e6cfd8a10624671909b6755e01761c6e · e6cfd8a10624671909b6755e01761c6e · e6cfd8a10624671909b6755e01761c6e · e6cfd8a10624671909b6755e01761c6e · e6cfd8a10624671909b6755e01761c6e · e6cfd8a10624671909b6755e01761c6e
14 changed files
--- a/module1/README.md
+++ b/module1/README.md
 Module 1
+je suis dans GitLab
\ No newline at end of file
--- a/module1/exo2/fichier-markdown.md
+++ b/module1/exo2/fichier-markdown.md
-test
+# Partie 1
\ No newline at end of file
+## 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
--- a/module2/exo1/Tutoriel.ipynb
+++ b/module2/exo1/Tutoriel.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titre du document"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2+2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Un petit exemple "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 100, 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "x=np.random.normal(loc=mu, scale=sigma, size= 10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "%matplotlib inline \n",
+    "plt.hist(x)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Utilisaton d'autre language "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%R\n",
+    "plot(cars)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": 4
+}
--- a/module2/exo1/Untitled.ipynb
+++ b/module2/exo1/Untitled.ipynb
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--- a/module2/exo1/toy_notebook_fr.ipynb
+++ b/module2/exo1/toy_notebook_fr.ipynb
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# À propos du calcul de $\\pi$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## En demandant à la lib maths\n",
+    "Mon ordinateur m'indique que $\\pi$ vaut *approximativement* "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.141592653589793\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math import *\n",
+    "print(pi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## En utilisant la méthodé des aiguilles de Buffon\n",
+    "Mais calculé avec la __méthode__ des [aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon), on obtiendrait comme __approximation__ : "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.128911138923655"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.random.seed(seed=42)\n",
+    "N = 10000\n",
+    "x = np.random.uniform(size=N, low=0, high=1)\n",
+    "theta = np.random.uniform(size=N, low=0, high=pi/2)\n",
+    "2/(sum((x+np.sin(theta))>1)/N)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Avec un argument \"fréquentiel\" de surface\n",
+    "Sinon, une méthode plus simple à comprendre et ne faisant pas intervenir d'appel à la fonction sinus se base sur le fait que si  $X\\sim U(0,1)$ et $Y\\sim U(0,1)$ alors $P[ X^2 + Y^2 \\leq 1] = \\pi /4$ (voir [méthode de Monte Carlo sur Wikipedia](https://fr.wikipedia.org/wiki/M%C3%A9thode_de_Monte-Carlo#D%C3%A9termination_de_la_valeur_de_%CF%80). Le code suivant illustre ce fait :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "np.random.seed(seed=42)\n",
+    "N = 1000\n",
+    "x = np.random.uniform(size=N, low=0, high=1)\n",
+    "y = np.random.uniform(size=N, low=0, high=1)\n",
+    "\n",
+    "accept = (x*x+y*y) <= 1\n",
+    "reject = np.logical_not(accept)\n",
+    "\n",
+    "fig, ax = plt.subplots(1)\n",
+    "ax.scatter(x[accept], y[accept], c='b', alpha=0.2, edgecolor=None)\n",
+    "ax.scatter(x[reject], y[reject], c='r', alpha=0.2, edgecolor=None)\n",
+    "ax.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il est alors aisé d'obtenir une approximation (pas terrible) de $\\pi$ en comptant de fois, en moyenne, $X^2 + Y^2$ est inférieur à 1 :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.112"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4*np.mean(accept)"
+   ]
+  }
+ ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
@@ -16,10 +154,9 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/module2/exo2/exercice.ipynb
+++ b/module2/exo2/exercice.ipynb
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Liste des données\n",
+    "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, \n",
+    "        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, \n",
+    "        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, \n",
+    "        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, \n",
+    "        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, \n",
+    "        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": [
+    {
+     "data": {
+      "text/plain": [
+       "14.113000000000001"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calcul des statistiques\n",
+    "moyenne = np.mean(data)\n",
+    "moyenne"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.334094455301447"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ecart_type = np.std(data, ddof=1)\n",
+    "ecart_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.5"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mediane = np.median(data)\n",
+    "mediane"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.8"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "minimum = np.min(data)\n",
+    "minimum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23.4"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "maximum = np.max(data)\n",
+    "maximum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f8ffa1093c8>]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ICCEoWdIO67TovokBzk0mzo7smmXIr1RbPZ8LBHPvk0QY9B3we393Dvfef37Qh9E3qk3NNjKj1kpW0w382l894enLApgG/WDFNOjcLJd6B3MeDz25dTP9/PZjUyjJWW6Qdie4jTjLcjEMiqamo2QFCN07gU3LuHkKi3J91tAj+rlcWDd3h6+9aR6droHFjWCmC+tFA5g6+lZLQ0N1Oi3ar2Ote7OpYrKQQ9anae/kBr60U4OuG8iJoOjwsVprj5xhi+LRy1v4zb89G/rzRVeqWFy9cFg4u1TDR7/xHL76lHfsYb3TxfyEKbn4syvamo5au2tq6Amm7zEeWayCEOD5C5P7I7lYRlzOZuyv210dlAJFVwofy/SptjQQ4gyaAJBo7IA3LDmq4+IlSz9//S3zAPiyy1bT8dBNyUX19EJnlJSsneXil1vcx9RLajIMipWaadCvxjDolFKz26IIig4Xmm5gra6mZhI8AHzu0SX8wVeeDdWJWUAUGD0PfdOeYuN4rWwrzppU+Q0o8+Zny7ITJEsoNxkAvr9YxXVzZUzkc6YHqfKrV+NSbWrISQSHJ/Oe4CDgeKvuG8dWU8VEPucZjZZo2qJ1DilS0EOvhXjo+VwGL7v+AADg3Eow06XaUm0PfaqQQ0PV7bUXfQa91jY9dPeOhGHHDnqc5xtN1W6pzPT9KNhzhYY+ZKRtig1gzpM0/+d7hqPsodtTbNxjyXxzJv2VmewznqsoyEkEhCSjJQOm5/bIYhW3H5sCYBqcsOrVuGy1zKrPqYITHHQGPTgpfO4slymfcbM99AQLi9zGbSIf7qFfXG/gxEwJlXwOR6cK3Fz0alOzYwBsLcxzLituycVcdy8PvdeNzB0IvbrV26Dz1pwkwqDHZKXG5kymyaBbjapCDPrlzSYmC6b3OGo3MuaZb7o8dCY1lJWsx7AxWGOu2bICQkii49iuVFtYb6i4zTLo+9EbnPVlmXCl77FgI9PQ3TeyTZcxZPRdQ1dMI8xzKi6uN+2S/hvmywHJpa3p6HQNR0O31sIMOruJma9jxih4nRaB+EFRZtAXpgu4Wu2d5aJZf09MLBoyrlmd8NJSYAM4HnqYEVncaOHYTCHxsWRJwAJ+bg+d3bhKitmoqu7LrHB76IDTeTAJ2M2DtRhg3uReAqObTRVTxRymirIdFGWSSyFEcgnP9khOcuEHRb0NxQyD4uJGEydnzZL+G+crOL/aQNclgbHP1slyMf+/bBn0kuyVXOpWHnqkht7LoFt24EUnpnGl2uKmW3rWLDz04WTF+iDT5KEzQ77d5rcvXdxs4th0EfmcZJeUjwosJc9dbMPWW85nuSXnTEM/UDINepLTe5iU4x/2sJfAaLWpYbIgY7KQtSUn9+Qe9n+k5JKkh65ba5aCQVG/h7683YbaNXB8hnnoFai6YVeOAq7WBSwPvcAkF/NaLSneLJe1egdtzQhUiQLxK2SXt9qQMgS3LUxB7RpY9w0TCayZk3ufJMKgx2QllRp6uORiGGYO+rGZYuKT4JOAeW/MUweCk+B5QdGpYs42sknuTPyNqni9ynfKVkvDdDFnp+9RSm2Pv+iRXFjpvxbqoSfhuISV/gPBdV+0UhZZ060z82UAwDmX7FL1DXv2a+glX1CUncP+Pi6Aq/Q/hoc+V1ZwzLrR9Mp04WX2JIkw6DFxJJfRMmxRMIPO0y9X6x2oXQML0wXTUx21oCgny4UZcDY42G9EWJUoI8kbmd+48XqV75SqlcI3VZChGxT1Ttfx0FlQVDYNmzmZKaihZ6UMshnSN8lFyhAUZSngVLCSf6ahX3/QNOhPLTuZLlVXHxfA8dR5Bt2dwsjz0ONKLte225ifzNsDUHrlotuZPcKgDxfMQ9cN6tHxRpntCA2dFXHYksuI3cg2LQ/d3XWw7vfQ1aCHPlt2V01KieWh+z230h499Lamo6XpmCrKtoHbammctEXz/2WrfN0vuQDWjSyBdbM1K76qSV6DrosbTeQkgiOW4SzKWRybKeDpFcdDZ60L2Hor+SwIcZwv1uqAvQbD3wsdiJ+HvrTVxuEJx6D38tA1XQRFB0Knq0cGOK5tOxWHSeYm9wvD8uAAvhFhJf92UDRBD103qO1J7hfMM2+quu1t2h66NezB7xX6PfREJ8Hbnpu/gdTu3md/10HA9NjZ++ouLAKcz5dr0HPJBIPDAoSVfDaQh35xvYFj00VPjvyZgxWv5NLySi6ZDMFkIQeDmjewjOt33d46L8vFSdeMfv+vbbVxaDKPiUIWJVnq7aGLoGj/oZTi1b/5VXzy25dCn7PimveYhsCoWcRifs0LijIPfWG6mNgFzvi/7nsML/jVL+A9H3kQn/jWBTvbZC9UW5qtB7snwQNOlovfeK7UOnZbAACJ3siCkou3cdZOsQc3uDz07ZZmr7GQc7JcAKcohskUbnYSO9hua9weKzx4kgsAlDkdFy+sOSmLjBvmK3hurWF7vdWmZg+aZrC1u1MWAW9O+m4rReudLmqdLuYn8iCE4Oh0oWdxkQiKDgBVN7C01caFtQb355puRrMPWulsadDR3RcQLyi6uNnEXEVBPichn80kWli0tNVGScni8mYL/+q+x/ET935rT39PNyi2WhqO+8aS1TtdZDNmfnlFyULVDfuCa3S6aKo6Dk64NPQEb2TMYLILnWncu5Vc3AFC5rFWWxpamo58LmN7uraHXo3w0GPGDjTdwKt+4yv4xLcuxDpGtWsgmyEerxswG3TVXU4FpRSXNpo4YQVEGTcdqkDTqV1gtNUyg7qEOH+PBXndGS7m9+a6MwSYyHMklxjBYJaDfthq7nZkqtCzuCjsJpYUwqDDnFsIIHTbzzxGlkKVhs6DboPOC4oubrRwzJrao+SkRHclatfA9QfL+PIvvwo/ec9xj7y1G7ZbGih1MiRYcVHDGnrAuu+xxwAnRsJu2kCyQVFbT7bSBDNWcHDXHrpbcnFp6O42sgDHQw+RH+Kc48tbbWw2NZwNmSbkJ6wvuF9DX2+oqHe6AQ/95TfMghDgi0+YHU+rLc3Tyx1wiotKAQ/d/H66KHukGEZOIvYxhsG0+fkJ06AfnepdXKQKDb3/sMBRmH7JLnaWqsTyaUeZWjsYLHSzuNm015u0h67qhlVqT3CgpKDe6cbexvNgxu3ULPPQnSk27ML2ByGZpOaWXMyxZMmmLbq34iVOoDYuW64iG2bQTQ1dR9HlrTLPlfW596ctAiz/vve6mSy3k66DXIOe99YEsAyXkz4Pfbas4MUnZ/C3jy8DsBpz+Y4/zEO3DTpHbgEQqzKYeeiHXB76RkONjP+ILJcB4Bh0/gfD7szMYx21IhsezCvP5zIByaVrSVALtoeebB66phu2B1PZhxJ45pGfmjVT3TxDD3xTbOp+D32iTx66FjJfc5dB0U3XOLlCToIsZbgeOls3M8ITHIOuxAwGs14/S3EHPYSMYisr3qAoy0H3e+gA8KZbD+Hscg0X1hqexlwMJiG5g6Du73k56IxehWSsSvSQy0MHEOmlC8llADDJJcw7Yt7bgu2hj75BZ4HQI5OFQFB0aasN3aA4Nm2uN8kSeMA86ZkHw/TNWkj1ahyYt3rS8tA3bYOueyomzceiJJfk1q3qpnHzZmJESy6PX92yW7f6qbbMTotFWQIhxO7n0tJ0u+zffA1z3ctbbUzkswE9G7Bu4DGcFtaNc6naitUlMkxymbCmFrG/cX61ASlDsDAdNOhvfN4hAOawmSqnFw0z8H7JpWR76MEbGKPXjWx5q43JQs5+P9kg8ajURZHlMgDYlol56n5Wah1kiGn8gHRp6EemCgFvmHletuSSS1Zy4XnoUXMme8G81cOTZlEUT3IJeuhtyFLG4/ElneXiv8hLcvjA5Fpbwzv+07fw4c8+wf05K/tnAcIpa2BymIfeNWio/BD3RsbOk4aqY7vV+/PqREgulDrX39nlGk7NlrjPPTpVwAsWJvH5x5ex1dQCHvpkD8mFl+HCkKVMdFB0u2175wDsHPkoyUnjSGtJIgw6gKbWW3KZqyj2nXnUGlXxYAbz8GQ+YESWrC0k21IqWQndBAuqNJ26DHp49724bNopfDlMh8yZ9Od9r9bM0XPujIm40gMArNc7uP/p1d5PtOh09WD6XsSQi7985Cqaqo6/f3rVNhJuzNa5jnFj5f9snihDyToZLzz9nD0nzjl+2ZWyF6uVbKjk4v3Mn7q2jZsOVUL/zhtvPYTvXaqi1ukGsnSYx+5PW5QyBNcfLOOmQxOhf7dXVtOylYPOmK8okDIk2kNn3RaFh94/Wj2Cote2zfzkuP0eho1nVmp48Py657Fa28zhnasoqLW9gxU2rIZDB6yqSSVGju5ecHurjoe+F8lFBbHS06aLciDLBXA8ONbljxl0N0xDjyMnfOybF/BTf/rt2AVSPOMWNrWIUopPPXgJSjaDeqeL71zYCDyn2tQ8gxtYT3S/QSeE2K10J0P05Lixg8WNJm6wSvKXYhp0XnDQ3XGx3ulicaOFmw+HG943WbILELwpse/LPg0dAP7ug6/C+156MvTvmh56+I3M76FnpQwOTeQjPXS7Ilh46P2jp4Ze62B+oj9zJpPgd//uHP63P/++57F6p4tKPotKPgfdoJ5A73pDRU5yCjbyVlFKUsFgdd8lF3MrzioH3Xno5UBQ1PzsV7Y7Hv0cMD02Sp2pM1Fc2WzBoPGm2ACWccsFDTovKPr9y1t4cmkbH3z9GchSJjBWD2Brdgy046F3PZN7AGft4R5677TFtqZjpdbBXadmAABXYvQGD9PQ2aDoWrtr55jfOB/uoV83V7ZvJH4NnXnsRZ/kEgczq4m/bnNiWQfzLg8dAI5M5SM/c1FYNACY5NJUda43trLdxlwlH3tM1bCxXu9gZbvjWVutzQx60CPetKa6MPkhybFkgHmxyFYecGUfgqLmmDHzQp8uyqi2VLPzoBoVFG17MlwA7GgMHcv0uBwzha/D8dDLIUHRTz14EYWchHfdfRx3n57Bl8+uBJ6z1fRKLhOFHLaaZqVoSeYX2fBGsQEsqyn6s2ZyywuPTyObIViKse6otEXAvOGeXd4GANx0ONygA46X7s9Dt7Nc5KCH3osoiW2l1gGlTlER42iP4iKWksvLfU8CYdABtCzPXDdowPtmVaLzE4prTFXyBr3R6eJ3vvBU7BzfKKpNDZ2u4dHKa20NFSXnGHRfYYe73wXzJJPy0DWO5LId4aGvbLfxH750LjRXfcs1CX6qmMOmtX7doLYxy0kZKNkMGp0u1K6BzabmyUEHXL3BYwRGmeRweQceeiAoqmTR0nTPurbbGv7q+0t4621HUMnn8OobD+KZlbqdA86otrw52VPFHGqdrpXlws/4CJNcZKm35MICoicOFDE/kY+VuhimoVdcY+ieWq6homTt+E0YP/rCBdxyeAI3+zTxo1NFvOjENO44PtXzePzIEU3J7Bz0Cb+HXsCylRXGQ+safSsqAoRBB+DNbvF7SKxKdH4in7inythqavjJjzyIf//lZ/BFq4hiLzANeb3utJLd9nno7lz0zaZq6+cAkM8mGwx2Sy55K4c6SnL55IOX8NtffBpPXN3m/tw9CHiqKGOrqXk6LTJY/vNqPZiyCMQfekAptQ1abMlFD+rJdqDWJf3d970raGk63nX3cQDAa286CAD4ylOOl97p6miquidrxZ394ffQe0ouMeoOLm84mVBHpvI9uw4C0ZWigOlUnF2q4cZDFU9wmsfJ2RL+xwde4QlSAuZkps/8o5fiBQu7M+hhHrq/SpRxsKJA06ndHM1P2K4kKYRBh6OhA8HUxWt2BaFie2xJSi4rtTZ+/N5v4bErWwD2VmADmMaGZX2wiTwAk1xy3JmOGyEeehLpmpRST5YLYFUOdsIllwesAO8TS1vcn5sBQia55KDqhn1j9g89aHS6gdFzDLsDX4/Pm+2AAKcCsxcdje+hA16n4tPfXsQthyfwgoVJAMCp2RJOHijiKy7Zxd9GFvD2aPFr6CwgzOvjApjr7tUm+vJmC3I2g7mygsOTvXuaAMy4BbXtiuscfHJ5u6fckhT+tMUvPXkN/+dfPoovn72GC1b1ql9y6SURhu1KkkIYdPg8dF9gdMXloccdJLtb2pqOH/+jB3DfdbZYAAAgAElEQVRxvYmP/tSLTU91jwa9pen2Seo16Boq1ig2wDvTcaOh4kDJ2xecHd9+wwKObuNWyWdDPfS2puN7i1UAwOMhHnq16fT4YEaLSSFlxVtk0+h0uWX/7mPqdQN3yw1xJbIOx7j5DXpb0/HE0jbe9LxDHo/1NTcdxDefXbcdEdbqwJ+2aP/dUA09PMsFiI4dLG42sTBVQCZDbNmh53zNEOPGbjDnrtVQa3dxY0RqYZL4exZ94oGL+M8PXMLPfOwh/Mbnn4KczQRugr2C+GG7kqTo+UqEkI8SQlYIIY+5HvtVQsgVQsgj1r+3JHuYyeKVXHwtVV0eelYyc3iT8tDPrzbw3FoDH37rrXjFDXMo58PzkuPCvHMAWHVJLv6gKNOsNd0wR5m5DHo+0TmTwSyAKIP+/cUq1K4ZaOJJLqoVK2DGimVBMM/Z7aFXrKZQvLJ/IH4wmOnnZ+bLO9LQg5ILS6U0X4/JGMdmvHrya248iE7XsHcq9rBkT5aL83UxRHLxBxQZ9rojdmSLGy27cvrIVB6aTrHWiG6q1gkxblkpg0JOstMxb47IQU8Sf+xgtdbBK26Yxcd/5i68++7j+F9eeTogBTEPPWwu7zBKLh8D8CbO479LKb3d+vc/9vew+ktLc4yH34Be2zarRA9Ygw/MDz3Zhk2zFatjHGcIw07ZdA2xXbMMF6XUlbbo1dCZcZjheOhJGHSNFV5IzoVSUXKhW9gHn9sAIcBbnn8YTy5tB7xCpmUyDZ0ZdqZteyUXyTbohMCzKwHir5t56HeenMFqrRNrJ8MrLPIPimbePqtQZtx9egaFnIQvsK6DvtmagNdD9xfZlHpq6L3XbQ4QN4/r8GTvniYAoHb10CZV5XwWz66assaZQRn0bNCgH50q4FVn5vBvfuT5+OAbbgz8TiwPfZgkF0rp/QCClQwpoqnq9pvuLwxZqZlVoqy6LslGVf5xVWUlt2cNvery0NctD6qpmpkUpobuLYFnRUUzHA89GcklWEkX5aE/+Nw6bpyv4KXXHUBD1XHJn+1hGbdJ20P3Sy5+DV3Haq2NAyVzB+ZGjuGpAs4k+DuOmYG4uAFChVNYBDifBfs7R3wZH0pWwj+47TA+893LuFJt9ZZcQsrgea1zzb8fvTOptTVUm5rdGoLpyr1SF6O8VZaLfnSqwO1X3g8UV3dN3aBYb6iBuIqfiR6VzcPooYfxTwghP7Akmel9O6IB0FR1e5akX3JhVaIMJaL4YK/4ixCYJLAXWIZLTiJYq5lfs5Ovks/a213mEdsGvdgfD51XSVfhTLABzPfn4YubuOf0Adxy2AwS+nV0d9k/4DLo1aDkwvpw84qKALeW3EtyaeNgRbH75ceRXXiFRWWfhn5ls4UMQSCTAwA+8LozAAV+74tPuzx0fpZLIef10N962xH8yptvCs9D933etbaGt//BN/C9S5sAnKZcrHmb3XWwR+pilLfKPN2bBxQQBbx56BsNFbpBexr0XpXNQ+ehh/CHAK4DcDuAJQC/HfZEQsj7CSEPEUIeWl2N3+siCeqdLn74P3zNziBhtFQds9YHF/TQvRd7rxabe0H1eavl/P4Z9FOzJTsoyk4+pv+5X8c26J5hycl76P6gKE+TfPRKFW3NwD2nZ3DDfBnZDAlkulRdbWQBR1dmkktZDma5rHDK/oH42T1LWy0cnszbmnKcwCivsMgfFL1SbWN+Is/NYz46VcB7XnICn/nuZXznwiayGeIJfsrZTGAwNOPYTBG/8KrrQlMD/Rr6s6sNPLJYxe988WkATjyCtVeeKuaQz2UidyZd3YBBw7sOsuKiGwcktwDOtU0pdTKfytEGvTxqQVEelNJrlFKdUmoA+GMAd0U8915K6Z2U0jvn5uZ2e5z7wqX1Jh67so1HrCwJRlPt2sOB/aXXrGqSoWSTm97j99BLSnYfNHTTMF43V7YN+rbLQ2f/s8c2mhwPPYamultYlovbaLF2qn59/IHzpvJ316kDyOckXH+wHAiMVn0pfHI2g5Is2Z6727iVlSyaqo7l7XaIhx5v3ctbbRyeLNjNmuKkLvIudH87gqvVVmSBzS++5nqU5Cy++MQ1TBXlgIFmGrlfQ++F7JNcWOzla+fW8MTVbSzaA8TNGxghBEcmC5H9XHq1kWVrj2qelTSylAGlZidKVpvQy0PP+Xa4ftydRPvBrl6JEHLY9e2PAHgs7LnDBPP6/HfTlqpjuiiDkKCHvtXSPNrkTgbo7hS/t7qXgQeMzaaKipLF/EQea3UmuZjvwwQz6K4bx4b1nOlS0ENPYt28XheVfA6UBlNIH3xuA2fmy/YN9pbDE3hiyWfQW8HjZ1KEks14dHJmRFZrnUCGCxA0bDxYUdGhyTyyUgaHJ6N7ezDMwiKv55zPZZAh3qCoXz93M1OS8f5XnrbWGJRPJkJayfbCX1DFYi9ShuCPv3YeixtNlGTJI9kc6TGOrVdPE1YPEdVlMWncaaphtQk8omI+YZk9SREnbfHTAL4F4EZCyGVCyM8C+A1CyKOEkB8AeA2Af5bwce4LLAPCfzdtajpKioSS7J0E3+nqaGm6R49MVHLxa+g9CmziUG2qmCrlMFcxR7u1Nd2loQcll82miko+6/EqHMml97oppXjtb30Vn3zwYqzj88tM5nEFt7GabuDhCxu4+9QB+7Fbjkzg2nbHk1+/2TQHPbjlBzbUwN+Bz62n+3PQAZeGHvF5b7fM8noWGFyYLvTU0Lu62YbAf6GzWadsd7K0FW3QAeBnXn4Ks2UlkKEDOEY+zynmiYLtyJz6BfMm+eMvPoa/+v5VPHRxA8dmip4dweHJfLSH3mNyz7Ql25yaLXF/3g+UBAx6v4OiPfdilNJ3ch7+SALHkjjbLb6H3lTNqS4lRfJ46OwGMFn0equJZ7lYJ0BJzqKtGejqRiADIy6bVtUkC/qu1Tu28bYlFyWH1VodgNnHxW8cnHmLvT301XoH59caePjCJt5994mez3cye1xpi5zMgceubKGh6rj79Iz92C1Wi9Unrm7jlWdMOa/aVD2DHgBHRw+OJXMMXVRQNOrzZhWSLHXv6FQR33x2LfT5QLT8wHqir9Y70HRqT8UJo6Rk8YmfvQs8OXyykENRlnbcGMq/I1uvqyjJEv7xq6/Df/3OIh67so3X3XzQ8zuHpwpYqXVCNeNOD4P+8688jTc+79Cuz/P9QHZJbKu1DspKNpZcVc7nwvPQOdlMSTJWlaI8D103KNSugWIua3roriIjXkm1nORYMp+HzgIuYX3a41BtqpgqynaMYK2u8oOilvHcbKjcSTZKROMiNywDYjFmCTwz6O785DInc+C7l8y4B2vXCpgeOgCP7OLvCw6Ez5l0e+w8ySWOhu4fHLwwXcDydjvSq48aHMwGRbPA6tGp4M7Bz82HJ7ja8/xEPnJCTxg8yWW2omBhuoh/8AJTbfWPhzs6lQelTqsMPyrnc/Yf64tPznB/1i88kkudHyjnMRHhoWsjlLY4crDAnztzhHnkRVlC0de+1PbQfWPJEguKsjJ4V9oiANT2ILtsWgbONui1DmrtLghxSsLdW0aehw6waS5xptg0rf/jV0wC3qAorwPk1WoLJVnySCNTRRlHpwqewKi7dS6DfV8OyccG+JIL2zVEGXRWVMQkl6PTBVDqGHoeUd4q64keloO+E37pdWfwsZ8OzVcIxQ6Ca07LCHZO/Lyl2V9n9SNnsB1KWNfFfvcF3w22Qdd1s2V2jwwXRiUiG00V3RaTg0ku7tasLSsVryBLKMreUnunpLo/QVG/zugvNNkNzMCx7onrDdOgl5WsLUtUlCzqqqnbbjaCBhEwA3ZxPHRmyJe327HeJ38xFeAEa91ez/J2O9DpDjC9U7+HHtojO0JD53ljcaSm5S0zV5xJNiyVLyrTJcq4sZ7oLLDaq41sFDMlGdf7DG8ceJILq5S+9cgk/uYDr8A7XrTg+Z0j1k4iLHWxl4Y+DLh3Jjvx0KMqm0cibbHfnF3e9nSX2y08DZ01OCrKkp3GxuB56FE9k/eKX092JJfdGfSubqDWNucuuiWX7bbmqcZjQ3obahcbDdWTg86IPTjYqtyktHcpOODsSnK+LBfAK7msbAcHUACm7HJ+te40quJKLnwNnXnolXzWnsrkp5fUdHWrjYOVvK39Lkz1Li5i76PCec2S5VRcrbbsiVL9xi+5rNVVOwYDmDdR//tll/+HBEZ7pS0OA+55B7yRhGEMU1B0eN9dF5984BI++GeP7PnvOGmLjqFougx6UZY8qXK8kmolKyU6WzNDYBsHu0/0LnPRq3ZfExn5nISKksWqJbkwWQNwDOi17TZU3fDkoDOUbCZWYdHiZhNZKwjnH8LAg6cn87Jc/PMcGXccm4JBgU88cAFAmORixQpCeprwAqKMnQ4OPjSZR4ZETy5ini/fQze371eq7T1553vBbdgMg2Kj0cGBUrRxKylmX6CVbX6DrlGQXFjwstbuotbu7sCg59BU9UC7YcMwW0OPQqVoX2E9N/bKFsdDZwa9IGdt78j/fLeXZHpsyeWh5zh50ruVXPxNm2YrCtbqHbt1rv91Lq6bBpgXSMv3MGyMy5st3G71NIkTGOVJLoWcBClD7BsvpRTXtjtcyeXVN87hTbcewq//zVn83RPX0OkasSUX9h7w9HOGv0e2HzO10Pl9OZvB/EQ+luQSGhTtmEHRgRl01iZa01FtaTAoPANPwohqVTEKkgs7NiZ37URDB4LXqWb0f83D++66KCumV7zXYOR2ywmKsvmabKteyJlB0abrxrHd0jCRz9qNuYDouYN7xV+EsFfJZdPXOXG2LFsGveu5SbETkjW64hn0OB66blBcrbbwopPTyEkkVmCUl7ZICPFsY6tNDWrX4Bp0Qgh+68duw3VzZfyvn/4egGCf76mQoKiSNdsh86Qc+zkR8zXtoqIJr+FdmC7EGhwcZtDrluSyl4DoXrBjB9ZgZAC2ZBdFQZY8w2Lc9EpbHAZYVtMVq+/PTiQXILiTHsSuZHjfXRcsF9RfxblTmOSiG9T2zN1ZLmUrZYwZ+2pTDXh7LA+dN0x6r5jDkoMe+m4lF9Y6lxm4AyUF63WVI7nsj4e+vN2GplOcmCnh6FRhR5KL/0J3G/RrNf74L0ZZyeKP33unfVPwa+jTIRo6IQQ3Harg+UcnQ48vKnaw3e6iqercwcFxNHR+HrpkjzTrlYOeJCx2wAx6HA+9KGdDr9FeaYvDAPs82Ge3E8kFCPZEH8SuZHjfXRd7lR4YWy3NbljEjIU/y8WgzgW31dI8QwMA88Oh1OlBsp/4I+IsrXC3694MSC5yiORi/ryXh94ra2XRnjNZwMJ00e75EYXKkVwAb+aAk+sdfoGdnC3h37/zDkwVc4HMjkMTeRybKeDmw8Fc7c/901fg515xOvTvRlUGs+M67MsVX5guYnm7HTrCLepCd990BuWhA07sgM2hjeOhmzEo/jnieKs7q1rtJ37JJSq24oaXlQUMJhA8Egbd6UK3e+1a0w00Vd1OK2PGwh0ULSleA1p1TY9nsG1ZErKLX0NnrW33KrkwD3W2rGCzqVkDooOSy0VrbmK45NJjEvwG68JXxLGZgj1IOAqt6829dx8TSy9lgbYorRsAXn3jQXzvX70e1x/09gMpyBK+9s9fa1eT7gR3j2w/S3aVqPe45icU6Aa1O1f6cbxVTpaLy6DHKSpKCnYDX2ceeowCpWKE5DJKGvrlzRYI4V8HPMI6Load20kyvO+uC7+h3Q0sZZEFmpixsA16zinzZTr6ViuY0+wMPUigURUnxWkvLXQ3mypkyWmjyrwsc7iFy0O3vl7cbCEnkUDPE4BJLtFrZhfCkak8FqaLWG+oPW9Gqq4jmyGB8nS35LLMxgBGaN2MXtPid0pUq4cle+fg9aSZUfYPHGfYWS4hpf+Mo1PFwM/7BVv3ekNFhoQPw3ATKblErHlYYHLQtVobB0py7DYEYYOiWR/9nPDQvfgb/+8GZsBZyTJ781vWCViQJTtoxlIXt5o8D51VkyUwX7NLA9JDWeHnuOoGxbef28BvfP5saO+QasPsFMmMnHvb7PbQWTqf2jUwUwq2YQXi9bBZ3Gzi0EQeSlayW6v26g2u6cE1s+Njn9G17TZmSjLXo00aJSuF5qEvbbVBSHBrzm6gYQY9juSSzZDYGm4SsHWv1VXMlBRPYkAYRVkKX/MI5aFTGk9iYoQFRXnDW5JmZ42SB8R+BEVtD92WXBwPPZsh1kAA53UoNQNTAYMec+jBbuB66EpwUPRvf+EpfOrBS1i3tvRfeWoVf/OBVwT+nj8n210cMuHy0DMZYuc/h06Cz0k9s1wub7TsKTZs3uTiRhNn5sNborKBz348QdGQfuX9ICx2QCnFFx5fxo3zlcANqdf5GpXlwpyKw1P5WEY0KVh2z1q94zlvoog06COQh+4+tp3cTMOmFkV9zkkxvO+uC3/j/92w5ZNcmIzBOi0CbmlHR0PV0TVoYJAuC+okNTBZ9hm3si+31zAo/uNXn8WxmSJ+/1134H9/4414cmkbT1+rBf5etent5e72OvyyCvs+LJshHyO7Z3GziQVrQj3bCfXKdAlrXsT6Y7AcdN4Ytn4Qlqb6tXNrOLtcw8+8/FTgZ7089OgsF/Pz8g+G7jfmMHQD6/VOrAwXACgqUZJLMD112NitQVeyEuRsJqihs95MwqB7YYZ2b5KLadD9QdGWqtsXoKOhd7ll/0C8Htm7heehl3ySy0bTnHX4I3ccxQ+/4Ah+7M5jkDIEf/m9K4G/F/DQK3zJxfzeXHuUh05puNTU6ZqTf5iHPluWUchJPTNdwmYuVvI56AZFSzP/7nyPgGhSKCE9bO69/zwOVhS87fYjgZ8V4hp0zrrZuT6ooiKGneXSUHtWiTKKOTPlUuOcIx3r3N7vGMd+kskQ+zPZqdw1kc96mskB/MZzSTMiBr132uLF9QZ+8VPfxaV1vkfIDPThyQIyxCW5aLptyG2tXtXt1rn+STBxptjsFt64qko+62lH4G+8P1dR8LLrZ3HfI1cDI9s2m5o93AEw0yDzOWd4hhsWGA3LZujVG3yp2galzg2TEIKF6d656JpucING7Pg2mxrW6h3MD8xDD+ahP3ZlC19/Zg0/8/JT/EyVPUku5u8OMgcdcLqKmo254nnoUTeyfvcF3y3s+u6VUeWHN9icBUWFh+5DyWaQzZBID/2zj1zF536whP/5P30TTy0H5QdWJTpZyHkCjS21i0KOeejshOzao8wm+umhc7zVsm+uKG+SyttvP4Ir1RYetqayA6bGy3qhMwghtrflN+jMY+f1QgeCLVX9sDJ/FgxlX/eqFo0KigLAc6sNUGqmAg4CmdMu+d77z6OsZPGuu49zfyeO5CJLfG91spDDP3r1dXjrbUHPv58o2Qy2WxrqnW7sAKGT3RO8TvvddXC3sGPcqYduxnxCCouEh+6FEBIZcAGAR69s4WBFASHAj9/7rcAg6O22BlnKIJ/LoOKaMNJ0SS7unQALovoLi5IcmBwmubh3Jrxp5G+49RDyuYxHdql3uugaNFA1yWSXgOSixPPQwwKjbLCF26AvTBd69nPphEou5vGcWzFvzgOTXKygKIsdXN5s4nOPLuGddx3zdKx0U4wwbEC0cSOE4P940024ISKQ3A+UrGSni+4kKAqEe+gjYdCZ5LKDLBeA33FxEO0Ohv8dtvAHB/08emUL95w+gP/2Cy/FRD6Hd//xA57pKVstDROFbKBPiDsoqmTNIb3Nju5Mj/dLLlJykgvPQ6/ks9B0ar8ebxp5WcniDbccwuceXbK9AruXu08TnyvL9u+4Yd+Heej5Hjeyxc0mchLxdEQ8Nl1Erd215SseYZILy8J5ZsUcjTfIoKhhTYIHgE986yIIgJ9+WTAYymA7vvAUPn2oS+ABM3agW2uOq6Hb6+YkL/S7jexuYVlsO/bQOT3RWVBUZLlwKHHS9xhr9Q6Wttp4wcIkjh8o4nd+7DY0VB0/uLxlP8dstGUaZ/f2qK05Hjob0ttQnaCoP8vFTltMIsslJG0RgC27rNY6KMlSoC/J2+84gmpTw/1PrwJwyv79Qc65ioKKr+EY4HjEYdVx/qEHfi5vms2k3H/3mJXxEuWlm/1reGmL5vt+zjLocYqKkkD2xQ4ev7qNW49ORpblSxmzuVVooypt+I2b2wjF1dB7Si6joKHvMiha5njoIigagV96cPPoFdNwP89qsnTigDk5/IrLkJgeOjPoOY+H7h4EW5Kzpofe0pDNENvYMxwPPRkN3f/h+wPCYY33X3HDHGZKMv7soUUA7rJ/7w3p515xGr/1jtsCv1+OadDDyv8XN5p2hgsjTupi2Fac3WCeXalDyhDMxvQS9xu71YP1eV+ptuzAbxTMMeAxCt6qO9gbV0O3g6IcWW5kJJdsBnI246nTiANPchlEdezwv8MWvAIbxmOWJ36rNTR4tixDyWY8Abntdtdl0PmSCwAUFQl1y0N3V1kymIceJyh63yNX8Mrf+ErsAKqm03APvYdBz0kZvOeeE/jCE9fwg8tVu9OiX3K5bq6MN956KPD77n4vPBzJJcxDbwYMHdPTowKjvMwewFn3ekPFwYqy48n1+4V7Z2IYFFc24xn0Qi66yGbYJRd5Nx66r3WGm1G4iQHmuufKyo7TKyv5HOqdri1TAaI5VyRFWQptzvWDK1s4PVeyt+mEEBydLnjKzmtWb3PAK7m4s1wA5qGbuq8/wwWINwme8dc/WMKljWbksAM3PA/dbp7PGlXV2qHbwZ97xSnMlGT828+fdUku8UaYvf2Oo/iT994ZatBtw+bz0DXdwB9+9Vms1VV7Z8SYLORQyWejhz2EZLmU5CzYNRXWNrcfuCuD1+odqLqBhRg54iUlujf4sBs39nkXrS6kcXBnifkJC34PG2UlG2i2FgdmW9wqgqb3vznXSJT+A7B7lfN47MoW7jo143lsYdqbMucu42eSC6XUykN3GXTFbAGq6cEqUaC3lswwDIrvXNgAYPYZPz0XPayXUhqa5QI4/WVWax28/PpZ7t+o5HP4J6+5Hr/210+goxkgJFgYFcZkIYfX3TIf+nPnRuas++GLm/gXf/EonrpWwxtvnce77gqm8U1w8nPdqF2de8KzdgS1dndgKYuAtzKYtVpYmO7dNKsgZyNbyQ67cWPneVzvHHAkl1aI5OJPlR1GfvWtt+5q1oG7/J9dc51u/6tjh/uschEWFGUBUf+QggWXh04pNQcjuySXrmH2aqEUHsmlZHWMq7ZUrjFkF2IvGeWpazU70+SC1ZY2CuduHiz9B8xCqLamY7vHrMN333McR6cKeOjiJibyudgd43qR9wWD1+odvPOPH8B2W8O973kR/ug9dwYyggDzBhmVbsqTmRgsiM2bJdov3HUH7HyKU/RTzEl24zc/puQyvH3BASc9N26GC+BILryd9CjITIApSfrbL8fB6bjofObsxt3P6tjhf4ctwuaK+gOijKNTBWw0VDTVLlqa6XG7PXQAuGb12S7m3Bq6+Tq8xlyA6TnmJNJTcnnw/DoAs2vexZDqVTdhept7XuEaJ2XRj5KV8MHXnwEQX26JA7vAWR76hbUG1K6B//cfPh9v4GjyjIKc5QbJGKaGzj/h7ZmfwyC5dHVbOopTll9SwiXCTlcfGcklbg46YN70CQH3RjYqGvpu4XVcHEQgeGTe4bC5oo9e3gIhTkCUwQJXVzZbdpUo8/iY3rVijTbzZrmYAyW2mlpoD2glK/X00B98bgNHpwo4M1+xB0dEoYWkODnDPbrcKlEeb7/jKG46VNnXiTf+0n9WdHK4RxOpKE8VCA+KAs5FMlAN3RUzubzZwnQxF0gZ5VGQs1zpgf2tYTdutuSyAw+dEIJijj+1aBRkpr3A64mu6v2/cQ+/qGVRlB3DJmcdQ/volS2cmi0FKh+ZQTfzo83HJgpOUBRwPHSP5GLpti1N5wZFgd7j2Cg1e5W/6sY5tFSd24rAT5iHXsxJIMQMijKD3qvPhJQh+PTP3wN9H+ee5n2l//ZYuB7GtihLWN4OLyyKMm6VIZBc3L17zAyXeEMnSrIUOV9z2OUHdiObrcT30AFrRzbClaK7heeha13a95vYyLzDYXNFH7uyxR3yyy68y9WWXeYflFyYh+7W0CXbs+IFRQFrzmREP/RnVupYb6i459QBnDhQwuJm05POxCOsCCGTISjLZic3XpVoGNMleUdN+nvhL/1f3mqjkJPsm2QYUZPggeBgbDeOhz7IQQ+Ohn55sxm7C2JBlrjpe8CIFBbldu6hA2wM3RhLLi77pOoGctn+ptuOzDvMG+u1WuMHRAGzF4MsZXB5s2n3ZXFXigLAimXQvXnojoEKyxAJ65HNeOA5M7vl7tMzOHmgCE2nuNpjck/UVHQWEF6tdXY063A/yWYIMsQruRyazPcM+PTqwRPWnAtwGfQBlf0D3oKquEVFgLVuTedmTIyGh77zLBcgfFC0KbkMdyB4L1QUjuQyAJlpuM8qF7y5oo9ZAVGeQc9kCI5M5XFlsxXobc68fTso6tPQGf7WuYxeHvqD59dxaCKP4zNFHD9g7hR6BUZZD2lukY017GGl1sFMUe5rKTGDEOKZK7q81Y7lOUfNmdQNCt0Iz3K5fq6M4zNFu3HYIGDHdnWrhbZmxG5rW5Sz0A3KDZ6PQpbLwlQRspTBjYd2lvERNig67ZJLPmd2hHVLLqac2N/PeWQ0dN5cUdaJ76bDE9zfYbnotoful1xqHMkllocuhXrolFI8cH4DL7v+AAghOGkV21zcaODl4OePA9GtNlk+tqbTAc+ZzNil/8vbbbz45EyP37Akl5DgYNRNDADe99KTeO9LTg50KAIzvOdXzcB2XA2dnVMtVbfjD4xRyHI5fqCIs//6TTuu0C3KwXqRsBqLNOE0/XM89LBpXEkyMu+wOyjKWKuryOfC+y4cnSrg8mYLW1aWC9V5E4kAABfHSURBVNvCs5vDCguKutMWXd56mIceFRQ9v9bAWr2Du08dAGAG9ORsJr6HHjL0gEkugzTozEM3DIqV7U6s7BM2xYaXFaTq0YUXhJCBlfwzmJZ8ftVsEhZXcrHL4MP6moxAxsdu3nuehx4lJ6YJ/5ALlTNSMmlG5h3mBUXXah3MRvRdWJguYK3ewUqtjZIs2Z6gZFUhrnA9dOfrsCyXKMnlwfOOfg6YF8XxmSIurEWnLkaNJWOtg1drnR33ad5PmIe+0VSh6gYOxZBcCi5P1c8ghujuFHZsz1oeelzJxW5U5Qvid3UDBh3uNe8FXsxkFAZE7wf+Bl2D2JWMzDvMDK0nKFrv4ECEgWMX39nlWsA4sz7jgNcrd3+9m6DoN55Zw8GKgtOzTl+TkweKuNRzFBsbKBu8OZXz5tSi1fpgPXRzHJvupCzGGGRsz2nVgjp6L8llGGBGaMvqBRQ21MJP2LCHQQw96Ce8QdFqytfM8EsuIigaAW+u6FpdtQc28GB659ml7YBxZvILIU5ZO+DsBAo5KTRwFeaha7qB+59exWtuPOjZNRyfKeHCeiOyR4TjxQRf09xNdKB2jQFLLuYkeMeg95ZceDdihtY1349hNuiEENsQHY2pnwMuiXDMjFuR02VyEF0HBwFXchEeOh/eXNG1eicy15p56A1VD3hWLDBayEke48s8q6imVmFB0YcubKLW6eI1Nx30PH5ytoi2ZmDFKgzi4WjoHA9dydoTcwbtobc13a4SjVPww+ITXMnFGqLLixsME0weiaufA96gqBtHTx7uLJfdUrSC4O6B5eMquZhB0f5+ziPzDtvThCyDbhgUGw010qDPVxRkrcCOvwCGeegFXwYC2wmEBUQBKyjKCXZ99akV5CSCl9/gzWZhbWWjAqORWS6uoO9ADbrloV/bbkPKkFjHYksuXA29/+1FdwMzvnGLioDwnQnb2aXVWy0qWVAKtF1JA2nflTAmXLOKAVNe62enRWCEDDpg5ojXreq7akuDbtDIwoeslMHhKdOLDGrolofum0jEPKuwgChgSS6crI0vn13BXadmAvM6T1iDHqK6LqoRerI7lfLgoDV0zcDSVhtzZSUwxo6HHRzk5KJr9lZ8sJksvdiNh16wb2Q+yUXv/xSbfsKLHaQ9bsCoWPUibHcyiAKynq9GCPkoIWSFEPKY67EZQsgXCSHnrP+nkz1ME7eHzjoP9ipvZ15VUHIxLzj/iDkm7YSV/ZvPCTbnWtxo4txKHa+58WDg+UenzVmbl2J46LwTwF1YM1cebOfBdlfHNatKNA5h0gMQfRMbJnYluYQMio7KZkoDvEHR46Khl63dCYubDGtQ9GMA3uR77EMAvkQpvQHAl6zvE8c9p3GtFs+gs8BoWFC04JvGQog5RzRKQ+d56F99agUAAvo5YBqshelCpIceWSlqGXRZyvTsnZIkihUMXt5qx26YFZbtATgdJofduMm2Qd9BUDRMcmE37txwr3m32C06NG9wEACUIf+c9wrb9W9bOvpQFhZRSu8HsOF7+G0APm59/XEAb9/n4+Liniu6Zk2PmevRDc720H0GmnnsxVwwaPGhN9+Md99zIvRvsrRFd9bKV55axYkDRU+6opvjM8V4GnpILxfA1M8HWTXJCouWt+J76JGDgyOKqYYJ1gt+Jxq6LGUgZUhoCl9ajVuBcwMfFw399Jx57T95dRsAf6Rk0uz21eYppUsAYP0fdEsTwD00IL6HziQXflDUL7kAwLvuPo7bj02F/k3FN72nren45rNrgXRFNycPRKcuRnno7FhnB6ifA+aNbKulodbp7kByMY+d14FvEDMXd4MiZVCSpchAuR/WGzy0yCalxo03KDrta2bcfmwKcjaDB59bh2FQdCP6FCVF4q9GCHk/IeQhQshDq6ure/pbJTlr56Gv1TvIZkjPQg+2TfYPq7DzzTkGvRf2GDrLCH/r2XW0NYMrtzBOHCii1u7aY+n8OO1z+WmLwGADooDpoTMjHFdyKYRoycDoXOh5WcLCdHHHu6OiEmyha0suKU5bBLzB4HHR0PM5Cbcfm8KDz20MbM27fbVrhJDDAGD9vxL2RErpvZTSOymld87Nze3y5Uw8Gnq9gwNluWe/ibtOzeBfv+1WvPKMN5WQ6V08D70Xim/Yw98/vYp8LoO7T4U3q2Itb1nnRz+qTkPnD7oll0HiDtjGnSIkZQiUbIavoY9IUPSDrz+DX3vbrTv+vSJn/N6o3MR2S6TkMuSf835wz6kZPHZlyx4oPoxBUR6fBfA+6+v3Abhvfw4nGm+WS3QOOkPKELznJScDHpEjuew8yMj0T9ag69nVOs7MVwJd9dyEVQ4yoqrKKvksMmSwk3sAr1d5eAc9yosh03t6NecaFm4/NoW7Tx/Y8e/xhj2kPW2xxKk7SPtNzM3dpw/AoOauHeh/z56e1owQ8mkArwYwSwi5DOD/BvDrAP6MEPKzAC4BeEeSB8koK07nvvUeVaK9cLJcduOhO1NsAHPM3S0hLXwZUdkeQPSw5HxOwsd++i5u3/d+4m6REFdDB1hP9AjJJaWeW1EODopmu7q0Nufi1R10xkRyAYAXHp9GTiL42jlTXu737rOnQaeUvjPkRz+0z8fSE/fA5LW6iusOlnf9t6KyXHrhHphsGBRXNlt4wy3zkb8T1dME6N334ZVn9iZX7QfMQ58s5CJ3I37Chh5oKb/QC3I2ILGlXU/mOS5OZk864wZuCrKE2xam8PVzawBGR0MfCGw7V7fma+6llexUMYecRDCzwxFbgPMhqV2zP4uqG1iYic5RtkvgO3zJxfTQh/vjYDeyncgtQPgYulHR0HdLSZYCn3fa5YeclIEsZcZWcgHM1tm2hi4MejjMQ7+23YbaNfYoueRw3y++HD/6woUd/y7zVDtdA4ubZm75sR5VhCVbQw8ZHDwCE12YVx43IMoIGxQdNhg7LRQ4NzInyyWdawbY5x0sLBr283u/YMNtgP7LiSMzgg5wZIsLVoHObI+iol7cciRa9w5DzjpB0VUrH/5YDw89qqcJYFZNDruWzIzQToOzRdkZJuJG1Vn73OEOiu6WkpwNjN9Le+k/YO5M3I6LquvIZkis3j9p4EUnpiFlCHSD9r1obqTOKpaPfckqod+Lh74XFJfksrjRAtC7irCnhj4CHjoLBu8kIArwPVXAKo0OSdVMA2ZQNCi5pHnNQHBHlvYB0X5KStZOYOh3RfBIvctMcrloTf85UBqUQXckl8ubTRysKD2DhPmsBEJGW0Nna9ypQS/mwiWXtHrngGnYOl0Duqs3eKerp1puAYKDosfNoAPOCEqhoUfAdOj9klx2izsourjZ7Cm3AOZs0UJOCtXQR2Fw8OHJPLIZgpt7pGj6iQqKpvlCL3Fa6I6DcfN/3qo+/Of2fvO6m+eRzZAdx5v2ykhq6BfXGyAEmCkOxqArLg19caOFF5+M1z04LB8bMPXkgjzcJ/3CdBGPffiNO0pZBMz0vbC0xWHflewF94BsVpk8LgZ9ra7a33fGYM1+XnxyZlfXyl4ZqXeZSS7VpoaZoozsgIwBOzkbHR1LW61YHjpg3pDCgqKj4KED2NUJWpTNkX0sTZGhdmmqDTovbtLp9n/oQb8pKkJyAXZ3reyVkXqX2fAJAJGTivpxHIA5gcigwLGYfbKLcjZQOcgw5Yd06slhVbKjEAjeC4VcsN3DOBg3f8xkVJyVNDBS7zKbKwoMLsMFcIKiz67WAcSfZBPW0wRI90nvlh7cjEKq5l7gTWtK+00MCGb3DGIU27gycu9yybpIBmnQWWbGsytm+mRcySUsOAikW0/mBQcBa80p3ZUAYZKLntrWuYyi4s2/H4ddybAwcu/yMHjohJgtYZe325AyJHYpfEnORnvoKT3peS1VAdNzS+tNDHAkl0CWS4rXDJiSC2uiB6T73B42Ru5dtg36gFIWGe6+JnGDs7zue4w0GzdbeuD0Bk+zceN56ONg3NwSG6UUi5vNPfVdEsRnpNIWAadadHZARUUMOSsB6MYOiALmBBu/UWOoKc5+CAuKarph36DTCDNsjTHLcnEPiq51NFzb7uBFJ+Kl9gr2xshdTczrGRYPPW5AFDAlF38pOCPNGjqTHoLDHgxMpXTNAH+e6jh46O4b+GNXtgAALxQGvS+M3JnFAmyD1NABx6DHDYgC5gXuLwUHgK5uwKDp7UYX6qF3aaolF9481XEosim6BkU/dGETJVnCTYd21whPsDNG7swahqAo4BjfYzPxPXTeAF3APYpt5D6OWERJLv3uRtdPpAxBPpfhFBalPMvFdZ4/fHETdxyfHptOi4Nm5K6msjU6jg1dHhRsUPRONXSA76kC6fXQw/LQOylvzgUEM5vUMWjOxT7vlVoHZ5e3hX7eR0ZOQ3/HixZwfKY4kLJaN6wt5k4kF3vIhU9H77DBwSk1bkXO4GDA9NDHwbg1O+NVWMTO828+uw6DQhj0PjJyBv30XBmn53Y/S3S/UHIZyNnMjtKxwqWHdHvoUoZAzmb4hUUplZkY7mIySulYZLmw8/xr51aRIcAdx6cGfETjQ7rPrAQpyhKOTReQ2YE2GOapjsOILl6VrKanuzkXYHXYtFJVuwYFpemeVgQ4Bv3yZgs3HpqwO00KkmfkPPRh4ZffcCPqISmIYTANvcHxVIH0BkUBpiUHb2RpXjNg3cis82QcbtyA47gAwItOCO+8nwiDvkvOzFd2/DslVzqXG/tCT7FxK8gSWppzI6OUjoWeXJSzqDbNMYXjMCAaAPK5DAgBKAXuPDEz6MMZK9J9Zg0ZPdMWU3yh+yWXrpWLn9ZAMKMoO9XBjoee7rRFQgiKVtKCCIj2F+Gh95HQvuDMc0uzh56TAj1NgHTLTIC3ley4SC6AOaWqpGR3VEkt2DvCoPcRVhQVqqGn+EL3jyVja067cSu6xu91uub/aZdcAGCuouDMfBmEpHsHNmwIg95HlGwGGRIssBkHDd2cp9q0v097dSyjKEtoqF07ZRFI/00MAP70p15sFxgJ+ocw6H2EEMIdQzcOWS4FOTiWDEj3TQwwM5sMagZE1THZlQDAoZgzAgT7S/rPrCGDN4ZuHDy3oizZ+dhA+oupGCw42FJ1bFiS0zhILoLBIDz0PlNSsp7+2IDLuKXYWy34slzGYVcCODnZ7/ijb+GZlToIgRj2IEgMYdD7TCEnBfuCj4OHnstC7Rro6gayUsaV5ZLuoNmpuRKkDMFEPosPvfkmvPHWQzg1Wxr0YQlSijDofaakBMfQOd5qeo2bnbKp6ZiQMmORew8ALz45g6f/nzeL9rGCvpDuq2kIKXIGRY+Dh+5voauNQe49QxhzQb9I/9U0ZJSUYJOqcUjh8w9MZnGDtHvoAkE/EVdTnynk+E2qgJQHRXOs06RVNWn1gE/zTUwg6DfiauozJUXiVopmM2RHrXhHjaJPclG76c/sEQj6jbia+kxRznK7LaZZPweCfWyc0v/03sQEgn6TbisyhJRkCapu2AYNGI/JPQWfQR+X5lwCQT8RV1Of8Rs2YDzmTLICG9YTfVyacwkE/WRPeeiEkAsAagB0AF1K6Z37cVBphnVcbKpdTBbM0Vxql6ZeSw6TXISHLhDsH/tRWPQaSunaPvydsYDXE30cPHR/HnpHSC4Cwb4jrqY+U+SModO6RqqrRAGnSZU/Dz3tOxOBoJ/s9WqiAL5ACHmYEPL+/TigtFOSg4Oix8FDz0oZyFKGk+WS7nULBP1kr5LLyyilVwkhBwF8kRByllJ6v/sJlqF/PwAcP358jy83+hQtDb3l6zw4DtJDwdU6WO0ayBBRFi8Q7Cd7siKU0qvW/ysA/juAuzjPuZdSeiel9M65ubm9vFwq4Hnona4xFtJDydVCd1xuYgJBP9n1FUUIKRFCKuxrAG8A8Nh+HVhasdMWO14PfRykh5KSxUbDHPIwDjKTQNBv9nJFzQP4OiHk+wC+DeBzlNLP789hpZeSHBwUrY6Jh/6y62fxtXOrWK93zJvYGKxZIOgnu9bQKaXnAdy2j8cyFhSVYNriuMgP77r7OD72zQv484cvQ+2Ox5oFgn4irqg+I0sZSBni6Yk+Dr1cAODMfAV3npjGp799aWzWLBD0E3FF9RlCCIqyd2qRptOx8VbfdfdxXFhv4pvPrqc+914g6DfjYUWGjJKc9aQtdsbIW33L8w9jspDDSq0zNjcxgaBfiCtqABR9PdHNAOF4eKv5nIR/+MKjAABlTG5iAkG/EFfUACjK3jF046Ynv+sus8BMeOgCwf6yH825BDukKGfR6Hg99HEybjfMV/CqM3OYKuYGfSgCQaoQBn0AlGQJ61aBjWFQdA06Vh46APzJ++6ERMZDZhII+sV4WZEhoag4Hro6pn3Bc1Im1TNUBYJBMF5WZEgo5hwNnRl0ESAUCAR7RViRAVBSsmK2pkAg2HeEFRkARVcbWdEXXCAQ7BfCigyAoixB0ynUriE8dIFAsG8IKzIA2Bi6lqoLD10gEOwbwooMgJLiDLlgw5LHpVJUIBAkh8hDHwDMQ//o15/DmfkKAOGhCwSCvSMM+gC48+Q07jo1g4984zlQaj4mNHSBQLBXhEEfAIcnC/izX3gJ1uodfOnJa3j86jbuOD496MMSCAQjjjDoA2S2rODHX3x80IchEAhSgtjnCwQCQUoQBl0gEAhSgjDoAoFAkBKEQRcIBIKUIAy6QCAQpARh0AUCgSAlCIMuEAgEKUEYdIFAIEgJhLLa8368GCGrAC7u8tdnAazt4+GMCuO47nFcMzCe6x7HNQM7X/cJSulcryf11aDvBULIQ5TSOwd9HP1mHNc9jmsGxnPd47hmILl1C8lFIBAIUoIw6AKBQJASRsmg3zvoAxgQ47jucVwzMJ7rHsc1Awmte2Q0dIFAIBBEM0oeukAgEAgiGAmDTgh5EyHkKULIM4SQDw36eJKAEHKMEPIVQsiThJDHCSEfsB6fIYR8kRByzvo/dZMwCCESIeR7hJC/tr4fhzVPEUL+nBBy1vrMX5L2dRNC/pl1bj9GCPk0ISSfxjUTQj5KCFkhhDzmeix0nYSQX7Fs21OEkDfu5bWH3qATQiQAfwDgzQBuAfBOQsgtgz2qROgC+GVK6c0A7gHwi9Y6PwTgS5TSGwB8yfo+bXwAwJOu78dhzf8OwOcppTcBuA3m+lO7bkLIUQD/FMCdlNLnAZAA/ATSueaPAXiT7zHuOq1r/CcA3Gr9zn+0bN6uGHqDDuAuAM9QSs9TSlUA/wXA2wZ8TPsOpXSJUvpd6+sazAv8KMy1ftx62scBvH0wR5gMhJAFAP8TgD9xPZz2NU8AeCWAjwAApVSllFaR8nXDnJBWIIRkARQBXEUK10wpvR/Ahu/hsHW+DcB/oZR2KKXPAXgGps3bFaNg0I8CWHR9f9l6LLUQQk4CuAPAgwDmKaVLgGn0ARwc3JElwu8B+OcADNdjaV/zaQCrAP7Ukpr+hBBSQorXTSm9AuC3AFwCsARgi1L6BaR4zT7C1rmv9m0UDDrhPJba1BxCSBnAZwD8EqV0e9DHkySEkB8GsEIpfXjQx9JnsgBeCOAPKaV3AGggHVJDKJZm/DYApwAcAVAihPzkYI9qKNhX+zYKBv0ygGOu7xdgbtVSByEkB9OYf5JS+hfWw9cIIYetnx8GsDKo40uAlwF4KyHkAkwp7bWEkP+MdK8ZMM/py5TSB63v/xymgU/zul8H4DlK6SqlVAPwFwBeinSv2U3YOvfVvo2CQf8OgBsIIacIITLMAMJnB3xM+w4hhMDUVJ+klP6O60efBfA+6+v3Abiv38eWFJTSX6GULlBKT8L8XL9MKf1JpHjNAEApXQawSAi50XrohwA8gXSv+xKAewghRetc/yGYcaI0r9lN2Do/C+AnCCEKIeQUgBsAfHvXr0IpHfp/AN4C4GkAzwL4l4M+noTW+HKYW60fAHjE+vcWAAdgRsXPWf/PDPpYE1r/qwH8tfV16tcM4HYAD1mf918CmE77ugF8GMBZAI8B+AQAJY1rBvBpmHECDaYH/rNR6wTwLy3b9hSAN+/ltUWlqEAgEKSEUZBcBAKBQBADYdAFAoEgJQiDLhAIBClBGHSBQCBICcKgCwQCQUoQBl0gEAhSgjDoAoFAkBKEQRcIBIKU8P8DRKN2O9aFL9kAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.figure()\n",
+    "plt.plot(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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": 17,
+     "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": [
+    "%matplotlib inline\n",
+    "plt.figure()\n",
+    "plt.hist(data, edgecolor='black')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "voici une des choses que j'ai vraiment aprecier dans ce module "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
@@ -16,10 +241,9 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/module2/exo5/Untitled.ipynb
+++ b/module2/exo5/Untitled.ipynb
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--- a/module3/exo1/analyse-syndrome-grippal.ipynb
+++ b/module3/exo1/analyse-syndrome-grippal.ipynb
--- a/module3/exo1/incidence-PAY-3.csv
+++ b/module3/exo1/incidence-PAY-3.csv
+# {"source":"r&#233;seau Sentinelles, INSERM, Sorbonne Universit&#233;, https:\/\/www.sentiweb.fr","meta":{"period":[198444,202518],"geo":["PAY",0],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false,"age_group":false,"span":"all"},"date":"2025-05-07T11:31:09+02:00","bundle":"1746610269"}
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
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+202517,3,22572,17604,27540,34,27,41,FR,France
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+202514,3,37579,31232,43926,56,47,65,FR,France
