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Commit dcb2a5df authored by 5491bc6b05566ce936e155347fc03a4e's avatar 5491bc6b05566ce936e155347fc03a4e
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csv local

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module2/exo2/exercice.ipynb
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{
"cells": [],
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"\n",
"A = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"count 100.00\n",
"mean 14.11\n",
"std 4.33\n",
"min 2.80\n",
"25% 11.85\n",
"50% 14.50\n",
"75% 16.80\n",
"max 23.40\n",
"dtype: float64\n"
]
}
],
"source": [
"print(pd.Series(A).describe().round(2))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f35f59a7588>]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(np.arange(len(A)), A)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 4., 3., 5., 9., 16., 20., 22., 9., 8., 4.]),\n",
" array([ 2.8 , 4.86, 6.92, 8.98, 11.04, 13.1 , 15.16, 17.22, 19.28,\n",
" 21.34, 23.4 ]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(A)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
......@@ -16,10 +130,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
module2/exo5/exo5_fr.ipynb
View file @ dcb2a5df
......@@ -2,14 +2,20 @@
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"# Analyse du risque de défaillance des joints toriques de la navette Challenger"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"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",
......@@ -32,7 +38,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"## Chargement des données\n",
"Nous commençons donc par charger ces données:"
......@@ -41,7 +50,10 @@
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"data": {
......@@ -301,7 +313,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"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",
......@@ -311,7 +326,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"## Inspection graphique des données\n",
"Les vols où aucun incident n'est relevé n'apportant aucun information\n",
......@@ -323,7 +341,10 @@
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"data": {
......@@ -431,13 +452,16 @@
}
],
"source": [
"data = data[data.Malfunction>0]\n",
"# data = data[data.Malfunction>0]\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"Très bien, nous avons une variabilité de température importante mais\n",
"la pression est quasiment toujours égale à 200, ce qui devrait\n",
......@@ -448,12 +472,15 @@
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"execution_count": 10,
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -476,7 +503,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"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",
......@@ -485,7 +515,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"## Estimation de l'influence de la température\n",
"\n",
......@@ -500,8 +533,36 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 11,
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Date', 'Count', 'Temperature', 'Pressure', 'Malfunction', 'Success',\n",
" 'Intercept', 'Frequency'],\n",
" dtype='object')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.columns"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"data": {
......@@ -509,10 +570,10 @@
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 7</td> \n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 23</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 5</td> \n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 21</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
......@@ -521,16 +582,16 @@
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -2.5250</td> \n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -3.9210</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 13 Apr 2019</td> <th> Deviance: </th> <td> 0.22231</td> \n",
" <th>Date:</th> <td>Sat, 23 Aug 2025</td> <th> Deviance: </th> <td> 3.0144</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>19:11:24</td> <th> Pearson chi2: </th> <td> 0.236</td> \n",
" <th>Time:</th> <td>15:27:42</td> <th> Pearson chi2: </th> <td> 5.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
" <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
......@@ -538,10 +599,10 @@
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> -1.3895</td> <td> 7.828</td> <td> -0.178</td> <td> 0.859</td> <td> -16.732</td> <td> 13.953</td>\n",
" <th>Intercept</th> <td> 5.0850</td> <td> 7.477</td> <td> 0.680</td> <td> 0.496</td> <td> -9.570</td> <td> 19.740</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Temperature</th> <td> 0.0014</td> <td> 0.122</td> <td> 0.012</td> <td> 0.991</td> <td> -0.238</td> <td> 0.240</td>\n",
" <th>Temperature</th> <td> -0.1156</td> <td> 0.115</td> <td> -1.004</td> <td> 0.316</td> <td> -0.341</td> <td> 0.110</td>\n",
"</tr>\n",
"</table>"
],
......@@ -550,24 +611,24 @@
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 7\n",
"Model: GLM Df Residuals: 5\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: -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",
"Method: IRLS Log-Likelihood: -3.9210\n",
"Date: Sat, 23 Aug 2025 Deviance: 3.0144\n",
"Time: 15:27:42 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 -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",
"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": 4,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
......@@ -585,7 +646,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"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",
......@@ -595,7 +659,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"source": [
"## Estimation de la probabilité de dysfonctionnant des joints toriques\n",
"La température prévue le jour du décollage est de 31°F. Essayons\n",
......@@ -606,11 +673,14 @@
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"data": {
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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -649,7 +719,10 @@
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"name": "stdout",
......@@ -666,7 +739,10 @@
},
{
"cell_type": "markdown",
"metadata": {},
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"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",
......@@ -686,10 +762,34 @@
"analyse et de regarder ce jeu de données sous tous les angles afin\n",
"d'expliquer ce qui ne va pas."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"hideCode": false,
"hidePrompt": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.012695889265624927"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 - (1 - 0.00425)**3"
]
}
],
"metadata": {
"celltoolbar": "Hide code",
"hide_code_all_hidden": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
......@@ -705,7 +805,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.6.4"
}
},
"nbformat": 4,
......
