2407 lines
477 KiB
Plaintext
2407 lines
477 KiB
Plaintext
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{
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"cell_type": "code",
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"hidden": true
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}
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}
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}
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"plt.style.use(\"seaborn-notebook\")\n",
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"\n",
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"from ipywidgets import interact, interactive, fixed\n",
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"import ipywidgets as widgets\n",
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"from IPython.display import display\n",
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"\n",
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"import notes_tools.tools as tools\n",
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"from notes_tools.tools.df_marks_manip import round_half_point"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"hidden": true
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}
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}
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}
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},
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"outputs": [],
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"source": [
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"ws = tools.get_class_ws(\"312\")\n",
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"flat = tools.extract_flat_marks(ws)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false,
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"hidden": true
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},
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"report_default": {
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"hidden": true
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}
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}
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}
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}
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},
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"outputs": [],
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"source": [
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"quest_pov, exo_pov, eval_pov = tools.digest_flat_df(flat)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false,
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"extensions": {
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"version": 1,
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"grid_default": {
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"hidden": true
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},
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"report_default": {
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"hidden": true
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}
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}
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}
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}
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},
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"outputs": [],
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"source": [
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"#eval_pov[\"Nom\"].unique()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"extensions": {
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}
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}
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}
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},
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"source": [
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"## All in"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false,
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"extensions": {
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"jupyter_dashboards": {
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"version": 1,
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"views": {
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"grid_default": {},
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"report_default": {
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"hidden": true
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}
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}
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}
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Eleve</th>\n",
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" <th>Date</th>\n",
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" <th>Bareme</th>\n",
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" <th>Mark</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>ABDALLAH Elza</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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" <td>11.5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>15</th>\n",
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" <td>ABDALLAH Nourayina</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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|
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" <td>10.5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>24</th>\n",
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" <td>ABDALLAH Roukia</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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" <td>4.5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>33</th>\n",
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" <td>AHAMADI Laila</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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" <td>11.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>42</th>\n",
