246 lines
4.6 KiB
Plaintext
246 lines
4.6 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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"# Advanced indexing"
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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": 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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"%matplotlib inline\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"try:\n",
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" import seaborn\n",
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"except ImportError:\n",
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" pass\n",
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"\n",
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"pd.options.display.max_rows = 10"
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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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"This dataset is borrowed from the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so all credit to him!). You can download these data from here: [`titles.csv`](https://drive.google.com/file/d/0B3G70MlBnCgKa0U4WFdWdGdVOFU/view?usp=sharing) and [`cast.csv`](https://drive.google.com/file/d/0B3G70MlBnCgKRzRmTWdQTUdjNnM/view?usp=sharing) and put them in the `/data` folder."
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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": null,
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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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"cast = pd.read_csv('data/cast.csv')\n",
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"cast.head()"
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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": null,
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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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"titles = pd.read_csv('data/titles.csv')\n",
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"titles.head()"
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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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"## Setting columns as the index"
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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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"Why is it useful to have an index?\n",
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"\n",
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"- Giving meaningful labels to your data -> easier to remember which data are where\n",
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"- Unleash some powerful methods, eg with a DatetimeIndex for time series\n",
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"- Easier and faster selection of data\n",
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"\n",
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"It is this last one we are going to explore here!"
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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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"Setting the `title` column as the 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": 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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"c = cast.set_index('title')"
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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": null,
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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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"c.head()"
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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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"Instead of doing:"
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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": null,
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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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"%%time\n",
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"cast[cast['title'] == 'Hamlet']"
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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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"we can now do:"
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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": null,
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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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"%%time\n",
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"c.loc['Hamlet']"
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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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"But you can also have multiple columns as the index, leading to a **multi-index or hierarchical 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": null,
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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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"c = cast.set_index(['title', 'year'])"
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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": null,
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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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"c.head()"
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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": null,
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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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"%%time\n",
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"c.loc[('Hamlet', 2000),:]"
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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": 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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"c2 = c.sort_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": null,
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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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"%%time\n",
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"c2.loc[('Hamlet', 2000),:]"
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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": 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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"celltoolbar": "Nbtutor - export exercises",
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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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