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import work from year 2015-2016
2017-06-16 06:48:54 +00:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 18,
"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": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"classe = \"313\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import et premiers traitements"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"scrolled": true
},
"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",
" 'DM_15_11_16',\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",
" 'Enclos',\n",
" 'DM_16_05_18',\n",
" 'BB_16_05_31']"
]
},
"execution_count": 20,
"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": "markdown",
"metadata": {},
"source": [
"## Par élève"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [
{
"ename": "XLRDError",
"evalue": "No sheet named <'notes'>",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m/home/lafrite/.virtualenvs/enseignement/lib/python3.5/site-packages/xlrd/book.py\u001b[0m in \u001b[0;36msheet_by_name\u001b[1;34m(self, sheet_name)\u001b[0m\n\u001b[0;32m 438\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 439\u001b[1;33m \u001b[0msheetx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sheet_names\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msheet_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 440\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: 'notes' is not in list",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[1;31mXLRDError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-4-4ee6ba5067a2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mds_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'notes'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnotes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mall_notes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mds_name\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/io/excel.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self, sheetname, header, skiprows, skip_footer, index_col, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, has_index_names, converters, **kwds)\u001b[0m\n\u001b[0;32m 237\u001b[0m \u001b[0mconvert_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconvert_float\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 238\u001b[0m \u001b[0mconverters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconverters\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 239\u001b[1;33m **kwds)\n\u001b[0m\u001b[0;32m 240\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 241\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_should_parse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparse_cols\u001b[0m\u001b[1;33m)\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/io/excel.py\u001b[0m in \u001b[0;36m_parse_excel\u001b[1;34m(self, sheetname, header, skiprows, skip_footer, index_col, has_index_names, parse_cols, parse_dates, date_parser, na_values, thousands, convert_float, verbose, **kwds)\u001b[0m\n\u001b[0;32m 370\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 371\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0masheetname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 372\u001b[1;33m \u001b[0msheet\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msheet_by_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0masheetname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 373\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# assume an integer if not a string\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 374\u001b[0m \u001b[0msheet\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msheet_by_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0masheetname\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/xlrd/book.py\u001b[0m in \u001b[0;36msheet_by_name\u001b[1;34m(self, sheet_name)\u001b[0m\n\u001b[0;32m 439\u001b[0m \u001b[0msheetx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sheet_names\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msheet_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 440\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 441\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mXLRDError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'No sheet named <%r>'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0msheet_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 442\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msheet_by_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msheetx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 443\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mXLRDError\u001b[0m: No sheet named <'notes'>"
]
}
],
"source": [
"ds_name = 'Notes'\n",
"notes = all_notes.parse(ds_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# 2e trimestre"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Connaissances pour le 2e trimestre"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds_name = \"Conn\"\n",
"notes = all_notes.parse(ds_name)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>MALIDE El-Anzize</th>\n",
" <th>MONNE Kevin</th>\n",
" <th>MOUSSA Roibouanti</th>\n",
" <th>OUSSENI Hilma</th>\n",
" <th>SAANLI Natali</th>\n",
" <th>SAID AHAMADA Roukaya</th>\n",
" <th>SANDA Issoufi</th>\n",
" <th>SOILIHI Soifia</th>\n",
" <th>SOUFIANI Laila</th>\n",
" <th>YOUSSOUF Sitirati</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Conn trimestre 1</th>\n",
" <td>20</td>\n",
" <td>7.5</td>\n",
" <td>16.500000</td>\n",
" <td>14.500000</td>\n",
" <td>4.000000</td>\n",
" <td>15.000000</td>\n",
" <td>15.500000</td>\n",
" <td>10.5</td>\n",
" <td>16.000000</td>\n",
" <td>10.000000</td>\n",
" <td>...</td>\n",
" <td>11.000000</td>\n",
" <td>16.000000</td>\n",
" <td>17.500000</td>\n",
" <td>10.500000</td>\n",
" <td>16.500000</td>\n",
" <td>17</td>\n",
" <td>9.000000</td>\n",
" <td>11.50</td>\n",
" <td>5.500000</td>\n",
" <td>6.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NaN</th>\n",
" <td>20</td>\n",
" <td>7.5</td>\n",
" <td>16.583333</td>\n",
" <td>14.333333</td>\n",
" <td>4.083333</td>\n",
" <td>14.833333</td>\n",
" <td>15.416667</td>\n",
" <td>10.5</td>\n",
" <td>15.833333</td>\n",
" <td>9.833333</td>\n",
" <td>...</td>\n",
" <td>10.916667</td>\n",
" <td>16.083333</td>\n",
" <td>17.666667</td>\n",
" <td>10.333333</td>\n",
" <td>16.416667</td>\n",
" <td>17</td>\n",
" <td>9.166667</td>\n",
" <td>11.25</td>\n",
" <td>5.333333</td>\n",
" <td>6.583333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Conn_15_09_09</th>\n",
" <td>4</td>\n",
" <td>1.5</td>\n",
" <td>3.500000</td>\n",
" <td>3.000000</td>\n",
" <td>1.500000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>2.5</td>\n",
" <td>3.000000</td>\n",
" <td>0.500000</td>\n",
" <td>...</td>\n",
" <td>1.500000</td>\n",
" <td>2.500000</td>\n",
" <td>4.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.500000</td>\n",
" <td>4</td>\n",
" <td>2.500000</td>\n",
" <td>1.50</td>\n",
" <td>1.000000</td>\n",
" <td>0.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Conn_15_09_16</th>\n",
" <td>4</td>\n",
" <td>2.0</td>\n",
" <td>4.000000</td>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>4.000000</td>\n",
" <td>3.500000</td>\n",
" <td>2.5</td>\n",
" <td>2.000000</td>\n",
" <td>2.500000</td>\n",
" <td>...</td>\n",
" <td>2.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4</td>\n",
" <td>2.500000</td>\n",
" <td>2.00</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Conn_15_09_30</th>\n",
" <td>5</td>\n",
" <td>2.0</td>\n",
" <td>3.500000</td>\n",
" <td>2.500000</td>\n",
" <td>1.500000</td>\n",
" <td>3.500000</td>\n",
" <td>3.500000</td>\n",
" <td>2.0</td>\n",
" <td>4.000000</td>\n",
" <td>3.500000</td>\n",
" <td>...</td>\n",
" <td>4.500000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4</td>\n",
" <td>2.500000</td>\n",
" <td>2.50</td>\n",
" <td>1.500000</td>\n",
" <td>2.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 31 columns</p>\n",
"</div>"
],
"text/plain": [
" Bareme ABDOU Farida ABOU BACAR Djaha AHAMADA Nabaouya \\\n",
"Conn trimestre 1 20 7.5 16.500000 14.500000 \n",
"NaN 20 7.5 16.583333 14.333333 \n",
"Conn_15_09_09 4 1.5 3.500000 3.000000 \n",
"Conn_15_09_16 4 2.0 4.000000 3.000000 \n",
"Conn_15_09_30 5 2.0 3.500000 2.500000 \n",
"\n",
" AHAMADI Faina ALI Mardhuia ALI SOULAIMANA Chamsia \\\n",
"Conn trimestre 1 4.000000 15.000000 15.500000 \n",
"NaN 4.083333 14.833333 15.416667 \n",
"Conn_15_09_09 1.500000 3.000000 3.000000 \n",
"Conn_15_09_16 1.000000 4.000000 3.500000 \n",
"Conn_15_09_30 1.500000 3.500000 3.500000 \n",
"\n",
" ALSENE ALI MADI Stela ANDRIATAHIANA Hoby ANLI Emeline \\\n",
"Conn trimestre 1 10.5 16.000000 10.000000 \n",
"NaN 10.5 15.833333 9.833333 \n",
"Conn_15_09_09 2.5 3.000000 0.500000 \n",
"Conn_15_09_16 2.5 2.000000 2.500000 \n",
"Conn_15_09_30 2.0 4.000000 3.500000 \n",
