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Muhammad Ikramullah Khan
Muhammad Ikramullah Khan

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Requests vs Selenium vs Scrapy: Which Web Scraping Tool Should You Actually Use?

#selenium #scrapy #python #programming

So you want to scrape some data from websites. You've probably already Googled "how to scrape websites with Python" and gotten completely overwhelmed by the options. Requests, BeautifulSoup, Selenium, Scrapy—everyone's talking about different tools like they're obvious choices.

Here's the truth: there's no single "best" tool. Each one is good at different things. It's like asking "should I use a hammer or a screwdriver?" Well... what are you trying to build?

Let me walk you through these tools like I'm talking to a friend who's just getting started. No jargon, no assumptions. Just real talk about when to use what.

The Three Main Options (Simplified)

Think of it this way:

Requests + BeautifulSoup = Your bicycle

  • Simple, lightweight, easy to learn
  • Great for getting from point A to point B
  • Perfect for quick trips

Selenium = Your SUV with 4-wheel drive

  • Heavier, uses more gas (resources)
  • Can handle tough terrain (JavaScript-heavy sites)
  • Overkill for simple trips

Scrapy = Your delivery truck

  • Built specifically for hauling lots of cargo (data)
  • Has all the professional features you need
  • Takes longer to learn to drive

Now let's dig into when you'd actually use each one.

Requests + BeautifulSoup: Start Here

If you're brand new to web scraping, start here. This is where everyone should begin.

What Are They?

Requests is a library that fetches web pages (makes HTTP requests)
BeautifulSoup is a library that parses HTML and lets you extract data from it

Together, they're like peanut butter and jelly—simple, classic, and they just work.

When to Use Requests + BeautifulSoup

Use this combo when:

  • The website is simple - Just plain HTML, no fancy JavaScript stuff
  • You're learning - This is the easiest to understand
  • It's a one-time scrape - Quick and dirty data extraction
  • The site doesn't have infinite scroll or dynamic loading
  • You need something working in the next 10 minutes

Real Example: Scraping a Blog

Let's say you want to scrape article titles from a simple blog:

import requests
from bs4 import BeautifulSoup

# Fetch the page
response = requests.get('https://example.com/blog')
html = response.text

# Parse it
soup = BeautifulSoup(html, 'html.parser')

# Extract titles
titles = soup.find_all('h2', class_='post-title')

for title in titles:
    print(title.text)
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That's it. Three steps: fetch, parse, extract.

The Limitations

Here's when Requests + BeautifulSoup falls short:

  • Can't handle JavaScript - If the page loads content dynamically (after page load), you won't see it
  • No built-in error handling - If something breaks, you write all the retry logic yourself
  • One page at a time - It's synchronous, meaning slow for scraping hundreds of pages
  • You build everything yourself - No pipelines, no automatic data storage

My Honest Opinion

This is perfect for:

  • Learning the basics
  • Small projects (scraping 10-100 pages)
  • Simple sites that don't use JavaScript heavily
  • Quick scripts you'll run once

I still use Requests + BeautifulSoup for quick one-off scraping tasks. When I just need to grab some data real quick, this is my go-to.

Selenium: When Sites Get Complicated

Selenium is different. It's not really a scraping tool—it's a browser automation tool that people use for scraping.

What Is Selenium?

Selenium actually opens a real browser (Chrome, Firefox, etc.) and controls it like a human would. It can:

  • Click buttons
  • Fill out forms
  • Wait for JavaScript to load
  • Scroll down pages
  • Handle pop-ups and alerts

When to Use Selenium

Use Selenium when:

  • The site uses heavy JavaScript - Content loads after the page (infinite scroll, lazy loading)
  • You need to interact with the site - Login forms, clicking buttons, scrolling
  • Content appears only after certain actions - Like clicking "Load More"
  • You're dealing with Single Page Applications (SPAs) - Sites built with React, Vue, Angular
  • You need to look like a human - Selenium mimics real browser behavior

Real Example: Scraping Twitter/X

Twitter loads tweets dynamically as you scroll. Requests won't see those tweets because they load via JavaScript:

