From d13160bee586c69ec264e088ba95b67a98972a70 Mon Sep 17 00:00:00 2001 From: acfbfe84e26f79674889b30a0f7426f4 Date: Wed, 19 Apr 2023 10:46:25 +0000 Subject: [PATCH] no commit message --- module2/exo2/exercice.ipynb | 98 +- module2/exo5/exo5_fr.ipynb | 251 ++-- module3/exo2/exercice.ipynb | 2334 ++++++++++++++++++++++++++++++++++- module3/exo3/exercice.ipynb | 842 ++++++++++++- 4 files changed, 3430 insertions(+), 95 deletions(-) diff --git a/module2/exo2/exercice.ipynb b/module2/exo2/exercice.ipynb index 0bbbe37..d48a37c 100644 --- a/module2/exo2/exercice.ipynb +++ b/module2/exo2/exercice.ipynb @@ -1,5 +1,98 @@ { - "cells": [], + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.array([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.8\n", + "23.4\n", + "14.113000000000001\n", + "4.334094455301447\n", + "14.5\n" + ] + } + ], + "source": [ + "print(f'{np.min(x)}')\n", + "print(f'{np.max(x)}')\n", + "print(f'{np.mean(x)}')\n", + "print(f'{np.std(x, ddof=1)}')\n", + "print(f'{np.median(x)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt \n", + "plt.figure()\n", + "plt.plot(x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.hist(x)\n", + "plt.show()" + ] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +109,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb index 26ad6d9..5503d63 100644 --- a/module2/exo5/exo5_fr.ipynb +++ b/module2/exo5/exo5_fr.ipynb @@ -261,30 +261,30 @@ "" ], "text/plain": [ - " Date Count Temperature Pressure Malfunction\n", - "0 4/12/81 6 66 50 0\n", - "1 11/12/81 6 70 50 1\n", - "2 3/22/82 6 69 50 0\n", - "3 11/11/82 6 68 50 0\n", - "4 4/04/83 6 67 50 0\n", - "5 6/18/82 6 72 50 0\n", - "6 8/30/83 6 73 100 0\n", - "7 11/28/83 6 70 100 0\n", - "8 2/03/84 6 57 200 1\n", - "9 4/06/84 6 63 200 1\n", - "10 8/30/84 6 70 200 1\n", - "11 10/05/84 6 78 200 0\n", - "12 11/08/84 6 67 200 0\n", - "13 1/24/85 6 53 200 2\n", - "14 4/12/85 6 67 200 0\n", - "15 4/29/85 6 75 200 0\n", - "16 6/17/85 6 70 200 0\n", - "17 7/29/85 6 81 200 0\n", - "18 8/27/85 6 76 200 0\n", - "19 10/03/85 6 79 200 0\n", - "20 10/30/85 6 75 200 2\n", - "21 11/26/85 6 76 200 0\n", - "22 1/12/86 6 58 200 1" + " Date Count Temperature Pressure Malfunction\n", + "0 4/12/81 6 66 50 0\n", + "1 11/12/81 6 70 50 1\n", + "2 3/22/82 6 69 50 0\n", + "3 11/11/82 6 68 50 0\n", + "4 4/04/83 6 67 50 0\n", + "5 6/18/82 6 72 50 0\n", + "6 8/30/83 6 73 100 0\n", + "7 11/28/83 6 70 100 0\n", + "8 2/03/84 6 57 200 1\n", + "9 4/06/84 6 63 200 1\n", + "10 8/30/84 6 70 200 1\n", + "11 10/05/84 6 78 200 0\n", + "12 11/08/84 6 67 200 0\n", + "13 1/24/85 6 53 200 2\n", + "14 4/12/85 6 67 200 0\n", + "15 4/29/85 6 75 200 0\n", + "16 6/17/85 6 70 200 0\n", + "17 7/29/85 6 81 200 0\n", + "18 8/27/85 6 76 200 0\n", + "19 10/03/85 6 79 200 0\n", + "20 10/30/85 6 75 200 2\n", + "21 11/26/85 6 76 200 0\n", + "22 1/12/86 6 58 200 1" ] }, "execution_count": 1, @@ -453,7 +453,7 @@ "outputs": [ { "data": { - "image/png": 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Generalized Linear Model Regression Results
Dep. Variable: Frequency No. Observations: 7
Model: GLM Df Residuals: 5
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -2.5250
Date: Sat, 13 Apr 2019 Deviance: 0.22231
Time: 19:11:24 Pearson chi2: 0.236
No. Iterations: 4 Covariance Type: nonrobust
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DateCountTemperaturePressureMalfunctionFrequencySuccessIntercept
111/12/816705010.16666751
82/03/8465720010.16666751
94/06/8466320010.16666751
108/30/8467020010.16666751
131/24/8565320020.33333341
2010/30/8567520020.33333341
221/12/8665820010.16666751
\n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "\n", - " \n", - "\n", - "
coef std err z P>|z| [0.025 0.975]
Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953
Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240
