1876 lines
218 KiB
Plaintext
1876 lines
218 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 23,
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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": 24,
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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': '313', 'date': '29 janvier 2016', 'titre': 'DM 4'}"
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]
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},
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"execution_count": 24,
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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 = \"313\"\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'] = \"313\"\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": 25,
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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": 26,
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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": 26,
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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": 27,
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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": 28,
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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": 29,
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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": 30,
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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>Malus</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>ABDALLAH Touraya</th>\n",
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" <td>8.5</td>\n",
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" <td>NaN</td>\n",
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" <td>4.0</td>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" <td>3</td>\n",
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" <td>1.666667</td>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>0.666667</td>\n",
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" <td>2</td>\n",
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" <td>NaN</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>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ABDOU Mariam</th>\n",
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" <td>20.0</td>\n",
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" <td>NaN</td>\n",
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" <td>6.0</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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" <td>3</td>\n",
|
|
" <td>4.000000</td>\n",
|
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" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
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" <td>3</td>\n",
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" <td>3.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</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>ABTOIHI SAID Yasmina</th>\n",
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" <td>13.5</td>\n",
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" <td>-2</td>\n",
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" <td>4.0</td>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
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" <td>3.000000</td>\n",
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" <td>0</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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" <td>2.333333</td>\n",
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" <td>3</td>\n",
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" <td>2</td>\n",
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" <td>6.000000</td>\n",
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" <td>0</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>AHAMED Anssuifidine</th>\n",
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" <td>16.0</td>\n",
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" <td>NaN</td>\n",
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" <td>5.0</td>\n",
