2017-2018/Notes/DB vers xlsx.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
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"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import xlsxwriter"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"from repytex.worksheets.worksheet import pull_datas\n",
"import pandas as pd\n",
"import numpy as np\n",
"import sqlite3"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"conn = sqlite3.connect('./recopytex.db')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'evaluation': id name term t_id\n",
" 0 1 DS1 mise en jambe 1 1,\n",
" 'exercises': id name eval_id date comment\n",
" 0 1 1 - Prendre la température 1 2017-09-05 00:00:00.000000 \n",
" 1 2 2 - Maladroite! 1 2017-09-05 00:00:00.000000 ,\n",
" 'question_scores': id question_id student_id value\n",
" 0 1 1 1 1\n",
" 1 2 2 1 2\n",
" 2 3 3 1 .\n",
" 3 4 1 2 2\n",
" 4 5 2 2 2\n",
" 5 6 3 2 1\n",
" 6 7 1 3 3\n",
" 7 8 2 3 0\n",
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" 24 25 1 8 3\n",
" 25 26 2 8 1\n",
" 26 27 3 8 .\n",
" 27 28 1 10 1\n",
" 28 29 2 10 2\n",
" 29 30 3 10 0\n",
" .. ... ... ... ...\n",
" 178 803 4 21 .\n",
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" \n",
" [200 rows x 4 columns],\n",
" 'questions': id name score_rate is_leveled exercise_id competence domain \\\n",
" 0 1 1 0 1 1 Cher Grandeurs \n",
" 1 2 2 0 1 1 Cal Grandeurs \n",
" 2 3 2.c 0 1 1 Cal Grandeurs \n",
" 0 4 0 1 2 Cher 4opérations \n",
" 1 5 0 1 2 Cher 4opérations \n",
" 2 6 0 1 2 Rai 4opérations \n",
" 3 7 0 1 2 Cal 4opérations \n",
" 4 8 0 1 2 Com 4opérations \n",
" \n",
" comment \n",
" 0 Lecture du thermomètre \n",
" 1 Suivre programme de calculs \n",
" 2 Renverser un programme de calculs \n",
" 0 Lire le tableau et le graphique \n",
" 1 Décomposer le problème \n",
" 2 Manipuler les grandeurs \n",
" 3 Mener à bien les calculs \n",
" 4 Rédaction ,\n",
" 'students': id name surname mail commment tribe_id\n",
" 0 1 ABDALLAH ALLAOUI Taiassima None 1\n",
" 1 2 ADANI Ismou None 1\n",
" 2 3 AHAMADA Dhoulkamal None 1\n",
" 3 4 AHAMADI Asbahati None 1\n",
" 4 5 AHAMADI OUSSENI Ansufiddine None 1\n",
" 5 6 AHAMED Fayadhi None 1\n",
" 6 7 AHMED SAID Hadaïta None 1\n",
" 7 8 ALI MADI Anissa None 1\n",
" 8 9 ALI Raydel None 1\n",
" 9 10 ATTOUMANE ALI Fatima None 1\n",
" 10 11 BACHIROU Elzame None 1\n",
" 11 12 BINALI Zalida None 1\n",
" 12 13 BOINA Abdillah Mze Limassi None 1\n",
" 13 14 BOUDRA Zaankidine None 1\n",
" 14 15 BOURA Kayssoiria None 1\n",
" 15 16 HALADI Asna None 1\n",
" 16 17 HALIDI Soibrata None 1\n",
" 17 18 HAMEDALY Doulkifly None 1\n",
" 18 19 IBRAHIM Nassur None 1\n",
" 19 20 INOUSSA Anchoura None 1\n",
" 20 21 MOHAMED Nadia None 1\n",
" 21 22 MOUHOUDHOIRE Izak None 1\n",
" 22 23 MOUSSRI Bakari None 1\n",
" 23 24 SAÏD Fatoumia None 1\n",
" 24 25 SAKOTRA Claudiana None 1\n",
" 25 26 TOUFAIL Salahou None 1\n",
" 26 76 Ibrahim Chaharzade None 1,\n",
" 'tribe': id name grade\n",
" 0 1 302 3e}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datas = pull_datas(1, conn)\n",
"datas"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'DS1 mise en jambe'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datas['evaluation'][\"name\"][0]"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"q_df = datas[\"questions\"]\n",
"q_df = q_df.assign(\n",
" Nom = q_df[[\"name\", \"comment\"]].apply(lambda x: \" \".join(x), 1)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 65,
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"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
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" <th></th>\n",
" <th>id</th>\n",
" <th>name</th>\n",
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" <th>is_leveled</th>\n",
" <th>exercise_id</th>\n",
" <th>competence</th>\n",
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" <th>Nom</th>\n",
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" <td>Grandeurs</td>\n",
" <td>Lecture du thermomètre</td>\n",
" <td>1 Lecture du thermomètre</td>\n",
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" </tr>\n",
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" <td>Suivre programme de calculs</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
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" <td>3</td>\n",
