2015-2016/notes/bilan313.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from opytex import texenv\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use(\"seaborn-notebook\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Information sur la classe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classe = \"313\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import et premiers traitements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Notes',\n",
       " 'Remarques',\n",
       " 'Conn',\n",
       " 'DM_15_09_18',\n",
       " 'DS_15_09_25',\n",
       " 'Pyramide',\n",
       " 'BB_15_10_31',\n",
       " 'DM_15_11_16',\n",
       " 'DS_15_11_27',\n",
       " 'DM_15_12_09',\n",
       " 'Boulettes',\n",
       " 'BB_16_01_23',\n",
       " 'DM_16_01_29',\n",
       " 'BB_16_02_15',\n",
       " 'DM_16_03_30',\n",
       " 'BB_16_04_02',\n",
       " 'BB_16_04_19',\n",
       " 'Enclos',\n",
       " 'DM_16_05_18',\n",
       " 'BB_16_05_31']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_notes = pd.ExcelFile(classe+\".xlsx\")\n",
    "all_notes.sheet_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1er trimestre "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Par élève"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "XLRDError",
     "evalue": "No sheet named <'notes'>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.5/site-packages/xlrd/book.py\u001b[0m in \u001b[0;36msheet_by_name\u001b[1;34m(self, sheet_name)\u001b[0m\n\u001b[0;32m    438\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 439\u001b[1;33m             \u001b[0msheetx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sheet_names\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msheet_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    440\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: 'notes' is not in list",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mXLRDError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-4ee6ba5067a2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mds_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'notes'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnotes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mall_notes\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[0m",
      "\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.5/site-packages/pandas/io/excel.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self, sheetname, header, skiprows, skip_footer, index_col, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, has_index_names, converters, **kwds)\u001b[0m\n\u001b[0;32m    237\u001b[0m                                  \u001b[0mconvert_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconvert_float\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    238\u001b[0m                                  \u001b[0mconverters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconverters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 239\u001b[1;33m                                  **kwds)\n\u001b[0m\u001b[0;32m    240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    241\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_should_parse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparse_cols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.5/site-packages/pandas/io/excel.py\u001b[0m in \u001b[0;36m_parse_excel\u001b[1;34m(self, sheetname, header, skiprows, skip_footer, index_col, has_index_names, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, verbose, **kwds)\u001b[0m\n\u001b[0;32m    370\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    371\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0masheetname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 372\u001b[1;33m                 \u001b[0msheet\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msheet_by_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0masheetname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    373\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# assume an integer if not a string\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    374\u001b[0m                 \u001b[0msheet\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msheet_by_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0masheetname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.5/site-packages/xlrd/book.py\u001b[0m in \u001b[0;36msheet_by_name\u001b[1;34m(self, sheet_name)\u001b[0m\n\u001b[0;32m    439\u001b[0m             \u001b[0msheetx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sheet_names\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msheet_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    440\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 441\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mXLRDError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'No sheet named <%r>'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0msheet_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    442\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msheet_by_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msheetx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    443\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mXLRDError\u001b[0m: No sheet named <'notes'>"
     ]
    }
   ],
   "source": [
    "ds_name = 'Notes'\n",
    "notes = all_notes.parse(ds_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 2e trimestre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connaissances pour le 2e trimestre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ds_name = \"Conn\"\n",
    "notes = all_notes.parse(ds_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Bareme</th>\n",
       "      <th>ABDOU Farida</th>\n",
       "      <th>ABOU BACAR Djaha</th>\n",
       "      <th>AHAMADA Nabaouya</th>\n",
       "      <th>AHAMADI Faina</th>\n",
       "      <th>ALI Mardhuia</th>\n",
       "      <th>ALI SOULAIMANA Chamsia</th>\n",
       "      <th>ALSENE ALI MADI Stela</th>\n",
       "      <th>ANDRIATAHIANA Hoby</th>\n",
       "      <th>ANLI Emeline</th>\n",
       "      <th>...</th>\n",
       "      <th>MALIDE El-Anzize</th>\n",
       "      <th>MONNE Kevin</th>\n",
       "      <th>MOUSSA Roibouanti</th>\n",
       "      <th>OUSSENI Hilma</th>\n",
       "      <th>SAANLI Natali</th>\n",
       "      <th>SAID AHAMADA Roukaya</th>\n",
       "      <th>SANDA Issoufi</th>\n",
       "      <th>SOILIHI Soifia</th>\n",
       "      <th>SOUFIANI Laila</th>\n",
       "      <th>YOUSSOUF Sitirati</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Conn trimestre 1</th>\n",
       "      <td>20</td>\n",
       "      <td>7.5</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>10.5</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>17</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>11.50</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>20</td>\n",
       "      <td>7.5</td>\n",
       "      <td>16.583333</td>\n",
       "      <td>14.333333</td>\n",
       "      <td>4.083333</td>\n",
       "      <td>14.833333</td>\n",
       "      <td>15.416667</td>\n",
       "      <td>10.5</td>\n",
       "      <td>15.833333</td>\n",
       "      <td>9.833333</td>\n",
       "      <td>...</td>\n",
       "      <td>10.916667</td>\n",
       "      <td>16.083333</td>\n",
       "      <td>17.666667</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>16.416667</td>\n",
       "      <td>17</td>\n",
       "      <td>9.166667</td>\n",
       "      <td>11.25</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>6.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conn_15_09_09</th>\n",
       "      <td>4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conn_15_09_16</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conn_15_09_30</th>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Bareme  ABDOU Farida  ABOU BACAR Djaha  AHAMADA Nabaouya  \\\n",
       "Conn trimestre 1      20           7.5         16.500000         14.500000   \n",
       "NaN                   20           7.5         16.583333         14.333333   \n",
       "Conn_15_09_09          4           1.5          3.500000          3.000000   \n",
       "Conn_15_09_16          4           2.0          4.000000          3.000000   \n",
       "Conn_15_09_30          5           2.0          3.500000          2.500000   \n",
       "\n",
       "                  AHAMADI Faina  ALI Mardhuia  ALI SOULAIMANA Chamsia  \\\n",
       "Conn trimestre 1       4.000000     15.000000               15.500000   \n",
       "NaN                    4.083333     14.833333               15.416667   \n",
       "Conn_15_09_09          1.500000      3.000000                3.000000   \n",
       "Conn_15_09_16          1.000000      4.000000                3.500000   \n",
       "Conn_15_09_30          1.500000      3.500000                3.500000   \n",
       "\n",
       "                  ALSENE ALI MADI Stela  ANDRIATAHIANA Hoby  ANLI Emeline  \\\n",
       "Conn trimestre 1                   10.5           16.000000     10.000000   \n",
       "NaN                                10.5           15.833333      9.833333   \n",
       "Conn_15_09_09                       2.5            3.000000      0.500000   \n",
       "Conn_15_09_16                       2.5            2.000000      2.500000   \n",
       "Conn_15_09_30                       2.0            4.000000      3.500000   \n",
       "\n",
       "                        ...          MALIDE El-Anzize  MONNE Kevin  \\\n",
       "Conn trimestre 1        ...                 11.000000    16.000000   \n",
       "NaN                     ...                 10.916667    16.083333   \n",
       "Conn_15_09_09           ...                  1.500000     2.500000   \n",
       "Conn_15_09_16           ...                  2.000000     4.000000   \n",
       "Conn_15_09_30           ...                  4.500000     4.000000   \n",
       "\n",
       "                  MOUSSA Roibouanti  OUSSENI Hilma  SAANLI Natali  \\\n",
       "Conn trimestre 1          17.500000      10.500000      16.500000   \n",
       "NaN                       17.666667      10.333333      16.416667   \n",
       "Conn_15_09_09              4.000000       1.000000       2.500000   \n",
       "Conn_15_09_16              4.000000       3.000000       4.000000   \n",
       "Conn_15_09_30              4.000000       3.000000       4.000000   \n",
       "\n",
       "                  SAID AHAMADA Roukaya  SANDA Issoufi  SOILIHI Soifia  \\\n",
       "Conn trimestre 1                    17       9.000000           11.50   \n",
       "NaN                                 17       9.166667           11.25   \n",
       "Conn_15_09_09                        4       2.500000            1.50   \n",
       "Conn_15_09_16                        4       2.500000            2.00   \n",
       "Conn_15_09_30                        4       2.500000            2.50   \n",
       "\n",
       "                  SOUFIANI Laila  YOUSSOUF Sitirati  \n",
       "Conn trimestre 1        5.500000           6.500000  \n",
       "NaN                     5.333333           6.583333  \n",
       "Conn_15_09_09           1.000000           0.500000  \n",
       "Conn_15_09_16           1.000000           1.000000  \n",
       "Conn_15_09_30           1.500000           2.500000  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Conn trimestre 1',                nan,    'Conn_15_09_09',\n",
       "          'Conn_15_09_16',    'Conn_15_09_30',    'Conn_15_10_07',\n",
       "          'Conn_15_11_04',    'Conn_15_11_12',                nan,\n",
       "                      nan,                nan, 'Conn trimestre 2',\n",
       "          'Conn_15_11_18',    'Conn_15_12_08',    'Conn_16_01_20',\n",
