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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

Summary

In this chapter, we examined several techniques of combining and reshaping data in one or more DataFrame objects. We started the chapter by examining how to combine data from multiple pandas objects. Then, we examined how to concatenate multiple DataFrame objects, both along the row and column axes. From this, we then examined how pandas can be used to perform database-like joins and merges of data based on values in multiple DataFrame objects.

We then examined how to reshape data in DataFrame using pivots, stacking, and melting. Through this, we saw how each of these processes provides several variations on how to move data around by changing the shape of the indexes, and by moving data in and out of indexes. This showed us how to organize data in formats that are efficient for lookup from other forms that may be more of convenience for the producer of the data.

In the...

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