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import work from year 2015-2016
2017-06-16 06:48:54 +00:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from opytex import texenv\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use(\"seaborn-notebook\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Information sur la classe"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"classe = \"309\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import et premiers traitements"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['notes',\n",
" 'Remarques',\n",
" 'Conn',\n",
" 'DM_15_09_18',\n",
" 'DS_15_09_25',\n",
" 'pyramide',\n",
" 'BB_15_10_31',\n",
" 'DS_15_11_27',\n",
" 'DM_15_12_09',\n",
" 'Boulettes',\n",
" 'BB_16_01_23',\n",
" 'DM_16_01_29',\n",
" 'BB_16_02_15',\n",
" 'DM_16_03_30',\n",
" 'BB_16_04_02',\n",
" 'BB_16_04_19',\n",
" 'DM_16_05_18',\n",
" 'BB_16_06_03']"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_notes = pd.ExcelFile(classe+\".xlsx\")\n",
"all_notes.sheet_names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1er trimestre"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ds_name = 'notes'\n",
"notes = all_notes.parse(ds_name)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>DM_15_09_18</th>\n",
" <th>DS_15_09_25</th>\n",
" <th>BB_15_10_31</th>\n",
" <th>Conn trimestre 1</th>\n",
" <th>Pyramide</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ABDOU Farida</th>\n",
" <td>4.5</td>\n",
" <td>9.0</td>\n",
" <td>10.5</td>\n",
" <td>7.5</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABOU BACAR Djaha</th>\n",
" <td>10.0</td>\n",
" <td>16.5</td>\n",
" <td>15.5</td>\n",
" <td>16.5</td>\n",
" <td>17.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADA Nabaouya</th>\n",
" <td>10.0</td>\n",
" <td>9.0</td>\n",
" <td>13.0</td>\n",
" <td>14.5</td>\n",
" <td>17.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADI Faina</th>\n",
" <td>4.0</td>\n",
" <td>4.5</td>\n",
" <td>5.5</td>\n",
" <td>4.0</td>\n",
" <td>18.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALI Mardhuia</th>\n",
" <td>8.5</td>\n",
" <td>11.5</td>\n",
" <td>11.0</td>\n",
" <td>15.0</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DM_15_09_18 DS_15_09_25 BB_15_10_31 Conn trimestre 1 \\\n",
"ABDOU Farida 4.5 9.0 10.5 7.5 \n",
"ABOU BACAR Djaha 10.0 16.5 15.5 16.5 \n",
"AHAMADA Nabaouya 10.0 9.0 13.0 14.5 \n",
"AHAMADI Faina 4.0 4.5 5.5 4.0 \n",
"ALI Mardhuia 8.5 11.5 11.0 15.0 \n",
"\n",
" Pyramide \n",
"ABDOU Farida 0.0 \n",
"ABOU BACAR Djaha 17.5 \n",
"AHAMADA Nabaouya 17.5 \n",
"AHAMADI Faina 18.0 \n",
"ALI Mardhuia 16.0 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trim1 = notes[1:6].T\n",
"baremT1 = trim1.iloc[0]\n",
"eleveT1 = trim1.iloc[1:31]\n",
"eleveT1.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Par élève"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from ipywidgets import interact, interactive"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Normalisation des notes de chaque exo\n",
"eleveT1_norm = eleveT1 / baremT1.values"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f042c143710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def f(x):\n",
" #notes_exo_norm\n",
" ax = eleveT1_norm.T.plot(color = \"gray\", legend = False, figsize = (16, 7))\n",
" d_norm = eleveT1_norm.describe()\n",
" d_norm.T[[\"min\", \"25%\", \"50%\", \"75%\", \"max\"]].plot(ax=ax, kind=\"area\", stacked = False, alpha=.1)\n",
" eleveT1_norm.loc[x].plot(ax=ax, color=\"red\", alpha = 1)\n",
"\n",
" \n",
"interact(f, x = list(eleveT2_norm.index))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2e trimestre"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Connaissances pour le 2e trimestre"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds_name = \"Conn\"\n",
"notes = all_notes.parse(ds_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes.index"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"conn_2nd_Tri = ['Conn_15_11_18', 'Conn_15_12_08', 'Conn_16_01_20',\n",
" 'Conn_16_02_03', 'Conn_16_02_10']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes_conn_2T = notes.T[conn_2nd_Tri]\n",
"notes_conn_2T.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"barem = notes_conn_2T[:1]\n",
"notes = notes_conn_2T[1:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes.head()\n",
"#barem"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes = notes.astype(float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Traitement des notes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"notes.T.index"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"len(notes.T.index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Un peu de statistiques"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>BAREME</th>\n",
" <th>ABDOU Farida</th>\n",
" <th>ABOU BACAR Djaha</th>\n",
" <th>AHAMADA Nabaouya</th>\n",
" <th>AHAMADI Faina</th>\n",
" <th>ALI Mardhuia</th>\n",
" <th>ALI SOULAIMANA Chamsia</th>\n",
" <th>ALSENE ALI MADI Stela</th>\n",
" <th>ANDRIATAHIANA Hoby</th>\n",
" <th>ANLI Emeline</th>\n",
" <th>...</th>\n",
" <th>SOILIHI Soifia</th>\n",
" <th>SOUFIANI Laila</th>\n",
" <th>YOUSSOUF Sitirati</th>\n",
" <th>Unnamed: 31</th>\n",
" <th>moyenne</th>\n",
" <th>min</th>\n",
" <th>q1</th>\n",
" <th>Me</th>\n",
" <th>Q3</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>12.000000</td>\n",
" <td>12.000000</td>\n",
" <td>12.00000</td>\n",
" <td>12.000000</td>\n",
" <td>12.000000</td>\n",
" <td>12.000000</td>\n",
" <td>12.000000</td>\n",
" <td>12.000000</td>\n",
" <td>12.000000</td>\n",
" <td>12.000000</td>\n",
" <td>...</td>\n",
" <td>12.000000</td>\n",
" <td>12.000000</td>\n",
" <td>12.000000</td>\n",
" <td>0</td>\n",
" <td>11.000000</td>\n",
" <td>11.000000</td>\n",
" <td>11.000000</td>\n",
" <td>11.000000</td>\n",
" <td>11.000000</td>\n",
" <td>11.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>22.583333</td>\n",
" <td>10.583333</td>\n",
" <td>15.37500</td>\n",
" <td>13.791667</td>\n",
" <td>8.125000</td>\n",
" <td>14.916667</td>\n",
" <td>15.208333</td>\n",
" <td>10.125000</td>\n",
" <td>14.166667</td>\n",
" <td>8.125000</td>\n",
" <td>...</td>\n",
" <td>12.416667</td>\n",
" <td>5.791667</td>\n",
" <td>8.750000</td>\n",
