{
 "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 données\n",
    "### Lecture fichier CVS obtenue à partir des fichiers excel annuels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hideOutput": true
   },
   "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": 3,
   "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"
     ]
    }
   ],
   "source": [
    "annees = Data[1][:] \n",
    "print(annees)\n",
    "nbr_annees=len(Data[1][:])\n",
    "print('Nombre d\\'années disponibles', nbr_annees)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construction d'un tableau 2D des notes triees par année\n",
    "**Attention la numérotation des colonnes débute à 0**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 40)"
      ]
     },
     "execution_count": 5,
     "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')\n",
    "plt.ylim(0, 40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcul de la moyenne des notes par année et affichage sous forme d'une courbe 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10.82, 15.93, 12.03, 12.02, 11.15, 12.0, 13.19, 11.25, 11.27, 12.03, 12.39, 12.0, 15.92, 14.12, 13.41, 13.86, 13.2, 12.77]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f0d7b5af9b0>"
      ]
     },
     "execution_count": 6,
     "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": [
    "moyenne= [round(np.mean(Data_triees[i][1]),2) for i in range(0,nbr_annees)]\n",
    "print(moyenne)\n",
    "#print (annees)\n",
    "plt.figure(2,figsize=(10, 8))\n",
    "plt.title(\"Moyenne des notes à l'examen du module Télécom 3A en fonction des années\")\n",
    "plt.plot(annees,moyenne,'o-',color='blue')\n",
    "plt.ylim(0, 20)\n",
    "plt.axhline(y=12,color='red',dashes=(3,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calcul des écart-types des notes par année et affichage sous forme d'une courbe 2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.68, 6.1, 5.25, 2.76, 2.46, 2.81, 2.1, 2.41, 1.66, 2.66, 2.6, 4.11, 2.18, 2.46, 2.88, 3.68, 3.57, 2.49]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 10)"
      ]
     },
     "execution_count": 9,
     "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": [
    "ecart_type= [round(np.std(Data_triees[i][1],0, ddof = 1),2) for i in range(0,nbr_annees)]\n",
    "print(ecart_type)\n",
    "#print (annees)\n",
    "plt.figure(3,figsize=(10, 8))\n",
    "plt.title(\"Écart-type des notes à l'examen du module Télécom 3A en fonction des années\")\n",
    "plt.plot(annees,ecart_type,'+-',color='green')\n",
    "plt.ylim(0, 10)\n",
    "#plt.axhline(y=12,color='red',dashes=(3,3))numpy.std(a, 0, ddof = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tracé de la moyenne des notes à l'examen en fonction des effectifs des année"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f0d7b5306d8>"
      ]
     },
     "execution_count": 10,
     "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": [
    "plt.figure(4,figsize=(10, 8))\n",
    "plt.title(\"Moyenne des notes à l'examen du module Télécom 3A en fonction des effectifs années\")\n",
    "plt.plot(effectif,moyenne,'.-',color='blue')\n",
    "plt.ylim(0, 20)\n",
    "plt.axhline(y=12,color='red',dashes=(3,3))"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Hide code",
  "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
}