551 lines
21 KiB
Plaintext
551 lines
21 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from opytex import texenv\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Informations sur le devoir"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'classe': '\\\\seconde', 'date': '27 mai 2015', 'titre': 'DS 8'}"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ds_name = 'DS_0527'\n",
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"classe = '2nd6'\n",
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"\n",
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"latex_info = {}\n",
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"latex_info['titre'] = 'DS 8' \n",
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"latex_info['classe'] = '\\\\seconde'\n",
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"latex_info['date'] = '27 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": 4,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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"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": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['DS_0527', 'av_arrondi', 'Exercice 1', '1 (Nuage points)', '2.a ',\n",
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" '2.b (Tracer droite)', '2.c (Estimation)', 'Exercice 2',\n",
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" '1 (equation droite)', '2.a (Tracer droite)', '2.b (Droite point)',\n",
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" '2.c (Parallèle)', '2.d (Parallèle)', 'Exercice 3',\n",
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" '1 (Domaine définition)', '2 (Valeur interdite)', '3 (Calcul)',\n",
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" 'Exercice 4'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 5,
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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": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"notes = notes.T"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"notes = notes.drop('av_arrondi', axis=1)\n",
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"#notes = notes.drop('num_sujet', axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"barem = notes[:1]\n",
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"notes = notes[1:]\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": 9,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"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": 10,
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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": 11,
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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(['DS_0527', 'Exercice 1', '1 (Nuage points)', '2.a ',\n",
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" '2.b (Tracer droite)', '2.c (Estimation)', 'Exercice 2',\n",
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" '1 (equation droite)', '2.a (Tracer droite)', '2.b (Droite point)',\n",
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" '2.c (Parallèle)', '2.d (Parallèle)', 'Exercice 3',\n",
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" '1 (Domaine définition)', '2 (Valeur interdite)', '3 (Calcul)',\n",
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" 'Exercice 4'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 11,
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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": 12,
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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": 13,
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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": 17,
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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 (Nuage points)',\n",
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" '2.a ',\n",
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" '2.b (Tracer droite)',\n",
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" '2.c (Estimation)',\n",
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" '1 (equation droite)',\n",
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" '2.a (Tracer droite)',\n",
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" '2.b (Droite point)',\n",
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" '2.c (Parallèle)',\n",
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" '2.d (Parallèle)',\n",
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" '1 (Domaine définition)',\n",
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" '2 (Valeur interdite)',\n",
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" '3 (Calcul)']"
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]
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},
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"execution_count": 17,
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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]\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": 18,
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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": 19,
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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": 20,
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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": 21,
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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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"17"
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]
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},
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"execution_count": 21,
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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": 22,
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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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},
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"outputs": [
|
||
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|
{
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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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||
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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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{
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"data": {
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"text/plain": [
|
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|
|
"<matplotlib.figure.Figure at 0x7f542600fa90>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
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"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": {
|
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|
"collapsed": false
|
||
|
|
},
|
||
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"outputs": [
|
||
|
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{
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"data": {
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"text/html": [
|
||
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
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|
"<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",
|
||
|
|
" <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",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" 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",
|
||
|
|
"count NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
|
||
|
|
"\n",
|
||
|
|
" 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",
|
||
|
|
"count NaN NaN NaN NaN NaN NaN NaN NaN NaN \n",
|
||
|
|
"\n",
|
||
|
|
" 4.B.3 \n",
|
||
|
|
"count NaN "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 36,
|
||
|
|
"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",
|
||
|
|
"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"
|
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