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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
Languages
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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

Microsoft Excel – hierarchical data

One of the major tasks with data analysis is to take very detailed information and aggregate it into a summary that is easy to digest. Rather than having to sift through thousands of orders, most executives at a company just want to know, “What have my sales looked like in the last X quarters?”

With Microsoft Excel, users will commonly summarize this information in a view like the one shown in Figure 4.3, which represents a hierarchy of Region/Sub-Region along the rows and Year/Quarter along the columns:

A screenshot of a spreadsheet

Figure 4.3: Workbook with hierarchical data – sales by Region and Quarter

While this summary does not seem too far-fetched, many analysis tools struggle to properly present this type of information. Taking a traditional SQL database as an example, there is no direct way to represent this Year/Quarter hierarchy in a table – your only option would be to concatenate all of the hierarchy fields together...

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