914 lines
19 KiB
Plaintext
914 lines
19 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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"# Indexing and selecting data"
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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"
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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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"# redefining the example objects\n",
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"\n",
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"# series\n",
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"population = pd.Series({'Germany': 81.3, 'Belgium': 11.3, 'France': 64.3, \n",
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" 'United Kingdom': 64.9, 'Netherlands': 16.9})\n",
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"\n",
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"# dataframe\n",
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"data = {'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],\n",
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" 'population': [11.3, 64.3, 81.3, 16.9, 64.9],\n",
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" 'area': [30510, 671308, 357050, 41526, 244820],\n",
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" 'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'London']}\n",
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"countries = pd.DataFrame(data)\n",
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"countries"
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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 index to the country names:"
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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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"countries = countries.set_index('country')\n",
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"countries"
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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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"## Some notes on selecting data\n",
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"\n",
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"One of pandas' basic features is the labeling of rows and columns, but this makes indexing also a bit more complex compared to numpy. We now have to distuinguish between:\n",
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"\n",
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"- selection by label\n",
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"- selection by position."
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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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"### `data[]` provides some convenience shortcuts "
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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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"For a DataFrame, basic indexing selects the columns.\n",
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"\n",
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"Selecting a single column:"
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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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"countries['area']"
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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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"or multiple columns:"
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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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"countries[['area', 'population']]"
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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, slicing accesses the rows:"
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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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"countries['France':'Netherlands']"
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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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"<div class=\"alert alert-danger\">\n",
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" <b>NOTE</b>: Unlike slicing in numpy, the end label is **included**.\n",
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"</div>"
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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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"So as a summary, `[]` provides the following convenience shortcuts:\n",
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"\n",
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"- Series: selecting a label: `s[label]`\n",
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"- DataFrame: selecting a single or multiple columns: `df['col']` or `df[['col1', 'col2']]`\n",
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"- DataFrame: slicing the rows: `df['row_label1':'row_label2']` or `df[mask]`"
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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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"### Systematic indexing with `loc` and `iloc`"
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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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"When using `[]` like above, you can only select from one axis at once (rows or columns, not both). For more advanced indexing, you have some extra attributes:\n",
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" \n",
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"* `loc`: selection by label\n",
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"* `iloc`: selection by position\n",
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"\n",
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"These methods index the different dimensions of the frame:\n",
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"\n",
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"* `df.loc[row_indexer, column_indexer]`\n",
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"* `df.iloc[row_indexer, column_indexer]`"
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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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"Selecting a single element:"
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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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"countries.loc['Germany', 'area']"
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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 the row or column indexer can also be a list, slice, boolean array, .."
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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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"countries.loc['France':'Germany', ['area', 'population']]"
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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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"---\n",
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"Selecting by position with `iloc` works similar as indexing numpy arrays:"
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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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"countries.iloc[0:2,1:3]"
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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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"The different indexing methods can also be used to assign data:"
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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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"countries2 = countries.copy()\n",
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"countries2.loc['Belgium':'Germany', 'population'] = 10"
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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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"countries2"
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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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"## Boolean indexing (filtering)"
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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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"Often, you want to select rows based on a certain condition. This can be done with 'boolean indexing' (like a where clause in SQL). \n",
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"\n",
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"The indexer (or boolean mask) should be 1-dimensional and the same length as the thing being indexed."
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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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"countries['area'] > 100000"
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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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"countries[countries['area'] > 100000]"
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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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"---\n",
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"\n",
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"<div class=\"alert alert-success\">\n",
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" <b>EXERCISE</b>: Add a column `density` with the population density (note: population column is expressed in millions)\n",
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"</div>"
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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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"clear_cell": true,
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"collapsed": false
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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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div class=\"alert alert-success\">\n",
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" <b>EXERCISE</b>: Select the capital and the population column of those countries where the density is larger than 300\n",
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"</div>"
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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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"clear_cell": true,
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"collapsed": false
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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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div class=\"alert alert-success\">\n",
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" <b>EXERCISE</b>: Add a column 'density_ratio' with the ratio of the density to the mean density\n",
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"</div>"
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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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"clear_cell": true,
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"collapsed": false
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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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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<div class=\"alert alert-success\">\n",
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" <b>EXERCISE</b>: Change the capital of the UK to Cambridge\n",
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"</div>"
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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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"clear_cell": true,
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"collapsed": false
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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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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"<div class=\"alert alert-success\">\n",
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" <b>EXERCISE</b>: Select all countries whose population density is between 100 and 300 people/km²\n",
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"</div>"
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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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"clear_cell": true,
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"collapsed": false
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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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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"## Some other useful methods: `isin` and string methods"
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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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"The `isin` method of Series is very useful to select rows that may contain certain values:"
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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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"s = countries['capital']"
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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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"s.isin?"
