recopytex/templates/tpl_evaluation.ipynb

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{
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"outputs": [],
"source": [
"from IPython.display import Markdown as md\n",
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"from IPython.display import display, HTML\n",
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"import pandas as pd\n",
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"import ipywidgets as widgets\n",
"import seaborn as sns\n",
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"from pathlib import Path\n",
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"from datetime import datetime\n",
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"from recopytex import flat_df_students, pp_q_scores\n",
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"#import prettytable as pt\n",
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"%matplotlib inline"
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]
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"source": [
"cm = sns.light_palette(\"green\", as_cmap=True)"
]
},
{
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"parameters"
]
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"outputs": [],
"source": [
"tribe = \"308\"\n",
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"assessment = \"DM1\"\n",
"date = \"15/09/16\"\n",
"csv_file = Path(f\"../sheets/{tribe}/160915_{assessment}.csv\")"
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]
},
{
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"outputs": [
{
"data": {
"text/markdown": [
"# DM1 (15/09/16) pour 308"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
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"source": [
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"if date is None:\n",
" display(md(f\"# {assessment} pour {tribe}\"))\n",
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"else:\n",
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" display(md(f\"# {assessment} ({date}) pour {tribe}\"))"
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]
},
{
"cell_type": "code",
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"execution_count": 32,
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"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
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"\n",
" .dataframe thead th {\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>Trimestre</th>\n",
" <th>Nom</th>\n",
" <th>Date</th>\n",
" <th>Exercice</th>\n",
" <th>Question</th>\n",
" <th>Competence</th>\n",
" <th>Domaine</th>\n",
" <th>Commentaire</th>\n",
" <th>Bareme</th>\n",
" <th>Est_nivele</th>\n",
" <th>Eleve</th>\n",
" <th>Score</th>\n",
" <th>Note</th>\n",
" <th>Niveau</th>\n",
" <th>Normalise</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>DM1</td>\n",
" <td>15/09/16</td>\n",
" <td>1</td>\n",
" <td>1.1</td>\n",
" <td>Cal</td>\n",
" <td>Prio</td>\n",
" <td></td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>ABDOU Asmahane</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>DM1</td>\n",
" <td>15/09/16</td>\n",
" <td>1</td>\n",
" <td>1.2</td>\n",
" <td>Cal</td>\n",
" <td>Prio</td>\n",
" <td></td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>ABDOU Asmahane</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
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" <th>2</th>\n",
" <td>1</td>\n",
" <td>DM1</td>\n",
" <td>15/09/16</td>\n",
" <td>1</td>\n",
" <td>1.3</td>\n",
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" <td>Prio</td>\n",
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" <td>1</td>\n",
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" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
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" <th>3</th>\n",
" <td>1</td>\n",
" <td>DM1</td>\n",
" <td>15/09/16</td>\n",
" <td>1</td>\n",
" <td>1.4</td>\n",
" <td>Cal</td>\n",
" <td>Prio</td>\n",
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" <td>1</td>\n",
" <td>ABDOU Asmahane</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>DM1</td>\n",
" <td>15/09/16</td>\n",
" <td>1</td>\n",
" <td>1.5</td>\n",
" <td>Cal</td>\n",
" <td>Prio</td>\n",
" <td></td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>ABDOU Asmahane</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
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"text/plain": [
" Trimestre Nom Date Exercice Question Competence Domaine Commentaire \\\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",
"\n",
" Bareme Est_nivele Eleve Score Note Niveau Normalise \n",
"0 1.0 1 ABDOU Asmahane 2.0 1.0 2.0 1.0 \n",
"1 1.0 1 ABDOU Asmahane 3.0 1.0 3.0 1.0 \n",
"2 1.0 1 ABDOU Asmahane 2.0 1.0 2.0 1.0 \n",
"3 1.0 1 ABDOU Asmahane 2.0 1.0 2.0 1.0 \n",
"4 1.0 1 ABDOU Asmahane 2.0 1.0 2.0 1.0 "
]
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"source": [
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"stack_scores = pd.read_csv(csv_file, encoding=\"latin_1\")\n",
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"scores = flat_df_students(stack_scores).dropna(subset=[\"Score\"])\n",
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"scores = pp_q_scores(scores)\n",
"scores.head()"
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]
},
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" <th></th>\n",
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" <th>Note</th>\n",
" <th>Bareme</th>\n",
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" <tr>\n",
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" <th>Exercice</th>\n",
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" <th>Eleve</th>\n",
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" <th rowspan=\"5\" valign=\"top\">1</th>\n",
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" <th>ABDOU Asmahane</th>\n",
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" <td>5.0</td>\n",
" <td>6.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>ABOU Roihim</th>\n",
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" <td>0.0</td>\n",
" <td>6.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>AHMED BOINALI Kouraichia</th>\n",
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" <td>2.0</td>\n",
" <td>6.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>AHMED Rahada</th>\n",
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" <td>3.0</td>\n",
" <td>6.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>ALI SAID Anchourati</th>\n",
