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Learning pandas

You're reading from   Learning pandas High performance data manipulation and analysis using Python

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Product type Paperback
Published in Jun 2017
Publisher
ISBN-13 9781787123137
Length 446 pages
Edition 2nd Edition
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Author (1):
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Michael Heydt Michael Heydt
Author Profile Icon Michael Heydt
Michael Heydt
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Table of Contents (16) Chapters Close

Preface 1. pandas and Data Analysis 2. Up and Running with pandas FREE CHAPTER 3. Representing Univariate Data with the Series 4. Representing Tabular and Multivariate Data with the DataFrame 5. Manipulating DataFrame Structure 6. Indexing Data 7. Categorical Data 8. Numerical and Statistical Methods 9. Accessing Data 10. Tidying Up Your Data 11. Combining, Relating, and Reshaping Data 12. Data Aggregation 13. Time-Series Modelling 14. Visualization 15. Historical Stock Price Analysis

Categorical Data

A categorical variable is a type of variable in statistics that represents a limited and often fixed set of values. This is in contrast to continuous variables, which can represent an infinite number of values. Common types of categorical variables include gender (where there are two values, male and female) or blood types (which can be one of the small sets of types of blood, such as A, B, and O).

pandas has the ability to represent Categorical variables using a type of pandas object known as Categorical. These pandas objects are designed to efficiently represent data that is grouped into a set of buckets, each represented by an integer code that represents one of the categories. The use of these underlying codes gives pandas the ability to efficiently represent sets of categories and to perform ordering and comparisons of data across multiple categorical variables...

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