       "          'Conn_16_02_03',    'Conn_16_02_10'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "conn_2nd_Tri = ['Conn_15_11_18',    'Conn_15_12_08',    'Conn_16_01_20',\n",
    "          'Conn_16_02_03',    'Conn_16_02_10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Conn_15_11_18</th>\n",
       "      <th>Conn_15_12_08</th>\n",
       "      <th>Conn_16_01_20</th>\n",
       "      <th>Conn_16_02_03</th>\n",
       "      <th>Conn_16_02_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bareme</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABDOU Farida</th>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABOU BACAR Djaha</th>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMADA Nabaouya</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMADI Faina</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Conn_15_11_18  Conn_15_12_08  Conn_16_01_20  Conn_16_02_03  \\\n",
       "Bareme                      5.0            5.0            5.0            4.0   \n",
       "ABDOU Farida                3.5            4.0            3.0            2.0   \n",
       "ABOU BACAR Djaha            4.5            5.0            2.5            3.0   \n",
       "AHAMADA Nabaouya            3.0            5.0            1.0            3.5   \n",
       "AHAMADI Faina               NaN            0.5            2.5            0.0   \n",
       "\n",
       "                  Conn_16_02_10  \n",
       "Bareme                      6.0  \n",
       "ABDOU Farida                2.0  \n",
       "ABOU BACAR Djaha            2.5  \n",
       "AHAMADA Nabaouya            2.0  \n",
       "AHAMADI Faina               3.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes_conn_2T = notes.T[conn_2nd_Tri]\n",
    "notes_conn_2T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "barem = notes_conn_2T[:1]\n",
    "notes = notes_conn_2T[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Conn_15_11_18</th>\n",
       "      <th>Conn_15_12_08</th>\n",
       "      <th>Conn_16_01_20</th>\n",
       "      <th>Conn_16_02_03</th>\n",
       "      <th>Conn_16_02_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ABDOU Farida</th>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABOU BACAR Djaha</th>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMADA Nabaouya</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMADI Faina</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALI Mardhuia</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Conn_15_11_18  Conn_15_12_08  Conn_16_01_20  Conn_16_02_03  \\\n",
       "ABDOU Farida                3.5            4.0            3.0            2.0   \n",
       "ABOU BACAR Djaha            4.5            5.0            2.5            3.0   \n",
       "AHAMADA Nabaouya            3.0            5.0            1.0            3.5   \n",
       "AHAMADI Faina               NaN            0.5            2.5            0.0   \n",
       "ALI Mardhuia                5.0            4.0            4.5            3.0   \n",
       "\n",
       "                  Conn_16_02_10  \n",
       "ABDOU Farida                2.0  \n",
       "ABOU BACAR Djaha            2.5  \n",
       "AHAMADA Nabaouya            2.0  \n",
       "AHAMADI Faina               3.0  \n",
       "ALI Mardhuia                4.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.head()\n",
    "#barem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes = notes.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Traitement des notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Conn_15_11_18', 'Conn_15_12_08', 'Conn_16_01_20', 'Conn_16_02_03',\n",
       "       'Conn_16_02_10'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.T.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(notes.T.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Un peu de statistiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Conn_15_11_18</th>\n",
       "      <th>Conn_15_12_08</th>\n",
       "      <th>Conn_16_01_20</th>\n",
       "      <th>Conn_16_02_03</th>\n",
       "      <th>Conn_16_02_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>30.00000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.206897</td>\n",
       "      <td>3.75000</td>\n",
       "      <td>2.379310</td>\n",
       "      <td>2.466667</td>\n",
       "      <td>3.344828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.161259</td>\n",
       "      <td>1.38184</td>\n",
       "      <td>1.300104</td>\n",
       "      <td>1.252125</td>\n",
       "      <td>1.518361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.50000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.62500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Conn_15_11_18  Conn_15_12_08  Conn_16_01_20  Conn_16_02_03  \\\n",
       "count      29.000000       30.00000      29.000000      30.000000   \n",
       "mean        3.206897        3.75000       2.379310       2.466667   \n",
       "std         1.161259        1.38184       1.300104       1.252125   \n",
       "min         0.500000        0.50000       0.000000       0.000000   \n",
       "25%         3.000000        2.62500       1.000000       1.500000   \n",
       "50%         3.000000        4.00000       2.500000       3.000000   \n",
       "75%         4.000000        5.00000       3.500000       3.500000   \n",
       "max         5.000000        5.00000       4.500000       4.000000   \n",
       "\n",
       "       Conn_16_02_10  \n",
       "count      29.000000  \n",
       "mean        3.344828  \n",
       "std         1.518361  \n",
       "min         1.000000  \n",
       "25%         2.000000  \n",
       "50%         3.000000  \n",
       "75%         4.500000  \n",
       "max         6.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f25ed408c88>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iGz6VW6vBMAwmp0bwouMBXG47BAIBmhp3Y/fug1DIi1b8nHxXc2XCUq2XKP7i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NXn9Fn9Yr7CnLW6P8qc6TOdnqouu1zOOYLB2OScc0Y5731eSMiASQKBsM21Vq0y2BJbQtra9/9/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6bORn5lGS699R2e4G90oikuzXksnIB7rBtwLZjusGDyvBTsbQ9Ajvuz8SodHx/a1viQpmnjbM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\ncPL6vIzMjTE0MxLsWz9OARYLZFnm197fmV6epTyvlA8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I+rhFUWR0bpz+8Ve4fR50Gi0N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pU8AzXn02AnlmNi6N9ZHskbVtz8xKgeeBjzvn3k6w6kFgcszzUqDRe3yPc+6XXn2e7Wtd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\niIiIiIgkyf8DKPYE/HvTqHMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25ed47cc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalisation des notes de chaque exo\n",
    "notes_exo_norm = notes / barem.values[0,:]\n",
    "#notes_exo_norm\n",
    "ax = notes_exo_norm.T.plot(color = \"gray\", legend = False, figsize = (16, 7))\n",
    "d_norm =  notes_exo_norm.describe()\n",
    "d_norm.T[[\"min\", \"25%\", \"50%\", \"75%\", \"max\"]].plot(ax=ax, kind=\"area\", stacked = False, alpha=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lDtAAWeoAyfk+oKMbeBEiAEmHAxe24SJEn/70F3j5y9eTH1DXZSvwk0x752df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\nQCVJkiRJM/H/AZ6n7ym7vO5gAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f25ed40cbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = notes.hist(figsize = (16,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def norma_mean(n):\n",
    "    return (n / barem).sum(axis=1)/len(n.dropna())*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes[\"Trim2\"] = notes.apply(norma_mean, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Conn_15_11_18</th>\n",
       "      <th>Conn_15_12_08</th>\n",
       "      <th>Conn_16_01_20</th>\n",
       "      <th>Conn_16_02_03</th>\n",
       "      <th>Conn_16_02_10</th>\n",
       "      <th>Trim2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ABDOU Farida</th>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABOU BACAR Djaha</th>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>14.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMADA Nabaouya</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.033333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMADI Faina</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ALI Mardhuia</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.466667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Conn_15_11_18  Conn_15_12_08  Conn_16_01_20  Conn_16_02_03  \\\n",
       "ABDOU Farida                3.5            4.0            3.0            2.0   \n",
       "ABOU BACAR Djaha            4.5            5.0            2.5            3.0   \n",
       "AHAMADA Nabaouya            3.0            5.0            1.0            3.5   \n",
       "AHAMADI Faina               NaN            0.5            2.5            0.0   \n",
       "ALI Mardhuia                5.0            4.0            4.5            3.0   \n",
       "\n",
       "                  Conn_16_02_10      Trim2  \n",
       "ABDOU Farida                2.0  11.733333  \n",
       "ABOU BACAR Djaha            2.5  14.266667  \n",
       "AHAMADA Nabaouya            2.0  12.033333  \n",
       "AHAMADI Faina               3.0   5.500000  \n",
       "ALI Mardhuia                4.0  16.466667  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brevet blanc février"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Exercice 7</th>\n",
       "      <th>Exercice 4</th>\n",
       "      <th>Exercice 5</th>\n",
       "      <th>Exercice 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bareme</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABDALLAH Touraya</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABDOU Mariam</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABTOIHI SAID Yasmina</th>\n",
       "      <td>6.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMED Anssuifidine</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Exercice 7  Exercice 4  Exercice 5  Exercice 2\n",
       "Bareme                       7.0         4.0         6.0         4.0\n",
       "ABDALLAH Touraya             4.0         0.5         1.5         1.0\n",
       "ABDOU Mariam                 5.0         2.5         6.0         4.0\n",
       "ABTOIHI SAID Yasmina         6.5         1.5         4.5         3.5\n",
       "AHAMED Anssuifidine          6.0         1.5         1.5         3.5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BB_fev = all_notes.parse('BB_16_02_15')\n",
    "BB_fev = BB_fev.T[[ 'Exercice 7', 'Exercice 4', 'Exercice 5', 'Exercice 2']]\n",
    "BB_fev.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Exercice 7</th>\n",
       "      <th>Exercice 4</th>\n",
       "      <th>Exercice 5</th>\n",
       "      <th>Exercice 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HALIDI Tomsoyère</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOUGNIDAHO Nouriana</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAGAF Amal</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABDALLAH Touraya</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMED Anssuifidine</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHMED ABDOU El-Karim</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOINA HASSANI Nahimi</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALIBOU Nafilati</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBRAHIM Laoura</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOENY MOKO Nadjma</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUMADI Himida</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUMADI Antufati</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABTOIHI SAID Yasmina</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BACO ABDALLAH Moustadirane</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DJADAR Ifrah</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YANCOUB Toufa</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANLI Koudoussia</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUSSOUF Asma</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATTOUMANI Hanissa</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOURTADJOU El-Fazar</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bareme</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABDOU Mariam</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMED Issihaka</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANDILI Chayhati</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANDJILANE Rachma</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BINALI Maoulida</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOINA Ainati</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAOUD El-Farouk</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUMADI ABDALLAH Abdallah</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALIDE ABDOU Nasser</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALIDE Younes</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAID Chamsoudine</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Exercice 7 Exercice 4 Exercice 5 Exercice 2\n",
