559 lines
19 KiB
Plaintext
559 lines
19 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 120,
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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": 121,
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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': '\\\\seconde', 'date': '6 mai 2015', 'titre': 'DM 4'}"
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]
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},
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"execution_count": 121,
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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 = \n",
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"classe = \n",
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"\n",
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"latex_info = {}\n",
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"latex_info['titre'] = \n",
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"latex_info['classe'] = \n",
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"latex_info['date'] = \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": 122,
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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": 123,
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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(['DM_0506', 'av_arrondi', 'num_sujet', 'Malus', 'Exercice 1', '1.1.a',\n",
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" '1.1.b', '1.1.c', '1.2.a', '1.2.b', '1.2.c', '1.2.d', '1.3.a', '1.3.b',\n",
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" '1.3.c', '1.3.d', 'Exercice 2', '2.1', '2.2', '2.3', 'Exercice 3',\n",
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" '3.1.a', '3.1.b', '3.1.c', '3.1.d', '3.2.a', '3.2.b', '3.2.c', '3.2.d'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 123,
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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": 124,
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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": 125,
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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": 126,
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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": 127,
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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] != 'abs']"
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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": 128,
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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": 129,
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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_0506', 'Malus', 'Exercice 1', '1.1.a', '1.1.b', '1.1.c', '1.2.a',\n",
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" '1.2.b', '1.2.c', '1.2.d', '1.3.a', '1.3.b', '1.3.c', '1.3.d',\n",
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" 'Exercice 2', '2.1', '2.2', '2.3', 'Exercice 3', '3.1.a', '3.1.b',\n",
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" '3.1.c', '3.1.d', '3.2.a', '3.2.b', '3.2.c', '3.2.d'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 129,
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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": 130,
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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\"]"
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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": 131,
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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": 132,
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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.a',\n",
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" '1.1.b',\n",
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" '1.1.c',\n",
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" '1.2.a',\n",
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" '1.2.b',\n",
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" '1.2.c',\n",
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" '1.2.d',\n",
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" '1.3.a',\n",
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" '1.3.b',\n",
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" '1.3.c',\n",
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" '1.3.d',\n",
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" '2.1',\n",
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" '2.2',\n",
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" '2.3',\n",
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" '3.1.a',\n",
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" '3.1.b',\n",
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" '3.1.c',\n",
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" '3.1.d',\n",
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" '3.2.a',\n",
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" '3.2.b',\n",
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" '3.2.c',\n",
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" '3.2.d']"
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]
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},
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"execution_count": 132,
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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, \"Malus\"]\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": 133,
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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": 134,
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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": 135,
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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": 136,
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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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"27"
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]
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},
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"execution_count": 136,
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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": 157,
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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": 138,
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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 28.000000\n",
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"mean 14.696429\n",
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"std 5.894661\n",
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"min 0.000000\n",
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"25% 14.375000\n",
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"50% 16.500000\n",
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"75% 18.625000\n",
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"max 20.000000\n",
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"Name: DM_0506, dtype: float64"
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]
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},
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"execution_count": 138,
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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": 139,
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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 0x7f761323dbe0>"
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]
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},
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"execution_count": 139,
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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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msahL0oBY1JeUPXVJk1jUJWlALOpLyp66pEks6pI0IBb1JWVPXdIkFnVJGpBWRT3JLUke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\nRtJfj8QdO38AAAAASUVORK5CYII=\n",
|
||
"text/plain": [
|
||
"<matplotlib.figure.Figure at 0x7f76133ec630>"
|
||
]
|
||
},
|
||
"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": 154,
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"notes_questions = notes[sous_exo]\n",
|
||
"notes_analysis = notes_questions.describe()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 155,
|
||
"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.a</th>\n",
|
||
" <th>1.1.b</th>\n",
|
||
" <th>1.1.c</th>\n",
|
||
" <th>1.2.a</th>\n",
|
||
" <th>1.2.b</th>\n",
|
||
" <th>1.2.c</th>\n",
|
||
" <th>1.2.d</th>\n",
|
||
" <th>1.3.a</th>\n",
|
||
" <th>1.3.b</th>\n",
|
||
" <th>1.3.c</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>2.2</th>\n",
|
||
" <th>2.3</th>\n",
|
||
" <th>3.1.a</th>\n",
|
||
" <th>3.1.b</th>\n",
|
||
" <th>3.1.c</th>\n",
|
||
" <th>3.1.d</th>\n",
|
||
" <th>3.2.a</th>\n",
|
||
" <th>3.2.b</th>\n",
|
||
" <th>3.2.c</th>\n",
|
||
" <th>3.2.d</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>25</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>1 rows × 22 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 1.1.a 1.1.b 1.1.c 1.2.a 1.2.b 1.2.c 1.2.d 1.3.a 1.3.b 1.3.c \\\n",
|
||
"count 25 25 25 25 NaN NaN NaN 25 25 NaN \n",
|
||
"\n",
|
||
" ... 2.2 2.3 3.1.a 3.1.b 3.1.c 3.1.d 3.2.a 3.2.b 3.2.c 3.2.d \n",
|
||
"count ... NaN 25 NaN NaN NaN NaN NaN NaN NaN NaN \n",
|
||
"\n",
|
||
"[1 rows x 22 columns]"
|
||
]
|
||
},
|
||
"execution_count": 155,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# J'aimerai récupérer le nom des questions qui ont été le moins répondus\n",
|
||
"notes_analysis[:1][notes_analysis[:1] == 25]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"collapsed": true
|
||
},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
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"language": "python",
|
||
"name": "python3"
|
||
},
|
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"language_info": {
|
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|
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|
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},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
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"nbconvert_exporter": "python",
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|
||
"version": "3.4.3"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 0
|
||
}
|