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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

Data Aggregation

Data aggregation is the process of grouping data based on some meaningful categories of the information. Analysis is then performed on each of the groups to report one or more summary statistics for each. This summarization in this sense is a general term in that summarization can literally be a summation (such as total number of units sold) or statistical calculation such as a mean or standard deviation.

This chapter will examine the facilities of pandas to perform data aggregation. This includes a powerful split-apply-combine pattern for grouping, performing group-level transformations and analyses, and reporting the results from every group within a summary pandas object. Within this framework, we will examine several techniques of grouping data, applying functions on a group level, and being able to filter data in or out of the analysis.

Specifically, in this...

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