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Python Web Scraping Tutorial - GeeksforGeeks

Web scraping is the process of extracting data from websites automatically. Python is widely used for web scraping because of its easy syntax and powerful libraries like BeautifulSoup, Scrapy, and Selenium.

In this tutorial, you'll learn how to use these Python tools to scrape data from websites and understand why Python 3 is a popular choice for web scraping tasks.

To install the required libraries in this article, run the following commands in the terminal.

pip install requests

pip install beautifulsoup4

pip install selenium

pip install lxml

pip install schedule

pip install pyautogui

• requests: Sends HTTP requests to get webpage content (used for static sites).
• beautifulsoup4: Parses and extracts HTML content (like tags, text, links).
• selenium: Automates browsers (needed for dynamic sites with JavaScript).
• lxml: A fast HTML/XML parser, useful for large or complex pages.
• schedule: Lets you run scraping tasks repeatedly at fixed intervals.
• pyautogui: Automates mouse and keyboard; useful when dealing with UI-based interactions.

The requests library is used for making HTTP requests to a specific URL and returns the response. Python requests provide inbuilt functionalities for managing both the request and response.

pip install requests

import requests

response = requests.get('https://www.geeksforgeeks.org/python-programming-language/')

print(response.status_code)

print(response.content)

Output:

Explanation:

• requests.get(url): Sends a GET request to the given URL.
• response.status_code: Returns HTTP status code (200 = success).
• response.content: Returns the raw HTML of the page in bytes.

For more information, refer to our Python Requests Tutorial .

Once the raw HTML is fetched, the next step is to parse it into a readable structure. That’s where BeautifulSoup comes in. It helps convert the raw HTML into a searchable tree of elements.

import requests
from bs4 import BeautifulSoup

response = requests.get('https://www.geeksforgeeks.org/python-programming-language/')

soup = BeautifulSoup(response.content, 'html.parser')

print(soup.prettify())

Output:

Explanation:

• BeautifulSoup(html, parser): Converts HTML into a searchable object. 'html.parser' is the built-in parser.
• soup.prettify(): Formats the HTML nicely for easier reading.

At this point, the HTML is ready to be searched for tags, classes or content.

Once we have parsed the HTML using BeautifulSoup, the next step is to locate and extract specific content from the page. Websites usually wrap their main article content inside tags with identifiable classes like <div class="article--viewer_content">. We can target such elements and pull out useful data like text, links or images.

In this example, we'll extract all paragraph (<p>) text from the main content section of the GeeksforGeeks Python Tutorial page.

import requests
from bs4 import BeautifulSoup

# Fetch and parse the page
response = requests.get('https://www.geeksforgeeks.org/python-programming-language-tutorial/')
soup = BeautifulSoup(response.content, 'html.parser')

# Find the main content container
content_div = soup.find('div', class_='article--viewer_content')
if content_div:
    for para in content_div.find_all('p'):
        print(para.text.strip())
else:
    print("No article content found.")

Output:

Image of the actual GeeksforGeeks Python Tutorial page:

Notice that the text output in the terminal contains the actual content from the web page.

For more information, refer to our Python BeautifulSoup .

Some websites load their content dynamically using JavaScript. This means the data you're trying to scrape may not be present in the initial HTML source. In such cases, BeautifulSoup alone won’t work, because it only reads static HTML.

To handle this, we use Selenium that can automate browsers like Chrome or Firefox, wait for content to load, click buttons, scroll and extract fully rendered web pages just like a real user.

A WebDriver is a software component that Selenium uses to interact with a web browser. It acts as the bridge between your Python script and the actual browser window.

Each browser (Chrome, Firefox, Edge, etc.) has its own WebDriver:

• Chrome: ChromeDriver
• Firefox: GeckoDriver
• Edge: EdgeDriver

Selenium uses this WebDriver to:

• Open and control the browser
• Load web pages
• Extract elements
• Simulate clicks, scrolls and inputs

You can either manually download the WebDriver or use webdriver-manager which handles the download and setup automatically.

In this example, we're directing the browser to the Google search page with the query parameter "geeksforgeeks". The browser will load this page and we can then proceed to interact with it programmatically using Selenium. This interaction could involve tasks like extracting search results, clicking on links or scraping specific content from the page.

# import webdriver 
from selenium import webdriver 

# create webdriver object 
driver = webdriver.Firefox() 

# get google.co.in 
driver.get("https://google.co.in / search?q = geeksforgeeks") 

Output

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
import time

element_list = []

# Set up Chrome options (optional)
options = webdriver.ChromeOptions()
options.add_argument("--headless")  # Run in headless mode (optional)
options.add_argument("--no-sandbox")
options.add_argument("--disable-dev-shm-usage")

# Use a proper Service object
service = Service(ChromeDriverManager().install())

for page in range(1, 3):
    # Initialize driver properly
    driver = webdriver.Chrome(service=service, options=options)

    # Load the URL
    url = f"https://webscraper.io/test-sites/e-commerce/static/computers/laptops?page={page}"
    driver.get(url)
    time.sleep(2)  # Optional wait to ensure page loads

