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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

Representing Univariate Data with the Series

The Series is the primary building block of pandas. It represents a one-dimensional array-like set of values of a single data type. It is often used to model zero or more measurements of a single variable. While it can appear like an array, a Series has an associated index that can be used to perform very efficient retrievals of values based upon labels.

A Series also performs automatic alignment of data between itself and other pandas objects. Alignment is a core feature of pandas where data is multiple pandas objects that are matched by label value before any operation is performed. This allows the simple application of operations without needing to explicitly code joins.

In this chapter, we will examine how to model measurements of a variable using a Series, including using an index to retrieve samples. This examination will include...

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