DEV Community: Raaga Priya Madhan

Python Regex Explained Simply — Extract Anything From Text

Raaga Priya Madhan — Fri, 12 Jun 2026 05:59:35 +0000

Regex sounds intimidating. It is not. Once you understand the 5 core concepts, you can extract any pattern from any text in seconds. Here is everything you need to know.

What is regex?

Regex is a pattern language. You describe what you are looking for using special characters and Python finds it for you — in any block of text, any size.

Real example: your client sends you a document with 500 customer records mixed with random text. They need all email addresses extracted into Excel. Without regex this takes hours. With regex it takes 3 lines.

import re

text = "Contact john@gmail.com or sales@company.com for details"
emails = re.findall(r'[\w.-]+@[\w.-]+\.\w+', text)
print(emails)
# ['john@gmail.com', 'sales@company.com']

The 5 patterns you need to know

1. \d — any digit

re.findall(r'\d', 'abc123def456')
# ['1', '2', '3', '4', '5', '6']

2. \w — any word character (letter, digit, underscore)

re.findall(r'\w+', 'hello world_123')
# ['hello', 'world_123']

3. + — one or more of the previous

re.findall(r'\d+', 'price is 45000 and tax is 8100')
# ['45000', '8100']

4. [] — any character in this set

re.findall(r'[aeiou]', 'hello world')
# ['e', 'o', 'o']

5. . — any single character

re.findall(r'c.t', 'cat cut cot bat')
# ['cat', 'cut', 'cot']

The 3 functions you will use constantly

re.findall — find all matches

Returns a list of everything that matches the pattern.

text = "Prices: ₹45,000 and ₹12,500 and ₹8,750"
prices = re.findall(r'[\d,]+', text)
print(prices)
# ['45,000', '12,500', '8,750']

re.sub — find and replace

Replaces every match with something else.

messy = "phone: 98-765-43210"
clean = re.sub(r'\D', '', messy)  # remove all non-digits
print(clean)
# '9876543210'

re.search — find first match

Returns just the first match with its position.

text = "Order #A12345 placed successfully"
match = re.search(r'#(\w+)', text)
if match:
    print(match.group(1))  # A12345

A real data cleaning example

Client problem: they have a spreadsheet with phone numbers in 6 different formats. They need them all standardised to 10 digits.

import pandas as pd
import re

df = pd.DataFrame({
    'Phone': ['9876543210', '+91-9876543210', 
              '(080) 4567-8901', '91 98765 43210']
})

def clean_phone(phone):
    digits = re.sub(r'\D', '', phone)
    if len(digits) == 10:
        return digits
    elif len(digits) == 12 and digits.startswith('91'):
        return digits[2:]
    return None

df['Clean'] = df['Phone'].apply(clean_phone)
print(df)

Output:
Phone Clean
0 9876543210 9876543210
1 +91-9876543210 9876543210
2 (080) 4567-8901 None
3 91 98765 43210 9876543210

The one-line summary

Regex is a pattern language — you describe what you are looking for and Python finds every instance of it in any text, any size.

Learn these 5 patterns and 3 functions and you can handle 90% of real data extraction gigs immediately.

Written by Raaga Priya Madhan — CSE student, Bangalore. I build Python automation and data extraction scripts. See my work on GitHub and connect on LinkedIn

What is NumPy? The Backbone of Data Science in Python

Raaga Priya Madhan — Thu, 11 Jun 2026 13:02:17 +0000

If you want to do data science, machine learning, or AI in Python — you will use NumPy constantly. Every major library from pandas to TensorFlow is built on top of it. Here is what it is and why it matters.

What is NumPy?

NumPy stands for Numerical Python. It is a library that gives Python the ability to work with large arrays of numbers extremely fast.

Python lists can store numbers. But they are slow for mathematical operations. NumPy arrays do the same thing 10 to 100 times faster.

