From a4464fd13484d6474a94b1c73cde03b72f392f02 Mon Sep 17 00:00:00 2001 From: 8765daa77f27c001b133a69561ec2403 <8765daa77f27c001b133a69561ec2403@app-learninglab.inria.fr> Date: Tue, 23 May 2023 14:53:50 +0000 Subject: [PATCH] Exercice 03.1 --- module2/exo1/toy_notebook_fr.ipynb | 381 +++- module2/exo5/exo5_fr-Copy1.ipynb | 713 ++++++ module2/exo5/exo5_fr.ipynb | 2 +- module3/exo1/analyse-syndrome-grippal.ipynb | 2237 ++++++++++++++++++- 4 files changed, 3291 insertions(+), 42 deletions(-) create mode 100644 module2/exo5/exo5_fr-Copy1.ipynb diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb index 0bbbe37..cc63977 100644 --- a/module2/exo1/toy_notebook_fr.ipynb +++ b/module2/exo1/toy_notebook_fr.ipynb @@ -1,6 +1,382 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "## Exemple de completion" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "x = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "30\n" + ] + } + ], + "source": [ + "x = x + 10\n", + "print(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "mu, sigma = 100, 15\n", + "x = np.random.normal(loc=mu, scale=sigma, size=10000)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(x)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "## Utilisation d'autres langages" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "%load_ext rpy2.ipython" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "%%R\n", + "Mu <- 100\n", + "y <- 10" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1] 50\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "y <- y + 10\n", + "print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[1] 50\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%R\n", + "plot(cars)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "application/json": { + "cell": { + "!": "OSMagics", + "HTML": "Other", + "R": "RMagics", + "SVG": "Other", + "bash": "Other", + "capture": "ExecutionMagics", + "debug": "ExecutionMagics", + "file": "Other", + "html": "DisplayMagics", + "javascript": "DisplayMagics", + "js": "DisplayMagics", + "latex": "DisplayMagics", + "markdown": "DisplayMagics", + "perl": "Other", + "prun": "ExecutionMagics", + "pypy": "Other", + "python": "Other", + "python2": "Other", + "python3": "Other", + "ruby": "Other", + "script": "ScriptMagics", + "sh": "Other", + "svg": "DisplayMagics", + "sx": "OSMagics", + "system": "OSMagics", + "time": "ExecutionMagics", + "timeit": "ExecutionMagics", + "writefile": "OSMagics" + }, + "line": { + "R": "RMagics", + "Rdevice": "RMagics", + "Rget": "RMagics", + "Rpull": "RMagics", + "Rpush": "RMagics", + "alias": "OSMagics", + "alias_magic": "BasicMagics", + "autoawait": "AsyncMagics", + "autocall": "AutoMagics", + "automagic": "AutoMagics", + "autosave": "KernelMagics", + "bookmark": "OSMagics", + "cat": "Other", + "cd": "OSMagics", + "clear": "KernelMagics", + "colors": "BasicMagics", + "conda": "PackagingMagics", + "config": "ConfigMagics", + "connect_info": "KernelMagics", + "cp": "Other", + "debug": "ExecutionMagics", + "dhist": "OSMagics", + "dirs": "OSMagics", + "doctest_mode": "BasicMagics", + "ed": "Other", + "edit": "KernelMagics", + "env": "OSMagics", + "gui": "BasicMagics", + "hist": "Other", + "history": "HistoryMagics", + "killbgscripts": "ScriptMagics", + "ldir": "Other", + "less": "KernelMagics", + "lf": "Other", + "lk": "Other", + "ll": "Other", + "load": "CodeMagics", + "load_ext": "ExtensionMagics", + "loadpy": "CodeMagics", + "logoff": "LoggingMagics", + "logon": "LoggingMagics", + "logstart": "LoggingMagics", + "logstate": "LoggingMagics", + "logstop": "LoggingMagics", + "ls": "Other", + "lsmagic": "BasicMagics", + "lx": "Other", + "macro": "ExecutionMagics", + "magic": "BasicMagics", + "man": "KernelMagics", + "matplotlib": "PylabMagics", + "mkdir": "Other", + "more": "KernelMagics", + "mv": "Other", + "notebook": "BasicMagics", + "page": "BasicMagics", + "pastebin": "CodeMagics", + "pdb": "ExecutionMagics", + "pdef": "NamespaceMagics", + "pdoc": "NamespaceMagics", + "pfile": "NamespaceMagics", + "pinfo": "NamespaceMagics", + "pinfo2": "NamespaceMagics", + "pip": "PackagingMagics", + "popd": "OSMagics", + "pprint": "BasicMagics", + "precision": "BasicMagics", + "prun": "ExecutionMagics", + "psearch": "NamespaceMagics", + "psource": "NamespaceMagics", + "pushd": "OSMagics", + "pwd": "OSMagics", + "pycat": "OSMagics", + "pylab": "PylabMagics", + "qtconsole": "KernelMagics", + "quickref": "BasicMagics", + "recall": "HistoryMagics", + "rehashx": "OSMagics", + "reload_ext": "ExtensionMagics", + "rep": "Other", + "rerun": "HistoryMagics", + "reset": "NamespaceMagics", + "reset_selective": "NamespaceMagics", + "rm": "Other", + "rmdir": "Other", + "run": "ExecutionMagics", + "save": "CodeMagics", + "sc": "OSMagics", + "set_env": "OSMagics", + "store": "StoreMagics", + "sx": "OSMagics", + "system": "OSMagics", + "tb": "ExecutionMagics", + "time": "ExecutionMagics", + "timeit": "ExecutionMagics", + "unalias": "OSMagics", + "unload_ext": "ExtensionMagics", + "who": "NamespaceMagics", + "who_ls": "NamespaceMagics", + "whos": "NamespaceMagics", + "xdel": "NamespaceMagics", + "xmode": "BasicMagics" + } + }, + "text/plain": [ + "Available line magics:\n", + "%R %Rdevice %Rget %Rpull %Rpush %alias %alias_magic %autoawait %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %conda %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %pip %popd %pprint %precision %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n", + "\n", + "Available cell magics:\n", + "%%! %%HTML %%R %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%js %%latex %%markdown %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n", + "\n", + "Automagic is ON, % prefix IS NOT needed for line magics." + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%lsmagic" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [] + } + ], "metadata": { + "hide_code_all_hidden": false, "kernelspec": { "display_name": "Python 3", "language": "python", @@ -16,10 +392,9 @@ "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-Copy1.ipynb b/module2/exo5/exo5_fr-Copy1.ipynb new file mode 100644 index 0000000..0089406 --- /dev/null +++ b/module2/exo5/exo5_fr-Copy1.ipynb @@ -0,0 +1,713 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analyse du risque de défaillance des joints toriques de la navette Challenger" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le 27 Janvier 1986, veille du décollage de la navette *Challenger*, eu\n", + "lieu une télé-conférence de trois heures entre les ingénieurs de la\n", + "Morton Thiokol (constructeur d'un des moteurs) et de la NASA. La\n", + "discussion portait principalement sur les conséquences de la\n", + "température prévue au moment du décollage de 31°F (juste en dessous de\n", + "0°C) sur le succès du vol et en particulier sur la performance des\n", + "joints toriques utilisés dans les moteurs. En effet, aucun test\n", + "n'avait été effectué à cette température.\n", + "\n", + "L'étude qui suit reprend donc une partie des analyses effectuées cette\n", + "nuit là et dont l'objectif était d'évaluer l'influence potentielle de\n", + "la température et de la pression à laquelle sont soumis les joints\n", + "toriques sur leur probabilité de dysfonctionnement. Pour cela, nous\n", + "disposons des résultats des expériences réalisées par les ingénieurs\n", + "de la NASA durant les 6 années précédant le lancement de la navette\n", + "Challenger.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chargement des données\n", + "Nous commençons donc par charger ces données:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateCountTemperaturePressureMalfunction
04/12/81666500
111/12/81670501
23/22/82669500
311/11/82668500
44/04/83667500
56/18/82672500
68/30/836731000
711/28/836701000
82/03/846572001
94/06/846632001
108/30/846702001
1110/05/846782000
1211/08/846672000
131/24/856532002
144/12/856672000
154/29/856752000
166/17/856702000
177/29/856812000
188/27/856762000
1910/03/856792000
2010/30/856752002
2111/26/856762000
221/12/866582001
\n", + "