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" <td>3</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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" <td>3.333333</td>\n",
|
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" <td>2</td>\n",
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" <td>3</td>\n",
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" <td>2</td>\n",
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" <td>3</td>\n",
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" <td>0.666667</td>\n",
|
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" <td>2</td>\n",
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|
" <td>NaN</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>AHAMED Issihaka</th>\n",
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|
" <td>16.0</td>\n",
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|
" <td>NaN</td>\n",
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|
" <td>5.5</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3.666667</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>6.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
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|
" <tr>\n",
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" <th>AHMED ABDOU El-Karim</th>\n",
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|
" <td>10.0</td>\n",
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|
" <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>3.666667</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>3</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>9.5</td>\n",
|
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" <td>NaN</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1.333333</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>5.000000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
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|
" </tr>\n",
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" <tr>\n",
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" <th>ANDJILANE Rachma</th>\n",
|
|
" <td>17.5</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>1</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",
|
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" <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",
|
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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>ANLI Koudoussia</th>\n",
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" <td>14.5</td>\n",
|
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" <td>NaN</td>\n",
|
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" <td>4.0</td>\n",
|
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" <td>3</td>\n",
|
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" <td>3</td>\n",
|
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" <td>2</td>\n",
|
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" <td>0</td>\n",
|
|
" <td>3.333333</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>6.333333</td>\n",
|
|
" <td>1</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>ATTOUMANI Hanissa</th>\n",
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" <td>18.5</td>\n",
|
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" <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>3.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</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",
|
|
" <th>BACO ABDALLAH Moustadirane</th>\n",
|
|
" <td>15.5</td>\n",
|
|
" <td>-2</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>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2.333333</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>5.333333</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>BINALI Maoulida</th>\n",
|
|
" <td>16.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>4.5</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3.333333</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2.333333</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>6.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>BOINA Ainati</th>\n",
|
|