This matters when you are working with thousands of rows of data or training a machine learning model on millions of examples.

Your first NumPy array

import numpy as np

sales = np.array([45000, 52000, 38000, 61000, 55000])

print(np.mean(sales))   # average
print(np.sum(sales))    # total
print(np.max(sales))    # highest
print(np.std(sales))    # how spread out the numbers are

Five lines. You get the average, total, maximum, and standard deviation of any list of numbers instantly. No loops. No manual calculation.

Why it is faster than Python lists

When you multiply a Python list by 2, Python loops through each item one by one. When you multiply a NumPy array by 2, it does all items simultaneously using optimised C code under the hood.

import numpy as np
import time

data = list(range(1_000_000))
np_data = np.array(data)

# Python loop
start = time.time()
result = [x * 2 for x in data]
print(f"Python loop: {time.time() - start:.4f} seconds")

# NumPy
start = time.time()
result = np_data * 2
print(f"NumPy: {time.time() - start:.4f} seconds")

On my machine NumPy is 50 times faster for this operation. At a million items. That gap only grows as data gets bigger.

The operations that make it powerful

Filter without loops

sales = np.array([45000, 52000, 38000, 61000, 55000])

# Get only months above 50,000
high_months = sales[sales > 50000]
print(high_months)  # [52000 61000 55000]

Apply conditions across all values

avg = np.mean(sales)
performance = np.where(sales >= avg, "Good", "Below average")
print(performance)
# ['Good' 'Good' 'Below average' 'Good' 'Good']

Work with 2D data like a spreadsheet

# 6 months of data: revenue, orders, avg order value
monthly = np.array([
    [45000, 120, 375],
    [52000, 138, 377],
    [38000, 102, 373],
])

print(monthly[:, 0].sum())   # total revenue across all months
print(monthly[:, 1].mean())  # average orders per month

The [:, 0] means "all rows, column 0". This is how you slice 2D data without writing nested loops.

Why every data scientist needs to know this

pandas DataFrames are built on NumPy arrays. When you call df['Revenue'].mean() in pandas, pandas calls NumPy internally. When you train a machine learning model in scikit-learn, it converts your data into NumPy arrays before processing.

Understanding NumPy means you understand what is happening under the hood in every data science tool you will ever use.

Install and get started

pip install numpy

Then open Google Colab and try this right now:

import numpy as np

data = np.array([10, 20, 30, 40, 50])
print("Mean:", np.mean(data))
print("Doubled:", data * 2)
print("Above 25:", data[data > 25])

Three lines and you have filtered, transformed, and analysed data without writing a single loop.

The one-line summary

NumPy makes Python fast enough for real data science — it is the foundation everything else is built on.

Written by Raaga Priya Madhan — CSE student, Bangalore. I write about Python, data science, and ML. See my code on GitHub and connect on LinkedIn

What is Web Scraping? A Beginner's Guide with Real Python Code

Raaga Priya Madhan — Wed, 10 Jun 2026 05:08:26 +0000

Every website you visit is full of data. Web scraping is how you extract that data automatically using code instead of copying it manually.

A real problem web scraping solves

Imagine you want a list of all books under £10 from an online bookstore. The website has 50 pages of books. Copying them manually would take hours.

A web scraper does it in 30 seconds.

How websites work — the quick version

Every webpage is just an HTML file. When you open a website, your browser downloads that HTML and displays it visually. Web scraping works by downloading that same HTML and extracting specific pieces of data from it.

Your first web scraper — 15 lines of code

import requests
from bs4 import BeautifulSoup
import pandas as pd

# Step 1: Download the webpage
url = "https://books.toscrape.com"
response = requests.get(url)

# Step 2: Parse the HTML
soup = BeautifulSoup(response.content, "html.parser")

# Step 3: Find all book items on the page
books = soup.find_all("article", class_="product_pod")

# Step 4: Extract the data we want
data = []
for book in books:
    title = book.find("h3").find("a")["title"]
    price = book.find("p", class_="price_color").text
    data.append({"Title": title, "Price": price})

# Step 5: Save to Excel
df = pd.DataFrame(data)
df.to_excel("books.xlsx", index=False)
print(f"Scraped {len(data)} books")

Run this and you get an Excel file with every book title and price from the page. books.toscrape.com is a safe practice website built specifically for learning scraping.

