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module1/exo2/fichier-markdown.md
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test
\ No newline at end of file
# Partie 1
## Sous-partie 1 : texte
Une phrase sans rien
*Une phrase en italique*
**Une phrase en gras**
Un lien vers [fun-mooc.fr](https://www.fun-mooc.fr/)
Une ligne de `code`
## Sous-partie 2 : listes
**Liste à puce**
- item
- sous-item
- sous-item
- item
- item
**Liste numérotée**
1. item
2. item
3. item
## Sous-partie 3 : code
```
# Extrait de code
```
\ No newline at end of file
module2/exo1/toy_notebook_fr.ipynb
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module2/exo2/module2_exo2_exercice_fr.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercice 02"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data = [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]\n",
"data = np.array(data)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean = 14.11 \n",
"Min = 2.80 \n",
"Max = 23.40 \n",
"Median = 14.50 \n",
"Std = 4.33\n"
]
}
],
"source": [
"print(f\"Mean = {np.mean(data):.2f} \\nMin = {np.min(data):.2f} \\nMax = {np.max(data):.2f} \\nMedian = {np.median(data):.2f} \\nStd = {np.std(data,ddof=1):.2f}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
module2/exo3/module2_exo3_exercice_fr.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "70f7489d",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "46f791f7",
"metadata": {},
"outputs": [],
"source": [
"data = [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]\n",
"data = np.array(data)\n",
"\n",
"x = np.linspace(0, len(data), len(data))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "a7184947",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1200x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Séquence plot : \n",
"\n",
"fig1, ax1 = plt.subplots(1,2, figsize=(12,5))\n",
"\n",
"ax1[0].plot(data, color='b', linewidth=0.8)\n",
"ax1[0].set_xlim(0,100)\n",
"ax1[0].grid(linestyle =':', color='black')\n",
"\n",
"ax1[1].hist(data, bins=10, color='b', edgecolor='black', alpha=0.9)\n",
"ax1[1].grid(linestyle =':', color='black')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "94d73297",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
module3/exo1/analyse-syndrome-grippal.ipynb
View file @ 930c5855
......@@ -9,13 +9,19 @@
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 18,
=======
"execution_count": 1,
>>>>>>> 7e8841b694ad2d15a27c499d9ba1c34b8cda2f54
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import os\n",
"import requests\n",
"import isoweek"
]
},
......@@ -28,7 +34,11 @@
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 19,
=======
"execution_count": 2,
>>>>>>> 7e8841b694ad2d15a27c499d9ba1c34b8cda2f54
"metadata": {},
"outputs": [],
"source": [
......@@ -59,7 +69,11 @@
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 20,
=======
"execution_count": 3,
>>>>>>> 7e8841b694ad2d15a27c499d9ba1c34b8cda2f54
"metadata": {},
"outputs": [
{
......@@ -162,6 +176,7 @@
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
<<<<<<< HEAD
" <th>5</th>\n",
" <td>202451</td>\n",
" <td>3</td>\n",
......@@ -487,6 +502,8 @@
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
=======
>>>>>>> 7e8841b694ad2d15a27c499d9ba1c34b8cda2f54
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
......@@ -500,6 +517,7 @@
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
<<<<<<< HEAD
