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)
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Categorical data

I broadly classify data into dates, continuous values, and categorical values. In this section, we will explore quantifying and visualizing categorical data.

How to do it…

  1. Pick out the columns with data types that are object:
    >>> fueleco.select_dtypes(object).columns
    Index(['drive', 'eng_dscr', 'fuelType', 'fuelType1', 'make', 'model',
           'mpgData', 'trany', 'VClass', 'guzzler', 'trans_dscr', 'tCharger',
           'sCharger', 'atvType', 'fuelType2', 'rangeA', 'evMotor', 'mfrCode',
           'c240Dscr', 'c240bDscr', 'createdOn', 'modifiedOn', 'startStop'],
  2. Use .nunique to determine the cardinality:
  3. ...