{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistique sur examen module 3A\n",
    "Je m'intéresse à l'évolution des notes sur l'ensemble des année soit de 2001 à 2020 (Il manque les notes des années 2002 et 2003 introuvables ???).\n",
    "## Création de la base de donnée\n",
    "### Lecture fichier CVS obtenue à partir des fichiers excel annuels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['Notes\\\\Année', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], ['2001', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020'], ['17.0833333333333', '26.2820512820513', '15', '11', '7.5', '10', '10', '9.5', '11.5', '7', '11', '13.5', '13.5', '15', '13.5', '7.5', '10.5', '14'], ['15.4166666666667', '23.7179487179487', '9', '12.5', '14', '9', '12', '12.5', '10.5', '11.5', '13', '15.5', '17', '10', '13', '16.5', '8.5', '11'], ['6.66666666666667', '10.2564102564103', '16.5', '11.5', '9', '13', '15', '10.5', '12.5', '12.5', '15.5', '10.5', '18', '13.5', '16.5', '15', '7.5', '11'], ['10', '15.3846153846154', '1', '11', '12', '15', '15', '16', '9', '8.5', '12.5', '6.5', '14', '17', '14', '17.5', '10.5', '11'], ['10', '15.3846153846154', '15.5', '16.5', '9', '14', '16', '10', '10', '12', '13', '14.5', '14.5', '11.5', '8', '12', '9', '15'], ['8.33333333333333', '12.8205128205128', '18.5', '8', '12.5', '12', '17', '10.5', '11.5', '10', '9', '16.5', '18.5', '16', '18', '16.5', '14.5', '10'], ['13.3333333333333', '20.5128205128205', '12.5', '12', '11', '11', '13', '9', '13.5', '11.5', '15', '5.5', '', '13', '15.5', '7.5', '16.5', '14.5'], ['10', '15.3846153846154', '15.5', '16.5', '9', '11', '12', '14.5', '13', '9.5', '10', '13.5', '', '12.5', '11', '10.5', '18', '11.5'], ['11.25', '17.3076923076923', '5', '6', '15', '15', '14', '14.5', '11', '12', '17.5', '', '', '13.5', '10', '10', '16', '12'], ['7.08333333333333', '10.8974358974359', '16.5', '13', '12.5', '8', '13', '9.5', '13', '17.5', '8.5', '', '', '15', '14', '17', '15.5', '18.5'], ['14.5833333333333', '22.4358974358974', '16', '10', '', '17', '11', '8.5', '8.5', '14', '12.5', '', '', '14.5', '14', '18', '17.5', '12'], ['7.91666666666667', '12.1794871794872', '12', '10.5', '', '14', '11', '13', '', '12', '14.5', '', '', '11.5', '', '15.5', '16', ''], ['2.91666666666667', '4.48717948717949', '6', '14.5', '', '13', '11', '9', '', '15', '11', '', '', '14.5', '', '16.5', '15.5', ''], ['12.9166666666667', '', '12.5', '13.5', '', '14', '16', '10.5', '', '14.5', '10.5', '', '', '19.5', '', '14', '11.5', ''], ['12.0833333333333', '', '13', '8', '', '11', '12', '', '', '13', '', '', '', '15', '', '', '8', ''], ['7.08333333333333', '', '16.5', '12.5', '', '6', '13', '', '', '', '', '', '', '17', '', '', '10.5', ''], ['14.5833333333333', '', '3.5', '15', '', '10', '', '', '', '', '', '', '', '11', '', '', '7.5', ''], ['13.75', '', '', '12.5', '', '15', '', '', '', '', '', '', '', '', '', '', '16.5', ''], ['12.0833333333333', '', '', '16', '', '10', '', '', '', '', '', '', '', '', '', '', '15', ''], ['5.41666666666667', '', '', '13', '', '', '', '', '', '', '', '', '', '', '', '', '17.5', ''], ['7.5', '', '', '11.5', '', '', '', '', '', '', '', '', '', '', '', '', '18', ''], ['13.75', '', '', '9.5', '', '', '', '', '', '', '', '', '', '', '', '', '13', ''], ['5.83333333333333', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '14', ''], ['9.58333333333333', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '12.5', ''], ['11.25', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '7.5', ''], ['13.75', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '15.5', ''], ['14.5833333333333', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '14', ''], ['13.75', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], ['6.25', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], ['7.08333333333333', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], ['12.9166666666667', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], ['7.08333333333333', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], ['16.25', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], ['17.5', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], ['10', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], ['6.66666666666667', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], ['14.5833333333333', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], ['10.4166666666667', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']]\n"
     ]
    }
   ],
   "source": [
    "# Récupération de données d'un ficier CSV\n",
    "# Attention!!!!!!!\n",
    "# Le separateur doit etre \";\" lors de la création du ficier CSV dans EXCEL\n",
    "\n",
    "###################################################\n",
    "# Importation des modules utilisés\n",
    "import csv  #module pour lecture format CSV\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Nom du fichier à traiter\n",
