diff --git a/module2/exo5/mes_calculs.ipynb b/module2/exo5/mes_calculs.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..322227e0bc0d07eb91bb642acf1c9ac61469da3e --- /dev/null +++ b/module2/exo5/mes_calculs.ipynb @@ -0,0 +1,608 @@ +{ + "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.~~ Au lieu de ne considérer que les cas de défaillances, on prend en compte **tous les essais.**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "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.~~ Dans la suite, on **néglige les effets de la pression.**\n", + "\n", + "Comment la fréquence ~~d'échecs~~ **des résultats** varie-t-elle avec la température ?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "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. ~~Beaucoup plus de points que lors de l'analyse fournie au début de l'exercice en incluant ceux correspondants aux essais réussis. On poursuit avec une estimation de la probabilité de défaillance.\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": 17, + "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: 23
Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -3.9210
Date: Tue, 25 Jan 2022 Deviance: 3.0144
Time: 17:21:38 Pearson chi2: 5.00
No. Iterations: 6 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 5.0850 7.477 0.680 0.496 -9.570 19.740
Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Generalized Linear Model Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Frequency No. Observations: 23\n", + "Model: GLM Df Residuals: 21\n", + "Model Family: Binomial Df Model: 1\n", + "Link Function: logit Scale: 1.0000\n", + "Method: IRLS Log-Likelihood: -3.9210\n", + "Date: Tue, 25 Jan 2022 Deviance: 3.0144\n", + "Time: 17:21:38 Pearson chi2: 5.00\n", + "No. Iterations: 6 Covariance Type: nonrobust\n", + "===============================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "-------------------------------------------------------------------------------\n", + "Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740\n", + "Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110\n", + "===============================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 17, + "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~~ **-0.1156**\n", + "et l'erreur standard de cet estimateur est de ~~0.122~~ **0.115**, autrement dit ~~on\n", + "ne peut pas distinguer d'impact particulier et il faut prendre nos\n", + "estimations avec des pincettes.~~ la température semble avoir un effet significatif mais l'incertitude est grande.\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": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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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:~~La température semble avoir un effet vraiment important, puisque la fréquence d'échec du joint semble être de l'ordre de 0.85 avec cependant de grandes incertitudes. **Ne pas tenir compte de ce qui se trouve ci-dessous.**\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "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/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..dca166a653a7e4fa951948972c3169a05f34b13c 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,6 +1,2271 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "# Incidence de la varicelle" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "## Chargement des données" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "On commence par récupérer les données depuis le site [Réseau Sentinelles](https://www.sentiweb.fr/france/fr/?) en naviguant dans le menu de gauche: `Surveillance continue`-> `Bases de données` puis `Accès aux données`. On sélectionne `Varicelle (1991 - en cours)` dans le menu déroulant intitulé `Maladie/Indicateur` puis, dans l'onglet `Télécharger` on prend soin de télécharger les données au format CSV afin de déterminer l'URL permettant d'accéder à ces données. Celle-ci est stockée sous la forme d'une chaîne de caractères dans la variable suivante:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "La lecture des données brutes donne:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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12022027850660341097813917FRFrance
22022017137931059716989211626FRFrance
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62021497136741036916979211626FRFrance
7202148711549850314595171222FRFrance
8202147711419837614462171222FRFrance
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1020214578965646811462141018FRFrance
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132021427944360371284914919FRFrance
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1720213874325226763837410FRFrance
18202137719647543174315FRFrance
192021367344117305152528FRFrance
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232021327410818956321639FRFrance
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252021307719041911018911616FRFrance
26202129768004109949110614FRFrance
272021287973402173115033FRFrance
282021277902643161373614721FRFrance
292021267728441081046011616FRFrance
.................................
