{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bilan pour les Tsmtg" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "from texenv import texenv\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes = pd.read_excel(\"./../../../notes_tstmg.xls\",sheetname=\"DST_03\")" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes = notes.T" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes = notes.drop(\"av_arrondi\", axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On extrait le barème et on l'enlève ainsi que les lignes du tableau qui ne servent à rien." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "barem = notes[:1]\n", "notes = notes[1:-6]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On enlèves les élèves absents" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes = notes[notes[\"DST_03\"].notnull()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On remplace les réponses NaN par \\NoRep" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "notes = notes.fillna(\"\\\\NoRep\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On arrondit les points de chacuns des exercices" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#notes['Exercice 1'] = notes['Exercice 1'].round(2)\n", "#notes['Exercice 2'] = notes['Exercice 2'].round(2)\n", "notes[['Exercice 1', 'Exercice 2']] = notes[['Exercice 1', 'Exercice 2']].applymap(lambda x:round(x,2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On remplace les notes par le code latex" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "toReplist = ['1.1 (Arbre)', '1.2.a (Décrire évènement)', '1.2.b (Calculer probabilité)', '1.2.c (Calculer probabiltié)', '1.3 (Calculer probabilté)', '1.4 (probabiltié conditionnelle)', '1.5.a (probabilité loi normale)', '1.5.b (probabilité loi normale)', '1.6.a (intervalle 95\\%)', '1.6.b (interprétation)', '2.1 (indice)', '2.2 (indice)', '2.3.a (taux évolution)', '2.3.b (taux évolution moyen)', '2.4 (projection)']" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def toRepVal(val):\n", " if val == \"\\\\NoRep\":\n", " return val\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": "markdown", "metadata": {}, "source": [ "Pour séléctionner plusieurs colonnes, il faut juste donner entre [] la liste des noms de colones! Facile!" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes[toReplist] = notes[toReplist].applymap(toRepVal)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On entre tout dans le fichier près à compiler!" ] }, { "cell_type": "code", "execution_count": 56, "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 = notes, barem = barem))" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEACAYAAABMEua6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAGKhJREFUeJzt3X/MXXV9B/D32xacxG1QjZ3SmkcGLOB0tG7YYDYORpPa\n", 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