" + ], + "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" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "data = pd.read_csv(\"shuttle.csv\")\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Le jeu de données nous indique la date de l'essai, le nombre de joints\n", + "toriques mesurés (il y en a 6 sur le lançeur principal), la\n", + "température (en Farenheit) et la pression (en psi), et enfin le\n", + "nombre de dysfonctionnements relevés. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspection graphique des données\n", + "Les vols où aucun incident n'est relevé n'apportant aucun information\n", + "sur l'influence de la température ou de la pression sur les\n", + "dysfonctionnements, nous nous concentrons sur les expériences où au\n", + "moins un joint a été défectueux.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateCountTemperaturePressureMalfunction
111/12/81670501
82/03/846572001
94/06/846632001
108/30/846702001
131/24/856532002
2010/30/856752002
221/12/866582001
\n", + "
" + ], + "text/plain": [ + " Date Count Temperature Pressure Malfunction\n", + "1 11/12/81 6 70 50 1\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", + "13 1/24/85 6 53 200 2\n", + "20 10/30/85 6 75 200 2\n", + "22 1/12/86 6 58 200 1" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = data[data.Malfunction>0]\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Très bien, nous avons une variabilité de température importante mais\n", + "la pression est quasiment toujours égale à 200, ce qui devrait\n", + "simplifier l'analyse.\n", + "\n", + "Comment la fréquence d'échecs varie-t-elle avec la température ?\n" + ] + }, + { + "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", + "pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n", + "import matplotlib.pyplot as plt\n", + "\n", + "data[\"Frequency\"]=data.Malfunction/data.Count\n", + "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "À première vue, ce n'est pas flagrant mais bon, essayons quand même\n", + "d'estimer l'impact de la température $t$ sur la probabilité de\n", + "dysfonctionnements d'un joint. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimation de l'influence de la température\n", + "\n", + "Supposons que chacun des 6 joints toriques est endommagé avec la même\n", + "probabilité et indépendamment des autres et que cette probabilité ne\n", + "dépend que de la température. Si on note $p(t)$ cette probabilité, le\n", + "nombre de joints $D$ dysfonctionnant lorsque l'on effectue le vol à\n", + "température $t$ suit une loi binomiale de paramètre $n=6$ et\n", + "$p=p(t)$. Pour relier $p(t)$ à $t$, on va donc effectuer une\n", + "régression logistique." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
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
\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", + "\"\"\"" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import statsmodels.api as sm\n", + "\n", + "data[\"Success\"]=data.Count-data.Malfunction\n", + "data[\"Intercept\"]=1\n", + "\n", + "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n", + "\n", + "logmodel.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "L'estimateur le plus probable du paramètre de température est 0.0014\n", + "et l'erreur standard de cet estimateur est de 0.122, autrement dit on\n", + "ne peut pas distinguer d'impact particulier et il faut prendre nos\n", + "estimations avec des pincettes.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimation de la probabilité de dysfonctionnant des joints toriques\n", + "La température prévue le jour du décollage est de 31°F. Essayons\n", + "d'estimer la probabilité de dysfonctionnement des joints toriques à\n", + "cette température à partir du modèle que nous venons de construire:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n", + "data_pred['Frequency'] = logmodel.predict(data_pred[['Intercept','Temperature']])\n", + "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n", + "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false, + "scrolled": true + }, + "source": [ + "Comme on pouvait s'attendre au vu des données initiales, la\n", + "température n'a pas d'impact notable