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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 FREE CHAPTER 2. Up and Running with pandas 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

Performing Boolean selection

Indexes give us a very powerful and efficient means of looking up values in a Series based upon their labels. But what if you want to look up entries in a Series based upon the values?

To handle this scenario pandas provides us with Boolean selection. A Boolean selection applies a logical expression to the values of the Series and returns a new series of Boolean values representing the result of that expression upon each value. This result can then be used to extract only values where True was a result.

To demonstrate Boolean selection, let's start with the following Series and apply the greater than operator to determine values greater than or equal to 3:

This results in a Series with matching index labels and the result of the expression as applied to the value of each label. The dtype of the values is bool.

This series can then be used to...

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