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Alex Spinov
Alex Spinov

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7 Python Libraries That Make Web Scraping Stupidly Easy (2026)

#python #webdev #tutorial #beginners

I've been scraping the web professionally for 3 years. I started with requests + BeautifulSoup like everyone else.

Then I discovered libraries that cut my code by 80%.

Here are 7 Python libraries I actually use in production — not theoretical picks, but tools I've shipped real projects with.

1. curl_cffi — The Requests Killer

requests gets blocked on half the internet. curl_cffi impersonates Chrome's TLS fingerprint.

from curl_cffi import requests as curl_requests

# This gets blocked with regular requests:
response = curl_requests.get(
    'https://example.com/api/data',
    impersonate='chrome'
)
print(response.json())  # Works!
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Why I switched: A client's scraper broke because the target site started checking TLS fingerprints. Switching from requests to curl_cffi was a one-line fix.

2. Selectolax — 20x Faster Than BeautifulSoup

BeautifulSoup is easy to learn. It's also painfully slow on large pages.

from selectolax.parser import HTMLParser

html = open('big_page.html').read()
tree = HTMLParser(html)

# CSS selectors, just like BeautifulSoup
for node in tree.css('.product-card'):
    title = node.css_first('h2').text()
    price = node.css_first('.price').text()
    print(f'{title}: {price}')
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Benchmark on a real 2MB HTML page:

  • BeautifulSoup: 3.2 seconds
  • Selectolax: 0.15 seconds

For scraping 10K pages, that's the difference between 9 hours and 25 minutes.

3. Trafilatura — Extract Article Text in One Line

Forget writing CSS selectors for every news site. Trafilatura uses ML to find the main content.

import trafilatura

html = trafilatura.fetch_url('https://example.com/article')
text = trafilatura.extract(html)
print(text)  # Clean article text, no boilerplate
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Use case: I built an LLM training pipeline that processes 1000 news articles/day. Trafilatura handles every site without custom selectors.

4. Botasaurus — Anti-Detection Built In

botasaurus handles anti-bot detection, caching, parallelism, and output formatting — all from decorators.

from botasaurus import browser, AntiDetectDriver

@browser(parallel=3, cache=True)
def scrape_product(driver: AntiDetectDriver, url):
    driver.get(url)
    return {
        'title': driver.text('h1'),
        'price': driver.text('.price'),
    }

# Scrapes 3 URLs in parallel, caches results
results = scrape_product(['url1', 'url2', 'url3'])
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Why it's special: Other libraries make you handle anti-detection, parallelism, and caching separately. Botasaurus does it all with decorators.

5. price-parser — Parse Any Price String

Prices on the web are messy: $1,299.99, € 1.299,99, ¥128,000, was $50 now $29.99.

from price_parser import Price

Price.fromstring('$1,299.99')       # Price(1299.99, 'USD')
Price.fromstring('€ 1.299,99')      # Price(1299.99, 'EUR')
Price.fromstring('was $50 now $29.99')  # Price(29.99, 'USD')
Price.fromstring('Free')            # Price(0, None)
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Time saved: Before price-parser, I had 40 lines of regex. Now it's one line.

6. Crawlee (Python) — Production-Grade Crawling

From the creators of Apify. Crawlee handles request queuing, retries, proxy rotation, and data storage.

from crawlee.playwright_crawler import PlaywrightCrawler, PlaywrightCrawlingContext

crawler = PlaywrightCrawler()

@crawler.router.default_handler
async def handler(context: PlaywrightCrawlingContext):
    title = await context.page.title()
    await context.push_data({'url': context.request.url, 'title': title})
    await context.enqueue_links(globs=['https://example.com/product/*'])

await crawler.run(['https://example.com/products'])
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Why not Scrapy? For new projects in 2026, Crawlee's async-first + Playwright integration is easier than Scrapy's callback-based architecture.

7. httpx — Async Requests Done Right

If you don't need browser rendering, httpx is the modern replacement for requests. HTTP/2 support + async.

import httpx
import asyncio

async def scrape_urls(urls):
    async with httpx.AsyncClient(http2=True) as client:
        tasks = [client.get(url) for url in urls]
        responses = await asyncio.gather(*tasks)
        return [r.json() for r in responses]

urls = [f'https://api.example.com/page/{i}' for i in range(100)]
results = asyncio.run(scrape_urls(urls))
print(f'Scraped {len(results)} pages')  # 100 pages in ~2 seconds
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Speed difference: Sequential requests: 100 pages in 60 seconds. Async httpx: same 100 pages in 2 seconds.

The Stack I Actually Use

Task Library
HTTP requests (APIs) httpx + curl_cffi
HTML parsing Selectolax
Article extraction Trafilatura
Browser scraping Crawlee or Botasaurus
Price parsing price-parser

Want more? I maintain a curated list of 130+ web scraping tools: awesome-web-scraping-2026

Which Python scraping library do you swear by? I'm genuinely curious — drop it in the comments 👇

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