Web Scraping with Python | Codanics Guide

Web Scraping with Python: The Ultimate Guide for Beginners & Pros 🚀

Web scraping is the process of automatically extracting data from websites. Whether you’re a data scientist, developer, or business analyst, web scraping with Python unlocks a world of possibilities—from gathering research data to monitoring prices and automating tedious tasks.

In this comprehensive guide by Codanics, you’ll learn how to scrape websites using Python’s most popular libraries, best practices for ethical scraping, and real-world use cases to supercharge your projects.

Watch our video: What Is Web Scraping? Key Terms + Best Python Libraries Explained

Why Web Scraping Matters in 2024
Prerequisites
Essential Python Libraries for Web Scraping
Data Collection Using API Libraries
Key Web Scraping Terms and Concepts
Step-by-Step: Scraping Your First Website
Real-World Example: Scraping Product Prices
Best Practices for Ethical Web Scraping
Handling Dynamic Content
Common Challenges & Solutions
Frequently Asked Questions
Disclaimer
Conclusion

Why Web Scraping Matters in 2024

Data is Power: Most valuable data is online—news, prices, reviews, research, and more.
Automation: Save hours by automating data collection.
Competitive Edge: Gain insights your competitors might miss.

Prerequisites

Basic knowledge of Python
Familiarity with HTML structure (helpful but not mandatory)

Essential Python Libraries for Web Scraping

Requests: For sending HTTP requests and fetching web pages.
BeautifulSoup: For parsing HTML and extracting data.
Pandas: For organizing and analyzing scraped data into tables, DataFrames, and exporting to various formats.
Selenium: For scraping dynamic websites (JavaScript-heavy) and automating browser interactions.
Scrapy: A powerful framework for large-scale web scraping projects with built-in features for following links and handling requests.
Regular Expressions (re): For pattern matching within text to extract specific data formats like emails, phone numbers, or custom patterns.

Data Collection Using API Libraries

APIs (Application Programming Interfaces) are a more structured way to gather data from websites. Some python libraries can help you access data from popular APIs:

wbdata: For accessing World Bank data.
FAOSTAT: For food and agriculture data.
Eurostat: For European statistics.
yfinance: For financial data from Yahoo Finance.
tweepy: For Twitter data.
google-api-python-client: For accessing Google APIs like Google Sheets, YouTube, etc.
openpyxl: For reading and writing Excel files.
pandas_datareader: For financial data from various sources like Yahoo Finance, Google Finance, etc.

Beyond traditional web scraping, Python offers specialized libraries to access structured data directly through APIs:

Data Gathering Using Python APIs | wbdata, FAOSTAT, Eurostat, yfinance for Analysts & Scientists

Watch our complete lecture: Data Gathering Using Python APIs | wbdata, FAOSTAT, Eurostat, yfinance

Key Web Scraping Terms and Concepts

Term	Description	Example
HTML	The standard markup language for web pages	`<div class="product">Product Name</div>`
CSS Selector	Pattern to target specific HTML elements	`soup.select('.product_price')` finds elements with class “product_price”
XPath	Query language for selecting nodes in XML/HTML	`//div[@class="product"]/span` selects all span elements inside divs with class “product”
User-Agent	Identifies the client accessing a website	`headers = {'User-Agent': 'Mozilla/5.0...'}`
robots.txt	File that tells scrapers which pages can be accessed	`User-agent: * Disallow: /private/` means no scraper should access “/private/”
Rate Limiting	Controlling request frequency to avoid server overload	`time.sleep(2)` adds a 2-second pause between requests
DOM	Document Object Model – the structure of HTML	Selenium accesses the DOM after JavaScript executes
API	Application Programming Interface – formal data access method	`response = requests.get("https://api.example.com/products")`
Regular Expression	Pattern matching for text	`re.findall(r'[\w\.-]+@[\w\.-]+', text)` extracts email addresses
Pagination	Navigating through multiple pages of content	`for page in range(1, 10): url = f"https://example.com/page/{page}"`
Throttling	Limiting request frequency to avoid being blocked	`from time import sleep; sleep(random.uniform(1, 3))`
Scraper Detection	Methods websites use to identify and block scrapers	IP tracking, request pattern analysis, browser fingerprinting
Headless Browser	Browser without GUI that can be controlled programmatically	`options.add_argument('--headless'); driver = webdriver.Chrome(options=options)`
Proxy Rotation	Switching between different proxy servers to hide your IP	`proxies = {'http': f'http://{proxy_ip}:{port}'}`
CAPTCHA	Challenge-response test to determine if user is human	Image recognition tests, 2captcha API integration
Web Crawler	Bot that systematically browses the web	Google’s Googlebot, scrapy spiders crawling multiple pages
ETL Pipeline	Extract, Transform, Load process for scraped data	Scrape data, clean it, store in database

