{ "cells": [ { "cell_type": "code", "execution_count": 18, "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": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'classe': '309', 'date': '25 septembre 2015', 'titre': 'DS 1'}" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_name = \"DS_15_09_25\"\n", "classe = \"309\"\n", "\n", "latex_info = {}\n", "latex_info['titre'] = \"DS 1\"\n", "latex_info['classe'] = classe\n", "latex_info['date'] = \"25 septembre 2015\"\n", "latex_info" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import et premiers traitements" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "notes = pd.ExcelFile(\"./../../../\"+classe+\".xlsx\")\n", "notes.sheet_names\n", "notes = notes.parse(ds_name)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([ 'DS_15_09_25', 'numero sujet', 'Presentation',\n", " 'Exercice 1', 1, 2,\n", " 'Exercice 2', '1 (Division)', '2.a (Division)',\n", " '2.b (PGCD)', 'Exercice 3', '1 (Vrai Faux)',\n", " '2 (Proba)', '3 (Proba)', 'Exercice 4',\n", " '1 (Modélisation)', '1 (Explication)'],\n", " dtype='object')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "notes.index" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "notes = notes.T" ] }, { "cell_type": "code", "execution_count": 23, "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": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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DS_15_09_25PresentationExercice 112Exercice 21 (Division)2.a (Division)2.b (PGCD)Exercice 31 (Vrai Faux)2 (Proba)3 (Proba)Exercice 41 (Modélisation)1 (Explication)
ABDOU Farida9.01.00.0NaNNaN2.0000000024.8333333321.020
ABOU BACAR Djaha16.51.02.5326.3333332335.3333334321.512
AHAMADA Nabaouya9.01.00.0003.0000000034.0000004211.011
AHAMADI Faina4.51.01.53NaN2.0000000020.0000000NaNNaN0.000
ALI Mardhuia11.51.02.5323.0000000034.6666674310.501
ALI SOULAIMANA Chamsia14.01.03.0333.0000000025.5000003331.521
ALSENE ALI MADI Stela8.51.00.0003.0000000033.8333331320.51NaN
ANDRIATAHIANA Hoby11.01.02.5324.3333330233.00000023NaN0.000
ANLI Emeline4.00.00.0003.0000000030.5000001000.501
ATHOUMANE Naouidat8.01.02.0312.0000000021.16666711NaN2.022
BOUDRA Nassifanya14.51.03.0333.0000000035.5000003332.013
CHANFI Nadhrati3.01.00.0000.0000000NaNNaN2.0000004000.00NaN
COMBO Moinécha12.51.03.0332.0000000024.8333333321.512
HALIDI Nisma10.00.52.5322.0000000024.8333333320.000
HAMZA Samianti4.51.00.0001.0000000011.0000002NaNNaN1.521
HOUMADI Mouslimati5.51.00.0003.0000000030.5000001001.011
HOUMADI Chaharazadi10.51.02.0222.6666670124.1666673310.510
HOUMADI Nasmi14.01.02.0224.3333330234.5000001332.022
HOUMADI Dhoirfia13.51.03.0332.0000000025.5000003332.022
LOUTOUFI Nachima4.01.00.0001.0000000010.5000001001.521
MALIDE El-Anzize9.51.00.0002.0000000025.5000003331.002
MONNE Kevin14.00.53.0334.0000000323.5000003033.033
MOUSSA Roibouanti13.01.03.0333.3333332305.0000002330.501
OUSSENI Hilma4.51.00.0001.0000000011.1666671011.512
SAANLI Natali19.01.03.0337.0000003335.0000002333.033
SAID AHAMADA Roukaya14.51.03.0334.6666671324.1666673311.521
