O9957r8A2q1atinHjxuUL9X71q19FkyZNYsyYMXH55ZcX+u2pb775psgXQTemj27lypXRq1evWLp0aVx77bXRrVu3qF69eowePTpycnLi0ksvzTfJ/bZKmLKd2nnnnWPSpEmx9957xwMPPBC9evXKPiRVqlQp+4ba6aefHgceeGD06tUrWrduHb17944rrrhig8+TyWTi1ltvjSuvvDJmz54dBx10UKF/8BSvypUrx/333x8VK1aMoUOHRsuWLaNPnz5x2GGHxd577x2dOnXKDm207pup//znP6Nu3brx4IMPRvPmzaN3795x9NFHx+677x61a9eOAw44YIuULy/5jojo169fkcNuNWrUKAYNGhS5ubnRrVu36Nq1a5xwwgmx6667xiWXXLJRdbVChQoxevToOO200+Ldd9+Ngw46KGbPnr3Zn4X8Jk2aFNddd11Urlw5xowZE1WqVImIHztWHnzwwahVq1b87W9/i8cff3yDjlemTJkYO3ZstGzZMsaPHx+NGzeOww47LPr27RudO3eOHXfcMc4777xC961atWqcccYZ2X+nvb14wQUXxLnnnhvfffdddO7cOdq2bRt9+vSJI488Mlq0aBH77bdffPrpp9nt27dvH3fddVeULVs2zj///GjWrFn06tUrTjjhhNhnn32iWbNmhb7NTel0++23x8cffxyVK1eOWbNmRf/+/Qv9b2OHmytKmTJl4rHHHos2bdrEvffeG82aNYsOHTpEnz59snWobt26cd555wlTitGmtp15LrjggujTp0/st99+ccopp8T+++8fvXr1itzc3Bg6dGj84he/2Owy1qlTJwYOHBhr166Nbt26RZcuXaJv376x7777xjHHHBOXXnppofvlvdHduHHjuOaaawqt30OGDCmw3wEHHBC1atWKli1bxtFHHx29e/eO5s2bx5gxY6JevXrZb91Q8mbNmhUnn3xy1K5dOzp27Bh9+vSJXr16xe677x7XXnttVKhQIYYOHZpvn8257+vcuXOUL18++ybwumFKxI9hS25ubvzwww/RsWPH1Ll82D5cddVVUaFChfjf//3faNmyZfTu3Ts7TNeqVavi+OOPT92/TJky+e7lBgwYYN6AbdBtt90WLVq0iBdffDGaNWsWJ5xwQvTs2TOaN28e3377baFDwG2oxo0bR7t27WLu3Lmx1157xamnnhpnn312oc+M5557bvzjH/+IXXfdNfr06RM9evSINm3axLfffhvHH398kc8XlC45OTnx8MMPR61ateKOO+6IXXbZJU4++eQ48sgjo1WrVjFjxow47LDD8s0nfPTRR8f5558f33//fXTq1Cm6du0affv2jdatW8eVV16ZvWf6qU3tR6tatWqMGzcudtxxxxg5cmTsvPPOcdRRR8XJJ58cHTp0iB133DH+8Ic/ZLfPZDJx//33R61ateKBBx7I1z7vtttuUatWrWz7vCHfMqbkNW3aNN+1La1fYsiQIdG3b9949dVXo02bNtG2bds48cQT4+STT46OHTtG7dq149hjj803DFyVKlXiqaeeiqZNm8bQoUOjcePG0blz5zjllFPi+OOPjz333DMaNmwY1157bZHn3dA+ukGDBsW7774bBx98cFx33XXZ5W3bto2//OUv8cMPP0Tv3r23yKg2JSphm9SkSZMkIpIZM2Zkl/Xr1y+JiGTkyJH5th08eHASEcngwYMLHGfhwoXJAQcckEREcsghhyTLli3Lrrv33nuTtm3bJhUrVkyqV6+eHHzwwclzzz2XzJgxI4mIpEmTJvmOVdTyPDfeeGMSEckOO+yQTJ06dYP2Yet65513kuOPPz6pVatWUrFixaRVq1bJn//852TVqlVJhQoVkjJlyiQrVqzIt8+sWbOS/v37J/Xr108qVKiQNG/ePLn88suTZcuWJZ07d04iIpkwYUK+fYpaXpSlS5cmZcuWTTKZTPLhhx+mbpubm5vcdNNNyR577JFUqFAhqVWrVnL00Ucnb7zxRjJhwoQkIpLOnTvn26eo5XnHO++887L18pNPPtmgMrN+c+fOTRo2bFjodSrPuHHjkkwmk+ywww7Z61vaNSzP0qVLkz/96U9J+/btk2rVqiU5OTlJkyZNkmOOOSZ58MEHi9xv/PjxSUQkjRo1StasWbPezzB+/Pjk2GOPTerXr5+UL18+qVu3brLffvsl119/fTJ//vwC20+fPj0566yzkmbNmiU5OTlJjRo1klatWiUDBw5Mpk+fvt7zsXWltaX33HNPdlleHVzff+te40aOHJlERNKvX79Cz70h9XrlypXJiBEjkq5duya1a9dOypUrl9SrVy9p06ZNcv755yfPPvvsZv4E2BQb23auW89Gjx6dHHDAAUnVqlWTKlWqJJ06dUqeeOKJQs+zvrazqDqUm5ub3HXXXcm+++6bVK1aNalRo0bSsWPH7LUwr76ua0Pq97pt5rrt6Pfff59ceumlSbNmzZIKFSok9evXT/r375988cUXG//DZaPl1a+ZM2dmlxX2TPD1118nQ4YMSY444oikWbNmSeXKlZPq1asnrVq1Ss4///wi77c25b4vT6dOnZKISHbdddcC66ZPn56tW3/6058K3X9911FKVmFt6Ibc86f9Xt99993kmGOOSRo0aJBUrFgx2XXXXZPLLrssWbJkSZHPuuv64IMPkohIKleunCxcuHCTykDxKawOJUmSfPfdd8mgQYOSnXfeOalQoUKy8847J+eee24yb968IuvBhtSPJPnxmta3b9+kQYMGSbly5Qr0Rax7nDfffDM5+uijk9q1ayc5OTnJnnvumdx8883J6tWrCxx3ffd16yvfxj4vs3G++OKL5Pzzz0+aN2+eVKhQIalWrVpywAEHJHfccUehv88kSZK777472XfffZOKFSsmNWrUSLp3755MmDAhtS8hSTa+Hy3PvHnzkmuuuSbZa6+9kipVqiSVKlVKmjdvnvTu3Tt55plnCmw/Y8aM5LTTTkvq1auXVKhQIWnRokVy1VVXJcuXL0+aN2+eRETy0Ucf5dvHtW/rKOzeel0b8vd9xx13JBGRHHDAARt0zqeffjrp2bNnstNOOyXly5dPatasmeyxxx7JySefnIwaNSpf326eJUuWJEOHDk0OOOCApGbNmkn58uWTBg0aJO3bt08uvfTS5OWXXy7yfBvSR3f//fcnEZHUr18/+frrrwvd5sQTT0wiIjn++OM36HOWVpkkMRg3kN+kSZOic+fOsddee23UuLRdunSJiRMnxoQJEzZrjPR//OMfcc4558Shhx4azz777CYfB9bn1FNPjQceeCBuuOGGuPLKK0u6OJQCJ510UowZMyZGjx4dvXr1KunisA3Z1LYTNke9evVi3rx5MXfu3Khbt25JFwdK1DXXXBN//OMf41e/+lXceeedJV0ctkH9+/ePe++9N0aOHBn9+/cv6eLARpsxY0bssssuUa1atViwYEGBScwpnTp27BhTpkyJUaNGRZ8+fUq6OAXoo8vPXxVsp+bNmxczZswosHzatGlxzjnnRETkGwKpuCxbtixuvPHGiAjjfbJVvffee/HQQw9F1apV43/+539KujiUAqtXr86Og9yyZcsSLg2lUWltO9k+ff755zFv3ryoVauWIIXt3tdffx233XZblClTJjvnAcDP0bJly2L69OkFls+aNStOOeWUyM3NjX79+glSthHjx4+PKVOmROPGjePEE08s6eIUoI+uoHIlXQCgZEyfPj074Xbz5s2jUqVKMWPGjHjzzTcjNzc3DjnkkC0yGe6G+vOf/xzTpk2LyZMnx+effx49evSIQw89tNjOz/bj7LPPjmXLlsX48eNjzZo1cc0110StWrVKuliUoO+++y4uuOCCeP311+Ozzz6Lfffdd4vMX8HPT2lrO9k+TZ48OW655ZaYMGFCRIS3p9muXXHFFfHll1/G888/H4sWLYpzzz03O5k9wM/RvHnzonXr1tGiRYvYbbfdonr16vHFF1/Em2++GatWrYq99947fv/735d0MUkxf/78uPzyy2PhwoXx9NNPR0TE0KFDo3z58iVcsv+jj65owhTYTu22225x/vnnx8SJE2PKlCmxdOnSqFatWhx44IHRt2/fOOecc6JcueK7RDz11FMxceLEqFOnTvTv3z9uvvnmYjs325e77rorypQpE40aNYpLLrkkLrvsspIuEiXs+++/j1GjRkWtWrXipJNOcv2hSKWt7WT79Omnn8bYsWOjQYMGcemll+owYbv24IMPxhdffBE77rhjXHjhhTFkyJCSLhLAVlWnTp245JJL4j//+U+89tprsWjRoqhcuXL84he/iBNOOCEGDRoUlStXLulikmLp0qVx1113Rbly5aJ58+Zx8cUXR+/evUu6WPnooyuaOVMAAAAAAABSGEAPAAAAAAAghTAFAAAAAAAghTAFAAAAAAAghTAFAAAAAAAghTAFAAAAAAAghTAFAADYJt1zzz2RyWSif//+m73PzJkzI5PJRNOmTQvs07Rp08hkMjFz5szNKi8AALDtEqYAAAAAAACkKFfSBQAAACguxx9/fOy///5Ro0aNki4KAACwDRGmAAAA240aNWoIUgAAgI1mmC8AAKBU+eSTT+LMM8+MZs2aRU5OTlStWjWaNGkSRx55ZIwcOXKDjvH555/H7rvvHplMJn7zm99Ebm5uRGzaPCtFWbx4cVxzzTWx1157RZUqVSInJycaNmwYHTp0iOuuuy5Wr1692ecAAABKB99MAQAASo1p06ZFhw4dYsmSJdGyZcs46qijomzZsjFnzpyYNGlSfPnll3HGGWekHuOVV16JY445JubPnx+33nprDBw4cIuXc/ny5dGxY8eYNm1a1K1bN7p16xZVqlSJb775Jj788MN4+eWX46KLLoqaNWtu8XMDAADFT5gCAACUGjfffHMsWbIk/vCHP8TVV1+db92KFSvitddeS93/kUceidNOOy0ymUyMHTs2jjnmmK1SzocffjimTZsWhx9+eDz++ONRvnz57Lrc3Nx46aWXonLlylvl3AAAQPEzzBcAAFBqfPvttxERccQRRxRYV6lSpTjooIOK3Pcvf/lL9OrVK6pXrx4TJ07cakHKuuU85JBD8gUpERFlypSJzp07R4UKFbba+QEAgOIlTAEAAEqN/fbbLyIizjvvvHj22Wdj5cqV691n7dq1MWDAgLj00ktj9913j1deeSXatWu3VcvZvn37iIgYOnRo3HfffbFgwYKtej4AAKBkCVMAAIBS49JLL43u3bvH1KlTo0ePHlG9evVo3759XHzxxUUO8fXggw/GHXfcEfXq1YspU6ZE06ZNt3o5u3TpEpdffnnMnTs3+vXrF3Xq1ImWLVvGmWeeGY8//nh2wnsAAODnQZgCAACUGpUrV47nnnsuXn311fjd734X3bp1i48//jhuvvnm2G+//eL8888vsE+nTp2iWbNmMXfu3Lj00kuLLcgYMmRIfPbZZzFs2LDo1atXLFu2LEaOHBnHHXdc7L///rFs2bJiKQcAALD1CVMAAIBSp3379nHttdfG+PHjY/78+TFmzJioVKlS3H777TFhwoR82zZu3DgmT54ce+yxR9x1113Rt2/fWLNmTbGUs2nTpjFo0KB46KGHYs6cOfHqq6/GbrvtFq+99loMHTq0WMoAAABsfcIUAACgVCtXrlyceOKJcdhhh0VExNtvv11gm4YNG8akSZNin332iYceeih69uwZq1atKuaS/hgCDRgwICIKLycAALBtEqYAAAClxu233x4fffRRgeXffPNNvP766xER0aRJk0L3rVOnTkyYMCE6dOgQTzzxRBx55JFbbaitsWPHxqRJkwoMKbZ69ep45plnUssJAABse8qVdAEAAADy/P3vf4/zzz8/mjVrFq1bt47q1avHvHnz4qWXXooVK1bEwQcfHMccc0yR+9eoUSOeffbZOO644+L555+PQw45JJ5++umoWbPmFi3nxIkT45Zbbok6derEPvvsE/Xq1YulS5fGK6+8EnPnzo2ddtopLrvssi16TgAAoOQIUwAAgFLjj3/8Yzz11FPxyiuvxCuvvBKLFy+OevXqxS9/+cs444wzok+fPlGuXPpjTJUqVeLJJ5+M3r17x+OPPx5du3aNf//731G3bt0tVs7+/ftHpUqVYvLkyfH+++/HxIkTo0aNGtG4ceO48MIL41e/+lXUrl17i50PAAAoWZkkSZKSLgQAAAAAAEBpZc4UAAAAAACAFMIUAAAAAACAFMIUAAAAAACAFMIUAAAAAACAFMIUAAAAAACAFMIUAAAAAACAFMIUAAAAAACAFMIUAAAAAACAFMIUAAAAAACAFMIUAAAAAACAFMIUAAAAAACAFMIUAAAAAACAFP8P0dPDtsNnZE4AAAAASUVORK5CYII=\n"},"metadata":{}}],"execution_count":148},{"cell_type":"code","source":"ax = sns.barplot(data=emp_self,\n x=\"skills\",\n y=\"level\",\n hue=\"year\")\nplt.legend(loc=(1.05, 0))\nplt.title('Skills development through self-assessment')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:05.812212Z","iopub.execute_input":"2025-02-13T11:43:05.812551Z","iopub.status.idle":"2025-02-13T11:43:06.331687Z","shell.execute_reply.started":"2025-02-13T11:43:05.812523Z","shell.execute_reply":"2025-02-13T11:43:06.330358Z"}},"outputs":[{"execution_count":149,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Skills development through self-assessment')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":149},{"cell_type":"code","source":"ax = sns.barplot(data=emp_users,\n x=\"skills\",\n y=\"level\",\n hue=\"year\")\nplt.legend(loc=(1.05, 0))\nplt.title('Skills development through user endorsement')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:06.332914Z","iopub.execute_input":"2025-02-13T11:43:06.333262Z","iopub.status.idle":"2025-02-13T11:43:06.835638Z","shell.execute_reply.started":"2025-02-13T11:43:06.333226Z","shell.execute_reply":"2025-02-13T11:43:06.834423Z"}},"outputs":[{"execution_count":150,"output_type":"execute_result","data":{"text/plain":"Text(0.5, 1.0, 'Skills development through user endorsement')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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month year self course user language technical soft skills \\\n0 2 2023 False True False False False True ilkukl \n1 7 2023 False True False False True False gqryuk \n2 2 2023 False True False True False False xfcycc \n3 12 2023 False True False False True False ijzffe \n4 12 2023 True False False False False True ieecmo \n5 10 2023 True False False True False False jnfqct \n6 10 2023 True False False False True False jbkamg \n7 10 2023 True False False False False True seevwz \n8 3 2024 False False True True False False xfcycc \n9 5 2024 True False False False False True oksacc \n10 6 2024 False False True False False True opswnk \n11 11 2024 False True False False True False gbhazb \n12 11 2024 False True False False False True jisdwl \n13 12 2024 False False True False True False gbhazb \n14 12 2024 False False True False False True jisdwl \n15 2 2024 False False True False True False gbhazb \n16 2 2024 False False True False False True jisdwl \n17 2 2024 True False False True False False wakpbw \n18 9 2025 False False True False True False tfncdy \n19 12 2025 True False False False True False gbhazb \n20 12 2025 True False False False False True jisdwl \n21 4 2025 False False True True False False xfcycc \n22 10 2025 False True False False False True iriayl \n23 4 2025 False False True False False True rqphnd \n24 5 2025 False False True False True False tfncdy \n25 3 2025 True False False True False False lfgljg \n26 10 2025 False True False False True False idrtpm \n27 9 2025 False True False False True False oducgf \n28 8 2025 False True False False True False nyeeyx \n\n level \n0 2 \n1 3 \n2 2 \n3 1 \n4 3 \n5 2 \n6 2 \n7 1 \n8 3 \n9 3 \n10 3 \n11 4 \n12 4 \n13 4 \n14 4 \n15 3 \n16 3 \n17 3 \n18 3 \n19 3 \n20 3 \n21 3 \n22 5 \n23 4 \n24 3 \n25 3 \n26 4 \n27 4 \n28 4 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
monthyearselfcourseuserlanguagetechnicalsoftskillslevel
022023FalseTrueFalseFalseFalseTrueilkukl2
172023FalseTrueFalseFalseTrueFalsegqryuk3
222023FalseTrueFalseTrueFalseFalsexfcycc2
3122023FalseTrueFalseFalseTrueFalseijzffe1
4122023TrueFalseFalseFalseFalseTrueieecmo3
5102023TrueFalseFalseTrueFalseFalsejnfqct2
6102023TrueFalseFalseFalseTrueFalsejbkamg2
7102023TrueFalseFalseFalseFalseTrueseevwz1
832024FalseFalseTrueTrueFalseFalsexfcycc3
952024TrueFalseFalseFalseFalseTrueoksacc3
1062024FalseFalseTrueFalseFalseTrueopswnk3
11112024FalseTrueFalseFalseTrueFalsegbhazb4
12112024FalseTrueFalseFalseFalseTruejisdwl4
13122024FalseFalseTrueFalseTrueFalsegbhazb4
14122024FalseFalseTrueFalseFalseTruejisdwl4
1522024FalseFalseTrueFalseTrueFalsegbhazb3
1622024FalseFalseTrueFalseFalseTruejisdwl3
1722024TrueFalseFalseTrueFalseFalsewakpbw3
1892025FalseFalseTrueFalseTrueFalsetfncdy3
19122025TrueFalseFalseFalseTrueFalsegbhazb3
20122025TrueFalseFalseFalseFalseTruejisdwl3
2142025FalseFalseTrueTrueFalseFalsexfcycc3
22102025FalseTrueFalseFalseFalseTrueiriayl5
2342025FalseFalseTrueFalseFalseTruerqphnd4
2452025FalseFalseTrueFalseTrueFalsetfncdy3
2532025TrueFalseFalseTrueFalseFalselfgljg3
26102025FalseTrueFalseFalseTrueFalseidrtpm4
2792025FalseTrueFalseFalseTrueFalseoducgf4
2882025FalseTrueFalseFalseTrueFalsenyeeyx4
\n
"},"metadata":{}}],"execution_count":151},{"cell_type":"markdown","source":"## Type of skills \n### Proportion","metadata":{}},{"cell_type":"code","source":"n_language = emp.loc[emp.language == True, :].shape[0]\nn_soft = emp.loc[emp.soft == True, :].shape[0]\nn_tech = emp.loc[emp.technical == True, :].shape[0]\nn_total = emp.shape[0]\n\nX = [\"language\", \"soft\",\"technical\"]\nY = [n_language/n_total, n_soft/n_total, n_tech/n_total]\nplt.figure(figsize=(20,14))\nplt.bar(X, Y)\n\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:06.856111Z","iopub.execute_input":"2025-02-13T11:43:06.856492Z","iopub.status.idle":"2025-02-13T11:43:07.134592Z","shell.execute_reply.started":"2025-02-13T11:43:06.856464Z","shell.execute_reply":"2025-02-13T11:43:07.133648Z"}},"outputs":[{"execution_count":152,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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skill role month year level self course user\n0 1010101525 ROLE_226242 2 2023 2 False True False\n1 1010101263 ROLE_226242 7 2023 3 False True False\n2 1010101122 ROLE_226242 2 2023 2 False True False\n3 1010101043 ROLE_226242 12 2023 1 False True False\n4 1010101954 ROLE_226242 12 2023 3 True False False\n5 1010101553 ROLE_226242 10 2023 2 True False False\n6 1010101363 ROLE_226242 10 2023 1 True False False\n0 1010101122 ROLE_713858 3 2024 3 False False True\n1 1010101762 ROLE_713858 5 2024 3 True False False\n2 1010101873 ROLE_713858 6 2024 3 False False True\n3 1010101767 ROLE_713858 11 2024 4 False True False\n4 1010101767 ROLE_713858 12 2024 4 False False True\n5 1010101767 ROLE_713858 2 2024 3 False False True\n6 1010101772 ROLE_713858 2 2024 3 True False False\n0 1010101867 ROLE_860638 9 2025 3 False False True\n1 1010101767 ROLE_860638 12 2025 3 True False False\n2 1010101122 ROLE_860638 4 2025 3 False False True\n3 1010101928 ROLE_860638 10 2025 5 False True False\n4 1010101679 ROLE_860638 4 2025 4 False False True\n5 1010101867 ROLE_860638 5 2025 3 False False True\n6 1010101721 ROLE_860638 3 2025 3 True False False\n0 1010101677 ROLE_860638 10 2025 4 False True False\n1 1010101693 ROLE_860638 9 2025 4 False True False\n2 1010101144 ROLE_860638 8 2025 4 False True False","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skillrolemonthyearlevelselfcourseuser
01010101525ROLE_226242220232FalseTrueFalse
11010101263ROLE_226242720233FalseTrueFalse
21010101122ROLE_226242220232FalseTrueFalse
31010101043ROLE_2262421220231FalseTrueFalse
41010101954ROLE_2262421220233TrueFalseFalse
51010101553ROLE_2262421020232TrueFalseFalse
61010101363ROLE_2262421020231TrueFalseFalse
01010101122ROLE_713858320243FalseFalseTrue
11010101762ROLE_713858520243TrueFalseFalse
21010101873ROLE_713858620243FalseFalseTrue
31010101767ROLE_7138581120244FalseTrueFalse
41010101767ROLE_7138581220244FalseFalseTrue
51010101767ROLE_713858220243FalseFalseTrue
61010101772ROLE_713858220243TrueFalseFalse
01010101867ROLE_860638920253FalseFalseTrue
11010101767ROLE_8606381220253TrueFalseFalse
21010101122ROLE_860638420253FalseFalseTrue
31010101928ROLE_8606381020255FalseTrueFalse
41010101679ROLE_860638420254FalseFalseTrue
51010101867ROLE_860638520253FalseFalseTrue
61010101721ROLE_860638320253TrueFalseFalse
01010101677ROLE_8606381020254FalseTrueFalse
11010101693ROLE_860638920254FalseTrueFalse
21010101144ROLE_860638820254FalseTrueFalse
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"},"metadata":{}}],"execution_count":153},{"cell_type":"code","source":"t =emp.loc[:,['language','technical','soft','skills','level']].groupby(['language','technical','soft','skills']).describe().reset_index()\nt.columns = t.columns.map('_'.join)\nt\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:07.152649Z","iopub.execute_input":"2025-02-13T11:43:07.153051Z","iopub.status.idle":"2025-02-13T11:43:07.247394Z","shell.execute_reply.started":"2025-02-13T11:43:07.153013Z","shell.execute_reply":"2025-02-13T11:43:07.246206Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/pandas/io/formats/format.py:1458: RuntimeWarning: invalid value encountered in greater\n has_large_values = (abs_vals > 1e6).any()\n/usr/local/lib/python3.10/dist-packages/pandas/io/formats/format.py:1459: RuntimeWarning: invalid value encountered in less\n has_small_values = ((abs_vals < 10 ** (-self.digits)) & (abs_vals > 0)).any()\n/usr/local/lib/python3.10/dist-packages/pandas/io/formats/format.py:1459: RuntimeWarning: invalid value encountered in greater\n has_small_values = ((abs_vals < 10 ** (-self.digits)) & (abs_vals > 0)).any()\n","output_type":"stream"},{"execution_count":154,"output_type":"execute_result","data":{"text/plain":" language_ technical_ soft_ skills_ level_count level_mean level_std \\\n0 False False True ieecmo 1.0 3.000000 NaN \n1 False False True ilkukl 1.0 2.000000 NaN \n2 False False True iriayl 1.0 5.000000 NaN \n3 False False True jisdwl 4.0 3.500000 0.57735 \n4 False False True oksacc 1.0 3.000000 NaN \n5 False False True opswnk 1.0 3.000000 NaN \n6 False False True rqphnd 1.0 4.000000 NaN \n7 False False True seevwz 1.0 1.000000 NaN \n8 False True False gbhazb 4.0 3.500000 0.57735 \n9 False True False gqryuk 1.0 3.000000 NaN \n10 False True False idrtpm 1.0 4.000000 NaN \n11 False True False ijzffe 1.0 1.000000 NaN \n12 False True False jbkamg 1.0 2.000000 NaN \n13 False True False nyeeyx 1.0 4.000000 NaN \n14 False True False oducgf 1.0 4.000000 NaN \n15 False True False tfncdy 2.0 3.000000 0.00000 \n16 True False False jnfqct 1.0 2.000000 NaN \n17 True False False lfgljg 1.0 3.000000 NaN \n18 True False False wakpbw 1.0 3.000000 NaN \n19 True False False xfcycc 3.0 2.666667 0.57735 \n\n level_min level_25% level_50% level_75% level_max \n0 3.0 3.0 3.0 3.0 3.0 \n1 2.0 2.0 2.0 2.0 2.0 \n2 5.0 5.0 5.0 5.0 5.0 \n3 3.0 3.0 3.5 4.0 4.0 \n4 3.0 3.0 3.0 3.0 3.0 \n5 3.0 3.0 3.0 3.0 3.0 \n6 4.0 4.0 4.0 4.0 4.0 \n7 1.0 1.0 1.0 1.0 1.0 \n8 3.0 3.0 3.5 4.0 4.0 \n9 3.0 3.0 3.0 3.0 3.0 \n10 4.0 4.0 4.0 4.0 4.0 \n11 1.0 1.0 1.0 1.0 1.0 \n12 2.0 2.0 2.0 2.0 2.0 \n13 4.0 4.0 4.0 4.0 4.0 \n14 4.0 4.0 4.0 4.0 4.0 \n15 3.0 3.0 3.0 3.0 3.0 \n16 2.0 2.0 2.0 2.0 2.0 \n17 3.0 3.0 3.0 3.0 3.0 \n18 3.0 3.0 3.0 3.0 3.0 \n19 2.0 2.5 3.0 3.0 3.0 ","text/html":"
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language_technical_soft_skills_level_countlevel_meanlevel_stdlevel_minlevel_25%level_50%level_75%level_max
0FalseFalseTrueieecmo1.03.000000NaN3.03.03.03.03.0
1FalseFalseTrueilkukl1.02.000000NaN2.02.02.02.02.0
2FalseFalseTrueiriayl1.05.000000NaN5.05.05.05.05.0
3FalseFalseTruejisdwl4.03.5000000.577353.03.03.54.04.0
4FalseFalseTrueoksacc1.03.000000NaN3.03.03.03.03.0
5FalseFalseTrueopswnk1.03.000000NaN3.03.03.03.03.0
6FalseFalseTruerqphnd1.04.000000NaN4.04.04.04.04.0
7FalseFalseTrueseevwz1.01.000000NaN1.01.01.01.01.0
8FalseTrueFalsegbhazb4.03.5000000.577353.03.03.54.04.0
9FalseTrueFalsegqryuk1.03.000000NaN3.03.03.03.03.0
10FalseTrueFalseidrtpm1.04.000000NaN4.04.04.04.04.0
11FalseTrueFalseijzffe1.01.000000NaN1.01.01.01.01.0
12FalseTrueFalsejbkamg1.02.000000NaN2.02.02.02.02.0
13FalseTrueFalsenyeeyx1.04.000000NaN4.04.04.04.04.0
14FalseTrueFalseoducgf1.04.000000NaN4.04.04.04.04.0
15FalseTrueFalsetfncdy2.03.0000000.000003.03.03.03.03.0
16TrueFalseFalsejnfqct1.02.000000NaN2.02.02.02.02.0
17TrueFalseFalselfgljg1.03.000000NaN3.03.03.03.03.0
18TrueFalseFalsewakpbw1.03.000000NaN3.03.03.03.03.0
19TrueFalseFalsexfcycc3.02.6666670.577352.02.53.03.03.0
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"},"metadata":{}}],"execution_count":154},{"cell_type":"code","source":"rows = t.soft_ == True\nt.loc[rows,'type'] = \"Soft skills\"\nrows = t.language_ == True\nt.loc[rows,'type'] = \"Language skills\"\nrows = t.technical_ == True\nt.loc[rows,'type'] = \"Technical skills\"\n\nt","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:07.248404Z","iopub.execute_input":"2025-02-13T11:43:07.248749Z","iopub.status.idle":"2025-02-13T11:43:07.281315Z","shell.execute_reply.started":"2025-02-13T11:43:07.248724Z","shell.execute_reply":"2025-02-13T11:43:07.280032Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.10/dist-packages/pandas/io/formats/format.py:1458: RuntimeWarning: invalid value encountered in greater\n has_large_values = (abs_vals > 1e6).any()\n/usr/local/lib/python3.10/dist-packages/pandas/io/formats/format.py:1459: RuntimeWarning: invalid value encountered in less\n has_small_values = ((abs_vals < 10 ** (-self.digits)) & (abs_vals > 0)).any()\n/usr/local/lib/python3.10/dist-packages/pandas/io/formats/format.py:1459: RuntimeWarning: invalid value encountered in greater\n has_small_values = ((abs_vals < 10 ** (-self.digits)) & (abs_vals > 0)).any()\n","output_type":"stream"},{"execution_count":155,"output_type":"execute_result","data":{"text/plain":" language_ technical_ soft_ skills_ level_count level_mean level_std \\\n0 False False True ieecmo 1.0 3.000000 NaN \n1 False False True ilkukl 1.0 2.000000 NaN \n2 False False True iriayl 1.0 5.000000 NaN \n3 False False True jisdwl 4.0 3.500000 0.57735 \n4 False False True oksacc 1.0 3.000000 NaN \n5 False False True opswnk 1.0 3.000000 NaN \n6 False False True rqphnd 1.0 4.000000 NaN \n7 False False True seevwz 1.0 1.000000 NaN \n8 False True False gbhazb 4.0 3.500000 0.57735 \n9 False True False gqryuk 1.0 3.000000 NaN \n10 False True False idrtpm 1.0 4.000000 NaN \n11 False True False ijzffe 1.0 1.000000 NaN \n12 False True False jbkamg 1.0 2.000000 NaN \n13 False True False nyeeyx 1.0 4.000000 NaN \n14 False True False oducgf 1.0 4.000000 NaN \n15 False True False tfncdy 2.0 3.000000 0.00000 \n16 True False False jnfqct 1.0 2.000000 NaN \n17 True False False lfgljg 1.0 3.000000 NaN \n18 True False False wakpbw 1.0 3.000000 NaN \n19 True False False xfcycc 3.0 2.666667 0.57735 \n\n level_min level_25% level_50% level_75% level_max type \n0 3.0 3.0 3.0 3.0 3.0 Soft skills \n1 2.0 2.0 2.0 2.0 2.0 Soft skills \n2 5.0 5.0 5.0 5.0 5.0 Soft skills \n3 3.0 3.0 3.5 4.0 4.0 Soft skills \n4 3.0 3.0 3.0 3.0 3.0 Soft skills \n5 3.0 3.0 3.0 3.0 3.0 Soft skills \n6 4.0 4.0 4.0 4.0 4.0 Soft skills \n7 1.0 1.0 1.0 1.0 1.0 Soft skills \n8 3.0 3.0 3.5 4.0 4.0 Technical skills \n9 3.0 3.0 3.0 3.0 3.0 Technical skills \n10 4.0 4.0 4.0 4.0 4.0 Technical skills \n11 1.0 1.0 1.0 1.0 1.0 Technical skills \n12 2.0 2.0 2.0 2.0 2.0 Technical skills \n13 4.0 4.0 4.0 4.0 4.0 Technical skills \n14 4.0 4.0 4.0 4.0 4.0 Technical skills \n15 3.0 3.0 3.0 3.0 3.0 Technical skills \n16 2.0 2.0 2.0 2.0 2.0 Language skills \n17 3.0 3.0 3.0 3.0 3.0 Language skills \n18 3.0 3.0 3.0 3.0 3.0 Language skills \n19 2.0 2.5 3.0 3.0 3.0 Language skills ","text/html":"
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language_technical_soft_skills_level_countlevel_meanlevel_stdlevel_minlevel_25%level_50%level_75%level_maxtype
0FalseFalseTrueieecmo1.03.000000NaN3.03.03.03.03.0Soft skills
1FalseFalseTrueilkukl1.02.000000NaN2.02.02.02.02.0Soft skills
2FalseFalseTrueiriayl1.05.000000NaN5.05.05.05.05.0Soft skills
3FalseFalseTruejisdwl4.03.5000000.577353.03.03.54.04.0Soft skills
4FalseFalseTrueoksacc1.03.000000NaN3.03.03.03.03.0Soft skills
5FalseFalseTrueopswnk1.03.000000NaN3.03.03.03.03.0Soft skills
6FalseFalseTruerqphnd1.04.000000NaN4.04.04.04.04.0Soft skills
7FalseFalseTrueseevwz1.01.000000NaN1.01.01.01.01.0Soft skills
8FalseTrueFalsegbhazb4.03.5000000.577353.03.03.54.04.0Technical skills
9FalseTrueFalsegqryuk1.03.000000NaN3.03.03.03.03.0Technical skills
10FalseTrueFalseidrtpm1.04.000000NaN4.04.04.04.04.0Technical skills
11FalseTrueFalseijzffe1.01.000000NaN1.01.01.01.01.0Technical skills
12FalseTrueFalsejbkamg1.02.000000NaN2.02.02.02.02.0Technical skills
13FalseTrueFalsenyeeyx1.04.000000NaN4.04.04.04.04.0Technical skills
14FalseTrueFalseoducgf1.04.000000NaN4.04.04.04.04.0Technical skills
15FalseTrueFalsetfncdy2.03.0000000.000003.03.03.03.03.0Technical skills
16TrueFalseFalsejnfqct1.02.000000NaN2.02.02.02.02.0Language skills
17TrueFalseFalselfgljg1.03.000000NaN3.03.03.03.03.0Language skills
18TrueFalseFalsewakpbw1.03.000000NaN3.03.03.03.03.0Language skills
19TrueFalseFalsexfcycc3.02.6666670.577352.02.53.03.03.0Language skills
\n
"},"metadata":{}}],"execution_count":155},{"cell_type":"code","source":"X = t.loc[:,'type'].to_list()\nY = t.loc[:,'level_mean'].to_list()\nerr = t.loc[:,'level_std'].to_list()\nplt.bar(X, Y, color=\"green\", label='mean skill level')\nplt.errorbar(X, Y, yerr=err, fmt=\"o\", color=\"black\", label='spread of skills level')\nplt.xticks(X)\nplt.xlabel(\"Year\")\nplt.ylabel(\"Expected skills levels\")\nplt.title(\"Expected skills level: average and range of \")\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:07.282469Z","iopub.execute_input":"2025-02-13T11:43:07.282847Z","iopub.status.idle":"2025-02-13T11:43:07.719860Z","shell.execute_reply.started":"2025-02-13T11:43:07.282813Z","shell.execute_reply":"2025-02-13T11:43:07.718564Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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FAvZr5JMyc/NT36o1j8/4/TqfjG6LHHHlu6WTlkyJAal6tY9zLLLFPjDfxRo0aVvlX785//vNK8mW/El/vm6YgRI0rbGjp0aGn622+/XZpeUxhTzpzcuPn6669Lj5v617/+VXZ9Fcdx7733rjS94mbY0ksvXeONu5mPU23ClOOPP750s2xuzXoc/va3v5W+QT67xwbNjzDlpZdeKi1X02Oa5kVN7/sHH3xQ2u8HH3yw7DoqehrN2mti4MCBZW9ITpkypdSTatab/HVxrs3umC8ogwYNKoVCs/riiy9KQd3f//73apev+JtZc801q8yrOG9q6u1WoV+/fsUkxSWXXLI4ffr0SvMqjlG50GtuLLXUUsUkxfvvv7/S9Oeee26OQpvf/e53NZ6XFY9BKhQKxXfeeafGdUyfPr30uMOLLrponvanJjXtZ7H4/9+XFi1aFMeNG1ft8jP/bT/22GOV5v3tb3+r9jo/q5kfgTevYcrMPSRq6+STTy4m1fe2mflzurpHZVZYe+21i0mKRx55ZKXpn3/+eelxaaecckqNy1955ZU1ftbXxXVtdm655ZbS9m+++eYa25133nk1hqTFojAFAH4MDEAPACzyLrnkkqy88sqZOnVqdt999/zzn/9Mkpx22mnp3r37bJfffffd07hx42rntWvXrrSOJ598stK8hx56KEmy7bbbZvLkyfn666+r/Wnbtm3atWuXJHn++eerLJ+kNOhxXXv66afzzTffJJkxwHJNNX799ddZe+21q9RYLBZL+73bbrulSZMm1W5n5uNUG+uuu26S5PXXX8/vf//7jB07dq7XUSwW85vf/CYnn3xyisViTj/99Fx55ZVp2LBhreuqjVVXXTXNmjVLkhx33HEZOnToAtnu4MGDUywW06xZs2y88cZl3+uK4z3ze50ku+66a5ZccskkyX/+858q2xg0aFDpvTnssMMqzZvXc21Be/jhh3PEEUdktdVWy2KLLZYGDRqUBozeddddkySfffZZJk6cWGm59u3bZ6eddkpS/TEaNWpU7r///iRVj9G3336bp556Kkmy3XbblT1G3bp1S5KMHTs2H3zwQbX7sMsuu8zRvn7yySc5/fTTKw1CPvMA2aNGjUqSvP3225WWm/mat+eee9a4/nLzKq5zK664Yjp27Fjj/n7zzTdZZ511ktT+vKjtfs5s0003zeKLL17tvFVXXbX0++eff15p3hNPPFHaz4rzuzr77rvvnO5OWW3bts0mm2wy23ZTp05N//79s8cee6Rz585p0aJFpWPyt7/9LUn5Y5IkO++8c43zKo7LrMfk6aefzvTp05PU/vypi+va7Dz++ONJkqZNm2aPPfaosd0BBxxQZRkA4MelUX0XAADwyCOPpEePHrVevlWrVvnvf/+bzTbbrHSzbLPNNkvfvn3naPnVV1+97Pw11lgjDz74YEaMGFFp+ltvvZUkueaaa3LNNdfM0bYq6kuS999/P0nSokWLrLzyynO0/NyqqDHJHG9j5hrHjx+fr776KsmcH6faOPTQQ3PppZfmpZdeyp///Of89a9/zUYbbZStttoq3bt3z7bbblsKKGpy5pln5qOPPkqDBg1y8cUX59hjj61VLfOqRYsWOfvss/Ob3/wmzzzzTNZdd9107do13bt3z1ZbbZUddtghyy23XJ1vt+K9njx5ctq0aTNHy8z8XidJkyZNsv/+++eKK67IDTfckPPPP79SGHXttdcmmXHzdOONN652+0ntzrUFZdq0afnJT36S/v37z1H78ePHZ7HFFqs07bDDDsugQYMydOjQvP7666XgI0luuOGGTJ06NQ0aNMghhxxSabkPPvgg33//fZLkyCOPzJFHHjlHNYwaNSorrrhilekrrLDCbJe94447cuihh+brr7+ebdvx48dXel1xzWvVqlWWWWaZGpdbbbXVapxXcV689957VY5jTWpzXszLfs6s3H62aNGi9Pu3335baV7FsZqT62RdmJP3/ssvv8zOO++cl156abZtyx2TZM6OS03HJKkcRM1q6aWXzuKLL176rJlZXVzXZufDDz9Mkqy00ko1fmEgSZZffvm0bNky33zzTZX/HwAAfhz0TAEAfhBWWGGFUu+PJDnooIPSqNGcfW+kVatWczR/1pt0s7v5VJ3JkyeXfp8wYUKSzPENxtqoTY1Tpkwp/T7zPs/pcaqNxo0bZ8iQITnttNPSoUOHTJ06NU8//XT++te/Ztddd83SSy+d0047rVJtsxo3blySpFAopG3btrWupS78+te/zi233FL65vjw4cPTr1+//OQnP0mXLl2y8847580336zTbc7r+VihojfFF198USkc++qrrzJo0KBKbeZ1++Xez/nl73//eylI2X333XPrrbfmrbfeyujRozNx4sRMnDixtJ/JjG/2z2qPPfZI69atk1TtnVIROG277bZZdtllK82rzTFKqn+fkso396szYsSIHHjggfn666+z/PLL5+KLL84LL7yQzz77LOPHjy/tb6dOnZJU3deKv/95+duvq/OynHndz5nNaU+2YrFY6XVdHKu5Mbv3PkmOOOKIvPTSS2nUqFGOP/74PPjggxk+fHjGjBlTOiannnpqkvLHJJmz41LTMUlqf1wWxPlT0ftsTj6LK+qctccaAPDjoGcKAPCD8LOf/SyfffZZ6fXvf//77LrrrnP07d3ZfZO5pptkrVq1yldffZWTTjop559//lzXXHEzdn7elJm55gkTJsx1cDPz8nN6nGprscUWyznnnJOzzz47r7/+ep5++ukMGTIkgwYNyvjx43Puuedm2LBhufPOO6td/txzz80tt9ySIUOG5NBDD02xWMxBBx00TzXNi3333Tf77rtvvvzyyzz99NN54oknMmjQoLz55pu577778vTTT+ell16ao3N0TlS8V0suuWTGjBlT6/VsvvnmWXHFFfP+++/nP//5T3r27JkkuemmmzJlypQUCoUceuihNW4/qd25tqBUPAbwgAMOyA033FBtm9mFPM2bN0+vXr1y9dVX5/rrr8+5556bQqGQd955J88991yS6gOnmY/RnXfemd133722uzFHrr766kyePDmtW7fOM888k6WXXrradhXB7qwq6q14fFtNyv3tV6xj/fXXz4svvjgnZc+1ed3PulBT6D6reb1OzqkPPvgg9913X5IZj8L8+c9/Xm27WXuT1KWZz/dvvvkmTZs2rbFtTcelrq5r5VRcq+bkvalos7Be3wCA+UvPFABgkfevf/0rt956a5LkD3/4Q9q1a5eJEyfm4IMPnu23bZPMtofAG2+8kWTGIz5mVnETvOJxXXNrpZVWSjLjZta7775bq3XMzsw36mtTZ5s2bUrjB8zpcZpXhUIha665Zn72s5/luuuuy6effpq99947SXLXXXfl5Zdfrna5li1bZtCgQdl2220zbdq0HHbYYbnuuuvqpKZ50b59++y5557529/+ljfeeCPXXnttCoVCxo8fn4suuqjOtlPxXo8bN65WY87MrCIsuf3220s3Dyt6XGy11Vbp0qVLjdtPav83Mb+NHTs2H3/8cZKUDdpee+212a6rIiz5+OOPM2TIkCT//xi1bNky++yzT5Vlll9++TRoMOOfYAviGL3yyitJZvSSqSlg+Oijj2oMGSre54kTJ1YKq2dVbryNivNi+PDhpfEz6tq87mddqPh8WFDXydmpOCbJvJ/rtTXzdaLcOfLll19W+4ivpG6vazWpeO/efffdfPfddzW2GzFiRClYnPX/BwCAHwdhCgCwSHvrrbdywgknJJkxQPqf/vSnXH311UmSZ599NmecccZs13HXXXeVxjGY1ejRo/Poo48mSbbYYotK8yoGoX7ooYdq9Y3Z7bffvvT7nI7fMLOKx5hNmzatxjbdu3cvfRu4pm/hl1MoFEr7PWjQoBpvNM18nOpay5Yt87vf/a70euaxOWbVokWL3H333dl+++0zbdq0HH744aUb3AuLQw45pDSuQrl9mVs77rhjkhmP2rnpppvmaV0VYcq3336bgQMHZsSIEaUBtg8//PBql5nXc21BmLnHSU1/N9OmTcv1118/23V17949nTt3TvL/H/VVca7tvffe1T62qE2bNqVHvy2IY1Sxv+WuEbM+pmxmM1/z7rjjjhrblZtXcZ0cN25cHnjggRrbzYt53c+6sOWWWyaZEZK9+uqrNbYbOHDgfK2jwpyc6x999FEee+yx+VbDZpttVgoPa3v+1OV1rSZbbbVVkhnH7O67766x3c0331xlGQDgx0WYAgAssr777rscdNBB+fbbb9OhQ4dSiLL77rvnF7/4RZLkL3/5S+lb4zUZOXJkzj333GrnnXTSSaUAYdbBoo899tg0a9Ys33zzTfr06TPbRwPNeuN85ZVXzq677ppkxjgOTz/9dI3LVtfDpmKMmJEjR9a4XOvWrfOzn/0sSXLhhRfmkUceKVvj5MmTS4PxVqjY788//zxnn312tcvNfJxqo9y3lpPK3+Kf3XgozZs3z1133ZUdd9wx06dPzxFHHJEBAwbUura59emnn5Z9XMy3335b+pZ/XY7tsuqqq2a33XZLMuMxd6+//nrZ9hMmTKixt8FKK62UzTbbLMmMgOC6665LsVhMs2bN0qtXr2qXqYtzbX5r3759KeSo6Qbun/70p9mej8mMoLFigPlbb701Dz30UIYPH56k+kd8Vfj1r3+dZEbYW9N1p0KxWJyjWmpS8a3+J598strAd9iwYTnvvPNqXH7jjTcuDZh+zjnnVDuw9wcffJBLLrmkxnXssMMOWXvttZMkv/jFL/Lpp5+WrfmLL74ojX80p+Z1P+vCoYcemsaNGydJTjzxxGqv2VdfffV8e9TZrGbuKVbduf7999/npz/9adkAal516NChFKb985//rLYH5pgxY2r8XEnq9rpWk912263Uo+nUU0+ttpfM8OHDS3+vG2ywQdZbb7252gYA8MMgTAEA6t2kSZPy9ddfz/Zn1sFtf/vb3+aVV15JoVDIgAEDstRSS5XmnX/++enWrVumT5+eww47rOzjQbp27Zq+ffvml7/8ZYYNG5axY8fmhRdeyAEHHFD6NvMhhxySjTbaqNJyyy67bC6++OIkM3q3bLDBBrn66qvz3nvv5auvvsrnn3+eZ599NhdffHG6d+9eZflkxg2mJZdcMlOmTMl2222X3/3ud3n55ZczduzYfPHFF3nsscfyu9/9rkqvmCTZcMMNk8zo1fLCCy9k0qRJmTp1aqZOnVrpWJ1zzjlZffXVM2XKlOy444459thj8/jjj+fLL7/MuHHj8t577+X222/Psccem+WWW67St2+TGd+y7969e5IZN5p/8Ytf5LXXXqtynLp27VrjMZ6do48+OmuuuWbOPvvsPProoxk5cmTGjRuXt956KxdddFHpef/LLLNMtt5669mur1mzZrnjjjvSs2fPTJ8+PX369Em/fv1qXd/cePDBB9OpU6f89Kc/zcCBA/Pee+9l3Lhx+eijj0q9ZipuFtf1mC6XXXZZll566YwdOzabbLJJTjvttDz33HMZPXp0xowZk7feeis33nhjevfunU6dOuXJJ5+scV0VgcDgwYNz5ZVXJpkx8HqbNm1qXGZez7XZGTFiRAqFQgqFQnr37j1XyyYzBtHed999kyQDBgzIr371qwwbNixjxozJ888/n969e+ess84qBQizU3GMJkyYUAqSOnbsmO22267GZfbdd99SCHPaaadl1113zZ133plPPvkk48ePz8cff5zBgwenb9++WX311UvhS20ccMABSWY83mynnXbKgw8+mC+//DLDhw/PRRddlK233jqtWrXKkksuWeM6LrjggiTJJ598ki233DIDBw7Ml19+mc8++yz/+c9/svXWW6d9+/Y1Ll8oFNK/f/+0aNEiw4cPz7rrrptzzz03Q4cOzdixYzN69OgMGzYsAwYMyP77758uXbrM9SPQ6mI/51WHDh1KPegefvjh7LjjjnnssccyZsyYvPPOOzn99NNz9NFHz9N1cm5suOGGpUDlV7/6VS6++OK8//77GTVqVO6777507949Dz744Byf67X1l7/8JY0bN87XX3+d7t27p3///hk5cmS+/PLL3H777dlyyy3z9ddflx4nWZ26vK5Vp0mTJqVHLr777rvZfPPNc/vtt+eLL77IyJEj079//2y55ZYZN25cGjVqVBp3CQD4ESoCANSDa665pphkrn6GDx9eWv7ee+8tFgqFYpLir3/962q38eqrrxabNWtWTFLce++9q8yvWO+VV15Z3G677Wrcbvfu3Ytff/11jfvy73//u9i8efPZ1r/EEktUu/zQoUOLyy+/fNllu3TpUmW5wYMHl47BrD/XXHNNpbaff/55sXv37nN0nC+66KIq2/ryyy+La665Zo3LHHroocW+ffvWWOvszEltbdu2LT711FNVlq1pn4vFYnHy5MnFXXbZpZik2KBBg+LVV19daX65mocPH15a9yOPPFJjzUcccUSl6XN6bp966qlzc4jmaH+LxWLxnXfeKa611lpzVMMdd9xR43bGjBlTbNKkSaX2d99992zrm5dzbXbHfOb5sx73OfX5558Xu3btWmNNW2+9dfGee+6p9rpTnQ022KDS8r/5zW9mW8N3331X/OUvfzlHx6jctaumc2Bmv/jFL8pekx5//PFily5dikmKffv2rXYd559/fo3XmrZt2xaff/750uv//Oc/1a7j2WefLXbu3HmO9vmVV16Z7X7V9X7W9Pc8q3LHfurUqcWDDz64xjrWWmut4sCBA+f43KrOEUccUUxmfC7NzqOPPlr2s+k3v/lN2WvgzNeyeanpxhtvLDZu3LjaGpo3b1685557SufGn/70p2rXUVfXtXIuvPDCYsOGDWtcb/PmzYsDBw6scfm5+bsEABZNeqYAAIucL774IkcccUSKxWLWX3/9/PnPf6623VprrZW//e1vSZLbbrut9O36WTVp0iT33Xdf/vGPf2SDDTbIYostlpYtW2aDDTbIJZdcksGDB6dly5Y11nPkkUdm+PDhOfPMM7PZZpulbdu2adiwYVq2bJlVV101BxxwQP71r3/lvffeq3b5tddeO2+++WYuvvjibLPNNmnXrl0aN26cDh06ZMMNN8ypp55a7WNatt122zz00EPZbbfd0qFDh9IYKtVZeumlM2TIkNx999056KCDsvzyy6d58+Zp3Lhx2rdvny233DK//e1v89RTT+X444+vsvxSSy2V559/Puecc07WXHPNNG/ePEsssUS22GKLXHPNNfM8HkH//v3zr3/9KwceeGDWWmuttGvXLg0bNsziiy+eTTbZJGeddVbefvvt0qOn5lTTpk1z2223Zbfddsv06dPzk5/8JP/3f/83T7XOzv7775+77747J554YjbddNMst9xyadq0aZo3b55VVlklvXv3ztNPPz3bRzzV1sorr5yXX3451113Xfbee+906tQpTZs2TZMmTbLMMstk2223Td++fTN06NDsscceNa5nySWXzC677FJ6vdRSS5Ue2VPOvJ5r89vSSy+d559/PieeeGK6du2axo0bZ8kll8xmm22WSy65JA8//HCaN28+x+ub9ZFe5R7xVaFx48a55JJLMnTo0Bx77LFZc80107p169I5v9566+Woo47K7bffPs9jq/zzn/9Mv379summm6Zly5Zp3rx5VlpppRx33HF5+eWXS2N9lHPSSSfl8ccfz5577pl27dqladOm6dq1a4455pi89NJLWXXVVUttW7duXe06Nt5447z99tu54oorsvPOO6djx45p0qRJmjVrls6dO2fnnXfOX/7yl7z33ntZZ5116mU/51XDhg1z3XXX5brrrstWW22V1q1bp2XLlunWrVvOPPPMPPPMM1liiSXmex0Vtt566zz33HM58MAD0759+9Lnym677Za777679Pk4v+2///556aWXcvDBB6dDhw5p0qRJOnXqlMMOOyzPP/98dt5559KjEWs6f+rqulbOr371qwwdOjQ/+9nPsuKKK6Z58+Zp2bJl1lhjjZx44ol55513svfee9f6OAAAi75CsTjL8zIAAH4kCoVCkuSaa66p1SODAEhefvnlrL/++kmSF198sfQ7zImxY8eWxo+69dZbs88++9RzRQAA1dMzBQAAgFqr6DnXtGnTrLnmmvVcDYuaO++8s/T7BhtsUI+VAACUJ0wBAACgRmPHjq1x3ltvvVUapH6vvfZKkyZNFlRZLCLKnT9ffPFFTj/99CTJJptski5duiyosgAA5lrND9YGAADgR2+HHXbIOuusk169emWdddZJy5Yt89lnn2XQoEE599xzM3HixDRt2rR0Uxxm1qdPnzRu3DgHH3xwNthgg7Rp0yajR4/O4MGDc8455+STTz5Jkpx99tn1XCkAQHnCFAAAAGo0ZcqUXHPNNbnmmmuqnd+sWbNce+216dat2wKujEXBtGnTcuedd+bWW2+tdn6DBg1y4YUXZvvtt1/AlQEAzB1hCgAAADW66KKLctttt+WJJ57I559/nrFjx6Z58+bp0qVLdthhh/zqV79K586d67tMFlJnnHFGVl999TzyyCMZOXJkxowZkyZNmmTZZZdNjx49ctxxxwniAIBFQqFYLBbruwgAAAAAAICF1Y+qZ8r06dMzcuTILLbYYikUCvVdDgAAAAAAUI+KxWImTpyYZZZZJg0aNKix3Y8qTBk5cmSWW265+i4DAAAAAABYiHz88cfp1KlTjfN/VGHKYostlmTGQWndunU9VwMAAAAAANSnCRMmZLnllivlBzX5UYUpFY/2at26tTAFAAAAAABIktkODVLzA8AAAAAAAAAQpgAAAAAAAJQjTAEAAAAAAChDmAIAAAAAAFCGMAUAAAAAAKAMYQoAAAAAAEAZwhQAAAAAAIAyGtV3AQAAAADAwun777/PtGnT6rsMgNlq2LBhGjduPN/WL0wBAAAAACqZMGFCRo8enSlTptR3KQBzrGnTpmnXrl1at25d5+sWpgAAAAAAJRMmTMinn36aVq1apV27dmncuHEKhUJ9lwVQo2KxmO+//z7jx4/Pp59+miR1HqgIUwAAAACAktGjR6dVq1bp1KmTEAVYZDRv3jyLLbZYPvnkk4wePbrOwxQD0AMAAAAASWaMkTJlypS0adNGkAIscgqFQtq0aZMpU6bk+++/r9N1C1MAAAAAgCQpDTY/PwdxBpifKq5fFdezuiJMAQAAAAAq0SsFWFTNr+uXMAUAAAAAAKAMYQoAAAAAAEAZwhQAAAAAAIAyGtV3AQAAAADAoqVw1qI1pkqxb7G+S2AWQ4YMyTbbbJPu3btnyJAh87xMxTgZxWLl97pHjx559NFH88gjj6RHjx5ztJ1+/fqlT58+OeKII9KvX785WmZhUZv9Zc7omQIAAAAAAFCGnikAAAAAACz0Nt5447z55ptp0aJFfZfCj5AwBQAAAACAhV6LFi2y2mqr1XcZ/Eh5zBcAAAAAwFwoFAqlMTquvfbabLzxxmnVqlWWWmqpHHTQQfnoo4+SzBi/49JLL826666bli1bpl27dundu3e+/PLLGtf9zjvv5Oijj86KK66YZs2apU2bNtl6661z7bXXVtv+ww8/zF/+8pdsu+226dy5c5o2bZrFF188W265Za688spMnz69yjIjRoxIoVDI8ssvn2KxmKuuuiobbLBBWrZsmTZt2mTHHXfM008/PdfH5bPPPsuvfvWrrLLKKmnWrFlatGiR5ZZbLtttt13+/ve/z/F6Ro0alc033zyFQiEHHHBApkyZkmTGmCmFQqHexwIZOXJkTjrppKy++upp0aJFFltssWy00Ua59NJLM3Xq1EptN9tssxQKhdxwww01ru/SSy9NoVDI3nvvXWXeiy++mEMOOaT03i655JLZaaedcs8999T5flGeMAUAAAAAoBZ+97vfpU+fPllsscWy8847p0WLFrnhhhuy5ZZbZty4cTnwwANz8sknp2PHjtlpp53SsGHD9O/fPzvssEO+++67Kuu7+eabs8466+Sqq65KkyZNsssuu2TDDTfMSy+9lMMOOyxHHnlklWX+85//5NRTT82IESOyyiqrZJ999sm6666b559/Pj//+c+z3377VRmUfWZ9+vTJL3/5yyy++OLZbbfd0qFDhzz44IPZZptt8uyzz87xsfj888+z4YYb5uKLL86UKVPSs2fP7LHHHunatWteeeWVnH322XO0nnfeeSebbbZZnn766Zxyyim54YYb0rRp0zmuY3577LHHsuaaa+Yf//hHJk+enB122CFbbLFF3n///Rx33HHZdddd8/3335fa9+nTJ0nKDmR/zTXXJEmV9/eiiy7KxhtvnOuvvz5t27bNHnvskW7dumXIkCHZdddd88c//rHud5AaecwXAAAAAEAt/Otf/8oLL7yQddZZJ0kyadKk7LjjjnniiSfSvXv3fPvtt3nrrbfSpUuXJMno0aOz2Wab5dVXX83NN9+cQw45pLSu1157LYcddlgKhUJuvfXW7LPPPqV5H374YXbfffdcc8016dGjRw4//PDSvJ122il77bVX1lxzzUq1jRw5MrvssksGDhyYW265Jfvtt1+V+j/88MMMGTIkw4YNyyqrrJIkmTZtWo466qhcffXVOeOMM3L//ffP0bG46qqrMnLkyBx11FG54oorSj13kuT777/PY489Ntt1PP7449lrr70yfvz4XHHFFTn66KPnaNsLyueff5599tknX331VS677LIcffTRadBgRn+FMWPGZP/9988DDzyQc889N2eccUaS5MADD8yJJ56YBx98MJ9++mmWXXbZSut89dVX89JLL2XppZfOzjvvXJp+//3358QTT0zbtm1z6623Zuutty7Ne+2117LLLrukb9++6d69e7p3774A9h49UwAAAAAAauGPf/xjKUhJkubNm+ekk05KMuOG98UXX1wKUpKkXbt2OeaYY5IkgwcPrrSuc845J1OmTMnZZ59dKUhJki5duuTf//53kuTiiy+uNG+jjTaqEqQkyTLLLJO//vWvSWb0eKnJJZdcUgpSkqRhw4Y555xzkiSPPvpopV4W5XzxxRdJkp49e1YKUpKkcePG2W677cou/9///rfUY+euu+5a6IKUJLnwwgszZsyYHHvssTnmmGNKQUqStG3bNgMGDEjjxo1z6aWXlnoDtW7dOvvuu2+mT5+eAQMGVFlnRa+Uww47LI0a/f++D3379k2xWMwVV1xRKUhJkrXWWisXXHBBkhnvHwuGMAUAAAAAoBZ22WWXKtNWXnnlJEmjRo2y44471jh/5MiRpWnTp0/PvffemyQ54IADqt3WhhtumFatWuXll1/O5MmTK82bMmVK7rrrrpxxxhn5+c9/nj59+qR379658sorkyRvv/12tets1KhRevbsWWV6hw4dssQSS2TKlCkZM2ZMtcvOauONN06SnHrqqRk4cGC+/vrrOVouSf785z/nkEMOSdu2bfP4449X6qGxMBk0aFCSmt+jZZddNiuvvHJGjRqVd999tzS94lFf/fv3r9T++++/z3XXXZek8iO+Ro8eneeeey7NmzfP7rvvXu22KsaNeeqpp2q3M8w1j/kCAAAAAKiFzp07V5nWqlWrJEnHjh0r9TSosNhiiyVJpUBkzJgxmTBhQpJkueWWm+12x4wZU3pc1DPPPJMDDjigNOh9dSrWPauOHTumcePG1c5r3bp1xo0bVyW4qclhhx2WBx98MNddd1323XffNGzYMGussUa23HLL9OrVK9tuu221yz355JN59NFH06xZszz22GNZccUV52h79eGDDz5Ikmy11VazbTtq1KhSj58ePXpkhRVWyNtvv52nnnoqm2++eZLk7rvvzqhRo7LJJptk9dVXLy07fPjwFIvFTJo0abbjxYwaNaq2u8NcEqYAAAAAANTCzI95mpt5s5o+fXrp9yOOOGK27StusH/77bfZa6+98sUXX6RPnz455phjstJKK6V169Zp2LBh3nnnnay66qo1DkA/NzXOToMGDXLttdfmtNNOy6BBg/Lkk0/mySefzOWXX57LL788u+++e2677bY0bNiw0nLdunVL48aN88ILL+S4447LrbfemubNm9dZXXWp4n3q1atXWrZsWbZt27ZtS78XCoX07t07Z5xxRvr161cKUyoe8VXRc2XW7bRq1Sr77rtvndXPvBGmAAAAAADUo3bt2qV58+aZNGlS/v73v6ddu3ZztNxjjz2WL774Iuuvv36uvvrqKvNnftTUgrLGGmtkjTXWyMknn5xisZiHH344Bx98cO66664MGDCgSnCw+OKL584778xuu+2We++9NzvvvHPuvvvuUg+fhclyyy2Xd999N7/97W+z4YYbztWyRxxxRM4888zceOONueiiizJhwoTce++9ad68eQ488MAq20lmhDBXX311nYZe1J53AQAAAACgHjVs2DA77LBDkuSmm26a4+XGjh2bpPrHjSXJtddeO+/FzYNCoZDtttsuBx98cJLklVdeqbZd69atc99992XHHXfMo48+mu233z7jxo1bgJXOmYqxXObmParQuXPnbLfddpkwYUIGDhyYa6+9NlOnTs0+++yTNm3aVGq7zDLLZO21187EiRNz33331UntzDthCgAAAABAPevbt2+aNGmSk08+Of3796/06K8Kw4YNy8CBA0uvK8bZGDx4cN54441Kba+66qrceOON87fomQwYMCAvvvhilekTJ07MkCFDkiRdunSpcfkWLVrkrrvuyj777JNnn302PXr0yBdffDG/yq2Vk08+OYsvvnguuOCCnH/++fnuu++qtBk+fHiNIVbFIPPXXHNNjY/4qnD22WeX5t91111V5heLxTz77LN54IEHarUvzD1hCgAAAABAPVt//fVLN+F79+6dLl26ZKeddsqhhx6aXXbZJcstt1zWWmutSr0i1ltvvey5556ZOHFi1ltvvey000456KCDsvrqq+fnP/95TjvttAVW/8CBA7Phhhtm2WWXza677ppDDz00u+66a5Zbbrm88sorWXPNNfOzn/2s7DqaNGmSm266KYcddlheffXVbL311vn4448X0B7MXqdOnXLHHXdkiSWWyG9+85sst9xy2W677XLooYdm9913z0orrZQVVlghl156abXL77XXXlliiSUyePDgvP7661l++eWz7bbbVtt29913z0UXXZSxY8dmjz32yMorr5zddtsthxxySHbcccd06NAhm266aR5++OH5ucvMxJgpAAAAAAALgf322y8bbbRRLr744jz44IN58sknM23atCy99NJZaaWV8stf/jK9evWqtMzNN9+ciy66KAMGDMgTTzyRZs2aZcMNN8zFF1+clVdeOeecc84Cqf3Xv/51unbtmqeeeiovvfRSxo4dmyWXXDJrrLFGDj744PTp02e2g7YnMx551r9//7Rq1SqXX355ttpqqzz00ENZaaWVFsBezN7WW2+d119/PZdeemkGDRqU559/PlOmTEn79u3TuXPnHHrooTUOGt+sWbMcdNBBueyyy5LMGEelUCjUuK3jjz8+2267bS655JI88sgjGTx4cBo0aJAOHTpkvfXWy6677mqA+gWoUCwWi/VdxIIyYcKEtGnTJuPHj0/r1q3ruxwAAAAAWKhMnjw5w4cPT9euXdOsWbP6Lgdgrs3tdWxOcwOP+QIAAAAAAChDmAIAAAAAAFCGMAUAAAAAAKAMYQoAAAAAAEAZwhQAAAAAAIAyhCkAAAAAAABlCFMAAAAAAADKWGjDlN69e6dQKJT9mTx5cn2XCQAAAAAA/MA1qu8CZmeLLbbISiutVO28hg0bLuBqAAAAAACAH5uFPkz56U9/mt69e9d3GcCP1LRp0/L444/ns88+S8eOHbPVVlsJcgEAAADgR2ahD1MA6svAgQPzq1/9Kp988klpWqdOnXLRRRdln332qcfKAAAAAIAFaaEdMwWgPg0cODC9evWqFKQkyaeffppevXpl4MCB9VQZAAAA/PB88803pXGSv/nmm/ouB6CKhb5nyiOPPJLXXnstEydOTNu2bbPxxhtnl112SdOmTeu7NOAHatq0afnVr36VYrFYZV6xWEyhUMgJJ5yQPffc0yO/AAAAAOBHYKEPUwYMGFBlWseOHXP11VenZ8+eZZedMmVKpkyZUno9YcKEOq8P+OF5/PHHq/RImVmxWMzHH3+cxx9/PD169FhwhQEAAAAA9WKhDVPWWWedXHTRRdluu+3SuXPnTJo0KUOHDs2ZZ56Zp556KnvssUceeOCBsjcyzz333Jx11lkLruhFWOGsQn2XAAuP1+as2TaXbpM8On9LgUVJsW/V3lwAAAD8OIwYMSJdu3ZNly5dMmLEiPm+vSlTpuSss87KzTffnI8++ijffffdfNl2jx498uijj+aRRx6Z4y/V1rTMmWeembPOOit9+/bNmWeeWZo+ZMiQbLPNNunevXuGDBlSp/UvyG3ML717907//v1zzTXXpHfv3vVWx0Ibppx44omVXi+22GLZYYcdsv3222fvvffOHXfckRNOOCGvvPJKjev43e9+l5NOOqn0esKECVluueXmV8nAD0WrOm4HAAAAQJ06/fTT87e//S1LL7109txzz7Ro0SLt2rWr77L4AVtow5SaFAqFnHXWWbnjjjsydOjQfPzxxzUGJE2bNjW2CjD3uiRpnaTckwFb/68dAAAAAAvcTTfdlGTG49pXXnnleq6msgEDBuTbb79N586d67sU6lCD+i6gNlZfffXS7+XGNQColQZJyg/JNGP+InkFBQAAgIXPtGnTSr8/9thjlV5DdT766KMkWeiClCTp3LlzVltttbRo0aK+S6EOLZK3AseMGVP6fbHFFqvHSoAfrDWS7J8ZPVBm1vp/09dY4BUBAADAD9LAgQOzxhr//x/au+yyS5ZffvkMHDiwHqsq7913382RRx6Zrl27pmnTpmnVqlW6dOmSXXfdNddcc02ltv369UuhUEjv3r0zZsyYHHvssencuXOaNm2aLl265MQTT8y4ceOqbGPIkCEpFArp0aNHvv3225xxxhlZffXV06JFiyy//PKV2r744os55JBDSutdcskls9NOO+Wee+6ptv433ngjffv2zRZbbJFll102TZo0Sdu2bbP99tuXenzU5O6770737t2z2GKLpU2bNtlqq61yxx13zN0BnMUnn3yS4447LiuvvHKaNWuWNm3aZIsttsiVV15ZJVhbfvnlUygUUizOGLezUCiUfvr16zdH27v55puz/fbbp23btmncuHHatm2bNdZYIz/72c/y6quvznHd11xzTZo0aZIlllgijzzySGl6jx49UigU6mRskhdffDEHHHBAOnXqlCZNmqR169ZZYYUVsu+++87zcZ/ZuHHj0rdv36y77rpZbLHF0qJFi6y11lo5++yz8+2331Zqe9BBB6VQKOS8886rcX133313CoVC1ltvvSrz3nnnnRx99NFZccUVS+/31ltvnWuvvbbO9md+WOQe85UkN9xwQ5KkdevWWXXVVeu5GuAHa40kqyX5MMnXmTFGSpcsojE0AAAALHwGDhyYXr16lW6MV/j000/Tq1ev3HLLLdlnn33qqbrqDRs2LFtssUUmTJiQVVddNbvttlsaNmyYTz75JI899lg+/fTT9OnTp8py48aNyyabbJIxY8ZUutl+4YUX5t57783jjz+epZZaqspykydPTo8ePfLGG29k6623zjrrrFPpy+YXXXRRTjrppEyfPj3rrrtuNtlkk3z++ecZMmRIHnjggZx11lk544wzKq3zggsuyL///e+sttpqWWuttbL44ovno48+yiOPPJLBgwfnmWeeyQUXXFClln/84x+lMao33njjrLjiinn33Xez1157VRq7em48//zz6dmzZ8aOHZvOnTtnr732yvjx4zNkyJA89dRTue2223LnnXemSZMmSZJevXpl9OjR6d+/f5LkiCOOKK1rpZVWmu32/vjHP6Zv375p1KhRNt988yy77LIZP358Pvroo/z73/9Ot27dsvbaa892PWeccUb+9Kc/Zfnll8+gQYMqBYJ1ZfDgwdl5553z/fffZ5111slmm22WadOm5dNPP82gQYMybdq07LnnnvO8nTfeeCM9e/bMxx9/nI4dO2bLLbdM48aN89xzz+X000/PrbfemiFDhqRNmzZJkj59+uSGG25I//79c+qpp1a7zopQ8cgjj6w0/eabb87hhx+eyZMnZ7XVVssuu+yS8ePH59lnn81hhx2Whx9+OFdfffU879P8sFCGKa+88ko++uij7LLLLmnU6P+XOH369FxzzTU57bTTkiTHH398GjduXF9lAj8GDZJ0re8iAAAA4Idn2rRp+dWvflUlSEmSYrGYQqGQE044IXvuuWcaNmxYDxVW74ILLsiECRNy9tln5/e//32leZMmTcrzzz9f7XJ33nlnNt100zz33HNZcsklkyRfffVVdt111zz11FM5/vjj89///rfKcs8++2zWXnvtvPfee+nQoUOleffff39OPPHEtG3bNrfeemu23nrr0rzXXnstu+yyS/r27Zvu3bune/fupXmHHXZYTjvttKywwgqV1vf2229n++23zz/+8Y8ceOCB2XjjjUvzXn311Zx88slp0KBBbrzxxvTq1as077rrrsthhx02u0NXxZQpU7Lffvtl7Nix+fnPf56LL764dL/3gw8+yHbbbZf7778/Z511Vs4555wkyd///vckKYUpc9obpWJ75513Xlq1apUXXnihyhf1P/zww0yaNKnsOr777rsceeSRue6667Lhhhvm7rvvztJLLz3HNcyNc845J99//32uvfbaHHLIIZXmjR8/Pm+++eY8b2PSpEnZY4898vHHH+cPf/hDTj/99FJw9e233+anP/1p/vvf/+bEE08shRzbb799OnfunLfeeivPPPNMNt1000rrHD16dO666640adIkBx98cGn6a6+9lsMOOyyFQiG33nprpaD0ww8/zO67755rrrkmPXr0yOGHHz7P+1bXFsrvV48YMSJ77rln2rdvn+233z6HHHJIdt1113Tt2jU//elPM3ny5Bx00EHp27dvfZcKAAAAANTC448/XnY85GKxmI8//jiPP/74Aqxq9r744oskMx5HNqvmzZtXCjRmdfnll5eClCRZfPHFc8UVV6RQKOSmm26q8XhceumlVYKUJOnbt2+KxWKuuOKKKttda621Sr1LLrnkkkrzunfvXiVISZJVV101p59+epLklltuqTTvkksuybRp07LffvtVClKS5JBDDskee+xR027X6Oabb86HH36YZZZZJhdeeGGlL86vsMIKpeDkkksuyeTJk+d6/bOaMGFCJk2alBVWWKHaJx516dIlq622Wo3Ljxs3LjvuuGOuu+667LHHHnn00UfnW5CSlD/X2rRpUyXEqI3+/fvn/fffz2677ZY//elPpSAlSVq0aJGrrroq7du3z3/+85/S4+gaNGhQ6hE062Ptkhnh2vfff5899tgjbdu2LU0/55xzMmXKlJx99tlVepx16dIl//73v5MkF1988Tzv1/ywUIYp66yzTk444YR069Ytb731VgYOHJjBgwcnmdGNa9CgQbn++usr9VoBAAAAABYdn332WZ22W1Aqemscc8wxuf/+++f4Jv8666yTddddt8r0tdZaK+utt16mT5+exx57rMr89u3bZ6uttqoyffTo0XnuuefSvHnz7L777tVus0ePHkmSp556qsq8r7/+OjfffHNOO+20HHXUUendu3d69+6dW2+9NcmMXiozqxj/49BDD612WzM/bmtOVazzwAMPTNOmTavM32effbLEEktk4sSJefHFF+d6/bNaaqmlsvzyy+fVV1/Nr3/967zxxhtzvOzw4cOz+eab59FHH80vf/nL3HbbbfN9gPmKc+2QQw7JE088kalTp9b5NgYNGpQkOeCAA6qd36pVq2y44YaZOnVqpV5XvXv3TqFQyI033lilN091j/iaPn167r333rLb2nDDDdOqVau8/PLLdRKe1bWFMo3o2rVr/vGPf9R3GQAAAADAfNKxY8c6bbegnHzyyXniiSfy0EMPpWfPnmncuHHWWWedbL311jnwwAOz0UYbVbtc1641P0e8a9eueemll6rtmTLrYPMVhg8fnmKxmEmTJlUbRMxs1KhRlV7fdddd6dOnT6WxV2Y1YcKESq8raqtpP8rtX00+/fTTsssWCoV07do148aNK7WdVwMGDEivXr1ywQUX5IILLsiSSy6ZTTbZJDvssEMOO+ywtGvXrtrljjrqqEydOjU//elPq/T0mV/OPffcvPrqq7n33ntz7733pnnz5ll//fXTo0ePHHLIIVl99dXneRsffPBBkhmPfpvdo9pmPo9WWGGFdO/ePUOGDMltt91WepzXyy+/nKFDh2aZZZbJjjvuWGo/ZsyY0jm13HLLzbauMWPGZNlll53r/ZmfFsowBQAAAAD4Ydtqq63SqVOnfPrpp9WOm1IoFNKpU6dqe2XUpxYtWuTBBx/M888/n/vuuy9PPfVUnnrqqbzwwgu54IIL8otf/CL//Oc/a7Xu6o5D8+bNq207ffr0JDN6Duy7775zvI1PP/00BxxwQCZNmpRTTjklhxxySJZffvm0atUqDRo0yAMPPJCddtqp2lp+CLbaaquMGDEigwYNyqOPPpqnnnoq999/f+6999707ds3t912W7bbbrsqyx166KEZMGBArrvuuuyzzz7Zeeed53utHTp0yAsvvJBHH300Dz30UJ588sk8++yzefLJJ/PnP/855557bn7729/O0zYqzqOePXvO9pFlXbp0qfT6yCOPzJAhQ9KvX79SmFLRK+Xwww+vNNZRxXaSOevFNLuAsD4IUwAAAACABa5hw4a56KKL0qtXrxQKhUo37wuFQpLkwgsvXKgGn5/ZRhttVOqFMnXq1Nx+++05/PDDc9lll6VXr17ZZpttKrUfPnx4jesaMWJEkqRTp05zvP2Kb/cXCoVcffXVadBgzkZ0uOuuuzJp0qTsvffe+ctf/lJl/rvvvlvtcssuu2zef//9jBgxIt26dasyv2If5kZFz4OK3hHVqThuddlLoXnz5unVq1dp7JdRo0blD3/4Q6666qoceeSR+fDDD6ssc8QRR2TnnXfOoYcemr322ivXX3/9XIVYtVUoFNKjR4/SI9smT56cfv365dhjj81pp52WXr16ZcUVV6z1+pdbbrm89dZb+clPflJlLJzZ2XffffPLX/4ygwcPzscff5yll146119/fZKkT58+ldq2a9cuzZs3z6RJk/L3v/+9xh5AC7OFcswUAAAAAOCHb5999sktt9ySZZZZptL0Tp065ZZbbqkySPXCqlGjRunVq1d22mmnJMkrr7xSpc2rr76aV199tcr0119/PS+99FIaNGhQdvD6WS2zzDJZe+21M3HixNx3331zvNzYsWOTVO1lkMzoGVNxM3xW3bt3TzJjcPHqDBgwYI5rqFARENx4443VjpFx2223Zdy4cVlsscWywQYbzPX659RSSy2Vv/71r0mSjz76qDTQ+qz233//3HbbbWnQoEEOOOCAWu3zvGrWrFl+/vOfZ+2118706dOrPafmRkUPm5tuummul23RokUOOOCATJ8+PQMGDMhdd92VMWPGZIsttsgqq6xSqW3Dhg2zww471HpbCwNhCgAAAABQb/bZZ59KA4Hfc889GT58+EIbpFx22WVVBmdPks8//zwvvPBCkpqDimOOOabSjfrx48fnmGOOSbFYzL777jtHY0nM7Oyzz04yoxfAXXfdVe02n3322TzwwAOlaRXjbNxyyy357LPPStOnTZuWM844o9rB6pPkuOOOS8OGDXPTTTfltttuqzTvhhtuyO233z5XtSfJfvvtl86dO2fkyJE56aSTKg2wPnz48Pz6178ubbtZs2Zzvf5Zffjhh/m///u/KuPBJCkdvyWWWCKtW7eucR277rpr7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\n"},"metadata":{}}],"execution_count":156},{"cell_type":"markdown","source":"# Skills comparison against role\n","metadata":{}},{"cell_type":"code","source":"roles = list(skills_employee_pd.role.unique())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:07.720894Z","iopub.execute_input":"2025-02-13T11:43:07.721206Z","iopub.status.idle":"2025-02-13T11:43:07.726430Z","shell.execute_reply.started":"2025-02-13T11:43:07.721181Z","shell.execute_reply":"2025-02-13T11:43:07.725378Z"}},"outputs":[],"execution_count":157},{"cell_type":"code","source":"skills_employee_pd.dtypes","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:07.727383Z","iopub.execute_input":"2025-02-13T11:43:07.727918Z","iopub.status.idle":"2025-02-13T11:43:07.748119Z","shell.execute_reply.started":"2025-02-13T11:43:07.727884Z","shell.execute_reply":"2025-02-13T11:43:07.747115Z"}},"outputs":[{"execution_count":158,"output_type":"execute_result","data":{"text/plain":"skill object\nrole object\nmonth int64\nyear int64\nlevel int64\nself bool\ncourse bool\nuser bool\ndtype: object"},"metadata":{}}],"execution_count":158},{"cell_type":"code","source":"skills_type = skills_employee_pd.loc[:,['self','user','course']].idxmax(axis=1)\nskills_employee_pd['source'] = skills_type\ncolumns = ['skill','role','month','year','level','source']\nskills_employee_pd = skills_employee_pd.loc[:, columns]\nskills_employee_pd.shape\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:07.749174Z","iopub.execute_input":"2025-02-13T11:43:07.749524Z","iopub.status.idle":"2025-02-13T11:43:07.771897Z","shell.execute_reply.started":"2025-02-13T11:43:07.749497Z","shell.execute_reply":"2025-02-13T11:43:07.770944Z"}},"outputs":[{"execution_count":159,"output_type":"execute_result","data":{"text/plain":"(24, 6)"},"metadata":{}}],"execution_count":159},{"cell_type":"code","source":"roles_pd.dtypes","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:07.772824Z","iopub.execute_input":"2025-02-13T11:43:07.773083Z","iopub.status.idle":"2025-02-13T11:43:07.791345Z","shell.execute_reply.started":"2025-02-13T11:43:07.773062Z","shell.execute_reply":"2025-02-13T11:43:07.790321Z"}},"outputs":[{"execution_count":160,"output_type":"execute_result","data":{"text/plain":"role id object\nrole object\nskill object\nlevel int64\ndtype: object"},"metadata":{}}],"execution_count":160},{"cell_type":"code","source":"job_skills_pd = roles_pd.merge(skills_employee_pd, left_on=['role id','skill'], right_on= ['role','skill'], how='inner')\njob_skills_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:07.792382Z","iopub.execute_input":"2025-02-13T11:43:07.792811Z","iopub.status.idle":"2025-02-13T11:43:07.826529Z","shell.execute_reply.started":"2025-02-13T11:43:07.792775Z","shell.execute_reply":"2025-02-13T11:43:07.825285Z"}},"outputs":[{"execution_count":161,"output_type":"execute_result","data":{"text/plain":" role id role_x skill level_x role_y month year \\\n0 ROLE_226242 egmhxghxbak 1010101525 4 ROLE_226242 2 2023 \n1 ROLE_860638 ymclhotoiml 1010101867 4 ROLE_860638 9 2025 \n2 ROLE_860638 ymclhotoiml 1010101867 4 ROLE_860638 5 2025 \n3 ROLE_713858 lewqumkrved 1010101122 5 ROLE_713858 3 2024 \n4 ROLE_226242 egmhxghxbak 1010101263 5 ROLE_226242 7 2023 \n5 ROLE_860638 ymclhotoiml 1010101767 5 ROLE_860638 12 2025 \n6 ROLE_713858 lewqumkrved 1010101762 4 ROLE_713858 5 2024 \n7 ROLE_226242 egmhxghxbak 1010101122 4 ROLE_226242 2 2023 \n8 ROLE_860638 ymclhotoiml 1010101122 4 ROLE_860638 4 2025 \n9 ROLE_713858 lewqumkrved 1010101873 4 ROLE_713858 6 2024 \n10 ROLE_226242 egmhxghxbak 1010101043 3 ROLE_226242 12 2023 \n11 ROLE_860638 ymclhotoiml 1010101928 4 ROLE_860638 10 2025 \n12 ROLE_713858 lewqumkrved 1010101767 3 ROLE_713858 11 2024 \n13 ROLE_713858 lewqumkrved 1010101767 3 ROLE_713858 12 2024 \n14 ROLE_713858 lewqumkrved 1010101767 3 ROLE_713858 2 2024 \n15 ROLE_226242 egmhxghxbak 1010101954 5 ROLE_226242 12 2023 \n16 ROLE_860638 ymclhotoiml 1010101679 3 ROLE_860638 4 2025 \n17 ROLE_713858 lewqumkrved 1010101767 5 ROLE_713858 11 2024 \n18 ROLE_713858 lewqumkrved 1010101767 5 ROLE_713858 12 2024 \n19 ROLE_713858 lewqumkrved 1010101767 5 ROLE_713858 2 2024 \n\n level_y source \n0 2 course \n1 3 user \n2 3 user \n3 3 user \n4 3 course \n5 3 self \n6 3 self \n7 2 course \n8 3 user \n9 3 user \n10 1 course \n11 5 course \n12 4 course \n13 4 user \n14 3 user \n15 3 self \n16 4 user \n17 4 course \n18 4 user \n19 3 user ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
role idrole_xskilllevel_xrole_ymonthyearlevel_ysource
0ROLE_226242egmhxghxbak10101015254ROLE_226242220232course
1ROLE_860638ymclhotoiml10101018674ROLE_860638920253user
2ROLE_860638ymclhotoiml10101018674ROLE_860638520253user
3ROLE_713858lewqumkrved10101011225ROLE_713858320243user
4ROLE_226242egmhxghxbak10101012635ROLE_226242720233course
5ROLE_860638ymclhotoiml10101017675ROLE_8606381220253self
6ROLE_713858lewqumkrved10101017624ROLE_713858520243self
7ROLE_226242egmhxghxbak10101011224ROLE_226242220232course
8ROLE_860638ymclhotoiml10101011224ROLE_860638420253user
9ROLE_713858lewqumkrved10101018734ROLE_713858620243user
10ROLE_226242egmhxghxbak10101010433ROLE_2262421220231course
11ROLE_860638ymclhotoiml10101019284ROLE_8606381020255course
12ROLE_713858lewqumkrved10101017673ROLE_7138581120244course
13ROLE_713858lewqumkrved10101017673ROLE_7138581220244user
14ROLE_713858lewqumkrved10101017673ROLE_713858220243user
15ROLE_226242egmhxghxbak10101019545ROLE_2262421220233self
16ROLE_860638ymclhotoiml10101016793ROLE_860638420254user
17ROLE_713858lewqumkrved10101017675ROLE_7138581120244course
18ROLE_713858lewqumkrved10101017675ROLE_7138581220244user
19ROLE_713858lewqumkrved10101017675ROLE_713858220243user
\n
"},"metadata":{}}],"execution_count":161},{"cell_type":"code","source":"columns = ['role_x', 'role id', 'skill','level_x','level_y', 'year']\njob_skills_pd = job_skills_pd.loc[:,columns]\njob_skills_pd.columns = ['role', 'role id', 'skill','level requirement','level', 'year']\n\njob_skills_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:07.827531Z","iopub.execute_input":"2025-02-13T11:43:07.827828Z","iopub.status.idle":"2025-02-13T11:43:07.854126Z","shell.execute_reply.started":"2025-02-13T11:43:07.827805Z","shell.execute_reply":"2025-02-13T11:43:07.852758Z"}},"outputs":[{"execution_count":162,"output_type":"execute_result","data":{"text/plain":" role role id skill level requirement level year\n0 egmhxghxbak ROLE_226242 1010101525 4 2 2023\n1 ymclhotoiml ROLE_860638 1010101867 4 3 2025\n2 ymclhotoiml ROLE_860638 1010101867 4 3 2025\n3 lewqumkrved ROLE_713858 1010101122 5 3 2024\n4 egmhxghxbak ROLE_226242 1010101263 5 3 2023\n5 ymclhotoiml ROLE_860638 1010101767 5 3 2025\n6 lewqumkrved ROLE_713858 1010101762 4 3 2024\n7 egmhxghxbak ROLE_226242 1010101122 4 2 2023\n8 ymclhotoiml ROLE_860638 1010101122 4 3 2025\n9 lewqumkrved ROLE_713858 1010101873 4 3 2024\n10 egmhxghxbak ROLE_226242 1010101043 3 1 2023\n11 ymclhotoiml ROLE_860638 1010101928 4 5 2025\n12 lewqumkrved ROLE_713858 1010101767 3 4 2024\n13 lewqumkrved ROLE_713858 1010101767 3 4 2024\n14 lewqumkrved ROLE_713858 1010101767 3 3 2024\n15 egmhxghxbak ROLE_226242 1010101954 5 3 2023\n16 ymclhotoiml ROLE_860638 1010101679 3 4 2025\n17 lewqumkrved ROLE_713858 1010101767 5 4 2024\n18 lewqumkrved ROLE_713858 1010101767 5 4 2024\n19 lewqumkrved ROLE_713858 1010101767 5 3 2024","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rolerole idskilllevel requirementlevelyear
0egmhxghxbakROLE_2262421010101525422023
1ymclhotoimlROLE_8606381010101867432025
2ymclhotoimlROLE_8606381010101867432025
3lewqumkrvedROLE_7138581010101122532024
4egmhxghxbakROLE_2262421010101263532023
5ymclhotoimlROLE_8606381010101767532025
6lewqumkrvedROLE_7138581010101762432024
7egmhxghxbakROLE_2262421010101122422023
8ymclhotoimlROLE_8606381010101122432025
9lewqumkrvedROLE_7138581010101873432024
10egmhxghxbakROLE_2262421010101043312023
11ymclhotoimlROLE_8606381010101928452025
12lewqumkrvedROLE_7138581010101767342024
13lewqumkrvedROLE_7138581010101767342024
14lewqumkrvedROLE_7138581010101767332024
15egmhxghxbakROLE_2262421010101954532023
16ymclhotoimlROLE_8606381010101679342025
17lewqumkrvedROLE_7138581010101767542024
18lewqumkrvedROLE_7138581010101767542024
19lewqumkrvedROLE_7138581010101767532024
\n
"},"metadata":{}}],"execution_count":162},{"cell_type":"code","source":"job_skills_pd['gap'] = job_skills_pd['level'] - job_skills_pd['level requirement']\njob_skills_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:07.855115Z","iopub.execute_input":"2025-02-13T11:43:07.855431Z","iopub.status.idle":"2025-02-13T11:43:07.882881Z","shell.execute_reply.started":"2025-02-13T11:43:07.855398Z","shell.execute_reply":"2025-02-13T11:43:07.881916Z"}},"outputs":[{"execution_count":163,"output_type":"execute_result","data":{"text/plain":" role role id skill level requirement level year gap\n0 egmhxghxbak ROLE_226242 1010101525 4 2 2023 -2\n1 ymclhotoiml ROLE_860638 1010101867 4 3 2025 -1\n2 ymclhotoiml ROLE_860638 1010101867 4 3 2025 -1\n3 lewqumkrved ROLE_713858 1010101122 5 3 2024 -2\n4 egmhxghxbak ROLE_226242 1010101263 5 3 2023 -2\n5 ymclhotoiml ROLE_860638 1010101767 5 3 2025 -2\n6 lewqumkrved ROLE_713858 1010101762 4 3 2024 -1\n7 egmhxghxbak ROLE_226242 1010101122 4 2 2023 -2\n8 ymclhotoiml ROLE_860638 1010101122 4 3 2025 -1\n9 lewqumkrved ROLE_713858 1010101873 4 3 2024 -1\n10 egmhxghxbak ROLE_226242 1010101043 3 1 2023 -2\n11 ymclhotoiml ROLE_860638 1010101928 4 5 2025 1\n12 lewqumkrved ROLE_713858 1010101767 3 4 2024 1\n13 lewqumkrved ROLE_713858 1010101767 3 4 2024 1\n14 lewqumkrved ROLE_713858 1010101767 3 3 2024 0\n15 egmhxghxbak ROLE_226242 1010101954 5 3 2023 -2\n16 ymclhotoiml ROLE_860638 1010101679 3 4 2025 1\n17 lewqumkrved ROLE_713858 1010101767 5 4 2024 -1\n18 lewqumkrved ROLE_713858 1010101767 5 4 2024 -1\n19 lewqumkrved ROLE_713858 1010101767 5 3 2024 -2","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
rolerole idskilllevel requirementlevelyeargap
0egmhxghxbakROLE_2262421010101525422023-2
1ymclhotoimlROLE_8606381010101867432025-1
2ymclhotoimlROLE_8606381010101867432025-1
3lewqumkrvedROLE_7138581010101122532024-2
4egmhxghxbakROLE_2262421010101263532023-2
5ymclhotoimlROLE_8606381010101767532025-2
6lewqumkrvedROLE_7138581010101762432024-1
7egmhxghxbakROLE_2262421010101122422023-2
8ymclhotoimlROLE_8606381010101122432025-1
9lewqumkrvedROLE_7138581010101873432024-1
10egmhxghxbakROLE_2262421010101043312023-2
11ymclhotoimlROLE_86063810101019284520251
12lewqumkrvedROLE_71385810101017673420241
13lewqumkrvedROLE_71385810101017673420241
14lewqumkrvedROLE_71385810101017673320240
15egmhxghxbakROLE_2262421010101954532023-2
16ymclhotoimlROLE_86063810101016793420251
17lewqumkrvedROLE_7138581010101767542024-1
18lewqumkrvedROLE_7138581010101767542024-1
19lewqumkrvedROLE_7138581010101767532024-2
\n
"},"metadata":{}}],"execution_count":163},{"cell_type":"code","source":"group_col = 'role'\nroles = list(job_skills_pd.role.unique())\nroles","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:07.883915Z","iopub.execute_input":"2025-02-13T11:43:07.884210Z","iopub.status.idle":"2025-02-13T11:43:07.902087Z","shell.execute_reply.started":"2025-02-13T11:43:07.884178Z","shell.execute_reply":"2025-02-13T11:43:07.900842Z"}},"outputs":[{"execution_count":164,"output_type":"execute_result","data":{"text/plain":"['egmhxghxbak', 'ymclhotoiml', 'lewqumkrved']"},"metadata":{}}],"execution_count":164},{"cell_type":"code","source":"rows = job_skills_pd.role.str.contains(roles[0])\nX = job_skills_pd.loc[rows,'skill'].to_list()\nY_level = job_skills_pd.loc[rows,'level'].to_list()\nY_req = job_skills_pd.loc[rows,'level requirement'].to_list()\nplt.bar(X, Y_level, color=\"green\", label='current skill level')\nplt.bar(X, Y_req,color='#cbf5dd', label= \"required level\")\nplt.xticks(X)\nplt.xlabel(\"Year\")\nplt.ylabel(\"Expected skills levels\")\nplt.title(\"Skills gap for role \" + roles[0])\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:07.903379Z","iopub.execute_input":"2025-02-13T11:43:07.903753Z","iopub.status.idle":"2025-02-13T11:43:08.227459Z","shell.execute_reply.started":"2025-02-13T11:43:07.903718Z","shell.execute_reply":"2025-02-13T11:43:08.226514Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":165},{"cell_type":"code","source":"rows = job_skills_pd.role.str.contains(roles[1])\nX = job_skills_pd.loc[rows,'skill'].to_list()\nY_level = job_skills_pd.loc[rows,'level'].to_list()\nY_req = job_skills_pd.loc[rows,'level requirement'].to_list()\nplt.bar(X, Y_level, color=\"green\", label='current skill level')\nplt.bar(X, Y_req,color='#cbf5dd', label= \"required level\")\nplt.xticks(X)\nplt.xlabel(\"Year\")\nplt.ylabel(\"Expected skills levels\")\nplt.title(\"Skills gap for role \" + roles[1])\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:08.228487Z","iopub.execute_input":"2025-02-13T11:43:08.228806Z","iopub.status.idle":"2025-02-13T11:43:08.552847Z","shell.execute_reply.started":"2025-02-13T11:43:08.228773Z","shell.execute_reply":"2025-02-13T11:43:08.551448Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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= job_skills_pd.role.str.contains(roles[2])\nX = job_skills_pd.loc[rows,'skill'].to_list()\nY_level = job_skills_pd.loc[rows,'level'].to_list()\nY_req = job_skills_pd.loc[rows,'level requirement'].to_list()\nplt.bar(X, Y_level, color=\"green\", label='current skill level')\nplt.bar(X, Y_req,color='#cbf5dd', label= \"required level\")\nplt.xticks(X)\nplt.xlabel(\"Year\")\nplt.ylabel(\"Expected skills levels\")\nplt.title(\"Skills gap for role \" + roles[2])\nplt.legend()\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:08.554008Z","iopub.execute_input":"2025-02-13T11:43:08.554331Z","iopub.status.idle":"2025-02-13T11:43:08.922901Z","shell.execute_reply.started":"2025-02-13T11:43:08.554304Z","shell.execute_reply":"2025-02-13T11:43:08.921813Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}],"execution_count":167},{"cell_type":"markdown","source":"# X-gov and societal analysis","metadata":{}},{"cell_type":"markdown","source":"## Dimensions\n","metadata":{}},{"cell_type":"code","source":"n_2023 = 100","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:08.923930Z","iopub.execute_input":"2025-02-13T11:43:08.924340Z","iopub.status.idle":"2025-02-13T11:43:08.929348Z","shell.execute_reply.started":"2025-02-13T11:43:08.924302Z","shell.execute_reply":"2025-02-13T11:43:08.927715Z"}},"outputs":[],"execution_count":168},{"cell_type":"markdown","source":"### employees features","metadata":{}},{"cell_type":"code","source":"ceis = random.choices(employees_pd.ceis, k = n_2023)\nrows = employees_pd.ceis.isin(ceis)\ncols = ['background','marital status','gender', 'carers']\nemp_details = employees_pd.loc[rows, cols].copy(deep = True)\nemp_details","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:08.930475Z","iopub.execute_input":"2025-02-13T11:43:08.930928Z","iopub.status.idle":"2025-02-13T11:43:08.957134Z","shell.execute_reply.started":"2025-02-13T11:43:08.930884Z","shell.execute_reply":"2025-02-13T11:43:08.956035Z"}},"outputs":[{"execution_count":169,"output_type":"execute_result","data":{"text/plain":" background marital status gender carers\n0 3 0 1 2\n1 2 1 1 0\n3 2 0 1 4\n9 4 0 0 0\n12 2 0 1 0\n.. ... ... ... ...\n285 4 0 0 0\n288 0 3 0 4\n290 0 3 1 2\n291 5 1 1 3\n299 3 3 0 1\n\n[84 rows x 4 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
backgroundmarital statusgendercarers
03012
12110
32014
94000
122010
...............
2854000
2880304
2900312
2915113
2993301
\n

