1826 lines
238 KiB
Plaintext
1826 lines
238 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from opytex import texenv\n",
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"plt.style.use(\"seaborn-notebook\")\n",
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"from IPython.core.pylabtools import figsize\n",
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"figsize = (16, 8)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Informations sur le devoir"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'classe': '309', 'date': '29 janvier 2016', 'titre': 'DM 4'}"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ds_name = \"DM_16_01_29\"\n",
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"classe = \"309\"\n",
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"\n",
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"latex_info = {}\n",
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"latex_info['titre'] = \"DM 4\"\n",
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"latex_info['classe'] = \"309\"\n",
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"latex_info['date'] = \"29 janvier 2016\"\n",
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"latex_info"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Import et premiers traitements"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"notes = pd.ExcelFile(\"./../../../../notes/\"+classe+\".xlsx\")\n",
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"notes.sheet_names\n",
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"notes = notes.parse(ds_name)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['DM_16_01_29', 'Malus', 'Exercice 1', '1.1 Developper',\n",
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" '1.2 Developper', '1.3 Double developpement', '1.4 Developpement carré',\n",
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" 'Exercice 2', '2.1 Addition fraction', '2.2 Addition fractions',\n",
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" '2.3 Multiplication Fraction', '2.4 Multiplication Fraction',\n",
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" 'Exercice 3', '1 (developper)', '2 (multiplication)', 'Exercice 4',\n",
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" 'Comparaison', 'Pythagore', 'Thalès'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"notes.index"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"notes = notes.drop(\"Malus\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"notes = notes.T"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"#notes = notes.drop('av_arrondi', axis=1)\n",
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"#notes = notes.drop('num_sujet', axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"barem = notes[:1]\n",
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"notes = notes[1:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>DM_16_01_29</th>\n",
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" <th>Exercice 1</th>\n",
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" <th>1.1 Developper</th>\n",
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" <th>1.2 Developper</th>\n",
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" <th>1.3 Double developpement</th>\n",
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" <th>1.4 Developpement carré</th>\n",
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" <th>Exercice 2</th>\n",
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" <th>2.1 Addition fraction</th>\n",
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" <th>2.2 Addition fractions</th>\n",
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" <th>2.3 Multiplication Fraction</th>\n",
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" <th>2.4 Multiplication Fraction</th>\n",
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" <th>Exercice 3</th>\n",
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" <th>1 (developper)</th>\n",
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" <th>2 (multiplication)</th>\n",
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" <th>Exercice 4</th>\n",
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" <th>Comparaison</th>\n",
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" <th>Pythagore</th>\n",