SANDA Issoufi5.00.50.5100.0000000004.1666673310.00NaN
SOILIHI Soifia9.01.01.5212.0000000024.1666673310.510
SOUFIANI Laila4.01.00.0002.0000000021.0000002NaNNaN0.00NaN
YOUSSOUF Sitirati3.51.00.0002.0000000020.5000001NaNNaN0.0NaNNaN
\n", "
" ], "text/plain": [ " DS_15_09_25 Presentation Exercice 1 1 2 \\\n", "ABDOU Farida 9.0 1.0 0.0 NaN NaN \n", "ABOU BACAR Djaha 16.5 1.0 2.5 3 2 \n", "AHAMADA Nabaouya 9.0 1.0 0.0 0 0 \n", "AHAMADI Faina 4.5 1.0 1.5 3 NaN \n", "ALI Mardhuia 11.5 1.0 2.5 3 2 \n", "ALI SOULAIMANA Chamsia 14.0 1.0 3.0 3 3 \n", "ALSENE ALI MADI Stela 8.5 1.0 0.0 0 0 \n", "ANDRIATAHIANA Hoby 11.0 1.0 2.5 3 2 \n", "ANLI Emeline 4.0 0.0 0.0 0 0 \n", "ATHOUMANE Naouidat 8.0 1.0 2.0 3 1 \n", "BOUDRA Nassifanya 14.5 1.0 3.0 3 3 \n", "CHANFI Nadhrati 3.0 1.0 0.0 0 0 \n", "COMBO Moinécha 12.5 1.0 3.0 3 3 \n", "HALIDI Nisma 10.0 0.5 2.5 3 2 \n", "HAMZA Samianti 4.5 1.0 0.0 0 0 \n", "HOUMADI Mouslimati 5.5 1.0 0.0 0 0 \n", "HOUMADI Chaharazadi 10.5 1.0 2.0 2 2 \n", "HOUMADI Nasmi 14.0 1.0 2.0 2 2 \n", "HOUMADI Dhoirfia 13.5 1.0 3.0 3 3 \n", "LOUTOUFI Nachima 4.0 1.0 0.0 0 0 \n", "MALIDE El-Anzize 9.5 1.0 0.0 0 0 \n", "MONNE Kevin 14.0 0.5 3.0 3 3 \n", "MOUSSA Roibouanti 13.0 1.0 3.0 3 3 \n", "OUSSENI Hilma 4.5 1.0 0.0 0 0 \n", "SAANLI Natali 19.0 1.0 3.0 3 3 \n", "SAID AHAMADA Roukaya 14.5 1.0 3.0 3 3 \n", "SANDA Issoufi 5.0 0.5 0.5 1 0 \n", "SOILIHI Soifia 9.0 1.0 1.5 2 1 \n", "SOUFIANI Laila 4.0 1.0 0.0 0 0 \n", "YOUSSOUF Sitirati 3.5 1.0 0.0 0 0 \n", "\n", " Exercice 2 1 (Division) 2.a (Division) 2.b (PGCD) \\\n", "ABDOU Farida 2.000000 0 0 2 \n", "ABOU BACAR Djaha 6.333333 2 3 3 \n", "AHAMADA Nabaouya 3.000000 0 0 3 \n", "AHAMADI Faina 2.000000 0 0 2 \n", "ALI Mardhuia 3.000000 0 0 3 \n", "ALI SOULAIMANA Chamsia 3.000000 0 0 2 \n", "ALSENE ALI MADI Stela 3.000000 0 0 3 \n", "ANDRIATAHIANA Hoby 4.333333 0 2 3 \n", "ANLI Emeline 3.000000 0 0 3 \n", "ATHOUMANE Naouidat 2.000000 0 0 2 \n", "BOUDRA Nassifanya 3.000000 0 0 3 \n", "CHANFI Nadhrati 0.000000 0 NaN NaN \n", "COMBO Moinécha 2.000000 0 0 2 \n", "HALIDI Nisma 2.000000 0 0 2 \n", "HAMZA Samianti 1.000000 0 0 1 \n", "HOUMADI Mouslimati 3.000000 0 0 3 \n", "HOUMADI Chaharazadi 2.666667 0 1 2 \n", "HOUMADI Nasmi 4.333333 0 2 3 \n", "HOUMADI Dhoirfia 2.000000 0 0 2 \n", "LOUTOUFI Nachima 1.000000 0 0 1 \n", "MALIDE El-Anzize 2.000000 0 0 2 \n", "MONNE Kevin 4.000000 0 3 2 \n", "MOUSSA Roibouanti 3.333333 2 3 0 \n", "OUSSENI Hilma 1.000000 0 0 1 \n", "SAANLI Natali 7.000000 3 3 3 \n", "SAID AHAMADA Roukaya 4.666667 1 3 2 \n", "SANDA Issoufi 0.000000 0 0 0 \n", "SOILIHI Soifia 2.000000 0 0 2 \n", "SOUFIANI Laila 2.000000 0 0 2 \n", "YOUSSOUF Sitirati 2.000000 0 0 2 \n", "\n", " Exercice 3 1 (Vrai Faux) 2 (Proba) 3 (Proba) \\\n", "ABDOU Farida 4.833333 3 3 2 \n", "ABOU BACAR Djaha 5.333333 4 3 2 \n", "AHAMADA Nabaouya 4.000000 4 2 1 \n", "AHAMADI Faina 0.000000 0 NaN NaN \n", "ALI Mardhuia 4.666667 4 3 1 \n", "ALI SOULAIMANA Chamsia 5.500000 3 3 3 \n", "ALSENE ALI MADI Stela 3.833333 1 3 2 \n", "ANDRIATAHIANA Hoby 3.000000 2 3 NaN \n", "ANLI Emeline 0.500000 1 0 0 \n", "ATHOUMANE Naouidat 1.166667 1 1 