764 lines
25 KiB
Plaintext
764 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 49,
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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 opytex 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": "markdown",
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"metadata": {},
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"source": [
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"# Informations sur le devoir"
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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": 50,
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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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"{'classe': '309', 'date': '18 septembre 2015', 'titre': 'DM 1'}"
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]
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},
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"execution_count": 50,
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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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"ds_name = \"DM_15_09_18\"\n",
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"classe = \"309\"\n",
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"\n",
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"latex_info = {}\n",
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"latex_info['titre'] = \"DM 1\" \n",
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"latex_info['classe'] = classe\n",
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"latex_info['date'] = \"18 septembre 2015\"\n",
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"latex_info"
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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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"# Import et premiers traitements"
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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": 51,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"notes = pd.ExcelFile(\"./../../../309.xlsx\")\n",
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"notes.sheet_names\n",
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"notes = notes.parse(ds_name)"
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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": 52,
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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([ 'DM_15_09_18', 'numero sujet', 'Exercice 1', '1 (PGCD)',\n",
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" '2 (Quantités)', 'Exercice 2', 1, 2,\n",
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" 3, 4, 5, 6],\n",
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" dtype='object')"
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]
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},
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"execution_count": 52,
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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": 53,
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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": 54,
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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)\n",
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"notes = notes.drop('numero sujet', 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": 55,
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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/html": [
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"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>DM_15_09_18</th>\n",
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" <th>Exercice 1</th>\n",
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" <th>1 (PGCD)</th>\n",
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" <th>2 (Quantités)</th>\n",
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" <th>Exercice 2</th>\n",
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" <th>1</th>\n",
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" <th>2</th>\n",
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" <th>3</th>\n",
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" <th>4</th>\n",
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" <th>5</th>\n",
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" <th>6</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>BAREME</th>\n",
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" <td>10</td>\n",
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" <td>4</td>\n",
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" <td>2</td>\n",
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" <td>2</td>\n",
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" <td>6</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" DM_15_09_18 Exercice 1 1 (PGCD) 2 (Quantités) Exercice 2 1 2 3 \\\n",
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"BAREME 10 4 2 2 6 1 1 1 \n",
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"\n",
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" 4 5 6 \n",
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"BAREME 1 1 1 "
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]
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},
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"execution_count": 55,
