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Regular expressions are powerful tools for working with text data in Python. They allow you to search, match, and manipulate strings based on patterns. The re.findall() function, part of the re module, is particularly useful for extracting all occurrences of a specific pattern from a string. In this tutorial, we will delve into the details of the re.findall() function, explore its syntax, and provide several examples to illustrate its usage.

Table of Contents

  • Introduction to re.findall()
  • Syntax of re.findall()
  • Flags for Modifying Behavior
  • Examples of re.findall()
    1. Simple Pattern Matching
    2. Extracting Email Addresses
  • Conclusion

Introduction to re.findall()

The re.findall() function is used to search for all non-overlapping occurrences of a pattern within a given string. It returns a list of all matches found, without considering overlapping matches. This function is particularly handy when you need to extract specific data from a text that follows a certain pattern, such as extracting phone numbers, email addresses, or URLs.

Syntax of re.findall()

The basic syntax of the re.findall() function is as follows:

re.findall(pattern, string, flags=0)
  • pattern: The regular expression pattern you want to search for.
  • string: The input string in which you want to search for the pattern.
  • flags (optional): Flags that modify the behavior of the regular expression engine. They are used to control various aspects such as case sensitivity, multiline matching, and more.

Flags for Modifying Behavior

Flags are optional parameters that can be used to modify the behavior of the regular expression engine. They are specified as constants from the re module, and they are combined using the bitwise OR operator (|). Here are some commonly used flags:

  • re.IGNORECASE or re.I: Ignore case while matching.
  • re.MULTILINE or re.M: Allow ^ and $ to match the start and end of each line (instead of just the start and end of the whole string).
  • re.DOTALL or re.S: Make the . special character match any character, including newline (\n).
  • re.UNICODE or re.U: Enable Unicode matching.
  • re.VERBOSE or re.X: Allow writing regular expressions in a more readable format with comments.

Examples of re.findall()

Now, let’s dive into some examples to understand how the re.findall() function works in practice.

Example 1: Simple Pattern Matching

Suppose we have a string containing various dates in the format “dd-mm-yyyy”, and we want to extract all the dates. We can use the re.findall() function to achieve this:

import re

text = "Some important dates are 15-02-2023, 28-07-2023, and 10-12-2022."

pattern = r"\d{2}-\d{2}-\d{4}"  # Matches the "dd-mm-yyyy" format

dates = re.findall(pattern, text)
print(dates)  # Output: ['15-02-2023', '28-07-2023', '10-12-2022']

In this example, we define the pattern r"\d{2}-\d{2}-\d{4}", which matches the “dd-mm-yyyy” format. The \d represents a digit, and {2} and {4} specify the exact number of occurrences. The re.findall() function returns a list containing all the matched dates.

Example 2: Extracting Email Addresses

Let’s consider a more complex example where we want to extract all email addresses from a given text. Email addresses typically follow a pattern of We’ll use the re.findall() function to extract all the email addresses from the text:

import re

text = "Contact us at or for more information."

pattern = r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b"

email_addresses = re.findall(pattern, text, re.IGNORECASE)

In this example, the pattern r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b" matches email addresses. Let’s break down the pattern:

  • \b: Word boundary to ensure we match complete email addresses.
  • [A-Za-z0-9._%+-]+: Matches the username part of the email address.
  • @[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}: Matches the domain part of the email address, including the top-level domain (TLD).
  • \b: Word boundary to complete the match.

The re.IGNORECASE flag is used to make the matching case-insensitive.


The re.findall() function is a versatile tool for extracting patterns from text data using regular expressions. By understanding its syntax, flags, and examples, you can harness its power to perform tasks such as pattern matching, data extraction, and more. Remember to experiment with different patterns and flags to meet your specific requirements. Regular expressions can be complex, but they provide immense flexibility when working with text-based data processing tasks in Python.

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