From 699360f0a0eabba6a5941abf97c99526b1934f99 Mon Sep 17 00:00:00 2001 From: Mariana Sousa Date: Mon, 14 Aug 2023 09:03:46 +0100 Subject: [PATCH] marianaslab --- your-code/continuous.ipynb | 469 ++++++++++++++++++++++++++++++++++--- your-code/discrete.ipynb | 245 +++++++++++++++++-- 2 files changed, 661 insertions(+), 53 deletions(-) diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb index 32218e9..ba6c92d 100644 --- a/your-code/continuous.ipynb +++ b/your-code/continuous.ipynb @@ -35,9 +35,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from scipy.stats import expon\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.82410285 2.88698719 2.35807773 2.93242575 2.28581205 2.49332811\n", + " 2.69577375 2.17878756 2.18542244 2.67962344]\n" + ] + } + ], "source": [ "from scipy.stats import uniform\n", "x = uniform.rvs(size=10)\n", @@ -73,11 +96,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "def numbers(a,b,c):\n", + " return np.random.randint(a,b,c)\n", + " \n", + "\n", + "\n", + "dist_1 = numbers(10,15,100)\n", + "\n", + "dist_2 = numbers(10, 60, 1000)\n", + "\n", + "plt.hist(dist_1, 10)\n", + "plt.show()\n", + "\n", + "plt.hist(dist_2, 10)\n", + "plt.show()" ] }, { @@ -93,7 +150,7 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# your answer here: they have different amount of datapoints and the scale is different\n" ] }, { @@ -114,11 +171,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "def normal_numbers(a,b,c): \n", + " return np.random.normal(loc = a, scale = b, size = 1000)\n", + "\n", + "normal_1 = normal_numbers(10, 1, 1000)\n", + "\n", + "normal_2 = normal_numbers(10, 50, 1000)\n", + "\n", + "plt.hist(normal_1)\n", + "plt.show()\n", + "\n", + "plt.hist(normal_2)\n", + "plt.show()\n" ] }, { @@ -134,7 +223,8 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# your answer here:\n", + "## one has more negative numbers then the others, other has more dispersion than the others" ] }, { @@ -158,11 +248,202 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "vehicles = pd.read_csv(\"vehicles.csv\")\n", + "\n", + "plt.hist(vehicles[\"Fuel Barrels/Year\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MakeModelYearEngine DisplacementCylindersTransmissionDrivetrainVehicle ClassFuel TypeFuel Barrels/YearCity MPGHighway MPGCombined MPGCO2 Emission Grams/MileFuel Cost/Year
0AM GeneralDJ Po Vehicle 2WD19842.54.0Automatic 3-spd2-Wheel DriveSpecial Purpose Vehicle 2WDRegular19.388824181717522.7647061950
1AM GeneralFJ8c Post Office19844.26.0Automatic 3-spd2-Wheel DriveSpecial Purpose Vehicle 2WDRegular25.354615131313683.6153852550
2AM GeneralPost Office DJ5 2WD19852.54.0Automatic 3-spdRear-Wheel DriveSpecial Purpose Vehicle 2WDRegular20.600625161716555.4375002100
3AM GeneralPost Office DJ8 2WD19854.26.0Automatic 3-spdRear-Wheel DriveSpecial Purpose Vehicle 2WDRegular25.354615131313683.6153852550
4ASC IncorporatedGNX19873.86.0Automatic 4-spdRear-Wheel DriveMidsize CarsPremium20.600625142116555.4375002550
\n", + "
" + ], + "text/plain": [ + " Make Model Year Engine Displacement \\\n", + "0 AM General DJ Po Vehicle 2WD 1984 2.5 \n", + "1 AM General FJ8c Post Office 1984 4.2 \n", + "2 AM General Post Office DJ5 2WD 1985 2.5 \n", + "3 AM General Post Office DJ8 2WD 1985 4.2 \n", + "4 ASC Incorporated GNX 1987 3.8 \n", + "\n", + " Cylinders Transmission Drivetrain Vehicle Class \\\n", + "0 4.0 Automatic 3-spd 2-Wheel Drive Special Purpose Vehicle 2WD \n", + "1 6.0 Automatic 3-spd 2-Wheel Drive Special Purpose Vehicle 2WD \n", + "2 4.0 Automatic 3-spd Rear-Wheel Drive Special Purpose Vehicle 2WD \n", + "3 6.0 Automatic 3-spd Rear-Wheel Drive Special Purpose Vehicle 2WD \n", + "4 6.0 Automatic 4-spd Rear-Wheel Drive Midsize Cars \n", + "\n", + " Fuel Type Fuel Barrels/Year City MPG Highway MPG Combined