{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse des mots-clés de mon journal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mise en forme de mon journal\n",
    "Mes paragraphes sont titrés \"Date:JJ/MM/AAAA\".\n",
    "\n",
    "La première ligne du paragraphe contient [Mots Clés:Xxxx,Yyyy,Zzzz] où Xxxx, Yyyy, Zzzz sont des mots-clés."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recupération des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "NomFichentree = \"../../journal/Readme.md\"\n",
    "NomFichsortie = \"./Info_Journal.csv\"\n",
    "\n",
    "with open(NomFichsortie, \"w\", encoding='utf-8') as sortie:\n",
    "    with open(NomFichentree, \"r\", encoding='utf-8') as entree:\n",
    "        for ligne in entree:\n",
    "            if \"Date:\" in ligne:\n",
    "                (rien,date) = ligne.lstrip('## ').strip().split(':')\n",
    "            if \"Mots Clés\" in ligne:\n",
    "                (rien,mots) = ligne.strip('Mots Clés').split(':')\n",
    "                listemots = mots.strip().split(',')\n",
    "                for mot in listemots:\n",
    "                    sortie.write(f\"{date};{mot}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyse des données\n",
    "On peut regarder l'aspect des données."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            0             1\n",
      "0  12/07/2020          Mooc\n",
      "1  12/07/2020  Installation\n",
      "2  12/07/2020        Python\n",
      "3  12/07/2020       Jupiter\n",
      "4  12/07/2020             R \n",
      "...\n",
      "              0        1\n",
      "33  22/08/2020  Réunion\n",
      "34  22/08/2020   Python\n",
      "35  28/08/2020  Lecture\n",
      "36  28/08/2020   Python\n",
      "37  28/08/2020      GUI\n"
     ]
    }
   ],
   "source": [
    "NomFichentree = \"./Info_Journal.csv\"\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "donnees = pd.read_csv(NomFichentree, sep=';', header=None,\n",
    "                      infer_datetime_format=True, keep_date_col=True,\n",
    "                      dayfirst=True)\n",
    "\n",
    "print(donnees.head(), '\\n...\\n', donnees.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous pouvons ensuite regarder:\n",
    "- le nombre total de données (le même que le nombre de lignes),\n",
    "- le nombre de données uniques (pour les dates et pour les mots-clés),\n",
    "- la date et le mot-clé les plus fréquents, avec leur fréquences respectives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 0     1\n",
      "count           38    38\n",
      "unique          13    15\n",
      "top     12/07/2020  Mooc\n",
      "freq             5     7\n"
     ]
    }
   ],
   "source": [
    "print(donnees.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous pouvons regarder, également les fréquences respectives de chaque date et de chaque mot-clé."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12/07/2020    5\n",
      "17/07/2020    5\n",
      "28/08/2020    3\n",
      "13/08/2020    3\n",
      "23/07/2020    3\n",
      "08/08/2020    3\n",
      "17/08/2020    3\n",
      "10/08/2020    3\n",
      "21/07/2020    2\n",
      "20/07/2020    2\n",
      "25/07/2020    2\n",
      "02/08/2020    2\n",
      "22/08/2020    2\n",
      "Name: 0, dtype: int64\n",
      "Mooc                    7\n",
      "Python                  5\n",
      "Lecture                 3\n",
      "Module 2                3\n",
      "Réunion                 3\n",
      "GUI                     3\n",
      "Exercices               3\n",
      "Module 1                2\n",
      "R                       2\n",
      "Jupiter                 2\n",
      "Git                     1\n",
      "Pannes oscillatoires    1\n",
      "Génération ACS          1\n",
      "Configuration           1\n",
      "Installation            1\n",
      "Name: 1, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(pd.value_counts(donnees[0]))\n",
    "print(pd.value_counts(donnees[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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