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Pandas 1.x Cookbook

Pandas 1.x Cookbook - Second Edition

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
Other Books You May Enjoy
16
Index

Improving the readability of Boolean indexing with the query method

Boolean indexing is not necessarily the most pleasant syntax to read or write, especially when using a single line to write a complex filter. pandas has an alternative string-based syntax through the DataFrame query method that can provide more clarity.

This recipe replicates the earlier recipe in this chapter, Translating SQL WHERE clauses, but instead takes advantage of the .query method of the DataFrame. The goal here is to filter the employee data for female employees from the police or fire departments who earn a salary of between 80 and 120 thousand dollars.

How to do it…

  1. Read in the employee data, assign the chosen departments, and import columns to variables:
    >>> employee = pd.read_csv("data/employee.csv")
    >>> depts = [
    ...     "Houston Police Department-HPD",
    ...     "Houston Fire Department (HFD)",
    ... ]
    >>> select_columns...
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