+202513,3,39673,33686,45660,59,50,68,FR,France
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+202401,3,120769,109452,132086,181,164,198,FR,France
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+199817,3,61148,51894,70402,105,89,121,FR,France
+199816,3,123195,109878,136512,211,188,234,FR,France
+199815,3,171603,156715,186491,294,268,320,FR,France
+199814,3,319092,298398,339786,547,512,582,FR,France
+199813,3,298212,278513,317911,512,478,546,FR,France
+199812,3,263585,246356,280814,452,422,482,FR,France
+199811,3,220578,204894,236262,378,351,405,FR,France
+199810,3,202354,186304,218404,347,319,375,FR,France
+199809,3,227553,209970,245136,390,360,420,FR,France
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+199807,3,190187,173694,206680,326,298,354,FR,France
+199806,3,88217,76829,99605,151,131,171,FR,France
+199805,3,45180,36970,53390,77,63,91,FR,France
+199804,3,30005,23272,36738,51,39,63,FR,France
+199803,3,23900,18221,29579,41,31,51,FR,France
+199802,3,31143,23994,38292,53,41,65,FR,France
+199801,3,24809,17786,31832,43,31,55,FR,France
+199752,3,29039,21984,36094,50,38,62,FR,France
+199751,3,34924,27737,42111,60,48,72,FR,France
+199750,3,33527,26388,40666,58,46,70,FR,France
+199749,3,15978,11147,20809,27,19,35,FR,France
+199748,3,11507,7744,15270,20,14,26,FR,France
+199747,3,11059,7151,14967,19,12,26,FR,France
+199746,3,8073,4613,11533,14,8,20,FR,France
+199745,3,18202,13113,23291,31,22,40,FR,France
+199744,3,19770,14405,25135,34,25,43,FR,France
+199743,3,16707,11945,21469,29,21,37,FR,France
+199742,3,15340,10351,20329,26,17,35,FR,France
+199741,3,16933,11941,21925,29,20,38,FR,France
+199740,3,13292,8709,17875,23,15,31,FR,France
+199739,3,10189,6279,14099,18,11,25,FR,France
+199738,3,3231,1061,5401,6,2,10,FR,France
+199737,3,4542,1949,7135,8,4,12,FR,France
+199736,3,5131,2205,8057,9,4,14,FR,France
+199735,3,1842,0,3740,3,0,6,FR,France
+199734,3,3226,0,6717,6,0,12,FR,France
+199733,3,1799,0,4368,3,0,7,FR,France
+199732,3,2086,0,4411,4,0,8,FR,France
+199731,3,1386,0,3332,2,0,5,FR,France
+199730,3,2116,0,4919,4,0,9,FR,France
+199729,3,1973,0,4347,3,0,7,FR,France
+199728,3,1560,216,2904,3,1,5,FR,France
+199727,3,2540,754,4326,4,1,7,FR,France
+199726,3,5582,2699,8465,10,5,15,FR,France
+199725,3,4012,1090,6934,7,2,12,FR,France
+199724,3,4696,1824,7568,8,3,13,FR,France
+199723,3,4709,2136,7282,8,4,12,FR,France
+199722,3,2283,665,3901,4,1,7,FR,France
+199721,3,2108,664,3552,4,2,6,FR,France
+199720,3,3257,1519,4995,6,3,9,FR,France
+199719,3,4646,2436,6856,8,4,12,FR,France
+199718,3,5246,2764,7728,9,5,13,FR,France
+199717,3,14236,9906,18566,24,17,31,FR,France
+199716,3,12169,8111,16227,21,14,28,FR,France
+199715,3,9516,6192,12840,16,10,22,FR,France
+199714,3,13602,9659,17545,23,16,30,FR,France
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+199712,3,17199,12707,21691,30,22,38,FR,France
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+199709,3,29860,24261,35459,51,41,61,FR,France
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+199646,3,32138,26358,37918,55,45,65,FR,France
+199645,3,25551,20136,30966,44,35,53,FR,France
+199644,3,10753,7230,14276,19,13,25,FR,France
+199643,3,12971,9311,16631,22,16,28,FR,France
+199642,3,11929,8472,15386,21,15,27,FR,France
+199641,3,9918,6674,13162,17,11,23,FR,France
+199640,3,10765,7067,14463,19,13,25,FR,France
+199639,3,8377,5183,11571,14,8,20,FR,France
+199638,3,9470,5965,12975,16,10,22,FR,France
+199637,3,2925,829,5021,5,1,9,FR,France
+199636,3,1122,23,2221,2,0,4,FR,France
+199635,3,1674,93,3255,3,0,6,FR,France
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+199633,3,540,0,1246,1,0,2,FR,France
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+199631,3,3010,752,5268,5,1,9,FR,France
+199630,3,2992,706,5278,5,1,9,FR,France
+199629,3,1559,144,2974,3,1,5,FR,France
+199628,3,3109,541,5677,5,1,9,FR,France
+199627,3,4456,1595,7317,8,3,13,FR,France
+199626,3,2871,1138,4604,5,2,8,FR,France
+199625,3,2308,856,3760,4,1,7,FR,France
+199624,3,2490,1067,3913,4,2,6,FR,France
+199623,3,2819,1262,4376,5,2,8,FR,France
+199622,3,4005,1984,6026,7,4,10,FR,France
+199621,3,7042,4291,9793,12,7,17,FR,France
+199620,3,10619,7365,13873,18,12,24,FR,France
+199619,3,11898,8065,15731,21,14,28,FR,France
+199618,3,18338,13352,23324,32,23,41,FR,France
+199617,3,24954,19100,30808,43,33,53,FR,France
+199616,3,29313,23868,34758,51,42,60,FR,France
+199615,3,51703,43644,59762,89,75,103,FR,France
+199614,3,44414,36774,52054,77,64,90,FR,France
+199613,3,30622,25062,36182,53,43,63,FR,France
+199612,3,31818,25852,37784,55,45,65,FR,France
+199611,3,24427,19499,29355,42,33,51,FR,France
+199610,3,20910,16198,25622,36,28,44,FR,France
+199609,3,19594,15228,23960,34,26,42,FR,France
+199608,3,19046,14850,23242,33,26,40,FR,France
+199607,3,23041,18338,27744,40,32,48,FR,France
+199606,3,21810,17153,26467,38,30,46,FR,France
+199605,3,25275,20410,30140,44,36,52,FR,France
+199604,3,43359,36948,49770,75,64,86,FR,France
+199603,3,54691,48016,61366,94,82,106,FR,France
+199602,3,105606,96106,115106,182,166,198,FR,France
+199601,3,223830,209274,238386,386,361,411,FR,France
+199552,3,397122,378252,415992,688,655,721,FR,France
+199551,3,749988,725434,774542,1299,1256,1342,FR,France
+199550,3,604926,583100,626752,1047,1009,1085,FR,France
+199549,3,375500,358200,392800,650,620,680,FR,France
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+199547,3,120472,110526,130418,209,192,226,FR,France
+199546,3,62380,55042,69718,108,95,121,FR,France
+199545,3,29791,24523,35059,52,43,61,FR,France
+199544,3,13168,9820,16516,23,17,29,FR,France
+199543,3,16188,12389,19987,28,21,35,FR,France
+199542,3,15178,11387,18969,26,19,33,FR,France
+199541,3,14980,10851,19109,26,19,33,FR,France
+199540,3,16245,11610,20880,28,20,36,FR,France
+199539,3,18499,13844,23154,32,24,40,FR,France
+199538,3,13123,9410,16836,23,17,29,FR,France
+199537,3,4895,2666,7124,8,4,12,FR,France
+199536,3,3659,1574,5744,6,2,10,FR,France
+199535,3,2496,644,4348,4,1,7,FR,France
+199534,3,1887,214,3560,3,0,6,FR,France
+199533,3,905,0,2007,2,0,4,FR,France
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+199531,3,1946,92,3800,3,0,6,FR,France
+199530,3,1898,46,3750,3,0,6,FR,France
+199529,3,2148,687,3609,4,1,7,FR,France
+199528,3,3312,1430,5194,6,3,9,FR,France
+199527,3,4853,2683,7023,8,4,12,FR,France
+199526,3,4986,2911,7061,9,5,13,FR,France
+199525,3,4571,2520,6622,8,4,12,FR,France
+199524,3,8058,5054,11062,14,9,19,FR,France
+199523,3,13820,9801,17839,24,17,31,FR,France
+199522,3,14124,9999,18249,24,17,31,FR,France
+199521,3,11931,8655,15207,21,15,27,FR,France
+199520,3,18846,14311,23381,33,25,41,FR,France
+199519,3,26261,21488,31034,45,37,53,FR,France
+199518,3,39231,33258,45204,68,58,78,FR,France
+199517,3,88640,79343,97937,153,137,169,FR,France
+199516,3,140488,129439,151537,243,224,262,FR,France
+199515,3,181007,169110,192904,313,292,334,FR,France
+199514,3,248704,235144,262264,431,408,454,FR,France
+199513,3,209839,197191,222487,363,341,385,FR,France
+199512,3,122434,112497,132371,212,195,229,FR,France
+199511,3,81730,73346,90114,142,127,157,FR,France
+199510,3,49387,42703,56071,86,74,98,FR,France
+199509,3,39696,33259,46133,69,58,80,FR,France
+199508,3,38008,32225,43791,66,56,76,FR,France
+199507,3,30550,25479,35621,53,44,62,FR,France
+199506,3,37351,32007,42695,65,56,74,FR,France
+199505,3,27876,23218,32534,48,40,56,FR,France
+199504,3,27643,22818,32468,48,40,56,FR,France
+199503,3,24844,20391,29297,43,35,51,FR,France
+199502,3,23480,19249,27711,41,34,48,FR,France
+199501,3,26556,21916,31196,46,38,54,FR,France
+199452,3,22036,17495,26577,38,30,46,FR,France
+199451,3,26912,21750,32074,47,38,56,FR,France
+199450,3,28831,23470,34192,50,41,59,FR,France
+199449,3,21161,16559,25763,37,29,45,FR,France
+199448,3,18487,14456,22518,32,25,39,FR,France
+199447,3,15206,11578,18834,26,20,32,FR,France
+199446,3,14252,10525,17979,25,19,31,FR,France
+199445,3,12152,8448,15856,21,15,27,FR,France
+199444,3,11307,7688,14926,20,14,26,FR,France
+199443,3,11254,8002,14506,20,14,26,FR,France
+199442,3,14406,10653,18159,25,18,32,FR,France
+199441,3,14073,10341,17805,24,18,30,FR,France
+199440,3,15962,11850,20074,28,21,35,FR,France
+199439,3,15878,11643,20113,28,21,35,FR,France
+199438,3,15366,11100,19632,27,20,34,FR,France
+199437,3,5949,3561,8337,10,6,14,FR,France
+199436,3,2806,1133,4479,5,2,8,FR,France
+199435,3,4556,2358,6754,8,4,12,FR,France
+199434,3,3459,1444,5474,6,2,10,FR,France
+199433,3,4272,977,7567,7,1,13,FR,France
+199432,3,4786,1613,7959,8,2,14,FR,France
+199431,3,5027,1580,8474,9,3,15,FR,France
+199430,3,2100,520,3680,4,1,7,FR,France
+199429,3,1188,0,2384,2,0,4,FR,France
+199428,3,1581,316,2846,3,1,5,FR,France
+199427,3,713,0,1459,1,0,2,FR,France
+199426,3,990,80,1900,2,0,4,FR,France
+199425,3,5431,3222,7640,9,5,13,FR,France
+199424,3,6288,3723,8853,11,7,15,FR,France
+199423,3,6440,1052,11828,11,2,20,FR,France
+199422,3,845,0,2449,1,0,4,FR,France
+199421,3,2197,395,3999,4,1,7,FR,France
+199420,3,2217,852,3582,4,2,6,FR,France
+199419,3,1483,406,2560,3,1,5,FR,France
+199418,3,3705,1940,5470,6,3,9,FR,France
+199417,3,2288,1064,3512,4,2,6,FR,France
+199416,3,5156,3176,7136,9,6,12,FR,France
+199415,3,6952,4678,9226,12,8,16,FR,France
+199414,3,4671,2784,6558,8,5,11,FR,France
+199413,3,3343,1641,5045,6,3,9,FR,France
+199412,3,4305,2412,6198,7,4,10,FR,France
+199411,3,5399,3327,7471,9,5,13,FR,France
+199410,3,4548,2529,6567,8,4,12,FR,France
+199409,3,5635,3251,8019,10,6,14,FR,France
+199408,3,9539,6652,12426,17,12,22,FR,France
+199407,3,14639,11188,18090,25,19,31,FR,France
+199406,3,17308,13395,21221,30,23,37,FR,France
+199405,3,18489,14290,22688,32,25,39,FR,France
+199404,3,23712,19048,28376,41,33,49,FR,France
+199403,3,29882,24817,34947,52,43,61,FR,France
+199402,3,47402,41415,53389,82,72,92,FR,France
+199401,3,118119,108182,128056,205,188,222,FR,France
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+199351,3,377472,359792,395152,658,627,689,FR,France
+199350,3,627639,607097,648181,1094,1058,1130,FR,France
+199349,3,898045,873419,922671,1565,1522,1608,FR,France
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+199342,3,19187,14893,23481,33,26,40,FR,France
+199341,3,18285,14384,22186,32,25,39,FR,France
+199340,3,13805,10348,17262,24,18,30,FR,France
+199339,3,14783,11173,18393,26,20,32,FR,France
+199338,3,10492,7329,13655,18,12,24,FR,France
+199337,3,7023,4503,9543,12,8,16,FR,France
+199336,3,4361,2445,6277,8,5,11,FR,France
+199335,3,5589,3066,8112,10,6,14,FR,France
+199334,3,2984,1209,4759,5,2,8,FR,France
+199333,3,3217,758,5676,6,2,10,FR,France
+199332,3,3755,1051,6459,7,2,12,FR,France
+199331,3,4163,1768,6558,7,3,11,FR,France
+199330,3,4055,1907,6203,7,3,11,FR,France
