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

DataFrame attributes

Each of the three DataFrame components–the index, columns, and data–may be accessed from a DataFrame. You might want to perform operations on the individual components and not on the DataFrame as a whole. In general, though we can pull out the data into a NumPy array, unless all the columns are numeric, we usually leave it in a DataFrame. DataFrames are ideal for managing heterogenous columns of data, NumPy arrays not so much.

This recipe pulls out the index, columns, and the data of the DataFrame into their own variables, and then shows how the columns and index are inherited from the same object.

How to do it…

  1. Use the DataFrame attributes index, columns, and values to assign the index, columns, and data to their own variables:
    >>> movies = pd.read_csv("data/movie.csv")
    >>> columns = movies.columns
    >>> index = movies.index
    >>> data = movies.to_numpy()
    
  2. Display...