XML, or Extensible Markup Language, is a web markup language that's similar to HTML but with significant flexibility (on the part of the user) built in, such as the ability to define your own tags. It was one of the most hyped technologies in the 1990s and early 2000s. It is a meta-language, that is, a language that allows us to define other languages using its mechanics, such as RSS, MathML (a mathematical markup language widely used for web publication and the display of math-heavy technical information), and so on. XML is also heavily used in regular data exchanges over the web, and as a data wrangling professional, you should have enough familiarity with its basic features to tap into the data flow pipeline whenever you need to extract data for your project.
Data Wrangling with Python
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Data Wrangling with Python
By:
Overview of this book
For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain.
The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into 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. This combination of Python tips and tricks will also demonstrate 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, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets.
By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.
Table of Contents (12 chapters)
Data Wrangling with Python
Preface
Free Chapter
Introduction to Data Wrangling with Python
Advanced Data Structures and File Handling
Introduction to NumPy, Pandas, and Matplotlib
A Deep Dive into Data Wrangling with Python
Getting Comfortable with Different Kinds of Data Sources
Learning the Hidden Secrets of Data Wrangling
Advanced Web Scraping and Data Gathering
RDBMS and SQL
Application of Data Wrangling in Real Life
Appendix
Customer Reviews