+199329,3,3714,1668,5760,6,2,10,FR,France
+199328,3,1774,484,3064,3,1,5,FR,France
+199327,3,2574,918,4230,4,1,7,FR,France
+199326,3,3048,1450,4646,5,2,8,FR,France
+199325,3,8348,5260,11436,15,10,20,FR,France
+199324,3,6624,3747,9501,12,7,17,FR,France
+199323,3,7424,4302,10546,13,8,18,FR,France
+199322,3,3432,1849,5015,6,3,9,FR,France
+199321,3,5942,3670,8214,10,6,14,FR,France
+199320,3,5137,3157,7117,9,6,12,FR,France
+199319,3,4593,2725,6461,8,5,11,FR,France
+199318,3,5027,3075,6979,9,6,12,FR,France
+199317,3,8111,5138,11084,14,9,19,FR,France
+199316,3,24932,19468,30396,43,33,53,FR,France
+199315,3,22181,17147,27215,39,30,48,FR,France
+199314,3,38266,30865,45667,67,54,80,FR,France
+199313,3,62987,54506,71468,110,95,125,FR,France
+199312,3,63493,55956,71030,111,98,124,FR,France
+199311,3,80548,72628,88468,140,126,154,FR,France
+199310,3,127910,117688,138132,223,205,241,FR,France
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+199308,3,222932,209051,236813,389,365,413,FR,France
+199307,3,248494,234936,262052,433,409,457,FR,France
+199306,3,286939,271830,302048,500,474,526,FR,France
+199305,3,255890,241516,270264,446,421,471,FR,France
+199304,3,193107,181261,204953,337,316,358,FR,France
+199303,3,147854,137256,158452,258,240,276,FR,France
+199302,3,90269,82164,98374,157,143,171,FR,France
+199301,3,75288,67244,83332,131,117,145,FR,France
+199253,3,56622,49107,64137,99,86,112,FR,France
+199252,3,49588,42568,56608,87,75,99,FR,France
+199251,3,41156,35024,47288,72,61,83,FR,France
+199250,3,40100,34699,45501,70,61,79,FR,France
+199249,3,39146,33847,44445,69,60,78,FR,France
+199248,3,32392,27499,37285,57,48,66,FR,France
+199247,3,26276,21797,30755,46,38,54,FR,France
+199246,3,22758,18462,27054,40,32,48,FR,France
+199245,3,22023,17804,26242,39,32,46,FR,France
+199244,3,27619,22945,32293,48,40,56,FR,France
+199243,3,24969,20704,29234,44,37,51,FR,France
+199242,3,29980,25261,34699,52,44,60,FR,France
+199241,3,24091,19953,28229,42,35,49,FR,France
+199240,3,17593,13885,21301,31,25,37,FR,France
+199239,3,13535,10301,16769,24,18,30,FR,France
+199238,3,9837,7106,12568,17,12,22,FR,France
+199237,3,7480,5130,9830,13,9,17,FR,France
+199236,3,5155,2958,7352,9,5,13,FR,France
+199235,3,6383,3687,9079,11,6,16,FR,France
+199234,3,2953,922,4984,5,1,9,FR,France
+199233,3,4046,1549,6543,7,3,11,FR,France
+199232,3,4963,2215,7711,9,4,14,FR,France
+199231,3,4233,1733,6733,7,3,11,FR,France
+199230,3,3091,1145,5037,5,2,8,FR,France
+199229,3,3586,1583,5589,6,2,10,FR,France
+199228,3,4179,2133,6225,7,3,11,FR,France
+199227,3,5568,3134,8002,10,6,14,FR,France
+199226,3,5886,3229,8543,10,5,15,FR,France
+199225,3,5304,2805,7803,9,5,13,FR,France
+199224,3,5928,3482,8374,10,6,14,FR,France
+199223,3,3914,1855,5973,7,3,11,FR,France
+199222,3,4899,2641,7157,9,5,13,FR,France
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+198624,3,11160,7252,15068,20,13,27,FR,France
+198623,3,14372,9896,18848,26,18,34,FR,France
+198622,3,9717,5594,13840,18,11,25,FR,France
+198621,3,14460,9575,19345,26,17,35,FR,France
+198620,3,17133,12106,22160,31,22,40,FR,France
+198619,3,22177,16008,28346,40,29,51,FR,France
+198618,3,31736,24115,39357,57,43,71,FR,France
+198617,3,34874,27440,42308,63,50,76,FR,France
+198616,3,41196,32894,49498,74,59,89,FR,France
+198615,3,42659,33537,51781,77,61,93,FR,France
+198614,3,49792,39728,59856,90,72,108,FR,France
+198613,3,87249,63562,110936,157,114,200,FR,France
+198612,3,124777,97237,152317,225,175,275,FR,France
+198611,3,168146,151035,185257,303,272,334,FR,France
+198610,3,257305,231225,283385,464,417,511,FR,France
+198609,3,321140,296644,345636,580,536,624,FR,France
+198608,3,448786,419219,478353,810,757,863,FR,France
+198607,3,490723,460150,521296,886,831,941,FR,France
+198606,3,439300,411764,466836,793,743,843,FR,France
+198605,3,364727,339868,389586,658,613,703,FR,France
+198604,3,348509,322464,374554,629,582,676,FR,France
+198603,3,271539,248654,294424,490,449,531,FR,France
+198602,3,178138,159344,196932,322,288,356,FR,France
+198601,3,112614,96539,128689,203,174,232,FR,France
+198552,3,86452,72250,100654,157,131,183,FR,France
+198551,3,114666,99538,129794,208,181,235,FR,France
+198550,3,86031,73604,98458,156,133,179,FR,France
+198549,3,65931,55326,76536,120,101,139,FR,France
+198548,3,68291,57581,79001,124,105,143,FR,France
+198547,3,66177,55221,77133,120,100,140,FR,France
+198546,3,70534,58788,82280,128,107,149,FR,France
+198545,3,57514,47167,67861,104,85,123,FR,France
+198544,3,67356,55744,78968,122,101,143,FR,France
+198543,3,87402,74208,100596,158,134,182,FR,France
+198542,3,76218,64604,87832,138,117,159,FR,France
+198541,3,45712,36755,54669,83,67,99,FR,France
+198540,3,49739,40335,59143,90,73,107,FR,France
+198539,3,39819,31234,48404,72,56,88,FR,France
+198538,3,27605,20614,34596,50,37,63,FR,France
+198537,3,22219,15626,28812,40,28,52,FR,France
+198536,3,27986,18992,36980,51,35,67,FR,France
+198535,3,30215,18546,41884,55,34,76,FR,France
+198534,3,40389,24786,55992,73,45,101,FR,France
+198533,3,22611,8819,36403,41,16,66,FR,France
+198532,3,7488,1851,13125,14,4,24,FR,France
+198531,3,16958,8479,25437,31,16,46,FR,France
+198530,3,11598,5507,17689,21,10,32,FR,France
+198529,3,13054,6474,19634,24,12,36,FR,France
+198528,3,14588,7659,21517,26,13,39,FR,France
+198527,3,19670,11761,27579,36,22,50,FR,France
+198526,3,18609,12637,24581,34,23,45,FR,France
+198525,3,19362,12454,26270,35,22,48,FR,France
+198524,3,19855,13577,26133,36,25,47,FR,France
+198523,3,19373,10010,28736,35,18,52,FR,France
+198522,3,24099,17190,31008,44,31,57,FR,France
+198521,3,26096,19621,32571,47,35,59,FR,France
+198520,3,27896,20885,34907,51,38,64,FR,France
+198519,3,43154,32821,53487,78,59,97,FR,France
+198518,3,40555,29935,51175,74,55,93,FR,France
+198517,3,34053,24366,43740,62,44,80,FR,France
+198516,3,50362,36451,64273,91,66,116,FR,France
+198515,3,63881,45538,82224,116,83,149,FR,France
+198514,3,134545,114400,154690,244,207,281,FR,France
+198513,3,197206,176080,218332,357,319,395,FR,France
+198512,3,245240,223304,267176,445,405,485,FR,France
+198511,3,276205,252399,300011,501,458,544,FR,France
+198510,3,353231,326279,380183,640,591,689,FR,France
+198509,3,369895,341109,398681,670,618,722,FR,France
+198508,3,389886,359529,420243,707,652,762,FR,France
+198507,3,471852,432599,511105,855,784,926,FR,France
+198506,3,565825,518011,613639,1026,939,1113,FR,France
+198505,3,637302,592795,681809,1155,1074,1236,FR,France
+198504,3,424937,390794,459080,770,708,832,FR,France
+198503,3,213901,174689,253113,388,317,459,FR,France
+198502,3,97586,80949,114223,177,147,207,FR,France
+198501,3,85489,65918,105060,155,120,190,FR,France
+198452,3,84830,60602,109058,154,110,198,FR,France
+198451,3,101726,80242,123210,185,146,224,FR,France
+198450,3,123680,101401,145959,225,184,266,FR,France
+198449,3,101073,81684,120462,184,149,219,FR,France
+198448,3,78620,60634,96606,143,110,176,FR,France
+198447,3,72029,54274,89784,131,99,163,FR,France
+198446,3,87330,67686,106974,159,123,195,FR,France
+198445,3,135223,101414,169032,246,184,308,FR,France
+198444,3,68422,20056,116788,125,37,213,FR,France
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
--- a/module3/exo2/inc-7-PAY.csv
+++ b/module3/exo2/inc-7-PAY.csv
+# {"source":"r&#233;seau Sentinelles, INSERM, Sorbonne Universit&#233;, https:\/\/www.sentiweb.fr","meta":{"period":[199049,202518],"geo":["PAY",0],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false,"age_group":false,"span":"all"},"date":"2025-05-07T11:31:22+02:00","bundle":"1746610269"}
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202518,7,5795,2714,8876,9,4,14,FR,France
+202517,7,6323,3436,9210,9,5,13,FR,France
+202516,7,6151,3193,9109,9,5,13,FR,France
+202515,7,5557,3262,7852,8,5,11,FR,France
+202514,7,4984,2858,7110,7,4,10,FR,France
+202513,7,5964,3608,8320,9,5,13,FR,France
+202512,7,3855,1995,5715,6,3,9,FR,France
+202511,7,5878,2747,9009,9,4,14,FR,France
+202510,7,2921,1421,4421,4,2,6,FR,France
+202509,7,3381,1468,5294,5,2,8,FR,France
+202508,7,2835,1286,4384,4,2,6,FR,France
+202507,7,4502,2382,6622,7,4,10,FR,France
+202506,7,3455,1958,4952,5,3,7,FR,France
+202505,7,2087,1056,3118,3,1,5,FR,France
+202504,7,6895,4466,9324,10,6,14,FR,France
+202503,7,2462,1161,3763,4,2,6,FR,France
+202502,7,5966,2757,9175,9,4,14,FR,France
+202501,7,6059,2451,9667,9,4,14,FR,France
+202452,7,4356,1776,6936,7,3,11,FR,France
+202451,7,4670,2239,7101,7,3,11,FR,France
+202450,7,7363,4438,10288,11,7,15,FR,France
+202449,7,6077,3631,8523,9,5,13,FR,France
+202448,7,4189,1454,6924,6,2,10,FR,France
+202447,7,1931,726,3136,3,1,5,FR,France
+202446,7,2260,863,3657,3,1,5,FR,France
+202445,7,2713,1216,4210,4,2,6,FR,France
+202444,7,2135,676,3594,3,1,5,FR,France
+202443,7,2124,641,3607,3,1,5,FR,France
+202442,7,2621,1246,3996,4,2,6,FR,France
+202441,7,2035,381,3689,3,1,5,FR,France
+202440,7,2125,725,3525,3,1,5,FR,France
+202439,7,2898,1333,4463,4,2,6,FR,France
+202438,7,751,0,1513,1,0,2,FR,France
+202437,7,916,28,1804,1,0,2,FR,France
+202436,7,2235,870,3600,3,1,5,FR,France
+202435,7,1623,284,2962,2,0,4,FR,France
+202434,7,2560,622,4498,4,1,7,FR,France
+202433,7,1971,536,3406,3,1,5,FR,France
+202432,7,4399,1944,6854,7,3,11,FR,France
+202431,7,4500,2213,6787,7,4,10,FR,France
+202430,7,7004,4278,9730,11,7,15,FR,France
+202429,7,9270,6303,12237,14,10,18,FR,France
+202428,7,9364,6498,12230,14,10,18,FR,France
+202427,7,10247,7090,13404,15,10,20,FR,France
+202426,7,14368,10399,18337,22,16,28,FR,France
+202425,7,11174,8039,14309,17,12,22,FR,France
+202424,7,12621,9357,15885,19,14,24,FR,France
+202423,7,14657,11339,17975,22,17,27,FR,France
+202422,7,11628,8361,14895,17,12,22,FR,France
+202421,7,9701,6851,12551,15,11,19,FR,France
+202420,7,13661,10209,17113,20,15,25,FR,France
+202419,7,10083,6413,13753,15,9,21,FR,France
+202418,7,13438,9514,17362,20,14,26,FR,France
+202417,7,15303,11219,19387,23,17,29,FR,France
+202416,7,18138,13540,22736,27,20,34,FR,France
+202415,7,24929,17315,32543,37,26,48,FR,France
+202414,7,16181,12544,19818,24,19,29,FR,France
+202413,7,18322,14206,22438,27,21,33,FR,France
+202412,7,12818,9128,16508,19,13,25,FR,France
+202411,7,15973,12400,19546,24,19,29,FR,France
+202410,7,14301,10761,17841,21,16,26,FR,France
+202409,7,14337,10871,17803,21,16,26,FR,France
+202408,7,15899,11991,19807,24,18,30,FR,France
+202407,7,11294,8226,14362,17,12,22,FR,France
+202406,7,12174,9020,15328,18,13,23,FR,France
+202405,7,8814,6110,11518,13,9,17,FR,France
+202404,7,9504,6566,12442,14,10,18,FR,France
+202403,7,6948,4633,9263,10,7,13,FR,France
+202402,7,7125,4852,9398,11,8,14,FR,France
+202401,7,13305,9214,17396,20,14,26,FR,France
+202352,7,11636,7354,15918,18,12,24,FR,France
+202351,7,6912,4227,9597,10,6,14,FR,France
+202350,7,8799,6215,11383,13,9,17,FR,France
