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Pandas 1.x Cookbook

Pandas 1.x Cookbook

By : Matthew Harrison, Theodore Petrou
4.5 (28)
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Pandas 1.x Cookbook

Pandas 1.x Cookbook

4.5 (28)
By: Matthew 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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15
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16
Index

Preserving Series size with the .where method

When you filter with Boolean arrays, the resulting Series or DataFrame is typically smaller. The .where method preserves the size of your Series or DataFrame and either sets the values that don't meet the criteria to missing or replaces them with something else. Instead of dropping all these values, it is possible to keep them.

When you combine this functionality with the other parameter, you can create functionality similar to coalesce found in databases.

In this recipe, we pass the .where method Boolean conditions to put a floor and ceiling on the minimum and maximum number of Facebook likes for actor 1 in the movie dataset.

How to do it…

  1. Read the movie dataset, set the movie title as the index, and select all the values in the actor_1_facebook_likes column that are not missing:
    >>> movie = pd.read_csv(
    ...     "data/movie.csv", index_col="movie_title"
    ... ...

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