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Pandas Cookbook

You're reading from   Pandas Cookbook Practical recipes for scientific computing, time series, and exploratory data analysis using Python

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781836205876
Length 404 pages
Edition 3rd Edition
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Authors (2):
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William Ayd William Ayd
Author Profile Icon William Ayd
William Ayd
Matthew Harrison Matthew Harrison
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Matthew Harrison
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Table of Contents (13) Chapters Close

Preface 1. pandas Foundations FREE CHAPTER 2. Selection and Assignment 3. Data Types 4. The pandas I/O System 5. Algorithms and How to Apply Them 6. Visualization 7. Reshaping DataFrames 8. Group By 9. Temporal Data Types and Algorithms 10. General Usage and Performance Tips 11. The pandas Ecosystem 12. Index

Aggregations

Aggregations (also referred to as reductions) help you to reduce multiple values from a series of values down to a single value. Even if the technical term is new to you, you have no doubt encountered many aggregations in your data journey. Things like the count of records, the sum or sales, or the average price are all very common aggregations.

In this recipe, we will explore many of the aggregations built into pandas, while also forming an understanding of how these aggregations are applied. Most analysis you will do throughout your data journey involves taking large datasets and aggregating the values therein into results that your audience can consume. Executives at most companies are not interested in receiving a data dump of transactions, they just want to know the sum, min, max, mean, and so on of values within those transactions. As such, effective use and application of aggregations is a key component to converting your complex data transformation pipelines...

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