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" <td>AHAMADI Satti</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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" <td>6.5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>51</th>\n",
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" <td>AHAMED El-Fahad</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>60</th>\n",
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" <td>AHMED Nachmie</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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|
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" <td>9.5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>69</th>\n",
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" <td>ALI ABDALLAH Raphael</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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" <td>8.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>78</th>\n",
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" <td>ATTOUMANE Nasrati</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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" <td>9.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>87</th>\n",
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" <td>ATTOUMANE Nedjima</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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|
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" <td>11.5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>96</th>\n",
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" <td>DARKAOUI Issouf</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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||
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>105</th>\n",
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" <td>DHOIMIR Hidayat</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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|
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" <td>3.5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>114</th>\n",
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" <td>DJAMAL Hounaissati</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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|
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" <td>7.0</td>\n",
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|
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" </tr>\n",
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" <tr>\n",
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" <th>123</th>\n",
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" <td>DJANFAR Houmadi</td>\n",
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||
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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||
|
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" <td>0.0</td>\n",
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|
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" </tr>\n",
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" <tr>\n",
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" <th>132</th>\n",
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" <td>FAIZ Aoufi Youssouf</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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|
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" <td>1.5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>141</th>\n",
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" <td>HAMZA El-Hadji</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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" <td>8.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>150</th>\n",
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" <td>HOUFRANE Soirta</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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" <td>12.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>159</th>\n",
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" <td>IBRAHIM Hindou</td>\n",
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>168</th>\n",
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" <td>IBRAHIM Rouiyati</td>\n",
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|
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" <td>2016-11-14</td>\n",
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" <td>13</td>\n",
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|
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" <td>9.0</td>\n",
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" </tr>\n",
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|
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" <tr>\n",
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|
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" <th>177</th>\n",
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|
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" <td>ISSOUF Toifia</td>\n",
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|
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" <td>2016-11-14</td>\n",
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|
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" <td>13</td>\n",
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|
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" <td>8.5</td>\n",
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|
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" </tr>\n",
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|
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" <tr>\n",
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|
|
" <th>186</th>\n",
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||
|
|
" <td>MOADJO Hachimia</td>\n",
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||
|
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" <td>2016-11-14</td>\n",
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|
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" <td>13</td>\n",
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|
|
" <td>3.0</td>\n",
