{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Projet Maman\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "donnees = pd.read_csv('donnees.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## D01FUN\n",
    "\n",
    "homme=len(donnees.loc[donnees.loc[:,'Indiquez votre genre :']=='Masculin','Indiquez votre genre :'])\n",
    "femme=len(donnees.loc[donnees.loc[:,'Indiquez votre genre :']=='Féminin','Indiquez votre genre :'])\n",
    "autre=len(donnees.loc[donnees.loc[:,'Indiquez votre genre :']=='Autre / Non renseigné','Indiquez votre genre :'])\n",
    "\n",
    "plt.figure(figsize = (8, 8))\n",
    "plt.pie([homme,femme,autre], colors = ['lightblue', 'lightgreen', 'salmon', 'blanchedalmond'], labels = ['Hommes','Femmes','Autres'],autopct='%1.1f%%',)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## D02FUN\n",
    "\n",
    "donnees_pays=donnees.loc[:,'Quel est votre pays de résidence ?']\n",
    "liste_pays=list(set(donnees_pays))\n",
    "dico_pays={}\n",
    "for pays in liste_pays:\n",
    "    dico_pays[pays]=len(donnees.loc[donnees.loc[:,'Quel est votre pays de résidence ?']==pays,'Quel est votre pays de résidence ?'])\n",
    "dico_pays_bis={}\n",
    "valeur_totale=0\n",
    "for key,value in dico_pays.items():\n",
    "    if value/(sum(dico_pays.values()))*100>1:\n",
    "        dico_pays_bis[key]=value\n",
    "    else:\n",
    "        valeur_totale=valeur_totale+value\n",
    "dico_pays_bis['Autres']=valeur_totale\n",
    "\n",
    "\n",
    "plt.figure(figsize = (8, 8))\n",
    "plt.pie(dico_pays_bis.values(), autopct='%1.1f%%', colors = ['lightblue', 'lightgreen', 'salmon', 'blanchedalmond'], labels = dico_pays_bis.keys())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## D04FUN\n",
    "\n",
    "donnees_age=donnees.loc[:,'Quelle est votre année de naissance ?'].count()\n",
    "a=donnees.loc[donnees.loc[:,'Quelle est votre année de naissance ?'] >= 2002,'Quelle est votre année de naissance ?'].count()\n",
    "b=donnees.loc[donnees.loc[:,'Quelle est votre année de naissance ?'] >= 2020-24,'Quelle est votre année de naissance ?'].count()\n",
    "c=donnees.loc[donnees.loc[:,'Quelle est votre année de naissance ?'] >= 2020-35,'Quelle est votre année de naissance ?'].count()\n",
    "d=donnees.loc[donnees.loc[:,'Quelle est votre année de naissance ?'] >= 2020-45,'Quelle est votre année de naissance ?'].count()\n",
    "e=donnees.loc[donnees.loc[:,'Quelle est votre année de naissance ?'] >= 2020-55,'Quelle est votre année de naissance ?'].count()\n",
    "f=donnees.loc[donnees.loc[:,'Quelle est votre année de naissance ?'] >= 2020-65,'Quelle est votre année de naissance ?'].count()\n",
    "g=donnees.loc[donnees.loc[:,'Quelle est votre année de naissance ?'] < 2020-65,'Quelle est votre année de naissance ?'].count()\n",
    "dico_age={'moins de 18ans':a,'19/24ans':b-a ,'25/35ans':c-b,'36/45ans':d-c,'46/55ans':e-d,'56/65ans':f-e,'plus de 65ans':g}\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "pd.Series(dico_age).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## D05FUN\n",
    "\n",
    "donnees_formation=donnees.loc[:,'Quel est votre niveau de formation ?']\n",
    "liste_formation=list(set(donnees_formation))\n",
    "dico_formation={}\n",
    "for formation in liste_formation:\n",
    "    dico_formation[formation]=len(donnees.loc[donnees.loc[:,'Quel est votre niveau de formation ?']==formation,'Quel est votre niveau de formation ?'])\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "pd.Series(dico_formation).sort_values(ascending=False).plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## D06FUN\n",
    "\n",
    "donnees_langue=donnees.loc[:,'Quelle est votre langue maternelle ? ']\n",
    "donnees_langue=donnees_langue.str.lower()\n",
    "liste_langue=list(set(donnees_langue))\n",
    "dico_langue={}\n",
    "for langue in liste_langue:\n",
    "    dico_langue[langue]=len(donnees.loc[donnees.loc[:,'Quelle est votre langue maternelle ? '].str.lower()==langue,'Quelle est votre langue maternelle ? '])\n",
    "\n",
    "\n",
    "del dico_langue['autre']\n",
    "donnees_langue_autre=donnees.loc[:,'Quelle est votre langue maternelle ?  [Autre]']\n",