Step-by-Step: Scraping Your First Website

1. Install Required Libraries

pip install requests beautifulsoup4 pandas

2. Fetch a Web Page

import requests

url = “https://example.com”
response = requests.get(url)
print(response.text) # HTML content

3. Parse HTML with BeautifulSoup

from bs4 import BeautifulSoup

soup = BeautifulSoup(response.text, “html.parser”)
title = soup.title.text
print(“Page Title:”, title)

4. Extract Specific Data

for link in soup.find_all(‘a’):

print(link.get(‘href’))

Real-World Example: Scraping Product Prices

Let’s scrape product names and prices from a sample e-commerce site.

Web Scraping Made Easy with Pandas – Few-Lines Data Extraction in Python

Watch our tutorial: Web Scraping Made Easy with Pandas

import requests
from bs4 import BeautifulSoup
import pandas as pd

url = “https://books.toscrape.com/”
response = requests.get(url)
soup = BeautifulSoup(response.text, “html.parser”)

books = []
for book in soup.select(‘.product_pod’):
title = book.h3.a[‘title’]
price = book.select_one(‘.price_color’).text
books.append({‘Title’: title, ‘Price’: price})

df = pd.DataFrame(books)
print(df.head())

Best Practices for Ethical Web Scraping

Respect robots.txt: Always check the site’s robots.txt file before scraping. This file contains instructions about which parts of a site should not be accessed by automated tools.
Don’t overload servers: Use reasonable delays between requests (e.g., 1-5 seconds) to avoid putting excessive load on web servers.
Identify your scraper: Set a user-agent string that identifies your bot and includes contact information.
headers = {
‘User-Agent’: ‘YourCompany Bot (yourname@example.com)’,
}
Avoid personal/sensitive data: Scrape only public information and never extract personally identifiable information without proper authorization.
Check legal policies: Ensure compliance with website terms of service, privacy policies, and applicable laws.
Cache results when possible: Store results locally to minimize duplicate requests.
import json
import os
# Check if we have cached data
if os.path.exists(‘cached_data.json’):
with open(‘cached_data.json’, ‘r’) as f:
data = json.load(f)
else:
# Scrape the website and save the data
data = scrape_website()
with open(‘cached_data.json’, ‘w’) as f:
json.dump(data, f)
Implement error handling: Build robust error handling to gracefully handle rate limits and network failures.
Schedule scraping during off-peak hours: Run your scrapers during periods of low traffic.

Handling Dynamic Content

Some sites load data with JavaScript. Use Selenium for such cases:

from selenium import webdriver

driver = webdriver.Chrome()
driver.get(“https://example.com”)
html = driver.page_source
# Parse with BeautifulSoup as before
driver.quit()

Common Challenges & Solutions

CAPTCHAs: Use APIs or manual intervention.
IP Blocking: Rotate proxies or use VPNs.
Changing HTML: Update your selectors regularly.

Frequently Asked Questions

Q: Is web scraping legal?
A: Scraping public data is generally legal, but always check the website’s terms and local laws.

Q: Can I scrape any website?
A: Not all sites allow scraping. Respect robots.txt and copyright.

Q: How do I handle websites that require login?
A: Use session management with the Requests library or Selenium to automate the login process.

Q: What’s the difference between web scraping and web crawling?
A: Web scraping extracts specific data from websites, while web crawling systematically browses websites to discover content.

Q: How can I avoid getting blocked while scraping?
A: Use delays between requests, rotate user agents, respect robots.txt, and consider using proxies.

Q: Are there alternatives to web scraping?
A: Yes, many websites offer APIs that provide structured data access.

Q: How do I handle data that loads through JavaScript?
A: Use Selenium or Playwright to execute JavaScript and access dynamically loaded content.

Q: Is web scraping scalable for big projects?
A: Yes, tools like Scrapy and frameworks like Selenium Grid can help scale scraping across multiple machines.

Disclaimer

Important: This guide is provided for educational purposes only. Web scraping can have legal and ethical implications. It is your responsibility to:

Ensure you have the right to scrape a particular website
Comply with each website’s terms of service and robots.txt directives
Respect copyright laws and intellectual property rights
Follow data protection regulations when storing scraped information
Use the scraped data in an ethical manner

Codanics does not endorse or encourage any use of web scraping techniques for illegal activities or in ways that violate website terms of service. The techniques shared in this guide should be applied responsibly and ethically.

Conclusion

Web scraping with Python is a powerful skill for data-driven professionals. By following best practices and using the right tools, you can unlock valuable insights from the web.

Ready to master Data Science?
Free Playlist to Learn and Master Data Science: Complete Courses on Data Science Python and Freelancing skills