{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "from opytex import texenv\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Informations sur le devoir" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'classe': '\\\\PSTMG', 'date': '29 mai 2015', 'titre': 'DS 7'}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_name = 'DS_0529'\n", "classe = '1stmg'\n", "\n", "latex_info = {}\n", "latex_info['titre'] = 'DS 7' \n", "latex_info['classe'] = '\\\\PSTMG'\n", "latex_info['date'] = '29 mai 2015'\n", "latex_info" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import et premiers traitements" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "notes = pd.ExcelFile(\"./../../../notes_\"+classe+\".xlsx\")\n", "notes.sheet_names\n", "notes = notes.parse(ds_name)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['DS_0529', 'av_arrondi', 'Exercice 1', '1 (Loi binomiale)',\n", " '2.a (Calcul proba)', '2.b (Calcul proba)', '3.a (Calcul proba)',\n", " '3.b (Calcul proba)', '4 (Espérance)', 'Exercice 2', '1 (Évolution)',\n", " '2 (taux évolution)', '3 (Évolution réciproque)',\n", " '4 (Évolution successive)', 'Exercice 3'],\n", " dtype='object')" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "notes.index" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "notes = notes.T" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes = notes.drop('av_arrondi', axis=1)\n", "#notes = notes.drop('subj_num', axis=1)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "barem = notes[:1]\n", "notes = notes[1:]\n", "#notes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Supression des notes inutiles " ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes = notes[notes[ds_name].notnull()]\n", "notes = notes[notes[ds_name] != 0]" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes = notes.astype(float)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Traitement des notes" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Index(['DS_0529', 'Exercice 1', '1 (Loi binomiale)', '2.a (Calcul proba)',\n", " '2.b (Calcul proba)', '3.a (Calcul proba)', '3.b (Calcul proba)',\n", " '4 (Espérance)', 'Exercice 2', '1 (Évolution)', '2 (taux évolution)',\n", " '3 (Évolution réciproque)', '4 (Évolution successive)', 'Exercice 3'],\n", " dtype='object')" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "notes.T.index" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "list_exo = [\"Exercice 1\", \"Exercice 2\", \"Exercice 3\"]" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n", "#notes[list_exo]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "['1 (Loi binomiale)',\n", " '2.a (Calcul proba)',\n", " '2.b (Calcul proba)',\n", " '3.a (Calcul proba)',\n", " '3.b (Calcul proba)',\n", " '4 (Espérance)',\n", " '1 (Évolution)',\n", " '2 (taux évolution)',\n", " '3 (Évolution réciproque)',\n", " '4 (Évolution successive)']" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "item_avec_note = list_exo + [ds_name]\n", "sous_exo = [i for i in notes.T.index if i not in item_avec_note]\n", "sous_exo" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def toRepVal(val):\n", " if pd.isnull(val):\n", " return \"\\\\NoRep\"\n", " elif val == 0:\n", " return \"\\\\RepZ\"\n", " elif val == 1:\n", " return \"\\\\RepU\"\n", " elif val == 2:\n", " return \"\\\\RepD\"\n", " elif val == 3:\n", " return \"\\\\RepT\"\n", " else:\n", " return val" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n", "#notes" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "eleves = notes.copy()\n", "eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "14" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(notes.T.index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Preparation du fichier .tex" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bilan = texenv.get_template(\"tpl_bilan.tex\")\n", "with open(\"./bilan.tex\",\"w\") as f:\n", " f.write(bilan.render(eleves = eleves, barem = barem, ds_name = ds_name, latex_info = latex_info, nbr_questions = len(barem.T)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Un peu de statistiques" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 27.000000\n", "mean 10.203704\n", "std 3.986176\n", "min 4.000000\n", "25% 6.750000\n", "50% 9.500000\n", "75% 13.500000\n", "max 18.500000\n", "Name: DS_0529, dtype: float64" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "notes[ds_name].describe()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "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": [ "
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" ], "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" } }, "nbformat": 4, "nbformat_minor": 0 }