    "donnees_langue_autre=donnees_langue_autre.str.lower()\n",
    "\n",
    "\n",
    "\n",
    "liste_langue_autre=list(set(donnees_langue_autre))\n",
    "del liste_langue_autre[liste_langue_autre.index(np.nan)]\n",
    "dico_langue_autre={'arabe':0}\n",
    "for langue in liste_langue_autre:\n",
    "    if langue=='arabic':\n",
    "        dico_langue_autre['arabe']+=len(donnees.loc[donnees.loc[:,'Quelle est votre langue maternelle ?  [Autre]'].str.lower()==langue,'Quelle est votre langue maternelle ?  [Autre]'])\n",
    "    else:\n",
    "        dico_langue_autre[langue]=len(donnees.loc[donnees.loc[:,'Quelle est votre langue maternelle ?  [Autre]'].str.lower()==langue,'Quelle est votre langue maternelle ?  [Autre]'])\n",
    "\n",
    "\n",
    "d_touteslangues={**dico_langue, **dico_langue_autre}\n",
    "plt.figure(figsize = (20, 10))\n",
    "pd.Series(d_touteslangues).sort_values(ascending=False).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## D07FUNC01\n",
    "\n",
    "donnees_raison=donnees.loc[:,'Pourquoi suivez-vous ce MOOC ?']\n",
    "liste_raison=list(set(donnees_raison))\n",
    "del liste_raison[liste_raison.index(np.nan)]\n",
    "dico_raison={}\n",
    "for raison in liste_raison:\n",
    "    dico_raison[raison]=len(donnees.loc[donnees.loc[:,'Pourquoi suivez-vous ce MOOC ?']==raison,'Pourquoi suivez-vous ce MOOC ?'])\n",
    "\n",
    "plt.figure(figsize = (10, 5))\n",
    "pd.Series(dico_raison).sort_values(ascending=False).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## D07FUNC02\n",
    "\n",
    "donnees_ects=donnees.loc[:,'Ce MOOC vous apporte-t-il des points ECTS par rapport à un cursus ?']\n",
    "liste_ects=list(set(donnees_ects))\n",
    "dico_ects={}\n",
    "del liste_ects[liste_ects.index(np.nan)]\n",
    "for ects in liste_ects:\n",
    "    dico_ects[ects]=len(donnees.loc[donnees.loc[:,'Ce MOOC vous apporte-t-il des points ECTS par rapport à un cursus ?']==ects,'Ce MOOC vous apporte-t-il des points ECTS par rapport à un cursus ?'])\n",
    "\n",
    "plt.figure(figsize = (8, 8))\n",
    "plt.pie(dico_ects.values(),labels=dico_ects.keys(),colors=['lightblue','salmon'],autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## D008FUN\n",
    "\n",
    "donnees_connaissance=donnees.loc[:,'Comment avez-vous eu connaissance de ce MOOC ?']\n",
    "liste_connaissance=list(set(donnees_connaissance))\n",
    "dico_connaissance={}\n",
    "for connaissance in liste_connaissance:\n",
    "    dico_connaissance[connaissance]=len(donnees.loc[donnees.loc[:,'Comment avez-vous eu connaissance de ce MOOC ?']==connaissance,'Comment avez-vous eu connaissance de ce MOOC ?'])\n",
    "d_connaissance=dict(sorted(dico_connaissance.items(),key=lambda t : t[1], reverse=True))\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "pd.Series(d_connaissance).plot(kind='bar', rot = 25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'the label [Est-ce le premier MOOC que vous suivez ? ] is not in the [columns]'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1505\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1506\u001b[0;31m                     \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1507\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36merror\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1500\u001b[0m                                .format(key=key,\n\u001b[0;32m-> 1501\u001b[0;31m                                        axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m   1502\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'the label [Est-ce le premier MOOC que vous suivez ? ] is not in the [columns]'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-d81232d7f840>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m## D009FUN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0moui\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdonnees\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdonnees\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Est-ce le premier MOOC que vous suivez ? '\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'Oui'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Est-ce