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" <th>Thalès</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>ABDOU Farida</th>\n",
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" <td>12.0</td>\n",
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" <td>3.5</td>\n",
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" <td>3</td>\n",
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" <td>3</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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|
" <td>2.000000</td>\n",
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" <td>0</td>\n",
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|
" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>3</td>\n",
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" <td>1.333333</td>\n",
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|
" <td>0</td>\n",
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|
" <td>2</td>\n",
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" <td>5.000000</td>\n",
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" <td>3</td>\n",
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|
" <td>2</td>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ABOU BACAR Djaha</th>\n",
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" <td>16.5</td>\n",
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" <td>4.0</td>\n",
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" <td>3</td>\n",
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|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
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|
" <td>2.333333</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>7.000000</td>\n",
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" <td>3</td>\n",
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" <td>3</td>\n",
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" <td>3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>AHAMADA Nabaouya</th>\n",
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" <td>14.5</td>\n",
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" <td>1.0</td>\n",
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" <td>2</td>\n",
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" <td>0</td>\n",
|
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" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
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" <td>3</td>\n",
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|
" <td>3</td>\n",
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" <td>2.333333</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
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" <td>7.000000</td>\n",
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" <td>3</td>\n",
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" <td>3</td>\n",
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" <td>3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>AHAMADI Faina</th>\n",
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" <td>14.5</td>\n",
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" <td>3.5</td>\n",
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" <td>3</td>\n",
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" <td>1</td>\n",
|
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>2.666667</td>\n",
|
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" <td>1</td>\n",
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|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
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" <td>3</td>\n",
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|
" <td>1.333333</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>7.000000</td>\n",
|
|
" <td>3</td>\n",
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" <td>3</td>\n",
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" <td>3</td>\n",
|
|
" </tr>\n",
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" <tr>\n",
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|
" <th>ALI Mardhuia</th>\n",
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|
" <td>19.0</td>\n",
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" <td>5.0</td>\n",
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|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>7.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
