Regex in Python

1. Introduction to Python re Library

1.1 🐍 What is re?

Python has a built-in library called re for working with regular expressions. Because it is built into Python, we do not need to install anything extra. We can simply import it: import re

1.2 What Can We Do with re?

The re library provides functions to: 👉 Search text 👉 Validate patterns 👉 Extract matching text 👉 Split text using patterns 👉 Replace matching text 📌 Regex gives us the pattern. Python's re library helps us apply that pattern to text.

2. re.search()

2.1 🔍 What is re.search()?

re.search() searches for a pattern inside a given text. It returns: 👉 The first match object if the pattern is found 👉 None if the pattern is not found Syntax: re.search(pattern, text)

2.2 Search Match Object Methods

Method	Meaning
`.group()`	Returns the matched text
`.start()`	Returns starting index
`.end()`	Returns ending index
`.span()`	Returns start and end index as a tuple

2.3 Example: Search Weight

import reusr_txt = "I bought 12 kg of apples."weight_regex = r"\d+(?= kg)"match = re.search(weight_regex, usr_txt)if match:print("Weight:", match.group())print("Start index:", match.start())print("End index:", match.end())print("Span:", match.span()) else:print("No weight found.")Output: Weight: 12 Start index: 9 End index: 11 Span: (9, 11) 📌 re.search() finds the first matching pattern.

3. Named Groups

3.1 🏷️ What is a Named Group?

A named group gives a name to a capturing group. Syntax: (?P<name>...) This allows us to access matched text by name instead of group number.

3.2 Example: Extract Date Parts

import reusr_txt = "Date of today is 2026-07-10. I have 3 classes today."date_regex = r"(?P<year>\d{4})-(?P<month>\d{2})-(?P<day>\d{2})"match = re.search(date_regex, usr_txt)if match:print("Year:", match.group("year"))print("Month:", match.group("month"))print("Day:", match.group("day")) else:print("No date found.")Output: Year: 2026 Month: 07 Day: 10 📌 Named groups make extracted data easier to understand.

3.3 Named Group vs Numbered Group

Using numbered groups: match.group(1)match.group(2)match.group(3) Using named groups: match.group("year")match.group("month")match.group("day") 📌 Named groups are easier to read, especially in larger patterns.

4. re.findall()

4.1 📋 What is re.findall()?

re.findall() finds all non-overlapping matches of a pattern in a string. It returns a list. Syntax: re.findall(pattern, text)

4.2 Example: Find All Weights

import reusr_txt = "I bought 10 kg apple, 5 kg mango and 12 litre curd for salad."weight_regex = r"\d+(?= kg)"weights = re.findall(weight_regex, usr_txt)print("Weights found:", weights)Output: Weights found: ['10', '5'] 📌 12 litre is not matched because the regex only looks for numbers followed by kg. 📌 re.findall() gives all matching results as a list.

5. re.split()

5.1 ✂️ What is re.split()?

re.split() splits a string wherever a pattern is found. It returns a list of substrings. Syntax: re.split(pattern, text)

5.2 Example: Split by Weight Values

import reusr_txt = "I bought 10 kg apple, 5 kg mango and 12 litre curd for salad."weight_regex = r"\d+ kg"parts = re.split(weight_regex, usr_txt)print("Parts after splitting:", parts)Output: Parts after splitting: ['I bought ', ' apple, ', ' mango and 12 litre curd for salad.'] 📌 Simple idea: re.split() cuts text wherever the pattern appears.

6. re.sub()

6.1 🔁 What is re.sub()?

re.sub() replaces matches of a pattern with another value. It returns the modified string. Syntax: re.sub(pattern, replacement, text)

6.2 Example: Replace Weight Values

import reusr_txt = "I bought 10 kg apple, 5 kg mango and 12 litre curd for salad."weight_regex = r"\d+ kg" replacement = "X kg"new_txt = re.sub(weight_regex, replacement, usr_txt)print("Modified text:", new_txt)Output: Modified text: I bought X kg apple, X kg mango and 12 litre curd for salad. 📌 re.sub() is used when we want to clean or replace matching text.

7. Quick Comparison of Common re Functions

Function	Purpose	Returns
`re.search()`	Finds the first match	Match object or None
`re.findall()`	Finds all matches	List
`re.split()`	Splits text by pattern	List
`re.sub()`	Replaces matched pattern	String

8. Practical Example: Extract Phone Numbers

8.1 Phone Number Example

import reusr_txt = "Ram: 9841000000, Sita: 9812345678, Hari: 014445566"phone_regex = r"9[78]\d{8}"phones = re.findall(phone_regex, usr_txt)print("Phone numbers:", phones)Output: Phone numbers: ['9841000000', '9812345678'] 📌 014445566 is not matched because it does not follow the mobile number pattern.

9. Practical Example: Extract Email-like Text

9.1 Email Example

import reusr_txt = "Contact us at [email protected] or [email protected]"email_regex = r"[\w.-]+@[\w.-]+\.\w+"emails = re.findall(email_regex, usr_txt)print("Emails:", emails)Output: Emails: ['[email protected]', '[email protected]'] 📌 Simple idea: Regex is useful for extracting structured information from messy text.

10. Practical Example: Replace Extra Spaces

10.1 Cleaning Extra Spaces

import reusr_txt = "Python is fun"space_regex = r"\s+"clean_txt = re.sub(space_regex, " ", usr_txt)print(clean_txt)Output: Python is fun 📌 This is useful for text cleaning.