84 rows × 4 columns

\n
"},"metadata":{}}],"execution_count":169},{"cell_type":"code","source":"emp = np.random.randint(100000, size=emp_details.shape[0])\nemp_details['employee'] = emp\nemp_details","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:08.958155Z","iopub.execute_input":"2025-02-13T11:43:08.958518Z","iopub.status.idle":"2025-02-13T11:43:08.977492Z","shell.execute_reply.started":"2025-02-13T11:43:08.958491Z","shell.execute_reply":"2025-02-13T11:43:08.976423Z"}},"outputs":[{"execution_count":170,"output_type":"execute_result","data":{"text/plain":" background marital status gender carers employee\n0 3 0 1 2 16160\n1 2 1 1 0 869\n3 2 0 1 4 72199\n9 4 0 0 0 3495\n12 2 0 1 0 2794\n.. ... ... ... ... ...\n285 4 0 0 0 13124\n288 0 3 0 4 7906\n290 0 3 1 2 37981\n291 5 1 1 3 78433\n299 3 3 0 1 52282\n\n[84 rows x 5 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
backgroundmarital statusgendercarersemployee
0301216160
12110869
3201472199
940003495
1220102794
..................
285400013124
28803047906
290031237981
291511378433
299330152282
\n

84 rows × 5 columns

\n
"},"metadata":{}}],"execution_count":170},{"cell_type":"code","source":"emp_details['organisation'] = random.choices(organisations_pd['organisation'], k = emp_details.shape[0])\nemp_details\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:08.978468Z","iopub.execute_input":"2025-02-13T11:43:08.978876Z","iopub.status.idle":"2025-02-13T11:43:09.005444Z","shell.execute_reply.started":"2025-02-13T11:43:08.978838Z","shell.execute_reply":"2025-02-13T11:43:09.003926Z"}},"outputs":[{"execution_count":171,"output_type":"execute_result","data":{"text/plain":" background marital status gender carers employee \\\n0 3 0 1 2 16160 \n1 2 1 1 0 869 \n3 2 0 1 4 72199 \n9 4 0 0 0 3495 \n12 2 0 1 0 2794 \n.. ... ... ... ... ... \n285 4 0 0 0 13124 \n288 0 3 0 4 7906 \n290 0 3 1 2 37981 \n291 5 1 1 3 78433 \n299 3 3 0 1 52282 \n\n organisation \n0 mzyxsukirqawtcybjvll \n1 mzyxsukirqawtcybjvll \n3 mzyxsukirqawtcybjvll \n9 nyfslxinrpbxjjmcvumo \n12 jcsurldsnmgzlowgykoi \n.. ... \n285 ogefzchrhslcymyobvml \n288 wuemqowtlvvboneqjkpm \n290 bsgsxowefpwemmycgrgd \n291 wuemqowtlvvboneqjkpm \n299 bsgsxowefpwemmycgrgd \n\n[84 rows x 6 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
backgroundmarital statusgendercarersemployeeorganisation
0301216160mzyxsukirqawtcybjvll
12110869mzyxsukirqawtcybjvll
3201472199mzyxsukirqawtcybjvll
940003495nyfslxinrpbxjjmcvumo
1220102794jcsurldsnmgzlowgykoi
.....................
285400013124ogefzchrhslcymyobvml
28803047906wuemqowtlvvboneqjkpm
290031237981bsgsxowefpwemmycgrgd
291511378433wuemqowtlvvboneqjkpm
299330152282bsgsxowefpwemmycgrgd
\n

84 rows × 6 columns

\n
"},"metadata":{}}],"execution_count":171},{"cell_type":"code","source":"roles_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.006704Z","iopub.execute_input":"2025-02-13T11:43:09.007027Z","iopub.status.idle":"2025-02-13T11:43:09.030652Z","shell.execute_reply.started":"2025-02-13T11:43:09.007002Z","shell.execute_reply":"2025-02-13T11:43:09.029434Z"}},"outputs":[{"execution_count":172,"output_type":"execute_result","data":{"text/plain":" role id role skill level\n0 ROLE_989543 naqdldozwyh 1010101043 3\n1 ROLE_329525 cqljjdixacw 1010101772 3\n2 ROLE_025947 sevplprmtbx 1010101176 4\n3 ROLE_743948 joyilptjvft 1010101652 5\n4 ROLE_482449 aedtathfnkb 1010101910 4\n.. ... ... ... ...\n95 ROLE_281011 enmugvlbsfl 1010101952 5\n96 ROLE_943766 kjqoddkwefj 1010101822 3\n97 ROLE_185077 pfapomtdxcc 1010101762 3\n98 ROLE_440020 zrztiwjkkiv 1010101987 4\n99 ROLE_060023 rrsdulwsydt 1010101789 4\n\n[500 rows x 4 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
role idroleskilllevel
0ROLE_989543naqdldozwyh10101010433
1ROLE_329525cqljjdixacw10101017723
2ROLE_025947sevplprmtbx10101011764
3ROLE_743948joyilptjvft10101016525
4ROLE_482449aedtathfnkb10101019104
...............
95ROLE_281011enmugvlbsfl10101019525
96ROLE_943766kjqoddkwefj10101018223
97ROLE_185077pfapomtdxcc10101017623
98ROLE_440020zrztiwjkkiv10101019874
99ROLE_060023rrsdulwsydt10101017894
\n

500 rows × 4 columns

\n
"},"metadata":{}}],"execution_count":172},{"cell_type":"code","source":"roles_ls = random.choices(roles_pd['role id'].unique(), k = emp_details.shape[0])\nemp_roles = pd.DataFrame()\nemp_roles['role'] = roles_ls\nemp_roles['employee'] = emp\nemp_roles\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.031468Z","iopub.execute_input":"2025-02-13T11:43:09.031862Z","iopub.status.idle":"2025-02-13T11:43:09.058524Z","shell.execute_reply.started":"2025-02-13T11:43:09.031826Z","shell.execute_reply":"2025-02-13T11:43:09.057359Z"}},"outputs":[{"execution_count":173,"output_type":"execute_result","data":{"text/plain":" role employee\n0 ROLE_908034 16160\n1 ROLE_576427 869\n2 ROLE_060023 72199\n3 ROLE_096991 3495\n4 ROLE_041208 2794\n.. ... ...\n79 ROLE_658943 13124\n80 ROLE_208288 7906\n81 ROLE_658943 37981\n82 ROLE_113664 78433\n83 ROLE_329525 52282\n\n[84 rows x 2 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
roleemployee
0ROLE_90803416160
1ROLE_576427869
2ROLE_06002372199
3ROLE_0969913495
4ROLE_0412082794
.........
79ROLE_65894313124
80ROLE_2082887906
81ROLE_65894337981
82ROLE_11366478433
83ROLE_32952552282
\n

84 rows × 2 columns

\n
"},"metadata":{}}],"execution_count":173},{"cell_type":"code","source":"rows = roles_pd['role id'].isin(roles_ls)\nroles = roles_pd.loc[rows,:]\nroles =roles.merge(emp_roles, left_on='role id', right_on='role')\nroles\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.059533Z","iopub.execute_input":"2025-02-13T11:43:09.059884Z","iopub.status.idle":"2025-02-13T11:43:09.089722Z","shell.execute_reply.started":"2025-02-13T11:43:09.059852Z","shell.execute_reply":"2025-02-13T11:43:09.088562Z"}},"outputs":[{"execution_count":174,"output_type":"execute_result","data":{"text/plain":" role id role_x skill level role_y employee\n0 ROLE_989543 naqdldozwyh 1010101043 3 ROLE_989543 16856\n1 ROLE_989543 naqdldozwyh 1010101043 3 ROLE_989543 4432\n2 ROLE_329525 cqljjdixacw 1010101772 3 ROLE_329525 98494\n3 ROLE_329525 cqljjdixacw 1010101772 3 ROLE_329525 52282\n4 ROLE_670811 tldkrglonlc 1010101525 3 ROLE_670811 92966\n.. ... ... ... ... ... ...\n415 ROLE_988646 qdyzkuyreat 1010101952 4 ROLE_988646 50156\n416 ROLE_875141 jldxlacorig 1010101987 3 ROLE_875141 59794\n417 ROLE_185077 pfapomtdxcc 1010101762 3 ROLE_185077 78023\n418 ROLE_060023 rrsdulwsydt 1010101789 4 ROLE_060023 72199\n419 ROLE_060023 rrsdulwsydt 1010101789 4 ROLE_060023 52964\n\n[420 rows x 6 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
role idrole_xskilllevelrole_yemployee
0ROLE_989543naqdldozwyh10101010433ROLE_98954316856
1ROLE_989543naqdldozwyh10101010433ROLE_9895434432
2ROLE_329525cqljjdixacw10101017723ROLE_32952598494
3ROLE_329525cqljjdixacw10101017723ROLE_32952552282
4ROLE_670811tldkrglonlc10101015253ROLE_67081192966
.....................
415ROLE_988646qdyzkuyreat10101019524ROLE_98864650156
416ROLE_875141jldxlacorig10101019873ROLE_87514159794
417ROLE_185077pfapomtdxcc10101017623ROLE_18507778023
418ROLE_060023rrsdulwsydt10101017894ROLE_06002372199
419ROLE_060023rrsdulwsydt10101017894ROLE_06002352964
\n

420 rows × 6 columns

\n
"},"metadata":{}}],"execution_count":174},{"cell_type":"code","source":"cols = ['role_x','skill','level','employee']\nroles = roles.loc[:,cols]\nroles","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.090949Z","iopub.execute_input":"2025-02-13T11:43:09.091346Z","iopub.status.idle":"2025-02-13T11:43:09.114302Z","shell.execute_reply.started":"2025-02-13T11:43:09.091310Z","shell.execute_reply":"2025-02-13T11:43:09.113301Z"}},"outputs":[{"execution_count":175,"output_type":"execute_result","data":{"text/plain":" role_x skill level employee\n0 naqdldozwyh 1010101043 3 16856\n1 naqdldozwyh 1010101043 3 4432\n2 cqljjdixacw 1010101772 3 98494\n3 cqljjdixacw 1010101772 3 52282\n4 tldkrglonlc 1010101525 3 92966\n.. ... ... ... ...\n415 qdyzkuyreat 1010101952 4 50156\n416 jldxlacorig 1010101987 3 59794\n417 pfapomtdxcc 1010101762 3 78023\n418 rrsdulwsydt 1010101789 4 72199\n419 rrsdulwsydt 1010101789 4 52964\n\n[420 rows x 4 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
role_xskilllevelemployee
0naqdldozwyh1010101043316856
1naqdldozwyh101010104334432
2cqljjdixacw1010101772398494
3cqljjdixacw1010101772352282
4tldkrglonlc1010101525392966
...............
415qdyzkuyreat1010101952450156
416jldxlacorig1010101987359794
417pfapomtdxcc1010101762378023
418rrsdulwsydt1010101789472199
419rrsdulwsydt1010101789452964
\n

420 rows × 4 columns

\n
"},"metadata":{}}],"execution_count":175},{"cell_type":"markdown","source":"Bring skills to record from the job role description. We are assuming many of level ranges between 1 and 4 for demo purposes.","metadata":{}},{"cell_type":"code","source":"taxonomy.columns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.115363Z","iopub.execute_input":"2025-02-13T11:43:09.115690Z","iopub.status.idle":"2025-02-13T11:43:09.133874Z","shell.execute_reply.started":"2025-02-13T11:43:09.115666Z","shell.execute_reply":"2025-02-13T11:43:09.132737Z"}},"outputs":[{"execution_count":176,"output_type":"execute_result","data":{"text/plain":"Index(['skill_id', 'skill', 'language', 'technical', 'soft', 'cluster',\n 'function', 'profession', 'guk'],\n dtype='object')"},"metadata":{}}],"execution_count":176},{"cell_type":"code","source":"roles_tax = roles.merge(taxonomy, left_on = 'skill', right_on = 'skill_id')\ncols = ['role_x','level','skill_y','language','technical','soft','cluster','function','profession','guk','skill_id','employee']\nroles_tax = roles_tax.loc[:, cols]\nroles_tax","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.134951Z","iopub.execute_input":"2025-02-13T11:43:09.135303Z","iopub.status.idle":"2025-02-13T11:43:09.171742Z","shell.execute_reply.started":"2025-02-13T11:43:09.135278Z","shell.execute_reply":"2025-02-13T11:43:09.170687Z"}},"outputs":[{"execution_count":177,"output_type":"execute_result","data":{"text/plain":" role_x level skill_y language technical soft cluster function \\\n0 naqdldozwyh 3 ijzffe False True False efit akvrfsup \n1 naqdldozwyh 3 ijzffe False True False efit akvrfsup \n2 cqljjdixacw 3 wakpbw True False False whxk easpvjiy \n3 cqljjdixacw 3 wakpbw True False False whxk easpvjiy \n4 tldkrglonlc 3 ilkukl False False True efit akvrfsup \n.. ... ... ... ... ... ... ... ... \n514 pfapomtdxcc 3 oksacc False False True dghm ckzgyvfj \n515 rrsdulwsydt 4 dypppp False True False spom hyhbuafh \n516 rrsdulwsydt 4 dypppp False True False dkhk akvrfsup \n517 rrsdulwsydt 4 dypppp False True False spom hyhbuafh \n518 rrsdulwsydt 4 dypppp False True False dkhk akvrfsup \n\n profession guk skill_id employee \n0 wrtttwd ldlmayhvvq 1010101043 16856 \n1 wrtttwd ldlmayhvvq 1010101043 4432 \n2 usxhuhz hpqmmbgtev 1010101772 98494 \n3 usxhuhz hpqmmbgtev 1010101772 52282 \n4 wrtttwd ldlmayhvvq 1010101525 92966 \n.. ... ... ... ... \n514 vmyvicq hpqmmbgtev 1010101762 78023 \n515 bnkimlk ldlmayhvvq 1010101789 72199 \n516 fcftnao ldlmayhvvq 1010101789 72199 \n517 bnkimlk ldlmayhvvq 1010101789 52964 \n518 fcftnao ldlmayhvvq 1010101789 52964 \n\n[519 rows x 12 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
role_xlevelskill_ylanguagetechnicalsoftclusterfunctionprofessiongukskill_idemployee
0naqdldozwyh3ijzffeFalseTrueFalseefitakvrfsupwrtttwdldlmayhvvq101010104316856
1naqdldozwyh3ijzffeFalseTrueFalseefitakvrfsupwrtttwdldlmayhvvq10101010434432
2cqljjdixacw3wakpbwTrueFalseFalsewhxkeaspvjiyusxhuhzhpqmmbgtev101010177298494
3cqljjdixacw3wakpbwTrueFalseFalsewhxkeaspvjiyusxhuhzhpqmmbgtev101010177252282
4tldkrglonlc3ilkuklFalseFalseTrueefitakvrfsupwrtttwdldlmayhvvq101010152592966
.......................................
514pfapomtdxcc3oksaccFalseFalseTruedghmckzgyvfjvmyvicqhpqmmbgtev101010176278023
515rrsdulwsydt4dyppppFalseTrueFalsespomhyhbuafhbnkimlkldlmayhvvq101010178972199
516rrsdulwsydt4dyppppFalseTrueFalsedkhkakvrfsupfcftnaoldlmayhvvq101010178972199
517rrsdulwsydt4dyppppFalseTrueFalsespomhyhbuafhbnkimlkldlmayhvvq101010178952964
518rrsdulwsydt4dyppppFalseTrueFalsedkhkakvrfsupfcftnaoldlmayhvvq101010178952964
\n

519 rows × 12 columns

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"},"metadata":{}}],"execution_count":177},{"cell_type":"code","source":"roles_tax['current level'] = random.choices([0,1,2,3,4], k = roles_tax.shape[0])\nroles_tax['self'] = True\nroles_tax['course'] = False\nroles_tax['user'] = False\nroles_tax\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.172512Z","iopub.execute_input":"2025-02-13T11:43:09.172821Z","iopub.status.idle":"2025-02-13T11:43:09.195259Z","shell.execute_reply.started":"2025-02-13T11:43:09.172797Z","shell.execute_reply":"2025-02-13T11:43:09.194081Z"}},"outputs":[{"execution_count":178,"output_type":"execute_result","data":{"text/plain":" role_x level skill_y language technical soft cluster function \\\n0 naqdldozwyh 3 ijzffe False True False efit akvrfsup \n1 naqdldozwyh 3 ijzffe False True False efit akvrfsup \n2 cqljjdixacw 3 wakpbw True False False whxk easpvjiy \n3 cqljjdixacw 3 wakpbw True False False whxk easpvjiy \n4 tldkrglonlc 3 ilkukl False False True efit akvrfsup \n.. ... ... ... ... ... ... ... ... \n514 pfapomtdxcc 3 oksacc False False True dghm ckzgyvfj \n515 rrsdulwsydt 4 dypppp False True False spom hyhbuafh \n516 rrsdulwsydt 4 dypppp False True False dkhk akvrfsup \n517 rrsdulwsydt 4 dypppp False True False spom hyhbuafh \n518 rrsdulwsydt 4 dypppp False True False dkhk akvrfsup \n\n profession guk skill_id employee current level self course \\\n0 wrtttwd ldlmayhvvq 1010101043 16856 0 True False \n1 wrtttwd ldlmayhvvq 1010101043 4432 3 True False \n2 usxhuhz hpqmmbgtev 1010101772 98494 2 True False \n3 usxhuhz hpqmmbgtev 1010101772 52282 0 True False \n4 wrtttwd ldlmayhvvq 1010101525 92966 0 True False \n.. ... ... ... ... ... ... ... \n514 vmyvicq hpqmmbgtev 1010101762 78023 2 True False \n515 bnkimlk ldlmayhvvq 1010101789 72199 0 True False \n516 fcftnao ldlmayhvvq 1010101789 72199 0 True False \n517 bnkimlk ldlmayhvvq 1010101789 52964 1 True False \n518 fcftnao ldlmayhvvq 1010101789 52964 1 True False \n\n user \n0 False \n1 False \n2 False \n3 False \n4 False \n.. ... \n514 False \n515 False \n516 False \n517 False \n518 False \n\n[519 rows x 16 columns]","text/html":"
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role_xlevelskill_ylanguagetechnicalsoftclusterfunctionprofessiongukskill_idemployeecurrent levelselfcourseuser
0naqdldozwyh3ijzffeFalseTrueFalseefitakvrfsupwrtttwdldlmayhvvq1010101043168560TrueFalseFalse
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2cqljjdixacw3wakpbwTrueFalseFalsewhxkeaspvjiyusxhuhzhpqmmbgtev1010101772984942TrueFalseFalse
3cqljjdixacw3wakpbwTrueFalseFalsewhxkeaspvjiyusxhuhzhpqmmbgtev1010101772522820TrueFalseFalse
4tldkrglonlc3ilkuklFalseFalseTrueefitakvrfsupwrtttwdldlmayhvvq1010101525929660TrueFalseFalse
...................................................
514pfapomtdxcc3oksaccFalseFalseTruedghmckzgyvfjvmyvicqhpqmmbgtev1010101762780232TrueFalseFalse
515rrsdulwsydt4dyppppFalseTrueFalsespomhyhbuafhbnkimlkldlmayhvvq1010101789721990TrueFalseFalse
516rrsdulwsydt4dyppppFalseTrueFalsedkhkakvrfsupfcftnaoldlmayhvvq1010101789721990TrueFalseFalse
517rrsdulwsydt4dyppppFalseTrueFalsespomhyhbuafhbnkimlkldlmayhvvq1010101789529641TrueFalseFalse
518rrsdulwsydt4dyppppFalseTrueFalsedkhkakvrfsupfcftnaoldlmayhvvq1010101789529641TrueFalseFalse
\n

519 rows × 16 columns

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"},"metadata":{}}],"execution_count":178},{"cell_type":"code","source":"roles_tax = roles_tax.merge(emp_details, left_on='employee',right_on='employee')\nroles_tax","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.196412Z","iopub.execute_input":"2025-02-13T11:43:09.196737Z","iopub.status.idle":"2025-02-13T11:43:09.235266Z","shell.execute_reply.started":"2025-02-13T11:43:09.196712Z","shell.execute_reply":"2025-02-13T11:43:09.234160Z"}},"outputs":[{"execution_count":179,"output_type":"execute_result","data":{"text/plain":" role_x level skill_y language technical soft cluster function \\\n0 naqdldozwyh 3 ijzffe False True False efit akvrfsup \n1 naqdldozwyh 3 ijzffe False True False efit akvrfsup \n2 cqljjdixacw 3 wakpbw True False False whxk easpvjiy \n3 cqljjdixacw 3 wakpbw True False False whxk easpvjiy \n4 tldkrglonlc 3 ilkukl False False True efit akvrfsup \n.. ... ... ... ... ... ... ... ... \n514 pfapomtdxcc 3 oksacc False False True dghm ckzgyvfj \n515 rrsdulwsydt 4 dypppp False True False spom hyhbuafh \n516 rrsdulwsydt 4 dypppp False True False dkhk akvrfsup \n517 rrsdulwsydt 4 dypppp False True False spom hyhbuafh \n518 rrsdulwsydt 4 dypppp False True False dkhk akvrfsup \n\n profession guk ... employee current level self course user \\\n0 wrtttwd ldlmayhvvq ... 16856 0 True False False \n1 wrtttwd ldlmayhvvq ... 4432 3 True False False \n2 usxhuhz hpqmmbgtev ... 98494 2 True False False \n3 usxhuhz hpqmmbgtev ... 52282 0 True False False \n4 wrtttwd ldlmayhvvq ... 92966 0 True False False \n.. ... ... ... ... ... ... ... ... \n514 vmyvicq hpqmmbgtev ... 78023 2 True False False \n515 bnkimlk ldlmayhvvq ... 72199 0 True False False \n516 fcftnao ldlmayhvvq ... 72199 0 True False False \n517 bnkimlk ldlmayhvvq ... 52964 1 True False False \n518 fcftnao ldlmayhvvq ... 52964 1 True False False \n\n background marital status gender carers organisation \n0 0 2 0 1 nyfslxinrpbxjjmcvumo \n1 2 1 1 2 ogefzchrhslcymyobvml \n2 1 0 0 2 nyfslxinrpbxjjmcvumo \n3 3 3 0 1 bsgsxowefpwemmycgrgd \n4 0 3 0 1 ctahjrijfpxqakqlneyk \n.. ... ... ... ... ... \n514 1 0 1 1 jcsurldsnmgzlowgykoi \n515 2 0 1 4 mzyxsukirqawtcybjvll \n516 2 0 1 4 mzyxsukirqawtcybjvll \n517 5 3 1 1 ktfqqxpnwoznbyttjmlo \n518 5 3 1 1 ktfqqxpnwoznbyttjmlo \n\n[519 rows x 21 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
role_xlevelskill_ylanguagetechnicalsoftclusterfunctionprofessionguk...employeecurrent levelselfcourseuserbackgroundmarital statusgendercarersorganisation
0naqdldozwyh3ijzffeFalseTrueFalseefitakvrfsupwrtttwdldlmayhvvq...168560TrueFalseFalse0201nyfslxinrpbxjjmcvumo
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2cqljjdixacw3wakpbwTrueFalseFalsewhxkeaspvjiyusxhuhzhpqmmbgtev...984942TrueFalseFalse1002nyfslxinrpbxjjmcvumo
3cqljjdixacw3wakpbwTrueFalseFalsewhxkeaspvjiyusxhuhzhpqmmbgtev...522820TrueFalseFalse3301bsgsxowefpwemmycgrgd
4tldkrglonlc3ilkuklFalseFalseTrueefitakvrfsupwrtttwdldlmayhvvq...929660TrueFalseFalse0301ctahjrijfpxqakqlneyk
..................................................................
514pfapomtdxcc3oksaccFalseFalseTruedghmckzgyvfjvmyvicqhpqmmbgtev...780232TrueFalseFalse1011jcsurldsnmgzlowgykoi
515rrsdulwsydt4dyppppFalseTrueFalsespomhyhbuafhbnkimlkldlmayhvvq...721990TrueFalseFalse2014mzyxsukirqawtcybjvll
516rrsdulwsydt4dyppppFalseTrueFalsedkhkakvrfsupfcftnaoldlmayhvvq...721990TrueFalseFalse2014mzyxsukirqawtcybjvll
517rrsdulwsydt4dyppppFalseTrueFalsespomhyhbuafhbnkimlkldlmayhvvq...529641TrueFalseFalse5311ktfqqxpnwoznbyttjmlo
518rrsdulwsydt4dyppppFalseTrueFalsedkhkakvrfsupfcftnaoldlmayhvvq...529641TrueFalseFalse5311ktfqqxpnwoznbyttjmlo
\n

519 rows × 21 columns

\n
"},"metadata":{}}],"execution_count":179},{"cell_type":"code","source":"columns = ['employee',\n 'marital status',\n 'gender',\n 'background',\n 'carers',\n 'organisation',\n 'role_x',\n 'level',\n 'current level',\n 'skill_y',\n 'language',\n 'technical',\n 'soft',\n 'self',\n 'user',\n 'course',\n 'cluster',\n 'function',\n 'profession',\n 'guk']\nroles_tax = roles_tax.loc[:,columns]\nroles_tax['month'] = random.choices([1,2,3,4,5,6,7,8,9,10,11,12], k=roles_tax.shape[0])\nroles_tax['year'] = np.repeat(2023, roles_tax.shape[0])\nnew = ['employee', 'marital status','gender','background','carer', 'organisation', 'role', 'level required', 'current level', 'skill', 'language', \"technical\",'soft', 'self', 'user', 'course', 'cluster', 'function', 'profession', 'guk', 'month', 'year']\nroles_tax.columns = new\nroles_tax.dtypes\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.236100Z","iopub.execute_input":"2025-02-13T11:43:09.236385Z","iopub.status.idle":"2025-02-13T11:43:09.250307Z","shell.execute_reply.started":"2025-02-13T11:43:09.236362Z","shell.execute_reply":"2025-02-13T11:43:09.249224Z"}},"outputs":[{"execution_count":180,"output_type":"execute_result","data":{"text/plain":"employee int64\nmarital status int64\ngender int64\nbackground int64\ncarer int64\norganisation object\nrole object\nlevel required int64\ncurrent level int64\nskill object\nlanguage bool\ntechnical bool\nsoft bool\nself bool\nuser bool\ncourse bool\ncluster object\nfunction object\nprofession object\nguk object\nmonth int64\nyear int64\ndtype: object"},"metadata":{}}],"execution_count":180},{"cell_type":"code","source":"roles_tax.groupby(['employee','role','skill']).count().describe()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.251404Z","iopub.execute_input":"2025-02-13T11:43:09.251773Z","iopub.status.idle":"2025-02-13T11:43:09.320837Z","shell.execute_reply.started":"2025-02-13T11:43:09.251736Z","shell.execute_reply":"2025-02-13T11:43:09.319700Z"}},"outputs":[{"execution_count":181,"output_type":"execute_result","data":{"text/plain":" marital status gender background carer organisation \\\ncount 445.000000 445.000000 445.000000 445.000000 445.000000 \nmean 1.166292 1.166292 1.166292 1.166292 1.166292 \nstd 0.407404 0.407404 0.407404 0.407404 0.407404 \nmin 1.000000 1.000000 1.000000 1.000000 1.000000 \n25% 1.000000 1.000000 1.000000 1.000000 1.000000 \n50% 1.000000 1.000000 1.000000 1.000000 1.000000 \n75% 1.000000 1.000000 1.000000 1.000000 1.000000 \nmax 4.000000 4.000000 4.000000 4.000000 4.000000 \n\n level required current level language technical soft \\\ncount 445.000000 445.000000 445.000000 445.000000 445.000000 \nmean 1.166292 1.166292 1.166292 1.166292 1.166292 \nstd 0.407404 0.407404 0.407404 0.407404 0.407404 \nmin 1.000000 1.000000 1.000000 1.000000 1.000000 \n25% 1.000000 1.000000 1.000000 1.000000 1.000000 \n50% 1.000000 1.000000 1.000000 1.000000 1.000000 \n75% 1.000000 1.000000 1.000000 1.000000 1.000000 \nmax 4.000000 4.000000 4.000000 4.000000 4.000000 \n\n self user course cluster function profession \\\ncount 445.000000 445.000000 445.000000 445.000000 445.000000 445.000000 \nmean 1.166292 1.166292 1.166292 1.166292 1.166292 1.166292 \nstd 0.407404 0.407404 0.407404 0.407404 0.407404 0.407404 \nmin 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n25% 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n50% 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \n75% 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 \nmax 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000 \n\n guk month year \ncount 445.000000 445.000000 445.000000 \nmean 1.166292 1.166292 1.166292 \nstd 0.407404 0.407404 0.407404 \nmin 1.000000 1.000000 1.000000 \n25% 1.000000 1.000000 1.000000 \n50% 1.000000 1.000000 1.000000 \n75% 1.000000 1.000000 1.000000 \nmax 4.000000 4.000000 4.000000 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
marital statusgenderbackgroundcarerorganisationlevel requiredcurrent levellanguagetechnicalsoftselfusercourseclusterfunctionprofessiongukmonthyear
count445.000000445.000000445.000000445.000000445.000000445.000000445.000000445.000000445.000000445.000000445.000000445.000000445.000000445.000000445.000000445.000000445.000000445.000000445.000000
mean1.1662921.1662921.1662921.1662921.1662921.1662921.1662921.1662921.1662921.1662921.1662921.1662921.1662921.1662921.1662921.1662921.1662921.1662921.166292
std0.4074040.4074040.4074040.4074040.4074040.4074040.4074040.4074040.4074040.4074040.4074040.4074040.4074040.4074040.4074040.4074040.4074040.4074040.407404
min1.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
25%1.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
50%1.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
75%1.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
max4.0000004.0000004.0000004.0000004.0000004.0000004.0000004.0000004.0000004.0000004.0000004.0000004.0000004.0000004.0000004.0000004.0000004.0000004.000000
\n
"},"metadata":{}}],"execution_count":181},{"cell_type":"markdown","source":"self assessed skills","metadata":{}},{"cell_type":"code","source":"roles_tax.dtypes\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.321937Z","iopub.execute_input":"2025-02-13T11:43:09.322256Z","iopub.status.idle":"2025-02-13T11:43:09.330836Z","shell.execute_reply.started":"2025-02-13T11:43:09.322231Z","shell.execute_reply":"2025-02-13T11:43:09.329500Z"}},"outputs":[{"execution_count":182,"output_type":"execute_result","data":{"text/plain":"employee int64\nmarital status int64\ngender int64\nbackground int64\ncarer int64\norganisation object\nrole object\nlevel required int64\ncurrent level int64\nskill object\nlanguage bool\ntechnical bool\nsoft bool\nself bool\nuser bool\ncourse bool\ncluster object\nfunction object\nprofession object\nguk object\nmonth int64\nyear int64\ndtype: object"},"metadata":{}}],"execution_count":182},{"cell_type":"code","source":"self = emp_details.copy(deep=True)\nself = self.merge(emp_roles, left_on='employee', right_on='employee')\nself = self.merge(roles_pd, left_on='role', right_on='role id')\nself = self.loc[:,['employee','role_y']]\nself = self.drop_duplicates()\nn = self.shape[0]\nself['level'] = np.repeat(0, n)\nself.columns =['employee','role','level']\nself\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.332017Z","iopub.execute_input":"2025-02-13T11:43:09.332343Z","iopub.status.idle":"2025-02-13T11:43:09.366866Z","shell.execute_reply.started":"2025-02-13T11:43:09.332317Z","shell.execute_reply":"2025-02-13T11:43:09.365742Z"}},"outputs":[{"execution_count":183,"output_type":"execute_result","data":{"text/plain":" employee role level\n0 16160 ckrqfhcfdvp 0\n5 869 oglugxxotod 0\n10 72199 rrsdulwsydt 0\n15 3495 tnpgicxmtco 0\n20 2794 syjprfieszh 0\n.. ... ... ...\n395 13124 mqxcxxjuodf 0\n400 7906 kbmzzbozsfw 0\n405 37981 mqxcxxjuodf 0\n410 78433 unnqapiiipf 0\n415 52282 cqljjdixacw 0\n\n[84 rows x 3 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
employeerolelevel
016160ckrqfhcfdvp0
5869oglugxxotod0
1072199rrsdulwsydt0
153495tnpgicxmtco0
202794syjprfieszh0
............
39513124mqxcxxjuodf0
4007906kbmzzbozsfw0
40537981mqxcxxjuodf0
41078433unnqapiiipf0
41552282cqljjdixacw0
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84 rows × 3 columns