sur la probabilité d'échec des\n", + "joints toriques. Elle sera d'environ 0.2, comme dans les essais\n", + "précédents où nous il y a eu défaillance d'au moins un joint. Revenons\n", + "à l'ensemble des données initiales pour estimer la probabilité de\n", + "défaillance d'un joint:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.06521739130434782\n" + ] + } + ], + "source": [ + "data = pd.read_csv(\"shuttle.csv\")\n", + "print(np.sum(data.Malfunction)/np.sum(data.Count))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cette probabilité est donc d'environ $p=0.065$, sachant qu'il existe\n", + "un joint primaire un joint secondaire sur chacune des trois parties du\n", + "lançeur, la probabilité de défaillance des deux joints d'un lançeur\n", + "est de $p^2 \\approx 0.00425$. La probabilité de défaillance d'un des\n", + "lançeur est donc de $1-(1-p^2)^3 \\approx 1.2%$. Ça serait vraiment\n", + "pas de chance... Tout est sous contrôle, le décollage peut donc avoir\n", + "lieu demain comme prévu.\n", + "\n", + "Seulement, le lendemain, la navette Challenger explosera et emportera\n", + "avec elle ses sept membres d'équipages. L'opinion publique est\n", + "fortement touchée et lors de l'enquête qui suivra, la fiabilité des\n", + "joints toriques sera directement mise en cause. Au delà des problèmes\n", + "de communication interne à la NASA qui sont pour beaucoup dans ce\n", + "fiasco, l'analyse précédente comporte (au moins) un petit\n", + "problème... Saurez-vous le trouver ? Vous êtes libre de modifier cette\n", + "analyse et de regarder ce jeu de données sous tous les angles afin\n", + "d'expliquer ce qui ne va pas." + ] + } + ], + "metadata": { + "celltoolbar": "Hide code", + "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": 2 +} diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb index 26ad6d9..0089406 100644 --- a/module2/exo5/exo5_fr.ipynb +++ b/module2/exo5/exo5_fr.ipynb @@ -705,7 +705,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5..faa4f1f 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": 3, + "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" @@ -61,10 +59,1000 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'raw_data' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\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[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'raw_data' is not defined" + ] + } + ], + "source": [ + "# Je teste que le fichier local n'existe pas avant de le télécharger\n", + "raw_data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020231931711212744.021480.02619.033.0FRFrance
120231831992915402.024456.03023.037.0FRFrance
220231732700721779.032235.04133.049.0FRFrance
320231632787522767.032983.04234.050.0FRFrance
420231533745530993.043917.05646.066.0FRFrance
520231434806040671.055449.07261.083.0FRFrance
620231336485956800.072918.09886.0110.0FRFrance
720231237275064499.081001.010997.0121.0FRFrance
820231137463866420.082856.0112100.0124.0FRFrance
920231037636868243.084493.0115103.0127.0FRFrance
1020230936206254778.069346.09382.0104.0FRFrance
1120230837639168065.084717.0115102.0128.0FRFrance
1220230738985180397.099305.0135121.0149.0FRFrance
1320230639736887636.0107100.0146131.0161.0FRFrance
1420230539546986268.0104670.0144130.0158.0FRFrance
1520230437490166916.082886.0113101.0125.0FRFrance
1620230336957061893.077247.010593.0117.0FRFrance
1720230237826070090.086430.0118106.0130.0FRFrance
182023013121773111024.0132522.0183167.0199.0FRFrance
192022523155371142004.0168738.0234214.0254.0FRFrance
202022513248319232128.0264510.0374350.0398.0FRFrance
212022503234143219402.0248884.0353331.0375.0FRFrance
222022493163384151691.0175077.0246228.0264.0FRFrance
232022483121691111744.0131638.0184169.0199.0FRFrance
2420224739641687230.0105602.0145131.0159.0FRFrance
2520224636773560075.075395.010290.0114.0FRFrance
2620224534530638909.051703.06858.078.0FRFrance
2720224433471328880.040546.05243.061.0FRFrance
2820224334476936884.052654.06856.080.0FRFrance
2920224234746240773.054151.07262.082.0FRFrance
.................................