" <td>NaN</td>\n",
" <td>12.530303</td>\n",
" <td>5.090909</td>\n",
" <td>9.363636</td>\n",
" <td>12.613636</td>\n",
" <td>15.784091</td>\n",
" <td>19.681818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>8.638796</td>\n",
" <td>5.546634</td>\n",
" <td>4.00071</td>\n",
" <td>3.933876</td>\n",
" <td>4.894919</td>\n",
" <td>4.425306</td>\n",
" <td>3.887382</td>\n",
" <td>4.871648</td>\n",
" <td>4.662877</td>\n",
" <td>4.205651</td>\n",
" <td>...</td>\n",
" <td>4.331876</td>\n",
" <td>4.125631</td>\n",
" <td>3.756812</td>\n",
" <td>NaN</td>\n",
" <td>3.542063</td>\n",
" <td>2.861976</td>\n",
" <td>3.142180</td>\n",
" <td>3.846043</td>\n",
" <td>4.025734</td>\n",
" <td>5.870574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>10.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.00000</td>\n",
" <td>9.000000</td>\n",
" <td>4.000000</td>\n",
" <td>8.500000</td>\n",
" <td>8.000000</td>\n",
" <td>4.000000</td>\n",
" <td>7.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>6.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.500000</td>\n",
" <td>NaN</td>\n",
" <td>7.033333</td>\n",
" <td>2.500000</td>\n",
" <td>4.625000</td>\n",
" <td>6.750000</td>\n",
" <td>8.875000</td>\n",
" <td>10.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>20.000000</td>\n",
" <td>8.250000</td>\n",
" <td>14.37500</td>\n",
" <td>10.500000</td>\n",
" <td>4.500000</td>\n",
" <td>11.375000</td>\n",
" <td>14.000000</td>\n",
" <td>7.375000</td>\n",
" <td>10.500000</td>\n",
" <td>6.000000</td>\n",
" <td>...</td>\n",
" <td>9.750000</td>\n",
" <td>3.750000</td>\n",
" <td>6.500000</td>\n",
" <td>NaN</td>\n",
" <td>10.016667</td>\n",
" <td>3.500000</td>\n",
" <td>7.687500</td>\n",
" <td>9.875000</td>\n",
" <td>13.187500</td>\n",
" <td>17.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>20.000000</td>\n",
" <td>11.000000</td>\n",
" <td>16.00000</td>\n",
" <td>13.750000</td>\n",
" <td>5.500000</td>\n",
" <td>15.250000</td>\n",
" <td>15.500000</td>\n",
" <td>10.250000</td>\n",
" <td>13.750000</td>\n",
" <td>8.750000</td>\n",
" <td>...</td>\n",
" <td>12.000000</td>\n",
" <td>5.000000</td>\n",
" <td>7.750000</td>\n",
" <td>NaN</td>\n",
" <td>12.133333</td>\n",
" <td>4.500000</td>\n",
" <td>9.250000</td>\n",
" <td>12.500000</td>\n",
" <td>16.000000</td>\n",
" <td>19.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>20.250000</td>\n",
" <td>12.375000</td>\n",
" <td>16.75000</td>\n",
" <td>15.000000</td>\n",
" <td>10.375000</td>\n",
" <td>17.125000</td>\n",
" <td>16.375000</td>\n",
" <td>11.875000</td>\n",
" <td>16.875000</td>\n",
" <td>10.000000</td>\n",
" <td>...</td>\n",
" <td>14.750000</td>\n",
" <td>6.625000</td>\n",
" <td>10.875000</td>\n",
" <td>NaN</td>\n",
" <td>14.583333</td>\n",
" <td>4.750000</td>\n",
" <td>10.875000</td>\n",
" <td>14.875000</td>\n",
" <td>17.312500</td>\n",
" <td>19.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>40.000000</td>\n",
" <td>22.000000</td>\n",
" <td>22.00000</td>\n",
" <td>23.000000</td>\n",
" <td>18.000000</td>\n",
" <td>23.000000</td>\n",
" <td>24.000000</td>\n",
" <td>19.500000</td>\n",
" <td>22.500000</td>\n",
" <td>16.000000</td>\n",
" <td>...</td>\n",
" <td>20.500000</td>\n",
" <td>16.000000</td>\n",
" <td>17.500000</td>\n",
" <td>NaN</td>\n",
" <td>19.300000</td>\n",
" <td>12.000000</td>\n",
" <td>14.875000</td>\n",
" <td>20.250000</td>\n",
" <td>22.875000</td>\n",
" <td>31.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" BAREME ABDOU Farida ABOU BACAR Djaha AHAMADA Nabaouya \\\n",
"count 12.000000 12.000000 12.00000 12.000000 \n",
"mean 22.583333 10.583333 15.37500 13.791667 \n",
"std 8.638796 5.546634 4.00071 3.933876 \n",
"min 10.000000 0.000000 7.00000 9.000000 \n",
"25% 20.000000 8.250000 14.37500 10.500000 \n",
"50% 20.000000 11.000000 16.00000 13.750000 \n",
"75% 20.250000 12.375000 16.75000 15.000000 \n",
"max 40.000000 22.000000 22.00000 23.000000 \n",
"\n",
" AHAMADI Faina ALI Mardhuia ALI SOULAIMANA Chamsia \\\n",
"count 12.000000 12.000000 12.000000 \n",
"mean 8.125000 14.916667 15.208333 \n",
"std 4.894919 4.425306 3.887382 \n",
"min 4.000000 8.500000 8.000000 \n",
"25% 4.500000 11.375000 14.000000 \n",
"50% 5.500000 15.250000 15.500000 \n",
"75% 10.375000 17.125000 16.375000 \n",
"max 18.000000 23.000000 24.000000 \n",
"\n",
" ALSENE ALI MADI Stela ANDRIATAHIANA Hoby ANLI Emeline ... \\\n",
"count 12.000000 12.000000 12.000000 ... \n",
"mean 10.125000 14.166667 8.125000 ... \n",
"std 4.871648 4.662877 4.205651 ... \n",
"min 4.000000 7.000000 0.000000 ... \n",
"25% 7.375000 10.500000 6.000000 ... \n",
"50% 10.250000 13.750000 8.750000 ... \n",
"75% 11.875000 16.875000 10.000000 ... \n",
"max 19.500000 22.500000 16.000000 ... \n",
"\n",
" SOILIHI Soifia SOUFIANI Laila YOUSSOUF Sitirati Unnamed: 31 \\\n",
"count 12.000000 12.000000 12.000000 0 \n",
"mean 12.416667 5.791667 8.750000 NaN \n",
"std 4.331876 4.125631 3.756812 NaN \n",
"min 6.000000 0.000000 3.500000 NaN \n",
"25% 9.750000 3.750000 6.500000 NaN \n",
"50% 12.000000 5.000000 7.750000 NaN \n",
"75% 14.750000 6.625000 10.875000 NaN \n",
"max 20.500000 16.000000 17.500000 NaN \n",
"\n",
" moyenne min q1 Me Q3 max \n",
"count 11.000000 11.000000 11.000000 11.000000 11.000000 11.000000 \n",
"mean 12.530303 5.090909 9.363636 12.613636 15.784091 19.681818 \n",
"std 3.542063 2.861976 3.142180 3.846043 4.025734 5.870574 \n",
"min 7.033333 2.500000 4.625000 6.750000 8.875000 10.000000 \n",
"25% 10.016667 3.500000 7.687500 9.875000 13.187500 17.500000 \n",
"50% 12.133333 4.500000 9.250000 12.500000 16.000000 19.000000 \n",
"75% 14.583333 4.750000 10.875000 14.875000 17.312500 19.250000 \n",
"max 19.300000 12.000000 14.875000 20.250000 22.875000 31.000000 \n",
"\n",
"[8 rows x 38 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.describe()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'barem' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-10-8fe0fc5f6073>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Normalisation des notes de chaque exo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnotes_exo_norm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnotes\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mbarem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;31m#notes_exo_norm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnotes_exo_norm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"gray\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlegend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m7\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0md_norm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnotes_exo_norm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'barem' is not defined"