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"},"metadata":{}}],"execution_count":183},{"cell_type":"code","source":"skills_pd.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.368039Z","iopub.execute_input":"2025-02-13T11:43:09.368366Z","iopub.status.idle":"2025-02-13T11:43:09.380404Z","shell.execute_reply.started":"2025-02-13T11:43:09.368339Z","shell.execute_reply":"2025-02-13T11:43:09.379322Z"}},"outputs":[{"execution_count":184,"output_type":"execute_result","data":{"text/plain":" skills_uid skills language technical soft cluster\n0 1010101263 gqryuk False True False 2020202265\n1 1010101987 goaylk True False False 2020202454\n2 1010101349 bdbluo True False False 2020202005\n3 1010101080 bsncme False True False 2020202005\n4 1010101952 bxxnst False False True 2020202703","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
skills_uidskillslanguagetechnicalsoftcluster
01010101263gqryukFalseTrueFalse2020202265
11010101987goaylkTrueFalseFalse2020202454
21010101349bdbluoTrueFalseFalse2020202005
31010101080bsncmeFalseTrueFalse2020202005
41010101952bxxnstFalseFalseTrue2020202703
\n
"},"metadata":{}}],"execution_count":184},{"cell_type":"code","source":"skills = random.choices(skills_pd.skills_uid, k=self.shape[0])\nself['skill'] = skills\nrows = skills_pd.skills_uid.isin(skills)\nskills = skills_pd.loc[rows, :]\nself = self.merge(skills_pd, left_on = 'skill', right_on=\"skills_uid\")\nself['current level'] = random.choices([3,4,5], k=self.shape[0])\nself['self'] = True\nself['user'] = False\nself['course'] = False\nself","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.381682Z","iopub.execute_input":"2025-02-13T11:43:09.382097Z","iopub.status.idle":"2025-02-13T11:43:09.418143Z","shell.execute_reply.started":"2025-02-13T11:43:09.382057Z","shell.execute_reply":"2025-02-13T11:43:09.417082Z"}},"outputs":[{"execution_count":185,"output_type":"execute_result","data":{"text/plain":" employee role level skill skills_uid skills language \\\n0 16160 ckrqfhcfdvp 0 1010101349 1010101349 bdbluo True \n1 869 oglugxxotod 0 1010101652 1010101652 dbbyzn False \n2 72199 rrsdulwsydt 0 1010101767 1010101767 gbhazb False \n3 72199 rrsdulwsydt 0 1010101767 1010101767 jisdwl False \n4 3495 tnpgicxmtco 0 1010101586 1010101586 jracpf False \n.. ... ... ... ... ... ... ... \n88 13124 mqxcxxjuodf 0 1010101070 1010101070 smkggd True \n89 7906 kbmzzbozsfw 0 1010101789 1010101789 dypppp False \n90 37981 mqxcxxjuodf 0 1010101691 1010101691 brxfmi False \n91 78433 unnqapiiipf 0 1010101881 1010101881 ndrenb False \n92 52282 cqljjdixacw 0 1010101952 1010101952 bxxnst False \n\n technical soft cluster current level self user course \n0 False False 2020202005 5 True False False \n1 True False 2020202005 3 True False False \n2 True False 2020202005 3 True False False \n3 False True 2020202043 5 True False False \n4 True False 2020202043 3 True False False \n.. ... ... ... ... ... ... ... \n88 False False 2020202005 4 True False False \n89 True False 2020202215 3 True False False \n90 False True 2020202378 3 True False False \n91 False True 2020202888 3 True False False \n92 False True 2020202703 3 True False False \n\n[93 rows x 14 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
employeerolelevelskillskills_uidskillslanguagetechnicalsoftclustercurrent levelselfusercourse
016160ckrqfhcfdvp010101013491010101349bdbluoTrueFalseFalse20202020055TrueFalseFalse
1869oglugxxotod010101016521010101652dbbyznFalseTrueFalse20202020053TrueFalseFalse
272199rrsdulwsydt010101017671010101767gbhazbFalseTrueFalse20202020053TrueFalseFalse
372199rrsdulwsydt010101017671010101767jisdwlFalseFalseTrue20202020435TrueFalseFalse
43495tnpgicxmtco010101015861010101586jracpfFalseTrueFalse20202020433TrueFalseFalse
.............................................
8813124mqxcxxjuodf010101010701010101070smkggdTrueFalseFalse20202020054TrueFalseFalse
897906kbmzzbozsfw010101017891010101789dyppppFalseTrueFalse20202022153TrueFalseFalse
9037981mqxcxxjuodf010101016911010101691brxfmiFalseFalseTrue20202023783TrueFalseFalse
9178433unnqapiiipf010101018811010101881ndrenbFalseFalseTrue20202028883TrueFalseFalse
9252282cqljjdixacw010101019521010101952bxxnstFalseFalseTrue20202027033TrueFalseFalse
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93 rows × 14 columns

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"},"metadata":{}}],"execution_count":185},{"cell_type":"code","source":"taxonomy.columns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.419103Z","iopub.execute_input":"2025-02-13T11:43:09.419399Z","iopub.status.idle":"2025-02-13T11:43:09.426842Z","shell.execute_reply.started":"2025-02-13T11:43:09.419367Z","shell.execute_reply":"2025-02-13T11:43:09.425487Z"}},"outputs":[{"execution_count":186,"output_type":"execute_result","data":{"text/plain":"Index(['skill_id', 'skill', 'language', 'technical', 'soft', 'cluster',\n 'function', 'profession', 'guk'],\n dtype='object')"},"metadata":{}}],"execution_count":186},{"cell_type":"code","source":"self = self.merge(taxonomy, left_on ='skills_uid', right_on='skill_id')\nself = self.merge(emp_details, left_on = \"employee\", right_on=\"employee\")\n\nself","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.427838Z","iopub.execute_input":"2025-02-13T11:43:09.428196Z","iopub.status.idle":"2025-02-13T11:43:09.469970Z","shell.execute_reply.started":"2025-02-13T11:43:09.428164Z","shell.execute_reply":"2025-02-13T11:43:09.469022Z"}},"outputs":[{"execution_count":187,"output_type":"execute_result","data":{"text/plain":" employee role level skill_x skills_uid skills language_x \\\n0 16160 ckrqfhcfdvp 0 1010101349 1010101349 bdbluo True \n1 869 oglugxxotod 0 1010101652 1010101652 dbbyzn False \n2 72199 rrsdulwsydt 0 1010101767 1010101767 gbhazb False \n3 72199 rrsdulwsydt 0 1010101767 1010101767 gbhazb False \n4 72199 rrsdulwsydt 0 1010101767 1010101767 jisdwl False \n.. ... ... ... ... ... ... ... \n114 7906 kbmzzbozsfw 0 1010101789 1010101789 dypppp False \n115 7906 kbmzzbozsfw 0 1010101789 1010101789 dypppp False \n116 37981 mqxcxxjuodf 0 1010101691 1010101691 brxfmi False \n117 78433 unnqapiiipf 0 1010101881 1010101881 ndrenb False \n118 52282 cqljjdixacw 0 1010101952 1010101952 bxxnst False \n\n technical_x soft_x cluster_x ... soft_y cluster_y function \\\n0 False False 2020202005 ... False dghm ckzgyvfj \n1 True False 2020202005 ... False dghm ckzgyvfj \n2 True False 2020202005 ... False dghm ckzgyvfj \n3 True False 2020202005 ... True plnm hyhbuafh \n4 False True 2020202043 ... False dghm ckzgyvfj \n.. ... ... ... ... ... ... ... \n114 True False 2020202215 ... False spom hyhbuafh \n115 True False 2020202215 ... False dkhk akvrfsup \n116 False True 2020202378 ... True efit akvrfsup \n117 False True 2020202888 ... True upnl akvrfsup \n118 False True 2020202703 ... True aywu hyhbuafh \n\n profession guk background marital status gender carers \\\n0 vmyvicq hpqmmbgtev 3 0 1 2 \n1 vmyvicq hpqmmbgtev 2 1 1 0 \n2 vmyvicq hpqmmbgtev 2 0 1 4 \n3 unsdcqf ldlmayhvvq 2 0 1 4 \n4 vmyvicq hpqmmbgtev 2 0 1 4 \n.. ... ... ... ... ... ... \n114 bnkimlk ldlmayhvvq 0 3 0 4 \n115 fcftnao ldlmayhvvq 0 3 0 4 \n116 wrtttwd ldlmayhvvq 0 3 1 2 \n117 wrtttwd ldlmayhvvq 5 1 1 3 \n118 unsdcqf ldlmayhvvq 3 3 0 1 \n\n organisation \n0 mzyxsukirqawtcybjvll \n1 mzyxsukirqawtcybjvll \n2 mzyxsukirqawtcybjvll \n3 mzyxsukirqawtcybjvll \n4 mzyxsukirqawtcybjvll \n.. ... \n114 wuemqowtlvvboneqjkpm \n115 wuemqowtlvvboneqjkpm \n116 bsgsxowefpwemmycgrgd \n117 wuemqowtlvvboneqjkpm \n118 bsgsxowefpwemmycgrgd \n\n[119 rows x 28 columns]","text/html":"
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employeerolelevelskill_xskills_uidskillslanguage_xtechnical_xsoft_xcluster_x...soft_ycluster_yfunctionprofessiongukbackgroundmarital statusgendercarersorganisation
016160ckrqfhcfdvp010101013491010101349bdbluoTrueFalseFalse2020202005...Falsedghmckzgyvfjvmyvicqhpqmmbgtev3012mzyxsukirqawtcybjvll
1869oglugxxotod010101016521010101652dbbyznFalseTrueFalse2020202005...Falsedghmckzgyvfjvmyvicqhpqmmbgtev2110mzyxsukirqawtcybjvll
272199rrsdulwsydt010101017671010101767gbhazbFalseTrueFalse2020202005...Falsedghmckzgyvfjvmyvicqhpqmmbgtev2014mzyxsukirqawtcybjvll
372199rrsdulwsydt010101017671010101767gbhazbFalseTrueFalse2020202005...Trueplnmhyhbuafhunsdcqfldlmayhvvq2014mzyxsukirqawtcybjvll
472199rrsdulwsydt010101017671010101767jisdwlFalseFalseTrue2020202043...Falsedghmckzgyvfjvmyvicqhpqmmbgtev2014mzyxsukirqawtcybjvll
..................................................................
1147906kbmzzbozsfw010101017891010101789dyppppFalseTrueFalse2020202215...Falsespomhyhbuafhbnkimlkldlmayhvvq0304wuemqowtlvvboneqjkpm
1157906kbmzzbozsfw010101017891010101789dyppppFalseTrueFalse2020202215...Falsedkhkakvrfsupfcftnaoldlmayhvvq0304wuemqowtlvvboneqjkpm
11637981mqxcxxjuodf010101016911010101691brxfmiFalseFalseTrue2020202378...Trueefitakvrfsupwrtttwdldlmayhvvq0312bsgsxowefpwemmycgrgd
11778433unnqapiiipf010101018811010101881ndrenbFalseFalseTrue2020202888...Trueupnlakvrfsupwrtttwdldlmayhvvq5113wuemqowtlvvboneqjkpm
11852282cqljjdixacw010101019521010101952bxxnstFalseFalseTrue2020202703...Trueaywuhyhbuafhunsdcqfldlmayhvvq3301bsgsxowefpwemmycgrgd
\n

119 rows × 28 columns

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"},"metadata":{}}],"execution_count":187},{"cell_type":"code","source":"self.head()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.470810Z","iopub.execute_input":"2025-02-13T11:43:09.471082Z","iopub.status.idle":"2025-02-13T11:43:09.492147Z","shell.execute_reply.started":"2025-02-13T11:43:09.471058Z","shell.execute_reply":"2025-02-13T11:43:09.490929Z"}},"outputs":[{"execution_count":188,"output_type":"execute_result","data":{"text/plain":" employee role level skill_x skills_uid skills language_x \\\n0 16160 ckrqfhcfdvp 0 1010101349 1010101349 bdbluo True \n1 869 oglugxxotod 0 1010101652 1010101652 dbbyzn False \n2 72199 rrsdulwsydt 0 1010101767 1010101767 gbhazb False \n3 72199 rrsdulwsydt 0 1010101767 1010101767 gbhazb False \n4 72199 rrsdulwsydt 0 1010101767 1010101767 jisdwl False \n\n technical_x soft_x cluster_x ... soft_y cluster_y function \\\n0 False False 2020202005 ... False dghm ckzgyvfj \n1 True False 2020202005 ... False dghm ckzgyvfj \n2 True False 2020202005 ... False dghm ckzgyvfj \n3 True False 2020202005 ... True plnm hyhbuafh \n4 False True 2020202043 ... False dghm ckzgyvfj \n\n profession guk background marital status gender carers \\\n0 vmyvicq hpqmmbgtev 3 0 1 2 \n1 vmyvicq hpqmmbgtev 2 1 1 0 \n2 vmyvicq hpqmmbgtev 2 0 1 4 \n3 unsdcqf ldlmayhvvq 2 0 1 4 \n4 vmyvicq hpqmmbgtev 2 0 1 4 \n\n organisation \n0 mzyxsukirqawtcybjvll \n1 mzyxsukirqawtcybjvll \n2 mzyxsukirqawtcybjvll \n3 mzyxsukirqawtcybjvll \n4 mzyxsukirqawtcybjvll \n\n[5 rows x 28 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
employeerolelevelskill_xskills_uidskillslanguage_xtechnical_xsoft_xcluster_x...soft_ycluster_yfunctionprofessiongukbackgroundmarital statusgendercarersorganisation
016160ckrqfhcfdvp010101013491010101349bdbluoTrueFalseFalse2020202005...Falsedghmckzgyvfjvmyvicqhpqmmbgtev3012mzyxsukirqawtcybjvll
1869oglugxxotod010101016521010101652dbbyznFalseTrueFalse2020202005...Falsedghmckzgyvfjvmyvicqhpqmmbgtev2110mzyxsukirqawtcybjvll
272199rrsdulwsydt010101017671010101767gbhazbFalseTrueFalse2020202005...Falsedghmckzgyvfjvmyvicqhpqmmbgtev2014mzyxsukirqawtcybjvll
372199rrsdulwsydt010101017671010101767gbhazbFalseTrueFalse2020202005...Trueplnmhyhbuafhunsdcqfldlmayhvvq2014mzyxsukirqawtcybjvll
472199rrsdulwsydt010101017671010101767jisdwlFalseFalseTrue2020202043...Falsedghmckzgyvfjvmyvicqhpqmmbgtev2014mzyxsukirqawtcybjvll
\n

5 rows × 28 columns

\n
"},"metadata":{}}],"execution_count":188},{"cell_type":"markdown","source":"columns = ['employee',\n 'organisation',\n 'role',\n 'level required',\n 'current level',\n 'skill',\n 'language',\n 'soft',\n 'self',\n 'user',\n 'course',\n 'cluster',\n 'function',\n 'profession',\n 'guk',\n 'month',\n 'year']\n","metadata":{"execution":{"iopub.status.busy":"2025-02-11T14:07:12.678420Z","iopub.execute_input":"2025-02-11T14:07:12.678814Z","iopub.status.idle":"2025-02-11T14:07:12.686461Z","shell.execute_reply.started":"2025-02-11T14:07:12.678775Z","shell.execute_reply":"2025-02-11T14:07:12.684933Z"}}},{"cell_type":"code","source":"columns = ['employee',\n 'marital status',\n 'gender',\n 'background',\n 'carers',\n 'organisation',\n 'role',\n 'level',\n 'current level',\n 'skill_y',\n 'language_x',\n 'technical_x',\n 'soft_x',\n 'self',\n 'user',\n 'course',\n 'cluster_y',\n 'function',\n 'profession',\n 'guk']\n\nself = self.loc[:,columns]\nself['month'] = random.choices([1,2,3,4,5,6,7,8,9,10,11,12], k=self.shape[0])\nself['year'] = np.repeat(2023, self.shape[0])\nnew = ['employee', 'marital status','gender','background','carer', 'organisation', 'role', 'level required', 'current level', 'skill', 'language', \"technical\",'soft', 'self', 'user', 'course', 'cluster', 'function', 'profession', 'guk', 'month', 'year']\nself.columns = new\nself.dtypes\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.493198Z","iopub.execute_input":"2025-02-13T11:43:09.493499Z","iopub.status.idle":"2025-02-13T11:43:09.518631Z","shell.execute_reply.started":"2025-02-13T11:43:09.493475Z","shell.execute_reply":"2025-02-13T11:43:09.517431Z"}},"outputs":[{"execution_count":189,"output_type":"execute_result","data":{"text/plain":"employee int64\nmarital status int64\ngender int64\nbackground int64\ncarer int64\norganisation object\nrole object\nlevel required int64\ncurrent level int64\nskill object\nlanguage bool\ntechnical bool\nsoft bool\nself bool\nuser bool\ncourse bool\ncluster object\nfunction object\nprofession object\nguk object\nmonth int64\nyear int64\ndtype: object"},"metadata":{}}],"execution_count":189},{"cell_type":"code","source":"self.dtypes","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.519773Z","iopub.execute_input":"2025-02-13T11:43:09.520115Z","iopub.status.idle":"2025-02-13T11:43:09.541904Z","shell.execute_reply.started":"2025-02-13T11:43:09.520068Z","shell.execute_reply":"2025-02-13T11:43:09.540792Z"}},"outputs":[{"execution_count":190,"output_type":"execute_result","data":{"text/plain":"employee int64\nmarital status int64\ngender int64\nbackground int64\ncarer int64\norganisation object\nrole object\nlevel required int64\ncurrent level int64\nskill object\nlanguage bool\ntechnical bool\nsoft bool\nself bool\nuser bool\ncourse bool\ncluster object\nfunction object\nprofession object\nguk object\nmonth int64\nyear int64\ndtype: object"},"metadata":{}}],"execution_count":190},{"cell_type":"code","source":"org_skills_pd = pd.concat([self,roles_tax])\norg_skills_pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.542986Z","iopub.execute_input":"2025-02-13T11:43:09.543315Z","iopub.status.idle":"2025-02-13T11:43:09.582268Z","shell.execute_reply.started":"2025-02-13T11:43:09.543285Z","shell.execute_reply":"2025-02-13T11:43:09.581150Z"}},"outputs":[{"execution_count":191,"output_type":"execute_result","data":{"text/plain":" employee marital status gender background carer \\\n0 16160 0 1 3 2 \n1 869 1 1 2 0 \n2 72199 0 1 2 4 \n3 72199 0 1 2 4 \n4 72199 0 1 2 4 \n.. ... ... ... ... ... \n514 78023 0 1 1 1 \n515 72199 0 1 2 4 \n516 72199 0 1 2 4 \n517 52964 3 1 5 1 \n518 52964 3 1 5 1 \n\n organisation role level required current level skill \\\n0 mzyxsukirqawtcybjvll ckrqfhcfdvp 0 5 bdbluo \n1 mzyxsukirqawtcybjvll oglugxxotod 0 3 dbbyzn \n2 mzyxsukirqawtcybjvll rrsdulwsydt 0 3 gbhazb \n3 mzyxsukirqawtcybjvll rrsdulwsydt 0 3 jisdwl \n4 mzyxsukirqawtcybjvll rrsdulwsydt 0 5 gbhazb \n.. ... ... ... ... ... \n514 jcsurldsnmgzlowgykoi pfapomtdxcc 3 2 oksacc \n515 mzyxsukirqawtcybjvll rrsdulwsydt 4 0 dypppp \n516 mzyxsukirqawtcybjvll rrsdulwsydt 4 0 dypppp \n517 ktfqqxpnwoznbyttjmlo rrsdulwsydt 4 1 dypppp \n518 ktfqqxpnwoznbyttjmlo rrsdulwsydt 4 1 dypppp \n\n ... soft self user course cluster function profession \\\n0 ... False True False False dghm ckzgyvfj vmyvicq \n1 ... False True False False dghm ckzgyvfj vmyvicq \n2 ... False True False False dghm ckzgyvfj vmyvicq \n3 ... False True False False plnm hyhbuafh unsdcqf \n4 ... True True False False dghm ckzgyvfj vmyvicq \n.. ... ... ... ... ... ... ... ... \n514 ... True True False False dghm ckzgyvfj vmyvicq \n515 ... False True False False spom hyhbuafh bnkimlk \n516 ... False True False False dkhk akvrfsup fcftnao \n517 ... False True False False spom hyhbuafh bnkimlk \n518 ... False True False False dkhk akvrfsup fcftnao \n\n guk month year \n0 hpqmmbgtev 3 2023 \n1 hpqmmbgtev 10 2023 \n2 hpqmmbgtev 1 2023 \n3 ldlmayhvvq 9 2023 \n4 hpqmmbgtev 4 2023 \n.. ... ... ... \n514 hpqmmbgtev 12 2023 \n515 ldlmayhvvq 6 2023 \n516 ldlmayhvvq 11 2023 \n517 ldlmayhvvq 10 2023 \n518 ldlmayhvvq 8 2023 \n\n[638 rows x 22 columns]","text/html":"
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employeemarital statusgenderbackgroundcarerorganisationrolelevel requiredcurrent levelskill...softselfusercourseclusterfunctionprofessiongukmonthyear
0161600132mzyxsukirqawtcybjvllckrqfhcfdvp05bdbluo...FalseTrueFalseFalsedghmckzgyvfjvmyvicqhpqmmbgtev32023
18691120mzyxsukirqawtcybjvlloglugxxotod03dbbyzn...FalseTrueFalseFalsedghmckzgyvfjvmyvicqhpqmmbgtev102023
2721990124mzyxsukirqawtcybjvllrrsdulwsydt03gbhazb...FalseTrueFalseFalsedghmckzgyvfjvmyvicqhpqmmbgtev12023
3721990124mzyxsukirqawtcybjvllrrsdulwsydt03jisdwl...FalseTrueFalseFalseplnmhyhbuafhunsdcqfldlmayhvvq92023
4721990124mzyxsukirqawtcybjvllrrsdulwsydt05gbhazb...TrueTrueFalseFalsedghmckzgyvfjvmyvicqhpqmmbgtev42023
..................................................................
514780230111jcsurldsnmgzlowgykoipfapomtdxcc32oksacc...TrueTrueFalseFalsedghmckzgyvfjvmyvicqhpqmmbgtev122023
515721990124mzyxsukirqawtcybjvllrrsdulwsydt40dypppp...FalseTrueFalseFalsespomhyhbuafhbnkimlkldlmayhvvq62023
516721990124mzyxsukirqawtcybjvllrrsdulwsydt40dypppp...FalseTrueFalseFalsedkhkakvrfsupfcftnaoldlmayhvvq112023
517529643151ktfqqxpnwoznbyttjmlorrsdulwsydt41dypppp...FalseTrueFalseFalsespomhyhbuafhbnkimlkldlmayhvvq102023
518529643151ktfqqxpnwoznbyttjmlorrsdulwsydt41dypppp...FalseTrueFalseFalsedkhkakvrfsupfcftnaoldlmayhvvq82023
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638 rows × 22 columns

\n
"},"metadata":{}}],"execution_count":191},{"cell_type":"code","source":"org_skills_pd.dtypes","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-13T11:43:09.583321Z","iopub.execute_input":"2025-02-13T11:43:09.583689Z","iopub.status.idle":"2025-02-13T11:43:09.591346Z","shell.execute_reply.started":"2025-02-13T11:43:09.583656Z","shell.execute_reply":"2025-02-13T11:43:09.590316Z"}},"outputs":[{"execution_count":192,"output_type":"execute_result","data":{"text/plain":"employee int64\nmarital status int64\ngender int64\nbackground int64\ncarer int64\norganisation object\nrole object\nlevel required int64\ncurrent level int64\nskill object\nlanguage bool\ntechnical bool\nsoft bool\nself bool\nuser bool\ncourse bool\ncluster object\nfunction object\nprofession object\nguk object\nmonth int64\nyear int64\ndtype: object"},"metadata":{}}],"execution_count":192}]} \ No newline at end of file diff --git a/Data engineering and science/Structuring data/structuring data.pdf b/Data engineering and science/Structuring data/structuring data.pdf new file mode 100644 index 0000000..9978eb8 Binary files /dev/null and b/Data engineering and science/Structuring data/structuring data.pdf differ diff --git a/Data engineering and science/global-emisssions.ipynb b/Data engineering and science/global-emisssions.ipynb new file mode 100644 index 0000000..bdfd430 --- /dev/null +++ b/Data engineering and science/global-emisssions.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3426904,"sourceType":"datasetVersion","datasetId":2065357},{"sourceId":4360255,"sourceType":"datasetVersion","datasetId":2432740},{"sourceId":4537501,"sourceType":"datasetVersion","datasetId":2651128},{"sourceId":4560499,"sourceType":"datasetVersion","datasetId":2661487},{"sourceId":5081220,"sourceType":"datasetVersion","datasetId":2950449},{"sourceId":5472882,"sourceType":"datasetVersion","datasetId":3106323},{"sourceId":5822810,"sourceType":"datasetVersion","datasetId":3346108},{"sourceId":6101670,"sourceType":"datasetVersion","datasetId":3495122},{"sourceId":7478877,"sourceType":"datasetVersion","datasetId":4353311}],"dockerImageVersionId":30746,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Global-co-emissions and other factors\n","metadata":{}},{"cell_type":"markdown","source":"Global emissions has risen - it is impacting the environment [1]. Demography and GDP may be factors to increase carbon dioxide emissions [2,3]. The latter has been discussed in the last two decades and various analytical model created. We use various data made available on Kaggle to explore those factors. We aim to answer the following questions: \n\n\n- Is there a trend of emissions throug time?\n- Has the GDP changed through time?\n\n\n\n__References:__ \n1. Töbelmann, Daniel, and Tobias Wendler. \"The impact of environmental innovation on carbon dioxide emissions.\" Journal of Cleaner Production 244 (2020): 118787\n2. O'Neill, Brian C., et al. \"Demographic change and carbon dioxide emissions.\" The Lancet 380.9837 (2012): 157-164.\n3. Tucker, Michael. \"Carbon dioxide emissions and global GDP.\" Ecological Economics 15.3 (1995): 215-223.","metadata":{}},{"cell_type":"markdown","source":"## Upload libraries and data","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom sklearn.mixture import GaussianMixture as GMM\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2025-02-20T19:38:14.978901Z","iopub.execute_input":"2025-02-20T19:38:14.979248Z","iopub.status.idle":"2025-02-20T19:38:17.403656Z","shell.execute_reply.started":"2025-02-20T19:38:14.979214Z","shell.execute_reply":"2025-02-20T19:38:17.402645Z"},"trusted":true},"outputs":[{"name":"stdout","text":"/kaggle/input/air-quality-per-country-with-gdp-and-population/air_gdp_density.csv\n/kaggle/input/2023-world-population-by-country/countries-table.csv\n/kaggle/input/2023-world-population-by-country/countries-table.json\n/kaggle/input/gdp-growth-around-the-globe/API_NY.GDP.MKTP.KD.ZG_DS2_en_csv_v2_4701072.csv\n/kaggle/input/gdp-growth-around-the-globe/Metadata_Indicator_API_NY.GDP.MKTP.KD.ZG_DS2_en_csv_v2_4701072.csv\n/kaggle/input/gdp-growth-around-the-globe/Metadata_Country_API_NY.GDP.MKTP.KD.ZG_DS2_en_csv_v2_4701072.csv\n/kaggle/input/global-co-emissions/GlobalCO2Emissions.csv\n/kaggle/input/countries-gdp-19602020/Countries GDP 1960-2020.csv\n/kaggle/input/world-population-dataset/world_population.csv\n/kaggle/input/countries-of-the-world-2023/world-data-2023.csv\n/kaggle/input/co2-emissions-by-country/co2_emissions_kt_by_country.csv\n/kaggle/input/countries-gdp-2012-to-2021/GDP.csv\n","output_type":"stream"}],"execution_count":1},{"cell_type":"code","source":"d = pd.read_csv('/kaggle/input/global-co-emissions/GlobalCO2Emissions.csv')\n\nd.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:17.405370Z","iopub.execute_input":"2025-02-20T19:38:17.405820Z","iopub.status.idle":"2025-02-20T19:38:17.430383Z","shell.execute_reply.started":"2025-02-20T19:38:17.405794Z","shell.execute_reply":"2025-02-20T19:38:17.429390Z"},"trusted":true},"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"Year int64\nEmissions float64\ndtype: object"},"metadata":{}}],"execution_count":2},{"cell_type":"code","source":"d.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:17.431816Z","iopub.execute_input":"2025-02-20T19:38:17.432186Z","iopub.status.idle":"2025-02-20T19:38:17.447189Z","shell.execute_reply.started":"2025-02-20T19:38:17.432156Z","shell.execute_reply":"2025-02-20T19:38:17.446121Z"},"trusted":true},"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":" Year Emissions\n0 1750 0.03\n1 1760 0.03\n2 1770 0.03\n3 1780 0.03\n4 1790 0.04","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearEmissions
017500.03
117600.03
217700.03
317800.03
417900.04
\n
"},"metadata":{}}],"execution_count":3},{"cell_type":"markdown","source":"## Data exploration\n\nThe dataset has two statistical variables - year and emissions. The observations appears to have occurred between 1750 and 2023. The emissions appears range from extremely low values to high ones. However, most occurences appear to be low. ","metadata":{}},{"cell_type":"code","source":"d.describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:17.449138Z","iopub.execute_input":"2025-02-20T19:38:17.449431Z","iopub.status.idle":"2025-02-20T19:38:17.468910Z","shell.execute_reply.started":"2025-02-20T19:38:17.449408Z","shell.execute_reply":"2025-02-20T19:38:17.467826Z"},"trusted":true},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":" Year Emissions\ncount 29.000000 29.000000\nmean 1889.827586 5.937586\nstd 84.855368 11.443084\nmin 1750.000000 0.030000\n25% 1820.000000 0.060000\n50% 1890.000000 0.430000\n75% 1960.000000 3.740000\nmax 2023.000000 40.900000","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
YearEmissions
count29.00000029.000000
mean1889.8275865.937586
std84.85536811.443084
min1750.0000000.030000
25%1820.0000000.060000
50%1890.0000000.430000
75%1960.0000003.740000
max2023.00000040.900000
\n
"},"metadata":{}}],"execution_count":4},{"cell_type":"code","source":"d.hist(grid = False)","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:17.471286Z","iopub.execute_input":"2025-02-20T19:38:17.471609Z","iopub.status.idle":"2025-02-20T19:38:17.930761Z","shell.execute_reply.started":"2025-02-20T19:38:17.471576Z","shell.execute_reply":"2025-02-20T19:38:17.929655Z"},"trusted":true},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"array([[,\n ]], dtype=object)"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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mqkCgE0FM+MAGj2kpOT9frrr2vnzp0KDQ11twcFBencuXMqKyvzmB0pLS1VUFBQrcdyOp1yOp1NXTIALzAzAqDZMsYoOTlZmzZt0rZt2xQeHu6xfdSoUerYsaNycnLcbYcPH1ZhYaFcLtfVLhdAAzEzAqDZSkpK0vr167V582Z17drV/RyIv7+/OnXqJH9/f82aNUvz589Xjx495Ofnp5///OdyuVx8kgZoQQgjAJqtlStXSpImTJjg0b5u3TrNnDlTkrR06VK1a9dOt912m6qqqhQbG6tnn332KlcK4EoQRgA0W8aYS/bx9fVVWlqa0tLSrkJFAJoCz4wAAACrvAojK1euVGRkpPz8/OTn5yeXy6U333yzzv7p6elyOBwei6+v7xUXDQAAWg+v3qYJDQ3V4sWLNXDgQBlj9Mc//lHTpk3TgQMHNHTo0Fr38fPz8/hhK4fDcWUVAwCAVsWrMDJ16lSP9SeffFIrV67U7t276wwjDoejzs/7AwAANPiZkerqamVkZKiysrLez/OfOXNGffv2VVhYmKZNm6b333//ksfmtyMAAGg7vA4jBw8eVJcuXeR0OjVnzhxt2rRJQ4YMqbVvRESE1q5dq82bN+uFF15QTU2NoqOjdfz48XrPwW9HAADQdngdRiIiIlRQUKA9e/bonnvuUWJioj744INa+7pcLiUkJGjEiBEaP368Nm7cqF69emn16tX1noPfjgAAoO3w+ntGfHx8NGDAAEn//CrmvXv3atmyZZcMGJLUsWNHXXfddTpy5Ei9/fjtCAAA2o4r/p6RmpoaVVVVXVbf6upqHTx4UMHBwVd6WgAA0Ep4NTOSkpKiuLg49enTR6dPn9b69euVm5urrVu3SpISEhLUu3dvpaamSpIWLlyoG264QQMGDFBZWZmeeuopHTt2THfffXfjjwQAALRIXoWREydOKCEhQcXFxfL391dkZKS2bt2qW265RZJUWFiodu3+Ndny1Vdfafbs2SopKVH37t01atQo7dq1q84HXgEAQNvjVRh5/vnn692em5vrsb506VItXbrU66IAAEDbwW/TAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKu8CiMrV65UZGSk/Pz85OfnJ5fLpTfffLPefTZs2KBBgwbJ19dXw4cP15YtW66oYAAA0Lp4FUZCQ0O1ePFi7du3T/n5+fr+97+vadOm6f3336+1/65duzRjxgzNmjVLBw4cUHx8vOLj43Xo0KFGKR4AALR8XoWRqVOn6tZbb9XAgQP13e9+V08++aS6dOmi3bt319p/2bJlmjx5shYsWKDBgwdr0aJFGjlypFasWNEoxQMAgJavwc+MVFdXKyMjQ5WVlXK5XLX2ycvLU0xMjEdbbGys8vLy6j12VVWVKioqPBYAANA6dfB2h4MHD8rlcuns2bPq0qWLNm3apCFDhtTat6SkRIGBgR5tgYGBKikpqfccqampeuKJJ7wtTZLU76E3GrRfS/PZ4imNery2ct2kxr12bem6AUBT8XpmJCIiQgUFBdqzZ4/uueceJSYm6oMPPmjUolJSUlReXu5eioqKGvX4AACg+fB6ZsTHx0cDBgyQJI0aNUp79+7VsmXLtHr16ov6BgUFqbS01KOttLRUQUFB9Z7D6XTK6XR6WxoAAGiBrvh7RmpqalRVVVXrNpfLpZycHI+27OzsOp8xAQAAbY9XMyMpKSmKi4tTnz59dPr0aa1fv165ubnaunWrJCkhIUG9e/dWamqqJGnu3LkaP368lixZoilTpigjI0P5+flas2ZN448EAAC0SF6FkRMnTighIUHFxcXy9/dXZGSktm7dqltuuUWSVFhYqHbt/jXZEh0drfXr1+uRRx7Rww8/rIEDByozM1PDhg1r3FEAAIAWy6sw8vzzz9e7PTc396K26dOna/r06V4VBQAA2g5+mwYAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAHQbO3cuVNTp05VSEiIHA6HMjMzPbbPnDlTDofDY5k8ebKdYgE0GGEEQLNVWVmpqKgopaWl1dln8uTJKi4udi9//vOfr2KFABpDB9sFAEBd4uLiFBcXV28fp9OpoKCgq1QRgKbAzAiAFi03N1cBAQGKiIjQPffco1OnTtXbv6qqShUVFR4LALsIIwBarMmTJ+tPf/qTcnJy9Jvf/EY7duxQXFycqqur69wnNTVV/v7+7iUsLOwqVgygNrxNA6DF+slPfuL+7+HDhysyMlLf+c53lJubq5tvvrnWfVJSUjR//nz3ekVFBYEEsIyZEQCtRv/+/dWzZ08dOXKkzj5Op1N+fn4eCwC7CCMAWo3jx4/r1KlTCg4Otl0KAC/wNg2AZuvMmTMesxxHjx5VQUGBevTooR49euiJJ57QbbfdpqCgIH3yySd68MEHNWDAAMXGxlqsGoC3CCMAmq38/HxNnDjRvX7hWY/ExEStXLlS7733nv74xz+qrKxMISEhmjRpkhYtWiSn02mrZAANQBgB0GxNmDBBxpg6t2/duvUqVgOgqfDMCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALDKqzCSmpqq66+/Xl27dlVAQIDi4+N1+PDhevdJT0+Xw+HwWHx9fa+oaAAA0Hp4FUZ27NihpKQk7d69W9nZ2Tp//rwmTZqkysrKevfz8/NTcXGxezl27NgVFQ0AAFoPr36bJisry2M9PT1dAQEB2rdvn2666aY693M4HAoKCmpYhQAAoFW7omdGysvLJUk9evSot9+ZM2fUt29fhYWFadq0aXr//ffr7V9VVaWKigqPBQAAtE4NDiM1NTWaN2+exo0bp2HDhtXZLyIiQmvXrtXmzZv1wgsvqKamRtHR0Tp+/Hid+6Smpsrf39+9hIWFNbRMAADQzDU4jCQlJenQoUPKyMiot5/L5VJCQoJGjBih8ePHa+PGjerVq5dWr15d5z4pKSkqLy93L0VFRQ0tEwAANHNePTNyQXJysl5//XXt3LlToaGhXu3bsWNHXXfddTpy5EidfZxOp5xOZ0NKAwAALYxXMyPGGCUnJ2vTpk3atm2bwsPDvT5hdXW1Dh48qODgYK/3BQAArY9XMyNJSUlav369Nm/erK5du6qkpESS5O/vr06dOkmSEhIS1Lt3b6WmpkqSFi5cqBtuuEEDBgxQWVmZnnrqKR07dkx33313Iw8FAAC0RF6FkZUrV0qSJkyY4NG+bt06zZw5U5JUWFiodu3+NeHy1Vdfafbs2SopKVH37t01atQo7dq1S0OGDLmyygEAQKvgVRgxxlyyT25ursf60qVLtXTpUq+KAgAAbQe/TQMAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACs8iqMpKam6vrrr1fXrl0VEBCg+Ph4HT58+JL7bdiwQYMGDZKvr6+GDx+uLVu2NLhgAADQungVRnbs2KGkpCTt3r1b2dnZOn/+vCZNmqTKyso699m1a5dmzJihWbNm6cCBA4qPj1d8fLwOHTp0xcUDAICWr4M3nbOysjzW09PTFRAQoH379ummm26qdZ9ly5Zp8uTJWrBggSRp0aJFys7O1ooVK7Rq1aoGlg0AAFqLK3pmpLy8XJLUo0ePOvvk5eUpJibGoy02NlZ5eXl17lNVVaWKigqPBQAAtE5ezYx8U01NjebNm6dx48Zp2LBhdfYrKSlRYGCgR1tgYKBKSkrq3Cc1NVVPPPFEQ0trE/o99IbtElosrh0ANC8NnhlJSkrSoUOHlJGR0Zj1SJJSUlJUXl7uXoqKihr9HAAAoHlo0MxIcnKyXn/9de3cuVOhoaH19g0KClJpaalHW2lpqYKCgurcx+l0yul0NqQ0AADQwng1M2KMUXJysjZt2qRt27YpPDz8kvu4XC7l5OR4tGVnZ8vlcnlXKQAAaJW8mhlJSkrS+vXrtXnzZnXt2tX93Ie/v786deokSUpISFDv3r2VmpoqSZo7d67Gjx+vJUuWaMqUKcrIyFB+fr7WrFnTyEMBAAAtkVczIytXrlR5ebkmTJig4OBg9/LSSy+5+xQWFqq4uNi9Hh0drfXr12vNmjWKiorSK6+8oszMzHofegUAAG2HVzMjxphL9snNzb2obfr06Zo+fbo3pwIAAG0Ev00DAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijABotnbu3KmpU6cqJCREDodDmZmZHtuNMXrssccUHBysTp06KSYmRh9//LGdYgE0GGEEQLNVWVmpqKgopaWl1br9t7/9rX7/+99r1apV2rNnj6655hrFxsbq7NmzV7lSAFeiwb/aCwBNLS4uTnFxcbVuM8bomWee0SOPPKJp06ZJkv70pz8pMDBQmZmZ+slPfnI1SwVwBZgZAdAiHT16VCUlJYqJiXG3+fv7a+zYscrLy6tzv6qqKlVUVHgsAOwijABokS78NlZgYKBHe2BgoHtbbVJTU+Xv7+9ewsLCmrROAJdGGAHQpqSkpKi8vNy9FBUV2S4JaPMIIwBapKCgIElSaWmpR3tpaal7W22cTqf8/Pw8FgB2EUYAtEjh4eEKCgpSTk6Ou62iokJ79uyRy+WyWBkAb/FpGgDN1pkzZ3TkyBH3+tGjR1VQUKAePXqoT58+mjdvnn79619r4MCBCg8P16OPPqqQkBDFx8fbKxqA1wgjAJqt/Px8TZw40b0+f/58SVJiYqLS09P14IMPqrKyUj/72c9UVlamG2+8UVlZWfL19bVVMoAGIIwAaLYmTJggY0yd2x0OhxYuXKiFCxdexaoANDaeGQEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVnkdRnbu3KmpU6cqJCREDodDmZmZ9fbPzc2Vw+G4aCkpKWlozQAAoBXxOoxUVlYqKipKaWlpXu13+PBhFRcXu5eAgABvTw0AAFqhDt7uEBcXp7i4OK9PFBAQoG7dunm9HwAAaN2u2jMjI0aMUHBwsG655Rb93//9X719q6qqVFFR4bEAAIDWqcnDSHBwsFatWqVXX31Vr776qsLCwjRhwgTt37+/zn1SU1Pl7+/vXsLCwpq6TAAAYInXb9N4KyIiQhEREe716OhoffLJJ1q6dKn+53/+p9Z9UlJSNH/+fPd6RUUFgQQAgFaqycNIbcaMGaN33nmnzu1Op1NOp/MqVgQAAGyx8j0jBQUFCg4OtnFqAADQzHg9M3LmzBkdOXLEvX706FEVFBSoR48e6tOnj1JSUvT555/rT3/6kyTpmWeeUXh4uIYOHaqzZ8/qD3/4g7Zt26a33nqr8UYBAABaLK/DSH5+viZOnOhev/BsR2JiotLT01VcXKzCwkL39nPnzun+++/X559/rs6dOysyMlJvv/22xzEAAEDb5XUYmTBhgowxdW5PT0/3WH/wwQf14IMPel0YAABoG/htGgAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGCV12Fk586dmjp1qkJCQuRwOJSZmXnJfXJzczVy5Eg5nU4NGDBA6enpDSgVAAC0Rl6HkcrKSkVFRSktLe2y+h89elRTpkzRxIkTVVBQoHnz5unuu+/W1q1bvS4WAAC0Ph283SEuLk5xcXGX3X/VqlUKDw/XkiVLJEmDBw/WO++8o6VLlyo2Ntbb0wMAgFamyZ8ZycvLU0xMjEdbbGys8vLy6tynqqpKFRUVHgsAAGidmjyMlJSUKDAw0KMtMDBQFRUV+vrrr2vdJzU1Vf7+/u4lLCysqcsEAACWNMtP06SkpKi8vNy9FBUV2S4JAAA0Ea+fGfFWUFCQSktLPdpKS0vl5+enTp061bqP0+mU0+ls6tIAAEAz0OQzIy6XSzk5OR5t2dnZcrlcTX1qAADQAngdRs6cOaOCggIVFBRI+udHdwsKClRYWCjpn2+xJCQkuPvPmTNHn376qR588EF99NFHevbZZ/Xyyy/rvvvua5wRAGjT/uu//ksOh8NjGTRokO2yAHjB67dp8vPzNXHiRPf6/PnzJUmJiYlKT09XcXGxO5hIUnh4uN544w3dd999WrZsmUJDQ/WHP/yBj/UCaDRDhw7V22+/7V7v0KHJ34EG0Ii8fsVOmDBBxpg6t9f27aoTJkzQgQMHvD0VAFyWDh06KCgoyHYZABqoWX6aBgC88fHHHyskJET9+/fXnXfe6TE7+218jxHQ/BBGALRoY8eOVXp6urKysrRy5UodPXpU3/ve93T69Ola+/M9RkDzQxgB0KLFxcVp+vTpioyMVGxsrLZs2aKysjK9/PLLtfbne4yA5oenvAC0Kt26ddN3v/tdHTlypNbtfI8R0PwwMwKgVTlz5ow++eQTBQcH2y4FwGUijABo0R544AHt2LFDn332mXbt2qUf/vCHat++vWbMmGG7NACXibdpALRox48f14wZM3Tq1Cn16tVLN954o3bv3q1evXrZLg3AZSKMAGjRMjIybJcA4ArxNg0AALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCqQWEkLS1N/fr1k6+vr8aOHat33323zr7p6elyOBwei6+vb4MLBgAArYvXYeSll17S/Pnz9fjjj2v//v2KiopSbGysTpw4Uec+fn5+Ki4udi/Hjh27oqIBAEDr4XUYefrppzV79mzdddddGjJkiFatWqXOnTtr7dq1de7jcDgUFBTkXgIDA6+oaAAA0Hp4FUbOnTunffv2KSYm5l8HaNdOMTExysvLq3O/M2fOqG/fvgoLC9O0adP0/vvv13ueqqoqVVRUeCwAAKB18iqMnDx5UtXV1RfNbAQGBqqkpKTWfSIiIrR27Vpt3rxZL7zwgmpqahQdHa3jx4/XeZ7U1FT5+/u7l7CwMG/KBAAALUiTf5rG5XIpISFBI0aM0Pjx47Vx40b16tVLq1evrnOflJQUlZeXu5eioqKmLhMAAFjSwZvOPXv2VPv27VVaWurRXlpaqqCgoMs6RseOHXXdddfpyJEjdfZxOp1yOp3elAYAAFoor2ZGfHx8NGrUKOXk5LjbampqlJOTI5fLdVnHqK6u1sGDBxUcHOxdpQAAoFXyamZEkubPn6/ExESNHj1aY8aM0TPPPKPKykrdddddkqSEhAT17t1bqampkqSFCxfqhhtu0IABA1RWVqannnpKx44d09133924IwEAAC2S12Hkjjvu0N/+9jc99thjKikp0YgRI5SVleV+qLWwsFDt2v1rwuWrr77S7NmzVVJSou7du2vUqFHatWuXhgwZ0nijAAAALZbXYUSSkpOTlZycXOu23Nxcj/WlS5dq6dKlDTkNAABoA/htGgAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgVYM+TQMAAFqWfg+90ajH+2zxlEY7FjMjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACs6mC7AABoi/o99IbtEq6azxZPadTjNfa1a8z6mnNtzRkzIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKxqUBhJS0tTv3795Ovrq7Fjx+rdd9+tt/+GDRs0aNAg+fr6avjw4dqyZUuDigWA2nh7TwLQvHgdRl566SXNnz9fjz/+uPbv36+oqCjFxsbqxIkTtfbftWuXZsyYoVmzZunAgQOKj49XfHy8Dh06dMXFA4C39yQAzY/XYeTpp5/W7Nmzddddd2nIkCFatWqVOnfurLVr19baf9myZZo8ebIWLFigwYMHa9GiRRo5cqRWrFhxxcUDgLf3JADNTwdvOp87d0779u1TSkqKu61du3aKiYlRXl5erfvk5eVp/vz5Hm2xsbHKzMys8zxVVVWqqqpyr5eXl0uSKioqLlljTdXfL9kHaCsu9Zq5sN0YczXKaXQNuSdxf7n6LufaeqOx/xwasz5qq73Ppe4xXoWRkydPqrq6WoGBgR7tgYGB+uijj2rdp6SkpNb+JSUldZ4nNTVVTzzxxEXtYWFh3pQLtHn+z1xev9OnT8vf379Ja2kKDbkncX+5+i7376Etzbm+1lLbpe4xXoWRqyUlJcVjNqWmpkZffvmlrr32WjkcDouVXb6KigqFhYWpqKhIfn5+tstpdrg+9bta18cYo9OnTyskJKTJztHcNPT+0tL/zlK/XW21/su9x3gVRnr27Kn27durtLTUo720tFRBQUG17hMUFORVf0lyOp1yOp0ebd26dfOm1GbDz8+vRf7Fu1q4PvW7GtenJc6IXNCQe9KV3l9a+t9Z6rerLdZ/OfcYrx5g9fHx0ahRo5STk+Nuq6mpUU5OjlwuV637uFwuj/6SlJ2dXWd/ALhcDbknAWh+vH6bZv78+UpMTNTo0aM1ZswYPfPMM6qsrNRdd90lSUpISFDv3r2VmpoqSZo7d67Gjx+vJUuWaMqUKcrIyFB+fr7WrFnTuCMB0CZd6p4EoPnzOozccccd+tvf/qbHHntMJSUlGjFihLKystwPkBUWFqpdu39NuERHR2v9+vV65JFH9PDDD2vgwIHKzMzUsGHDGm8UzZDT6dTjjz9+0XQw/onrUz+uz+W71D2psbT0PxPqt4v66+cwLfUzfQAAoFXgt2kAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEkXrs3LlTU6dOVUhIiBwOx0U/7nfmzBklJycrNDRUnTp1cv9i6DedPXtWSUlJuvbaa9WlSxfddtttF31bZGFhoaZMmaLOnTsrICBACxYs0D/+8Y+mHt4Vu9T1KS0t1cyZMxUSEqLOnTtr8uTJ+vjjjz36tObrk5qaquuvv15du3ZVQECA4uPjdfjwYY8+jTX+3NxcjRw5Uk6nUwMGDFB6enpTD69NSUtLU79+/eTr66uxY8fq3XfftV1SrS71mjTG6LHHHlNwcLA6deqkmJiYi16TNjXWa8aWlStXKjIy0v0tpS6XS2+++aZ7e3OuvTaLFy+Ww+HQvHnz3G1NNgaDOm3ZssX86le/Mhs3bjSSzKZNmzy2z54923znO98x27dvN0ePHjWrV6827du3N5s3b3b3mTNnjgkLCzM5OTkmPz/f3HDDDSY6Otq9/R//+IcZNmyYiYmJMQcOHDBbtmwxPXv2NCkpKVdrmA1W3/WpqakxN9xwg/ne975n3n33XfPRRx+Zn/3sZ6ZPnz7mzJkz7n6t+frExsaadevWmUOHDpmCggJz6623Nsn4P/30U9O5c2czf/5888EHH5jly5eb9u3bm6ysrKs63tYqIyPD+Pj4mLVr15r333/fzJ4923Tr1s2UlpbaLu0il7pnLV682Pj7+5vMzEzzl7/8xfzgBz8w4eHh5uuvv7ZT8Lc0xmvGptdee8288cYb5q9//as5fPiwefjhh03Hjh3NoUOHjDHNu/Zve/fdd02/fv1MZGSkmTt3rru9qcZAGLlMtb2whw4dahYuXOjRNnLkSPOrX/3KGGNMWVmZ6dixo9mwYYN7+4cffmgkmby8PGPMP28e7dq1MyUlJe4+K1euNH5+fqaqqqqJRtP4vn19Dh8+bCS5X4TGGFNdXW169eplnnvuOWNM27o+xhhz4sQJI8ns2LHDGNN443/wwQfN0KFDPc51xx13mNjY2KYeUpswZswYk5SU5F6vrq42ISEhJjU11WJVl1bbPxCCgoLMU0895W4rKyszTqfT/PnPf7ZQ4aU15DXT3HTv3t384Q9/aFG1nz592gwcONBkZ2eb8ePHu8NIU46Bt2muQHR0tF577TV9/vnnMsZo+/bt+utf/6pJkyZJkvbt26fz588rJibGvc+gQYPUp08f5eXlSZLy8vI0fPhwj2+LjI2NVUVFhd5///2rO6BGVFVVJUny9fV1t7Vr105Op1PvvPOOpLZ3fcrLyyVJPXr0kNR448/Ly/M4xoU+F46Bhjt37pz27dvncX3btWunmJiYFnd9jx49qpKSEo+x+Pv7a+zYsc12LA15zTQX1dXVysjIUGVlpVwuV4uqPSkpSVOmTLnovtKUYyCMXIHly5dryJAhCg0NlY+PjyZPnqy0tDTddNNNkqSSkhL5+Phc9IuggYGBKikpcff59tdWX1i/0KcluvAXNCUlRV999ZXOnTun3/zmNzp+/LiKi4slta3rU1NTo3nz5mncuHHun0JorPHX1aeiokJff/11UwynzTh58qSqq6trvb4t6e+f9K+/Ly1lLA19zdh28OBBdenSRU6nU3PmzNGmTZs0ZMiQFlG7JGVkZGj//v3u35f7pqYcg9e/TYN/Wb58uXbv3q3XXntNffv21c6dO5WUlKSQkJCLEmVb07FjR23cuFGzZs1Sjx491L59e8XExCguLk6mDf4CQVJSkg4dOuSeFQJQv5b6momIiFBBQYHKy8v1yiuvKDExUTt27LBd1mUpKirS3LlzlZ2d7TGrfTUwM9JAX3/9tR5++GE9/fTTmjp1qiIjI5WcnKw77rhDv/vd7yRJQUFBOnfunMrKyjz2LS0tVVBQkLvPt59EvrB+oU9LNWrUKBUUFKisrEzFxcXKysrSqVOn1L9/f0lt5/okJyfr9ddf1/bt2xUaGupub6zx19XHz89PnTp1auzhtCk9e/ZU+/bta72+LeXv3wUX6m0JY7mS14xtPj4+GjBggEaNGqXU1FRFRUVp2bJlLaL2ffv26cSJExo5cqQ6dOigDh06aMeOHfr973+vDh06KDAwsMnGQBhpoPPnz+v8+fMev1AsSe3bt1dNTY2kf/7PuGPHjsrJyXFvP3z4sAoLC+VyuSRJLpdLBw8e1IkTJ9x9srOz5efnpyFDhlyFkTQ9f39/9erVSx9//LHy8/M1bdo0Sa3/+hhjlJycrE2bNmnbtm0KDw/32N5Y43e5XB7HuNDnwjHQcD4+Pho1apTH9a2pqVFOTk6Lu77h4eEKCgryGEtFRYX27NnTbMbSGK+Z5qampkZVVVUtovabb75ZBw8eVEFBgXsZPXq07rzzTvd/N9kYrujx11bu9OnT5sCBA+bAgQNGknn66afNgQMHzLFjx4wxxowfP94MHTrUbN++3Xz66adm3bp1xtfX1zz77LPuY8yZM8f06dPHbNu2zeTn5xuXy2VcLpd7+4WPbk6aNMkUFBSYrKws06tXrxbx0dVLXZ+XX37ZbN++3XzyyScmMzPT9O3b1/zoRz/yOEZrvj733HOP8ff3N7m5uaa4uNi9/P3vf3f3aYzxX/ho74IFC8yHH35o0tLS+GhvI8rIyDBOp9Okp6ebDz74wPzsZz8z3bp18/iEU3Nxqdfk4sWLTbdu3czmzZvNe++9Z6ZNm9asPtrbGK8Zmx566CGzY8cOc/ToUfPee++Zhx56yDgcDvPWW28ZY5p37XX55qdpjGm6MRBG6rF9+3Yj6aIlMTHRGGNMcXGxmTlzpgkJCTG+vr4mIiLCLFmyxNTU1LiP8fXXX5t7773XdO/e3XTu3Nn88Ic/NMXFxR7n+eyzz0xcXJzp1KmT6dmzp7n//vvN+fPnr+ZQG+RS12fZsmUmNDTUdOzY0fTp08c88sgjF30ctzVfn9qujSSzbt06d5/GGv/27dvNiBEjjI+Pj+nfv7/HOXDlli9fbvr06WN8fHzMmDFjzO7du22XVKtLvSZramrMo48+agIDA43T6TQ333yzOXz4sN2iv6GxXjO2/Md//Ifp27ev8fHxMb169TI333yzO4gY07xrr8u3w0hTjcFhTBt8mhAAADQbPDMCAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAqv8HipSgQIJJBuIAAAAASUVORK5CYII="},"metadata":{}}],"execution_count":5},{"cell_type":"markdown","source":"## Is there a trends of emissions produced through time?","metadata":{}},{"cell_type":"code","source":"import seaborn as sns\nsns.set_style(\"whitegrid\", {'axes.grid' : False})\nsns.scatterplot(x='Year', y='Emissions', data=d)","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:17.931940Z","iopub.execute_input":"2025-02-20T19:38:17.932222Z","iopub.status.idle":"2025-02-20T19:38:18.432932Z","shell.execute_reply.started":"2025-02-20T19:38:17.932200Z","shell.execute_reply":"2025-02-20T19:38:18.431860Z"},"trusted":true},"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":6},{"cell_type":"markdown","source":"We model the relationship using the exponential equation and predict the observations to check accuracy.\n\nFor fitting an exponential equation (equation 1.0), we take the logarithm of both side to fit log of y against x. (see equation 2.0). Fitting _log(y)_ - as if it is linear - emphasizes small values of _y_, causing large deviation for large value of _y_. A linear regression seeks finding a gradient ($\\delta y$) that minimises the distance between the predicted and known values of _y_ (see equation 3.0). We assume $Y_i = \\log y_i$. We also assume an approximation of the $\\Delta Y_i$ is the difference betweeh _y_ and its absolute values, causing favouring small values (see equation 4.0). For that reason, the reduce large values with a square root. \n\n$y = Ae^{Bx}$ (1.0)\n\n$log y = log (A + Bx)$ (2.0) \n\n$\\sum_{i} \\Delta y{2} = \\sum{i} \\left(y_{i} -\\hat{y_{i}} \\right)^2$ (3.0)\n\n$\\Delta Y_i = \\Delta (\\log y_i) ≈ \\Delta y_i / |y_i|$ (4.0)\n\n\nWe use two models one with the reduction of values of _y_ and without.\n","metadata":{}},{"cell_type":"code","source":"fit = np.polyfit(d['Year'], np.log(d['Emissions']), 1)\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:18.434136Z","iopub.execute_input":"2025-02-20T19:38:18.434396Z","iopub.status.idle":"2025-02-20T19:38:18.442355Z","shell.execute_reply.started":"2025-02-20T19:38:18.434375Z","shell.execute_reply":"2025-02-20T19:38:18.441411Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.02869030176345192\nCoefficient B = -54.781444681281656\n","output_type":"stream"}],"execution_count":7},{"cell_type":"code","source":"d['model_A'] = np.exp(fit[1]) * np.exp(fit[0] * d['Year']) \nd['errors_model_A'] = np.abs(d['Emissions'] - d['model_A'])\nd.errors_model_A.hist(grid = False)\nd.errors_model_A.describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:18.443384Z","iopub.execute_input":"2025-02-20T19:38:18.443706Z","iopub.status.idle":"2025-02-20T19:38:18.756035Z","shell.execute_reply.started":"2025-02-20T19:38:18.443681Z","shell.execute_reply":"2025-02-20T19:38:18.755051Z"},"trusted":true},"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"count 29.000000\nmean 1.958857\nstd 3.997135\nmin 0.003330\n25% 0.019677\n50% 0.150126\n75% 0.864781\nmax 14.875568\nName: errors_model_A, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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"},"metadata":{}}],"execution_count":8},{"cell_type":"markdown","source":"This could be alleviated by giving each entry a \"weight\" proportional to y. polyfit supports weighted-least-squares via the w keyword argument.","metadata":{}},{"cell_type":"code","source":"np.sqrt(d.Emissions).describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:18.757082Z","iopub.execute_input":"2025-02-20T19:38:18.757354Z","iopub.status.idle":"2025-02-20T19:38:18.766763Z","shell.execute_reply.started":"2025-02-20T19:38:18.757332Z","shell.execute_reply":"2025-02-20T19:38:18.765769Z"},"trusted":true},"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"count 29.000000\nmean 1.555659\nstd 1.908700\nmin 0.173205\n25% 0.244949\n50% 0.655744\n75% 1.933908\nmax 6.395311\nName: Emissions, dtype: float64"},"metadata":{}}],"execution_count":9},{"cell_type":"code","source":"fit = np.polyfit(d['Year'], np.log(d['Emissions']), 1, w = np.sqrt(d.Emissions))\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:18.767923Z","iopub.execute_input":"2025-02-20T19:38:18.768191Z","iopub.status.idle":"2025-02-20T19:38:18.777682Z","shell.execute_reply.started":"2025-02-20T19:38:18.768170Z","shell.execute_reply":"2025-02-20T19:38:18.776674Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.03430748369375951\nCoefficient B = -65.67787308498968\n","output_type":"stream"}],"execution_count":10},{"cell_type":"code","source":"d['model_B'] = np.exp(fit[1]) * np.exp(fit[0] * d['Year']) \nd['errors_model_B'] = np.abs(d['Emissions'] - d['model_B'])\nd.errors_model_B.hist(grid=False)\nd.errors_model_B.describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:18.778902Z","iopub.execute_input":"2025-02-20T19:38:18.779172Z","iopub.status.idle":"2025-02-20T19:38:19.052751Z","shell.execute_reply.started":"2025-02-20T19:38:18.779150Z","shell.execute_reply":"2025-02-20T19:38:19.051622Z"},"trusted":true},"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"count 29.000000\nmean 0.449098\nstd 0.745753\nmin 0.000193\n25% 0.020054\n50% 0.026446\n75% 0.697636\nmax 2.969357\nName: errors_model_B, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":11},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.scatter(x=d['Year'], y= d['Emissions'])\nplt.scatter(x=d['Year'], y= d['model_A'], marker = \"+\")\nplt.scatter(x=d['Year'], y= d['model_B'], marker = \"x\")\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.054091Z","iopub.execute_input":"2025-02-20T19:38:19.054457Z","iopub.status.idle":"2025-02-20T19:38:19.333516Z","shell.execute_reply.started":"2025-02-20T19:38:19.054423Z","shell.execute_reply":"2025-02-20T19:38:19.332455Z"},"trusted":true},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":12},{"cell_type":"markdown","source":"Accuracy of Model A:","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import mean_absolute_error\nprint(\"MAE\",mean_absolute_error(d['Emissions'],d['model_A']))\n\n\nfrom sklearn.metrics import r2_score\nr2 = r2_score(d['Emissions'], d['model_A'])\nprint('R2 ' , r2)\nk=2\nn = d.shape[0]\nadj_r2_score = 1 - ((1-r2)*(n-1)/(n-k-1))\nprint('Adj R2 score ' ,adj_r2_score)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.335151Z","iopub.execute_input":"2025-02-20T19:38:19.335538Z","iopub.status.idle":"2025-02-20T19:38:19.343838Z","shell.execute_reply.started":"2025-02-20T19:38:19.335505Z","shell.execute_reply":"2025-02-20T19:38:19.342620Z"},"trusted":true},"outputs":[{"name":"stdout","text":"MAE 1.9588565646273792\nR2 0.8476354892671631\nAdj R2 score 0.8359151422877141\n","output_type":"stream"}],"execution_count":13},{"cell_type":"markdown","source":"Accuracy of model B","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import mean_absolute_error\nprint(\"MAE\",mean_absolute_error(d['Emissions'],d['model_B']))\n\n\nfrom sklearn.metrics import r2_score\nr2 = r2_score(d['Emissions'], d['model_B'])\nprint('R2 ' , r2)\nk=2\nn = d.shape[0]\nadj_r2_score = 1 - ((1-r2)*(n-1)/(n-k-1))\nprint('Adj R2 score ' ,adj_r2_score)\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.348309Z","iopub.execute_input":"2025-02-20T19:38:19.348636Z","iopub.status.idle":"2025-02-20T19:38:19.358624Z","shell.execute_reply.started":"2025-02-20T19:38:19.348610Z","shell.execute_reply":"2025-02-20T19:38:19.357629Z"},"trusted":true},"outputs":[{"name":"stdout","text":"MAE 0.44909784325335167\nR2 0.9941575105083272\nAdj R2 score 0.993708088239737\n","output_type":"stream"}],"execution_count":14},{"cell_type":"markdown","source":"We have demonstrated that exponential relationship may exists in yearly carbon dioxide measurements through time. The relationship shows it is likely the carbon dioxide emissions should increase even more rapidly.","metadata":{}},{"cell_type":"markdown","source":"# Has the GDP changed through time?\n\nWe model the relationship using the exponential equation and predict the observations to check accuracy.\n\nFor fitting an exponential equation (equation 1.0), we take the logarithm of both side to fit log of y against x. (see equation 2.0). Fitting _log(y)_ - as if it is linear - emphasizes small values of _y_, causing large deviation for large value of _y_. A linear regression seeks finding a gradient ($\\delta y$) that minimises the distance between the predicted and known values of _y_ (see equation 3.0). We assume $Y_i = \\log y_i$. We also assume an approximation of the $\\Delta Y_i$ is the difference betweeh _y_ and its absolute values, causing favouring small values (see equation 4.0). For that reason, the reduce large values with a square root. \n\n$y = Ae^{Bx}$ (1.0)\n\n$log y = log (A + Bx)$ (2.0) \n\n$\\sum_{i} \\Delta y{2} = \\sum{i} \\left(y_{i} -\\hat{y_{i}} \\right)^2$ (3.0)\n\n$\\Delta Y_i = \\Delta (\\log y_i) ≈ \\Delta y_i / |y_i|$ (4.0)\n\n","metadata":{}},{"cell_type":"code","source":"for dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.359801Z","iopub.execute_input":"2025-02-20T19:38:19.360113Z","iopub.status.idle":"2025-02-20T19:38:19.374346Z","shell.execute_reply.started":"2025-02-20T19:38:19.360090Z","shell.execute_reply":"2025-02-20T19:38:19.373390Z"},"trusted":true},"outputs":[{"name":"stdout","text":"/kaggle/input/air-quality-per-country-with-gdp-and-population/air_gdp_density.csv\n/kaggle/input/2023-world-population-by-country/countries-table.csv\n/kaggle/input/2023-world-population-by-country/countries-table.json\n/kaggle/input/gdp-growth-around-the-globe/API_NY.GDP.MKTP.KD.ZG_DS2_en_csv_v2_4701072.csv\n/kaggle/input/gdp-growth-around-the-globe/Metadata_Indicator_API_NY.GDP.MKTP.KD.ZG_DS2_en_csv_v2_4701072.csv\n/kaggle/input/gdp-growth-around-the-globe/Metadata_Country_API_NY.GDP.MKTP.KD.ZG_DS2_en_csv_v2_4701072.csv\n/kaggle/input/global-co-emissions/GlobalCO2Emissions.csv\n/kaggle/input/countries-gdp-19602020/Countries GDP 1960-2020.csv\n/kaggle/input/world-population-dataset/world_population.csv\n/kaggle/input/countries-of-the-world-2023/world-data-2023.csv\n/kaggle/input/co2-emissions-by-country/co2_emissions_kt_by_country.csv\n/kaggle/input/countries-gdp-2012-to-2021/GDP.csv\n","output_type":"stream"}],"execution_count":15},{"cell_type":"code","source":"gdp = pd.read_csv('/kaggle/input/countries-gdp-19602020/Countries GDP 1960-2020.csv')\ngdp.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.375668Z","iopub.execute_input":"2025-02-20T19:38:19.375973Z","iopub.status.idle":"2025-02-20T19:38:19.395242Z","shell.execute_reply.started":"2025-02-20T19:38:19.375950Z","shell.execute_reply":"2025-02-20T19:38:19.394121Z"},"trusted":true},"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"Country Name object\nCountry Code object\n1960 float64\n1961 float64\n1962 float64\n ... \n2016 float64\n2017 float64\n2018 float64\n2019 float64\n2020 float64\nLength: 63, dtype: object"},"metadata":{}}],"execution_count":16},{"cell_type":"code","source":"value_vars = [ str(year) for year in range(1960, 2021)]\nvalue_vars","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.396837Z","iopub.execute_input":"2025-02-20T19:38:19.397166Z","iopub.status.idle":"2025-02-20T19:38:19.404770Z","shell.execute_reply.started":"2025-02-20T19:38:19.397133Z","shell.execute_reply":"2025-02-20T19:38:19.403581Z"},"trusted":true},"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"['1960',\n '1961',\n '1962',\n '1963',\n '1964',\n '1965',\n '1966',\n '1967',\n '1968',\n '1969',\n '1970',\n '1971',\n '1972',\n '1973',\n '1974',\n '1975',\n '1976',\n '1977',\n '1978',\n '1979',\n '1980',\n '1981',\n '1982',\n '1983',\n '1984',\n '1985',\n '1986',\n '1987',\n '1988',\n '1989',\n '1990',\n '1991',\n '1992',\n '1993',\n '1994',\n '1995',\n '1996',\n '1997',\n '1998',\n '1999',\n '2000',\n '2001',\n '2002',\n '2003',\n '2004',\n '2005',\n '2006',\n '2007',\n '2008',\n '2009',\n '2010',\n '2011',\n '2012',\n '2013',\n '2014',\n '2015',\n '2016',\n '2017',\n '2018',\n '2019',\n '2020']"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"\ngdp_long = gdp.melt(id_vars='Country Code', value_vars = value_vars)\ngdp_long['log_10_value'] = np.log10(gdp_long['value'])\ngdp_long['variable'] = gdp_long['variable'].astype(int)\ngdp_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.406141Z","iopub.execute_input":"2025-02-20T19:38:19.406788Z","iopub.status.idle":"2025-02-20T19:38:19.431025Z","shell.execute_reply.started":"2025-02-20T19:38:19.406761Z","shell.execute_reply":"2025-02-20T19:38:19.429930Z"},"trusted":true},"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"Country Code object\nvariable int64\nvalue float64\nlog_10_value float64\ndtype: object"},"metadata":{}}],"execution_count":18},{"cell_type":"code","source":"gdp_long.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.432315Z","iopub.execute_input":"2025-02-20T19:38:19.432963Z","iopub.status.idle":"2025-02-20T19:38:19.443344Z","shell.execute_reply.started":"2025-02-20T19:38:19.432927Z","shell.execute_reply":"2025-02-20T19:38:19.442361Z"},"trusted":true},"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":" Country Code variable value log_10_value\n0 AFE 1960 1.931311e+10 10.285852\n1 AFW 1960 1.040428e+10 10.017212\n2 AUS 1960 1.860679e+10 10.269671\n3 AUT 1960 6.592694e+09 9.819063\n4 BDI 1960 1.960000e+08 8.292256","text/html":"
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Country Codevariablevaluelog_10_value
0AFE19601.931311e+1010.285852
1AFW19601.040428e+1010.017212
2AUS19601.860679e+1010.269671
3AUT19606.592694e+099.819063
4BDI19601.960000e+088.292256
\n
"},"metadata":{}}],"execution_count":19},{"cell_type":"markdown","source":"The yearly distributions appears to vary greatly, but the point of centrality appears to increase through time. The quartile of the log values appears to grow linear, which suggest an exponential relationship. The errors distributions is skewed to the left and near 0, suggesting the model fitting process was quite accurate. ","metadata":{}},{"cell_type":"code","source":"temp = np.log10(gdp.loc[:, value_vars])\ntemp.boxplot(rot=45, fontsize = 12, grid = False, figsize=[20,20])\ngdp.describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:19.444640Z","iopub.execute_input":"2025-02-20T19:38:19.445029Z","iopub.status.idle":"2025-02-20T19:38:20.971626Z","shell.execute_reply.started":"2025-02-20T19:38:19.444996Z","shell.execute_reply":"2025-02-20T19:38:20.970498Z"},"trusted":true},"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":" 1960 1961 1962 1963 1964 \\\ncount 1.190000e+02 1.200000e+02 1.200000e+02 1.200000e+02 1.200000e+02 \nmean 7.737603e+10 7.918769e+10 8.417576e+10 9.092311e+10 9.976049e+10 \nstd 2.252438e+11 2.335094e+11 2.507523e+11 2.696523e+11 2.944479e+11 \nmin 1.201201e+07 1.159201e+07 1.254156e+07 1.283323e+07 1.341655e+07 \n25% 4.362676e+08 4.747200e+08 4.716388e+08 5.081636e+08 5.418066e+08 \n50% 2.723593e+09 2.667191e+09 3.050546e+09 3.570681e+09 3.184116e+09 \n75% 2.926200e+10 3.041073e+10 3.294838e+10 3.774822e+10 3.686680e+10 \nmax 1.390000e+12 1.440000e+12 1.550000e+12 1.670000e+12 1.820000e+12 \n\n 1965 1966 1967 1968 1969 \\\ncount 1.200000e+02 1.200000e+02 1.200000e+02 1.200000e+02 1.200000e+02 \nmean 1.091809e+11 1.177544e+11 1.244980e+11 1.338719e+11 1.480550e+11 \nstd 3.211343e+11 3.498230e+11 3.731073e+11 4.036768e+11 4.443198e+11 \nmin 1.359393e+07 1.446908e+07 1.583518e+07 1.460000e+07 1.585000e+07 \n25% 6.561320e+08 6.940201e+08 7.417202e+08 7.688965e+08 7.862815e+08 \n50% 3.590080e+09 4.230784e+09 4.194304e+09 4.571298e+09 5.726672e+09 \n75% 4.106156e+10 4.430473e+10 4.379965e+10 4.683330e+10 5.355294e+10 \nmax 1.990000e+12 2.160000e+12 2.290000e+12 2.480000e+12 2.730000e+12 \n\n ... 2011 2012 2013 2014 \\\ncount ... 1.200000e+02 1.200000e+02 1.200000e+02 1.200000e+02 \nmean ... 4.266303e+12 4.403837e+12 4.564958e+12 4.711580e+12 \nstd ... 1.111174e+13 1.135331e+13 1.167128e+13 1.199261e+13 \nmin ... 6.761296e+08 6.929333e+08 7.212074e+08 7.277148e+08 \n25% ... 1.775774e+10 1.768566e+10 1.878204e+10 1.958781e+10 \n50% ... 2.365000e+11 2.250000e+11 2.345000e+11 2.395000e+11 \n75% ... 1.610000e+12 1.680000e+12 1.790000e+12 1.850000e+12 \nmax ... 7.370000e+13 7.530000e+13 7.740000e+13 7.960000e+13 \n\n 2015 2016 2017 2018 2019 \\\ncount 1.200000e+02 1.200000e+02 1.200000e+02 1.200000e+02 1.200000e+02 \nmean 4.499309e+12 4.564773e+12 4.886253e+12 5.207231e+12 5.303437e+12 \nstd 1.139102e+13 1.158322e+13 1.231733e+13 1.311345e+13 1.332070e+13 \nmin 7.554000e+08 7.744296e+08 7.921778e+08 8.113000e+08 8.250407e+08 \n25% 1.941603e+10 2.070647e+10 2.203821e+10 2.353911e+10 2.328174e+10 \n50% 2.165000e+11 2.310000e+11 2.525000e+11 2.750000e+11 2.740000e+11 \n75% 1.680000e+12 1.560000e+12 1.670000e+12 1.750000e+12 1.800000e+12 \nmax 7.510000e+13 7.630000e+13 8.120000e+13 8.630000e+13 8.760000e+13 \n\n 2020 \ncount 1.200000e+02 \nmean 5.146832e+12 \nstd 1.292930e+13 \nmin 8.074741e+08 \n25% 2.052449e+10 \n50% 2.580000e+11 \n75% 1.702500e+12 \nmax 8.470000e+13 \n\n[8 rows x 61 columns]","text/html":"
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1960196119621963196419651966196719681969...2011201220132014201520162017201820192020
count1.190000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+02...1.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+021.200000e+02
mean7.737603e+107.918769e+108.417576e+109.092311e+109.976049e+101.091809e+111.177544e+111.244980e+111.338719e+111.480550e+11...4.266303e+124.403837e+124.564958e+124.711580e+124.499309e+124.564773e+124.886253e+125.207231e+125.303437e+125.146832e+12
std2.252438e+112.335094e+112.507523e+112.696523e+112.944479e+113.211343e+113.498230e+113.731073e+114.036768e+114.443198e+11...1.111174e+131.135331e+131.167128e+131.199261e+131.139102e+131.158322e+131.231733e+131.311345e+131.332070e+131.292930e+13
min1.201201e+071.159201e+071.254156e+071.283323e+071.341655e+071.359393e+071.446908e+071.583518e+071.460000e+071.585000e+07...6.761296e+086.929333e+087.212074e+087.277148e+087.554000e+087.744296e+087.921778e+088.113000e+088.250407e+088.074741e+08
25%4.362676e+084.747200e+084.716388e+085.081636e+085.418066e+086.561320e+086.940201e+087.417202e+087.688965e+087.862815e+08...1.775774e+101.768566e+101.878204e+101.958781e+101.941603e+102.070647e+102.203821e+102.353911e+102.328174e+102.052449e+10
50%2.723593e+092.667191e+093.050546e+093.570681e+093.184116e+093.590080e+094.230784e+094.194304e+094.571298e+095.726672e+09...2.365000e+112.250000e+112.345000e+112.395000e+112.165000e+112.310000e+112.525000e+112.750000e+112.740000e+112.580000e+11
75%2.926200e+103.041073e+103.294838e+103.774822e+103.686680e+104.106156e+104.430473e+104.379965e+104.683330e+105.355294e+10...1.610000e+121.680000e+121.790000e+121.850000e+121.680000e+121.560000e+121.670000e+121.750000e+121.800000e+121.702500e+12
max1.390000e+121.440000e+121.550000e+121.670000e+121.820000e+121.990000e+122.160000e+122.290000e+122.480000e+122.730000e+12...7.370000e+137.530000e+137.740000e+137.960000e+137.510000e+137.630000e+138.120000e+138.630000e+138.760000e+138.470000e+13
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8 rows × 61 columns