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import time

# Start browser
driver = webdriver.Chrome()

# Go to Twitter profile
driver.get('https://twitter.com/someuser')

# Wait for tweets to load
time.sleep(3)

# Scroll down to load more tweets
for _ in range(5):
    driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
    time.sleep(2)

# Now extract tweets
tweets = driver.find_elements(By.CSS_SELECTOR, 'article[data-testid="tweet"]')

for tweet in tweets:
    print(tweet.text)

driver.quit()
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The Downsides

Selenium has some serious drawbacks:

  • SLOW - Like, really slow. Opening a browser takes time, loading pages takes time
  • Resource hungry - Uses a lot of RAM and CPU
  • More complex code - Lots of waiting, handling timeouts, dealing with browser quirks
  • Harder to run at scale - Running 100 browser instances? Good luck with that
  • Fragile - Websites change their structure, and your Selenium code breaks easily

My Honest Opinion

Use Selenium when you have to, not because you want to.

I see beginners reaching for Selenium too quickly. They think "I'll just use Selenium for everything!" and then wonder why their scraper takes 5 minutes to scrape 20 pages.

Use Selenium only when:

  • You've tried Requests + BeautifulSoup and it doesn't work
  • The site legitimately needs JavaScript to function
  • You need to interact with the page (login, click buttons, etc.)

For everything else, there are better options.

Scrapy: The Professional's Choice

Scrapy is a framework, not just a library. It's an entire system built specifically for web scraping at scale.

What Is Scrapy?

Think of Scrapy as an assembly line for data. It has:

  • Spiders - Define what to scrape and how
  • Pipelines - Process and clean your data
  • Middlewares - Handle requests, rotate proxies, add delays
  • Built-in tools - Export data, handle errors, respect robots.txt

When to Use Scrapy

Use Scrapy when:

  • You're scraping lots of pages - Hundreds, thousands, or millions
  • You need it to be FAST - Scrapy is asynchronous (parallel requests)
  • It's a serious project - Not just a quick script
  • You want structure - Organized, maintainable code
  • You need features - Automatic retries, data pipelines, throttling
  • You're building a production scraper - Something that runs regularly

Real Example: Scraping an E-commerce Site 

# myspider.py
import scrapy

class ProductSpider(scrapy.Spider):
    name = 'products'
    start_urls = ['https://example.com/products']

    def parse(self, response):
        # Extract products from listing page
        for product in response.css('div.product'):
            yield {
                'name': product.css('h3.title::text').get(),
                'price': product.css('span.price::text').get(),
                'url': product.css('a::attr(href)').get(),
            }

        # Follow pagination
        next_page = response.css('a.next-page::attr(href)').get()
        if next_page:
            yield response.follow(next_page, self.parse)
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Run it:

scrapy crawl products -o products.json
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The Learning Curve

Here's the thing about Scrapy: it's harder to learn at first.

With Requests + BeautifulSoup, you write one script and you're done. With Scrapy, you need to understand:

  • How spiders work
  • How to configure settings
  • How pipelines process data
  • How middlewares modify requests

But once you get it? It's incredibly powerful.

The Advantages

Why people love Scrapy:

  • Asynchronous - Scrapes multiple pages simultaneously (FAST!)
  • Built-in features - Don't reinvent the wheel
  • Respect rate limits - Auto-throttle built in
  • Data pipelines - Clean, validate, and store data automatically
  • Easy to maintain - Well-organized project structure
  • Scales well - From 100 pages to 10 million

The Disadvantages

  • Steeper learning curve - Not beginner-friendly
  • Overkill for small projects - Don't use a sledgehammer to crack a nut
  • Can't handle JavaScript natively - You need Splash or Playwright integration
  • More setup required - Can't just write one file and run it

My Honest Opinion

Scrapy is what you "grow into."

If you're just starting out, it might feel overwhelming. But if you're serious about web scraping—if you're thinking "I want to scrape data professionally" or "I need to build scrapers for my job"—learn Scrapy.

I use Scrapy for:

  • Any project with 500+ pages
  • Scrapers that need to run regularly
  • When I need the data in a database
  • Projects where I want proper error handling and logging

The Decision Tree (When to Use What)

Let me make this super simple. Ask yourself these questions:

Question 1: Does the site use heavy JavaScript?