" <td>16.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>4.5</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1</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>7.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>BOINA HASSANI Nahimi</th>\n",
|
|
" <td>11.5</td>\n",
|
|
" <td>-2</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>2</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>3.000000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>DAOUD El-Farouk</th>\n",
|
|
" <td>16.0</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>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2.333333</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>4.333333</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>DJADAR Ifrah</th>\n",
|
|
" <td>11.5</td>\n",
|
|
" <td>-2</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3.333333</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>5.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>HALIBOU Nafilati</th>\n",
|
|
" <td>8.5</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>2.5</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1.666667</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0.333333</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>HALIDI Tomsoyère</th>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>0.0</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",
|
|
" <td>NaN</td>\n",
|
|
" <td>0.000000</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>HOUMADI Himida</th>\n",
|
|
" <td>11.5</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1.333333</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>HOUMADI Antufati</th>\n",
|
|
" <td>17.5</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>3.333333</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>1</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 ABDALLAH Abdallah</th>\n",
|
|
" <td>16.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3.333333</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>5.666667</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>IBRAHIM Laoura</th>\n",
|
|
" <td>12.5</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>4.333333</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>MALIDE ABDOU Nasser</th>\n",
|
|
" <td>17.5</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>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>0</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>MALIDE Younes</th>\n",
|
|
" <td>18.5</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>4.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</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>MOENY MOKO Nadjma</th>\n",
|
|
" <td>18.5</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>3.000000</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</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>MOUGNIDAHO Nouriana</th>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>0.0</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",
|
|
" <td>NaN</td>\n",
|
|
" <td>0.000000</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>MOURTADJOU El-Fazar</th>\n",
|
|
" <td>15.5</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>4.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1.666667</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>4.000000</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>SAGAF Amal</th>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>0.0</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",
|
|
" <td>NaN</td>\n",
|
|
" <td>0.000000</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>SAID Chamsoudine</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>3.666667</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>6.666667</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>YANCOUB Toufa</th>\n",
|
|
" <td>17.5</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>5.5</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>2</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>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>YOUSSOUF Asma</th>\n",
|
|
" <td>12.0</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>3.5</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1.666667</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>6.000000</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" DM_16_01_29 Malus Exercice 1 1.1 Developper \\\n",
