{
 "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\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('journal_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['24/09/2020' '25/09/2020' '26/09/2020' '27/09/2020' '05/10/2020']\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",
    "date = np.array(data['date'])\n",
    "\n",
    "date_time_obj = [datetime.strptime(date[i], '%d/%m/%Y') for i in range(len(date))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse graphique\n",
    "\n",
    "Etude graphie présentant le nombre de quizzs et d'exercices réalisés en fonction de la date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x384 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "plt.figure(num=None, figsize=(10, 4), dpi=96, facecolor='w', edgecolor='k')\n",
    "p1 = plt.bar(date_time_obj, nb_quizzs, 0.9, color='blue')\n",
    "plt.bar(date_time_obj, nb_exos, 0.9, bottom=nb_quizzs, color='orange')\n",
    "plt.yticks([0, 1, 2, 3 , 4])\n",
    "plt.xlabel('date')\n",
    "plt.ylabel('number of quizzs / exercises')\n",
    "plt.legend(['quizz', 'exercise'], loc='upper center')\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
}