198119852132609619621.032571.04735.059.0FRFrance
198219852032789620885.034907.05138.064.0FRFrance
198319851934315432821.053487.07859.097.0FRFrance
198419851834055529935.051175.07455.093.0FRFrance
198519851733405324366.043740.06244.080.0FRFrance
198619851635036236451.064273.09166.0116.0FRFrance
198719851536388145538.082224.011683.0149.0FRFrance
19881985143134545114400.0154690.0244207.0281.0FRFrance
19891985133197206176080.0218332.0357319.0395.0FRFrance
19901985123245240223304.0267176.0445405.0485.0FRFrance
19911985113276205252399.0300011.0501458.0544.0FRFrance
19921985103353231326279.0380183.0640591.0689.0FRFrance
19931985093369895341109.0398681.0670618.0722.0FRFrance
19941985083389886359529.0420243.0707652.0762.0FRFrance
19951985073471852432599.0511105.0855784.0926.0FRFrance
19961985063565825518011.0613639.01026939.01113.0FRFrance
19971985053637302592795.0681809.011551074.01236.0FRFrance
19981985043424937390794.0459080.0770708.0832.0FRFrance
19991985033213901174689.0253113.0388317.0459.0FRFrance
200019850239758680949.0114223.0177147.0207.0FRFrance
200119850138548965918.0105060.0155120.0190.0FRFrance
200219845238483060602.0109058.0154110.0198.0FRFrance
2003198451310172680242.0123210.0185146.0224.0FRFrance
20041984503123680101401.0145959.0225184.0266.0FRFrance
2005198449310107381684.0120462.0184149.0219.0FRFrance
200619844837862060634.096606.0143110.0176.0FRFrance
200719844737202954274.089784.013199.0163.0FRFrance
200819844638733067686.0106974.0159123.0195.0FRFrance
20091984453135223101414.0169032.0246184.0308.0FRFrance
201019844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2011 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202319 3 17112 12744.0 21480.0 26 19.0 \n", + "1 202318 3 19929 15402.0 24456.0 30 23.0 \n", + "2 202317 3 27007 21779.0 32235.0 41 33.0 \n", + "3 202316 3 27875 22767.0 32983.0 42 34.0 \n", + "4 202315 3 37455 30993.0 43917.0 56 46.0 \n", + "5 202314 3 48060 40671.0 55449.0 72 61.0 \n", + "6 202313 3 64859 56800.0 72918.0 98 86.0 \n", + "7 202312 3 72750 64499.0 81001.0 109 97.0 \n", + "8 202311 3 74638 66420.0 82856.0 112 100.0 \n", + "9 202310 3 76368 68243.0 84493.0 115 103.0 \n", + "10 202309 3 62062 54778.0 69346.0 93 82.0 \n", + "11 202308 3 76391 68065.0 84717.0 115 102.0 \n", + "12 202307 3 89851 80397.0 99305.0 135 121.0 \n", + "13 202306 3 97368 87636.0 107100.0 146 131.0 \n", + "14 202305 3 95469 86268.0 104670.0 144 130.0 \n", + "15 202304 3 74901 66916.0 82886.0 113 101.0 \n", + "16 202303 3 69570 61893.0 77247.0 105 93.0 \n", + "17 202302 3 78260 70090.0 86430.0 118 106.0 \n", + "18 202301 3 121773 111024.0 132522.0 183 167.0 \n", + "19 202252 3 155371 142004.0 168738.0 234 214.0 \n", + "20 202251 3 248319 232128.0 264510.0 374 350.0 \n", + "21 202250 3 234143 219402.0 248884.0 353 331.0 \n", + "22 202249 3 163384 151691.0 175077.0 246 228.0 \n", + "23 202248 3 121691 111744.0 131638.0 184 169.0 \n", + "24 202247 3 96416 87230.0 105602.0 145 131.0 \n", + "25 202246 3 67735 60075.0 75395.0 102 90.0 \n", + "26 202245 3 45306 38909.0 51703.0 68 58.0 \n", + "27 202244 3 34713 28880.0 40546.0 52 43.0 \n", + "28 202243 3 44769 36884.0 52654.0 68 56.0 \n", + "29 202242 3 47462 40773.0 54151.0 72 62.0 \n", + "... ... ... ... ... ... ... ... \n", + "1981 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1982 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1983 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1984 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1985 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1986 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1987 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1988 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1989 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1990 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1991 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1992 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1993 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1994 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1995 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1996 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1997 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1998 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1999 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2000 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2001 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2002 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2003 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2004 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2005 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2006 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2007 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2008 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2009 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2010 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 33.0 FR France \n", + "1 37.0 FR France \n", + "2 49.0 FR France \n", + "3 50.0 FR France \n", + "4 66.0 FR France \n", + "5 83.0 FR France \n", + "6 110.0 FR France \n", + "7 121.0 FR France \n", + "8 124.0 FR France \n", + "9 127.0 FR France \n", + "10 104.0 FR France \n", + "11 128.0 FR France \n", + "12 149.0 FR France \n", + "13 161.0 FR France \n", + "14 158.0 FR France \n", + "15 125.0 FR France \n", + "16 117.0 FR France \n", + "17 130.0 FR France \n", + "18 199.0 FR France \n", + "19 254.0 FR France \n", + "20 398.0 FR France \n", + "21 375.0 FR France \n", + "22 264.0 FR France \n", + "23 199.0 FR France \n", + "24 159.0 FR France \n", + "25 114.0 FR France \n", + "26 78.0 FR France \n", + "27 61.0 FR France \n", + "28 80.0 FR France \n", + "29 82.0 FR France \n", + "... ... ... ... \n", + "1981 59.0 FR France \n", + "1982 64.0 FR France \n", + "1983 97.0 FR France \n", + "1984 93.0 FR France \n", + "1985 80.0 FR France \n", + "1986 116.0 FR France \n", + "1987 149.0 FR France \n", + "1988 281.0 FR France \n", + "1989 395.0 FR France \n", + "1990 485.0 FR France \n", + "1991 544.0 FR France \n", + "1992 689.0 FR France \n", + "1993 722.0 FR France \n", + "1994 762.0 FR France \n", + "1995 926.0 FR France \n", + "1996 1113.0 FR France \n", + "1997 1236.0 FR France \n", + "1998 832.0 FR France \n", + "1999 459.0 FR France \n", + "2000 207.0 FR France \n", + "2001 190.0 FR France \n", + "2002 198.0 FR France \n", + "2003 224.0 FR France \n", + "2004 266.0 FR France \n", + "2005 219.0 FR France \n", + "2006 176.0 FR France \n", + "2007 163.0 FR France \n", + "2008 195.0 FR France \n", + "2009 308.0 FR France \n", + "2010 213.0 FR France \n", + "\n", + "[2011 rows x 10 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "# Je télécharge les données dans un fichier local\n", "raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data" ] @@ -78,9 +1066,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
177419891930NaNNaN0NaNNaNFRFrance
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
020231931711212744.021480.02619.033.0FRFrance
120231831992915402.024456.03023.037.0FRFrance
220231732700721779.032235.04133.049.0FRFrance
320231632787522767.032983.04234.050.0FRFrance
420231533745530993.043917.05646.066.0FRFrance
520231434806040671.055449.07261.083.0FRFrance
620231336485956800.072918.09886.0110.0FRFrance
720231237275064499.081001.010997.0121.0FRFrance
820231137463866420.082856.0112100.0124.0FRFrance
920231037636868243.084493.0115103.0127.0FRFrance
1020230936206254778.069346.09382.0104.0FRFrance
1120230837639168065.084717.0115102.0128.0FRFrance
1220230738985180397.099305.0135121.0149.0FRFrance
1320230639736887636.0107100.0146131.0161.0FRFrance
1420230539546986268.0104670.0144130.0158.0FRFrance
1520230437490166916.082886.0113101.0125.0FRFrance
1620230336957061893.077247.010593.0117.0FRFrance
1720230237826070090.086430.0118106.0130.0FRFrance
182023013121773111024.0132522.0183167.0199.0FRFrance
192022523155371142004.0168738.0234214.0254.0FRFrance
202022513248319232128.0264510.0374350.0398.0FRFrance
212022503234143219402.0248884.0353331.0375.0FRFrance
222022493163384151691.0175077.0246228.0264.0FRFrance
232022483121691111744.0131638.0184169.0199.0FRFrance
2420224739641687230.0105602.0145131.0159.0FRFrance
2520224636773560075.075395.010290.0114.0FRFrance
2620224534530638909.051703.06858.078.0FRFrance
2720224433471328880.040546.05243.061.0FRFrance
2820224334476936884.052654.06856.080.0FRFrance
2920224234746240773.054151.07262.082.0FRFrance
.................................
