diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 59b955a..b6b0deb 100755 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -12,12 +12,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# import numpy and pandas\n", - "\n" + "import pandas as pd\n", + "import numpy as np\n", + "import scipy.stats as st\n", + "import seaborn as sns" ] }, { @@ -31,11 +34,219 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameJob TitlesDepartmentFull or Part-TimeSalary or HourlyTypical HoursAnnual SalaryHourly Rate
0AARON, JEFFERY MSERGEANTPOLICEFSalaryNaN101442.0NaN
1AARON, KARINAPOLICE OFFICER (ASSIGNED AS DETECTIVE)POLICEFSalaryNaN94122.0NaN
2AARON, KIMBERLEI RCHIEF CONTRACT EXPEDITERGENERAL SERVICESFSalaryNaN101592.0NaN
3ABAD JR, VICENTE MCIVIL ENGINEER IVWATER MGMNTFSalaryNaN110064.0NaN
4ABASCAL, REECE ETRAFFIC CONTROL AIDE-HOURLYOEMCPHourly20.0NaN19.86
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33178ZYLINSKA, KATARZYNAPOLICE OFFICERPOLICEFSalaryNaN72510.0NaN
33179ZYMANTAS, LAURA CPOLICE OFFICERPOLICEFSalaryNaN48078.0NaN
33180ZYMANTAS, MARK EPOLICE OFFICERPOLICEFSalaryNaN90024.0NaN
33181ZYRKOWSKI, CARLO EPOLICE OFFICERPOLICEFSalaryNaN93354.0NaN
33182ZYSKOWSKI, DARIUSZCHIEF DATA BASE ANALYSTDoITFSalaryNaN115932.0NaN
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33183 rows × 8 columns

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" + ], + "text/plain": [ + " Name Job Titles \\\n", + "0 AARON, JEFFERY M SERGEANT \n", + "1 AARON, KARINA POLICE OFFICER (ASSIGNED AS DETECTIVE) \n", + "2 AARON, KIMBERLEI R CHIEF CONTRACT EXPEDITER \n", + "3 ABAD JR, VICENTE M CIVIL ENGINEER IV \n", + "4 ABASCAL, REECE E TRAFFIC CONTROL AIDE-HOURLY \n", + "... ... ... \n", + "33178 ZYLINSKA, KATARZYNA POLICE OFFICER \n", + "33179 ZYMANTAS, LAURA C POLICE OFFICER \n", + "33180 ZYMANTAS, MARK E POLICE OFFICER \n", + "33181 ZYRKOWSKI, CARLO E POLICE OFFICER \n", + "33182 ZYSKOWSKI, DARIUSZ CHIEF DATA BASE ANALYST \n", + "\n", + " Department Full or Part-Time Salary or Hourly Typical Hours \\\n", + "0 POLICE F Salary NaN \n", + "1 POLICE F Salary NaN \n", + "2 GENERAL SERVICES F Salary NaN \n", + "3 WATER MGMNT F Salary NaN \n", + "4 OEMC P Hourly 20.0 \n", + "... ... ... ... ... \n", + "33178 POLICE F Salary NaN \n", + "33179 POLICE F Salary NaN \n", + "33180 POLICE F Salary NaN \n", + "33181 POLICE F Salary NaN \n", + "33182 DoIT F Salary NaN \n", + "\n", + " Annual Salary Hourly Rate \n", + "0 101442.0 NaN \n", + "1 94122.0 NaN \n", + "2 101592.0 NaN \n", + "3 110064.0 NaN \n", + "4 NaN 19.86 \n", + "... ... ... \n", + "33178 72510.0 NaN \n", + "33179 48078.0 NaN \n", + "33180 90024.0 NaN \n", + "33181 93354.0 NaN \n", + "33182 115932.0 NaN \n", + "\n", + "[33183 rows x 8 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Your code here:\n" + "# Your code here:\n", + "salaries = pd.read_csv(\"Current_Employee_Names__Salaries__and_Position_Titles.csv\")\n", + "salaries" ] }, { @@ -47,12 +258,131 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameJob TitlesDepartmentFull or Part-TimeSalary or HourlyTypical HoursAnnual SalaryHourly Rate
0AARON, JEFFERY MSERGEANTPOLICEFSalaryNaN101442.0NaN