+202349,7,7817,5362,10272,12,8,16,FR,France
+202348,7,7351,4749,9953,11,7,15,FR,France
+202347,7,6537,4277,8797,10,7,13,FR,France
+202346,7,5229,2973,7485,8,5,11,FR,France
+202345,7,5007,2675,7339,8,4,12,FR,France
+202344,7,3688,1664,5712,6,3,9,FR,France
+202343,7,3891,1675,6107,6,3,9,FR,France
+202342,7,3968,1212,6724,6,2,10,FR,France
+202341,7,3356,1764,4948,5,3,7,FR,France
+202340,7,2845,1410,4280,4,2,6,FR,France
+202339,7,1739,629,2849,3,1,5,FR,France
+202338,7,1663,274,3052,3,1,5,FR,France
+202337,7,1122,223,2021,2,1,3,FR,France
+202336,7,726,10,1442,1,0,2,FR,France
+202335,7,961,96,1826,1,0,2,FR,France
+202334,7,1168,9,2327,2,0,4,FR,France
+202333,7,3308,1184,5432,5,2,8,FR,France
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+202324,7,11115,7968,14262,17,12,22,FR,France
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+202321,7,11349,7598,15100,17,11,23,FR,France
+202320,7,9000,4615,13385,14,7,21,FR,France
+202319,7,9344,6091,12597,14,9,19,FR,France
+202318,7,10671,7291,14051,16,11,21,FR,France
+202317,7,9184,6162,12206,14,9,19,FR,France
+202316,7,11387,8014,14760,17,12,22,FR,France
+202315,7,14040,7613,20467,21,11,31,FR,France
+202314,7,15247,11032,19462,23,17,29,FR,France
+202313,7,13322,9700,16944,20,15,25,FR,France
+202312,7,10374,7218,13530,16,11,21,FR,France
+202311,7,4919,2880,6958,7,4,10,FR,France
+202310,7,4854,2731,6977,7,4,10,FR,France
+202309,7,7004,4548,9460,11,7,15,FR,France
+202308,7,8175,5316,11034,12,8,16,FR,France
+202307,7,6595,3782,9408,10,6,14,FR,France
+202306,7,9595,6017,13173,14,9,19,FR,France
+202305,7,6237,3907,8567,9,5,13,FR,France
+202304,7,6299,3973,8625,9,6,12,FR,France
+202303,7,6063,3798,8328,9,6,12,FR,France
+202302,7,6576,3060,10092,10,5,15,FR,France
+202301,7,8153,5470,10836,12,8,16,FR,France
+202252,7,5171,2717,7625,8,4,12,FR,France
+202251,7,6226,3822,8630,9,5,13,FR,France
+202250,7,6590,3100,10080,10,5,15,FR,France
+202249,7,5095,3212,6978,8,5,11,FR,France
+202248,7,4985,3043,6927,8,5,11,FR,France
+202247,7,6087,3733,8441,9,5,13,FR,France
+202246,7,3033,1392,4674,5,3,7,FR,France
+202245,7,3827,1720,5934,6,3,9,FR,France
+202244,7,4271,2231,6311,6,3,9,FR,France
+202243,7,5863,3302,8424,9,5,13,FR,France
+202242,7,3770,1950,5590,6,3,9,FR,France
+202241,7,4177,2219,6135,6,3,9,FR,France
+202240,7,4883,1472,8294,7,2,12,FR,France
+202239,7,2041,331,3751,3,0,6,FR,France
+202238,7,1771,419,3123,3,1,5,FR,France
+202237,7,1725,499,2951,3,1,5,FR,France
+202236,7,1069,178,1960,2,1,3,FR,France
+202235,7,1581,400,2762,2,0,4,FR,France
+202234,7,2266,788,3744,3,1,5,FR,France
+202233,7,7340,0,17399,11,0,26,FR,France
+202232,7,7801,4086,11516,12,6,18,FR,France
+202231,7,6896,4170,9622,10,6,14,FR,France
+202230,7,9039,5770,12308,14,9,19,FR,France
+202229,7,14851,10060,19642,22,15,29,FR,France
+202228,7,15471,11028,19914,23,16,30,FR,France
+202227,7,21191,16198,26184,32,24,40,FR,France
+202226,7,16854,12806,20902,25,19,31,FR,France
+202225,7,22266,18027,26505,34,28,40,FR,France
+202224,7,22458,18105,26811,34,27,41,FR,France
+202223,7,18772,14875,22669,28,22,34,FR,France
+202222,7,18965,14985,22945,29,23,35,FR,France
+202221,7,20310,16307,24313,31,25,37,FR,France
+202220,7,23585,19004,28166,36,29,43,FR,France
+202219,7,18593,14181,23005,28,21,35,FR,France
+202218,7,17851,13963,21739,27,21,33,FR,France
+202217,7,20314,16001,24627,31,24,38,FR,France
+202216,7,19660,14860,24460,30,23,37,FR,France
+202215,7,17799,13715,21883,27,21,33,FR,France
+202214,7,17005,13162,20848,26,20,32,FR,France
+202213,7,15448,11659,19237,23,17,29,FR,France
+202212,7,14702,10794,18610,22,16,28,FR,France
+202211,7,11729,8347,15111,18,13,23,FR,France
+202210,7,13314,10036,16592,20,15,25,FR,France
+202209,7,10485,7600,13370,16,12,20,FR,France
+202208,7,12088,8741,15435,18,13,23,FR,France
+202207,7,14003,10789,17217,21,16,26,FR,France
+202206,7,9798,7048,12548,15,11,19,FR,France
+202205,7,10851,7797,13905,16,11,21,FR,France
+202204,7,9547,6721,12373,14,10,18,FR,France
+202203,7,13972,10680,17264,21,16,26,FR,France
+202202,7,8495,6026,10964,13,9,17,FR,France
+202201,7,13793,10597,16989,21,16,26,FR,France
+202152,7,13239,9611,16867,20,15,25,FR,France
+202151,7,13326,9629,17023,20,14,26,FR,France
+202150,7,14128,10312,17944,21,15,27,FR,France
+202149,7,13674,10369,16979,21,16,26,FR,France
+202148,7,11549,8503,14595,17,12,22,FR,France
+202147,7,11419,8376,14462,17,12,22,FR,France
+202146,7,8216,5724,10708,12,8,16,FR,France
+202145,7,8965,6468,11462,14,10,18,FR,France
+202144,7,8736,5636,11836,13,8,18,FR,France
+202143,7,8145,5164,11126,12,7,17,FR,France
+202142,7,9443,6037,12849,14,9,19,FR,France
+202141,7,4021,2239,5803,6,3,9,FR,France
+202140,7,4441,2454,6428,7,4,10,FR,France
+202139,7,2291,1056,3526,3,1,5,FR,France
+202138,7,4325,2267,6383,7,4,10,FR,France
+202137,7,1964,754,3174,3,1,5,FR,France
+202136,7,3441,1730,5152,5,2,8,FR,France
+202135,7,2562,1107,4017,4,2,6,FR,France
+202134,7,1429,378,2480,2,0,4,FR,France
+202133,7,3829,1830,5828,6,3,9,FR,France
+202132,7,4108,1895,6321,6,3,9,FR,France
+202131,7,4793,2301,7285,7,3,11,FR,France
+202130,7,7190,4191,10189,11,6,16,FR,France
+202129,7,6800,4109,9491,10,6,14,FR,France
+202128,7,9734,0,21731,15,0,33,FR,France
+202127,7,9026,4316,13736,14,7,21,FR,France
+202126,7,7284,4108,10460,11,6,16,FR,France
+202125,7,9351,6540,12162,14,10,18,FR,France
+202124,7,12034,8937,15131,18,13,23,FR,France
+202123,7,9116,6420,11812,14,10,18,FR,France
+202122,7,4817,2752,6882,7,4,10,FR,France
+202121,7,6092,3458,8726,9,5,13,FR,France
+202120,7,7485,4601,10369,11,7,15,FR,France
+202119,7,6654,4370,8938,10,7,13,FR,France
+202118,7,3912,2110,5714,6,3,9,FR,France
+202117,7,4686,2878,6494,7,4,10,FR,France
+202116,7,4780,2891,6669,7,4,10,FR,France
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+199111,7,15574,11184,19964,27,19,35,FR,France
+199110,7,16643,11372,21914,29,20,38,FR,France
+199109,7,13741,8780,18702,24,15,33,FR,France
+199108,7,13289,8813,17765,23,15,31,FR,France
+199107,7,12337,8077,16597,22,15,29,FR,France
+199106,7,10877,7013,14741,19,12,26,FR,France
+199105,7,10442,6544,14340,18,11,25,FR,France
+199104,7,7913,4563,11263,14,8,20,FR,France
+199103,7,15387,10484,20290,27,18,36,FR,France
+199102,7,16277,11046,21508,29,20,38,FR,France
+199101,7,15565,10271,20859,27,18,36,FR,France
+199052,7,19375,13295,25455,34,23,45,FR,France
+199051,7,19080,13807,24353,34,25,43,FR,France
+199050,7,11079,6660,15498,20,12,28,FR,France
+199049,7,1143,0,2610,2,0,5,FR,France
--- a/module3/exo3/Subject6_smoking.csv
+++ b/module3/exo3/Subject6_smoking.csv
+"Smoker","Status","Age"
+"Yes","Alive",21
+"Yes","Alive",19.3
+"No","Dead",57.5
+"No","Alive",47.1
+"Yes","Alive",81.4
+"No","Alive",36.8
+"No","Alive",23.8
+"Yes","Dead",57.5
+"Yes","Alive",24.8
+"Yes","Alive",49.5
+"Yes","Alive",30
+"No","Dead",66
+"Yes","Alive",49.2
+"No","Alive",58.4
+"No","Dead",60.6
+"No","Alive",25.1
+"No","Alive",43.5
+"No","Alive",27.1
+"No","Alive",58.3
+"Yes","Alive",65.7
+"No","Dead",73.2
+"Yes","Alive",38.3
+"No","Alive",33.4
+"Yes","Dead",62.3
+"No","Alive",18
+"No","Alive",56.2
+"Yes","Alive",59.2
+"No","Alive",25.8
+"No","Dead",36.9
+"No","Alive",20.2
+"Yes","Alive",34.6
+"Yes","Alive",51.9
+"Yes","Alive",49.9
+"No","Alive",19.4
+"No","Alive",56.9
+"Yes","Alive",46.7
+"Yes","Alive",44.4
+"Yes","Alive",29.5
+"Yes","Dead",33
+"Yes","Alive",35.6
+"Yes","Alive",39.1
+"No","Dead",69.7
+"Yes","Alive",35.7
+"No","Dead",75.8
+"No","Alive",25.3
+"No","Dead",83
+"Yes","Dead",44.3
+"No","Alive",18.5
+"Yes","Alive",37.5
+"Yes","Alive",22.1
+"No","Alive",82.8
+"No","Alive",45
+"No","Dead",73.3
+"Yes","Alive",39
+"No","Alive",28.4
+"No","Dead",73.7
+"Yes","Alive",40.1
+"No","Alive",51.2
+"No","Alive",22.9
+"No","Alive",41.9
+"Yes","Alive",58.1
+"Yes","Alive",37.3
+"No","Alive",41.7
+"Yes","Dead",36.3
+"Yes","Dead",80.7
+"Yes","Alive",33
+"Yes","Alive",38.6
+"Yes","Alive",27.9
+"No","Alive",47.6
+"No","Dead",77.6
+"No","Dead",58.1
+"Yes","Alive",26.2
+"No","Alive",45.4
+"No","Alive",62.4
+"No","Alive",62.5
+"No","Alive",39.5
+"No","Alive",27.6
+"Yes","Alive",31.4
+"No","Dead",85
+"No","Alive",18.9
+"No","Alive",35.3
+"Yes","Alive",25.4
+"No","Dead",72.8
+"Yes","Alive",58.3
+"No","Alive",27.3
+"No","Dead",55.9
+"No","Alive",32.8
+"Yes","Dead",53.6
+"No","Alive",55.9
+"Yes","Alive",48
+"Yes","Alive",56.1
+"No","Alive",18.3
+"Yes","Alive",20.2
+"No","Alive",62.8
+"Yes","Alive",18.6
+"No","Alive",46.3
+"No","Alive",36
+"Yes","Dead",55.5
+"Yes","Alive",18.6
+"No","Dead",65.7
+"No","Dead",76.5
+"Yes","Dead",61
+"No","Alive",26.8
+"Yes","Alive",47.6
+"No","Dead",70.5
+"No","Dead",81.8
+"Yes","Alive",32.5
+"No","Alive",23
+"No","Alive",83.7
+"Yes","Dead",62.8
+"Yes","Alive",45.9
+"No","Alive",59.9
+"Yes","Dead",66.5
+"No","Alive",47.5
+"No","Dead",89.3
+"No","Alive",57.2
+"Yes","Alive",21.3
+"Yes","Alive",34
+"No","Alive",59.5
+"Yes","Alive",50.1
+"No","Alive",56.1
+"Yes","Alive",30.6
+"Yes","Dead",63.8
+"Yes","Alive",27.4
+"Yes","Alive",32.5
+"No","Alive",22.5
+"Yes","Alive",24.2
+"No","Alive",56.8
+"Yes","Alive",28.9
+"Yes","Dead",87.8
+"Yes","Alive",19.4
+"No","Dead",88.4
+"Yes","Dead",35.7
+"No","Alive",33.7
+"No","Dead",62.3
+"Yes","Alive",63.6
+"Yes","Dead",71.7
+"No","Alive",48.5
+"No","Alive",82
+"Yes","Dead",40.8
+"Yes","Alive",31.3
+"No","Alive",25.1
+"No","Alive",24.4
+"No","Alive",32.2
+"No","Alive",53.1
+"No","Alive",47.5
+"No","Dead",26.3
+"No","Dead",66
+"Yes","Alive",41
+"No","Dead",58.3
+"No","Dead",86.8
+"Yes","Alive",49.7
+"No","Alive",21
+"No","Dead",75.8
+"No","Alive",50.5
+"No","Dead",63.5
+"Yes","Alive",33.1
+"No","Alive",30.7
+"Yes","Dead",59.4
+"No","Alive",67.2
+"Yes","Alive",20.7
+"No","Alive",41.6
+"No","Alive",37.7
+"No","Dead",89.7
+"No","Alive",45.2
+"Yes","Dead",59.9
+"No","Alive",55.4
+"Yes","Alive",44.8
+"No","Alive",18.3
+"No","Dead",76.7
+"Yes","Dead",62.3
+"Yes","Dead",48.4
+"No","Alive",82.7
+"No","Alive",27
+"No","Alive",30.9
+"No","Dead",82.9
+"No","Alive",40.5
+"Yes","Alive",56.8
+"No","Alive",26.5
+"Yes","Alive",41.9
+"Yes","Alive",29.9
+"No","Dead",75
+"No","Dead",66.3
+"No","Dead",87
+"No","Dead",79.5
+"Yes","Alive",51.7
+"No","Alive",30
+"No","Alive",78.2
+"No","Alive",80
+"Yes","Alive",62.9
+"Yes","Dead",78.3
+"Yes","Alive",49.8
+"Yes","Alive",36.5
+"No","Dead",60.1
+"Yes","Alive",62
+"No","Alive",19
+"No","Dead",87.6
+"No","Alive",24.3
+"No","Alive",22.2
+"Yes","Dead",68.4
+"No","Alive",32.5
+"Yes","Dead",89.2
+"Yes","Alive",44.5
+"Yes","Alive",43.3
+"Yes","Dead",57.4
+"Yes","Alive",45.6
+"Yes","Alive",18.1
+"Yes","Dead",63.4
+"No","Alive",67
+"Yes","Alive",55.6
+"Yes","Alive",51.9
+"No","Alive",23.3
+"Yes","Dead",57.6
+"No","Alive",38.4
+"No","Dead",35.2
+"Yes","Alive",26.2
+"No","Alive",60.3
+"Yes","Alive",48.7