       "HALIDI Tomsoyère                False      False      False      False\n",
       "MOUGNIDAHO Nouriana             False      False      False      False\n",
       "SAGAF Amal                      False      False      False      False\n",
       "ABDALLAH Touraya                 True      False      False      False\n",
       "AHAMED Anssuifidine              True      False      False       True\n",
       "AHMED ABDOU El-Karim             True      False      False       True\n",
       "BOINA HASSANI Nahimi             True      False      False       True\n",
       "HALIBOU Nafilati                 True      False      False       True\n",
       "IBRAHIM Laoura                   True      False      False       True\n",
       "MOENY MOKO Nadjma                True      False      False       True\n",
       "HOUMADI Himida                   True      False       True      False\n",
       "HOUMADI Antufati                 True      False       True      False\n",
       "ABTOIHI SAID Yasmina             True      False       True       True\n",
       "BACO ABDALLAH Moustadirane       True      False       True       True\n",
       "DJADAR Ifrah                     True      False       True       True\n",
       "YANCOUB Toufa                    True      False       True       True\n",
       "ANLI Koudoussia                  True       True      False       True\n",
       "YOUSSOUF Asma                    True       True      False       True\n",
       "ATTOUMANI Hanissa                True       True       True      False\n",
       "MOURTADJOU El-Fazar              True       True       True      False\n",
       "Bareme                           True       True       True       True\n",
       "ABDOU Mariam                     True       True       True       True\n",
       "AHAMED Issihaka                  True       True       True       True\n",
       "ANDILI Chayhati                  True       True       True       True\n",
       "ANDJILANE Rachma                 True       True       True       True\n",
       "BINALI Maoulida                  True       True       True       True\n",
       "BOINA Ainati                     True       True       True       True\n",
       "DAOUD El-Farouk                  True       True       True       True\n",
       "HOUMADI ABDALLAH Abdallah        True       True       True       True\n",
       "MALIDE ABDOU Nasser              True       True       True       True\n",
       "MALIDE Younes                    True       True       True       True\n",
       "SAID Chamsoudine                 True       True       True       True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((BB_fev / BB_fev.T['Bareme']) > 0.4).sort_values(['Exercice 7', 'Exercice 4', 'Exercice 5',  'Exercice 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Bilan 2e trimestre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ds_name = 'Notes'\n",
    "notes = all_notes.parse(ds_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DS_15_11_27</th>\n",
       "      <th>DM_15_12_09</th>\n",
       "      <th>Boulettes</th>\n",
       "      <th>BB_16_01_23</th>\n",
       "      <th>DM_16_01_29</th>\n",
       "      <th>Brevet blanc Fevrier</th>\n",
       "      <th>Connaissance trimestre 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ABDALLAH Touraya</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABDOU Mariam</th>\n",
       "      <td>17.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABTOIHI SAID Yasmina</th>\n",
       "      <td>10.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMED Anssuifidine</th>\n",
       "      <td>12.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>21.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMED Issihaka</th>\n",
       "      <td>12.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHMED ABDOU El-Karim</th>\n",
       "      <td>7.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANDILI Chayhati</th>\n",
       "      <td>6.5</td>\n",
       "      <td>11.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>29.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>32.5</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANDJILANE Rachma</th>\n",
       "      <td>9.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>24.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>21.5</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANLI Koudoussia</th>\n",
       "      <td>9.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>23.5</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATTOUMANI Hanissa</th>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>19.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BACO ABDALLAH Moustadirane</th>\n",
       "      <td>5.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BINALI Maoulida</th>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOINA Ainati</th>\n",
       "      <td>9.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOINA HASSANI Nahimi</th>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>11.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAOUD El-Farouk</th>\n",
       "      <td>14.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DJADAR Ifrah</th>\n",
       "      <td>5.5</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>11.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALIBOU Nafilati</th>\n",
       "      <td>3.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUMADI Himida</th>\n",
       "      <td>8.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>11.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUMADI Antufati</th>\n",
       "      <td>7.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUMADI ABDALLAH Abdallah</th>\n",
       "      <td>7.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBRAHIM Laoura</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALIDE ABDOU Nasser</th>\n",
       "      <td>18.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>28.5</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALIDE Younes</th>\n",
       "      <td>16.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>35.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>37.5</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOENY MOKO Nadjma</th>\n",
       "      <td>7.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOURTADJOU El-Fazar</th>\n",
       "      <td>7.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>21.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAID Chamsoudine</th>\n",
       "      <td>13.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YANCOUB Toufa</th>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>17.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUSSOUF Asma</th>\n",
       "      <td>10.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            DS_15_11_27  DM_15_12_09  Boulettes  BB_16_01_23  \\\n",
       "ABDALLAH Touraya                    4.0          0.0        8.5         10.0   \n",
       "ABDOU Mariam                       17.5         14.0       18.0         35.0   \n",
       "ABTOIHI SAID Yasmina               10.0         13.5       18.0         23.0   \n",
       "AHAMED Anssuifidine                12.0         14.5       10.5         21.5   \n",
       "AHAMED Issihaka                    12.0         13.0       10.5         21.0   \n",
       "AHMED ABDOU El-Karim                7.5          9.5       17.5         13.5   \n",
       "ANDILI Chayhati                     6.5         11.5       17.5         29.5   \n",
       "ANDJILANE Rachma                    9.0         17.5       17.5         24.0   \n",
       "ANLI Koudoussia                     9.5         14.0       17.5         23.0   \n",
       "ATTOUMANI Hanissa                  14.0         14.0       10.5         19.5   \n",
       "BACO ABDALLAH Moustadirane          5.0         18.0       10.5         17.0   \n",
       "BINALI Maoulida                     5.0         17.0       18.0         25.5   \n",
       "BOINA Ainati                        9.5         14.0       18.0         25.5   \n",
       "BOINA HASSANI Nahimi                3.0         15.0       12.0         12.5   \n",
       "DAOUD El-Farouk                    14.0         15.5       12.0         20.5   \n",
       "DJADAR Ifrah                        5.5         11.0       12.0         19.5   \n",
       "HALIBOU Nafilati                    3.5          7.5       12.0         16.5   \n",
       "HOUMADI Himida                      8.0         14.5       12.0         17.5   \n",
       "HOUMADI Antufati                    7.5         19.0       18.5         22.0   \n",
       "HOUMADI ABDALLAH Abdallah           7.5         14.0       12.0         19.0   \n",
       "IBRAHIM Laoura                      5.0          4.5       18.5         18.0   \n",
       "MALIDE ABDOU Nasser                18.0         19.0       18.5         28.0   \n",
       "MALIDE Younes                      16.0         17.5       18.5         35.5   \n",
       "MOENY MOKO Nadjma                   7.5         10.0        8.5         18.5   \n",
       "MOURTADJOU El-Fazar                 7.5         17.5        8.5         21.0   \n",
       "SAID Chamsoudine                   13.0         19.5       12.0         24.5   \n",
       "YANCOUB Toufa                      16.0         16.0       12.0         27.0   \n",
       "YOUSSOUF Asma                      10.5          0.0        8.5         16.5   \n",
       "\n",
       "                            DM_16_01_29  Brevet blanc Fevrier  \\\n",
       "ABDALLAH Touraya                    8.5                  15.0   \n",
       "ABDOU Mariam                       20.0                  31.0   \n",
       "ABTOIHI SAID Yasmina               13.5                  24.5   \n",
       "AHAMED Anssuifidine                16.0                  22.0   \n",
       "AHAMED Issihaka                    16.0                  23.0   \n",
       "AHMED ABDOU El-Karim               10.0                  17.5   \n",
       "ANDILI Chayhati                     9.5                  32.5   \n",
       "ANDJILANE Rachma                   17.5                  21.5   \n",
       "ANLI Koudoussia                    14.5                  23.5   \n",
       "ATTOUMANI Hanissa                  18.5                  25.0   \n",
       "BACO ABDALLAH Moustadirane         15.5                  21.0   \n",
       "BINALI Maoulida                    16.0                  26.0   \n",
       "BOINA Ainati                       16.0                  28.5   \n",
       "BOINA HASSANI Nahimi               11.5                  17.0   \n",
       "DAOUD El-Farouk                    16.0                  26.0   \n",
       "DJADAR Ifrah                       11.5                  22.0   \n",
