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2015-2016/3e/DM/DM_15_09_11/Bilan/Bilan309.ipynb

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{
"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": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DM_15_09_18</th>\n",
" <th>Exercice 1</th>\n",
" <th>1 (PGCD)</th>\n",
" <th>2 (Quantités)</th>\n",
" <th>Exercice 2</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>BAREME</th>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Exercice 1</th>\n",
" <th>Exercice 2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ABDOU Farida</th>\n",
" <td>4.00</td>\n",
" <td>0.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABOU BACAR Djaha</th>\n",
" <td>4.00</td>\n",
" <td>6.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADA Nabaouya</th>\n",
" <td>4.00</td>\n",
" <td>6.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADI Faina</th>\n",
" <td>1.33</td>\n",
" <td>2.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALI Mardhuia</th>\n",
" <td>2.67</td>\n",
" <td>6.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALI SOULAIMANA Chamsia</th>\n",
" <td>2.00</td>\n",
" <td>6.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALSENE ALI MADI Stela</th>\n",
" <td>0.67</td>\n",
" <td>3.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANDRIATAHIANA Hoby</th>\n",
" <td>4.00</td>\n",
" <td>3.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANLI Emeline</th>\n",
" <td>2.00</td>\n",
" <td>4.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ATHOUMANE Naouidat</th>\n",
" <td>3.33</td>\n",
" <td>5.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BOUDRA Nassifanya</th>\n",
" <td>4.00</td>\n",
" <td>6.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CHANFI Nadhrati</th>\n",
" <td>3.33</td>\n",
" <td>2.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COMBO Moinécha</th>\n",
" <td>4.00</td>\n",
" <td>6.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HALIDI Nisma</th>\n",
" <td>4.00</td>\n",
" <td>5.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HAMZA Samianti</th>\n",
" <td>3.33</td>\n",
" <td>1.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Mouslimati</th>\n",
" <td>2.67</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Chaharazadi</th>\n",
" <td>2.00</td>\n",
" <td>4.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Nasmi</th>\n",
" <td>4.00</td>\n",
" <td>6.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Dhoirfia</th>\n",
" <td>2.00</td>\n",
" <td>3.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LOUTOUFI Nachima</th>\n",
" <td>1.33</td>\n",
" <td>2.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MALIDE El-Anzize</th>\n",
" <td>2.00</td>\n",
" <td>2.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MONNE Kevin</th>\n",
" <td>4.00</td>\n",
" <td>4.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MOUSSA Roibouanti</th>\n",
" <td>4.00</td>\n",
" <td>6.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OUSSENI Hilma</th>\n",
" <td>2.00</td>\n",
" <td>3.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SAANLI Natali</th>\n",
" <td>4.00</td>\n",
" <td>1.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SAID AHAMADA Roukaya</th>\n",
" <td>4.00</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SANDA Issoufi</th>\n",
" <td>2.00</td>\n",
" <td>4.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SOILIHI Soifia</th>\n",
" <td>2.00</td>\n",
" <td>4.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SOUFIANI Laila</th>\n",
" <td>2.00</td>\n",
" <td>2.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YOUSSOUF Sitirati</th>\n",
" <td>2.00</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7efd747b1630>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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2b6yyP2b7xtJ5BrF9q+0V23tX/exHbN9n+1u2d9merVuj9bCOiO9Jujwi5iTNSbrC9vva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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efd747b1a90>"
]
},
"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": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>1 (PGCD)</th>\n",
" <th>2 (Quantités)</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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
}
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