+"Yes","Alive",23.7
+"Yes","Alive",46.9
+"Yes","Alive",18
+"No","Dead",65.8
+"Yes","Alive",33
+"Yes","Dead",83.1
+"No","Dead",58.4
+"Yes","Alive",23.2
+"No","Alive",66.7
+"No","Alive",58.8
+"Yes","Alive",56.7
+"Yes","Alive",21.5
+"No","Dead",78.3
+"No","Alive",51.5
+"Yes","Alive",63.5
+"Yes","Alive",57.8
+"Yes","Alive",59.5
+"Yes","Dead",47.8
+"Yes","Alive",53.9
+"Yes","Alive",45.5
+"Yes","Alive",24.2
+"No","Alive",63.9
+"Yes","Alive",37.5
+"No","Alive",20.6
+"No","Alive",22.9
+"No","Alive",46.1
+"No","Alive",49.6
+"No","Alive",31.4
+"No","Alive",25.9
+"Yes","Alive",46.8
+"Yes","Dead",81
+"No","Dead",84.3
+"No","Alive",30.8
+"Yes","Alive",52.4
+"No","Alive",20.1
+"Yes","Dead",58.9
+"Yes","Alive",72.1
+"No","Alive",19.6
+"No","Alive",52.6
+"Yes","Alive",35
+"Yes","Dead",35.4
+"No","Dead",55.1
+"Yes","Alive",23.7
+"No","Alive",49.1
+"Yes","Alive",39.7
+"Yes","Alive",33.7
+"No","Dead",66.4
+"No","Alive",24.2
+"No","Dead",67.2
+"No","Alive",19.4
+"No","Alive",52.4
+"No","Dead",58.6
+"Yes","Alive",36.2
+"Yes","Alive",38.8
+"Yes","Alive",47.9
+"No","Alive",36.5
+"Yes","Alive",24.3
+"No","Alive",38.8
+"No","Alive",38.4
+"No","Alive",55.3
+"No","Dead",87.7
+"Yes","Dead",56.7
+"No","Alive",74.1
+"Yes","Alive",62.3
+"No","Alive",18.5
+"Yes","Dead",59.3
+"No","Alive",39.8
+"Yes","Dead",55
+"No","Alive",42.8
+"No","Alive",34.2
+"Yes","Alive",33.7
+"No","Alive",30.6
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+"Yes","Alive",62
+"No","Alive",61.3
+"Yes","Alive",58.5
+"No","Alive",41.6
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+"No","Alive",52.9
+"Yes","Alive",34
+"No","Dead",52.4
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+"No","Alive",23.7
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+"No","Alive",31.9
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+"No","Alive",22.6
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+"Yes","Alive",31
+"Yes","Alive",55.3
+"No","Alive",40.8
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+"No","Alive",52.3
+"Yes","Alive",51.9
+"No","Alive",28.3
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+"No","Alive",55.4
+"No","Dead",43.3
+"No","Alive",60.1
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+"No","Dead",79
+"No","Dead",65.1
+"Yes","Alive",40.1
+"No","Alive",46
+"No","Alive",40.2
+"No","Dead",89.2
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+"No","Alive",43.4
+"No","Alive",48.8
+"No","Alive",19.8
+"Yes","Alive",27.8
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+"No","Dead",28.5
+"No","Alive",26.7
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+"No","Dead",74.4
+"Yes","Alive",40.8
+"Yes","Alive",20.4
+"No","Dead",42.1
+"No","Alive",41.2
+"Yes","Alive",20.9
+"Yes","Alive",45.5
+"No","Alive",26.7
+"No","Alive",41.8
+"No","Alive",33.7
+"No","Alive",56.5
+"Yes","Alive",38.8
+"Yes","Alive",55.5
+"Yes","Alive",24.9
+"No","Alive",33
+"Yes","Alive",55.7
+"No","Alive",25.7
+"No","Alive",19.5
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+"No","Dead",83.9
+"No","Alive",34.9
+"Yes","Alive",51.2
+"No","Dead",86.3
+"Yes","Dead",36
+"Yes","Alive",48.3
+"No","Alive",63.1
+"No","Alive",60.8
+"Yes","Dead",39.3
+"No","Alive",36.7
+"No","Alive",63.8
+"No","Dead",71.3
+"No","Alive",57.7
+"No","Alive",63.2
+"No","Alive",46.6
+"Yes","Dead",82.4
+"Yes","Alive",38.3
+"Yes","Alive",32.7
+"No","Alive",39.7
+"Yes","Dead",60
+"No","Dead",71
+"No","Alive",20.5
+"No","Alive",44.4
+"Yes","Alive",31.2
+"Yes","Alive",47.8
+"Yes","Alive",60.9
+"No","Dead",61.4
+"Yes","Alive",43
+"No","Alive",42.1
+"Yes","Alive",35.9
+"No","Alive",22.3
+"Yes","Dead",62.1
+"No","Dead",88.6
+"No","Alive",39.1
--- a/module3/exo3/exercice_fr.ipynb
+++ b/module3/exo3/exercice_fr.ipynb
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sujet 6 : Autour du Paradoxe de Simpson"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline \n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Maintenant nous allons telecharger nos données en se servant du lien de téléchargement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url= \"https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv?inline=false\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "maintenant nous importons nos données localement dans Jupyter "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = \"Subject6_smoking.csv\"\n",
+    "\n",
+    "import os\n",
+    "import urllib.request\n",
+    "if not os.path.exists(data_file):\n",
+    "    urllib.request.urlretrieve(data_url,data_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data = pd.read_csv(data_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mission 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous effectuons le calcule des personnes qui ne fumme pas et sont en vie; qui fument et sont en vie; qui ne fument pas et sont mort; qui fument et sont mort "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum_alive_NotSmoking = raw_data[(raw_data[\"Smoker\"] == \"No\") & (raw_data[\"Status\"] == \"Alive\")].shape[0]\n",
+    "sum_alive_Smoking = raw_data[(raw_data[\"Smoker\"] == \"Yes\") & (raw_data[\"Status\"] == \"Alive\")].shape[0]\n",
+    "sum_death_NotSmoking = raw_data[(raw_data[\"Smoker\"] == \"No\") & (raw_data[\"Status\"] == \"Dead\")].shape[0]\n",
+    "sum_death_Smoking = raw_data[(raw_data[\"Smoker\"] == \"Yes\") & (raw_data[\"Status\"] == \"Dead\")].shape[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Maintenant on presente le tableau demandé"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calcule du taux de mortalité"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Pour les femmes qui fumment \n",
+    "taux_fume = (sum_death_Smoking / (sum_death_Smoking+sum_alive_Smoking))*100\n",
+    "taux_fume = round(taux_fume, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Pour les femmes qui ne fument pas \n",
+    "taux_fume_pas = (sum_death_NotSmoking / (sum_death_NotSmoking+sum_alive_NotSmoking))*100\n",
+    "taux_fume_pas = round(taux_fume_pas, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tableau de mortalité"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "données_tab1 = {\"En vie\":[sum_alive_Smoking , sum_alive_NotSmoking], \"Mort\": [sum_death_Smoking, sum_death_NotSmoking], \"Taux mortalité en %\": [taux_fume, taux_fume_pas]}\n",
+    "tab1 = pd.DataFrame(données_tab1)\n",
+    "tab1.index = [\"fume\", \"Ne fume pas\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Affichage du premier tableau "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>En vie</th>\n",
+       "      <th>Mort</th>\n",
+       "      <th>Taux mortalité en %</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>fume</th>\n",
+       "      <td>443</td>\n",
+       "      <td>139</td>\n",
+       "      <td>23.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ne fume pas</th>\n",
+       "      <td>502</td>\n",
+       "      <td>230</td>\n",
+       "      <td>31.42</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             En vie  Mort  Taux mortalité en %\n",
+       "fume            443   139                23.88\n",
+       "Ne fume pas     502   230                31.42"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tab1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa9ad02518>"
+      ]
+     },
+     "execution_count": 62,
+     "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": [
+    "tab1.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mission 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous commençons d'abord par calculer les nombres de personnes par tranche d'age. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Tranche d'age entre 18 à 34 ans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Tranche d'age allant de 18 à 34 ans et fument\n",
+    "age_18_34f = raw_data[(raw_data[\"Age\"] <= 34) & (raw_data[\"Smoker\"] == \"Yes\")].shape[0] # f designe fume\n",
+    "\n",
+    "#Tranche d'age allant de 18 à 34 ans, fument et vivent\n",
+    "age_18_34fv = raw_data[(raw_data[\"Age\"] <= 34) & (raw_data[\"Smoker\"] == \"Yes\")& (raw_data[\"Status\"] == \"Alive\")].shape[0] # fv designe fume et vie \n",
+    "\n",
+    "#Tranche d'age allant de 18 à 34 ans, fument et mort\n",
+    "age_18_34fm = raw_data[(raw_data[\"Age\"] <= 34) & (raw_data[\"Smoker\"] == \"Yes\")& (raw_data[\"Status\"] == \"Dead\")].shape[0] # fm designe fume et mort \n",
+    "\n",
+    "#Tranche d'age allant de 18 à 34 ans et ne fument pas\n",
+    "age_18_34fp = raw_data[(raw_data[\"Age\"] <= 34) & (raw_data[\"Smoker\"] == \"No\")].shape[0] # fp désigne ne fume pas\n",
+    "\n",
+    "#Tranche d'age allant de 18 à 34 ans, fument pas et vivent\n",
+    "age_18_34fpv = raw_data[(raw_data[\"Age\"] <= 34) & (raw_data[\"Smoker\"] == \"No\")& (raw_data[\"Status\"] == \"Alive\")].shape[0] # fpv designe fume pas et vie \n",
+    "\n",
+    "#Tranche d'age allant de 18 à 34 ans, fument pas et mort\n",
+    "age_18_34fpm = raw_data[(raw_data[\"Age\"] <= 34) & (raw_data[\"Smoker\"] == \"No\")& (raw_data[\"Status\"] == \"Dead\")].shape[0] # fpm designe fume pas et mort \n",
+    "\n",
+    "#Tranche d'age allant de 18 à 34 ans et sont en vie\n",
+    "age_18_34v = raw_data[(raw_data[\"Age\"] <= 34) & (raw_data[\"Status\"] == \"Alive\")].shape[0] # v désigne vie\n",
+    "\n",
+    "#Tranche d'age allant de 18 à 34 ans et sont mort\n",
+    "age_18_34m = raw_data[(raw_data[\"Age\"] <= 34) & (raw_data[\"Status\"] == \"Dead\")].shape[0] # m désigne mort "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Tranche d'age allant de 34 à 54 ans (34 exclut mais 54 inclut) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Tranche d'age allant de 34 à 54 ans et fument\n",
+    "age_34_54f = raw_data[(raw_data[\"Age\"] > 34) & (raw_data[\"Age\"] <= 54) & (raw_data[\"Smoker\"] == \"Yes\")].shape[0] # f designe fume\n",
+    "\n",
+    "#Tranche d'age allant de 34 à 54 ans, fument et vivent\n",
+    "age_34_54fv = raw_data[(raw_data[\"Age\"] > 34) & (raw_data[\"Age\"] <= 54) & (raw_data[\"Smoker\"] == \"Yes\")& (raw_data[\"Status\"] == \"Alive\")].shape[0] # fv designe fume et vie \n",
+    "\n",
+    "#Tranche d'age allant de 34 à 54 ans, fument et mort\n",
+    "age_34_54fm = raw_data[(raw_data[\"Age\"] > 34) & (raw_data[\"Age\"] <= 54) & (raw_data[\"Smoker\"] == \"Yes\")& (raw_data[\"Status\"] == \"Dead\")].shape[0] # fm designe fume et mort \n",
+    "\n",
+    "#Tranche d'age allant de 34 à 54 ans et ne fument pas\n",
+    "age_34_54fp = raw_data[(raw_data[\"Age\"] > 34) & (raw_data[\"Age\"] <= 54) & (raw_data[\"Smoker\"] == \"No\")].shape[0] # fp désigne ne fume pas\n",
+    "\n",
+    "#Tranche d'age allant de 34 à 54 ans, fument pas et vivent\n",
