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notes_l3_2020_2021.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bilan résultats examen L3 Python 2020 - 2021 "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"\n",
"sns.set_theme(\n",
" style=\"ticks\",\n",
" rc={\"figure.figsize\": (8, 8), \"axes.spines.top\": False, \"axes.spines.right\": False},\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lecture du fichier"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
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"\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>0</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",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>11</th>\n",
" <th>notes</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>23/04/2021 12:32:09</td>\n",
" <td>h305-01</td>\n",
" <td>25%</td>\n",
" <td>100%</td>\n",
" <td>0%</td>\n",
" <td>0%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
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" <td>100%</td>\n",
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" <td>9.00</td>\n",
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" <td>23/04/2021 12:36:20</td>\n",
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" <td>100%</td>\n",
" <td>0%</td>\n",
" <td>12.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>23/04/2021 12:44:33</td>\n",
" <td>h305-06</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>0%</td>\n",
" <td>75%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>50%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>75%</td>\n",
" <td>15.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>23/04/2021 13:41:51</td>\n",
" <td>h305-08</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>75%</td>\n",
" <td>50%</td>\n",
" <td>75%</td>\n",
" <td>100%</td>\n",
" <td>25%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>50%</td>\n",
" <td>14.50</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>23/04/2021 13:46:00</td>\n",
" <td>h305-10</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>75%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>25%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>50%</td>\n",
" <td>16.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>23/04/2021 13:49:16</td>\n",
" <td>h305-11</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>0%</td>\n",
" <td>100%</td>\n",
" <td>50%</td>\n",
" <td>100%</td>\n",
" <td>0%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>0%</td>\n",
" <td>11.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>23/04/2021 13:52:45</td>\n",
" <td>h305-14</td>\n",
" <td>50%</td>\n",
" <td>100%</td>\n",
" <td>50%</td>\n",
" <td>100%</td>\n",
" <td>75%</td>\n",
" <td>100%</td>\n",
" <td>0%</td>\n",
" <td>25%</td>\n",
" <td>0%</td>\n",
" <td>0%</td>\n",
" <td>10.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>23/04/2021 13:57:35</td>\n",
" <td>h305-16</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>0%</td>\n",
" <td>0%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>75%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>0%</td>\n",
" <td>12.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>23/04/2021 14:00:14</td>\n",
" <td>h305-18</td>\n",
" <td>25%</td>\n",
" <td>100%</td>\n",
" <td>75%</td>\n",
" <td>50%</td>\n",
" <td>0%</td>\n",
" <td>100%</td>\n",
" <td>0%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>50%</td>\n",
" <td>10.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>23/04/2021 14:05:08</td>\n",
" <td>h305-19</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>75%</td>\n",
" <td>75%</td>\n",
" <td>75%</td>\n",
" <td>100%</td>\n",
" <td>50%</td>\n",
" <td>100%</td>\n",
" <td>75%</td>\n",
" <td>75%</td>\n",
" <td>16.25</td>\n",
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" <td>23/04/2021 14:07:40</td>\n",
" <td>h305-21</td>\n",
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" <td>50%</td>\n",
" <td>75%</td>\n",
" <td>100%</td>\n",
" <td>50%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>25%</td>\n",
" <td>13.50</td>\n",
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" <th>11</th>\n",
" <td>23/04/2021 14:10:23</td>\n",
" <td>h305-24</td>\n",
" <td>100%</td>\n",
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" <td>75%</td>\n",
" <td>100%</td>\n",
" <td>50%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>50%</td>\n",
" <td>15.75</td>\n",
" </tr>\n",
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" <td>23/04/2021 14:12:31</td>\n",
" <td>h305-25</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
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" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>50%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>25%</td>\n",
" <td>16.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>23/04/2021 14:15:10</td>\n",
" <td>h305-28</td>\n",
" <td>100%</td>\n",
" <td>75%</td>\n",
" <td>0%</td>\n",
" <td>100%</td>\n",
" <td>75%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>25%</td>\n",
" <td>15.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>23/04/2021 14:18:31</td>\n",
" <td>h305-30</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>0%</td>\n",
" <td>0%</td>\n",
" <td>75%</td>\n",
" <td>100%</td>\n",
" <td>50%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>50%</td>\n",
" <td>12.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>23/04/2021 14:22:32</td>\n",
