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

Filling values with unequal indexes

When two Series are added together using the plus operator and one of the index labels does not appear in the other, the resulting value is always missing. pandas has the .add method, which provides an option to fill the missing value. Note that these Series do not include duplicate entries, hence there is no need to worry about a Cartesian product exploding the number of entries.

In this recipe, we add together multiple Series from the baseball dataset with unequal (but unique) indexes using the .add method with the fill_value parameter to ensure that there are no missing values in the result.

How to do it…

  1. Read in the three baseball datasets and set playerID as the index:
    >>> baseball_14 = pd.read_csv(
    ...     "data/baseball14.csv", index_col="playerID"
    ... )
    >>> baseball_15 = pd.read_csv(
    ...     "data/baseball15.csv", index_col="playerID"
    ... )
    >&gt...