diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..b183087d7e275f1cdb1d1c067515bc4b9d20586b 100644 --- a/module2/exo1/toy_notebook_fr.ipynb +++ b/module2/exo1/toy_notebook_fr.ipynb @@ -1,5 +1,150 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. À propos du calcul de $\\pi$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.1 En demandant à la lib maths\n", + "Mon ordinateur m’indique que $\\pi$ vaut *approximativement*" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.141592653589793\n" + ] + } + ], + "source": [ + "from math import *\n", + "print(pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.2 En utilisant la méthode des aiguilles de Buffon\n", + "Mais calculé avec la **méthode** des [aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon), on obtiendrait comme **approximation** :" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.128911138923655" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.random.seed(seed=42)\n", + "N = 10000\n", + "x = np.random.uniform(size=N, low=0, high=1)\n", + "theta = np.random.uniform(size=N, low=0, high=pi/2)\n", + "2/(sum((x+np.sin(theta))>1)/N)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.3 Avec un argument \"fréquentiel\" de surface\n", + "Sinon, une méthode plus simple à comprendre et ne faisant pas intervenir d'appel à la fonction sinus se base sur le fait que si $X\\sim U(0,1)$ et $Y\\sim U(0,1)$ alors $P[X^2+Y^2\\leq 1] = \\pi/4$ (voir [méthode de Monte Carlo sur Wikipedia](https://fr.wikipedia.org/wiki/M%C3%A9thode_de_Monte-Carlo#D%C3%A9termination_de_la_valeur_de_%CF%80)). Le code suivant illustre ce fait :" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline \n", + "import matplotlib.pyplot as plt\n", + "\n", + "np.random.seed(seed=42)\n", + "N = 1000\n", + "x = np.random.uniform(size=N, low=0, high=1)\n", + "y = np.random.uniform(size=N, low=0, high=1)\n", + "\n", + "accept = (x*x+y*y) <= 1\n", + "reject = np.logical_not(accept)\n", + "\n", + "fig, ax = plt.subplots(1)\n", + "ax.scatter(x[accept], y[accept], c='b', alpha=0.2, edgecolor=None)\n", + "ax.scatter(x[reject], y[reject], c='r', alpha=0.2, edgecolor=None)\n", + "ax.set_aspect('equal')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il est alors aisé d'obtenir une approximation (pas terrible) de $\\pi$ en comptant combien de fois, en moyenne, $X^2 + Y^2$ est inférieur à 1 :" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.112" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "4*np.mean(accept)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +161,9 @@ "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 59d72b5b58a3ae26346460dd39e62a39c55243d7..770f5b4588aad64e9f8a071016231f3e47424d37 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": 18, "metadata": {}, "outputs": [], "source": [ @@ -28,10 +28,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" @@ -61,14 +59,1019 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02025043375118356288.0393948.0560532.0588.0FRFrance
12025033253215239337.0267093.0378357.0399.0FRFrance
22025023257247242991.0271503.0384363.0405.0FRFrance
32025013231549214627.0248471.0345320.0370.0FRFrance
42024523201726185870.0217582.0302278.0326.0FRFrance
52024513201697187843.0215551.0302281.0323.0FRFrance
62024503136694126369.0147019.0205190.0220.0FRFrance
7202449310848799037.0117937.0163149.0177.0FRFrance
820244838738178687.096075.0131118.0144.0FRFrance
920244737628667626.084946.0114101.0127.0FRFrance
1020244635639949006.063792.08574.096.0FRFrance
1120244534734740843.053851.07161.081.0FRFrance
1220244433603930122.041956.05445.063.0FRFrance
1320244334657239928.053216.07060.080.0FRFrance
1420244236778560009.075561.010290.0114.0FRFrance
1520244137943571386.087484.0119107.0131.0FRFrance
1620244038496576555.093375.0127114.0140.0FRFrance
1720243939166082937.0100383.0137124.0150.0FRFrance
1820243839178682903.0100669.0138125.0151.0FRFrance
1920243735646049319.063601.08574.096.0FRFrance
2020243633365727906.039408.05041.059.0FRFrance
2120243532740422036.032772.04133.049.0FRFrance
2220243432671721003.032431.04031.049.0FRFrance
2320243332062315349.025897.03123.039.0FRFrance
2420243232318717532.028842.03527.043.0FRFrance
2520243132603520267.031803.03930.048.0FRFrance
2620243033639328593.044193.05543.067.0FRFrance
2720242933956032592.046528.05949.069.0FRFrance
2820242835434245781.062903.08168.094.0FRFrance
2920242734736440234.054494.07160.082.0FRFrance
.................................
