Book Image

Pandas Cookbook

By : Theodore Petrou
Book Image

Pandas Cookbook

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)

Comparing missing values

Pandas uses the NumPy NaN (np.nan) object to represent a missing value. This is an unusual object, as it is not equal to itself. Even Python's None object evaluates as True when compared to itself:

>>> np.nan == np.nan
False
>>> None == None
True

All other comparisons against np.nan also return False, except not equal to:

>>> np.nan > 5
False
>>> 5 > np.nan
False
>>> np.nan != 5
True

Getting ready

Series and DataFrames use the equals operator, ==, to make element-by-element comparisons that return an object of the same size. This recipe shows you how to use the equals operator, which is very different from the equals method.

As in the previous recipe...