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|
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" </tr>\n",
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|
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" <tr>\n",
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|
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" <th>195</th>\n",
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||
|
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" <td>MOHAMED Abderemane</td>\n",
|
||
|
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" <td>2016-11-14</td>\n",
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|
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" <td>13</td>\n",
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|
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" <td>7.5</td>\n",
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|
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" </tr>\n",
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|
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" <tr>\n",
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|
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" <th>204</th>\n",
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||
|
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" <td>MOHAMED Nadhir-Eddine</td>\n",
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||
|
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" <td>2016-11-14</td>\n",
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|
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" <td>13</td>\n",
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|
|
" <td>10.5</td>\n",
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|
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" </tr>\n",
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|
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" <tr>\n",
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" <th>213</th>\n",
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|
|
" <td>MOUSSA Faize</td>\n",
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|
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" <td>2016-11-14</td>\n",
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|
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" <td>13</td>\n",
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" <td>0.0</td>\n",
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|
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" </tr>\n",
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|
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" <tr>\n",
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" <th>222</th>\n",
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||
|
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" <td>SAID Fatima</td>\n",
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||
|
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" <td>2016-11-14</td>\n",
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||
|
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" <td>13</td>\n",
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||
|
|
" <td>10.5</td>\n",
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|
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" </tr>\n",
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||
|
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" <tr>\n",
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" <th>231</th>\n",
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||
|
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" <td>SAID Ramiati</td>\n",
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||
|
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" <td>2016-11-14</td>\n",
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||
|
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" <td>13</td>\n",
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||
|
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" <td>0.0</td>\n",
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||
|
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" </tr>\n",
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" <tr>\n",
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||
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" <th>240</th>\n",
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||
|
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" <td>SAÏD Latif</td>\n",
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||
|
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" <td>2016-11-14</td>\n",
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||
|
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" <td>13</td>\n",
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||
|
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" <td>0.0</td>\n",
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||
|
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" </tr>\n",
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" <tr>\n",
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||
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" <th>249</th>\n",
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||
|
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" <td>YOUSSOUF Ouldine</td>\n",
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||
|
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" <td>2016-11-14</td>\n",
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||
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" <td>13</td>\n",
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|
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" <td>10.0</td>\n",
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||
|
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" </tr>\n",
|
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|
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" </tbody>\n",
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||
|
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"</table>\n",
|
||
|