|
|
"ABDALLAH Touraya 8.5 NaN 4.0 2 \n",
|
|
"ABDOU Mariam 20.0 NaN 6.0 3 \n",
|
|
"ABTOIHI SAID Yasmina 13.5 -2 4.0 2 \n",
|
|
"AHAMED Anssuifidine 16.0 NaN 5.0 3 \n",
|
|
"AHAMED Issihaka 16.0 NaN 5.5 3 \n",
|
|
"AHMED ABDOU El-Karim 10.0 NaN 5.5 3 \n",
|
|
"ANDILI Chayhati 9.5 NaN 3.0 3 \n",
|
|
"ANDJILANE Rachma 17.5 NaN 5.0 1 \n",
|
|
"ANLI Koudoussia 14.5 NaN 4.0 3 \n",
|
|
"ATTOUMANI Hanissa 18.5 NaN 5.5 3 \n",
|
|
"BACO ABDALLAH Moustadirane 15.5 -2 6.0 3 \n",
|
|
"BINALI Maoulida 16.0 NaN 4.5 3 \n",
|
|
"BOINA Ainati 16.0 NaN 4.5 2 \n",
|
|
"BOINA HASSANI Nahimi 11.5 -2 6.0 3 \n",
|
|
"DAOUD El-Farouk 16.0 NaN 5.5 2 \n",
|
|
"DJADAR Ifrah 11.5 -2 5.0 2 \n",
|
|
"HALIBOU Nafilati 8.5 NaN 2.5 3 \n",
|
|
"HALIDI Tomsoyère NaN NaN 0.0 NaN \n",
|
|
"HOUMADI Himida 11.5 NaN 3.0 3 \n",
|
|
"HOUMADI Antufati 17.5 NaN 6.0 3 \n",
|
|
"HOUMADI ABDALLAH Abdallah 16.0 NaN 4.0 2 \n",
|
|
"IBRAHIM Laoura 12.5 NaN 5.0 2 \n",
|
|
"MALIDE ABDOU Nasser 17.5 NaN 6.0 3 \n",
|
|
"MALIDE Younes 18.5 NaN 6.0 3 \n",
|
|
"MOENY MOKO Nadjma 18.5 NaN 6.0 3 \n",
|
|
"MOUGNIDAHO Nouriana NaN NaN 0.0 NaN \n",
|
|
"MOURTADJOU El-Fazar 15.5 NaN 6.0 3 \n",
|
|
"SAGAF Amal NaN NaN 0.0 NaN \n",
|
|
"SAID Chamsoudine 19.0 NaN 5.5 3 \n",
|
|
"YANCOUB Toufa 17.5 NaN 5.5 3 \n",
|
|
"YOUSSOUF Asma 12.0 NaN 3.5 3 \n",
|
|
"\n",
|
|
" 1.2 Developper 1.3 Double developpement \\\n",
|
|
"ABDALLAH Touraya 2 1 \n",
|
|
"ABDOU Mariam 3 3 \n",
|
|
"ABTOIHI SAID Yasmina 2 2 \n",
|
|
"AHAMED Anssuifidine 3 2 \n",
|
|
"AHAMED Issihaka 3 3 \n",
|
|
"AHMED ABDOU El-Karim 2 3 \n",
|
|
"ANDILI Chayhati 3 0 \n",
|
|
"ANDJILANE Rachma 3 3 \n",
|
|
"ANLI Koudoussia 3 2 \n",
|
|
"ATTOUMANI Hanissa 2 3 \n",
|
|
"BACO ABDALLAH Moustadirane 3 3 \n",
|
|
"BINALI Maoulida 3 3 \n",
|
|
"BOINA Ainati 1 3 \n",
|
|
"BOINA HASSANI Nahimi 3 3 \n",
|
|
"DAOUD El-Farouk 3 3 \n",
|
|
"DJADAR Ifrah 2 3 \n",
|
|
"HALIBOU Nafilati 2 0 \n",
|
|
"HALIDI Tomsoyère NaN NaN \n",
|
|
"HOUMADI Himida 3 0 \n",
|
|
"HOUMADI Antufati 3 3 \n",
|
|
"HOUMADI ABDALLAH Abdallah 2 2 \n",
|
|
"IBRAHIM Laoura 2 3 \n",
|
|
"MALIDE ABDOU Nasser 3 3 \n",
|
|
"MALIDE Younes 3 3 \n",
|
|
"MOENY MOKO Nadjma 3 3 \n",
|
|
"MOUGNIDAHO Nouriana NaN NaN \n",
|
|
"MOURTADJOU El-Fazar 3 3 \n",
|
|
"SAGAF Amal NaN NaN \n",
|
|
"SAID Chamsoudine 2 3 \n",
|
|
"YANCOUB Toufa 3 3 \n",
|
|
"YOUSSOUF Asma 2 2 \n",
|
|
"\n",
|
|
" 1.4 Developpement carré Exercice 2 \\\n",
|
|
"ABDALLAH Touraya 3 1.666667 \n",
|
|
"ABDOU Mariam 3 4.000000 \n",
|
|
"ABTOIHI SAID Yasmina 2 3.000000 \n",
|
|
"AHAMED Anssuifidine 2 3.333333 \n",
|
|
"AHAMED Issihaka 2 3.666667 \n",
|
|
"AHMED ABDOU El-Karim 3 3.666667 \n",
|
|
"ANDILI Chayhati 0 1.333333 \n",
|
|
"ANDJILANE Rachma 3 4.000000 \n",
|
|
"ANLI Koudoussia 0 3.333333 \n",
|
|
"ATTOUMANI Hanissa 3 3.000000 \n",
|
|
"BACO ABDALLAH Moustadirane 3 3.666667 \n",
|
|
"BINALI Maoulida 0 3.333333 \n",
|
|
"BOINA Ainati 3 4.000000 \n",
|
|
"BOINA HASSANI Nahimi 3 3.666667 \n",
|
|
"DAOUD El-Farouk 3 3.666667 \n",
|
|
"DJADAR Ifrah 3 3.333333 \n",
|
|
"HALIBOU Nafilati 0 1.666667 \n",
|
|
"HALIDI Tomsoyère NaN 0.000000 \n",
|
|
"HOUMADI Himida 0 3.000000 \n",
|
|
"HOUMADI Antufati 3 3.333333 \n",
|
|
"HOUMADI ABDALLAH Abdallah 2 3.333333 \n",
|
|
"IBRAHIM Laoura 3 3.000000 \n",
|
|
"MALIDE ABDOU Nasser 3 4.000000 \n",
|
|
"MALIDE Younes 3 4.000000 \n",
|
|
"MOENY MOKO Nadjma 3 3.000000 \n",
|
|
"MOUGNIDAHO Nouriana NaN 0.000000 \n",
|
|
"MOURTADJOU El-Fazar 3 4.000000 \n",
|
|
"SAGAF Amal NaN 0.000000 \n",
|
|
"SAID Chamsoudine 3 3.666667 \n",
|
|
"YANCOUB Toufa 2 3.000000 \n",
|
|
"YOUSSOUF Asma 0 1.666667 \n",
|
|
"\n",
|
|
" 2.1 Addition fraction 2.2 Addition fractions \\\n",
|
|
"ABDALLAH Touraya 2 2 \n",
|
|
"ABDOU Mariam 3 3 \n",
|
|
"ABTOIHI SAID Yasmina 0 3 \n",
|
|
"AHAMED Anssuifidine 2 3 \n",
|
|
"AHAMED Issihaka 3 3 \n",
|
|
"AHMED ABDOU El-Karim 3 3 \n",
|
|
"ANDILI Chayhati 0 2 \n",
|
|
"ANDJILANE Rachma 3 3 \n",
|
|
"ANLI Koudoussia 3 2 \n",
|
|
"ATTOUMANI Hanissa 3 2 \n",
|
|
"BACO ABDALLAH Moustadirane 3 3 \n",
|
|
"BINALI Maoulida 3 3 \n",
|
|
"BOINA Ainati 3 3 \n",
|
|
"BOINA HASSANI Nahimi 2 3 \n",
