Book Image

Data Wrangling with Python

By : Dr. Tirthajyoti Sarkar, Shubhadeep Roychowdhury
Book Image

Data Wrangling with Python

By: Dr. Tirthajyoti Sarkar, Shubhadeep Roychowdhury

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
Appendix

Fundamentals of Regular Expressions (RegEx)


Regular expressions or regex are used to identify whether a pattern exists in a given sequence of characters a (string) or not. They help in manipulating textual data, which is often a prerequisite for data science projects that involve text mining.

Regex in the Context of Web Scraping

Web pages are often full of text and while there are some methods in BeautifulSoup or XML parser to extract raw text, there is no method for the intelligent analysis of that text. If, as a data wrangler, you are looking for a particular piece of data (for example, email IDs or phone numbers in a special format), you have to do a lot of string manipulation on a large corpus to extract email IDs or phone numbers. RegEx are very powerful and save data wrangling professional a lot of time and effort with string manipulation because they can search for complex textual patterns with wildcards of an arbitrary length.

RegEx is like a mini-programming language in itself and...