`re`

The Python re module provides support for working with regular expressions, allowing you to search, match, and manipulate strings using complex pattern descriptions.

Regular expressions are a powerful tool for text processing and can be used for tasks such as validation, parsing, and string manipulation.

Here’s a quick example:

>>> import re
>>> pattern = r"\d+"
>>> re.findall(pattern, "There are 42 apples and 100 oranges")
['42', '100']

Key Features

Supports Perl-style regular expressions
Provides functions for searching, matching, and replacing text
Allows compilation of regular expressions for reuse
Supports advanced pattern features like lookaheads and backreferences

Frequently Used Classes and Functions

Object	Type	Description
`re.compile()`	Function	Compiles a regular expression pattern for reuse
`re.match()`	Function	Determines if the regex matches at the start of a string
`re.search()`	Function	Searches for a pattern match anywhere in the string
`re.findall()`	Function	Finds all non-overlapping matches in a string
`re.sub()`	Function	Replaces matches with a specified string
`re.Match`	Class	Contains match details and results

Examples

Compile a regular expression pattern for reuse:

>>> pattern = re.compile(r"\b\w+\b")
>>> pattern.findall("This is a test.")
['This', 'is', 'a', 'test']

Search for a pattern match anywhere in the string:

>>> match = re.search(r"\d+", "There are 42 apples")
>>> match.group()
'42'

Replace matches with a specified string:

>>> re.sub(r"\d+", "#", "Room 123, Floor 4")
'Room #, Floor #'

Common Use Cases

Validating input data, such as email addresses, phone numbers, and other similar data
Parsing and extracting information from text files
Replacing or reformatting strings based on patterns
Tokenizing text for natural language processing

Real-World Example

Suppose you want to extract all email addresses from a block of text. You can use the re module to accomplish this:

>>> text = "Contact us at support@example.com or sales@example.org"
>>> email_pattern = r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}"
>>> emails = re.findall(email_pattern, text)
>>> emails
['support@example.com', 'sales@example.org']

In this example, the re module efficiently extracts email addresses from the provided text, showcasing its power in text processing tasks.

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