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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

Slicing a Series into subsets

pandas Series support a feature called slicing. Slicing is a powerful way to retrieve subsets of data from a pandas object. Through slicing, we can select data based upon position or index labels and have greater control over the sequencing of the items that result (forwards or reverse) and the interval (every item, every other).

Slicing overloads the normal array [] operator (and also .loc[], .iloc[], and .ix[]) to accept a slice object. A slice object is created using a syntax of start:end:step, the components representing the first item, last item, and the increment between each item that you would like as the step.

Each component of the slice is optional and provides a convenient means to select entire rows by omitting a component of the slice specifier.

To start demonstrating slicing, we will use the following Series:

We can select consecutive...

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