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" <td>0.0</td>\n",
" <td>6.0</td>\n",
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" </tr>\n",
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"text/plain": [
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" Note Bareme\n",
"Exercice Eleve \n",
"1 ABDOU Asmahane 5.0 6.0\n",
" ABOU Roihim 0.0 6.0\n",
" AHMED BOINALI Kouraichia 2.0 6.0\n",
" AHMED Rahada 3.0 6.0\n",
" ALI SAID Anchourati 0.0 6.0"
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]
},
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"execution_count": 6,
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"output_type": "execute_result"
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}
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"source": [
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"exercises_scores = scores.groupby([\"Exercice\", \"Eleve\"]).agg({\"Note\": \"sum\", \"Bareme\": \"sum\"})\n",
"exercises_scores.head()"
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]
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" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row0\" class=\"row_heading level0 row0\" >ABDOU Asmahane</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow0_col0\" class=\"data row0 col0\" >6.5</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow0_col1\" class=\"data row0 col1\" >12</td>\n",
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" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row1\" class=\"row_heading level0 row1\" >ABOU Roihim</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow1_col0\" class=\"data row1 col0\" >0</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow1_col1\" class=\"data row1 col1\" >12</td>\n",
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" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row2\" class=\"row_heading level0 row2\" >AHMED BOINALI Kouraichia</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow2_col0\" class=\"data row2 col0\" >3.5</td>\n",
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" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow3_col0\" class=\"data row3 col0\" >7</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow3_col1\" class=\"data row3 col1\" >12</td>\n",
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" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row4\" class=\"row_heading level0 row4\" >ALI SAID Anchourati</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow4_col0\" class=\"data row4 col0\" >0</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow4_col1\" class=\"data row4 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row5\" class=\"row_heading level0 row5\" >ASSANE Noussouraniya</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow5_col0\" class=\"data row5 col0\" >5.5</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow5_col1\" class=\"data row5 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row6\" class=\"row_heading level0 row6\" >BACAR Issiaka</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow6_col0\" class=\"data row6 col0\" >0</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow6_col1\" class=\"data row6 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row7\" class=\"row_heading level0 row7\" >BACAR Samina</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow7_col0\" class=\"data row7 col0\" >4</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow7_col1\" class=\"data row7 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row8\" class=\"row_heading level0 row8\" >CHAIHANE Said</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow8_col0\" class=\"data row8 col0\" >6</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow8_col1\" class=\"data row8 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row9\" class=\"row_heading level0 row9\" >COMBO Houzaimati</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow9_col0\" class=\"data row9 col0\" >6</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow9_col1\" class=\"data row9 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row10\" class=\"row_heading level0 row10\" >DAOUD Anzilati</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow10_col0\" class=\"data row10 col0\" >7.5</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow10_col1\" class=\"data row10 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row11\" class=\"row_heading level0 row11\" >DAOUD Talaenti</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow11_col0\" class=\"data row11 col0\" >8</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow11_col1\" class=\"data row11 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row12\" class=\"row_heading level0 row12\" >DARKAOUI Rachma</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow12_col0\" class=\"data row12 col0\" >8</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow12_col1\" class=\"data row12 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row13\" class=\"row_heading level0 row13\" >DHAKIOINE Nabaouya</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow13_col0\" class=\"data row13 col0\" >1</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow13_col1\" class=\"data row13 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row14\" class=\"row_heading level0 row14\" >DJANFAR Soioutinour</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow14_col0\" class=\"data row14 col0\" >7.5</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow14_col1\" class=\"data row14 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row15\" class=\"row_heading level0 row15\" >DRISSA Ibrahim</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow15_col0\" class=\"data row15 col0\" >0</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow15_col1\" class=\"data row15 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row16\" class=\"row_heading level0 row16\" >HACHIM SIDI Assani</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow16_col0\" class=\"data row16 col0\" >8.5</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow16_col1\" class=\"data row16 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row17\" class=\"row_heading level0 row17\" >HAFIDHUI Zalifa</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow17_col0\" class=\"data row17 col0\" >8</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow17_col1\" class=\"data row17 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row18\" class=\"row_heading level0 row18\" >HOUMADI