Indexing and selecting data



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%matplotlib inline

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
try:
    import seaborn
except ImportError:
    pass



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data = {'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],
        'population': [11.3, 64.3, 81.3, 16.9, 64.9],
        'area': [30510, 671308, 357050, 41526, 244820],
        'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'London']}
countries = pd.DataFrame(data)
countries



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countries = countries.set_index('country')
countries

Some notes on selecting data

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:

selection by label
selection by position.

`data[]` provides some convenience shortcuts

For a DataFrame, basic indexing selects the columns.

Selecting a single column:



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countries['area']

or multiple columns:



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countries[['area', 'population']]

But, slicing accesses the rows:



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countries['France':'Netherlands']

So as a summary, [] provides the following convenience shortcuts:

Series: selecting a label: s[label]
DataFrame: selecting a single or multiple columns: df['col'] or df[['col1', 'col2']]
DataFrame: slicing the rows: df['row_label1':'row_label2'] or df[mask]

Systematic indexing with `loc` and `iloc`

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:

loc: selection by label
iloc: selection by position

These methods index the different dimensions of the frame:

df.loc[row_indexer, column_indexer]
df.iloc[row_indexer, column_indexer]

Selecting a single element:



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countries.loc['Germany', 'area']

But the row or column indexer can also be a list, slice, boolean array, ..



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countries.loc['France':'Germany', ['area', 'population']]

Selecting by position with iloc works similar as indexing numpy arrays:



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countries.iloc[0:2,1:3]

The different indexing methods can also be used to assign data:



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countries2 = countries.copy()
countries2.loc['Belgium':'Germany', 'population'] = 10



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countries2

Boolean indexing (filtering)

Like a where clause in SQL. The indexer (or boolean mask) should be 1-dimensional and the same length as the thing being indexed.



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countries['area'] > 100000

EXERCISE: Add a column `density` with the population density (note: population column is expressed in millions)



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EXERCISE: Select the capital and the population column of those countries where the density is larger than 300



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EXERCISE: Add a column 'density_ratio' with the ratio of the density to the mean density



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EXERCISE: Change the capital of the UK to Cambridge



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EXERCISE: Select all countries whose population density is between 100 and 300 people/km²



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Some other useful methods: `isin` and string methods

The isin method of Series is very useful to select rows that may contain certain values:



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s = countries['capital']



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s.isin?



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s.isin(['Berlin', 'London'])

This can then be used to filter the dataframe with boolean indexing:



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countries[countries['capital'].isin(['Berlin', 'London'])]

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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'Berlin'.startswith('B')

In pandas, these are available on a Series through the str namespace:



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countries['capital'].str.startswith('B')

For an overview of all string methods, see: http://pandas.pydata.org/pandas-docs/stable/api.html#string-handling

EXERCISE: Select all countries that have capital names with more than 7 characters



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EXERCISE: Select all countries that have capital names that contain the character sequence 'am'



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Pitfall: chained indexing (and the 'SettingWithCopyWarning')



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countries.loc['Belgium', 'capital'] = 'Ghent'



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countries



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countries['capital']['Belgium'] = 'Antwerp'



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countries



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countries[countries['capital'] == 'Antwerp']['capital'] = 'Brussels'



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countries

How to avoid this?

Use loc instead of chained indexing if possible!
Or copy explicitly if you don't want to change the original data.

More exercises!

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 (so all credit to him!) and the datasets he prepared for that. You can download these data from here: titles.csv and cast.csv and put them in the /data folder.



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cast = pd.read_csv('data/cast.csv')
cast.head()



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titles = pd.read_csv('data/titles.csv')
titles.head()

EXERCISE: How many movies are listed in the titles dataframe?



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EXERCISE: What are the earliest two films listed in the titles dataframe?



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EXERCISE: How many movies have the title "Hamlet"?



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EXERCISE: List all of the "Treasure Island" movies from earliest to most recent.



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EXERCISE: How many movies were made from 1950 through 1959?



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EXERCISE: How many roles in the movie "Inception" are NOT ranked by an "n" value?



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EXERCISE: But how many roles in the movie "Inception" did receive an "n" value?



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EXERCISE: Display the cast of "North by Northwest" in their correct "n"-value order, ignoring roles that did not earn a numeric "n" value.



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EXERCISE: How many roles were credited in the silent 1921 version of Hamlet?



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EXERCISE: List the supporting roles (having n=2) played by Cary Grant in the 1940s, in order by year.



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