No JavaScript / Simple HTML?
→ Start with Requests + BeautifulSoup

Heavy JavaScript / Dynamic content?
→ You probably need Selenium
→ OR use Scrapy with Splash/Playwright

Question 2: How many pages are you scraping?

1-100 pages?
→ Requests + BeautifulSoup is fine

100-1,000 pages?
→ Consider Scrapy (it'll be much faster)

1,000+ pages?
→ Definitely use Scrapy

Question 3: Is this a one-time thing or recurring?

One-time scrape?
→ Requests + BeautifulSoup (quick and dirty)

Regular scraping / Production use?
→ Scrapy (proper structure)

Question 4: Do you need to interact with the site?

No interaction needed?
→ Requests + BeautifulSoup or Scrapy

Need to login / click / fill forms?
→ Selenium

Real-World Scenarios

Let me give you some concrete examples:

Scenario 1: Scraping Product Prices

The Task: Scrape prices from 50 e-commerce product pages

Best Tool: Requests + BeautifulSoup

Why: Simple HTML, small number of pages, one-time task. No need for complexity.

import requests
from bs4 import BeautifulSoup

urls = ['https://example.com/product/' + str(i) for i in range(1, 51)]

for url in urls:
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    price = soup.find('span', class_='price').text
    print(f"{url}: {price}")
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Scenario 2: Monitoring Job Listings Daily

The Task: Scrape 500+ job postings every day from multiple sites

Best Tool: Scrapy

Why: Large scale, recurring task, needs to be fast and reliable. You'll want pipelines to store data in a database.

Scenario 3: Scraping Instagram Posts

The Task: Collect posts and comments from Instagram

Best Tool: Selenium (or a specialized tool)

Why: Instagram is heavy JavaScript, requires login, has anti-bot measures. You need a real browser.

Scenario 4: Academic Research Data

The Task: Scrape 10,000 research paper abstracts from a university database

Best Tool: Scrapy

Why: Large volume, static HTML, needs to be respectful with delays. Scrapy's auto-throttle is perfect.

Scenario 5: Scraping a News Article

The Task: Grab today's headline and summary from one news site

Best Tool: Requests + BeautifulSoup

Why: Single page, one-time scrape. Writing a Scrapy spider would take longer than just using Requests.

Quick Comparison Table

Here's everything side-by-side:

Feature Requests + BS4 Selenium Scrapy
Learning Curve Easy Medium Hard
Speed Medium Slow Very Fast
JavaScript Support No Yes No (needs plugins)
Setup Time 5 minutes 15 minutes 30+ minutes
Best For 1-100 pages JS-heavy sites 500+ pages
Resource Usage Low Very High Low
Maintenance Easy Medium Easy (once set up)
Parallel Requests No (default) Very hard Yes (built-in)
Error Handling Manual Manual Built-in
Data Pipelines Manual Manual Built-in

My Recommendation for Beginners

If you're just starting out, here's my advice:

Week 1-2: Learn Requests + BeautifulSoup

  • Start here. Period.
  • Scrape some simple sites
  • Get comfortable with HTML, CSS selectors
  • Build 3-5 small projects

Week 3-4: Try Selenium

  • Pick a JavaScript-heavy site
  • Learn how to wait for elements
  • Try scraping a site that needs interaction
  • Understand its limitations

Month 2-3: Dive into Scrapy

  • Only after you're comfortable with the basics
  • Build a proper multi-page scraper
  • Learn pipelines and middlewares
  • This is when scraping gets professional

Common Mistakes Beginners Make

Let me save you some time by pointing out mistakes I see all the time:

Mistake 1: Using Selenium for Everything

"I'll just use Selenium so I never have to worry about JavaScript!"

Bad idea. Selenium is slow and resource-intensive. It's like buying a monster truck to commute to work. Sure, it'll get you there, but you'll waste a lot of time and gas.

Fix: Always try Requests + BeautifulSoup first. Only use Selenium when you actually need it.

Mistake 2: Not Checking robots.txt

Every website has a robots.txt file (like example.com/robots.txt) that tells scrapers what they're allowed to scrape.

Ignoring it can get your IP banned or even lead to legal issues.