"\n",
" ... MALIDE El-Anzize MONNE Kevin \\\n",
"Conn trimestre 1 ... 11.000000 16.000000 \n",
"NaN ... 10.916667 16.083333 \n",
"Conn_15_09_09 ... 1.500000 2.500000 \n",
"Conn_15_09_16 ... 2.000000 4.000000 \n",
"Conn_15_09_30 ... 4.500000 4.000000 \n",
"\n",
" MOUSSA Roibouanti OUSSENI Hilma SAANLI Natali \\\n",
"Conn trimestre 1 17.500000 10.500000 16.500000 \n",
"NaN 17.666667 10.333333 16.416667 \n",
"Conn_15_09_09 4.000000 1.000000 2.500000 \n",
"Conn_15_09_16 4.000000 3.000000 4.000000 \n",
"Conn_15_09_30 4.000000 3.000000 4.000000 \n",
"\n",
" SAID AHAMADA Roukaya SANDA Issoufi SOILIHI Soifia \\\n",
"Conn trimestre 1 17 9.000000 11.50 \n",
"NaN 17 9.166667 11.25 \n",
"Conn_15_09_09 4 2.500000 1.50 \n",
"Conn_15_09_16 4 2.500000 2.00 \n",
"Conn_15_09_30 4 2.500000 2.50 \n",
"\n",
" SOUFIANI Laila YOUSSOUF Sitirati \n",
"Conn trimestre 1 5.500000 6.500000 \n",
"NaN 5.333333 6.583333 \n",
"Conn_15_09_09 1.000000 0.500000 \n",
"Conn_15_09_16 1.000000 1.000000 \n",
"Conn_15_09_30 1.500000 2.500000 \n",
"\n",
"[5 rows x 31 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Conn trimestre 1', nan, 'Conn_15_09_09',\n",
" 'Conn_15_09_16', 'Conn_15_09_30', 'Conn_15_10_07',\n",
" 'Conn_15_11_04', 'Conn_15_11_12', nan,\n",
" nan, nan, 'Conn trimestre 2',\n",
" 'Conn_15_11_18', 'Conn_15_12_08', 'Conn_16_01_20',\n",
" 'Conn_16_02_03', 'Conn_16_02_10'],\n",
" dtype='object')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.index"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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": 8,
"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>Conn_15_11_18</th>\n",
" <th>Conn_15_12_08</th>\n",
" <th>Conn_16_01_20</th>\n",
" <th>Conn_16_02_03</th>\n",
" <th>Conn_16_02_10</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Bareme</th>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABDOU Farida</th>\n",
" <td>3.5</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABOU BACAR Djaha</th>\n",
" <td>4.5</td>\n",
" <td>5.0</td>\n",
" <td>2.5</td>\n",
" <td>3.0</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADA Nabaouya</th>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>3.5</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADI Faina</th>\n",
" <td>NaN</td>\n",
" <td>0.5</td>\n",
" <td>2.5</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Conn_15_11_18 Conn_15_12_08 Conn_16_01_20 Conn_16_02_03 \\\n",
"Bareme 5.0 5.0 5.0 4.0 \n",
"ABDOU Farida 3.5 4.0 3.0 2.0 \n",
"ABOU BACAR Djaha 4.5 5.0 2.5 3.0 \n",
"AHAMADA Nabaouya 3.0 5.0 1.0 3.5 \n",
"AHAMADI Faina NaN 0.5 2.5 0.0 \n",
"\n",
" Conn_16_02_10 \n",
"Bareme 6.0 \n",
"ABDOU Farida 2.0 \n",
"ABOU BACAR Djaha 2.5 \n",
"AHAMADA Nabaouya 2.0 \n",
"AHAMADI Faina 3.0 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes_conn_2T = notes.T[conn_2nd_Tri]\n",
"notes_conn_2T.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"barem = notes_conn_2T[:1]\n",
"notes = notes_conn_2T[1:]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>Conn_15_11_18</th>\n",
" <th>Conn_15_12_08</th>\n",
" <th>Conn_16_01_20</th>\n",
" <th>Conn_16_02_03</th>\n",
" <th>Conn_16_02_10</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ABDOU Farida</th>\n",
" <td>3.5</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABOU BACAR Djaha</th>\n",
" <td>4.5</td>\n",
" <td>5.0</td>\n",
" <td>2.5</td>\n",
" <td>3.0</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADA Nabaouya</th>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>3.5</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADI Faina</th>\n",
" <td>NaN</td>\n",
" <td>0.5</td>\n",
" <td>2.5</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALI Mardhuia</th>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>4.5</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Conn_15_11_18 Conn_15_12_08 Conn_16_01_20 Conn_16_02_03 \\\n",
"ABDOU Farida 3.5 4.0 3.0 2.0 \n",
"ABOU BACAR Djaha 4.5 5.0 2.5 3.0 \n",
"AHAMADA Nabaouya 3.0 5.0 1.0 3.5 \n",
"AHAMADI Faina NaN 0.5 2.5 0.0 \n",
"ALI Mardhuia 5.0 4.0 4.5 3.0 \n",
"\n",
" Conn_16_02_10 \n",
"ABDOU Farida 2.0 \n",
"ABOU BACAR Djaha 2.5 \n",
"AHAMADA Nabaouya 2.0 \n",
"AHAMADI Faina 3.0 \n",
"ALI Mardhuia 4.0 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.head()\n",
"#barem"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes = notes.astype(float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Traitement des notes"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Conn_15_11_18', 'Conn_15_12_08', 'Conn_16_01_20', 'Conn_16_02_03',\n",
" 'Conn_16_02_10'],\n",
" dtype='object')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.T.index"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(notes.T.index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Un peu de statistiques"
]
},
{
"cell_type": "code",
"execution_count": 14,
"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>Conn_15_11_18</th>\n",
" <th>Conn_15_12_08</th>\n",
" <th>Conn_16_01_20</th>\n",
" <th>Conn_16_02_03</th>\n",
" <th>Conn_16_02_10</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>29.000000</td>\n",
" <td>30.00000</td>\n",
" <td>29.000000</td>\n",
" <td>30.000000</td>\n",
" <td>29.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3.206897</td>\n",
" <td>3.75000</td>\n",
" <td>2.379310</td>\n",
" <td>2.466667</td>\n",
" <td>3.344828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.161259</td>\n",
" <td>1.38184</td>\n",
" <td>1.300104</td>\n",
" <td>1.252125</td>\n",
" <td>1.518361</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.500000</td>\n",
" <td>0.50000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>3.000000</td>\n",
" <td>2.62500</td>\n",
" <td>1.000000</td>\n",
" <td>1.500000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.000000</td>\n",
" <td>4.00000</td>\n",
" <td>2.500000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>4.000000</td>\n",
" <td>5.00000</td>\n",
" <td>3.500000</td>\n",
" <td>3.500000</td>\n",
" <td>4.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>5.000000</td>\n",
" <td>5.00000</td>\n",
" <td>4.500000</td>\n",
" <td>4.000000</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Conn_15_11_18 Conn_15_12_08 Conn_16_01_20 Conn_16_02_03 \\\n",
"count 29.000000 30.00000 29.000000 30.000000 \n",
"mean 3.206897 3.75000 2.379310 2.466667 \n",
"std 1.161259 1.38184 1.300104 1.252125 \n",
"min 0.500000 0.50000 0.000000 0.000000 \n",
"25% 3.000000 2.62500 1.000000 1.500000 \n",
"50% 3.000000 4.00000 2.500000 3.000000 \n",
"75% 4.000000 5.00000 3.500000 3.500000 \n",
"max 5.000000 5.00000 4.500000 4.000000 \n",
"\n",
" Conn_16_02_10 \n",
"count 29.000000 \n",
"mean 3.344828 \n",
"std 1.518361 \n",
"min 1.000000 \n",
"25% 2.000000 \n",
"50% 3.000000 \n",
"75% 4.500000 \n",
"max 6.000000 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.describe()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f25ed408c88>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA9kAAAGrCAYAAADUwVQJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVfVv7D/z+rywWdng7bA
"text/plain": [
"<matplotlib.figure.Figure at 0x7f25ed47cc88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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": 16,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f25ed40cbe0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = notes.hist(figsize = (16,8))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes[\"Trim2\"] = notes.apply(norma_mean, axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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>Conn_15_11_18</th>\n",
" <th>Conn_15_12_08</th>\n",
" <th>Conn_16_01_20</th>\n",
" <th>Conn_16_02_03</th>\n",
" <th>Conn_16_02_10</th>\n",
" <th>Trim2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ABDOU Farida</th>\n",
" <td>3.5</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>11.733333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABOU BACAR Djaha</th>\n",
" <td>4.5</td>\n",
" <td>5.0</td>\n",
" <td>2.5</td>\n",
" <td>3.0</td>\n",
" <td>2.5</td>\n",
" <td>14.266667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADA Nabaouya</th>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>1.0</td>\n",
" <td>3.5</td>\n",
" <td>2.0</td>\n",
" <td>12.033333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMADI Faina</th>\n",
" <td>NaN</td>\n",
" <td>0.5</td>\n",
" <td>2.5</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>5.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ALI Mardhuia</th>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>4.5</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>16.466667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Conn_15_11_18 Conn_15_12_08 Conn_16_01_20 Conn_16_02_03 \\\n",
"ABDOU Farida 3.5 4.0 3.0 2.0 \n",
"ABOU BACAR Djaha 4.5 5.0 2.5 3.0 \n",
"AHAMADA Nabaouya 3.0 5.0 1.0 3.5 \n",
