Python
import requests
from bs4 import BeautifulSoup
import pandas as pd

# Basic web scraping
url = "https://example.com"
response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')

# Extract specific elements
titles = soup.find_all('h2', class_='title')
data = [title.get_text().strip() for title in titles]
print(data)

Progress:

• Identify target website and data requirements
• Choose scraping method (requests vs selenium)
• Inspect HTML structure and identify selectors
• Handle rate limiting and headers
• Extract and clean data
• Store results in structured format

Step-by-step Process:

1. Setup and Headers

   Python
   headers = {
       'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
   }
   session = requests.Session()
   session.headers.update(headers)

2. Handle Dynamic Content (if needed)

   Python
   from selenium import webdriver
   from selenium.webdriver.common.by import By
   from selenium.webdriver.support.ui import WebDriverWait
   
   driver = webdriver.Chrome()
   driver.get(url)
   WebDriverWait(driver, 10).until(
       EC.presence_of_element_located((By.CLASS_NAME, "target-class"))
   )

3. Extract and Structure Data

   Python
   results = []
   for item in soup.find_all('div', class_='item'):
       data = {
           'title': item.find('h3').get_text().strip(),
           'price': item.find('span', class_='price').get_text(),
           'url': item.find('a')['href']
       }
       results.append(data)
   
   df = pd.DataFrame(results)