{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Markdown as md\n",
"from IPython.display import display\n",
"import pandas as pd\n",
"from pathlib import Path\n",
"from datetime import datetime\n",
"from recopytex import flat_df_for, pp_q_scores\n",
"#import prettytable as pt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"tribe = 308\n",
"student = \"ABDOU Asmahane\"\n",
"source = Path(f\"../sheets/{tribe}/\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"dfs = []\n",
"for file in source.glob(\"*.csv\"):\n",
" df = pd.read_csv(file)\n",
" df = flat_df_for(df, student)\n",
" dfs.append(df)\n",
"scores = pd.concat(dfs) \n",
"scores = pp_q_scores(scores)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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" Bareme | \n",
" Est_nivele | \n",
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],
"text/plain": [
" Trimestre Nom Date Exercice Question Competence Domaine \\\n",
"0 1 DS2 05/11/16 1 1 Mod Frac \n",
"1 1 DS2 05/11/16 1 2 Cal Frac \n",
"2 1 DS2 05/11/16 2 Com Geo \n",
"3 1 DS2 05/11/16 2 Con Geo \n",
"4 1 DS2 05/11/16 2 Cal Geo \n",
"5 1 DS2 05/11/16 3 1 à 3 Che Ope \n",
"6 1 DS2 05/11/16 3 1 à 3 Cal Ope \n",
"7 1 DS2 05/11/16 3 1 à 3 Com Ope \n",
"8 1 DS2 05/11/16 4 1 Che Geo3D \n",
"9 1 DS2 05/11/16 4 1 Cal Litt \n",
"0 1 DM1 15/09/16 1 1.1 Cal Prio \n",
"1 1 DM1 15/09/16 1 1.2 Cal Prio \n",
"2 1 DM1 15/09/16 1 1.3 Cal Prio \n",
"3 1 DM1 15/09/16 1 1.4 Cal Prio \n",
"4 1 DM1 15/09/16 1 1.5 Cal Prio \n",
"5 1 DM1 15/09/16 1 1.6 Cal Prio \n",
"6 1 DM1 15/09/16 2 2.1 Com Proba \n",
"7 1 DM1 15/09/16 2 2.2 à 2.4 Com Proba \n",
"8 1 DM1 15/09/16 2 2.2 à 2.4 Rep Proba \n",
"9 1 DM1 15/09/16 2 2.2 à 2.4 Div Proba \n",
"10 1 DM1 15/09/16 2 2.5 Rai Proba \n",
"11 1 DM1 15/09/16 Malus Retard Div Div \n",
"12 1 DM1 15/09/16 Presentation Com Div \n",
"0 1 DS1 24/09/16 1 1.a Cal Prio \n",
"1 1 DS1 24/09/16 1 1.b Cal Prio \n",
"2 1 DS1 24/09/16 1 1.c Cal Prio \n",
"3 1 DS1 24/09/16 1 1.d Cal Prio \n",
"4 1 DS1 24/09/16 2 1 Rec Proba \n",
"5 1 DS1 24/09/16 2 2 Cal Proba \n",
"6 1 DS1 24/09/16 2 3 à 5 Com Proba \n",
"7 1 DS1 24/09/16 2 3 à 5 Rep Proba \n",
"8 1 DS1 24/09/16 2 3 à 5 Div Proba \n",
"9 1 DS1 24/09/16 3 Com Scra \n",
"10 1 DS1 24/09/16 3 Che Scra \n",
"11 1 DS1 24/09/16 4 1 et 2 Che Ope \n",
"12 1 DS1 24/09/16 4 1 et 2 Com Ope \n",
"13 1 DS1 24/09/16 4 1 et 2 Cal Ope \n",
"\n",
" Commentaire Bareme Est_nivele Score Note \\\n",
"0 Figure -> fraction 2.0 1 -1 0.00 \n",
"1 Égalité fractions 2.0 1 -1 0.00 \n",
"2 Communication 2.0 1 -1 0.00 \n",
"3 Tableau 2.0 1 -1 0.00 \n",
"4 Calculs 2.0 1 -1 0.00 \n",
"5 Mettre en valeur les informations 1.5 1 -1 0.00 \n",
"6 Choisir et faire les calculs 1.0 1 -1 0.00 \n",
"7 Phrases et étapes 1.5 1 -1 0.00 \n",
"8 Remplir le tableau 2.0 1 -1 0.00 \n",
"9 Calcul s+f-a 1.0 1 -1 0.00 \n",
"0 1.0 1 2 0.67 \n",
"1 1.0 1 3 1.00 \n",
"2 1.0 1 2 0.67 \n",
"3 1.0 1 2 0.67 \n",
"4 1.0 1 2 0.67 \n",
"5 1.0 1 0 0.00 \n",
"6 1.0 1 1 0.33 \n",
"7 Notation P(...) 1.0 1 0 0.00 \n",
"8 Fractions 1.0 1 0 0.00 \n",
"9 résultat 1.0 1 -1 0.00 \n",
"10 1.0 1 -1 0.00 \n",
"11 0.0 0 -1 0.00 \n",
"12 1.0 0 1 1.00 \n",
"0 1.5 1 3 1.50 \n",
"1 1.5 1 0 0.00 \n",
"2 1.5 1 3 1.50 \n",
"3 1.5 1 3 1.50 \n",
"4 1.0 1 1 0.33 \n",
"5 1.0 1 2 0.67 \n",
"6 Notation 1.5 1 1 0.50 \n",
"7 Fraction 1.5 1 2 1.00 \n",
"8 Résultat 1.0 1 0 0.00 \n",
"9 Explication 1.5 1 1 0.50 \n",
"10 Trouver erreur 1.5 1 1 0.50 \n",