15951991267176081130423912312042FRFrance
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1625 rows × 10 columns

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3829 1830 5828 6 3 \n", + "23 202132 7 4108 1895 6321 6 3 \n", + "24 202131 7 4793 2301 7285 7 3 \n", + "25 202130 7 7190 4191 10189 11 6 \n", + "26 202129 7 6800 4109 9491 10 6 \n", + "27 202128 7 9734 0 21731 15 0 \n", + "28 202127 7 9026 4316 13736 14 7 \n", + "29 202126 7 7284 4108 10460 11 6 \n", + "... ... ... ... ... ... ... ... \n", + "1595 199126 7 17608 11304 23912 31 20 \n", + "1596 199125 7 16169 10700 21638 28 18 \n", + "1597 199124 7 16171 10071 22271 28 17 \n", + "1598 199123 7 11947 7671 16223 21 13 \n", + "1599 199122 7 15452 9953 20951 27 17 \n", + "1600 199121 7 14903 8975 20831 26 16 \n", + "1601 199120 7 19053 12742 25364 34 23 \n", + "1602 199119 7 16739 11246 22232 29 19 \n", + "1603 199118 7 21385 13882 28888 38 25 \n", + "1604 199117 7 13462 8877 18047 24 16 \n", + "1605 199116 7 14857 10068 19646 26 18 \n", + "1606 199115 7 13975 9781 18169 25 18 \n", + "1607 199114 7 12265 7684 16846 22 14 \n", + "1608 199113 7 9567 6041 13093 17 11 \n", + "1609 199112 7 10864 7331 14397 19 13 \n", + "1610 199111 7 15574 11184 19964 27 19 \n", + "1611 199110 7 16643 11372 21914 29 20 \n", + "1612 199109 7 13741 8780 18702 24 15 \n", + "1613 199108 7 13289 8813 17765 23 15 \n", + "1614 199107 7 12337 8077 16597 22 15 \n", + "1615 199106 7 10877 7013 14741 19 12 \n", + "1616 199105 7 10442 6544 14340 18 11 \n", + "1617 199104 7 7913 4563 11263 14 8 \n", + "1618 199103 7 15387 10484 20290 27 18 \n", + "1619 199102 7 16277 11046 21508 29 20 \n", + "1620 199101 7 15565 10271 20859 27 18 \n", + "1621 199052 7 19375 13295 25455 34 23 \n", + "1622 199051 7 19080 13807 24353 34 25 \n", + "1623 199050 7 11079 6660 15498 20 12 \n", + "1624 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 27 FR France \n", + "1 17 FR France \n", + "2 26 FR France \n", + "3 25 FR France \n", + "4 26 FR France \n", + "5 27 FR France \n", + "6 26 FR France \n", + "7 22 FR France \n", + "8 22 FR France \n", + "9 16 FR France \n", + "10 18 FR France \n", + "11 18 FR France \n", + "12 17 FR France \n", + "13 19 FR France \n", + "14 9 FR France \n", + "15 10 FR France \n", + "16 5 FR France \n", + "17 10 FR France \n", + "18 5 FR France \n", + "19 8 FR France \n", + "20 6 FR France \n", + "21 4 FR France \n", + "22 9 FR France \n", + "23 9 FR France \n", + "24 11 FR France \n", + "25 16 FR France \n", + "26 14 FR France \n", + "27 33 FR France \n", + "28 21 FR France \n", + "29 16 FR France \n", + "... ... ... ... \n", + "1595 42 FR France \n", + "1596 38 FR France \n", + "1597 39 FR France \n", + "1598 29 FR France \n", + "1599 37 FR France \n", + "1600 36 FR France \n", + "1601 45 FR France \n", + "1602 39 FR France \n", + "1603 51 FR France \n", + "1604 32 FR France \n", + "1605 34 FR France \n", + "1606 32 FR France \n", + "1607 30 FR France \n", + "1608 23 FR France \n", + "1609 25 FR France \n", + "1610 35 FR France \n", + "1611 38 FR France \n", + "1612 33 FR France \n", + "1613 31 FR France \n", + "1614 29 FR France \n", + "1615 26 FR France \n", + "1616 25 FR France \n", + "1617 20 FR France \n", + "1618 36 FR France \n", + "1619 38 FR France \n", + "1620 36 FR France \n", + "1621 45 FR France \n", + "1622 43 FR France \n", + "1623 28 FR France \n", + "1624 5 FR France \n", + "\n", + "[1625 rows x 10 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "## Reformatage des données" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Dans un premier