Web Scraping: What, How, and When

What is Web Scraping?
Web scraping is a data extraction technique used to gather information from websites. It involves automated processes that retrieve data from web pages, allowing users to collect, analyze, and use the data for various purposes. Web scraping is particularly valuable for obtaining large datasets from the internet efficiently.

How Does Web Scraping Work?
Web scraping works by sending HTTP requests to a target website, downloading the web page’s HTML content, and then parsing that content to extract specific data. Here’s a simplified step-by-step process:

  1. Sending Requests: A web scraper sends HTTP requests to the URL(s) of the web page(s) containing the desired data.
  2. Downloading HTML: Once the request is processed by the web server, the scraper receives the HTML source code of the web page.
  3. Parsing HTML: The scraper parses the HTML to identify and extract the relevant data, such as text, images, links, or structured information.
  4. Data Storage: Extracted data can be stored in various formats, including CSV, JSON, or a database, for analysis or other purposes.
  5. Automation: Web scraping can be automated to fetch data from multiple pages or websites, saving time and effort.

When to Use Web Scraping?
Web scraping is a valuable tool in various scenarios:

  1. Data Collection: When you need to gather data from websites, such as product prices, news articles, or stock market data.
  2. Research and Analysis: For conducting market research, sentiment analysis, trend monitoring, and competitive analysis.
  3. Price Comparison: To track price changes for products or services across different websites.
  4. Data Integration: To integrate data from various sources into a single dataset or application.
  5. Monitoring and Alerts: For real-time monitoring of websites for changes, updates, or specific events.
  6. Automated Tasks: When you want to automate repetitive tasks like data entry or content extraction.
  7. Academic Research: In academic studies for data collection and analysis.
  8. Business Intelligence: To support decision-making processes by providing up-to-date information.
  9. Lead Generation: For extracting contact information from websites for sales and marketing purposes.

NOTE: It’s essential to note that while web scraping offers many benefits, it should be conducted ethically and in compliance with the terms of use of the target websites. Some websites may have legal restrictions on scraping, so it’s crucial to review and respect their policies. Additionally, the frequency and volume of scraping should be mindful of server load and bandwidth usage.

You can access the code at this GitHub repository: GitHub – Web Scraping with Pandas DataFrames

A simplified Python code for web scraping company data from a webpage using BeautifulSoup and storing it in a Pandas DataFrame:

import pandas as pd
import requests
from bs4 import BeautifulSoup

# Create empty lists to store data
names, ratings, reviews, company_types, headquarters, ages, employee_counts = [], [], [], [], [], [], []

# Loop through multiple pages (1 to 1000 in this example)
for page_number in range(1, 1001):
    url = f'https://www.ambitionbox.com/list-of-companies?page={page_number}'
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')

    # Find company containers
    company_containers = soup.find_all('div', class_='company-content-wrapper')

    # Extract data from each container
    for company in company_containers:
        ratings.append(company.find('p', class_='rating').text.strip())
        reviews.append(company.find('a', class_='review-count').text.strip())
        company_info = company.find_all('p', class_='infoEntity')

# Create a DataFrame to organize the extracted data
data = {
    'Name': names,
    'Rating': ratings,
    'Reviews': reviews,
    'Company Type': company_types,
    'Headquarters': headquarters,
    'Company Age': ages,
    'Employee Count': employee_counts

df = pd.DataFrame(data)

# Print sample data and DataFrame shape
print(f"Shape of DataFrame: {df.shape}")


  • We import the necessary libraries, including Pandas, requests, and BeautifulSoup.
  • We create empty lists to store data for each attribute we want to scrape.
  • We loop through multiple pages (from 1 to 1000 in this example) using a for loop.
  • For each page, we send a request to the URL, parse the HTML content using BeautifulSoup, and find all company containers.
  • Inside the loop, we extract data such as company names, ratings, reviews, and other information from each container and append them to their respective lists.
  • After scraping data from all pages, we organize it into a dictionary.
  • Finally, we create a Pandas DataFrame from the dictionary and print a sample of the data along with the shape of the DataFrame.

This code simplifies the web scraping process and demonstrates how to extract and store data from web pages into a structured DataFrame.

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