198119852132609619621.032571.04735.059.0FRFrance
198219852032789620885.034907.05138.064.0FRFrance
198319851934315432821.053487.07859.097.0FRFrance
198419851834055529935.051175.07455.093.0FRFrance
198519851733405324366.043740.06244.080.0FRFrance
198619851635036236451.064273.09166.0116.0FRFrance
198719851536388145538.082224.011683.0149.0FRFrance
19881985143134545114400.0154690.0244207.0281.0FRFrance
19891985133197206176080.0218332.0357319.0395.0FRFrance
19901985123245240223304.0267176.0445405.0485.0FRFrance
19911985113276205252399.0300011.0501458.0544.0FRFrance
19921985103353231326279.0380183.0640591.0689.0FRFrance
19931985093369895341109.0398681.0670618.0722.0FRFrance
19941985083389886359529.0420243.0707652.0762.0FRFrance
19951985073471852432599.0511105.0855784.0926.0FRFrance
19961985063565825518011.0613639.01026939.01113.0FRFrance
19971985053637302592795.0681809.011551074.01236.0FRFrance
19981985043424937390794.0459080.0770708.0832.0FRFrance
19991985033213901174689.0253113.0388317.0459.0FRFrance
200019850239758680949.0114223.0177147.0207.0FRFrance
200119850138548965918.0105060.0155120.0190.0FRFrance
200219845238483060602.0109058.0154110.0198.0FRFrance
2003198451310172680242.0123210.0185146.0224.0FRFrance
20041984503123680101401.0145959.0225184.0266.0FRFrance
2005198449310107381684.0120462.0184149.0219.0FRFrance
200619844837862060634.096606.0143110.0176.0FRFrance
200719844737202954274.089784.013199.0163.0FRFrance
200819844638733067686.0106974.0159123.0195.0FRFrance
20091984453135223101414.0169032.0246184.0308.0FRFrance
201019844436842220056.0116788.012537.0213.0FRFrance
\n", + "

2010 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202319 3 17112 12744.0 21480.0 26 19.0 \n", + "1 202318 3 19929 15402.0 24456.0 30 23.0 \n", + "2 202317 3 27007 21779.0 32235.0 41 33.0 \n", + "3 202316 3 27875 22767.0 32983.0 42 34.0 \n", + "4 202315 3 37455 30993.0 43917.0 56 46.0 \n", + "5 202314 3 48060 40671.0 55449.0 72 61.0 \n", + "6 202313 3 64859 56800.0 72918.0 98 86.0 \n", + "7 202312 3 72750 64499.0 81001.0 109 97.0 \n", + "8 202311 3 74638 66420.0 82856.0 112 100.0 \n", + "9 202310 3 76368 68243.0 84493.0 115 103.0 \n", + "10 202309 3 62062 54778.0 69346.0 93 82.0 \n", + "11 202308 3 76391 68065.0 84717.0 115 102.0 \n", + "12 202307 3 89851 80397.0 99305.0 135 121.0 \n", + "13 202306 3 97368 87636.0 107100.0 146 131.0 \n", + "14 202305 3 95469 86268.0 104670.0 144 130.0 \n", + "15 202304 3 74901 66916.0 82886.0 113 101.0 \n", + "16 202303 3 69570 61893.0 77247.0 105 93.0 \n", + "17 202302 3 78260 70090.0 86430.0 118 106.0 \n", + "18 202301 3 121773 111024.0 132522.0 183 167.0 \n", + "19 202252 3 155371 142004.0 168738.0 234 214.0 \n", + "20 202251 3 248319 232128.0 264510.0 374 350.0 \n", + "21 202250 3 234143 219402.0 248884.0 353 331.0 \n", + "22 202249 3 163384 151691.0 175077.0 246 228.0 \n", + "23 202248 3 121691 111744.0 131638.0 184 169.0 \n", + "24 202247 3 96416 87230.0 105602.0 145 131.0 \n", + "25 202246 3 67735 60075.0 75395.0 102 90.0 \n", + "26 202245 3 45306 38909.0 51703.0 68 58.0 \n", + "27 202244 3 34713 28880.0 40546.0 52 43.0 \n", + "28 202243 3 44769 36884.0 52654.0 68 56.0 \n", + "29 202242 3 47462 40773.0 54151.0 72 62.0 \n", + "... ... ... ... ... ... ... ... \n", + "1981 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1982 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1983 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1984 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1985 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1986 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1987 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1988 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1989 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1990 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1991 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1992 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1993 