Book Image

Pandas Cookbook

By : Theodore Petrou
Book Image

Pandas Cookbook

By: Theodore Petrou

Overview of this book

This book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas 0.20. 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. 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 like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas 0.20 library to generate results.
Table of Contents (12 chapters)

Speeding up scalar selection

Both the .iloc and .loc indexers are capable of selecting a single element, a scalar value, from a Series or DataFrame. However, there exist the indexers, .iat and .at, which respectively achieve the same thing at faster speeds. Like .iloc, the .iat indexer uses integer location to make its selection and must be passed two integers separated by a comma. Similar to .loc, the .at index uses labels to make its selection and must be passed an index and column label separated by a comma.

Getting ready

This recipe is valuable if computational time is of utmost importance. It shows the performance improvement of .iat and .at over .iloc and .loc when using scalar selection.

...