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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

Selecting rows using Boolean selection

Rows can be selected by using Boolean selection. When applied to a data frame, a Boolean selection can utilize data from multiple columns. Consider the following query, which identifies all stocks with a price less than 100:

This result can then be applied to the DataFrame using the [] operator to return only the rows where the result was True:

Multiple conditions can be put together using parentheses. The following retrieves the symbols and price for all stocks with a price between 6 and 10:

It is common to perform selection using multiple variables. The following demonstrates this by finding all rows where the Sector is Health Care and the Price is greater than or equal to 100.00:

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