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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

The goal of this chapter is to introduce a foundation of pandas by thoroughly inspecting the Series and DataFrame data structures. It is vital for pandas users to know each component of the Series and the DataFrame, and to understand that each column of data in pandas holds precisely one data type.

In this chapter, you will learn how to select a single column of data from a DataFrame, which is returned as a Series. Working with this one-dimensional object makes it easy to show how different methods and operators work. Many Series methods return another Series as output. This leads to the possibility of calling further methods in succession, which is known as method chaining.

The Index component of the Series and DataFrame is what separates pandas from most other data analysis libraries and is the key to understanding how many operations work. We will get a glimpse of this powerful object when we use it as a meaningful label for Series values. The final two recipes contain simple tasks that frequently occur during a data analysis.

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