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|
" <th>ALI SOULAIMANA Chamsia</th>\n",
|
|
" <td>15.5</td>\n",
|
|
" <td>3.5</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>6.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>ALSENE ALI MADI Stela</th>\n",
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|
" <td>11.0</td>\n",
|
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" <td>2.5</td>\n",
|
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" <td>2</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3.333333</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
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" <td>NaN</td>\n",
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|
" </tr>\n",
|
|
" <tr>\n",
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|
" <th>ANDRIATAHIANA Hoby</th>\n",
|
|
" <td>14.0</td>\n",
|
|
" <td>3.5</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0.333333</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>7.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
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" <td>3</td>\n",
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" </tr>\n",
|
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" <tr>\n",
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|
" <th>ANLI Emeline</th>\n",
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" <td>7.0</td>\n",
|
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" <td>4.0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0.666667</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0.333333</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
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|
" </tr>\n",
|
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" <tr>\n",
|
|
" <th>ATHOUMANE Naouidat</th>\n",
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|
" <td>15.0</td>\n",
|
|
" <td>5.5</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2.666667</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1.666667</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>5.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>BOUDRA Nassifanya</th>\n",
|
|
" <td>19.0</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>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2.666667</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>7.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>CHANFI Nadhrati</th>\n",
|
|
" <td>16.0</td>\n",
|
|
" <td>6.0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>5.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>COMBO Moinécha</th>\n",
|
|
" <td>18.5</td>\n",
|
|
" <td>5.5</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3.666667</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>6.333333</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>HALIDI Nisma</th>\n",
|
|
" <td>18.5</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>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>6.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>HAMZA Samianti</th>\n",
|
|
" <td>8.5</td>\n",
|
|
" <td>3.5</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0.666667</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1.333333</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>HOUMADI Mouslimati</th>\n",
|
|
" <td>10.0</td>\n",
|
|
" <td>4.5</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2.666667</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>HOUMADI Chaharazadi</th>\n",
|
|
" <td>16.5</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>4.000000</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>3</td>\n",
|
|
" <td>6.666667</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>HOUMADI Nasmi</th>\n",
|
|
" <td>17.5</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>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1.666667</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>7.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>HOUMADI Dhoirfia</th>\n",
|
|
" <td>17.5</td>\n",
|
|
" <td>6.0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3.666667</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>5.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>LOUTOUFI Nachima</th>\n",