le premier MOOC que vous suivez ? '\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mnon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdonnees\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdonnees\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Est-ce le premier MOOC que vous suivez ? '\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'Non'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Est-ce le premier MOOC que vous suivez ? '\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1365\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1366\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1368\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1369\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    856\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    857\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 858\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    859\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    860\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    989\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    990\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_label_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 991\u001b[0;31m                 \u001b[0msection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    993\u001b[0m                 \u001b[0;31m# we have yielded a scalar ?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1625\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1626\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1627\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1628\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1512\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1513\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1514\u001b[0;31m                 \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1516\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36merror\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 raise KeyError(u\"the label [{key}] is not in the [{axis}]\"\n\u001b[1;32m   1500\u001b[0m                                .format(key=key,\n\u001b[0;32m-> 1501\u001b[0;31m                                        axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m   1502\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'the label [Est-ce le premier MOOC que vous suivez ? ] is not in the [columns]'"
     ]
    }
   ],
   "source": [
    "## D009FUN\n",
    "\n",
    "oui=donnees.loc[donnees.loc[:,'Est-ce le premier MOOC que vous suivez ? ']=='Oui','Est-ce le premier MOOC que vous suivez ? '].count()\n",
    "non=donnees.loc[donnees.loc[:,'Est-ce le premier MOOC que vous suivez ? ']=='Non','Est-ce le premier MOOC que vous suivez ? '].count()\n",
    "\n",
    "plt.pie([oui,non],labels=['Oui','Non'],colors=['lightblue','salmon'],autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## D010FUN\n",
    "\n",
    "apprendre=donnees.loc[donnees.loc[:,\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [Parce que j'éprouve du plaisir et de la satisfaction à apprendre de nouvelles choses]\"]=='Oui',\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [Parce que j'éprouve du plaisir et de la satisfaction à apprendre de nouvelles choses]\"].count()\n",
    "sujet_interessant=donnees.loc[donnees.loc[:,\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [Parce que ce sujet m'intéresse particulièrement]\"]=='Oui',\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [Parce que ce sujet m'intéresse particulièrement]\"].count()\n",
    "poursuite_carriere=donnees.loc[donnees.loc[:,\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [Parce que selon moi il va m'aider dans la poursuite de ma carrière]\"]=='Oui',\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [Parce que selon moi il va m'aider dans la poursuite de ma carrière]\"].count()\n",
    "insertion_professionelle=donnees.loc[donnees.loc[:,\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [ Pour m'aider dans mon insertion professionnelle ]\"]=='Oui',\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [ Pour m'aider dans mon insertion professionnelle ]\"].count()\n",
