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)

Defining an aggregation

The most common use of the groupby method is to perform an aggregation. What actually is an aggregation? In our data analysis world, an aggregation takes place when a sequence of many inputs get summarized or combined into a single value output. For example, summing up all the values of a column or finding its maximum are common aggregations applied on a single sequence of data. An aggregation simply takes many values and converts them down to a single value.

In addition to the grouping columns defined during the introduction, most aggregations have two other components, the aggregating columns and aggregating functions. The aggregating columns are those whose values will be aggregated. The aggregating functions define how the aggregation takes place. Major aggregation functions include sum, min, max, mean, count, variance, std, and so on.

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