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

Concatenating multiple DataFrames together

The concat function enables concatenating two or more DataFrames (or Series) together, both vertically and horizontally. As per usual, when dealing with multiple pandas objects simultaneously, concatenation doesn't happen haphazardly but aligns each object by their index.

In this recipe, we combine DataFrames both horizontally and vertically with the concat function and then change the parameter values to yield different results.

How to do it…

  1. Read in the 2016 and 2017 stock datasets, and make their ticker symbol the index:
    >>> stocks_2016 = pd.read_csv('data/stocks_2016.csv',
    ...     index_col='Symbol')
    >>> stocks_2017 = pd.read_csv('data/stocks_2017.csv',
    ...     index_col='Symbol')
    >>> stocks_2016
            Shares  Low  High
    Symbol                   
    AAPL        80   95   110
    TSLA        50   80   130
    WMT         40   55    70
    ...