    "trouver_emploi=donnees.loc[donnees.loc[:,\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [ Pour m’aider à retrouver un emploi]\"]=='Oui',\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [ Pour m’aider à retrouver un emploi]\"].count()\n",
    "defi=donnees.loc[donnees.loc[:,\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [Pour relever un défi]\"]=='Oui',\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [Pour relever un défi]\"].count()\n",
    "aucune_raison=donnees.loc[donnees.loc[:,\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [Je n’ai pas de raison particulière]\"]=='Oui',\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [Je n’ai pas de raison particulière]\"].count()\n",
    "\n",
    "liste_raison_autres=list(donnees.loc[donnees.loc[:,\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [Autre]\"]!=np.nan,\"Pour quelles raisons ce MOOC vous intéresse-t-il ? [Autre]\"].dropna())\n",
    "dico_raison_autres={\"coronavirus\":0,\"curiosité\":0,\"école doctorale\":0,\"recherche\":0}\n",
    "for raison_autres in liste_raison_autres:\n",
    "    if \"coro\" in raison_autres.lower() or \"covi\" in raison_autres.lower() or \"confi\" in raison_autres.lower():\n",
    "        dico_raison_autres[\"coronavirus\"]+=1\n",
    "    if 'docto' in raison_autres.lower():\n",
    "        dico_raison_autres[\"école doctorale\"]+=1\n",
    "    if 'rch' in raison_autres.lower() or 'thèse' in raison_autres.lower():\n",
    "        dico_raison_autres[\"recherche\"]+=1\n",
    "    if 'curiosit' in raison_autres.lower() or 'générale' in raison_autres.lower() or \"découverte\" in raison_autres.lower() or \"approche\" in raison_autres.lower():\n",
    "        dico_raison_autres[\"curiosité\"]+=1\n",
    "\n",
    "\n",
    "raisons=[\"apprendre\",\"sujet intéressant\",\"poursuite de carrière\",\"insertion professionelle\",\"trouver un emploi\",\"défi\",\"aucune raison\"]+list(dico_raison_autres.keys())\n",
    "nbr_raisons=[apprendre,sujet_interessant,poursuite_carriere,insertion_professionelle,trouver_emploi,defi,aucune_raison]+list(dico_raison_autres.values())\n",
    "plt.figure(figsize=(10,5))\n",
    "pd.Series(nbr_raisons,index=raisons).sort_values(ascending=False).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## D011FUN\n",
    "\n",
    "donnees_niveau=donnees.loc[:,'Quel est votre niveau de connaissance sur le sujet ?']\n",
    "liste_niveau=list(set(donnees_niveau))\n",
    "dico_niveau={}\n",
    "for niveau in liste_niveau:\n",
    "    dico_niveau[niveau]=donnees.loc[donnees.loc[:,'Quel est votre niveau de connaissance sur le sujet ?']==niveau,'Quel est votre niveau de connaissance sur le sujet ?'].count()\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.pie(dico_niveau.values(),labels=dico_niveau.keys(),colors=['lightblue','salmon','lightgreen'],autopct='%1.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## D012FUN\n",
    "\n",
    "donnees_heures=donnees.loc[:,\"Combien d'heures par semaine pensez-vous passer sur le MOOC ?\"]\n",
    "liste_heures=[\"Moins d'1h\",'1h à 2h', '4h à 5h', 'Plus de 5h']\n",
    "dico_heures={}\n",
    "for heures in liste_heures:\n",
    "    dico_heures[heures]=len(donnees.loc[donnees.loc[:,\"Combien d'heures par semaine pensez-vous passer sur le MOOC ?\"]==heures,\"Combien d'heures par semaine pensez-vous passer sur le MOOC ?\"])\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.pie(dico_heures.values(),labels=dico_heures.keys(),colors=['lightblue','salmon','lightgreen','violet','blanchedalmond'],autopct='%1.1f%%')\n",
    "plt.show()\n",
    "plt.figure(figsize=(10,5))\n",
    "pd.Series(dico_heures).plot(kind='bar', rot = 25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## D016FUN\n",
    "\n",
    "donnees_attestation=donnees.loc[:,\"Avez-vous l'intention d'obtenir une attestation de suivi avec succès gratuite pour ce MOOC ?\"]\n",
    "liste_attestation=list(set(donnees_attestation))\n",
    "dico_attestation={}\n",
    "for attestation in liste_attestation:\n",