" + "" ], "text/plain": [ - "\n", - "\"\"\"\n", - " Generalized Linear Model Regression Results \n", - "==============================================================================\n", - "Dep. Variable: Frequency No. Observations: 7\n", - "Model: GLM Df Residuals: 5\n", - "Model Family: Binomial Df Model: 1\n", - "Link Function: logit Scale: 1.0000\n", - "Method: IRLS Log-Likelihood: -2.5250\n", - "Date: Sat, 13 Apr 2019 Deviance: 0.22231\n", - "Time: 19:11:24 Pearson chi2: 0.236\n", - "No. Iterations: 4 Covariance Type: nonrobust\n", - "===============================================================================\n", - " coef std err z P>|z| [0.025 0.975]\n", - "-------------------------------------------------------------------------------\n", - "Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953\n", - "Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240\n", - "===============================================================================\n", - "\"\"\"" + " Date Count Temperature Pressure Malfunction Frequency Success \\\n", + "1 11/12/81 6 70 50 1 0.166667 5 \n", + "8 2/03/84 6 57 200 1 0.166667 5 \n", + "9 4/06/84 6 63 200 1 0.166667 5 \n", + "10 8/30/84 6 70 200 1 0.166667 5 \n", + "13 1/24/85 6 53 200 2 0.333333 4 \n", + "20 10/30/85 6 75 200 2 0.333333 4 \n", + "22 1/12/86 6 58 200 1 0.166667 5 \n", + "\n", + " Intercept \n", + "1 1 \n", + "8 1 \n", + "9 1 \n", + "10 1 \n", + "13 1 \n", + "20 1 \n", + "22 1 " ] }, - "execution_count": 4, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" + }, + { + "ename": "AttributeError", + "evalue": "module 'statsmodels.api' has no attribute 'GLsM'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Intercept\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mlogmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGLsM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Frequency'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Intercept'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Temperature'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfamilies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBinomial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfamilies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mlogmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: module 'statsmodels.api' has no attribute 'GLsM'" + ] } ], "source": [ @@ -577,7 +656,7 @@ "\n", "data[\"Success\"]=data.Count-data.Malfunction\n", "data[\"Intercept\"]=1\n", - "\n", + "display(data)\n", "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n", "\n", "logmodel.summary()" @@ -705,7 +784,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe37..6b32872 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2334 @@ { - "cells": [], + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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19881985093369895341109.0398681.0670618.0722.0FRFrance
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19931985043424937390794.0459080.0770708.0832.0FRFrance
19941985033213901174689.0253113.0388317.0459.0FRFrance
199519850239758680949.0114223.0177147.0207.0FRFrance
199619850138548965918.0105060.0155120.0190.0FRFrance
199719845238483060602.0109058.0154110.0198.0FRFrance
1998198451310172680242.0123210.0185146.0224.0FRFrance
19991984503123680101401.0145959.0225184.0266.0FRFrance
2000198449310107381684.0120462.0184149.0219.0FRFrance
200119844837862060634.096606.0143110.0176.0FRFrance
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2006 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202314 3 48829 41324.0 56334.0 73 62.0 \n", + "1 202313 3 64859 56800.0 72918.0 98 86.0 \n", + "2 202312 3 72750 64499.0 81001.0 109 97.0 \n", + "3 202311 3 74638 66420.0 82856.0 112 100.0 \n", + "4 202310 3 76368 68243.0 84493.0 115 103.0 \n", + "5 202309 3 62062 54778.0 69346.0 93 82.0 \n", + "6 202308 3 76391 68065.0 84717.0 115 102.0 \n", + "7 202307 3 89851 80397.0 99305.0 135 121.0 \n", + "8 202306 3 97368 87636.0 107100.0 146 131.0 \n", + "9 202305 3 95469 86268.0 104670.0 144 130.0 \n", + "10 202304 3 74901 66916.0 82886.0 113 101.0 \n", + "11 202303 3 69570 61893.0 77247.0 105 93.0 \n", + "12 202302 3 78260 70090.0 86430.0 118 106.0 \n", + "13 202301 3 121773 111024.0 132522.0 183 167.0 \n", + "14 202252 3 155371 142004.0 168738.0 234 214.0 \n", + "15 202251 3 248319 232128.0 264510.0 374 350.0 \n", + "16 202250 3 234143 219402.0 248884.0 353 331.0 \n", + "17 202249 3 163384 151691.0 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3 63881 45538.0 