"AHAMADI Faina NaN 0.5 2.5 0.0 \n",
"ALI Mardhuia 5.0 4.0 4.5 3.0 \n",
"\n",
" Conn_16_02_10 Trim2 \n",
"ABDOU Farida 2.0 11.733333 \n",
"ABOU BACAR Djaha 2.5 14.266667 \n",
"AHAMADA Nabaouya 2.0 12.033333 \n",
"AHAMADI Faina 3.0 5.500000 \n",
"ALI Mardhuia 4.0 16.466667 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Brevet blanc février"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>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>ABDALLAH Touraya</th>\n",
" <td>4.0</td>\n",
" <td>0.5</td>\n",
" <td>1.5</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABDOU Mariam</th>\n",
" <td>5.0</td>\n",
" <td>2.5</td>\n",
" <td>6.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABTOIHI SAID Yasmina</th>\n",
" <td>6.5</td>\n",
" <td>1.5</td>\n",
" <td>4.5</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMED Anssuifidine</th>\n",
" <td>6.0</td>\n",
" <td>1.5</td>\n",
" <td>1.5</td>\n",
" <td>3.5</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",
"ABDALLAH Touraya 4.0 0.5 1.5 1.0\n",
"ABDOU Mariam 5.0 2.5 6.0 4.0\n",
"ABTOIHI SAID Yasmina 6.5 1.5 4.5 3.5\n",
"AHAMED Anssuifidine 6.0 1.5 1.5 3.5"
]
},
"execution_count": 4,
"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": "code",
"execution_count": 8,
"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>HALIDI Tomsoyère</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MOUGNIDAHO Nouriana</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SAGAF Amal</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABDALLAH Touraya</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMED Anssuifidine</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHMED ABDOU El-Karim</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BOINA HASSANI Nahimi</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HALIBOU Nafilati</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IBRAHIM Laoura</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MOENY MOKO Nadjma</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Himida</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Antufati</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABTOIHI SAID Yasmina</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BACO ABDALLAH Moustadirane</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DJADAR Ifrah</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YANCOUB Toufa</th>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANLI Koudoussia</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YOUSSOUF Asma</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ATTOUMANI Hanissa</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MOURTADJOU El-Fazar</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>ABDOU Mariam</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMED Issihaka</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANDILI Chayhati</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANDJILANE Rachma</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BINALI Maoulida</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BOINA Ainati</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DAOUD El-Farouk</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI ABDALLAH Abdallah</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MALIDE ABDOU Nasser</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MALIDE Younes</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SAID Chamsoudine</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",
"HALIDI Tomsoyère False False False False\n",
"MOUGNIDAHO Nouriana False False False False\n",
"SAGAF Amal False False False False\n",
"ABDALLAH Touraya True False False False\n",
"AHAMED Anssuifidine True False False True\n",
"AHMED ABDOU El-Karim True False False True\n",
"BOINA HASSANI Nahimi True False False True\n",
"HALIBOU Nafilati True False False True\n",
"IBRAHIM Laoura True False False True\n",
"MOENY MOKO Nadjma True False False True\n",
"HOUMADI Himida True False True False\n",
"HOUMADI Antufati True False True False\n",
"ABTOIHI SAID Yasmina True False True True\n",
"BACO ABDALLAH Moustadirane True False True True\n",
"DJADAR Ifrah True False True True\n",
"YANCOUB Toufa True False True True\n",
"ANLI Koudoussia True True False True\n",
"YOUSSOUF Asma True True False True\n",
"ATTOUMANI Hanissa True True True False\n",
"MOURTADJOU El-Fazar True True True False\n",
"Bareme True True True True\n",
"ABDOU Mariam True True True True\n",
"AHAMED Issihaka True True True True\n",
"ANDILI Chayhati True True True True\n",
"ANDJILANE Rachma True True True True\n",
"BINALI Maoulida True True True True\n",
"BOINA Ainati True True True True\n",
"DAOUD El-Farouk True True True True\n",
"HOUMADI ABDALLAH Abdallah True True True True\n",
"MALIDE ABDOU Nasser True True True True\n",
"MALIDE Younes True True True True\n",
"SAID Chamsoudine True True True True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"((BB_fev / BB_fev.T['Bareme']) > 0.4).sort_values(['Exercice 7', 'Exercice 4', 'Exercice 5', 'Exercice 2'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Bilan 2e trimestre"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds_name = 'Notes'\n",
"notes = all_notes.parse(ds_name)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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>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>Connaissance trimestre 2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ABDALLAH Touraya</th>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>8.5</td>\n",
" <td>10.0</td>\n",
" <td>8.5</td>\n",
" <td>15.0</td>\n",
" <td>3.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABDOU Mariam</th>\n",
" <td>17.5</td>\n",
" <td>14.0</td>\n",
" <td>18.0</td>\n",
" <td>35.0</td>\n",
" <td>20.0</td>\n",
" <td>31.0</td>\n",
" <td>13.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABTOIHI SAID Yasmina</th>\n",
" <td>10.0</td>\n",
" <td>13.5</td>\n",
" <td>18.0</td>\n",
" <td>23.0</td>\n",
" <td>13.5</td>\n",
" <td>24.5</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMED Anssuifidine</th>\n",
" <td>12.0</td>\n",
" <td>14.5</td>\n",
" <td>10.5</td>\n",
" <td>21.5</td>\n",
" <td>16.0</td>\n",
" <td>22.0</td>\n",
" <td>17.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMED Issihaka</th>\n",
" <td>12.0</td>\n",
" <td>13.0</td>\n",
" <td>10.5</td>\n",
" <td>21.0</td>\n",
" <td>16.0</td>\n",
" <td>23.0</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHMED ABDOU El-Karim</th>\n",
" <td>7.5</td>\n",
" <td>9.5</td>\n",
" <td>17.5</td>\n",
" <td>13.5</td>\n",
" <td>10.0</td>\n",
" <td>17.5</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANDILI Chayhati</th>\n",
" <td>6.5</td>\n",
" <td>11.5</td>\n",
" <td>17.5</td>\n",
" <td>29.5</td>\n",
" <td>9.5</td>\n",
" <td>32.5</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANDJILANE Rachma</th>\n",
" <td>9.0</td>\n",
" <td>17.5</td>\n",
" <td>17.5</td>\n",
" <td>24.0</td>\n",
" <td>17.5</td>\n",
" <td>21.5</td>\n",
" <td>17.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANLI Koudoussia</th>\n",
" <td>9.5</td>\n",
" <td>14.0</td>\n",
" <td>17.5</td>\n",
" <td>23.0</td>\n",
" <td>14.5</td>\n",
" <td>23.5</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ATTOUMANI Hanissa</th>\n",
" <td>14.0</td>\n",
" <td>14.0</td>\n",
" <td>10.5</td>\n",
" <td>19.5</td>\n",
" <td>18.5</td>\n",
" <td>25.0</td>\n",
" <td>15.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BACO ABDALLAH Moustadirane</th>\n",
" <td>5.0</td>\n",
" <td>18.0</td>\n",
" <td>10.5</td>\n",
" <td>17.0</td>\n",
" <td>15.5</td>\n",
" <td>21.0</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BINALI Maoulida</th>\n",
" <td>5.0</td>\n",
" <td>17.0</td>\n",
" <td>18.0</td>\n",
" <td>25.5</td>\n",
" <td>16.0</td>\n",
" <td>26.0</td>\n",
" <td>13.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BOINA Ainati</th>\n",
" <td>9.5</td>\n",
" <td>14.0</td>\n",
" <td>18.0</td>\n",
" <td>25.5</td>\n",
" <td>16.0</td>\n",
" <td>28.5</td>\n",
" <td>16.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BOINA HASSANI Nahimi</th>\n",
" <td>3.0</td>\n",
" <td>15.0</td>\n",
" <td>12.0</td>\n",
" <td>12.5</td>\n",
" <td>11.5</td>\n",
" <td>17.0</td>\n",
" <td>13.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DAOUD El-Farouk</th>\n",
" <td>14.0</td>\n",
" <td>15.5</td>\n",
" <td>12.0</td>\n",
" <td>20.5</td>\n",
" <td>16.0</td>\n",
" <td>26.0</td>\n",
" <td>11.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DJADAR Ifrah</th>\n",
" <td>5.5</td>\n",
" <td>11.0</td>\n",
" <td>12.0</td>\n",
" <td>19.5</td>\n",
" <td>11.5</td>\n",
" <td>22.0</td>\n",
" <td>15.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HALIBOU Nafilati</th>\n",
" <td>3.5</td>\n",
" <td>7.5</td>\n",
" <td>12.0</td>\n",
" <td>16.5</td>\n",
" <td>8.5</td>\n",
" <td>17.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Himida</th>\n",
" <td>8.0</td>\n",
