Book Image

Pandas 1.x Cookbook - Second Edition

By : Matt Harrison, Theodore Petrou
Book Image

Pandas 1.x Cookbook - Second Edition

By: Matt Harrison, Theodore Petrou

Overview of this book

The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter. This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.
Table of Contents (17 chapters)
Other Books You May Enjoy

Replicating pivot_table with a groupby aggregation

At first glance, it may seem that the .pivot_table method provides a unique way to analyze data. However, after a little massaging, it is possible to replicate its functionality with the .groupby method. Knowing this equivalence can help shrink the universe of pandas functionality.

In this recipe, we use the flights dataset to create a pivot table and then recreate it using the .groupby method.

How to do it…

  1. Read in the flights dataset, and use the .pivot_table method to find the total number of canceled flights per origin airport for each airline:
    >>> flights = pd.read_csv('data/flights.csv')
    >>> fpt = flights.pivot_table(index='AIRLINE',
    ...     columns='ORG_AIR',
    ...     values='CANCELLED',
    ...     aggfunc='sum',
    ...     fill_value=0)
    >>> fpt
    AA   ...