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1994 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1995 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1996 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1997 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1998 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1999 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "2000 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "2001 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "2002 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "2003 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "2004 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "2005 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "2006 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "2007 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "2008 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "2009 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "2010 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 33.0 FR France \n", + "1 37.0 FR France \n", + "2 49.0 FR France \n", + "3 50.0 FR France \n", + "4 66.0 FR France \n", + "5 83.0 FR France \n", + "6 110.0 FR France \n", + "7 121.0 FR France \n", + "8 124.0 FR France \n", + "9 127.0 FR France \n", + "10 104.0 FR France \n", + "11 128.0 FR France \n", + "12 149.0 FR France \n", + "13 161.0 FR France \n", + "14 158.0 FR France \n", + "15 125.0 FR France \n", + "16 117.0 FR France \n", + "17 130.0 FR France \n", + "18 199.0 FR France \n", + "19 254.0 FR France \n", + "20 398.0 FR France \n", + "21 375.0 FR France \n", + "22 264.0 FR France \n", + "23 199.0 FR France \n", + "24 159.0 FR France \n", + "25 114.0 FR France \n", + "26 78.0 FR France \n", + "27 61.0 FR France \n", + "28 80.0 FR France \n", + "29 82.0 FR France \n", + "... ... ... ... \n", + "1981 59.0 FR France \n", + "1982 64.0 FR France \n", + "1983 97.0 FR France \n", + "1984 93.0 FR France \n", + "1985 80.0 FR France \n", + "1986 116.0 FR France \n", + "1987 149.0 FR France \n", + "1988 281.0 FR France \n", + "1989 395.0 FR France \n", + "1990 485.0 FR France \n", + "1991 544.0 FR France \n", + "1992 689.0 FR France \n", + "1993 722.0 FR France \n", + "1994 762.0 FR France \n", + "1995 926.0 FR France \n", + "1996 1113.0 FR France \n", + "1997 1236.0 FR France \n", + "1998 832.0 FR France \n", + "1999 459.0 FR France \n", + "2000 207.0 FR France \n", + "2001 190.0 FR France \n", + "2002 198.0 FR France \n", + "2003 224.0 FR France \n", + "2004 266.0 FR France \n", + "2005 219.0 FR France \n", + "2006 176.0 FR France \n", + "2007 163.0 FR France \n", + "2008 195.0 FR France \n", + "2009 308.0 FR France \n", + "2010 213.0 FR France \n", + "\n", + "[2010 rows x 10 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2141,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -143,7 +2162,7 @@ "Il restent deux petites modifications à faire.\n", "\n", "Premièrement, nous définissons les périodes d'observation\n", - "comme nouvel index de notre jeux de données. Ceci en fait\n", + "comme nouvel index de notre jeu de données. Ceci en fait\n", "une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Deuxièmement, nous trions les points par période, dans\n", @@ -152,10 +2171,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,9 +2196,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "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 +2224,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.plot(style='*')" ] @@ -314,9 +2406,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "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": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -331,9 +2471,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "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 +2504,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -364,7 +2525,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, -- 2.18.1