207019852132609619621.032571.04735.059.0FRFrance
207119852032789620885.034907.05138.064.0FRFrance
207219851934315432821.053487.07859.097.0FRFrance
207319851834055529935.051175.07455.093.0FRFrance
207419851733405324366.043740.06244.080.0FRFrance
207519851635036236451.064273.09166.0116.0FRFrance
207619851536388145538.082224.011683.0149.0FRFrance
20771985143134545114400.0154690.0244207.0281.0FRFrance
20781985133197206176080.0218332.0357319.0395.0FRFrance
20791985123245240223304.0267176.0445405.0485.0FRFrance
20801985113276205252399.0300011.0501458.0544.0FRFrance
20811985103353231326279.0380183.0640591.0689.0FRFrance
20821985093369895341109.0398681.0670618.0722.0FRFrance
20831985083389886359529.0420243.0707652.0762.0FRFrance
20841985073471852432599.0511105.0855784.0926.0FRFrance
20851985063565825518011.0613639.01026939.01113.0FRFrance
20861985053637302592795.0681809.011551074.01236.0FRFrance
20871985043424937390794.0459080.0770708.0832.0FRFrance
20881985033213901174689.0253113.0388317.0459.0FRFrance
208919850239758680949.0114223.0177147.0207.0FRFrance
209019850138548965918.0105060.0155120.0190.0FRFrance
209119845238483060602.0109058.0154110.0198.0FRFrance
2092198451310172680242.0123210.0185146.0224.0FRFrance
20931984503123680101401.0145959.0225184.0266.0FRFrance
2094198449310107381684.0120462.0184149.0219.0FRFrance
209519844837862060634.096606.0143110.0176.0FRFrance
209619844737202954274.089784.013199.0163.0FRFrance
209719844638733067686.0106974.0159123.0195.0FRFrance
20981984453135223101414.0169032.0246184.0308.0FRFrance
209919844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2100 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202504 3 375118 356288.0 393948.0 560 532.0 \n", + "1 202503 3 253215 239337.0 267093.0 378 357.0 \n", + "2 202502 3 257247 242991.0 271503.0 384 363.0 \n", + "3 202501 3 231549 214627.0 248471.0 345 320.0 \n", + "4 202452 3 201726 185870.0 217582.0 302 278.0 \n", + "5 202451 3 201697 187843.0 215551.0 302 281.0 \n", + "6 202450 3 136694 126369.0 147019.0 205 190.0 \n", + "7 202449 3 108487 99037.0 117937.0 163 149.0 \n", + "8 202448 3 87381 78687.0 96075.0 131 118.0 \n", + "9 202447 3 76286 67626.0 84946.0 114 101.0 \n", + "10 202446 3 56399 49006.0 63792.0 85 74.0 \n", + "11 202445 3 47347 40843.0 53851.0 71 61.0 \n", + "12 202444 3 36039 30122.0 41956.0 54 45.0 \n", + "13 202443 3 46572 39928.0 53216.0 70 60.0 \n", + "14 202442 3 67785 60009.0 75561.0 102 90.0 \n", + "15 202441 3 79435 71386.0 87484.0 119 107.0 \n", + "16 202440 3 84965 76555.0 93375.0 127 114.0 \n", + "17 202439 3 91660 82937.0 100383.0 137 124.0 \n", + "18 202438 3 91786 82903.0 100669.0 138 125.0 \n", + "19 202437 3 56460 49319.0 63601.0 85 74.0 \n", + "20 202436 3 33657 27906.0 39408.0 50 41.0 \n", + "21 202435 3 27404 22036.0 32772.0 41 33.0 \n", + "22 202434 3 26717 21003.0 32431.0 40 31.0 \n", + "23 202433 3 20623 15349.0 25897.0 31 23.0 \n", + "24 202432 3 23187 17532.0 28842.0 35 27.0 \n", + "25 202431 3 26035 20267.0 31803.0 39 30.0 \n", + "26 202430 3 36393 28593.0 44193.0 55 43.0 \n", + "27 202429 3 39560 32592.0 46528.0 59 49.0 \n", + "28 202428 3 54342 45781.0 62903.0 81 68.0 \n", + "29 202427 3 47364 40234.0 54494.0 71 60.0 \n", + "... ... ... ... ... ... ... ... \n", + "2070 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2071 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2072 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2073 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2074 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2075 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2076 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2077 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2078 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2079 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2080 