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"</div>"
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||
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],
|
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|
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"text/plain": [
|
||
|
|
" Eleve Date Bareme Mark\n",
|
||
|
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"6 ABDALLAH Elza 2016-11-14 13 11.5\n",
|
||
|
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"15 ABDALLAH Nourayina 2016-11-14 13 10.5\n",
|
||
|
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"24 ABDALLAH Roukia 2016-11-14 13 4.5\n",
|
||
|
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"33 AHAMADI Laila 2016-11-14 13 11.0\n",
|
||
|
|
"42 AHAMADI Satti 2016-11-14 13 6.5\n",
|
||
|
|
"51 AHAMED El-Fahad 2016-11-14 13 0.0\n",
|
||
|
|
"60 AHMED Nachmie 2016-11-14 13 9.5\n",
|
||
|
|
"69 ALI ABDALLAH Raphael 2016-11-14 13 8.0\n",
|
||
|
|
"78 ATTOUMANE Nasrati 2016-11-14 13 9.0\n",
|
||
|
|
"87 ATTOUMANE Nedjima 2016-11-14 13 11.5\n",
|
||
|
|
"96 DARKAOUI Issouf 2016-11-14 13 0.0\n",
|
||
|
|
"105 DHOIMIR Hidayat 2016-11-14 13 3.5\n",
|
||
|
|
"114 DJAMAL Hounaissati 2016-11-14 13 7.0\n",
|
||
|
|
"123 DJANFAR Houmadi 2016-11-14 13 0.0\n",
|
||
|
|
"132 FAIZ Aoufi Youssouf 2016-11-14 13 1.5\n",
|
||
|
|
"141 HAMZA El-Hadji 2016-11-14 13 8.0\n",
|
||
|
|
"150 HOUFRANE Soirta 2016-11-14 13 12.0\n",
|
||
|
|
"159 IBRAHIM Hindou 2016-11-14 13 0.0\n",
|
||
|
|
"168 IBRAHIM Rouiyati 2016-11-14 13 9.0\n",
|
||
|
|
"177 ISSOUF Toifia 2016-11-14 13 8.5\n",
|
||
|
|
"186 MOADJO Hachimia 2016-11-14 13 3.0\n",
|
||
|
|
"195 MOHAMED Abderemane 2016-11-14 13 7.5\n",
|
||
|
|
"204 MOHAMED Nadhir-Eddine 2016-11-14 13 10.5\n",
|
||
|
|
"213 MOUSSA Faize 2016-11-14 13 0.0\n",
|
||
|
|
"222 SAID Fatima 2016-11-14 13 10.5\n",
|
||
|
|
"231 SAID Ramiati 2016-11-14 13 0.0\n",
|
||
|
|
"240 SAÏD Latif 2016-11-14 13 0.0\n",
|
||
|
|
"249 YOUSSOUF Ouldine 2016-11-14 13 10.0"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"eval_pov[eval_pov[\"Nom\"] == \"DM2\"][[\"Eleve\",\"Date\",\"Bareme\", \"Mark\"]]\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"------"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 0,
|
||
|
|
"height": 3,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 0,
|
||
|
|
"width": 12
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"# Conn\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"hidden": true
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"quest_conn = quest_pov[quest_pov[\"Nom\"].str.contains(\"Conn\")]\n",
|
||
|
|
"exo_conn = exo_pov[exo_pov[\"Nom\"].str.contains(\"Conn\")]\n",
|
||
|
|
"eval_conn = eval_pov[eval_pov[\"Nom\"].str.contains(\"Conn\")]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"hidden": true
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/home/lafrite/.virtualenvs/enseignement/lib/python3.5/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
|
||
|
|
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||
|
|
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||
|
|
"\n",
|
||
|
|
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
|
||
|
|
" from ipykernel import kernelapp as app\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#eval_conn[\"Normalized\"] = eval_conn[\"Mark\"] / eval_conn[\"Bareme\"]\n",
|
||
|
|
"eval_conn[\"NonZero\"] = (eval_conn[\"Mark\"] != 0).astype(int)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"hidden": true
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"conn_sum = eval_conn.groupby(\"Eleve\").sum()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"hidden": true
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"conn_sum[\"Final\"] = (conn_sum[\"Normalized\"] / conn_sum[\"NonZero\"] * 20).apply(round_half_point)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 0,
|
||
|
|
"height": 4,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 3,
|
||
|
|
"width": 4
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Analyse de notes finales des devoirs de connaissances"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 0,
|
||
|
|
"height": 7,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 7,
|
||
|
|
"width": 4
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"count 26.000000\n",
|
||
|
|
"mean 11.615385\n",
|
||
|
|
"std 3.832252\n",
|
||
|
|
"min 2.500000\n",
|
||
|
|
"25% 9.375000\n",
|
||
|
|
"50% 12.250000\n",
|
||
|
|
"75% 13.875000\n",
|
||
|
|
"max 18.000000\n",
|
||
|
|
"Name: Final, dtype: float64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"conn_sum[\"Final\"].describe()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 0,
|
||
|
|
"height": 11,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 14,
|
||
|
|
"width": 4
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f7866760748>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f7866760c50>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"conn_sum[\"Final\"].hist(bins = 40, range = (0,20))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 4,
|
||
|
|
"height": 2,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 3,
|
||
|
|
"width": 8
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Analyse de l'évolution sur le temps pour chaque élève"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#exo_conn[\"Normalized\"] = exo_conn[\"Mark\"] / exo_conn[\"Bareme\"]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
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|
"jupyter_dashboards": {
|
||
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"version": 1,
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||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"hidden": true
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f786641df60>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAp8AAALXCAYAAADL4ysJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ne2FlZQVdXd0O24+MjMT
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f786644de80>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pd.pivot_table(exo_conn,\n",
|
||
|
|
" index = [\"Date\"],\n",
|
||
|
|
" values = [\"Normalized\"],\n",
|
||
|
|
