{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sujet 6 : Autour du Paradoxe de Simpson\n",
    "---\n",
    "**Prérequis :** calcul de moyennes et de ratios, techniques de présentations graphiques simples, éventuellement régression logistique\n",
    "\n",
    "---\n",
    "\n",
    "En 1972-1974, à Whickham, une ville du nord-est de l'Angleterre, située à environ 6,5 kilomètres au sud-ouest de Newcastle upon Tyne, un sondage d'un sixième des électeurs a été effectué afin d'éclairer des travaux sur les maladies thyroïdiennes et cardiaques (Tunbridge et al. 1977). Une suite de cette étude a été menée vingt ans plus tard (Vanderpump et al. 1995). Certains des résultats avaient trait au tabagisme et cherchaient à savoir si les individus étaient toujours en vie lors de la seconde étude. Par simplicité, nous nous restreindrons aux femmes et parmi celles-ci aux 1314 qui ont été catégorisées comme \"fumant actuellement\" ou \"n'ayant jamais fumé\". Il y avait relativement peu de femmes dans le sondage initial ayant fumé et ayant arrêté depuis (162) et très peu pour lesquelles l'information n'était pas disponible (18). La survie à 20 ans a été déterminée pour l'ensemble des femmes du premier sondage.\n",
    "\n",
    "---\n",
    "\n",
    "**Votre mission si vous l'acceptez :**\n",
    "\n",
    "1. Représentez dans un tableau le nombre total de femmes vivantes et décédées sur la période en fonction de leur habitude de tabagisme. Calculez dans chaque groupe (fumeuses / non fumeuses) le taux de mortalité (le rapport entre le nombre de femmes décédées dans un groupe et le nombre total de femmes dans ce groupe). Vous pourrez proposer une représentation graphique de ces données et calculer des intervalles de confiance si vous le souhaitez. En quoi ce résultat est-il surprenant ?\n",
    "2. Reprenez la question 1 (effectifs et taux de mortalité) en rajoutant une nouvelle catégorie liée à la classe d'âge. On considérera par exemple les classes suivantes : 18-34 ans, 34-54 ans, 55-64 ans, plus de 65 ans. En quoi ce résultat est-il surprenant ? Arrivez-vous à expliquer ce paradoxe ? De même, vous pourrez proposer une représentation graphique de ces données pour étayer vos explications.\n",
    "3. Afin d'éviter un biais induit par des regroupements en tranches d'âges arbitraires et non régulières, il est envisageable d'essayer de réaliser une régression logistique. Si on introduit une variable Death valant 1 ou 0 pour indiquer si l'individu est décédé durant la période de 20 ans, on peut étudier le modèle Death ~ Age pour étudier la probabilité de décès en fonction de l'âge selon que l'on considère le groupe des fumeuses ou des non fumeuses. Ces régressions vous permettent-elles de conclure sur la nocivité du tabagisme ? Vous pourrez proposer une représentation graphique de ces régressions (en n'omettant pas les régions de confiance).\n",
    "4. Déposez votre étude dans FUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os\n",
    "import urllib.request"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En suivant la technique apprise dans le precédent exercice, on vérifie si le jeu de données existe déjà sous la forme d'un fichier local .csv et si non, on le télécharge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "datas_file = 'simpson_paradox.csv'\n",
    "datas_url = 'https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv'\n",
    "\n",
    "if not os.path.exists(datas_file):\n",
    "    urllib.request.urlretrieve(datas_url, datas_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_datas = pd.read_csv(datas_file, header=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On vérifie l'absence de données nulles par ligne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Smoker</th>\n",
       "      <th>Status</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Smoker, Status, Age]\n",
       "Index: []"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_datas[raw_datas.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Exercice 1\n",
