Regular Expressions (Regex) using Python

Perfect — this is an essential and very practical topic. Let’s go step-by-step so you build complete clarity + hands-on skill in Regular Expressions (Regex) using Python.

🧩 What Are Regular Expressions?

Regular Expressions (often called regex or regexp) are patterns used to match, search, and manipulate text.
They act like “smart filters” for strings.

Think of regex as:

A language within a language that lets you find patterns in text — like phone numbers, email addresses, dates, etc.

💡 Why Use Regular Expressions?

✅ Text cleaning / preprocessing — remove unwanted characters, extract words, etc.
✅ Validation — check if an email, phone number, or password format is valid.
✅ Data extraction — extract numbers, dates, or specific patterns from logs or documents.
✅ Search and replace — find certain text and replace with something else.
✅ Log analysis / parsing — extract fields from server logs, error traces, etc.
✅ ETL pipelines — clean and standardize data before loading.

🧠 Regex in Python — re Module

Python’s built-in re module is used for regex operations.

Import it first:

import re

🎯 Step-by-Step Learning

Let’s go from simple → powerful patterns with clear examples.

🔹 1. Basic Matching

import re

text = "I love Python programming"
pattern = "Python"

match = re.search(pattern, text)
print(match)

✅ Output:

<re.Match object; span=(7, 13), match='Python'>

re.search() returns the first match.
span=(7, 13) means Python is found between index 7 and 13.

🔹 2. Check if pattern exists

if re.search("Python", text):
    print("Found!")
else:
    print("Not found.")

Output → ✅ Found!

🔹 3. Find all matches

re.findall("a", "Banana")

Output → ['a', 'a', 'a']

🔹 4. Replace text

re.sub("Python", "Java", text)

Output → 'I love Java programming'

🔹 5. Split text

re.split("\s", "Split this sentence by spaces")

Output → ['Split', 'this', 'sentence', 'by', 'spaces']

🔤 6. Meta Characters (Powerful Part!)

Example:

text = "Email me at code@example.com or admin@test.org"
emails = re.findall(r"[\w\.-]+@[\w\.-]+\.\w+", text)
print(emails)

✅ Output:

['code@example.com', 'admin@test.org']

🔧 7. Grouping & Capturing

You can extract sub-parts of matched text.

text = "My phone number is 98765-43210"
pattern = r"(\d{5})-(\d{5})"

match = re.search(pattern, text)
if match:
    print("Full match:", match.group(0))
    print("First part:", match.group(1))
    print("Second part:", match.group(2))

Output:

Full match: 98765-43210
First part: 98765
Second part: 43210

🔍 8. Real-World / Industry Examples

✅ 1. Email Validation

def is_valid_email(email):
    return bool(re.match(r"^[\w\.-]+@[\w\.-]+\.\w+$", email))

print(is_valid_email("raj@example.com"))

✅ 2. Extract Phone Numbers

text = "Call me at 9876543210 or 9123456789"
phones = re.findall(r"\b\d{10}\b", text)
print(phones)

Output → ['9876543210', '9123456789']

✅ 3. Clean Text (Data Cleaning in NLP)

text = "Hello!!! This@@ is ## messy### text$$$"
clean = re.sub(r"[^A-Za-z\s]", "", text)
print(clean)

Output → Hello This is messy text

✅ 4. Extract Log Information

log = "2025-10-29 12:30:22, INFO: User Rajeev logged in"
pattern = r"(\d{4}-\d{2}-\d{2}) (\d{2}:\d{2}:\d{2}), (\w+): (.+)"

match = re.match(pattern, log)
if match:
    date, time, level, message = match.groups()
    print(date, time, level, message)

Output:

2025-10-29 12:30:22 INFO User Rajeev logged in

✅ 5. Extract URLs

text = "Visit https://openai.com or http://example.org for info."
urls = re.findall(r"https?://[A-Za-z0-9./]+", text)
print(urls)

Output → ['https://openai.com', 'http://example.org']

🏗️ How Regex Is Used in Industry

🧩 Interactive Practice Tasks (Try Yourself!)

1. Extract all hashtags from:
   "Loving #Python #Regex #AI"
2. Extract all capitalized words from a sentence.
3. Replace all multiple spaces with a single space.
4. Validate if a string is a valid Indian PIN code (6 digits).
5. Extract domain names from email list.

Would you like me to make these into small interactive coding challenges (with step-by-step hints and solutions like a mini Regex lab)?

💬 Top Interview Questions

Would you like me to now turn this into an interactive mini-lab —
where you learn by solving progressively harder regex exercises with guided hints and real-world context (e.g., validating emails, parsing logs, cleaning data)?