]
}
],
"source": [
"# Normalisation des notes de chaque exo\n",
"notes_exo_norm = notes / barem.values[0,:]\n",
"#notes_exo_norm\n",
"ax = notes_exo_norm.T.plot(color = \"gray\", legend = False, figsize = (16, 7))\n",
"d_norm = notes_exo_norm.describe()\n",
"d_norm.T[[\"min\", \"25%\", \"50%\", \"75%\", \"max\"]].plot(ax=ax, kind=\"area\", stacked = False, alpha=.1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0441e3a588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = notes.hist(figsize = (16,8))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def norma_mean(n):\n",
" return (n / barem).sum(axis=1)/len(n.dropna())*20"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "(\"name 'barem' is not defined\", 'occurred at index Trimestre 1')",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-14-050bc6750a18>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnotes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Trim2\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnotes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnorma_mean\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, axis, broadcast, raw, reduce, args, **kwds)\u001b[0m\n\u001b[0;32m 3970\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mreduce\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3971\u001b[0m \u001b[0mreduce\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3972\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreduce\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3973\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3974\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_broadcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.5/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_apply_standard\u001b[1;34m(self, func, axis, ignore_failures, reduce)\u001b[0m\n\u001b[0;32m 4062\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4063\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4064\u001b[1;33m \u001b[0mresults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4065\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4066\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m<ipython-input-12-1d9385d63cf7>\u001b[0m in \u001b[0;36mnorma_mean\u001b[1;34m(n)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mnorma_mean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mbarem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: (\"name 'barem' is not defined\", 'occurred at index Trimestre 1')"
]
}
],
"source": [
"notes[\"Trim2\"] = notes.apply(norma_mean, axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Brevet blanc février"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Exercice 7</th>\n",
" <th>Exercice 4</th>\n",
" <th>Exercice 5</th>\n",
" <th>Exercice 2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>bareme</th>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABDOU Farida</th>\n",
" <td>3.5</td>\n",
" <td>2.5</td>\n",
" <td>2.5</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABOU BACAR Djaha</th>\n",
" <td>3.0</td>\n",
" <td>2.5</td>\n",
" <td>4.5</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADA Nabaouya</th>\n",
" <td>5.5</td>\n",
" <td>2.5</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADI Faina</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Exercice 7 Exercice 4 Exercice 5 Exercice 2\n",
"bareme 7.0 4.0 6.0 4.0\n",
"ABDOU Farida 3.5 2.5 2.5 0.5\n",
"ABOU BACAR Djaha 3.0 2.5 4.5 3.5\n",
"AHAMADA Nabaouya 5.5 2.5 2.0 0.0\n",
"AHAMADI Faina 0.0 0.0 0.0 0.0"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BB_fev = all_notes.parse('BB_16_02_15')\n",
"BB_fev = BB_fev.T[[ 'Exercice 7', 'Exercice 4', 'Exercice 5', 'Exercice 2']]\n",
"BB_fev.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Récupération des élèves n'ayant pas la moyenne trié par priorité"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Exercice 7</th>\n",
" <th>Exercice 4</th>\n",
" <th>Exercice 5</th>\n",
" <th>Exercice 2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AHAMADI Faina</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CHANFI Nadhrati</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SOILIHI Soifia</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SOUFIANI Laila</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YOUSSOUF Sitirati</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OUSSENI Hilma</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALSENE ALI MADI Stela</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANLI Emeline</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HAMZA Samianti</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Nasmi</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SANDA Issoufi</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALI SOULAIMANA Chamsia</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Dhoirfia</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Mouslimati</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LOUTOUFI Nachima</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MALIDE El-Anzize</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALI Mardhuia</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ATHOUMANE Naouidat</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HALIDI Nisma</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Chaharazadi</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SAANLI Natali</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SAID AHAMADA Roukaya</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADA Nabaouya</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MOUSSA Roibouanti</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABDOU Farida</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANDRIATAHIANA Hoby</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bareme</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABOU BACAR Djaha</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BOUDRA Nassifanya</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>COMBO Moinécha</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MONNE Kevin</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Exercice 7 Exercice 4 Exercice 5 Exercice 2\n",