    "    dico_attestation[attestation]=len(donnees.loc[donnees.loc[:,\"Avez-vous l'intention d'obtenir une attestation de suivi avec succès gratuite pour ce MOOC ?\"]==attestation,\"Avez-vous l'intention d'obtenir une attestation de suivi avec succès gratuite pour ce MOOC ?\"])\n",
    "\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.pie(dico_attestation.values(),colors=['lightblue','salmon','lightgreen','violet','blanchedalmond'],autopct='%1.1f%%')\n",
    "plt.legend(dico_attestation.keys(),loc=\"upper center\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## DILLCurrentStatus\n",
    "\n",
    "donnees_situation=donnees.loc[:,'Merci de nous préciser votre situation professionnelle.  Vous êtes actuellement : ']\n",
    "liste_situation=list(set(donnees_situation))\n",
    "del liste_situation[liste_situation.index(np.nan)]\n",
    "dico_situation={}\n",
    "for situation in liste_situation:\n",
    "    dico_situation[situation]=len(donnees.loc[donnees.loc[:,'Merci de nous préciser votre situation professionnelle.  Vous êtes actuellement : ']==situation,'Merci de nous préciser votre situation professionnelle.  Vous êtes actuellement : '])\n",
    "\n",
    "a=pd.Series(dico_situation)\n",
    "a_clé=list(a.keys())\n",
    "a_clé[8]='Salarié(e) de la fonction publique'\n",
    "a_clé[9]=\"Salarié(e) d'une entreprise\"\n",
    "plt.figure(figsize=(10,5))\n",
    "pd.Series(a.values,index=a_clé).sort_values(ascending=False).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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q2p7kF4GPAnsCf1pV1y5aMkmSpDHQdG++qvpb4G8XKcvuYCIOZ+6m/OzGm5/fePPzG19+dkCqHnLOuCRJkobk7WQkSZIaWKYkSZIaWKYaJXlikud2j/dJ8ui+M0mSpOVjmWqQ5OeBDwDTy4gdDPzf/hJpWEn2SHJN3zm0cEkOSvLMJMdNf/WdSdpdJdncbV0uehZNV/OJVzO4R+GlAFV1Y5ID+42kYVTVA0n+KckTquorfefR/CR5K/AzwPXA/d1wAf+ht1AaWpKVwFuBx1XVyUmOAI6tqnf1HE0Pb1WS5wAvTvI+droLSlVd0U+s0WCZanNvVd2X7vYESVYwyy11NLJWAdcmuQy4e3qwql7cXyQN6VTgyVX13b6DaEH+DHg38Gvd/v8D3g9YpkbXbwJnMTgC8/s7PVfACcueaIRYptp8MsmbgH2SnAS8Cvhwz5k0vLP7DqAF+xKepjDODqiq85K8Eb6/CPT9c32T+lNVHwA+kOQ3quq3+84zaixTbc4CzgSuBl7JYAHTd/aaSEOrqk/2nUELdhdwZZK/A+6dHqyqN/QXSfNwd5LH0s3kJ3kW8K1+I2lXkhxWVdcDf5PkmJ2fn/TDfC7auUBJ9gQ2VtXP9J1FC5PkLh48LLsX8Ajg7qrat79UGkaSM2cb95yb8dD9Y/xHwJHANcAUcFpVfb7XYHpYSTZU1fokn5jl6aoqD/Np/qrq/iRTSfaqqvv6zqP5q6odlrFIspbBBQUacVX1ru4G60+oqpv6zqPhJdkD2Bt4DvAUBicy31BV3+s1mHapqtZ3D0/e+VzFJHv3EGmkODPVIMnbgWOATex4AvPOJ+dpTCT5h6p6Vt85tGtJXsDgJNi9quqQJEcDv1VVL+k5moaQ5HNVdWzfOTR/Sa6oqmPmGps0zky1+Vr3tQfgYp1jJslLZ+zuAazBqzHHxVuAHwM+AVBVVyX5kX4jaR4+luRU4EPlX/RjIclBwOMZXHD1DB5cGmFf4JG9BRsRlqkGVeXVYOPtRTMebwduAU7pJ4rm6XtV9c3pZUk6/qM8Pt4APArYnuS7DP5hLs9XHGk/CfwsD10a4S7gTX0EGiUe5luAJB9mF7+4XadIWlpJ3g18hME6RWuB1wGPnHFeh6QlkOTUqvpg3zlGjWVqAbpVYAFeChwE/EW3/wrglqqa+JY+DpI8GTgXWFlVRyZ5OvDiqvrvPUfTHJI8isEigs9jMKvxUeDsqrqn12AaSpLNVXXiXGMaTd05i09lcCEBAFX1lv4S9c8y1SDJp6rq2XONaTQl+STwK8Dbq+oZ3dg1VXVkv8mk3VN31dcjGZzr9hPseN7NR6rq8J6iaUhJ/oTBZ3g8g3UVTwMuq6pZlyuZFJ4z1WYqyQ9X1c0ASQ5hsF6KxsMjq+qync672d5XGM0tye9V1S8nOZ9ZDrVX1Utn+TaNjlcCrwceB8xc5PFO4G29JNJ8HVdVT0/y+ao6O8nvAR/qO1TfLFNtfgm4JMnN3f5qBr8sNB6+keRJPLgK82nA1n4jaQ7v77Z/3GsKLUhV/QHwB0leU1V/1HceLcj0GlP3JHkccAdwSI95RoJlqkFVXZTkUOCwbuj6qrp3V9+jkfJqYANwWJKvMrjfmyvaj7Cquqx7+Bngvqp6AL6/EORevQXTfH0ryX/aebCq3tNHGM3Lh5P8IPA/GcwuFvCOfiP1z3OmGiU5EjiCHU/E8xfCGOlOZt6jqu7qO4uGk+RzwPOmP7MkjwY+WlXH9ZtMw0gyc1Zqb+BE4IqqOq2nSBpC90fLs6rqs93+DwB7V9XE31fRmakGSX6LwUmURzC4yfHJwKcBy9QY6H4RnMrg8OyK6XOnJv2qlDGxz8zyW1V3JZn4hQPHRVW9ZuZ+kscAf95THA2pqh7ozpE6ttu/lxk3Gp9ke/QdYMydxuAvqq9X1c8BRwE/0G8kzcMFDBbp3M7gdkDTXxp99yQ5anqnu53Md3fxeo22e4An9x1CQ/lYklOz05U7k86ZqTbf6Zr69iT7ArcDP9x3KA3t4Kp6ft8htCC/BJyf5Mvd/hMYrPOmMbDTwsd7AocD5/WXSPPg6vWzsEy12dKdiPcO4HLg28Blu/4WjZDPJnlaVV3ddxDNT1VdmuRwBv8IB7i2qu7rOZaG979mPN7O4DO0DI+BqvI+tLPwBPRFkmQ1sG9Vfb7nKJpDkqsZ/FW8AjgUuJnBcf/pv7Ce3mM87UKS51TVJ5PMesumqtq03Jm0MN2h2Z8CXsbgStoPVpVLXow4V6+fnTNTC5DkJ4FHV9UHpseq6pYkP51kZVVd3GM8ze2FfQfQgp0EfBI4fZbnCrBMjbDuFk5nMJiFuoPBumGpquN7DaY5zVi9/oAk+7Hj6vWP6y3YiHBmagGS/APwoqrattP4QcD5VXVsP8k0H0mexeDw0MzL64+oqkv7TSbtnpI8APw9cGZV3dSN3VxVnms64pK8jgdXr/8qD5apO4F3TPqsomVqAbpl9Gc9FLSr5zRaklwJHFPdD0G3hsqWqjqm32SaS5K9gLV0y1pMj1fVW/vKpLkleQmDmanjgIuA9wHvrKqJX0F7XLh6/excGmFh9k7ykEOkSR4B7NNDHi1MasZfE91q2h76Hg/nAy9n8HndP+NLI6yqzq+qlzO4a8QlDK7KXJnk3CTP6zWchvX1bhafJL+e5ENJJv4PUGemFiDJOcBK4Ber6u5u7FHAHwLfqKpf7TOfhpPkQwx+oZ/bDb0KOL6q1vYWSkNJck1VHdl3DrVLsj+Dc+BeXlUn9J1HuzZ99CXJvwP+B4MrM99UVT/Wc7ReOTO1ML8O3AZ8OcnlSS4HbgG2dc9pPPwCg8MNXwVuBX4MWN9rIg3rH5Ic0XcItauqf6mqt1ukxsb0DPALgHOr6gK8L6YzUy2S7AP8SLd7U1V9p888Gl6SPYHXVtX/7juL5q9b3uLJwE3suKzFxB9ukJZSkgsZ/AH6XOBHge8Al1XVUbv8xt2cZUoTK8klVfUTfefQ/CV50mzjVfXF5c4iTZLuHpjPB66uqhuTrAKeVlUf6zlaryxTmlhJfgd4DIO1br5/T76quqK3UBpad2+wKXa8mu9r/SWSdl9J9q2qO7tz3B6iqv5luTONEsvUAiRZUVXb+86hNkk+Mctwee7G6EvyKuAtDBZ+fKAbrqryPCppCSS5sKpemORLDBbInXmj45r0tcIsUwuQZAuDE5YvAi6qqlv6TSRNliQ3AcfuvHCuJPXBMrVASZ4InMzg2PHjgU8DHwE+WVX39plNw0myEngr8LiqOrm7OuzYqnpXz9E0hySXACdWlWtLScssyeOBJ7LjIfZP9Zeof5apRdAt1vnvGRSrnwC2VdULeg2lOSX5CPBu4Neq6qhuIdYrq+ppPUfTw0jy2u7h0xncpPpCBlfzAVBVf9hHLmlSJPldBgvmfoEHl0moqpr15uOTwtWeF0FVfQ/4ePc13do1+g6oqvOSvBGgqrYncaZjtE11263d1749ZpEm0VrgKR6B2ZFlaglU1Vf7zqCh3J3ksQxOppy+8fG3+o2kXamq3+g7gzThbgYewYwZYVmmNNneAGwCnpTkMwxmPU7rN5KGkeQi4Iyq+ma3vx/wFx5el5bcPcBVSTaz4yH21z78t+z+LFML1K2gfU5V/UrfWbQwVXVFkucAT2Fwme8N3SFbjb6DposUQFX9a5LH9RlImhCbui/NYJlaoKq6P8mPJkl5Fv84eyawmsHPwjFJqKr39BtJQ7g/ycFVdStAkif0HUiaBFW1se8Mo8gy1eZK4IIkf82OK2h/qL9IGlaSPweeBFzFjKtSAMvU6PtN4DNJPt7tHw/8lx7zSLu1JOdV1cu6+2I+ZAKhqp7eQ6yR4dIIDZK8e5bhqqr/vOxhNG9JrgOOcGZxPHXrhB3L4BDtZ6rq9p4jSbutJKuqamu3xuJDVNWXlzvTKLFMaWJ1M4qvraqtfWfR/CU5A3hSVf1Okh8CDqyqy/vOJU2CJPuy46Kd3ptPC9PNTM023enM1AhL8mEGn9ujgaOBy9jxqpSJXnxuHCT5YwaXZz+7qg7vbr760ar6tz1Hk3ZrSV7J4L6Y3+HBf/8m/t58njPV5sIZj/cGXgJ41/rRtwlYCfz9TuPPAVwjbDwcV1XHJLkSBn8VJ9mr71DSBPivwFOr6ht9BxkllqkGVfXBmftJ3gv8XU9xNLxTgDdV1ednDia5G/gtwHvzjb7vJdmDBxdcfSzwQL+RpInwRQZrTWkGy9TiOhTwEu3Rt3rnIgVQVVuSrF7+OBpWkhVVtR14G/BBYCrJ2cDLgLN7DSdNhjcCn01yKS7a+X2WqQZJ7mLwl3G67deBX+01lIax9y6e22fZUmghLgOOqar3JLkceC6Dn7/Tq+qafqNJE+HtDO5DezXOBn+fZapBVT267wxakH9M8vNV9Y6Zg0nOBLwabLRl+kFVXQtc22MWaRJtr6o39B1i1Hg1X4MkAX4aOKSqfrtbhfmgqrqs52jahW59ovOB+3iwPK0B9gJeUlVf7yubdi3JrcDvP9zzVfWwz0lql+R3gC8DH2bHw3wujaCFSXIug2nOE7rLs/cDPubl2eMhyfHAkd3utVX18V29Xv1LshU4lxkzVDNVledNSUsoyZdmGZ74pREsUw2SXDF9eXZVPaMb+6eqOqrvbNLuaPpnru8ckjST50y1+V6SPXnw8uwpPCFPWkqzzkhJWlpJXrqr5yf9nrSWqTZ/yODcmwO748inAb/ebyRpt3Zi3wGkCfWibnsgcByDK/pgcJPxS4CJLlMe5muU5DAGv+ADbK6q63qOJEnSkkhyIfDz0/c0TbIKeFtV7XLmandnmVqAJPtW1Z3d/cAeYtKvapAk7Z6SXFNVR87Y3wO4uqqe2mOs3nmYb2H+Cnghg8vqZy7aOb2d6KsaJEm7rUuSfBR4L4N/784ANvcbqX/OTEmSpKEleQnw7G73X4GVVfXqHiP1zpmpRkkeDzyRGf8vq+pT/SWSJGlJfQk4lsE9Mb/E4D6ZE80y1SDJ7wIvB74A3N8NF2CZkiTtNpI8mcEhvVcAdwDvZ3B06/heg40ID/M1SHID8PSqunfOF0uSNKaSPAD8PXBmVd3Ujd086SufT9uj7wBj7mbgEX2HkCRpiZ0KfB34RJJ3JJleEkg4M9UkyQeBoxhcyTDzho+v7S2UJElLJMmjgLUMDvedAGwEzq+qj/UarGeWqQZJ1s02XlUblzuLJEnLqVtr8XTg5VV1Qt95+mSZapRkH+AJVXVD31kkSdLy85ypeUrymBmPXwRcBVzU7R+dZFNf2SRJ0vKzTM3fy5Oc2j1+M/BM4JsAVXUVcEhPuSRJUg8sU/NUVRuAI7rd7VX1rZ1fssyRJElSj1y0cwGq6re7h9ck+SlgzySHAq8FPttfMkmStNycmWrzGuCpDJZFeC9wJ/D6XhNJkqRl5dV8kiRJDTzM1yDJh3noOVLfArYAb6+q7y5/KkmStJw8zNfmZuDbwDu6rzuB24And/uSJGk352G+Bkk+VVXPnm0sybVV9dS+skmSpOXhzFSbqSRPmN7pHh/Q7d7XTyRJkrScPGeqzS8Dn07yRQZ3zz4EeFV3I0jvzydJ0gTwMF+jJD8AHMagTF3vSeeSJE0Wy1SjJMcBq5kxy1dV7+ktkCRJWlYe5muQ5M+BJzG42fH93XABlilJkiaEM1MNklwHHFH+T5QkaWJ5NV+ba4CD+g4hSZL642G+NgcAX0hyGYP78wFQVS/uL5IkSVpOlqk2b+47gCRJ6pfnTEmSJDVwZmoBktzFQ29wDIO1pqqq9l3mSJIkqSfOTEmSJDXwaj5JkqQGlilJkqQGlilJkqQGlilJkqQGlilJkqQGlilJkqQG/x+W2oPM6952aAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## D0xxFUN DILLPrivatePublic\n",
    "\n",
    "donnees_fonction=donnees.loc[:,'Quelle est votre fonction ?']\n",
    "liste_fonction=list(set(donnees_fonction))\n",
    "del liste_fonction[liste_fonction.index(np.nan)]\n",
    "dico_fonction={}\n",
    "for fonction in liste_fonction:\n",
    "    dico_fonction[fonction]=len(donnees.loc[donnees.loc[:,'Quelle est votre fonction ?']==fonction,'Quelle est votre fonction ?'])\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "pd.Series(dico_fonction).sort_values(ascending=False).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## DILLDiscipline\n",
    "\n",
    "chimie=donnees.loc[donnees.loc[:,'Quelle est votre discipline ? [Chimie]']=='Oui','Quelle est votre discipline ? [Chimie]'].count()\n",
    "physique_SI=donnees.loc[donnees.loc[:,\"Quelle est votre discipline ? [Physique, sciences de l'ingénieur]\"]=='Oui',\"Quelle est votre discipline ? [Physique, sciences de l'ingénieur]\"].count()\n",