" columns = [\"Eleve\"],\n",
|
||
|
|
" ).plot()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
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|
|
"version": 1,
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|
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"views": {
|
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|
|
"grid_default": {
|
||
|
|
"col": 0,
|
||
|
|
"height": 3,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 25,
|
||
|
|
"width": 11
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"------------\n",
|
||
|
|
"## DM1"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 0,
|
||
|
|
"height": 3,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 28,
|
||
|
|
"width": 11
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"-------------\n",
|
||
|
|
"## DS1"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"hidden": true
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"-------------------\n",
|
||
|
|
"## DS2"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"quest_DS2, exo_DS2, eval_DS2 = tools.select_eval(quest_pov, exo_pov, eval_pov, \"DS2\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"abs_pres = tools.get_present_absent(eval_DS2)\n",
|
||
|
|
"absents = abs_pres[\"absents\"]\n",
|
||
|
|
"presents = abs_pres[\"presents\"]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Absents"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"IBRAHIM Hindou\n",
|
||
|
|
"MOUSSA Faize\n",
|
||
|
|
"SAID Ramiati\n",
|
||
|
|
"SAÏD Latif\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for a in absents:\n",
|
||
|
|
" print(a)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# On enlève les absents\n",
|
||
|
|
"quest_DS2, exo_DS2, eval_DS2 = tools.keep_only_presents(quest_DS2, exo_DS2, eval_DS2, presents)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 4,
|
||
|
|
"height": 4,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 5,
|
||
|
|
"width": 4
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"### Vision globale"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 8,
|
||
|
|
"height": 7,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 31,
|
||
|
|
"width": 4
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"count 24.000000\n",
|
||
|
|
"mean 11.083333\n",
|
||
|
|
"std 3.052678\n",
|
||
|
|
"min 2.500000\n",
|
||
|
|
"25% 9.500000\n",
|
||
|
|
"50% 11.250000\n",
|
||
|
|
"75% 13.125000\n",
|
||
|
|
"max 16.000000\n",
|
||
|
|
"Name: Mark, dtype: float64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"eval_DS2[\"Mark\"].describe()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 4,
|
||
|
|
"height": 11,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 31,
|
||
|
|
"width": 4
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f78662b0208>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABMYAAAG2CAYAAACUF4qqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2bVr16bEGhsbrSS7d+/
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f78663d6c88>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"eval_DS2[\"Mark\"].hist(bins = 20, range = (0,20), figsize = (15,5))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 8,
|
||
|
|
"height": 4,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 5,
|
||
|
|
"width": 4
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"### Diagramme en boite par exercice"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 20,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 4,
|
||
|
|
"height": 11,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 9,
|
||
|
|
"width": 4
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f786477d198>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 20,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAq8AAAHqCAYAAAAqHKffAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfZKmZ3iVFo5EPtkK8lj
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f7864759630>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pd.pivot_table(exo_DS2,\n",
|
||
|
|
" index = [\"Eleve\"],\n",
|
||
|
|
" values = [\"Normalized\"],\n",
|
||
|
|
" columns = [\"Exercice\"]\n",
|
||
|
|
" ).plot.box(title=\"Diagramme boite par exercice\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 8,
|
||
|
|
"height": 4,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 9,
|
||
|
|
"width": 4
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"### Questions mieux/moins réussites"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"hidden": true
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def question_uniq_formater(row):\n",
|
||
|
|
" ans = \"\"\n",
|
||
|
|
" try:\n",
|
||
|
|
" int(row['Exercice'])\n",
|
||
|
|
" except ValueError:\n",
|
||
|
|
" ans += str(row[\"Exercice\"])\n",
|
||
|
|
" else:\n",
|
||
|
|
" ans += \"Ex\"+str(row[\"Exercice\"])\n",
|
||
|
|
" \n",
|
||
|
|
" try:\n",
|
||
|
|
" int(row[\"Question\"])\n",
|
||
|
|
" except ValueError:\n",
|
||
|
|
" if not pd.isnull(row[\"Question\"]):\n",
|
||
|
|
" ans += str(row[\"Question\"])\n",
|
||
|
|
" else:\n",
|
||
|
|
" ans += \"Qu\"+str(row[\"Question\"])\n",
|
||
|
|
" \n",
|
||
|
|
" if not pd.isnull(row[\"Commentaire\"]):\n",
|
||
|
|
" ans += \" ({})\".format(row[\"Commentaire\"])\n",
|
||
|
|
" return ans\n",
|
||
|
|
" "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"hidden": true
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#quest_DS2[\"Uniq_quest\"] = quest_DS2.apply(question_uniq_formater, axis=1)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"hidden": true
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#quest_DS2[\"Normalized\"] = quest_DS2[\"Mark\"] / quest_DS2[\"Bareme\"]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"hidden": true
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"quest = quest_DS2.groupby([\"Uniq_quest\"]).mean()[\"Normalized\"].copy()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"hidden": true
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/home/lafrite/.virtualenvs/enseignement/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: sort is deprecated, use sort_values(inplace=True) for for INPLACE sorting\n",
|
