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

Grouping and aggregating with multiple columns and functions

It is possible to group and aggregate with multiple columns. The syntax is slightly different than it is for grouping and aggregating with a single column. As usual with any kind of grouping operation, it helps to identify the three components: the grouping columns, aggregating columns, and aggregating functions.

In this recipe, we showcase the flexibility of the .groupby method by answering the following queries:

  • Finding the number of canceled flights for every airline per weekday
  • Finding the number and percentage of canceled and diverted flights for every airline per weekday
  • For each origin and destination, finding the total number of flights, the number and percentage of canceled flights, and the average and variance of the airtime

How to do it…

  1. Read in the flights dataset, and answer the first query by defining the grouping columns (AIRLINE, WEEKDAY), the aggregating...