" <td>h306-01</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>100%</td>\n",
" <td>75%</td>\n",
" <td>100%</td>\n",
" <td>75%</td>\n",
" <td>100%</td>\n",
" <td>75%</td>\n",
" <td>0%</td>\n",
" <td>15.75</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 \\\n",
"0 23/04/2021 12:32:09 h305-01 25% 100% 0% 0% 100% 100% 0% \n",
"1 23/04/2021 12:36:20 h305-03 100% 75% 0% 100% 75% 100% 25% \n",
"2 23/04/2021 12:44:33 h305-06 100% 100% 0% 75% 100% 100% 50% \n",
"3 23/04/2021 13:41:51 h305-08 100% 100% 75% 50% 75% 100% 25% \n",
"4 23/04/2021 13:46:00 h305-10 100% 100% 75% 100% 100% 100% 25% \n",
"5 23/04/2021 13:49:16 h305-11 100% 100% 0% 100% 50% 100% 0% \n",
"6 23/04/2021 13:52:45 h305-14 50% 100% 50% 100% 75% 100% 0% \n",
"7 23/04/2021 13:57:35 h305-16 100% 100% 0% 0% 100% 100% 75% \n",
"8 23/04/2021 14:00:14 h305-18 25% 100% 75% 50% 0% 100% 0% \n",
"9 23/04/2021 14:05:08 h305-19 100% 100% 75% 75% 75% 100% 50% \n",
"10 23/04/2021 14:07:40 h305-21 100% 100% 25% 50% 75% 100% 50% \n",
"11 23/04/2021 14:10:23 h305-24 100% 100% 100% 50% 75% 100% 50% \n",
"12 23/04/2021 14:12:31 h305-25 100% 100% 75% 100% 100% 100% 50% \n",
"13 23/04/2021 14:15:10 h305-28 100% 75% 0% 100% 75% 100% 100% \n",
"14 23/04/2021 14:18:31 h305-30 100% 100% 0% 0% 75% 100% 50% \n",
"15 23/04/2021 14:22:32 h306-01 100% 100% 100% 100% 75% 100% 75% \n",
"\n",
" 9 10 11 notes \n",
"0 100% 100% 0% 9.00 \n",
"1 100% 100% 0% 12.00 \n",
"2 100% 100% 75% 15.75 \n",
"3 100% 100% 50% 14.50 \n",
"4 100% 100% 50% 16.25 \n",
"5 100% 100% 0% 11.00 \n",
"6 25% 0% 0% 10.00 \n",
"7 100% 100% 0% 12.75 \n",
"8 100% 100% 50% 10.00 \n",
"9 100% 75% 75% 16.25 \n",
"10 100% 100% 25% 13.50 \n",
"11 100% 100% 50% 15.75 \n",
"12 100% 100% 25% 16.25 \n",
"13 100% 100% 25% 15.00 \n",
"14 100% 100% 50% 12.75 \n",
"15 100% 75% 0% 15.75 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"df = pd.read_csv(\n",
" \"https://docs.google.com/spreadsheets/d/15poSZ2DrtZDt_urFYrRjeBseloAOFqvT87HCyPvkqEE/export?gid=1793266644&format=csv\",\n",
" skiprows=2,\n",
" header=None,\n",
")\n",
"\n",
"df.rename(columns={12: \"notes\", 13: \"nom\", 14: \"prenom\", 25: \"sexe\"}, inplace=True)\n",
"df[\"notes\"] = df[\"notes\"].str.replace(\",\", \".\").astype(float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Résultats par exercice"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def plot_mark_by_exercice(cols):\n",
" ex = df.iloc[:, cols].melt()\n",
" ex[\"variable\"] -= cols[0]\n",
"\n",
" ex[\"value\"] = ex[\"value\"].str.strip(\"%\").astype(float)\n",
" ex.rename(columns={\"variable\": \"question\", \"value\": \"note [%]\"}, inplace=True)\n",
"\n",
" sns.catplot(data=ex, x=\"note [%]\", col=\"question\", kind=\"count\", col_wrap=5)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
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"text/plain": [
"<Figure size 1800x720 with 10 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_mark_by_exercice(range(2, 12))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Moyenne générale"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <td>0%</td>\n",
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" <td>h305-14</td>\n",
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" <td>0%</td>\n",
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" 0 1 2 3 4 5 6 7 8 \\\n",
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"4 23/04/2021 13:46:00 h305-10 100% 100% 75% 100% 100% 100% 25% \n",
"5 23/04/2021 13:49:16 h305-11 100% 100% 0% 100% 50% 100% 0% \n",
"6 23/04/2021 13:52:45 h305-14 50% 100% 50% 100% 75% 100% 0% \n",
"7 23/04/2021 13:57:35 h305-16 100% 100% 0% 0% 100% 100% 75% \n",
"8 23/04/2021 14:00:14 h305-18 25% 100% 75% 50% 0% 100% 0% \n",
"9 23/04/2021 14:05:08 h305-19 100% 100% 75% 75% 75% 100% 50% \n",
"10 23/04/2021 14:07:40 h305-21 100% 100% 25% 50% 75% 100% 50% \n",
"11 23/04/2021 14:10:23 h305-24 100% 100% 100% 50% 75% 100% 50% \n",
"12 23/04/2021 14:12:31 h305-25 100% 100% 75% 100% 100% 100% 50% \n",
"13 23/04/2021 14:15:10 h305-28 100% 75% 0% 100% 75% 100% 100% \n",
"14 23/04/2021 14:18:31 h305-30 100% 100% 0% 0% 75% 100% 50% \n",
"15 23/04/2021 14:22:32 h306-01 100% 100% 100% 100% 75% 100% 75% \n",
"\n",
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]
},
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"metadata": {},
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{
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{
"data": {
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" Summary\n",
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"pd.DataFrame(\n",
" {\n",
" \"Summary\": {\n",
" **df.notes.describe(),\n",
" **{f\"< {val:2}\": (df.notes < val).sum() for val in [5, 7, 10, 12]},\n",
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")"
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},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.histplot(data=df, x=\"notes\", kde=True, element=\"step\")\n",
"g.set(ylabel=\"\");"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.violinplot(data=df, x=\"notes\", y=\"sexe\", cut=0, inner=\"quartile\")\n",
"sns.swarmplot(data=df, x=\"notes\", y=\"sexe\", color=\"0.3\");"
]
}
],
"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": "3.9.3"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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