|
|
"DAOUD El-Farouk 3 3 \n",
|
|
"DJADAR Ifrah 3 2 \n",
|
|
"HALIBOU Nafilati 0 3 \n",
|
|
"HALIDI Tomsoyère NaN NaN \n",
|
|
"HOUMADI Himida 1 3 \n",
|
|
"HOUMADI Antufati 1 3 \n",
|
|
"HOUMADI ABDALLAH Abdallah 3 3 \n",
|
|
"IBRAHIM Laoura 0 3 \n",
|
|
"MALIDE ABDOU Nasser 3 3 \n",
|
|
"MALIDE Younes 3 3 \n",
|
|
"MOENY MOKO Nadjma 2 2 \n",
|
|
"MOUGNIDAHO Nouriana NaN NaN \n",
|
|
"MOURTADJOU El-Fazar 3 3 \n",
|
|
"SAGAF Amal NaN NaN \n",
|
|
"SAID Chamsoudine 3 3 \n",
|
|
"YANCOUB Toufa 2 2 \n",
|
|
"YOUSSOUF Asma 3 0 \n",
|
|
"\n",
|
|
" 2.3 Multiplication Fraction \\\n",
|
|
"ABDALLAH Touraya 1 \n",
|
|
"ABDOU Mariam 3 \n",
|
|
"ABTOIHI SAID Yasmina 3 \n",
|
|
"AHAMED Anssuifidine 2 \n",
|
|
"AHAMED Issihaka 2 \n",
|
|
"AHMED ABDOU El-Karim 2 \n",
|
|
"ANDILI Chayhati 2 \n",
|
|
"ANDJILANE Rachma 3 \n",
|
|
"ANLI Koudoussia 2 \n",
|
|
"ATTOUMANI Hanissa 2 \n",
|
|
"BACO ABDALLAH Moustadirane 3 \n",
|
|
"BINALI Maoulida 2 \n",
|
|
"BOINA Ainati 3 \n",
|
|
"BOINA HASSANI Nahimi 3 \n",
|
|
"DAOUD El-Farouk 2 \n",
|
|
"DJADAR Ifrah 3 \n",
|
|
"HALIBOU Nafilati 2 \n",
|
|
"HALIDI Tomsoyère NaN \n",
|
|
"HOUMADI Himida 2 \n",
|
|
"HOUMADI Antufati 3 \n",
|
|
"HOUMADI ABDALLAH Abdallah 2 \n",
|
|
"IBRAHIM Laoura 3 \n",
|
|
"MALIDE ABDOU Nasser 3 \n",
|
|
"MALIDE Younes 3 \n",
|
|
"MOENY MOKO Nadjma 3 \n",
|
|
"MOUGNIDAHO Nouriana NaN \n",
|
|
"MOURTADJOU El-Fazar 3 \n",
|
|
"SAGAF Amal NaN \n",
|
|
"SAID Chamsoudine 2 \n",
|
|
"YANCOUB Toufa 2 \n",
|
|
"YOUSSOUF Asma 2 \n",
|
|
"\n",
|
|
" 2.4 Multiplication Fraction Exercice 3 \\\n",
|
|
"ABDALLAH Touraya 0 0.666667 \n",
|
|
"ABDOU Mariam 3 3.000000 \n",
|
|
"ABTOIHI SAID Yasmina 3 2.333333 \n",
|
|
"AHAMED Anssuifidine 3 0.666667 \n",
|
|
"AHAMED Issihaka 3 1.000000 \n",
|
|
"AHMED ABDOU El-Karim 3 1.000000 \n",
|
|
"ANDILI Chayhati NaN 0.000000 \n",
|
|
"ANDJILANE Rachma 3 1.666667 \n",
|
|
"ANLI Koudoussia 3 1.000000 \n",
|
|
"ATTOUMANI Hanissa 2 3.000000 \n",
|
|
"BACO ABDALLAH Moustadirane 2 2.333333 \n",
|
|
"BINALI Maoulida 2 2.333333 \n",
|
|
"BOINA Ainati 3 0.666667 \n",
|
|
"BOINA HASSANI Nahimi 3 1.000000 \n",
|
|
"DAOUD El-Farouk 3 2.333333 \n",
|
|
"DJADAR Ifrah 2 0.000000 \n",
|
|
"HALIBOU Nafilati 0 0.333333 \n",
|
|
"HALIDI Tomsoyère NaN 0.000000 \n",
|
|
"HOUMADI Himida 3 1.333333 \n",
|
|
"HOUMADI Antufati 3 1.000000 \n",
|
|
"HOUMADI ABDALLAH Abdallah 2 3.000000 \n",
|
|
"IBRAHIM Laoura 3 0.000000 \n",
|
|
"MALIDE ABDOU Nasser 3 0.666667 \n",
|
|
"MALIDE Younes 3 1.666667 \n",
|
|
"MOENY MOKO Nadjma 2 2.666667 \n",
|
|
"MOUGNIDAHO Nouriana NaN 0.000000 \n",
|
|
"MOURTADJOU El-Fazar 3 1.666667 \n",
|
|
"SAGAF Amal NaN 0.000000 \n",
|
|
"SAID Chamsoudine 3 3.000000 \n",
|
|
"YANCOUB Toufa 3 3.000000 \n",
|
|
"YOUSSOUF Asma 0 1.000000 \n",
|
|
"\n",
|
|
" 1 (developper) 2 (multiplication) Exercice 4 \\\n",
|
|
"ABDALLAH Touraya 2 NaN 2.000000 \n",
|
|
"ABDOU Mariam 3 3 7.000000 \n",
|
|
"ABTOIHI SAID Yasmina 3 2 6.000000 \n",
|
|
"AHAMED Anssuifidine 2 NaN 7.000000 \n",
|
|
"AHAMED Issihaka 3 NaN 6.000000 \n",
|
|
"AHMED ABDOU El-Karim 3 NaN 0.000000 \n",
|
|
"ANDILI Chayhati NaN NaN 5.000000 \n",
|
|
"ANDJILANE Rachma 3 1 7.000000 \n",
|
|
"ANLI Koudoussia 3 0 6.333333 \n",
|
|
"ATTOUMANI Hanissa 3 3 7.000000 \n",
|
|
"BACO ABDALLAH Moustadirane 3 2 5.333333 \n",
|
|
"BINALI Maoulida 3 2 6.000000 \n",
|
|
"BOINA Ainati 2 0 7.000000 \n",
|
|
"BOINA HASSANI Nahimi 3 0 3.000000 \n",
|
|
"DAOUD El-Farouk 3 2 4.333333 \n",
|
|
"DJADAR Ifrah 0 NaN 5.000000 \n",
|
|
"HALIBOU Nafilati 1 NaN 4.000000 \n",
|
|
"HALIDI Tomsoyère NaN NaN 0.000000 \n",
|
|
"HOUMADI Himida 2 1 4.000000 \n",
|
|
"HOUMADI Antufati 1 1 7.000000 \n",
|
|
"HOUMADI ABDALLAH Abdallah 3 3 5.666667 \n",
|
|
"IBRAHIM Laoura 0 0 4.333333 \n",
|
|
"MALIDE ABDOU Nasser 0 1 7.000000 \n",
|
|
"MALIDE Younes 3 1 7.000000 \n",
|
|
"MOENY MOKO Nadjma 2 3 7.000000 \n",
|
|
"MOUGNIDAHO Nouriana NaN NaN 0.000000 \n",
|
|
"MOURTADJOU El-Fazar 3 1 4.000000 \n",
|
|
"SAGAF Amal NaN NaN 0.000000 \n",
|
|
"SAID Chamsoudine 3 3 6.666667 \n",
|
|
"YANCOUB Toufa 3 3 6.000000 \n",
|
|
"YOUSSOUF Asma 1 1 6.000000 \n",
|
|
"\n",
|
|
" Comparaison Pythagore Thalès \n",
|
|
"ABDALLAH Touraya 0 1 1 \n",
|
|
"ABDOU Mariam 3 3 3 \n",