NaN \n", "BOUDRA Nassifanya 5.500000 3 3 3 \n", "CHANFI Nadhrati 2.000000 4 0 0 \n", "COMBO Moinécha 4.833333 3 3 2 \n", "HALIDI Nisma 4.833333 3 3 2 \n", "HAMZA Samianti 1.000000 2 NaN NaN \n", "HOUMADI Mouslimati 0.500000 1 0 0 \n", "HOUMADI Chaharazadi 4.166667 3 3 1 \n", "HOUMADI Nasmi 4.500000 1 3 3 \n", "HOUMADI Dhoirfia 5.500000 3 3 3 \n", "LOUTOUFI Nachima 0.500000 1 0 0 \n", "MALIDE El-Anzize 5.500000 3 3 3 \n", "MONNE Kevin 3.500000 3 0 3 \n", "MOUSSA Roibouanti 5.000000 2 3 3 \n", "OUSSENI Hilma 1.166667 1 0 1 \n", "SAANLI Natali 5.000000 2 3 3 \n", "SAID AHAMADA Roukaya 4.166667 3 3 1 \n", "SANDA Issoufi 4.166667 3 3 1 \n", "SOILIHI Soifia 4.166667 3 3 1 \n", "SOUFIANI Laila 1.000000 2 NaN NaN \n", "YOUSSOUF Sitirati 0.500000 1 NaN NaN \n", "\n", " Exercice 4 1 (Modélisation) 1 (Explication) \n", "ABDOU Farida 1.0 2 0 \n", "ABOU BACAR Djaha 1.5 1 2 \n", "AHAMADA Nabaouya 1.0 1 1 \n", "AHAMADI Faina 0.0 0 0 \n", "ALI Mardhuia 0.5 0 1 \n", "ALI SOULAIMANA Chamsia 1.5 2 1 \n", "ALSENE ALI MADI Stela 0.5 1 NaN \n", "ANDRIATAHIANA Hoby 0.0 0 0 \n", "ANLI Emeline 0.5 0 1 \n", "ATHOUMANE Naouidat 2.0 2 2 \n", "BOUDRA Nassifanya 2.0 1 3 \n", "CHANFI Nadhrati 0.0 0 NaN \n", "COMBO Moinécha 1.5 1 2 \n", "HALIDI Nisma 0.0 0 0 \n", "HAMZA Samianti 1.5 2 1 \n", "HOUMADI Mouslimati 1.0 1 1 \n", "HOUMADI Chaharazadi 0.5 1 0 \n", "HOUMADI Nasmi 2.0 2 2 \n", "HOUMADI Dhoirfia 2.0 2 2 \n", "LOUTOUFI Nachima 1.5 2 1 \n", "MALIDE El-Anzize 1.0 0 2 \n", "MONNE Kevin 3.0 3 3 \n", "MOUSSA Roibouanti 0.5 0 1 \n", "OUSSENI Hilma 1.5 1 2 \n", "SAANLI Natali 3.0 3 3 \n", "SAID AHAMADA Roukaya 1.5 2 1 \n", "SANDA Issoufi 0.0 0 NaN \n", "SOILIHI Soifia 0.5 1 0 \n", "SOUFIANI Laila 0.0 0 NaN \n", "YOUSSOUF Sitirati 0.0 NaN NaN " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "barem = notes[:1]\n", "notes = notes[1:]\n", "notes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Supression des notes inutiles " ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "invalid type comparison", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mnotes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnotes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnotes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mds_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnotnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnotes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnotes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnotes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mds_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m'abs'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.4/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(self, other, axis)\u001b[0m\n\u001b[0;32m 612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 613\u001b[0m \u001b[1;31m# scalars\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 614\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mna_op\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mother\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 