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

Using the pandas profiling library

There is a third-party library, pandas Profiling (https://pandas-profiling.github.io/pandas-profiling/docs/), that creates reports for each column. These reports are similar to the output of the .describe method, but include plots and other descriptive statistics.

In this section, we will use the pandas Profiling library on the fuel economy data. Use pip install pandas-profiling to install the library.

How to do it…

  1. Run the profile_report function to create an HTML report:
    >>> import pandas_profiling as pp
    >>> pp.ProfileReport(fueleco)
    
pandas profiling summary

pandas profiling summary

pandas profiling details

pandas profiling details

How it works…

The pandas Profiling library generates an HTML report. If you are using Jupyter, it will create it inline. If you want to save this report to a file (or if you are not using Jupyter), you can use the .to_file method:

>>> report = pp.ProfileReport...