1AARON, KARINAPOLICE OFFICER (ASSIGNED AS DETECTIVE)POLICEFSalaryNaN94122.0NaN
2AARON, KIMBERLEI RCHIEF CONTRACT EXPEDITERGENERAL SERVICESFSalaryNaN101592.0NaN
3ABAD JR, VICENTE MCIVIL ENGINEER IVWATER MGMNTFSalaryNaN110064.0NaN
4ABASCAL, REECE ETRAFFIC CONTROL AIDE-HOURLYOEMCPHourly20.0NaN19.86
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" + ], + "text/plain": [ + " Name Job Titles \\\n", + "0 AARON, JEFFERY M SERGEANT \n", + "1 AARON, KARINA POLICE OFFICER (ASSIGNED AS DETECTIVE) \n", + "2 AARON, KIMBERLEI R CHIEF CONTRACT EXPEDITER \n", + "3 ABAD JR, VICENTE M CIVIL ENGINEER IV \n", + "4 ABASCAL, REECE E TRAFFIC CONTROL AIDE-HOURLY \n", + "\n", + " Department Full or Part-Time Salary or Hourly Typical Hours \\\n", + "0 POLICE F Salary NaN \n", + "1 POLICE F Salary NaN \n", + "2 GENERAL SERVICES F Salary NaN \n", + "3 WATER MGMNT F Salary NaN \n", + "4 OEMC P Hourly 20.0 \n", + "\n", + " Annual Salary Hourly Rate \n", + "0 101442.0 NaN \n", + "1 94122.0 NaN \n", + "2 101592.0 NaN \n", + "3 110064.0 NaN \n", + "4 NaN 19.86 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "salaries.head()\n" ] }, { @@ -64,12 +394,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Name 0\n", + "Job Titles 0\n", + "Department 0\n", + "Full or Part-Time 0\n", + "Salary or Hourly 0\n", + "Typical Hours 25161\n", + "Annual Salary 8022\n", + "Hourly Rate 25161\n", + "dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "salaries.isna().sum()\n" ] }, { @@ -81,12 +430,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['Name',\n", + " 'Job Titles',\n", + " 'Department',\n", + " 'Full or Part-Time',\n", + " 'Salary or Hourly',\n", + " 'Typical Hours',\n", + " 'Annual Salary',\n", + " 'Hourly Rate']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(salaries.columns.values)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Salary or Hourly\n", + "Salary 25161\n", + "Hourly 8022\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "salaries[\"Salary or Hourly\"].value_counts()" ] }, { @@ -105,12 +495,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Department\n", + "POLICE 13414\n", + "FIRE 4641\n", + "STREETS & SAN 2198\n", + "OEMC 2102\n", + "WATER MGMNT 1879\n", + "AVIATION 1629\n", + "TRANSPORTN 1140\n", + "PUBLIC LIBRARY 1015\n", + "GENERAL SERVICES 980\n", + "FAMILY & SUPPORT 615\n", + "FINANCE 560\n", + "HEALTH 488\n", + "CITY COUNCIL 411\n", + "LAW 407\n", + "BUILDINGS 269\n", + "COMMUNITY DEVELOPMENT 207\n", + "BUSINESS AFFAIRS 171\n", + "COPA 116\n", + "BOARD OF ELECTION 107\n", + "DoIT 99\n", + "PROCUREMENT 92\n", + "INSPECTOR GEN 87\n", + "MAYOR'S OFFICE 85\n", + "CITY CLERK 84\n", + "ANIMAL CONTRL 81\n", + "HUMAN RESOURCES 79\n", + "CULTURAL AFFAIRS 65\n", + "BUDGET & MGMT 46\n", + "ADMIN HEARNG 39\n", + "DISABILITIES 28\n", + "TREASURER 22\n", + "HUMAN RELATIONS 16\n", + "BOARD OF ETHICS 8\n", + "POLICE BOARD 2\n", + "LICENSE APPL COMM 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "salaries[\"Department\"].value_counts()\n" ] }, { @@ -124,12 +561,163 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameJob TitlesDepartmentFull or Part-TimeSalary or HourlyTypical HoursAnnual SalaryHourly Rate
