555 lines
21 KiB
Plaintext
555 lines
21 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 74,
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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"
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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": 75,
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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': '\\\\TSTMG', 'date': '16 mai 2015', 'titre': 'DST 04'}"
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]
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},
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"execution_count": 75,
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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 = 'DST_04'\n",
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"classe = 'tstmg'\n",
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"\n",
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"latex_info = {}\n",
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"latex_info['titre'] = 'DST 04' \n",
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"latex_info['classe'] = '\\\\TSTMG'\n",
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"latex_info['date'] = '16 mai 2015'\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": 76,
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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+\".xls\")\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": 77,
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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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"Index(['DST_04', 'av_arrondi', 'Exercice 1', 'QCM', '1.1', '1.2', 'Exercice 2',\n",
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" '2.A.1', '2.A.2', '2.A.3.a', '2.A.3.b', '2.A.3.c', '2.B.1', '2.B.2',\n",
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" 'Exercice 3', '3.A.1', '3.A.2', '3.B.1', '3.B.2', '3.B.3', 'Exercice 4',\n",
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" '4.A.1', '4.A.1.a', '4.A.1.b', '4.B.1', '4.B.2', '4.B.3'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 77,
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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": 78,
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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": 79,
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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": 80,
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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:]\n",
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"#notes"
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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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"## Supression des notes inutiles "
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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": 81,
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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[notes[ds_name].notnull()]\n",
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"notes = notes[notes[ds_name] != 0]"
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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": 82,
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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.astype(float)"
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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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"## Traitement des notes"
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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": 83,
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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(['DST_04', 'Exercice 1', 'QCM', '1.1', '1.2', 'Exercice 2', '2.A.1',\n",
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" '2.A.2', '2.A.3.a', '2.A.3.b', '2.A.3.c', '2.B.1', '2.B.2',\n",
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" 'Exercice 3', '3.A.1', '3.A.2', '3.B.1', '3.B.2', '3.B.3', 'Exercice 4',\n",
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" '4.A.1', '4.A.1.a', '4.A.1.b', '4.B.1', '4.B.2', '4.B.3'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 83,
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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.T.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": 84,
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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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"list_exo = [\"Exercice 1\", \"Exercice 2\", \"Exercice 3\", \"Exercice 4\"]"
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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": 85,
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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[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
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"#notes[list_exo]"
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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": 86,
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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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"['1.1',\n",
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" '1.2',\n",
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" '2.A.1',\n",
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" '2.A.2',\n",
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" '2.A.3.a',\n",
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" '2.A.3.b',\n",
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" '2.A.3.c',\n",
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" '2.B.1',\n",
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" '2.B.2',\n",
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" '3.A.1',\n",
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" '3.A.2',\n",
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" '3.B.1',\n",
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" '3.B.2',\n",
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" '3.B.3',\n",
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" '4.A.1',\n",
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" '4.A.1.a',\n",
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" '4.A.1.b',\n",
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" '4.B.1',\n",
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" '4.B.2',\n",
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" '4.B.3']"
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]
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},
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"execution_count": 86,
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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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"item_avec_note = list_exo + [ds_name, \"QCM\"]\n",
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"sous_exo = [i for i in notes.T.index if i not in item_avec_note]\n",
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"sous_exo"
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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": 87,
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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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"def toRepVal(val):\n",
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" if pd.isnull(val):\n",
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" return \"\\\\NoRep\"\n",
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" elif val == 0:\n",
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" return \"\\\\RepZ\"\n",
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" elif val == 1:\n",
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" return \"\\\\RepU\"\n",
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" elif val == 2:\n",
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" return \"\\\\RepD\"\n",
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" elif val == 3:\n",
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" return \"\\\\RepT\"\n",
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" else:\n",
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" return val"
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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": 88,
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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[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
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"#notes"
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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": 89,
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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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"eleves = notes.copy()\n",
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"eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
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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": 90,
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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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"26"
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]
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},
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"execution_count": 90,
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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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"len(notes.T.index)"
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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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"# Preparation du fichier .tex"
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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": 91,
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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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"bilan = texenv.get_template(\"tpl_bilan.tex\")\n",
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"with open(\"./bilan.tex\",\"w\") as f:\n",
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" f.write(bilan.render(eleves = eleves, barem = barem, ds_name = ds_name, latex_info = latex_info, nbr_questions = len(barem.T)))"
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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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"# Un peu de statistiques"
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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": 92,
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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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"count 20.000000\n",
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"mean 11.425000\n",
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"std 4.733072\n",
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"min 3.000000\n",
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"25% 8.500000\n",
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"50% 10.750000\n",
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"75% 15.125000\n",
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"max 20.000000\n",
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"Name: DST_04, dtype: float64"
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]
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},
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"execution_count": 92,
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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[ds_name].describe()"
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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": 34,
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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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"<matplotlib.axes._subplots.AxesSubplot at 0x7f54263a3d30>"
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]
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},
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"execution_count": 34,
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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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"data": {
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"image/png": 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MztugP1Ga8T2pGTNZuYzHz5nKZc3VxKCLuplZ3wy6qGfsn2XMZOUyHj9nKpc1VxODLupm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\nAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7f542600fa90>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"notes_seules = notes[ds_name]\n",
|
|
"notes_seules.hist(bins = (notes_seules.max() - notes_seules.min())*2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"notes_questions = notes[sous_exo]\n",
|
|
"notes_analysis = notes_questions.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>1.1</th>\n",
|
|
" <th>1.2</th>\n",
|
|
" <th>2.A.1</th>\n",
|
|
" <th>2.A.2</th>\n",
|
|
" <th>2.A.3.a</th>\n",
|
|
" <th>2.A.3.b</th>\n",
|
|
" <th>2.A.3.c</th>\n",
|
|
" <th>2.B.1</th>\n",
|
|
" <th>2.B.2</th>\n",
|
|
" <th>3.A.1</th>\n",
|
|
" <th>3.A.2</th>\n",
|
|
" <th>3.B.1</th>\n",
|
|
" <th>3.B.2</th>\n",
|
|
" <th>3.B.3</th>\n",
|
|
" <th>4.A.1</th>\n",
|
|
" <th>4.A.1.a</th>\n",
|
|
" <th>4.A.1.b</th>\n",
|
|
" <th>4.B.1</th>\n",
|
|
" <th>4.B.2</th>\n",
|
|
" <th>4.B.3</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
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" <td>NaN</td>\n",
|
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" <td>NaN</td>\n",
|
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" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
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" <td>NaN</td>\n",
|
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" <td>NaN</td>\n",
|
|
" </tr>\n",
|
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 1.1 1.2 2.A.1 2.A.2 2.A.3.a 2.A.3.b 2.A.3.c 2.B.1 2.B.2 3.A.1 \\\n",
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"count NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
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"\n",
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" 3.A.2 3.B.1 3.B.2 3.B.3 4.A.1 4.A.1.a 4.A.1.b 4.B.1 4.B.2 \\\n",
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"count NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
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"\n",
|
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" 4.B.3 \n",
|
|
"count NaN "
|
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]
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},
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|
"execution_count": 36,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
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"source": [
|
|
"# J'aimerai récupérer le nom des questions qui ont été le moins répondus\n",
|
|
"notes_analysis[:1][notes_analysis[:1] == 25]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
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"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
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"source": []
|
|
}
|
|
],
|
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"metadata": {
|
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"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.4.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
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"nbformat_minor": 0
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}
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