" <th>2070</th>\n",
" <td>198521</td>\n",
" <td>3</td>\n",
......@@ -825,6 +843,8 @@
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
=======
>>>>>>> 7e8841b694ad2d15a27c499d9ba1c34b8cda2f54
" <th>2095</th>\n",
" <td>198448</td>\n",
" <td>3</td>\n",
......@@ -901,6 +921,7 @@
"2 202502 3 257247 242991.0 271503.0 384 363.0 \n",
"3 202501 3 231549 214627.0 248471.0 345 320.0 \n",
"4 202452 3 201726 185870.0 217582.0 302 278.0 \n",
<<<<<<< HEAD
"5 202451 3 201697 187843.0 215551.0 302 281.0 \n",
"6 202450 3 136694 126369.0 147019.0 205 190.0 \n",
"7 202449 3 108487 99037.0 117937.0 163 149.0 \n",
......@@ -952,6 +973,9 @@
"2092 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2093 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2094 198449 3 101073 81684.0 120462.0 184 149.0 \n",
=======
"... ... ... ... ... ... ... ... \n",
>>>>>>> 7e8841b694ad2d15a27c499d9ba1c34b8cda2f54
"2095 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2096 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2097 198446 3 87330 67686.0 106974.0 159 123.0 \n",
......@@ -964,6 +988,7 @@
"2 405.0 FR France \n",
"3 370.0 FR France \n",
"4 326.0 FR France \n",
<<<<<<< HEAD
"5 323.0 FR France \n",
"6 220.0 FR France \n",
"7 177.0 FR France \n",
......@@ -1015,6 +1040,9 @@
"2092 224.0 FR France \n",
"2093 266.0 FR France \n",
"2094 219.0 FR France \n",
=======
"... ... ... ... \n",
>>>>>>> 7e8841b694ad2d15a27c499d9ba1c34b8cda2f54
"2095 176.0 FR France \n",
"2096 163.0 FR France \n",
"2097 195.0 FR France \n",
......@@ -1024,7 +1052,11 @@
"[2100 rows x 10 columns]"
]
},
<<<<<<< HEAD
"execution_count": 20,
=======
"execution_count": 3,
>>>>>>> 7e8841b694ad2d15a27c499d9ba1c34b8cda2f54
"metadata": {},
"output_type": "execute_result"
}
......@@ -1038,11 +1070,18 @@
"cell_type": "markdown",
"metadata": {},
"source": [
<<<<<<< HEAD
"Modification du code pour utiliser le fichier local contenant les données :"
=======
"Modification du code pour utiliser le fichier local contenant les données :\n",
"1. Vérifie si une copie locale des données existe déjà\n",
"2. Si elle n'existe pas, télécharge les données depuis le Réseau Sentinelles"
>>>>>>> 7e8841b694ad2d15a27c499d9ba1c34b8cda2f54
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 5,
"metadata": {},
"outputs": [
......@@ -1069,6 +1108,247 @@
],
"source": [
"raw_data = pd.read_csv('https://app-learninglab.inria.fr/moocrr/gitlab/5212fa3d0a7441c34b57f854081c7450/mooc-rr/blob/master/module3/exo1/inc-25-PAY.csv', encoding = 'iso-8859-1', skiprows=1)\n",
=======
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Téléchargement des données et sauvegarde locale.\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <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",
" <th>geo_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>202504</td>\n",
" <td>3</td>\n",
" <td>375118</td>\n",
" <td>356288.0</td>\n",
" <td>393948.0</td>\n",
" <td>560</td>\n",
" <td>532.0</td>\n",
" <td>588.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202503</td>\n",
" <td>3</td>\n",
" <td>253215</td>\n",
" <td>239337.0</td>\n",
" <td>267093.0</td>\n",
" <td>378</td>\n",
" <td>357.0</td>\n",
" <td>399.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202502</td>\n",
" <td>3</td>\n",
" <td>257247</td>\n",
" <td>242991.0</td>\n",
" <td>271503.0</td>\n",
" <td>384</td>\n",
" <td>363.0</td>\n",
" <td>405.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202501</td>\n",
