{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      dep  sexe        jour  hosp  rea   rad   dc\n",
      "0      01     0  2020-03-18     2    0     1    0\n",
      "1      01     1  2020-03-18     1    0     1    0\n",
      "2      01     2  2020-03-18     1    0     0    0\n",
      "3      02     0  2020-03-18    41   10    18   11\n",
      "4      02     1  2020-03-18    19    4    11    6\n",
      "5      02     2  2020-03-18    22    6     7    5\n",
      "6      03     0  2020-03-18     4    0     1    0\n",
      "7      03     1  2020-03-18     1    0     0    0\n",
      "8      03     2  2020-03-18     3    0     1    0\n",
      "9      04     0  2020-03-18     3    1     2    0\n",
      "10     04     1  2020-03-18     3    1     0    0\n",
      "11     04     2  2020-03-18     0    0     2    0\n",
      "12     05     0  2020-03-18     8    1     9    0\n",
      "13     05     1  2020-03-18     1    0     7    0\n",
      "14     05     2  2020-03-18     7    1     2    0\n",
      "15     06     0  2020-03-18    25    1    47    2\n",
      "16     06     1  2020-03-18    15    1    19    0\n",
      "17     06     2  2020-03-18    10    0    28    2\n",
      "18     07     0  2020-03-18    12    1     0    0\n",
      "19     07     1  2020-03-18     7    1     0    0\n",
      "20     07     2  2020-03-18     3    0     0    0\n",
      "21     08     0  2020-03-18     0    0     1    0\n",
      "22     08     1  2020-03-18     0    0     1    0\n",
      "23     08     2  2020-03-18     0    0     0    0\n",
      "24     09     0  2020-03-18     1    1     2    0\n",
      "25     09     1  2020-03-18     0    0     1    0\n",
      "26     09     2  2020-03-18     1    1     1    0\n",
      "27     10     0  2020-03-18     5    0     0    0\n",
      "28     10     1  2020-03-18     4    0     0    0\n",
      "29     10     2  2020-03-18     1    0     0    0\n",
      "...   ...   ...         ...   ...  ...   ...  ...\n",
      "9063   91     0  2020-04-16  1171  200   747  273\n",
      "9064   91     1  2020-04-16   598  138   452  162\n",
      "9065   91     2  2020-04-16   552   57   292  109\n",
      "9066   92     0  2020-04-16  1977  406  1856  620\n",
      "9067   92     1  2020-04-16  1114  297  1080  363\n",
      "9068   92     2  2020-04-16   847  105   768  252\n",
      "9069   93     0  2020-04-16  1569  242  1384  609\n",
      "9070   93     1  2020-04-16   852  170   830  383\n",
      "9071   93     2  2020-04-16   703   67   542  222\n",
      "9072   94     0  2020-04-16  2249  327  1407  681\n",
      "9073   94     1  2020-04-16  1176  234   791  390\n",
      "9074   94     2  2020-04-16  1051   92   599  275\n",
      "9075   95     0  2020-04-16   937  173  1248  423\n",
      "9076   95     1  2020-04-16   550  124   724  277\n",
      "9077   95     2  2020-04-16   381   48   520  144\n",
      "9078  971     0  2020-04-16    34   17    59   10\n",
      "9079  971     1  2020-04-16    24   13    27    4\n",
      "9080  971     2  2020-04-16    10    4    32    6\n",
      "9081  972     0  2020-04-16    40   17    55    6\n",
      "9082  972     1  2020-04-16    26   14    28    4\n",
      "9083  972     2  2020-04-16    14    3    27    2\n",
      "9084  973     0  2020-04-16     8    1    24    0\n",
      "9085  973     1  2020-04-16     3    0    12    0\n",
      "9086  973     2  2020-04-16     5    1    12    0\n",
      "9087  974     0  2020-04-16    19    4    83    0\n",
      "9088  974     1  2020-04-16    13    4    38    0\n",
      "9089  974     2  2020-04-16     6    0    45    0\n",
      "9090  976     0  2020-04-16    22    6    39    4\n",
      "9091  976     1  2020-04-16    16    5    23    4\n",
      "9092  976     2  2020-04-16     6    1    16    0\n",
      "\n",
      "[9093 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import csv\n",
    "headers = ['dep','sexe','jour','hosp','rea','rad','dc']\n",
    "data = pd.read_csv (r'https://app-learninglab.inria.fr/moocrr/gitlab/fb603eb03b4a7730590df7596fd58966/mooc-rr/raw/master/module2/exo4/donnees-hospitalieres-covid19-2020-04-17-08h44.csv', names = headers, delimiter=';')\n",
    "print (data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "jour = data['jour']\n",
    "hosp = data['hosp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "mp.pyplot.plot_date(jour, hosp, xdate=True)\n",
    "mp.pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3281"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(data['hosp'])"
   ]
  },
  {
   "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": 2
}