" <td>2.c</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cal</td>\n",
" <td>Grandeurs</td>\n",
" <td>Renverser un programme de calculs</td>\n",
" <td>2.c Renverser un programme de calculs</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4</td>\n",
" <td></td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>2</td>\n",
" <td>Cher</td>\n",
" <td>4opérations</td>\n",
" <td>Lire le tableau et le graphique</td>\n",
" <td>Lire le tableau et le graphique</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>5</td>\n",
" <td></td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>2</td>\n",
" <td>Cher</td>\n",
" <td>4opérations</td>\n",
" <td>Décomposer le problème</td>\n",
" <td>Décomposer le problème</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id name score_rate is_leveled exercise_id competence domain \\\n",
"0 1 1 0 1 1 Cher Grandeurs \n",
"1 2 2 0 1 1 Cal Grandeurs \n",
"2 3 2.c 0 1 1 Cal Grandeurs \n",
"0 4 0 1 2 Cher 4opérations \n",
"1 5 0 1 2 Cher 4opérations \n",
"\n",
" comment Nom \n",
"0 Lecture du thermomètre 1 Lecture du thermomètre \n",
"1 Suivre programme de calculs 2 Suivre programme de calculs \n",
"2 Renverser un programme de calculs 2.c Renverser un programme de calculs \n",
"0 Lire le tableau et le graphique Lire le tableau et le graphique \n",
"1 Décomposer le problème Décomposer le problème "
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"q_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>name</th>\n",
" <th>eval_id</th>\n",
" <th>date</th>\n",
" <th>comment</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <tr>\n",
" <th>0</th>\n",
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" <td>1</td>\n",
" <td>1 - Prendre la température</td>\n",
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" <td>1</td>\n",
" <td>2017-09-05 00:00:00.000000</td>\n",
" <td></td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
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" <td>2</td>\n",
" <td>2 - Maladroite!</td>\n",
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" <td>1</td>\n",
" <td>2017-09-05 00:00:00.000000</td>\n",
" <td></td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
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],
"text/plain": [
" id name eval_id date comment\n",
"0 1 1 - Prendre la température 1 2017-09-05 00:00:00.000000 \n",
"1 2 2 - Maladroite! 1 2017-09-05 00:00:00.000000 "
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]
},
"execution_count": 66,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datas[\"exercises\"]"
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]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
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" text-align: left;\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>score_rate</th>\n",
" <th>competence</th>\n",
" <th>id</th>\n",
" <th>name</th>\n",
" <th>eval_id</th>\n",
" <th>date</th>\n",
" <th>comment</th>\n",
" <th>Nom</th>\n",
" <th>exercise_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>Cher-Cal-Cal</td>\n",
" <td>1</td>\n",
" <td>1 - Prendre la température</td>\n",
" <td>1</td>\n",
" <td>2017-09-05 00:00:00.000000</td>\n",
" <td></td>\n",
" <td>Ex 1 - Prendre la température</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>Cher-Cher-Rai-Cal-Com</td>\n",
" <td>2</td>\n",
" <td>2 - Maladroite!</td>\n",
" <td>1</td>\n",
" <td>2017-09-05 00:00:00.000000</td>\n",
" <td></td>\n",
" <td>Ex 2 - Maladroite!</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" score_rate competence id name eval_id \\\n",
"0 0 Cher-Cal-Cal 1 1 - Prendre la température 1 \n",
"1 0 Cher-Cher-Rai-Cal-Com 2 2 - Maladroite! 1 \n",
"\n",
" date comment Nom \\\n",
"0 2017-09-05 00:00:00.000000 Ex 1 - Prendre la température \n",
"1 2017-09-05 00:00:00.000000 Ex 2 - Maladroite! \n",
"\n",
" exercise_id \n",
"0 1 \n",
"1 2 "
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ex_q = q_df.groupby(\"exercise_id\")\\\n",
" .agg({\"score_rate\": np.sum, \"competence\": lambda x: '-'.join(list(x))}) \\\n",
" .merge(datas[\"exercises\"], left_index=True, right_on='id')\n",
"ex_q = ex_q.assign(\n",
" Nom = ex_q[\"name\"].apply(lambda x: f\"Ex {x}\"),\n",
" exercise_id = ex_q[\"id\"]\n",
")\n",
