2014-2015/T_STMG/DS/DST_04/Bilan/Bilan.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 74,
   "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": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classe': '\\\\TSTMG', 'date': '16 mai 2015', 'titre': 'DST 04'}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_name = 'DST_04'\n",
    "classe = 'tstmg'\n",
    "\n",
    "latex_info = {}\n",
    "latex_info['titre'] = 'DST 04' \n",
    "latex_info['classe'] = '\\\\TSTMG'\n",
    "latex_info['date'] = '16 mai 2015'\n",
    "latex_info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import et premiers traitements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DST_04', 'av_arrondi', 'Exercice 1', 'QCM', '1.1', '1.2', 'Exercice 2',\n",
       "       '2.A.1', '2.A.2', '2.A.3.a', '2.A.3.b', '2.A.3.c', '2.B.1', '2.B.2',\n",
       "       'Exercice 3', '3.A.1', '3.A.2', '3.B.1', '3.B.2', '3.B.3', 'Exercice 4',\n",
       "       '4.A.1', '4.A.1.a', '4.A.1.b', '4.B.1', '4.B.2', '4.B.3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "notes = notes.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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": 80,
   "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": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes = notes[notes[ds_name].notnull()]\n",
    "notes = notes[notes[ds_name] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes = notes.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traitement des notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DST_04', 'Exercice 1', 'QCM', '1.1', '1.2', 'Exercice 2', '2.A.1',\n",
       "       '2.A.2', '2.A.3.a', '2.A.3.b', '2.A.3.c', '2.B.1', '2.B.2',\n",
       "       'Exercice 3', '3.A.1', '3.A.2', '3.B.1', '3.B.2', '3.B.3', 'Exercice 4',\n",
       "       '4.A.1', '4.A.1.a', '4.A.1.b', '4.B.1', '4.B.2', '4.B.3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.T.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "list_exo = [\"Exercice 1\", \"Exercice 2\", \"Exercice 3\", \"Exercice 4\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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": 86,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1.1',\n",
       " '1.2',\n",
       " '2.A.1',\n",
       " '2.A.2',\n",
       " '2.A.3.a',\n",
       " '2.A.3.b',\n",
       " '2.A.3.c',\n",
       " '2.B.1',\n",
       " '2.B.2',\n",
       " '3.A.1',\n",
       " '3.A.2',\n",
       " '3.B.1',\n",
       " '3.B.2',\n",
       " '3.B.3',\n",
       " '4.A.1',\n",
       " '4.A.1.a',\n",
       " '4.A.1.b',\n",
       " '4.B.1',\n",
       " '4.B.2',\n",
       " '4.B.3']"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_avec_note = list_exo + [ds_name,  \"QCM\"]\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": 87,
   "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": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
    "#notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eleves = notes.copy()\n",
    "eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(notes.T.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparation du fichier .tex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    20.000000\n",
       "mean     11.425000\n",
       "std       4.733072\n",
       "min       3.000000\n",
       "25%       8.500000\n",
       "50%      10.750000\n",
       "75%      15.125000\n",
       "max      20.000000\n",
       "Name: DST_04, dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes[ds_name].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f54263a3d30>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEbhR0sHq6x2SdknaBRA
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f542600fa90>"
      ]
     },
     "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": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes_questions = notes[sous_exo]\n",
    "notes_analysis = notes_questions.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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</th>\n",
       "      <th>1.2</th>\n",
       "      <th>2.A.1</th>\n",
       "      <th>2.A.2</th>\n",
       "      <th>2.A.3.a</th>\n",
       "      <th>2.A.3.b</th>\n",
       "      <th>2.A.3.c</th>\n",
       "      <th>2.B.1</th>\n",
       "      <th>2.B.2</th>\n",
       "      <th>3.A.1</th>\n",
       "      <th>3.A.2</th>\n",
       "      <th>3.B.1</th>\n",
       "      <th>3.B.2</th>\n",
       "      <th>3.B.3</th>\n",
       "      <th>4.A.1</th>\n",
       "      <th>4.A.1.a</th>\n",
       "      <th>4.A.1.b</th>\n",
       "      <th>4.B.1</th>\n",
       "      <th>4.B.2</th>\n",
       "      <th>4.B.3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\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",
       "      <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",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       1.1  1.2  2.A.1  2.A.2  2.A.3.a  2.A.3.b  2.A.3.c  2.B.1  2.B.2  3.A.1  \\\n",
       "count  NaN  NaN    NaN    NaN      NaN      NaN      NaN    NaN    NaN    NaN   \n",
       "\n",
       "       3.A.2  3.B.1  3.B.2  3.B.3  4.A.1  4.A.1.a  4.A.1.b  4.B.1  4.B.2  \\\n",
       "count    NaN    NaN    NaN    NaN    NaN      NaN      NaN    NaN    NaN   \n",
       "\n",
       "       4.B.3  \n",
       "count    NaN  "
      ]
     },
     "execution_count": 36,
     "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
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}