From d2991c4e7a3551a0e8620395903cfa1b60c6567e Mon Sep 17 00:00:00 2001 From: carolina0987 Date: Thu, 10 Aug 2023 16:20:52 +0100 Subject: [PATCH] lab done --- your-code/continuous.ipynb | 495 ++++++++++++++++++++++++++++++++++--- your-code/discrete.ipynb | 258 +++++++++++++++++-- 2 files changed, 694 insertions(+), 59 deletions(-) diff --git a/your-code/continuous.ipynb b/your-code/continuous.ipynb index 32218e9..4e5c3fb 100644 --- a/your-code/continuous.ipynb +++ b/your-code/continuous.ipynb @@ -11,6 +11,25 @@ " Happy learning!" ] }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "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", + "from scipy.stats import uniform\n", + "from scipy.stats import expon\n", + "from scipy.stats import norm" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -35,9 +54,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2.64895343 2.74878495 2.58785098 2.20239956 2.97383585 2.99449499\n", + " 2.62506504 2.32611344 2.12366964 2.68090901]\n" + ] + } + ], "source": [ "from scipy.stats import uniform\n", "x = uniform.rvs(size=10)\n", @@ -73,11 +101,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 90., 100., 102., 117., 84., 102., 114., 95., 91., 105.]),\n", + " array([10.02908031, 15.01409371, 19.99910712, 24.98412052, 29.96913392,\n", + " 34.95414733, 39.93916073, 44.92417413, 49.90918754, 54.89420094,\n", + " 59.87921434]),\n", + " )" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "def random_numbers(a, b, c):\n", + " x = uniform.rvs(size = c)\n", + " random = a + (b - a) * x\n", + " return random\n", + "\n", + "random_numbers(10, 15, 100)\n", + "random_numbers(10, 60, 1000)\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize = (8, 4))\n", + "\n", + "axs[0].hist(random_numbers(10, 15, 100), bins = 10)\n", + "axs[1].hist(random_numbers(10, 60, 1000), bins = 10)" ] }, { @@ -89,11 +153,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "#They have different scales, and that make them look different (?)" ] }, { @@ -114,11 +178,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 24., 62., 148., 243., 239., 166., 78., 33., 3., 4.]),\n", + " array([-119.40066134, -88.36761725, -57.33457316, -26.30152907,\n", + " 4.73151502, 35.76455911, 66.79760319, 97.83064728,\n", + " 128.86369137, 159.89673546, 190.92977955]),\n", + " )" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "def generate_random_numbers(mean, std, size):\n", + " random_numbers = np.random.normal(loc=mean, scale=std, size=size)\n", + " return random_numbers\n", + "\n", + "number1 = generate_random_numbers(mean=10, std=1, size=1000)\n", + "number2 = generate_random_numbers(mean=10, std=50, size=1000)\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(8, 4))\n", + "axs[0].hist(number1)\n", + "axs[1].hist(number2)" ] }, { @@ -130,11 +228,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "#The scale is different, but the two graphics are still pretty similar. We have a lot of zero values. \n", + "#They appear to be normally distributed." ] }, { @@ -146,6 +245,140 @@ "In this challenge we are going to take a look the real data. We will use vehicles.csv file for this exercise" ] }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "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", + "
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
\n", + "
" + ], + "text/plain": [ + " Make Model Year Engine Displacement Cylinders \\\n", + "0 AM General DJ Po Vehicle 2WD 1984 2.5 4.0 \n", + "1 AM General FJ8c Post Office 1984 4.2 6.0 \n", + "2 AM General Post Office DJ5 2WD 1985 2.5 4.0 \n", + "\n", + " Transmission Drivetrain Vehicle Class Fuel Type \\\n", + "0 Automatic 3-spd 2-Wheel Drive Special Purpose Vehicle 2WD Regular \n", + "1 Automatic 3-spd 2-Wheel Drive Special Purpose Vehicle 2WD Regular \n", + "2 Automatic 3-spd Rear-Wheel Drive Special Purpose Vehicle 2WD Regular \n", + "\n", + " Fuel Barrels/Year City MPG Highway MPG Combined MPG \\\n", + "0 19.388824 18 17 17 \n", + "1 25.354615 13 13 13 \n", + "2 20.600625 16 17 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 " + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vehicles = pd.read_csv(\"vehicles.csv\")\n", + "vehicles.head(3)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -158,11 +391,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(vehicles[\"Fuel Barrels/Year\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "17.609055502328133" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "vehicles[\"Fuel Barrels/Year\"].mean()" ] }, { @@ -174,11 +439,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(vehicles[\"CO2 Emission Grams/Mile\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "475.3163392572124" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "vehicles[\"CO2 Emission Grams/Mile\"].mean()" ] }, { @@ -190,11 +487,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(vehicles[\"Combined MPG\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "19.92932242990654" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "vehicles[\"Combined MPG\"].mean()" ] }, { @@ -206,11 +535,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ - "# you answer here:\n" + "#They all look kinda normally distributed, we can see that because, they have a bell-shaped symmetrical curve centered around the mean" ] }, { @@ -237,11 +566,76 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(array([99., 87., 66., 66., 68., 71., 49., 55., 52., 47., 28., 26., 27.,\n", + " 21., 18., 27., 17., 13., 16., 10., 13., 8., 10., 5., 11., 9.,\n", + " 11., 6., 5., 5., 2., 4., 1., 4., 2., 3., 4., 6., 4.,\n", + " 2., 3., 0., 0., 4., 2., 3., 0., 1., 0., 1., 0., 2.,\n", + " 0., 0., 1., 1., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 1.]),\n", + " array([1.68636908e-01, 1.01062177e+01, 2.00437985e+01, 2.99813793e+01,\n", + " 3.99189601e+01, 4.98565408e+01, 5.97941216e+01, 6.97317024e+01,\n", + " 7.96692832e+01, 8.96068640e+01, 9.95444448e+01, 1.09482026e+02,\n", + " 1.19419606e+02, 1.29357187e+02, 1.39294768e+02, 1.49232349e+02,\n", + " 1.59169929e+02, 1.69107510e+02, 1.79045091e+02, 1.88982672e+02,\n", + " 1.98920253e+02, 2.08857833e+02, 2.18795414e+02, 2.28732995e+02,\n", + " 2.38670576e+02, 2.48608157e+02, 2.58545737e+02, 2.68483318e+02,\n", + " 2.78420899e+02, 2.88358480e+02, 2.98296060e+02, 3.08233641e+02,\n", + " 3.18171222e+02, 3.28108803e+02, 3.38046384e+02, 3.47983964e+02,\n", + " 3.57921545e+02, 3.67859126e+02, 3.77796707e+02, 3.87734288e+02,\n", + " 3.97671868e+02, 4.07609449e+02, 4.17547030e+02, 4.27484611e+02,\n", + " 4.37422191e+02, 4.47359772e+02, 4.57297353e+02, 4.67234934e+02,\n", + " 4.77172515e+02, 4.87110095e+02, 4.97047676e+02, 5.06985257e+02,\n", + " 5.16922838e+02, 5.26860419e+02, 5.36797999e+02, 5.46735580e+02,\n", + " 5.56673161e+02, 5.66610742e+02, 5.76548322e+02, 5.86485903e+02,\n", + " 5.96423484e+02, 6.06361065e+02, 6.16298646e+02, 6.26236226e+02,\n", + " 6.36173807e+02, 6.46111388e+02, 6.56048969e+02, 6.65986550e+02,\n", + " 6.75924130e+02, 6.85861711e+02, 6.95799292e+02, 7.05736873e+02,\n", + " 7.15674453e+02, 7.25612034e+02, 7.35549615e+02, 7.45487196e+02,\n", + " 7.55424777e+02, 7.65362357e+02, 7.75299938e+02, 7.85237519e+02,\n", + " 7.95175100e+02, 8.05112681e+02, 8.15050261e+02, 8.24987842e+02,\n", + " 8.34925423e+02, 8.44863004e+02, 8.54800584e+02, 8.64738165e+02,\n", + " 8.74675746e+02, 8.84613327e+02, 8.94550908e+02, 9.04488488e+02,\n", + " 9.14426069e+02, 9.24363650e+02, 9.34301231e+02, 9.44238812e+02,\n", + " 9.54176392e+02, 9.64113973e+02, 9.74051554e+02, 9.83989135e+02,\n", + " 9.93926715e+02]),\n", + " )" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# your code here\n" + "def generate_exponential(mean, size):\n", + " scale = mean\n", + " random_numbers = np.random.exponential(scale=scale, size=size)\n", + " return random_numbers\n", + "\n", + "number1 = generate_exponential(mean=1, size=1000)\n", + "number2 = generate_exponential(mean=100, size=1000)\n", + "\n", + "fig, axs = plt.subplots(1, 2)\n", + "axs[0].hist(number1, bins=100 )\n", + "axs[1].hist(number2, bins=100)" ] }, { @@ -257,7 +651,7 @@ "metadata": {}, "outputs": [], "source": [ - "# your answer here:\n" + "#The scale is different, but the two graphics are still pretty similar. We have a lot of zero values. " ] }, { @@ -273,12 +667,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.7534030360583935" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# your answer here\n", - "# Hint: This is same as saying P(x<15)\n" + "# Hint: This is same as saying P(x<15)\n", + "\n", + "lambda_inv = 10/1\n", + "exp_dist = expon(scale = lambda_inv)\n", + "x = exp_dist.cdf(14)\n", + "x\n", + "\n", + "#The probability that the customer will spend less than 15min in the bank is 75%." ] }, { @@ -290,17 +702,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.2231301601484298" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your answer here\n" + "1 - exp_dist.cdf(15)\n", + "\n", + "#The probability that the customer will spend more than 15min is 22%." ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -314,7 +739,7 @@ "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..91f015d 100644 --- a/your-code/discrete.ipynb +++ b/your-code/discrete.ipynb @@ -1,5 +1,25 @@ { "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "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", + "from scipy.stats import uniform\n", + "from scipy.stats import expon\n", + "from scipy.stats import norm" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -33,17 +53,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The probability that the fruit is an apple is 0.6\n", + "The probability that the fruit is an orange is 0.4\n" + ] + } + ], "source": [ "\"\"\"\n", "Calculate:\n", "p = probability that the fruit is an apple \n", "q = probability that the fruit is an orange\n", "\"\"\"\n", + "p = 60/100\n", + "q = 40/100\n", "\n", - "# your code here\n" + "print(f\"The probability that the fruit is an apple is {p}\")\n", + "print(f\"The probability that the fruit is an orange is {q}\")" ] }, { @@ -61,11 +93,53 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.07776" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "# 1.\n", + "n = 20\n", + "p = 0.6\n", + "\n", + "\n", + "0.6 * 0.6 * 0.6 * 0.6 * 0.6\n", + "#There is a 7% chance that the first 5 fruits are all apples. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.073741824000001e-06" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 2.\n", + "n = 20\n", + "q = 0.4\n", + "\n", + "0.4**15\n", + "#There is a 0.0000001% chance that the next 15 fruits are all oranges. " ] }, { @@ -83,11 +157,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.0012944935222876583" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n = 20\n", + "p = 0.60\n", + "\n", + "binomial_dist = binom(n, p)\n", + "apples = binomial_dist.pmf(5)\n", + "apples" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.0012944935222876583" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "n = 20\n", + "q = 0.40\n", + "\n", + "binomial_dist = binom(n, q)\n", + "oranges = binomial_dist.pmf(15)\n", + "oranges" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.675713479244708e-06" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "apples*oranges\n", + "#There is a 0.0000001% chance that we pick 5 apples and 15 oranges" ] }, { @@ -101,11 +237,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.00031703112116863004" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "n = 20\n", + "p = 0.60\n", + "\n", + "binomial_dist = binom(n, p)\n", + "binomial_dist.cdf(4)\n", + "#There is a 0.0003% chance that less than 5 fruits picked are apples" ] }, { @@ -119,12 +271,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "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" + "x = range(0,n+1)\n", + "plt.plot(x, binomial_dist.pmf(x),\"x\", c = \"purple\")\n", + "plt.ylabel(\"Probability\")\n", + "plt.xlabel(\"Apples\")\n", + "plt.title(\"Binomial Distribution - Apples\")\n", + "\n", + "plt.show()" ] }, { @@ -151,11 +319,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.053775025581946814" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "mu = 2.3\n", + "poisson_dist = poisson(mu)\n", + "\n", + "poisson_dist.pmf(5)" ] }, { @@ -167,18 +349,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "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" + "# Please label the axes and give a title to the plot \n", + "\n", + "poisson_dist.pmf(10)\n", + "\n", + "X = np.arange(0, 11)\n", + "\n", + "plt.plot(X, poisson_dist.pmf(X), \"-o\", c = \"green\")\n", + "plt.xlabel(\"goals\")\n", + "plt.ylabel(\"probability\")\n", + "plt.title(\"Probability of goals\")\n", + "plt.show()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -192,7 +402,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.10.9" } }, "nbformat": 4,