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Python Data Cleaning Cookbook

You're reading from   Python Data Cleaning Cookbook Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781803239873
Length 486 pages
Edition 2nd Edition
Languages
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Author (1):
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Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
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Table of Contents (14) Chapters Close

Preface 1. Anticipating Data Cleaning Issues When Importing Tabular Data with pandas 2. Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data FREE CHAPTER 3. Taking the Measure of Your Data 4. Identifying Outliers in Subsets of Data 5. Using Visualizations for the Identification of Unexpected Values 6. Cleaning and Exploring Data with Series Operations 7. Identifying and Fixing Missing Values 8. Encoding, Transforming, and Scaling Features 9. Fixing Messy Data When Aggregating 10. Addressing Data Issues When Combining DataFrames 11. Tidying and Reshaping Data 12. Automate Data Cleaning with User-Defined Functions, Classes, and Pipelines 13. Index

Importing more complicated JSON data from an API

In the previous recipe, we discussed one significant advantage (and challenge) of working with JSON data – its flexibility. A JSON file can have just about any structure its authors can imagine. This often means that this data does not have the tabular structure of the data sources we have discussed so far and that pandas DataFrames have. Often, analysts and application developers use JSON precisely because it does not insist on a tabular structure. I know I do!

Retrieving data from multiple tables often requires us to do a one-to-many merge. Saving that data to one table or file means duplicating data on the “one” side of the one-to-many relationship. For example, student demographic data is merged with data on the courses studied, and the demographic data is repeated for each course. With JSON, duplication is not required to capture these items of data in one file. We can have data on the courses studied nested...

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Python Data Cleaning Cookbook - Second Edition
Published in: May 2024
Publisher: Packt
ISBN-13: 9781803239873
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