       "HALIBOU Nafilati                    8.5                  17.0   \n",
       "HOUMADI Himida                     11.5                  15.5   \n",
       "HOUMADI Antufati                   17.5                  20.0   \n",
       "HOUMADI ABDALLAH Abdallah          16.0                  26.5   \n",
       "IBRAHIM Laoura                     12.5                  16.0   \n",
       "MALIDE ABDOU Nasser                17.5                  28.5   \n",
       "MALIDE Younes                      18.5                  37.5   \n",
       "MOENY MOKO Nadjma                  18.5                  17.0   \n",
       "MOURTADJOU El-Fazar                15.5                  24.5   \n",
       "SAID Chamsoudine                   19.0                  28.5   \n",
       "YANCOUB Toufa                      17.5                  26.0   \n",
       "YOUSSOUF Asma                      12.0                  19.0   \n",
       "\n",
       "                            Connaissance trimestre 2  \n",
       "ABDALLAH Touraya                                 3.5  \n",
       "ABDOU Mariam                                    13.5  \n",
       "ABTOIHI SAID Yasmina                            15.0  \n",
       "AHAMED Anssuifidine                             17.5  \n",
       "AHAMED Issihaka                                 19.0  \n",
       "AHMED ABDOU El-Karim                            10.0  \n",
       "ANDILI Chayhati                                 12.0  \n",
       "ANDJILANE Rachma                                17.0  \n",
       "ANLI Koudoussia                                 14.0  \n",
       "ATTOUMANI Hanissa                               15.5  \n",
       "BACO ABDALLAH Moustadirane                      13.0  \n",
       "BINALI Maoulida                                 13.5  \n",
       "BOINA Ainati                                    16.0  \n",
       "BOINA HASSANI Nahimi                            13.5  \n",
       "DAOUD El-Farouk                                 11.0  \n",
       "DJADAR Ifrah                                    15.5  \n",
       "HALIBOU Nafilati                                 7.0  \n",
       "HOUMADI Himida                                  12.0  \n",
       "HOUMADI Antufati                                10.5  \n",
       "HOUMADI ABDALLAH Abdallah                       15.0  \n",
       "IBRAHIM Laoura                                   8.0  \n",
       "MALIDE ABDOU Nasser                             19.0  \n",
       "MALIDE Younes                                   19.5  \n",
       "MOENY MOKO Nadjma                               12.5  \n",
       "MOURTADJOU El-Fazar                             15.0  \n",
       "SAID Chamsoudine                                15.5  \n",
       "YANCOUB Toufa                                   17.5  \n",
       "YOUSSOUF Asma                                   10.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trim2 = notes[8:].T\n",
    "barem = trim2.iloc[0]\n",
    "eleveT2 = trim2.iloc[1:32].dropna().astype('float')\n",
    "eleveT2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse par élève "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact, interactive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DS_15_11_27</th>\n",
       "      <th>DM_15_12_09</th>\n",
       "      <th>Boulettes</th>\n",
       "      <th>BB_16_01_23</th>\n",
       "      <th>DM_16_01_29</th>\n",
       "      <th>Brevet blanc Fevrier</th>\n",
       "      <th>Connaissance trimestre 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.471429</td>\n",
       "      <td>0.662500</td>\n",
       "      <td>0.692857</td>\n",
       "      <td>0.539732</td>\n",
       "      <td>0.741071</td>\n",
       "      <td>0.582143</td>\n",
       "      <td>0.680357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.215994</td>\n",
       "      <td>0.256625</td>\n",
       "      <td>0.191347</td>\n",
       "      <td>0.149616</td>\n",
       "      <td>0.167389</td>\n",
       "      <td>0.139586</td>\n",
       "      <td>0.188483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.312500</td>\n",
       "      <td>0.568750</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>0.446875</td>\n",
       "      <td>0.593750</td>\n",
       "      <td>0.465625</td>\n",
       "      <td>0.587500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.425000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.525000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.581250</td>\n",
       "      <td>0.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.612500</td>\n",
       "      <td>0.856250</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.618750</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.653125</td>\n",
       "      <td>0.781250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.887500</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.937500</td>\n",
       "      <td>0.975000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DS_15_11_27  DM_15_12_09  Boulettes  BB_16_01_23  DM_16_01_29  \\\n",
       "count    28.000000    28.000000  28.000000    28.000000    28.000000   \n",
       "mean      0.471429     0.662500   0.692857     0.539732     0.741071   \n",
       "std       0.215994     0.256625   0.191347     0.149616     0.167389   \n",
       "min       0.150000     0.000000   0.425000     0.250000     0.425000   \n",
       "25%       0.312500     0.568750   0.525000     0.446875     0.593750   \n",
       "50%       0.425000     0.700000   0.600000     0.525000     0.800000   \n",
       "75%       0.612500     0.856250   0.900000     0.618750     0.875000   \n",
       "max       0.900000     0.975000   0.925000     0.887500     1.000000   \n",
       "\n",
       "       Brevet blanc Fevrier  Connaissance trimestre 2  \n",
       "count             28.000000                 28.000000  \n",
       "mean               0.582143                  0.680357  \n",
       "std                0.139586                  0.188483  \n",
       "min                0.375000                  0.175000  \n",
       "25%                0.465625                  0.587500  \n",
       "50%                0.581250                  0.687500  \n",
       "75%                0.653125                  0.781250  \n",
       "max                0.937500                  0.975000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalisation des notes de chaque exo\n",
    "eleveT2_norm = eleveT2 / barem.values.astype('float')\n",
    "eleveT2_norm.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MdITIjFdZka6bY5Qe01EjKtdvX6fmfg0NDxty27wy/JVcuvuLUoudvYbqSwHQEmxh34l9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\nFX8ffz4///mOrNyWVpcoqytjbHoMuVxORmIG2ce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s4lzVbnh4eNtV1+1V8k1xt5/6uevu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\n/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C3NMbs0y+zi7EucRiaT4evlS7BfMEG+QaQXpX8m7K/AURN2BTCE1XRuAWgFfiuEGHjh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\nrbvp6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75biKKAwWBEFEWFJabRaChzPzOMUxoPmzGL0+ngezRL/1yQZZmx2XFuPr5DOpPG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\nNyOHIksB+dl525J6ZFmmZ7yP3vF+hBCU55XSV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foXMf+VlqYj7Kvb/966gX6/l7HxQYZH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\nw59YiXbqLSYzB8rrd6zC+7mhqApT85HkntGZsYgrP68eu6tpGour6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eJ255KRno3BYESS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\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VAqK0lcRvktnvFIZhcOHSZ8zMjtPSvI8jh87s+GefF4ZhcOPW94yM9gKmGenpkx9QUV6b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FISYigujwcOpbmimprqGsupnKulayUmKZmpWEzWrxdcxJwdtDZ3OBEsMwqgAURfkf4ArgRMFu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\nqy+jTc0GHj/vCiqPOL5Ul+g4zse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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d11c61ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def f(x):\n",
    "    ax = eleveT2_norm.T.plot(color = \"gray\", legend = False, figsize = (16, 7))\n",
    "    d_norm =  eleveT2_norm.describe()\n",
    "    d_norm.T[[\"min\", \"25%\", \"50%\", \"75%\", \"max\"]].plot(ax=ax, kind=\"area\", stacked = False, alpha=.1)\n",
    "    eleveT2_norm.loc[x].plot(ax=ax, color=\"red\", alpha = 1)\n",
    "\n",
    "\n",
    "    \n",
    "interact(f, x = list(eleveT2_norm.index))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# 3e trimestre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DM_16_03_30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ds_name = 'DM_16_03_30'\n",
    "notes = all_notes.parse(ds_name).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classe': '313', 'date': '30 mars 2016', 'titre': 'Devoir maison 5'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latex_info = {}\n",
    "latex_info['titre'] =  \"Devoir maison 5\"\n",
    "latex_info['classe'] = \"313\"\n",
    "latex_info['date'] = \"30 mars 2016\"\n",
    "latex_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "barem = notes[:1]\n",
    "notes = notes[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DM_16_03_30</th>\n",
       "      <th>Malus</th>\n",
       "      <th>Exercice 1</th>\n",
       "      <th>1.1 Developper</th>\n",
       "      <th>1.2 Developper</th>\n",
       "      <th>1.3 Double developpement</th>\n",
       "      <th>1.4 Developpement carré</th>\n",
       "      <th>Exercice 2</th>\n",
       "      <th>2.1 Addition fraction</th>\n",
       "      <th>2.2 Multiplication fractions</th>\n",
       "      <th>...</th>\n",
       "      <th>Equation *</th>\n",
       "      <th>Equation fraction</th>\n",
       "      <th>equation simple</th>\n",
       "      <th>equation complexe</th>\n",
       "      <th>Exercice 4</th>\n",
       "      <th>Calcul angle</th>\n",
       "      <th>Angle alterne</th>\n",
       "      <th>Calcul longueur</th>\n",
       "      <th>2 methodes</th>\n",
       "      <th>arrondi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ABDALLAH Touraya</th>\n",
       "      <td>10.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABDOU Mariam</th>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABTOIHI SAID Yasmina</th>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMED Anssuifidine</th>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMED Issihaka</th>\n",
       "      <td>14.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3.166667</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      DM_16_03_30  Malus  Exercice 1  1.1 Developper  \\\n",
       "ABDALLAH Touraya             10.5    NaN         6.0               3   \n",
       "ABDOU Mariam                 19.0    NaN         5.5               3   \n",
       "ABTOIHI SAID Yasmina         17.0    NaN         6.0               3   \n",
       "AHAMED Anssuifidine          15.0    NaN         5.5               2   \n",
       "AHAMED Issihaka              14.5    NaN         5.0               3   \n",
       "\n",
       "                      1.2 Developper  1.3 Double developpement  \\\n",
       "ABDALLAH Touraya                   3                         3   \n",
       "ABDOU Mariam                       2                         3   \n",
       "ABTOIHI SAID Yasmina               3                         3   \n",
       "AHAMED Anssuifidine                3                         3   \n",
       "AHAMED Issihaka                    3                         2   \n",
       "\n",
       "                      1.4 Developpement carré  Exercice 2  \\\n",
       "ABDALLAH Touraya                            3    2.666667   \n",
       "ABDOU Mariam                                3    4.000000   \n",
       "ABTOIHI SAID Yasmina                        3    3.333333   \n",
       "AHAMED Anssuifidine                         3    3.666667   \n",
