418 lines
16 KiB
Plaintext
418 lines
16 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Bilan pour les Tsmtg"
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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": 37,
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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": 38,
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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_tstmg.xls\",sheetname=\"DST_03\")"
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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": 39,
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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.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": 40,
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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": "markdown",
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"metadata": {},
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"source": [
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"On extrait le barème et on l'enlève ainsi que les lignes du tableau qui ne servent à rien."
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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": 41,
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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:-6]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"On enlèves les élèves absents"
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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": 42,
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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[\"DST_03\"].notnull()]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"On remplace les réponses NaN par \\NoRep"
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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": 43,
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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.fillna(\"\\\\NoRep\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"On arrondit les points de chacuns des exercices"
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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": 44,
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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['Exercice 1'] = notes['Exercice 1'].round(2)\n",
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"#notes['Exercice 2'] = notes['Exercice 2'].round(2)\n",
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"notes[['Exercice 1', 'Exercice 2']] = notes[['Exercice 1', 'Exercice 2']].applymap(lambda x:round(x,2))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"On remplace les notes par le code latex"
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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": 45,
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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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"toReplist = ['1.1 (Arbre)', '1.2.a (Décrire évènement)', '1.2.b (Calculer probabilité)', '1.2.c (Calculer probabiltié)', '1.3 (Calculer probabilté)', '1.4 (probabiltié conditionnelle)', '1.5.a (probabilité loi normale)', '1.5.b (probabilité loi normale)', '1.6.a (intervalle 95\\%)', '1.6.b (interprétation)', '2.1 (indice)', '2.2 (indice)', '2.3.a (taux évolution)', '2.3.b (taux évolution moyen)', '2.4 (projection)']"
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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": 46,
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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 val == \"\\\\NoRep\":\n",
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" return val\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"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Pour séléctionner plusieurs colonnes, il faut juste donner entre [] la liste des noms de colones! Facile!"
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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": 47,
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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[toReplist] = notes[toReplist].applymap(toRepVal)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"On entre tout dans le fichier près à compiler!"
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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": 56,
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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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"bilan = texenv.get_template(\"tpl_bilan.tex\")\n",
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"with open(\"./bilan.tex\",\"w\") as f:\n",
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" f.write(bilan.render(eleves = notes, barem = barem))"
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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": 48,
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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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"<matplotlib.axes._subplots.AxesSubplot at 0x7f7645bcca58>"
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]
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},
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"execution_count": 48,
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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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"data": {
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"image/png": [
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],
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"text/plain": [
|
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"<matplotlib.figure.Figure at 0x7f7645bcc2e8>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"notes[\"DST_03\"].hist(bins = 21)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 51,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"count 21.000000\n",
|
|
"mean 11.833333\n",
|
|
"std 3.461695\n",
|
|
"min 5.500000\n",
|
|
"25% 10.000000\n",
|
|
"50% 12.000000\n",
|
|
"75% 15.000000\n",
|
|
"max 18.000000\n",
|
|
"Name: DST_03, dtype: float64"
|
|
]
|
|
},
|
|
"execution_count": 51,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"notes.DST_03.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
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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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.4.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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