|
|
" <td>8.5</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2.666667</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0.666667</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>MALIDE El-Anzize</th>\n",
|
|
" <td>14.0</td>\n",
|
|
" <td>3.5</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2.666667</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>5.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>MONNE Kevin</th>\n",
|
|
" <td>9.5</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>2.666667</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3.666667</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>MOUSSA Roibouanti</th>\n",
|
|
" <td>17.5</td>\n",
|
|
" <td>6.0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3.666667</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>5.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>OUSSENI Hilma</th>\n",
|
|
" <td>17.5</td>\n",
|
|
" <td>5.5</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>6.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>SAANLI Natali</th>\n",
|
|
" <td>18.5</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>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>7.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>SAID AHAMADA Roukaya</th>\n",
|
|
" <td>12.5</td>\n",
|
|
" <td>6.0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0.666667</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>SANDA Issoufi</th>\n",
|
|
" <td>9.5</td>\n",
|
|
" <td>2.5</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>2.333333</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0.666667</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>SOILIHI Soifia</th>\n",
|
|
" <td>15.5</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2.666667</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>6.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>SOUFIANI Laila</th>\n",
|
|
" <td>2.5</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2.333333</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0.000000</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>YOUSSOUF Sitirati</th>\n",
|
|
" <td>6.5</td>\n",
|
|
" <td>2.0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2.333333</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" DM_16_01_29 Exercice 1 1.1 Developper \\\n",
|
|
"ABDOU Farida 12.0 3.5 3 \n",
|
|
"ABOU BACAR Djaha 16.5 4.0 3 \n",
|
|
"AHAMADA Nabaouya 14.5 1.0 2 \n",
|
|
"AHAMADI Faina 14.5 3.5 3 \n",
|
|
"ALI Mardhuia 19.0 5.0 3 \n",
|
|
"ALI SOULAIMANA Chamsia 15.5 3.5 2 \n",
|
|
"ALSENE ALI MADI Stela 11.0 2.5 2 \n",
|
|
"ANDRIATAHIANA Hoby 14.0 3.5 3 \n",
|
|
"ANLI Emeline 7.0 4.0 2 \n",
|
|
"ATHOUMANE Naouidat 15.0 5.5 3 \n",
|
|
"BOUDRA Nassifanya 19.0 6.0 3 \n",
|
|
"CHANFI Nadhrati 16.0 6.0 3 \n",
|
|
"COMBO Moinécha 18.5 5.5 3 \n",
|
|
"HALIDI Nisma 18.5 6.0 3 \n",
|
|
"HAMZA Samianti 8.5 3.5 3 \n",
|
|
"HOUMADI Mouslimati 10.0 4.5 3 \n",
|
|
"HOUMADI Chaharazadi 16.5 3.0 2 \n",
|
|
"HOUMADI Nasmi 17.5 6.0 3 \n",
|
|
"HOUMADI Dhoirfia 17.5 6.0 3 \n",
|
|
"LOUTOUFI Nachima 8.5 2.0 1 \n",
|
|
"MALIDE El-Anzize 14.0 3.5 2 \n",
|
|
"MONNE Kevin 9.5 3.0 3 \n",
|
|
"MOUSSA Roibouanti 17.5 6.0 3 \n",
|
|
"OUSSENI Hilma 17.5 5.5 2 \n",
|
|
"SAANLI Natali 18.5 5.5 3 \n",
|
|
"SAID AHAMADA Roukaya 12.5 6.0 3 \n",
|
|
"SANDA Issoufi 9.5 2.5 3 \n",
|
|
"SOILIHI Soifia 15.5 5.0 3 \n",
|
|
"SOUFIANI Laila 2.5 0.0 0 \n",
|
|
"YOUSSOUF Sitirati 6.5 2.0 3 \n",
|
|
"\n",
|
|
" 1.2 Developper 1.3 Double developpement \\\n",
|
|
"ABDOU Farida 3 1 \n",
|
|
"ABOU BACAR Djaha 1 2 \n",
|
|
"AHAMADA Nabaouya 0 0 \n",
|
|
"AHAMADI Faina 1 1 \n",
|
|
"ALI Mardhuia 2 2 \n",
|
|
"ALI SOULAIMANA Chamsia 3 2 \n",
|
|
"ALSENE ALI MADI Stela 1 1 \n",
|
|
"ANDRIATAHIANA Hoby 2 1 \n",
|
|
"ANLI Emeline 2 2 \n",
|
|
"ATHOUMANE Naouidat 3 2 \n",
|
|
"BOUDRA Nassifanya 3 3 \n",
|
|
"CHANFI Nadhrati 3 3 \n",
|
|
"COMBO Moinécha 2 3 \n",
|
|
"HALIDI Nisma 3 3 \n",
|
|
"HAMZA Samianti 1 1 \n",
|
|
"HOUMADI Mouslimati 2 2 \n",
|
|
"HOUMADI Chaharazadi NaN 2 \n",
|
|
"HOUMADI Nasmi 3 3 \n",
|
|
"HOUMADI Dhoirfia 3 3 \n",
|
|
"LOUTOUFI Nachima 1 2 \n",
|
|
"MALIDE El-Anzize 3 1 \n",
|
|
"MONNE Kevin 1 2 \n",
|
|
"MOUSSA Roibouanti 3 3 \n",
|
|
"OUSSENI Hilma 3 3 \n",
|
|
"SAANLI Natali 2 3 \n",
|
|
"SAID AHAMADA Roukaya 3 3 \n",
