{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Around Simpson's Paradox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data are available in the MOOC repository. The CSV file contains data for the 1314 women that were polled in Whickham, England, in 1972-1974 and were categorized as \"currently smoking\" or \"never smoked\". Each line is related to a person and contains whether she smokes or not, whether alive or dead twenty year after the survey and her age at the time of the survey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 1314\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Smoker</th>\n",
       "      <th>Status</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>19.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Dead</td>\n",
       "      <td>57.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>47.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>81.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Smoker Status   Age\n",
       "0    Yes  Alive  21.0\n",
       "1    Yes  Alive  19.3\n",
       "2     No   Dead  57.5\n",
       "3     No  Alive  47.1\n",
       "4    Yes  Alive  81.4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_url = \"https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv\"\n",
    "data = pd.read_csv(data_url)\n",
    "print(\"Number of rows:\", len(data))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CSV file does not contain any missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows with missing values: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of rows with missing values:\", data.isnull().any(axis=1).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the number of women alive and dead after twenty years, according to their smoking habits. A heatmap is effective in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "count = np.array(data.groupby(['Smoker', 'Status']).count())\n",
    "count = np.reshape(count, (2, 2))\n",
    "annots = np.array([f\"{v}\\n{v/len(data):.2%}\" for v in count.flatten()]).reshape(2,2)\n",
    "\n",
    "sns.heatmap(count, annot=annots, fmt=\"\", cmap='Blues', cbar=False,\n",
    "           xticklabels=['Alive', 'Dead'], yticklabels=['No', 'Yes'])\n",
    "plt.title(\"Number and percentage of alive/dead women after 20 years, according to smoking habits\")\n",
    "plt.xlabel(\"Status\")\n",
    "plt.ylabel(\"Smoker\")\n",
    "plt.show()"
   ]
  }
 ],
 "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": 2
}