||
|
|
" if __name__ == '__main__':\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"quest.sort()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 8,
|
||
|
|
"height": 9,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 13,
|
||
|
|
"width": 4
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Uniq_quest\n",
|
||
|
|
"Exo3 Qu3 (Réduire l'expression) 0.027778\n",
|
||
|
|
"Exo3 Qu2 (Créer des formules) 0.125000\n",
|
||
|
|
"Exo1 Qu3 (Problème Fraction) 0.236111\n",
|
||
|
|
"Exo1 Qu1 (Création Fractions) 0.388889\n",
|
||
|
|
"Exo2 (Calculs) 0.583333\n",
|
||
|
|
"Exo2 (Communication) 0.597222\n",
|
||
|
|
"Exo2 (Connaissance de Thalès) 0.611111\n",
|
||
|
|
"Exo1 Qu2 (Calculs Fractions) 0.750000\n",
|
||
|
|
"Exo3 Qu1 (Remplir le tableau) 0.750000\n",
|
||
|
|
"Presentation 0.802083\n",
|
||
|
|
"Name: Normalized, dtype: float64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"quest"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 0,
|
||
|
|
"height": 9,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 48,
|
||
|
|
"width": 12
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"#### Exo 1\n",
|
||
|
|
"Le calcul avec les fractions semble ok. \n",
|
||
|
|
"Par contre le problème avec les fractions n'est pas du tout réussi. \n",
|
||
|
|
"#### Exo 2\n",
|
||
|
|
"Plutôt décevant. Aucune des 3 choses attendues ne ressort comme plus problématique que les autres.\n",
|
||
|
|
"#### Exo 3\n",
|
||
|
|
"Comme prévu, les questions sur les conjectures est très peu réussi.\n",
|
||
|
|
"Par contre le tableau est relativement bien rempli."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"col": 4,
|
||
|
|
"height": 4,
|
||
|
|
"hidden": false,
|
||
|
|
"row": 20,
|
||
|
|
"width": 4
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"### Élèves à surveiller par exercice"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {
|
||
|
|
"hidden": true
|
||
|
|
},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"inquietant = exo_DS2[exo_DS2[\"Normalized\"] < 0.4]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>Bareme</th>\n",
|
||
|
|
" <th>Mark</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=\"4\" valign=\"top\">1</th>\n",
|
||
|
|
" <th>ABDALLAH Roukia</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>2.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>AHAMADI Satti</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>1.5</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>DJAMAL Hounaissati</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>2.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>DJANFAR Houmadi</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>1.5</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th rowspan=\"7\" valign=\"top\">2</th>\n",
|
||
|
|
" <th>ABDALLAH Nourayina</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>2.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>AHAMED El-Fahad</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>0.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>AHMED Nachmie</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>1.5</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>DHOIMIR Hidayat</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>1.5</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>DJANFAR Houmadi</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>0.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>FAIZ Aoufi Youssouf</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>2.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>IBRAHIM Rouiyati</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>2.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th rowspan=\"10\" valign=\"top\">3</th>\n",
|
||
|
|
" <th>ABDALLAH Nourayina</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>2.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>ABDALLAH Roukia</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>2.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>AHAMADI Satti</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>2.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>DARKAOUI Issouf</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>1.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>DHOIMIR Hidayat</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>2.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>DJAMAL Hounaissati</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>1.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>DJANFAR Houmadi</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>1.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>FAIZ Aoufi Youssouf</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>2.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>MOADJO Hachimia</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>2.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>MOHAMED Nadhir-Eddine</th>\n",
|
||
|
|
" <td>6</td>\n",
|
||
|
|
" <td>1.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th rowspan=\"3\" valign=\"top\">Presentation</th>\n",
|
||
|
|
" <th>DJANFAR Houmadi</th>\n",
|
||
|
|
" <td>2</td>\n",
|
||
|
|
" <td>0.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>MOHAMED Abderemane</th>\n",
|
||
|
|
" <td>2</td>\n",
|
||
|
|
" <td>0.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>SAID Fatima</th>\n",
|
||
|
|
" <td>2</td>\n",
|
||
|
|
" <td>0.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" Bareme Mark\n",
|
||
|
|
"Exercice Eleve \n",
|
||
|
|
"1 ABDALLAH Roukia 6 2.0\n",
|
||
|
|
" AHAMADI Satti 6 1.5\n",
|
||
|
|
" DJAMAL Hounaissati 6 2.0\n",
|
||
|
|
" DJANFAR Houmadi 6 1.5\n",
|
||
|
|
"2 ABDALLAH Nourayina 6 2.0\n",
|
||
|
|
" AHAMED El-Fahad 6 0.0\n",
|
||
|
|
" AHMED Nachmie 6 1.5\n",
|
||
|
|
" DHOIMIR Hidayat 6 1.5\n",
|
||
|
|
" DJANFAR Houmadi 6 0.0\n",
|
||
|
|
" FAIZ Aoufi Youssouf 6 2.0\n",
|
||
|
|
" IBRAHIM Rouiyati 6 2.0\n",
|
||
|
|
"3 ABDALLAH Nourayina 6 2.0\n",
|
||
|
|
" ABDALLAH Roukia 6 2.0\n",
|
||
|
|
" AHAMADI Satti 6 2.0\n",
|
||
|
|
" DARKAOUI Issouf 6 1.0\n",
|
||
|
|
" DHOIMIR Hidayat 6 2.0\n",
|
||
|
|
" DJAMAL Hounaissati 6 1.0\n",