" <td>14.5</td>\n",
" <td>12.0</td>\n",
" <td>17.5</td>\n",
" <td>11.5</td>\n",
" <td>15.5</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Antufati</th>\n",
" <td>7.5</td>\n",
" <td>19.0</td>\n",
" <td>18.5</td>\n",
" <td>22.0</td>\n",
" <td>17.5</td>\n",
" <td>20.0</td>\n",
" <td>10.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI ABDALLAH Abdallah</th>\n",
" <td>7.5</td>\n",
" <td>14.0</td>\n",
" <td>12.0</td>\n",
" <td>19.0</td>\n",
" <td>16.0</td>\n",
" <td>26.5</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>IBRAHIM Laoura</th>\n",
" <td>5.0</td>\n",
" <td>4.5</td>\n",
" <td>18.5</td>\n",
" <td>18.0</td>\n",
" <td>12.5</td>\n",
" <td>16.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MALIDE ABDOU Nasser</th>\n",
" <td>18.0</td>\n",
" <td>19.0</td>\n",
" <td>18.5</td>\n",
" <td>28.0</td>\n",
" <td>17.5</td>\n",
" <td>28.5</td>\n",
" <td>19.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MALIDE Younes</th>\n",
" <td>16.0</td>\n",
" <td>17.5</td>\n",
" <td>18.5</td>\n",
" <td>35.5</td>\n",
" <td>18.5</td>\n",
" <td>37.5</td>\n",
" <td>19.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MOENY MOKO Nadjma</th>\n",
" <td>7.5</td>\n",
" <td>10.0</td>\n",
" <td>8.5</td>\n",
" <td>18.5</td>\n",
" <td>18.5</td>\n",
" <td>17.0</td>\n",
" <td>12.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MOURTADJOU El-Fazar</th>\n",
" <td>7.5</td>\n",
" <td>17.5</td>\n",
" <td>8.5</td>\n",
" <td>21.0</td>\n",
" <td>15.5</td>\n",
" <td>24.5</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SAID Chamsoudine</th>\n",
" <td>13.0</td>\n",
" <td>19.5</td>\n",
" <td>12.0</td>\n",
" <td>24.5</td>\n",
" <td>19.0</td>\n",
" <td>28.5</td>\n",
" <td>15.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YANCOUB Toufa</th>\n",
" <td>16.0</td>\n",
" <td>16.0</td>\n",
" <td>12.0</td>\n",
" <td>27.0</td>\n",
" <td>17.5</td>\n",
" <td>26.0</td>\n",
" <td>17.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YOUSSOUF Asma</th>\n",
" <td>10.5</td>\n",
" <td>0.0</td>\n",
" <td>8.5</td>\n",
" <td>16.5</td>\n",
" <td>12.0</td>\n",
" <td>19.0</td>\n",
" <td>10.0</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",
"ABDALLAH Touraya 4.0 0.0 8.5 10.0 \n",
"ABDOU Mariam 17.5 14.0 18.0 35.0 \n",
"ABTOIHI SAID Yasmina 10.0 13.5 18.0 23.0 \n",
"AHAMED Anssuifidine 12.0 14.5 10.5 21.5 \n",
"AHAMED Issihaka 12.0 13.0 10.5 21.0 \n",
"AHMED ABDOU El-Karim 7.5 9.5 17.5 13.5 \n",
"ANDILI Chayhati 6.5 11.5 17.5 29.5 \n",
"ANDJILANE Rachma 9.0 17.5 17.5 24.0 \n",
"ANLI Koudoussia 9.5 14.0 17.5 23.0 \n",
"ATTOUMANI Hanissa 14.0 14.0 10.5 19.5 \n",
"BACO ABDALLAH Moustadirane 5.0 18.0 10.5 17.0 \n",
"BINALI Maoulida 5.0 17.0 18.0 25.5 \n",
"BOINA Ainati 9.5 14.0 18.0 25.5 \n",
"BOINA HASSANI Nahimi 3.0 15.0 12.0 12.5 \n",
"DAOUD El-Farouk 14.0 15.5 12.0 20.5 \n",
"DJADAR Ifrah 5.5 11.0 12.0 19.5 \n",
"HALIBOU Nafilati 3.5 7.5 12.0 16.5 \n",
"HOUMADI Himida 8.0 14.5 12.0 17.5 \n",
"HOUMADI Antufati 7.5 19.0 18.5 22.0 \n",
"HOUMADI ABDALLAH Abdallah 7.5 14.0 12.0 19.0 \n",
"IBRAHIM Laoura 5.0 4.5 18.5 18.0 \n",
"MALIDE ABDOU Nasser 18.0 19.0 18.5 28.0 \n",
"MALIDE Younes 16.0 17.5 18.5 35.5 \n",
"MOENY MOKO Nadjma 7.5 10.0 8.5 18.5 \n",
"MOURTADJOU El-Fazar 7.5 17.5 8.5 21.0 \n",
"SAID Chamsoudine 13.0 19.5 12.0 24.5 \n",
"YANCOUB Toufa 16.0 16.0 12.0 27.0 \n",
"YOUSSOUF Asma 10.5 0.0 8.5 16.5 \n",
"\n",
" DM_16_01_29 Brevet blanc Fevrier \\\n",
"ABDALLAH Touraya 8.5 15.0 \n",
"ABDOU Mariam 20.0 31.0 \n",
"ABTOIHI SAID Yasmina 13.5 24.5 \n",
"AHAMED Anssuifidine 16.0 22.0 \n",
"AHAMED Issihaka 16.0 23.0 \n",
"AHMED ABDOU El-Karim 10.0 17.5 \n",
"ANDILI Chayhati 9.5 32.5 \n",
"ANDJILANE Rachma 17.5 21.5 \n",
"ANLI Koudoussia 14.5 23.5 \n",
"ATTOUMANI Hanissa 18.5 25.0 \n",
"BACO ABDALLAH Moustadirane 15.5 21.0 \n",
"BINALI Maoulida 16.0 26.0 \n",
"BOINA Ainati 16.0 28.5 \n",
"BOINA HASSANI Nahimi 11.5 17.0 \n",
"DAOUD El-Farouk 16.0 26.0 \n",
"DJADAR Ifrah 11.5 22.0 \n",
"HALIBOU Nafilati 8.5 17.0 \n",
"HOUMADI Himida 11.5 15.5 \n",
"HOUMADI Antufati 17.5 20.0 \n",
"HOUMADI ABDALLAH Abdallah 16.0 26.5 \n",
"IBRAHIM Laoura 12.5 16.0 \n",
"MALIDE ABDOU Nasser 17.5 28.5 \n",
"MALIDE Younes 18.5 37.5 \n",
"MOENY MOKO Nadjma 18.5 17.0 \n",
"MOURTADJOU El-Fazar 15.5 24.5 \n",
"SAID Chamsoudine 19.0 28.5 \n",
"YANCOUB Toufa 17.5 26.0 \n",
"YOUSSOUF Asma 12.0 19.0 \n",
"\n",
" Connaissance trimestre 2 \n",
"ABDALLAH Touraya 3.5 \n",
"ABDOU Mariam 13.5 \n",
"ABTOIHI SAID Yasmina 15.0 \n",
"AHAMED Anssuifidine 17.5 \n",
"AHAMED Issihaka 19.0 \n",
"AHMED ABDOU El-Karim 10.0 \n",
"ANDILI Chayhati 12.0 \n",
"ANDJILANE Rachma 17.0 \n",
"ANLI Koudoussia 14.0 \n",
"ATTOUMANI Hanissa 15.5 \n",
"BACO ABDALLAH Moustadirane 13.0 \n",
"BINALI Maoulida 13.5 \n",
"BOINA Ainati 16.0 \n",
"BOINA HASSANI Nahimi 13.5 \n",
"DAOUD El-Farouk 11.0 \n",
"DJADAR Ifrah 15.5 \n",
"HALIBOU Nafilati 7.0 \n",
"HOUMADI Himida 12.0 \n",
"HOUMADI Antufati 10.5 \n",
"HOUMADI ABDALLAH Abdallah 15.0 \n",
"IBRAHIM Laoura 8.0 \n",
"MALIDE ABDOU Nasser 19.0 \n",
"MALIDE Younes 19.5 \n",
"MOENY MOKO Nadjma 12.5 \n",
"MOURTADJOU El-Fazar 15.0 \n",
"SAID Chamsoudine 15.5 \n",
"YANCOUB Toufa 17.5 \n",
"YOUSSOUF Asma 10.0 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trim2 = notes[8:].T\n",
"barem = trim2.iloc[0]\n",
"eleveT2 = trim2.iloc[1:32].dropna().astype('float')\n",
"eleveT2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyse par élève "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from ipywidgets import interact, interactive"
]
},
{
"cell_type": "code",
"execution_count": 34,
"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>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>Connaissance trimestre 2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>28.000000</td>\n",
" <td>28.000000</td>\n",
" <td>28.000000</td>\n",
" <td>28.000000</td>\n",
" <td>28.000000</td>\n",
" <td>28.000000</td>\n",
" <td>28.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.471429</td>\n",
" <td>0.662500</td>\n",
" <td>0.692857</td>\n",
" <td>0.539732</td>\n",
" <td>0.741071</td>\n",
" <td>0.582143</td>\n",
" <td>0.680357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.215994</td>\n",
" <td>0.256625</td>\n",
" <td>0.191347</td>\n",
" <td>0.149616</td>\n",
" <td>0.167389</td>\n",
" <td>0.139586</td>\n",
" <td>0.188483</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.150000</td>\n",
" <td>0.000000</td>\n",
" <td>0.425000</td>\n",
" <td>0.250000</td>\n",
" <td>0.425000</td>\n",
" <td>0.375000</td>\n",
" <td>0.175000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.312500</td>\n",
" <td>0.568750</td>\n",
" <td>0.525000</td>\n",
" <td>0.446875</td>\n",
" <td>0.593750</td>\n",
" <td>0.465625</td>\n",
" <td>0.587500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.425000</td>\n",
" <td>0.700000</td>\n",
" <td>0.600000</td>\n",
" <td>0.525000</td>\n",
" <td>0.800000</td>\n",
" <td>0.581250</td>\n",
" <td>0.687500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.612500</td>\n",
" <td>0.856250</td>\n",
" <td>0.900000</td>\n",
" <td>0.618750</td>\n",
" <td>0.875000</td>\n",
" <td>0.653125</td>\n",
" <td>0.781250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.900000</td>\n",
" <td>0.975000</td>\n",
" <td>0.925000</td>\n",
" <td>0.887500</td>\n",
" <td>1.000000</td>\n",
" <td>0.937500</td>\n",
" <td>0.975000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DS_15_11_27 DM_15_12_09 Boulettes BB_16_01_23 DM_16_01_29 \\\n",
"count 28.000000 28.000000 28.000000 28.000000 28.000000 \n",
"mean 0.471429 0.662500 0.692857 0.539732 0.741071 \n",
"std 0.215994 0.256625 0.191347 0.149616 0.167389 \n",
"min 0.150000 0.000000 0.425000 0.250000 0.425000 \n",
"25% 0.312500 0.568750 0.525000 0.446875 0.593750 \n",
"50% 0.425000 0.700000 0.600000 0.525000 0.800000 \n",
"75% 0.612500 0.856250 0.900000 0.618750 0.875000 \n",
"max 0.900000 0.975000 0.925000 0.887500 1.000000 \n",
"\n",
" Brevet blanc Fevrier Connaissance trimestre 2 \n",
"count 28.000000 28.000000 \n",
"mean 0.582143 0.680357 \n",
"std 0.139586 0.188483 \n",
"min 0.375000 0.175000 \n",
"25% 0.465625 0.587500 \n",
"50% 0.581250 0.687500 \n",
"75% 0.653125 0.781250 \n",
"max 0.937500 0.975000 "
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Normalisation des notes de chaque exo\n",
"eleveT2_norm = eleveT2 / barem.values.astype('float')\n",
"eleveT2_norm.describe()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA+wAAAGrCAYAAABaGx11AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7j5s8LDnfOTPn8bmRva
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7d11c61ef0>"
]
},
"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": {
"collapsed": false
},
"source": [
"# 3e trimestre"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DM_16_03_30"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds_name = 'DM_16_03_30'\n",
