Regular Expression (Regex) in Python

Regular Expressions (Regex) in Python are used to search, match, extract, and manipulate text based on patterns. Regex is widely used in data validation, log analysis, automation testing, and text processing.

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Why Use Regular Expressions?

• Validate user input (email, phone, password)
• Search and extract text patterns
• Replace or clean unwanted data
• Parse logs and reports

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Regex Module in Python

Python provides the built-in re module for working with regular expressions.

import re

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Basic Regex Functions

Function	Description
re.match()	Matches pattern at beginning
re.search()	Searches pattern anywhere
re.findall()	Returns all matches
re.finditer()	Returns iterator of matches
re.sub()	Replaces matched text

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re.match()

import re

text = "Python is powerful"
result = re.match("Python", text)

if result:
    print("Match found")

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re.search()

text = "Learning Python Regex"
result = re.search("Python", text)

if result:
    print("Found at position:", result.start())

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re.findall()

text = "Python Java Python Selenium"
result = re.findall("Python", text)
print(result)

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re.sub()

text = "My phone number is 9876543210"
new_text = re.sub(r"\d", "*", text)
print(new_text)

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Common Regex Patterns

Pattern	Description
.	Any character except newline
\d	Digit (0-9)
\D	Non-digit
\w	Word character
\W	Non-word character
\s	Whitespace
\S	Non-whitespace

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Quantifiers

Symbol	Meaning
*	0 or more
+	1 or more
?	0 or 1
{n}	Exactly n times

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Character Sets

[a-z]     # lowercase letters
[A-Z]     # uppercase letters
[0-9]     # digits

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Validating Email Using Regex

email = "test@example.com"
pattern = r"^[\w\.-]+@[\w\.-]+\.\w+$"

if re.match(pattern, email):
    print("Valid Email")
else:
    print("Invalid Email")

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Regex Groups

text = "Order ID: 12345"
match = re.search(r"(\d+)", text)

print(match.group())

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Flags in Regex

Flag	Description
re.I	Ignore case
re.M	Multiline
re.S	Dot matches newline

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Regex in Automation Testing

import re

response = "Order ID: 98765 confirmed"
order_id = re.search(r"\d+", response).group()
print(order_id)

Used in:

• Validating API responses
• Extracting dynamic data
• Log file analysis

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Best Practices

• Use raw strings (r"...")
• Keep patterns simple and readable
• Test regex patterns before use
• Use comments for complex regex

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Common Interview Questions

• What is regex?
• Difference between match and search?
• What is re.findall()?
• What are regex groups?
• What are regex flags?

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Conclusion

Regular Expressions in Python provide powerful text processing capabilities. They are essential for data validation, automation testing, and real-time applications.

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