       "AHAMED Issihaka                             2    3.333333   \n",
       "\n",
       "                      2.1 Addition fraction  2.2 Multiplication fractions  \\\n",
       "ABDALLAH Touraya                          2                             2   \n",
       "ABDOU Mariam                              3                             3   \n",
       "ABTOIHI SAID Yasmina                      2                             3   \n",
       "AHAMED Anssuifidine                       3                             3   \n",
       "AHAMED Issihaka                           3                             2   \n",
       "\n",
       "                       ...     Equation *  Equation fraction  equation simple  \\\n",
       "ABDALLAH Touraya       ...              2                  2                0   \n",
       "ABDOU Mariam           ...              3                  2                3   \n",
       "ABTOIHI SAID Yasmina   ...              3                  3                3   \n",
       "AHAMED Anssuifidine    ...              1                  1                3   \n",
       "AHAMED Issihaka        ...              3                NaN                2   \n",
       "\n",
       "                      equation complexe  Exercice 4  Calcul angle  \\\n",
       "ABDALLAH Touraya                      0    1.000000             2   \n",
       "ABDOU Mariam                          3    5.500000             3   \n",
       "ABTOIHI SAID Yasmina                  3    3.500000             3   \n",
       "AHAMED Anssuifidine                   3    2.666667             3   \n",
       "AHAMED Issihaka                       3    3.166667             3   \n",
       "\n",
       "                      Angle alterne  Calcul longueur  2 methodes  arrondi  \n",
       "ABDALLAH Touraya                NaN              NaN         NaN      NaN  \n",
       "ABDOU Mariam                      3                3           3        0  \n",
       "ABTOIHI SAID Yasmina              0                3           1        0  \n",
       "AHAMED Anssuifidine               2                0           1        0  \n",
       "AHAMED Issihaka                   2                2         NaN      NaN  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#barem\n",
    "notes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suppression des notes inutiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes = notes[notes[ds_name].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "notes = notes.astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Traitement des notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DM_16_03_30', 'Malus', 'Exercice 1', '1.1 Developper',\n",
       "       '1.2 Developper', '1.3 Double developpement', '1.4 Developpement carré',\n",
       "       'Exercice 2', '2.1 Addition fraction', '2.2 Multiplication fractions',\n",
       "       '2.3 Addition fractions', '2.4 Multiplication Fraction', 'Exercice 3',\n",
       "       'Equation +', 'Equation - ', 'Equation *', 'Equation fraction',\n",
       "       'equation simple', 'equation complexe', 'Exercice 4', 'Calcul angle',\n",
       "       'Angle alterne', 'Calcul longueur', '2 methodes', 'arrondi'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.T.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Exercice 1', 'Exercice 2', 'Exercice 3', 'Exercice 4']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_exo = [\"Exercice \"+str(i+1) for i in range(4)]\n",
    "list_exo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
    "#notes[list_exo]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autres_notes = ['Malus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1.1 Developper',\n",
       " '1.2 Developper',\n",
       " '1.3 Double developpement',\n",
       " '1.4 Developpement carré',\n",
       " '2.1 Addition fraction',\n",
       " '2.2 Multiplication fractions',\n",
       " '2.3 Addition fractions',\n",
       " '2.4 Multiplication Fraction',\n",
       " 'Equation +',\n",
       " 'Equation - ',\n",
       " 'Equation *',\n",
       " 'Equation fraction',\n",
       " 'equation simple',\n",
       " 'equation complexe',\n",
       " 'Calcul angle',\n",
       " 'Angle alterne',\n",
       " 'Calcul longueur',\n",
       " '2 methodes',\n",
       " 'arrondi']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_avec_note = list_exo + [ds_name] + autres_notes\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": 35,
   "metadata": {
    "collapsed": true
   },
   "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": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
    "#notes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eleves = notes.copy()\n",
    "eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#eleves.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    28.000000\n",
       "mean     14.767857\n",
       "std       3.854968\n",
       "min       6.500000\n",
       "25%      13.000000\n",
       "50%      15.750000\n",
       "75%      17.125000\n",
       "max      20.000000\n",
       "Name: DM_16_03_30, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes[ds_name].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f852d1302e8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ASIbwRjF0brlRv8yYebeG8AYAIBnCG8XQueVG/TJj5t0awhsAgGQIbxRD55Yb9cuMmXdr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\nQ3gDAJAM4Q0AQDKENwAAyfw/j9p+l0vda8QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f85122dacf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#notes_seules = notes[ds_name]\n",
    "ax = notes[ds_name].hist(bins = barem[ds_name][0], range=(0,barem[ds_name][0]), )\n",
    "ax.set_xlabel(\"Notes\")\n",
    "ax.set_ylabel(\"Effectif\")\n",
    "#notes_seules.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8512238cc0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cv3kW3+QoaimPMJ8MaLS429fw/smfLttzKSOdTvNs5CGTUyNEwsU2osWq1kvHhVarw2K2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\nEolEIpHsQf5/AJjLRN8fl60AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8512226a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalisation des notes de chaque exo\n",
    "notes_exo_norm = notes[list_exo] / barem[list_exo].values[0,:]\n",
    "#notes_exo_norm\n",
    "ax = notes_exo_norm.T.plot(color = \"gray\", legend = False, )\n",
    "d_norm =  notes_exo_norm.describe()\n",
    "d_norm.T[[\"min\", \"25%\", \"50%\", \"75%\", \"max\"]].plot(ax=ax, kind=\"area\", stacked = False, alpha=.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparation des bilans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "bilan = texenv.get_template(\"./tpl_bilan.tex\")\n",
    "cible_bilan = \"../3e/DM/DM_16_03_23/Bilan/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bilan = texenv.get_template(\"tpl_bilan.tex\")\n",
    "with open(cible_bilan+\"./bilan\"+classe+\".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": {
    "collapsed": true
   },
   "source": [
    "## BB_16_04_02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds_name = 'BB_16_04_02'\n",
    "notes = all_notes.parse(ds_name).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "barem = notes[:1]\n",
    "notes = notes[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "notes = notes[notes[ds_name].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes = notes.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['BB_16_04_02', 'Présentation', 'Exercice 1', '1.1.a Tableau',\n",
       "       '1.1.b formule', '1.1.c Nom fonction', '1.2.a Nombre machine',\n",
       "       '1.2.b Tableau', '1.2.c Formule', '1.3.a Graphique',\n",
       "       '1.3.b Comparaison', 'Exercice 2', '2.1 Trignonométrie',\n",
       "       '2.1  Arrondis', '2.2 Réponse', '2.2 Méthode', 'Exercice 3',\n",
       "       '3.1 Probabilité', '3.2 Nbr issues', '3.3 ', 'Exercice 4', '4 Sophie',\n",
       "       '4 Martin', '4 Gabriel', '4 Faiza', 'Exercice 5'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.T.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Exercice 1', 'Exercice 2', 'Exercice 3', 'Exercice 4', 'Exercice 5']"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_exo = [\"Exercice \"+str(i+1) for i in range(5)]\n",
    "list_exo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
    "#notes[list_exo].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autres_notes = ['Présentation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "item_avec_note = list_exo + [ds_name] + autres_notes\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": 71,
   "metadata": {
    "collapsed": true
   },
   "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": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
    "#notes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eleves = notes.copy()\n",
    "eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    27.000000\n",
       "mean     14.314815\n",
       "std       4.834070\n",
       "min       6.500000\n",
       "25%      11.000000\n",
       "50%      13.500000\n",
       "75%      16.500000\n",
       "max      26.500000\n",
       "Name: BB_16_04_02, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes[ds_name].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fbc15d222e8>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ieu4AbkdP4bLLLuPyy+9m/r+XI8DdbGy8ln379jVfv6fDPAU4fPgwWy3aea9ieL8T+D+Z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\nkEseZsz4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc30bc6a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#notes_seules = notes[ds_name]\n",
    "ax = notes[ds_name].hist(bins = barem[ds_name][0], range=(0,barem[ds_name][0]), )\n",
    "ax.set_xlabel(\"Notes\")\n",
    "ax.set_ylabel(\"Effectif\")\n",
    "#notes_seules.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classe': '313', 'date': '02 Avril 2016', 'titre': 'Brevet Blanc'}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latex_info = {}\n",
    "latex_info['titre'] =  \"Brevet Blanc\"\n",
    "latex_info['classe'] = \"313\"\n",
    "latex_info['date'] = \"02 Avril 2016\"\n",
    "latex_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bilan = texenv.get_template(\"./tpl_bilan.tex\")\n",
    "cible_bilan = \"../3e/DS/\"+ds_name+\"/Bilan/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(cible_bilan+\"bilan\"+classe+\".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": {
    "collapsed": true
   },
   "source": [
    "## BB_16_04_19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds_name = 'BB_16_04_19'\n",
    "notes = all_notes.parse(ds_name).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "barem = notes[:1]\n",
    "notes = notes[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes = notes[notes[\"BB_16_04_19\"]!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    28.000000\n",
       "mean     17.071429\n",
       "std       6.353352\n",
       "min       7.000000\n",
       "25%      12.750000\n",
       "50%      16.500000\n",
       "75%      21.125000\n",
       "max      31.500000\n",
       "Name: BB_16_04_19, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes['BB_16_04_19'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2d2d5f3c8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K5DZ7e3V1ddTzzzuftHrK+Iaerxyre97Wjzf+9bixw/hu3MbxVuc8vnLK+WqYLzVt8/fH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\nqAEAqBiFGgCAilGoAQCo2P8HJLG44LQNMh0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc2d2d65b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notes[\"BB_16_04_19\"].hist(bins = barem[ds_name][0], range=(0,barem[ds_name][0]),)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## DM_16_05_28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ds_name = 'DM_16_05_18'\n",
    "notes = all_notes.parse(ds_name).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DM_16_05_18</th>\n",
