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)
15
Other Books You May Enjoy
16
Index

Unstacking after a groupby aggregation

Grouping data by a single column and performing an aggregation on a single column returns a result that is easy to consume. When grouping by more than one column, a resulting aggregation might not be structured in a manner that makes consumption easy. Since .groupby operations, by default, put the unique grouping columns in the index, the .unstack method can be beneficial to rearrange the data so that it is presented in a manner that is more useful for interpretation.

In this recipe, we use the employee dataset to perform an aggregation, grouping by multiple columns. We then use the .unstack method to reshape the result into a format that makes for easier comparisons of different groups.

How to do it…

  1. Read in the employee dataset and find the mean salary by race:
    >>> employee = pd.read_csv('data/employee.csv')
    >>> (employee
    ...     .groupby('RACE')
    ...     ['BASE_SALARY...