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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":20},{"cell_type":"code","source":"col = ['variable', 'value']\ntemp = gdp_long.loc[:,col] \nstats = temp.groupby(['variable']).describe().droplevel(axis =1 , level= 0).reset_index()\nstats","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:20.972985Z","iopub.execute_input":"2025-02-20T19:38:20.973353Z","iopub.status.idle":"2025-02-20T19:38:21.119163Z","shell.execute_reply.started":"2025-02-20T19:38:20.973319Z","shell.execute_reply":"2025-02-20T19:38:21.118137Z"},"trusted":true},"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":" variable count mean std min 25% \\\n0 1960 119.0 7.737603e+10 2.252438e+11 1.201201e+07 4.362676e+08 \n1 1961 120.0 7.918769e+10 2.335094e+11 1.159201e+07 4.747200e+08 \n2 1962 120.0 8.417576e+10 2.507523e+11 1.254156e+07 4.716388e+08 \n3 1963 120.0 9.092311e+10 2.696523e+11 1.283323e+07 5.081636e+08 \n4 1964 120.0 9.976049e+10 2.944479e+11 1.341655e+07 5.418066e+08 \n.. ... ... ... ... ... ... \n56 2016 120.0 4.564773e+12 1.158322e+13 7.744296e+08 2.070647e+10 \n57 2017 120.0 4.886253e+12 1.231733e+13 7.921778e+08 2.203821e+10 \n58 2018 120.0 5.207231e+12 1.311345e+13 8.113000e+08 2.353911e+10 \n59 2019 120.0 5.303437e+12 1.332070e+13 8.250407e+08 2.328174e+10 \n60 2020 120.0 5.146832e+12 1.292930e+13 8.074741e+08 2.052449e+10 \n\n 50% 75% max \n0 2.723593e+09 2.926200e+10 1.390000e+12 \n1 2.667191e+09 3.041073e+10 1.440000e+12 \n2 3.050546e+09 3.294838e+10 1.550000e+12 \n3 3.570681e+09 3.774822e+10 1.670000e+12 \n4 3.184116e+09 3.686680e+10 1.820000e+12 \n.. ... ... ... \n56 2.310000e+11 1.560000e+12 7.630000e+13 \n57 2.525000e+11 1.670000e+12 8.120000e+13 \n58 2.750000e+11 1.750000e+12 8.630000e+13 \n59 2.740000e+11 1.800000e+12 8.760000e+13 \n60 2.580000e+11 1.702500e+12 8.470000e+13 \n\n[61 rows x 9 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
variablecountmeanstdmin25%50%75%max
01960119.07.737603e+102.252438e+111.201201e+074.362676e+082.723593e+092.926200e+101.390000e+12
11961120.07.918769e+102.335094e+111.159201e+074.747200e+082.667191e+093.041073e+101.440000e+12
21962120.08.417576e+102.507523e+111.254156e+074.716388e+083.050546e+093.294838e+101.550000e+12
31963120.09.092311e+102.696523e+111.283323e+075.081636e+083.570681e+093.774822e+101.670000e+12
41964120.09.976049e+102.944479e+111.341655e+075.418066e+083.184116e+093.686680e+101.820000e+12
..............................
562016120.04.564773e+121.158322e+137.744296e+082.070647e+102.310000e+111.560000e+127.630000e+13
572017120.04.886253e+121.231733e+137.921778e+082.203821e+102.525000e+111.670000e+128.120000e+13
582018120.05.207231e+121.311345e+138.113000e+082.353911e+102.750000e+111.750000e+128.630000e+13
592019120.05.303437e+121.332070e+138.250407e+082.328174e+102.740000e+111.800000e+128.760000e+13
602020120.05.146832e+121.292930e+138.074741e+082.052449e+102.580000e+111.702500e+128.470000e+13
\n

61 rows × 9 columns

\n
"},"metadata":{}}],"execution_count":21},{"cell_type":"code","source":"plt.scatter(stats['variable'],stats['50%'], s= 1, label='Median GDP - USD')\nplt.scatter(stats['variable'],stats['25%'], s= 1, label='Q1 GDP - USD')\nplt.scatter(stats['variable'],stats['75%'], s= 1, label='Q3 GDP - USD')\nplt.legend()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:21.120515Z","iopub.execute_input":"2025-02-20T19:38:21.120933Z","iopub.status.idle":"2025-02-20T19:38:21.496241Z","shell.execute_reply.started":"2025-02-20T19:38:21.120899Z","shell.execute_reply":"2025-02-20T19:38:21.495216Z"},"trusted":true},"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":22},{"cell_type":"code","source":"fit = np.polyfit(stats['variable'], np.log(stats['50%']), 1,\n w =np.sqrt(stats['50%']))\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:21.497684Z","iopub.execute_input":"2025-02-20T19:38:21.498323Z","iopub.status.idle":"2025-02-20T19:38:21.505081Z","shell.execute_reply.started":"2025-02-20T19:38:21.498288Z","shell.execute_reply":"2025-02-20T19:38:21.504125Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.07111806201145401\nCoefficient B = -117.09232591432259\n","output_type":"stream"}],"execution_count":23},{"cell_type":"code","source":"stats['model_50%'] = np.exp(fit[1]) * np.exp(fit[0] * stats['variable']) \nstats['errors_model_50%'] = np.abs(stats['50%'] - stats['model_50%'])\nstats['errors_model_50%'].hist(grid=False)\nstats['errors_model_50%'].describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:21.506338Z","iopub.execute_input":"2025-02-20T19:38:21.506713Z","iopub.status.idle":"2025-02-20T19:38:21.796238Z","shell.execute_reply.started":"2025-02-20T19:38:21.506688Z","shell.execute_reply":"2025-02-20T19:38:21.795274Z"},"trusted":true},"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"count 6.100000e+01\nmean 1.109316e+10\nstd 1.591221e+10\nmin 2.165587e+08\n25% 2.494373e+09\n50% 3.968645e+09\n75% 1.374232e+10\nmax 8.677551e+10\nName: errors_model_50%, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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"},"metadata":{}}],"execution_count":24},{"cell_type":"code","source":"fit = np.polyfit(stats['variable'], np.log(stats['75%']), 1,\n w = np.sqrt(stats['75%']))\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:21.797356Z","iopub.execute_input":"2025-02-20T19:38:21.797673Z","iopub.status.idle":"2025-02-20T19:38:21.804498Z","shell.execute_reply.started":"2025-02-20T19:38:21.797649Z","shell.execute_reply":"2025-02-20T19:38:21.803621Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.0646318667262896\nCoefficient B = -102.0929023995858\n","output_type":"stream"}],"execution_count":25},{"cell_type":"code","source":"stats['model_75%'] = np.exp(fit[1]) * np.exp(fit[0] * stats['variable']) \nstats['errors_model_75%'] = np.abs(stats['75%'] - stats['model_75%'])\nstats['errors_model_75%'].hist(grid=False)\nstats['errors_model_75%'].describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:21.805630Z","iopub.execute_input":"2025-02-20T19:38:21.806455Z","iopub.status.idle":"2025-02-20T19:38:22.124313Z","shell.execute_reply.started":"2025-02-20T19:38:21.806429Z","shell.execute_reply":"2025-02-20T19:38:22.123301Z"},"trusted":true},"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"count 6.100000e+01\nmean 8.975987e+10\nstd 1.171396e+11\nmin 3.714321e+08\n25% 1.831220e+10\n50% 3.356206e+10\n75% 1.093801e+11\nmax 5.964380e+11\nName: errors_model_75%, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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"},"metadata":{}}],"execution_count":26},{"cell_type":"code","source":"fit = np.polyfit(stats['variable'], np.log(stats['25%']), 1,\n w = np.sqrt(stats['25%']))\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:22.125636Z","iopub.execute_input":"2025-02-20T19:38:22.125915Z","iopub.status.idle":"2025-02-20T19:38:22.132406Z","shell.execute_reply.started":"2025-02-20T19:38:22.125893Z","shell.execute_reply":"2025-02-20T19:38:22.131350Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.06031305728310042\nCoefficient B = -97.86712787332907\n","output_type":"stream"}],"execution_count":27},{"cell_type":"code","source":"stats['model_25%'] = np.exp(fit[1]) * np.exp(fit[0] * stats['variable']) \nstats['errors_model_25%'] = np.abs(stats['25%'] - stats['model_25%'])\nstats['errors_model_25%'].hist(grid=False)\nstats['errors_model_25%'].describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:22.133630Z","iopub.execute_input":"2025-02-20T19:38:22.133979Z","iopub.status.idle":"2025-02-20T19:38:22.457668Z","shell.execute_reply.started":"2025-02-20T19:38:22.133947Z","shell.execute_reply":"2025-02-20T19:38:22.456698Z"},"trusted":true},"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"count 6.100000e+01\nmean 7.988075e+08\nstd 8.209953e+08\nmin 3.983350e+07\n25% 3.445744e+08\n50% 5.018512e+08\n75% 8.281231e+08\nmax 5.059890e+09\nName: errors_model_25%, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
","image/png":"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"},"metadata":{}}],"execution_count":28},{"cell_type":"code","source":"plt.scatter(stats['variable'],stats['50%'], s= 2, c = 'b', label='Median GDP - USD')\nplt.scatter(stats['variable'],stats['model_50%'], s= 1, c ='b', marker = \"+\", label='Modelled Median GDP - USD')\n\nplt.scatter(stats['variable'],stats['25%'], s= 2, c='r', label='Q1 GDP - USD')\nplt.scatter(stats['variable'],stats['model_25%'], s= 1, c ='r', marker = \"+\", label='Modelled Q1 GDP - USD')\n\nplt.scatter(stats['variable'],stats['75%'], s= 1, c= 'magenta', label='Q3 GDP - USD')\nplt.scatter(stats['variable'],stats['model_75%'], s= 1, c ='magenta', marker = \"+\", label='Modelled Median GDP - USD')\n\nplt.legend()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:22.458816Z","iopub.execute_input":"2025-02-20T19:38:22.459078Z","iopub.status.idle":"2025-02-20T19:38:22.907583Z","shell.execute_reply.started":"2025-02-20T19:38:22.459056Z","shell.execute_reply":"2025-02-20T19:38:22.906613Z"},"trusted":true},"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":29},{"cell_type":"code","source":"plt.scatter(stats['variable'],stats['50%'], s= 1)\nplt.scatter(stats['variable'],stats['model_50%'], c='black', s = 1, marker = '+')","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:22.908996Z","iopub.execute_input":"2025-02-20T19:38:22.909471Z","iopub.status.idle":"2025-02-20T19:38:23.180297Z","shell.execute_reply.started":"2025-02-20T19:38:22.909437Z","shell.execute_reply":"2025-02-20T19:38:23.179272Z"},"trusted":true},"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":30},{"cell_type":"code","source":"plt.scatter(stats['variable'],stats['25%'], s= 1)\nplt.scatter(stats['variable'],stats['model_25%'], c='black', s = 1, marker = '+')","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:23.181794Z","iopub.execute_input":"2025-02-20T19:38:23.182165Z","iopub.status.idle":"2025-02-20T19:38:23.492379Z","shell.execute_reply.started":"2025-02-20T19:38:23.182132Z","shell.execute_reply":"2025-02-20T19:38:23.491383Z"},"trusted":true},"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":31},{"cell_type":"code","source":"plt.scatter(stats['variable'],stats['75%'], s= 1)\nplt.scatter(stats['variable'],stats['model_75%'], c='black', s = 1, marker = '+')","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:23.493428Z","iopub.execute_input":"2025-02-20T19:38:23.493740Z","iopub.status.idle":"2025-02-20T19:38:23.761167Z","shell.execute_reply.started":"2025-02-20T19:38:23.493716Z","shell.execute_reply":"2025-02-20T19:38:23.760191Z"},"trusted":true},"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":32},{"cell_type":"markdown","source":"# World population\n\nWe discover again the population per country has some great variation. The median population per country appears to exponentially.","metadata":{}},{"cell_type":"code","source":"file = '/kaggle/input/world-population-dataset/world_population.csv'\npop = pd.read_csv(file)\nprint(pop.shape)\nprint(pop.dtypes)","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:23.762397Z","iopub.execute_input":"2025-02-20T19:38:23.762741Z","iopub.status.idle":"2025-02-20T19:38:23.776399Z","shell.execute_reply.started":"2025-02-20T19:38:23.762716Z","shell.execute_reply":"2025-02-20T19:38:23.775399Z"},"trusted":true},"outputs":[{"name":"stdout","text":"(234, 17)\nRank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\n2022 Population int64\n2020 Population int64\n2015 Population int64\n2010 Population int64\n2000 Population int64\n1990 Population int64\n1980 Population int64\n1970 Population int64\nArea (km²) int64\nDensity (per km²) float64\nGrowth Rate float64\nWorld Population Percentage float64\ndtype: object\n","output_type":"stream"}],"execution_count":33},{"cell_type":"code","source":"pop.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:23.777758Z","iopub.execute_input":"2025-02-20T19:38:23.778141Z","iopub.status.idle":"2025-02-20T19:38:23.793903Z","shell.execute_reply.started":"2025-02-20T19:38:23.778107Z","shell.execute_reply":"2025-02-20T19:38:23.792699Z"},"trusted":true},"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":" Rank CCA3 Country/Territory Capital Continent 2022 Population \\\n0 36 AFG Afghanistan Kabul Asia 41128771 \n1 138 ALB Albania Tirana Europe 2842321 \n2 34 DZA Algeria Algiers Africa 44903225 \n3 213 ASM American Samoa Pago Pago Oceania 44273 \n4 203 AND Andorra Andorra la Vella Europe 79824 \n\n 2020 Population 2015 Population 2010 Population 2000 Population \\\n0 38972230 33753499 28189672 19542982 \n1 2866849 2882481 2913399 3182021 \n2 43451666 39543154 35856344 30774621 \n3 46189 51368 54849 58230 \n4 77700 71746 71519 66097 \n\n 1990 Population 1980 Population 1970 Population Area (km²) \\\n0 10694796 12486631 10752971 652230 \n1 3295066 2941651 2324731 28748 \n2 25518074 18739378 13795915 2381741 \n3 47818 32886 27075 199 \n4 53569 35611 19860 468 \n\n Density (per km²) Growth Rate World Population Percentage \n0 63.0587 1.0257 0.52 \n1 98.8702 0.9957 0.04 \n2 18.8531 1.0164 0.56 \n3 222.4774 0.9831 0.00 \n4 170.5641 1.0100 0.00 ","text/html":"
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RankCCA3Country/TerritoryCapitalContinent2022 Population2020 Population2015 Population2010 Population2000 Population1990 Population1980 Population1970 PopulationArea (km²)Density (per km²)Growth RateWorld Population Percentage
036AFGAfghanistanKabulAsia411287713897223033753499281896721954298210694796124866311075297165223063.05871.02570.52
1138ALBAlbaniaTiranaEurope284232128668492882481291339931820213295066294165123247312874898.87020.99570.04
234DZAAlgeriaAlgiersAfrica4490322543451666395431543585634430774621255180741873937813795915238174118.85311.01640.56
3213ASMAmerican SamoaPago PagoOceania4427346189513685484958230478183288627075199222.47740.98310.00
4203ANDAndorraAndorra la VellaEurope7982477700717467151966097535693561119860468170.56411.01000.00
\n
"},"metadata":{}}],"execution_count":34},{"cell_type":"code","source":"pop.columns","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:23.795254Z","iopub.execute_input":"2025-02-20T19:38:23.795539Z","iopub.status.idle":"2025-02-20T19:38:23.806311Z","shell.execute_reply.started":"2025-02-20T19:38:23.795516Z","shell.execute_reply":"2025-02-20T19:38:23.805391Z"},"trusted":true},"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"Index(['Rank', 'CCA3', 'Country/Territory', 'Capital', 'Continent',\n '2022 Population', '2020 Population', '2015 Population',\n '2010 Population', '2000 Population', '1990 Population',\n '1980 Population', '1970 Population', 'Area (km²)', 'Density (per km²)',\n 'Growth Rate', 'World Population Percentage'],\n dtype='object')"},"metadata":{}}],"execution_count":35},{"cell_type":"code","source":"cols = ['1970 Population', '1980 Population', \n '1990 Population','2000 Population',\n '2010 Population','2015 Population',\n '2020 Population','2022 Population']\ntemp = pop.loc[:, cols].copy(deep = True)\ntemp = np.log10(temp)\ntemp.boxplot(grid = False, rot =45)","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:23.807446Z","iopub.execute_input":"2025-02-20T19:38:23.807862Z","iopub.status.idle":"2025-02-20T19:38:24.094768Z","shell.execute_reply.started":"2025-02-20T19:38:23.807826Z","shell.execute_reply":"2025-02-20T19:38:24.093699Z"},"trusted":true},"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":36},{"cell_type":"code","source":"years_label =['2022 Population', \n '2020 Population', \n '2015 Population',\n '2010 Population', \n '2000 Population', \n '1990 Population',\n '1980 Population', \n '1970 Population']\n\nid_vars = ['Rank', 'CCA3', 'Country/Territory', \n 'Capital', 'Continent', 'Area (km²)']\npop_long = pop.melt(id_vars = id_vars, value_vars = years_label)\npop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.096154Z","iopub.execute_input":"2025-02-20T19:38:24.096528Z","iopub.status.idle":"2025-02-20T19:38:24.110981Z","shell.execute_reply.started":"2025-02-20T19:38:24.096494Z","shell.execute_reply":"2025-02-20T19:38:24.110022Z"},"trusted":true},"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"Rank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\nArea (km²) int64\nvariable object\nvalue int64\ndtype: object"},"metadata":{}}],"execution_count":37},{"cell_type":"code","source":"pop_long.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.112243Z","iopub.execute_input":"2025-02-20T19:38:24.113035Z","iopub.status.idle":"2025-02-20T19:38:24.128686Z","shell.execute_reply.started":"2025-02-20T19:38:24.113001Z","shell.execute_reply":"2025-02-20T19:38:24.127639Z"},"trusted":true},"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":" Rank CCA3 Country/Territory Capital Continent Area (km²) \\\n0 36 AFG Afghanistan Kabul Asia 652230 \n1 138 ALB Albania Tirana Europe 28748 \n2 34 DZA Algeria Algiers Africa 2381741 \n3 213 ASM American Samoa Pago Pago Oceania 199 \n4 203 AND Andorra Andorra la Vella Europe 468 \n\n variable value \n0 2022 Population 41128771 \n1 2022 Population 2842321 \n2 2022 Population 44903225 \n3 2022 Population 44273 \n4 2022 Population 79824 ","text/html":"
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RankCCA3Country/TerritoryCapitalContinentArea (km²)variablevalue
036AFGAfghanistanKabulAsia6522302022 Population41128771
1138ALBAlbaniaTiranaEurope287482022 Population2842321
234DZAAlgeriaAlgiersAfrica23817412022 Population44903225
3213ASMAmerican SamoaPago PagoOceania1992022 Population44273
4203ANDAndorraAndorra la VellaEurope4682022 Population79824
\n
"},"metadata":{}}],"execution_count":38},{"cell_type":"code","source":"len(pop_long['Country/Territory'].unique())","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.130003Z","iopub.execute_input":"2025-02-20T19:38:24.130680Z","iopub.status.idle":"2025-02-20T19:38:24.142614Z","shell.execute_reply.started":"2025-02-20T19:38:24.130646Z","shell.execute_reply":"2025-02-20T19:38:24.141526Z"},"trusted":true},"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"234"},"metadata":{}}],"execution_count":39},{"cell_type":"code","source":"current = ['2022 Population', \n '2020 Population', \n '2015 Population',\n '2010 Population', \n '2000 Population', \n '1990 Population',\n '1980 Population', \n '1970 Population']\n\nnew = [2022, 2020,2015,2010,2000,1990,1980,1970]\n\npop_long = pop_long.replace(current, new)\npop_long.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.143979Z","iopub.execute_input":"2025-02-20T19:38:24.144361Z","iopub.status.idle":"2025-02-20T19:38:24.168699Z","shell.execute_reply.started":"2025-02-20T19:38:24.144329Z","shell.execute_reply":"2025-02-20T19:38:24.167798Z"},"trusted":true},"outputs":[{"name":"stderr","text":"/tmp/ipykernel_33/2926351034.py:12: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n pop_long = pop_long.replace(current, new)\n","output_type":"stream"},{"execution_count":40,"output_type":"execute_result","data":{"text/plain":" Rank CCA3 Country/Territory Capital Continent Area (km²) \\\n0 36 AFG Afghanistan Kabul Asia 652230 \n1 138 ALB Albania Tirana Europe 28748 \n2 34 DZA Algeria Algiers Africa 2381741 \n3 213 ASM American Samoa Pago Pago Oceania 199 \n4 203 AND Andorra Andorra la Vella Europe 468 \n\n variable value \n0 2022 41128771 \n1 2022 2842321 \n2 2022 44903225 \n3 2022 44273 \n4 2022 79824 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
RankCCA3Country/TerritoryCapitalContinentArea (km²)variablevalue
036AFGAfghanistanKabulAsia652230202241128771
1138ALBAlbaniaTiranaEurope2874820222842321
234DZAAlgeriaAlgiersAfrica2381741202244903225
3213ASMAmerican SamoaPago PagoOceania199202244273
4203ANDAndorraAndorra la VellaEurope468202279824
\n
"},"metadata":{}}],"execution_count":40},{"cell_type":"code","source":"pop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.174725Z","iopub.execute_input":"2025-02-20T19:38:24.175041Z","iopub.status.idle":"2025-02-20T19:38:24.181989Z","shell.execute_reply.started":"2025-02-20T19:38:24.175017Z","shell.execute_reply":"2025-02-20T19:38:24.181001Z"},"trusted":true},"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"Rank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\nArea (km²) int64\nvariable int64\nvalue int64\ndtype: object"},"metadata":{}}],"execution_count":41},{"cell_type":"code","source":"pop_long['Area_log_10'] = np.log10(pop_long['Area (km²)'])\npop_long['value_log_10'] = np.log10(pop_long['value'])\npop_long.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.183132Z","iopub.execute_input":"2025-02-20T19:38:24.183411Z","iopub.status.idle":"2025-02-20T19:38:24.204824Z","shell.execute_reply.started":"2025-02-20T19:38:24.183389Z","shell.execute_reply":"2025-02-20T19:38:24.203842Z"},"trusted":true},"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":" Rank CCA3 Country/Territory Capital Continent Area (km²) \\\n0 36 AFG Afghanistan Kabul Asia 652230 \n1 138 ALB Albania Tirana Europe 28748 \n2 34 DZA Algeria Algiers Africa 2381741 \n3 213 ASM American Samoa Pago Pago Oceania 199 \n4 203 AND Andorra Andorra la Vella Europe 468 \n\n variable value Area_log_10 value_log_10 \n0 2022 41128771 5.814401 7.614146 \n1 2022 2842321 4.458608 6.453673 \n2 2022 44903225 6.376895 7.652278 \n3 2022 44273 2.298853 4.646139 \n4 2022 79824 2.670246 4.902133 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
RankCCA3Country/TerritoryCapitalContinentArea (km²)variablevalueArea_log_10value_log_10
036AFGAfghanistanKabulAsia6522302022411287715.8144017.614146
1138ALBAlbaniaTiranaEurope28748202228423214.4586086.453673
234DZAAlgeriaAlgiersAfrica23817412022449032256.3768957.652278
3213ASMAmerican SamoaPago PagoOceania1992022442732.2988534.646139
4203ANDAndorraAndorra la VellaEurope4682022798242.6702464.902133
\n
"},"metadata":{}}],"execution_count":42},{"cell_type":"code","source":"pop.head()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.205830Z","iopub.execute_input":"2025-02-20T19:38:24.206088Z","iopub.status.idle":"2025-02-20T19:38:24.221006Z","shell.execute_reply.started":"2025-02-20T19:38:24.206067Z","shell.execute_reply":"2025-02-20T19:38:24.220021Z"},"trusted":true},"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":" Rank CCA3 Country/Territory Capital Continent 2022 Population \\\n0 36 AFG Afghanistan Kabul Asia 41128771 \n1 138 ALB Albania Tirana Europe 2842321 \n2 34 DZA Algeria Algiers Africa 44903225 \n3 213 ASM American Samoa Pago Pago Oceania 44273 \n4 203 AND Andorra Andorra la Vella Europe 79824 \n\n 2020 Population 2015 Population 2010 Population 2000 Population \\\n0 38972230 33753499 28189672 19542982 \n1 2866849 2882481 2913399 3182021 \n2 43451666 39543154 35856344 30774621 \n3 46189 51368 54849 58230 \n4 77700 71746 71519 66097 \n\n 1990 Population 1980 Population 1970 Population Area (km²) \\\n0 10694796 12486631 10752971 652230 \n1 3295066 2941651 2324731 28748 \n2 25518074 18739378 13795915 2381741 \n3 47818 32886 27075 199 \n4 53569 35611 19860 468 \n\n Density (per km²) Growth Rate World Population Percentage \n0 63.0587 1.0257 0.52 \n1 98.8702 0.9957 0.04 \n2 18.8531 1.0164 0.56 \n3 222.4774 0.9831 0.00 \n4 170.5641 1.0100 0.00 ","text/html":"
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RankCCA3Country/TerritoryCapitalContinent2022 Population2020 Population2015 Population2010 Population2000 Population1990 Population1980 Population1970 PopulationArea (km²)Density (per km²)Growth RateWorld Population Percentage
036AFGAfghanistanKabulAsia411287713897223033753499281896721954298210694796124866311075297165223063.05871.02570.52
1138ALBAlbaniaTiranaEurope284232128668492882481291339931820213295066294165123247312874898.87020.99570.04
234DZAAlgeriaAlgiersAfrica4490322543451666395431543585634430774621255180741873937813795915238174118.85311.01640.56
3213ASMAmerican SamoaPago PagoOceania4427346189513685484958230478183288627075199222.47740.98310.00
4203ANDAndorraAndorra la VellaEurope7982477700717467151966097535693561119860468170.56411.01000.00
\n
"},"metadata":{}}],"execution_count":43},{"cell_type":"code","source":"plt.scatter(pop_long['Area (km²)'],pop_long['value'], s=1)\nplt.xlabel('Area in square KM')\nplt.ylabel('Population')\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.222343Z","iopub.execute_input":"2025-02-20T19:38:24.222762Z","iopub.status.idle":"2025-02-20T19:38:24.553824Z","shell.execute_reply.started":"2025-02-20T19:38:24.222728Z","shell.execute_reply":"2025-02-20T19:38:24.552790Z"},"trusted":true},"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Population')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":44},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"plt.scatter(pop_long['Area_log_10'],\n pop_long['value_log_10'], s=1)\nplt.xlabel('Area in square KM (log scale)')\nplt.ylabel('Population (log scale)')\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.554938Z","iopub.execute_input":"2025-02-20T19:38:24.555196Z","iopub.status.idle":"2025-02-20T19:38:24.847877Z","shell.execute_reply.started":"2025-02-20T19:38:24.555175Z","shell.execute_reply":"2025-02-20T19:38:24.846850Z"},"trusted":true},"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Population (log scale)')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":45},{"cell_type":"code","source":"pop_long.groupby(['CCA3']).describe()['value']","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:24.849285Z","iopub.execute_input":"2025-02-20T19:38:24.849731Z","iopub.status.idle":"2025-02-20T19:38:26.890032Z","shell.execute_reply.started":"2025-02-20T19:38:24.849695Z","shell.execute_reply":"2025-02-20T19:38:26.888984Z"},"trusted":true},"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% \\\nCCA3 \nABW 8.0 8.672675e+04 2.099067e+04 59106.0 64850.75 94721.0 \nAFG 8.0 2.444019e+07 1.272585e+07 10694796.0 12053216.00 23866327.0 \nAGO 8.0 2.038648e+07 1.140585e+07 6029700.0 10953990.25 19879123.5 \nAIA 8.0 1.141812e+04 3.955593e+03 6283.0 7877.00 12109.5 \nALB 8.0 2.906065e+06 2.860820e+05 2324731.0 2860717.00 2897940.0 \n... ... ... ... ... ... ... \nWSM 8.0 1.869280e+05 2.718362e+04 142771.0 167365.75 189340.0 \nYEM 8.0 2.091169e+07 1.043535e+07 6843607.0 12332575.25 21686323.0 \nZAF 8.0 4.560999e+07 1.392213e+07 22368306.0 37274064.75 49299093.5 \nZMB 8.0 1.207067e+07 6.042178e+06 4281671.0 7194910.25 11841611.0 \nZWE 8.0 1.164829e+07 3.978175e+06 5202918.0 9347901.25 12337223.5 \n\n 75% max \nCCA3 \nABW 104804.00 106585.0 \nAFG 35058181.75 41128771.0 \nAGO 29452912.00 35588987.0 \nAIA 14790.00 15857.0 \nALB 3001743.50 3295066.0 \n... ... ... \nWSM 206410.50 222382.0 \nYEM 29458420.25 33696614.0 \nZAF 56607859.75 59893885.0 \nZMB 16918101.25 20017675.0 \nZWE 14533619.25 16320537.0 \n\n[234 rows x 8 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
CCA3
ABW8.08.672675e+042.099067e+0459106.064850.7594721.0104804.00106585.0
AFG8.02.444019e+071.272585e+0710694796.012053216.0023866327.035058181.7541128771.0
AGO8.02.038648e+071.140585e+076029700.010953990.2519879123.529452912.0035588987.0
AIA8.01.141812e+043.955593e+036283.07877.0012109.514790.0015857.0
ALB8.02.906065e+062.860820e+052324731.02860717.002897940.03001743.503295066.0
...........................
WSM8.01.869280e+052.718362e+04142771.0167365.75189340.0206410.50222382.0
YEM8.02.091169e+071.043535e+076843607.012332575.2521686323.029458420.2533696614.0
ZAF8.04.560999e+071.392213e+0722368306.037274064.7549299093.556607859.7559893885.0
ZMB8.01.207067e+076.042178e+064281671.07194910.2511841611.016918101.2520017675.0
ZWE8.01.164829e+073.978175e+065202918.09347901.2512337223.514533619.2516320537.0
\n

234 rows × 8 columns

\n
"},"metadata":{}}],"execution_count":46},{"cell_type":"code","source":"fit = np.polyfit(pop_long['Area_log_10'], np.log(pop_long.value_log_10), 1,\n w = np.sqrt(pop_long['Area_log_10']))\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:26.891291Z","iopub.execute_input":"2025-02-20T19:38:26.891548Z","iopub.status.idle":"2025-02-20T19:38:26.898459Z","shell.execute_reply.started":"2025-02-20T19:38:26.891525Z","shell.execute_reply":"2025-02-20T19:38:26.897413Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.11403525470531985\nCoefficient B = 1.3153462719013784\n","output_type":"stream"}],"execution_count":47},{"cell_type":"code","source":"pop_long['model_a'] = np.exp(fit[1]) * np.exp(fit[0] * pop_long['Area_log_10']) \nstats['errors_model_a'] = np.abs(pop_long['Area_log_10'] - pop_long['model_a'])\nstats['errors_model_a'].hist(grid=False)\nstats['errors_model_a'].describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:26.899638Z","iopub.execute_input":"2025-02-20T19:38:26.899947Z","iopub.status.idle":"2025-02-20T19:38:27.234065Z","shell.execute_reply.started":"2025-02-20T19:38:26.899925Z","shell.execute_reply":"2025-02-20T19:38:27.233032Z"},"trusted":true},"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"count 61.000000\nmean 1.771376\nstd 0.424304\nmin 1.277968\n25% 1.425505\n50% 1.665613\n75% 1.960580\nmax 2.807485\nName: errors_model_a, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":48},{"cell_type":"code","source":"# gaussian mixture clustering\nfrom sklearn.mixture import GaussianMixture as GMM\nfrom matplotlib import pyplot\nfrom sklearn import metrics\n# define dataset\nX = np.array(pop_long['Area_log_10']).reshape(-1,1)\ny = np.array(pop_long['value_log_10']).reshape(-1,1)\n\nprint('X \\n' , X)\nprint('y \\n', y)","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:27.235240Z","iopub.execute_input":"2025-02-20T19:38:27.235537Z","iopub.status.idle":"2025-02-20T19:38:27.242905Z","shell.execute_reply.started":"2025-02-20T19:38:27.235504Z","shell.execute_reply":"2025-02-20T19:38:27.241894Z"},"trusted":true},"outputs":[{"name":"stdout","text":"X \n [[5.81440077]\n [4.45860764]\n [6.37689453]\n ...\n [5.7226076 ]\n [5.87657114]\n [5.59190677]]\ny \n [[7.61414573]\n [6.45367312]\n [7.65227753]\n ...\n [6.83528506]\n [6.63161329]\n [6.71624698]]\n","output_type":"stream"}],"execution_count":49},{"cell_type":"code","source":"def SelBest(arr:list, X:int)->list:\n '''\n returns the set of X configurations with shorter distance\n '''\n dx=np.argsort(arr)[:X]\n return arr[dx]","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:27.244032Z","iopub.execute_input":"2025-02-20T19:38:27.244296Z","iopub.status.idle":"2025-02-20T19:38:27.254440Z","shell.execute_reply.started":"2025-02-20T19:38:27.244275Z","shell.execute_reply":"2025-02-20T19:38:27.253387Z"},"trusted":true},"outputs":[],"execution_count":50},{"cell_type":"markdown","source":"An early investigation suggested a high level of clusterisation may be suitable. Therefore, we start from 100 to 230, to lower time and computations resources.","metadata":{}},{"cell_type":"code","source":"n_clusters=np.arange(100,230 , 10)\nbic=[]\nbics_err=[]\niterations=20\nfor n in n_clusters:\n tmp_bic=[]\n for _ in range(iterations):\n gmm=GMM(n, n_init=2).fit(X,y) \n labels=gmm.predict(X)\n tmp_bic.append(gmm.bic(X)) \n val=np.mean(SelBest(np.array(tmp_bic), int(iterations)))\n err=np.std(tmp_bic)\n bic.append(val)\n bics_err.append(err)\n print('n_cluster : ' , n , 'mean_bic : ', val, 'std_bic : ', err)\n \nplt.errorbar(n_clusters, bic, yerr=bics_err)\nplt.title(\"Silhouette Scores\", fontsize=20)\nplt.xticks(n_clusters)\nplt.xlabel(\"N. of clusters\")\nplt.ylabel(\"Score\")","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:38:27.255849Z","iopub.execute_input":"2025-02-20T19:38:27.256218Z","iopub.status.idle":"2025-02-20T19:40:37.806662Z","shell.execute_reply.started":"2025-02-20T19:38:27.256184Z","shell.execute_reply":"2025-02-20T19:40:37.805687Z"},"trusted":true},"outputs":[{"name":"stdout","text":"n_cluster : 100 mean_bic : 3856.636008099107 std_bic : 55.47173557029389\nn_cluster : 110 mean_bic : 3565.0920937256656 std_bic : 51.752097688005044\nn_cluster : 120 mean_bic : 3238.5537606329126 std_bic : 50.77634947393244\nn_cluster : 130 mean_bic : 2970.963428753665 std_bic : 29.957044359627815\nn_cluster : 140 mean_bic : 2718.7738015616546 std_bic : 36.56849723532222\nn_cluster : 150 mean_bic : 2461.7434436986937 std_bic : 28.967210977215455\nn_cluster : 160 mean_bic : 2270.44758783618 std_bic : 20.198361504024515\nn_cluster : 170 mean_bic : 2145.6805203386057 std_bic : 12.24340901736794\nn_cluster : 180 mean_bic : 2077.753389432272 std_bic : 7.143630339477631\nn_cluster : 190 mean_bic : 2103.315153452777 std_bic : 5.4441102760476765\nn_cluster : 200 mean_bic : 2201.952212797958 std_bic : 1.697177350126278\nn_cluster : 210 mean_bic : 2420.0825722867294 std_bic : 3.6913212538211284e-05\nn_cluster : 220 mean_bic : 2646.1275722457517 std_bic : 0.0003102267595525465\n","output_type":"stream"},{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Score')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":51},{"cell_type":"code","source":"n_clusters=np.arange(180,200)\nbic=[]\nbics_err=[]\niterations=20\nfor n in n_clusters:\n tmp_bic=[]\n for _ in range(iterations):\n gmm=GMM(n, n_init=2).fit(X,y) \n labels=gmm.predict(X)\n tmp_bic.append(gmm.bic(X)) \n val=np.mean(SelBest(np.array(tmp_bic), int(iterations)))\n err=np.std(tmp_bic)\n bic.append(val)\n bics_err.append(err)\n print('n_cluster : ' , n , 'mean_bic : ', val, 'std_bic : ', err)\n \nplt.errorbar(n_clusters, bic, yerr=bics_err)\nplt.title(\"Silhouette Scores\", fontsize=20)\nplt.xticks(n_clusters)\nplt.xlabel(\"N. of clusters\")\nplt.ylabel(\"Score\")","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:40:37.807799Z","iopub.execute_input":"2025-02-20T19:40:37.808080Z","iopub.status.idle":"2025-02-20T19:46:21.845896Z","shell.execute_reply.started":"2025-02-20T19:40:37.808058Z","shell.execute_reply":"2025-02-20T19:46:21.844812Z"},"trusted":true},"outputs":[{"name":"stdout","text":"n_cluster : 180 mean_bic : 2077.3715072718583 std_bic : 6.009613137236369\nn_cluster : 181 mean_bic : 2074.610155198689 std_bic : 6.132013300665143\nn_cluster : 182 mean_bic : 2076.9811505087523 std_bic : 7.742041551849214\nn_cluster : 183 mean_bic : 2072.006716491217 std_bic : 4.6829381188961\nn_cluster : 184 mean_bic : 2075.581410104163 std_bic : 4.8345628408635255\nn_cluster : 185 mean_bic : 2078.718600132686 std_bic : 5.410150134557857\nn_cluster : 186 mean_bic : 2082.679191338651 std_bic : 4.369518481868247\nn_cluster : 187 mean_bic : 2086.374873318467 std_bic : 4.443574187828375\nn_cluster : 188 mean_bic : 2091.777437198426 std_bic : 5.27966678892769\nn_cluster : 189 mean_bic : 2097.306969274856 std_bic : 4.407326580151155\nn_cluster : 190 mean_bic : 2101.285361999117 std_bic : 4.428161881020638\nn_cluster : 191 mean_bic : 2109.4111420235167 std_bic : 3.964093365119614\nn_cluster : 192 mean_bic : 2114.2438137323797 std_bic : 3.0248979834840086\nn_cluster : 193 mean_bic : 2124.2408702586276 std_bic : 3.666845164893888\nn_cluster : 194 mean_bic : 2133.906395659718 std_bic : 3.72052880851393\nn_cluster : 195 mean_bic : 2141.462880793213 std_bic : 1.796548480564042\nn_cluster : 196 mean_bic : 2151.2642721469065 std_bic : 3.2553922108512405\nn_cluster : 197 mean_bic : 2163.631598992382 std_bic : 2.8430415838810754\nn_cluster : 198 mean_bic : 2175.5214552718066 std_bic : 1.9922646827976598\nn_cluster : 199 mean_bic : 2189.2082085183433 std_bic : 2.2941454065542075\nn_cluster : 200 mean_bic : 2203.244380306086 std_bic : 3.0120442417340234\nn_cluster : 201 mean_bic : 2218.485116993871 std_bic : 4.0680801004301905\nn_cluster : 202 mean_bic : 2239.044799942589 std_bic : 2.5570123980141233\nn_cluster : 203 mean_bic : 2261.244248494023 std_bic : 1.8265364708540335\nn_cluster : 204 mean_bic : 2283.1917096295947 std_bic : 0.18798540760169105\nn_cluster : 205 mean_bic : 2305.855992595264 std_bic : 0.33339569586501683\nn_cluster : 206 mean_bic : 2329.0116064023596 std_bic : 0.6541869234109525\nn_cluster : 207 mean_bic : 2352.116475599726 std_bic : 0.39658898224762856\nn_cluster : 208 mean_bic : 2374.874062142659 std_bic : 1.2101533414852998e-05\nn_cluster : 209 mean_bic : 2397.4783528092335 std_bic : 0.00019030039784818077\n","output_type":"stream"},{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Score')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":52},{"cell_type":"markdown","source":"The minimum and optimum number of clusters may vary each run. It is unknown whether it is a global or local optimum. However, it has been approximately around 180 more than 31 times.","metadata":{}},{"cell_type":"code","source":"from sklearn.mixture import GaussianMixture \ngmm = GaussianMixture(n_components=180)\ngmm.fit(X)\nlabels = gmm.predict(X)\nprobs = gmm.predict_proba(X)\n\nplt.scatter(X, y, c=labels, s=1, cmap='viridis',)\nplt.xlabel('Area in square KM (log scale)')\nplt.ylabel('Population (log scale)')\nlabels\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:21.847119Z","iopub.execute_input":"2025-02-20T19:46:21.847414Z","iopub.status.idle":"2025-02-20T19:46:22.410766Z","shell.execute_reply.started":"2025-02-20T19:46:21.847392Z","shell.execute_reply":"2025-02-20T19:46:22.409466Z"},"trusted":true},"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"array([ 54, 122, 76, ..., 120, 110, 135])"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":53},{"cell_type":"markdown","source":"Earlier we have discovered there are 234 countries. The number of components is quite close from the number of unique territories. The yearly observations made for each country may lead to a cluster for each country. However, some countries may have similar population and areas and form a cluster. \n\nWe propose to use instead the expected population through time to try to overcome this observation. We use the median point.","metadata":{}},{"cell_type":"code","source":"pop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:22.412261Z","iopub.execute_input":"2025-02-20T19:46:22.412675Z","iopub.status.idle":"2025-02-20T19:46:22.421820Z","shell.execute_reply.started":"2025-02-20T19:46:22.412638Z","shell.execute_reply":"2025-02-20T19:46:22.420627Z"},"trusted":true},"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"Rank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\nArea (km²) int64\nvariable int64\nvalue int64\nArea_log_10 float64\nvalue_log_10 float64\nmodel_a float64\ndtype: object"},"metadata":{}}],"execution_count":54},{"cell_type":"code","source":"means = pop_long.loc[: , ['CCA3','Area (km²)','value']].copy(deep = True)\nmeans.describe()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:22.423183Z","iopub.execute_input":"2025-02-20T19:46:22.423552Z","iopub.status.idle":"2025-02-20T19:46:22.446528Z","shell.execute_reply.started":"2025-02-20T19:46:22.423520Z","shell.execute_reply":"2025-02-20T19:46:22.445452Z"},"trusted":true},"outputs":[{"execution_count":55,"output_type":"execute_result","data":{"text/plain":" Area (km²) value\ncount 1.872000e+03 1.872000e+03\nmean 5.814494e+05 2.661274e+07\nstd 1.758542e+06 1.133600e+08\nmin 1.000000e+00 5.100000e+02\n25% 2.586000e+03 3.180410e+05\n50% 8.119950e+04 4.225097e+06\n75% 4.383170e+05 1.546753e+07\nmax 1.709824e+07 1.425887e+09","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Area (km²)value
count1.872000e+031.872000e+03
mean5.814494e+052.661274e+07
std1.758542e+061.133600e+08
min1.000000e+005.100000e+02
25%2.586000e+033.180410e+05
50%8.119950e+044.225097e+06
75%4.383170e+051.546753e+07
max1.709824e+071.425887e+09
\n
"},"metadata":{}}],"execution_count":55},{"cell_type":"code","source":"tmp = means.groupby(['CCA3','Area (km²)']).median().reset_index()\ntmp","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:22.447660Z","iopub.execute_input":"2025-02-20T19:46:22.448037Z","iopub.status.idle":"2025-02-20T19:46:22.465989Z","shell.execute_reply.started":"2025-02-20T19:46:22.448001Z","shell.execute_reply":"2025-02-20T19:46:22.465046Z"},"trusted":true},"outputs":[{"execution_count":56,"output_type":"execute_result","data":{"text/plain":" CCA3 Area (km²) value\n0 ABW 180 94721.0\n1 AFG 652230 23866327.0\n2 AGO 1246700 19879123.5\n3 AIA 91 12109.5\n4 ALB 28748 2897940.0\n.. ... ... ...\n229 WSM 2842 189340.0\n230 YEM 527968 21686323.0\n231 ZAF 1221037 49299093.5\n232 ZMB 752612 11841611.0\n233 ZWE 390757 12337223.5\n\n[234 rows x 3 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
CCA3Area (km²)value
0ABW18094721.0
1AFG65223023866327.0
2AGO124670019879123.5
3AIA9112109.5
4ALB287482897940.0
............
229WSM2842189340.0
230YEM52796821686323.0
231ZAF122103749299093.5
232ZMB75261211841611.0
233ZWE39075712337223.5
\n