       "      <th>Malus</th>\n",
       "      <th>Exercice 1</th>\n",
       "      <th>Dev 1</th>\n",
       "      <th>Dev 2</th>\n",
       "      <th>Dev 3</th>\n",
       "      <th>Dev 4</th>\n",
       "      <th>Facto 1</th>\n",
       "      <th>Facto 2</th>\n",
       "      <th>Facto 3</th>\n",
       "      <th>...</th>\n",
       "      <th>Equation deux cotes</th>\n",
       "      <th>Exercice 3</th>\n",
       "      <th>Proba</th>\n",
       "      <th>Proba ou</th>\n",
       "      <th>Proba comparaison</th>\n",
       "      <th>Tableau</th>\n",
       "      <th>Fréquence</th>\n",
       "      <th>Nombre points</th>\n",
       "      <th>Moyenne</th>\n",
       "      <th>Médiane</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ABDALLAH Touraya</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABDOU Mariam</th>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABTOIHI SAID Yasmina</th>\n",
       "      <td>14.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMED Anssuifidine</th>\n",
       "      <td>16.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMED Issihaka</th>\n",
       "      <td>16.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      DM_16_05_18  Malus  Exercice 1  Dev 1  Dev 2  Dev 3  \\\n",
       "ABDALLAH Touraya              6.0    NaN    2.666667      3      3      1   \n",
       "ABDOU Mariam                 17.0    NaN    8.000000      3      3      3   \n",
       "ABTOIHI SAID Yasmina         14.5    NaN    7.000000      3      3      3   \n",
       "AHAMED Anssuifidine          16.5    NaN    8.000000      3      3      3   \n",
       "AHAMED Issihaka              16.5    NaN    6.333333      2      3      3   \n",
       "\n",
       "                      Dev 4  Facto 1  Facto 2  Facto 3   ...     \\\n",
       "ABDALLAH Touraya          1      NaN      NaN      NaN   ...      \n",
       "ABDOU Mariam              3        3        3        3   ...      \n",
       "ABTOIHI SAID Yasmina      3        3        3        3   ...      \n",
       "AHAMED Anssuifidine       3        3        3        3   ...      \n",
       "AHAMED Issihaka           3        3        3        2   ...      \n",
       "\n",
       "                      Equation deux cotes  Exercice 3  Proba   Proba ou  \\\n",
       "ABDALLAH Touraya                      NaN    1.666667       1         1   \n",
       "ABDOU Mariam                            2    6.000000       3         3   \n",
       "ABTOIHI SAID Yasmina                    3    3.666667       3         3   \n",
       "AHAMED Anssuifidine                     1    5.333333       3         3   \n",
       "AHAMED Issihaka                         3    6.666667       3         3   \n",
       "\n",
       "                      Proba comparaison  Tableau  Fréquence  Nombre points  \\\n",
       "ABDALLAH Touraya                      3        0        NaN              0   \n",
       "ABDOU Mariam                          3        3          0              3   \n",
       "ABTOIHI SAID Yasmina                  1        3          1              0   \n",
       "AHAMED Anssuifidine                   0        3          2              3   \n",
       "AHAMED Issihaka                       3        3          3              3   \n",
       "\n",
       "                      Moyenne  Médiane  \n",
       "ABDALLAH Touraya            0        0  \n",
       "ABDOU Mariam                3        0  \n",
       "ABTOIHI SAID Yasmina        0        0  \n",
       "AHAMED Anssuifidine         1        1  \n",
       "AHAMED Issihaka             1        1  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barem = notes[:1]\n",
    "notes = notes[1:]\n",
    "notes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "notes = notes[notes[ds_name]!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    28.000000\n",
       "mean     14.839286\n",
       "std       3.291554\n",
       "min       6.000000\n",
       "25%      13.500000\n",
       "50%      16.000000\n",
       "75%      16.625000\n",
       "max      19.500000\n",
       "Name: DM_16_05_18, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes[ds_name].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc826fa7390>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CYtlr9/a1GGPdZMxfcRrr15C+P8lPJPmbVfV4VT1WVXdc6yY53Hr54c3lmV/PdMBzc3S/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\nqp4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc826fa68d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notes[ds_name].hist(bins = barem[ds_name][0], range=(0,barem[ds_name][0]),)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Traitement et bilan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DM_16_05_18', 'Malus', 'Exercice 1', 'Dev 1', 'Dev 2', 'Dev 3',\n",
       "       'Dev 4', 'Facto 1', 'Facto 2', 'Facto 3', 'Facto identité remarquable',\n",
       "       'Exercice 2', 'Equation simple', 'Equation fraction',\n",
       "       'Equation simple 2', 'Equation deux cotes', 'Exercice 3', 'Proba ',\n",
       "       'Proba ou', 'Proba comparaison', 'Tableau', 'Fréquence',\n",
       "       'Nombre points', 'Moyenne', 'Médiane'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Exercice 1', 'Exercice 2', 'Exercice 3']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_exo = [\"Exercice \"+str(i+1) for i in range(3)]\n",
    "list_exo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
    "#notes[list_exo].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BB_16_05_31"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ds_name = 'BB_16_05_31'\n",
    "notes = all_notes.parse(ds_name).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "barem = notes[:1]\n",
    "notes = notes[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notes = notes[pd.notnull(notes['BB_16_05_31'])]\n",
    "#notes = notes[notes[ds_name]!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BB_16_05_31</th>\n",
       "      <th>Présentation</th>\n",
       "      <th>Exercice 1</th>\n",
       "      <th>Lecture graphique</th>\n",
       "      <th>Calcul</th>\n",
       "      <th>Maximum</th>\n",
       "      <th>Exercice 2</th>\n",
       "      <th>Application pgm calcul</th>\n",
       "      <th>Proposition 1</th>\n",
       "      <th>Proposition 2</th>\n",
       "      <th>...</th>\n",
       "      <th>Total</th>\n",
       "      <th>Exercice 5</th>\n",
       "      <th>Prix lettre métro</th>\n",
       "      <th>Prix lettre mayotte</th>\n",
       "      <th>Tache complexe</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Exercice 6</th>\n",
       "      <th>Couleur présente</th>\n",
       "      <th>Formule tableur</th>\n",
       "      <th>Nombre boule (équation)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ABDALLAH Touraya</th>\n",
       "      <td>8.5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABDOU Mariam</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ABTOIHI SAID Yasmina</th>\n",
       "      <td>10.5</td>\n",
       "      <td>4</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMED Anssuifidine</th>\n",
       "      <td>12.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHAMED Issihaka</th>\n",
       "      <td>15.5</td>\n",
       "      <td>2</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AHMED ABDOU El-Karim</th>\n",
       "      <td>7.5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANDILI Chayhati</th>\n",
       "      <td>16.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANDJILANE Rachma</th>\n",
       "      <td>10.5</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANLI Koudoussia</th>\n",
       "      <td>15.5</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATTOUMANI Hanissa</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BACO ABDALLAH Moustadirane</th>\n",
       "      <td>8.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BINALI Maoulida</th>\n",
       "      <td>10.5</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOINA Ainati</th>\n",
       "      <td>19.5</td>\n",
       "      <td>3</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOINA HASSANI Nahimi</th>\n",
       "      <td>8.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAOUD El-Farouk</th>\n",
       "      <td>19.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DJADAR Ifrah</th>\n",
       "      <td>10.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HALIBOU Nafilati</th>\n",
       "      <td>9.5</td>\n",
       "      <td>3</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUMADI Himida</th>\n",
       "      <td>6.5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUMADI Antufati</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HOUMADI ABDALLAH Abdallah</th>\n",
       "      <td>18.5</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALIDE ABDOU Nasser</th>\n",
       "      <td>17.5</td>\n",
       "      <td>4</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MALIDE Younes</th>\n",
       "      <td>28.5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOENY MOKO Nadjma</th>\n",
       "      <td>5.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MOURTADJOU El-Fazar</th>\n",
       "      <td>15.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SAID Chamsoudine</th>\n",
       "      <td>14.5</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YANCOUB Toufa</th>\n",
       "      <td>16.5</td>\n",
       "      <td>3</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUSSOUF Asma</th>\n",
       "      <td>8.5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            BB_16_05_31  Présentation  Exercice 1  \\\n",
       "ABDALLAH Touraya                    8.5             4    0.000000   \n",
       "ABDOU Mariam                       25.0             4    3.666667   \n",
       "ABTOIHI SAID Yasmina               10.5             4    3.666667   \n",
       "AHAMED Anssuifidine                12.5             3    1.333333   \n",
       "AHAMED Issihaka                    15.5             2    3.333333   \n",
       "AHMED ABDOU El-Karim                7.5             3    0.666667   \n",
       "ANDILI Chayhati                    16.0             4    4.000000   \n",
       "ANDJILANE Rachma                   10.5             4    2.000000   \n",
       "ANLI Koudoussia                    15.5             4    2.000000   \n",
       "ATTOUMANI Hanissa                   9.0             3    0.000000   \n",
       "BACO ABDALLAH Moustadirane          8.5             2    0.666667   \n",
       "BINALI Maoulida                    10.5             3    1.000000   \n",
       "BOINA Ainati                       19.5             3    4.000000   \n",
       "BOINA HASSANI Nahimi                8.5             2    1.666667   \n",
       "DAOUD El-Farouk                    19.0             4    0.000000   \n",
       "DJADAR Ifrah                       10.5             2    1.333333   \n",
       "HALIBOU Nafilati                    9.5             3    2.000000   \n",
       "HOUMADI Himida                      6.5             4    0.000000   \n",
       "HOUMADI Antufati                    9.0             4    0.000000   \n",
       "HOUMADI ABDALLAH Abdallah          18.5             4    2.000000   \n",
       "MALIDE ABDOU Nasser                17.5             4    4.333333   \n",
       "MALIDE Younes                      28.5             4    5.000000   \n",
       "MOENY MOKO Nadjma                   5.5             1    2.000000   \n",
       "MOURTADJOU El-Fazar                15.0             4    1.000000   \n",
       "SAID Chamsoudine                   14.5             4    2.000000   \n",
       "YANCOUB Toufa                      16.5             3    4.333333   \n",
       "YOUSSOUF Asma                       8.5             2    0.000000   \n",
       "\n",