    "informatique=donnees.loc[donnees.loc[:,\"Quelle est votre discipline ? [Informatique]\"]=='Oui',\"Quelle est votre discipline ? [Informatique]\"].count()\n",
    "maths=donnees.loc[donnees.loc[:,\"Quelle est votre discipline ? [Mathématiques]\"]=='Oui',\"Quelle est votre discipline ? [Mathématiques]\"].count()\n",
    "science_cognitives=donnees.loc[donnees.loc[:,\"Quelle est votre discipline ? [Sciences cognitives]\"]=='Oui',\"Quelle est votre discipline ? [Sciences cognitives]\"].count()\n",
    "environnement_planete_univers=donnees.loc[donnees.loc[:,\"Quelle est votre discipline ? [Sciences de l'environnement, Planète et Univers]\"]=='Oui',\"Quelle est votre discipline ? [Sciences de l'environnement, Planète et Univers]\"].count()\n",
    "sciences_du_vivant=donnees.loc[donnees.loc[:,\"Quelle est votre discipline ? [Sciences du Vivant (Biologie, Sciences agricoles, Sciences pharmaceutiques,  Alimentation, Médecine, ...)]\"]=='Oui',\"Quelle est votre discipline ? [Sciences du Vivant (Biologie, Sciences agricoles, Sciences pharmaceutiques,  Alimentation, Médecine, ...)]\"].count()\n",
    "homme_société=donnees.loc[donnees.loc[:,\"Quelle est votre discipline ? [Sciences de l'Homme et Société]\"]=='Oui',\"Quelle est votre discipline ? [Sciences de l'Homme et Société]\"].count()\n",
    "economie=donnees.loc[donnees.loc[:,\"Quelle est votre discipline ? [Économie et finance quantitative]\"]=='Oui',\"Quelle est votre discipline ? [Économie et finance quantitative]\"].count()\n",
    "autre=donnees.loc[donnees.loc[:,\"Quelle est votre discipline ? [Autre]\"]!=np.nan,\"Quelle est votre discipline ? [Autre]\"].count()\n",
    "\n",
    "discipline=[\"Chimie\",\"Physique et Scince de l'ingénieur\",\"Informatique\",\"Mathématiques\",\"Sciences cognitives\",\"Sciences de l'environnement, Planète et Univers\",\"Sciences du vivant\",\"Sciences de l'homme et société\",\"Economie et finance quantitative\",\"Autre\"]\n",
    "nbr_discipline=[chimie,physique_SI,informatique,maths,science_cognitives,environnement_planete_univers,sciences_du_vivant,homme_société,economie,autre]\n",
    "plt.figure(figsize=(10,5))\n",
    "pd.Series(nbr_discipline,index=discipline).sort_values(ascending=False).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## DILLlevel\n",
    "\n",
    "liste_niveau_info=[\"Inconnu\",\"Débutant\",\"Intermédiaire\",\"Confirmé\"]\n",
    "\n",
    "donnees_python=donnees.loc[:,'Quel est votre niveau de connaissance de :  [Langage Python]']\n",
    "dico_python={}\n",
    "for niveau in liste_niveau_info:\n",
    "    dico_python[niveau]=len(donnees.loc[donnees.loc[:,'Quel est votre niveau de connaissance de :  [Langage Python]']==niveau,'Quel est votre niveau de connaissance de :  [Langage Python]'])\n",
    "\n",
    "donnees_R=donnees.loc[:,'Quel est votre niveau de connaissance de :  [langage R]']\n",
    "dico_R={}\n",
    "for niveau in liste_niveau_info:\n",
    "    dico_R[niveau]=len(donnees.loc[donnees.loc[:,'Quel est votre niveau de connaissance de :  [langage R]']==niveau,'Quel est votre niveau de connaissance de :  [langage R]'])\n",
    "\n",
    "donnees_git=donnees.loc[:,'Quel est votre niveau de connaissance de :  [Git / Gitlab]']\n",
    "dico_git={}\n",
    "for niveau in liste_niveau_info:\n",
    "    dico_git[niveau]=len(donnees.loc[donnees.loc[:,'Quel est votre niveau de connaissance de :  [Git / Gitlab]']==niveau,'Quel est votre niveau de connaissance de :  [Git / Gitlab]'])\n",
    "\n",
    "pd.DataFrame({\"Git/Gitlab\":dico_git,\"Python\":dico_python,\"R\":dico_R}).T.plot(kind='bar',figsize=(15,7))\n",
    "plt.legend(loc='upper center')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## DILLEPST\n",
    "\n",
    "EPST=donnees.loc[donnees.loc[:,\"Si vous travaillez dans un institut de recherche ou une université, précisez son type et son nom (acronyme si possible) [EPST (établissement public à caractère scientifique et technologique)]\"]=='Oui',\"Si vous travaillez dans un institut de recherche ou une université, précisez son type et son nom (acronyme si possible) [EPST (établissement public à caractère scientifique et technologique)]\"].count()\n",
    "EPIC=donnees.loc[donnees.loc[:,\"Si vous travaillez dans un institut de recherche ou une université, précisez son type et son nom (acronyme si possible) [EPIC (établissements publics industriels et commerciaux)]\"]=='Oui',\"Si vous travaillez dans un institut de recherche ou une université, précisez son type et son nom (acronyme si possible) [EPIC (établissements publics industriels et commerciaux)]\"].count()\n",
    "Universite=donnees.loc[donnees.loc[:,\"Si vous travaillez dans un institut de recherche ou une université, précisez son type et son nom (acronyme si possible) [Université]\"]=='Oui',\"Si vous travaillez dans un institut de recherche ou une université, précisez son type et son nom (acronyme si possible) [Université]\"].count()\n",
    "autre=donnees.loc[donnees.loc[:,\"Si vous travaillez dans un institut de recherche ou une université, précisez son type et son nom (acronyme si possible) [Autre]\"]=='Oui',\"Si vous travaillez dans un institut de recherche ou une université, précisez son type et son nom (acronyme si possible) [Autre]\"].count()\n",
    "\n",
    "etablissement=[\"EPST\",\"EPIC\",\"Université\",\"Autre\"]\n",
    "nbr_etablissement=[EPST,EPIC,Universite,autre]\n",
    "pd.Series(nbr_etablissement,index=etablissement).sort_values(ascending=False).plot(kind='bar',figsize=(15,7))\n",
    "plt.show()\n",
    "\n",
    "liste_EPST=list(donnees.loc[:,\"Si vous travaillez dans un institut de recherche ou une université, précisez son type et son nom (acronyme si possible) [EPST (établissement public à caractère scientifique et technologique)][Commentaire]\"].dropna())\n",
    "dico_EPST={\"INRA\":0,\"CNRS\":0,\"IFSTTAR\":0,\"INED\":0,\"INRIA\":0,\"INSERM\":0,\"IRD\":0,\"Autre\":0}\n",
    "liste_autres=[]\n",
    "for n_EPST in liste_EPST:\n",
    "    if \"INRA\" in n_EPST.upper():\n",
    "        dico_EPST[\"INRA\"]+=1\n",
    "    elif \"CNRS\" in n_EPST.upper():\n",
    "        dico_EPST[\"CNRS\"]+=1\n",
    "    elif \"IFSTTAR\" in n_EPST.upper():\n",
    "        dico_EPST[\"IFSTTAR\"]+=1\n",
    "    elif \"INED\" in n_EPST.upper():\n",
    "        dico_EPST[\"INED\"]+=1\n",
    "    elif \"INRIA\" in n_EPST.upper():\n",
    "        dico_EPST[\"INRIA\"]+=1\n",
    "    elif \"INSERM\" in n_EPST.upper():\n",
    "        dico_EPST[\"INSERM\"]+=1\n",
    "    elif \"IRD\" in n_EPST.upper():\n",
    "        dico_EPST[\"IRD\"]+=1\n",
    "    else:\n",
    "        liste_autres+=[n_EPST]\n",
    "        dico_EPST[\"Autre\"]+=1\n",
    "        \n",
    "pd.Series(dico_EPST).sort_values(ascending=False).plot(kind=\"bar\",figsize=(15,7))\n",
    "plt.show()\n",
    "print(\"Autres :\\n\")\n",
    "print(liste_autres)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## ILLOs\n",
    "\n",
    "linux=donnees.loc[donnees.loc[:,\"Sur quel système d'exploitation travaillez-vous ? [Linux]\"]=='Oui',\"Sur quel système d'exploitation travaillez-vous ? [Linux]\"].count()\n",
    "windows=donnees.loc[donnees.loc[:,\"Sur quel système d'exploitation travaillez-vous ? [Windows]\"]=='Oui',\"Sur quel système d'exploitation travaillez-vous ? [Windows]\"].count()\n",
    "macOS=donnees.loc[donnees.loc[:,\"Sur quel système d'exploitation travaillez-vous ? [macOS]\"]=='Oui',\"Sur quel système d'exploitation travaillez-vous ? [macOS]\"].count()\n",
    "Android=donnees.loc[donnees.loc[:,\"Sur quel système d'exploitation travaillez-vous ? [Android]\"]=='Oui',\"Sur quel système d'exploitation travaillez-vous ? [Android]\"].count()\n",
    "iOS=donnees.loc[donnees.loc[:,\"Sur quel système d'exploitation travaillez-vous ? [iOS]\"]=='Oui',\"Sur quel système d'exploitation travaillez-vous ? [iOS]\"].count()\n",
    "autre=donnees.loc[donnees.loc[:,\"Sur quel système d'exploitation travaillez-vous ? [Autre]\"]=='Oui',\"Sur quel système d'exploitation travaillez-vous ? [Autre]\"].count()\n",
    "\n",
    "OS=[\"linus\",\"windows\",\"macOS\",\"android\",\"iOS\",\"Autre\"]\n",
    "nbr_OS=[linux,windows,macOS,Android,iOS,autre]\n",
    "pd.Series(nbr_OS,index=OS).sort_values(ascending=False).plot(kind='bar',figsize=(10,5))\n",
    "plt.show()"
   ]
  }
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