module3/exo1/analyse-syndrome-grippal.ipynb
View file @ dcb2a5df
......@@ -9,14 +9,15 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import isoweek"
"import isoweek\n",
"import os"
]
},
{
......@@ -28,10 +29,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
......@@ -61,14 +60,1020 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 6,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"csv loaded from local\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>week</th>\n",
" <th>indicator</th>\n",
" <th>inc</th>\n",
" <th>inc_low</th>\n",
" <th>inc_up</th>\n",
" <th>inc100</th>\n",
" <th>inc100_low</th>\n",
" <th>inc100_up</th>\n",
" <th>geo_insee</th>\n",
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" <td>17312.0</td>\n",
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" <td>202533</td>\n",
" <td>3</td>\n",
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" <td>12133.0</td>\n",
" <td>21741.0</td>\n",
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" <td>32.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202532</td>\n",
" <td>3</td>\n",
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" <td>14303.0</td>\n",
" <td>25497.0</td>\n",
" <td>30</td>\n",
" <td>22.0</td>\n",
" <td>38.0</td>\n",
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" <td>202531</td>\n",
" <td>3</td>\n",
" <td>18470</td>\n",
" <td>12625.0</td>\n",
" <td>24315.0</td>\n",
" <td>28</td>\n",
" <td>19.0</td>\n",
" <td>37.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202530</td>\n",
" <td>3</td>\n",
" <td>19166</td>\n",
" <td>14283.0</td>\n",
" <td>24049.0</td>\n",
" <td>29</td>\n",
" <td>22.0</td>\n",
" <td>36.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>202529</td>\n",
" <td>3</td>\n",
" <td>18673</td>\n",
" <td>13815.0</td>\n",
" <td>23531.0</td>\n",
" <td>28</td>\n",
" <td>21.0</td>\n",
" <td>35.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>202528</td>\n",
" <td>3</td>\n",
" <td>23285</td>\n",
" <td>18131.0</td>\n",
" <td>28439.0</td>\n",
" <td>35</td>\n",
" <td>27.0</td>\n",
" <td>43.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>202527</td>\n",
" <td>3</td>\n",
" <td>21453</td>\n",
" <td>17129.0</td>\n",
" <td>25777.0</td>\n",
" <td>32</td>\n",
" <td>26.0</td>\n",
" <td>38.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>202526</td>\n",
" <td>3</td>\n",
" <td>21945</td>\n",
" <td>17422.0</td>\n",
" <td>26468.0</td>\n",
" <td>33</td>\n",
" <td>26.0</td>\n",
" <td>40.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>202525</td>\n",
" <td>3</td>\n",
" <td>23323</td>\n",
" <td>18546.0</td>\n",
" <td>28100.0</td>\n",
" <td>35</td>\n",
" <td>28.0</td>\n",
" <td>42.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>202524</td>\n",
" <td>3</td>\n",
" <td>23154</td>\n",
" <td>18577.0</td>\n",
" <td>27731.0</td>\n",
" <td>35</td>\n",
" <td>28.0</td>\n",
" <td>42.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>202523</td>\n",
" <td>3</td>\n",
" <td>24391</td>\n",
" <td>19307.0</td>\n",
" <td>29475.0</td>\n",
" <td>36</td>\n",
" <td>28.0</td>\n",
" <td>44.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>202522</td>\n",
" <td>3</td>\n",
" <td>18755</td>\n",
" <td>14333.0</td>\n",
" <td>23177.0</td>\n",
" <td>28</td>\n",
" <td>21.0</td>\n",
