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Pandas Cookbook

You're reading from   Pandas Cookbook Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781784393878
Length 532 pages
Edition 1st Edition
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Author (1):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
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Table of Contents (12) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Beginning Data Analysis 4. Selecting Subsets of Data 5. Boolean Indexing 6. Index Alignment 7. Grouping for Aggregation, Filtration, and Transformation 8. Restructuring Data into a Tidy Form 9. Combining Pandas Objects 10. Time Series Analysis 11. Visualization with Matplotlib, Pandas, and Seaborn

Introduction

One of the most fundamental tasks during a data analysis involves splitting data into independent groups before performing a calculation on each group. This methodology has been around for quite some time but has more recently been referred to as split-apply-combine. This chapter covers the powerful groupby method, which allows you to group your data in any way imaginable and apply any type of function independently to each group before returning a single dataset.

Hadley Wickham coined the term split-apply-combine to describe the common data analysis pattern of breaking up data into independent manageable chunks, independently applying functions to these chunks, and then combining the results back together. More details can be found in his paper (http://bit.ly/2isFuL9).

Before we get started with the recipes, we will need to know just a little terminology. All basic...

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