Learn to extract, parse, and structure web data using Python. From HTML basics to advanced scraping techniques.
Start Learning ↓Understanding where data comes from and how to collect it responsibly is the foundation of every data science project.
"Garbage In, Garbage Out" — The quality of your insights depends entirely on the quality of data you collect. Always prioritize accuracy and reliability.
Structured access to real-time information. The cleanest way to get data when available—always check for an API first.
SQL and NoSQL databases house large volumes of structured data. Essential for enterprise data collection.
Unstructured data from blogs, e-commerce, and news sites. Requires parsing HTML to extract meaningful information.
CSV, JSON, and Excel sheets serve as local or cloud-based repositories for structured datasets.
Real-time inputs from IoT devices and application events for streaming data collection.
With great power comes great responsibility. Ethical scraping protects both you and the websites you interact with.
Always review and comply with website terms before scraping.
Respect the rules that indicate what can be crawled.
Data must be collected with proper user permission.
Comply with GDPR, CCPA, and data protection laws.
Properly cite sources and respect licensing agreements.
Never use data without proper permissions.
Web scraping is the automated process of extracting information from websites by parsing HTML code instead of manual copying.
Track competitor pricing across e-commerce platforms automatically.
Collect articles from multiple sources for sentiment analysis.
Aggregate postings from multiple boards into a unified database.
HTML (HyperText Markup Language) structures web content using nested tags. Understanding this hierarchy is crucial for effective scraping.
<!-- Every webpage follows this basic structure --> <!DOCTYPE html> <html> <head> <title>Page Title</title> </head> <body> <h1 class="main-title">Welcome!</h1> <p id="description">This is a paragraph.</p> </body> </html>
| Tag | Purpose | Scraping Use |
|---|---|---|
<div> |
Container/division | Group related content sections |
<p> |
Paragraph | Extract text blocks |
<h1>-<h6> |
Headings | Identify section titles |
<a> |
Anchor/Link | Extract URLs (href attribute) |
<table> |
Table structure | Structured data extraction |
<span> |
Inline container | Target specific text snippets |
<!-- Use class and id to target specific elements --> <div class="product-card" id="item-123"> <h2 class="title">Product Name</h2> <span class="price">$29.99</span> <a href="/buy-now">Purchase</a> </div> /* CSS Selectors to target these elements: .product-card → Selects the container #item-123 → Selects specific item .price → Selects price text a[href] → Selects links */
CSS selectors are patterns used to select HTML elements. They're the primary tool for targeting specific data in your scraper.
| Selector | Meaning | Example Target |
|---|---|---|
.class |
By class name | .price → All elements with class="price" |
#id |
By ID | #main → Element with id="main" |
tag |
By tag name | h1 → All heading 1 elements |
tag.class |
Tag with class | p.intro → Paragraphs with class "intro" |
[attr] |
Has attribute | [href] → Elements with href attribute |
parent > child |
Direct child | div > p → Paragraphs directly inside divs |
Python offers powerful libraries for every step of the scraping process—from fetching pages to parsing complex HTML.
HTTP library for fetching webpage content. Handles headers, sessions, and authentication.
Parses HTML/XML and provides Pythonic idioms for navigating the parse tree.
High-performance XML/HTML parser. Faster than html.parser for large documents.
Automates browsers for JavaScript-rendered pages that require interaction.
import requests from bs4 import BeautifulSoup # Step 1: Fetch the page url = "https://example.com" headers = {"User-Agent": "Mozilla/5.0"} response = requests.get(url, headers=headers) # Step 2: Check status if response.status_code == 200: # Step 3: Parse HTML soup = BeautifulSoup(response.text, 'lxml') # Step 4: Extract data using CSS selectors title = soup.select_one('h1').text items = soup.select('.product-item') for item in items: name = item.select_one('.name').text price = item.select_one('.price').text print(f"{name}: {price}")
200 "OK" → Success 404 "Not Found" → Page doesn't exist 403 "Forbidden" → Access denied (check headers) 500 "Server Error"→ Problem with the server 429 "Too Many" → Rate limited (slow down!)
Test your understanding by writing HTML and extracting data with CSS selectors.
Enter HTML code below:
Enter a CSS selector to extract data:
soup.find('tag') |
Find first matching element |
soup.find_all('tag') |
Find all matching elements (returns list) |
soup.select_one('.class') |
Find first using CSS selector |
soup.select('.class') |
Find all using CSS selector |
element.text |
Get text content inside tag |
element['href'] |
Get attribute value |