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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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"barem = notes[:1]\n",
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"notes = notes[1:]\n",
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"notes\n",
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"barem"
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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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"## Supression des notes inutiles "
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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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"notes = notes[notes[ds_name].notnull()]\n",
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"#notes = notes[notes[ds_name] != 'abs']"
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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": 57,
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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.astype(float)"
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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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"## Traitement des notes"
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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": 58,
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"metadata": {
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|
"collapsed": false,
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|
"scrolled": true
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},
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|
"outputs": [
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{
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|
"data": {
|
|
"text/plain": [
|
|
"Index([ 'DM_15_09_18', 'Exercice 1', '1 (PGCD)', '2 (Quantités)',\n",
|
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" 'Exercice 2', 1, 2, 3,\n",
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" 4, 5, 6],\n",
|
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" dtype='object')"
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]
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|
},
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|
"execution_count": 58,
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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.T.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": 59,
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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\"]"
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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": 60,
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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/html": [
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
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" <th>Exercice 1</th>\n",
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" <th>Exercice 2</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>ABDOU Farida</th>\n",
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" <td>4.00</td>\n",
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" <td>0.33</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ABOU BACAR Djaha</th>\n",
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" <td>4.00</td>\n",
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" <td>6.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>AHAMADA Nabaouya</th>\n",
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" <td>4.00</td>\n",
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" <td>6.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>AHAMADI Faina</th>\n",
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" <td>1.33</td>\n",
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" <td>2.67</td>\n",
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|
" </tr>\n",
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" <tr>\n",
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" <th>ALI Mardhuia</th>\n",
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" <td>2.67</td>\n",
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" <td>6.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ALI SOULAIMANA Chamsia</th>\n",
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" <td>2.00</td>\n",
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" <td>6.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ALSENE ALI MADI Stela</th>\n",
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" <td>0.67</td>\n",
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" <td>3.33</td>\n",
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|
" </tr>\n",
|
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" <tr>\n",
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" <th>ANDRIATAHIANA Hoby</th>\n",
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" <td>4.00</td>\n",
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" <td>3.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ANLI Emeline</th>\n",
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" <td>2.00</td>\n",
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" <td>4.33</td>\n",
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|
" </tr>\n",
|
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" <tr>\n",
|
|
" <th>ATHOUMANE Naouidat</th>\n",
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|
" <td>3.33</td>\n",
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|
" <td>5.33</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