" <td>3</td>\n",
" <td>231549</td>\n",
" <td>214627.0</td>\n",
" <td>248471.0</td>\n",
" <td>345</td>\n",
" <td>320.0</td>\n",
" <td>370.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202452</td>\n",
" <td>3</td>\n",
" <td>201726</td>\n",
" <td>185870.0</td>\n",
" <td>217582.0</td>\n",
" <td>302</td>\n",
" <td>278.0</td>\n",
" <td>326.0</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>2095</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>2096</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>2097</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>2098</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>2099</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>2100 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202504 3 375118 356288.0 393948.0 560 532.0 \n",
"1 202503 3 253215 239337.0 267093.0 378 357.0 \n",
"2 202502 3 257247 242991.0 271503.0 384 363.0 \n",
"3 202501 3 231549 214627.0 248471.0 345 320.0 \n",
"4 202452 3 201726 185870.0 217582.0 302 278.0 \n",
"... ... ... ... ... ... ... ... \n",
"2095 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2096 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2097 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2098 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2099 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 588.0 FR France \n",
"1 399.0 FR France \n",
"2 405.0 FR France \n",
"3 370.0 FR France \n",
"4 326.0 FR France \n",
"... ... ... ... \n",
"2095 176.0 FR France \n",
"2096 163.0 FR France \n",
"2097 195.0 FR France \n",
"2098 308.0 FR France \n",
"2099 213.0 FR France \n",
"\n",
"[2100 rows x 10 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"file_path = r\"C:\\Users\\33612\\Downloads\\inc-25-PAY.csv\"\n",
"\n",
"if not os.path.exists(file_path):\n",
" response = requests.get(data_url)\n",
" with open(file_path, \"wb\") as file:\n",
" file.write(response.content)\n",
" print(\"Téléchargement des données et sauvegarde locale.\")\n",
"else:\n",
" print(\"Données locales déjà existantes.\")\n",
"\n",
"raw_data = pd.read_csv(file_path, encoding = 'iso-8859-1', skiprows=1)\n",
>>>>>>> 7e8841b694ad2d15a27c499d9ba1c34b8cda2f54
"raw_data"
]
},
......@@ -2521,7 +2801,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
......@@ -2535,7 +2815,11 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
<<<<<<< HEAD
"version": "3.6.4"
=======
"version": "3.10.9"
>>>>>>> 7e8841b694ad2d15a27c499d9ba1c34b8cda2f54
}
},
"nbformat": 4,
......
module3/exo1/inc-25-PAY.csv 0 → 100644
View file @ 930c5855
# {"source":"r&#233;seau Sentinelles, INSERM, Sorbonne Universit&#233;, https:\/\/www.sentiweb.fr","meta":{"period":[202012,202504],"geo":["PAY",0],"geo_ref":"insee","indicator":"25","type":"all","conf_int":true,"compact":false,"age_group":false,"span":"all"},"date":"2025-01-29T17:06:29+01:00","bundle":"1738166770"}
week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
202504,25,479584,458288,500880,716,684,748,FR,France
202503,25,331873,315941,347805,495,471,519,FR,France
202502,25,341954,325501,358407,510,485,535,FR,France
202501,25,323517,303504,343530,483,453,513,FR,France
202452,25,288527,269519,307535,433,405,461,FR,France
202451,25,303406,286288,320524,455,429,481,FR,France
202450,25,224823,211490,238156,337,317,357,FR,France
202449,25,192040,179321,204759,288,269,307,FR,France
202448,25,167988,155804,180172,252,234,270,FR,France