|
|
"SANDA Issoufi 1 1 \n",
|
|
"SOILIHI Soifia 3 2 \n",
|
|
"SOUFIANI Laila 0 0 \n",
|
|
"YOUSSOUF Sitirati 1 0 \n",
|
|
"\n",
|
|
" 1.4 Developpement carré Exercice 2 \\\n",
|
|
"ABDOU Farida 0 2.000000 \n",
|
|
"ABOU BACAR Djaha 2 3.000000 \n",
|
|
"AHAMADA Nabaouya 0 4.000000 \n",
|
|
"AHAMADI Faina 2 2.666667 \n",
|
|
"ALI Mardhuia 3 4.000000 \n",
|
|
"ALI SOULAIMANA Chamsia 0 4.000000 \n",
|
|
"ALSENE ALI MADI Stela 1 3.333333 \n",
|
|
"ANDRIATAHIANA Hoby 1 3.000000 \n",
|
|
"ANLI Emeline 2 0.666667 \n",
|
|
"ATHOUMANE Naouidat 3 2.666667 \n",
|
|
"BOUDRA Nassifanya 3 3.333333 \n",
|
|
"CHANFI Nadhrati 3 4.000000 \n",
|
|
"COMBO Moinécha 3 3.666667 \n",
|
|
"HALIDI Nisma 3 3.333333 \n",
|
|
"HAMZA Samianti 2 3.000000 \n",
|
|
"HOUMADI Mouslimati 2 2.666667 \n",
|
|
"HOUMADI Chaharazadi 2 4.000000 \n",
|
|
"HOUMADI Nasmi 3 2.666667 \n",
|
|
"HOUMADI Dhoirfia 3 3.666667 \n",
|
|
"LOUTOUFI Nachima 0 2.666667 \n",
|
|
"MALIDE El-Anzize 1 3.000000 \n",
|
|
"MONNE Kevin NaN 2.666667 \n",
|
|
"MOUSSA Roibouanti 3 3.666667 \n",
|
|
"OUSSENI Hilma 3 4.000000 \n",
|
|
"SAANLI Natali 3 4.000000 \n",
|
|
"SAID AHAMADA Roukaya 3 4.000000 \n",
|
|
"SANDA Issoufi NaN 2.333333 \n",
|
|
"SOILIHI Soifia 2 2.000000 \n",
|
|
"SOUFIANI Laila 0 2.333333 \n",
|
|
"YOUSSOUF Sitirati 0 2.000000 \n",
|
|
"\n",
|
|
" 2.1 Addition fraction 2.2 Addition fractions \\\n",
|
|
"ABDOU Farida 0 1 \n",
|
|
"ABOU BACAR Djaha 2 3 \n",
|
|
"AHAMADA Nabaouya 3 3 \n",
|
|
"AHAMADI Faina 1 2 \n",
|
|
"ALI Mardhuia 3 3 \n",
|
|
"ALI SOULAIMANA Chamsia 3 3 \n",
|
|
"ALSENE ALI MADI Stela 3 2 \n",
|
|
"ANDRIATAHIANA Hoby 3 2 \n",
|
|
"ANLI Emeline 0 0 \n",
|
|
"ATHOUMANE Naouidat 2 2 \n",
|
|
"BOUDRA Nassifanya 3 2 \n",
|
|
"CHANFI Nadhrati 3 3 \n",
|
|
"COMBO Moinécha 3 2 \n",
|
|
"HALIDI Nisma 3 2 \n",
|
|
"HAMZA Samianti 1 3 \n",
|
|
"HOUMADI Mouslimati 2 2 \n",
|
|
"HOUMADI Chaharazadi 3 3 \n",
|
|
"HOUMADI Nasmi 3 0 \n",
|
|
"HOUMADI Dhoirfia 3 2 \n",
|
|
"LOUTOUFI Nachima 1 1 \n",
|
|
"MALIDE El-Anzize 3 3 \n",
|
|
"MONNE Kevin 3 3 \n",
|
|
"MOUSSA Roibouanti 3 2 \n",
|
|
"OUSSENI Hilma 3 3 \n",
|
|
"SAANLI Natali 3 3 \n",
|
|
"SAID AHAMADA Roukaya 3 3 \n",
|
|
"SANDA Issoufi 0 2 \n",
|
|
"SOILIHI Soifia 3 0 \n",
|
|
"SOUFIANI Laila 2 3 \n",
|
|
"YOUSSOUF Sitirati 0 3 \n",
|
|
"\n",
|
|
" 2.3 Multiplication Fraction \\\n",
|
|
"ABDOU Farida 2 \n",
|
|
"ABOU BACAR Djaha 2 \n",
|
|
"AHAMADA Nabaouya 3 \n",
|
|
"AHAMADI Faina 2 \n",
|
|
"ALI Mardhuia 3 \n",
|
|
"ALI SOULAIMANA Chamsia 3 \n",
|
|
"ALSENE ALI MADI Stela 2 \n",
|
|
"ANDRIATAHIANA Hoby 2 \n",
|
|
"ANLI Emeline 1 \n",
|
|
"ATHOUMANE Naouidat 2 \n",
|
|
"BOUDRA Nassifanya 2 \n",
|
|
"CHANFI Nadhrati 3 \n",
|
|
"COMBO Moinécha 3 \n",
|
|
"HALIDI Nisma 2 \n",
|
|
"HAMZA Samianti 2 \n",
|
|
"HOUMADI Mouslimati 2 \n",
|
|
"HOUMADI Chaharazadi 3 \n",
|
|
"HOUMADI Nasmi 2 \n",
|
|
"HOUMADI Dhoirfia 3 \n",
|
|
"LOUTOUFI Nachima 3 \n",
|
|
"MALIDE El-Anzize 2 \n",
|
|
"MONNE Kevin 2 \n",
|
|
"MOUSSA Roibouanti 3 \n",
|
|
"OUSSENI Hilma 3 \n",
|
|
"SAANLI Natali 3 \n",
|
|
"SAID AHAMADA Roukaya 3 \n",
|
|
"SANDA Issoufi 2 \n",
|
|
"SOILIHI Soifia 0 \n",
|
|
"SOUFIANI Laila 2 \n",
|
|
"YOUSSOUF Sitirati 3 \n",
|
|
"\n",
|
|
" 2.4 Multiplication Fraction Exercice 3 \\\n",
|
|
"ABDOU Farida 3 1.333333 \n",
|
|
"ABOU BACAR Djaha 2 2.333333 \n",
|
|
"AHAMADA Nabaouya 3 2.333333 \n",
|
|
"AHAMADI Faina 3 1.333333 \n",
|
|
"ALI Mardhuia 3 3.000000 \n",
|
|
"ALI SOULAIMANA Chamsia 3 2.000000 \n",
|
|
"ALSENE ALI MADI Stela 3 2.000000 \n",
|
|
"ANDRIATAHIANA Hoby 2 0.333333 \n",
|
|
"ANLI Emeline 1 0.333333 \n",
|
|
"ATHOUMANE Naouidat 2 1.666667 \n",
|
|
"BOUDRA Nassifanya 3 2.666667 \n",
|
|
"CHANFI Nadhrati 3 1.000000 \n",
|
|
"COMBO Moinécha 3 3.000000 \n",
|
|
"HALIDI Nisma 3 3.000000 \n",
|
|
"HAMZA Samianti 3 0.666667 \n",
|
|
"HOUMADI Mouslimati 2 0.000000 \n",
|
|
"HOUMADI Chaharazadi 3 2.666667 \n",
|
|
"HOUMADI Nasmi 3 1.666667 \n",
|
|
"HOUMADI Dhoirfia 3 3.000000 \n",
|
|
"LOUTOUFI Nachima 3 0.666667 \n",
|
|
"MALIDE El-Anzize 1 2.666667 \n",
|
|
"MONNE Kevin 0 0.000000 \n",
|
|
"MOUSSA Roibouanti 3 3.000000 \n",
|
|
"OUSSENI Hilma 3 2.000000 \n",
|
|
"SAANLI Natali 3 2.000000 \n",
|
|
"SAID AHAMADA Roukaya 3 0.666667 \n",
|
|
"SANDA Issoufi 3 0.666667 \n",
|
|
"SOILIHI Soifia 3 2.666667 \n",
|
|