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2081 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2082 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2083 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2084 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2085 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2086 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2087 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2088 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2089 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2090 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2091 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2092 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2093 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2094 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2095 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2096 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2097 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2098 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2099 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 588.0 FR France \n", + "1 399.0 FR France \n", + "2 405.0 FR France \n", + "3 370.0 FR France \n", + "4 326.0 FR France \n", + "5 323.0 FR France \n", + "6 220.0 FR France \n", + "7 177.0 FR France \n", + "8 144.0 FR France \n", + "9 127.0 FR France \n", + "10 96.0 FR France \n", + "11 81.0 FR France \n", + "12 63.0 FR France \n", + "13 80.0 FR France \n", + "14 114.0 FR France \n", + "15 131.0 FR France \n", + "16 140.0 FR France \n", + "17 150.0 FR France \n", + "18 151.0 FR France \n", + "19 96.0 FR France \n", + "20 59.0 FR France \n", + "21 49.0 FR France \n", + "22 49.0 FR France \n", + "23 39.0 FR France \n", + "24 43.0 FR France \n", + "25 48.0 FR France \n", + "26 67.0 FR France \n", + "27 69.0 FR France \n", + "28 94.0 FR France \n", + "29 82.0 FR France \n", + "... ... ... ... \n", + "2070 59.0 FR France \n", + "2071 64.0 FR France \n", + "2072 97.0 FR France \n", + "2073 93.0 FR France \n", + "2074 80.0 FR France \n", + "2075 116.0 FR France \n", + "2076 149.0 FR France \n", + "2077 281.0 FR France \n", + "2078 395.0 FR France \n", + "2079 485.0 FR France \n", + "2080 544.0 FR France \n", + "2081 689.0 FR France \n", + "2082 722.0 FR France \n", + "2083 762.0 FR France \n", + "2084 926.0 FR France \n", + "2085 1113.0 FR France \n", + "2086 1236.0 FR France \n", + "2087 832.0 FR France \n", + "2088 459.0 FR France \n", + "2089 207.0 FR France \n", + "2090 190.0 FR France \n", + "2091 198.0 FR France \n", + "2092 224.0 FR France \n", + "2093 266.0 FR France \n", + "2094 219.0 FR France \n", + "2095 176.0 FR France \n", + "2096 163.0 FR France \n", + "2097 195.0 FR France \n", + "2098 308.0 FR France \n", + "2099 213.0 FR France \n", + "\n", + "[2100 rows x 10 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modification du code pour utiliser le fichier local contenant les données :" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "ename": "ParserError", + "evalue": "Error tokenizing data. C error: Expected 1 fields in line 30, saw 21\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mParserError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mraw_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'https://app-learninglab.inria.fr/moocrr/gitlab/5212fa3d0a7441c34b57f854081c7450/mooc-rr/blob/master/module3/exo1/inc-25-PAY.