202447,25,153124,140532,165716,230,211,249,FR,France
202446,25,110985,100589,121381,166,150,182,FR,France
202445,25,95469,86201,104737,143,129,157,FR,France
202444,25,78626,69865,87387,118,105,131,FR,France
202443,25,100509,90709,110309,151,136,166,FR,France
202442,25,132611,121676,143546,199,183,215,FR,France
202441,25,154693,143389,165997,232,215,249,FR,France
202440,25,160178,148549,171807,240,223,257,FR,France
202439,25,165030,153301,176759,247,229,265,FR,France
202438,25,172776,160575,184977,259,241,277,FR,France
202437,25,114404,104186,124622,172,157,187,FR,France
202436,25,69076,60823,77329,104,92,116,FR,France
202435,25,56945,49181,64709,85,73,97,FR,France
202434,25,54854,46661,63047,82,70,94,FR,France
202433,25,42722,35041,50403,64,52,76,FR,France
202432,25,51364,42728,60000,77,64,90,FR,France
202431,25,58547,49907,67187,88,75,101,FR,France
202430,25,78760,67253,90267,118,101,135,FR,France
202429,25,76144,66493,85795,114,100,128,FR,France
202428,25,103510,91761,115259,155,137,173,FR,France
202427,25,95397,85288,105506,143,128,158,FR,France
202426,25,93551,82989,104113,140,124,156,FR,France
202425,25,104489,94128,114850,157,141,173,FR,France
202424,25,93416,83247,103585,140,125,155,FR,France
202423,25,87657,79302,96012,131,118,144,FR,France
202422,25,83921,75089,92753,126,113,139,FR,France
202421,25,57374,50188,64560,86,75,97,FR,France
202420,25,58995,51600,66390,88,77,99,FR,France
202419,25,45311,38190,52432,68,57,79,FR,France
202418,25,60414,52520,68308,91,79,103,FR,France
202417,25,68239,59172,77306,102,88,116,FR,France
202416,25,67240,58605,75875,101,88,114,FR,France
202415,25,79320,70126,88514,119,105,133,FR,France
202414,25,80827,72223,89431,121,108,134,FR,France
202413,25,85838,77103,94573,129,116,142,FR,France
202412,25,87946,78990,96902,132,119,145,FR,France
202411,25,96942,87243,106641,145,130,160,FR,France
202410,25,108432,98329,118535,163,148,178,FR,France
202409,25,116863,106317,127409,175,159,191,FR,France
202408,25,158832,146426,171238,238,219,257,FR,France
202407,25,196928,183754,210102,295,275,315,FR,France
202406,25,264450,250119,278781,396,375,417,FR,France
202405,25,301224,286226,316222,452,430,474,FR,France
202404,25,312596,297185,328007,469,446,492,FR,France
202403,25,260433,246176,274690,390,369,411,FR,France
202402,25,223175,209907,236443,335,315,355,FR,France
202401,25,207680,192579,222781,311,288,334,FR,France
202352,25,204801,189186,220416,308,285,331,FR,France
202351,25,281235,264279,298191,423,397,449,FR,France
202350,25,281168,265632,296704,423,400,446,FR,France
202349,25,286802,271137,302467,431,407,455,FR,France
202348,25,247301,232152,262450,372,349,395,FR,France
202347,25,225044,210484,239604,339,317,361,FR,France
202346,25,170637,158010,183264,257,238,276,FR,France
202345,25,150125,137230,163020,226,207,245,FR,France
202344,25,108608,98016,119200,163,147,179,FR,France
202343,25,105159,94706,115612,158,142,174,FR,France
202342,25,123120,111942,134298,185,168,202,FR,France
202341,25,126477,115697,137257,190,174,206,FR,France
202340,25,145262,132323,158201,219,200,238,FR,France
202339,25,156932,144286,169578,236,217,255,FR,France
202338,25,140770,128842,152698,212,194,230,FR,France
202337,25,109440,98918,119962,165,149,181,FR,France
202336,25,90802,81444,100160,137,123,151,FR,France
202335,25,75353,66521,84185,113,100,126,FR,France
202334,25,62298,53692,70904,94,81,107,FR,France
202333,25,51315,41862,60768,77,63,91,FR,France