"notes = all_notes.parse(ds_name).T"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'classe': '313', 'date': '30 mars 2016', 'titre': 'Devoir maison 5'}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"latex_info = {}\n",
"latex_info['titre'] = \"Devoir maison 5\"\n",
"latex_info['classe'] = \"313\"\n",
"latex_info['date'] = \"30 mars 2016\"\n",
"latex_info"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"barem = notes[:1]\n",
"notes = notes[1:]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"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>ABDALLAH Touraya</th>\n",
" <td>10.5</td>\n",
" <td>NaN</td>\n",
" <td>6.0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2.666667</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.000000</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABDOU Mariam</th>\n",
" <td>19.0</td>\n",
" <td>NaN</td>\n",
" <td>5.5</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>4.000000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>5.500000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABTOIHI SAID Yasmina</th>\n",
" <td>17.0</td>\n",
" <td>NaN</td>\n",
" <td>6.0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3.333333</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3.500000</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMED Anssuifidine</th>\n",
" <td>15.0</td>\n",
" <td>NaN</td>\n",
" <td>5.5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3.666667</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2.666667</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMED Issihaka</th>\n",
" <td>14.5</td>\n",
" <td>NaN</td>\n",
" <td>5.0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3.333333</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3.166667</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" DM_16_03_30 Malus Exercice 1 1.1 Developper \\\n",
"ABDALLAH Touraya 10.5 NaN 6.0 3 \n",
"ABDOU Mariam 19.0 NaN 5.5 3 \n",
"ABTOIHI SAID Yasmina 17.0 NaN 6.0 3 \n",
"AHAMED Anssuifidine 15.0 NaN 5.5 2 \n",
"AHAMED Issihaka 14.5 NaN 5.0 3 \n",
"\n",
" 1.2 Developper 1.3 Double developpement \\\n",
"ABDALLAH Touraya 3 3 \n",
"ABDOU Mariam 2 3 \n",
"ABTOIHI SAID Yasmina 3 3 \n",
"AHAMED Anssuifidine 3 3 \n",
"AHAMED Issihaka 3 2 \n",
"\n",
" 1.4 Developpement carré Exercice 2 \\\n",
"ABDALLAH Touraya 3 2.666667 \n",
"ABDOU Mariam 3 4.000000 \n",
"ABTOIHI SAID Yasmina 3 3.333333 \n",
"AHAMED Anssuifidine 3 3.666667 \n",
"AHAMED Issihaka 2 3.333333 \n",
"\n",
" 2.1 Addition fraction 2.2 Multiplication fractions \\\n",
"ABDALLAH Touraya 2 2 \n",
"ABDOU Mariam 3 3 \n",
"ABTOIHI SAID Yasmina 2 3 \n",
"AHAMED Anssuifidine 3 3 \n",
"AHAMED Issihaka 3 2 \n",
"\n",
" ... Equation * Equation fraction equation simple \\\n",
"ABDALLAH Touraya ... 2 2 0 \n",
"ABDOU Mariam ... 3 2 3 \n",
"ABTOIHI SAID Yasmina ... 3 3 3 \n",
"AHAMED Anssuifidine ... 1 1 3 \n",
"AHAMED Issihaka ... 3 NaN 2 \n",
"\n",
" equation complexe Exercice 4 Calcul angle \\\n",
"ABDALLAH Touraya 0 1.000000 2 \n",
"ABDOU Mariam 3 5.500000 3 \n",
"ABTOIHI SAID Yasmina 3 3.500000 3 \n",
"AHAMED Anssuifidine 3 2.666667 3 \n",
"AHAMED Issihaka 3 3.166667 3 \n",
"\n",
" Angle alterne Calcul longueur 2 methodes arrondi \n",
"ABDALLAH Touraya NaN NaN NaN NaN \n",
"ABDOU Mariam 3 3 3 0 \n",
"ABTOIHI SAID Yasmina 0 3 1 0 \n",
"AHAMED Anssuifidine 2 0 1 0 \n",
"AHAMED Issihaka 2 2 NaN NaN \n",
"\n",
"[5 rows x 25 columns]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#barem\n",
"notes.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Suppression des notes inutiles"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes = notes[notes[ds_name].notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes = notes.astype(float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Traitement des notes"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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 fractions', '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": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.T.index"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Exercice 1', 'Exercice 2', 'Exercice 3', 'Exercice 4']"
]
},
"execution_count": 31,
"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": 33,
"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": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"autres_notes = ['Malus']"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"['1.1 Developper',\n",
" '1.2 Developper',\n",
" '1.3 Double developpement',\n",
" '1.4 Developpement carré',\n",
" '2.1 Addition fraction',\n",
" '2.2 Multiplication fractions',\n",
" '2.3 Addition fractions',\n",
" '2.4 Multiplication Fraction',\n",
" 'Equation +',\n",
" 'Equation - ',\n",
" 'Equation *',\n",
" 'Equation fraction',\n",
" 'equation simple',\n",
" 'equation complexe',\n",
" 'Calcul angle',\n",
" 'Angle alterne',\n",
" 'Calcul longueur',\n",
" '2 methodes',\n",
" 'arrondi']"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"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": 35,
"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": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
"#notes.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"eleves = notes.copy()\n",
"eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#eleves.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Statistiques"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 28.000000\n",
"mean 14.767857\n",
"std 3.854968\n",
"min 6.500000\n",
"25% 13.000000\n",
"50% 15.750000\n",
"75% 17.125000\n",
"max 20.000000\n",
"Name: DM_16_03_30, dtype: float64"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes[ds_name].describe()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f852d1302e8>"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFmCAYAAABENhLdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nABiBQTPvY2z/ijpddu/
"text/plain": [
"<matplotlib.figure.Figure at 0x7f85122dacf8>"
]
},
"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": 48,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f8512238cc0>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAFXCAYAAADUG/YoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJWS5wPreLezdcwyNZun
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8512226a58>"
]
},
"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": "markdown",
"metadata": {},
"source": [
"### Preparation des bilans"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"bilan = texenv.get_template(\"./tpl_bilan.tex\")\n",
"cible_bilan = \"../3e/DM/DM_16_03_23/Bilan/\""
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"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": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ds_name = 'BB_16_04_02'\n",
"notes = all_notes.parse(ds_name).T"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"barem = notes[:1]\n",
"notes = notes[1:]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes = notes[notes[ds_name].notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes = notes.astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"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',\n",
" '2.1 Arrondis', '2.2 Réponse', '2.2 Méthode', 'Exercice 3',\n",
" '3.1 Probabilité', '3.2 Nbr issues', '3.3 ', 'Exercice 4', '4 Sophie',\n",
" '4 Martin', '4 Gabriel', '4 Faiza', 'Exercice 5'],\n",
" dtype='object')"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.T.index"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Exercice 1', 'Exercice 2', 'Exercice 3', 'Exercice 4', 'Exercice 5']"
]
},
"execution_count": 67,
"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": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
"#notes[list_exo].head()"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"autres_notes = ['Présentation']"
]
},
{
"cell_type": "code",
"execution_count": 70,
"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": 71,
"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": 72,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
"#notes.head()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"eleves = notes.copy()\n",
"eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 27.000000\n",
"mean 14.314815\n",
"std 4.834070\n",
"min 6.500000\n",
"25% 11.000000\n",
"50% 13.500000\n",
"75% 16.500000\n",
"max 26.500000\n",
"Name: BB_16_04_02, dtype: float64"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes[ds_name].describe()"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fbc15d222e8>"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAAFmCAYAAABENhLdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\na4AfBZ7X16IkSdLpbdt
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbc30bc6a20>"
]
},
"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": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'classe': '313', 'date': '02 Avril 2016', 'titre': 'Brevet Blanc'}"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"latex_info = {}\n",
