finelybook
The Data Wrangling Workshop: Create your own actionable insights using data from multiple raw sources,2nd Edition
29 July 2020
by 作者: Brian Lipp ,Shubhadeep Roychowdhury ,Dr. Tirthajyoti Sarkar
pages 页数: 576 pages
Publisher Finelybook 出版社: Packt Publishing; 2nd edition edition (29 July 2020)
Language 语言: English
ISBN-10 书号: 1839215003
ISBN-13 书号: 9781839215001
Book Description
Simplify your ETL processes with these hands-on tips,tricks,and best practices
Though 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. The Data Wrangling Workshop teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain.
The workshop begins by 作者: demonstrating how to work with data structures using Python. Through examples and activities,you’ll understand why you should stay away from traditional ways of data cleaning as done in other languages and take advantage of the specialized pre-built routines in Python. As you progress,you’ll learn how to use the same Python backend and extract/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 workshop teaches you how to handle missing or wrong data,and reformat it based on the requirements from the downstream analytics tool.
By the end of the workshop,you would have learned several data wrangling techniques and best practices that’ll give you the confidence to extract,clean,transform,and format your data efficiently from a diverse array of sources.
What you will learn
Get to grips with the fundamentals of data wrangling
Learn 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 deep into the commonly used data science libraries of Python
Learn how to handle and compensate for missing data