Marie</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow18_col0\" class=\"data row18 col0\" >7.5</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow18_col1\" class=\"data row18 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row19\" class=\"row_heading level0 row19\" >HOUMADI Sania</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow19_col0\" class=\"data row19 col0\" >5.5</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow19_col1\" class=\"data row19 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row20\" class=\"row_heading level0 row20\" >MAANDHUI Halouoi</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow20_col0\" class=\"data row20 col0\" >8.5</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow20_col1\" class=\"data row20 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row21\" class=\"row_heading level0 row21\" >MASSONDI Nasma</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow21_col0\" class=\"data row21 col0\" >8</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow21_col1\" class=\"data row21 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row22\" class=\"row_heading level0 row22\" >SAIDALI Irichad</th>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow22_col0\" class=\"data row22 col0\" >7</td>\n",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow22_col1\" class=\"data row22 col1\" >12</td>\n",
" </tr>\n",
" </tbody></table>"
],
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"text/plain": [
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"<pandas.io.formats.style.Styler at 0x7f92fc46a9d0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assessment_scores = scores.groupby([\"Eleve\"]).agg({\"Note\": \"sum\", \"Bareme\": \"sum\"})\n",
"#assessment_scores.style.background_gradient(cmap=cm, subset=[\"Note\"])\n",
"assessment_scores.style.bar(subset=[\"Note\"])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12.0"
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]
},
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"execution_count": 8,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"assessment_scores.Bareme.max()"
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]
},
{
"cell_type": "code",
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"execution_count": 9,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
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"outputs": [
{
"data": {
"text/plain": [
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"count 23.00\n",
"mean 5.37\n",
"std 3.08\n",
"min 0.00\n",
"25% 3.75\n",
"50% 6.50\n",
"75% 7.75\n",
"max 8.50\n",
"Name: Note, dtype: float64"
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]
},
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"execution_count": 9,
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"metadata": {},
"output_type": "execute_result"
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}
],
"source": [
"assessment_scores[\"Note\"].describe()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f92c5d2a450>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
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},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
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"dp = sns.distplot(assessment_scores.Note, bins=int(assessment_scores.Bareme[0])*2)\n",
"dp.set(xlim=(0, assessment_scores.Bareme[0]))\n",
"dp"
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]
},
{
"cell_type": "code",
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"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Note</th>\n",
" <th>Bareme</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Exercice</th>\n",
" <th>Eleve</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">1</th>\n",
" <th>ABDOU Asmahane</th>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABOU Roihim</th>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHMED BOINALI Kouraichia</th>\n",
" <td>2.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHMED Rahada</th>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALI SAID Anchourati</th>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Note Bareme\n",
"Exercice Eleve \n",
"1 ABDOU Asmahane 5.0 6.0\n",
" ABOU Roihim 0.0 6.0\n",
" AHMED BOINALI Kouraichia 2.0 6.0\n",
" AHMED Rahada 3.0 6.0\n",
" ALI SAID Anchourati 0.0 6.0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exercises_scores.head()"
]
},
{
"cell_type": "code",
"execution_count": 79,
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"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
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"outputs": [],
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"source": [
"def st_results(student):\n",
" html = \"<div class='card'>\"\n",
" html += \"<div class='container'>\"\n",
" \n",
" ass = assessment_scores.loc[student]\n",
" exercices = exercises_scores.xs(student, level=\"Eleve\")\n",
" \n",
" html += f\"<h2> {student} -- {assessment}</h2>\"\n",
" html += f\"<p> {ass.Note} / {ass.Bareme}</p>\"\n",
" for (i, ex) in exercices.iterrows():\n",
" if not ex.Bareme == 0:\n",
" html += f\"<h3> Exercice {i} ({ex.Note}/{ex.Bareme})</h3>\"\n",
" html += scores.loc[(scores.Exercice==i) & (scores.Eleve==student)][[\"Question\", \"Commentaire\", \"Note\", \"Bareme\"]].to_html()\n",
" html += \"</div>\"\n",
" html += \"</div>\"\n",
" return HTML(html)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "788cdf676be445669085d7de593a8eeb",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(Dropdown(description='student', options=('ABDOU Asmahane', 'ABOU Roihim', 'AHMED BOINALI…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function __main__.st_results(student)>"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"widgets.interact(st_results, student=list(assessment_scores.index))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
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"source": []
}
],
"metadata": {
"celltoolbar": "Tags",
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"extensions": {
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"cellMargin": 10,
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"name": "grid",
"type": "grid"
},
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"name": "report",
"type": "report"
}
}
}
},
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"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",
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"version": "3.7.4"
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