234 rows × 3 columns

\n
"},"metadata":{}}],"execution_count":56},{"cell_type":"code","source":"# gaussian mixture clustering\nfrom sklearn.mixture import GaussianMixture as GMM\nfrom matplotlib import pyplot\nfrom sklearn import metrics\n# define dataset\nX = np.array(np.log10(tmp['Area (km²)'])).reshape(-1,1)\ny = np.array(np.log10(tmp['value'])).reshape(-1,1)\n\nprint('X \\n' , len(X))\nprint('y \\n', len(y))\nplt.scatter(X,\n y, s=1)\nplt.xlabel('Area in square KM (log scale)')\nplt.ylabel('Population (log scale)')","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:22.467073Z","iopub.execute_input":"2025-02-20T19:46:22.467358Z","iopub.status.idle":"2025-02-20T19:46:22.755772Z","shell.execute_reply.started":"2025-02-20T19:46:22.467335Z","shell.execute_reply":"2025-02-20T19:46:22.754618Z"},"trusted":true},"outputs":[{"name":"stdout","text":"X \n 234\ny \n 234\n","output_type":"stream"},{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Population (log scale)')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":57},{"cell_type":"markdown","source":"We reduced the number of dimension from three to two - we removed the temporal dimension. We now discover with the log scale that an exponential tend may exist. \n\nA Gaussian mixture model appears not to fit properly. A regression has demonstrated that a exponential equation is likely to model the relationship between the size of a country and an expected population. The exponential equation suggests the size of the territories are likely to impact on the population residing in the country. Other factors such as geographical features, for example, should be considered. This suggestion is not exhaustive. \n","metadata":{}},{"cell_type":"code","source":"n_clusters=np.arange(2,10,1)\nbic=[]\nbics_err=[]\niterations=20\nfor n in n_clusters:\n tmp_bic=[]\n for _ in range(iterations):\n gmm=GMM(n, n_init=2).fit(X,y) \n labels=gmm.predict(X)\n tmp_bic.append(gmm.bic(X)) \n val=np.mean(SelBest(np.array(tmp_bic), int(iterations)))\n err=np.std(tmp_bic)\n bic.append(val)\n bics_err.append(err)\n print('n_cluster : ' , n , 'mean_bic : ', val, 'std_bic : ', err)\n \nplt.errorbar(n_clusters, bic, yerr=bics_err)\nplt.title(\"Silhouette Scores\", fontsize=20)\nplt.xticks(n_clusters)\nplt.xlabel(\"N. of clusters\")\nplt.ylabel(\"Score\")","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:22.757496Z","iopub.execute_input":"2025-02-20T19:46:22.757817Z","iopub.status.idle":"2025-02-20T19:46:25.513013Z","shell.execute_reply.started":"2025-02-20T19:46:22.757795Z","shell.execute_reply":"2025-02-20T19:46:25.511905Z"},"trusted":true},"outputs":[{"name":"stdout","text":"n_cluster : 2 mean_bic : 810.7546783544826 std_bic : 1.1368683772161603e-13\nn_cluster : 3 mean_bic : 826.0248752749292 std_bic : 0.04386275255791023\nn_cluster : 4 mean_bic : 836.9482665520421 std_bic : 2.4987458886256007\nn_cluster : 5 mean_bic : 851.1583710650709 std_bic : 0.3326180440302201\nn_cluster : 6 mean_bic : 864.5621764585967 std_bic : 2.986333140880444\nn_cluster : 7 mean_bic : 877.8200894841821 std_bic : 3.4934227400776683\nn_cluster : 8 mean_bic : 889.3653062190849 std_bic : 3.624720032953729\nn_cluster : 9 mean_bic : 900.9320916437897 std_bic : 1.271264802422914\n","output_type":"stream"},{"execution_count":58,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Score')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":58},{"cell_type":"code","source":"from sklearn.mixture import GaussianMixture \ngmm = GaussianMixture(n_components=7)\ngmm.fit(X)\nlabels = gmm.predict(X)\nprobs = gmm.predict_proba(X)\n\nplt.scatter(X, y, c=labels, s=1, cmap='viridis',)\nplt.xlabel('Area in square KM (log scale)')\nplt.ylabel('Population (log scale)')\n#labels\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:25.514418Z","iopub.execute_input":"2025-02-20T19:46:25.515061Z","iopub.status.idle":"2025-02-20T19:46:25.822648Z","shell.execute_reply.started":"2025-02-20T19:46:25.515023Z","shell.execute_reply":"2025-02-20T19:46:25.821481Z"},"trusted":true},"outputs":[{"execution_count":59,"output_type":"execute_result","data":{"text/plain":"Text(0, 0.5, 'Population (log scale)')"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":59},{"cell_type":"code","source":"\nfit = np.polyfit(np.log10(tmp['Area (km²)']), \n np.log10(tmp['value']), 1,\n w = np.sqrt(np.log10(tmp['Area (km²)'])))\n\n#view the output of the model\nprint('Coefficient A = ', fit[0])\nprint('Coefficient B = ', fit[1])\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:25.824137Z","iopub.execute_input":"2025-02-20T19:46:25.824522Z","iopub.status.idle":"2025-02-20T19:46:25.832211Z","shell.execute_reply.started":"2025-02-20T19:46:25.824486Z","shell.execute_reply":"2025-02-20T19:46:25.830989Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Coefficient A = 0.6973094427440153\nCoefficient B = 3.233127897973355\n","output_type":"stream"}],"execution_count":60},{"cell_type":"code","source":"import matplotlib.pyplot as plt\ntmp['model'] = fit[1] + fit[0] * np.log10(tmp['Area (km²)'])\n\n\nplt.scatter(x=np.log10(tmp['Area (km²)']), y= np.log10(tmp['value']), label = 'Area in square km')\nplt.scatter(x=np.log10(tmp['Area (km²)']), y= tmp['model'], marker = \"+\")\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:25.833821Z","iopub.execute_input":"2025-02-20T19:46:25.834459Z","iopub.status.idle":"2025-02-20T19:46:26.178116Z","shell.execute_reply.started":"2025-02-20T19:46:25.834426Z","shell.execute_reply":"2025-02-20T19:46:26.177071Z"},"trusted":true},"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":61},{"cell_type":"code","source":"import matplotlib.pyplot as plt\ntmp['model'] = np.exp(fit[1]) * np.exp(fit[0] * np.log10(tmp['Area (km²)'])) \n\n\nplt.scatter(x=tmp['Area (km²)'], y= tmp['value'], label = 'Area in square km')\nplt.scatter(x=tmp['Area (km²)'], y= tmp['model'], marker = \"+\")\n\n\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:26.179183Z","iopub.execute_input":"2025-02-20T19:46:26.179445Z","iopub.status.idle":"2025-02-20T19:46:26.501953Z","shell.execute_reply.started":"2025-02-20T19:46:26.179425Z","shell.execute_reply":"2025-02-20T19:46:26.500943Z"},"trusted":true},"outputs":[{"execution_count":62,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":62},{"cell_type":"code","source":"tmp['errors_model'] = np.abs(tmp['Area (km²)'] - tmp['model'])\ntmp['errors_model'].hist(grid=False)\ntmp['errors_model'].describe()","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:26.503445Z","iopub.execute_input":"2025-02-20T19:46:26.503856Z","iopub.status.idle":"2025-02-20T19:46:26.776192Z","shell.execute_reply.started":"2025-02-20T19:46:26.503811Z","shell.execute_reply":"2025-02-20T19:46:26.775226Z"},"trusted":true},"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"count 2.340000e+02\nmean 5.805673e+05\nstd 1.761308e+06\nmin 8.985719e-01\n25% 2.374108e+03\n50% 8.042166e+04\n75% 4.291368e+05\nmax 1.709431e+07\nName: errors_model, dtype: float64"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":63},{"cell_type":"markdown","source":"We explore next the population concentration in countries. We surmise the more individuals living closely, the global emissions may occur. We will explore hot the population concentration has evolved between 1970 and 2022.\n\nWe are going find the population per km square - $pop_per_km_square = value/ area in km square$.","metadata":{}},{"cell_type":"code","source":"pop_long.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:26.777544Z","iopub.execute_input":"2025-02-20T19:46:26.777964Z","iopub.status.idle":"2025-02-20T19:46:26.785706Z","shell.execute_reply.started":"2025-02-20T19:46:26.777929Z","shell.execute_reply":"2025-02-20T19:46:26.784617Z"},"trusted":true},"outputs":[{"execution_count":64,"output_type":"execute_result","data":{"text/plain":"Rank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\nArea (km²) int64\nvariable int64\nvalue int64\nArea_log_10 float64\nvalue_log_10 float64\nmodel_a float64\ndtype: object"},"metadata":{}}],"execution_count":64},{"cell_type":"markdown","source":"We population per country","metadata":{}},{"cell_type":"code","source":"pop_long['pop_per_km_square'] = pop_long.value/pop_long['Area (km²)']\npop_long.groupby(['CCA3']).describe()['pop_per_km_square']","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:46:26.786929Z","iopub.execute_input":"2025-02-20T19:46:26.787214Z","iopub.status.idle":"2025-02-20T19:46:29.468258Z","shell.execute_reply.started":"2025-02-20T19:46:26.787192Z","shell.execute_reply":"2025-02-20T19:46:29.467189Z"},"trusted":true},"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":" count mean std min 25% 50% \\\nCCA3 \nABW 8.0 481.815278 116.614859 328.366667 360.281944 526.227778 \nAFG 8.0 37.471742 19.511296 16.397277 18.480009 36.591888 \nAGO 8.0 16.352353 9.148836 4.836528 8.786388 15.945395 \nAIA 8.0 125.473901 43.468054 69.043956 86.560440 133.071429 \nALB 8.0 101.087550 9.951370 80.865834 99.510122 100.804926 \n... ... ... ... ... ... ... \nWSM 8.0 65.773399 9.564960 50.236101 58.890130 66.622097 \nYEM 8.0 39.607873 19.765118 12.962162 23.358566 41.075071 \nZAF 8.0 37.353488 11.401890 18.319106 30.526565 40.374774 \nZMB 8.0 16.038369 8.028277 5.689081 9.559920 15.734018 \nZWE 8.0 29.809550 10.180688 13.314971 23.922543 31.572623 \n\n 75% max \nCCA3 \nABW 582.244444 592.138889 \nAFG 53.751256 63.058692 \nAGO 23.624699 28.546552 \nAIA 162.527473 174.252747 \nALB 104.415733 114.618965 \n... ... ... \nWSM 72.628607 78.248417 \nYEM 55.795844 63.823213 \nZAF 46.360479 49.051654 \nZMB 22.479181 26.597603 \nZWE 37.193497 41.766461 \n\n[234 rows x 8 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
countmeanstdmin25%50%75%max
CCA3
ABW8.0481.815278116.614859328.366667360.281944526.227778582.244444592.138889
AFG8.037.47174219.51129616.39727718.48000936.59188853.75125663.058692
AGO8.016.3523539.1488364.8365288.78638815.94539523.62469928.546552
AIA8.0125.47390143.46805469.04395686.560440133.071429162.527473174.252747
ALB8.0101.0875509.95137080.86583499.510122100.804926104.415733114.618965
...........................
WSM8.065.7733999.56496050.23610158.89013066.62209772.62860778.248417
YEM8.039.60787319.76511812.96216223.35856641.07507155.79584463.823213
ZAF8.037.35348811.40189018.31910630.52656540.37477446.36047949.051654
ZMB8.016.0383698.0282775.6890819.55992015.73401822.47918126.597603
ZWE8.029.80955010.18068813.31497123.92254331.57262337.19349741.766461
\n

234 rows × 8 columns

\n
"},"metadata":{}}],"execution_count":65},{"cell_type":"code","source":"pop_long.dtypes","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-02-20T19:48:54.001825Z","iopub.execute_input":"2025-02-20T19:48:54.002228Z","iopub.status.idle":"2025-02-20T19:48:54.010270Z","shell.execute_reply.started":"2025-02-20T19:48:54.002199Z","shell.execute_reply":"2025-02-20T19:48:54.009300Z"}},"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"Rank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\nArea (km²) int64\nvariable int64\nvalue int64\nArea_log_10 float64\nvalue_log_10 float64\nmodel_a float64\npop_per_km_square float64\ndtype: object"},"metadata":{}}],"execution_count":67},{"cell_type":"code","source":"pop_long.loc[pop_long['Area (km²)'] > pop_long['value'],:]","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:49:15.633976Z","iopub.execute_input":"2025-02-20T19:49:15.634349Z","iopub.status.idle":"2025-02-20T19:49:15.654116Z","shell.execute_reply.started":"2025-02-20T19:49:15.634324Z","shell.execute_reply":"2025-02-20T19:49:15.653078Z"},"trusted":true},"outputs":[{"execution_count":68,"output_type":"execute_result","data":{"text/plain":" Rank CCA3 Country/Territory Capital Continent Area (km²) \\\n64 231 FLK Falkland Islands Stanley South America 12173 \n78 208 GRL Greenland Nuuk North America 2166086 \n298 231 FLK Falkland Islands Stanley South America 12173 \n312 208 GRL Greenland Nuuk North America 2166086 \n532 231 FLK Falkland Islands Stanley South America 12173 \n546 208 GRL Greenland Nuuk North America 2166086 \n766 231 FLK Falkland Islands Stanley South America 12173 \n780 208 GRL Greenland Nuuk North America 2166086 \n1000 231 FLK Falkland Islands Stanley South America 12173 \n1014 208 GRL Greenland Nuuk North America 2166086 \n1234 231 FLK Falkland Islands Stanley South America 12173 \n1248 208 GRL Greenland Nuuk North America 2166086 \n1400 172 ESH Western Sahara El Aaiún Africa 266000 \n1468 231 FLK Falkland Islands Stanley South America 12173 \n1473 184 GUF French Guiana Cayenne South America 83534 \n1482 208 GRL Greenland Nuuk North America 2166086 \n1634 172 ESH Western Sahara El Aaiún Africa 266000 \n1702 231 FLK Falkland Islands Stanley South America 12173 \n1707 184 GUF French Guiana Cayenne South America 83534 \n1716 208 GRL Greenland Nuuk North America 2166086 \n1773 134 MNG Mongolia Ulaanbaatar Asia 1564110 \n1779 145 NAM Namibia Windhoek Africa 825615 \n1868 172 ESH Western Sahara El Aaiún Africa 266000 \n\n variable value Area_log_10 value_log_10 model_a \\\n64 2022 3780 4.085398 3.577492 5.937129 \n78 2022 56466 6.335676 4.751787 7.673990 \n298 2020 3747 4.085398 3.573684 5.937129 \n312 2020 56026 6.335676 4.748390 7.673990 \n532 2015 3408 4.085398 3.532500 5.937129 \n546 2015 55895 6.335676 4.747373 7.673990 \n766 2010 3187 4.085398 3.503382 5.937129 \n780 2010 56351 6.335676 4.750902 7.673990 \n1000 2000 3080 4.085398 3.488551 5.937129 \n1014 2000 56184 6.335676 4.749613 7.673990 \n1234 1990 2332 4.085398 3.367729 5.937129 \n1248 1990 55599 6.335676 4.745067 7.673990 \n1400 1990 178529 5.424882 5.251709 6.916944 \n1468 1980 2240 4.085398 3.350248 5.937129 \n1473 1980 66825 4.921863 4.824939 6.531340 \n1482 1980 50106 6.335676 4.699890 7.673990 \n1634 1980 116775 5.424882 5.067350 6.916944 \n1702 1970 2274 4.085398 3.356790 5.937129 \n1707 1970 46484 4.921863 4.667303 6.531340 \n1716 1970 45434 6.335676 4.657381 7.673990 \n1773 1970 1293880 6.194267 6.111894 7.551235 \n1779 1970 754467 5.916778 5.877640 7.316028 \n1868 1970 76371 5.424882 4.882928 6.916944 \n\n pop_per_km_square \n64 0.310523 \n78 0.026068 \n298 0.307812 \n312 0.025865 \n532 0.279964 \n546 0.025805 \n766 0.261809 \n780 0.026015 \n1000 0.253019 \n1014 0.025938 \n1234 0.191572 \n1248 0.025668 \n1400 0.671162 \n1468 0.184014 \n1473 0.799974 \n1482 0.023132 \n1634 0.439004 \n1702 0.186807 \n1707 0.556468 \n1716 0.020975 \n1773 0.827231 \n1779 0.913824 \n1868 0.287109 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
RankCCA3Country/TerritoryCapitalContinentArea (km²)variablevalueArea_log_10value_log_10model_apop_per_km_square
64231FLKFalkland IslandsStanleySouth America12173202237804.0853983.5774925.9371290.310523
78208GRLGreenlandNuukNorth America21660862022564666.3356764.7517877.6739900.026068
298231FLKFalkland IslandsStanleySouth America12173202037474.0853983.5736845.9371290.307812
312208GRLGreenlandNuukNorth America21660862020560266.3356764.7483907.6739900.025865
532231FLKFalkland IslandsStanleySouth America12173201534084.0853983.5325005.9371290.279964
546208GRLGreenlandNuukNorth America21660862015558956.3356764.7473737.6739900.025805
766231FLKFalkland IslandsStanleySouth America12173201031874.0853983.5033825.9371290.261809
780208GRLGreenlandNuukNorth America21660862010563516.3356764.7509027.6739900.026015
1000231FLKFalkland IslandsStanleySouth America12173200030804.0853983.4885515.9371290.253019
1014208GRLGreenlandNuukNorth America21660862000561846.3356764.7496137.6739900.025938
1234231FLKFalkland IslandsStanleySouth America12173199023324.0853983.3677295.9371290.191572
1248208GRLGreenlandNuukNorth America21660861990555996.3356764.7450677.6739900.025668
1400172ESHWestern SaharaEl AaiúnAfrica26600019901785295.4248825.2517096.9169440.671162
1468231FLKFalkland IslandsStanleySouth America12173198022404.0853983.3502485.9371290.184014
1473184GUFFrench GuianaCayenneSouth America835341980668254.9218634.8249396.5313400.799974
1482208GRLGreenlandNuukNorth America21660861980501066.3356764.6998907.6739900.023132
1634172ESHWestern SaharaEl AaiúnAfrica26600019801167755.4248825.0673506.9169440.439004
1702231FLKFalkland IslandsStanleySouth America12173197022744.0853983.3567905.9371290.186807
1707184GUFFrench GuianaCayenneSouth America835341970464844.9218634.6673036.5313400.556468
1716208GRLGreenlandNuukNorth America21660861970454346.3356764.6573817.6739900.020975
1773134MNGMongoliaUlaanbaatarAsia1564110197012938806.1942676.1118947.5512350.827231
1779145NAMNamibiaWindhoekAfrica82561519707544675.9167785.8776407.3160280.913824
1868172ESHWestern SaharaEl AaiúnAfrica2660001970763715.4248824.8829286.9169440.287109
\n
"},"metadata":{}}],"execution_count":68},{"cell_type":"code","source":"rows = pop_long['Area (km²)'] > pop_long['value']\ntemp = pop_long.loc[rows,['CCA3','Country/Territory','Continent','variable']]\ntemp = temp.groupby(['CCA3','Country/Territory','Continent']).count().reset_index()\ntemp","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:49:19.803960Z","iopub.execute_input":"2025-02-20T19:49:19.804300Z","iopub.status.idle":"2025-02-20T19:49:19.820536Z","shell.execute_reply.started":"2025-02-20T19:49:19.804276Z","shell.execute_reply":"2025-02-20T19:49:19.819466Z"},"trusted":true},"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":" CCA3 Country/Territory Continent variable\n0 ESH Western Sahara Africa 3\n1 FLK Falkland Islands South America 8\n2 GRL Greenland North America 8\n3 GUF French Guiana South America 2\n4 MNG Mongolia Asia 1\n5 NAM Namibia Africa 1","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
CCA3Country/TerritoryContinentvariable
0ESHWestern SaharaAfrica3
1FLKFalkland IslandsSouth America8
2GRLGreenlandNorth America8
3GUFFrench GuianaSouth America2
4MNGMongoliaAsia1
5NAMNamibiaAfrica1
\n
"},"metadata":{}}],"execution_count":69},{"cell_type":"code","source":"pop.dtypes","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:49:23.538044Z","iopub.execute_input":"2025-02-20T19:49:23.538435Z","iopub.status.idle":"2025-02-20T19:49:23.546279Z","shell.execute_reply.started":"2025-02-20T19:49:23.538403Z","shell.execute_reply":"2025-02-20T19:49:23.545239Z"},"trusted":true},"outputs":[{"execution_count":70,"output_type":"execute_result","data":{"text/plain":"Rank int64\nCCA3 object\nCountry/Territory object\nCapital object\nContinent object\n2022 Population int64\n2020 Population int64\n2015 Population int64\n2010 Population int64\n2000 Population int64\n1990 Population int64\n1980 Population int64\n1970 Population int64\nArea (km²) int64\nDensity (per km²) float64\nGrowth Rate float64\nWorld Population Percentage float64\ndtype: object"},"metadata":{}}],"execution_count":70},{"cell_type":"code","source":"tmp = pd.DataFrame()\ntmp['1970'] = np.log10(pop['1970 Population']/pop['Area (km²)'])\ntmp['1980'] = np.log10(pop['1980 Population']/pop['Area (km²)'])\ntmp['1990'] = np.log10(pop['1990 Population']/pop['Area (km²)'])\ntmp['2000'] = np.log10(pop['2000 Population']/pop['Area (km²)'])\ntmp['2015'] = np.log10(pop['2015 Population']/pop['Area (km²)'])\ntmp['2020'] = np.log10(pop['2020 Population']/pop['Area (km²)'])\ntmp['2022'] = np.log10(pop['2022 Population']/pop['Area (km²)'])\nax = tmp.boxplot(grid = False, rot =45)\nplt.title('Population concentration (log scale)')\ntmp.describe()\n","metadata":{"execution":{"iopub.status.busy":"2025-02-20T19:49:27.142435Z","iopub.execute_input":"2025-02-20T19:49:27.143534Z","iopub.status.idle":"2025-02-20T19:49:27.502297Z","shell.execute_reply.started":"2025-02-20T19:49:27.143496Z","shell.execute_reply":"2025-02-20T19:49:27.501278Z"},"trusted":true},"outputs":[{"execution_count":71,"output_type":"execute_result","data":{"text/plain":" 1970 1980 1990 2000 2015 2020 \\\ncount 234.000000 234.000000 234.000000 234.000000 234.000000 234.000000 \nmean 1.594789 1.682442 1.769069 1.834934 1.922655 1.946820 \nstd 0.784700 0.765499 0.754869 0.742757 0.729774 0.724947 \nmin -1.678295 -1.635786 -1.590609 -1.586063 -1.588303 -1.587286 \n25% 1.120070 1.224847 1.313055 1.417633 1.529090 1.579103 \n50% 1.629144 1.736050 1.850794 1.895206 1.965093 1.972973 \n75% 2.098137 2.164787 2.230460 2.269495 2.356745 2.381238 \nmax 4.084040 4.131555 4.180828 4.210385 4.311923 4.353007 \n\n 2022 \ncount 234.000000 \nmean 1.954727 \nstd 0.723207 \nmin -1.583889 \n25% 1.584492 \n50% 1.979262 \n75% 2.378272 \nmax 4.364969 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
1970198019902000201520202022
count234.000000234.000000234.000000234.000000234.000000234.000000234.000000
mean1.5947891.6824421.7690691.8349341.9226551.9468201.954727
std0.7847000.7654990.7548690.7427570.7297740.7249470.723207
min-1.678295-1.635786-1.590609-1.586063-1.588303-1.587286-1.583889
25%1.1200701.2248471.3130551.4176331.5290901.5791031.584492
50%1.6291441.7360501.8507941.8952061.9650931.9729731.979262
75%2.0981372.1647872.2304602.2694952.3567452.3812382.378272
max4.0840404.1315554.1808284.2103854.3119234.3530074.364969
\n
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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"},"metadata":{}}],"execution_count":71},{"cell_type":"markdown","source":"The median population concentration appears to increase through time. The number of outliers appears to both become smaller or larger. It may indicates some countries may be becoming less populated while other increase. \n\nAll the values are shown in log scale 10. A negative value suggests a very low density - the area in square km is greater than the population. We found five countries above. ","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]} \ No newline at end of file diff --git a/Data engineering and science/uk-house-prices-and-population.ipynb b/Data engineering and science/uk-house-prices-and-population.ipynb new file mode 100644 index 0000000..25e83b9 --- /dev/null +++ b/Data engineering and science/uk-house-prices-and-population.ipynb @@ -0,0 +1,4964 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d97a33e8", + "metadata": { + "papermill": { + "duration": 0.013511, + "end_time": "2025-02-16T13:26:20.262900", + "exception": false, + "start_time": "2025-02-16T13:26:20.249389", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# population across the UK" + ] + }, + { + "cell_type": "markdown", + "id": "155623cd", + "metadata": { + "papermill": { + "duration": 0.014118, + "end_time": "2025-02-16T13:26:20.290484", + "exception": false, + "start_time": "2025-02-16T13:26:20.276366", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Introduction \n", + "This notebook aims to explore the Housing prices and population across the UK. We use a wide range of analytical techniques that includes visualisation as well as application of advanced statistical techniques. It is aimed for teaching those techniques, rather being produced as a reference. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f96d3ac1", + "metadata": { + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", + "execution": { + "iopub.execute_input": "2025-02-16T13:26:20.318684Z", + "iopub.status.busy": "2025-02-16T13:26:20.318243Z", + "iopub.status.idle": "2025-02-16T13:26:24.868432Z", + "shell.execute_reply": "2025-02-16T13:26:24.867392Z" + }, + "papermill": { + "duration": 4.566687, + "end_time": "2025-02-16T13:26:24.870614", + "exception": false, + "start_time": "2025-02-16T13:26:20.303927", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/kaggle/input/united-kingdom-real-estate-and-population-data/data.csv\n" + ] + } + ], + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n", + "# For example, here's several helpful packages to load\n", + "\n", + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.cluster import KMeans\n", + "\n", + "# Input data files are available in the read-only \"../input/\" directory\n", + "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", + "\n", + "import os\n", + "for dirname, _, filenames in os.walk('/kaggle/input'):\n", + " for filename in filenames:\n", + " print(os.path.join(dirname, filename))\n", + "\n", + "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n", + "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session" + ] + }, + { + "cell_type": "markdown", + "id": "d57ecf9b", + "metadata": { + "papermill": { + "duration": 0.013082, + "end_time": "2025-02-16T13:26:24.897236", + "exception": false, + "start_time": "2025-02-16T13:26:24.884154", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Exploration of the data and its features" + ] + }, + { + "cell_type": "markdown", + "id": "fa8e521b", + "metadata": { + "papermill": { + "duration": 0.012808, + "end_time": "2025-02-16T13:26:24.923229", + "exception": false, + "start_time": "2025-02-16T13:26:24.910421", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The data is substantially large - more than 7 millions observations. Numerical data brings a high level of variability. Analysis may suffer from some large dispersion within the numerical values. The datasets appears to have some categorical values as well as time series data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "38f8860b", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:26:24.951862Z", + "iopub.status.busy": "2025-02-16T13:26:24.950786Z", + "iopub.status.idle": "2025-02-16T13:26:46.528615Z", + "shell.execute_reply": "2025-02-16T13:26:46.527388Z" + }, + "papermill": { + "duration": 21.594396, + "end_time": "2025-02-16T13:26:46.530723", + "exception": false, + "start_time": "2025-02-16T13:26:24.936327", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(7853837, 13)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "source = '/kaggle/input/united-kingdom-real-estate-and-population-data/data.csv'\n", + "data = pd.read_csv(source)\n", + "data.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5ed0eef6", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:26:46.560899Z", + "iopub.status.busy": "2025-02-16T13:26:46.560533Z", + "iopub.status.idle": "2025-02-16T13:26:46.569831Z", + "shell.execute_reply": "2025-02-16T13:26:46.568888Z" + }, + "papermill": { + "duration": 0.027658, + "end_time": "2025-02-16T13:26:46.571814", + "exception": false, + "start_time": "2025-02-16T13:26:46.544156", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Unnamed: 0 int64\n", + "Code object\n", + "Name object\n", + "Geography object\n", + "Area (sq km) float64\n", + "Price int64\n", + "Date of Transfer object\n", + "Property Type object\n", + "Old/New object\n", + "Duration object\n", + "PPDCategory Type object\n", + "pp_sq_m float64\n", + "est_pop float64\n", + "dtype: object" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.dtypes\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a368cca1", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:26:46.600309Z", + "iopub.status.busy": "2025-02-16T13:26:46.599916Z", + "iopub.status.idle": "2025-02-16T13:27:54.061022Z", + "shell.execute_reply": "2025-02-16T13:27:54.059829Z" + }, + "papermill": { + "duration": 67.479754, + "end_time": "2025-02-16T13:27:54.064966", + "exception": false, + "start_time": "2025-02-16T13:26:46.585212", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "data['Date of Transfer'] = pd.to_datetime(data['Date of Transfer'])\n", + "data['Year'] = data['Date of Transfer'].dt.year\n", + "data['Month'] = data['Date of Transfer'].dt.month\n", + "data['Day'] = data['Date of Transfer'].dt.day\n", + "data.dtypes\n", + "data.to_csv('data_with_year.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "651686fd", + "metadata": { + "papermill": { + "duration": 0.043762, + "end_time": "2025-02-16T13:27:54.138750", + "exception": false, + "start_time": "2025-02-16T13:27:54.094988", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Numerical values and their features\n", + "It appears some large disparity in the data occurs in the data. A large standard deviation exists. The difference between arithmetical mean and median may indicate some large prices and area skewness to the right. However, the kustosis is only large for the prices. We will need to investigate further the presence of data in the tails for the Prices.\n", + "\n", + "The people per square metres and populations appears to be skewed to the left. A low kurtosis indicates they may not many observations in the tails. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d32fd570", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:54.288716Z", + "iopub.status.busy": "2025-02-16T13:27:54.288124Z", + "iopub.status.idle": "2025-02-16T13:27:56.720363Z", + "shell.execute_reply": "2025-02-16T13:27:56.719207Z" + }, + "papermill": { + "duration": 2.512935, + "end_time": "2025-02-16T13:27:56.722558", + "exception": false, + "start_time": "2025-02-16T13:27:54.209623", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0Area (sq km)PriceDate of Transferpp_sq_mest_popYearMonthDay
count7.853837e+067.853837e+067.853837e+0678538377.853837e+065.208853e+067.853837e+067.853837e+067.853837e+06
mean5.431394e+064.194698e+022.209744e+052008-06-24 11:25:39.1786214402.599774e+031.372225e+062.007962e+036.711091e+001.691465e+01
min1.282500e+041.631770e+011.000000e+002001-01-01 00:00:009.704739e+012.140000e+032.001000e+031.000000e+001.000000e+00
25%2.674884e+067.461320e+011.025000e+052004-03-05 00:00:009.843264e+021.340490e+052.004000e+034.000000e+009.000000e+00
50%5.370908e+061.581281e+021.550000e+052007-06-15 00:00:002.608139e+032.380160e+052.007000e+037.000000e+001.700000e+01
75%8.102541e+064.144144e+022.407510e+052013-03-08 00:00:004.011644e+034.418580e+052.013000e+031.000000e+012.500000e+01
max1.083096e+071.572031e+039.890000e+072017-06-29 00:00:005.218986e+037.322403e+062.017000e+031.200000e+013.100000e+01
std3.154738e+065.338312e+025.708082e+05NaN1.613246e+032.584126e+064.943425e+003.349538e+009.011419e+00
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Area (sq km) Price \\\n", + "count 7.853837e+06 7.853837e+06 7.853837e+06 \n", + "mean 5.431394e+06 4.194698e+02 2.209744e+05 \n", + "min 1.282500e+04 1.631770e+01 1.000000e+00 \n", + "25% 2.674884e+06 7.461320e+01 1.025000e+05 \n", + "50% 5.370908e+06 1.581281e+02 1.550000e+05 \n", + "75% 8.102541e+06 4.144144e+02 2.407510e+05 \n", + "max 1.083096e+07 1.572031e+03 9.890000e+07 \n", + "std 3.154738e+06 5.338312e+02 5.708082e+05 \n", + "\n", + " Date of Transfer pp_sq_m est_pop \\\n", + "count 7853837 7.853837e+06 5.208853e+06 \n", + "mean 2008-06-24 11:25:39.178621440 2.599774e+03 1.372225e+06 \n", + "min 2001-01-01 00:00:00 9.704739e+01 2.140000e+03 \n", + "25% 2004-03-05 00:00:00 9.843264e+02 1.340490e+05 \n", + "50% 2007-06-15 00:00:00 2.608139e+03 2.380160e+05 \n", + "75% 2013-03-08 00:00:00 4.011644e+03 4.418580e+05 \n", + "max 2017-06-29 00:00:00 5.218986e+03 7.322403e+06 \n", + "std NaN 1.613246e+03 2.584126e+06 \n", + "\n", + " Year Month Day \n", + "count 7.853837e+06 7.853837e+06 7.853837e+06 \n", + "mean 2.007962e+03 6.711091e+00 1.691465e+01 \n", + "min 2.001000e+03 1.000000e+00 1.000000e+00 \n", + "25% 2.004000e+03 4.000000e+00 9.000000e+00 \n", + "50% 2.007000e+03 7.000000e+00 1.700000e+01 \n", + "75% 2.013000e+03 1.000000e+01 2.500000e+01 \n", + "max 2.017000e+03 1.200000e+01 3.100000e+01 \n", + "std 4.943425e+00 3.349538e+00 9.011419e+00 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4611f670", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:56.754619Z", + "iopub.status.busy": "2025-02-16T13:27:56.754201Z", + "iopub.status.idle": "2025-02-16T13:27:57.357834Z", + "shell.execute_reply": "2025-02-16T13:27:57.356742Z" + }, + "papermill": { + "duration": 0.62429, + "end_time": "2025-02-16T13:27:57.360605", + "exception": false, + "start_time": "2025-02-16T13:27:56.736315", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Area (sq km) 0.622937\n", + "Price 7994.700840\n", + "pp_sq_m -1.376422\n", + "est_pop 1.475516\n", + "dtype: float64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols= ['Area (sq km)', 'Price', 'pp_sq_m','est_pop']\n", + "data_num = data.loc[:, cols]\n", + "data_num.kurt()" + ] + }, + { + "cell_type": "markdown", + "id": "346c6381", + "metadata": { + "papermill": { + "duration": 0.014271, + "end_time": "2025-02-16T13:27:57.388762", + "exception": false, + "start_time": "2025-02-16T13:27:57.374491", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Non-numerical values\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "511c40ad", + "metadata": { + "papermill": { + "duration": 0.013573, + "end_time": "2025-02-16T13:27:57.416395", + "exception": false, + "start_time": "2025-02-16T13:27:57.402822", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Code object\n", + "Name object\n", + "Geography object\n", + "Date of Transfer object\n", + "Property Type object\n", + "Old/New object\n", + "Duration object\n", + "PPDCategory Type object" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3ed5962d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:57.445236Z", + "iopub.status.busy": "2025-02-16T13:27:57.444854Z", + "iopub.status.idle": "2025-02-16T13:27:57.883365Z", + "shell.execute_reply": "2025-02-16T13:27:57.882187Z" + }, + "papermill": { + "duration": 0.455462, + "end_time": "2025-02-16T13:27:57.885499", + "exception": false, + "start_time": "2025-02-16T13:27:57.430037", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "135" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data.Code.unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fb1dec5e", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:57.914913Z", + "iopub.status.busy": "2025-02-16T13:27:57.914563Z", + "iopub.status.idle": "2025-02-16T13:27:58.432270Z", + "shell.execute_reply": "2025-02-16T13:27:58.430474Z" + }, + "papermill": { + "duration": 0.534925, + "end_time": "2025-02-16T13:27:58.434425", + "exception": false, + "start_time": "2025-02-16T13:27:57.899500", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Unitary Authority', 'Metropolitan District',\n", + " 'Non-metropolitan District', 'Region', 'London Borough'],\n", + " dtype=object)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.Geography.unique()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "11fb7d23", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:58.464166Z", + "iopub.status.busy": "2025-02-16T13:27:58.463773Z", + "iopub.status.idle": "2025-02-16T13:27:58.822498Z", + "shell.execute_reply": "2025-02-16T13:27:58.821328Z" + }, + "papermill": { + "duration": 0.376451, + "end_time": "2025-02-16T13:27:58.824968", + "exception": false, + "start_time": "2025-02-16T13:27:58.448517", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['T', 'D', 'S', 'F', 'O'], dtype=object)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Property Type'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "b3356c20", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:58.916577Z", + "iopub.status.busy": "2025-02-16T13:27:58.916135Z", + "iopub.status.idle": "2025-02-16T13:27:59.272355Z", + "shell.execute_reply": "2025-02-16T13:27:59.271400Z" + }, + "papermill": { + "duration": 0.435033, + "end_time": "2025-02-16T13:27:59.274519", + "exception": false, + "start_time": "2025-02-16T13:27:58.839486", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['N', 'Y'], dtype=object)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Old/New'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "954ab8d8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:59.305692Z", + "iopub.status.busy": "2025-02-16T13:27:59.305286Z", + "iopub.status.idle": "2025-02-16T13:27:59.663731Z", + "shell.execute_reply": "2025-02-16T13:27:59.662728Z" + }, + "papermill": { + "duration": 0.376514, + "end_time": "2025-02-16T13:27:59.665930", + "exception": false, + "start_time": "2025-02-16T13:27:59.289416", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['A', 'B'], dtype=object)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['PPDCategory Type'].unique()" + ] + }, + { + "cell_type": "markdown", + "id": "1facb4b7", + "metadata": { + "papermill": { + "duration": 0.013865, + "end_time": "2025-02-16T13:27:59.694468", + "exception": false, + "start_time": "2025-02-16T13:27:59.680603", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Is there a relationship between an estimated population and an area?\n" + ] + }, + { + "cell_type": "markdown", + "id": "90b81d05", + "metadata": { + "papermill": { + "duration": 0.014052, + "end_time": "2025-02-16T13:27:59.722789", + "exception": false, + "start_time": "2025-02-16T13:27:59.708737", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We explore the area and estimated population by Geography. Some large differences in centrality and dispersion exist. Consequently, we explore the relationship for each type of geography and across all types of geography." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "257ffd8d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:27:59.754561Z", + "iopub.status.busy": "2025-02-16T13:27:59.754154Z", + "iopub.status.idle": "2025-02-16T13:28:02.839270Z", + "shell.execute_reply": "2025-02-16T13:28:02.838137Z" + }, + "papermill": { + "duration": 3.104522, + "end_time": "2025-02-16T13:28:02.841491", + "exception": false, + "start_time": "2025-02-16T13:27:59.736969", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Geography\n", + "London Borough 152901\n", + "Metropolitan District 1490010\n", + "Non-metropolitan District 1384878\n", + "Region 823056\n", + "Unitary Authority 1358008\n", + "Name: est_pop, dtype: int64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby(['Geography']).count()['est_pop']" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a49eda45", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:28:02.872660Z", + "iopub.status.busy": "2025-02-16T13:28:02.872274Z", + "iopub.status.idle": "2025-02-16T13:28:07.238873Z", + "shell.execute_reply": "2025-02-16T13:28:07.237895Z" + }, + "papermill": { + "duration": 4.384846, + "end_time": "2025-02-16T13:28:07.241107", + "exception": false, + "start_time": "2025-02-16T13:28:02.856261", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanmin25%50%75%maxstd
Geography
London Borough227540.077.39757237.259350.464580.821186.4887150.13263.455596e+01
Metropolitan District2191246.0232.88426669.4365115.6485142.3450338.6198568.00641.518014e+02
Non-metropolitan District2135570.0219.22949421.430540.553076.6971331.32881307.93772.619611e+02
Region1261082.01572.0308001572.03081572.03081572.03081572.03081572.03081.591616e-12
Unitary Authority2038399.0154.96948416.317773.342179.8497230.09331125.82261.300433e+02
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" + ], + "text/plain": [ + " count mean min 25% \\\n", + "Geography \n", + "London Borough 227540.0 77.397572 37.2593 50.4645 \n", + "Metropolitan District 2191246.0 232.884266 69.4365 115.6485 \n", + "Non-metropolitan District 2135570.0 219.229494 21.4305 40.5530 \n", + "Region 1261082.0 1572.030800 1572.0308 1572.0308 \n", + "Unitary Authority 2038399.0 154.969484 16.3177 73.3421 \n", + "\n", + " 50% 75% max std \n", + "Geography \n", + "London Borough 80.8211 86.4887 150.1326 3.455596e+01 \n", + "Metropolitan District 142.3450 338.6198 568.0064 1.518014e+02 \n", + "Non-metropolitan District 76.6971 331.3288 1307.9377 2.619611e+02 \n", + "Region 1572.0308 1572.0308 1572.0308 1.591616e-12 \n", + "Unitary Authority 79.8497 230.0933 1125.8226 1.300433e+02 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.groupby(['Geography']).describe()['Area (sq km)']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bc0c017d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:28:07.272382Z", + "iopub.status.busy": "2025-02-16T13:28:07.271256Z", + "iopub.status.idle": "2025-02-16T13:28:11.530741Z", + "shell.execute_reply": "2025-02-16T13:28:11.529546Z" + }, + "papermill": { + "duration": 4.277242, + "end_time": "2025-02-16T13:28:11.532919", + "exception": false, + "start_time": "2025-02-16T13:28:07.255677", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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countmeanmin25%50%75%maxstd
Geography
London Borough152901.02.548180e+05149045.0210044.0277266.0296218.0335112.060986.888813
Metropolitan District1490010.04.403575e+05176826.0266241.0422915.0513102.0984642.0237751.740921
Non-metropolitan District1384878.01.083403e+0555802.089836.0107264.0122366.0165895.023795.293115
Region823056.07.322403e+067322403.07322403.07322403.07322403.07322403.00.000000
Unitary Authority1358008.02.031151e+052140.0150334.0191202.0240954.0310088.057311.131448
