{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from opytex import texenv\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Informations sur le devoir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classe': '313', 'date': '18 septembre 2015', 'titre': 'DM 1'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_name = \"DM_15_09_18\"\n",
    "classe = \"313\"\n",
    "\n",
    "latex_info = {}\n",
    "latex_info['titre'] = \"DM 1\"  \n",
    "latex_info['classe'] = \"313\"\n",
    "latex_info['date'] = \"18 septembre 2015\"\n",
    "latex_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import et premiers traitements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-5d994dc2c0df>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnotes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mExcelFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"./../../../313_.xlsx\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mnotes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msheet_names\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mnotes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnotes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mds_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.4/site-packages/pandas/io/excel.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, io, **kwds)\u001b[0m\n\u001b[0;32m    186\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_contents\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 188\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    189\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'xlrd'\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBook\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    190\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.4/site-packages/xlrd/__init__.py\u001b[0m in \u001b[0;36mopen_workbook\u001b[1;34m(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows)\u001b[0m\n\u001b[0;32m    414\u001b[0m                 \u001b[0mformatting_info\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mformatting_info\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    415\u001b[0m                 \u001b[0mon_demand\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mon_demand\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 416\u001b[1;33m                 \u001b[0mragged_rows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mragged_rows\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    417\u001b[0m                 )\n\u001b[0;32m    418\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mbk\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.4/site-packages/xlrd/xlsx.py\u001b[0m in \u001b[0;36mopen_workbook_2007_xml\u001b[1;34m(zf, component_names, logfile, verbosity, use_mmap, formatting_info, on_demand, ragged_rows)\u001b[0m\n\u001b[0;32m    789\u001b[0m         \u001b[0mx12sheet\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX12Sheet\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msheet\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogfile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbosity\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    790\u001b[0m         \u001b[0mheading\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Sheet %r (sheetx=%d) from %r\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msheet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msheetx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 791\u001b[1;33m         \u001b[0mx12sheet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess_stream\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzflo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheading\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    792\u001b[0m         \u001b[1;32mdel\u001b[0m \u001b[0mzflo\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    793\u001b[0m         \u001b[0mcomments_fname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'xl/comments%d.xml'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msheetx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.4/site-packages/xlrd/xlsx.py\u001b[0m in \u001b[0;36mown_process_stream\u001b[1;34m(self, stream, heading)\u001b[0m\n\u001b[0;32m    529\u001b[0m                 \u001b[0melem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# destroy all child elements (cells)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    530\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0melem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtag\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mU_SSML12\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"dimension\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 531\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_dimension\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0melem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    532\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0melem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtag\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mU_SSML12\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"mergeCell\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    533\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_merge_cell\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0melem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.4/site-packages/xlrd/xlsx.py\u001b[0m in \u001b[0;36mdo_dimension\u001b[1;34m(self, elem)\u001b[0m\n\u001b[0;32m    563\u001b[0m             \u001b[1;31m# print >> self.logfile, \"dimension: ref=%r\" % ref\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    564\u001b[0m             \u001b[0mlast_cell_ref\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mref\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m':'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;31m# example: \"Z99\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 565\u001b[1;33m             \u001b[0mrowx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcell_name_to_rowx_colx\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlast_cell_ref\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    566\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msheet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dimnrows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrowx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    567\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msheet\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dimncols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.4/site-packages/xlrd/xlsx.py\u001b[0m in \u001b[0;36mcell_name_to_rowx_colx\u001b[1;34m(cell_name, letter_value)\u001b[0m\n\u001b[0;32m     87\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# start of row number; can't be '0'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m                 \u001b[0mcolx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolx\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 89\u001b[1;33m                 \u001b[1;32massert\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mcolx\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mX12_MAX_COLS\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     90\u001b[0m                 \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "notes = pd.ExcelFile(\"./../../../313_.xlsx\")\n",
    "notes.sheet_names\n",
    "notes = notes.parse(ds_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'notes' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-a47dece6e491>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnotes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'notes' is not defined"
     ]
    }
   ],
   "source": [
    "notes.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "notes = notes.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes = notes.drop('av_arrondi', axis=1)\n",
    "notes = notes.drop('num_sujet', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "barem = notes[:1]\n",
    "notes = notes[1:]\n",
    "#notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supression des notes inutiles "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes = notes[notes[ds_name].notnull()]\n",
    "notes = notes[notes[ds_name] != 'abs']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes = notes.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traitement des notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DM_0506', 'Malus', 'Exercice 1', '1.1.a', '1.1.b', '1.1.c', '1.2.a',\n",
       "       '1.2.b', '1.2.c', '1.2.d', '1.3.a', '1.3.b', '1.3.c', '1.3.d',\n",
       "       'Exercice 2', '2.1', '2.2', '2.3', 'Exercice 3', '3.1.a', '3.1.b',\n",
       "       '3.1.c', '3.1.d', '3.2.a', '3.2.b', '3.2.c', '3.2.d'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.T.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "list_exo = [\"Exercice 1\", \"Exercice 2\", \"Exercice 3\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
    "#notes[list_exo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1.1.a',\n",
       " '1.1.b',\n",
       " '1.1.c',\n",
       " '1.2.a',\n",
       " '1.2.b',\n",
       " '1.2.c',\n",
       " '1.2.d',\n",
       " '1.3.a',\n",
       " '1.3.b',\n",
       " '1.3.c',\n",
       " '1.3.d',\n",
       " '2.1',\n",
       " '2.2',\n",
       " '2.3',\n",
       " '3.1.a',\n",
       " '3.1.b',\n",
       " '3.1.c',\n",
       " '3.1.d',\n",
       " '3.2.a',\n",
       " '3.2.b',\n",
       " '3.2.c',\n",
       " '3.2.d']"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_avec_note = list_exo + [ds_name,  \"Malus\"]\n",
    "sous_exo = [i for i in notes.T.index if i not in item_avec_note]\n",
    "sous_exo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def toRepVal(val):\n",
    "    if pd.isnull(val):\n",
    "        return \"\\\\NoRep\"\n",
    "    elif val == 0:\n",
    "        return \"\\\\RepZ\"\n",
    "    elif val == 1:\n",
    "        return \"\\\\RepU\"\n",
    "    elif val == 2:\n",
    "        return \"\\\\RepD\"\n",
    "    elif val == 3:\n",
    "        return \"\\\\RepT\"\n",
    "    else:\n",
    "        return val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
    "#notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eleves = notes.copy()\n",
    "eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(notes.T.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparation du fichier .tex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bilan = texenv.get_template(\"tpl_bilan.tex\")\n",
    "with open(\"./bilan.tex\",\"w\") as f:\n",
    "    f.write(bilan.render(eleves = eleves, barem = barem, ds_name = ds_name, latex_info = latex_info, nbr_questions = len(barem.T)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Un peu de statistiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    28.000000\n",
       "mean     14.696429\n",
       "std       5.894661\n",
       "min       0.000000\n",
       "25%      14.375000\n",
       "50%      16.500000\n",
       "75%      18.625000\n",
       "max      20.000000\n",
       "Name: DM_0506, dtype: float64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes[ds_name].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f761323dbe0>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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msahL0oBY1JeUPXVJk1jUJWlALOpLyp66pEks6pI0IBb1JWVPXdIkFnVJGpBWRT3JLUke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\nRtJfj8QdO38AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f76133ec630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notes_seules = notes[ds_name]\n",
    "notes_seules.hist(bins = (notes_seules.max() - notes_seules.min())*2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes_questions = notes[sous_exo]\n",
    "notes_analysis = notes_questions.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1.1.a</th>\n",
       "      <th>1.1.b</th>\n",
       "      <th>1.1.c</th>\n",
       "      <th>1.2.a</th>\n",
       "      <th>1.2.b</th>\n",
       "      <th>1.2.c</th>\n",
       "      <th>1.2.d</th>\n",
       "      <th>1.3.a</th>\n",
       "      <th>1.3.b</th>\n",
       "      <th>1.3.c</th>\n",
       "      <th>...</th>\n",
       "      <th>2.2</th>\n",
       "      <th>2.3</th>\n",
       "      <th>3.1.a</th>\n",
       "      <th>3.1.b</th>\n",
       "      <th>3.1.c</th>\n",
       "      <th>3.1.d</th>\n",
       "      <th>3.2.a</th>\n",
       "      <th>3.2.b</th>\n",
       "      <th>3.2.c</th>\n",
       "      <th>3.2.d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       1.1.a  1.1.b  1.1.c  1.2.a  1.2.b  1.2.c  1.2.d  1.3.a  1.3.b  1.3.c  \\\n",
       "count     25     25     25     25    NaN    NaN    NaN     25     25    NaN   \n",
       "\n",
       "       ...    2.2  2.3  3.1.a  3.1.b  3.1.c  3.1.d  3.2.a  3.2.b  3.2.c  3.2.d  \n",
       "count  ...    NaN   25    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# J'aimerai récupérer le nom des questions qui ont été le moins répondus\n",
    "notes_analysis[:1][notes_analysis[:1] == 25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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