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

Understanding the differences between seaborn and pandas

The seaborn library is a popular Python library for creating visualizations. Like pandas, it does not do any actual plotting itself and is a wrapper around matplotlib. Seaborn plotting functions work with pandas DataFrames to create aesthetically pleasing visualizations.

While seaborn and pandas both reduce the overhead of matplotlib, the way they approach data is completely different. Nearly all of the seaborn plotting functions require tidy (or long) data.

Processing tidy data during data analysis often creates aggregated or wide data. This data, in wide format, is what pandas uses to make its plots.

In this recipe, we will build similar plots with both seaborn and pandas to show the types of data (tidy versus wide) that they accept.

How to do it…

  1. Read in the employee dataset:
    >>> employee = pd.read_csv('data/employee.csv',
    ...     parse_dates=['HIRE_DATE...