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" + ], + "text/plain": [ + " count mean min 25% \\\n", + "Geography \n", + "London Borough 152901.0 2.548180e+05 149045.0 210044.0 \n", + "Metropolitan District 1490010.0 4.403575e+05 176826.0 266241.0 \n", + "Non-metropolitan District 1384878.0 1.083403e+05 55802.0 89836.0 \n", + "Region 823056.0 7.322403e+06 7322403.0 7322403.0 \n", + "Unitary Authority 1358008.0 2.031151e+05 2140.0 150334.0 \n", + "\n", + " 50% 75% max std \n", + "Geography \n", + "London Borough 277266.0 296218.0 335112.0 60986.888813 \n", + "Metropolitan District 422915.0 513102.0 984642.0 237751.740921 \n", + "Non-metropolitan District 107264.0 122366.0 165895.0 23795.293115 \n", + "Region 7322403.0 7322403.0 7322403.0 0.000000 \n", + "Unitary Authority 191202.0 240954.0 310088.0 57311.131448 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "group_by_geo = data.groupby(['Geography'])\n", + "group_by_geo.describe()['est_pop']" + ] + }, + { + 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nn6958+Zp27Zt+uijj9z3jgALbdmyRZKUlpZW4/zo0aP1zjvvuOIAAADgnex2uwoLC7VgwQL5+vqaGnG+vr5KT0/XVVddJbvdrn79+lmXKACgQdTrCDpJevvttzVq1CjNmzdPI0eOVHl5uXJychQXF3fS5wUHB2vp0qUaNmyY3nzzTT399NPy9fXVxx9/rEGDBp32GwA8SdeuXSVJ06ZNq3E+IyPDFAcAAADvVHkd76ioqBrnK8cr4wAATVu9jqCTfj9Nb+rUqZo6dWqtMcuXL69xPCwsTG+99VZ9Nwk0GvPmzVNoaKj+9a9/6bXXXjPdFOX48eOaP3++Kw4AAADeKzw8XJK0YcMGXXHFFdXmN2zYYIoDADRt9T6CDkDtmjdvrr59+8owDIWEhOjee+/Vli1bdO+99yokJESGYahv375q3ry51akCAADAQrGxsYqIiNCkSZPkdDpNc06nU5MnT1ZkZKRiY2MtyhAA0JBo0AFutmbNGleTbv78+UpNTdX8+fNdzbk1a9ZYnSIAAAAsZrPZNH36dOXk5CgpKUn5+fkqKSlRfn6+kpKSlJOTo2nTppnOyAAANF31PsUVwKmtWbNGR44c0bBhw/Tdd9+pZ8+emj9/PkfOAQAAwCU5OVmZmZlKTU01XdM7MjJSmZmZSk5OtjA7AEBDokEHnCXNmzfXBx98oEWLFmnw4MHy9/e3OiUAAAB4mOTkZCUmJmrZsmVavHixBg0apP79+3PkHAB4GRp0AAAAAGAhm82m+Ph4HT16VPHx8TTnAMALcQ06AAAAAAAAwEI06AAAAAAAAAAL0aADAAAAAAAALESDDgAAAAAs5HA4lJeXpxUrVigvL08Oh8PqlAAADYwGHQAAAABYJCsrS926dVNCQoIyMjKUkJCgbt26KSsry+rUAAANiAYdAAAAAFggKytLKSkpio6Olt1u14IFC2S32xUdHa2UlBSadADgRWjQAQAAAEADczgcSk1N1ZAhQ5Sdna2YmBgFBwcrJiZG2dnZGjJkiNLS0jjdFQC8BA06AAAAAGhgdrtdhYWFGjt2rEpLSzVy5EiNHz9eI0eOVGlpqdLT01VQUCC73W51qgCABuBndQIAAAAA4G2KiookSRMnTtTHH3/sGl+3bp1mz56tG264wRQHAGjaaNABAAAAQAMLDw+XJH388ccKCAjQqFGjFBkZqYKCAs2cOdPVtKuMAwA0bZziCgAAAAAN7A9/+IMkycfHR4cOHdLEiRMVHh6uiRMn6tChQ/Lx8THFAQCaNhp0wFnicDiUl5enFStWKC8vjwv8AgAAwOX//u//JEmGYejWW29Vfn6+SkpKlJ+fr1tvvVWGYZjiAABNGw064CzIyspSt27dlJCQoIyMDCUkJKhbt27KysqyOjUAAAB4gM2bN0uSXn75Za1fv15xcXG64447FBcXpw0bNuill14yxQEAmjYadICbZWVlKSUlRdHR0bLb7VqwYIHsdruio6OVkpJCkw4AAAC64IILJEk7d+7Uzz//rNzcXI0ePVq5ubnavHmzduzYYYoDADRtNOgAN3I4HEpNTdWQIUOUnZ2tmJgYBQcHKyYmRtnZ2RoyZIjS0tI43RUAAMDLTZ06VZKUkZGhsrIy01xZWZlmzpxpigMANG3cxRVwI7vdrsLCQi1YsEC+vr6mRpyvr6/S09N11VVXyW63q1+/ftYlCgAAAEsFBwcrMTFRCxcuVEhIiGs8IyPD9XViYqKCg4OtSA8A0MA4gg5wo6KiIklSVFRUjfOV45VxAAAA8F733HPPGc0DAJoOGnSAG4WHh0uSNmzYUON85XhlHAAAALxT5aVRhg4dqiNHjujhhx9Wr1699PDDD+vIkSMaOnQol0YBAC9Cgw5wo9jYWEVERGjSpElyOp2mOafTqcmTJysyMlKxsbEWZQgAAABPUHlplLFjx6pZs2aaNWuWxo8fr1mzZqlZs2ZKT09XQUGB7Ha71akCABoADTrAjWw2m6ZPn66cnBwlJSUpPz9fJSUlys/PV1JSknJycjRt2jTZbDarUwUAAICFuDQKAKAqbhIBuFlycrIyMzOVmpqquLg413hkZKQyMzOVnJxsYXYAAADwBFUvjXLFFVdUm+fSKADgXTiCDjgLkpOT9fPPPys3N1ejR49Wbm6uNm/eTHMOAAAAkrg0CgDAjCPogLPEZrMpPj5eR48eVXx8PKe1AgAAwKXy0igpKSlKTExUQkKCNm/erG3btik3N1cff/yxMjMzWUMCgJegQQcAAAAAFkhOTlZaWppmzJihnJwc17ifn5/S0tI4+wIAvAgNOgAAAACwQFZWlqZNm6YbbrjBdQTdBRdcoNzcXE2bNk1XXHEFTToA8BI06AAAAACggTkcDqWmpmrIkCHKzs6Ww+HQokWLNHjwYD3++ONKSkpSWlqaEhMTOc0VALwAN4kAAAAAgAZmt9tVWFiosWPHytfX/GeZr6+v0tPTVVBQILvdblGGAICGRIMOAAAAABpYUVGRJCkqKqrG+crxyjgAQNNGgw4AAAAAGlh4eLgkacOGDTXOV45XxgEAmjYadAAAAADQwGJjYxUREaFJkybJ6XSa5pxOpyZPnqzIyEjFxsZalCEAoCHRoAMAAACABmaz2TR9+nTl5OQoKSlJ+fn5KikpUX5+vpKSkpSTk6Np06ZxgwgA8BLcxRUAAAAALJCcnKzMzEylpqYqLi7ONR4ZGanMzEwlJydbmB0AoCHRoAMAAAAAiyQnJysxMVHLli3T4sWLNWjQIPXv358j5wDAy9CgAwAAAAAL2Ww2xcfH6+jRo4qPj6c5BwBeiGvQAQAAAAAAABaiQQcAAAAAAABYiAYdAAAAAFjI4XAoLy9PK1asUF5enhwOh9UpAQAaGA06AAAAALBIVlaWunXrpoSEBGVkZCghIUHdunVTVlaW1akBABoQDToAAAAAsEBWVpZSUlK0Z88e0/iePXuUkpJCkw4AvAgNOgAAAABoYA6HQ4888ogMw9C1114ru92uBQsWyG6369prr5VhGHrkkUc43RUAvAQNOgAAAABoYMuXL9fevXt19dVXa+HChYqJiVFwcLBiYmK0cOFC/fGPf9TevXu1fPlyq1MFADQAGnQAAAAA0MAqG28TJkyQr6/5zzJfX1+NHz/eFAcAaNpo0AEAAAAAAAAWokEHAAAAAA2sX79+kqRx48apuLhYN998s5588kndfPPNKi4u1oQJE0xxAICmzc/qBAAAAADA2/Tr10/t2rXTypUr1bJlS9f4tm3bXI/DwsJo0AGAl+AIOgAAAABoYDabTS1atDhpTGhoqGw2WwNlBACwEg06AAAAAGhgR44c0ZYtW+Tj46Nzzz3XNHfeeefJx8dHW7Zs0ZEjRyzKEADQkGjQAQAAAEADu/vuuyVJd911l7Zt26bc3FyNHj1aubm5Kiws1LBhw0xxAICmjQYdAAAAADSwLVu2SJLS0tJks9kUHx+vuLg4xcfHy2azafTo0aY4AEDTRoMOAAAAABpY165dJUnTpk2rcT4jI8MUBwBo2mjQAQAAAEADmzdvniTpX//6l44fP26aO378uObPn2+KAwA0bTToAAAAAKCBNW/eXH379pVhGAoJCdHAgQP1/vvva+DAgQoJCZFhGOrbt6+aN29udaoAgAbgZ3UCAAAAAOCN1qxZo27dumnLli1aunSpaa5r165as2aNRZkBABoaR9ABAAAAgAWysrK0detWDRw4UFFRUTrnnHMUFRWlgQMHauvWrcrKyrI6RQBAA+EIOgAAAABoYA6HQ6mpqerTp482btyowsJCSdJvv/2mI0eOqE+fPkpLS1NiYqJsNpu1yQIAzjoadAAAAADQwOx2uwoLC7Vt2zYFBQWZ5vbs2aNt27bJMAzZ7Xb169fPmiQBAA2GU1wBAAAAoIHt2rVLkmQYhq699lrZ7XYtWLBAdrtd1157rQzDMMUBAJo2GnQAAAAA0MB++eUXSVLPnj21cOFCxcTEKDg4WDExMVq4cKGio6NNcQCApo0GHQAAAAA0sN9++02S1KxZsxrnK8cr4wAATRsNOgAAAABoYL6+v/8p9sUXXygpKUn5+fkqKSlRfn6+63HVOABA08ZPewAAAABoYJU3frj44ov13XffKS4uTnfccYfi4uK0fv16de/e3RQHAGjauIsrAAAAADSwfv36qV27dvrxxx81ePBg9e7dW1u2bFHXrl1VVlamRYsWKSwsjAYdAHiJeh9BV1paqmeeeUYdO3Z0XcQ0Nze33htOSEiQj4+PHn/88Xo/FwAAAAAaM5vNptmzZ0uSFi1apOzsbK1fv17Z2dlatGiRJOnVV1+VzWazMk0AQAOpd4Pu3nvvVUZGhu688069+OKLstlsGjx4sFauXFnn18jKytIXX3xR300DjUpJSYlGjhyp8ePHa+TIkSopKbE6JQAAAHiQyuvMne48AKDpqFeDbs2aNXr33Xc1efJkTZ06VQ8++KCWLl2qzp07a8yYMXV6jePHjys1NVXPPPPMaSUMNAZJSUkKCQnR7NmztW7dOs2ePVshISFKSkqyOjUAAAB4gLKyMk2fPl2SFBQUZJqrfDx9+nSVlZU1eG4AgIZXrwZdZmambDabHnzwQddYUFCQRowYoS+++EI7duw45WtMmTJFTqdTaWlp9c8WaASSkpK0cOFCBQQEaMyYMXr11Vc1ZswYBQQEaOHChTTpAAAAoJdeeklOp1OSdM011+iJJ57QgAED9MQTT+iaa66RJDmdTr300ktWpgkAaCD1uknE2rVrdeGFF6pFixam8csvv1yStG7dOnXq1KnW52/fvl3/7//9P7355psKDg4+jXQBz1ZSUuJqzh0+fFg+Pj5atGiRRowYoeeff16hoaFauHChSkpK+B4AAADwYna7XZLUvn17LVmyRBUVFZKkJUuWyM/PT+3bt9eePXtkt9uVmppqZaoAgAZQrwZdUVGRwsPDq41Xju3evfukz09NTVXv3r11++2312ezKi0tVWlpqetxcXGxJKm8vFzl5eX1ei13qty2lTl4EuohjR49WpI0atQo+fj4mGri7++vkSNHatq0aRo9erRmzZplZaqWYB8xox5m1MOMeph5Uj08IQecHGvHxsHb63HkyBFJ0p49exQWFqZx48YpJCREx44d04QJE7Rnzx5XnDfWyNv3jxNRj+qoiRn1MPOUetRn+/Vq0JWUlCgwMLDaeOU1Ek52Efxly5bpgw8+0OrVq+uzSUnS5MmTNWHChGrjS5YsUUhISL1fz91O5y62TZk316PyQr6RkZGuu29J/6tJZGSkK67qvLfx5n2kJtTDjHqYUQ8zT6jHsWPHrE4Bp8DasXHx1npUPStp5syZ2rJli7Zs2aLWrVtr5syZGjZsmCuOdSMqUY/qqIkZ9TCzuh71WTfWq0EXHBxs+jSy0vHjx13zNamoqNDIkSN19913q2/fvvXZpCQpPT3ddWSS9PunoJ06ddKAAQOqnW7bkMrLy5Wbm6uEhAT5+/tbloenoB7SJ598onXr1qmgoEAjRoyoVpOxY8dKkq644goNHjzY4mwbHvuIGfUwox5m1MPMk+pReTQWPBdrx8bB2+uxePFi19d33XWX63p0kuTr+79LhYeFhbFu9ML940TUozpqYkY9zDylHvVZN9arQRceHq5du3ZVGy8qKpIkdezYscbnvf322/rpp5/0j3/8Q4WFhaa5w4cPq7CwUGFhYbV+ohkYGFjjkXv+/v4eseN5Sh6ewpvrkZGRodmzZ2vmzJl6/vnnXXXw9/eXYRiu01ozMjK8tkaSd+8jNaEeZtTDjHqYeUI9rN4+To21Y+PirfWw2Wyur6s25058bLPZvLI+lbx1/6gN9aiOmphRDzOr61GfbdfrLq69evXSpk2bqnUAK09b7dWrV43P2759u8rLy/XHP/5RkZGRrv+k35t3kZGRWrJkSX1SATxScHCwEhMTVVZWptDQUI0dO1a7du3S2LFjFRoaqrKyMiUmJnKDCAAAAC/XtWtXt8YBABq3eh1Bl5KSomnTpum1115TWlqapN8vwjtnzhzFxMS47uC6fft2HTt2TN27d5ck3X777TU272666SYNHjxYf/rTnxQTE3OGbwXwDNnZ2UpKStLChQs1bdo001xiYqKys7OtSQwAAAAeo1u3bm6NAwA0bvVq0MXExOiWW25Renq69u7dq27dumnu3LkqLCzUG2+84Yq75557lJeXJ8MwJEndu3d3NetOFBkZqaSkpNN/B4AHys7OVklJiUaPHq38/HxdccUVysjI4Mg5AAAASJLpRiYBAQG66aabFBwcrJKSEn344YcqKytzxd14441WpQkAaCD1atBJv5+S+txzz2nevHk6cOCAevbsqZycHMXFxZ2N/IBGKzg4WLNmzdKiRYs0ePBgrgMAAAAAl927d0uSWrVqpSNHjui9995zzfn5+alVq1Y6ePCgKw4A0LTVu0EXFBSkqVOnaurUqbXGLF++vE6vVXmEHQAAAAB4k9DQUP3yyy/y8/PTwYMHlZaW5jrzYtq0aYqIiHDFAQCavno36AAAAAAAZyYxMVHTpk3Tvn371K5dO5WUlEiS1q1bp7lz57oeJyYmWpkmAKCB1OsurgAAAACAMzdw4EDX15XNuJoeV40DADRdNOgAAAAAoIHFxsbK1/fkf475+voqNja2gTICAFiJBh1wlpSVlWnWrFl67bXXNGvWLNeduAAAAIBVq1bJ6XRK+v0urlUFBgZKkpxOp1atWtXguQEAGh4NOuAsGDNmjJo1a6a0tDQtWrRIaWlpatasmcaMGWN1agAAAPAARUVFkqR//etfCg8PN82Fh4frX//6lykOANC0cZMIwM3GjBmjqVOnqn379powYYICAwNVWlqqcePGue5+PGXKFIuzBAAAgJUqm3I7duxQeXm5aa6srEzbt283xQEAmjaOoAPcqKysTDNmzFD79u21c+dO3X///WrdurXuv/9+7dy5U+3bt9eMGTM43RUAAMDLxcbGKiwsTOnp6dq9e7dpbvfu3Ro7dqzCwsK4Bh0AeAkadIAbvfLKK6qoqNDEiRPl52c+QNXPz09//etfVVFRoVdeecWiDAEAAOAp9u3b5/o6JiZGEyZMUExMTI3zAICmjVNcATfasmWLJGnIkCE1zleOV8YBAADAO+Xk5LhuEtG5c2etXr1aq1evliRFRESosLBQTqdTOTk5SkxMtDJVAEAD4Ag6wI26du0q6fcFV00qxyvjAAAA4J2efvppSdJ1112nLVu2KDc3V6NHj1Zubq5+/vlnXXPNNaY4AEDTRoMOcKNHH31Ufn5++vOf/6yKigrTXEVFhf7yl7/Iz89Pjz76qEUZAgAAwBMcPHhQknTrrbfKZrMpPj5ecXFxio+Pl81m080332yKAwA0bTToADcKCAjQU089pT179ujcc8/VmDFjtGjRIo0ZM0bnnnuu9uzZo6eeekoBAQFWpwoAAAALdevWTZL07LPP6vjx45o1a5Zee+01zZo1S8ePH9e4ceNMcQCApo1r0AFuNmXKFG3atEkLFy7UzJkzTXOJiYmaMmWKNYkBAADAYyxevFitWrXSr7/+quDgYNf4okWLlJaWZooDADR9NOgAN8vKytJ//vMf3XDDDerSpYt++uknXXTRRdq6dav+85//KCsrS8nJyVanCQAAAAu1bNlSLVu21KFDh04ZAwBo+jjFFXAjh8Oh1NRUDRkyRB9++KFuvPFGRUVF6cYbb9SHH36oIUOGKC0tTQ6Hw+pUAQAAYKGysjIdPXpUPj4+Nc77+Pjo6NGjKisra+DMAABWoEEHuJHdbldhYaGuuuoqXXjhhUpISFBGRoYSEhJ04YUX6sorr1RBQYHsdrvVqQIAAMBCr7zyiioqKmQYhq6//npFRESoWbNmioiI0PXXXy/DMFRRUaFXXnnF6lQBAA2AU1wBNyoqKpIkpaenm64lIkl79uzR2LFjTXEAAADwTps3b5Yk9ezZUz/99JMKCwslSUePHnWNf/fdd644AEDTxhF0gBuFhYW5vr722mtlt9u1YMEC2e12XXvttTXGAQAAwPtUntr63XffqUePHnriiSc0YMAAPfHEE+rRo4e+++47UxwAoGnjCDrAjSqvLXfOOefoww8/lGEY2r9/v2JiYvThhx8qLCxMBw4c4Bp0AAAAXq5v376SJF9fX33yySeu9eGSJUtks9nk6+srp9PpigMANG0cQQe4UeW15Q4cOKDk5GTl5+erpKRE+fn5Sk5O1sGDB01xAAAA8E4HDhyQJDmdTjmdTt15552aPn267rzzTtdY1TgAQNPGEXTAWTBu3Di99dZbiouLc41FRkbqueee01//+lcLMwMAAIAnaN26taTfj6Dz8fHRO++8o3feeUeSZLPZZBiGnE6nKw4A0LRxBB3gRv369ZMk/fe//9WmTZuUm5ur0aNHKzc3Vz/99JOWLl1qigMAAIB3+vLLLyX9fgTd9ddfr6SkJEVHRyspKUnXX3+96wi6yjgAQNPGEXSAG/Xr10/t2rXTypUrddNNN2nAgAEqKyvTDz/8oJkzZ2rlypUKCwujQQcAAODlDMOQJHXu3FmffPKJqyG3fv16+fr6qnPnztq2bZsrDgDQtNGgA9zIZrNp9uzZuvnmm7Vo0SJ9/PHHrrnKO3C9+uqrstlsVqUIAAAAD3DBBRdIkrZt2yZf3+onNm3bts0UBwBo2jjFFTgLfHx8FBgYaBoLDAx0NekAAADg3R566CHX1wEBAaa5qo+rxgEAmi4adIAbORwOpaamqk+fPurQoYNprkOHDurTp4/S0tLkcDgsyhAAAACeYNWqVa6vy8rKTHNVH1eNAwA0XTToADey2+0qLCzU119/rejoaNntdi1YsEB2u13R0dH6+uuvVVBQILvdbnWqAAAAsNDy5ctdX1def66mx1XjAABNFw06wI127dolSRo4cKA++OADHT9+XF9++aWOHz+uDz74QAMHDjTFAQAAwDtVbcINHjxYN910k6Kjo3XTTTdp8ODBNcYBAJoubhIBuNGvv/4qSYqIiNCFF16owsJCSVJGRoYiIiJcDbrKOAAAAHinVq1aSZKCg4P1/fffu24KsX79enXu3FnBwcEqKSlxxQEAmjYadIAbtWvXTtLvd2odMmSI5s2bp507d+q8887TCy+8oNmzZ5viAAAA4J0OHjwoSSopKVFJSYmeeuopHT16VM2aNdM777yjkpISUxwAoGmjQQe40Yk3hjAMw/T/2uIAAADgvfbu3asZM2ZYnQYAwEI06ICz4OKLL9b69esVFxfnGouIiFD37t21ceNGCzMDAACAJzjnnHNcX1eezlrT46pxAICmiwYd4EZ79+6VJG3cuFE33HCDRo8erc2bN+uCCy5Qbm6uPv74Y1McAAAAvFNYWJjr66rNuRMfV40DADRdNOgANwoPD5ckTZo0Sf/4xz+Uk5PjmouMjNTf/vY3jR071hUHAAAA77R//363xgEAGjdfqxMAmpLY2FhFRERo1apV2rRpk3JzczV69Gjl5ubqp59+0hdffKHIyEjFxsZanSoAAAAs1Lp1a7fGAQAaNxp0gBvZbDZNnz5dOTk5uvnmmxUYGKi+ffsqMDBQN998s3JycjRt2jTZbDarUwUAAICFVq9e7fq6TZs26tmzp84991z17NlTbdq0qTEOANB0cYor4GbJycnKzMxUamqq6SYRkZGRyszMVHJysoXZAQAAwBPs3LlTkuTr66v9+/e7TmXdtWuXa9zpdLriAABNGw064CxITk5WYmKili1bpsWLF2vQoEHq378/R84BAABAknT8+HFJktPprHG+crwyDgDQtHGKK3CW2Gw2xcfHKy4uTvHx8TTnAAAA4HLppZe6NQ4A0LjRoAMAAACABrZ+/XrX1z4+Pqa5qo+rxgEAmi4adAAAAADQwL744gvX14ZhmOaqPq4aBwBoumjQAQAAAEADq6iocGscAKBxo0EHAAAAAA2sc+fObo0DADRuNOgAAAAAoIE98cQTpsedO3fW0KFDqzXkTowDADRNNOgAAAAAoIF9/PHHpsfbtm3TRx99pG3btp00DgDQNNGgAwAAAIAGtnr1arfGAQAaNz+rEwCaqrKyMr300ktaunSpfv75Zz3xxBMKCAiwOi0AAAB4AKfT6dY4AEDjxhF0wFkwZswYhYSEKC0tTYsWLVJaWppCQkI0ZswYq1MDAACAB7j88svdGgcAaNxo0AFuNmbMGE2dOlWGYZjGDcPQ1KlTadIBAABAcXFxbo0DADRuNOgANyorK9P06dMlSYMHD5bdbteCBQtkt9s1ePBgSdL06dNVVlZmZZoAAACwWH5+vlvjAACNGw06wI1efvllOZ1OXXrppXr//fe1evVqzZs3T6tXr9b777+vnj17yul06uWXX7Y6VQAAAFho586dbo0DADRuNOgAN7Lb7ZKkLl26KDQ01HQNutDQUHXt2tUUBwAAAO8UGBjo1jgAQONGgw5wo9DQUEnShx9+WO2OW06nUx9++KEpDgAAAN4pKCjIrXEAgMaNBh3gRnfccYfr64EDB5quQTdw4MAa4wAAAOB9Dhw44NY4AEDjRoMOcKMNGza4vv7yyy+1fv16lZSUaP369fryyy9rjAMAAID3+fXXX90aBwBo3PysTgBoSlatWuX6et++fXr00Uddj318fGqMAwAAgPfx86vbn2J1jQMANG4cQQe4UfPmzSVJI0aM0Pnnn2+a69y5s+6//35THAAAALzTOeec49Y4AEDjRoMOcKO7775bkvTee+/JMAzTnNPp1L///W9THAAAALzTeeed59Y4AEDjxvHSgBtde+21Cg4O1pEjR1RWVqbbbrtNwcHBKikp0YcffqiysjIFBwfr2muvtTpVAAAAWCgsLMytcQCAxo0GHeBmoaGhKikpUVlZmd57770a5wEAAODdvvrqK7fGAQAaN05xBdzIbrdr7969kqSAgADTXGBgoCRp7969stvtDZ4bAAAAPMeOHTvcGgcAaNxo0AFutGvXLklS79691bFjR9NceHi4evfubYoDAACAd+IurgCAqmjQAW7066+/SpLWrVun6Oho2e12LViwQHa7XdHR0Vq3bp0pDgAAAN6Ja9ABAKri4xjAjdq0aSNJateunbKysmQYhvbv36+YmBhlZWXp3HPP1d69e11xAAAA8E6HDx92axwAoHGjQQe40f79+yX9fp25m266SQkJCdq8ebO2bdum3Nxc1/XpKuMAAADgncrLy90aBwBo3GjQAW7Url07SVJkZKQWL16snJwc15yfn58iIyNVUFDgigMAAIB38vHxcWscAKBxo0EHuNG5554rSSooKFBYWJjuvPNOHT16VM2aNdM777yjgoICUxwAAAC8U1hYWJ1uHMY16ADAO9T7JhGlpaV65pln1LFjRwUHBysmJka5ubmnfF5WVpZuu+02denSRSEhIbrooouUmpqqgwcPnk7egEe66qqr5Ofnp5YtWyo4OFgzZszQa6+9phkzZigkJEQtW7aUn5+frrrqKqtTBQAAgIXCw8PdGgcAaNzq3aC79957lZGRoTvvvFMvvviibDabBg8erJUrV570eQ8++KB+/PFH3XXXXZo1a5YGDhyol19+WVdeeaVKSkpO+w0AnmTVqlWqqKhQcXGxLr74YkVFRemcc85RVFSUunfvruLiYlVUVGjVqlVWpwoAAAAL1fVvIP5WAgDvUK9TXNesWaN3331XU6dOVVpamiTpnnvuUVRUlMaMGXPSpkNmZqb69etnGuvTp4+GDx+ud955Rw888ED9swc8TFFRkSQpIiJCn3zyiWv8t99+04YNG1zXoKuMAwAAgHcqLi52axwAoHGr1xF0mZmZstlsevDBB11jQUFBGjFihL744gvt2LGj1uee2JyTpJtuukmS9OOPP9YnDcBjVZ6CUHmtucsuu0xXX321LrvsMtM4pyoAAAB4t44dO7o1DgDQuNWrQbd27VpdeOGFatGihWn88ssvlyStW7euXhv/5ZdfJElt27at1/MAT9W7d2/X1+eff76++eYbrVy5Ut98843OP//8GuMAAADgfYYOHerWOABA41avU1yLiopqPPKncmz37t312vgLL7wgm82mlJSUk8aVlpaqtLTU9bjyMO/y8nKVl5fXa5vuVLltK3PwJNRDuvvuu11fHz9+XCNHjtTx48cVFBSkd9991xT3wQcfWJGipdhHzKiHGfUwox5mnlQPT8gBJ8fasXHw9npkZ2fXOe7ee+89q7l4Im/fP05EPaqjJmbUw8xT6lGf7fsYhmHUNbhr16666KKLtGjRItP41q1b1bVrV82YMUOjRo2q02vNnz9fd955p8aMGaMXXnjhpLHjx4/XhAkTanyNkJCQuqYPnHUjR47U9u3b1aZNG+3fv7/a/DnnnKPffvtN559/vmbNmmVBhgCAM3Xs2DENGzZMhw4dqnZWATwDa0c0BsOHD9ehQ4dOGdeyZUvNnTu3ATICALhbfdaN9WrQRUVFqX379vrss89M4z/88IN69Oih2bNn66GHHjrl69jtdg0YMEDx8fHKycmRn9/JD+Sr6VPQTp06ad++fZYujMvLy5Wbm6uEhAT5+/tbloenoB5SfHy8vvjiC0lScHCw6a5bVR9feeWVysvLsyRHK7GPmFEPM+phRj3MPKkexcXFatu2LQ06D8basXHw9noEBQXJ6XSeMs7X11fHjx9vgIw8i7fvHyeiHtVREzPqYeYp9ajPurFep7iGh4dr165d1cYr70hZlwuYfvvtt7rxxhsVFRWlzMzMUzbnJCkwMFCBgYHVxv39/T1ix/OUPDyFN9ej8oYpktS/f3+lp6dr586dOu+88zR58mTX0acjRozw2hpJ3r2P1IR6mFEPM+ph5gn1sHr7ODXWjo2Lt9ajLs25yjhvrE8lb90/akM9qqMmZtTDzOp61Gfb9WrQ9erVS8uWLVNxcbGp87d69WrX/Mls2bJFAwcOVFhYmBYtWqTmzZvXZ/OAx6t6msLixYvVqlUr9enTRx999JEWL15cYxwAAADQsmVLGYYhHx8f1ooA4IXqdRfXlJQUORwOvfbaa66x0tJSzZkzRzExMerUqZMkafv27dq4caPpub/88osGDBggX19fffrpp2rXrp0b0gc8S+V155o1aybDMDR//nylpqZq/vz5MgxDzZo1M8UBAAAA0u8f4BYXF9OcAwAvVa8j6GJiYnTLLbcoPT1de/fuVbdu3TR37lwVFhbqjTfecMXdc889ysvLU9XL2w0cOFBbt27VmDFjtHLlSq1cudI11759eyUkJLjh7QDW8vX9ved99OhRBQYGmq5/ExgYqKNHj5riAAAAAAAA6tWgk6S3335bzz33nObNm6cDBw6oZ8+eysnJUVxc3Emf9+2330qSpkyZUm0uPj6eBh2ahH79+mnixImSqjfhbDabKQ4AAADeq0OHDvrll1/qFAcAaPrq3aALCgrS1KlTNXXq1Fpjli9fXm2sHjeLBRqt2NhY+fr6yul0qn///rr++uu1efNmXXDBBfr000+1aNEi+fr6KjY21upUAQAAYKGWLVvWqUHXsmXLBsgGAGC1ejfoANRu1apVcjqd8vHx0bJly1x3bZWkkJAQ+fj4yOl0atWqVRxFBwAA4MV+++03t8YBABo3LoQFuFFRUZEkad68eQoLCzPNhYWFad68eaY4AAAAeKfDhw+7NQ4A0LjRoAPcKDw8XJK0Y8cO+fj4VJvfvn27KQ4AAADeyeFwuDUOANC4cYor4EaxsbFq166d0tPTdcMNN+ipp55yXYNuyZIlGjt2rMLCwrgGHQAAgJerqKhwaxwAoHGjQQe4WeWRc5999pk+/vhj13hQUJBVKQEAAMDD+Pr61unoOF9fTnoCAG/AT3vAjex2u/bu3StJ1U5xrVxc7d27V3a7vcFzAwAAgOcIDAx0axwAoHGjQQe40a5duyRJgwYN0qFDh5Sbm6vRo0crNzdXBw8e1KBBg0xxAAAA8E7l5eVujQMANG406AA3+vXXXyVJycnJMgxD3377rTZu3Khvv/1WhmEoKSnJFAcAAADvRIMOAFAV16AD3Khdu3aSpEmTJunhhx92XVdk0aJFeuaZZ3T++eeb4gAAAAAAAGjQAW507rnnSpIKCgqqzTkcDtd4ZRwAAAAAAACnuAJuFBMT4/q6tptEnBgHAAAA78NNIgAAVXEEHeBGL7/8suvrgQMHqmvXrtq0aZMuvPBCbdmyRYsXL3bFPf3001alCQAAAIuVlpa6NQ4A0LjRoAPcaOHChZKk+Ph4LVmyxHUNuiVLlsjPz09xcXFasWKFFi5cSIMOAAAAAABI4hRX4KzIy8tTQECAaczf318rVqywKCMAAAB4EpvN5tY4AEDjxhF0gBsNHTpUn3/+uSQpLi5OISEh+vnnn9WtWzcdO3ZMn376qSsOAAAA3svf3991tsWp4gAATR8NOsCNevXq5fq6shknSevXr681DgAAAN6Ha9ABAKriFFfAjVauXOnWOAAAAAAA0PTRoAPcqKyszK1xAAAAaJq4Bh0AoCoadIAbff/9926NAwAAQNPk51e3qw3VNQ4A0LjRoAPcaNOmTW6NAwAAQNNUXl7u1jgAQONGgw5wo6oLKF9f87dX1ccstAAAALxbXe7gWp84AEDjRoMOcKMWLVq4vnY6naa5qo+rxgEAAAAAAO/GBQ0AN2revLnp8UUXXaRWrVrp4MGD+umnn2qNAwAAAAAA3osGHeBGnTt31qpVq1yPqzblTowDAAAAAACQOMUVcKuWLVu6NQ4AAAAAADR9NOgAAAAAAAAAC9GgA9yooqLCrXEAAABomoKCgtwaBwBo3GjQAW70zTffuL728fExzVV9XDUOAAAA3uf48eNujQMANG406AA3OnDggOtrwzBMc1UfV40DAAAAAADejQYd4EYdOnRwaxwAAAAAAGj6aNABbnTllVeaHrdu3VoRERFq3br1SeMAAAAAAID38rM6AaAp+frrr02PDxw4UOPprCf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Area (sq km)Pricepp_sq_mest_pop
Area (sq km)1.0000000.1699820.4098600.945912
Price0.1699821.0000000.1174330.335731
pp_sq_m0.4098600.1174331.0000000.618912
est_pop0.9459120.3357310.6189121.000000
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" + ], + "text/plain": [ + " Area (sq km) Price pp_sq_m est_pop\n", + "Area (sq km) 1.000000 0.169982 0.409860 0.945912\n", + "Price 0.169982 1.000000 0.117433 0.335731\n", + "pp_sq_m 0.409860 0.117433 1.000000 0.618912\n", + "est_pop 0.945912 0.335731 0.618912 1.000000" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_num.corr()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "568c670a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:15.429628Z", + "iopub.status.busy": "2025-02-16T13:29:15.428850Z", + "iopub.status.idle": "2025-02-16T13:29:39.447836Z", + "shell.execute_reply": "2025-02-16T13:29:39.446262Z" + }, + "papermill": { + "duration": 24.047255, + "end_time": "2025-02-16T13:29:39.453531", + "exception": false, + "start_time": "2025-02-16T13:29:15.406276", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Area in (sq km) (log 10)')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(np.log10(data_num.est_pop), np.log10(data_num['Area (sq km)']))\n", + "plt.xlabel('estimated population (log10)')\n", + "plt.ylabel('Area in (sq km) (log 10)')" + ] + }, + { + "cell_type": "markdown", + "id": "2773476a", + "metadata": { + "papermill": { + "duration": 0.017758, + "end_time": "2025-02-16T13:29:39.492841", + "exception": false, + "start_time": "2025-02-16T13:29:39.475083", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Methods\n", + "\n", + "We have applied an analytical methodology based on linear regression. We used the estimated popuplation, area in squared km, and people per square km. We remove any unknown values. \n", + "\n", + "We explored linear, polynomial, and cluster relationship between the estimated population and the areas in square meter. We mostly used a linear regression, a polynomial regression, and K-means to analyse possible clusters.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "91cb1d21", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:39.531522Z", + "iopub.status.busy": "2025-02-16T13:29:39.530415Z", + "iopub.status.idle": "2025-02-16T13:29:39.537863Z", + "shell.execute_reply": "2025-02-16T13:29:39.536632Z" + }, + "papermill": { + "duration": 0.029383, + "end_time": "2025-02-16T13:29:39.540013", + "exception": false, + "start_time": "2025-02-16T13:29:39.510630", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def extract(geography : str)-> pd.DataFrame:\n", + " rows = data.Geography.str.contains(geography)\n", + " cols = ['Area (sq km)', 'est_pop', 'pp_sq_m']\n", + " geo = data.loc[rows, cols]\n", + " geo = geo.dropna()\n", + " geo = geo.drop_duplicates()\n", + " return geo" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "cfa107e8", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:39.577722Z", + "iopub.status.busy": "2025-02-16T13:29:39.577296Z", + "iopub.status.idle": "2025-02-16T13:29:39.582481Z", + "shell.execute_reply": "2025-02-16T13:29:39.581474Z" + }, + "papermill": { + "duration": 0.026602, + "end_time": "2025-02-16T13:29:39.584666", + "exception": false, + "start_time": "2025-02-16T13:29:39.558064", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def print_summary(df : pd.DataFrame) -> None:\n", + " print(df.shape) \n", + " print(df.dtypes)\n", + " print(df.describe())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "13e60de0", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:39.622916Z", + "iopub.status.busy": "2025-02-16T13:29:39.622541Z", + "iopub.status.idle": "2025-02-16T13:29:39.628626Z", + "shell.execute_reply": "2025-02-16T13:29:39.627567Z" + }, + "papermill": { + "duration": 0.027706, + "end_time": "2025-02-16T13:29:39.630594", + "exception": false, + "start_time": "2025-02-16T13:29:39.602888", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def analyse_linear(df : pd.DataFrame) -> None:\n", + " x = df[['Area (sq km)']] #numpy.linalg.solve(f.A, b)\n", + " y = df[['est_pop']]\n", + " x = sm.add_constant(x)\n", + " model = sm.OLS(y, x).fit()\n", + " print(model.summary())\n", + " print(\"---- params / coeficient -------\")\n", + " print(model.params)\n", + " print('------------p values----------')\n", + " print(model.pvalues)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "d9241b52", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:39.669101Z", + "iopub.status.busy": "2025-02-16T13:29:39.668723Z", + "iopub.status.idle": "2025-02-16T13:29:39.674014Z", + "shell.execute_reply": "2025-02-16T13:29:39.672929Z" + }, + "papermill": { + "duration": 0.027634, + "end_time": "2025-02-16T13:29:39.676177", + "exception": false, + "start_time": "2025-02-16T13:29:39.648543", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def visualise(df : pd.DataFrame)-> None:\n", + " plt.scatter(df['Area (sq km)'], df.est_pop)\n", + " plt.xlabel(\"Area (sq kn)\")\n", + " plt.ylabel(\"Estimated population\")\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "77dce3fe", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:39.715165Z", + "iopub.status.busy": "2025-02-16T13:29:39.714412Z", + "iopub.status.idle": "2025-02-16T13:29:39.720066Z", + "shell.execute_reply": "2025-02-16T13:29:39.718914Z" + }, + "papermill": { + "duration": 0.028122, + "end_time": "2025-02-16T13:29:39.722540", + "exception": false, + "start_time": "2025-02-16T13:29:39.694418", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def complete_analysis(geography : str) -> None:\n", + " df = extract(geography)\n", + " print_summary(df)\n", + " visualise(df)\n", + " analyse_linear(df)" + ] + }, + { + "cell_type": "markdown", + "id": "1bda7e50", + "metadata": { + "papermill": { + "duration": 0.017562, + "end_time": "2025-02-16T13:29:39.758306", + "exception": false, + "start_time": "2025-02-16T13:29:39.740744", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Analysis and interpretation" + ] + }, + { + "cell_type": "markdown", + "id": "289a5feb", + "metadata": { + "papermill": { + "duration": 0.017422, + "end_time": "2025-02-16T13:29:39.793752", + "exception": false, + "start_time": "2025-02-16T13:29:39.776330", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### London Borough" + ] + }, + { + "cell_type": "markdown", + "id": "b08834ee", + "metadata": { + "papermill": { + "duration": 0.017523, + "end_time": "2025-02-16T13:29:39.829101", + "exception": false, + "start_time": "2025-02-16T13:29:39.811578", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The null hypothesis $H_0$ suggests there is no correlation between the area and the estimated population. The alternative hypothesis $H_1$ suggests the contrary. The p-value appears to be in range $ 0.01 \\leq 0.02 \\leq 0.05$. It appears some moderate evidence to reject the null hypothesis in favour of the alternative hypothesis. It is, therefore inconclusive a linear relationship exists between an area and an estimated population in London Borough.\n", + "\n", + "\n", + "Other types of geography has no clear pattern in their data. Some strong collinearity suggests the area and estimated population are dependent variables. Therefore, no regression analysis was successfully completed." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "522d94b1", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:39.867919Z", + "iopub.status.busy": "2025-02-16T13:29:39.867551Z", + "iopub.status.idle": "2025-02-16T13:29:42.377428Z", + "shell.execute_reply": "2025-02-16T13:29:42.376219Z" + }, + "papermill": { + "duration": 2.532556, + "end_time": "2025-02-16T13:29:42.379648", + "exception": false, + "start_time": "2025-02-16T13:29:39.847092", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameArea (sq km)est_pop
3930985BARNET86.7667319481.0
3942717BEXLEY60.5784218757.0
3946217BROMLEY150.1326296218.0
3967398CROYDON86.4887335112.0
3992967ENFIELD80.8211277266.0
4018729HARROW50.4645210044.0
4041706HOUNSLOW55.9628215976.0
4052419KINGSTON UPON THAMES37.2593149045.0
4064371SUTTON43.8477181461.0
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" + ], + "text/plain": [ + " Name Area (sq km) est_pop\n", + "3930985 BARNET 86.7667 319481.0\n", + "3942717 BEXLEY 60.5784 218757.0\n", + "3946217 BROMLEY 150.1326 296218.0\n", + "3967398 CROYDON 86.4887 335112.0\n", + "3992967 ENFIELD 80.8211 277266.0\n", + "4018729 HARROW 50.4645 210044.0\n", + "4041706 HOUNSLOW 55.9628 215976.0\n", + "4052419 KINGSTON UPON THAMES 37.2593 149045.0\n", + "4064371 SUTTON 43.8477 181461.0" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = data.Geography.str.contains('London Borough')\n", + "cols = ['Name', 'Area (sq km)', 'est_pop']\n", + "data.loc[rows, cols].drop_duplicates().dropna()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "36d98203", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:42.420063Z", + "iopub.status.busy": "2025-02-16T13:29:42.419309Z", + "iopub.status.idle": "2025-02-16T13:29:45.101289Z", + "shell.execute_reply": "2025-02-16T13:29:45.100078Z" + }, + "papermill": { + "duration": 2.703926, + "end_time": "2025-02-16T13:29:45.103642", + "exception": false, + "start_time": "2025-02-16T13:29:42.399716", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(9, 3)\n", + "Area (sq km) float64\n", + "est_pop float64\n", + "pp_sq_m float64\n", + "dtype: object\n", + " Area (sq km) est_pop pp_sq_m\n", + "count 9.00000 9.000000 9.000000\n", + "mean 72.48020 244817.777778 3636.848486\n", + "std 34.38624 64545.766174 668.873753\n", + "min 37.25930 149045.000000 1973.042497\n", + "25% 50.46450 210044.000000 3611.138624\n", + "50% 60.57840 218757.000000 3859.277949\n", + "75% 86.48870 296218.000000 4000.209344\n", + "max 150.13260 335112.000000 4162.213041\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: est_pop R-squared: 0.564\n", + "Model: OLS Adj. R-squared: 0.502\n", + "Method: Least Squares F-statistic: 9.050\n", + "Date: Sun, 16 Feb 2025 Prob (F-statistic): 0.0197\n", + "Time: 13:29:44 Log-Likelihood: -108.18\n", + "No. Observations: 9 AIC: 220.4\n", + "Df Residuals: 7 BIC: 220.8\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------\n", + "const 1.427e+05 3.72e+04 3.835 0.006 5.47e+04 2.31e+05\n", + "Area (sq km) 1409.5066 468.543 3.008 0.020 301.579 2517.434\n", + "==============================================================================\n", + "Omnibus: 0.461 Durbin-Watson: 1.944\n", + "Prob(Omnibus): 0.794 Jarque-Bera (JB): 0.488\n", + "Skew: 0.368 Prob(JB): 0.783\n", + "Kurtosis: 2.128 Cond. No. 194.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "---- params / coeficient -------\n", + "const 142656.456677\n", + "Area (sq km) 1409.506611\n", + "dtype: float64\n", + "------------p values----------\n", + "const 0.006419\n", + "Area (sq km) 0.019710\n", + "dtype: float64\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=9\n", + " warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "complete_analysis('London Borough')\n" + ] + }, + { + "cell_type": "markdown", + "id": "13a565d7", + "metadata": { + "papermill": { + "duration": 0.018618, + "end_time": "2025-02-16T13:29:45.141616", + "exception": false, + "start_time": "2025-02-16T13:29:45.122998", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Our analysis has shown the London Borough may have a polynomial relationship of degree 3 relationship between an area and the estimated population. Our results are not statiscally significant - there is not enough observations. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "adcc95df", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:45.181160Z", + "iopub.status.busy": "2025-02-16T13:29:45.180797Z", + "iopub.status.idle": "2025-02-16T13:29:47.730968Z", + "shell.execute_reply": "2025-02-16T13:29:47.729901Z" + }, + "papermill": { + "duration": 2.572416, + "end_time": "2025-02-16T13:29:47.732929", + "exception": false, + "start_time": "2025-02-16T13:29:45.160513", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(8, 3)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.preprocessing import PolynomialFeatures\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error\n", + "\n", + "\n", + "geo = extract('London Borough')\n", + "\n", + "geo = geo.loc[geo['Area (sq km)'] < 140, :]\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "print(geo.shape)\n", + "visualise(geo)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3e2ee0ce", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:47.773276Z", + "iopub.status.busy": "2025-02-16T13:29:47.772907Z", + "iopub.status.idle": "2025-02-16T13:29:47.857266Z", + "shell.execute_reply": "2025-02-16T13:29:47.856152Z" + }, + "papermill": { + "duration": 0.107331, + "end_time": "2025-02-16T13:29:47.859514", + "exception": false, + "start_time": "2025-02-16T13:29:47.752183", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error: 26185868.997357935\n", + "Coefficient : [-766835.3079051] [[ 4.74722740e+04 -7.76448590e+02 4.31938558e+00]]\n" + ] + } + ], + "source": [ + "x = geo['Area (sq km)']\n", + "y = geo['est_pop']\n", + "poly = PolynomialFeatures(degree=3, include_bias=False)\n", + "\n", + "#reshape data to work properly with sklearn\n", + "poly_features = poly.fit_transform(x.values.reshape(-1, 1))\n", + "poly_reg_model = LinearRegression()\n", + "poly_reg_model.fit(poly_features, y.values.reshape(-1,1))\n", + "\n", + "\n", + "y_pred = poly_reg_model.predict(poly_features)\n", + "\n", + "print(\"Mean Squared Error: \" ,mean_squared_error(y.values.reshape(-1, 1),y_pred, multioutput = 'uniform_average'))\n", + "print(\"Coefficient : \", poly_reg_model.intercept_, poly_reg_model.coef_)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "363ed8a0", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:47.899487Z", + "iopub.status.busy": "2025-02-16T13:29:47.899056Z", + "iopub.status.idle": "2025-02-16T13:29:48.157672Z", + "shell.execute_reply": "2025-02-16T13:29:48.156551Z" + }, + "papermill": { + "duration": 0.281209, + "end_time": "2025-02-16T13:29:48.159905", + "exception": false, + "start_time": "2025-02-16T13:29:47.878696", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#use model to make predictions on response variable\n", + "y_predicted = poly_reg_model.predict(poly_features)\n", + "\n", + "#create scatterplot of x vs. y\n", + "plt.scatter(x, y)\n", + "\n", + "#add line to show fitted polynomial regression model\n", + "plt.plot(x, y_predicted, color='purple')" + ] + }, + { + "cell_type": "markdown", + "id": "ac8269a2", + "metadata": { + "papermill": { + "duration": 0.019655, + "end_time": "2025-02-16T13:29:48.199823", + "exception": false, + "start_time": "2025-02-16T13:29:48.180168", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Metropolitain district" + ] + }, + { + "cell_type": "markdown", + "id": "30da8335", + "metadata": { + "papermill": { + "duration": 0.019483, + "end_time": "2025-02-16T13:29:48.239288", + "exception": false, + "start_time": "2025-02-16T13:29:48.219805", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The null hypothesis $H_0$ suggests there is no correlation between the area and the estimated population. The alternative hypothesis $H_1$ suggests the contrary. The p-value appears to be in range $ 0.01 \\leq 0.02 \\leq 0.05$. It appears some moderate evidence to reject the null hypothesis in favour of the alternative hypothesis. It is, therefore inconclusive a linear relationship exists between an area and an estimated population across Metropolitain districts. The number of observations is lower than 31, and therefore more observations is required to bring some statistical significance." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "43708752", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:48.280303Z", + "iopub.status.busy": "2025-02-16T13:29:48.279882Z", + "iopub.status.idle": "2025-02-16T13:29:51.060951Z", + "shell.execute_reply": "2025-02-16T13:29:51.060031Z" + }, + "papermill": { + "duration": 2.804369, + "end_time": "2025-02-16T13:29:51.063167", + "exception": false, + "start_time": "2025-02-16T13:29:48.258798", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameArea (sq km)est_pop
110237GATESHEAD142.3391191178.0
132800NEWCASTLE UPON TYNE113.4515266241.0
201862SUNDERLAND137.4365284601.0
340360BOLTON139.7923261302.0
390312BURY99.4604180655.0
410640MANCHESTER115.6485422915.0
602278OLDHAM142.3450218537.0
637390ROCHDALE158.1281206440.0
657571SALFORD97.1974216978.0
677149STOCKPORT126.0404284557.0
722923WIGAN188.1711301453.0
885067LIVERPOOL111.8357441858.0
995898ST. HELENS136.3587176826.0
1014514WIRRAL160.9220315004.0
1146767BARNSLEY329.0776218124.0
1182952DONCASTER568.0064286900.0
1238766ROTHERHAM286.5345248349.0
1272975SHEFFIELD367.9302513102.0
1378950BRADFORD366.4197470753.0
1447613LEEDS551.7067715609.0
1572830WAKEFIELD338.6198315380.0
2168458BIRMINGHAM267.7912984642.0
2334015COVENTRY98.6390302804.0
2396909DUDLEY97.9584305052.0
2409882SOLIHULL178.2821199574.0
2438332WALSALL103.9734253333.0
2468145WOLVERHAMPTON69.4365238016.0
\n", + "
" + ], + "text/plain": [ + " Name Area (sq km) est_pop\n", + "110237 GATESHEAD 142.3391 191178.0\n", + "132800 NEWCASTLE UPON TYNE 113.4515 266241.0\n", + "201862 SUNDERLAND 137.4365 284601.0\n", + "340360 BOLTON 139.7923 261302.0\n", + "390312 BURY 99.4604 180655.0\n", + "410640 MANCHESTER 115.6485 422915.0\n", + "602278 OLDHAM 142.3450 218537.0\n", + "637390 ROCHDALE 158.1281 206440.0\n", + "657571 SALFORD 97.1974 216978.0\n", + "677149 STOCKPORT 126.0404 284557.0\n", + "722923 WIGAN 188.1711 301453.0\n", + "885067 LIVERPOOL 111.8357 441858.0\n", + "995898 ST. HELENS 136.3587 176826.0\n", + "1014514 WIRRAL 160.9220 315004.0\n", + "1146767 BARNSLEY 329.0776 218124.0\n", + "1182952 DONCASTER 568.0064 286900.0\n", + "1238766 ROTHERHAM 286.5345 248349.0\n", + "1272975 SHEFFIELD 367.9302 513102.0\n", + "1378950 BRADFORD 366.4197 470753.0\n", + "1447613 LEEDS 551.7067 715609.0\n", + "1572830 WAKEFIELD 338.6198 315380.0\n", + "2168458 BIRMINGHAM 267.7912 984642.0\n", + "2334015 COVENTRY 98.6390 302804.0\n", + "2396909 DUDLEY 97.9584 305052.0\n", + "2409882 SOLIHULL 178.2821 199574.0\n", + "2438332 WALSALL 103.9734 253333.0\n", + "2468145 WOLVERHAMPTON 69.4365 238016.0" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = data.Geography.str.contains('Metropolitan District')\n", + "cols = ['Name', 'Area (sq km)', 'est_pop']\n", + "data.loc[rows, cols].drop_duplicates().dropna()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "4c68c7f4", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:51.106718Z", + "iopub.status.busy": "2025-02-16T13:29:51.106303Z", + "iopub.status.idle": "2025-02-16T13:29:54.059627Z", + "shell.execute_reply": "2025-02-16T13:29:54.058585Z" + }, + "papermill": { + "duration": 2.976993, + "end_time": "2025-02-16T13:29:54.061800", + "exception": false, + "start_time": "2025-02-16T13:29:51.084807", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(27, 3)\n", + "Area (sq km) float64\n", + "est_pop float64\n", + "pp_sq_m float64\n", + "dtype: object\n", + " Area (sq km) est_pop pp_sq_m\n", + "count 27.000000 27.000000 27.000000\n", + "mean 203.463044 326673.444444 1964.005356\n", + "std 135.911305 178115.620112 973.121974\n", + "min 69.436500 176826.000000 505.099943\n", + "25% 112.643600 218330.500000 1296.926626\n", + "50% 142.339100 284557.000000 1816.351030\n", + "75% 277.162850 315192.000000 2391.627946\n", + "max 568.006400 984642.000000 3950.956627\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: est_pop R-squared: 0.195\n", + "Model: OLS Adj. R-squared: 0.162\n", + "Method: Least Squares F-statistic: 6.041\n", + "Date: Sun, 16 Feb 2025 Prob (F-statistic): 0.0213\n", + "Time: 13:29:53 Log-Likelihood: -361.32\n", + "No. Observations: 27 AIC: 726.6\n", + "Df Residuals: 25 BIC: 729.2\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------\n", + "const 2.09e+05 5.72e+04 3.653 0.001 9.12e+04 3.27e+05\n", + "Area (sq km) 578.1413 235.222 2.458 0.021 93.692 1062.591\n", + "==============================================================================\n", + "Omnibus: 29.735 Durbin-Watson: 2.000\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 69.985\n", + "Skew: 2.155 Prob(JB): 6.35e-16\n", + "Kurtosis: 9.606 Cond. No. 444.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "---- params / coeficient -------\n", + "const 209043.053986\n", + "Area (sq km) 578.141307\n", + "dtype: float64\n", + "------------p values----------\n", + "const 0.001201\n", + "Area (sq km) 0.021256\n", + "dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geo = extract('Metropolitan District')\n", + "\n", + "geo = geo.loc[geo['Area (sq km)'] < 400, :]\n", + "geo = geo.loc[geo['est_pop'] < 9e5, :]\n", + "\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "print(geo.shape)\n", + "visualise(geo)\n" + ] + }, + { + "cell_type": "markdown", + "id": "84479113", + "metadata": { + "papermill": { + "duration": 0.021624, + "end_time": "2025-02-16T13:29:57.002764", + "exception": false, + "start_time": "2025-02-16T13:29:56.981140", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A polynomial relationship may exists between the area and the estimated population. However, it has been been established clearly in our analysis below. Some cluster of data may suggest some cluster may exist for the metropolitain district." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "f81a5eeb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:57.047876Z", + "iopub.status.busy": "2025-02-16T13:29:57.047524Z", + "iopub.status.idle": "2025-02-16T13:29:57.058368Z", + "shell.execute_reply": "2025-02-16T13:29:57.057377Z" + }, + "papermill": { + "duration": 0.03662, + "end_time": "2025-02-16T13:29:57.060908", + "exception": false, + "start_time": "2025-02-16T13:29:57.024288", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mean Squared Error: 3820156977.9389973\n", + "Coefficient : [-1379587.1745336] [[ 5.11222610e+04 -5.86372387e+02 3.13474961e+00 -7.90235273e-03\n", + " 7.58399631e-06]]\n" + ] + } + ], + "source": [ + "x = geo['Area (sq km)']\n", + "y = geo['est_pop']\n", + "poly = PolynomialFeatures(degree=5, include_bias=False)\n", + "\n", + "#reshape data to work properly with sklearn\n", + "poly_features = poly.fit_transform(x.values.reshape(-1, 1))\n", + "poly_reg_model = LinearRegression()\n", + "poly_reg_model.fit(poly_features, y.values.reshape(-1,1))\n", + "\n", + "\n", + "y_pred = poly_reg_model.predict(poly_features)\n", + "\n", + "print(\"Mean Squared Error: \" ,mean_squared_error(y.values.reshape(-1, 1),y_pred, multioutput = 'uniform_average'))\n", + "print(\"Coefficient : \", poly_reg_model.intercept_, poly_reg_model.coef_)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "4320fd13", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:57.107100Z", + "iopub.status.busy": "2025-02-16T13:29:57.106747Z", + "iopub.status.idle": "2025-02-16T13:29:57.296671Z", + "shell.execute_reply": "2025-02-16T13:29:57.295527Z" + }, + "papermill": { + "duration": 0.216865, + "end_time": "2025-02-16T13:29:57.299152", + "exception": false, + "start_time": "2025-02-16T13:29:57.082287", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#use model to make predictions on response variable\n", + "y_predicted = poly_reg_model.predict(poly_features)\n", + "\n", + "#create scatterplot of x vs. y\n", + "plt.scatter(x, y)\n", + "\n", + "#add line to show fitted polynomial regression model\n", + "plt.plot(x, y_predicted, color='purple')" + ] + }, + { + "cell_type": "markdown", + "id": "d2c832bb", + "metadata": { + "papermill": { + "duration": 0.021727, + "end_time": "2025-02-16T13:29:57.343293", + "exception": false, + "start_time": "2025-02-16T13:29:57.321566", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A cluster analysis - KMeans - suggest 5 centroids may be suitable. The population appears to impact on the centroid over the area. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "94f19d82", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:57.389309Z", + "iopub.status.busy": "2025-02-16T13:29:57.388943Z", + "iopub.status.idle": "2025-02-16T13:29:57.897313Z", + "shell.execute_reply": "2025-02-16T13:29:57.896240Z" + }, + "papermill": { + "duration": 0.534004, + "end_time": "2025-02-16T13:29:57.899458", + "exception": false, + "start_time": "2025-02-16T13:29:57.365454", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.datasets import make_blobs\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "geo = geo.loc[data['Area (sq km)'] < 350, :]\n", + "x = geo['Area (sq km)'].values\n", + "y = geo['est_pop'].values\n", + "data_to_fit = list(zip(x, y))\n", + "inertias = []\n", + "\n", + "for i in range(2,7):\n", + " kmeans = KMeans(n_clusters=i)\n", + " kmeans.fit(data_to_fit)\n", + " inertias.append(kmeans.inertia_)\n", + "\n", + "plt.plot(range(2,7), inertias, marker='o')\n", + "plt.title('Elbow method')\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('Inertia')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "14de0ed9", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:29:57.945991Z", + "iopub.status.busy": "2025-02-16T13:29:57.945579Z", + "iopub.status.idle": "2025-02-16T13:29:58.186458Z", + "shell.execute_reply": "2025-02-16T13:29:58.185283Z" + }, + "papermill": { + "duration": 0.26699, + "end_time": "2025-02-16T13:29:58.188768", + "exception": false, + "start_time": "2025-02-16T13:29:57.921778", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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NameArea (sq km)est_pop
306108BARROW-IN-FURNESS77.897171960.0
319086CARLISLE1038.2969100764.0
759658BURNLEY110.684089521.0
787134CHORLEY202.7622100559.0
806848LANCASTER566.9315134049.0
............
4875912EXETER47.0330111180.0
4906479CHELTENHAM46.5961110024.0
4942597GLOUCESTER40.5530109947.0
4979207STROUD460.5422108060.0
4990633TEWKESBURY414.414476524.0
\n", + "

