323 lines
14 KiB
Plaintext
323 lines
14 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 87,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from texenv import texenv\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 88,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"notes = pd.read_excel(\"./../../../notes_1stmg.xls\",sheetname=\"DS_0320\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 89,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['DS_0320', 'av_arrondi', 'Exercice 1', '1 (double inclusion)', '2 (double inclusion)', '3 (proportion)', 'Exercice 2', '1 (Remplir tableau)', '2.a (calculer proportion)', '2.b (calculer proportion)', '2.c (proportion parmi)', '2.d (proportion parmi)', 'Exercice 3', '1 (completer arbre)', '2.a (proportion feuille)', '2.b (proportion feuille)', '2.c (proportion ou)', '3 (calculer quantité)', 'Exercice 4', '1 (tracer fonction)', '2 (maximum)'], dtype='object')"
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]
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},
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"execution_count": 89,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"notes.index"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 90,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"notes = notes.T"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 91,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"notes = notes.drop(\"av_arrondi\", axis=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 92,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"barem = notes[:1]\n",
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"notes = notes[1:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 93,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"notes = notes[notes[\"DS_0320\"].notnull()]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 94,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "could not convert string to float: ",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32m<ipython-input-94-7ea0c14a88f0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnotes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnotes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[1;32m/home/lafrite/.virtualenvs/data_analysis/lib/python3.4/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, raise_on_error)\u001b[0m\n\u001b[0;32m 2212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2213\u001b[0m mgr = self._data.astype(\n\u001b[1;32m-> 2214\u001b[1;33m dtype=dtype, copy=copy, raise_on_error=raise_on_error)\n\u001b[0m\u001b[0;32m 2215\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2216\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m/home/lafrite/.virtualenvs/data_analysis/lib/python3.4/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[0;32m 2500\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2501\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2502\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'astype'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2503\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2504\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m/home/lafrite/.virtualenvs/data_analysis/lib/python3.4/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, axes, filter, do_integrity_check, **kwargs)\u001b[0m\n\u001b[0;32m 2455\u001b[0m copy=align_copy)\n\u001b[0;32m 2456\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2457\u001b[1;33m \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2458\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2459\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;32m/home/lafrite/.virtualenvs/data_analysis/lib/python3.4/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, raise_on_error, values)\u001b[0m\n\u001b[0;32m 369\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_on_error\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 370\u001b[0m return self._astype(dtype, copy=copy, raise_on_error=raise_on_error,\n\u001b[1;32m--> 371\u001b[1;33m values=values)\n\u001b[0m\u001b[0;32m 372\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 373\u001b[0m def _astype(self, dtype, copy=False, raise_on_error=True, values=None,\n",
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"\u001b[1;32m/home/lafrite/.virtualenvs/data_analysis/lib/python3.4/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36m_astype\u001b[1;34m(self, dtype, copy, raise_on_error, values, klass)\u001b[0m\n\u001b[0;32m 399\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 400\u001b[0m \u001b[1;31m# _astype_nansafe works fine with 1-d only\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 401\u001b[1;33m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_astype_nansafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 402\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 403\u001b[0m newb = make_block(values,\n",
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"\u001b[1;32m/home/lafrite/.virtualenvs/data_analysis/lib/python3.4/site-packages/pandas/core/common.py\u001b[0m in \u001b[0;36m_astype_nansafe\u001b[1;34m(arr, dtype, copy)\u001b[0m\n\u001b[0;32m 2627\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2628\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2629\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2630\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2631\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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"\u001b[1;31mValueError\u001b[0m: could not convert string to float: "
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]
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}
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],
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"source": [
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"notes = notes.astype(float)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 95,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"count 31\n",
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"unique 18\n",
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"top 11\n",
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"freq 5\n",
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"Name: DS_0320, dtype: float64"
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]
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},
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"execution_count": 95,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"notes['DS_0320'].describe()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 96,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"list_exo = [\"Exercice 1\", \"Exercice 2\", \"Exercice 3\", \"Exercice 4\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 97,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 98,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"def toRepVal(val):\n",
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" if pd.isnull(val):\n",
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" return \"\\\\NoRep\"\n",
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" elif val == 0:\n",
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" return \"\\\\RepZ\"\n",
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" elif val == 1:\n",
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" return \"\\\\RepU\"\n",
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" elif val == 2:\n",
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" return \"\\\\RepD\"\n",
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" elif val == 3:\n",
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" return \"\\\\RepT\"\n",
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" else:\n",
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|
" return val"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 99,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"['1 (double inclusion)',\n",
|
||
|
|
" '2 (double inclusion)',\n",
|
||
|
|
" '3 (proportion)',\n",
|
||
|
|
" '1 (Remplir tableau)',\n",
|
||
|
|
" '2.a (calculer proportion)',\n",
|
||
|
|
" '2.b (calculer proportion)',\n",
|
||
|
|
" '2.c (proportion parmi)',\n",
|
||
|
|
" '2.d (proportion parmi)',\n",
|
||
|
|
" '1 (completer arbre)',\n",
|
||
|
|
" '2.a (proportion feuille)',\n",
|
||
|
|
" '2.b (proportion feuille)',\n",
|
||
|
|
" '2.c (proportion ou)',\n",
|
||
|
|
" '3 (calculer quantité)',\n",
|
||
|
|
" '1 (tracer fonction)',\n",
|
||
|
|
" '2 (maximum)']"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 99,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"sous_exo = [i for i in notes.T.index if i not in list_exo + ['DS_0320']]\n",
|
||
|
|
"sous_exo"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 100,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"notes[sous_exo] = notes[sous_exo].applymap(toRepVal)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 101,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Index(['DS_0320', 'Exercice 1', '1 (double inclusion)', '2 (double inclusion)', '3 (proportion)', 'Exercice 2', '1 (Remplir tableau)', '2.a (calculer proportion)', '2.b (calculer proportion)', '2.c (proportion parmi)', '2.d (proportion parmi)', 'Exercice 3', '1 (completer arbre)', '2.a (proportion feuille)', '2.b (proportion feuille)', '2.c (proportion ou)', '3 (calculer quantité)', 'Exercice 4', '1 (tracer fonction)', '2 (maximum)'], dtype='object')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 101,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"notes.T.index"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 102,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"bilan = texenv.get_template(\"tpl_bilan.tex\")\n",
|
||
|
|
"with open(\"./bilan.tex\",\"w\") as f:\n",
|
||
|
|
" f.write(bilan.render(eleves = notes, barem = barem))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 3
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.4.3"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 0
|
||
|
|
}
|