Fix: Always check robots.txt. Scrapy respects it by default. With Requests, you need to check manually.

Mistake 3: Scraping Too Fast

Hitting a site with 100 requests per second is a great way to:

  • Get your IP banned
  • Crash the website (if it's small)
  • Look like a jerk

Fix: Add delays between requests. Be respectful. The website owner is paying for bandwidth.

import time

for url in urls:
    scrape(url)
    time.sleep(2)  # Wait 2 seconds between requests
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Mistake 4: Not Handling Errors

Your scraper will break. Pages will return 404s. Servers will time out. That's normal.

Not handling these errors means your scraper crashes and you lose all your data.

Fix: Use try-except blocks:

try:
    response = requests.get(url, timeout=10)
    response.raise_for_status()
except requests.exceptions.RequestException as e:
    print(f"Error scraping {url}: {e}")
    continue
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Mistake 5: Choosing Scrapy for a 10-Page Project

I've seen people set up entire Scrapy projects with spiders, pipelines, and settings... to scrape 10 pages.

That's like building a factory to make a single sandwich.

Fix: Choose the right tool for the job. Small project? Keep it simple.

The Bottom Line

Here's what you need to remember:

Use Requests + BeautifulSoup when:

  • You're learning
  • The site is simple (no JavaScript)
  • You're scraping fewer than 100 pages
  • It's a one-time thing

Use Selenium when:

  • The site uses heavy JavaScript
  • You need to interact (login, click, scroll)
  • Content loads dynamically
  • You've tried Requests and it didn't work

Use Scrapy when:

  • You're scraping hundreds or thousands of pages
  • It's a recurring job
  • You need professional features (pipelines, middlewares)
  • Speed and structure matter

What I Wish Someone Told Me When I Started

When I first started scraping, I jumped straight to Scrapy because "it's what the pros use." I struggled for weeks trying to understand spiders, pipelines, and settings when all I needed to do was scrape 20 pages.

Looking back, I should have:

  1. Started with Requests + BeautifulSoup
  2. Built 10 simple scrapers
  3. Hit the limits of what Requests could do
  4. THEN moved to Scrapy

Don't skip steps. Master the basics first.

Also, remember: web scraping isn't always the answer. Many sites have APIs. Always check if there's an official API before scraping. APIs are:

  • Faster
  • More reliable
  • Legal and ethical
  • Less likely to break

Next Steps

Ready to start scraping? Here's what to do:

1. Pick a simple site

  • Start with something easy like a blog or simple news site
  • Avoid sites with heavy JavaScript at first
  • Check robots.txt

2. Use Requests + BeautifulSoup

  • Write a simple scraper
  • Extract just one piece of data (like titles)
  • Get it working, then expand

3. Add error handling

  • Handle 404s and timeouts
  • Add logging
  • Test what happens when things break

4. Respect the website

  • Add delays between requests
  • Don't scrape personal data
  • Follow robots.txt

5. Scale up gradually

  • Once you're comfortable, try more pages
  • When Requests feels slow, consider Scrapy
  • When you hit JavaScript, try Selenium

Final Thoughts

There's no "best" web scraping tool—only the best tool for your specific situation.

Start simple. Master the basics. Add complexity only when you need it.

And remember: with great scraping power comes great responsibility. Just because you can scrape something doesn't mean you should. Be ethical, be respectful, and be smart about it.

Happy scraping!

Questions? Drop a comment below! I'm always happy to help beginners navigate this stuff. We've all been there.

Top comments (1)

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onlineproxyio profile image
OnlineProxy •

Start with Requests - BeautifulSoup - Scrapy and only whip out Playwright/Selenium when the site truly needs JS or button‑clicking. For a ~1,000‑page run, Scrapy’s the move-it’s got concurrency, retries, throttling, caching, and pipelines baked in. Selenium is overkill if the data’s in the HTML or a JSON/XHR-hit those endpoints with Requests/Scrapy, and save Scrapy+Playwright for truly JS‑locked pages. Scrapy can absolutely replace Requests+BS4 for pro or recurring scrapes, but for tiny 10–50 page grabs, Requests+BS4 ships faster. TLDR: most pain is about HTTP, selectors, and anti‑bot-start simple, then level up only when you must.