Web Scraper (Enhanced)

🐍 Lesson 46: Web Scraper Project (Python)

1. Project Overview

In short → Web scraping is a way of automatically pulling data (like news headlines, prices, or lists of information) out of a website using a Python script.

In detail → Web scraping is the process of extracting data from websites automatically using Python scripts.

2. Required Libraries

requests → for sending requests to a website
beautifulsoup4 → for parsing HTML

Install: pip install requests beautifulsoup4

3. Summary

✅ Web Scraping = pulling website data with Python

✅ Libraries → requests + BeautifulSoup

✅ Example → news headlines, product prices

✅ Error handling → network error, HTML changes

python

# ===== 1. Basic Web Scraper Setup =====
import requests
from bs4 import BeautifulSoup

# ===== 2. Example: Scraping News Headlines =====
print("===== Web Scraping Example =====")

# Note: This is a demonstration. In real use, replace URL with target site.
# URL = "https://news.ycombinator.com/"
# response = requests.get(URL)
# soup = BeautifulSoup(response.text, "html.parser")
# headlines = soup.find_all("a", class_="storylink")
# 
# for i, headline in enumerate(headlines[:10], 1):
#     print(f"{i}. {headline.text}")

print("\n===== Scraping Process =====")
print("1. Use requests.get(URL) to fetch HTML")
print("2. Parse HTML with BeautifulSoup")
print("3. Find elements with soup.find_all()")
print("4. Extract text/data from elements")
print("5. Save or process the data")

# ===== 3. Error Handling =====
print(f"\n===== Error Handling =====")
print("✅ Handle requests.exceptions.RequestException")
print("✅ Check response status code")
print("✅ Validate HTML structure changes")

# ===== 4. Best Practices =====
print(f"\n===== Best Practices =====")
print("✅ Check robots.txt before scraping")
print("✅ Use headers to identify your bot")
print("✅ Add delays between requests")
print("✅ Respect website terms of service")

You should see

===== Web Scraping Example ===== ===== Scraping Process ===== 1. Use requests.get(URL) to fetch HTML 2. Parse HTML with BeautifulSoup 3. Find elements with soup.find_all() 4. Extract text/data from elements 5. Save or process the data ===== Error Handling ===== ✅ Handle requests.exceptions.RequestException ✅ Check response status code ✅ Validate HTML structure changes ===== Best Practices ===== ✅ Check robots.txt before scraping ✅ Use headers to identify your bot ✅ Add delays between requests ✅ Respect website terms of service