Gain insights into Python's regular expressions with practical examples, web scraping projects, coding exercises, and beginner-friendly lectures. Delve into real-world data and enhance your Python skills effectively.

Regex with Python? Many of you do not find this combination easy, which partly happens because of the Python's obfuscated regex documentation too! In this course, to make your life easy, I have used some real-world examples.  The goals are:

- Teach you how to use the Python regular expressions (re) module and relevant functions by running interactive examples;
- Educate you to deal with projects with real world data examples (e.g., pdf and web scraping, more coming soon!);
- Challenge you with coding exercises and quizzes; and
- Beginner friendly video lectures to explain the basic REGEX concepts.

This is a short course,  should be pretty quick and easy to complete. However, it assumes that you have basic Python skills (see the other Python courses on this platform). This course should get you going with Python Regex in less than 30mins. This course is a core component of the 'Learn Scientific Programming' project.

Python Regular Expressions with Data Scraping Projects

There are times when you want to match a pattern only optionally! The `?` character flags the group that precedes it as an optional part of the pattern. For example, enter the following into the interactive shell:

import re

Regex = re.compile(r'(scientific )?programming')
m1 = Regex.search('Learn programming')
m2 = Regex.search('Learn scientific programming')

print m1.group()
print m2.group()

The output will be:

```
programming 
scientific programming
```

This means that the `(scientific )?` part of the regular expression means that the pattern `scientific ` (notice the white space!) is an **optional** group. The regex will match text that has zero instances or one instance of `scientific ` in it. This is why the regex matches both 'programming' and 'scientific programming'.



Note that the '`*`', '`+`', and '`?`' qualifiers are all **greedy**; they match as much text as possible. Sometimes this behavior isn’t desired; if the RE pattern `<.*>` is matched against '`<H1>Learn Scientific Programming</H1>`', it will match the entire string, and not just '`<H1>`'. Adding '`?`' after the qualifier makes it perform the match in non-greedy or minimal fashion; as few characters as possible will be matched. Using `.*?` in the previous expression will match only '`<H1>`'.


Python Greedy and non-greedy matching explained with examples.

Introduction

Python Regular Expressions Funtions

Python Regex - Look ahead and Look behind

Python Lazy and Greedy Search

Python Regex by Examples

References

Greedy/Non-Greedy Matching