0AARON, JEFFERY MSERGEANTPOLICEFSalaryNaN101442.0NaN
1AARON, KARINAPOLICE OFFICER (ASSIGNED AS DETECTIVE)POLICEFSalaryNaN94122.0NaN
2AARON, KIMBERLEI RCHIEF CONTRACT EXPEDITERGENERAL SERVICESFSalaryNaN101592.0NaN
3ABAD JR, VICENTE MCIVIL ENGINEER IVWATER MGMNTFSalaryNaN110064.0NaN
4ABASCAL, REECE ETRAFFIC CONTROL AIDE-HOURLYOEMCPHourly20.0NaN19.86
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" + ], + "text/plain": [ + " Name Job Titles \\\n", + "0 AARON, JEFFERY M SERGEANT \n", + "1 AARON, KARINA POLICE OFFICER (ASSIGNED AS DETECTIVE) \n", + "2 AARON, KIMBERLEI R CHIEF CONTRACT EXPEDITER \n", + "3 ABAD JR, VICENTE M CIVIL ENGINEER IV \n", + "4 ABASCAL, REECE E TRAFFIC CONTROL AIDE-HOURLY \n", + "\n", + " Department Full or Part-Time Salary or Hourly Typical Hours \\\n", + "0 POLICE F Salary NaN \n", + "1 POLICE F Salary NaN \n", + "2 GENERAL SERVICES F Salary NaN \n", + "3 WATER MGMNT F Salary NaN \n", + "4 OEMC P Hourly 20.0 \n", + "\n", + " Annual Salary Hourly Rate \n", + "0 101442.0 NaN \n", + "1 94122.0 NaN \n", + "2 101592.0 NaN \n", + "3 110064.0 NaN \n", + "4 NaN 19.86 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "TtestResult(statistic=20.6198057854942, pvalue=4.3230240486229894e-92, df=8021)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "# H0= hourly wage of all hourly workers are = $30/h\n", + "# H1= hourly wage of all hourly workers are != $30/h\n", + "# alpha= 0.05 \n", + "hourly_wage= salaries[salaries['Salary or Hourly']== 'Hourly']['Hourly Rate']\n", + "\n", + "st.ttest_1samp(hourly_wage, 30)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The p-value is very low so he can say tha we reject the null hypothesis. Conclusion: Hourly wage of all workers are significantly different from $30/hr " ] }, { @@ -143,12 +731,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-statistic: 5.932870515690814\n", + "P-value: 0.9999999984921207\n", + "We are not able to reject the null hypothesis\n" + ] + } + ], "source": [ "# Your code here:\n", - "\n" + "# H0= mu annual salary >= 86000$\n", + "# H1= mu annual salary > 86000$\n", + "alpha = 0.05\n", + "salary = salaries[salaries[\"Salary or Hourly\"] ==\"Salary\"]\n", + "mu = 86000\n", + "annual = salary[\"Annual Salary\"]\n", + "\n", + "t_statistic, p_value = st.ttest_1samp(annual, mu, alternative=\"less\")\n", + "print(f\"T-statistic: {t_statistic}\")\n", + "print(f\"P-value: {p_value}\")\n", + "\n", + "if p_value < alpha:\n", + " print(\"We are able to reject the null hypothesis\")\n", + "else:\n", + " print(\"We are not able to reject the null hypothesis\")" ] }, { @@ -160,12 +772,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'STREETS & SAN'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "table = pd.crosstab(salaries[\"Department\"],salaries[\"Salary or Hourly\"])\n", + "department = table[\"Hourly\"].idxmax()\n", + "department" ] }, { @@ -177,12 +802,156 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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NameJob TitlesDepartmentFull or Part-TimeSalary or HourlyTypical HoursAnnual SalaryHourly Rate
0AARON, JEFFERY MSERGEANTPOLICEFSalaryNaN101442.0NaN
