From 13ff5de8c25335a0dea5ae6344957482892ef9ca Mon Sep 17 00:00:00 2001 From: d76bb037743742eedff662532efb18c3 Date: Wed, 24 Jul 2024 13:11:07 +0000 Subject: [PATCH] Changes made by transforming column 'inc' into a numerical object --- module2/exo1/Getting_started_FUN_mooc.ipynb | 6 + module2/exo1/Untitled.ipynb | 6 + module2/exo1/V1_tutorial.ipynb | 190 ++ module2/exo1/toy_notebook_fr.ipynb | 16 +- module3/exo1/analyse-syndrome-grippal.ipynb | 2288 ++++++++++++++++++- 5 files changed, 2466 insertions(+), 40 deletions(-) create mode 100644 module2/exo1/Getting_started_FUN_mooc.ipynb create mode 100644 module2/exo1/Untitled.ipynb create mode 100644 module2/exo1/V1_tutorial.ipynb diff --git a/module2/exo1/Getting_started_FUN_mooc.ipynb b/module2/exo1/Getting_started_FUN_mooc.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/module2/exo1/Getting_started_FUN_mooc.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/module2/exo1/Untitled.ipynb b/module2/exo1/Untitled.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/module2/exo1/Untitled.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/module2/exo1/V1_tutorial.ipynb b/module2/exo1/V1_tutorial.ipynb new file mode 100644 index 0000000..5b4eaba --- /dev/null +++ b/module2/exo1/V1_tutorial.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Titre du document\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "2+2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n" + ] + } + ], + "source": [ + "x=10\n", + "print(x)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "x = x+10" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20\n" + ] + } + ], + "source": [ + "print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "y =12" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12\n" + ] + } + ], + "source": [ + "print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "mu, sigma = 100,15" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "x= np.random.normal(loc=mu, scale=sigma,size=100000)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "## SHOW a plot \n", + "%matplotlib inline\n", + "plt.hist(x)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb index 0bbbe37..91fb9db 100644 --- a/module2/exo1/toy_notebook_fr.ipynb +++ b/module2/exo1/toy_notebook_fr.ipynb @@ -1,5 +1,16 @@ { - "cells": [], + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "1+1\n", + "\n" + ] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +27,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5..c3e393c 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,10 +28,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" @@ -61,9 +59,976 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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220242634421936956.051482.06655.077.0FRFrance
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520242333587530610.041140.05446.062.0FRFrance
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720242132196317556.026370.03326.040.0FRFrance
820242032005715780.024334.03024.036.0FRFrance
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1020241832240917653.027165.03427.041.0FRFrance
1120241732704221410.032674.04133.049.0FRFrance
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1320241533022924648.035810.04537.053.0FRFrance
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212024073138078127050.0149106.0207190.0224.0FRFrance
222024063190062177955.0202169.0285267.0303.0FRFrance
232024053216237203595.0228879.0324305.0343.0FRFrance
242024043213196200547.0225845.0320301.0339.0FRFrance
252024033163457152276.0174638.0245228.0262.0FRFrance
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.................................
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2072 rows × 10 columns

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63881 45538.0 82224.0 116 83.0 \n", + "2049 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2050 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2051 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2052 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2053 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2054 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2055 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2056 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2057 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2058 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2059 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2060 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2061 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2062 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2063 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2064 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2065 198450 3 123680 101401.0 145959.0 225 184.0 \n", 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308.0 FR France \n", + "2071 213.0 FR France \n", + "\n", + "[2072 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" @@ -78,9 +1043,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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2071 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202428 3 60997 50901.0 71093.0 91 76.0 \n", + "1 202427 3 47389 40227.0 54551.0 71 60.0 \n", + "2 202426 3 44219 36956.0 51482.0 66 55.0 \n", + "3 202425 3 47204 40300.0 54108.0 71 61.0 \n", + "4 202424 3 41110 34671.0 47549.0 62 52.0 \n", + "5 202423 3 35875 30610.0 41140.0 54 46.0 \n", + "6 202422 3 33772 28274.0 39270.0 51 43.0 \n", + "7 202421 3 21963 17556.0 26370.0 33 26.0 \n", + "8 202420 3 20057 15780.0 24334.0 30 24.0 \n", + "9 202419 3 15375 11274.0 19476.0 23 17.0 \n", + "10 202418 3 22409 17653.0 27165.0 34 27.0 \n", + "11 202417 3 27042 21410.0 32674.0 41 33.0 \n", + "12 202416 3 28882 23305.0 34459.0 43 35.0 \n", + "13 202415 3 30229 24648.0 35810.0 45 37.0 \n", + "14 202414 3 31813 26529.0 37097.0 48 40.0 \n", + "15 202413 3 35090 29607.0 40573.0 53 45.0 \n", + "16 202412 3 40639 34582.0 46696.0 61 52.0 \n", + "17 202411 3 50268 43331.0 57205.0 75 65.0 \n", + "18 202410 3 60107 52623.0 67591.0 90 79.0 \n", + "19 202409 3 71121 62920.0 79322.0 107 95.0 \n", + "20 202408 3 104566 94520.0 114612.0 157 142.0 \n", + "21 202407 3 138078 127050.0 149106.0 207 190.0 \n", + "22 202406 3 190062 177955.0 202169.0 285 267.0 \n", + "23 202405 3 216237 203595.0 228879.0 324 305.0 \n", + "24 202404 3 213196 200547.0 225845.0 320 301.0 \n", + "25 