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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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"s.isin(['Berlin', 'London'])"
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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 can then be used to filter the dataframe with boolean 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": false
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},
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"outputs": [],
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"source": [
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"countries[countries['capital'].isin(['Berlin', 'London'])]"
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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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"Let's say we want to select all data for which the capital starts with a 'B'. In Python, when having a string, we could use the `startswith` method:"
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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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"'Berlin'.startswith('B')"
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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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"In pandas, these are available on a Series through the `str` namespace:"
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]
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},
|
|
{
|
|
"cell_type": "code",
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|
"execution_count": null,
|
|
"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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"countries['capital'].str.startswith('B')"
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]
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},
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{
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"cell_type": "markdown",
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|
"metadata": {},
|
|
"source": [
|
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"For an overview of all string methods, see: http://pandas.pydata.org/pandas-docs/stable/api.html#string-handling"
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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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"<div class=\"alert alert-success\">\n",
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" <b>EXERCISE</b>: Select all countries that have capital names with more than 7 characters\n",
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"</div>"
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]
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},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
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"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: Select all countries that have capital names that contain the character sequence 'am'\n",
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"</div>"
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]
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|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
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|
"metadata": {},
|
|
"source": [
|
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"## Pitfall: chained indexing (and the 'SettingWithCopyWarning')"
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|
]
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|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"countries.loc['Belgium', 'capital'] = 'Ghent' "
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]
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|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"countries"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
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|
"source": [
|
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"countries['capital']['Belgium'] = 'Antwerp' "
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|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"countries"
|
|
]
|
|
},
|
|
{
|
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"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
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"outputs": [],
|
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"source": [
|
|
"countries[countries['capital'] == 'Antwerp']['capital'] = 'Brussels' "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"countries"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"How to avoid this?\n",
|
|
"\n",
|
|
"* Use `loc` instead of chained indexing if possible!\n",
|
|
"* Or `copy` explicitly if you don't want to change the original data."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"source": [
|
|
"## More exercises!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"For the quick ones among you, here are some more exercises with some larger dataframe with film data. These exercises are based on the [PyCon tutorial of Brandon Rhodes](https://github.com/brandon-rhodes/pycon-pandas-tutorial/) (so all credit to him!) and the datasets he prepared for that. You can download these data from here: [`titles.csv`](https://drive.google.com/open?id=0B3G70MlBnCgKajNMa1pfSzN6Q3M) and [`cast.csv`](https://drive.google.com/open?id=0B3G70MlBnCgKal9UYTJSR2ZhSW8) and put them in the `/data` folder."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"cast = pd.read_csv('data/cast.csv')\n",
|
|
"cast.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"titles = pd.read_csv('data/titles.csv')\n",
|
|
"titles.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: How many movies are listed in the titles dataframe?\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: What are the earliest two films listed in the titles dataframe?\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: How many movies have the title \"Hamlet\"?\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: List all of the \"Treasure Island\" movies from earliest to most recent.\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: How many movies were made from 1950 through 1959?\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: How many roles in the movie \"Inception\" are NOT ranked by an \"n\" value?\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: But how many roles in the movie \"Inception\" did receive an \"n\" value?\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: Display the cast of \"North by Northwest\" in their correct \"n\"-value order, ignoring roles that did not earn a numeric \"n\" value.\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: How many roles were credited in the silent 1921 version of Hamlet?\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"<div class=\"alert alert-success\">\n",
|
|
" <b>EXERCISE</b>: List the supporting roles (having n=2) played by Cary Grant in the 1940s, in order by year.\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"clear_cell": true,
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"celltoolbar": "Nbtutor - export exercises",
|
|
"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": "3.5.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 0
|
|
}
|