202332,25,44411,36803,52019,67,56,78,FR,France
202331,25,45046,37089,53003,68,56,80,FR,France
202330,25,43645,35319,51971,66,53,79,FR,France
202329,25,36079,28820,43338,54,43,65,FR,France
202328,25,32466,25871,39061,49,39,59,FR,France
202327,25,32116,25876,38356,48,39,57,FR,France
202326,25,29974,24339,35609,45,37,53,FR,France
202325,25,33506,27383,39629,50,41,59,FR,France
202324,25,36369,29696,43042,55,45,65,FR,France
202323,25,42752,36381,49123,64,54,74,FR,France
202322,25,50539,43068,58010,76,65,87,FR,France
202321,25,48716,41316,56116,73,62,84,FR,France
202320,25,44849,37807,51891,67,56,78,FR,France
202319,25,48729,41382,56076,73,62,84,FR,France
202318,25,51222,43946,58498,77,66,88,FR,France
202317,25,65181,56998,73364,98,86,110,FR,France
202316,25,67943,59918,75968,102,90,114,FR,France
202315,25,82866,73291,92441,125,111,139,FR,France
202314,25,98529,87900,109158,148,132,164,FR,France
202313,25,119724,108822,130626,180,164,196,FR,France
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202311,25,129390,118530,140250,195,179,211,FR,France
202310,25,131370,120657,142083,198,182,214,FR,France
202309,25,107669,98011,117327,162,147,177,FR,France
202308,25,124258,113620,134896,187,171,203,FR,France
202307,25,142739,130843,154635,215,197,233,FR,France
202306,25,159334,147000,171668,240,221,259,FR,France
202305,25,152820,141240,164400,230,213,247,FR,France
202304,25,121411,111309,131513,183,168,198,FR,France
202303,25,114848,105018,124678,173,158,188,FR,France
202302,25,130074,119576,140572,196,180,212,FR,France
202301,25,193195,179660,206730,291,271,311,FR,France
202252,25,232035,215718,248352,350,325,375,FR,France
202251,25,372445,352616,392274,562,532,592,FR,France
202250,25,382405,363398,401412,577,548,606,FR,France
202249,25,296183,280349,312017,447,423,471,FR,France
202248,25,235044,221092,248996,354,333,375,FR,France
202247,25,199061,185808,212314,300,280,320,FR,France
202246,25,147262,135893,158631,222,205,239,FR,France
202245,25,103238,93558,112918,156,141,171,FR,France
202244,25,86318,77100,95536,130,116,144,FR,France
202243,25,116462,103782,129142,176,157,195,FR,France
202242,25,120953,110276,131630,182,166,198,FR,France
202241,25,123539,113639,133439,186,171,201,FR,France
202240,25,117976,108270,127682,178,163,193,FR,France
202239,25,115474,105715,125233,174,159,189,FR,France
202238,25,82053,73524,90582,124,111,137,FR,France
202237,25,62482,55099,69865,94,83,105,FR,France
202236,25,43450,37073,49827,66,56,76,FR,France
202235,25,33304,27967,38641,50,42,58,FR,France
202234,25,32173,26435,37911,49,40,58,FR,France
202233,25,29216,23034,35398,44,35,53,FR,France
202232,25,40805,32605,49005,62,50,74,FR,France
202231,25,42848,35020,50676,65,53,77,FR,France
202230,25,49485,41725,57245,75,63,87,FR,France
202229,25,61242,52724,69760,92,79,105,FR,France
202228,25,72660,63141,82179,110,96,124,FR,France
202227,25,105803,95020,116586,160,144,176,FR,France
202226,25,86210,77443,94977,130,117,143,FR,France
202225,25,66068,58718,73418,100,89,111,FR,France
202224,25,53312,46832,59792,80,70,90,FR,France
202223,25,48915,42661,55169,74,65,83,FR,France
202222,25,44745,38871,50619,67,58,76,FR,France
202221,25,35684,30219,41149,54,46,62,FR,France
202220,25,46874,40696,53052,71,62,80,FR,France
202219,25,42681,36818,48544,64,55,73,FR,France
202218,25,61938,54472,69404,93,82,104,FR,France
202217,25,75024,66766,83282,113,101,125,FR,France