|
|
"ABTOIHI SAID Yasmina 0 3 3 \n",
|
|
"AHAMED Anssuifidine 3 3 3 \n",
|
|
"AHAMED Issihaka 3 3 2 \n",
|
|
"AHMED ABDOU El-Karim NaN NaN NaN \n",
|
|
"ANDILI Chayhati NaN 3 2 \n",
|
|
"ANDJILANE Rachma 3 3 3 \n",
|
|
"ANLI Koudoussia 1 3 3 \n",
|
|
"ATTOUMANI Hanissa 3 3 3 \n",
|
|
"BACO ABDALLAH Moustadirane 1 3 2 \n",
|
|
"BINALI Maoulida 3 3 2 \n",
|
|
"BOINA Ainati 3 3 3 \n",
|
|
"BOINA HASSANI Nahimi NaN 2 1 \n",
|
|
"DAOUD El-Farouk 1 3 1 \n",
|
|
"DJADAR Ifrah 3 3 1 \n",
|
|
"HALIBOU Nafilati NaN 3 1 \n",
|
|
"HALIDI Tomsoyère NaN NaN NaN \n",
|
|
"HOUMADI Himida NaN 3 1 \n",
|
|
"HOUMADI Antufati 3 3 3 \n",
|
|
"HOUMADI ABDALLAH Abdallah 2 3 2 \n",
|
|
"IBRAHIM Laoura 1 3 1 \n",
|
|
"MALIDE ABDOU Nasser 3 3 3 \n",
|
|
"MALIDE Younes 3 3 3 \n",
|
|
"MOENY MOKO Nadjma 3 3 3 \n",
|
|
"MOUGNIDAHO Nouriana NaN NaN NaN \n",
|
|
"MOURTADJOU El-Fazar NaN 3 1 \n",
|
|
"SAGAF Amal NaN NaN NaN \n",
|
|
"SAID Chamsoudine 2 3 3 \n",
|
|
"YANCOUB Toufa 3 3 2 \n",
|
|
"YOUSSOUF Asma 3 3 2 "
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"notes\n",
|
|
"#barem"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Supression des notes inutiles "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 31,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"notes = notes[notes[ds_name].notnull()]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 32,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"notes = notes.astype(float)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Traitement des notes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 33,
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"Index(['DM_16_01_29', '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 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": 33,
|
|
"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": 34,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['Exercice 1', 'Exercice 2', 'Exercice 3', 'Exercice 4']"
|
|
]
|
|
},
|
|
"execution_count": 34,
|
|
"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": 35,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"autres_notes = [\"Malus\"]\n",
|
|
"#notes = notes.T.drop(\"Malus\").T"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"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": 37,
|
|
"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": 37,
|
|
"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": 38,
|
|
"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": 39,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
|
|
"#notes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 40,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"eleves = notes.copy()\n",
|
|
"eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 41,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"19"
|
|
]
|
|
},
|
|
"execution_count": 41,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"len(notes.T.index)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Un peu de statistiques"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 42,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"count 28.000000\n",
|
|
"mean 14.821429\n",
|
|
"std 3.347786\n",
|
|
"min 8.500000\n",
|
|
"25% 11.875000\n",
|
|
"50% 16.000000\n",
|
|
"75% 17.500000\n",
|
|
"max 20.000000\n",
|
|
"Name: DM_16_01_29, dtype: float64"
|
|
]
|
|
},
|
|
"execution_count": 42,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"notes[ds_name].describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.text.Text at 0x7f325c3d2748>"
|
|
]
|
|
},
|
|
"execution_count": 43,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFmCAYAAABENhLdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nY0sAAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f325c428be0>"
|
|
]
|
|
},
|
|
"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": 44,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f32415bbef0>"
|
|
]
|
|
},
|
|
"execution_count": 44,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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9Q95e3ns/+Q6orrLxyOf+SIm4raws8pvf/ghRFJViP5vl/G+GHAFIN2IfEIA0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\nIcQuJA0AIYQQYheSBoAQQgixC/1/oaAMbsO6aHkAAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f32415dc5f8>"