82224.0 116 83.0 \n", + "1983 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1984 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1985 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1986 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1987 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1988 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1989 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1990 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1991 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1992 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1993 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1994 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1995 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1996 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1997 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1998 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1999 198450 3 123680 101401.0 145959.0 225 184.0 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720230738985180397.099305.0135121.0149.0FRFrance
820230639736887636.0107100.0146131.0161.0FRFrance
920230539546986268.0104670.0144130.0158.0FRFrance
1020230437490166916.082886.0113101.0125.0FRFrance
1120230336957061893.077247.010593.0117.0FRFrance
1220230237826070090.086430.0118106.0130.0FRFrance
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142022523155371142004.0168738.0234214.0254.0FRFrance
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2020224636773560075.075395.010290.0114.0FRFrance
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2220224433471328880.040546.05243.061.0FRFrance
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2820223832878123733.033829.04335.051.0FRFrance
2920223732139517076.025714.03225.039.0FRFrance
.................................
197619852132609619621.032571.04735.059.0FRFrance
197719852032789620885.034907.05138.064.0FRFrance
197819851934315432821.053487.07859.097.0FRFrance
197919851834055529935.051175.07455.093.0FRFrance
198019851733405324366.043740.06244.080.0FRFrance
198119851635036236451.064273.09166.0116.0FRFrance
198219851536388145538.082224.011683.0149.0FRFrance
19831985143134545114400.0154690.0244207.0281.0FRFrance
19841985133197206176080.0218332.0357319.0395.0FRFrance
19851985123245240223304.0267176.0445405.0485.0FRFrance
19861985113276205252399.0300011.0501458.0544.0FRFrance
19871985103353231326279.0380183.0640591.0689.0FRFrance
19881985093369895341109.0398681.0670618.0722.0FRFrance
19891985083389886359529.0420243.0707652.0762.0FRFrance
19901985073471852432599.0511105.0855784.0926.0FRFrance
19911985063565825518011.0613639.01026939.01113.0FRFrance
19921985053637302592795.0681809.011551074.01236.0FRFrance
19931985043424937390794.0459080.0770708.0832.0FRFrance
19941985033213901174689.0253113.0388317.0459.0FRFrance
199519850239758680949.0114223.0177147.0207.0FRFrance
199619850138548965918.0105060.0155120.0190.0FRFrance
199719845238483060602.0109058.0154110.0198.0FRFrance
1998198451310172680242.0123210.0185146.0224.0FRFrance
19991984503123680101401.0145959.0225184.0266.0FRFrance
2000198449310107381684.0120462.0184149.0219.0FRFrance
200119844837862060634.096606.0143110.0176.0FRFrance
200219844737202954274.089784.013199.0163.0FRFrance
200319844638733067686.0106974.0159123.0195.0FRFrance
20041984453135223101414.0169032.0246184.0308.0FRFrance
200519844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2005 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202314 3 48829 41324.0 56334.0 73 62.0 \n", + "1 202313 3 64859 56800.0 72918.0 98 86.0 \n", + "2 202312 3 72750 64499.0 81001.0 109 97.0 \n", + "3 202311 3 74638 66420.0 82856.0 112 100.0 \n", + "4 202310 3 76368 68243.0 84493.0 115 103.0 \n", + "5 202309 3 62062 54778.0 69346.0 93 82.0 \n", + "6 202308 3 76391 68065.0 84717.0 115 102.0 \n", + "7 202307 3 89851 80397.0 99305.0 135 121.0 \n", + "8 202306 3 97368 87636.0 107100.0 146 131.0 \n", + "9 202305 3 95469 86268.0 104670.0 144 130.0 \n", + "10 202304 3 74901 66916.0 82886.0 113 101.0 \n", + "11 202303 3 69570 61893.0 77247.0 105 93.0 \n", + "12 202302 3 78260 70090.0 86430.0 118 106.0 \n", + "13 202301 3 