"AHAMADI Faina False False False False\n",
"CHANFI Nadhrati False False False False\n",
"SOILIHI Soifia False False False False\n",
"SOUFIANI Laila False False False False\n",
"YOUSSOUF Sitirati False False True False\n",
"OUSSENI Hilma False False True True\n",
"ALSENE ALI MADI Stela True False False False\n",
"ANLI Emeline True False False False\n",
"HAMZA Samianti True False False False\n",
"HOUMADI Nasmi True False False False\n",
"SANDA Issoufi True False False False\n",
"ALI SOULAIMANA Chamsia True False False True\n",
"HOUMADI Dhoirfia True False False True\n",
"HOUMADI Mouslimati True False True False\n",
"LOUTOUFI Nachima True False True False\n",
"MALIDE El-Anzize True False True False\n",
"ALI Mardhuia True False True True\n",
"ATHOUMANE Naouidat True False True True\n",
"HALIDI Nisma True False True True\n",
"HOUMADI Chaharazadi True False True True\n",
"SAANLI Natali True False True True\n",
"SAID AHAMADA Roukaya True False True True\n",
"AHAMADA Nabaouya True True False False\n",
"MOUSSA Roibouanti True True False True\n",
"ABDOU Farida True True True False\n",
"ANDRIATAHIANA Hoby True True True False\n",
"bareme True True True True\n",
"ABOU BACAR Djaha True True True True\n",
"BOUDRA Nassifanya True True True True\n",
"COMBO Moinécha True True True True\n",
"MONNE Kevin True True True True"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"((BB_fev / BB_fev.T['bareme']) > 0.4).sort(['Exercice 7', 'Exercice 4', 'Exercice 5', 'Exercice 2'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Élèves à soucis dans exo 1 (Calcul littéral)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['ABDOU Farida', 'AHAMADA Nabaouya', 'AHAMADI Faina', 'ALI Mardhuia',\n",
" 'ALI SOULAIMANA Chamsia', 'ALSENE ALI MADI Stela', 'ANLI Emeline',\n",
" 'ATHOUMANE Naouidat', 'CHANFI Nadhrati', 'HAMZA Samianti',\n",
" 'HOUMADI Mouslimati', 'HOUMADI Nasmi', 'MALIDE El-Anzize',\n",
" 'MOUSSA Roibouanti', 'OUSSENI Hilma', 'SANDA Issoufi', 'SOUFIANI Laila',\n",
" 'YOUSSOUF Sitirati'],\n",
" dtype='object')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BB_fev.T[BB_fev.T['Exercice 1']<3].index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Élèves à soucis dans exo 2 (géométrie)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['ABDOU Farida', 'AHAMADA Nabaouya', 'AHAMADI Faina',\n",
" 'ALSENE ALI MADI Stela', 'ANDRIATAHIANA Hoby', 'ANLI Emeline',\n",
" 'CHANFI Nadhrati', 'HAMZA Samianti', 'HOUMADI Mouslimati',\n",
" 'HOUMADI Nasmi', 'LOUTOUFI Nachima', 'MALIDE El-Anzize',\n",
" 'SANDA Issoufi', 'SOILIHI Soifia', 'SOUFIANI Laila',\n",
" 'YOUSSOUF Sitirati'],\n",
" dtype='object')"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BB_fev.T[BB_fev.T['Exercice 2']<2].index"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['AHAMADI Faina', 'ALI Mardhuia', 'ALI SOULAIMANA Chamsia',\n",
" 'ALSENE ALI MADI Stela', 'ANLI Emeline', 'ATHOUMANE Naouidat',\n",
" 'CHANFI Nadhrati', 'HALIDI Nisma', 'HAMZA Samianti',\n",
" 'HOUMADI Mouslimati', 'HOUMADI Chaharazadi', 'HOUMADI Nasmi',\n",
" 'HOUMADI Dhoirfia', 'LOUTOUFI Nachima', 'MALIDE El-Anzize',\n",
" 'OUSSENI Hilma', 'SAANLI Natali', 'SAID AHAMADA Roukaya',\n",
" 'SANDA Issoufi', 'SOILIHI Soifia', 'SOUFIANI Laila',\n",
" 'YOUSSOUF Sitirati'],\n",
" dtype='object')"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BB_fev.T[BB_fev.T['Exercice 4']<1.5].index"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['ABDOU Farida', 'AHAMADA Nabaouya', 'AHAMADI Faina',\n",
" 'ALI SOULAIMANA Chamsia', 'ALSENE ALI MADI Stela', 'ANDRIATAHIANA Hoby',\n",
" 'ANLI Emeline', 'ATHOUMANE Naouidat', 'CHANFI Nadhrati',\n",
" 'HAMZA Samianti', 'HOUMADI Nasmi', 'HOUMADI Dhoirfia',\n",
" 'LOUTOUFI Nachima', 'MALIDE El-Anzize', 'MOUSSA Roibouanti',\n",
" 'OUSSENI Hilma', 'SANDA Issoufi', 'SOILIHI Soifia', 'SOUFIANI Laila',\n",
" 'YOUSSOUF Sitirati'],\n",
" dtype='object')"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BB_fev.T[BB_fev.T['Exercice 5']<3].index"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['ABDOU Farida', 'ABOU BACAR Djaha', 'AHAMADA Nabaouya', 'AHAMADI Faina',\n",
" 'ALI Mardhuia', 'ALI SOULAIMANA Chamsia', 'ALSENE ALI MADI Stela',\n",
" 'ANDRIATAHIANA Hoby', 'ANLI Emeline', 'ATHOUMANE Naouidat',\n",
" 'BOUDRA Nassifanya', 'CHANFI Nadhrati', 'HALIDI Nisma',\n",
" 'HAMZA Samianti', 'HOUMADI Mouslimati', 'HOUMADI Chaharazadi',\n",
" 'HOUMADI Nasmi', 'HOUMADI Dhoirfia', 'LOUTOUFI Nachima',\n",
" 'MALIDE El-Anzize', 'MONNE Kevin', 'MOUSSA Roibouanti', 'OUSSENI Hilma',\n",
" 'SAANLI Natali', 'SAID AHAMADA Roukaya', 'SANDA Issoufi',\n",
" 'SOILIHI Soifia', 'SOUFIANI Laila', 'YOUSSOUF Sitirati'],\n",
" dtype='object')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BB_fev.T[BB_fev.T['Exercice 6']<2].index"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['AHAMADI Faina', 'CHANFI Nadhrati', 'OUSSENI Hilma', 'SOILIHI Soifia',\n",
" 'SOUFIANI Laila', 'YOUSSOUF Sitirati'],\n",
" dtype='object')"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BB_fev.T[BB_fev.T['Exercice 7']<3].index"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"BB_fev.T[]"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Bilan 2e trimestre"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds_name = 'notes'\n",
"notes = all_notes.parse(ds_name)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": 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>DS_15_11_27</th>\n",
" <th>DM_15_12_09</th>\n",
" <th>Boulettes</th>\n",
" <th>BB_16_01_23</th>\n",
" <th>DM_16_01_29</th>\n",
" <th>Brevet blanc Fevrier</th>\n",
" <th>Conn trimestre 2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ABDOU Farida</th>\n",
" <td>8.5</td>\n",
" <td>12.0</td>\n",
" <td>13.5</td>\n",
" <td>22.0</td>\n",
" <td>12.0</td>\n",
" <td>16.0</td>\n",