"latex_info['titre'] = \"Brevet Blanc\"\n",
"latex_info['classe'] = \"313\"\n",
"latex_info['date'] = \"02 Avril 2016\"\n",
"latex_info"
]
},
{
"cell_type": "code",
"execution_count": 77,
"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": 78,
"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": {
"collapsed": true
},
"source": [
"## BB_16_04_19"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ds_name = 'BB_16_04_19'\n",
"notes = all_notes.parse(ds_name).T"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"barem = notes[:1]\n",
"notes = notes[1:]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes = notes[notes[\"BB_16_04_19\"]!=0]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 28.000000\n",
"mean 17.071429\n",
"std 6.353352\n",
"min 7.000000\n",
"25% 12.750000\n",
"50% 16.500000\n",
"75% 21.125000\n",
"max 31.500000\n",
"Name: BB_16_04_19, dtype: float64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes['BB_16_04_19'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc2d2d5f3c8>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc2d2d65b38>"
]
},
"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_28"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ds_name = 'DM_16_05_18'\n",
"notes = all_notes.parse(ds_name).T"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>ABDALLAH Touraya</th>\n",
" <td>6.0</td>\n",
" <td>NaN</td>\n",
" <td>2.666667</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>1.666667</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABDOU Mariam</th>\n",
" <td>17.0</td>\n",
" <td>NaN</td>\n",
" <td>8.000000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>6.000000</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>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABTOIHI SAID Yasmina</th>\n",
" <td>14.5</td>\n",
" <td>NaN</td>\n",
" <td>7.000000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>3.666667</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMED Anssuifidine</th>\n",
" <td>16.5</td>\n",
" <td>NaN</td>\n",
" <td>8.000000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</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>5.333333</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMED Issihaka</th>\n",
" <td>16.5</td>\n",
" <td>NaN</td>\n",
" <td>6.333333</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>6.666667</td>\n",
" <td>3</td>\n",
" <td>3</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",
" </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 \\\n",
"ABDALLAH Touraya 6.0 NaN 2.666667 3 3 1 \n",
"ABDOU Mariam 17.0 NaN 8.000000 3 3 3 \n",
"ABTOIHI SAID Yasmina 14.5 NaN 7.000000 3 3 3 \n",
"AHAMED Anssuifidine 16.5 NaN 8.000000 3 3 3 \n",
"AHAMED Issihaka 16.5 NaN 6.333333 2 3 3 \n",
"\n",
" Dev 4 Facto 1 Facto 2 Facto 3 ... \\\n",
"ABDALLAH Touraya 1 NaN NaN NaN ... \n",
"ABDOU Mariam 3 3 3 3 ... \n",
"ABTOIHI SAID Yasmina 3 3 3 3 ... \n",
"AHAMED Anssuifidine 3 3 3 3 ... \n",
"AHAMED Issihaka 3 3 3 2 ... \n",
"\n",
" Equation deux cotes Exercice 3 Proba Proba ou \\\n",
"ABDALLAH Touraya NaN 1.666667 1 1 \n",
"ABDOU Mariam 2 6.000000 3 3 \n",
"ABTOIHI SAID Yasmina 3 3.666667 3 3 \n",
"AHAMED Anssuifidine 1 5.333333 3 3 \n",
"AHAMED Issihaka 3 6.666667 3 3 \n",
"\n",
" Proba comparaison Tableau Fréquence Nombre points \\\n",
"ABDALLAH Touraya 3 0 NaN 0 \n",
"ABDOU Mariam 3 3 0 3 \n",
"ABTOIHI SAID Yasmina 1 3 1 0 \n",
"AHAMED Anssuifidine 0 3 2 3 \n",
"AHAMED Issihaka 3 3 3 3 \n",
"\n",
" Moyenne Médiane \n",
"ABDALLAH Touraya 0 0 \n",
"ABDOU Mariam 3 0 \n",
"ABTOIHI SAID Yasmina 0 0 \n",
"AHAMED Anssuifidine 1 1 \n",
"AHAMED Issihaka 1 1 \n",
"\n",
"[5 rows x 25 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"barem = notes[:1]\n",
"notes = notes[1:]\n",
"notes.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes = notes[notes[ds_name]!=0]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 28.000000\n",
"mean 14.839286\n",
"std 3.291554\n",
"min 6.000000\n",
"25% 13.500000\n",
"50% 16.000000\n",
"75% 16.625000\n",
"max 19.500000\n",
"Name: DM_16_05_18, dtype: float64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes[ds_name].describe()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc826fa7390>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fc826fa68d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"notes[ds_name].hist(bins = barem[ds_name][0], range=(0,barem[ds_name][0]),)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Traitement et bilan"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.columns"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Exercice 1', 'Exercice 2', 'Exercice 3']"
]
},
"execution_count": 10,
"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": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
"#notes[list_exo].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## BB_16_05_31"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds_name = 'BB_16_05_31'\n",
"notes = all_notes.parse(ds_name).T"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"barem = notes[:1]\n",
"notes = notes[1:]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notes = notes[pd.notnull(notes['BB_16_05_31'])]\n",
"#notes = notes[notes[ds_name]!=0]"
]
},
{
"cell_type": "code",
"execution_count": 55,
"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>BB_16_05_31</th>\n",
" <th>Présentation</th>\n",
" <th>Exercice 1</th>\n",
" <th>Lecture graphique</th>\n",
" <th>Calcul</th>\n",
" <th>Maximum</th>\n",
" <th>Exercice 2</th>\n",
" <th>Application pgm calcul</th>\n",
" <th>Proposition 1</th>\n",
" <th>Proposition 2</th>\n",
" <th>...</th>\n",
" <th>Total</th>\n",
" <th>Exercice 5</th>\n",
" <th>Prix lettre métro</th>\n",
" <th>Prix lettre mayotte</th>\n",
" <th>Tache complexe</th>\n",
" <th>Volume</th>\n",
" <th>Exercice 6</th>\n",
" <th>Couleur présente</th>\n",
" <th>Formule tableur</th>\n",
" <th>Nombre boule (équation)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ABDALLAH Touraya</th>\n",
" <td>8.5</td>\n",
" <td>4</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1.000000</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABDOU Mariam</th>\n",
" <td>25.0</td>\n",
" <td>4</td>\n",
" <td>3.666667</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3.666667</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.666667</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ABTOIHI SAID Yasmina</th>\n",
" <td>10.5</td>\n",
" <td>4</td>\n",
" <td>3.666667</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1.333333</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMED Anssuifidine</th>\n",
" <td>12.5</td>\n",
" <td>3</td>\n",
" <td>1.333333</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1.333333</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</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>1.000000</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHAMED Issihaka</th>\n",
" <td>15.5</td>\n",
" <td>2</td>\n",
" <td>3.333333</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2.000000</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>1.333333</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>1.333333</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHMED ABDOU El-Karim</th>\n",
" <td>7.5</td>\n",
" <td>3</td>\n",
" <td>0.666667</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>2.000000</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</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>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANDILI Chayhati</th>\n",
" <td>16.0</td>\n",
" <td>4</td>\n",
" <td>4.000000</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2.000000</td>\n",
" <td>3</td>\n",
" <td>3</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>1.666667</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANDJILANE Rachma</th>\n",
" <td>10.5</td>\n",
" <td>4</td>\n",
" <td>2.000000</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2.000000</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</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>2.333333</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ANLI Koudoussia</th>\n",
" <td>15.5</td>\n",
" <td>4</td>\n",
" <td>2.000000</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>3.333333</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.666667</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ATTOUMANI Hanissa</th>\n",
" <td>9.0</td>\n",
" <td>3</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.000000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</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>1.000000</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BACO ABDALLAH Moustadirane</th>\n",
" <td>8.5</td>\n",
" <td>2</td>\n",
