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Pandas Cookbook

Pandas Cookbook

By : Theodore Petrou
4.3 (32)
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Pandas Cookbook

Pandas Cookbook

4.3 (32)
By: Theodore Petrou

Overview of this book

This book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas 0.20. 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. 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 like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas 0.20 library to generate results.
Table of Contents (12 chapters)
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Stacking multiple groups of variables simultaneously

Some datasets contain multiple groups of variables as column names that need to be stacked simultaneously into their own columns. An example with the movie dataset can help clarify this. Let's begin by selecting all columns containing the actor names and their corresponding Facebook likes:

>>> movie = pd.read_csv('data/movie.csv')
>>> actor = movie[['movie_title', 'actor_1_name',
'actor_2_name', 'actor_3_name',
'actor_1_facebook_likes',
'actor_2_facebook_likes',
'actor_3_facebook_likes']]
>>> actor.head()

If we define our variables as the title of the movie, the actor name, and the number of Facebook likes, then we will need to stack independently...

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