" <td>11.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABOU BACAR Djaha</th>\n",
" <td>7.0</td>\n",
" <td>14.0</td>\n",
" <td>14.5</td>\n",
" <td>20.0</td>\n",
" <td>16.5</td>\n",
" <td>22.0</td>\n",
" <td>14.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADA Nabaouya</th>\n",
" <td>10.5</td>\n",
" <td>10.5</td>\n",
" <td>14.5</td>\n",
" <td>23.0</td>\n",
" <td>14.5</td>\n",
" <td>16.5</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADI Faina</th>\n",
" <td>4.5</td>\n",
" <td>8.5</td>\n",
" <td>14.5</td>\n",
" <td>9.0</td>\n",
" <td>14.5</td>\n",
" <td>5.0</td>\n",
" <td>5.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALI Mardhuia</th>\n",
" <td>12.5</td>\n",
" <td>10.0</td>\n",
" <td>15.5</td>\n",
" <td>20.5</td>\n",
" <td>19.0</td>\n",
" <td>23.0</td>\n",
" <td>16.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DS_15_11_27 DM_15_12_09 Boulettes BB_16_01_23 \\\n",
"ABDOU Farida 8.5 12.0 13.5 22.0 \n",
"ABOU BACAR Djaha 7.0 14.0 14.5 20.0 \n",
"AHAMADA Nabaouya 10.5 10.5 14.5 23.0 \n",
"AHAMADI Faina 4.5 8.5 14.5 9.0 \n",
"ALI Mardhuia 12.5 10.0 15.5 20.5 \n",
"\n",
" DM_16_01_29 Brevet blanc Fevrier Conn trimestre 2 \n",
"ABDOU Farida 12.0 16.0 11.5 \n",
"ABOU BACAR Djaha 16.5 22.0 14.5 \n",
"AHAMADA Nabaouya 14.5 16.5 12.0 \n",
"AHAMADI Faina 14.5 5.0 5.5 \n",
"ALI Mardhuia 19.0 23.0 16.5 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trim2 = notes[8:].T\n",
"barem = trim2.iloc[0]\n",
"eleveT2 = trim2.iloc[1:31]\n",
"eleveT2.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Par élève"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from ipywidgets import interact, interactive"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Normalisation des notes de chaque exo\n",
"eleveT2_norm = eleveT2 / barem.values"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA9YAAAGrCAYAAAAlyg+EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGBiop3PvkWf3di/2dKb
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0427fcc320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def f(x):\n",
" ax = eleveT2_norm.T.plot(color = \"gray\", legend = False, figsize = (16, 7))\n",
" d_norm = eleveT2_norm.describe()\n",
" d_norm.T[[\"min\", \"25%\", \"50%\", \"75%\", \"max\"]].plot(ax=ax, kind=\"area\", stacked = False, alpha=.1)\n",
" eleveT2_norm.loc[x].plot(ax=ax, color=\"red\", alpha = 1)\n",
"\n",
"\n",
" \n",
"interact(f, x = list(eleveT2_norm.index))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyse par élève "
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from ipywidgets import interact, interactive"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IndexError",
"evalue": "too many indices for array",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-46-fc504a96f3c6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# Normalisation des notes de chaque exo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnotes_exo_norm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnotes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mregex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Conn\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mbarem\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m#notes_exo_norm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnotes_exo_norm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"gray\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlegend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m7\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mIndexError\u001b[0m: too many indices for array"
]
}
],
"source": [
"# Normalisation des notes de chaque exo\n",
"notes_exo_norm = notes.filter(regex=\"Conn\") / barem.values[0,:]\n",
"def f(x):\n",
" #notes_exo_norm\n",
" ax = notes_exo_norm.T.plot(color = \"gray\", legend = False, figsize = (16, 7))\n",
" d_norm = notes_exo_norm.describe()\n",
" d_norm.T[[\"min\", \"25%\", \"50%\", \"75%\", \"max\"]].plot(ax=ax, kind=\"area\", stacked = False, alpha=.1)\n",
" notes_exo_norm.loc[x].plot(ax=ax, color=\"red\", alpha = 1)\n",
"\n",
" \n",
"interact(f, x = list(notes.index))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3e trimestre"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DM_16_03_30"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ds_name = 'DM_16_03_30'\n",
"notes = all_notes.parse(ds_name).T"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'classe': '309', 'date': '30 mars 2016', 'titre': 'Devoir maison 5'}"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"latex_info = {}\n",
"latex_info['titre'] = \"Devoir maison 5\"\n",
"latex_info['classe'] = \"309\"\n",
"latex_info['date'] = \"30 mars 2016\"\n",
"latex_info"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"barem = notes[:1]\n",
"notes = notes[1:]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": 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>DM_16_03_30</th>\n",
" <th>Malus</th>\n",
" <th>Exercice 1</th>\n",
" <th>1.1 Developper</th>\n",
" <th>1.2 Developper</th>\n",
" <th>1.3 Double developpement</th>\n",
" <th>1.4 Developpement carré</th>\n",
" <th>Exercice 2</th>\n",
" <th>2.1 Addition fraction</th>\n",
" <th>2.2 Multiplication fractions</th>\n",
" <th>...</th>\n",
" <th>Equation *</th>\n",
" <th>Equation fraction</th>\n",
" <th>equation simple</th>\n",
" <th>equation complexe</th>\n",
" <th>Exercice 4</th>\n",
" <th>Calcul angle</th>\n",
" <th>Angle alterne</th>\n",
" <th>Calcul longueur</th>\n",
" <th>2 methodes</th>\n",
" <th>Arrondi</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Bareme</th>\n",
" <td>20</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.5</td>\n",
" <td>0.5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1.5</td>\n",
" <td>1</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>0.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" DM_16_03_30 Malus Exercice 1 1.1 Developper 1.2 Developper \\\n",
"Bareme 20 NaN 6 1.5 1.5 \n",
"\n",
" 1.3 Double developpement 1.4 Developpement carré Exercice 2 \\\n",
"Bareme 1.5 1.5 4 \n",
"\n",
" 2.1 Addition fraction 2.2 Multiplication fractions ... \\\n",
"Bareme 1 1 ... \n",
"\n",
" Equation * Equation fraction equation simple equation complexe \\\n",
"Bareme 0.5 0.5 1 1 \n",