" <td>35.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>202521</td>\n",
" <td>3</td>\n",
" <td>23760</td>\n",
" <td>18671.0</td>\n",
" <td>28849.0</td>\n",
" <td>35</td>\n",
" <td>27.0</td>\n",
" <td>43.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>202520</td>\n",
" <td>3</td>\n",
" <td>20265</td>\n",
" <td>15814.0</td>\n",
" <td>24716.0</td>\n",
" <td>30</td>\n",
" <td>23.0</td>\n",
" <td>37.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>202519</td>\n",
" <td>3</td>\n",
" <td>16264</td>\n",
" <td>12394.0</td>\n",
" <td>20134.0</td>\n",
" <td>24</td>\n",
" <td>18.0</td>\n",
" <td>30.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>202518</td>\n",
" <td>3</td>\n",
" <td>18115</td>\n",
" <td>13975.0</td>\n",
" <td>22255.0</td>\n",
" <td>27</td>\n",
" <td>21.0</td>\n",
" <td>33.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>202517</td>\n",
" <td>3</td>\n",
" <td>22150</td>\n",
" <td>17291.0</td>\n",
" <td>27009.0</td>\n",
" <td>33</td>\n",
" <td>26.0</td>\n",
" <td>40.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>202516</td>\n",
" <td>3</td>\n",
" <td>28564</td>\n",
" <td>22550.0</td>\n",
" <td>34578.0</td>\n",
" <td>43</td>\n",
" <td>34.0</td>\n",
" <td>52.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>202515</td>\n",
" <td>3</td>\n",
" <td>35721</td>\n",
" <td>29592.0</td>\n",
" <td>41850.0</td>\n",
" <td>53</td>\n",
" <td>44.0</td>\n",
" <td>62.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>202514</td>\n",
" <td>3</td>\n",
" <td>37579</td>\n",
" <td>31232.0</td>\n",
" <td>43926.0</td>\n",
" <td>56</td>\n",
" <td>47.0</td>\n",
" <td>65.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>202513</td>\n",
" <td>3</td>\n",
" <td>39673</td>\n",
" <td>33686.0</td>\n",
" <td>45660.0</td>\n",
" <td>59</td>\n",
" <td>50.0</td>\n",
" <td>68.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>202512</td>\n",
" <td>3</td>\n",
" <td>52543</td>\n",
" <td>45627.0</td>\n",
" <td>59459.0</td>\n",
" <td>78</td>\n",
" <td>68.0</td>\n",
" <td>88.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>202511</td>\n",
" <td>3</td>\n",
" <td>59469</td>\n",
" <td>52154.0</td>\n",
" <td>66784.0</td>\n",
" <td>89</td>\n",
" <td>78.0</td>\n",
" <td>100.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>202510</td>\n",
" <td>3</td>\n",
" <td>60334</td>\n",
" <td>53048.0</td>\n",
" <td>67620.0</td>\n",
" <td>90</td>\n",
" <td>79.0</td>\n",
" <td>101.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>202509</td>\n",
" <td>3</td>\n",
" <td>84531</td>\n",
" <td>74994.0</td>\n",
" <td>94068.0</td>\n",
" <td>126</td>\n",
" <td>112.0</td>\n",
" <td>140.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>202508</td>\n",
" <td>3</td>\n",
" <td>136020</td>\n",
" <td>124824.0</td>\n",
" <td>147216.0</td>\n",
" <td>203</td>\n",
" <td>186.0</td>\n",
" <td>220.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>202507</td>\n",
" <td>3</td>\n",
" <td>208952</td>\n",
" <td>195988.0</td>\n",
" <td>221916.0</td>\n",
" <td>312</td>\n",
" <td>293.0</td>\n",
" <td>331.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>202506</td>\n",
" <td>3</td>\n",
" <td>273519</td>\n",
" <td>258159.0</td>\n",
" <td>288879.0</td>\n",
" <td>408</td>\n",
" <td>385.0</td>\n",
