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

Comparing continuous values across categories

The previous sections discussed looking at a single column. This section will show how to compare continuous variables in different categories. We will look at mileage numbers in different brands: Ford, Honda, Tesla, and BMW.

How to do it…

  1. Make a mask for the brands we want and then use a group by operation to look at the mean and standard deviation for the city08 column for each group of cars:
    >>> mask = fueleco.make.isin(
    ...     ["Ford", "Honda", "Tesla", "BMW"]
    ... )
    >>> fueleco[mask].groupby("make").city08.agg(
    ...     ["mean", "std"]
    ... )
                mean       std
    make
    BMW    17.817377  7.372907
    Ford   16.853803  6.701029
    Honda  24.372973  9.154064
    Tesla  92.826087  5.538970
    
  2. Visualize the city08 values for each make with seaborn:
    >>> g = sns.catplot(
    ...     x="make", y=...