202216,25,103633,93419,113847,156,141,171,FR,France
202215,25,178168,164846,191490,269,249,289,FR,France
202214,25,250778,236085,265471,378,356,400,FR,France
202213,25,288803,273650,303956,436,413,459,FR,France
202212,25,247537,233872,261202,373,352,394,FR,France
202211,25,183347,171632,195062,276,258,294,FR,France
202210,25,140366,129989,150743,212,196,228,FR,France
202209,25,82748,74732,90764,125,113,137,FR,France
202208,25,61352,54240,68464,93,82,104,FR,France
202207,25,70083,62324,77842,106,94,118,FR,France
202206,25,94774,85822,103726,143,130,156,FR,France
202205,25,135656,125440,145872,205,190,220,FR,France
202204,25,144768,134227,155309,218,202,234,FR,France
202203,25,152651,141883,163419,230,214,246,FR,France
202202,25,131009,121146,140872,198,183,213,FR,France
202201,25,140289,129234,151344,212,195,229,FR,France
202152,25,119284,108280,130288,181,164,198,FR,France
202151,25,95962,86205,105719,145,130,160,FR,France
202150,25,98252,89107,107397,149,135,163,FR,France
202149,25,97448,88847,106049,148,135,161,FR,France
202148,25,102691,94009,111373,155,142,168,FR,France
202147,25,95781,87247,104315,145,132,158,FR,France
202146,25,85173,77239,93107,129,117,141,FR,France
202145,25,60241,53646,66836,91,81,101,FR,France
202144,25,57800,50959,64641,87,77,97,FR,France
202143,25,66246,58313,74179,100,88,112,FR,France
202142,25,85033,76428,93638,129,116,142,FR,France
202141,25,75193,67494,82892,114,102,126,FR,France
202140,25,72779,65395,80163,110,99,121,FR,France
202139,25,64110,57120,71100,97,86,108,FR,France
202138,25,46691,40979,52403,71,62,80,FR,France
202137,25,38158,32682,43634,58,50,66,FR,France
202136,25,26963,22369,31557,41,34,48,FR,France
202135,25,24219,19591,28847,37,30,44,FR,France
202134,25,28967,23539,34395,44,36,52,FR,France
202133,25,25885,20391,31379,39,31,47,FR,France
202132,25,30016,23788,36244,45,36,54,FR,France
202131,25,39744,32220,47268,60,49,71,FR,France
202130,25,35434,28666,42202,54,44,64,FR,France
202129,25,33514,27312,39716,51,42,60,FR,France
202128,25,24111,18384,29838,36,27,45,FR,France
202127,25,25266,19878,30654,38,30,46,FR,France
202126,25,20220,15692,24748,31,24,38,FR,France
202125,25,16520,13155,19885,25,20,30,FR,France
202124,25,16195,12787,19603,25,20,30,FR,France
202123,25,17284,13767,20801,26,21,31,FR,France
202122,25,21595,17630,25560,33,27,39,FR,France
202121,25,25571,21204,29938,39,32,46,FR,France
202120,25,28635,24051,33219,43,36,50,FR,France
202119,25,22076,18039,26113,33,27,39,FR,France
202118,25,19490,15721,23259,30,24,36,FR,France
202117,25,24090,19722,28458,36,29,43,FR,France
202116,25,38224,32624,43824,58,50,66,FR,France
202115,25,46758,40754,52762,71,62,80,FR,France
202114,25,51604,45378,57830,78,69,87,FR,France
202113,25,65268,58302,72234,99,88,110,FR,France
202112,25,75578,68194,82962,114,103,125,FR,France
202111,25,57271,50883,63659,87,77,97,FR,France
202110,25,50943,45071,56815,77,68,86,FR,France
202109,25,41917,36224,47610,63,54,72,FR,France
202108,25,51836,45450,58222,78,68,88,FR,France
202107,25,57614,50967,64261,87,77,97,FR,France
202106,25,61368,54487,68249,93,83,103,FR,France
202105,25,62174,55528,68820,94,84,104,FR,France
202104,25,72814,65305,80323,110,99,121,FR,France
202103,25,61740,55001,68479,93,83,103,FR,France
202102,25,50443,44430,56456,76,67,85,FR,France
202101,25,56078,49481,62675,85,75,95,FR,France
202053,25,42094,35402,48786,64,54,74,FR,France
202052,25,35747,29356,42138,54,44,64,FR,France