"ex_q"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Nom</th>\n",
" <th>competence</th>\n",
" <th>score_rate</th>\n",
" <th>exercise_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Ex 1 - Prendre la température</td>\n",
" <td>Cher-Cal-Cal</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1 Lecture du thermomètre</td>\n",
" <td>Cher</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2 Suivre programme de calculs</td>\n",
" <td>Cal</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2.c Renverser un programme de calculs</td>\n",
" <td>Cal</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Ex 2 - Maladroite!</td>\n",
" <td>Cher-Cher-Rai-Cal-Com</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Lire le tableau et le graphique</td>\n",
" <td>Cher</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Décomposer le problème</td>\n",
" <td>Cher</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Manipuler les grandeurs</td>\n",
" <td>Rai</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Mener à bien les calculs</td>\n",
" <td>Cal</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Rédaction</td>\n",
" <td>Com</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Nom competence score_rate \\\n",
"0 Ex 1 - Prendre la température Cher-Cal-Cal 0 \n",
"0 1 Lecture du thermomètre Cher 0 \n",
"1 2 Suivre programme de calculs Cal 0 \n",
"2 2.c Renverser un programme de calculs Cal 0 \n",
"1 Ex 2 - Maladroite! Cher-Cher-Rai-Cal-Com 0 \n",
"0 Lire le tableau et le graphique Cher 0 \n",
"1 Décomposer le problème Cher 0 \n",
"2 Manipuler les grandeurs Rai 0 \n",
"3 Mener à bien les calculs Cal 0 \n",
"4 Rédaction Com 0 \n",
"\n",
" exercise_id \n",
"0 1 \n",
"0 1 \n",
"1 1 \n",
"2 1 \n",
"1 2 \n",
"0 2 \n",
"1 2 \n",
"2 2 \n",
"3 2 \n",
"4 2 "
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sel_fields = ['Nom', 'competence', 'score_rate', 'exercise_id']\n",
"xls_df = pd.concat([ex_q[sel_fields], q_df[sel_fields]]).sort_values(\"exercise_id\")\n",
"xls_df"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [],
"source": [
"writer = pd.ExcelWriter(\"./test.xlsx\", engine=\"xlsxwriter\")\n",
"xls_df.to_excel(writer, index=False, sheet_name='DNB blanc 2')"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"workbook = writer.book\n",
"worksheet = writer.sheets['DNB blanc 2']"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"first_line_format = workbook.add_format({\"align\": 'center', \"rotation\":90})\n",
"\n",
"worksheet.set_column('A:D', 20)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"writer.save()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fait au DNB blanc 1"
]
},
{
"cell_type": "code",
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"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
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"wb = xlsxwriter.Workbook(\"DNB_BLANC_pro1.xlsx\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Formatages"
]
},
{
"cell_type": "code",
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"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"eval_format = wb.add_format({'bold': True, 'font_color': 'red'})\n",
"eval_cell = wb.add_format()\n",
"eval_cell.set_bg_color(\"red\")\n",
"# exo_cell"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La feuille de calcul"
]
},
{
"cell_type": "code",
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"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"wb = xlsxwriter.Workbook(\"DS4_302.xlsx\")"
]
},
{
"cell_type": "code",
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"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ws = wb.add_worksheet()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En tête du tableau"
]
},
{
"cell_type": "code",
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"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
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"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ws.write(0,1,\"Competence\")\n",
"ws.write(0,2,\"Barème\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Toutes les questions et exercices"
]
},
{
"cell_type": "code",
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"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def q_line(q, row, ws):\n",
" ws.write(row, 0, f\"{q.name} - {q.comment}\")\n",