    "Représentez dans un tableau le nombre total de femmes vivantes et décédées sur la période en fonction de leur habitude de tabagisme. Calculez dans chaque groupe (fumeuses / non fumeuses) le taux de mortalité (le rapport entre le nombre de femmes décédées dans un groupe et le nombre total de femmes dans ce groupe). Vous pourrez proposer une représentation graphique de ces données et calculer des intervalles de confiance si vous le souhaitez. En quoi ce résultat est-il surprenant ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- D'après l'énoncé, il faudrait classer les données selon que les femmes aient été fummeuses, donc on copie la DataFrame `raw_datas` en modifiant son index par la colonne `Smoker`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_datas1 = raw_datas.set_index('Smoker').sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- On peut ensuite utiliser la fonction `.loc` pour avoir uniquement les fumeuses (`Yes`) ou les non fumeuses (`No`) et créer deux nouveau objets de type DataFrame `smokers` & `non_smokers`\n",
    "    - le total de lignes étant le nombre total de femmes appartenant à ce groupe et peut être obtenu via la fonction `len`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hideOutput": true
   },
   "outputs": [],
   "source": [
    "smokers  = sorted_datas1.loc['Yes']\n",
    "non_smokers = sorted_datas1.loc['No']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total fumeuses = 582 / Total non-fumeuses = 732\n"
     ]
    }
   ],
   "source": [
    "smokers_total = len(smokers)\n",
    "non_smokers_total = len(non_smokers)\n",
    "print('Total fumeuses = {} / Total non-fumeuses = {}'.format(smokers_total, non_smokers_total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- On peut accéder au nombre total de femmes décédées par groupe  via `(non_)smokers.loc[smokers['Status'] == 'Dead']`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fumeuses mortes = 139 / Non-fumeuses mortes = 230\n"
     ]
    }
   ],
   "source": [
    "smokers_dead = len(smokers.loc[smokers['Status'] == 'Dead'])\n",
    "non_smokers_dead = len(non_smokers.loc[non_smokers['Status'] == 'Dead'])\n",
    "print('Fumeuses mortes = {} / Non-fumeuses mortes = {}'.format(smokers_dead, non_smokers_dead))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- On calcule le taux de mortalité par groupe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mortalité chez les femmes fumeuses : 4.19\n",
      "Mortalité chez les femmes non-fumeuses : 3.18\n"
     ]
    }
   ],
   "source": [
    "smokers_mortality = smokers_total / smokers_dead\n",
    "non_smokers_mortality = non_smokers_total / non_smokers_dead\n",
    "print('Mortalité chez les femmes fumeuses : {:.2f}'.format(smokers_mortality))\n",
    "print('Mortalité chez les femmes non-fumeuses : {:.2f}'.format(non_smokers_mortality))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enfin on affiche les proportions de femmes vivantes/decédée, on peut choisir différent type de graphiques, j'ai finalement choisit d'utiliser des camemberts car je trouvais cela plus parlant. En effet avec de tels graphiques on pourrait penser que la mortalité chez les non-fumeuses ets plus important que chez les non-fumeuses alors que la moprtalité est effectiveent plus grande chez les fumeuses. Si on choisit de plotter les même séries de données en hisogramme par exemple on a la même illusion visuelle, si on applique la même échelle en y aux trois graphiques, l'illusion est toujours persistante. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e579fb128>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=3, sharey=False, figsize=(20, 6))\n",
    "smokers['Status'].value_counts().plot.pie(title='Fumeuses', ax=axes[0])\n",
    "non_smokers['Status'].value_counts().plot.pie(title='Non-fumeuses', ax=axes[1])\n",
    "sorted_datas1['Status'].value_counts().plot.pie(title='Total', ax=axes[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e57818668>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=3, sharey=False, figsize=(20, 6))\n",
    "smokers['Status'].value_counts().plot.bar(title='Fumeuses', ax=axes[0])\n",
    "non_smokers['Status'].value_counts().plot.bar(title='Non-fumeuses', ax=axes[1])\n",