"SOUFIANI Laila 0 0.000000 \n",
|
|
"YOUSSOUF Sitirati 0 2.333333 \n",
|
|
"\n",
|
|
" 1 (developper) 2 (multiplication) Exercice 4 \\\n",
|
|
"ABDOU Farida 0 2 5.000000 \n",
|
|
"ABOU BACAR Djaha 3 2 7.000000 \n",
|
|
"AHAMADA Nabaouya 3 2 7.000000 \n",
|
|
"AHAMADI Faina 2 1 7.000000 \n",
|
|
"ALI Mardhuia 3 3 7.000000 \n",
|
|
"ALI SOULAIMANA Chamsia 2 2 6.000000 \n",
|
|
"ALSENE ALI MADI Stela 2 2 3.000000 \n",
|
|
"ANDRIATAHIANA Hoby 1 NaN 7.000000 \n",
|
|
"ANLI Emeline 1 0 2.000000 \n",
|
|
"ATHOUMANE Naouidat 1 2 5.000000 \n",
|
|
"BOUDRA Nassifanya 2 3 7.000000 \n",
|
|
"CHANFI Nadhrati 3 0 5.000000 \n",
|
|
"COMBO Moinécha 3 3 6.333333 \n",
|
|
"HALIDI Nisma 3 3 6.000000 \n",
|
|
"HAMZA Samianti 2 0 1.333333 \n",
|
|
"HOUMADI Mouslimati 0 NaN 3.000000 \n",
|
|
"HOUMADI Chaharazadi 2 3 6.666667 \n",
|
|
"HOUMADI Nasmi 3 1 7.000000 \n",
|
|
"HOUMADI Dhoirfia 3 3 5.000000 \n",
|
|
"LOUTOUFI Nachima 0 1 3.000000 \n",
|
|
"MALIDE El-Anzize 2 3 5.000000 \n",
|
|
"MONNE Kevin 0 0 3.666667 \n",
|
|
"MOUSSA Roibouanti 3 3 5.000000 \n",
|
|
"OUSSENI Hilma 2 2 6.000000 \n",
|
|
"SAANLI Natali 2 2 7.000000 \n",
|
|
"SAID AHAMADA Roukaya 2 0 2.000000 \n",
|
|
"SANDA Issoufi 0 1 4.000000 \n",
|
|
"SOILIHI Soifia 2 3 6.000000 \n",
|
|
"SOUFIANI Laila 0 0 0.000000 \n",
|
|
"YOUSSOUF Sitirati 3 2 0.000000 \n",
|
|
"\n",
|
|
" Comparaison Pythagore Thalès \n",
|
|
"ABDOU Farida 3 2 2 \n",
|
|
"ABOU BACAR Djaha 3 3 3 \n",
|
|
"AHAMADA Nabaouya 3 3 3 \n",
|
|
"AHAMADI Faina 3 3 3 \n",
|
|
"ALI Mardhuia 3 3 3 \n",
|
|
"ALI SOULAIMANA Chamsia 0 3 3 \n",
|
|
"ALSENE ALI MADI Stela 0 3 NaN \n",
|
|
"ANDRIATAHIANA Hoby 3 3 3 \n",
|
|
"ANLI Emeline 0 1 1 \n",
|
|
"ATHOUMANE Naouidat 0 3 2 \n",
|
|
"BOUDRA Nassifanya 3 3 3 \n",
|
|
"CHANFI Nadhrati 0 3 2 \n",
|
|
"COMBO Moinécha 1 3 3 \n",
|
|
"HALIDI Nisma 0 3 3 \n",
|
|
"HAMZA Samianti 1 1 0 \n",
|
|
"HOUMADI Mouslimati 0 2 1 \n",
|
|
"HOUMADI Chaharazadi 2 3 3 \n",
|
|
"HOUMADI Nasmi 3 3 3 \n",
|
|
"HOUMADI Dhoirfia 0 3 2 \n",
|
|
"LOUTOUFI Nachima 0 2 1 \n",
|
|
"MALIDE El-Anzize 0 2 3 \n",
|
|
"MONNE Kevin 2 3 0 \n",
|
|
"MOUSSA Roibouanti 3 2 2 \n",
|
|
"OUSSENI Hilma 0 3 3 \n",
|
|
"SAANLI Natali 3 3 3 \n",
|
|
"SAID AHAMADA Roukaya NaN 2 NaN \n",
|
|
"SANDA Issoufi 0 2 2 \n",
|
|
"SOILIHI Soifia 0 3 3 \n",
|
|
"SOUFIANI Laila 0 0 0 \n",
|
|
"YOUSSOUF Sitirati NaN NaN NaN "
|
|
]
|
|
},
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"notes\n",
|
|
"#barem"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Supression des notes inutiles "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"notes = notes[notes[ds_name].notnull()]"
|
|
]
|
|
},
|
|
{
|
|
"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(['DM_16_01_29', 'Exercice 1', '1.1 Developper', '1.2 Developper',\n",
|
|
" '1.3 Double developpement', '1.4 Developpement carré', 'Exercice 2',\n",
|
|
" '2.1 Addition fraction', '2.2 Addition fractions',\n",
|
|
" '2.3 Multiplication Fraction', '2.4 Multiplication Fraction',\n",
|
|
" 'Exercice 3', '1 (developper)', '2 (multiplication)', 'Exercice 4',\n",
|
|
" 'Comparaison', 'Pythagore', 'Thalès'],\n",
|
|
" dtype='object')"
|
|
]
|
|
},
|
|
"execution_count": 12,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"notes.T.index"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Liste des exercices (non noté en compétences)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['Exercice 1', 'Exercice 2', 'Exercice 3', 'Exercice 4']"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"list_exo = [\"Exercice \"+str(i+1) for i in range(4)]\n",
|
|
"list_exo"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Les autres types de notes (presentation, malus...) qui ne sont pas en compétences"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"autres_notes = []\n",
|
|
"#notes = notes.T.drop(\"Malus\").T"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
|
|
"#notes[list_exo]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Les éléments avec notes et les éléments par compétences (sous_exo)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"scrolled": 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 Addition fractions',\n",
|
|
" '2.3 Multiplication Fraction',\n",
|
|
" '2.4 Multiplication Fraction',\n",
|
|
" '1 (developper)',\n",
|
|
" '2 (multiplication)',\n",
|
|