MPG \\\n", + "0 Regular 19.388824 18 17 17 \n", + "1 Regular 25.354615 13 13 13 \n", + "2 Regular 20.600625 16 17 16 \n", + "3 Regular 25.354615 13 13 13 \n", + "4 Premium 20.600625 14 21 16 \n", + "\n", + " CO2 Emission Grams/Mile Fuel Cost/Year \n", + "0 522.764706 1950 \n", + "1 683.615385 2550 \n", + "2 555.437500 2100 \n", + "3 683.615385 2550 \n", + "4 555.437500 2550 " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "vehicles.head()" ] }, { @@ -174,11 +455,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "plt.hist(vehicles[\"CO2 Emission Grams/Mile\"])\n", + "plt.show()\n" ] }, { @@ -190,11 +483,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "plt.hist(vehicles[\"Combined MPG\"])\n", + "plt.show()" ] }, { @@ -210,7 +515,8 @@ "metadata": {}, "outputs": [], "source": [ - "# you answer here:\n" + "# you answer here:\n", + "## maybe Combined MPG, because it looks more simetric." ] }, { @@ -237,11 +543,89 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 4.90975163, 3.67674667, 21.34634522, 12.92211479, 6.90317124,\n", + " 3.03132028, 9.19296527, 3.51561902, 48.29292725, 8.45635916,\n", + " 4.56152493, 2.11157678, 4.49549693, 9.48927872, 3.69911199,\n", + " 16.59990611, 16.81988897, 8.43328015, 20.19161028, 25.62505188,\n", + " 0.47419958, 2.32316969, 1.44010232, 5.51898109, 17.13276068,\n", + " 45.86394918, 14.35242751, 24.80425318, 7.76550562, 48.10040892,\n", + " 22.72240154, 0.50127644, 1.02464948, 6.17949263, 4.00024444,\n", + " 13.76639423, 9.69154397, 3.21073504, 8.624062 , 0.1441838 ,\n", + " 25.66096234, 17.64912595, 8.61174062, 25.33673077, 4.5611982 ,\n", + " 10.94832278, 7.58546466, 3.98152751, 27.76404364, 9.20942764,\n", + " 1.23184202, 0.65341439, 12.7663127 , 6.9953399 , 16.50573119,\n", + " 1.95487292, 9.6337003 , 1.09009309, 0.34780099, 1.02010157,\n", + " 5.24157256, 0.20915514, 0.79261919, 2.39327862, 46.85863632,\n", + " 2.17435704, 50.45384769, 1.50766382, 1.17959712, 1.6516833 ,\n", + " 1.29571489, 0.93949751, 8.94952056, 4.72542351, 8.40135069,\n", + " 11.26680249, 0.39084559, 8.56145857, 17.21654247, 0.53489505,\n", + " 18.83193193, 2.86946834, 32.40619276, 7.37969248, 17.49774829,\n", + " 2.90186705, 0.26642165, 2.73133672, 1.60904661, 4.01927516,\n", + " 0.12628866, 16.03820013, 2.55955996, 2.26666981, 12.61976358,\n", + " 10.55009989, 18.6302799 , 16.49896672, 0.19866463, 7.51776863])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def expon_numbers(a, b):\n", + " return np.random.exponential(a, b)\n", + "\n", + "expon_numbers(10, 100)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "expon_1 = expon_numbers(1, 1000)\n", + "\n", + "expon_2 = expon_numbers(100, 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(expon_1, 100)\n", + "plt.show()\n", + "\n", + "plt.hist(expon_2, 100)\n", + "plt.show()" ] }, { @@ -257,7 +641,8 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "# your answer here:\n", + "# the scales are different, their distribution is similiar." ] }, { @@ -273,11 +658,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7768698398515702\n" + ] + } + ], "source": [ - "# your answer here\n", + "\n", + "lambda_inv = 10/1 # on average you wait 10 min for your appoitment\n", + "\n", + "expon_dist = expon(scale = lambda_inv)\n", + "\n", + "print(expon_dist.cdf(15)) \n", + "\n", + "\n", + "\n", "# Hint: This is same as saying P(x<15)\n" ] }, @@ -290,17 +691,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2231301601484298\n" + ] + } + ], "source": [ - "# your answer here\n" + "print(1-expon_dist.cdf(15))\n" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -314,7 +723,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.9" } }, "nbformat": 4, diff --git a/your-code/discrete.ipynb b/your-code/discrete.ipynb index 310a3c6..d49c5fd 100644 --- a/your-code/discrete.ipynb +++ b/your-code/discrete.ipynb @@ -33,9 +33,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], + "source": [ + "import random\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from scipy.stats import bernoulli\n", + "from scipy.stats import binom\n", + "from scipy.stats import geom\n", + "from scipy.stats import poisson\n", + "\n", + "from scipy.stats import uniform\n", + "from scipy.stats import expon\n", + "from scipy.stats import norm" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "probability of getting and apple 0.6\n", + "probability of getting and orange 0.4\n" + ] + } + ], "source": [ "\"\"\"\n", "Calculate:\n", @@ -43,7 +73,12 @@ "q = probability that the fruit is an orange\n", "\"\"\"\n", "\n", - "# your code here\n" + "p = 60/100\n", + "q = 40/100\n", + "\n", + "print(f\"probability of getting and apple {60/100}\")\n", + "print(f\"probability of getting and orange {40/100}\")\n", + "\n" ] }, { @@ -61,11 +96,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6\n", + "chance os 5 apples in a row 0.07776\n" + ] + } + ], + "source": [ + "# 1) considering 20 fruits\n", + "\n", + "# 1 --> apples 0 --> oranges\n", + "\n", + "p = 0.6\n", + "\n", + "bernoulli_dist = bernoulli(p)\n", + "\n", + "print(bernoulli_dist.pmf(1))\n", + "\n", + "apples = 0.6*0.6*0.6*0.6*0.6\n", + "apples\n", + "print(f\"chance os 5 apples in a row {apples}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "chance os 5 apples in a row 1.073741824000001e-06\n" + ] + } + ], "source": [ - "# your code here\n" + "q\n", + "\n", + "oranges = q**15\n", + "oranges\n", + "\n", + "print(f\"chance os 5 apples in a row {oranges}\")" ] }, { @@ -83,11 +161,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1.6757134792447097e-06" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "# probability of 5 apples\n", + "\n", + "n = 20\n", + "p = 0.6\n", + "\n", + "binomial_dist = binom(n,p)\n", + "apples = binomial_dist.pmf(5)\n", + "\n", + "q = 0.4\n", + "\n", + "binomial_dist2 = binom(n,q)\n", + "oranges = binomial_dist2.pmf(15)\n", + "\n", + "apples*oranges" ] }, { @@ -101,11 +203,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.00031703112116863004" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "n = 20\n", + "p =0.6\n", + "\n", + "binomial_dist = binom(n,p)\n", + "\n", + "less_than_5_apples = binomial_dist.cdf(4)\n", + "\n", + "less_than_5_apples " ] }, { @@ -119,12 +239,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "\n", + "### ainda nao demmos!!\n", + "\n", + "#binomial_dist = binom(n,p)\n", + "#binomial_dist.pdf(20)\n", + "\n", "# your code here\n", - "# Please label the axes and give a title to the plot\n" + "# Please label the axes and give a title to the plot\n", + "\n", + "X = np.arange(1,20)\n", + "\n", + "plt.plot(X, binomial_dist.pmf(X), \"b-\")\n", + "plt.show()\n" ] }, { @@ -151,11 +293,49 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2432902008176640000" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "import math\n", + "math.factorial(n)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.053775025581946814" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mu = 2.3\n", + "\n", + "poisson_dist = poisson(mu)\n", + "\n", + "poisson_dist.pmf(5)\n", + "\n", + "\n" ] }, { @@ -167,18 +347,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n", - "# Please label the axes and give a title to the plot \n" + "\n", + "X = np.arange(0,10)\n", + "\n", + "plt.plot(X, poisson_dist.pmf(X), \"o\")\n", + "plt.title(\"probability of goals\")\n", + "plt.xlabel(\"number of goals\")\n", + "plt.ylabel(\"probability\")\n", + "plt.show()\n", + "\n", + "\n" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -192,7 +391,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.9" } }, "nbformat": 4,