Book Image

Pandas 1.x Cookbook - Second Edition

By : Matthew Harrison, Theodore Petrou
Book Image

Pandas 1.x Cookbook - Second Edition

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)
15
Other Books You May Enjoy
16
Index

Stacking area charts to discover emerging trends

Stacked area charts are great visualizations to discover emerging trends, especially in the marketplace. It is a common choice to show the percentage of the market share for things such as internet browsers, cell phones, or vehicles.

In this recipe, we will use data gathered from the popular website meetup.com. Using a stacked area chart, we will show membership distribution between five data science-related meetup groups.

How to do it…

  1. Read in the meetup dataset, convert the join_date column into a Timestamp, and set it as the index:
    >>> meetup = pd.read_csv('data/meetup_groups.csv',
    ...     parse_dates=['join_date'],
    ...     index_col='join_date')
    >>> meetup
                                               group  ... country
    join_date                                         ...
    2016-11-18 02:41:29     houston machine learning  ...      us
    2017-05-09 14:16...