" 'Comparaison',\n",
|
|
" 'Pythagore',\n",
|
|
" 'Thalès']"
|
|
]
|
|
},
|
|
"execution_count": 16,
|
|
"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": 17,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def toRepVal(val):\n",
|
|
" if pd.isnull(val):\n",
|
|
" return \"\\\\NoRep\"\n",
|
|
" elif val == 0:\n",
|
|
" return \"\\\\RepZ\"\n",
|
|
" elif val == 1:\n",
|
|
" return \"\\\\RepU\"\n",
|
|
" elif val == 2:\n",
|
|
" return \"\\\\RepD\"\n",
|
|
" elif val == 3:\n",
|
|
" return \"\\\\RepT\"\n",
|
|
" else:\n",
|
|
" return val"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
|
|
"#notes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 19,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"eleves = notes.copy()\n",
|
|
"eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"18"
|
|
]
|
|
},
|
|
"execution_count": 20,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"len(notes.T.index)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Un peu de statistiques"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 21,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"count 30.000000\n",
|
|
"mean 13.766667\n",
|
|
"std 4.356393\n",
|
|
"min 2.500000\n",
|
|
"25% 10.250000\n",
|
|
"50% 14.750000\n",
|
|
"75% 17.500000\n",
|
|
"max 19.000000\n",
|
|
"Name: DM_16_01_29, dtype: float64"
|
|
]
|
|
},
|
|
"execution_count": 21,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"notes[ds_name].describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.text.Text at 0x7fbcc16939b0>"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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t49vveKfPxyWt6fO6D2j2/tHPdR/Sli3Tx6Mfv2/jkq7UIYmInk5q/9U4pdf9O75uRNLt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\nADQURR4AgIb6/4D9HMauLF08AAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fbcc16d3128>"
|
|
]
|
|
},
|
|
"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": 23,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbcc1613eb8>"
|
|
]
|
|
},
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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j5PK5FSNEZWi1WmpMNbjtbnraetjcuRm9fmNkSJIkpuemGRgfYGRqRNVy1NfW09/dz5bu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\nACgUCoVCsQX5/7HchO4FSoPKAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fbcc175c128>"
|
|
]
|
|
},
|
|
"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": "code",
|
|
"execution_count": 24,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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+dyzwUUmLY4hsV9aRwLcGDRgR35f0F8B3JD0zIr47JAezNmtjvW8FLo79bS1L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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fbcc16f7668>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"ax = notes[list_exo].hist()"
|
|
]
|
|
},
|
|
{
|
|
"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>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>2.1 Addition fraction</th>\n",
|
|
" <th>2.2 Addition fractions</th>\n",
|
|
" <th>2.3 Multiplication Fraction</th>\n",
|
|
" <th>2.4 Multiplication Fraction</th>\n",
|
|
" <th>1 (developper)</th>\n",
|
|
" <th>2 (multiplication)</th>\n",
|
|
" <th>Comparaison</th>\n",
|
|
" <th>Pythagore</th>\n",
|
|
" <th>Thalès</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>30.000000</td>\n",
|
|
" <td>29.000000</td>\n",
|
|
" <td>30.000000</td>\n",
|
|
" <td>28.000000</td>\n",
|
|
" <td>30.000000</td>\n",
|
|
" <td>30.000000</td>\n",
|
|
" <td>30.000000</td>\n",
|
|
" <td>30.000000</td>\n",
|
|
" <td>30.000000</td>\n",
|
|
" <td>28.000000</td>\n",
|
|
" <td>28.000000</td>\n",
|
|
" <td>29.000000</td>\n",
|
|
" <td>27.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>2.600000</td>\n",
|
|
" <td>2.034483</td>\n",
|
|
" <td>1.900000</td>\n",
|
|
" <td>1.892857</td>\n",
|
|
" <td>2.266667</td>\n",
|
|
" <td>2.200000</td>\n",
|
|
" <td>2.333333</td>\n",
|
|
" <td>2.433333</td>\n",
|
|
" <td>1.833333</td>\n",
|
|
" <td>1.750000</td>\n",
|
|
" <td>1.285714</td>\n",
|
|
" <td>2.517241</td>\n",
|
|
" <td>2.222222</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>0.723974</td>\n",
|
|
" <td>1.017095</td>\n",
|
|
" <td>0.994814</td>\n",
|
|
" <td>1.196887</td>\n",
|
|
" <td>1.112107</td>\n",
|
|
" <td>0.961321</td>\n",
|
|
" <td>0.711159</td>\n",
|
|