" <td>431.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>202505</td>\n",
" <td>3</td>\n",
" <td>334395</td>\n",
" <td>318416.0</td>\n",
" <td>350374.0</td>\n",
" <td>499</td>\n",
" <td>475.0</td>\n",
" <td>523.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2100</th>\n",
" <td>198521</td>\n",
" <td>3</td>\n",
" <td>26096</td>\n",
" <td>19621.0</td>\n",
" <td>32571.0</td>\n",
" <td>47</td>\n",
" <td>35.0</td>\n",
" <td>59.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2101</th>\n",
" <td>198520</td>\n",
" <td>3</td>\n",
" <td>27896</td>\n",
" <td>20885.0</td>\n",
" <td>34907.0</td>\n",
" <td>51</td>\n",
" <td>38.0</td>\n",
" <td>64.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2102</th>\n",
" <td>198519</td>\n",
" <td>3</td>\n",
" <td>43154</td>\n",
" <td>32821.0</td>\n",
" <td>53487.0</td>\n",
" <td>78</td>\n",
" <td>59.0</td>\n",
" <td>97.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2103</th>\n",
" <td>198518</td>\n",
" <td>3</td>\n",
" <td>40555</td>\n",
" <td>29935.0</td>\n",
" <td>51175.0</td>\n",
" <td>74</td>\n",
" <td>55.0</td>\n",
" <td>93.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2104</th>\n",
" <td>198517</td>\n",
" <td>3</td>\n",
" <td>34053</td>\n",
" <td>24366.0</td>\n",
" <td>43740.0</td>\n",
" <td>62</td>\n",
" <td>44.0</td>\n",
" <td>80.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2105</th>\n",
" <td>198516</td>\n",
" <td>3</td>\n",
" <td>50362</td>\n",
" <td>36451.0</td>\n",
" <td>64273.0</td>\n",
" <td>91</td>\n",
" <td>66.0</td>\n",
" <td>116.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2106</th>\n",
" <td>198515</td>\n",
" <td>3</td>\n",
" <td>63881</td>\n",
" <td>45538.0</td>\n",
" <td>82224.0</td>\n",
" <td>116</td>\n",
" <td>83.0</td>\n",
" <td>149.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2107</th>\n",
" <td>198514</td>\n",
" <td>3</td>\n",
" <td>134545</td>\n",
" <td>114400.0</td>\n",
" <td>154690.0</td>\n",
" <td>244</td>\n",
" <td>207.0</td>\n",
" <td>281.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2108</th>\n",
" <td>198513</td>\n",
" <td>3</td>\n",
" <td>197206</td>\n",
" <td>176080.0</td>\n",
" <td>218332.0</td>\n",
" <td>357</td>\n",
" <td>319.0</td>\n",
" <td>395.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2109</th>\n",
" <td>198512</td>\n",
" <td>3</td>\n",
" <td>245240</td>\n",
" <td>223304.0</td>\n",
" <td>267176.0</td>\n",
" <td>445</td>\n",
" <td>405.0</td>\n",
" <td>485.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2110</th>\n",
" <td>198511</td>\n",
" <td>3</td>\n",
" <td>276205</td>\n",
" <td>252399.0</td>\n",
" <td>300011.0</td>\n",
" <td>501</td>\n",
" <td>458.0</td>\n",
" <td>544.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2111</th>\n",
" <td>198510</td>\n",
" <td>3</td>\n",
" <td>353231</td>\n",
" <td>326279.0</td>\n",
" <td>380183.0</td>\n",
" <td>640</td>\n",
" <td>591.0</td>\n",
" <td>689.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2112</th>\n",
" <td>198509</td>\n",
" <td>3</td>\n",
" <td>369895</td>\n",
" <td>341109.0</td>\n",
" <td>398681.0</td>\n",
" <td>670</td>\n",
" <td>618.0</td>\n",
" <td>722.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2113</th>\n",
" <td>198508</td>\n",
" <td>3</td>\n",
" <td>389886</td>\n",