"11 1.0 1 3 1.00 \n",
"12 1.5 1 2 1.00 \n",
"13 1.5 1 3 1.50 \n",
"\n",
" Niveau Normalise \n",
"0 NaN 0.00 \n",
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"13 3.0 1.00 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scores"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" | \n",
" Note | \n",
" Bareme | \n",
"
\n",
" \n",
" Nom | \n",
" Exercice | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" DM1 | \n",
" 1 | \n",
" 3.67 | \n",
" 6.0 | \n",
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\n",
" \n",
" 2 | \n",
" 0.33 | \n",
" 5.0 | \n",
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" Malus | \n",
" 0.00 | \n",
" 0.0 | \n",
"
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" \n",
" Presentation | \n",
" 1.00 | \n",
" 1.0 | \n",
"
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" \n",
" DS1 | \n",
" 1 | \n",
" 4.50 | \n",
" 6.0 | \n",
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" \n",
" 2 | \n",
" 2.50 | \n",
" 6.0 | \n",
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" 3 | \n",
" 1.00 | \n",
" 3.0 | \n",
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" 4 | \n",
" 3.50 | \n",
" 4.0 | \n",
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" \n",
" DS2 | \n",
" 1 | \n",
" 0.00 | \n",
" 4.0 | \n",
"
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" \n",
" 2 | \n",
" 0.00 | \n",
" 6.0 | \n",
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" 3 | \n",
" 0.00 | \n",
" 4.0 | \n",
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"text/plain": [
" Note Bareme\n",
"Nom Exercice \n",
"DM1 1 3.67 6.0\n",
" 2 0.33 5.0\n",
" Malus 0.00 0.0\n",
" Presentation 1.00 1.0\n",
"DS1 1 4.50 6.0\n",
" 2 2.50 6.0\n",
" 3 1.00 3.0\n",
" 4 3.50 4.0\n",
"DS2 1 0.00 4.0\n",
" 2 0.00 6.0\n",
" 3 0.00 4.0\n",
" 4 0.00 3.0"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exercises_scores = scores.groupby([\"Nom\", \"Exercice\"]).agg({\"Note\": \"sum\", \"Bareme\": \"sum\"})\n",
"exercises_scores"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Note | \n",
" Bareme | \n",
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" \n",
" Nom | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" DM1 | \n",
" 5.0 | \n",
" 12.0 | \n",
"
\n",
" \n",
" DS1 | \n",
" 11.5 | \n",
" 19.0 | \n",
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" \n",
" DS2 | \n",
" 0.0 | \n",
" 17.0 | \n",
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"text/plain": [
" Note Bareme\n",
"Nom \n",
"DM1 5.0 12.0\n",
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"DS2 0.0 17.0"
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},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assessment_scores = scores.groupby([\"Nom\"]).agg({\"Note\": \"sum\", \"Bareme\": \"sum\"})\n",
"assessment_scores"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"# ABDOU Asmahane en 308"
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"md(f\"# {student} en {tribe}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Tags",
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"display_name": "Python 3",
"language": "python",
"name": "python3"
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"file_extension": ".py",
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"name": "python",
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"pygments_lexer": "ipython3",
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