Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781803239873
Length 486 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Michael Walker Michael Walker
Author Profile Icon Michael Walker
Michael Walker
Arrow right icon
View More author details
Toc

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 simple JSON data

JavaScript Object Notation (JSON) has turned out to be an incredibly useful standard for transferring data from one machine, process, or node to another. Often, a client sends a data request to a server, upon which that server queries the data in local storage and then converts it from something like an SQL Server, MySQL, or PostgreSQL table or tables into JSON, which the client can consume. This is sometimes complicated further by the first server (say, a web server) forwarding the request to a database server. JSON facilitates this, as does XML, by doing the following:

  • Being readable by humans
  • Being consumable by most client devices
  • Not being limited in structure

JSON is quite flexible, which means that it can accommodate just about anything, no matter how unwise. The structure can even change within a JSON file, so different keys might be present at different points. For example, the file might begin with some explanatory...

You have been reading a chapter from
Python Data Cleaning Cookbook - Second Edition
Published in: May 2024
Publisher: Packt
ISBN-13: 9781803239873
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime