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Data Wrangling with Python

You're reading from   Data Wrangling with Python Creating actionable data from raw sources

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789800111
Length 452 pages
Edition 1st Edition
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Authors (2):
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Shubhadeep Roychowdhury Shubhadeep Roychowdhury
Author Profile Icon Shubhadeep Roychowdhury
Shubhadeep Roychowdhury
Dr. Tirthajyoti Sarkar Dr. Tirthajyoti Sarkar
Author Profile Icon Dr. Tirthajyoti Sarkar
Dr. Tirthajyoti Sarkar
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Table of Contents (12) Chapters Close

Data Wrangling with Python
Preface
1. Introduction to Data Wrangling with Python FREE CHAPTER 2. Advanced Data Structures and File Handling 3. Introduction to NumPy, Pandas, and Matplotlib 4. A Deep Dive into Data Wrangling with Python 5. Getting Comfortable with Different Kinds of Data Sources 6. Learning the Hidden Secrets of Data Wrangling 7. Advanced Web Scraping and Data Gathering 8. RDBMS and SQL 9. Application of Data Wrangling in Real Life 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...

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