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'iso-8859-1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskiprows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mraw_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 707\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 708\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 709\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 710\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 711\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 453\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 454\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 455\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 456\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 457\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 1067\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'skipfooter not supported for iteration'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1069\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1070\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1071\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'as_recarray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 1837\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1838\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1839\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1840\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1841\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_first_chunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.read\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_low_memory\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.raise_parser_error\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mParserError\u001b[0m: Error tokenizing data. C error: Expected 1 fields in line 30, saw 21\n" + ] + } + ], + "source": [ + "raw_data = pd.read_csv('https://app-learninglab.inria.fr/moocrr/gitlab/5212fa3d0a7441c34b57f854081c7450/mooc-rr/blob/master/module3/exo1/inc-25-PAY.csv', encoding = 'iso-8859-1', skiprows=1)\n", + "raw_data" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -78,9 +1081,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "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", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
18631989193-NaNNaN-NaNNaNFRFrance
\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n", + "1863 198919 3 - NaN NaN - NaN NaN \n", + "\n", + " geo_insee geo_name \n", + "1863 FR France " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] @@ -94,9 +1161,976 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02025043375118356288.0393948.0560532.0588.0FRFrance
12025033253215239337.0267093.0378357.0399.0FRFrance
22025023257247242991.0271503.0384363.0405.0FRFrance
32025013231549214627.0248471.0345320.0370.0FRFrance
42024523201726185870.0217582.0302278.0326.0FRFrance
52024513201697187843.0215551.0302281.0323.0FRFrance
62024503136694126369.0147019.0205190.0220.0FRFrance
7202449310848799037.0117937.0163149.0177.0FRFrance
820244838738178687.096075.0131118.0144.0FRFrance
920244737628667626.084946.0114101.0127.0FRFrance
1020244635639949006.063792.08574.096.0FRFrance
1120244534734740843.053851.07161.081.0FRFrance
1220244433603930122.041956.05445.063.0FRFrance
1320244334657239928.053216.07060.080.0FRFrance
1420244236778560009.075561.010290.0114.0FRFrance
1520244137943571386.087484.0119107.0131.0FRFrance
1620244038496576555.093375.0127114.0140.0FRFrance
1720243939166082937.0100383.0137124.0150.0FRFrance
1820243839178682903.0100669.0138125.0151.0FRFrance
1920243735646049319.063601.08574.096.0FRFrance
2020243633365727906.039408.05041.059.0FRFrance
2120243532740422036.032772.04133.049.0FRFrance
2220243432671721003.032431.04031.049.0FRFrance
2320243332062315349.025897.03123.039.0FRFrance
2420243232318717532.028842.03527.043.0FRFrance
2520243132603520267.031803.03930.048.0FRFrance
2620243033639328593.044193.05543.067.0FRFrance
2720242933956032592.046528.05949.069.0FRFrance
2820242835434245781.062903.08168.094.0FRFrance
2920242734736440234.054494.07160.082.0FRFrance
.................................