66 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " Name Area (sq km) est_pop\n", + "306108 BARROW-IN-FURNESS 77.8971 71960.0\n", + "319086 CARLISLE 1038.2969 100764.0\n", + "759658 BURNLEY 110.6840 89521.0\n", + "787134 CHORLEY 202.7622 100559.0\n", + "806848 LANCASTER 566.9315 134049.0\n", + "... ... ... ...\n", + "4875912 EXETER 47.0330 111180.0\n", + "4906479 CHELTENHAM 46.5961 110024.0\n", + "4942597 GLOUCESTER 40.5530 109947.0\n", + "4979207 STROUD 460.5422 108060.0\n", + "4990633 TEWKESBURY 414.4144 76524.0\n", + "\n", + "[66 rows x 3 columns]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = data.Geography.str.contains('Non-metropolitan District')\n", + "cols = ['Name', 'Area (sq km)', 'est_pop']\n", + "data.loc[rows, cols].drop_duplicates().dropna()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "ff29a701", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:01.220154Z", + "iopub.status.busy": "2025-02-16T13:30:01.219705Z", + "iopub.status.idle": "2025-02-16T13:30:04.281157Z", + "shell.execute_reply": "2025-02-16T13:30:04.280018Z" + }, + "papermill": { + "duration": 3.092493, + "end_time": "2025-02-16T13:30:04.283555", + "exception": false, + "start_time": "2025-02-16T13:30:01.191062", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(66, 3)\n", + "Area (sq km) float64\n", + "est_pop float64\n", + "pp_sq_m float64\n", + "dtype: object\n", + " Area (sq km) est_pop pp_sq_m\n", + "count 66.000000 66.000000 66.000000\n", + "mean 258.307398 103076.636364 1180.008029\n", + "std 268.691958 24246.021386 1082.819562\n", + "min 21.430500 55802.000000 97.047386\n", + "25% 46.705325 86267.750000 257.300281\n", + "50% 157.164950 100661.500000 508.837899\n", + "75% 362.460375 117088.500000 2199.312867\n", + "max 1307.937700 165895.000000 3751.569025\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: est_pop R-squared: 0.069\n", + "Model: OLS Adj. R-squared: 0.055\n", + "Method: Least Squares F-statistic: 4.767\n", + "Date: Sun, 16 Feb 2025 Prob (F-statistic): 0.0327\n", + "Time: 13:30:03 Log-Likelihood: -757.11\n", + "No. Observations: 66 AIC: 1518.\n", + "Df Residuals: 64 BIC: 1523.\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------\n", + "const 9.694e+04 4039.801 23.996 0.000 8.89e+04 1.05e+05\n", + "Area (sq km) 23.7574 10.882 2.183 0.033 2.019 45.496\n", + "==============================================================================\n", + "Omnibus: 2.722 Durbin-Watson: 1.696\n", + "Prob(Omnibus): 0.256 Jarque-Bera (JB): 2.193\n", + "Skew: 0.444 Prob(JB): 0.334\n", + "Kurtosis: 3.099 Cond. No. 517.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "---- params / coeficient -------\n", + "const 96939.931907\n", + "Area (sq km) 23.757370\n", + "dtype: float64\n", + "------------p values----------\n", + "const 1.055017e-33\n", + "Area (sq km) 3.269236e-02\n", + "dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geo = extract('Non-metropolitan District')\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "print(geo.shape)\n", + "visualise(geo)\n" + ] + }, + { + "cell_type": "markdown", + "id": "48f67d0f", + "metadata": { + "papermill": { + "duration": 0.025854, + "end_time": "2025-02-16T13:30:07.216404", + "exception": false, + "start_time": "2025-02-16T13:30:07.190550", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A cluster analysis - KMeans - suggest 5 centroids may be suitable. The population appears to impact on the centroid over the area. \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "55290cfb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:07.268572Z", + "iopub.status.busy": "2025-02-16T13:30:07.268166Z", + "iopub.status.idle": "2025-02-16T13:30:07.276152Z", + "shell.execute_reply": "2025-02-16T13:30:07.274574Z" + }, + "papermill": { + "duration": 0.036654, + "end_time": "2025-02-16T13:30:07.278545", + "exception": false, + "start_time": "2025-02-16T13:30:07.241891", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "\n", + "from sklearn.datasets import make_blobs\n", + "from sklearn.cluster import KMeans\n", + "from sklearn.metrics import silhouette_score\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "if False: \n", + " x = geo['Area (sq km)'].values\n", + " y = geo['est_pop'].values\n", + " data_to_fit = list(zip(x, y))\n", + " inertias = []\n", + "\n", + " for i in range(1,11):\n", + " kmeans = KMeans(n_clusters=i)\n", + " kmeans.fit(data_to_fit)\n", + " inertias.append(kmeans.inertia_)\n", + "\n", + " plt.plot(range(1,11), inertias, marker='o')\n", + " plt.title('Elbow method')\n", + " plt.xlabel('Number of clusters')\n", + " plt.ylabel('Inertia')\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "455cd9ee", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:07.331454Z", + "iopub.status.busy": "2025-02-16T13:30:07.330576Z", + "iopub.status.idle": "2025-02-16T13:30:07.578241Z", + "shell.execute_reply": "2025-02-16T13:30:07.577171Z" + }, + "papermill": { + "duration": 0.275936, + "end_time": "2025-02-16T13:30:07.580171", + "exception": false, + "start_time": "2025-02-16T13:30:07.304235", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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aNFFUVCT27t0rwsLCxMqVK5War7/+Wvj7+4tly5aJ8+fPi/Xr1wutViv27t2r1GzevFno9XqRk5Mjzp07J1588UURFBTkdJVdR710hFfDERER9Tyu/v7u8gDvjrz11lvQaDSYOXMmmpqakJ6ejj/84Q/Kcq1Wi507d+Kll16CyWRCv379MHfuXLzxxhtKzdChQ7Fr1y4sXboUa9euxaBBg/D+++8jPT1dqZk9ezauXLmCVatWwWw2IykpCXv37nUa9N1RL0REREQdkYQQwtNNeCur1YrAwEBYLBaekiMiIuohXP39zXvDEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRii6FpQ0bNmDMmDEwGo0wGo0wmUzYs2ePsvyhhx6CJElOjwULFjhto7y8HBkZGfD390d4eDiWLVuGlpYWp5qDBw9i7Nix0Ov1iIuLQ05Ozm29rF+/HkOGDIGfnx9SU1Nx9OhRp+WNjY3IzMxESEgIAgICMHPmTFRWVnbl7RIRERF1LSwNGjQIb775JgoLC3H8+HE8/PDDePzxx3H27Fml5oUXXsDly5eVx5o1a5RldrsdGRkZsNlsOHz4MDZu3IicnBysWrVKqSkrK0NGRgamTJmCoqIiLFmyBPPnz8e+ffuUmi1btiArKwurV6/GiRMnkJiYiPT0dFRVVSk1S5cuxY4dO7Bt2zbk5+ejoqICM2bMuKOdRERERH2YuEsDBgwQ77//vhBCiMmTJ4vFixe3W7t7926h0WiE2WxWXtuwYYMwGo2iqalJCCHEq6++Ku677z6n9WbPni3S09OV5ykpKSIzM1N5brfbRVRUlMjOzhZCCFFTUyN8fX3Ftm3blJrz588LAKKgoKDT781isQgAwmKxdHodIiIi8ixXf3/f8Zglu92OzZs3o66uDiaTSXn9o48+QmhoKBISErBy5UrU19crywoKCjB69GhEREQor6Wnp8NqtSpHpwoKCpCWlub0s9LT01FQUAAAsNlsKCwsdKrRaDRIS0tTagoLC9Hc3OxUM3LkSMTExCg1bWlqaoLVanV6EBERUd/m09UVTp8+DZPJhMbGRgQEBGD79u2Ij48HADz99NMYPHgwoqKicOrUKSxfvhzFxcX45JNPAABms9kpKAFQnpvNZtUaq9WKhoYGXL9+HXa7vc2aCxcuKNvQ6XQICgq6rab157QlOzsbr7/+ehf3CBEREfVmXQ5LI0aMQFFRESwWC/70pz9h7ty5yM/PR3x8PF588UWlbvTo0Rg4cCCmTp2K0tJSxMbGurTx7rBy5UpkZWUpz61WK6Kjoz3YEREREXlal0/D6XQ6xMXFITk5GdnZ2UhMTMTatWvbrE1NTQUAlJSUAAAiIyNvuyKt9XlkZKRqjdFohMFgQGhoKLRabZs1N2/DZrOhpqam3Zq26PV65Uq/1gcRERH1bXc9z5LD4UBTU1Oby4qKigAAAwcOBACYTCacPn3a6aq13NxcGI1G5VSeyWRCXl6e03Zyc3OVcVE6nQ7JyclONQ6HA3l5eUpNcnIyfH19nWqKi4tRXl7uNL6KiIiIqENdGQ2+YsUKkZ+fL8rKysSpU6fEihUrhCRJ4i9/+YsoKSkRb7zxhjh+/LgoKysTn376qRg2bJiYNGmSsn5LS4tISEgQ06ZNE0VFRWLv3r0iLCxMrFy5Uqn5+uuvhb+/v1i2bJk4f/68WL9+vdBqtWLv3r1KzebNm4Verxc5OTni3Llz4sUXXxRBQUFOV9ktWLBAxMTEiP3794vjx48Lk8kkTCZTl0a/82o4IiKinsfV399dCkvPP/+8GDx4sNDpdCIsLExMnTpV/OUvfxFCCFFeXi4mTZokgoODhV6vF3FxcWLZsmW3NfrNN9+I6dOnC4PBIEJDQ8Urr7wimpubnWoOHDggkpKShE6nE8OGDRMffvjhbb288847IiYmRuh0OpGSkiKOHDnitLyhoUG8/PLLYsCAAcLf3188+eST4vLly115uwxLREREPZCrv78lIYTw7LEt72W1WhEYGAiLxcLxS0RERD2Eq7+/eW84IiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERF5hM1uhxDC0210yMfTDRCRazmEwBfffoMvv/8OAgLjowZh8uAh0Gr4uxEReV6dzYackyfwn6eKUFVXB51Wi4zhI/BPyeNxb0iop9trkyR6QqTzEKvVisDAQFgsFhiNRk+3Q9Shr69fwws7/oyymuvw+Xs4anE4EG0MxPs/fhLDQ0I83CER9WW1TU146pMtuHD1Khw3xQ+tJMFHo8HGJ2Yh5Z5Bd/1zXP39zV81iXoJS2Mjnv7vrSi31ACQQ1KLwwEAqKi14qlPtuBaQ70HOySivu6tLw+j+JagBAB2IdDscCBz9w402+0e6q59DEtEvcS2c2dwpb4O9jYOFtuFQE1jIzafOe2BzoiIgIbmZmw5c7rNf6MAeQhBdUM9cr8udXNnHWNYIuoldvztAtTOqTuEwI6/XXBbP0REN/vOakVDS7NqjY9Gg/NXq9zUUecxLBH1ErW2po5rmjquISLqDjqttsMaIQT0Wu+79oxhiaiXGB4cAq0ktbtcK0mIC+YAbyLyjJjAQAwJCkL7/0rJQwYeHjrMbT11FsMSUS/xdEJiu2MBAPkfoZ+MSXRjR0REP5AkCQvHT2h3uIBWkjAxOgbxYeFu7aszGJaIeolJg4dgxsj4dpc/NnwEHh4a68aOiIiczRh1H7ImTIQEORxJkJQj4mMiIrFu+o8922A7OM+SCs6zRD2NQwjkFJ3A+18dh/nGDQBAeL9+mHd/Mp5PSubElETkFb6tqcHWc6fxTU0NAnQ6PDZ8BCbGDIZGZShBV7j6+5thSQXDEvVUdocDFbW1EBC4p7+RIYmI+hRXf39735BzIrprWo0G0YGBnm6DiKhX4K+bRERERCp4ZIk6rc5mw+azp7H5zCmYb9xAiMGA/3VfAp4ZnYggP4On2yMiIuoWHLOkgmOWfnC9oQFz/nsLSq5VA4By6adGkhAZEICts+Ygqn/f3kdEROQdeCNd8ojVB/Pw9fVrEIDTHBkOIVB54wZe+cseT7VGnSUEUF8P1NTIf/L3JCKiTuFpOOrQlbo67C752213iW5lFwJffv8dLlZXY3gIZ4j2Oo2NQFERcOgQUFICtLQAPj5AXBzwwANAUhLg5+fpLomIvBbDEnXodFVlu0HpZh+fOYlVkx92Q0fUaSUlwAcfAKWlgCQBISFAv35AczNw7Bhw9CgQGwvMmyeHJw8SQuCEuQLf1tTAqNdjYvRgGHx9PdoTERHAsESdoHa/sZttOXsayydOgt6HHyuvUFICrF0LmM3A8OGATue8PDwcsNmAixflusWLPRaYjlV8hxWf/QVlNdeV1wJ0OixKmYD594+D5KKJ6oiI7gTHLFGHxg6Mgq4Tkxo2tLRgT8lFN3REHWpslI8omc1AfPztQamVTicvN5vl+sZG9/YJ4GSlGT/5ZBu+tdQ4vX7DZkP2oc+x7tgRt/dERHSzLoWlDRs2YMyYMTAajTAajTCZTNiz54eBvY2NjcjMzERISAgCAgIwc+ZMVFZWOm2jvLwcGRkZ8Pf3R3h4OJYtW4aWlhanmoMHD2Ls2LHQ6/WIi4tDTk7Obb2sX78eQ4YMgZ+fH1JTU3H06FGn5Z3phTqnv16Px+4d2WGdj0aDi9euuqEj6lBRkXzqbfhw+fSbGkmSjyiVlgInT7qlvZv99q+fwy5Eu6d63zl6BNca6t3cFRHRD7oUlgYNGoQ333wThYWFOH78OB5++GE8/vjjOHv2LABg6dKl2LFjB7Zt24b8/HxUVFRgxowZyvp2ux0ZGRmw2Ww4fPgwNm7ciJycHKxatUqpKSsrQ0ZGBqZMmYKioiIsWbIE8+fPx759+5SaLVu2ICsrC6tXr8aJEyeQmJiI9PR0VFVVKTUd9UJd8/L41A5rhBAw+HCMiccJIQ/mlqT2jyjdSq+X67/4wq1XyVXeuIHD311SHRNndziw6+Lf3NYTEdGt7nqepeDgYPz2t7/FrFmzEBYWhk2bNmHWrFkAgAsXLmDUqFEoKCjAhAkTsGfPHjz22GOoqKhAREQEAODdd9/F8uXLceXKFeh0Oixfvhy7du3CmTNnlJ8xZ84c1NTUYO/evQCA1NRUjB8/HuvWrQMAOBwOREdHY9GiRVixYgUsFkuHvXQG51n6gRACj276I/5WfRVqH5g9z8zFiJBQt/VFbaivB7KyAI1GHpfUWVVVgMMB/O53gL9/9/V3k3NXqvDYx/+pWuOj0eClcSlYOmGiW3oiop7Pa+ZZstvt2Lx5M+rq6mAymVBYWIjm5makpaUpNSNHjkRMTAwKCgoAAAUFBRg9erQSlAAgPT0dVqtVOTpVUFDgtI3WmtZt2Gw2FBYWOtVoNBqkpaUpNZ3ppS1NTU2wWq1OD5JJkoSfp5raDUoaScKUIUMZlLyBzSZPD9DVK8l8fOT1bLbu6asNYf790NHQbbvDgYh+AW7ph4ioLV0OS6dPn0ZAQAD0ej0WLFiA7du3Iz4+HmazGTqdDkFBQU71ERERMJvNAACz2ewUlFqXty5Tq7FarWhoaMDVq1dht9vbrLl5Gx310pbs7GwEBgYqj+jo6M7tlD5iety9+JfJD0MrSdBIEnwkDXz+PvD7HwbFYO0jj3m4QwIgn3rz8ZGnB+iK1vmXOnvqzgXC+vXDAzGDVa+49NVqkTF8hNt6IiK6VZev8R4xYgSKiopgsVjwpz/9CXPnzkV+fn539OZ2K1euRFZWlvLcarUyMN3ip4n3Y3rcvfjv82fxTc11BOj0yBh+L5IiB/Lybm9hMMgDto8d69ppuOpqYPx4eX03WjFxEmZs/RjCYW9z7FLWhIkI5KSZRORBXQ5LOp0OcX+fiyU5ORnHjh3D2rVrMXv2bNhsNtTU1Dgd0amsrERkZCQAIDIy8rar1lqvULu55tar1iorK2E0GmEwGKDVaqHVatusuXkbHfXSFr1eD71e34W90TeF9euHBeNSPN0GtUeS5Jm5jx6VT6l15khRU5M8sPvBBzu+es7FRoWFY/Os2Xhtfy7OXvnhIo0BfgYsnfAP+MmYJLf2Q0R0q7ueZ8nhcKCpqQnJycnw9fVFXl6esqy4uBjl5eUwmUwAAJPJhNOnTztdtZabmwuj0Yj4+Hil5uZttNa0bkOn0yE5OdmpxuFwIC8vT6npTC9EvVpSkjwz98WLHV/dJoQ8gWVsLJCY6Jb2bpUYEYkdTz2LnU89i/WP/hh/fGIWCub9E4MSEXmFLh1ZWrlyJaZPn46YmBjU1tZi06ZNOHjwIPbt24fAwEDMmzcPWVlZCA4OhtFoxKJFi2AymZSrz6ZNm4b4+Hg8++yzWLNmDcxmM1577TVkZmYqR3QWLFiAdevW4dVXX8Xzzz+P/fv3Y+vWrdi1a5fSR1ZWFubOnYtx48YhJSUFb7/9Nurq6vDcc88BQKd6IerV/PzkW5isXQucOyeflmvrqGlTkxyUIiOB+fM9fo+4+LBwxId14dQhEZEbdCksVVVV4ac//SkuX76MwMBAjBkzBvv27cOPfvQjAMBbb70FjUaDmTNnoqmpCenp6fjDH/6grK/VarFz50689NJLMJlM6NevH+bOnYs33nhDqRk6dCh27dqFpUuXYu3atRg0aBDef/99pKenKzWzZ8/GlStXsGrVKpjNZiQlJWHv3r1Og7476oWo14uLk29hcuu94Vqvequulo8qxcXJQSk21tMdExF5pbueZ6k34zxL1Cs0Nsozc3/xhXwUqfWqt7g4eYxSYqLHjygREbmSq7+/ecdTot7Ozw9ITQVSUoCGhh8GfRsMbh/MTUTUEzEsEfUVkiTPzO2m2bmJiHqLu74ajoiIiKg3Y1giIiIiUsGwRERERKSCYYmIiIhIBcMSERERkQqGJaK70NaNX4mIqHfh1AHUJ9Q3N2PT6ZP4+MwpXL5RiyA/P8wcdR9+mng/wvz7dWlbDc3N2HjyK3x0+iS+r7XC4OODH987Ei8mj8ewAcHd9A6IiMhTOIO3Cs7g3TtYmxox57+3ovjqFQBA6wdeI0kINhiwddYcDAka0Klt1dlsePqTrThTVYmb/+JoJQm+Wi3++MQsjIu6x7VvgIiIusTV3988DUe93q8/P4iL1VchAKeA4xAC1xsa8PM9Ozu9rbeOHMbZK1W49TcMuxCw2e3I3L0DzXa7K9omIiIvwbBEvdr1hgb8ufg87O0cQLULgTNXqnCy0tzhthpbmrH57Kl2xyk5hMCV+jrklX19Vz0TEZF3YViiXq24+ipaHA7VGgnASfPlDrd1yWJFfXOzao2PRoOzVyq70iIREXk5hiXq1bSazt0oVqvp+K+Cr7bjGiEEdFptp34mERH1DAxL1KuNDo9AgE7XYd0D0YM7rBkcGISYwECoxS+7EJgyZFgXOiQiIm/HsES9mp+PL55LGttuwNFKEtKGxWJwUFCH25IkCZnjJ9w2uPvmbU24JxoJ4RF32i4REXkhhiXq9RalmPDje0cCkAMNIE8bAABjIiLx2x9N7/S2Zo26D4tSJjhtq/XP+8LCsf7RH7usbyIi8g6cZ0kF51nqPYQQOFbxPbaePY1LVgtC/P3x5Ih4TBk6DD6dGK90q9Jr1dhy9jS+tdSgv06PjHtHYFLMkE6NfSIiou7l6u9vhiUVDEtEREQ9DyelJCIiInIjhiUiIiIiFQxLRERERCoYloiIiIhUMCwRERERqWBYIiIiIlLBsERERESkgmGJiIiISAXDEhEREZEKhiUiIiIiFQxLRERERCoYloiIiIhU+Hi6AaJbCSFQdOAMcv+Yj6vfVyP0nhD86KeTkTQlAZIkebo9IiLqYxiWyKvYGm1443//Dl/uLIRGq4HD7oBGq0HuH/Mx4cfJ+NXWV6DT+3q6TSIi6kN4Go68yruvbMTR3ScAAA67w+nPL3edwL//4o8e642IiPomhiXyGtbqWux5Pw/CIdpcLhwCu/7jM9Rev+HmzoiIqC9jWCKvcTL/HFqa7ao1LbYWnMo/56aOiIiIGJbIi7TYWlxaR0RE5Aoc4E1e495xwzpZF9vNnXSdrdGGgv85jstlVTAGB2DikykIDDV6ui0iInIBhiXyGvfEDUTyj8ag6MAZ2Fscty3X+GgwdupoDBwW4YHu2pe/9TDeXvDvuFFTB62PBna7A+8sfB+zXvlHPPfrOdBoeACXiKgn47/i5FVe+eBlhEQFQ6N1/mhqtBqE3ROCVz542UOdte3LXYX49VNv4YalDgDkkCeAlmY7Nr+5HR++ttnDHRIR0d1iWCKvEjYoBBsK1+Anr81C6KBg+PhqETYoBD/51Sz84fj/h9CoYE+3qBBC4P2VH0GCBLR9AR/+9G//A8tVq3sbIyIil5KEEO38M09WqxWBgYGwWCwwGjn+hJyVX/ge8+KXqBdJwJINLyLjxR+5pSciInL99zePLBHdodprHc/3pNFqOlVHRETei2GJ6A5FDA4FOrhVnaPFgcih4e5piIiIugXDEtEdCr0nBON+lHjbYHSFBAQE+eMfJt8L1NcDPONNRNQjMSwR3YUFb/0Mfv30twUmSQIggJ+P1UD32kogKwv4t38DjhwBGhs90ywREd0RhiWiuzB41CD8/vBvMDZtjNMpucH9gV8/IGHKvQagXz9AowGOHQN+9zvg9deBkhLPNU1ERF3Cq+FU8Gq4OyeEwNm/XlBmtL5/6mjo/HSebqtbXTlUiMq3/x39a68hZmwcJL3+9iKbDbh4EYiMBBYvBuLi3N8oEVEv5+rvb87gTS5XdOAM3nrxXVSUViqvBQT1w8/+zxz848vpkKQORkX3RI2NCNv1J4RJ14AJ8X8/D9cGnQ6IjwfOnQM++ABYvRrw83Nvr0RE1CU8DUcudebQeaxI/zUul1U5vX6jpg7rFn2A/35rp4c662ZFRUBpKTB8ePtBqZUkyUeUSkuBkyfd0h4REd05hiVyqX9/9T8hHA4IR9tnd3N+tRl11no3d9XNhAAOHZJDkK6Tpxr1ern+iy94lRwRkZdjWCKXufx1Jc4fuQhHO0EJAJoabTj0yZdu7MoNGhrkAdshIV1bLyREXq+hoXv6IiIil2BYIpe5Zq7psEaj1eB6J+p6FJsNaGkBfH27tp6Pj7yezdY9fRERkUt0KSxlZ2dj/Pjx6N+/P8LDw/HEE0+guLjYqeahhx6CJElOjwULFjjVlJeXIyMjA/7+/ggPD8eyZcvQ0tLiVHPw4EGMHTsWer0ecXFxyMnJua2f9evXY8iQIfDz80NqaiqOHj3qtLyxsRGZmZkICQlBQEAAZs6cicrKytu2Q64RPDCowxpHiwMhXnQzXJfQ6eTg09zctfVaWuT1OnvqjojczuFwoKGuEbxwvG/rUljKz89HZmYmjhw5gtzcXDQ3N2PatGmoq6tzqnvhhRdw+fJl5bFmzRplmd1uR0ZGBmw2Gw4fPoyNGzciJycHq1atUmrKysqQkZGBKVOmoKioCEuWLMH8+fOxb98+pWbLli3IysrC6tWrceLECSQmJiI9PR1VVT8MLF66dCl27NiBbdu2IT8/HxUVFZgxY0aXdxJ1zsChEUh4YCQ0mvYHOPv10+OBGSlu7MoNDAZ5wHZ1ddfWq66W1zMYuqcvIrpj35dcxu9e2IAfB/wE/9j/WTwZ/DO894s/4nqVxdOtkQfc1TxLV65cQXh4OPLz8zFp0iQA8pGlpKQkvP32222us2fPHjz22GOoqKhAREQEAODdd9/F8uXLceXKFeh0Oixfvhy7du3CmTNnlPXmzJmDmpoa7N27FwCQmpqK8ePHY926dQDk9B8dHY1FixZhxYoVsFgsCAsLw6ZNmzBr1iwAwIULFzBq1CgUFBRgwoQJHb4/zrPUdee/vIisyavgaLG3OXZp4Tvz8HjmIx7orJsdOSJPOBkX17kjRU1N8tVwr7wCpKZ2f39E1GmlJ79B1uRVaKpvgr3Fobyu0WoQEjUAvz/8G4Te08UxiuRWrv7+vqsxSxaLnLCDg51Pq3z00UcIDQ1FQkICVq5cifr6H65+KigowOjRo5WgBADp6emwWq04e/asUpOWlua0zfT0dBQUFAAAbDYbCgsLnWo0Gg3S0tKUmsLCQjQ3NzvVjBw5EjExMUrNrZqammC1Wp0e1DWjUofjt3mrET1qkNPrQeGByPqPBb0zKAFAUhIQGytPONnR7x9CyAO7Y2OBxES3tEdEnSOEwL8+/TYa65yDEgA47A5UX76OdYv+r4e6I0+540kpHQ4HlixZgokTJyIhIUF5/emnn8bgwYMRFRWFU6dOYfny5SguLsYnn3wCADCbzU5BCYDy3Gw2q9ZYrVY0NDTg+vXrsNvtbdZcuHBB2YZOp0NQUNBtNa0/51bZ2dl4/fXXu7gn6FYJE0fiP079G/5W+DXMZVXoHxyAMZNGwce3F8+B6ucHzJsHrF0rTzgZFydPD3CrpiY5KEVGAvPnc0JKIi9z9nAxys9/3+5yR4sDh//nGK5+X82jS33IHX97ZWZm4syZMzh06JDT6y+++KLy36NHj8bAgQMxdepUlJaWIjY29s47dYOVK1ciKytLeW61WhEdHe3BjnouSZIwYlwsRozz7v/nLhUXJ9/C5IMP5FNskiRPD9B61Vt1tXxUKS5ODkpe/veBqC/6+uS3kCT1A8TCIfDtue8YlvqQOwpLCxcuxM6dO/H5559j0KBBqrWpfx+PUVJSgtjYWERGRt521VrrFWqRkZHKn7detVZZWQmj0QiDwQCtVgutVttmzc3bsNlsqKmpcTq6dHPNrfR6PfRtHQ0g6qy4OPkWJidPyhNOlpQA9fVyYBo/HnjwQfnUG48oEXklnUHXqXlie/u9LslZl8KSEAKLFi3C9u3bcfDgQQwdOrTDdYqKigAAAwcOBACYTCb85je/QVVVFcLDwwEAubm5MBqNiI+PV2p2797ttJ3c3FyYTCYAgE6nQ3JyMvLy8vDEE08AkE8L5uXlYeHChQCA5ORk+Pr6Ii8vDzNnzgQAFBcXo7y8XNkOUbfw85MHbaekyBNO2mzyoG+DoeNboRCRR41/JAkarQYOu6PdGmNIf4xM5U2w+5IuhaXMzExs2rQJn376Kfr376+M/QkMDITBYEBpaSk2bdqERx99FCEhITh16hSWLl2KSZMmYcyYMQCAadOmIT4+Hs8++yzWrFkDs9mM1157DZmZmcpRnQULFmDdunV49dVX8fzzz2P//v3YunUrdu3apfSSlZWFuXPnYty4cUhJScHbb7+Nuro6PPfcc0pP8+bNQ1ZWFoKDg2E0GrFo0SKYTKZOXQlHdNckCfD3lx9E1COEDByA9J89hL0fHmj3tk2zX30cvrouTkJLPZvoAgBtPj788EMhhBDl5eVi0qRJIjg4WOj1ehEXFyeWLVsmLBaL03a++eYbMX36dGEwGERoaKh45ZVXRHNzs1PNgQMHRFJSktDpdGLYsGHKz7jZO++8I2JiYoROpxMpKSniyJEjTssbGhrEyy+/LAYMGCD8/f3Fk08+KS5fvtzp92uxWASA2/onIqLeq6mhSayesUakSbNEuu9sMc3nf4t03/8t0qRZ4g9LPhQOh8PTLVIHXP39fVfzLPV2nGeJiKjvKj5Wgs/+83PUXLUiIiYU6c9NQfSIezzdFnWCq7+/e/G13ERERHduxPg4jBjPsUnEG+kSERERqWJYIiIiIlLB03BuZLfbceDjv+LTdXvwzdlL0Bl0mDzLhBlLMjDo3ihPt0dERERt4ABvFa4cIGa32/GbOW/hi//+EhqNpNxkVuujgcZHi9/sXIn7Hx7tiraJiIj6NK+6kS513v+s34dDn8gzlztumrvD3uJAi60Fr8/8/9FQ1+ip9oiIiKgdDEtuIITAJ2t3QaDtg3jCIVBnqcfBzX91c2dERETUEYYlN6i9fgPmsiq0k5UAAFofLc4fuei+poiIiKhTGJbcQOuj7WQd/3cQERF5G347u0E/oz/uHRcLjab9m6jaW+wY/8j9buyKiIiIOoNhyU3mLH/CaWD3zTRaDQYOi0Bqxlg3d0VEREQdYVhykwdnTsDP/s8cAIDm76fbJEk+0hQSNQDZe/+506friIiIyH04z5KK7riRbtmZcux6Lxdfn/4Whn5+eGBGKh6aMxGGfn4u2T4REVFf5+rvb4YlFd0RloiIiKh7cVJKIiIiIjdiWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCoYlIiIiIhUMS0REREQqGJaIiIiIVDAsEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxIRERGRCh9PN0DUW9W11OG7hu+hhQYx/QZDp/H1dEtERHQHGJaIXKy+pR4fl2/F4erDaBF2AIC/1h/pkT/CP0Y9Bo3EA7pE1LNdqv8OB6vycbnRDIPWgJTg8UgecD98NL0zVvTOd0Xdwi7sKLtRhkZHEwb6RSJEH+LplrxOk70J2RfW4Lv67+GAQ3m93l6P7d9/iqrGKrwwbB4kSfJgl0REd0YIgT999wl2Xt4NDTRwwAEJEo5fL0SUXxSWj/wFgnSBnm7T5RiWqENCCOyvOog/f/8/sLZYlddHBybgp4N/gnC/MA92510OXMnHpfrvICDaXP7X6gI8FD4Z9/Yf7ubOiIju3hdXD2Hn5d0AoPxC2PrvnbnRjN9fXIdfxf+y1/1CyPMB1KEdl3fhj9/+l1NQAoCzlnN449yvcbXpqoc68z4Hqg62G5QAQAMN8q984caOiIhcQwiBHRW7213ugAOldV+j9EapG7tyD4YlUlVjq8H27z5tc5kDDtS11OPP3+9wc1fe65rtmupyBxyoaqxyUzdERK5z1VaNqib1f7800OCU5YybOnIfhiVS9dfqAtUjJQ44UFB9BE32Jjd25b38tf1Ul2uggdHX6KZuvIAQQH09UFMj/yna/ywRkXezO1o6rJEkCS2i47qehmOWSFV10zVoJA3sf7+qqy0togU3Wm5Ar9W7sTPv9GDoROy6vMdpcPfNHHBgYqjJzV15QGMjUFQEHDoElJQALS2Ajw8QFwc88ACQlAT4+Xm6SyLqglB9KPy1/qi317dbYxd2DO03xH1NuQnDEqnq7xMA0cHRAAkS/H383dSRd0uLmIqDVz5HXUvdbYFJAw2G9BuCxKAxHurOTUpKgA8+AEpLAUkCQkKAfv2A5mbg2DHg6FEgNhaYN08OT0TUI/hofDA1fAp2Xt7d5hkHCRKMvkbcH5Tk/ua6GU/DkSpT6IR2j5IAcgC4PygJBq3BjV15ryBdIF4btQL3GKIAyP94SJCvChkTNBq/GLEUWknryRa7V0kJsHat/GdcHDBqFBAeDgwYIP85apT8+s11RNRj/OM9j7V5Na8GGug0Ovx8eGavnGtJEh0dNujDrFYrAgMDYbFYYDT2oXEmt/i/ZRvx+ZUvbvtNQoIEH8kHq+77Z8T4R3uoO+8khEDpjVJ8XVcGjaRFQuB9iPSL8HRb3auxEXj9dTkAxcfLR5XaIwRw7pwcnFav5ik5oh6k2dGM/CtfIK9yP6qarkCv0WNCSCrSI3+ECL9wT7cHwPXf370v/pHLzR3yE/hp9Pisar/T2KVQfQheHDafQakNkiQhrn8c4vr3odNMRUXyqbfhw9WDEiAvj4uT60+eBFJT3dIiEd09X40v0iIeRlrEw55uxW0YlqhDWkmLpwfPwY+jMnCy5hQaHU2IMgzEyP4jeOsOkgkhD+aWJECn69w6er1c/8UXQEpKxwGLiMhDGJao0/r79scDYRM93QZ5o4YG+fRbSBdvgRMSIq/X0AD48yIBIvJOPCxARHfPZpOnB/D17dp6Pj7yejZb9/RFROQCDEtEdPd0Ojn4NDd3bb3W+Zc6e+qOiMgDeBqOqAersdUgr+oADl89gnp7PSL9IvBw+EMwhUxw7+W7BoM8YPvYMXmKgM6qrgbGj5fXJyLyUjyyRNRDfVf/PX55ehV2VuzGVdtV1NvrUVb3Dd4v+xC/+9taNDu6eJTnbkiSPDO3EJ0/pdbUJNc/+CAHdxORV2NYIuqBHMKBtRffQYO9wWnS0Na5sM5Zz+PTCjff4DgpSZ6Z++LFju8BJ4Q8sDs2FkhMdEt7RER3imGJqAc6azmHqqYr7c6uLiCQV7nfvUeX/PzkW5hERsoTTja1c3PlpiZ5eWQkMH8+J6QkIq/HMUtEPVDJjVJooFG9FU29vQGVjZUY5D/IfY3FxQGLF99+b7jWq96qq+WjSnFxclCKjXVfb0REd4hhiagH6uxkoB6ZNLT1FiYnT8oTTpaUAPX1cmAaP14eo5SYyCNKRNRjMCwR9UD3GUfhk+//rFoT5BuESL9I9zR0Kz8/+RYmKSnyhJM2mzw9gMHAwdxE1ONwzBJRDxQbEIuh/YZAo/JXePrAdM/fjkaS5Jm5g4LkPxmUiKgHYlgi6oEkScLPhy9EmD5Mfg45hLSGp8lhD2JaRJpbe7ILO8rrL6Gs7hs02dsZ3E1E1AN1KSxlZ2dj/Pjx6N+/P8LDw/HEE0+guLjYqaaxsRGZmZkICQlBQEAAZs6cicrKSqea8vJyZGRkwN/fH+Hh4Vi2bBlaWlqcag4ePIixY8dCr9cjLi4OOTk5t/Wzfv16DBkyBH5+fkhNTcXRo0e73AtRTxWsG4Bfj/4XzBv6HOKNozDEfzBSQ1KwcuSreG7IXLcdVXIIB3Zf3oslX/0CvzrzL/iXs/8Hi75aio++3czQRES9Qpf+Nc3Pz0dmZiaOHDmC3NxcNDc3Y9q0aairq1Nqli5dih07dmDbtm3Iz89HRUUFZsyYoSy32+3IyMiAzWbD4cOHsXHjRuTk5GDVqlVKTVlZGTIyMjBlyhQUFRVhyZIlmD9/Pvbt26fUbNmyBVlZWVi9ejVOnDiBxMREpKeno6qqqtO9EPV0Oo0Ok8IewKsjX8HrCauwIPYFjDSOgOSm011CCOR880dsubQN1har8nqTowm5lZ9hTfG/webO6QuIiLqBJERHs8e178qVKwgPD0d+fj4mTZoEi8WCsLAwbNq0CbNmzQIAXLhwAaNGjUJBQQEmTJiAPXv24LHHHkNFRQUiIiIAAO+++y6WL1+OK1euQKfTYfny5di1axfOnDmj/Kw5c+agpqYGe/fuBQCkpqZi/PjxWLduHQDA4XAgOjoaixYtwooVKzrVS0esVisCAwNhsVhgNBrvdDcR9VoXa0vw6/PZqjU/HfwMpkY87KaOiIhc//19V8fpLRYLACA4OBgAUFhYiObmZqSl/TBWYuTIkYiJiUFBQQEAoKCgAKNHj1aCEgCkp6fDarXi7NmzSs3N22itad2GzWZDYWGhU41Go0FaWppS05lebtXU1ASr1er0IKL25V/5XHWQOQDsrzronmaIiLrJHYclh8OBJUuWYOLEiUhISAAAmM1m6HQ6BAUFOdVGRETAbDYrNTcHpdblrcvUaqxWKxoaGnD16lXY7fY2a27eRke93Co7OxuBgYHKIzo6upN7g6hvMjdWqk6MCQBXmq66qRsiou5xx2EpMzMTZ86cwebNm13Zj0etXLkSFotFeVy6dMnTLRF5tQCfAOVKvPYYtAY3dUNE1D3uKCwtXLgQO3fuxIEDBzBo0A+3UoiMjITNZkNNTY1TfWVlJSIjI5WaW69Ia33eUY3RaITBYEBoaCi0Wm2bNTdvo6NebqXX62E0Gp0eRNQ+U0iqcvPetmigwcTQf3BjR0RErtelsCSEwMKFC7F9+3bs378fQ4cOdVqenJwMX19f5OXlKa8VFxejvLwcJpMJAGAymXD69Gmnq9Zyc3NhNBoRHx+v1Ny8jdaa1m3odDokJyc71TgcDuTl5Sk1nemFiO5O8oCxiDYManPckgYaGLQG/IiDu4moh+vS1XAvv/wyNm3ahE8//RQjRoxQXg8MDITBIB9qf+mll7B7927k5OTAaDRi0aJFAIDDhw8DkKcOSEpKQlRUFNasWQOz2Yxnn30W8+fPx7/+678CkKcOSEhIQGZmJp5//nns378fP//5z7Fr1y6kp6cDkKcOmDt3Lt577z2kpKTg7bffxtatW3HhwgVlLFNHvXSEV8MRdczaXIs/lLyL87UXoIEEQIIDDkT6RWBRXCYG+d/j6RaJqI9x+fe36AIAbT4+/PBDpaahoUG8/PLLYsCAAcLf3188+eST4vLly07b+eabb8T06dOFwWAQoaGh4pVXXhHNzc1ONQcOHBBJSUlCp9OJYcOGOf2MVu+8846IiYkROp1OpKSkiCNHjjgt70wvaiwWiwAgLBZLp9ch6qu+rSsXuyr2iP/5fqc4azknHA6Hp1sioj7K1d/fdzXPUm/HI0t0p4SwAY1/gWg+BUg+kHSTAF2q2yaLJCLqy1z9/e3jgp6I6CbCVghx/WVAXEfrXzFR9z7gMxIY8O+QtG1fYEBERN6JN9IlciHR8i3EtecBYfn7Ky1/fwBouQhx7afyUSciIuoxGJaIXEjUfwjABrQ5UaMdsH8DNO5rYxkREXkrhiUiV2rYBcCuUqCBaNzrrm6IiMgFGJaIXEk0dFDgAEStW1ohIiLXYFgiciWfoYDq7T+0gE+cu7ohIiIXYFgiciHJ/xlA5fYfgB2SYba72iEiIhdgWKK+RQigvh6oqZH/dPU0Y4aZgG4ibj+69Pfn/TIh+Y64dS0iIvJinGeJ+obGRqCoCDh0CCgpAVpaAB8fIC4OeOABICkJ8PO76x8jSb7AgPeAuv+AqP8vwFEtL9DGQgp4EZLhibv+GURE5F6cwVsFZ/DuJUpKgA8+AEpLAUkCQkIAX1+guRmorpaPLsXGAvPmyeHJRYRoARxVAHwATRhn7yYichPO4E3UFSUlwNq1gNkMDB8O6HTOy8PDAZsNuHhRrlu82GWBSZJ8AG2US7ZFRESewzFL1Hs1NspHlMxmID7+9qDUSqeTl5vNcn1jo3v7JCIir8awRHdMCBtE8xkI20kIR72n27ldUZF86m34cPn0mxpJko8olZYCJ0+6pT0iIuoZeBqOukyIFqDuXYi6jT/cA00yQBhmQ+q/FJJk8GyDgDwO6dAhOQS1d0TpVnq9XP/FF0BKSscBi4iI+gQeWaIuEUJAWJZB3HjnppvFQp65uv6PENfmeceNYhsa5PFKISFdWy8kRF6voaOZuImIqK9gWKKusR0GGneh7YkXHUDzcaDhU3d3dTubTZ4ewNe3a+v5+Mjr2bwg8BERkVdgWKIuEfVbAWhVKiSI+o/d1U77dDo5+DQ3d2291vmXOnvqjoiIej2GJeoa+zcA7CoFArBfclMzKgwGecB2dXXX1quultcztD/uSrSUQDT8GaJhF4Tj2l02SkRE3o4DvKlrNMGQM7ZDpSbITc2okCR5Zu6jR+VTap05UtTUJA8Mf/DBNgd3i5bvICwrgOajN73qA2GYCcn4GiRJ77r+iYjIa/DIEnWJZPhHqAYlaCAZnnRXO+qSkuSZuS9e7PgecELIA7tjY4HExNsX26shrs0BmgtvWdICNGyDuL4QnAyfiKh3YliirvF7FPCJQ9vjlrTykSf/Oe7uqm1+fvItTCIjgXPn5CNHbWlqkpdHRgLz57d5jzhR/8e/3+etrVOQDsCWD9iOtrGMiIh6OoYl6hJJ0kMa8EfAN/nvr2igfIx84iAFb4KkCfZUe7eLi/vhFialpcD580BVFXDtmvzn+fPy63FxwJIl8pGltjRsg/pYLS1E4/ZueANERORpHLNEXSZpQyGF/BdE83l5KgHhAHTJgO/93nmz2Lg4YPVqeWbuL76QT7fV18tXvY0fL49RSkxs84iSwnG9gx9iB+xVLm2biIi8A8MS3THJdxTgO8rTbXSOnx+QmirPzN3Q8MOgb4OhczN1a0IBR6VKgRbQRrqsXSIi8h48DUd9iyQB/v5AUJD8Z2ePhBn+F9T/utghGWa4oEEiIvI2DEtEnSD1+ymgHYi2B7ZLgD79pnFcRETUmzAsEXWCpAmCFLwF0E8CcPPRKD3g/xykoN9553gtIiK6axyzRNRJkjYc0oD3IOzfA81nAfgCuvGQNAGebo2IiLoRwxJRF0naewDtPZ5ug4iI3ISn4YiIiIhUMCwRERERqWBYIiIiIlLBsERERESkgmGJiIiISAXDEhEREZEKhiUiIiIiFQxLRERERCoYloiIiIhUcAZvFUIIAIDVavVwJ0RERNRZrd/brd/jd4thSUVtbS0AIDo62sOdEBERUVfV1tYiMDDwrrcjCVfFrl7I4XCgoqIC/fv35x3lVVitVkRHR+PSpUswGo2ebqfH4/50He5L1+L+dC3uT9e6eX/2798ftbW1iIqKgkZz9yOOeGRJhUajwaBBgzzdRo9hNBr5F96FuD9dh/vStbg/XYv707Va96crjii14gBvIiIiIhUMS0REREQqGJborun1eqxevRp6vd7TrfQK3J+uw33pWtyfrsX96VrduT85wJuIiIhIBY8sEREREalgWCIiIiJSwbBEREREpIJhiYiIiEgFwxK16fPPP8ePf/xjREVFQZIk/PnPf3ZaLoTAqlWrMHDgQBgMBqSlpeHixYtONdeuXcMzzzwDo9GIoKAgzJs3Dzdu3HDju/AeHe3Pn/3sZ5AkyenxyCOPONVwf8qys7Mxfvx49O/fH+Hh4XjiiSdQXFzsVNPY2IjMzEyEhIQgICAAM2fORGVlpVNNeXk5MjIy4O/vj/DwcCxbtgwtLS3ufCteoTP786GHHrrt87lgwQKnGu5P2YYNGzBmzBhlYkSTyYQ9e/Yoy/nZ7JqO9qe7PpsMS9Smuro6JCYmYv369W0uX7NmDX7/+9/j3XffxZdffol+/fohPT0djY2NSs0zzzyDs2fPIjc3Fzt37sTnn3+OF1980V1vwat0tD8B4JFHHsHly5eVx8cff+y0nPtTlp+fj8zMTBw5cgS5ublobm7GtGnTUFdXp9QsXboUO3bswLZt25Cfn4+KigrMmDFDWW6325GRkQGbzYbDhw9j48aNyMnJwapVqzzxljyqM/sTAF544QWnz+eaNWuUZdyfPxg0aBDefPNNFBYW4vjx43j44Yfx+OOP4+zZswD42eyqjvYn4KbPpiDqAACxfft25bnD4RCRkZHit7/9rfJaTU2N0Ov14uOPPxZCCHHu3DkBQBw7dkyp2bNnj5AkSXz//fdu690b3bo/hRBi7ty54vHHH293He7P9lVVVQkAIj8/XwghfxZ9fX3Ftm3blJrz588LAKKgoEAIIcTu3buFRqMRZrNZqdmwYYMwGo2iqanJvW/Ay9y6P4UQYvLkyWLx4sXtrsP9qW7AgAHi/fff52fTRVr3pxDu+2zyyBJ1WVlZGcxmM9LS0pTXAgMDkZqaioKCAgBAQUEBgoKCMG7cOKUmLS0NGo0GX375pdt77gkOHjyI8PBwjBgxAi+99BKqq6uVZdyf7bNYLACA4OBgAEBhYSGam5udPp8jR45ETEyM0+dz9OjRiIiIUGrS09NhtVqdfmPti27dn60++ugjhIaGIiEhAStXrkR9fb2yjPuzbXa7HZs3b0ZdXR1MJhM/m3fp1v3Zyh2fTd5Il7rMbDYDgNOHr/V56zKz2Yzw8HCn5T4+PggODlZq6AePPPIIZsyYgaFDh6K0tBS//OUvMX36dBQUFECr1XJ/tsPhcGDJkiWYOHEiEhISAMifPZ1Oh6CgIKfaWz+fbX1+W5f1VW3tTwB4+umnMXjwYERFReHUqVNYvnw5iouL8cknnwDg/rzV6dOnYTKZ0NjYiICAAGzfvh3x8fEoKiriZ/MOtLc/Afd9NhmWiLzAnDlzlP8ePXo0xowZg9jYWBw8eBBTp071YGfeLTMzE2fOnMGhQ4c83Uqv0N7+vHls3OjRozFw4EBMnToVpaWliI2NdXebXm/EiBEoKiqCxWLBn/70J8ydOxf5+fmebqvHam9/xsfHu+2zydNw1GWRkZEAcNsVHJWVlcqyyMhIVFVVOS1vaWnBtWvXlBpq37BhwxAaGoqSkhIA3J9tWbhwIXbu3IkDBw5g0KBByuuRkZGw2Wyoqalxqr/189nW57d1WV/U3v5sS2pqKgA4fT65P3+g0+kQFxeH5ORkZGdnIzExEWvXruVn8w61tz/b0l2fTYYl6rKhQ4ciMjISeXl5ymtWqxVffvmlch7ZZDKhpqYGhYWFSs3+/fvhcDiUDzO177vvvkN1dTUGDhwIgPvzZkIILFy4ENu3b8f+/fsxdOhQp+XJycnw9fV1+nwWFxejvLzc6fN5+vRppwCam5sLo9GoHN7vKzran20pKioCAKfPJ/dn+xwOB5qamvjZdJHW/dmWbvts3uFgdOrlamtrxVd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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------ Centers --------\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[1.42637640e+02, 2.53448200e+05],\n", + " [1.13742100e+02, 4.32386500e+05],\n", + " [1.63969600e+02, 3.01264429e+05],\n", + " [1.81006040e+02, 2.11930600e+05],\n", + " [1.26052733e+02, 1.82886333e+05]])" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans = KMeans(n_clusters=5)\n", + "kmeans.fit(data_to_fit)\n", + "centers = kmeans.cluster_centers_\n", + "plt.scatter(centers[:, 0], centers[:, 1], c='red', s=100, alpha=0.5);\n", + "plt.scatter(x, y, c=kmeans.labels_)\n", + "plt.show()\n", + "print(\"------ Centers --------\")\n", + "centers" + ] + }, + { + "cell_type": "markdown", + "id": "5d3507ce", + "metadata": { + "papermill": { + "duration": 0.025334, + "end_time": "2025-02-16T13:30:07.631027", + "exception": false, + "start_time": "2025-02-16T13:30:07.605693", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Unitary Authority" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "df01d38d", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:07.687718Z", + "iopub.status.busy": "2025-02-16T13:30:07.686337Z", + "iopub.status.idle": "2025-02-16T13:30:10.573565Z", + "shell.execute_reply": "2025-02-16T13:30:10.572429Z" + }, + "papermill": { + "duration": 2.918096, + "end_time": "2025-02-16T13:30:10.576039", + "exception": false, + "start_time": "2025-02-16T13:30:07.657943", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameArea (sq km)est_pop
0DARLINGTON197.477997894.0
24298HARTLEPOOL93.717090152.0
45279MIDDLESBROUGH53.8816141233.0
79564STOCKTON-ON-TEES204.9331183795.0
232679BLACKPOOL34.8709142270.0
265981WARRINGTON180.6279191202.0
1043317YORK271.9314181291.0
1600231DERBY78.0311230726.0
1661744LEICESTER73.3421282757.0
1755801NOTTINGHAM74.6132268939.0
2020112STOKE-ON-TRENT93.4485240422.0
2559930BEDFORD476.4082148113.0
2601891LUTON43.3525185889.0
2642153PETERBOROUGH343.3782157439.0
2695524SOUTHEND-ON-SEA41.6742160362.0
4083886MILTON KEYNES308.6267212707.0
4140194PORTSMOUTH40.3816188043.0
4165294READING40.3888144684.0
4231578SLOUGH32.5419120577.0
4262177SOUTHAMPTON49.8808219539.0
4347818WOKINGHAM178.9690150334.0
4766901ISLES OF SCILLY16.31772140.0
4767083PLYMOUTH79.8497240954.0
4821786SWINDON230.0933180129.0
4997110CONWY1125.8226109674.0
4999239WREXHAM503.7739128540.0
5019603SWANSEA377.6145223463.0
5065066BRIDGEND250.7852128735.0
5084286CARDIFF140.9144310088.0
5153665MERTHYR TYDFIL111.446356207.0
5160445CAERPHILLY277.3879169546.0
5169995NEWPORT190.4311137642.0
\n", + "
" + ], + "text/plain": [ + " Name Area (sq km) est_pop\n", + "0 DARLINGTON 197.4779 97894.0\n", + "24298 HARTLEPOOL 93.7170 90152.0\n", + "45279 MIDDLESBROUGH 53.8816 141233.0\n", + "79564 STOCKTON-ON-TEES 204.9331 183795.0\n", + "232679 BLACKPOOL 34.8709 142270.0\n", + "265981 WARRINGTON 180.6279 191202.0\n", + "1043317 YORK 271.9314 181291.0\n", + "1600231 DERBY 78.0311 230726.0\n", + "1661744 LEICESTER 73.3421 282757.0\n", + "1755801 NOTTINGHAM 74.6132 268939.0\n", + "2020112 STOKE-ON-TRENT 93.4485 240422.0\n", + "2559930 BEDFORD 476.4082 148113.0\n", + "2601891 LUTON 43.3525 185889.0\n", + "2642153 PETERBOROUGH 343.3782 157439.0\n", + "2695524 SOUTHEND-ON-SEA 41.6742 160362.0\n", + "4083886 MILTON KEYNES 308.6267 212707.0\n", + "4140194 PORTSMOUTH 40.3816 188043.0\n", + "4165294 READING 40.3888 144684.0\n", + "4231578 SLOUGH 32.5419 120577.0\n", + "4262177 SOUTHAMPTON 49.8808 219539.0\n", + "4347818 WOKINGHAM 178.9690 150334.0\n", + "4766901 ISLES OF SCILLY 16.3177 2140.0\n", + "4767083 PLYMOUTH 79.8497 240954.0\n", + "4821786 SWINDON 230.0933 180129.0\n", + "4997110 CONWY 1125.8226 109674.0\n", + "4999239 WREXHAM 503.7739 128540.0\n", + "5019603 SWANSEA 377.6145 223463.0\n", + "5065066 BRIDGEND 250.7852 128735.0\n", + "5084286 CARDIFF 140.9144 310088.0\n", + "5153665 MERTHYR TYDFIL 111.4463 56207.0\n", + "5160445 CAERPHILLY 277.3879 169546.0\n", + "5169995 NEWPORT 190.4311 137642.0" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rows = data.Geography.str.contains('Unitary Authority')\n", + "cols = ['Name', 'Area (sq km)', 'est_pop']\n", + "data.loc[rows, cols].drop_duplicates().dropna()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "86541075", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:10.630127Z", + "iopub.status.busy": "2025-02-16T13:30:10.629427Z", + "iopub.status.idle": "2025-02-16T13:30:13.460318Z", + "shell.execute_reply": "2025-02-16T13:30:13.458783Z" + }, + "papermill": { + "duration": 2.861789, + "end_time": "2025-02-16T13:30:13.464277", + "exception": false, + "start_time": "2025-02-16T13:30:10.602488", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(32, 3)\n", + "Area (sq km) float64\n", + "est_pop float64\n", + "pp_sq_m float64\n", + "dtype: object\n", + " Area (sq km) est_pop pp_sq_m\n", + "count 32.000000 32.000000 32.000000\n", + "mean 194.278537 169546.437500 1874.322193\n", + "std 214.988748 65004.272087 1571.624909\n", + "min 16.317700 2140.000000 97.416769\n", + "25% 52.881400 135415.250000 572.163541\n", + "50% 126.180350 164954.000000 929.406800\n", + "75% 256.071750 214415.000000 3587.820838\n", + "max 1125.822600 310088.000000 4656.650554\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: est_pop R-squared: 0.020\n", + "Model: OLS Adj. R-squared: -0.012\n", + "Method: Least Squares F-statistic: 0.6248\n", + "Date: Sun, 16 Feb 2025 Prob (F-statistic): 0.435\n", + "Time: 13:30:13 Log-Likelihood: -399.20\n", + "No. Observations: 32 AIC: 802.4\n", + "Df Residuals: 30 BIC: 805.3\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------\n", + "const 1.779e+05 1.57e+04 11.337 0.000 1.46e+05 2.1e+05\n", + "Area (sq km) -43.1873 54.637 -0.790 0.435 -154.772 68.397\n", + "==============================================================================\n", + "Omnibus: 2.149 Durbin-Watson: 2.032\n", + "Prob(Omnibus): 0.341 Jarque-Bera (JB): 1.004\n", + "Skew: -0.293 Prob(JB): 0.605\n", + "Kurtosis: 3.640 Cond. No. 390.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "---- params / coeficient -------\n", + "const 177936.800432\n", + "Area (sq km) -43.187287\n", + "dtype: float64\n", + "------------p values----------\n", + "const 2.277620e-12\n", + "Area (sq km) 4.354778e-01\n", + "dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geo = extract('Unitary Authority')\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "print(geo.shape)\n", + "visualise(geo)\n" + ] + }, + { + "cell_type": "markdown", + "id": "cf1505a4", + "metadata": { + "papermill": { + "duration": 0.026583, + "end_time": "2025-02-16T13:30:16.428036", + "exception": false, + "start_time": "2025-02-16T13:30:16.401453", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A cluster analysis - KMeans - suggest 5 centroids may be suitable. The population appears to impact on the centroid over the area. " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "3218ecd6", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:16.483867Z", + "iopub.status.busy": "2025-02-16T13:30:16.482786Z", + "iopub.status.idle": "2025-02-16T13:30:16.866963Z", + "shell.execute_reply": "2025-02-16T13:30:16.865883Z" + }, + "papermill": { + "duration": 0.414679, + "end_time": "2025-02-16T13:30:16.869336", + "exception": false, + "start_time": "2025-02-16T13:30:16.454657", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = geo['Area (sq km)'].values\n", + "y = geo['est_pop'].values\n", + "data_to_fit = list(zip(x, y))\n", + "inertias = []\n", + "\n", + "for i in range(1,11):\n", + " kmeans = KMeans(n_clusters=i)\n", + " kmeans.fit(data_to_fit)\n", + " inertias.append(kmeans.inertia_)\n", + "\n", + "plt.plot(range(1,11), inertias, marker='o')\n", + "plt.title('Elbow method')\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('Inertia')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "045b53fb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:16.926883Z", + "iopub.status.busy": "2025-02-16T13:30:16.925924Z", + "iopub.status.idle": "2025-02-16T13:30:17.172277Z", + "shell.execute_reply": "2025-02-16T13:30:17.171293Z" + }, + "papermill": { + "duration": 0.277234, + "end_time": "2025-02-16T13:30:17.174719", + "exception": false, + "start_time": "2025-02-16T13:30:16.897485", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------ Centers --------\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[2.64922342e+02, 1.28320667e+05],\n", + " [1.64575217e+02, 2.27968500e+05],\n", + " [6.38820000e+01, 2.91735000e+04],\n", + " [1.81528900e+02, 1.77521778e+05],\n", + " [9.62899000e+01, 2.87261333e+05]])" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans = KMeans(n_clusters=5)\n", + "kmeans.fit(data_to_fit)\n", + "centers = kmeans.cluster_centers_\n", + "plt.scatter(centers[:, 0], centers[:, 1], c='red', s=100, alpha=0.5);\n", + "plt.scatter(x, y, c=kmeans.labels_)\n", + "plt.show()\n", + "print(\"------ Centers --------\")\n", + "centers" + ] + }, + { + "cell_type": "markdown", + "id": "aa621d2a", + "metadata": { + "papermill": { + "duration": 0.027809, + "end_time": "2025-02-16T13:30:17.230548", + "exception": false, + "start_time": "2025-02-16T13:30:17.202739", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### All data" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "f37c0bdd", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:17.288901Z", + "iopub.status.busy": "2025-02-16T13:30:17.288514Z", + "iopub.status.idle": "2025-02-16T13:30:20.348950Z", + "shell.execute_reply": "2025-02-16T13:30:20.347844Z" + }, + "papermill": { + "duration": 3.092874, + "end_time": "2025-02-16T13:30:20.351303", + "exception": false, + "start_time": "2025-02-16T13:30:17.258429", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(315669, 4)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geo = data_num\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "geo = geo.drop_duplicates().dropna()\n", + "geo = geo.loc[geo.est_pop < 9e5]\n", + "print(geo.shape)\n", + "visualise(geo)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "f9b66fcb", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:20.411142Z", + "iopub.status.busy": "2025-02-16T13:30:20.410173Z", + "iopub.status.idle": "2025-02-16T13:30:39.848527Z", + "shell.execute_reply": "2025-02-16T13:30:39.847436Z" + }, + "papermill": { + "duration": 19.470328, + "end_time": "2025-02-16T13:30:39.850622", + "exception": false, + "start_time": "2025-02-16T13:30:20.380294", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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2qWxMoU8FAIC6RlC5DPpUAAAwD0HlMuJjz42oOByGydUAAOBZCCqX0SUqSH4+VmWeKdKBk7lmlwMAgEchqFyGj5dVPaNDJLGeCgAAdY2gUgVllylvJKgAAFCnCCpV0CemtKE2hYZaAADqEkGlCnrHhMpikVJOnVF6Tr7Z5QAA4DFMDSrTpk2TxWIpd+vYsaOZJVUqyM9HHSOCJEmbmP4BAKDOmD6i0qVLFx07dsx5W716tdklVSq+dPqHhloAAOqOt+kFeHsrIiLC7DIuKz42VB+vS6FPBQCAOmT6iMq+ffsUFRWl1q1ba9y4cTp06JDZJVWqbOG3nUezdaaw2ORqAADwDKYGlf79+2vBggVavHix5s6dq+TkZA0ZMkQ5OTmVnl9QUKDs7Oxyt7rSIsRfUcF+sjsMJR3KrLP3BQDAk5kaVEaPHq3bbrtN3bt318iRI/Xvf/9bmZmZ+uKLLyo9f/r06QoODnbeoqOj67Te85fTBwAAtc/0qZ/zhYSEqH379vrll18qfTwhIUFZWVnOW2pqap3WV7ZBYSIbFAIAUCfcKqjk5uZq//79ioyMrPRxm82moKCgcre6FB9TMqKy5VCm7GxQCABArTM1qDz55JNasWKFDh48qJ9++kn/8z//Iy8vL91xxx1mlnVRHSICFWjzVm5BsXan1V1/DAAAnsrUoHL48GHdcccd6tChg26//XY1adJE69atU7Nmzcws66K8rBb1KltOn/VUAACodaauo7Jw4UIz375G+saEauXeE9qYkqG7B8aaXQ4AAA2aW/Wo1Ad9yhpqk0/LMOhTAQCgNhFUqqlndIi8rRalZefrSOZZs8sBAKBBI6hUUyNfb3VpESxJ2sR6KgAA1CqCSg2c26CQ9VQAAKhNBJUa6BvLlT8AANQFgkoN9Cld+G3P8RxlnS0yuRoAABougkoNNAu0KbZJIxmGtPkQoyoAANQWgkoNlW1QuInpHwAAag1BpYZoqAUAoPYRVGqobEQlKTVThcUOk6sBAKBhIqjUUJtmAQpt5KOCYod2Hs0yuxwAABokgkoNWSwW59U/XKYMAEDtIKhcAed6Kin0qQAAUBsIKlcg/ryF39igEAAA1yOoXIGuLYLl623VqbxCJZ/MM7scAAAaHILKFbB5e6lnyxBJ0kY2KAQAwOUIKleoj3P6hz4VAABcjaByhc411DKiAgCAqxFUrlDvViVB5cCJPJ3KLTC5GgAAGhaCyhUKaeSr9s0bS2JUBQAAVyOouIBzg0KCCgAALkVQcQE2KAQAoHYQVFygb+mIyo4jWcovsptcDQAADQdBxQVahvorPNCmIruhramZZpcDAECDQVBxAYvF4hxVoaEWAADXIai4SDwLvwEA4HIEFReJjzk3ouJwsEEhAACuQFBxkU6RgWrk66Wc/GLtTc8xuxwAABoEgoqLeHtZ1atViCRp40H6VAAAcAWCigs5p3/oUwEAwCUIKi7ElT8AALgWQcWFerYKkdUiHc44q2NZZ80uBwCAeo+g4kKNbd7qHBUkiT4VAABcgaDiYmV9KmxQCADAlSOouFjZwm9sUAgAwJUjqLhY2YjKrmPZyi0oNrkaAADqN4KKi0UE+6llqL8chrTlENM/AABcCYJKLSi7TDmRhloAAK4IQaUWlPWpbEqhTwUAgCtBUKkFZX0qWw5lqsjuMLkaAADqL4JKLWgX3lhBft46U2jXrmPZZpcDAEC9RVCpBVarRX1iSqZ/WPgNAICaI6jUknjnvj/0qQAAUFMElVri3KDwYIYMwzC5GgAA6ieCSi3p3jJYPl4WpecUKPU0GxQCAFATBJVa4ufjpW4tgiWxnD4AADVFUKlF5/pUaKgFAKAmCCq1KN555Q8jKgAA1ARBpRaVXaK8Lz1XmWcKTa4GAID6h6BSi5o0tql1swBJ0iamfwAAqDaCSi3rG8MGhQAA1BRBpZb1YYNCAABqzG2CyowZM2SxWDRx4kSzS3GpsoXfth7OUkGx3eRqAACoX9wiqCQmJur9999X9+7dzS7F5WKbNFLTxr4qLHZox5Ess8sBAKBeMT2o5Obmaty4cfrggw8UGhpqdjkuZ7Gc26CQPhUAAKrH9KAyfvx43XDDDRoxYoTZpdSac/v+0KcCAEB1eJv55gsXLtTmzZuVmJhYpfMLCgpUUFDgvJ+dnV1bpblU2YjKppQMORyGrFaLyRUBAFA/mDaikpqaqscee0x/+9vf5OfnV6XnTJ8+XcHBwc5bdHR0LVfpGl2iguXnY1XGmSIdOJlrdjkAANQbpgWVTZs2KT09Xb1795a3t7e8vb21YsUKzZ49W97e3rLbK14hk5CQoKysLOctNTXVhMqrz9fbqp7RIZLoUwEAoDpMm/oZPny4tm/fXu7Yvffeq44dO+qPf/yjvLy8KjzHZrPJZrPVVYkuFR8TpnUHTmvjwQzd0a+V2eUAAFAvmBZUAgMD1bVr13LHAgIC1KRJkwrHG4L40oXfNrLwGwAAVWb6VT+eondMqCwWKeXUGaXn5JtdDgAA9YKpV/1caPny5WaXUGuC/HzUoXmgdqflaNPBDI3uFml2SQAAuD1GVOpQ2XoqNNQCAFA1BJU6FM8GhQAAVAtBpQ7Fl46o7DiarTOFxSZXAwCA+yOo1KEWIf6KCvaT3WEoKTXT7HIAAHB7BJU61se57w99KgAAXA5BpY71jS3bSZk+FQAALoegUsfKNijccihTdodhcjUAALg3gkod6xgRpMY2b+UWFGt3Wv3Y/RkAALMQVOqYl9Wi3qWjKvSpAABwaQQVE8SXBZUUggoAAJdCUDGBc4NCGmoBALgkgooJekaHyMtq0bGsfB3JPGt2OQAAuC2Cigka+Xqra1SQJEZVAAC4FIKKSeKdGxQSVAAAuBiCikniufIHAIDLIqiYpE9pQ+2e4znKOltkcjUAALgngopJwgP9FNOkkQxD2nKIURUAACpDUDFRfAwbFAIAcCkEFROxQSEAAJdGUDFR2cJvWw9nqrDYYXI1AAC4H4KKido0a6zQRj7KL3Jo59Ess8sBAMDtEFRMZLFY1Ke0T2UT+/4AAFABQcVk8fSpAABwUQQVk/WNPbfwm2EYJlcDAIB7IaiYrGuLYPl6W3Uqr1AHT50xuxwAANwKQcVkNm8v9WgZLInpHwAALkRQcQNlGxSykzIAAOURVNyAc4NCrvwBAKAcgoob6FMaVA6cyNOp3AKTqwEAwH0QVNxASCNftQtvLIn1VAAAOB9BxU04+1QIKgAAOBFU3AQbFAIAUBFBxU3Ely6lv+NIlvKL7CZXAwCAeyCouInoMH+FB9pUZDe0NTXT7HIAAHALBBU3YbFYnPv+0KcCAEAJgoobKZv+YeE3AABKEFTcSN/SK382pWTI4WCDQgAACCpupFNkoBr5eik7v1j70nPNLgcAANMRVNyIt5dVvVqFSOIyZQAAJIKK2+kTc276BwAAT0dQcTMs/AYAwDkEFTfTq1WorBbpcMZZpWXlm10OAACmIqi4mcY2b3WKDJIkbUxhVAUA4NkIKm6o7DLljQfpUwEAeDaCihuKp08FAABJBBW3VLZC7a5j2cotKDa5GgAAzENQcUMRwX5qGeovhyFtOcT0DwDAcxFU3FR8TOkGhfSpAAA8GEHFTcWXNdRy5Q8AwIMRVNxU2ZU/Ww5lqtjuMLkaAADMQVBxU+3CGyvIz1tnCu3adSzH7HIAADAFQcVNWa0W9YnhMmUAgGczNajMnTtX3bt3V1BQkIKCgjRgwAB9//33ZpbkVsr6VNigEADgqUwNKi1bttSMGTO0adMmbdy4Uddcc41uvvlm7dy508yy3Eb8eSMqhmGYXA0AAHXP1KAyZswYXX/99WrXrp3at2+vl19+WY0bN9a6devMLMtt9IgOkY+XRek5BUo9fdbscgAAqHNu06Nit9u1cOFC5eXlacCAAZWeU1BQoOzs7HK3hszPx0tdWwRL4jJlAIBnMj2obN++XY0bN5bNZtNDDz2kRYsWqXPnzpWeO336dAUHBztv0dHRdVxt3Su7TDmRhd8AAB7IYtSw+WHjxo364osvdOjQIRUWFpZ77Kuvvqry6xQWFurQoUPKysrSP/7xD3344YdasWJFpWGloKBABQUFzvvZ2dmKjo5WVlaWgoKCavIx3N5/dqbpwY83qV14Yy2ZdLXZ5QAAcMWys7MVHBxcpd/fNRpRWbhwoQYOHKhdu3Zp0aJFKioq0s6dO7Vs2TIFBwdX67V8fX3Vtm1b9enTR9OnT1ePHj301ltvVXquzWZzXiFUdmvoyhpq96XnKvNM4WXOBgCgYalRUHnllVf0xhtv6LvvvpOvr6/eeust7d69W7fffrtatWp1RQU5HI5yoyaerkljm1o3C5DEZcoAAM9To6Cyf/9+3XDDDZJKRkTy8vJksVj0+OOPa968eVV+nYSEBK1cuVIHDx7U9u3blZCQoOXLl2vcuHE1KavBcm5QSFABAHiYGgWV0NBQ5eSULOveokUL7dixQ5KUmZmpM2fOVPl10tPTddddd6lDhw4aPny4EhMT9Z///EfXXnttTcpqsJwbFLJCLQDAw3jX5ElDhw7VkiVL1K1bN91222167LHHtGzZMi1ZskTDhw+v8uv8+c9/rsnbe5yyEZWth7NUUGyXzdvL5IoAAKgbNQoq77zzjvLz8yVJzzzzjHx8fPTTTz/p1ltv1f/7f//PpQVCimsaoCYBvjqVV6gdR7LUJybM7JIAAKgTNQoqYWHnflFarVZNmTLFZQWhIovFovjYUP1n53ElHswgqAAAPEaVg0p2drbzcuDLrQjrCZcN17X4mDD9Z+dxbTyYIbGcCgDAQ1Q5qISGhurYsWMKDw9XSEiILBZLhXMMw5DFYpHdbndpkZDiY0v6VDalnJbDYchqrfj9BwCgoalyUFm2bJlzyufHH3+stYJQuS5RwfLzsSrjTJEOnMxV2/BAs0sCAKDWVTmoXH31ufmGuLg4RUdHVxhVMQxDqamprqsOTr7eVvVoGaL1yae18WAGQQUA4BFqtI5KXFycTpw4UeH46dOnFRcXd8VFoXJsUAgA8DQ1CiplvSgXys3NlZ+f3xUXhcr1Oa9PBQAAT1Cty5MnTZokqeRy2WeffVaNGjVyPma327V+/Xr17NnTpQXinN6tQmWxSAdPnVF6Tr7CAwmFAICGrVpBZcuWLZJKRlS2b98uX19f52O+vr7q0aOHnnzySddWCKdgfx91aB6o3Wk52nQwQ6O7RZpdEgAAtapaQaXsap97771Xs2fPVmAgDZ11LT42VLvTcrQxhaACAGj4qt2jUlRUpI8//lgpKSm1UQ8uoy8bFAIAPEi1g4qPj49atWrFom4m6VO6QeGOo9k6U1hscjUAANSuGl3188wzz+jpp5/W6dP8q76utQjxV2Swn+wOQ0mpmWaXAwBArarx7sm//PKLoqKiFBMTo4CAgHKPb9682SXFoaKSDQrD9N3Wo9p4MEMD2zQ1uyQAAGpNjYLK2LFjXVwGqiM+JrQkqKSw8BsAoGGrUVCZOnWqq+tANZRtULg5JUN2hyEvNigEADRQNepRkaTMzEx9+OGHSkhIcPaqbN68WUeOHHFZcahcx4ggNbZ5K7egWLvTss0uBwCAWlOjoLJt2za1b99eM2fO1J/+9CdlZmZKkr766islJCS4sj5UwstqUa9WIZKkTUz/AAAasBoFlUmTJumee+7Rvn37yu3tc/3112vlypUuKw4XxwaFAABPUKOgkpiYqAcffLDC8RYtWigtLe2Ki8LlxZeup7KJhd8AAA1YjYKKzWZTdnbF3oi9e/eqWbNmV1wULq9nqxB5WS06mpWvI5lnzS4HAIBaUaOgctNNN+mFF15QUVGRpJK1PQ4dOqQ//vGPuvXWW11aICrXyNdbXaKCJLGcPgCg4apRUHnttdeUm5ur8PBwnT17VldffbXatm2rwMBAvfzyy66uERcRH1O27w99KgCAhqlG66gEBwdryZIlWr16tbZt26bc3Fz17t1bI0aMcHV9uIS+saH6y5pkJTKiAgBooGoUVMoMHjxYgwcPdlUtqKY+pQu/7Tmeo+z8IgX5+ZhcEQAArlXjoLJ06VItXbpU6enpcjgc5R77y1/+csWF4fLCA/0U06SRUk6d0eaUDA3rEG52SQAAuFSNelSef/55XXfddVq6dKlOnjypjIyMcjfUHfpUAAANWY1GVN577z0tWLBAv//9711dD6opPjZUX24+rI0p9KkAABqeGo2oFBYWauDAga6uBTXQt7RPJSk1U4XFjsucDQBA/VKjoHL//ffr008/dXUtqIHWTRsrpJGP8osc2nk0y+xyAABwqRpN/eTn52vevHn673//q+7du8vHp/zVJq+//rpLisPlWa0WxceE6r+70rUpJUO9WoWaXRIAAC5To6Cybds29ezZU5K0Y8cOV9aDGoiPDdN/d6Ur8eBp3T+ktdnlAADgMjUKKj/++KOr68AVcG5QmJIhwzBksVhMrggAANeoVlC55ZZbLnuOxWLRl19+WeOCUH3dWgbL19uqk7mFOnjqjOKaBphdEgAALlGtoBIcHFxbdeAK2Ly91L1FsDamZCjx4GmCCgCgwahWUJk/f35t1YErFB8bpo0pGdp0MEO3x0ebXQ4AAC5Ro8uT4X7K1lNJZOE3AEADQlBpIPqUNtQeOJGnU7kFJlcDAIBrEFQaiJBGvmoX3lhSydU/AAA0BASVBiS+dPpnI0EFANBAEFQakHM7KdOnAgBoGAgqDUjf2JKgsv1IlvKL7CZXAwDAlSOoNCDRYf5qFmhTkd3QtsNsUAgAqP8IKg2IxWI5d5ky0z8AgAaAoNLA9KFPBQDQgBBUGpiyEZVNKRlyOAyTqwEA4MoQVBqYzpFBauTrpez8Yu1LzzW7HAAArghBpYHx9rKqZ3SIJPpUAAD1H0GlAYovvUyZFWoBAPUdQaUB4sofAEBDQVBpgHq1CpXVIh3OOKu0rHyzywEAoMZMDSrTp09X3759FRgYqPDwcI0dO1Z79uwxs6QGobHNW50igyRJG1MYVQEA1F+mBpUVK1Zo/PjxWrdunZYsWaKioiJdd911ysvLM7OsBiE+pnSDwoP0qQAA6i9vM9988eLF5e4vWLBA4eHh2rRpk4YOHWpSVQ1DfGyYPlqbouV70/VNUojCA/3ULy5MXlaL2aUBAFBlpgaVC2VllexPExYWVunjBQUFKigocN7Pzs6uk7rqo9yCIknSwZNn9NjCJElSZLCfpo7prFFdI02sDACAqnObZlqHw6GJEydq0KBB6tq1a6XnTJ8+XcHBwc5bdHR0HVdZPyzecUxPf7WjwvG0rHz94ZPNWrzjmAlVAQBQfW4TVMaPH68dO3Zo4cKFFz0nISFBWVlZzltqamodVlg/2B2Gnv/uZ1W2eH7Zsee/+1l2ltcHANQDbjH1M2HCBP3zn//UypUr1bJly4ueZ7PZZLPZ6rCy+mdD8mkdu8QlyYakY1n52pB8WgPaNKm7wgAAqAFTg4phGHrkkUe0aNEiLV++XHFxcWaW0yCk51Rt3ZSqngcAgJlMDSrjx4/Xp59+qm+++UaBgYFKS0uTJAUHB8vf39/M0uqt8EA/l54HAICZTO1RmTt3rrKysjRs2DBFRkY6b59//rmZZdVr/eLCFBnsp4tdhGxRydU//eIqv7IKAAB3YvrUD1zLy2rR1DGd9YdPNssiVWiqNSRNHdOZ9VQAAPWC21z1A9cZ1TVSc3/XWxHBFad3gvy8NaBNUxOqAgCg+tziqh+43qiukbq2c4Q2JJ9Wek6+Qhr5aNo3O5V86oxe+ufPevW2HmaXCADAZTGi0oB5WS0a0KaJbu7ZQle3D9ert/WQxSL9fdNhLd+TbnZ5AABcFkHFg8THhumegbGSpISvtisnv8jcggAAuAyCioeZPLKDWoU10rGsfL3y791mlwMAwCURVDxMI19vzby1uyTpsw2HtOaXkyZXBADAxRFUPNCANk30u6taSZL++OU25RUUm1wRAACVI6h4qCmjO6lFiL8OZ5zVrMVMAQEA3BNBxUM1tnlrxq3dJEkfrU3R+gOnTK4IAICKCCoebEi7ZvpNfLSkkimgs4V2kysCAKA8goqHe+bGTooI8tPBU2f02g97zC4HAIByCCoeLsjPR9NvKZkC+vOaZG1KyTC5IgAAziGoQL/qGK5bereQYUhP/WOr8ouYAgIAuAeCCiRJz93YWc0Cbdp/Ik9vLd1ndjkAAEgiqKBUSCNfvTS2qyRp3soD2nY409yCAAAQQQXnGdklQmN6RMnuMDT579tUUMwUEADAXAQVlPP8TV3UJMBXe47naM6yX8wuBwDg4QgqKCcswFcv3FwyBfTu8v3aeTTL5IoAAJ6MoIIKru8WoVFdIlRcOgVUZHeYXRIAwEMRVFCBxWLRi2O7KqSRj34+lq33lu83uyQAgIciqKBSzQJtmjamiyRp9rJ92pOWY3JFAABPRFDBRd3cM0ojOoWryG5o8j+2qpgpIABAHSOo4KIsFote/p9uCvTz1rbDWfpwdbLZJQEAPAxBBZfUPMhPz97YWZL0+pK9+iU91+SKAACehKCCy7qtT0sNbd9MhcUOPfWPrbI7DLNLAgB4CIIKLstisWjGLd3U2OatzYcyNX8NU0AAgLpBUEGVRIX46+nrO0mS/vTDHh08mWdyRQAAT0BQQZXd0S9ag9o2UX6RQ099uU0OpoAAALWMoIIqK5kC6q5Gvl7akHxan6xPMbskAEADR1BBtUSHNdIfR3WUJM34frdST58xuSIAQENGUEG1/f6qGPWLC9OZQrumfLVNhsEUEACgdhBUUG1Wq0Wzbu0uPx+r1vxySgsTU80uCQDQQBFUUCOxTQP05HUdJEkv/2uXjmaeNbkiAEBDRFBBjd07KE69W4Uot6BYCV9tZwoIAOByBBXUmJfVolm/7iFfb6tW7D2hf2w6bHZJAIAGhqCCK9I2vLEeH9FekvTiP3/W8ex8kysCADQkBBVcsf8bEqfuLYOVnV+sZxYxBQQAcB2CCq6Yt5dVr/66h3y8LPrvrnR9u/Wo2SUBABoIggpcokNEoB65pp0kaeq3O3Uip8DkigAADQFBBS7zh2Ft1DkySJlnivTcNzvMLgcA0AAQVOAyPl5WvXpbd3lbLfp+R5r+vf2Y2SUBAOo5ggpcqktUsB4e1kaS9OzXO3Q6r9DkigAA9RlBBS43/pq2at+8sU7lFWratzvNLgcAUI8RVOByNm8vvfrrHrJapG+3HtUPO9PMLgkAUE8RVFArekSH6IGhJVNAz3y9Q5lnmAICAFQfQQW1ZuKIdmrTLEAncgr04j93mV0OAKAeIqig1vj5eGnWr3vIYpG+3HxYP+5JN7skAEA9Q1BBreoTE6r7BsVJkhK+3K7s/CKTKwIA1CcEFdS6J6/roNgmjZSWna9X/sUUEACg6ggqqHX+vl6aeWt3SdLCxFSt2nfC5IoAAPUFQQV1on/rJrp7QIwkacqX25VbUGxyRQCA+oCggjrz1KiOahnqryOZZzXz+91mlwMAqAdMDSorV67UmDFjFBUVJYvFoq+//trMclDLAmzezimgj9elaO3+UyZXBABwd6YGlby8PPXo0UNz5swxswzUoUFtm+qOfq0kSX/8cpvOFDIFBAC4OG8z33z06NEaPXq0mSXABE9f31Er9qTr0Okz+tN/9uq5MZ3NLgkA4KbqVY9KQUGBsrOzy91Q/wT6+eiVW7pJkub/lKyNB0+bXBEAwF3Vq6Ayffp0BQcHO2/R0dFml4QaGtYhXL/u01KGIT31j23KL7KbXRIAwA3Vq6CSkJCgrKws5y01NdXsknAFnr2hs8IDbTpwMk9vLNlrdjkAADdUr4KKzWZTUFBQuRvqr+BGPnrlf0qmgD5YdUBJqZnmFgQAcDv1Kqig4RnRubnG9oySw5Am/32rCoqZAgIAnGNqUMnNzVVSUpKSkpIkScnJyUpKStKhQ4fMLAt1bOqYLmra2Ff70nP19tJfzC4HAOBGTA0qGzduVK9evdSrVy9J0qRJk9SrVy8999xzZpaFOhYa4KsXb+4qSZq7Yr92HMkyuSIAgLswNagMGzZMhmFUuC1YsMDMsmCC0d0idUO3SNkdhp78+1YVFjvMLgkA4AboUYHbeP7mLgoL8NXutBzNXb7f7HIAAG6AoAK30bSxTdNu6iJJeufHfdqdxoJ+AODpCCpwK2O6R+q6zs1VZDc0+e/bVGxnCggAPBlBBW7FYrHopbFdFezvo+1HsvT+ygNmlwQAMBFBBW4nPMhPz91YslHhW//dpz1p2Vq7/5S+STqitftPye4wTK4QAFBXTN09GbiYW3q30D+3HdWPe07oxrdXq8h+LpxEBvtp6pjOGtU10sQKAQB1gREVuCWLxaLrujSXpHIhRZLSsvL1h082a/GOY2aUBgCoQwQVuCW7w9Dsi6xSWxZbnv/uZ6aBAKCBI6jALW1IPq1jWfkXfdyQdCwrXxuST9ddUQCAOkdQgVtKz7l4SKnJeQCA+omgArcUHuhXpfOW7UpXejZhBQAaKoIK3FK/uDBFBvvJcpnzvtl6VINmLtPkv2/V3uM5dVIbAKDuEFTglrysFk0dU7KWyoVhxVJ6e+jq1uobG6oiu6G/bzqs695YqXvmb9BP+0/KMGiyBYCGwGLU47/Rs7OzFRwcrKysLAUFBZldDmrB4h3H9Px3P5drrL1wHZXNhzL0wcoDWrwzTWX/N3dtEaT/G9JaN3SLlLcXeRwA3El1fn8TVOD27A5DG5JPKz0nX+GBfuoXFyYva8VJoZRTefrz6mR9sTFV+UUlewS1CPHXfYPj9Ju+0WpsY31DAHAHBBV4tIy8Qn28LkUf/XRQp/IKJUmBft4a1z9G9w6KVfOgqjXqAgBqB0EFkJRfZNeiLUf0waoDOnAiT5Lk42XRzT1b6P+GtFaHiECTKwQAz0RQAc7jcBhaujtdH6w8oA0Hzy0Qd3X7ZnpgaGsNbNNEFsvlri8CALgKQQW4iC2HMvTBqgNavCNNZavvd4kK0gNDW+v6bpHyofEWAGodQQW4jJRTefrL6mR9sfGwzhbZJUlRwX66b3CcftuvFY23AFCLCCpAFWXkFeqTdSn6aO1Bncw913h7Z/9WundgnCKCabwFAFcjqADVlF9k19dbjmjeeY233laLbuoZpf8b0lqdIvn/CwBchaAC1JDDYWjZ7nTNW3Wg3M7MQ9s30wNDWmtQWxpvAeBKEVQAF0hKzdQHqw7o++3HnI23nSKD9MDQON3YPYrGWwCoIYIK4EKHTp3RX9Yk6/PEVGfjbWSwn+4bFKff9otWoJ+PyRUCQP1CUAFqQeaZQv1t/SHNX3NQJ3MLJEmBNm/d0b+V7h0Uq8hgf5MrBID6gaAC1KL8Iru+STqieSsPaP/5jbc9onT/kNbqHMX/iwBwKQQVoA44HIaW703X+ysOaP15jbdD2jXV/w1prSHtmpZrvK3q5ooA0NARVIA6trW08fbf5zXedowI1ANDW+vG7lFatvu4nv/uZx3Lync+JzLYT1PHdNaorpEmVQ0A5iCoACZJPX1Gf16drC82pupMYUnjbYi/jzLPFlU4t2wsZe7vehNWAHgUggpgsnONt8nOFW8rY5EUEeyn1X+8hmkgAB6jOr+/WQgCqAUhjXw1/ldt9frtPS55niHpWFa+1h04VTeFAUA9w85rQC3KOFNxyqcy/7sgUf1bN1Hf2FDFx4apZ3SI/Hy8ark6AHB/BBWgFoUHVm1Tw/xih1bsPaEVe09Ikny8LOraIlj9YsMUHxum+JhQhQb41mapAOCWCCpALeoXF6bIYD+lZeWrsmawsh6V937XR1sOZSjxYIYSD55Wek6BthzK1JZDmXp/5QFJUtvwxuobG6q+sWHqGxumlqH+7DsEoMGjmRaoZYt3HNMfPtksSeXCysWu+jEMQ6mnzyrx4GltTDmtxIMZ+iU9t8LrNg+yKT42rHTUJVQdI4JoyAVQL3DVD+BmFu84dkXrqJzKLdCmlAxtTCkZcdl+OEvFjvJ/dBvbvNU7JlT96HMB4OYIKoAbcuXKtGcL7UpKzdTGg6eVmJKhzSkZyi0oLncOfS4A3BVBBfAwdoeh3WnZSkwuCS6JySV9LheizwWAOyCoAB7OMAwdzjirDclV63PpGxOqvnFhVepzYc8iAFeKoAKggtN5hdpU2uOSePC0dhzJUpG98j6XsuByYZ/LlfbaAIBEUAFQBWcL7dp6uLTP5WBJn0vORfpc+saGycti0Xsr9le4zJo9iwBUF0EFQLWV9blsPHhu1OV4dsU+l4uJZM8iAFVEUAFwxcr6XBIPntY/tx3Tst3pl31OgM1LUcH+Cg+yqVljm5oFltzCA/2cXzdrbFNIIx/Tm3jptQHMU53f36xMC6BSFotF0WGNFB3WSF5WS5WCSl6BXfvSc7Wvksbd8/l4WcoFmWYXBJnzg05trAVDrw1QfxBUAFxWVfcsevXX3RUV4q8TOQVKz8nXiZyCkltugdKzS/6beaZIRXZDR7PydfS8oHAxgX7epaMypYGmse28++duYY18Za3CiEjZSsEXDiWnZeXrD59srne9NowMoaEjqAC4rKruWXRL75aX/SVZUGzXqdzC0jBTcF6YyXeGmbLHCosdyskvVk5+sQ6cyLvk63pZLWra2PfcqMz5ozSlwSYswFdTv91Z6WcwSj/H89/9rGs7R9SLX/aMDMET0KMCoEqqu2fRlTIMQ9n5xReMyuQ7g8z5t9NnCuXKv8lu7d1C7ZoHys/bKj8fr/Nu5+77n3/f20t+vlb5elnrrPfmYiND9fUqLEaGPAvNtABqhbv+C77I7tDpvMKKU07nj9rkFuho5tkKa8e4ksWiktDiYy0NMl6y+ZS/7+djLQ02Xs5zzw8+tosEIX9fq2zeJY/5eFk06s1VSsuufOqsbISrvlyF5a7/X9UUoevyCCoAak19/kt47f5TuuODdZc9b0SncAX7+yq/2K6CIrvOFtmVX+RQfpG99Hbu67NFdjnc9G/R3q1CFBni7ww6ft7lR4ZsPl7OUSP/CqNG54JR2TEfL6vLa2xoI0MNKXTV5p91ggoAVMLuMDR45rLL9tpUZyTCMAwV2Q3lF5cEl4IiR2mwORdoyu4XFDmUX2zX2cLSx4rtFw0/ZfcLih0l5xefO88s3lbLBSHm/KmxklEjm0/5kSLnCJJP2SjSuef4elk06YutOpVXWOn71ceRoYYSumo7cBFUAOAi6rrXxtUMw9DKfSd0918SL3vu/w2JU1SI/7kQVFwals4LT+cec6jg/GOlgaqg2LxgdD5fL6u8vSzytlrk4/zaKh8vi7y9rM7jZfd9zn/cWnK+T+l5FR4v/drXu+LjJc+79GuV9CZJ98zfoJO59T901UXgqndBZc6cOXr11VeVlpamHj166O2331a/fv0u+zyCCoCaqO/D87UxMnQxhmGooNhxQagp+bpspKfgwqmx4nNhqODCKbPzRobSs6t2iXpDYrWoNFBdGLYs8rFePIBdeNzHWhqwnF9by71GZaGsKqHLapH+768baz1w1asF3z7//HNNmjRJ7733nvr3768333xTI0eO1J49exQeHm52eQAaoFFdI3Vt54h622vjZbVo6pjO+sMnm2VR5SNDU8d0dsnnsVgszqkaV6tqz9Dc3/VWj5YhKrYbKnI4Sv5rd6jI7lCxo+TrYruhYodDRXbjgq8dKnIYKip2XPTx81+j6PznlZ5X9n7FjorHC0ufl1dQpJwC+2U/i8OQCoodbjNSVV2GpGNZ+dqQfFoD2jSpk/c0fUSlf//+6tu3r9555x1JksPhUHR0tB555BFNmTLlks9lRAWAJ2NkyH1UNXTNubO3urcMrhB6KgtNzuNVDFjF5wWzYufxS4SucueV/Df7bJEyzxZd9nO89dueurlnixp/v+rNiEphYaE2bdqkhIQE5zGr1aoRI0Zo7dq1Fc4vKChQQcG5TdKys7PrpE4AcEeMDLmPqi6KOKqrey8mWNXAVdXVql3B9deaVcPJkydlt9vVvHnzcsebN2+utLS0CudPnz5dwcHBzlt0dHRdlQoAbsnLatGANk10c88WGtCmiVv/EqzMqK6Rmvu73ooILv+LLyLYz+0bm89XFrqkcyGrTH0KXWWB62JVWlQyatcvLqzOajK9R6U6EhISNGnSJOf97OxswgoA1HP1fWSoTFnounA6LqIeTce54yiXqUGladOm8vLy0vHjx8sdP378uCIiIiqcb7PZZLPZ6qo8AEAdKRsZqu8aQuhyt8BlalDx9fVVnz59tHTpUo0dO1ZSSTPt0qVLNWHCBDNLAwCgRhpC6HKnwGX61M+kSZN09913Kz4+Xv369dObb76pvLw83XvvvWaXBgCAx3KXwGV6UPnNb36jEydO6LnnnlNaWpp69uypxYsXV2iwBQAAnsf0dVSuBOuoAABQ/1Tn97eplycDAABcCkEFAAC4LYIKAABwWwQVAADgtggqAADAbRFUAACA2yKoAAAAt2X6gm9XomwJmOzsbJMrAQAAVVX2e7sqS7nV66CSk5MjSeygDABAPZSTk6Pg4OBLnlOvV6Z1OBw6evSoAgMDZbHUn50p61J2draio6OVmprK6r1ugJ+He+Hn4V74ebif2vqZGIahnJwcRUVFyWq9dBdKvR5RsVqtatmypdll1AtBQUH8wXcj/DzcCz8P98LPw/3Uxs/kciMpZWimBQAAbougAgAA3BZBpYGz2WyaOnWqbDab2aVA/DzcDT8P98LPw/24w8+kXjfTAgCAho0RFQAA4LYIKgAAwG0RVAAAgNsiqAAAALdFUGmApk+frr59+yowMFDh4eEaO3as9uzZY3ZZKDVjxgxZLBZNnDjR7FI82pEjR/S73/1OTZo0kb+/v7p166aNGzeaXZZHstvtevbZZxUXFyd/f3+1adNGL774YpX2gcGVW7lypcaMGaOoqChZLBZ9/fXX5R43DEPPPfecIiMj5e/vrxEjRmjfvn11Vh9BpQFasWKFxo8fr3Xr1mnJkiUqKirSddddp7y8PLNL83iJiYl6//331b17d7NL8WgZGRkaNGiQfHx89P333+vnn3/Wa6+9ptDQULNL80gzZ87U3Llz9c4772jXrl2aOXOmZs2apbffftvs0jxCXl6eevTooTlz5lT6+KxZszR79my99957Wr9+vQICAjRy5Ejl5+fXSX1cnuwBTpw4ofDwcK1YsUJDhw41uxyPlZubq969e+vdd9/VSy+9pJ49e+rNN980uyyPNGXKFK1Zs0arVq0yuxRIuvHGG9W8eXP9+c9/dh679dZb5e/vr08++cTEyjyPxWLRokWLNHbsWEkloylRUVF64okn9OSTT0qSsrKy1Lx5cy1YsEC//e1va70mRlQ8QFZWliQpLCzM5Eo82/jx43XDDTdoxIgRZpfi8b799lvFx8frtttuU3h4uHr16qUPPvjA7LI81sCBA7V06VLt3btXkrR161atXr1ao0ePNrkyJCcnKy0trdzfW8HBwerfv7/Wrl1bJzXU600JcXkOh0MTJ07UoEGD1LVrV7PL8VgLFy7U5s2blZiYaHYpkHTgwAHNnTtXkyZN0tNPP63ExEQ9+uij8vX11d133212eR5nypQpys7OVseOHeXl5SW73a6XX35Z48aNM7s0j5eWliZJat68ebnjzZs3dz5W2wgqDdz48eO1Y8cOrV692uxSPFZqaqoee+wxLVmyRH5+fmaXA5UE+Pj4eL3yyiuSpF69emnHjh167733CCom+OKLL/S3v/1Nn376qbp06aKkpCRNnDhRUVFR/DzA1E9DNmHCBP3zn//Ujz/+qJYtW5pdjsfatGmT0tPT1bt3b3l7e8vb21srVqzQ7Nmz5e3tLbvdbnaJHicyMlKdO3cud6xTp046dOiQSRV5tsmTJ2vKlCn67W9/q27duun3v/+9Hn/8cU2fPt3s0jxeRESEJOn48ePljh8/ftz5WG0jqDRAhmFowoQJWrRokZYtW6a4uDizS/Jow4cP1/bt25WUlOS8xcfHa9y4cUpKSpKXl5fZJXqcQYMGVbhkf+/evYqJiTGpIs925swZWa3lfx15eXnJ4XCYVBHKxMXFKSIiQkuXLnUey87O1vr16zVgwIA6qYGpnwZo/Pjx+vTTT/XNN98oMDDQOY8YHBwsf39/k6vzPIGBgRX6gwICAtSkSRP6hkzy+OOPa+DAgXrllVd0++23a8OGDZo3b57mzZtndmkeacyYMXr55ZfVqlUrdenSRVu2bNHrr7+u++67z+zSPEJubq5++eUX5/3k5GQlJSUpLCxMrVq10sSJE/XSSy+pXbt2iouL07PPPquoqCjnlUG1zkCDI6nS2/z5880uDaWuvvpq47HHHjO7DI/23XffGV27djVsNpvRsWNHY968eWaX5LGys7ONxx57zGjVqpXh5+dntG7d2njmmWeMgoICs0vzCD/++GOlvzPuvvtuwzAMw+FwGM8++6zRvHlzw2azGcOHDzf27NlTZ/WxjgoAAHBb9KgAAAC3RVABAABui6ACAADcFkEFAAC4LYIKAABwWwQVAADgtggqAADAbRFUAFTq4MGDslgsSkpKMrsUp927d+uqq66Sn5+fevbsWe3nu+NnAnBpBBXATd1zzz2yWCyaMWNGueNff/21LBaLSVWZa+rUqQoICNCePXvK7T1ilgULFigkJMTsMoAGjaACuDE/Pz/NnDlTGRkZZpfiMoWFhTV+7v79+zV48GDFxMSoSZMmLqzKXHa7nQ34gIsgqABubMSIEYqIiLjkdvfTpk2rMA3y5ptvKjY21nn/nnvu0dixY/XKK6+oefPmCgkJ0QsvvKDi4mJNnjxZYWFhatmypebPn1/h9Xfv3q2BAwfKz89PXbt21YoVK8o9vmPHDo0ePVqNGzdW8+bN9fvf/14nT550Pj5s2DBNmDBBEydOVNOmTTVy5MhKP4fD4dALL7ygli1bymazqWfPnlq8eLHzcYvFok2bNumFF16QxWLRtGnTLvo6s2bNUtu2bWWz2dSqVSu9/PLLlZ5b2YjIhSNWW7du1a9+9SsFBgYqKChIffr00caNG7V8+XLde++9ysrKksViKVdTQUGBnnzySbVo0UIBAQHq37+/li9fXuF9v/32W3Xu3Fk2m02HDh3S8uXL1a9fPwUEBCgkJESDBg1SSkpKpbUDnoKgArgxLy8vvfLKK3r77bd1+PDhK3qtZcuW6ejRo1q5cqVef/11TZ06VTfeeKNCQ0O1fv16PfTQQ3rwwQcrvM/kyZP1xBNPaMuWLRowYIDGjBmjU6dOSZIyMzN1zTXXqFevXtq4caMWL16s48eP6/bbby/3Gh999JF8fX21Zs0avffee5XW99Zbb+m1117Tn/70J23btk0jR47UTTfdpH379kmSjh07pi5duuiJJ57QsWPH9OSTT1b6OgkJCZoxY4aeffZZ/fzzz/r000/VvHnzGn/fxo0bp5YtWyoxMVGbNm3SlClT5OPjo4EDB+rNN99UUFCQjh07Vq6mCRMmaO3atVq4cKG2bdum2267TaNGjXJ+Fkk6c+aMZs6cqQ8//FA7d+5UWFiYxo4dq6uvvlrbtm3T2rVr9cADD3jsNB/gVGfbHwKolrvvvtu4+eabDcMwjKuuusq47777DMMwjEWLFhnn/9GdOnWq0aNHj3LPfeONN4yYmJhyrxUTE2PY7XbnsQ4dOhhDhgxx3i8uLjYCAgKMzz77zDAMw0hOTjYkGTNmzHCeU1RUZLRs2dKYOXOmYRiG8eKLLxrXXXddufdOTU01JDl3V7366quNXr16XfbzRkVFGS+//HK5Y3379jUefvhh5/0ePXoYU6dOvehrZGdnGzabzfjggw8qfbzsM23ZssUwDMOYP3++ERwcXO6cC7+/gYGBxoIFCyp9vcqen5KSYnh5eRlHjhwpd3z48OFGQkKC83mSjKSkJOfjp06dMiQZy5cvv+jnAzwRIypAPTBz5kx99NFH2rVrV41fo0uXLrJaz/2Rb968ubp16+a87+XlpSZNmig9Pb3c8wYMGOD82tvbW/Hx8c46tm7dqh9//FGNGzd23jp27CippJ+kTJ8+fS5ZW3Z2to4ePapBgwaVOz5o0KBqfeZdu3apoKBAw4cPr/JzLmfSpEm6//77NWLECM2YMaPc56rM9u3bZbfb1b59+3LflxUrVpR7rq+vr7p37+68HxYWpnvuuUcjR47UmDFj9NZbb+nYsWMu+xxAfUVQAeqBoUOHauTIkUpISKjwmNVqlWEY5Y4VFRVVOM/Hx6fcfYvFUumx6jR15ubmasyYMUpKSip327dvn4YOHeo8LyAgoMqveSX8/f2rdX5VvnfTpk3Tzp07dcMNN2jZsmXq3LmzFi1adNHXzM3NlZeXlzZt2lTue7Jr1y699dZb5Wq9cFpn/vz5Wrt2rQYOHKjPP/9c7du317p166r1mYCGhqAC1BMzZszQd999p7Vr15Y73qxZM6WlpZX7hevKdULO/0VZXFysTZs2qVOnTpKk3r17a+fOnYqNjVXbtm3L3aoTToKCghQVFaU1a9aUO75mzRp17ty5yq/Trl07+fv7V/nS5WbNmiknJ0d5eXnOY5V979q3b6/HH39cP/zwg2655RZn07Gvr6/sdnu5c3v16iW73a709PQK35OIiIjL1tSrVy8lJCTop59+UteuXfXpp59W6bMADRVBBagnunXrpnHjxmn27Nnljg8bNkwnTpzQrFmztH//fs2ZM0fff/+9y953zpw5WrRokXbv3q3x48crIyND9913nyRp/PjxOn36tO644w4lJiZq//79+s9//qN77723wi/wy5k8ebJmzpypzz//XHv27NGUKVOUlJSkxx57rMqv4efnpz/+8Y966qmn9Ne//lX79+/XunXr9Oc//7nS8/v3769GjRrp6aef1v79+/Xpp59qwYIFzsfPnj2rCRMmaPny5UpJSdGaNWuUmJjoDGqxsbHKzc3V0qVLdfLkSZ05c0bt27fXuHHjdNddd+mrr75ScnKyNmzYoOnTp+tf//rXRWtPTk5WQkKC1q5dq5SUFP3www/at2+f870AT0VQAeqRF154ocLUTKdOnfTuu+9qzpw56tGjhzZs2HDRK2JqYsaMGZoxY4Z69Oih1atX69tvv1XTpk0lyTkKYrfbdd1116lbt26aOHGiQkJCyvXDVMWjjz6qSZMm6YknnlC3bt20ePFiffvtt2rXrl21XufZZ5/VE088oeeee06dOnXSb37zmwp9N2XCwsL0ySef6N///re6deumzz77rNxlz15eXjp16pTuuusutW/fXrfffrtGjx6t559/XpI0cOBAPfTQQ/rNb36jZs2aadasWZJKpnDuuusuPfHEE+rQoYPGjh2rxMREtWrV6qJ1N2rUSLt379att96q9u3b64EHHtD48eP14IMPVuvzAw2NxbhwghYAAMBNMKICAADcFkEFAAC4LYIKAABwWwQVAADgtggqAADAbRFUAACA2yKoAAAAt0VQAQAAbougAgAA3BZBBQAAuC2CCgAAcFsEFQAA4Lb+P9YQscoDNifLAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = geo['Area (sq km)'].values\n", + "y = geo['est_pop'].values\n", + "data_to_fit = list(zip(x, y))\n", + "inertias = []\n", + "\n", + "for i in range(1,11):\n", + " kmeans = KMeans(n_clusters=i)\n", + " kmeans.fit(data_to_fit)\n", + " inertias.append(kmeans.inertia_)\n", + "\n", + "plt.plot(range(1,11), inertias, marker='o')\n", + "plt.title('Elbow method')\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('Inertia')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "84bb47c6", + "metadata": { + "papermill": { + "duration": 0.028807, + "end_time": "2025-02-16T13:30:39.909567", + "exception": false, + "start_time": "2025-02-16T13:30:39.880760", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A cluster analysis - KMeans - suggest 5 centroids may be suitable. The population appears to impact on the centroid over the area. " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "bb080ce0", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:39.969741Z", + "iopub.status.busy": "2025-02-16T13:30:39.968884Z", + "iopub.status.idle": "2025-02-16T13:30:45.145799Z", + "shell.execute_reply": "2025-02-16T13:30:45.144645Z" + }, + "papermill": { + "duration": 5.209848, + "end_time": "2025-02-16T13:30:45.148537", + "exception": false, + "start_time": "2025-02-16T13:30:39.938689", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------ Centers --------\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[2.16001231e+02, 1.07181088e+05],\n", + " [1.46898757e+02, 2.79987631e+05],\n", + " [5.51706700e+02, 7.15609000e+05],\n", + " [2.10517597e+02, 4.55918908e+05],\n", + " [2.20252450e+02, 1.91956420e+05]])" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans = KMeans(n_clusters=5)\n", + "kmeans.fit(data_to_fit)\n", + "centers = kmeans.cluster_centers_\n", + "plt.scatter(centers[:, 0], centers[:, 1], c='red', s=100, alpha=0.5);\n", + "plt.scatter(x, y, c=kmeans.labels_, s= 0.5)\n", + "plt.xlabel('Area square km')\n", + "plt.ylabel('Estimated population')\n", + "plt.show()\n", + "print(\"------ Centers --------\")\n", + "centers" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6f7bf4a", + "metadata": { + "papermill": { + "duration": 0.031206, + "end_time": "2025-02-16T13:30:45.210579", + "exception": false, + "start_time": "2025-02-16T13:30:45.179373", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "392c6f54", + "metadata": { + "papermill": { + "duration": 0.031454, + "end_time": "2025-02-16T13:30:45.272196", + "exception": false, + "start_time": "2025-02-16T13:30:45.240742", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Density of population" + ] + }, + { + "cell_type": "markdown", + "id": "2b3c8d80", + "metadata": { + "papermill": { + "duration": 0.029676, + "end_time": "2025-02-16T13:30:45.332229", + "exception": false, + "start_time": "2025-02-16T13:30:45.302553", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "The metric _people per sq_km_ is skewed to the right. The difference between the arithmetical mean and median is rather large. The dispersion is also quite large. A 3D representation at a logarithm scale suggest the size of an area may limit its population density. \n", + "\n", + "We surmise the density of population may be restricted to its size, but is can vary immensely for a certain area. Certain factors such as use of the land and the geography itself may impact on the relationship between the population and its area. None of those factors were clearly captured in the data. " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "81a4b55c", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:45.394669Z", + "iopub.status.busy": "2025-02-16T13:30:45.394290Z", + "iopub.status.idle": "2025-02-16T13:30:45.970724Z", + "shell.execute_reply": "2025-02-16T13:30:45.969707Z" + }, + "papermill": { + "duration": 0.610783, + "end_time": "2025-02-16T13:30:45.972974", + "exception": false, + "start_time": "2025-02-16T13:30:45.362191", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Area (sq km)est_poppp_sq_m
count135.0000001.350000e+02135.000000
mean229.5041212.264777e+051690.937385
std255.5694676.274943e+051349.913246
min16.3177002.140000e+0397.047386
25%58.2706009.844850e+04473.015434
50%139.7923001.353810e+051343.116544
75%328.6507502.183305e+052835.794533
max1572.0308007.322403e+064657.925913
\n", + "
" + ], + "text/plain": [ + " Area (sq km) est_pop pp_sq_m\n", + "count 135.000000 1.350000e+02 135.000000\n", + "mean 229.504121 2.264777e+05 1690.937385\n", + "std 255.569467 6.274943e+05 1349.913246\n", + "min 16.317700 2.140000e+03 97.047386\n", + "25% 58.270600 9.844850e+04 473.015434\n", + "50% 139.792300 1.353810e+05 1343.116544\n", + "75% 328.650750 2.183305e+05 2835.794533\n", + "max 1572.030800 7.322403e+06 4657.925913" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols = ['Area (sq km)', 'est_pop', 'pp_sq_m']\n", + "geo = data.loc[:, cols]\n", + "geo = geo.dropna()\n", + "geo = geo.drop_duplicates()\n", + "geo.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "841ba07a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:46.035020Z", + "iopub.status.busy": "2025-02-16T13:30:46.034609Z", + "iopub.status.idle": "2025-02-16T13:30:46.307950Z", + "shell.execute_reply": "2025-02-16T13:30:46.306901Z" + }, + "papermill": { + "duration": 0.307108, + "end_time": "2025-02-16T13:30:46.310114", + "exception": false, + "start_time": "2025-02-16T13:30:46.003006", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(6,6))\n", + "\n", + "#ax = Axes3D(fig) # Method 1\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "\n", + "x = np.log10(geo['Area (sq km)'])\n", + "y = np.log10(geo.est_pop)\n", + "z = np.log10(geo.pp_sq_m)\n", + "\n", + "plt.figure(figsize = [20,16])\n", + "ax.scatter(x, y, z, c=x, marker='o')\n", + "ax.set_xlabel('Area - sq km')\n", + "ax.set_ylabel('Estimated pop')\n", + "ax.set_zlabel('Person per km')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "68611191", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:30:46.376292Z", + "iopub.status.busy": "2025-02-16T13:30:46.375896Z", + "iopub.status.idle": "2025-02-16T13:31:23.361371Z", + "shell.execute_reply": "2025-02-16T13:31:23.360155Z" + }, + "papermill": { + "duration": 37.055479, + "end_time": "2025-02-16T13:31:23.397784", + "exception": false, + "start_time": "2025-02-16T13:30:46.342305", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Area (sq km) Price pp_sq_m est_pop\n", + "count 7.853837e+06 7.853837e+06 7.853837e+06 5.208853e+06\n", + "mean 4.194698e+02 2.209744e+05 2.599774e+03 1.372225e+06\n", + "std 5.338312e+02 5.708082e+05 1.613246e+03 2.584126e+06\n", + "min 1.631770e+01 1.000000e+00 9.704739e+01 2.140000e+03\n", + "25% 7.461320e+01 1.025000e+05 9.843264e+02 1.340490e+05\n", + "50% 1.581281e+02 1.550000e+05 2.608139e+03 2.380160e+05\n", + "75% 4.144144e+02 2.407510e+05 4.011644e+03 4.418580e+05\n", + "max 1.572031e+03 9.890000e+07 5.218986e+03 7.322403e+06\n", + " OLS Regression Results \n", + "==============================================================================\n", + "Dep. Variable: pp_sq_m R-squared: 0.168\n", + "Model: OLS Adj. R-squared: 0.168\n", + "Method: Least Squares F-statistic: 1.586e+06\n", + "Date: Sun, 16 Feb 2025 Prob (F-statistic): 0.00\n", + "Time: 13:30:50 Log-Likelihood: -6.8430e+07\n", + "No. Observations: 7853837 AIC: 1.369e+08\n", + "Df Residuals: 7853835 BIC: 1.369e+08\n", + "Df Model: 1 \n", + "Covariance Type: nonrobust \n", + "================================================================================\n", + " coef std err t P>|t| [0.025 0.975]\n", + "--------------------------------------------------------------------------------\n", + "const 2080.2172 0.668 3115.083 0.000 2078.908 2081.526\n", + "Area (sq km) 1.2386 0.001 1259.249 0.000 1.237 1.241\n", + "==============================================================================\n", + "Omnibus: 2898113.039 Durbin-Watson: 0.000\n", + "Prob(Omnibus): 0.000 Jarque-Bera (JB): 500464.485\n", + "Skew: -0.305 Prob(JB): 0.00\n", + "Kurtosis: 1.925 Cond. No. 863.\n", + "==============================================================================\n", + "\n", + "Notes:\n", + "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", + "---- params / coeficient -------\n", + "const 2080.217194\n", + "Area (sq km) 1.238605\n", + "dtype: float64\n", + "------------p values----------\n", + "const 0.0\n", + "Area (sq km) 0.0\n", + "dtype: float64\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Population density (pp/km^2)')" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geo = data_num\n", + "print(geo.describe())\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "\n", + "x = geo[['Area (sq km)']] \n", + "y = geo[['pp_sq_m']]\n", + "x = sm.add_constant(x)\n", + "model = sm.OLS(y, x).fit()\n", + "print(model.summary())\n", + "print(\"---- params / coeficient -------\")\n", + "print(model.params)\n", + "print('------------p values----------')\n", + "print(model.pvalues)\n", + "\n", + "\n", + "plt.scatter(geo[['Area (sq km)']] , geo[['pp_sq_m']])\n", + "plt.xlabel(\"Area (sq kn)\")\n", + "plt.ylabel(\"Population density (pp/km^2)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "f05bd10e", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:31:23.470230Z", + "iopub.status.busy": "2025-02-16T13:31:23.469849Z", + "iopub.status.idle": "2025-02-16T13:31:46.283598Z", + "shell.execute_reply": "2025-02-16T13:31:46.282549Z" + }, + "papermill": { + "duration": 22.855241, + "end_time": "2025-02-16T13:31:46.285501", + "exception": false, + "start_time": "2025-02-16T13:31:23.430260", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(7853837, 4)\n" + ] + }, + { + "data": { + "image/png": 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AgCvx+OqhWrVquXx/8eJFnTt3TsHBwapWrZpz4rnywNVDAABYT0mP3x5PLnfmzBmXR25urg4cOKCuXbsyjT8AACgzHoeWorRo0UKpqakaOXKkN3YHAABQiFdCiyQFBQXpxIkT3todAACAC48H4l6679AlxhidPHlS06ZNU5cuXbxWGAAAwOU8Di29evVy+d5ms6levXq69dZbNWnSJG/VBQAA4MLj0OJwOMqiDgAAgGKVakyLMeaqU/sDAAB4Q4lCS1pamtq2bauqVauqatWqatu2rd5++21v1wYAAODk8emh5557TpMnT9Zjjz2mxMRESdLGjRs1evRopaen64UXXvB6kQAAAB7PiFuvXj1NnTpVffv2dVk+f/58PfbYY/rf//7n1QKLw4y4AABYT7nNiHvx4kUlJCQUWt6xY0fl5+d7ujsAAAC3eBxa+vfvrxkzZhRaPmvWLPXr188rRQEAAPxaqQbiPvzww3r44YfVrl07vfXWWwoICNDjjz/ufHgiNTVVNptNo0aNKklJAADAz3k8EHfPnj267rrrJElHjhyRJNWtW1d169bVnj17nNvZbDa397llyxb9/e9/V1xcnKflAACASsLj0LJ69WqvFpCbm6t+/frprbfe0ksvveTVfQMAAP9RqsnlfvjhB/3www+lKmD48OFKTk5WUlLSVbfNy8tTTk6OywMAAFQOHocWh8OhF154QWFhYYqJiVFMTIzCw8P14osvejzF/4IFC7Rt2zalpKS4tX1KSorCwsKcj6ioKE/LBwAAFuXx6aHx48crLS1Nqampzrs6r1+/XhMmTND58+f18ssvu7WfY8eOaeTIkVqxYoWqVq3q1nOefvpplwG+OTk5BBcAACoJjyeXi4yM1MyZM3X33Xe7LF+2bJmGDRum48ePu7WfpUuXqnfv3goMDHQuKygokM1mU0BAgPLy8lzWFYXJ5QAAsJ6SHr897mnJzMxUbGxsoeWxsbHKzMx0ez+33Xabdu/e7bJs0KBBio2N1VNPPXXVwAIAACoXj0NLfHy8pk2bpqlTp7osnzZtmuLj493eT82aNdW2bVuXZdWrV1edOnUKLQcAAPA4tLz22mtKTk7WypUrXW6YeOzYMX388cdeLxAAAEAqwZgWSTpx4oSmT5+u/fv3S5Jat26tYcOGKTIy0usFFocxLQAAWE9Jj98lCi0VBaEFAADrKbeBuJJ05swZpaWlad++fZKkNm3aaNCgQapdu3ZJdgcAAHBVHk8ut27dOjVp0kRTp07VmTNndObMGU2dOlVNmzbVunXryqJGAAAAz08PtWvXTomJiZoxY4bzsuSCggINGzZMGzZsKHQZc1ni9BAAANZT0uO3xz0thw8f1hNPPOEyj0pgYKAef/xxHT582NPdAQAAuMXj0HLdddc5x7Jcbt++fR7N0wIAAOAJjwfijhgxQiNHjtThw4d14403SpI2bdqk6dOnKzU1Vbt27XJuGxcX571KAQBApebxmJaAgOI7Z2w2m4wxstlsKigoKFVxV8OYFgAArKfcLnk+evSop08BAAAoNY9DS0xMTFnUAQAAUCyPB+ICAAD4AqEFAABYAqEFAABYAqEFAABYAqEFAABYgltXD9WqVUs2m82tHWZmZpaqIAAAgKK4FVqmTJni/Pr06dN66aWX1L17dyUmJkqSNm7cqOXLl+vZZ58tkyIBAAA8nhH33nvvVbdu3fToo4+6LJ82bZpWrlyppUuXerO+YjEjLgAA1lNud3levny5evToUWh5jx49tHLlSk93BwAA4BaPQ0udOnW0bNmyQsuXLVumOnXqeKUoAACAX/N4Gv+JEyfq4Ycf1po1a9SpUydJ0ubNm/Xpp5/qrbfe8nqBAAAAUglCy8CBA9W6dWtNnTpVixcvliS1bt1a69evd4YYAAAAb/N4IG5FwkBcAACsp9wG4krSkSNH9Mwzz+gPf/iDMjIyJEmffPKJvvnmm5LsDgAA4Ko8Di1r165Vu3bttHnzZv3nP/9Rbm6uJGnnzp16/vnnvV4gAACAVILQMm7cOL300ktasWKFgoODnctvvfVWbdq0yavFAQAAXOJxaNm9e7d69+5daHlERIT+97//eaUoAACAX/M4tISHh+vkyZOFlm/fvl2NGjXySlEAAAC/5nFo+f3vf6+nnnpKp06dks1mk8Ph0JdffqkxY8bowQcfLIsaAQAAPA8tr7zyimJjYxUVFaXc3Fy1adNGN998szp37qxnnnmmLGoEAAAo+Twtx44d0+7du5Wbm6sOHTqoRYsW3q7tqpinBQAA6ym3eVpeeOEFnTt3TlFRUbrzzjv1u9/9Ti1atNDPP/+sF154wdPdAQAAuMXjnpbAwECdPHlSERERLstPnz6tiIgIFRQUeLXA4tDTAgCA9ZRbT4sxRjabrdDynTt3qnbt2p7uDgAAwC1u3zCxVq1astlsstlsatmypUtwKSgoUG5uroYMGVImRQIAALgdWqZMmSJjjAYPHqyJEycqLCzMuS44OFhNmjRRYmKiRy8+Y8YMzZgxQ999950k6dprr9Vzzz2nnj17erQfAADg/9wOLQMGDJAkNW3aVJ07d1aVKlVK/eKNGzdWamqqWrRoIWOM3nnnHd1zzz3avn27rr322lLvHwAA+I8SX/IsSefPn9eFCxdclpV2QGzt2rX1+uuv66GHHrrqtgzEBQDAekp6/Ha7p+WSc+fO6cknn9TChQt1+vTpQutLevVQQUGBFi1apJ9++umKp5ny8vKUl5fn/D4nJ6dErwUAAKzH46uHxo4dq88//1wzZsyQ3W7X22+/rYkTJyoyMlLz5s3zuIDdu3erRo0astvtGjJkiJYsWaI2bdoUuW1KSorCwsKcj6ioKI9fDwAAWJPHp4eio6M1b948/eY3v1FoaKi2bdum5s2b6x//+Ifmz5+vjz/+2KMCLly4oPT0dGVnZ+u9997T22+/rbVr1xYZXIrqaYmKiuL0EAAAFlJup4cyMzN1zTXXSPpl/EpmZqYkqWvXrho6dKinu1NwcLCaN28uSerYsaO2bNmiN954Q3//+98LbWu322W32z1+DQAAYH0enx665pprdPToUUlSbGysFi5cKEn64IMPFB4eXuqCHA6HS28KAACAVIKelkGDBmnnzp265ZZbNG7cON11112aNm2aLl68qMmTJ3u0r6efflo9e/ZUdHS0zp49q3fffVdr1qzR8uXLPS0LAAD4OY9Dy+jRo51fJyUlaf/+/dq6dauaN2+uuLg4j/aVkZGhBx98UCdPnlRYWJji4uK0fPly3X777Z6WBQAA/Fyp5mnxNeZpAQDAesptIK4kbdmyRatXr1ZGRoYcDofLOk9PEQEAALjD49Dyyiuv6JlnnlGrVq1Uv359lxsnFnX3ZwAAAG/wOLS88cYbmj17tgYOHFgG5QAAABTN40ueAwIC1KVLl7KoBQAA4Io8Di2jR4/W9OnTy6IWAACAK/L49NCYMWOUnJysZs2aqU2bNqpSpYrL+sWLF3utOAAAgEs8Di0jRozQ6tWr1a1bN9WpU4fBtwAAoFx4HFreeecd/ec//1FycnJZ1AMAAFAkj8e01K5dW82aNSuLWgAAAK7I49AyYcIEPf/88zp37lxZ1AMAAFAkj08PTZ06VUeOHFH9+vXVpEmTQgNxt23b5rXiAAAALvE4tPTq1asMygAAACgeN0wEAADlqqTHb4/HtAAAAPiCW6eHateurYMHD6pu3bqqVatWsXOzZGZmeq04AACAS9wKLX/9619Vs2ZN59dMKAcAAMobY1oAAEC5KrcxLYGBgcrIyCi0/PTp0woMDPR0dwAAAG7xOLRcqWMmLy9PwcHBpS4IAACgKG7P0zJ16lRJks1m09tvv60aNWo41xUUFGjdunWKjY31foUAAADyILT89a9/lfRLT8vMmTNdTgUFBwerSZMmmjlzpvcrBAAAkAeh5ejRo5Kkbt26afHixapVq1aZFQUAAPBrHo9pWb16tUtgKSgo0I4dO3TmzBmvFgYAAHA5j0PLqFGjlJaWJumXwHLzzTfruuuuU1RUlNasWePt+gAAACSVILQsWrRI8fHxkqQPPvhA3333nfbv36/Ro0dr/PjxXi8QAABAKkFoOX36tBo0aCBJ+vjjj3XfffepZcuWGjx4sHbv3u31AgEAAKQShJb69etr7969Kigo0Keffqrbb79dknTu3DkmlwMAAGXG7auHLhk0aJB+97vfqWHDhrLZbEpKSpIkbd68mXlaAABAmfE4tEyYMEFt27bVsWPHdN9998lut0v6ZXr/cePGeb1AAAAAiRsmAgCAclbmN0y88847lZ2d7fw+NTVVWVlZzu9Pnz6tNm3auP3CAAAAnnA7tCxfvlx5eXnO71955RVlZmY6v8/Pz9eBAwe8Wx0AAMD/53Zo+fVZJAufVQIAABbk8SXPAAAAvuB2aLHZbLLZbIWWAQAAlAe3L3k2xmjgwIHOS5zPnz+vIUOGqHr16pLkMt7FXSkpKVq8eLH279+vkJAQde7cWa+++qpatWrl8b4AAIB/c/uS50GDBrm1wzlz5rj94j169NDvf/97XX/99crPz9ef//xn7dmzR3v37nWGoeJwyTMAANZT0uN3hZqn5ccff1RERITWrl2rm2+++arbE1oAALCekh6/PZ4Rtyxdmgemdu3aRa7Py8tzOQ2Vk5NTLnUBAADfqzBXDzkcDo0aNUpdunRR27Zti9wmJSVFYWFhzkdUVFQ5VwkAAHylwpweGjp0qD755BOtX79ejRs3LnKbonpaoqKiOD0EAICFWPr00KOPPqoPP/xQ69atu2JgkSS73e68egkAAFQuPg0txhg99thjWrJkidasWaOmTZv6shwAAFCB+TS0DB8+XO+++66WLVummjVr6tSpU5KksLAwhYSE+LI0AABQwfh0TMuVZtSdM2eOBg4ceNXnc8kzAADWY8kxLRVkDDAAALCACnPJMwAAQHEILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBIILQAAwBKCfPni69at0+uvv66tW7fq5MmTWrJkiXr16uXLkuCmJuM+KrTsu9RkH1QCAKgsfNrT8tNPPyk+Pl7Tp0/3ZRnwUFGBpbjlAAB4g097Wnr27KmePXv6sgR46GrBpMm4j+hxAQCUCUuNacnLy1NOTo7LA+XH3Z4UelwAAGXBUqElJSVFYWFhzkdUVJSvSwIAAOXEUqHl6aefVnZ2tvNx7NgxX5cEAADKiU/HtHjKbrfLbrf7ugwAAOADluppAQAAlZdPQ0tubq527NihHTt2SJKOHj2qHTt2KD093Zdl4QrcvSqIq4cAAGXBZowxvnrxNWvWqFu3boWWDxgwQHPnzr3q83NychQWFqbs7GyFhoaWQYUoSnFXBxFYAABXU9Ljt09DS2kRWnyHGXEBACVV0uO3pQbiouIgoAAAyhuhBfAheqwAwH1cPQT4CPdwAgDPEFoAH3DnHk4AAFeEFqCccQ8nACgZQgsAALAEQgsAALAEQgsAALAEQgsAALAEQgtQzriHEwCUDJPLwTL8aSK271KTuYcTAHiInhZYgj9OxHalYEJgAYCiccNEVHjuBBMO9ABgHSU9ftPTggqNidgAAJcwpsXH/GmcBgAAZYmeFh/yx3Ea3sT7AAC4HKHFR7hhHgAAniG0+ADjNAAA8ByhpYIjuLiHcUAA4P8YiFsOCB4AAJQeoaWMeSOweLKPy3scrHRl0pVqZY4WAMAlTC5XBnzds2K1KeKvVquV2uINVgqbAFASTC5XQfg6sLhTQ0Wo8RJ3aq1M091zGTwAXBk9LV5ktQOLtw/6nvYQlPS0l7/iVBiAyqKkx2/GtJSQ1QJKWfO0h6A0B19/PH3iyWXwVm8rAJQUp4c81GTcRwSWXynJ+1HS95DTJwBQedHT4gF/OzD++r/2sjy94w2lGf8CALA+xrS4yd8Cy+VKcoVORX4/yiK4lPUpKcb3AGXLH08rW1lJj9+EFjdU5AN0ZeDufC1FPc8b3Al0V/tALMn60uDDGPg/lW3aBCsgtJRRaCGwWFtJPpC8+TP3pBfLF8Glov336U49Fa3m8lYe7fen95ir8iomQouXQwthxX948oHki597SXuS3N33lVS0/z5L2qP16238WXm035/eY067VlyEFi+GFgKLf7vSh5O//txLOiapPD/EvfXe+/OBpzx+ZhXt96K0CC0VFzPieom/Hrjwf4r6GVemn7snc8KUB2++jr/+HMvjZ1bRfi+AohBaLsMfY+XBzxoArIfQgkqL4AIA1lIhQsv06dPVpEkTVa1aVZ06ddJXX33l65IAABbn7jgVxrNYh89Dy7///W89/vjjev7557Vt2zbFx8ere/fuysjI8HVpgOXxYYzK7mp/A/yNWIvPQ8vkyZP1yCOPaNCgQWrTpo1mzpypatWqafbs2eVaB6cKCqtMf8wlaet3qckV+gPxSq9d0f779Obr+OvvbHn8zCra74U3lfZvARWHTy95vnDhgqpVq6b33ntPvXr1ci4fMGCAsrKytGzZMpft8/LylJeX5/w+JydHUVFRXrnkmdDiqiwnPfO20sxz4s7EZVfa9mrP8/Q99OZ8LSWdWM6T53sb87RcHfO0wF9Ycp6WEydOqFGjRtqwYYMSExOdy5988kmtXbtWmzdvdtl+woQJmjhxYqH9EFq8qzQfTOX9PpZmzhVPnuuND+uSHAzK+vYFFW3mU2bEvTpmxIU/qBShpTL1tHjjv+6r3RenPA/OngYEb0xyVRH/Y/TGe14R2wUAnrBkaPH09NCveXNGXE8Dgrfu6+Kt0wr+6krtd7fN/vofo7+2C0DlYMnQIkmdOnXSDTfcoDfffFOS5HA4FB0drUcffVTjxo0r9rnensa/sgcEAADKg2Wn8X/88cf11ltv6Z133tG+ffs0dOhQ/fTTTxo0aFC511KRrwQBAKCyC/J1Affff79+/PFHPffcczp16pTat2+vTz/9VPXr1/dJPWU51gMAAJScz08PlUZZ3eUZAACUHcueHgIAAHAHoQUAAFgCoQUAAFgCoQUAAFgCoQUAAFgCoQUAAFgCoQUAAFgCoQUAAFgCoQUAAFiCz6fxL41Lk/nm5OT4uBIAAOCuS8dtTyflt3RoOXv2rCQpKirKx5UAAABPnT17VmFhYW5vb+l7DzkcDp04cUI1a9aUzWYr8X5ycnIUFRWlY8eO+fU9jGinf6Gd/oV2+hfaWTxjjM6ePavIyEgFBLg/UsXSPS0BAQFq3Lix1/YXGhrq179cl9BO/0I7/Qvt9C+088o86WG5hIG4AADAEggtAADAEggtkux2u55//nnZ7XZfl1KmaKd/oZ3+hXb6F9pZNiw9EBcAAFQe9LQAAABLILQAAABLILQAAABLqPShZfr06WrSpImqVq2qTp066auvvvJ1SR5JSUnR9ddfr5o1ayoiIkK9evXSgQMHXLY5f/68hg8frjp16qhGjRq699579d///tdlm/T0dCUnJ6tatWqKiIjQ2LFjlZ+fX55NcVtqaqpsNptGjRrlXOZPbTx+/LgeeOAB1alTRyEhIWrXrp2+/vpr53pjjJ577jk1bNhQISEhSkpK0qFDh1z2kZmZqX79+ik0NFTh4eF66KGHlJubW95NuaKCggI9++yzatq0qUJCQtSsWTO9+OKLLlN6W7Gd69at01133aXIyEjZbDYtXbrUZb232rRr1y7ddNNNqlq1qqKiovTaa6+VddNcFNfOixcv6qmnnlK7du1UvXp1RUZG6sEHH9SJEydc9mH1dv7akCFDZLPZNGXKFJfl/tLOffv26e6771ZYWJiqV6+u66+/Xunp6c715fYZbCqxBQsWmODgYDN79mzzzTffmEceecSEh4eb//73v74uzW3du3c3c+bMMXv27DE7duwwd955p4mOjja5ubnObYYMGWKioqLMqlWrzNdff21uvPFG07lzZ+f6/Px807ZtW5OUlGS2b99uPv74Y1O3bl3z9NNP+6JJxfrqq69MkyZNTFxcnBk5cqRzub+0MTMz08TExJiBAweazZs3m2+//dYsX77cHD582LlNamqqCQsLM0uXLjU7d+40d999t2natKn5+eefndv06NHDxMfHm02bNpkvvvjCNG/e3PTt29cXTSrSyy+/bOrUqWM+/PBDc/ToUbNo0SJTo0YN88Ybbzi3sWI7P/74YzN+/HizePFiI8ksWbLEZb032pSdnW3q169v+vXrZ/bs2WPmz59vQkJCzN///vfyamax7czKyjJJSUnm3//+t9m/f7/ZuHGjueGGG0zHjh1d9mH1dl5u8eLFJj4+3kRGRpq//vWvLuv8oZ2HDx82tWvXNmPHjjXbtm0zhw8fNsuWLXM5VpbXZ3ClDi033HCDGT58uPP7goICExkZaVJSUnxYVelkZGQYSWbt2rXGmF8+QKpUqWIWLVrk3Gbfvn1Gktm4caMx5pdf2ICAAHPq1CnnNjNmzDChoaEmLy+vfBtQjLNnz5oWLVqYFStWmFtuucUZWvypjU899ZTp2rXrFdc7HA7ToEED8/rrrzuXZWVlGbvdbubPn2+MMWbv3r1GktmyZYtzm08++cTYbDZz/PjxsiveA8nJyWbw4MEuy/r06WP69etnjPGPdv76w99bbfrb3/5matWq5fJ7+9RTT5lWrVqVcYuKVtzB/JKvvvrKSDLff/+9Mca/2vnDDz+YRo0amT179piYmBiX0OIv7bz//vvNAw88cMXnlOdncKU9PXThwgVt3bpVSUlJzmUBAQFKSkrSxo0bfVhZ6WRnZ0uSateuLUnaunWrLl686NLO2NhYRUdHO9u5ceNGtWvXTvXr13du0717d+Xk5Oibb74px+qLN3z4cCUnJ7u0RfKvNr7//vtKSEjQfffdp4iICHXo0EFvvfWWc/3Ro0d16tQpl7aGhYWpU6dOLm0NDw9XQkKCc5ukpCQFBARo8+bN5deYYnTu3FmrVq3SwYMHJUk7d+7U+vXr1bNnT0n+087LeatNGzdu1M0336zg4GDnNt27d9eBAwd05syZcmqNZ7Kzs2Wz2RQeHi7Jf9rpcDjUv39/jR07Vtdee22h9f7QTofDoY8++kgtW7ZU9+7dFRERoU6dOrmcQirPz+BKG1r+97//qaCgwOUNlKT69evr1KlTPqqqdBwOh0aNGqUuXbqobdu2kqRTp04pODjY+WFxyeXtPHXqVJHvw6V1FcGCBQu0bds2paSkFFrnL22UpG+//VYzZsxQixYttHz5cg0dOlQjRozQO++8I+n/ai3u9/bUqVOKiIhwWR8UFKTatWtXmLaOGzdOv//97xUbG6sqVaqoQ4cOGjVqlPr16yfJf9p5OW+1ySq/y5ecP39eTz31lPr27eu8N42/tPPVV19VUFCQRowYUeR6f2hnRkaGcnNzlZqaqh49euizzz5T79691adPH61du1ZS+X4GW/qGiXA1fPhw7dmzR+vXr/d1KV517NgxjRw5UitWrFDVqlV9XU6ZcjgcSkhI0CuvvCJJ6tChg/bs2aOZM2dqwIABPq7OexYuXKh//etfevfdd3Xttddqx44dGjVqlCIjI/2qnZXdxYsX9bvf/U7GGM2YMcPX5XjV1q1b9cYbb2jbtm2y2Wy+LqfMOBwOSdI999yj0aNHS5Lat2+vDRs2aObMmbrlllvKtZ5K29NSt25dBQYGFhrd/N///lcNGjTwUVUl9+ijj+rDDz/U6tWrXe583aBBA124cEFZWVku21/ezgYNGhT5Plxa52tbt25VRkaGrrvuOgUFBSkoKEhr167V1KlTFRQUpPr161u+jZc0bNhQbdq0cVnWunVr5yj9S7UW93vboEEDZWRkuKzPz89XZmZmhWnr2LFjnb0t7dq1U//+/TV69GhnT5q/tPNy3mqTVX6XLwWW77//XitWrHC5A7A/tPOLL75QRkaGoqOjnZ9L33//vZ544gk1adJEkn+0s27dugoKCrrq51J5fQZX2tASHBysjh07atWqVc5lDodDq1atUmJiog8r84wxRo8++qiWLFmizz//XE2bNnVZ37FjR1WpUsWlnQcOHFB6erqznYmJidq9e7fLH9elD5lf/6L6wm233abdu3drx44dzkdCQoL69evn/NrqbbykS5cuhS5ZP3jwoGJiYiRJTZs2VYMGDVzampOTo82bN7u0NSsrS1u3bnVu8/nnn8vhcKhTp07l0IqrO3funAICXD9+AgMDnf/V+Us7L+etNiUmJmrdunW6ePGic5sVK1aoVatWqlWrVjm1pniXAsuhQ4e0cuVK1alTx2W9P7Szf//+2rVrl8vnUmRkpMaOHavly5dL8o92BgcH6/rrry/2c6lcjzNuD9n1QwsWLDB2u93MnTvX7N271/zxj3804eHhLqObK7qhQ4easLAws2bNGnPy5Enn49y5c85thgwZYqKjo83nn39uvv76a5OYmGgSExOd6y9dinbHHXeYHTt2mE8//dTUq1evwl0OfLnLrx4yxn/a+NVXX5mgoCDz8ssvm0OHDpl//etfplq1auaf//ync5vU1FQTHh5uli1bZnbt2mXuueeeIi+b7dChg9m8ebNZv369adGiRYW65HnAgAGmUaNGzkueFy9ebOrWrWuefPJJ5zZWbOfZs2fN9u3bzfbt240kM3nyZLN9+3bnVTPeaFNWVpapX7++6d+/v9mzZ49ZsGCBqVatWrleIltcOy9cuGDuvvtu07hxY7Njxw6Xz6XLrxKxejuL8uurh4zxj3YuXrzYVKlSxcyaNcscOnTIvPnmmyYwMNB88cUXzn2U12dwpQ4txhjz5ptvmujoaBMcHGxuuOEGs2nTJl+X5BFJRT7mzJnj3Obnn382w4YNM7Vq1TLVqlUzvXv3NidPnnTZz3fffWd69uxpQkJCTN26dc0TTzxhLl68WM6tcd+vQ4s/tfGDDz4wbdu2NXa73cTGxppZs2a5rHc4HObZZ5819evXN3a73dx2223mwIEDLtucPn3a9O3b19SoUcOEhoaaQYMGmbNnz5ZnM4qVk5NjRo4caaKjo03VqlXNNddcY8aPH+9yULNiO1evXl3k3+OAAQOMMd5r086dO03Xrl2N3W43jRo1MqmpqeXVRGNM8e08evToFT+XVq9e7TftLEpRocVf2pmWlmaaN29uqlatauLj483SpUtd9lFen8Hc5RkAAFhCpR3TAgAArIXQAgAALIHQAgAALIHQAgAALIHQAgAALIHQAgAALIHQAgAALIHQAgAALIHQAqBCefbZZ/XHP/6xzPY/d+5chYeHl2ofM2fO1F133eWdggC4jdACVHIbN25UYGCgkpOTfV2KTp06pTfeeEPjx4/3dSnFGjx4sLZt26YvvvjC16UAlQqhBajk0tLS9Nhjj2ndunU6ceJEsdsaY5Sfn19mtbz99tvq3Lmz8+6xFVVwcLD+8Ic/aOrUqb4uBahUCC1AJZabm6t///vfGjp0qJKTkzV37lyX9WvWrJHNZtMnn3yijh07ym63a/369XI4HEpJSVHTpk0VEhKi+Ph4vffee87nFRQU6KGHHnKub9Wqld54442r1rNgwYJCp13ee+89tWvXTiEhIapTp46SkpL0008/OV/n8ccfV3h4uOrUqaMnn3xSAwYMUK9evdx+D3788UclJCSod+/eysvLc7Z51apVSkhIULVq1dS5c2cdOHDA5Xl33XWX3n//ff38889uvxaA0iG0AJXYwoULFRsbq1atWumBBx7Q7NmzVdQ9VMeNG6fU1FTt27dPcXFxSklJ0bx58zRz5kx98803Gj16tB544AGtXbtWkuRwONS4cWMtWrRIe/fu1XPPPac///nPWrhw4RVryczM1N69e5WQkOBcdvLkSfXt21eDBw/Wvn37tGbNGvXp08dZ46RJkzR37lzNnj1b69evV2ZmppYsWeJ2+48dO6abbrpJbdu21XvvvSe73e5cN378eE2aNElff/21goKCNHjwYJfnJiQkKD8/X5s3b3b79QCUUkluYw3AP3Tu3NlMmTLFGGPMxYsXTd26dc3q1aud6y/dsv7y29CfP3/eVKtWzWzYsMFlXw899JDp27fvFV9r+PDh5t57773i+u3btxtJJj093bls69atRpL57rvvinxOw4YNzWuvveb8/uLFi6Zx48bmnnvuueLrzJkzx4SFhZn9+/ebqKgoM2LECONwOAq1eeXKlc5lH330kZFkfv75Z5d91apVy8ydO/eKrwXAu4J8mpgA+MyBAwf01VdfOXsmgoKCdP/99ystLU2/+c1vXLa9vPfj8OHDOnfunG6//XaXbS5cuKAOHTo4v58+fbpmz56t9PR0/fzzz7pw4YLat29/xXounWapWrWqc1l8fLxuu+02tWvXTt27d9cdd9yh3/72t6pVq5ays7N18uRJderUybl9UFCQEhISiuwt+vVr3XTTTfrDH/6gKVOmFLlNXFyc8+uGDRtKkjIyMhQdHe1cHhISonPnzhX7WgC8h9ACVFJpaWnKz89XZGSkc5kxRna7XdOmTVNYWJhzefXq1Z1f5+bmSpI++ugjNWrUyGWfl06vLFiwQGPGjNGkSZOUmJiomjVr6vXXXy/2VErdunUlSWfOnFG9evUkSYGBgVqxYoU2bNigzz77TG+++abGjx+vzZs3q3bt2iVuu91uV1JSkj788EONHTu2UDskqUqVKs6vbTabpF9Oe10uMzPTWSuAsseYFqASys/P17x58zRp0iTt2LHD+di5c6ciIyM1f/78Kz63TZs2stvtSk9PV/PmzV0eUVFRkqQvv/xSnTt31rBhw9ShQwc1b95cR44cKbamZs2aKTQ0VHv37nVZbrPZ1KVLF02cOFHbt29XcHCwlixZorCwMDVs2NAlCOXn52vr1q1XbX9AQID+8Y9/qGPHjurWrdtVr5oqypEjR3T+/HmX3iUAZYueFqAS+vDDD3XmzBk99NBDLj0qknTvvfcqLS1NQ4YMKfK5NWvW1JgxYzR69Gg5HA517dpV2dnZ+vLLLxUaGqoBAwaoRYsWmjdvnpYvX66mTZvqH//4h7Zs2aKmTZtesaaAgAAlJSVp/fr1zqt/Nm/erFWrVumOO+5QRESENm/erB9//FGtW7eWJI0cOVKpqalq0aKFYmNjNXnyZGVlZbn1HgQGBupf//qX+vbtq1tvvVVr1qxRgwYN3HquJH3xxRe65ppr1KxZM7efA6B06GkBKqG0tDQlJSUVCizSL6Hl66+/1q5du674/BdffFHPPvusUlJS1Lp1a/Xo0UMfffSRM5T86U9/Up8+fXT//ferU6dOOn36tIYNG3bVuh5++GEtWLDAeRomNDRU69at05133qmWLVvqmWee0aRJk9SzZ09J0hNPPKH+/ftrwIABztNQvXv3dvt9CAoK0vz583Xttdfq1ltvVUZGhtvPnT9/vh555BG3twdQejZztRFrAFBOjDHq1KmTRo8erb59+5ZoHwMHDlRWVpaWLl3q3eIu88033+jWW2/VwYMHiwx+AMoGPS0AKgybzaZZs2aV6ay73nDy5EnNmzePwAKUM8a0AKhQ2rdvX+yl0RVBUlKSr0sAKiVODwEAAEvg9BAAALAEQgsAALAEQgsAALAEQgsAALAEQgsAALAEQgsAALAEQgsAALAEQgsAALAEQgsAALCE/wdtY/MXyhUDNwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "geo = data_num\n", + "geo = geo.sort_values(by='Area (sq km)', ascending=True)\n", + "print(geo.shape)\n", + "visualise(geo)\n" + ] + }, + { + "cell_type": "markdown", + "id": "02891f7f", + "metadata": { + "papermill": { + "duration": 0.032246, + "end_time": "2025-02-16T13:31:46.351048", + "exception": false, + "start_time": "2025-02-16T13:31:46.318802", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "A cluster analysis - KMeans - suggest 5 centroids may be suitable. The population appears to impact on the centroid over the area. " + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "df194312", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:31:46.419615Z", + "iopub.status.busy": "2025-02-16T13:31:46.419213Z", + "iopub.status.idle": "2025-02-16T13:36:26.344167Z", + "shell.execute_reply": "2025-02-16T13:36:26.342953Z" + }, + "papermill": { + "duration": 279.996234, + "end_time": "2025-02-16T13:36:26.380813", + "exception": false, + "start_time": "2025-02-16T13:31:46.384579", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n", + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = geo['Area (sq km)'].values\n", + "y = geo['pp_sq_m'].values\n", + "data_to_fit = list(zip(x, y))\n", + "inertias = []\n", + "\n", + "for i in range(1,11):\n", + " kmeans = KMeans(n_clusters=i)\n", + " kmeans.fit(data_to_fit)\n", + " inertias.append(kmeans.inertia_)\n", + "\n", + "plt.plot(range(1,11), inertias, marker='o')\n", + "plt.title('Elbow method')\n", + "plt.xlabel('Number of clusters')\n", + "plt.ylabel('Inertia')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "99bc0f0c", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:36:26.453507Z", + "iopub.status.busy": "2025-02-16T13:36:26.453066Z", + "iopub.status.idle": "2025-02-16T13:39:04.629263Z", + "shell.execute_reply": "2025-02-16T13:39:04.628146Z" + }, + "papermill": { + "duration": 158.254367, + "end_time": "2025-02-16T13:39:04.669109", + "exception": false, + "start_time": "2025-02-16T13:36:26.414742", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------ Centers --------\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[ 236.8852271 , 1496.3982078 ],\n", + " [ 98.17824303, 3939.25590807],\n", + " [ 367.61878674, 529.88931171],\n", + " [1572.03080001, 4852.80537318],\n", + " [ 71.12833405, 2706.46815779]])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kmeans = KMeans(n_clusters=5)\n", + "kmeans.fit(data_to_fit)\n", + "centers = kmeans.cluster_centers_\n", + "plt.scatter(centers[:, 0], centers[:, 1], c='red', s=100, alpha=0.5);\n", + "plt.scatter(x, y, c=kmeans.labels_)\n", + "plt.xlabel(\"Area (km^2)\")\n", + "plt.ylabel(\"density of population (pp/km^2)\")\n", + "plt.show()\n", + "print(\"------ Centers --------\")\n", + "centers" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "9b1cd3e2", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:39:04.742910Z", + "iopub.status.busy": "2025-02-16T13:39:04.742509Z", + "iopub.status.idle": "2025-02-16T13:39:05.162040Z", + "shell.execute_reply": "2025-02-16T13:39:05.160979Z" + }, + "papermill": { + "duration": 0.459915, + "end_time": "2025-02-16T13:39:05.164430", + "exception": false, + "start_time": "2025-02-16T13:39:04.704515", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "76211.43788470532\n", + "Chi2 value= \n", + "p-value= 0.0\n", + "Degrees of freedom= 134\n", + "\n" + ] + } + ], + "source": [ + "from scipy.stats import chi2_contingency\n", + "from scipy.stats import chi2\n", + "geo = geo.loc[:,['Area (sq km)','est_pop']]\n", + "geo = geo.drop_duplicates()\n", + "geo = geo.dropna()\n", + "\n", + "nl = \"\\n\"\n", + "stat, p, dof, expected = chi2_contingency(geo)\n", + "print(stat)\n", + "\n", + "print(f\"Chi2 value= {chi2}{nl}p-value= {p}{nl}Degrees of freedom= {dof}{nl}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "d280b31a", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:39:05.238236Z", + "iopub.status.busy": "2025-02-16T13:39:05.237181Z", + "iopub.status.idle": "2025-02-16T13:40:13.869905Z", + "shell.execute_reply": "2025-02-16T13:40:13.868975Z" + }, + "papermill": { + "duration": 68.672649, + "end_time": "2025-02-16T13:40:13.872543", + "exception": false, + "start_time": "2025-02-16T13:39:05.199894", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "data['label'] = kmeans.labels_\n", + "data.to_csv('data_with_labels.csv')" + ] + }, + { + "cell_type": "markdown", + "id": "1c9d65fe", + "metadata": { + "papermill": { + "duration": 0.035585, + "end_time": "2025-02-16T13:40:13.943468", + "exception": false, + "start_time": "2025-02-16T13:40:13.907883", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "# Is there a relationship between houses prices and clusters of density and areas?" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "2a8ba5fe", + "metadata": { + "execution": { + "iopub.execute_input": "2025-02-16T13:40:14.015891Z", + "iopub.status.busy": "2025-02-16T13:40:14.015515Z", + "iopub.status.idle": "2025-02-16T13:40:46.775130Z", + "shell.execute_reply": "2025-02-16T13:40:46.774042Z" + }, + "papermill": { + "duration": 32.832975, + "end_time": "2025-02-16T13:40:46.811890", + "exception": false, + "start_time": "2025-02-16T13:40:13.978915", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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HELENS',\n", + " 'STAFFORD',\n", + " 'STOCKTON-ON-TEES',\n", + " 'TAMWORTH',\n", + " 'WALSALL',\n", + " 'WARRINGTON',\n", + " 'WARWICK',\n", + " 'WIGAN',\n", + " 'WIRRAL',\n", + " 'WOLVERHAMPTON',\n", + " 'WORCESTER',\n", + " 'YORK'}" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grouping = data.groupby(['label'])['Name'].apply(set).reset_index()\n", + "g_4 = grouping.loc[4,'Name']\n", + "g_4\n" + ] + }, + { + "cell_type": "markdown", + "id": "7b21e246", + "metadata": { + "papermill": { + "duration": 0.043893, + "end_time": "2025-02-16T13:40:48.704639", + "exception": false, + "start_time": "2025-02-16T13:40:48.660746", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Findings and conclusion\n", + "\n", + "We applied a chi-square test. We use the H0 (Null Hypothesis): There is no relationship between the area and estimated population. We also used the the H1 (Alternative Hypothesis): There is a relationship between an area and an estimated population. $p-value = 0$ and $p < 0.01$ suggest we can reject the Null hupothesis. Therefore, we surmise there is a relationship between an area and an estimated population. \n", + "\n", + "We have shown a strong Pearson correlation between the area (square km) and estimated population may exist - i.e., 0.94. A weaker Pearson correlation between the area and the people per square km was approximated to 0.40. Some KMeans analysis suggests the 5 centroids across each type of geography and across the whole dataset. Each cluster appears to be mostly guided by the estimated population or population density. Therefore, we surmise and confirm that an estimated population may vary across an area. The latter may impact on the population density. Other factors may impact on the population density. Further research based on employment, geography features, and other aspects should be further conducted.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "deee784c", + "metadata": { + "papermill": { + "duration": 0.037409, + "end_time": "2025-02-16T13:40:48.777667", + "exception": false, + "start_time": "2025-02-16T13:40:48.740258", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kaggle": { + "accelerator": "none", + "dataSources": [ + { + "datasetId": 4404514, + "sourceId": 8376714, + "sourceType": "datasetVersion" + } + ], + "dockerImageVersionId": 30664, + "isGpuEnabled": false, + "isInternetEnabled": true, + "language": "python", + "sourceType": "notebook" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "papermill": { + "default_parameters": {}, + "duration": 873.639834, + "end_time": "2025-02-16T13:40:50.743126", + "environment_variables": {}, + "exception": null, + "input_path": "__notebook__.ipynb", + "output_path": "__notebook__.ipynb", + "parameters": {}, + "start_time": "2025-02-16T13:26:17.103292", + "version": "2.5.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}