|
|
]
|
|
},
|
|
"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": 45,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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Pa2qViHhQ0nXAerLjQ5bVxP5ezfq2yMaNWznyyCOTz09T7pe2K/uW03yJ+yr624smfP3S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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f325c3e02e8>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"ax = notes[list_exo].hist()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"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>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",
|
|
" <td>27.000000</td>\n",
|
|
" <td>27.000000</td>\n",
|
|
" <td>21.000000</td>\n",
|
|
" <td>22.000000</td>\n",
|
|
" <td>27.000000</td>\n",
|
|
" <td>27.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>mean</th>\n",
|
|
" <td>2.678571</td>\n",
|
|
" <td>2.571429</td>\n",
|
|
" <td>2.428571</td>\n",
|
|
" <td>2.178571</td>\n",
|
|
" <td>2.250000</td>\n",
|
|
" <td>2.642857</td>\n",
|
|
" <td>2.428571</td>\n",
|
|
" <td>2.444444</td>\n",
|
|
" <td>2.259259</td>\n",
|
|
" <td>1.571429</td>\n",
|
|
" <td>2.272727</td>\n",
|
|
" <td>2.888889</td>\n",
|
|
" <td>2.148148</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>std</th>\n",
|
|
" <td>0.547964</td>\n",
|
|
" <td>0.572750</td>\n",
|
|
" <td>0.997351</td>\n",
|
|
" <td>1.218790</td>\n",
|
|
" <td>1.109721</td>\n",
|
|
" <td>0.678467</td>\n",
|
|
" <td>0.572750</td>\n",
|
|
" <td>0.974022</td>\n",
|
|
" <td>1.059484</td>\n",
|
|
" <td>1.121224</td>\n",
|
|
" <td>1.077113</td>\n",
|
|
" <td>0.423659</td>\n",
|
|
" <td>0.863967</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>min</th>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>25%</th>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2.000000</td>\n",
|
|
" <td>2.000000</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>1.250000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</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>2.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>3.000000</td>\n",
|
|
" <td>2.000000</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 28.000000 28.000000 28.000000 \n",
|
|
"mean 2.678571 2.571429 2.428571 \n",
|
|
"std 0.547964 0.572750 0.997351 \n",
|
|
"min 1.000000 1.000000 0.000000 \n",
|
|
"25% 2.000000 2.000000 2.000000 \n",
|
|
"50% 3.000000 3.000000 3.000000 \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 28.000000 28.000000 \n",
|
|
"mean 2.178571 2.250000 2.642857 \n",
|
|
"std 1.218790 1.109721 0.678467 \n",
|
|
"min 0.000000 0.000000 0.000000 \n",
|
|
"25% 2.000000 2.000000 2.000000 \n",
|
|
"50% 3.000000 3.000000 3.000000 \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 28.000000 27.000000 \n",
|
|
"mean 2.428571 2.444444 \n",
|
|
"std 0.572750 0.974022 \n",
|
|
"min 1.000000 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 27.000000 21.000000 22.000000 27.000000 27.000000 \n",
|
|
"mean 2.259259 1.571429 2.272727 2.888889 2.148148 \n",
|
|
"std 1.059484 1.121224 1.077113 0.423659 0.863967 \n",
|
|
"min 0.000000 0.000000 0.000000 1.000000 1.000000 \n",
|
|
"25% 2.000000 1.000000 1.250000 3.000000 1.000000 \n",
|
|
"50% 3.000000 1.000000 3.000000 3.000000 2.000000 \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": 46,
|
|
"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": 47,
|
|
"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>28</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>28</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>27</td>\n",
|
|
" <td>21</td>\n",
|
|
" <td>22</td>\n",
|
|
" <td>27</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 28 28 28 \n",
|
|
"\n",
|
|
" 1.4 Developpement carré 2.1 Addition fraction 2.2 Addition fractions \\\n",
|
|
"count 28 28 28 \n",
|
|
"\n",
|
|
" 2.3 Multiplication Fraction 2.4 Multiplication Fraction \\\n",
|
|
"count 28 27 \n",
|
|
"\n",
|
|
" 1 (developper) 2 (multiplication) Comparaison Pythagore Thalès \n",
|
|
"count 27 21 22 27 27 "
|
|
]
|
|
},
|
|
"execution_count": 47,
|
|
"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": 48,
|
|
"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
|
|
}
|