207019852132609619621.032571.04735.059.0FRFrance
207119852032789620885.034907.05138.064.0FRFrance
207219851934315432821.053487.07859.097.0FRFrance
207319851834055529935.051175.07455.093.0FRFrance
207419851733405324366.043740.06244.080.0FRFrance
207519851635036236451.064273.09166.0116.0FRFrance
207619851536388145538.082224.011683.0149.0FRFrance
20771985143134545114400.0154690.0244207.0281.0FRFrance
20781985133197206176080.0218332.0357319.0395.0FRFrance
20791985123245240223304.0267176.0445405.0485.0FRFrance
20801985113276205252399.0300011.0501458.0544.0FRFrance
20811985103353231326279.0380183.0640591.0689.0FRFrance
20821985093369895341109.0398681.0670618.0722.0FRFrance
20831985083389886359529.0420243.0707652.0762.0FRFrance
20841985073471852432599.0511105.0855784.0926.0FRFrance
20851985063565825518011.0613639.01026939.01113.0FRFrance
20861985053637302592795.0681809.011551074.01236.0FRFrance
20871985043424937390794.0459080.0770708.0832.0FRFrance
20881985033213901174689.0253113.0388317.0459.0FRFrance
208919850239758680949.0114223.0177147.0207.0FRFrance
209019850138548965918.0105060.0155120.0190.0FRFrance
209119845238483060602.0109058.0154110.0198.0FRFrance
2092198451310172680242.0123210.0185146.0224.0FRFrance
20931984503123680101401.0145959.0225184.0266.0FRFrance
2094198449310107381684.0120462.0184149.0219.0FRFrance
209519844837862060634.096606.0143110.0176.0FRFrance
209619844737202954274.089784.013199.0163.0FRFrance
209719844638733067686.0106974.0159123.0195.0FRFrance
20981984453135223101414.0169032.0246184.0308.0FRFrance
209919844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2099 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202504 3 375118 356288.0 393948.0 560 532.0 \n", + "1 202503 3 253215 239337.0 267093.0 378 357.0 \n", + "2 202502 3 257247 242991.0 271503.0 384 363.0 \n", + "3 202501 3 231549 214627.0 248471.0 345 320.0 \n", + "4 202452 3 201726 185870.0 217582.0 302 278.0 \n", + "5 202451 3 201697 187843.0 215551.0 302 281.0 \n", + "6 202450 3 136694 126369.0 147019.0 205 190.0 \n", + "7 202449 3 108487 99037.0 117937.0 163 149.0 \n", + "8 202448 3 87381 78687.0 96075.0 131 118.0 \n", + "9 202447 3 76286 67626.0 84946.0 114 101.0 \n", + "10 202446 3 56399 49006.0 63792.0 85 74.0 \n", + "11 202445 3 47347 40843.0 53851.0 71 61.0 \n", + "12 202444 3 36039 30122.0 41956.0 54 45.0 \n", + "13 202443 3 46572 39928.0 53216.0 70 60.0 \n", + "14 202442 3 67785 60009.0 75561.0 102 90.0 \n", + "15 202441 3 79435 71386.0 87484.0 119 107.0 \n", + "16 202440 3 84965 76555.0 93375.0 127 114.0 \n", + "17 202439 3 91660 82937.0 100383.0 137 124.0 \n", + "18 202438 3 91786 82903.0 100669.0 138 125.0 \n", + "19 202437 3 56460 49319.0 63601.0 85 74.0 \n", + "20 202436 3 33657 27906.0 39408.0 50 41.0 \n", + "21 202435 3 27404 22036.0 32772.0 41 33.0 \n", + "22 202434 3 26717 21003.0 32431.0 40 31.0 \n", + "23 202433 3 20623 15349.0 25897.0 31 23.0 \n", + "24 202432 3 23187 17532.0 28842.0 35 27.0 \n", + "25 202431 3 26035 20267.0 31803.0 39 30.0 \n", + "26 202430 3 36393 28593.0 44193.0 55 43.0 \n", + "27 202429 3 39560 32592.0 46528.0 59 49.0 \n", + "28 202428 3 54342 45781.0 62903.0 81 68.0 \n", + "29 202427 3 47364 40234.0 54494.0 71 60.0 \n", + "... ... ... ... ... ... ... ... \n", + "2070 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "2071 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "2072 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "2073 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "2074 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "2075 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "2076 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "2077 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "2078 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "2079 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "2080 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "2081 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "2082 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "2083 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "2084 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "2085 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "2086 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "2087 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "2088 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2089 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2090 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2091 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2092 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2093 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2094 