+    "age_34_54fpv = raw_data[(raw_data[\"Age\"] > 34) & (raw_data[\"Age\"] <= 54) & (raw_data[\"Smoker\"] == \"No\")& (raw_data[\"Status\"] == \"Alive\")].shape[0] # fpv designe fume pas et vie \n",
+    "\n",
+    "#Tranche d'age allant de 34 à 54 ans, fument pas et mort\n",
+    "age_34_54fpm = raw_data[(raw_data[\"Age\"] > 34) & (raw_data[\"Age\"] <= 54) & (raw_data[\"Smoker\"] == \"No\")& (raw_data[\"Status\"] == \"Dead\")].shape[0] # fpm designe fume pas et mort \n",
+    "\n",
+    "#Tranche d'age allant de 34 à 54 ans et sont en vie\n",
+    "age_34_54v = raw_data[(raw_data[\"Age\"] > 34) & (raw_data[\"Age\"] <= 54) & (raw_data[\"Status\"] == \"Alive\")].shape[0] # v désigne vie\n",
+    "\n",
+    "#Tranche d'age allant de 34 à 54 ans et sont mort\n",
+    "age_34_54m = raw_data[(raw_data[\"Age\"] > 34) & (raw_data[\"Age\"] <= 54) & (raw_data[\"Status\"] == \"Dead\")].shape[0] # m désigne mort"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Tranche d'age allant de 54 à 64 ans (54 exclut mais 64 inclut) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Tranche d'age allant de 54 à 64 ans et fument\n",
+    "age_54_64f = raw_data[(raw_data[\"Age\"] > 54) & (raw_data[\"Age\"] <= 64) & (raw_data[\"Smoker\"] == \"Yes\")].shape[0] # f designe fume\n",
+    "\n",
+    "#Tranche d'age allant de 54 à 64 ans, fument et vivent\n",
+    "age_54_64fv = raw_data[(raw_data[\"Age\"] > 54) & (raw_data[\"Age\"] <= 64) & (raw_data[\"Smoker\"] == \"Yes\")& (raw_data[\"Status\"] == \"Alive\")].shape[0] # fv designe fume et vie \n",
+    "\n",
+    "#Tranche d'age allant de 54 à 64 ans, fument et mort\n",
+    "age_54_64fm = raw_data[(raw_data[\"Age\"] > 54) & (raw_data[\"Age\"] <= 64) & (raw_data[\"Smoker\"] == \"Yes\")& (raw_data[\"Status\"] == \"Dead\")].shape[0] # fm designe fume et mort \n",
+    "\n",
+    "#Tranche d'age allant de 54 à 64 ans et ne fument pas\n",
+    "age_54_64fp = raw_data[(raw_data[\"Age\"] > 54) & (raw_data[\"Age\"] <= 64) & (raw_data[\"Smoker\"] == \"No\")].shape[0] # fp désigne ne fume pas\n",
+    "\n",
+    "#Tranche d'age allant de 54 à 64 ans, fument pas et vivent\n",
+    "age_54_64fpv = raw_data[(raw_data[\"Age\"] > 54) & (raw_data[\"Age\"] <= 64) & (raw_data[\"Smoker\"] == \"No\")& (raw_data[\"Status\"] == \"Alive\")].shape[0] # fpv designe fume pas et vie \n",
+    "\n",
+    "#Tranche d'age allant de 54 à 64 ans, fument pas et mort\n",
+    "age_54_64fpm = raw_data[(raw_data[\"Age\"] > 54) & (raw_data[\"Age\"] <= 64) & (raw_data[\"Smoker\"] == \"No\")& (raw_data[\"Status\"] == \"Dead\")].shape[0] # fpm designe fume pas et mort\n",
+    "\n",
+    "#Tranche d'age allant de 54 à 64 ans et sont en vie\n",
+    "age_54_64v = raw_data[(raw_data[\"Age\"] > 54) & (raw_data[\"Age\"] <= 64) & (raw_data[\"Status\"] == \"Alive\")].shape[0] # v désigne vie\n",
+    "\n",
+    "#Tranche d'age allant de 54 à 64 ans et sont mort\n",
+    "age_54_64m = raw_data[(raw_data[\"Age\"] > 54) & (raw_data[\"Age\"] <= 64) & (raw_data[\"Status\"] == \"Dead\")].shape[0] # m désigne mort"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Tranche d'age plus grand que 65 ans ( 65 inclut) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Tranche d'age plus grand que 65 ans et fument\n",
+    "age_65f = raw_data[(raw_data[\"Age\"] >= 65) & (raw_data[\"Smoker\"] == \"Yes\")].shape[0] # f designe fume\n",
+    "\n",
+    "#Tranche d'age plus grand que 65 ans, fument et vivent\n",
+    "age_65fv = raw_data[(raw_data[\"Age\"] >= 65) & (raw_data[\"Smoker\"] == \"Yes\")& (raw_data[\"Status\"] == \"Alive\")].shape[0] # fv designe fume et vie \n",
+    "\n",
+    "#Tranche d'age plus grand que 65 ans, fument et mort\n",
+    "age_65fm = raw_data[(raw_data[\"Age\"] >= 65) & (raw_data[\"Smoker\"] == \"Yes\")& (raw_data[\"Status\"] == \"Dead\")].shape[0] # fm designe fume et mort \n",
+    "\n",
+    "#Tranche d'age plus grand que 65 ans et ne fument pas\n",
+    "age_65fp = raw_data[(raw_data[\"Age\"] >= 65) & (raw_data[\"Smoker\"] == \"No\")].shape[0] # fp désigne ne fume pas\n",
+    "\n",
+    "#Tranche d'age plus grand que 65 ans, fument pas et vivent\n",
+    "age_65fpv = raw_data[(raw_data[\"Age\"] >= 65) & (raw_data[\"Smoker\"] == \"No\")& (raw_data[\"Status\"] == \"Alive\")].shape[0] # fpv designe fume pas et vie \n",
+    "\n",
+    "#Tranche d'age plus grand que 65 ans, fument pas et mort\n",
+    "age_65fpm = raw_data[(raw_data[\"Age\"] >= 65) & (raw_data[\"Smoker\"] == \"No\")& (raw_data[\"Status\"] == \"Dead\")].shape[0] # fpm designe fume pas et mort\n",
+    "\n",
+    "#Tranche d'age plus grand que 65 ans et sont en vie\n",
+    "age_65v = raw_data[(raw_data[\"Age\"] >= 65) & (raw_data[\"Status\"] == \"Alive\")].shape[0] # v désigne vie\n",
+    "\n",
+    "#Tranche d'age plus grand que 65 ans et sont mort\n",
+    "age_65m = raw_data[(raw_data[\"Age\"] >= 65) & (raw_data[\"Status\"] == \"Dead\")].shape[0] # m désigne mort"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Calcule des taux de mortalité"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Pour les d'age allant de 18 à 34 ans qui fument\n",
+    "taux_18_34f = (age_18_34fm/ (age_18_34fm+age_18_34fv))*100\n",
+    "taux_18_34f = round(taux_18_34f, 2)\n",
+    "\n",
+    "#Pour les d'age allant de 18 à 34 ans qui ne fument pas\n",
+    "taux_18_34fp = (age_18_34fpm/ (age_18_34fpm+age_18_34fpv))*100\n",
+    "taux_18_34fp = round(taux_18_34fp, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Pour les d'age allant de 34 à 54 ans qui fument\n",
+    "taux_34_54f = (age_34_54fm/ (age_34_54fm+age_34_54fv))*100\n",
+    "taux_34_54f = round(taux_34_54f, 2)\n",
+    "\n",
+    "#Pour les d'age allant de 34 à 54 ans qui ne fument pas\n",
+    "taux_34_54fp = (age_34_54fpm/ (age_34_54fpm+age_34_54fpv))*100\n",
+    "taux_34_54fp = round(taux_34_54fp, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Pour les d'age allant de 54 à 64 ans qui fument\n",
+    "taux_54_64f = (age_54_64fm/ (age_54_64fm+age_54_64fv))*100\n",
+    "taux_54_64f = round(taux_54_64f, 2)\n",
+    "\n",
+    "#Pour les d'age allant de 54 à 64 ans qui ne fument pas\n",
+    "taux_54_64fp = (age_54_64fpm/ (age_54_64fpm+age_54_64fpv))*100\n",
+    "taux_54_64fp = round(taux_54_64fp, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Pour les d'age plus grand que 65 ans qui fument\n",
+    "taux_65f = (age_65fm/ (age_65fm+age_65fv))*100\n",
+    "taux_65f = round(taux_65f, 2)\n",
+    "\n",
+    "#Pour les d'age plus grand que 65 ans qui ne fument pas\n",
+    "taux_65fp = (age_65fpm/ (age_65fpm+age_65fpv))*100\n",
+    "taux_65fp = round(taux_65fp, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tableau de mortalité "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "données_tab2 = {\"Fume\":[age_18_34f, age_34_54f, age_54_64f, age_65f],\"Ne fume pas\":[age_18_34fp, age_34_54fp, age_54_64fp, age_65fp],\"En vie\":[age_18_34v, age_34_54v, age_54_64v, age_65v],\"Mort\":[age_18_34m, age_34_54m, age_54_64m, age_65m], \"Taux mortalité de ceux qui fument en %\": [taux_18_34f, taux_34_54f, taux_54_64f, taux_65f], \"Taux mortalité de ceux qui ne fument pas en %\": [taux_18_34fp, taux_34_54fp, taux_54_64fp, taux_65fp]}\n",
+    "tab2 = pd.DataFrame(données_tab2)\n",
+    "tab2.index = [\"18 à 34\", \"34 à 54\", \"54 à 64\", \">=65\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Afichage du deuxième tableau"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "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>En vie</th>\n",
+       "      <th>Fume</th>\n",
+       "      <th>Mort</th>\n",
+       "      <th>Ne fume pas</th>\n",
+       "      <th>Taux mortalité de ceux qui fument en %</th>\n",
+       "      <th>Taux mortalité de ceux qui ne fument pas en %</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>18 à 34</th>\n",
+       "      <td>389</td>\n",
+       "      <td>181</td>\n",
+       "      <td>11</td>\n",
+       "      <td>219</td>\n",
+       "      <td>2.76</td>\n",
+       "      <td>2.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34 à 54</th>\n",
+       "      <td>376</td>\n",
+       "      <td>237</td>\n",
+       "      <td>60</td>\n",
+       "      <td>199</td>\n",
+       "      <td>17.30</td>\n",
+       "      <td>9.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54 à 64</th>\n",
+       "      <td>145</td>\n",
+       "      <td>115</td>\n",
+       "      <td>91</td>\n",
+       "      <td>121</td>\n",
+       "      <td>44.35</td>\n",
+       "      <td>33.06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>&gt;=65</th>\n",
+       "      <td>35</td>\n",
+       "      <td>49</td>\n",
+       "      <td>207</td>\n",
+       "      <td>193</td>\n",
+       "      <td>85.71</td>\n",
+       "      <td>85.49</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         En vie  Fume  Mort  Ne fume pas  \\\n",
+       "18 à 34     389   181    11          219   \n",
+       "34 à 54     376   237    60          199   \n",
+       "54 à 64     145   115    91          121   \n",
+       ">=65         35    49   207          193   \n",
+       "\n",
+       "         Taux mortalité de ceux qui fument en %  \\\n",
+       "18 à 34                                    2.76   \n",
+       "34 à 54                                   17.30   \n",
+       "54 à 64                                   44.35   \n",
+       ">=65                                      85.71   \n",
+       "\n",
+       "         Taux mortalité de ceux qui ne fument pas en %  \n",
+       "18 à 34                                           2.74  \n",
+       "34 à 54                                           9.55  \n",
+       "54 à 64                                          33.06  \n",
+       ">=65                                             85.49  "
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tab2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Le taux de mortalité de ceux qui on plus de 65 ans est élevé qu'elle fument ou pas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generation du graphique "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa9adcf7b8>"
+      ]
+     },
+     "execution_count": 93,
+     "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": [
+    "tab2.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Mission 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
@@ -16,10 +686,9 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/readme.md
+++ b/readme.md
@@ -7,3 +7,4 @@ Ce dépôt contient les fichiers utiles au MOOC Recherche Reproductible.
 EN
 This repository contains useful files for the Reproducible Research MOOC.
+hello
\ No newline at end of file