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|
" <th>BOUDRA Nassifanya</th>\n",
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" <td>4.00</td>\n",
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" <td>6.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
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" <th>CHANFI Nadhrati</th>\n",
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" <td>3.33</td>\n",
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" <td>2.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
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|
" <th>COMBO Moinécha</th>\n",
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" <td>4.00</td>\n",
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" <td>6.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>HALIDI Nisma</th>\n",
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" <td>4.00</td>\n",
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" <td>5.33</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>HAMZA Samianti</th>\n",
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" <td>3.33</td>\n",
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" <td>1.67</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>HOUMADI Mouslimati</th>\n",
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" <td>2.67</td>\n",
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" <td>5.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>HOUMADI Chaharazadi</th>\n",
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" <td>2.00</td>\n",
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" <td>4.67</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>HOUMADI Nasmi</th>\n",
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" <td>4.00</td>\n",
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" <td>6.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>HOUMADI Dhoirfia</th>\n",
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" <td>2.00</td>\n",
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" <td>3.67</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>LOUTOUFI Nachima</th>\n",
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" <td>1.33</td>\n",
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" <td>2.33</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>MALIDE El-Anzize</th>\n",
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" <td>2.00</td>\n",
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" <td>2.67</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>MONNE Kevin</th>\n",
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" <td>4.00</td>\n",
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" <td>4.33</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>MOUSSA Roibouanti</th>\n",
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" <td>4.00</td>\n",
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" <td>6.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>OUSSENI Hilma</th>\n",
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" <td>2.00</td>\n",
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" <td>3.33</td>\n",
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" </tr>\n",
|
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" <tr>\n",
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|
" <th>SAANLI Natali</th>\n",
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" <td>4.00</td>\n",
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" <td>1.67</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>SAID AHAMADA Roukaya</th>\n",
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" <td>4.00</td>\n",
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" <td>5.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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|
" <th>SANDA Issoufi</th>\n",
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" <td>2.00</td>\n",
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" <td>4.67</td>\n",
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|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>SOILIHI Soifia</th>\n",
|
|
" <td>2.00</td>\n",
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" <td>4.00</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>SOUFIANI Laila</th>\n",
|
|
" <td>2.00</td>\n",
|
|
" <td>2.67</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>YOUSSOUF Sitirati</th>\n",
|
|
" <td>2.00</td>\n",
|
|
" <td>5.00</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" Exercice 1 Exercice 2\n",
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|
"ABDOU Farida 4.00 0.33\n",
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|
"ABOU BACAR Djaha 4.00 6.00\n",
|
|
"AHAMADA Nabaouya 4.00 6.00\n",
|
|
"AHAMADI Faina 1.33 2.67\n",
|
|
"ALI Mardhuia 2.67 6.00\n",
|
|
"ALI SOULAIMANA Chamsia 2.00 6.00\n",
|
|
"ALSENE ALI MADI Stela 0.67 3.33\n",
|
|
"ANDRIATAHIANA Hoby 4.00 3.00\n",
|
|
"ANLI Emeline 2.00 4.33\n",
|
|
"ATHOUMANE Naouidat 3.33 5.33\n",
|
|
"BOUDRA Nassifanya 4.00 6.00\n",
|
|
"CHANFI Nadhrati 3.33 2.00\n",
|
|
"COMBO Moinécha 4.00 6.00\n",
|
|
"HALIDI Nisma 4.00 5.33\n",
|
|
"HAMZA Samianti 3.33 1.67\n",
|
|
"HOUMADI Mouslimati 2.67 5.00\n",
|
|
"HOUMADI Chaharazadi 2.00 4.67\n",
|
|
"HOUMADI Nasmi 4.00 6.00\n",
|
|
"HOUMADI Dhoirfia 2.00 3.67\n",
|
|
"LOUTOUFI Nachima 1.33 2.33\n",
|
|
"MALIDE El-Anzize 2.00 2.67\n",
|
|
"MONNE Kevin 4.00 4.33\n",
|
|
"MOUSSA Roibouanti 4.00 6.00\n",
|
|
"OUSSENI Hilma 2.00 3.33\n",
|
|
"SAANLI Natali 4.00 1.67\n",
|
|
