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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

The split, apply, and combine (SAC) pattern

Many data analysis problems utilize a pattern of processing data referred to as split-apply-combine. In this pattern, three steps are taken to analyze data:

  • A dataset is split into smaller pieces based on certain criteria
  • Each of these pieces are operated upon independently
  • All the results are then combined back and presented as a single unit

The following diagram demonstrates a simple split-apply-combine process to calculate the mean of values grouped by a character-based key (a or b):

The data is then split by the index label into two groups (one each for a and b). The mean of the values in each group is calculated. The resulting values from the group are then combined into a single pandas object, which is indexed by the label representing each group.

Splitting in pandas is performed using the .groupby() method of a Series or DataFrame...

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