" ws.write(row, 1, f\"{q.competence}\")\n",
" ws.write(row, 2, q.score_rate)"
]
},
{
"cell_type": "code",
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"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def exo_block(exo, row, ws):\n",
" qrow = row\n",
" for q in exo[\"questions\"]:\n",
" qrow += 1\n",
" q_line(q, qrow, ws)\n",
" ws.write(row, 0, f\"Ex {exo['name']}\")\n",
" row_range = \", \".join([f\"C{i}\" for i in range(row+2, qrow+2)])\n",
" for i in range(30):\n",
" ws.write(row, i+2, f\"=SUM({row_range})\")\n",
" return qrow"
]
},
{
"cell_type": "code",
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"execution_count": 16,
"metadata": {
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"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"row = 2\n",
"exo_rows = []\n",
"for exo in ev.exercises:\n",
" exo_rows.append(row)\n",
" row = exo_block(exo, row, ws) + 1\n",
"ws.write(1,0, ev.name, eval_format)\n",
"exo_xls_range = \", \".join([f\"C{i+1}\" for i in exo_rows])\n",
"for i in range(30):\n",
" ws.write(1,i+2, f\"=SUM({exo_xls_range})\", eval_format)"
]
},
{
"cell_type": "code",
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"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"wb.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Avec openpyxl"
]
},
{
"cell_type": "code",
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"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from openpyxl import Workbook\n",
"from openpyxl.styles import PatternFill, Border, Side, Alignment, Protection, Font, colors"
]
},
{
"cell_type": "code",
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"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'00FF0000'"
]
},
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"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"colors.RED"
]
},
{
"cell_type": "code",
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"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"wb = Workbook()"
]
},
{
"cell_type": "code",
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"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ws = wb.create_sheet(ev.name)"
]
},
{
"cell_type": "code",
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"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"<Cell 'DNB blanc1'.C1>"
]
},
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"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ws.cell(1,2,\"Competence\")\n",
"ws.cell(1,3,\"Barème\")"
]
},
{
"cell_type": "code",
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"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def q_line(q, row, ws):\n",
" ws.cell(row, 1, f\"{q.name} - {q.comment}\")\n",
" ws.cell(row, 2, f\"{q.competence}\")\n",
" ws.cell(row, 3, q.score_rate)"
]
},
{
"cell_type": "code",
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"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def exo_block(exo, row, ws):\n",
" qrow = row\n",
" for q in exo[\"questions\"]:\n",
" qrow += 1\n",
" q_line(q, qrow, ws)\n",
" ws.cell(row, 1, f\"Ex {exo['name']}\")\n",
" row_range = \", \".join([f\"C{i}\" for i in range(row+1, qrow+1)])\n",
" ws.cell(row, 3, f\"=SUM({row_range})\")\n",
" return qrow"
]
},
{
"cell_type": "code",
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"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"<Cell 'DNB blanc1'.C2>"
]
},
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"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"row = 3\n",
"exo_rows = []\n",
"for exo in ev.exercises:\n",
" exo_rows.append(row)\n",
" row = exo_block(exo, row, ws) + 1\n",
"eval_row = ws.row_dimensions[2]\n",
"eval_row.fill = PatternFill(bgColor=colors.RED)\n",
"ws.cell(2,1, ev.name)\n",
"exo_xls_range = \", \".join([f\"C{i}\" for i in exo_rows])\n",
"ws.cell(2,3, f\"=SUM({exo_xls_range})\")"
]
},
{
"cell_type": "code",
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"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'302'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ev.tribe"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
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"wb.save(f\"./{ev.name}-{ev.tribe}.xlsx\")"
]
},
{
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}