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2095 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2096 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2097 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2098 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2099 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 588.0 FR France \n", + "1 399.0 FR France \n", + "2 405.0 FR France \n", + "3 370.0 FR France \n", + "4 326.0 FR France \n", + "5 323.0 FR France \n", + "6 220.0 FR France \n", + "7 177.0 FR France \n", + "8 144.0 FR France \n", + "9 127.0 FR France \n", + "10 96.0 FR France \n", + "11 81.0 FR France \n", + "12 63.0 FR France \n", + "13 80.0 FR France \n", + "14 114.0 FR France \n", + "15 131.0 FR France \n", + "16 140.0 FR France \n", + "17 150.0 FR France \n", + "18 151.0 FR France \n", + "19 96.0 FR France \n", + "20 59.0 FR France \n", + "21 49.0 FR France \n", + "22 49.0 FR France \n", + "23 39.0 FR France \n", + "24 43.0 FR France \n", + "25 48.0 FR France \n", + "26 67.0 FR France \n", + "27 69.0 FR France \n", + "28 94.0 FR France \n", + "29 82.0 FR France \n", + "... ... ... ... \n", + "2070 59.0 FR France \n", + "2071 64.0 FR France \n", + "2072 97.0 FR France \n", + "2073 93.0 FR France \n", + "2074 80.0 FR France \n", + "2075 116.0 FR France \n", + "2076 149.0 FR France \n", + "2077 281.0 FR France \n", + "2078 395.0 FR France \n", + "2079 485.0 FR France \n", + "2080 544.0 FR France \n", + "2081 689.0 FR France \n", + "2082 722.0 FR France \n", + "2083 762.0 FR France \n", + "2084 926.0 FR France \n", + "2085 1113.0 FR France \n", + "2086 1236.0 FR France \n", + "2087 832.0 FR France \n", + "2088 459.0 FR France \n", + "2089 207.0 FR France \n", + "2090 190.0 FR France \n", + "2091 198.0 FR France \n", + "2092 224.0 FR France \n", + "2093 266.0 FR France \n", + "2094 219.0 FR France \n", + "2095 176.0 FR France \n", + "2096 163.0 FR France \n", + "2097 195.0 FR France \n", + "2098 308.0 FR France \n", + "2099 213.0 FR France \n", + "\n", + "[2099 rows x 10 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2156,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2186,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 24, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,9 +2211,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "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", @@ -199,11 +2239,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "sorted_data['inc'].plot()" + "sorted_data['inc_up'].plot()" ] }, { @@ -215,11 +2278,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "sorted_data['inc'][-200:].plot()" + "sorted_data['inc_up'][-200:].plot()" ] }, { @@ -252,13 +2338,11 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 29, + "metadata": {}, "outputs": [], "source": [ - "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", + " first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\n", " sorted_data.index[-1].year)]" ] @@ -274,15 +2358,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ - "year = []\n", + " 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", + " one_year = sorted_data['inc_up'][week1:week2-1]\n", " assert abs(len(one_year)-52) < 2\n", " yearly_incidence.append(one_year.sum())\n", " year.append(week2.year)\n", @@ -298,9 +2382,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 33, + "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.plot(style='*')" ] @@ -314,9 +2421,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2021 910589.0\n", + "2014 1911251.0\n", + "1991 1980417.0\n", + "1995 2090418.0\n", + "2020 2226761.0\n", + "2022 2338967.0\n", + "2019 2481665.0\n", + "2012 2536425.0\n", + "2017 2593378.0\n", + "2006 2719258.0\n", + "2003 2734405.0\n", + "1992 2921510.0\n", + "1993 2986279.0\n", + "2018 2991551.0\n", + "2001 3040167.0\n", + "1988 3131459.0\n", + "2016 3177327.0\n", + "2007 3181219.0\n", + "2011 3205326.0\n", + "2023 3217613.0\n", + "1987 3253239.0\n", + "2008 3403787.0\n", + "1998 3410332.0\n", + "2002 3688034.0\n", + "1994 3765327.0\n", + "1996 3837601.0\n", + "1997 3923810.0\n", + "2009 3965230.0\n", + "2015 4002562.0\n", + "2024 4075202.0\n", + "2004 4133721.0\n", + "2000 4288499.0\n", + "2005 4326419.0\n", + "1999 4350757.0\n", + "2010 4601495.0\n", + "2013 4657613.0\n", + "1990 5675038.0\n", + "1986 5758913.0\n", + "1989 5840764.0\n", + "dtype: float64" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -331,21 +2488,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "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)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { @@ -364,7 +2535,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4,