    "nomfichier='base_notes.csv'\n",
    "\n",
    "###################################################\n",
    "# Lecture des données du fichier stockées alors dans Data\n",
    "\n",
    "Data=[]\n",
    "with open(nomfichier, newline='') as csvfile:\n",
    "    DataBrute = csv.reader(csvfile, delimiter=';')\n",
    "    Data = [row for row in DataBrute] \n",
    "print(Data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mise en forme des donnée pour exploitation dans python\n",
    "### Vérification du format des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notes\\Année\n"
     ]
    }
   ],
   "source": [
    "nom = Data[0][0]\n",
    "print(nom)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Affichage des années disponibles et de leur nombre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2001', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020']\n",
      "Nombre d'années disponibles 18\n",
      "2008\n"
     ]
    }
   ],
   "source": [
    "annees = Data[1][:] \n",
    "print(annees)\n",
    "nbr_annees=len(Data[1][:])\n",
    "print('Nombre d\\'années disponibles', nbr_annees)\n",
    "print(str(annees[5]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construction d'un tableau 2D des notes tries par année"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Attention la numérotation des colonnes débute à 0**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Tableau final rangé : \n",
      "\n",
      "[['2001', [17.08, 15.42, 6.67, 10.0, 10.0, 8.33, 13.33, 10.0, 11.25, 7.08, 14.58, 7.92, 2.92, 12.92, 12.08, 7.08, 14.58, 13.75, 12.08, 5.42, 7.5, 13.75, 5.83, 9.58, 11.25, 13.75, 14.58, 13.75, 6.25, 7.08, 12.92, 7.08, 16.25, 17.5, 10.0, 6.67, 14.58, 10.42]], ['2004', [26.28, 23.72, 10.26, 15.38, 15.38, 12.82, 20.51, 15.38, 17.31, 10.9, 22.44, 12.18, 4.49]], ['2005', [15.0, 9.0, 16.5, 1.0, 15.5, 18.5, 12.5, 15.5, 5.0, 16.5, 16.0, 12.0, 6.0, 12.5, 13.0, 16.5, 3.5]], ['2006', [11.0, 12.5, 11.5, 11.0, 16.5, 8.0, 12.0, 16.5, 6.0, 13.0, 10.0, 10.5, 14.5, 13.5, 8.0, 12.5, 15.0, 12.5, 16.0, 13.0, 11.5, 9.5]], ['2007', [7.5, 14.0, 9.0, 12.0, 9.0, 12.5, 11.0, 9.0, 15.0, 12.5]], ['2008', [10.0, 9.0, 13.0, 15.0, 14.0, 12.0, 11.0, 11.0, 15.0, 8.0, 17.0, 14.0, 13.0, 14.0, 11.0, 6.0, 10.0, 15.0, 10.0]], ['2009', [10.0, 12.0, 15.0, 15.0, 16.0, 17.0, 13.0, 12.0, 14.0, 13.0, 11.0, 11.0, 11.0, 16.0, 12.0, 13.0]], ['2010', [9.5, 12.5, 10.5, 16.0, 10.0, 10.5, 9.0, 14.5, 14.5, 9.5, 8.5, 13.0, 9.0, 10.5]], ['2011', [11.5, 10.5, 12.5, 9.0, 10.0, 11.5, 13.5, 13.0, 11.0, 13.0, 8.5]], ['2012', [7.0, 11.5, 12.5, 8.5, 12.0, 10.0, 11.5, 9.5, 12.0, 17.5, 14.0, 12.0, 15.0, 14.5, 13.0]], ['2013', [11.0, 13.0, 15.5, 12.5, 13.0, 9.0, 15.0, 10.0, 17.5, 8.5, 12.5, 14.5, 11.0, 10.5]], ['2014', [13.5, 15.5, 10.5, 6.5, 14.5, 16.5, 5.5, 13.5]], ['2015', [13.5, 17.0, 18.0, 14.0, 14.5, 18.5]], ['2016', [15.0, 10.0, 13.5, 17.0, 11.5, 16.0, 13.0, 12.5, 13.5, 15.0, 14.5, 11.5, 14.5, 19.5, 15.0, 17.0, 11.0]], ['2017', [13.5, 13.0, 16.5, 14.0, 8.0, 18.0, 15.5, 11.0, 10.0, 14.0, 14.0]], ['2018', [7.5, 16.5, 15.0, 17.5, 12.0, 16.5, 7.5, 10.5, 10.0, 17.0, 18.0, 15.5, 16.5, 14.0]], ['2019', [10.5, 8.5, 7.5, 10.5, 9.0, 14.5, 16.5, 18.0, 16.0, 15.5, 17.5, 16.0, 15.5, 11.5, 8.0, 10.5, 7.5, 16.5, 15.0, 17.5, 18.0, 13.0, 14.0, 12.5, 7.5, 15.5, 14.0]], ['2020', [14.0, 11.0, 11.0, 11.0, 15.0, 10.0, 14.5, 11.5, 12.0, 18.5, 12.0]]]\n"
     ]
    }
   ],
   "source": [
    "Data_triees=[]\n",
    "for i in range(0,nbr_annees):    \n",
    "    # Recherche de la colonne de l'année dans la base de notes\n",
    "    colonne_annee = ''    \n",
    "    if Data[1][i] == str(annees[i]):\n",
    "        colonne_annee = i\n",
    "        text = ['notes_'+ str(annees[i])]\n",
    "    \n",
    "    Data_annee = [round(float(Data[j][colonne_annee]),2) for j in range(2,len(Data)) if Data[j][colonne_annee]!='']\n",
    "    Data_triees.append([annees[i],Data_annee])\n",
    "    #print('colonne de l\\'année',colonne_annee+1,'\\n',text, Data_annee)\n",
    "print('\\n Tableau final rangé : \\n')\n",
    "print(Data_triees)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploitation des données (notes/annees)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcul du nombre d'élèves par année et affichage sous forme d'une courbe 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7effd49efe48>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "effectif=[len(Data_triees[i][1]) for i in range(0,nbr_annees)]\n",
    "#print(effectif)\n",
    "#print (annees)\n",
    "plt.figure(1,figsize=(10, 8))\n",
    "plt.title(\"Effectif du module télécom 3A en fonction des années\")\n",
    "plt.plot(annees,effectif,'*-',color='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcul de la moyenne des notes par année et affichage sous forme d'une courbe 2D"
   ]
  }
 ],
 "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
}