    "sorted_datas1['Status'].value_counts().plot.bar(title='Total', ax=axes[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e574d5438>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=3, sharey=True, figsize=(20, 6))\n",
    "smokers['Status'].value_counts().plot.bar(title='Fumeuses', ax=axes[0])\n",
    "non_smokers['Status'].value_counts().plot.bar(title='Non-fumeuses', ax=axes[1])\n",
    "sorted_datas1['Status'].value_counts().plot.bar(title='Total', ax=axes[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Exercice 2\n",
    "Reprenez la question 1 (effectifs et taux de mortalité) en rajoutant une nouvelle catégorie liée à la classe d'âge. On considérera par exemple les classes suivantes : 18-34 ans, 34-54 ans, 55-64 ans, plus de 65 ans. En quoi ce résultat est-il surprenant ? Arrivez-vous à expliquer ce paradoxe ? De même, vous pourrez proposer une représentation graphique de ces données pour étayer vos explications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cette fois-ci, on choisit de s'intéresser à des catégories d'âge arbitraires, ce niveau de classement est introduit après la séparation fumeuses/non-fumeuses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e5717c470>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=4, sharey=True, figsize=(15, 5))\n",
    "smokers.set_index('Age').sort_index().loc[18 : 34]['Status'].value_counts().plot.bar(title='Fumeuses 18-34 ans', ax=axes[0])\n",
    "smokers.set_index('Age').sort_index().loc[34 : 54]['Status'].value_counts().plot.bar(title='Fumeuses 34-54 ans', ax=axes[1])\n",
    "smokers.set_index('Age').sort_index().loc[55 : 64]['Status'].value_counts().plot.bar(title='Fumeuses 55-64 ans', ax=axes[2])\n",
    "smokers.set_index('Age').sort_index().loc[65:]['Status'].value_counts().plot.bar(title='Fumeuses 65+ ans', ax=axes[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3e56e8e7f0>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=4, sharey=True, figsize=(15, 5))\n",
    "non_smokers.set_index('Age').sort_index().loc[18 : 34]['Status'].value_counts().plot.bar(title='Non-fumeuses 18-34 ans', ax=axes[0])\n",
    "non_smokers.set_index('Age').sort_index().loc[34 : 54]['Status'].value_counts().plot.bar(title='Fumeuses 34-54 ans', ax=axes[1])\n",
    "non_smokers.set_index('Age').sort_index().loc[55 : 64]['Status'].value_counts().plot.bar(title='Fumeuses 55-64 ans', ax=axes[2])\n",
    "non_smokers.set_index('Age').sort_index().loc[65:]['Status'].value_counts().plot.bar(title='Fumeuses 65+ ans', ax=axes[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-58-e55079056663>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;31m#print(non_smokers.set_index('Age').sort_index().loc[i[1][0] : i[1][1]]['Status'])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mnon_smokers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAge\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'yes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1119\u001b[0m         raise ValueError(\"The truth value of a {0} is ambiguous. \"\n\u001b[1;32m   1120\u001b[0m                          \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1121\u001b[0;31m                          .format(self.__class__.__name__))\n\u001b[0m\u001b[1;32m   1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m     \u001b[0m__bool__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "ages = [(18, 34), (34,54), (55,64), 65]\n",
    "\n",
    "for i in enumerate(cat):\n",
    "    #print(i[0]) 0\n",
    "    #print(i[1]) (18,34)\n",
    "    #print(i[1][0]) #18\n",
    "    #print(i[1][1]) 34\n",
    "    #isinstance(i[1], tuple)\n",
    "    \n",
    "    if isinstance(i[1], tuple):\n",
    "        #print(non_smokers.set_index('Age').sort_index().loc[i[1][0] : i[1][1]]['Status'])\n",
    "        if i[1][0] <= non_smokers.Age <= i[1][1]:\n",
    "            print('yes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il semble assez clair que l'énoncé cherche à nous faire relever que le taux de mortalité est plus faible chez les fumeuses de 65+ que chez celles de 55-64ans mais on peut assez simplement expliquer ce paradoxe part le fait que le total des femmes fumeuses de 65+ soit beaucoup plus faible"
   ]
  }
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