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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

Alignment via index labels

Alignment of Series data by index labels is a fundamental concept in pandas, as well as being one of its most powerful concepts. Alignment provides automatic correlation of related values in multiple Series objects based upon index labels. This saves a lot of error-prone effort matching data in multiple sets using standard procedural techniques.

To demonstrate alignment, let's perform an example of adding values in two Series objects. Let's start with the following two Series objects representing two different samples of a set of variables (a and b):

Now suppose we would like to total the values for each variable. We can express this simply as s1 + s2:

pandas has matched the measurement for each variable in each series, added those values, and returned us the sum for each in one succinct statement.

It is also possible to apply a scalar value...

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