" <td>359529.0</td>\n",
" <td>420243.0</td>\n",
" <td>707</td>\n",
" <td>652.0</td>\n",
" <td>762.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2114</th>\n",
" <td>198507</td>\n",
" <td>3</td>\n",
" <td>471852</td>\n",
" <td>432599.0</td>\n",
" <td>511105.0</td>\n",
" <td>855</td>\n",
" <td>784.0</td>\n",
" <td>926.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2115</th>\n",
" <td>198506</td>\n",
" <td>3</td>\n",
" <td>565825</td>\n",
" <td>518011.0</td>\n",
" <td>613639.0</td>\n",
" <td>1026</td>\n",
" <td>939.0</td>\n",
" <td>1113.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2116</th>\n",
" <td>198505</td>\n",
" <td>3</td>\n",
" <td>637302</td>\n",
" <td>592795.0</td>\n",
" <td>681809.0</td>\n",
" <td>1155</td>\n",
" <td>1074.0</td>\n",
" <td>1236.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2117</th>\n",
" <td>198504</td>\n",
" <td>3</td>\n",
" <td>424937</td>\n",
" <td>390794.0</td>\n",
" <td>459080.0</td>\n",
" <td>770</td>\n",
" <td>708.0</td>\n",
" <td>832.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2118</th>\n",
" <td>198503</td>\n",
" <td>3</td>\n",
" <td>213901</td>\n",
" <td>174689.0</td>\n",
" <td>253113.0</td>\n",
" <td>388</td>\n",
" <td>317.0</td>\n",
" <td>459.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2119</th>\n",
" <td>198502</td>\n",
" <td>3</td>\n",
" <td>97586</td>\n",
" <td>80949.0</td>\n",
" <td>114223.0</td>\n",
" <td>177</td>\n",
" <td>147.0</td>\n",
" <td>207.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2120</th>\n",
" <td>198501</td>\n",
" <td>3</td>\n",
" <td>85489</td>\n",
" <td>65918.0</td>\n",
" <td>105060.0</td>\n",
" <td>155</td>\n",
" <td>120.0</td>\n",
" <td>190.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2121</th>\n",
" <td>198452</td>\n",
" <td>3</td>\n",
" <td>84830</td>\n",
" <td>60602.0</td>\n",
" <td>109058.0</td>\n",
" <td>154</td>\n",
" <td>110.0</td>\n",
" <td>198.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2122</th>\n",
" <td>198451</td>\n",
" <td>3</td>\n",
" <td>101726</td>\n",
" <td>80242.0</td>\n",
" <td>123210.0</td>\n",
" <td>185</td>\n",
" <td>146.0</td>\n",
" <td>224.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2123</th>\n",
" <td>198450</td>\n",
" <td>3</td>\n",
" <td>123680</td>\n",
" <td>101401.0</td>\n",
" <td>145959.0</td>\n",
" <td>225</td>\n",
" <td>184.0</td>\n",
" <td>266.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2124</th>\n",
" <td>198449</td>\n",
" <td>3</td>\n",
" <td>101073</td>\n",
" <td>81684.0</td>\n",
" <td>120462.0</td>\n",
" <td>184</td>\n",
" <td>149.0</td>\n",
" <td>219.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2125</th>\n",
" <td>198448</td>\n",
" <td>3</td>\n",
" <td>78620</td>\n",
" <td>60634.0</td>\n",
" <td>96606.0</td>\n",
" <td>143</td>\n",
" <td>110.0</td>\n",
" <td>176.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2126</th>\n",
" <td>198447</td>\n",
" <td>3</td>\n",
" <td>72029</td>\n",
" <td>54274.0</td>\n",
" <td>89784.0</td>\n",
" <td>131</td>\n",
" <td>99.0</td>\n",
" <td>163.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2127</th>\n",
" <td>198446</td>\n",
" <td>3</td>\n",
" <td>87330</td>\n",
" <td>67686.0</td>\n",
" <td>106974.0</td>\n",
" <td>159</td>\n",
" <td>123.0</td>\n",