"SAID AHAMADA Roukaya 4.00 5.00\n",
|
|
"SANDA Issoufi 2.00 4.67\n",
|
|
"SOILIHI Soifia 2.00 4.00\n",
|
|
"SOUFIANI Laila 2.00 2.67\n",
|
|
"YOUSSOUF Sitirati 2.00 5.00"
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|
]
|
|
},
|
|
"execution_count": 60,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"notes[list_exo] = notes[list_exo].applymap(lambda x:round(x,2))\n",
|
|
"notes[list_exo]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 61,
|
|
"metadata": {
|
|
"collapsed": false,
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['1 (PGCD)', '2 (Quantités)', 1, 2, 3, 4, 5, 6]"
|
|
]
|
|
},
|
|
"execution_count": 61,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"item_avec_note = list_exo + [ds_name]\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": 62,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def toRepVal(val):\n",
|
|
" if pd.isnull(val):\n",
|
|
" return \"\\\\NoRep\"\n",
|
|
" elif val == 0:\n",
|
|
" return \"\\\\RepZ\"\n",
|
|
" elif val == 1:\n",
|
|
" return \"\\\\RepU\"\n",
|
|
" elif val == 2:\n",
|
|
" return \"\\\\RepD\"\n",
|
|
" elif val == 3:\n",
|
|
" return \"\\\\RepT\"\n",
|
|
" else:\n",
|
|
" return val"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 63,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"notes[item_avec_note] = notes[item_avec_note].fillna(\".\")\n",
|
|
"#notes"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 64,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"eleves = notes.copy()\n",
|
|
"eleves[sous_exo] = notes[sous_exo].applymap(toRepVal)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 65,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"11"
|
|
]
|
|
},
|
|
"execution_count": 65,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"len(notes.T.index)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Preparation du fichier .tex"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 66,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"bilan = texenv.get_template(\"tpl_bilan.tex\")\n",
|
|
"with open(\"./bilan309.tex\",\"w\") as f:\n",
|
|
" f.write(bilan.render(eleves = eleves, barem = barem, ds_name = ds_name, latex_info = latex_info, nbr_questions = len(barem.T)))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Un peu de statistiques"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 43,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"count 29.000000\n",
|
|
"mean 7.120690\n",
|
|
"std 2.149149\n",
|
|
"min 3.500000\n",
|
|
"25% 5.500000\n",
|
|
"50% 7.000000\n",
|
|
"75% 9.000000\n",
|
|
"max 10.000000\n",
|
|
"Name: DM_15_09_18, dtype: float64"
|
|
]
|
|
},
|
|
"execution_count": 43,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"notes[ds_name].describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 44,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7efd747b1630>"
|
|
]
|
|
},
|
|
"execution_count": 44,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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2b6yyP2b7xtJ5BrF9q+0V23tX/exHbN9n+1u2d9merVuj9bCOiO9Jujwi5iTNSbrC9vva\nrlfQjZKe0AS88qWFkDQfEedHxEWlwzT0F5LujYh3SjpPUpp6LSK+WR3z8yVdKOkVSXcVjjU022+X\ndL2kCyPiPepXnNeWTTU82z8j6XckvVfSz0q6yvZPlU010Hb131y42k2S7ouIcyX9S7W9rg19NkhE\nvFJdPFHSCZIOb2S9cbN9tqQPSvqCJunXvs2ky237NEmXRsStUv93IxHxUuFYbW2T9FREHBh4y8ky\nI2lL9SKCLeq/8iuLn5a0OyK+FxGvS7pf0q8VzlQrIh6Q9OIxP75G0m3V5dskfahujQ0Na9vH2V6W\ntCJpV0Q8tJH1CvispE8q2T8yq4SkXba/YftjpcM0sFXSC7a3237E9udtbykdqqVrJd1ROkQTEfGs\npFsk/bf6r/L6TkT8c9lUjTwm6dKqRtgi6ZclnV04UxtnRMRKdXlF0hl1N97oI+vDVQ1ytqT32X73\nRtYbJ9tXSXo+IvYo4aPTyiURcaGkKyX9vu1LSwca0oykCyT9VURcIOm7GvAUcBLZPlHS1ZL+rnSW\nJmz/sPqP6nqS3ibpLbZ/o2ioBiLiSUl/LmmXpK9K2qO8D7gkSdF/pUdtFbspH5FaPYVd1Js7mUn2\n85Kusf20pDsl/aLtLxXO1EhEfLv6/oL6nWmW3voZSc+seia2U/3hnc2Vkh6ujn8m2yQ9HRH/GxGH\nJH1F/ftDGhFxa0T8XERcJuk76r8fJJsV22dKku2zJD1fd+ONvBrk9CO/vbR9sqT3K9cvif4kIs6J\niK3qP5X9WkR8uHSuYdneYvvU6vIpkj4gaW/9fzUZIuI5SQdsn1v9aJukxwtGaus69f+hz+a/JF1s\n+2T33368Tf1fsqdh+8er7z8h6VeVrIqq/KOkj1SXPyLpH+puPOjt5nXOknRb9TGqx0n6m4i4dwPr\nlZbt1SBnSLqreqv/jKTbI2JX2UiNXC/p9qpKeErSRwvnaaT6B3KbpEy/K5AkRcSDtndKekTSoer7\nX5dN1dhO2z8q6TVJvxcRB0sHqmP7TkmXSTrd9gFJn5L0Z5L+1vZvS9ov6ddr1+BNMQAw+fjfegFA\nAgxrAEiAYQ0ACTCsASABhjUAJMCwBoAEGNYAkADDGgAS+H+jULq3dz6RcwAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7efd747b1a90>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"notes_seules = notes[ds_name]\n",
|
|
"notes_seules.hist(bins = (notes_seules.max() - notes_seules.min())*2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"notes_questions = notes[sous_exo]\n",
|
|
"notes_analysis = notes_questions.describe()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>1 (PGCD)</th>\n",
|
|
" <th>2 (Quantités)</th>\n",
|
|
" <th>1</th>\n",
|
|
" <th>2</th>\n",
|
|
" <th>3</th>\n",
|
|
" <th>4</th>\n",
|
|
" <th>5</th>\n",
|
|
" <th>6</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>count</th>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" <td>NaN</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 1 (PGCD) 2 (Quantités) 1 2 3 4 5 6\n",
|
|
"count NaN NaN NaN NaN NaN NaN NaN NaN"
|
|
]
|
|
},
|
|
"execution_count": 46,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# J'aimerai récupérer le nom des questions qui ont été le moins répondus\n",
|
|
"notes_analysis[:1][notes_analysis[:1] == 25]"
|
|
]
|
|
},
|
|
{
|
|
"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",
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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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}
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