|
||
|
|
" DJANFAR Houmadi 6 1.0\n",
|
||
|
|
" FAIZ Aoufi Youssouf 6 2.0\n",
|
||
|
|
" MOADJO Hachimia 6 2.0\n",
|
||
|
|
" MOHAMED Nadhir-Eddine 6 1.0\n",
|
||
|
|
"Presentation DJANFAR Houmadi 2 0.0\n",
|
||
|
|
" MOHAMED Abderemane 2 0.0\n",
|
||
|
|
" SAID Fatima 2 0.0"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pd.pivot_table(inquietant,\n",
|
||
|
|
" index = [\"Exercice\", \"Eleve\"],\n",
|
||
|
|
" values = [\"Mark\", \"Bareme\"])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"-------\n",
|
||
|
|
"## DM2"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"quest_DM2, exo_DM2, eval_DM2 = tools.select_eval(quest_pov, exo_pov, eval_pov, \"DM2\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 30,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"abs_pres = tools.get_present_absent(eval_DM2)\n",
|
||
|
|
"absents = abs_pres[\"absents\"]\n",
|
||
|
|
"presents = abs_pres[\"presents\"]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 31,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Non rendus\n",
|
||
|
|
"AHAMED El-Fahad\n",
|
||
|
|
"DARKAOUI Issouf\n",
|
||
|
|
"DJANFAR Houmadi\n",
|
||
|
|
"IBRAHIM Hindou\n",
|
||
|
|
"MOUSSA Faize\n",
|
||
|
|
"SAID Ramiati\n",
|
||
|
|
"SAÏD Latif\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(\"Non rendus\")\n",
|
||
|
|
"for e in absents:\n",
|
||
|
|
" print(e)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": true
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# On enlève les absents\n",
|
||
|
|
"quest_DM2, exo_DM2, eval_DM2 = tools.keep_only_presents(quest_DM2, exo_DM2, eval_DM2, presents)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"### Vision globale"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 33,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Devoir sur 13.0\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(\"Devoir sur {}\".format(eval_DM2[\"Bareme\"].iloc[0]))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 34,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"count 21.000000\n",
|
||
|
|
"mean 8.238095\n",
|
||
|
|
"std 2.990063\n",
|
||
|
|
"min 1.500000\n",
|
||
|
|
"25% 7.000000\n",
|
||
|
|
"50% 9.000000\n",
|
||
|
|
"75% 10.500000\n",
|
||
|
|
"max 12.000000\n",
|
||
|
|
"Name: Mark, dtype: float64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 34,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"eval_DM2[\"Mark\"].describe()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 35,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f7864760a20>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 35,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f78661f4be0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"eval_DM2[\"Mark\"].hist(bins = 26, range = (0,13), figsize = (15,5))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"### Diagramme par exercice"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 36,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/home/lafrite/.virtualenvs/enseignement/lib/python3.5/site-packages/numpy/lib/function_base.py:3403: RuntimeWarning: Invalid value encountered in median\n",
|
||
|
|
" RuntimeWarning)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7f786468ac88>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 36,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAp8AAAHqCAYAAABC7yXnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncEbKw0F1XZ+03Bk+pd6
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f78645db4a8>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pd.pivot_table(exo_DM2,\n",
|
||
|
|
" index = [\"Eleve\"],\n",
|
||
|
|
" values = [\"Normalized\"],\n",
|
||
|
|
" columns = [\"Exercice\"]\n",
|
||
|
|
" ).plot.box(title=\"Diagramme boite par exercice\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
|
|
"hidden": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"### Questions moins/mieux réussites"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true,
|
||
|
|
"extensions": {
|
||
|
|
"jupyter_dashboards": {
|
||
|
|
"version": 1,
|
||
|
|
"views": {
|
||
|
|
"grid_default": {},
|
||
|
|
"report_default": {
|
||
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"outputs": [],
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"source": [
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||
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"quest = quest_DS2.groupby([\"Uniq_quest\"]).mean()[\"Normalized\"].copy()"
|
||
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]
|
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},
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{
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"/home/lafrite/.virtualenvs/enseignement/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: sort is deprecated, use sort_values(inplace=True) for for INPLACE sorting\n",
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" if __name__ == '__main__':\n"
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{
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||
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"data": {
|
||
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|
"text/plain": [
|
||
|
|
"Uniq_quest\n",
|
||
|
|
"Exo3 Qu3 (Réduire l'expression) 0.027778\n",
|
||
|
|
"Exo3 Qu2 (Créer des formules) 0.125000\n",
|
||
|
|
"Exo1 Qu3 (Problème Fraction) 0.236111\n",
|
||
|
|
"Exo1 Qu1 (Création Fractions) 0.388889\n",
|
||
|
|
"Exo2 (Calculs) 0.583333\n",
|
||
|
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"Exo2 (Communication) 0.597222\n",
|
||
|
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"Exo2 (Connaissance de Thalès) 0.611111\n",
|
||
|
|
"Exo1 Qu2 (Calculs Fractions) 0.750000\n",
|
||
|
|
"Exo3 Qu1 (Remplir le tableau) 0.750000\n",
|
||
|
|
"Presentation 0.802083\n",
|
||
|
|
"Name: Normalized, dtype: float64"
|
||
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|
]
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||
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},
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"execution_count": 39,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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||
