Python Data Cleaning Cookbook
Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI
2nd Edition
Published on 23. September 2024
486 pages
978-1-80324-629-1 (ISBN)
Description
No detailed description available for "Python Data Cleaning Cookbook".
More details
Edition
2nd Revised edition
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Revised edition
File size
17,65 MB
ISBN-13
978-1-80324-629-1 (9781803246291)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Michael Walker
Python Data Cleaning Cookbook
Prepare your data for analysis with pandas, NumPy, Matplotlib, scikit-learn, and OpenAI
Book
05/2024
2nd Edition
Packt Publishing
€58.50
Shipment within 3-4 weeks
Previous edition
Michael Walker | Michael B. Walker
Python Data Cleaning Cookbook
Modern techniques and Python tools to detect and remove dirty data and extract key insights
E-Book
09/2024
Packt Publishing
€29.49
Available for download
Person
Walker Michael :
Michael Walker has worked as a data analyst for over 30 years at a variety of educational institutions. He is currently the CIO at College Unbound in Providence, Rhode Island, in the United States. He has also taught data science, research methods, statistics, and computer programming to undergraduates since 2006.
Michael Walker has worked as a data analyst for over 30 years at a variety of educational institutions. He is currently the CIO at College Unbound in Providence, Rhode Island, in the United States. He has also taught data science, research methods, statistics, and computer programming to undergraduates since 2006.
Content
Table of Contents
Anticipating Data Cleaning Issues When Importing Tabular Data with pandas
Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data
Taking the Measure of Your Data
Identifying Outliers in Subsets of Data
Using Visualizations for the Identification of Unexpected Values
Cleaning and Exploring Data with Series Operations
Identifying and Fixing Missing Values
Encoding, Transforming, and Scaling Features
Fixing Messy Data When Aggregating
Addressing Data Issues When Combining DataFrames
Tidying and Reshaping Data
Automate Data Cleaning with User-Defined Functions, Classes, and Pipelines
Anticipating Data Cleaning Issues When Importing Tabular Data with pandas
Anticipating Data Cleaning Issues When Working with HTML, JSON, and Spark Data
Taking the Measure of Your Data
Identifying Outliers in Subsets of Data
Using Visualizations for the Identification of Unexpected Values
Cleaning and Exploring Data with Series Operations
Identifying and Fixing Missing Values
Encoding, Transforming, and Scaling Features
Fixing Messy Data When Aggregating
Addressing Data Issues When Combining DataFrames
Tidying and Reshaping Data
Automate Data Cleaning with User-Defined Functions, Classes, and Pipelines