What each part does

requests.get(url) — downloads the raw HTML of the webpage, the same way your browser does

BeautifulSoup — reads the HTML and lets you search through it like a document

find_all("article", class_="product_pod") — finds every book item on the page by looking for its HTML tag and class name

title = book.find("h3").find("a")["title"] — digs into each book item and pulls out just the title text

df.to_excel() — saves everything neatly into an Excel file

Real uses of web scraping

Price monitoring — track competitor prices on Amazon or Flipkart automatically
Lead generation — extract business names and contact details from directories
Research — collect hundreds of data points from multiple websites for analysis
Job listings — scrape job boards and filter by your criteria automatically
News aggregation — pull headlines from multiple news sites into one place

Is web scraping legal?

Generally yes, with conditions. Scraping publicly visible data is usually fine. The rules are:

Only scrape public pages — never pages that require login
Respect the site's robots.txt file
Add delays between requests so you don't overload the server
Never scrape personal or private data

When in doubt, check the website's Terms of Service.

How to install the libraries

Open your terminal and run:

pip install requests beautifulsoup4 pandas openpyxl

Then run the script above. It works immediately.

The one-line summary

Web scraping is code that reads a webpage the same way your browser does — but instead of displaying it, it extracts specific data and saves it for you.

Written by Raaga Priya Madhan — CSE student, Bangalore. I build Python automation scripts for businesses. See my scraping code on GitHub or connect on LinkedIn

How Python Automation Saves Businesses Hours Every Week

Raaga Priya Madhan — Wed, 10 Jun 2026 05:07:08 +0000

Most small businesses waste hours every week on tasks that a Python script could finish in seconds. Here's exactly what that looks like in real life.

The problem

A business owner has 500 rows of sales data in Excel every Monday morning. They manually:

Delete empty rows
Remove duplicates
Copy totals into a summary sheet
Email the summary to their manager

This takes 2 hours. Every single week. That's 100 hours a year spent on copy-pasting.

The solution — a Python script

Here's the actual code that replaces those 2 hours:

import pandas as pd

# Read the messy sales file
df = pd.read_excel("weekly_sales.xlsx")

# Clean it up
df = df.dropna(how='all')
df = df.drop_duplicates()

# Generate the summary
summary = df.groupby("Product")["Revenue"].sum()
summary = summary.sort_values(ascending=False)

# Save the clean report
summary.to_excel("weekly_summary.xlsx")

print("Done. Report saved.")

Run this script. It does the entire 2-hour job in 4 seconds.

More real examples

Invoice processing

A freelancer receives 200 invoices a month as PDFs and manually enters data into a spreadsheet. A Python script reads each PDF, extracts the numbers, and fills the spreadsheet automatically.

Time saved: 6 hours per month.

Website price monitoring

An ecommerce business manually checks 5 competitor websites every day to compare prices. A Python web scraper checks all 5 sites automatically every morning and sends an email summary.

Time saved: 1 hour per day.

File organisation

A photography studio has 10,000 photos named IMG_4829.jpg, IMG_4830.jpg and so on. A Python script renames all of them by date and category in under a minute.

Time saved: 3 hours of manual work done instantly.

Why businesses pay for this

The scripts above are not complicated. A CSE student can build each one in a few hours. But to a business owner who doesn't code, these scripts are worth paying for because:

They save hours of work every week
They eliminate human errors
They run automatically without supervision

A script that saves 2 hours per week is worth thousands of rupees per month to the business that uses it.

