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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

Creating Categoricals

A pandas Categorical is used to represent a categorical variable. A categorical variable consists of a finite set of values and is often used to map values into a set of categories and track how many values are present in each category. Another purpose is to map sections of continuous values into a discrete set of named labels, an example of which is mapping a numeric grade to a letter grade. We will examine how to perform this mapping at the end of the chapter.

There are several ways to create a pandas Categorical. The following screenshot demonstrates creating a Categorical directly from a list:

This Categorical is created from a list consisting of five strings and three distinct values: low, medium, and high. When creating the Categorical, pandas determines each unique value in the list and uses those as the categories.

These categories can be examined...

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