       "                            Lecture graphique  Calcul  Maximum  Exercice 2  \\\n",
       "ABDALLAH Touraya                          NaN     NaN      NaN    1.000000   \n",
       "ABDOU Mariam                                3       1        3    3.666667   \n",
       "ABTOIHI SAID Yasmina                        3       1        3    1.333333   \n",
       "AHAMED Anssuifidine                         0       2        0    1.333333   \n",
       "AHAMED Issihaka                             3       2        0    2.000000   \n",
       "AHMED ABDOU El-Karim                      NaN       1      NaN    2.000000   \n",
       "ANDILI Chayhati                             0       3        3    2.000000   \n",
       "ANDJILANE Rachma                            3       0        0    2.000000   \n",
       "ANLI Koudoussia                             3     NaN        0    3.333333   \n",
       "ATTOUMANI Hanissa                         NaN     NaN      NaN    3.000000   \n",
       "BACO ABDALLAH Moustadirane                  1     NaN      NaN    4.000000   \n",
       "BINALI Maoulida                             0       0        3    3.000000   \n",
       "BOINA Ainati                                3       3        0    1.666667   \n",
       "BOINA HASSANI Nahimi                        1       0        3    2.333333   \n",
       "DAOUD El-Farouk                           NaN     NaN      NaN    4.333333   \n",
       "DJADAR Ifrah                                2     NaN      NaN    3.666667   \n",
       "HALIBOU Nafilati                            1       2        0    1.000000   \n",
       "HOUMADI Himida                              0       0      NaN    1.666667   \n",
       "HOUMADI Antufati                          NaN     NaN      NaN    1.000000   \n",
       "HOUMADI ABDALLAH Abdallah                   1       2        0    3.666667   \n",
       "MALIDE ABDOU Nasser                         3       2        3    2.666667   \n",
       "MALIDE Younes                               3       3        3    4.333333   \n",
       "MOENY MOKO Nadjma                           1       2        0    1.666667   \n",
       "MOURTADJOU El-Fazar                       NaN     NaN        3    2.000000   \n",
       "SAID Chamsoudine                            1       2        0    3.000000   \n",
       "YANCOUB Toufa                               3       2        3    2.000000   \n",
       "YOUSSOUF Asma                             NaN     NaN      NaN    1.666667   \n",
       "\n",
       "                            Application pgm calcul  Proposition 1  \\\n",
       "ABDALLAH Touraya                                 3              0   \n",
       "ABDOU Mariam                                     3              3   \n",
       "ABTOIHI SAID Yasmina                             3              0   \n",
       "AHAMED Anssuifidine                              3              0   \n",
       "AHAMED Issihaka                                  3              0   \n",
       "AHMED ABDOU El-Karim                             3              0   \n",
       "ANDILI Chayhati                                  3              3   \n",
       "ANDJILANE Rachma                                 3              0   \n",
       "ANLI Koudoussia                                  3              2   \n",
       "ATTOUMANI Hanissa                                3              3   \n",
       "BACO ABDALLAH Moustadirane                       3              3   \n",
       "BINALI Maoulida                                  3              0   \n",
       "BOINA Ainati                                     3              0   \n",
       "BOINA HASSANI Nahimi                             3              0   \n",
       "DAOUD El-Farouk                                  3              3   \n",
       "DJADAR Ifrah                                     3              3   \n",
       "HALIBOU Nafilati                                 3              0   \n",
       "HOUMADI Himida                                   3              1   \n",
       "HOUMADI Antufati                                 3            NaN   \n",
       "HOUMADI ABDALLAH Abdallah                        3              3   \n",
       "MALIDE ABDOU Nasser                              3              2   \n",
       "MALIDE Younes                                    3              3   \n",
       "MOENY MOKO Nadjma                                3              1   \n",
       "MOURTADJOU El-Fazar                              3              3   \n",
       "SAID Chamsoudine                                 3              3   \n",
       "YANCOUB Toufa                                    3              3   \n",
       "YOUSSOUF Asma                                    3              0   \n",
       "\n",
       "                            Proposition 2           ...             Total  \\\n",
       "ABDALLAH Touraya                        0           ...                 0   \n",
       "ABDOU Mariam                            1           ...                 3   \n",
       "ABTOIHI SAID Yasmina                  NaN           ...               NaN   \n",
       "AHAMED Anssuifidine                     1           ...               NaN   \n",
       "AHAMED Issihaka                         3           ...               NaN   \n",
       "AHMED ABDOU El-Karim                    3           ...               NaN   \n",
       "ANDILI Chayhati                       NaN           ...               NaN   \n",
       "ANDJILANE Rachma                        3           ...               NaN   \n",
       "ANLI Koudoussia                         3           ...               NaN   \n",
       "ATTOUMANI Hanissa                       3           ...               NaN   \n",
       "BACO ABDALLAH Moustadirane              3           ...               NaN   \n",
       "BINALI Maoulida                         3           ...               NaN   \n",
       "BOINA Ainati                            2           ...               NaN   \n",
       "BOINA HASSANI Nahimi                    3           ...                 0   \n",
       "DAOUD El-Farouk                         2           ...                 3   \n",
       "DJADAR Ifrah                            2           ...               NaN   \n",
       "HALIBOU Nafilati                        0           ...               NaN   \n",
       "HOUMADI Himida                          1           ...               NaN   \n",
       "HOUMADI Antufati                      NaN           ...               NaN   \n",
       "HOUMADI ABDALLAH Abdallah               3           ...                 3   \n",
       "MALIDE ABDOU Nasser                     3           ...               NaN   \n",
       "MALIDE Younes                           3           ...                 3   \n",
       "MOENY MOKO Nadjma                       1           ...               NaN   \n",
       "MOURTADJOU El-Fazar                   NaN           ...                 3   \n",
       "SAID Chamsoudine                        1           ...               NaN   \n",
       "YANCOUB Toufa                         NaN           ...               NaN   \n",
       "YOUSSOUF Asma                           2           ...               NaN   \n",
       "\n",
       "                            Exercice 5  Prix lettre métro  \\\n",
       "ABDALLAH Touraya              1.000000                  3   \n",
       "ABDOU Mariam                  0.000000                  0   \n",
       "ABTOIHI SAID Yasmina          0.000000                  0   \n",
       "AHAMED Anssuifidine           0.000000                NaN   \n",
       "AHAMED Issihaka               1.333333                NaN   \n",
       "AHMED ABDOU El-Karim          0.000000                NaN   \n",
       "ANDILI Chayhati               0.000000                NaN   \n",
       "ANDJILANE Rachma              0.000000                NaN   \n",
       "ANLI Koudoussia               1.000000                  3   \n",
       "ATTOUMANI Hanissa             0.000000                NaN   \n",
       "BACO ABDALLAH Moustadirane    0.000000                NaN   \n",
       "BINALI Maoulida               0.000000                NaN   \n",
       "BOINA Ainati                  5.000000                  3   \n",
       "BOINA HASSANI Nahimi          0.000000                NaN   \n",
       "DAOUD El-Farouk               0.000000                NaN   \n",
       "DJADAR Ifrah                  0.000000                  0   \n",
       "HALIBOU Nafilati              0.000000                NaN   \n",
       "HOUMADI Himida                0.000000                NaN   \n",
       "HOUMADI Antufati              0.000000                NaN   \n",
       "HOUMADI ABDALLAH Abdallah     0.666667                  2   \n",
       "MALIDE ABDOU Nasser           0.000000                NaN   \n",
       "MALIDE Younes                 1.000000                NaN   \n",
       "MOENY MOKO Nadjma             0.000000                  0   \n",
       "MOURTADJOU El-Fazar           2.000000                  0   \n",
       "SAID Chamsoudine              0.000000                NaN   \n",
       "YANCOUB Toufa                 1.666667                  3   \n",
       "YOUSSOUF Asma                 0.666667                  0   \n",
       "\n",
       "                            Prix lettre mayotte  Tache complexe  Volume  \\\n",
       "ABDALLAH Touraya                            NaN             NaN     NaN   \n",
       "ABDOU Mariam                                  0             NaN     NaN   \n",
       "ABTOIHI SAID Yasmina                        NaN             NaN     NaN   \n",
       "AHAMED Anssuifidine                         NaN             NaN     NaN   \n",
       "AHAMED Issihaka                               2               1     NaN   \n",
       "AHMED ABDOU El-Karim                        NaN             NaN     NaN   \n",
       "ANDILI Chayhati                             NaN             NaN     NaN   \n",
       "ANDJILANE Rachma                            NaN             NaN     NaN   \n",
       "ANLI Koudoussia                             NaN             NaN     NaN   \n",
       "ATTOUMANI Hanissa                           NaN             NaN     NaN   \n",
       "BACO ABDALLAH Moustadirane                  NaN               0       0   \n",
       "BINALI Maoulida                             NaN             NaN     NaN   \n",
       "BOINA Ainati                                  3               3       3   \n",
       "BOINA HASSANI Nahimi                        NaN             NaN       0   \n",
       "DAOUD El-Farouk                             NaN             NaN     NaN   \n",
       "DJADAR Ifrah                                NaN               0       0   \n",
       "HALIBOU Nafilati                            NaN             NaN     NaN   \n",
       "HOUMADI Himida                              NaN             NaN     NaN   \n",
       "HOUMADI Antufati                            NaN             NaN     NaN   \n",
       "HOUMADI ABDALLAH Abdallah                   NaN             NaN     NaN   \n",
       "MALIDE ABDOU Nasser                         NaN             NaN     NaN   \n",
       "MALIDE Younes                               NaN             NaN       3   \n",
       "MOENY MOKO Nadjma                             0               0       0   \n",
       "MOURTADJOU El-Fazar                           1               1       3   \n",
       "SAID Chamsoudine                            NaN             NaN     NaN   \n",
       "YANCOUB Toufa                                 2             NaN     NaN   \n",
       "YOUSSOUF Asma                                 0               0       2   \n",
       "\n",
       "                            Exercice 6  Couleur présente  Formule tableur  \\\n",
       "ABDALLAH Touraya              1.000000                 3                0   \n",