What you actually need to build these

Just two Python libraries:

pandas — reads, cleans, and analyses Excel and CSV data
requests + BeautifulSoup — fetches and extracts data from websites

Both are free. Both can be learned in a weekend.

The one-line summary

Python automation turns hours of manual work into seconds of automatic work. Businesses know this. That is why they pay developers to build these scripts.

Written by Raaga Priya Madhan — CSE student, Bangalore. I build Python automation scripts for businesses. Connect with me on LinkedIn or see my work at GitHub

What is an Algorithm? Explained with Everyday Examples

Raaga Priya Madhan — Tue, 09 Jun 2026 04:58:22 +0000

The word "algorithm" sounds intimidating. It's not. You follow algorithms every day without realizing it.

An algorithm is just a set of steps

When you make tea, you follow an algorithm:

Boil water
Add tea bag to cup
Pour hot water into cup
Wait 3 minutes
Remove tea bag
Add milk and sugar if needed
Drink

That's it. A fixed set of steps that produces a result. Algorithms in programming work exactly the same way.

A real programming example

Say you want to find the largest number in a list. Here's the algorithm:

numbers = [4, 7, 2, 9, 1]

largest = numbers[0]

for number in numbers:
    if number > largest:
        largest = number

print(largest)

Step by step:

Assume the first number is the largest
Go through every number in the list
If a number is bigger than your current largest, update largest
Print the result

Output: 9

Why algorithms matter

Every app you use runs on algorithms. Google's search results, Instagram's feed, Spotify's recommendations — all algorithms deciding what to show you.

The two things every algorithm needs

Correctness — it must produce the right answer
Efficiency — it must do it without wasting time or memory

The one-line summary

An algorithm is a step-by-step recipe for solving a problem. Every program you've ever used runs on them.

Written by Raaga Priya Madhan — CSE student, Bangalore. I write about CS concepts simply. Connect with me on LinkedIn

Python vs Java — Which Should Beginners Learn First?

Raaga Priya Madhan — Tue, 09 Jun 2026 04:56:12 +0000

This is one of the most common questions beginners ask. Here's an honest answer with no fluff.

Python — the case for starting here

Python reads almost like English. Compare these two ways of printing "Hello World":

Python:

print("Hello World")

Java:

public class Main {
    public static void main(String[] args) {
        System.out.println("Hello World");
    }
}

Same result. Python does it in 1 line. Java needs 5.

When you're learning, that simplicity matters enormously. Python lets you focus on learning programming concepts instead of fighting with syntax.

What Python is great for

Data analysis and automation
Web scraping
Machine learning and AI
Quick scripts that solve real problems
Backend web development

What Java is great for

Android app development
Large enterprise software
Banking and financial systems
Performance-critical applications

The honest answer

Start with Python if: you want to get results quickly, you're interested in data/AI/automation, or you just want to learn programming as a skill.

Start with Java if: you specifically want to build Android apps or you're going into a field that requires Java.

What most CSE students do

Learn Python first for speed and simplicity. Add Java later when your university requires it or a job demands it. Knowing one well makes learning the other much faster.

The one-line summary

Python for beginners. Java when you have a specific reason to use it.

Written by Raaga Priya Madhan — CSE student, Bangalore. I write about CS concepts simply. Connect with me on LinkedIn

How Does the Internet Actually Work? A Simple Explanation

Raaga Priya Madhan — Tue, 09 Jun 2026 04:51:17 +0000

You use the internet every second of your life. But what actually happens when you type google.com and hit Enter?

Step 1 — Your browser asks for directions

Your computer doesn't know where google.com lives. So it asks a DNS server — think of it as the internet's phone book — "where is google.com?"

The DNS server replies with an IP address like 142.250.194.46. That's Google's actual address on the internet.

Step 2 — Your browser knocks on Google's door

Your browser sends an HTTP request to that IP address. It's basically saying: "Hello Google, please send me your homepage."