" <td>195.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2128</th>\n",
" <td>198445</td>\n",
" <td>3</td>\n",
" <td>135223</td>\n",
" <td>101414.0</td>\n",
" <td>169032.0</td>\n",
" <td>246</td>\n",
" <td>184.0</td>\n",
" <td>308.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2129</th>\n",
" <td>198444</td>\n",
" <td>3</td>\n",
" <td>68422</td>\n",
" <td>20056.0</td>\n",
" <td>116788.0</td>\n",
" <td>125</td>\n",
" <td>37.0</td>\n",
" <td>213.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2130 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202534 3 23555 17312.0 29798.0 35 26.0 \n",
"1 202533 3 16937 12133.0 21741.0 25 18.0 \n",
"2 202532 3 19900 14303.0 25497.0 30 22.0 \n",
"3 202531 3 18470 12625.0 24315.0 28 19.0 \n",
"4 202530 3 19166 14283.0 24049.0 29 22.0 \n",
"5 202529 3 18673 13815.0 23531.0 28 21.0 \n",
"6 202528 3 23285 18131.0 28439.0 35 27.0 \n",
"7 202527 3 21453 17129.0 25777.0 32 26.0 \n",
"8 202526 3 21945 17422.0 26468.0 33 26.0 \n",
"9 202525 3 23323 18546.0 28100.0 35 28.0 \n",
"10 202524 3 23154 18577.0 27731.0 35 28.0 \n",
"11 202523 3 24391 19307.0 29475.0 36 28.0 \n",
"12 202522 3 18755 14333.0 23177.0 28 21.0 \n",
"13 202521 3 23760 18671.0 28849.0 35 27.0 \n",
"14 202520 3 20265 15814.0 24716.0 30 23.0 \n",
"15 202519 3 16264 12394.0 20134.0 24 18.0 \n",
"16 202518 3 18115 13975.0 22255.0 27 21.0 \n",
"17 202517 3 22150 17291.0 27009.0 33 26.0 \n",
"18 202516 3 28564 22550.0 34578.0 43 34.0 \n",
"19 202515 3 35721 29592.0 41850.0 53 44.0 \n",
"20 202514 3 37579 31232.0 43926.0 56 47.0 \n",
"21 202513 3 39673 33686.0 45660.0 59 50.0 \n",
"22 202512 3 52543 45627.0 59459.0 78 68.0 \n",
"23 202511 3 59469 52154.0 66784.0 89 78.0 \n",
"24 202510 3 60334 53048.0 67620.0 90 79.0 \n",
"25 202509 3 84531 74994.0 94068.0 126 112.0 \n",
"26 202508 3 136020 124824.0 147216.0 203 186.0 \n",
"27 202507 3 208952 195988.0 221916.0 312 293.0 \n",
"28 202506 3 273519 258159.0 288879.0 408 385.0 \n",
"29 202505 3 334395 318416.0 350374.0 499 475.0 \n",
"... ... ... ... ... ... ... ... \n",
"2100 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2101 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2102 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2103 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2104 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2105 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2106 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2107 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2108 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2109 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2110 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2111 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2112 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2113 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2114 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2115 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2116 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2117 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2118 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2119 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2120 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2121 