202403 3 163457 152276.0 174638.0 245 228.0 \n", + "26 202402 3 129436 119453.0 139419.0 194 179.0 \n", + "27 202401 3 120769 109452.0 132086.0 181 164.0 \n", + "28 202352 3 115446 103738.0 127154.0 174 156.0 \n", + "29 202351 3 148755 136546.0 160964.0 224 206.0 \n", + "... ... ... ... ... ... ... ... \n", + "2042 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2043 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2044 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2045 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2046 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2047 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2048 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2049 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2050 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2051 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2052 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2053 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2054 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2055 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2056 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2057 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2058 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2059 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2060 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2061 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2062 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2063 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2064 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2065 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2066 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2067 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2068 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2069 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2070 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2071 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 106.0 FR France \n", + "1 82.0 FR France \n", + "2 77.0 FR France \n", + "3 81.0 FR France \n", + "4 72.0 FR France \n", + "5 62.0 FR France \n", + "6 59.0 FR France \n", + "7 40.0 FR France \n", + "8 36.0 FR France \n", + "9 29.0 FR France \n", + "10 41.0 FR France \n", + "11 49.0 FR France \n", + "12 51.0 FR France \n", + "13 53.0 FR France \n", + "14 56.0 FR France \n", + "15 61.0 FR France \n", + "16 70.0 FR France \n", + "17 85.0 FR France \n", + "18 101.0 FR France \n", + "19 119.0 FR France \n", + "20 172.0 FR France \n", + "21 224.0 FR France \n", + "22 303.0 FR France \n", + "23 343.0 FR France \n", + "24 339.0 FR France \n", + "25 262.0 FR France \n", + "26 209.0 FR France \n", + "27 198.0 FR France \n", + "28 192.0 FR France \n", + "29 242.0 FR France \n", + "... ... ... ... \n", + "2042 59.0 FR France \n", + "2043 64.0 FR France \n", + "2044 97.0 FR France \n", + "2045 93.0 FR France \n", + "2046 80.0 FR France \n", + "2047 116.0 FR France \n", + "2048 149.0 FR France \n", + "2049 281.0 FR France \n", + "2050 395.0 FR France \n", + "2051 485.0 FR France \n", + "2052 544.0 FR France \n", + "2053 689.0 FR France \n", + "2054 722.0 FR France \n", + "2055 762.0 FR France \n", + "2056 926.0 FR France \n", + "2057 1113.0 FR France \n", + "2058 1236.0 FR France \n", + "2059 832.0 FR France \n", + "2060 459.0 FR France \n", + "2061 207.0 FR France \n", + "2062 190.0 FR France \n", + "2063 198.0 FR France \n", + "2064 224.0 FR France \n", + "2065 266.0 FR France \n", + "2066 219.0 FR France \n", + "2067 176.0 FR France \n", + "2068 163.0 FR France \n", + "2069 195.0 FR France \n", + "2070 308.0 FR France \n", + "2071 213.0 FR France \n", + "\n", + "[2071 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2118,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2148,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,9 +2173,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "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", @@ -199,9 +2201,107 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " week indicator inc inc_low inc_up inc100 \\\n", + "period \n", + "1984-10-29/1984-11-04 198444 3 68422 20056.0 116788.0 125 \n", + "1984-11-05/1984-11-11 198445 3 135223 101414.0 169032.0 246 \n", + "1984-11-12/1984-11-18 198446 3 87330 67686.0 106974.0 159 \n", + "1984-11-19/1984-11-25 198447 3 72029 54274.0 89784.0 131 \n", + "1984-11-26/1984-12-02 198448 3 78620 60634.0 96606.0 143 \n", + "\n", + " inc100_low inc100_up geo_insee geo_name \n", + "period \n", + "1984-10-29/1984-11-04 37.0 213.0 FR France \n", + "1984-11-05/1984-11-11 184.0 308.0 FR France \n", + "1984-11-12/1984-11-18 123.0 195.0 FR France \n", + "1984-11-19/1984-11-25 99.0 163.0 FR France \n", + "1984-11-26/1984-12-02 110.0 176.0 FR France \n", + "object\n", + "period\n", + "1984-10-29/1984-11-04 68422\n", + "1984-11-05/1984-11-11 135223\n", + "1984-11-12/1984-11-18 87330\n", + "1984-11-19/1984-11-25 72029\n", + "1984-11-26/1984-12-02 78620\n", + "Freq: W-SUN, Name: inc, dtype: object\n" + ] + } + ], + "source": [ + "# Vérifiez le contenu de sorted_data\n", + "print(sorted_data.head())\n", + "\n", + "# Vérifiez si la colonne 'inc' existe et contient des données numériques\n", + "print(sorted_data['inc'].dtype)\n", + "print(sorted_data['inc'].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "int64\n", + "period\n", + "1984-10-29/1984-11-04 68422\n", + "1984-11-05/1984-11-11 135223\n", + "1984-11-12/1984-11-18 87330\n", + "1984-11-19/1984-11-25 72029\n", + "1984-11-26/1984-12-02 78620\n", + "Freq: W-SUN, Name: inc, dtype: int64\n" + ] + } + ], + "source": [ + "# Convertir la colonne 'inc' en type numérique\n", + "sorted_data['inc'] = pd.to_numeric(sorted_data['inc'], errors='coerce')\n", + "\n", + "# Vérifiez les données après conversion\n", + "print(sorted_data['inc'].dtype)\n", + "print(sorted_data['inc'].head())" + ] + }, + { + "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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\n", 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\n", 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4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -331,9 +2524,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.hist(xrot=20)" ] @@ -341,9 +2557,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -364,7 +2578,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, -- 2.18.1