{ "cells": [ { "cell_type": "code", "execution_count": 49, "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": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'classe': '309', 'date': '18 septembre 2015', 'titre': 'DM 1'}" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_name = \"DM_15_09_18\"\n", "classe = \"309\"\n", "\n", "latex_info = {}\n", "latex_info['titre'] = \"DM 1\" \n", "latex_info['classe'] = classe\n", "latex_info['date'] = \"18 septembre 2015\"\n", "latex_info" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import et premiers traitements" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "notes = pd.ExcelFile(\"./../../../309.xlsx\")\n", "notes.sheet_names\n", "notes = notes.parse(ds_name)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([ 'DM_15_09_18', 'numero sujet', 'Exercice 1', '1 (PGCD)',\n", " '2 (Quantités)', 'Exercice 2', 1, 2,\n", " 3, 4, 5, 6],\n", " dtype='object')" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "notes.index" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "notes = notes.T" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#notes = notes.drop('av_arrondi', axis=1)\n", "notes = notes.drop('numero sujet', axis=1)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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DM_15_09_18Exercice 11 (PGCD)2 (Quantités)Exercice 2123456
BAREME104226111111
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" ], "text/plain": [ " DM_15_09_18 Exercice 1 1 (PGCD) 2 (Quantités) Exercice 2 1 2 3 \\\n", "BAREME 10 4 2 2 6 1 1 1 \n", "\n", " 4 5 6 \n", "BAREME 1 1 1 " ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "barem = notes[:1]\n", "notes = notes[1:]\n", "notes\n", "barem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Supression des notes inutiles " ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes = notes[notes[ds_name].notnull()]\n", "#notes = notes[notes[ds_name] != 'abs']" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes = notes.astype(float)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Traitement des notes" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Index([ 'DM_15_09_18', 'Exercice 1', '1 (PGCD)', '2 (Quantités)',\n", " 'Exercice 2', 1, 2, 3,\n", " 4, 5, 6],\n", " dtype='object')" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "notes.T.index" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [], "source": [ "list_exo = [\"Exercice 1\", \"Exercice 2\"]" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Exercice 1Exercice 2
ABDOU Farida4.000.33
ABOU BACAR Djaha4.006.00
AHAMADA Nabaouya4.006.00
AHAMADI Faina1.332.67
ALI Mardhuia2.676.00
ALI SOULAIMANA Chamsia2.006.00
ALSENE ALI MADI Stela0.673.33
ANDRIATAHIANA Hoby4.003.00
ANLI Emeline2.004.33
ATHOUMANE Naouidat3.335.33
BOUDRA Nassifanya4.006.00
CHANFI Nadhrati3.332.00
COMBO Moinécha4.006.00
HALIDI Nisma4.005.33
HAMZA Samianti3.331.67
HOUMADI Mouslimati2.675.00
HOUMADI Chaharazadi2.004.67
HOUMADI Nasmi4.006.00
HOUMADI Dhoirfia2.003.67
LOUTOUFI Nachima1.332.33
MALIDE El-Anzize2.002.67
MONNE Kevin4.004.33
MOUSSA Roibouanti4.006.00
OUSSENI Hilma2.003.33
SAANLI Natali4.001.67
SAID AHAMADA Roukaya4.005.00
SANDA Issoufi2.004.67
SOILIHI Soifia2.004.00
SOUFIANI Laila2.002.67
YOUSSOUF Sitirati2.005.00
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" ], "text/plain": [ " Exercice 1 Exercice 2\n", "ABDOU Farida 4.00 0.33\n", "ABOU BACAR Djaha 4.00 6.00\n", "AHAMADA Nabaouya 4.00 6.00\n", "AHAMADI Faina 1.33 2.67\n", "ALI Mardhuia 2.67 6.00\n", "ALI SOULAIMANA Chamsia 2.00 6.00\n", "ALSENE ALI MADI Stela 0.67 3.33\n", "ANDRIATAHIANA Hoby 4.00 3.00\n", "ANLI Emeline 2.00 4.33\n", "ATHOUMANE Naouidat 3.33 5.33\n", "BOUDRA Nassifanya 4.00 6.00\n", "CHANFI Nadhrati 3.33 2.00\n", "COMBO Moinécha 4.00 6.00\n", "HALIDI Nisma 4.00 5.33\n", "HAMZA Samianti 3.33 1.67\n", "HOUMADI Mouslimati 2.67 5.00\n", "HOUMADI Chaharazadi 2.00 4.67\n", "HOUMADI Nasmi 4.00 6.00\n", "HOUMADI Dhoirfia 2.00 3.67\n", "LOUTOUFI Nachima 1.33 2.33\n", "MALIDE El-Anzize 2.00 2.67\n", "MONNE Kevin 4.00 4.33\n", "MOUSSA Roibouanti 4.00 6.00\n", "OUSSENI Hilma 2.00 3.33\n", "SAANLI Natali 4.00 1.67\n", "SAID AHAMADA Roukaya 4.00 5.00\n", "SANDA Issoufi 2.00 4.67\n", "SOILIHI Soifia 2.00 4.00\n", "SOUFIANI Laila 2.00 2.67\n", "YOUSSOUF Sitirati 2.00 5.00" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n", "notes[list_exo]" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "['1 (PGCD)', '2 (Quantités)', 1, 2, 3, 4, 5, 6]" ] }, "execution_count": 61, "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": 62, "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": 63, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n", "#notes" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "eleves = notes.copy()\n", "eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "11" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(notes.T.index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Preparation du fichier .tex" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bilan = texenv.get_template(\"tpl_bilan.tex\")\n", "with open(\"./bilan309.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": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 29.000000\n", "mean 7.120690\n", "std 2.149149\n", "min 3.500000\n", "25% 5.500000\n", "50% 7.000000\n", "75% 9.000000\n", "max 10.000000\n", "Name: DM_15_09_18, dtype: float64" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "notes[ds_name].describe()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 44, "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": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes_questions = notes[sous_exo]\n", "notes_analysis = notes_questions.describe()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
1 (PGCD)2 (Quantités)123456
countNaNNaNNaNNaNNaNNaNNaNNaN
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" ], "text/plain": [ " 1 (PGCD) 2 (Quantités) 1 2 3 4 5 6\n", "count NaN NaN NaN NaN NaN NaN NaN NaN" ] }, "execution_count": 46, "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 }