"\n",
" Exercice 4 Calcul angle Angle alterne Calcul longueur 2 methodes \\\n",
"Bareme 6 1.5 1 1.5 1.5 \n",
"\n",
" Arrondi \n",
"Bareme 0.5 \n",
"\n",
"[1 rows x 25 columns]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"barem\n",
"#notes.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Suppression des notes inutiles"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes = notes[notes[ds_name].notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes = notes.astype(float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Traitement des notes"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['DM_16_03_30', 'Malus', 'Exercice 1', '1.1 Developper',\n",
" '1.2 Developper', '1.3 Double developpement', '1.4 Developpement carré',\n",
" 'Exercice 2', '2.1 Addition fraction', '2.2 Multiplication fractions',\n",
" '2.3 addition Fraction', '2.4 Multiplication Fraction', 'Exercice 3',\n",
" 'Equation +', 'Equation - ', 'Equation *', 'Equation fraction',\n",
" 'equation simple', 'equation complexe', 'Exercice 4', 'Calcul angle',\n",
" 'Angle alterne', 'Calcul longueur', '2 methodes', 'Arrondi'],\n",
" dtype='object')"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.T.index"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Exercice 1', 'Exercice 2', 'Exercice 3', 'Exercice 4']"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_exo = [\"Exercice \"+str(i+1) for i in range(4)]\n",
"list_exo"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
"#notes[list_exo]"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"autres_notes = ['Malus']"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"item_avec_note = list_exo + [ds_name] + autres_notes\n",
"sous_exo = [i for i in notes.T.index if i not in item_avec_note]\n",
"#sous_exo"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def toRepVal(val):\n",
" if pd.isnull(val):\n",
" return \"\\\\NoRep\"\n",
" elif val == 0:\n",
" return \"\\\\RepZ\"\n",
" elif val == 1:\n",
" return \"\\\\RepU\"\n",
" elif val == 2:\n",
" return \"\\\\RepD\"\n",
" elif val == 3:\n",
" return \"\\\\RepT\"\n",
" else:\n",
" return val"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
"#notes.head()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"eleves = notes.copy()\n",
"eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#eleves.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Statistiques"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"count 29.000000\n",
"mean 13.034483\n",
"std 4.638602\n",
"min 2.000000\n",
"25% 10.000000\n",
"50% 14.500000\n",
"75% 16.500000\n",
"max 19.000000\n",
"Name: DM_16_03_30, dtype: float64"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes[ds_name].describe()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f8fd391c1d0>"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfkAAAFmCAYAAABuhuNyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlXzO6OnmjfqVZsmQt/2
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8fd394f898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#notes_seules = notes[ds_name]\n",
"ax = notes[ds_name].hist(bins = barem[ds_name][0], range=(0,barem[ds_name][0]), )\n",
"ax.set_xlabel(\"Notes\")\n",
"ax.set_ylabel(\"Effectif\")\n",
"#notes_seules.hist()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f8fd3989c50>"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f8fd391c5f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Normalisation des notes de chaque exo\n",
"notes_exo_norm = notes[list_exo] / barem[list_exo].values[0,:]\n",
"#notes_exo_norm\n",
"ax = notes_exo_norm.T.plot(color = \"gray\", legend = False, )\n",
"d_norm = notes_exo_norm.describe()\n",
"d_norm.T[[\"min\", \"25%\", \"50%\", \"75%\", \"max\"]].plot(ax=ax, kind=\"area\", stacked = False, alpha=.1)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bilan = texenv.get_template(\"./tpl_bilan.tex\")\n",
"cible_bilan = \"../3e/DM/DM_16_03_23/Bilan/\""
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bilan = texenv.get_template(\"tpl_bilan.tex\")\n",
"with open(cible_bilan+\"./bilan\"+classe+\".tex\",\"w\") as f:\n",
" f.write(bilan.render(eleves = eleves, barem = barem, ds_name = ds_name, latex_info = latex_info, nbr_questions = len(barem.T)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## BB_16_04_02"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ds_name = 'BB_16_04_02'\n",
"notes = all_notes.parse(ds_name).T"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"barem = notes[:1]\n",
"notes = notes[1:]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#barem\n",
"#notes.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Suppression des notes inutiles"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes = notes[notes[ds_name].notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes = notes.astype(float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Traitement des notes"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['BB_16_04_02', 'Présentation', 'Exercice 1', '1.1.a Tableau',\n",
" '1.1.b formule', '1.1.c Nom fonction', '1.2.a Nombre machine',\n",
" '1.2.b Tableau', '1.2.c Formule', '1.3.a Graphique',\n",
" '1.3.b Comparaison', 'Exercice 2', '2.1 Trignonométrie', '2.1 Arrondi',\n",
" '2.2 Réponse', '2.2 Méthode', 'Exercice 3', '3.1 Probabilité',\n",
" '3.2 Nbr issues', '3.3 Points restants', 'Exercice 4', '4 Sophie',\n",
" '4 Martin', '4 Gabriel', '4 Faiza', 'Exercice 5'],\n",
" dtype='object')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.T.index"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Exercice 1', 'Exercice 2', 'Exercice 3', 'Exercice 4', 'Exercice 5']"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_exo = [\"Exercice \"+str(i+1) for i in range(5)]\n",
"list_exo"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
"#notes[list_exo].head()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"autres_notes = ['Présentation']"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"item_avec_note = list_exo + [ds_name] + autres_notes\n",
"sous_exo = [i for i in notes.T.index if i not in item_avec_note]\n",
"#sous_exo"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def toRepVal(val):\n",
" if pd.isnull(val):\n",
" return \"\\\\NoRep\"\n",