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"quest"
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||
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]
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},
|
||
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{
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"cell_type": "markdown",
|
||
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"metadata": {
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"extensions": {
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}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"#### Exo 1 (calculs avec des fractions)\n",
|
||
|
|
"C'est convenable même si l'addition de fraction est à retravailler.\n",
|
||
|
|
"\n",
|
||
|
|
"#### Exo 2 (exercice technique avec Thalès)\n",
|
||
|
|
"La rédaction commence à rentrer mais le tableur n'est pas assez souvent bien fait.\n",
|
||
|
|
"\n",
|
||
|
|
"#### Exo 3 (Probabilité)\n",
|
||
|
|
"Trop de raté quand il s'agit de calculer un probabilité."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
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|
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"cell_type": "markdown",
|
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"metadata": {
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}
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}
|
||
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|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"### ÉLèves à surveiller"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 40,
|
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"metadata": {
|
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|
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"collapsed": true,
|
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}
|
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|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"inquietant = exo_DM2[exo_DM2[\"Normalized\"] < 0.4]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
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|
|
"cell_type": "code",
|
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|
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"execution_count": 41,
|
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|
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|
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}
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}
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}
|
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|
|
}
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||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>Bareme</th>\n",
|
||
|
|
" <th>Mark</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>1</th>\n",
|
||
|
|
" <th>FAIZ Aoufi Youssouf</th>\n",
|
||
|
|
" <td>4.5</td>\n",
|
||
|
|
" <td>1.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th rowspan=\"4\" valign=\"top\">2</th>\n",
|
||
|
|
" <th>DHOIMIR Hidayat</th>\n",
|
||
|
|
" <td>4.5</td>\n",
|
||
|
|
" <td>0.5</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>FAIZ Aoufi Youssouf</th>\n",
|
||
|
|
" <td>4.5</td>\n",
|
||
|
|
" <td>0.5</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>MOADJO Hachimia</th>\n",
|
||
|
|
" <td>4.5</td>\n",
|
||
|
|
" <td>0.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>MOHAMED Abderemane</th>\n",
|
||
|
|
" <td>4.5</td>\n",
|
||
|
|
" <td>0.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th rowspan=\"8\" valign=\"top\">3</th>\n",
|
||
|
|
" <th>ABDALLAH Roukia</th>\n",
|
||
|
|
" <td>4.0</td>\n",
|
||
|
|
" <td>0.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>AHAMADI Satti</th>\n",
|
||
|
|
" <td>4.0</td>\n",
|
||
|
|
" <td>1.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>ALI ABDALLAH Raphael</th>\n",
|
||
|
|
" <td>4.0</td>\n",
|
||
|
|
" <td>1.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>DHOIMIR Hidayat</th>\n",
|
||
|
|
" <td>4.0</td>\n",
|
||
|
|
" <td>0.5</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>FAIZ Aoufi Youssouf</th>\n",
|
||
|
|
" <td>4.0</td>\n",
|
||
|
|
" <td>0.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>HAMZA El-Hadji</th>\n",
|
||
|
|
" <td>4.0</td>\n",
|
||
|
|
" <td>1.5</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>MOADJO Hachimia</th>\n",
|
||
|
|
" <td>4.0</td>\n",
|
||
|
|
" <td>0.0</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>MOHAMED Nadhir-Eddine</th>\n",
|
||
|
|
" <td>4.0</td>\n",
|
||
|
|
" <td>1.5</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" Bareme Mark\n",
|
||
|
|
"Exercice Eleve \n",
|
||
|
|
"1 FAIZ Aoufi Youssouf 4.5 1.0\n",
|
||
|
|
"2 DHOIMIR Hidayat 4.5 0.5\n",
|
||
|
|
" FAIZ Aoufi Youssouf 4.5 0.5\n",
|
||
|
|
" MOADJO Hachimia 4.5 0.0\n",
|
||
|
|
" MOHAMED Abderemane 4.5 0.0\n",
|
||
|
|
"3 ABDALLAH Roukia 4.0 0.0\n",
|
||
|
|
" AHAMADI Satti 4.0 1.0\n",
|
||
|
|
" ALI ABDALLAH Raphael 4.0 1.0\n",
|
||
|
|
" DHOIMIR Hidayat 4.0 0.5\n",
|
||
|
|
" FAIZ Aoufi Youssouf 4.0 0.0\n",
|
||
|
|
" HAMZA El-Hadji 4.0 1.5\n",
|
||
|
|
" MOADJO Hachimia 4.0 0.0\n",
|
||
|
|
" MOHAMED Nadhir-Eddine 4.0 1.5"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 41,
|
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|
|
"metadata": {},
|
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|
|
"output_type": "execute_result"
|
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|
|
}
|
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|
|
],
|
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|
|
"source": [
|
||
|
|
"pd.pivot_table(inquietant,\n",
|
||
|
|
" index = [\"Exercice\", \"Eleve\"],\n",
|
||
|
|
" values = [\"Mark\", \"Bareme\"])"
|
||
|
|
]
|
||
|
|
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|
||
|
|
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|
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|
|
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|
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|
|
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