{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse statistique du journal de bord du MOOC\n",
    "Le but de cette analyse est de mettre en avant quelques statistiques sur le journal écrit en __Markdown__ et disponible [ici](https://app-learninglab.inria.fr/moocrr/jupyter/user/0f544bda50e5a7465a15bc07714abde6/edit/work/journal/journal.md).\n",
    "\n",
    "Bien que le document soit bien vide, nous allons quand même essayer de faire quelques statistiques qui viendront s'éttofer avec l'avancement dans le MOOC."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importation et filtrage des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('journal_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 2, 3, 3, 0] [0, 2, 0, 0, 4]\n"
     ]
    }
   ],
   "source": [
    "quizzs = np.array(data['quizzs'])\n",
    "exos = np.array(data['exercises'])\n",
    "nb_quizzs = []\n",
    "nb_exos = []\n",
    "\n",
    "for i in range(len(quizzs)):\n",
    "    nb_quizz_day = np.ndarray.tolist(np.char.split(quizzs[i]))\n",
    "    if nb_quizz_day[0] != '0':\n",
    "        nb_quizzs.append(len(nb_quizz_day))\n",
    "    else:\n",
    "        nb_quizzs.append(0)\n",
    "        \n",
    "    nb_exo_day = np.ndarray.tolist(np.char.split(exos[i]))\n",
    "    if nb_exo_day[0] != '0':\n",
    "        nb_exos.append(len(nb_exo_day))\n",
    "    else:\n",
    "        nb_exos.append(0)\n",
    "\n",
    "print(nb_quizzs, nb_exos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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",
    "\n",
    "fig, ax = plt.subplots(1)\n",
    "p1 = plt.bar(data['date'], nb_quizzs, 0.9)\n",
    "plt.bar(data['date'], nb_exos, 0.9, bottom=nb_quizzs)\n",
    "plt.yticks([0, 1, 2, 3 , 4])\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
}