       "ABDOU Mariam                  0.666667                 0                2   \n",
       "ABTOIHI SAID Yasmina          1.000000                 3              NaN   \n",
       "AHAMED Anssuifidine           1.000000                 3              NaN   \n",
       "AHAMED Issihaka               1.333333                 3                1   \n",
       "AHMED ABDOU El-Karim          0.000000               NaN              NaN   \n",
       "ANDILI Chayhati               1.666667                 3                2   \n",
       "ANDJILANE Rachma              2.333333                 3                2   \n",
       "ANLI Koudoussia               1.666667                 3                2   \n",
       "ATTOUMANI Hanissa             1.000000                 3              NaN   \n",
       "BACO ABDALLAH Moustadirane    0.000000                 0                0   \n",
       "BINALI Maoulida               1.000000                 3              NaN   \n",
       "BOINA Ainati                  0.000000               NaN              NaN   \n",
       "BOINA HASSANI Nahimi          0.666667                 0                2   \n",
       "DAOUD El-Farouk               0.000000               NaN              NaN   \n",
       "DJADAR Ifrah                  1.000000                 3              NaN   \n",
       "HALIBOU Nafilati              1.000000                 3              NaN   \n",
       "HOUMADI Himida                0.000000                 0                0   \n",
       "HOUMADI Antufati              1.666667                 3                2   \n",
       "HOUMADI ABDALLAH Abdallah     1.333333                 3                1   \n",
       "MALIDE ABDOU Nasser           1.000000                 3              NaN   \n",
       "MALIDE Younes                 1.333333                 0                3   \n",
       "MOENY MOKO Nadjma             1.000000                 3              NaN   \n",
       "MOURTADJOU El-Fazar           1.000000                 0                3   \n",
       "SAID Chamsoudine              0.000000               NaN              NaN   \n",
       "YANCOUB Toufa                 0.000000               NaN              NaN   \n",
       "YOUSSOUF Asma                 1.333333                 3                1   \n",
       "\n",
       "                            Nombre boule (équation)  \n",
       "ABDALLAH Touraya                                  0  \n",
       "ABDOU Mariam                                      0  \n",
       "ABTOIHI SAID Yasmina                              0  \n",
       "AHAMED Anssuifidine                               0  \n",
       "AHAMED Issihaka                                   0  \n",
       "AHMED ABDOU El-Karim                            NaN  \n",
       "ANDILI Chayhati                                   0  \n",
       "ANDJILANE Rachma                                  2  \n",
       "ANLI Koudoussia                                 NaN  \n",
       "ATTOUMANI Hanissa                                 0  \n",
       "BACO ABDALLAH Moustadirane                        0  \n",
       "BINALI Maoulida                                 NaN  \n",
       "BOINA Ainati                                    NaN  \n",
       "BOINA HASSANI Nahimi                              0  \n",
       "DAOUD El-Farouk                                 NaN  \n",
       "DJADAR Ifrah                                      0  \n",
       "HALIBOU Nafilati                                  0  \n",
       "HOUMADI Himida                                    0  \n",
       "HOUMADI Antufati                                NaN  \n",
       "HOUMADI ABDALLAH Abdallah                         0  \n",
       "MALIDE ABDOU Nasser                             NaN  \n",
       "MALIDE Younes                                     1  \n",
       "MOENY MOKO Nadjma                                 0  \n",
       "MOURTADJOU El-Fazar                               0  \n",
       "SAID Chamsoudine                                NaN  \n",
       "YANCOUB Toufa                                   NaN  \n",
       "YOUSSOUF Asma                                   NaN  \n",
       "\n",
       "[27 rows x 32 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    27.000000\n",
       "mean     13.203704\n",
       "std       5.653895\n",
       "min       5.500000\n",
       "25%       8.750000\n",
       "50%      10.500000\n",
       "75%      16.250000\n",
       "max      28.500000\n",
       "Name: BB_16_05_31, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes[ds_name].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe0192f67f0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y7CAGg8FMH4tJDR+7tvVN8riVeSwm29aDmJ+fn/oc+mTWv3ulUV/dmt7Xqvcun9p+j/qP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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe0190fa940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "notes[ds_name].hist(bins = barem[ds_name][0], range=(0,barem[ds_name][0]),)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bilan personnels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "notes = notes.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['BB_16_05_31', 'Présentation', 'Exercice 1', 'Lecture graphique',\n",
       "       'Calcul', 'Maximum', 'Exercice 2', 'Application pgm calcul',\n",
       "       'Proposition 1', 'Proposition 2', 'Proposition 3', 'Proposition 4',\n",
       "       'Exercice 3', 'Construction géométrique', 'Thalès', 'Périmètre',\n",
       "       'Exercice 4', 'Proportionnalité temps', 'Pythagore',\n",
       "       'Utilisation formule', 'Vitesse', 'Tableau', 'Total', 'Exercice 5',\n",
       "       'Prix lettre métro', 'Prix lettre mayotte', 'Tache complexe', 'Volume',\n",
       "       'Exercice 6', 'Couleur présente', 'Formule tableur',\n",
       "       'Nombre boule (équation)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes.T.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Exercice 1',\n",
       " 'Exercice 2',\n",
       " 'Exercice 3',\n",
       " 'Exercice 4',\n",
       " 'Exercice 5',\n",
       " 'Exercice 6']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_exo = [\"Exercice \"+str(i+1) for i in range(6)]\n",
    "list_exo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
    "#notes[list_exo].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "autres_notes = ['Présentation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "item_avec_note = list_exo + [ds_name] + autres_notes\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": 66,
   "metadata": {
    "collapsed": true
   },
   "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": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
    "#notes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eleves = notes.copy()\n",
    "eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    27.000000\n",
       "mean     13.203704\n",
       "std       5.653895\n",
       "min       5.500000\n",
       "25%       8.750000\n",
       "50%      10.500000\n",
       "75%      16.250000\n",
       "max      28.500000\n",
       "Name: BB_16_05_31, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes[ds_name].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classe': '313', 'date': '31 mai 2016', 'titre': 'Dernier Brevet Blanc!'}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "latex_info = {}\n",
    "latex_info['titre'] =  \"Dernier Brevet Blanc!\"\n",
    "latex_info['classe'] = \"313\"\n",
    "latex_info['date'] = \"31 mai 2016\"\n",
    "latex_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bilan = texenv.get_template(\"./tpl_bilan.tex\")\n",
    "cible_bilan = \"../3e/DS/BB_16_05_18/Bilan/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open(cible_bilan+\"bilan\"+classe+\".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": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Bilan 3e trimestre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ds_name = \"Notes\"\n",
    "notes = all_notes.parse(ds_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DM_16_03_30', 'BB_16_04_02', 'BB_16_04_19', 'Enclos', 'DM_16_05_18',\n",
       "       'BB_16_05_31', 'Connaissance trimestre 3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notes[17:].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trim3 = notes[17:].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "barem = trim3[:1]\n",
    "notesT3 = trim3[1:32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "notesT3 = notesT3.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DM_16_03_30</th>\n",
       "      <th>BB_16_04_02</th>\n",
       "      <th>BB_16_04_19</th>\n",
       "      <th>Enclos</th>\n",
       "      <th>DM_16_05_18</th>\n",
       "      <th>BB_16_05_31</th>\n",
       "      <th>Connaissance trimestre 3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BAREME</th>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>41</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>37</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DM_16_03_30 BB_16_04_02 BB_16_04_19 Enclos DM_16_05_18 BB_16_05_31  \\\n",
       "BAREME          20          31          41     19          20          37   \n",
       "\n",
       "       Connaissance trimestre 3  \n",
       "BAREME                       20  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ABDALLAH Touraya                 4\n",
       "ABDOU Mariam                  18.5\n",
       "ABTOIHI SAID Yasmina          16.5\n",
       "AHAMED Anssuifidine           10.5\n",
       "AHAMED Issihaka               12.5\n",
       "AHMED ABDOU El-Karim             7\n",
       "ANDILI Chayhati                 11\n",
       "ANDJILANE Rachma                13\n",
       "ANLI Koudoussia                 11\n",
       "ATTOUMANI Hanissa             15.5\n",
       "BACO ABDALLAH Moustadirane    10.5\n",
       "BINALI Maoulida                 16\n",
       "BOINA Ainati                  14.5\n",
       "BOINA HASSANI Nahimi            13\n",
       "DAOUD El-Farouk                 13\n",
       "DJADAR Ifrah                    13\n",
       "HALIBOU Nafilati                 6\n",
       "HOUMADI Himida                11.5\n",
       "HOUMADI Antufati               6.5\n",
       "HOUMADI ABDALLAH Abdallah       18\n",
       "IBRAHIM Laoura                   4\n",
       "MALIDE ABDOU Nasser             18\n",
       "MALIDE Younes                   19\n",
       "MOENY MOKO Nadjma              5.5\n",
       "MOURTADJOU El-Fazar             14\n",
       "SAID Chamsoudine                15\n",
       "YANCOUB Toufa                 13.5\n",
       "YOUSSOUF Asma                  4.5\n",
       "Name: Connaissance trimestre 3, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "notesT3['Connaissance trimestre 3']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preparation du fichier .tex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "latex_info = {}\n",
    "latex_info['titre'] =  \"\"\n",
    "latex_info['classe'] = \"\"\n",
    "latex_info['date'] = \"\"\n",
    "latex_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#eleves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bilan = texenv.get_template(\"tpl_bilan.tex\")\n",
    "with open(\"./bilan\"+classe+\".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": {
    "collapsed": true
   },
   "source": [
    "## Bilan à remplir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bilan = texenv.get_template(\"tpl_bilan.tex\")\n",
    "with open(\"./fill_bilan.tex\",\"w\") as f:\n",
    "    f.write(bilan.render(eleves = [(\"Nom\",, barem = barem, ds_name = ds_name, latex_info = latex_info, nbr_questions = len(barem.T)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
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   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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