Step 3 — Google's server responds

Google's server receives your request and sends back the HTML, CSS, and JavaScript files that make up the page.

Step 4 — Your browser builds the page

Your browser reads those files and renders them into the visual page you see — with colors, images, buttons, and text.

The whole thing happens in under 1 second

DNS lookup → HTTP request → Server response → Browser renders. Every time you visit a website.

Key terms to know

IP Address — A unique number identifying every device on the internet
DNS — The phone book that converts website names to IP addresses
HTTP/HTTPS — The language browsers and servers use to talk to each other
Server — A computer that stores website files and sends them on request

The one-line summary

You type a URL → DNS finds its address → your browser requests the page → the server sends it back → your browser shows it to you.

Written by Raaga Priya Madhan — CSE student, Bangalore. I write about CS concepts simply. Connect with me on LinkedIn

What is Git? A Beginner's Guide with Real Examples

Raaga Priya Madhan — Tue, 09 Jun 2026 04:44:02 +0000

Every developer talks about Git. But if you're just starting out, it can feel overwhelming. Let me break it down simply.

What is Git?

Git is a tool that tracks changes to your code over time.

Think of it like a save system in a video game. Every time you hit save, Git remembers exactly what your code looked like at that moment. If you break something later, you can go back to any previous save.

Why do developers use it?

Without Git, if you accidentally delete important code — it's gone forever. With Git, you just roll back to the last working version.

It also lets multiple developers work on the same project without overwriting each other's work.

The 5 commands you actually need

1. git init

Starts tracking a folder with Git.

git init

2. git add

Tells Git which files to save.

git add filename.py

3. git commit

Actually saves the snapshot with a message.

git commit -m "Added data cleaning function"

4. git push

Uploads your code to GitHub so others can see it.

git push

5. git clone

Downloads someone else's project to your computer.

git clone https://github.com/username/repo

GitHub vs Git

Git is the tool. GitHub is the website where you store your Git projects online — like Google Drive but for code.

The one-line summary

Git saves snapshots of your code so you never lose work and can always go back in time.

Start using it on your next project and it'll become second nature within a week.

Written by Raaga Priya Madhan — CSE student, Bangalore. I write about CS concepts simply. Connect with me on LinkedIn

What is an API? A Simple Explanation with Real Examples

Raaga Priya Madhan — Tue, 09 Jun 2026 04:42:01 +0000

You've heard the word API everywhere. In job descriptions, in tutorials, in tech Twitter. But what actually is it?

The waiter analogy

Think of an API like a waiter at a restaurant.

You (the app) don't walk into the kitchen (the server) yourself. You tell the waiter what you want. The waiter goes to the kitchen, gets your food, and brings it back.

That waiter is the API.

A real example you use every day

When you open Zomato and it shows a map of nearby restaurants — that map comes from Google Maps. Zomato didn't build its own maps. It asked Google's Maps API: "Give me a map for this location." Google sent it back. Zomato displayed it.

You use APIs dozens of times a day without knowing it.

Another example — weather apps

Your phone's weather app doesn't have its own satellites. It calls a weather API, asks "what's the weather in Bangalore right now?", and displays whatever comes back.

What does an API actually look like?

Here's a real API call in Python — just 3 lines:

import requests
response = requests.get("https://api.open-meteo.com/v1/forecast?latitude=12.97&longitude=77.59&current_weather=true")
print(response.json())

Run this and you get live weather data for Bangalore returned as text your program can read. That's an API call.

Why developers love APIs

You don't rebuild what already exists
Google Maps, payments, weather, SMS — all available as APIs
You focus on your app, APIs handle the hard parts

The one-line summary

An API is a messenger that lets two apps talk to each other — you send a request, it sends back data.

That's it. Next time someone says "we integrated an API" — you know exactly what happened.

Written by Raaga Priya Madhan — CSE student, Bangalore. I write about CS concepts simply. Connect with me on LinkedIn