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

Persisting JSON data

There are several reasons why we might want to serialize a JSON file:

  • We may have retrieved the data with an API but need to keep a snapshot of the data.
  • The data in the JSON file is relatively static and informs our data cleaning and analysis over multiple phases of a project.
  • We might decide that the flexibility of a schema-less format such as JSON helps us solve many data cleaning and analysis problems.

It is worth highlighting this last reason to use JSON – that it can solve many data problems. Although tabular data structures clearly have many benefits, particularly for operational data, they are often not the best way to store data for analysis purposes. In preparing data for analysis, a substantial amount of time is spent either merging data from different tables or dealing with data redundancy when working with flat files. Not only are these processes time-consuming but every merge or reshaping leaves the door open...

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