" <td>0.666667</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.000000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BINALI Maoulida</th>\n",
" <td>10.5</td>\n",
" <td>3</td>\n",
" <td>1.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3.000000</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</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>1.000000</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BOINA Ainati</th>\n",
" <td>19.5</td>\n",
" <td>3</td>\n",
" <td>4.000000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1.666667</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>5.000000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BOINA HASSANI Nahimi</th>\n",
" <td>8.5</td>\n",
" <td>2</td>\n",
" <td>1.666667</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2.333333</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0.666667</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DAOUD El-Farouk</th>\n",
" <td>19.0</td>\n",
" <td>4</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.333333</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DJADAR Ifrah</th>\n",
" <td>10.5</td>\n",
" <td>2</td>\n",
" <td>1.333333</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.666667</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.000000</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HALIBOU Nafilati</th>\n",
" <td>9.5</td>\n",
" <td>3</td>\n",
" <td>2.000000</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1.000000</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</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>1.000000</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Himida</th>\n",
" <td>6.5</td>\n",
" <td>4</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1.666667</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</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>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI Antufati</th>\n",
" <td>9.0</td>\n",
" <td>4</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>3</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>1.666667</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HOUMADI ABDALLAH Abdallah</th>\n",
" <td>18.5</td>\n",
" <td>4</td>\n",
" <td>2.000000</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>3.666667</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>0.666667</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.333333</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MALIDE ABDOU Nasser</th>\n",
" <td>17.5</td>\n",
" <td>4</td>\n",
" <td>4.333333</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2.666667</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</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>1.000000</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MALIDE Younes</th>\n",
" <td>28.5</td>\n",
" <td>4</td>\n",
" <td>5.000000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>4.333333</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>1.333333</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MOENY MOKO Nadjma</th>\n",
" <td>5.5</td>\n",
" <td>1</td>\n",
" <td>2.000000</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1.666667</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.000000</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MOURTADJOU El-Fazar</th>\n",
" <td>15.0</td>\n",
" <td>4</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>2.000000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2.000000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1.000000</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SAID Chamsoudine</th>\n",
" <td>14.5</td>\n",
" <td>4</td>\n",
" <td>2.000000</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>3.000000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</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>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YANCOUB Toufa</th>\n",
" <td>16.5</td>\n",
" <td>3</td>\n",
" <td>4.333333</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2.000000</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>1.666667</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YOUSSOUF Asma</th>\n",
" <td>8.5</td>\n",
" <td>2</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.666667</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>0.666667</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1.333333</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>27 rows × 32 columns</p>\n",
"</div>"
],
"text/plain": [
" BB_16_05_31 Présentation Exercice 1 \\\n",
"ABDALLAH Touraya 8.5 4 0.000000 \n",
"ABDOU Mariam 25.0 4 3.666667 \n",
"ABTOIHI SAID Yasmina 10.5 4 3.666667 \n",
"AHAMED Anssuifidine 12.5 3 1.333333 \n",
"AHAMED Issihaka 15.5 2 3.333333 \n",
"AHMED ABDOU El-Karim 7.5 3 0.666667 \n",
"ANDILI Chayhati 16.0 4 4.000000 \n",
"ANDJILANE Rachma 10.5 4 2.000000 \n",
"ANLI Koudoussia 15.5 4 2.000000 \n",
"ATTOUMANI Hanissa 9.0 3 0.000000 \n",
"BACO ABDALLAH Moustadirane 8.5 2 0.666667 \n",
"BINALI Maoulida 10.5 3 1.000000 \n",
"BOINA Ainati 19.5 3 4.000000 \n",
"BOINA HASSANI Nahimi 8.5 2 1.666667 \n",
"DAOUD El-Farouk 19.0 4 0.000000 \n",
"DJADAR Ifrah 10.5 2 1.333333 \n",
"HALIBOU Nafilati 9.5 3 2.000000 \n",
"HOUMADI Himida 6.5 4 0.000000 \n",
"HOUMADI Antufati 9.0 4 0.000000 \n",
"HOUMADI ABDALLAH Abdallah 18.5 4 2.000000 \n",
"MALIDE ABDOU Nasser 17.5 4 4.333333 \n",
"MALIDE Younes 28.5 4 5.000000 \n",
"MOENY MOKO Nadjma 5.5 1 2.000000 \n",
"MOURTADJOU El-Fazar 15.0 4 1.000000 \n",
"SAID Chamsoudine 14.5 4 2.000000 \n",
"YANCOUB Toufa 16.5 3 4.333333 \n",
"YOUSSOUF Asma 8.5 2 0.000000 \n",
"\n",
" Lecture graphique Calcul Maximum Exercice 2 \\\n",
"ABDALLAH Touraya NaN NaN NaN 1.000000 \n",
"ABDOU Mariam 3 1 3 3.666667 \n",
"ABTOIHI SAID Yasmina 3 1 3 1.333333 \n",
"AHAMED Anssuifidine 0 2 0 1.333333 \n",
"AHAMED Issihaka 3 2 0 2.000000 \n",
"AHMED ABDOU El-Karim NaN 1 NaN 2.000000 \n",
"ANDILI Chayhati 0 3 3 2.000000 \n",
"ANDJILANE Rachma 3 0 0 2.000000 \n",
"ANLI Koudoussia 3 NaN 0 3.333333 \n",
"ATTOUMANI Hanissa NaN NaN NaN 3.000000 \n",
"BACO ABDALLAH Moustadirane 1 NaN NaN 4.000000 \n",
"BINALI Maoulida 0 0 3 3.000000 \n",
"BOINA Ainati 3 3 0 1.666667 \n",
"BOINA HASSANI Nahimi 1 0 3 2.333333 \n",
"DAOUD El-Farouk NaN NaN NaN 4.333333 \n",
"DJADAR Ifrah 2 NaN NaN 3.666667 \n",
"HALIBOU Nafilati 1 2 0 1.000000 \n",
"HOUMADI Himida 0 0 NaN 1.666667 \n",
"HOUMADI Antufati NaN NaN NaN 1.000000 \n",
"HOUMADI ABDALLAH Abdallah 1 2 0 3.666667 \n",
"MALIDE ABDOU Nasser 3 2 3 2.666667 \n",
"MALIDE Younes 3 3 3 4.333333 \n",
"MOENY MOKO Nadjma 1 2 0 1.666667 \n",
"MOURTADJOU El-Fazar NaN NaN 3 2.000000 \n",
"SAID Chamsoudine 1 2 0 3.000000 \n",
"YANCOUB Toufa 3 2 3 2.000000 \n",
"YOUSSOUF Asma NaN NaN NaN 1.666667 \n",
"\n",
" Application pgm calcul Proposition 1 \\\n",
"ABDALLAH Touraya 3 0 \n",
"ABDOU Mariam 3 3 \n",
"ABTOIHI SAID Yasmina 3 0 \n",
"AHAMED Anssuifidine 3 0 \n",
"AHAMED Issihaka 3 0 \n",
"AHMED ABDOU El-Karim 3 0 \n",
"ANDILI Chayhati 3 3 \n",
"ANDJILANE Rachma 3 0 \n",
"ANLI Koudoussia 3 2 \n",
"ATTOUMANI Hanissa 3 3 \n",
"BACO ABDALLAH Moustadirane 3 3 \n",
"BINALI Maoulida 3 0 \n",
"BOINA Ainati 3 0 \n",
"BOINA HASSANI Nahimi 3 0 \n",
"DAOUD El-Farouk 3 3 \n",
"DJADAR Ifrah 3 3 \n",
"HALIBOU Nafilati 3 0 \n",
"HOUMADI Himida 3 1 \n",
"HOUMADI Antufati 3 NaN \n",
"HOUMADI ABDALLAH Abdallah 3 3 \n",
"MALIDE ABDOU Nasser 3 2 \n",
"MALIDE Younes 3 3 \n",
"MOENY MOKO Nadjma 3 1 \n",
"MOURTADJOU El-Fazar 3 3 \n",
"SAID Chamsoudine 3 3 \n",
"YANCOUB Toufa 3 3 \n",
"YOUSSOUF Asma 3 0 \n",
"\n",
" Proposition 2 ... Total \\\n",
"ABDALLAH Touraya 0 ... 0 \n",
"ABDOU Mariam 1 ... 3 \n",
"ABTOIHI SAID Yasmina NaN ... NaN \n",
"AHAMED Anssuifidine 1 ... NaN \n",
"AHAMED Issihaka 3 ... NaN \n",
"AHMED ABDOU El-Karim 3 ... NaN \n",
"ANDILI Chayhati NaN ... NaN \n",
"ANDJILANE Rachma 3 ... NaN \n",
"ANLI Koudoussia 3 ... NaN \n",
"ATTOUMANI Hanissa 3 ... NaN \n",
"BACO ABDALLAH Moustadirane 3 ... NaN \n",
"BINALI Maoulida 3 ... NaN \n",
"BOINA Ainati 2 ... NaN \n",
"BOINA HASSANI Nahimi 3 ... 0 \n",
"DAOUD El-Farouk 2 ... 3 \n",
"DJADAR Ifrah 2 ... NaN \n",
"HALIBOU Nafilati 0 ... NaN \n",
"HOUMADI Himida 1 ... NaN \n",
"HOUMADI Antufati NaN ... NaN \n",
"HOUMADI ABDALLAH Abdallah 3 ... 3 \n",
"MALIDE ABDOU Nasser 3 ... NaN \n",
"MALIDE Younes 3 ... 3 \n",
"MOENY MOKO Nadjma 1 ... NaN \n",
"MOURTADJOU El-Fazar NaN ... 3 \n",
"SAID Chamsoudine 1 ... NaN \n",
"YANCOUB Toufa NaN ... NaN \n",
"YOUSSOUF Asma 2 ... NaN \n",
"\n",
" Exercice 5 Prix lettre métro \\\n",
"ABDALLAH Touraya 1.000000 3 \n",
"ABDOU Mariam 0.000000 0 \n",
"ABTOIHI SAID Yasmina 0.000000 0 \n",
"AHAMED Anssuifidine 0.000000 NaN \n",
"AHAMED Issihaka 1.333333 NaN \n",
"AHMED ABDOU El-Karim 0.000000 NaN \n",
"ANDILI Chayhati 0.000000 NaN \n",
"ANDJILANE Rachma 0.000000 NaN \n",
"ANLI Koudoussia 1.000000 3 \n",
"ATTOUMANI Hanissa 0.000000 NaN \n",
"BACO ABDALLAH Moustadirane 0.000000 NaN \n",
"BINALI Maoulida 0.000000 NaN \n",
"BOINA Ainati 5.000000 3 \n",
"BOINA HASSANI Nahimi 0.000000 NaN \n",
"DAOUD El-Farouk 0.000000 NaN \n",
"DJADAR Ifrah 0.000000 0 \n",
"HALIBOU Nafilati 0.000000 NaN \n",
"HOUMADI Himida 0.000000 NaN \n",
"HOUMADI Antufati 0.000000 NaN \n",
"HOUMADI ABDALLAH Abdallah 0.666667 2 \n",
"MALIDE ABDOU Nasser 0.000000 NaN \n",
"MALIDE Younes 1.000000 NaN \n",
"MOENY MOKO Nadjma 0.000000 0 \n",
"MOURTADJOU El-Fazar 2.000000 0 \n",
"SAID Chamsoudine 0.000000 NaN \n",
"YANCOUB Toufa 1.666667 3 \n",
"YOUSSOUF Asma 0.666667 0 \n",
"\n",
" Prix lettre mayotte Tache complexe Volume \\\n",
"ABDALLAH Touraya NaN NaN NaN \n",
"ABDOU Mariam 0 NaN NaN \n",
"ABTOIHI SAID Yasmina NaN NaN NaN \n",
"AHAMED Anssuifidine NaN NaN NaN \n",
"AHAMED Issihaka 2 1 NaN \n",
"AHMED ABDOU El-Karim NaN NaN NaN \n",
"ANDILI Chayhati NaN NaN NaN \n",
"ANDJILANE Rachma NaN NaN NaN \n",
"ANLI Koudoussia NaN NaN NaN \n",
"ATTOUMANI Hanissa NaN NaN NaN \n",
"BACO ABDALLAH Moustadirane NaN 0 0 \n",
"BINALI Maoulida NaN NaN NaN \n",
"BOINA Ainati 3 3 3 \n",
"BOINA HASSANI Nahimi NaN NaN 0 \n",
"DAOUD El-Farouk NaN NaN NaN \n",
"DJADAR Ifrah NaN 0 0 \n",
"HALIBOU Nafilati NaN NaN NaN \n",
"HOUMADI Himida NaN NaN NaN \n",
"HOUMADI Antufati NaN NaN NaN \n",
"HOUMADI ABDALLAH Abdallah NaN NaN NaN \n",
"MALIDE ABDOU Nasser NaN NaN NaN \n",
"MALIDE Younes NaN NaN 3 \n",
"MOENY MOKO Nadjma 0 0 0 \n",
"MOURTADJOU El-Fazar 1 1 3 \n",
"SAID Chamsoudine NaN NaN NaN \n",
"YANCOUB Toufa 2 NaN NaN \n",
"YOUSSOUF Asma 0 0 2 \n",
"\n",
" Exercice 6 Couleur présente Formule tableur \\\n",
"ABDALLAH Touraya 1.000000 3 0 \n",
"ABDOU Mariam 0.666667 0 2 \n",
"ABTOIHI SAID Yasmina 1.000000 3 NaN \n",
"AHAMED Anssuifidine 1.000000 3 NaN \n",
"AHAMED Issihaka 1.333333 3 1 \n",
"AHMED ABDOU El-Karim 0.000000 NaN NaN \n",
"ANDILI Chayhati 1.666667 3 2 \n",
"ANDJILANE Rachma 2.333333 3 2 \n",
"ANLI Koudoussia 1.666667 3 2 \n",
"ATTOUMANI Hanissa 1.000000 3 NaN \n",
"BACO ABDALLAH Moustadirane 0.000000 0 0 \n",
"BINALI Maoulida 1.000000 3 NaN \n",
"BOINA Ainati 0.000000 NaN NaN \n",
"BOINA HASSANI Nahimi 0.666667 0 2 \n",
"DAOUD El-Farouk 0.000000 NaN NaN \n",
"DJADAR Ifrah 1.000000 3 NaN \n",
"HALIBOU Nafilati 1.000000 3 NaN \n",
"HOUMADI Himida 0.000000 0 0 \n",
"HOUMADI Antufati 1.666667 3 2 \n",
"HOUMADI ABDALLAH Abdallah 1.333333 3 1 \n",
"MALIDE ABDOU Nasser 1.000000 3 NaN \n",
"MALIDE Younes 1.333333 0 3 \n",
"MOENY MOKO Nadjma 1.000000 3 NaN \n",
"MOURTADJOU El-Fazar 1.000000 0 3 \n",
"SAID Chamsoudine 0.000000 NaN NaN \n",
"YANCOUB Toufa 0.000000 NaN NaN \n",
"YOUSSOUF Asma 1.333333 3 1 \n",
"\n",
" Nombre boule (équation) \n",
"ABDALLAH Touraya 0 \n",
"ABDOU Mariam 0 \n",
"ABTOIHI SAID Yasmina 0 \n",
"AHAMED Anssuifidine 0 \n",
"AHAMED Issihaka 0 \n",
"AHMED ABDOU El-Karim NaN \n",
"ANDILI Chayhati 0 \n",
"ANDJILANE Rachma 2 \n",
"ANLI Koudoussia NaN \n",
"ATTOUMANI Hanissa 0 \n",
"BACO ABDALLAH Moustadirane 0 \n",
"BINALI Maoulida NaN \n",
"BOINA Ainati NaN \n",
"BOINA HASSANI Nahimi 0 \n",
"DAOUD El-Farouk NaN \n",
"DJADAR Ifrah 0 \n",
"HALIBOU Nafilati 0 \n",
"HOUMADI Himida 0 \n",
"HOUMADI Antufati NaN \n",
"HOUMADI ABDALLAH Abdallah 0 \n",
"MALIDE ABDOU Nasser NaN \n",
"MALIDE Younes 1 \n",
"MOENY MOKO Nadjma 0 \n",
"MOURTADJOU El-Fazar 0 \n",
"SAID Chamsoudine NaN \n",
"YANCOUB Toufa NaN \n",
"YOUSSOUF Asma NaN \n",
"\n",
"[27 rows x 32 columns]"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 27.000000\n",
"mean 13.203704\n",
"std 5.653895\n",
"min 5.500000\n",
"25% 8.750000\n",
"50% 10.500000\n",
"75% 16.250000\n",
"max 28.500000\n",
"Name: BB_16_05_31, dtype: float64"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes[ds_name].describe()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe0192f67f0>"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAFXCAYAAABtOQ2RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noy4kf86SN8Mdyltf9uc
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe0190fa940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"notes[ds_name].hist(bins = barem[ds_name][0], range=(0,barem[ds_name][0]),)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bilan personnels"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes = notes.astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['BB_16_05_31', 'Présentation', 'Exercice 1', 'Lecture graphique',\n",
" 'Calcul', 'Maximum', 'Exercice 2', 'Application pgm calcul',\n",
" 'Proposition 1', 'Proposition 2', 'Proposition 3', 'Proposition 4',\n",
" 'Exercice 3', 'Construction géométrique', 'Thalès', 'Périmètre',\n",
" 'Exercice 4', 'Proportionnalité temps', 'Pythagore',\n",
" 'Utilisation formule', 'Vitesse', 'Tableau', 'Total', 'Exercice 5',\n",
" 'Prix lettre métro', 'Prix lettre mayotte', 'Tache complexe', 'Volume',\n",
" 'Exercice 6', 'Couleur présente', 'Formule tableur',\n",
" 'Nombre boule (équation)'],\n",
" dtype='object')"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes.T.index"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Exercice 1',\n",
" 'Exercice 2',\n",
" 'Exercice 3',\n",
" 'Exercice 4',\n",
" 'Exercice 5',\n",
" 'Exercice 6']"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_exo = [\"Exercice \"+str(i+1) for i in range(6)]\n",
"list_exo"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
"#notes[list_exo].head()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"autres_notes = ['Présentation']"
]
},
{
"cell_type": "code",
"execution_count": 65,
"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": 66,
"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": 67,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
"#notes.head()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"eleves = notes.copy()\n",
"eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 27.000000\n",
"mean 13.203704\n",
"std 5.653895\n",
"min 5.500000\n",
"25% 8.750000\n",
"50% 10.500000\n",
"75% 16.250000\n",
"max 28.500000\n",
"Name: BB_16_05_31, dtype: float64"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes[ds_name].describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'classe': '313', 'date': '31 mai 2016', 'titre': 'Dernier Brevet Blanc!'}"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"latex_info = {}\n",
"latex_info['titre'] = \"Dernier Brevet Blanc!\"\n",
"latex_info['classe'] = \"313\"\n",
"latex_info['date'] = \"31 mai 2016\"\n",
"latex_info"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bilan = texenv.get_template(\"./tpl_bilan.tex\")\n",
"cible_bilan = \"../3e/DS/BB_16_05_18/Bilan/\""
]
},
{
"cell_type": "code",
"execution_count": 87,
"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": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Bilan 3e trimestre"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ds_name = \"Notes\"\n",
"notes = all_notes.parse(ds_name)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['DM_16_03_30', 'BB_16_04_02', 'BB_16_04_19', 'Enclos', 'DM_16_05_18',\n",
" 'BB_16_05_31', 'Connaissance trimestre 3'],\n",
" dtype='object')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notes[17:].index"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trim3 = notes[17:].T"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"barem = trim3[:1]\n",
"notesT3 = trim3[1:32]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notesT3 = notesT3.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>Enclos</th>\n",
" <th>DM_16_05_18</th>\n",
" <th>BB_16_05_31</th>\n",
" <th>Connaissance 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>19</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 Enclos DM_16_05_18 BB_16_05_31 \\\n",
"BAREME 20 31 41 19 20 37 \n",
"\n",
" Connaissance trimestre 3 \n",
"BAREME 20 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"barem"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"ABDALLAH Touraya 4\n",
"ABDOU Mariam 18.5\n",
"ABTOIHI SAID Yasmina 16.5\n",
"AHAMED Anssuifidine 10.5\n",
"AHAMED Issihaka 12.5\n",
"AHMED ABDOU El-Karim 7\n",
"ANDILI Chayhati 11\n",
"ANDJILANE Rachma 13\n",
"ANLI Koudoussia 11\n",
"ATTOUMANI Hanissa 15.5\n",
"BACO ABDALLAH Moustadirane 10.5\n",
"BINALI Maoulida 16\n",
"BOINA Ainati 14.5\n",
"BOINA HASSANI Nahimi 13\n",
"DAOUD El-Farouk 13\n",
"DJADAR Ifrah 13\n",
"HALIBOU Nafilati 6\n",
"HOUMADI Himida 11.5\n",
"HOUMADI Antufati 6.5\n",
"HOUMADI ABDALLAH Abdallah 18\n",
"IBRAHIM Laoura 4\n",
"MALIDE ABDOU Nasser 18\n",
"MALIDE Younes 19\n",
"MOENY MOKO Nadjma 5.5\n",
"MOURTADJOU El-Fazar 14\n",
"SAID Chamsoudine 15\n",
"YANCOUB Toufa 13.5\n",
"YOUSSOUF Asma 4.5\n",
"Name: Connaissance trimestre 3, dtype: object"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"notesT3['Connaissance trimestre 3']\n"
]
},
{
"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)))"
]
}
],
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