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2122 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2123 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2124 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2125 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2126 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2127 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2128 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2129 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 44.0 FR France \n",
"1 32.0 FR France \n",
"2 38.0 FR France \n",
"3 37.0 FR France \n",
"4 36.0 FR France \n",
"5 35.0 FR France \n",
"6 43.0 FR France \n",
"7 38.0 FR France \n",
"8 40.0 FR France \n",
"9 42.0 FR France \n",
"10 42.0 FR France \n",
"11 44.0 FR France \n",
"12 35.0 FR France \n",
"13 43.0 FR France \n",
"14 37.0 FR France \n",
"15 30.0 FR France \n",
"16 33.0 FR France \n",
"17 40.0 FR France \n",
"18 52.0 FR France \n",
"19 62.0 FR France \n",
"20 65.0 FR France \n",
"21 68.0 FR France \n",
"22 88.0 FR France \n",
"23 100.0 FR France \n",
"24 101.0 FR France \n",
"25 140.0 FR France \n",
"26 220.0 FR France \n",
"27 331.0 FR France \n",
"28 431.0 FR France \n",
"29 523.0 FR France \n",
"... ... ... ... \n",
"2100 59.0 FR France \n",
"2101 64.0 FR France \n",
"2102 97.0 FR France \n",
"2103 93.0 FR France \n",
"2104 80.0 FR France \n",
"2105 116.0 FR France \n",
"2106 149.0 FR France \n",
"2107 281.0 FR France \n",
"2108 395.0 FR France \n",
"2109 485.0 FR France \n",
"2110 544.0 FR France \n",
"2111 689.0 FR France \n",
"2112 722.0 FR France \n",
"2113 762.0 FR France \n",
"2114 926.0 FR France \n",
"2115 1113.0 FR France \n",
"2116 1236.0 FR France \n",
"2117 832.0 FR France \n",
"2118 459.0 FR France \n",
"2119 207.0 FR France \n",
"2120 190.0 FR France \n",
"2121 198.0 FR France \n",
"2122 224.0 FR France \n",
"2123 266.0 FR France \n",
"2124 219.0 FR France \n",
"2125 176.0 FR France \n",
"2126 163.0 FR France \n",
"2127 195.0 FR France \n",
"2128 308.0 FR France \n",
"2129 213.0 FR France \n",
"\n",
"[2130 rows x 10 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n",
"if \"incidence-PAY-3.csv\" not in os.listdir(): \n",
" raw_data = pd.read_csv(data_url, skiprows=1)\n",
" raw_data.to_csv(\"incidence-PAY-3.csv\", index=False)\n",
" print(\"downloaded from source URL\")\n",
"else:\n",
" raw_data = pd.read_csv(\"incidence-PAY-3.csv\")\n",
" print(\"csv loaded from local\")\n",
"raw_data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['influenza-like-illness-analysis.org',\n",
" 'influenza-like-illness-analysis.Rmd',\n",
" 'influenza-like-illness-analysis.ipynb',\n",
" 'analyse-syndrome-grippal.Rmd',\n",
" 'analyse-syndrome-grippal.org',\n",
" 'analyse-syndrome-grippal.ipynb',\n",
" '.ipynb_checkpoints']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.listdir()"
]
},
{
"cell_type": "markdown",
"metadata": {},
......@@ -364,7 +1369,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.6.4"
}
},
"nbformat": 4,
......
module3/exo1/incidence-PAY-3.csv 0 → 100644
View file @ dcb2a5df
This source diff could not be displayed because it is too large. You can view the blob instead.
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