" elif val == 0:\n",
" return \"\\\\RepZ\"\n",
" elif val == 1:\n",
" return \"\\\\RepU\"\n",
" elif val == 2:\n",
" return \"\\\\RepD\"\n",
" elif val == 3:\n",
" return \"\\\\RepT\"\n",
" else:\n",
" return val"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
"#notes.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"eleves = notes.copy()\n",
"eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Statistiques"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 30.000000\n",
"mean 12.850000\n",
"std 4.512721\n",
"min 0.000000\n",
"25% 10.500000\n",
"50% 12.750000\n",
"75% 15.250000\n",
"max 21.000000\n",
"Name: BB_16_04_02, dtype: float64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes[ds_name].describe()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f3a4de345f8>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFmCAYAAABENhLdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6wuy8y6p1W651XV1Yec
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3a4deabbe0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#notes_seules = notes[ds_name]\n",
"ax = notes[ds_name].hist(bins = barem[ds_name][0], range=(0,barem[ds_name][0]), )\n",
"ax.set_xlabel(\"Notes\")\n",
"ax.set_ylabel(\"Effectif\")\n",
"#notes_seules.hist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bilan tex"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'classe': '309', 'date': '02 Avril 2016', 'titre': 'Brevet Blanc'}"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"latex_info = {}\n",
"latex_info['titre'] = \"Brevet Blanc\"\n",
"latex_info['classe'] = \"309\"\n",
"latex_info['date'] = \"02 Avril 2016\"\n",
"latex_info"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bilan = texenv.get_template(\"./tpl_bilan.tex\")\n",
"cible_bilan = \"../3e/DS/\"+ds_name+\"/Bilan/\""
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open(cible_bilan+\"bilan\"+classe+\".tex\",\"w\") as f:\n",
" f.write(bilan.render(eleves = eleves, barem = barem, ds_name = ds_name, latex_info = latex_info, nbr_questions = len(barem.T)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## BB_16_04_19"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds_name = 'BB_16_04_19'\n",
"notes = all_notes.parse(ds_name).T"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"barem = notes[:1]\n",
"notes = notes[1:]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes = notes[notes[\"BB_16_04_19\"]!=0]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 29.000000\n",
"mean 15.431034\n",
"std 6.528405\n",
"min 5.000000\n",
"25% 12.000000\n",
"50% 15.000000\n",
"75% 17.500000\n",
"max 30.000000\n",
"Name: BB_16_04_19, dtype: float64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes['BB_16_04_19'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7ff5953731d0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAFXCAYAAAB6G51YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBwzACR0wAACNYAB3one
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff5953912e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"notes[\"BB_16_04_19\"].hist(bins = barem[ds_name][0], range=(0,barem[ds_name][0]),)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## DM_16_05_18"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds_name = 'DM_16_05_18'\n",
"notes = all_notes.parse(ds_name).T"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": 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>DM_16_05_18</th>\n",
" <th>Malus</th>\n",
" <th>Exercice 1</th>\n",
" <th>Dev 1</th>\n",
" <th>Dev 2</th>\n",
" <th>Dev 3</th>\n",
" <th>Dev 4</th>\n",
" <th>Facto 1</th>\n",
" <th>Facto 2</th>\n",
" <th>Facto 3</th>\n",
" <th>...</th>\n",
" <th>Equation deux cotes</th>\n",
" <th>Exercice 3</th>\n",
" <th>Proba</th>\n",
" <th>Proba ou</th>\n",
" <th>Proba comparaison</th>\n",
" <th>Tableau</th>\n",
" <th>Fréquence</th>\n",
" <th>Nombre points</th>\n",
" <th>Moyenne</th>\n",
" <th>Médiane</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ABDOU Farida</th>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABOU BACAR Djaha</th>\n",
" <td>11</td>\n",
" <td>NaN</td>\n",
" <td>3.333333</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>5.666667</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADA Nabaouya</th>\n",
" <td>13</td>\n",
" <td>NaN</td>\n",
" <td>5.333333</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>5.666667</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADI Faina</th>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALI Mardhuia</th>\n",
" <td>11</td>\n",
" <td>NaN</td>\n",
" <td>0.666667</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7.333333</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" DM_16_05_18 Malus Exercice 1 Dev 1 Dev 2 Dev 3 Dev 4 \\\n",
"ABDOU Farida 0 NaN 0.000000 NaN NaN NaN NaN \n",
"ABOU BACAR Djaha 11 NaN 3.333333 3 1 0 0 \n",
"AHAMADA Nabaouya 13 NaN 5.333333 2 1 1 3 \n",
"AHAMADI Faina 0 NaN 0.000000 NaN NaN NaN NaN \n",
"ALI Mardhuia 11 NaN 0.666667 0 2 0 0 \n",
"\n",
" Facto 1 Facto 2 Facto 3 ... Equation deux cotes \\\n",
"ABDOU Farida NaN NaN NaN ... NaN \n",
"ABOU BACAR Djaha 2 2 2 ... 1 \n",
"AHAMADA Nabaouya 3 3 3 ... 1 \n",
"AHAMADI Faina NaN NaN NaN ... NaN \n",
"ALI Mardhuia NaN NaN NaN ... 2 \n",
"\n",
" Exercice 3 Proba Proba ou Proba comparaison Tableau \\\n",
"ABDOU Farida 0.000000 NaN NaN NaN NaN \n",
"ABOU BACAR Djaha 5.666667 3 NaN 3 3 \n",
"AHAMADA Nabaouya 5.666667 3 3 3 3 \n",
"AHAMADI Faina 0.000000 NaN NaN NaN NaN \n",
"ALI Mardhuia 7.333333 3 3 3 3 \n",
"\n",
" Fréquence Nombre points Moyenne Médiane \n",
"ABDOU Farida NaN NaN NaN NaN \n",
"ABOU BACAR Djaha 3 3 1 1 \n",
"AHAMADA Nabaouya 0 3 1 1 \n",
"AHAMADI Faina NaN NaN NaN NaN \n",
"ALI Mardhuia 1 3 3 3 \n",
"\n",
"[5 rows x 25 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"barem = notes[:1]\n",
"notes = notes[1:]\n",
"notes.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes = notes[notes[ds_name]!=0]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 25.000000\n",
"mean 13.300000\n",
"std 3.455069\n",
"min 3.500000\n",
"25% 11.000000\n",
"50% 13.000000\n",
"75% 16.500000\n",
"max 18.500000\n",
