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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

The .index and .values properties

Each Series object consists of a series of values and an index. The values can be accessed through the .values property:

The result from is a NumPy array object, as the following verifies:

This is called out for informational purposes. We will not examine NumPy arrays in this book. Historically, pandas did use NumPy arrays under the covers, so NumPy arrays were more important in the past, but this dependency has been removed in recent versions. But as a convenience, .values returns a NumPy array even if the underlying representation is not a NumPy array.

In addition, the index for the series can be retrieved using .index:

The type of index created by pandas is RangeIndex. This is a change in pandas from the previous version of this book, when this type of index did not exist. The RangeIndex object represents a range of values from the start...

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