Web Scraping

Web Scraping is a community skill for extracting data from websites, covering HTTP requests, HTML parsing, dynamic content handling, rate limiting, and data extraction patterns for journalism and research data collection.

What Is This?

Overview

Web Scraping provides guidance on extracting structured data from web pages for research and journalism purposes. It covers HTTP request handling that fetches web pages with proper headers, session management, and retry logic for reliable data retrieval, HTML parsing that extracts specific elements from page structure using CSS selectors and XPath expressions, dynamic content handling that processes JavaScript-rendered pages using headless browsers when static HTML parsing is insufficient, rate limiting that spaces requests to avoid overwhelming target servers and comply with robots.txt directives, and data extraction patterns that transform raw HTML into structured datasets with proper cleaning, validation, and storage. The skill helps researchers and journalists collect web data responsibly and efficiently.

Who Should Use This

This skill serves data journalists collecting public information for reporting, researchers building datasets from web sources, and analysts extracting structured data from unstructured web pages. It is also useful for developers automating routine data gathering tasks that would otherwise require significant manual effort.

Why Use It?

Problems It Solves

Manual data collection from websites is impractical when hundreds or thousands of pages need processing. JavaScript-rendered content is invisible to simple HTTP request tools. Websites change structure frequently breaking extraction logic that depends on specific HTML patterns. Collecting data at scale requires rate limiting and error handling to avoid server disruption and IP blocking. Without structured tooling, even modest scraping projects become difficult to maintain reliably over time.

Core Highlights

Request handler fetches pages with sessions and retry logic. Parser engine extracts elements using CSS and XPath selectors. Dynamic renderer handles JavaScript-generated page content. Rate limiter spaces requests for responsible data collection.

How to Use It?

Basic Usage

import requests
from bs4 import (
    BeautifulSoup
)

def scrape_articles(url):
    resp = requests.get(
        url, headers={
            'User-Agent':
                'ResearchBot/1.0'
        })
    resp.raise_for_status()
    soup = BeautifulSoup(
        resp.text, 'html.parser')

    articles = []
    for item in soup.select(
        'article.post'
    ):
        title = item.select_one(
            'h2').text.strip()
        link = item.select_one(
            'a')['href']
        date = item.select_one(
            'time')['datetime']
        articles.append({
            'title': title,
            'url': link,
            'date': date
        })
    return articles

Real-World Examples

import time
import requests
from bs4 import (
    BeautifulSoup
)

class RateLimitedScraper:
    def __init__(
        self, delay=2.0
    ):
        self.delay = delay
        self.session = (
            requests.Session())
        self.session.headers[
            'User-Agent'] = (
            'ResearchBot/1.0')

    def fetch(self, url):
        time.sleep(
            self.delay)
        resp = (
            self.session.get(
                url))
        resp.raise_for_status()
        return BeautifulSoup(
            resp.text,
            'html.parser')

    def extract_table(
        self, url
    ):
        soup = self.fetch(url)
        rows = []
        for tr in (
            soup.select(
                'table tr')
        ):
            cells = [
                td.text.strip()
                for td in
                tr.select(
                    'td')]
            if cells:
                rows.append(
                    cells)
        return rows

Advanced Tips

Use sessions to maintain cookies and authentication state across multiple page requests. Check robots.txt before scraping to respect site crawling directives. Save raw HTML responses to disk before parsing so you can reprocess data without re-fetching if extraction logic needs adjustment. Implementing exponential backoff on failed requests further improves reliability when servers return temporary errors or throttle responses under load.

When to Use It?

Use Cases

Collect public government data published as HTML tables for research analysis. Extract news article metadata from media websites for journalism investigations. Build a dataset of product information from e-commerce listings. Monitor publicly available regulatory filings or court records that are updated on a recurring schedule.

Related Topics

BeautifulSoup, requests, Selenium, data journalism, HTML parsing, robots.txt, and data collection.

Important Notes

Requirements

Python with requests and BeautifulSoup libraries for HTTP fetching and HTML parsing of static web pages. Selenium or Playwright for scraping pages that require JavaScript execution to render dynamic content. Understanding of HTML structure and CSS selectors for targeting specific page elements to accurately extract.

Usage Recommendations

Do: respect robots.txt directives and rate limit requests to avoid disrupting target servers. Identify your scraper with a descriptive User-Agent string that includes contact information. Cache fetched pages locally to reduce repeated requests during development.

Don't: scrape websites that explicitly prohibit automated access in their terms of service. Send rapid-fire requests without delays since this resembles a denial-of-service attack. Ignore error responses and retry immediately since backing off on errors is essential for responsible scraping.

Limitations

Website structure changes can break selectors requiring ongoing maintenance when target pages are redesigned or updated. Anti-scraping measures like CAPTCHAs and rate limiting may block automated access entirely. JavaScript-heavy single-page applications require headless browser rendering which is significantly slower than static HTML parsing.