121773 111024.0 132522.0 183 167.0 \n", + "14 202252 3 155371 142004.0 168738.0 234 214.0 \n", + "15 202251 3 248319 232128.0 264510.0 374 350.0 \n", + "16 202250 3 234143 219402.0 248884.0 353 331.0 \n", + "17 202249 3 163384 151691.0 175077.0 246 228.0 \n", + "18 202248 3 121691 111744.0 131638.0 184 169.0 \n", + "19 202247 3 96416 87230.0 105602.0 145 131.0 \n", + "20 202246 3 67735 60075.0 75395.0 102 90.0 \n", + "21 202245 3 45306 38909.0 51703.0 68 58.0 \n", + "22 202244 3 34713 28880.0 40546.0 52 43.0 \n", + "23 202243 3 44769 36884.0 52654.0 68 56.0 \n", + "24 202242 3 47462 40773.0 54151.0 72 62.0 \n", + "25 202241 3 48583 42388.0 54778.0 73 64.0 \n", + "26 202240 3 41927 36115.0 47739.0 63 54.0 \n", + "27 202239 3 39902 34168.0 45636.0 60 51.0 \n", + "28 202238 3 28781 23733.0 33829.0 43 35.0 \n", + "29 202237 3 21395 17076.0 25714.0 32 25.0 \n", + "... ... ... ... ... ... ... ... \n", + "1976 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1977 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1978 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1979 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1980 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1981 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1982 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1983 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1984 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1985 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1986 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1987 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1988 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1989 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1990 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1991 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1992 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1993 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1994 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1995 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1996 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1997 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1998 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1999 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2000 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2001 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2002 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2003 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2004 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2005 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 84.0 FR France \n", + "1 110.0 FR France \n", + "2 121.0 FR France \n", + "3 124.0 FR France \n", + "4 127.0 FR France \n", + "5 104.0 FR France \n", + "6 128.0 FR France \n", + "7 149.0 FR France \n", + "8 161.0 FR France \n", + "9 158.0 FR France \n", + "10 125.0 FR France \n", + "11 117.0 FR France \n", + "12 130.0 FR France \n", + "13 199.0 FR France \n", + "14 254.0 FR France \n", + "15 398.0 FR France \n", + "16 375.0 FR France \n", + "17 264.0 FR France \n", + "18 199.0 FR France \n", + "19 159.0 FR France \n", + "20 114.0 FR France \n", + "21 78.0 FR France \n", + "22 61.0 FR France \n", + "23 80.0 FR France \n", + "24 82.0 FR France \n", + "25 82.0 FR France \n", + "26 72.0 FR France \n", + "27 69.0 FR France \n", + "28 51.0 FR France \n", + "29 39.0 FR France \n", + "... ... ... ... \n", + "1976 59.0 FR France \n", + "1977 64.0 FR France \n", + "1978 97.0 FR France \n", + "1979 93.0 FR France \n", + "1980 80.0 FR France \n", + "1981 116.0 FR France \n", + "1982 149.0 FR France \n", + "1983 281.0 FR France \n", + "1984 395.0 FR France \n", + "1985 485.0 FR France \n", + "1986 544.0 FR France \n", + "1987 689.0 FR France \n", + "1988 722.0 FR France \n", + "1989 762.0 FR France \n", + "1990 926.0 FR France \n", + "1991 1113.0 FR France \n", + "1992 1236.0 FR France \n", + "1993 832.0 FR France \n", + "1994 459.0 FR France \n", + "1995 207.0 FR France \n", + "1996 190.0 FR France \n", + "1997 198.0 FR France \n", + "1998 224.0 FR France \n", + "1999 266.0 FR France \n", + "2000 219.0 FR France \n", + "2001 176.0 FR France \n", + "2002 163.0 FR France \n", + "2003 195.0 FR France \n", + "2004 308.0 FR France \n", + "2005 213.0 FR France \n", + "\n", + "[2005 rows x 10 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " week = int(year_and_week_str[4:])\n", + " w = isoweek.Week(year, week)\n", + " return pd.Period(w.day(0), 'W')\n", + "\n", + "data['period'] = [convert_week(yw) for yw in data['week']]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" + ] + } + ], + "source": [ + "periods = sorted_data.index\n", + "for p1, p2 in zip(periods[:-1], periods[1:]):\n", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['inc'][-200:].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " for y in range(1985,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_august_week[:-1],\n", + " first_august_week[1:]):\n", + " one_year = sorted_data['inc'][week1:week2-1]\n", + " assert abs(len(one_year)-52) < 2\n", + " yearly_incidence.append(one_year.sum())\n", + " year.append(week2.year)\n", + "yearly_incidence = pd.Series(data=yearly_incidence, index=year)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2021 743449\n", + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2020 2010315\n", + "2022 2060304\n", + "2012 2175217\n", + "2003 2234584\n", + "2019 2254386\n", + "2006 2307352\n", + "2017 2321583\n", + "2001 2529279\n", + "1992 2574578\n", + "1993 2703886\n", + "2018 2705325\n", + "1988 2765617\n", + "2007 2780164\n", + "1987 2855570\n", + "2016 2856393\n", + "2011 2857040\n", + "2008 2973918\n", + "1998 3034904\n", + "2002 3125418\n", + "2009 3444020\n", + "1994 3514763\n", + "1996 3539413\n", + "2004 3567744\n", + "1997 3620066\n", + "2015 3654892\n", + "2000 3826372\n", + "2005 3835025\n", + "1999 3908112\n", + "2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The history saving thread hit an unexpected error (OperationalError('disk I/O error',)).History will not be written to the database.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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SmokerStatusAge
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SmokerStatusAge
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [Smoker, Status, Age]\n", + "Index: []" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)] " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Donc ici pas de point manquant -> pas besoin de modifier les données." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On compte les morts." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number alive = 945\n", + "number dead = 369\n", + "total number = 1314\n", + "number smoker = 582\n", + "number non smoker = 732\n" + ] + } + ], + "source": [ + "dead = raw_data['Status'].value_counts()['Dead']\n", + "alive = raw_data['Status'].value_counts()['Alive']\n", + "print(f'number alive = {alive}')\n", + "print(f'number dead = {dead}')\n", + "print(f'total number = {alive + dead}')\n", + "smoker = raw_data['Smoker'].value_counts()['Yes']\n", + "non_smoker = raw_data['Smoker'].value_counts()['No']\n", + "print(f'number smoker = {smoker}')\n", + "print(f'number non smoker = {non_smoker}')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 1" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Dead230139
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" + ], + "text/plain": [ + "Smoker No Yes\n", + "Status \n", + "Alive 502 443\n", + "Dead 230 139" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tab = pd.crosstab(raw_data.Status, raw_data.Smoker) # on a bien le bon nombre de vivants et de morts\n", + "tab" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[502 443]\n", + " [230 139]]\n", + "ratio_smoker = 0.238832\n", + "ratio_non_smoker = 0.314208\n" + ] + } + ], + "source": [ + "numpy_data = np.array(tab)\n", + "print(numpy_data)\n", + "ratio_smoker = numpy_data[1,1]/smoker\n", + "ratio_non_smoker = numpy_data[1,0]/non_smoker\n", + "print(f'ratio_smoker = {ratio_smoker:.6f}')\n", + "print(f'ratio_non_smoker = {ratio_non_smoker:.6f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ah bah c'est dommage, les gens qui ne fument pas meurent plus que les gens qui fument... embêtant." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Question 2 " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SmokerStatusAgeAgeGroup