615\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 616\u001b[0m raise TypeError('Could not compare %s type with Series'\n", "\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.4/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m 566\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 567\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 568\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"invalid type comparison\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 569\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 570\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: invalid type comparison" ] } ], "source": [ "notes = notes[notes[ds_name].notnull()]\n", "#notes = notes[notes[ds_name] != 'abs']" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes = notes.astype(float)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Traitement des notes" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Index([ 'DS_15_09_25', 'Presentation', 'Exercice 1',\n", " 1, 2, 'Exercice 2',\n", " '1 (Division)', '2.a (Division)', '2.b (PGCD)',\n", " 'Exercice 3', '1 (Vrai Faux)', '2 (Proba)',\n", " '3 (Proba)', 'Exercice 4', '1 (Modélisation)',\n", " '1 (Explication)'],\n", " dtype='object')" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "notes.T.index" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "list_exo = [\"Exercice 1\", \"Exercice 2\", \"Exercice 3\", \"Exercice 4\"]" ] }, { "cell_type": "code", "execution_count": 29, "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": 30, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[1,\n", " 2,\n", " '1 (Division)',\n", " '2.a (Division)',\n", " '2.b (PGCD)',\n", " '1 (Vrai Faux)',\n", " '2 (Proba)',\n", " '3 (Proba)',\n", " '1 (Modélisation)',\n", " '1 (Explication)']" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "item_avec_note = list_exo + [ds_name, \"Presentation\"]\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": 31, "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": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n", "#notes" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "eleves = notes.copy()\n", "eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "16" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(notes.T.index)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Preparation du fichier .tex" ] }, { "cell_type": "code", "execution_count": 35, "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": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 30.000000\n", "mean 9.466667\n", "std 4.468150\n", "min 3.000000\n", "25% 4.625000\n", "50% 9.250000\n", "75% 13.375000\n", "max 19.000000\n", "Name: DS_15_09_25, dtype: float64" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "notes[ds_name].describe()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1.1.a1.1.b1.1.c1.2.a1.2.b1.2.c1.2.d1.3.a1.3.b1.3.c...2.22.33.1.a3.1.b3.1.c3.1.d3.2.a3.2.b3.2.c3.2.d
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" ], "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", "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 }