temps, on vérifie s'il n'y a pas de lignes manquantes dans le tableau ci-dessus:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", + "Index: []" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "missing_lines = raw_data[raw_data.isnull().any(axis=1)]\n", + "missing_lines" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "On voit que la variable `missing_lines` est vide, ce qui indique que le jeux de données ne souffre pas de \"trous\". On copie le jeu de données dans une nouvelle variable, qui est celle sur laquelle les traitements seront effectués:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "data = raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Ensuite, on reformule la numérotation des semaines. En effet, dans le tableau ci-dessus, les semaines sont numérotées avec six chiffres: les quatres premiers chiffres correspondent à l'année, et les deux derniers au numéro de la semaine, ce qui donne l'impression à `pandas` qu'il s'agit d'un entier alors que ce n'est pas le cas. De plus, une telle numérotation ne peut pas être interprétée par `pandas`, il faut donc la reformuler. Cela est réalisé avec la librairie `isoweek`. On écrit une fonction `conversionDate`qui sera appliquée à l'ensemble de la première colonne du jeu de données:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "def conversionDate(dateInt):\n", + " \n", + " dateStr = str(dateInt)\n", + " annee = int(dateStr[:4])\n", + " semaine = int(dateStr[4:])\n", + " s = isoweek.Week(annee, semaine)\n", + " return pd.Period(s.day(0), 'W')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_nameperiod
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4202151713326962917023201426FRFrance2021-12-20/2021-12-26
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62021497136741036916979211626FRFrance2021-12-06/2021-12-12
7202148711549850314595171222FRFrance2021-11-29/2021-12-05
8202147711419837614462171222FRFrance2021-11-22/2021-11-28
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142021417402122395803639FRFrance2021-10-11/2021-10-17
1520214074441245464287410FRFrance2021-10-04/2021-10-10
162021397229110563526315FRFrance2021-09-27/2021-10-03
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18202137719647543174315FRFrance2021-09-13/2021-09-19
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202021357256211074017426FRFrance2021-08-30/2021-09-05
21202134714293782480204FRFrance2021-08-23/2021-08-29
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292021267728441081046011616FRFrance2021-06-28/2021-07-04
....................................
15951991267176081130423912312042FRFrance1991-06-24/1991-06-30
15961991257161691070021638281838FRFrance1991-06-17/1991-06-23
15971991247161711007122271281739FRFrance1991-06-10/1991-06-16
1598199123711947767116223211329FRFrance1991-06-03/1991-06-09
1599199122715452995320951271737FRFrance1991-05-27/1991-06-02
1600199121714903897520831261636FRFrance1991-05-20/1991-05-26
16011991207190531274225364342345FRFrance1991-05-13/1991-05-19
16021991197167391124622232291939FRFrance1991-05-06/1991-05-12
16031991187213851388228888382551FRFrance1991-04-29/1991-05-05
1604199117713462887718047241632FRFrance1991-04-22/1991-04-28
16051991167148571006819646261834FRFrance1991-04-15/1991-04-21
1606199115713975978118169251832FRFrance1991-04-08/1991-04-14
1607199114712265768416846221430FRFrance1991-04-01/1991-04-07
160819911379567604113093171123FRFrance1991-03-25/1991-03-31
1609199112710864733114397191325FRFrance1991-03-18/1991-03-24
16101991117155741118419964271935FRFrance1991-03-11/1991-03-17