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1YesAlive19.31
2NoDead57.53
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6NoAlive23.81
7YesDead57.53
8YesAlive24.81
9YesAlive49.52
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" + ], + "text/plain": [ + " Smoker Status Age AgeGroup\n", + "0 Yes Alive 21.0 1\n", + "1 Yes Alive 19.3 1\n", + "2 No Dead 57.5 3\n", + "3 No Alive 47.1 2\n", + "4 Yes Alive 81.4 4\n", + "5 No Alive 36.8 2\n", + "6 No Alive 23.8 1\n", + "7 Yes Dead 57.5 3\n", + "8 Yes Alive 24.8 1\n", + "9 Yes Alive 49.5 2" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data.loc[((raw_data.Age < 34) & (raw_data.Age >= 18) ), 'AgeGroup'] = '1'\n", + "raw_data.loc[((raw_data.Age < 55) & (raw_data.Age >= 34) ), 'AgeGroup'] = '2'\n", + "raw_data.loc[((raw_data.Age < 65) & (raw_data.Age >= 55) ), 'AgeGroup'] = '3'\n", + "raw_data.loc[(raw_data.Age >= 65), 'AgeGroup'] = '4'\n", + "\n", + "raw_data[:10]" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "##################\n", + "Groupe d'age : 1\n", + "number alive = 387\n", + "number dead = 11\n", + "total number = 398\n", + "number smoker = 179\n", + "number non smoker = 219\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Smoker No Yes\n", + "Status \n", + "Alive 81 64\n", + "Dead 40 51" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ratio_smoker = 0.443478\n", + "ratio_non_smoker = 0.330579\n", + "##################\n", + "\n", + "##################\n", + "Groupe d'age : 4\n", + "number alive = 35\n", + "number dead = 207\n", + "total number = 242\n", + "number smoker = 49\n", + "number non smoker = 193\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Smoker No Yes\n", + "Status \n", + "Alive 28 7\n", + "Dead 165 42" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ratio_smoker = 0.857143\n", + "ratio_non_smoker = 0.854922\n", + "##################\n", + "\n" + ] + } + ], + "source": [ + "for age_group in ['1', '2', '3', '4']:\n", + " print('##################')\n", + " print(f'Groupe d\\'age : {age_group}')\n", + " tab_class = raw_data.loc[(raw_data.AgeGroup == age_group)]\n", + " dead = tab_class['Status'].value_counts()['Dead']\n", + " alive = tab_class['Status'].value_counts()['Alive']\n", + " print(f'number alive = {alive}')\n", + " print(f'number dead = {dead}')\n", + " print(f'total number = {alive + dead}')\n", + " smoker = tab_class['Smoker'].value_counts()['Yes']\n", + " non_smoker = tab_class['Smoker'].value_counts()['No']\n", + " print(f'number smoker = {smoker}')\n", + " print(f'number non smoker = {non_smoker}')\n", + " tab_class = pd.crosstab(tab_class.Status, tab_class.Smoker) # on a bien le bon nombre de vivants et de morts\n", + " display(tab_class)\n", + " numpy_data = np.array(tab_class)\n", + " \n", + " ratio_smoker = numpy_data[1,1]/smoker\n", + " ratio_non_smoker = numpy_data[1,0]/non_smoker\n", + " print(f'ratio_smoker = {ratio_smoker:.6f}')\n", + " print(f'ratio_non_smoker = {ratio_non_smoker:.6f}')\n", + " print('##################\\n')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Analyse :** \n", + "- Dans le groupe 1 : chez les plus jeunes (18-34 ans), le fait de fumer n'influe pas énormément sur le taux de mortalité. \n", + "- Dans le groupe 2 : fumer tue, le taux de mortalité est presque 2 fois plus élévé chez les fumeurs. \n", + "- Dans le groupe 3 : tout le monde meurt. Mais un peu plus souvent chez les fumeurs. \n", + "- Dans le groupe 4 : c'est catastrophique (mais normal, ils sont vieux), tout le monde meurt. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C'est donc le nombre de membres de la catégorie 4 qui biaise les données globales : il y en a beaucoup, dont une grande partie de non-fumeurs, là où pour les autres catégories, la proportion de fumeurs et de non-fumeurs est presque équivalente. Puisque beaucoup de vieux meurent, la consommation de tabac ne semble pas faire varier le taux, ce qui biaise le ratio global." + ] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +853,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - -- 2.18.1