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{
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{
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"source": [
"from IPython.display import Markdown as md\n",
"from IPython.display import display, HTML\n",
"import pandas as pd\n",
"import ipywidgets as widgets\n",
"import seaborn as sns\n",
"from pathlib import Path\n",
"from datetime import datetime\n",
"from recopytex import flat_df_students, pp_q_scores\n",
"#import prettytable as pt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"cm = sns.light_palette(\"green\", as_cmap=True)"
]
},
{
"cell_type": "code",
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"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"tribe = \"308\"\n",
"assessment = \"DM1\"\n",
"date = \"15/09/16\"\n",
"csv_file = Path(f\"../sheets/{tribe}/160915_{assessment}.csv\")"
]
},
{
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"outputs": [
{
"data": {
"text/markdown": [
"# DM1 (15/09/16) pour 308"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"if date is None:\n",
" display(md(f\"# {assessment} pour {tribe}\"))\n",
"else:\n",
" display(md(f\"# {assessment} ({date}) pour {tribe}\"))"
]
},
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"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>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",
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" <th>Niveau</th>\n",
" <th>Normalise</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
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" <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",
" </tr>\n",
" <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",
" </tr>\n",
" <tr>\n",
" <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",
" <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",
" </tr>\n",
" <tr>\n",
" <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",
" <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",
" </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",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stack_scores = pd.read_csv(csv_file, encoding=\"latin_1\")\n",
"scores = flat_df_students(stack_scores).dropna(subset=[\"Score\"])\n",
"scores = pp_q_scores(scores)\n",
"scores.head()"
]
},
{
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"<div>\n",
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"\n",
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"\n",
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" 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": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"exercises_scores = scores.groupby([\"Exercice\", \"Eleve\"]).agg({\"Note\": \"sum\", \"Bareme\": \"sum\"})\n",
"exercises_scores.head()"
]
},
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" }</style><table id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11d\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Note</th> <th class=\"col_heading level0 col1\" >Bareme</th> </tr> <tr> <th class=\"index_name level0\" >Eleve</th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
" <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",
" </tr>\n",
" <tr>\n",
" <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",
" </tr>\n",
" <tr>\n",
" <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",
" <td id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11drow2_col1\" class=\"data row2 col1\" >12</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_996dd6e8_c3d5_11e9_aaaf_902b34d9e11dlevel0_row3\" class=\"row_heading level0 row3\" >AHMED Rahada</th>\n",
" <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",
" </tr>\n",
" <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>"
],
"text/plain": [
"<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"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"assessment_scores.Bareme.max()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": false
}
}
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"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"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"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"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dp = sns.distplot(assessment_scores.Note, bins=int(assessment_scores.Bareme[0])*2)\n",
"dp.set(xlim=(0, assessment_scores.Bareme[0]))\n",
"dp"
]
},
{
"cell_type": "code",
"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,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {},
"report_default": {
"hidden": true
}
}
}
}
},
"outputs": [],
"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": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Tags",
"extensions": {
"jupyter_dashboards": {
"activeView": "grid_default",
"version": 1,
"views": {
"grid_default": {
"cellMargin": 10,
"defaultCellHeight": 20,
"maxColumns": 12,
"name": "grid",
"type": "grid"
},
"report_default": {
"name": "report",
"type": "report"
}
}
}
},
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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