"Name: DM_16_05_18, dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes[ds_name].describe()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0f34cacd30>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAeAAAAFXCAYAAAB6G51YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n73WSPNhu390xIh3wqG6
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0f22831c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Traitement et bilan"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['DM_16_05_18', 'Malus', 'Exercice 1', 'Dev 1', 'Dev 2', 'Dev 3',\n",
" 'Dev 4', 'Facto 1', 'Facto 2', 'Facto 3', 'Facto identité remarquable',\n",
" 'Exercice 2', 'Equation simple', 'Equation fraction',\n",
" 'Equation simple 2', 'Equation deux cotes', 'Exercice 3', 'Proba ',\n",
" 'Proba ou', 'Proba comparaison', 'Tableau', 'Fréquence',\n",
" 'Nombre points', 'Moyenne', 'Médiane'],\n",
" dtype='object')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.columns"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Exercice 1', 'Exercice 2', 'Exercice 3']"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_exo = [\"Exercice \"+str(i+1) for i in range(3)]\n",
"list_exo"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/lafrite/.virtualenvs/enseignement/lib/python3.5/site-packages/pandas/core/frame.py:2320: 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",
" self[k1] = value[k2]\n"
]
}
],
"source": [
"notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
"#notes[list_exo].head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"autres_notes = ['Malus']"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"item_avec_note = list_exo + [ds_name] + autres_notes\n",
"sous_exo = [i for i in notes.T.index if i not in item_avec_note]\n",
"#sous_exo"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def toRepVal(val):\n",
" if pd.isnull(val):\n",
" return \"\\\\NoRep\"\n",
" elif val == 0:\n",
" return \"\\\\RepZ\"\n",
" elif val == 1:\n",
" return \"\\\\RepU\"\n",
" elif val == 2:\n",
" return \"\\\\RepD\"\n",
" elif val == 3:\n",
" return \"\\\\RepT\"\n",
" else:\n",
" return val"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
"#notes.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"eleves = notes.copy()\n",
"eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'classe': '309', 'date': '28 mai 2016', 'titre': 'DM 6'}"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"latex_info = {}\n",
"latex_info['titre'] = \"DM 6\"\n",
"latex_info['classe'] = \"309\"\n",
"latex_info['date'] = \"28 mai 2016\"\n",
"latex_info"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bilan = texenv.get_template(\"./tpl_bilan.tex\")\n",
"cible_bilan = \"../3e/DM/\"+ds_name+\"/Bilan/\""
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(cible_bilan+\"bilan\"+classe+\".tex\",\"w\") as f:\n",
" f.write(bilan.render(eleves = eleves, barem = barem, ds_name = ds_name, latex_info = latex_info, nbr_questions = len(barem.T)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bilan 3e trimestre"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds_name = \"notes\"\n",
"notes = all_notes.parse(ds_name)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['DM_16_03_30', 'BB_16_04_02', 'BB_16_04_19', 'DM_16_05_18',\n",
" 'BB_16_06_03', 'Conn trimestre 3'],\n",
" dtype='object')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes[17:].index"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trim3 = notes[17:].T"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"barem = trim3[:1]\n",
"notesT3 = trim3[1:31]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": 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>DM_16_03_30</th>\n",
" <th>BB_16_04_02</th>\n",
" <th>BB_16_04_19</th>\n",
" <th>DM_16_05_18</th>\n",
" <th>BB_16_06_03</th>\n",
" <th>Conn trimestre 3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>BAREME</th>\n",
" <td>20</td>\n",
" <td>31</td>\n",
" <td>41</td>\n",
" <td>20</td>\n",
" <td>37</td>\n",
" <td>20</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DM_16_03_30 BB_16_04_02 BB_16_04_19 DM_16_05_18 BB_16_06_03 \\\n",
"BAREME 20 31 41 20 37 \n",
"\n",
" Conn trimestre 3 \n",
"BAREME 20 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"barem"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"ABDOU Farida 11.5\n",
"ABOU BACAR Djaha 14.5\n",
"AHAMADA Nabaouya 8.0\n",
"AHAMADI Faina 10.5\n",
"ALI Mardhuia 11.0\n",
"ALI SOULAIMANA Chamsia 13.5\n",
"ALSENE ALI MADI Stela 3.5\n",
"ANDRIATAHIANA Hoby 13.5\n",
"ANLI Emeline 10.5\n",
"ATHOUMANE Naouidat 11.5\n",
"BOUDRA Nassifanya 18.0\n",
"CHANFI Nadhrati 2.5\n",
"COMBO Moinécha 15.5\n",
"HALIDI Nisma 15.0\n",
"HAMZA Samianti 8.5\n",
"HOUMADI Chaharazadi 14.0\n",
"HOUMADI Dhoirfia 15.0\n",
"HOUMADI Mouslimati 8.0\n",
"HOUMADI Nasmi 17.0\n",
"LOUTOUFI Nachima 10.0\n",
"MALIDE El-Anzize 16.5\n",
"MONNE Kevin 11.0\n",
"MOUSSA Roibouanti 16.0\n",
"OUSSENI Hilma 13.5\n",
"SAANLI Natali 19.0\n",
"SAID AHAMADA Roukaya 15.5\n",
"SANDA Issoufi 8.5\n",
"SOILIHI Soifia 11.0\n",
"SOUFIANI Laila 4.0\n",
"YOUSSOUF Sitirati 6.0\n",
"Name: Conn trimestre 3, dtype: float64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notesT3['Conn trimestre 3'].sort_index()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preparation du fichier .tex"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"latex_info = {}\n",
"latex_info['titre'] = \"\"\n",
"latex_info['classe'] = \"\"\n",
"latex_info['date'] = \"\"\n",
"latex_info"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#eleves"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bilan = texenv.get_template(\"tpl_bilan.tex\")\n",
"with open(\"./bilan\"+classe+\".tex\",\"w\") as f:\n",
" f.write(bilan.render(eleves = eleves, barem = barem, ds_name = ds_name, latex_info = latex_info, nbr_questions = len(barem.T)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Bilan à remplir"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"bilan = texenv.get_template(\"tpl_bilan.tex\")\n",
"with open(\"./fill_bilan.tex\",\"w\") as f:\n",
" f.write(bilan.render(eleves = [(\"Nom\",, barem = barem, ds_name = ds_name, latex_info = latex_info, nbr_questions = len(barem.T)))"
]
}
],
"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": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}