The Data Wrangling Workshop – Second Edition
eBook Details:
- Paperback: 576 pages
- Publisher: WOW! eBook (July 29, 2020)
- Language: English
- ISBN-10: 1839215003
- ISBN-13: 978-1839215001
eBook Description:
The Data Wrangling Workshop, 2nd Edition: A beginner’s guide to simplifying Extract, Transform, Load (ETL) processes with the help of hands-on tips, tricks, and best practices, in a fun and interactive way
While a huge amount of data is readily available to us, it is not useful in its raw form. For data to be meaningful, it must be curated and refined.
If you’re a beginner, then The Data Wrangling Workshop, Second Edition will help to break down the process for you. You’ll start with the basics and build your knowledge, progressing from the core aspects behind data wrangling, to using the most popular tools and techniques.
This book starts by showing you how to work with data structures using Python. Through examples and activities, you’ll understand why you should stay away from traditional methods of data cleaning used in other languages and take advantage of the specialized pre-built routines in Python. Later, you’ll learn how to use the same Python backend to extract and transform data from an array of sources, including the internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, the book teaches you how to handle missing or incorrect data, and reformat it based on the requirements from your downstream analytics tool.
- Get to grips with the fundamentals of data wrangling
- Understand how to model data with random data generation and data integrity checks
- Discover how to examine data with descriptive statistics and plotting techniques
- Explore how to search and retrieve information with regular expressions
- Delve into commonly-used Python data science libraries
- Become well-versed with how to handle and compensate for missing data
By the end of this The Data Wrangling Workshop, 2nd Edition book, you will have developed a solid understanding of how to perform data wrangling with Python, and learned several techniques and best practices to extract, clean, transform, and format your data efficiently, from a diverse array of sources.