16111991107166431137221914292038FRFrance1991-03-04/1991-03-10
1612199109713741878018702241533FRFrance1991-02-25/1991-03-03
1613199108713289881317765231531FRFrance1991-02-18/1991-02-24
1614199107712337807716597221529FRFrance1991-02-11/1991-02-17
1615199106710877701314741191226FRFrance1991-02-04/1991-02-10
1616199105710442654414340181125FRFrance1991-01-28/1991-02-03
16171991047791345631126314820FRFrance1991-01-21/1991-01-27
16181991037153871048420290271836FRFrance1991-01-14/1991-01-20
16191991027162771104621508292038FRFrance1991-01-07/1991-01-13
16201991017155651027120859271836FRFrance1990-12-31/1991-01-06
16211990527193751329525455342345FRFrance1990-12-24/1990-12-30
16221990517190801380724353342543FRFrance1990-12-17/1990-12-23
1623199050711079666015498201228FRFrance1990-12-10/1990-12-16
16241990497114302610205FRFrance1990-12-03/1990-12-09
\n", + "

1625 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202203 7 14299 10896 17702 22 17 \n", + "1 202202 7 8506 6034 10978 13 9 \n", + "2 202201 7 13793 10597 16989 21 16 \n", + "3 202152 7 13239 9611 16867 20 15 \n", + "4 202151 7 13326 9629 17023 20 14 \n", + "5 202150 7 14128 10312 17944 21 15 \n", + "6 202149 7 13674 10369 16979 21 16 \n", + "7 202148 7 11549 8503 14595 17 12 \n", + "8 202147 7 11419 8376 14462 17 12 \n", + "9 202146 7 8216 5724 10708 12 8 \n", + "10 202145 7 8965 6468 11462 14 10 \n", + "11 202144 7 8736 5636 11836 13 8 \n", + "12 202143 7 8145 5164 11126 12 7 \n", + "13 202142 7 9443 6037 12849 14 9 \n", + "14 202141 7 4021 2239 5803 6 3 \n", + "15 202140 7 4441 2454 6428 7 4 \n", + "16 202139 7 2291 1056 3526 3 1 \n", + "17 202138 7 4325 2267 6383 7 4 \n", + "18 202137 7 1964 754 3174 3 1 \n", + "19 202136 7 3441 1730 5152 5 2 \n", + "20 202135 7 2562 1107 4017 4 2 \n", + "21 202134 7 1429 378 2480 2 0 \n", + "22 202133 7 3829 1830 5828 6 3 \n", + "23 202132 7 4108 1895 6321 6 3 \n", + "24 202131 7 4793 2301 7285 7 3 \n", + "25 202130 7 7190 4191 10189 11 6 \n", + "26 202129 7 6800 4109 9491 10 6 \n", + "27 202128 7 9734 0 21731 15 0 \n", + "28 202127 7 9026 4316 13736 14 7 \n", + "29 202126 7 7284 4108 10460 11 6 \n", + "... ... ... ... ... ... ... ... \n", + "1595 199126 7 17608 11304 23912 31 20 \n", + "1596 199125 7 16169 10700 21638 28 18 \n", + "1597 199124 7 16171 10071 22271 28 17 \n", + "1598 199123 7 11947 7671 16223 21 13 \n", + "1599 199122 7 15452 9953 20951 27 17 \n", + "1600 199121 7 14903 8975 20831 26 16 \n", + "1601 199120 7 19053 12742 25364 34 23 \n", + "1602 199119 7 16739 11246 22232 29 19 \n", + "1603 199118 7 21385 13882 28888 38 25 \n", + "1604 199117 7 13462 8877 18047 24 16 \n", + "1605 199116 7 14857 10068 19646 26 18 \n", + "1606 199115 7 13975 9781 18169 25 18 \n", + "1607 199114 7 12265 7684 16846 22 14 \n", + "1608 199113 7 9567 6041 13093 17 11 \n", + "1609 199112 7 10864 7331 14397 19 13 \n", + "1610 199111 7 15574 11184 19964 27 19 \n", + "1611 199110 7 16643 11372 21914 29 20 \n", + "1612 199109 7 13741 8780 18702 24 15 \n", + "1613 199108 7 13289 8813 17765 23 15 \n", + "1614 199107 7 12337 8077 16597 22 15 \n", + "1615 199106 7 10877 7013 14741 19 12 \n", + "1616 199105 7 10442 6544 14340 18 11 \n", + "1617 199104 7 7913 4563 11263 14 8 \n", + "1618 199103 7 15387 10484 20290 27 18 \n", + "1619 199102 7 16277 11046 21508 29 20 \n", + "1620 199101 7 15565 10271 20859 27 18 \n", + "1621 199052 7 19375 13295 25455 34 23 \n", + "1622 199051 7 19080 13807 24353 34 25 \n", + "1623 199050 7 11079 6660 15498 20 12 \n", + "1624 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name period \n", + "0 27 FR France 2022-01-17/2022-01-23 \n", + "1 17 FR France 2022-01-10/2022-01-16 \n", + "2 26 FR France 2022-01-03/2022-01-09 \n", + "3 25 FR France 2021-12-27/2022-01-02 \n", + "4 26 FR France 2021-12-20/2021-12-26 \n", + "5 27 FR France 2021-12-13/2021-12-19 \n", + "6 26 FR France 2021-12-06/2021-12-12 \n", + "7 22 FR France 2021-11-29/2021-12-05 \n", + "8 22 FR France 2021-11-22/2021-11-28 \n", + "9 16 FR France 2021-11-15/2021-11-21 \n", + "10 18 FR France 2021-11-08/2021-11-14 \n", + "11 18 FR France 2021-11-01/2021-11-07 \n", + "12 17 FR France 2021-10-25/2021-10-31 \n", + "13 19 FR France 2021-10-18/2021-10-24 \n", + "14 9 FR France 2021-10-11/2021-10-17 \n", + "15 10 FR France 2021-10-04/2021-10-10 \n", + "16 5 FR France 2021-09-27/2021-10-03 \n", + "17 10 FR France 2021-09-20/2021-09-26 \n", + "18 5 FR France 2021-09-13/2021-09-19 \n", + "19 8 FR France 2021-09-06/2021-09-12 \n", + "20 6 FR France 2021-08-30/2021-09-05 \n", + "21 4 FR France 2021-08-23/2021-08-29 \n", + "22 9 FR France 2021-08-16/2021-08-22 \n", + "23 9 FR France 2021-08-09/2021-08-15 \n", + "24 11 FR France 2021-08-02/2021-08-08 \n", + "25 16 FR France 2021-07-26/2021-08-01 \n", + "26 14 FR France 2021-07-19/2021-07-25 \n", + "27 33 FR France 2021-07-12/2021-07-18 \n", + "28 21 FR France 2021-07-05/2021-07-11 \n", + "29 16 FR France 2021-06-28/2021-07-04 \n", + "... ... ... ... ... \n", + "1595 42 FR France 1991-06-24/1991-06-30 \n", + "1596 38 FR France 1991-06-17/1991-06-23 \n", + "1597 39 FR France 1991-06-10/1991-06-16 \n", + "1598 29 FR France 1991-06-03/1991-06-09 \n", + "1599 37 FR France 1991-05-27/1991-06-02 \n", + "1600 36 FR France 1991-05-20/1991-05-26 \n", + "1601 45 FR France 1991-05-13/1991-05-19 \n", + "1602 39 FR France 1991-05-06/1991-05-12 \n", + "1603 51 FR France 1991-04-29/1991-05-05 \n", + "1604 32 FR France 1991-04-22/1991-04-28 \n", + "1605 34 FR France 1991-04-15/1991-04-21 \n", + "1606 32 FR France 1991-04-08/1991-04-14 \n", + "1607 30 FR France 1991-04-01/1991-04-07 \n", + "1608 23 FR France 1991-03-25/1991-03-31 \n", + "1609 25 FR France 1991-03-18/1991-03-24 \n", + "1610 35 FR France 1991-03-11/1991-03-17 \n", + "1611 38 FR France 1991-03-04/1991-03-10 \n", + "1612 33 FR France 1991-02-25/1991-03-03 \n", + "1613 31 FR France 1991-02-18/1991-02-24 \n", + "1614 29 FR France 1991-02-11/1991-02-17 \n", + "1615 26 FR France 1991-02-04/1991-02-10 \n", + "1616 25 FR France 1991-01-28/1991-02-03 \n", + "1617 20 FR France 1991-01-21/1991-01-27 \n", + "1618 36 FR France 1991-01-14/1991-01-20 \n", + "1619 38 FR France 1991-01-07/1991-01-13 \n", + "1620 36 FR France 1990-12-31/1991-01-06 \n", + "1621 45 FR France 1990-12-24/1990-12-30 \n", + "1622 43 FR France 1990-12-17/1990-12-23 \n", + "1623 28 FR France 1990-12-10/1990-12-16 \n", + "1624 5 FR France 1990-12-03/1990-12-09 \n", + "\n", + "[1625 rows x 11 columns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"period\"] = data[\"week\"].apply(conversionDate)\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Ensuite, on définit la colonne nouvellement créée comme le nouvel index de nos données:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.set_index(\"period\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Puis on trie les données par ordre chronologique:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "data = data.sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { + "hide_code_all_hidden": false, "kernelspec": { "display_name": "Python 3", "language": "python", @@ -16,10 +2281,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -