{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 120,
   "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": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classe': '\\\\seconde', 'date': '6 mai 2015', 'titre': 'DM 4'}"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_name = \n",
    "classe = \n",
    "\n",
    "latex_info = {}\n",
    "latex_info['titre'] =  \n",
    "latex_info['classe'] = \n",
    "latex_info['date'] = \n",
    "latex_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import et premiers traitements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "notes = pd.ExcelFile(\"./../../../notes_\"+classe+\".xls\")\n",
    "notes.sheet_names\n",
    "notes = notes.parse(ds_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DM_0506', 'av_arrondi', 'num_sujet', 'Malus', 'Exercice 1', '1.1.a',\n",
       "       '1.1.b', '1.1.c', '1.2.a', '1.2.b', '1.2.c', '1.2.d', '1.3.a', '1.3.b',\n",
       "       '1.3.c', '1.3.d', 'Exercice 2', '2.1', '2.2', '2.3', 'Exercice 3',\n",
       "       '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",
       "      dtype='object')"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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