1AARON, KARINAPOLICE OFFICER (ASSIGNED AS DETECTIVE)POLICEFSalaryNaN94122.0NaN
2AARON, KIMBERLEI RCHIEF CONTRACT EXPEDITERGENERAL SERVICESFSalaryNaN101592.0NaN
3ABAD JR, VICENTE MCIVIL ENGINEER IVWATER MGMNTFSalaryNaN110064.0NaN
4ABASCAL, REECE ETRAFFIC CONTROL AIDE-HOURLYOEMCPHourly20.0NaN19.86
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" + ], + "text/plain": [ + " Name Job Titles \\\n", + "0 AARON, JEFFERY M SERGEANT \n", + "1 AARON, KARINA POLICE OFFICER (ASSIGNED AS DETECTIVE) \n", + "2 AARON, KIMBERLEI R CHIEF CONTRACT EXPEDITER \n", + "3 ABAD JR, VICENTE M CIVIL ENGINEER IV \n", + "4 ABASCAL, REECE E TRAFFIC CONTROL AIDE-HOURLY \n", + "\n", + " Department Full or Part-Time Salary or Hourly Typical Hours \\\n", + "0 POLICE F Salary NaN \n", + "1 POLICE F Salary NaN \n", + "2 GENERAL SERVICES F Salary NaN \n", + "3 WATER MGMNT F Salary NaN \n", + "4 OEMC P Hourly 20.0 \n", + "\n", + " Annual Salary Hourly Rate \n", + "0 101442.0 NaN \n", + "1 94122.0 NaN \n", + "2 101592.0 NaN \n", + "3 110064.0 NaN \n", + "4 NaN 19.86 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "salaries.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "TtestResult(statistic=-5.0380388645913685, pvalue=0.9999996882408217, df=599)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "import scipy.stats as stats\n", + "\n", "# Your code here:\n", - "\n" + "# H0= mu hourly wage <= 35\n", + "# H1= mu hourly wagw > 35\n", + "hourly_sample = salaries[(salaries[\"Department\"]==\"STREETS & SAN\") & (salaries[\"Salary or Hourly\"] ==\"Hourly\")][\"Hourly Rate\"].sample(600)\n", + "st.ttest_1samp(hourly_sample, 35, alternative=\"greater\")\n" ] }, { @@ -206,12 +975,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(32.52345834488425, 33.05365708767623)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "hourly_workers = salaries[salaries['Salary or Hourly'] == 'Hourly']\n", + "\n", + "mean_hourly_wage = hourly_workers['Hourly Rate'].mean()\n", + "standard_error = st.sem(hourly_workers['Hourly Rate'])\n", + "\n", + "confidence_level = 0.95\n", + "\n", + "degrees_of_freedom = len(hourly_workers) - 1\n", + "\n", + "confidence_interval = st.t.interval(confidence_level, degrees_of_freedom, loc=mean_hourly_wage, scale=standard_error)\n", + "\n", + "confidence_interval" ] }, { @@ -223,12 +1014,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(86476.5176546444, 86496.31135162238)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Your code here:\n", - "\n" + "salaried_employeed = salaries[(salaries[\"Department\"]==\"POLICE\") & (salaries[\"Salary or Hourly\"] ==\"Salary\")][\"Annual Salary\"]\n", + "\n", + "confidance_level = 0.95\n", + "alpha = 1 - confidance_level\n", + "\n", + "ddof = len(salaried_employeed) - 1\n", + "\n", + "mean = salaried_employeed.mean()\n", + "standard_error = st.sem(salaried_employeed)\n", + "\n", + "confidence_interval = st.t.interval(alpha, df=ddof, loc=mean, scale=standard_error)\n", + "confidence_interval" ] }, { @@ -246,7 +1059,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -271,7 +1084,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.9" } }, "nbformat": 4,