" <td>1.006302</td>\n",
|
|
" <td>1.116748</td>\n",
|
|
" <td>1.142609</td>\n",
|
|
" <td>1.410467</td>\n",
|
|
" <td>0.784706</td>\n",
|
|
" <td>1.050031</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>0.500000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 1.1 Developper 1.2 Developper 1.3 Double developpement \\\n",
|
|
"count 30.000000 29.000000 30.000000 \n",
|
|
"mean 2.600000 2.034483 1.900000 \n",
|
|
"std 0.723974 1.017095 0.994814 \n",
|
|
"min 0.000000 0.000000 0.000000 \n",
|
|
"25% 2.000000 1.000000 1.000000 \n",
|
|
"50% 3.000000 2.000000 2.000000 \n",
|
|
"75% 3.000000 3.000000 3.000000 \n",
|
|
"max 3.000000 3.000000 3.000000 \n",
|
|
"\n",
|
|
" 1.4 Developpement carré 2.1 Addition fraction 2.2 Addition fractions \\\n",
|
|
"count 28.000000 30.000000 30.000000 \n",
|
|
"mean 1.892857 2.266667 2.200000 \n",
|
|
"std 1.196887 1.112107 0.961321 \n",
|
|
"min 0.000000 0.000000 0.000000 \n",
|
|
"25% 1.000000 2.000000 2.000000 \n",
|
|
"50% 2.000000 3.000000 2.000000 \n",
|
|
"75% 3.000000 3.000000 3.000000 \n",
|
|
"max 3.000000 3.000000 3.000000 \n",
|
|
"\n",
|
|
" 2.3 Multiplication Fraction 2.4 Multiplication Fraction \\\n",
|
|
"count 30.000000 30.000000 \n",
|
|
"mean 2.333333 2.433333 \n",
|
|
"std 0.711159 1.006302 \n",
|
|
"min 0.000000 0.000000 \n",
|
|
"25% 2.000000 2.000000 \n",
|
|
"50% 2.000000 3.000000 \n",
|
|
"75% 3.000000 3.000000 \n",
|
|
"max 3.000000 3.000000 \n",
|
|
"\n",
|
|
" 1 (developper) 2 (multiplication) Comparaison Pythagore Thalès \n",
|
|
"count 30.000000 28.000000 28.000000 29.000000 27.000000 \n",
|
|
"mean 1.833333 1.750000 1.285714 2.517241 2.222222 \n",
|
|
"std 1.116748 1.142609 1.410467 0.784706 1.050031 \n",
|
|
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
|
|
"25% 1.000000 1.000000 0.000000 2.000000 2.000000 \n",
|
|
"50% 2.000000 2.000000 0.500000 3.000000 3.000000 \n",
|
|
"75% 3.000000 3.000000 3.000000 3.000000 3.000000 \n",
|
|
"max 3.000000 3.000000 3.000000 3.000000 3.000000 "
|
|
]
|
|
},
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"notes_questions = notes[sous_exo]\n",
|
|
"notes_analysis = notes_questions.describe()\n",
|
|
"notes_analysis"
|
|
]
|
|
},
|
|
{
|
|
"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>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>2.1 Addition fraction</th>\n",
|
|
" <th>2.2 Addition fractions</th>\n",
|
|
" <th>2.3 Multiplication Fraction</th>\n",
|
|
" <th>2.4 Multiplication Fraction</th>\n",
|
|
" <th>1 (developper)</th>\n",
|
|
" <th>2 (multiplication)</th>\n",
|
|
" <th>Comparaison</th>\n",
|
|
" <th>Pythagore</th>\n",
|
|
" <th>Thalès</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>30</td>\n",
|
|
" <td>29</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>30</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>29</td>\n",
|
|
" <td>27</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 1.1 Developper 1.2 Developper 1.3 Double developpement \\\n",
|
|
"count 30 29 30 \n",
|
|
"\n",
|
|
" 1.4 Developpement carré 2.1 Addition fraction 2.2 Addition fractions \\\n",
|
|
"count 28 30 30 \n",
|
|
"\n",
|
|
" 2.3 Multiplication Fraction 2.4 Multiplication Fraction \\\n",
|
|
"count 30 30 \n",
|
|
"\n",
|
|
" 1 (developper) 2 (multiplication) Comparaison Pythagore Thalès \n",
|
|
"count 30 28 28 29 27 "
|
|
]
|
|
},
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# J'aimerai récupérer le nom des questions qui ont été le moins répondus\n",
|
|
"notes_analysis[:1]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Preparation du fichier .tex"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 28,
|
|
"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": 28,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "SyntaxError",
|
|
"evalue": "invalid syntax (<ipython-input-28-5b3ec646b48a>, line 3)",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-28-5b3ec646b48a>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m f.write(bilan.render(eleves = [(\"Nom\", barem = barem, ds_name = ds_name, latex_info = latex_info, nbr_questions = len(barem.T)))\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
|
|
]
|
|
}
|
|
],
|
|
"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": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.5.1"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|