202051,25,37600,32121,43079,57,49,65,FR,France
202050,25,43682,37897,49467,66,57,75,FR,France
202049,25,36337,31052,41622,55,47,63,FR,France
202048,25,44146,38122,50170,67,58,76,FR,France
202047,25,43834,37975,49693,67,58,76,FR,France
202046,25,58132,51454,64810,88,78,98,FR,France
202045,25,94838,86206,103470,144,131,157,FR,France
202044,25,106153,96692,115614,161,147,175,FR,France
202043,25,103830,94353,113307,158,144,172,FR,France
202042,25,96541,87664,105418,147,134,160,FR,France
202041,25,81954,73921,89987,124,112,136,FR,France
202040,25,67761,60310,75212,103,92,114,FR,France
202039,25,68999,61350,76648,105,93,117,FR,France
202038,25,88638,80122,97154,135,122,148,FR,France
202037,25,56454,49776,63132,86,76,96,FR,France
202036,25,24149,19991,28307,37,31,43,FR,France
202035,25,19831,15528,24134,30,23,37,FR,France
202034,25,11690,7876,15504,18,12,24,FR,France
202033,25,10848,7145,14551,16,10,22,FR,France
202032,25,11409,7422,15396,17,11,23,FR,France
202031,25,13366,9412,17320,20,14,26,FR,France
202030,25,21428,16609,26247,33,26,40,FR,France
202029,25,22240,17578,26902,34,27,41,FR,France
202028,25,17581,13715,21447,27,21,33,FR,France
202027,25,10817,7957,13677,16,12,20,FR,France
202026,25,7126,4846,9406,11,8,14,FR,France
202025,25,7053,4747,9359,11,7,15,FR,France
202024,25,5269,3493,7045,8,5,11,FR,France
202023,25,6768,4615,8921,10,7,13,FR,France
202022,25,8748,6192,11304,13,9,17,FR,France
202021,25,14651,11353,17949,22,17,27,FR,France
202020,25,28416,23875,32957,43,36,50,FR,France
202019,25,27347,22880,31814,42,35,49,FR,France
202018,25,34618,29499,39737,53,45,61,FR,France
202017,25,45903,40032,51774,70,61,79,FR,France
202016,25,60969,54118,67820,93,83,103,FR,France
202015,25,95364,86665,104063,145,132,158,FR,France
202014,25,213772,201334,226210,325,306,344,FR,France
202013,25,297819,283636,312002,452,430,474,FR,France
202012,25,273062,259400,286724,415,394,436,FR,France
module3/exo2/exercice_fr.ipynb
View file @ 930c5855
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module3/exo3/exercice_fr.ipynb
View file @ 930c5855
......@@ -1094,7 +1094,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 11,
"metadata": {},
"outputs": [
{
......@@ -1146,7 +1146,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
......@@ -1162,7 +1162,7 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
......@@ -1178,7 +1178,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
......@@ -1198,7 +1198,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
......@@ -1207,7 +1207,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 16,
"metadata": {},
"outputs": [
{
......@@ -1236,7 +1236,7 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 17,
"metadata": {},
"outputs": [
{
......@@ -1271,7 +1271,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 18,
"metadata": {},
"outputs": [
{
......@@ -1925,7 +1925,7 @@
"[3358 rows x 6 columns]"
]
},
"execution_count": 19,
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
......@@ -1937,7 +1937,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
......@@ -1957,7 +1957,7 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 20,
"metadata": {},
"outputs": [
{
......@@ -1984,7 +1984,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 21,
"metadata": {
"scrolled": true
},
......@@ -2015,7 +2015,7 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 22,
"metadata": {},
"outputs": [
{
......
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