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Pandas Cookbook

You're reading from   Pandas Cookbook Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781784393878
Length 532 pages
Edition 1st Edition
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Author (1):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
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Table of Contents (12) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Beginning Data Analysis 4. Selecting Subsets of Data 5. Boolean Indexing 6. Index Alignment 7. Grouping for Aggregation, Filtration, and Transformation 8. Restructuring Data into a Tidy Form 9. Combining Pandas Objects 10. Time Series Analysis 11. Visualization with Matplotlib, Pandas, and Seaborn

Customizing aggregating functions with *args and **kwargs

When writing your own user-defined customized aggregation function, pandas implicitly passes it each of the aggregating columns one at a time as a Series. Occasionally, you will need to pass more arguments to your function than just the Series itself. To do so, you need to be aware of Python's ability to pass an arbitrary number of arguments to functions. Let's take a look at the signature of the groupby object's agg method with help from the inspect module:

>>> college = pd.read_csv('data/college.csv')
>>> grouped = college.groupby(['STABBR', 'RELAFFIL'])

>>> import inspect
>>> inspect.signature(grouped.agg)
<Signature (arg, *args, **kwargs)>

The argument *args allow you to pass an arbitrary number of non-keyword arguments to your customized...

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