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)
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Examining the groupby object

The immediate result from using the .groupby method on a DataFrame is a groupby object. Usually, we chain operations on this object to do aggregations or transformations without ever storing the intermediate values in variables.

In this recipe, we examine the groupby object to examine individual groups.

How to do it…

  1. Let's get started by grouping the state and religious affiliation columns from the college dataset, saving the result to a variable and confirming its type:
    >>> college = pd.read_csv('data/college.csv')
    >>> grouped = college.groupby(['STABBR', 'RELAFFIL'])
    >>> type(grouped)
    <class 'pandas.core.groupby.generic.DataFrameGroupBy'>
  2. Use the dir function to discover the attributes of a groupby object:
    >>> print([attr for attr in dir(grouped) if not
    ...     attr.startswith('_')])