    # Extract product details
    titles = driver.find_elements(By.CLASS_NAME, "title")
    prices = driver.find_elements(By.CLASS_NAME, "price")
    descriptions = driver.find_elements(By.CLASS_NAME, "description")
    ratings = driver.find_elements(By.CLASS_NAME, "ratings")

    # Store results in a list
    for i in range(len(titles)):
        element_list.append([
            titles[i].text,
            prices[i].text,
            descriptions[i].text,
            ratings[i].text
        ])

    driver.quit()

# Display extracted data
for row in element_list:
    print(row)

Output:

Explanation:

• ChromeOptions() + --headless: Runs the browser in the background without opening a visible window — ideal for automation and speed.
• ChromeDriverManager().install(): Automatically downloads the correct version of ChromeDriver based on your Chrome browser.
• Service(...): Wraps the ChromeDriver path for proper configuration with Selenium 4+.
• webdriver.Chrome(service=..., options=...): Launches a Chrome browser instance with the given setup.
• driver.get(url): Navigates to the specified page URL.
• find_elements(By.CLASS_NAME, "class"): Extracts all elements matching the given class name like titles, prices, etc.
• .text: Retrieves the visible text content from an HTML element.
• element_list.append([...]): Stores each product's extracted data in a structured list.
• driver.quit(): Closes the browser to free system resources.

For more information, refer to our Python Selenium .

lxml is a high-speed parser that supports XPath queries, ideal when you need precision.

Below is a simple example demonstrating how to use the lxml module for Python web scraping:

1. We import the html module from lxml along with the requests module for sending HTTP requests.
2. We define the URL of the website we want to scrape.
3. We send an HTTP GET request to the website using the requests.get() function and retrieve the HTML content of the page.
4. We parse the HTML content using the html.fromstring() function from lxml which returns an HTML element tree.
5. We use XPath expressions to extract specific elements from the HTML tree. In this case, we're extracting the text content of all the <a> (anchor) elements on the page.
6. We iterate over the extracted link titles and print them out.

from lxml import html
import requests

url = 'https://example.com'
response = requests.get(url)
tree = html.fromstring(response.content)

# Extract all link texts
link_titles = tree.xpath('//a/text()')

for title in link_titles:
    print(title)

Output in the Terminal

More information...

Below is the snapshot of the actual webpage of the URL: 'https://example.com'

Code Explanation:

• html.fromstring(): Parses HTML into an element tree.
• tree.xpath(): Uses XPath to extract specific tags or data.

For more information, refer to our lxml

The urllib module in Python is a built-in library that provides functions for working with URLs. It allows you to interact with web pages by fetching URLs (Uniform Resource Locators), opening and reading data from them and performing other URL-related tasks like encoding and parsing. Urllib is a package that collects several modules for working with URLs such as:

• urllib.request for opening and reading.
• urllib.parse for parsing URLs
• urllib.error for the exceptions raised
• urllib.robotparser for parsing robot.txt files

If urllib is not present in your environment, execute the below code to install it.

pip install urllib3

Here's a simple example demonstrating how to use the urllib module to fetch the content of a web page:

1. We define the URL of the web page we want to fetch.
2. We use urllib.request.urlopen() function to open the URL and obtain a response object.
3. We read the content of the response object using the read() method.
4. Since the content is returned as bytes, we decode it to a string using the decode() method with 'utf-8' encoding.
5. Finally, we print the HTML content of the web page.

import urllib.request

# URL of the web page to fetch
url = 'https://www.example.com'

try:
    response = urllib.request.urlopen(url)
    data = response.read()
    
    # Decode the data (if it's in bytes) to a string
    html_content = data.decode('utf-8')
    
    # Print the HTML content of the web page
    print(html_content)

except Exception as e:
    print("Error fetching URL:", e)

Output:

For more information, refer to urllib module

PyAutoGUI lets you simulate mouse and keyboard actions. It’s useful if elements aren’t reachable via Selenium like special pop-ups or custom scrollbars.

In this example, pyautogui is used to perform scrolling and take a screenshot of the search results page obtained by typing a query into the search input field and clicking the search button using Selenium.

import pyautogui

# moves to (519,1060) in 1 sec
pyautogui.moveTo(519, 1060, duration = 1)

# simulates a click at the present mouse position 
pyautogui.click()

pyautogui.moveTo(1717, 352, duration = 1) 

pyautogui.click()

Output

Explanation:

• moveTo(x, y): Moves the mouse to a screen position.
• click(): Clicks at the current mouse location.

For more information, refer to PyAutoGUI

The schedule module in Python is a simple library that allows you to schedule Python functions to run at specified intervals. It's particularly useful in web scraping in Python when you need to regularly scrape data from a website at predefined intervals such as hourly, daily or weekly.

import schedule 
import time 

def func(): 
    print("Geeksforgeeks") 

schedule.every(1).minutes.do(func) 

while True: 
    schedule.run_pending() 
    time.sleep(1) 

Output:

Explanation:

You can notice in the output that the pragram is call the function "func" every minute, so you can implement the code for timely web scrapping in similar way.

• schedule.every().minutes.do(): Schedules your function.
• run_pending(): Checks if any job is due.
• time.sleep(): Prevents the loop from hogging CPU.

Python 3 is the most modern and supported version of Python and it's ideal for web scraping because:

• Readable syntax: Easy to learn and write.
• Strong library support: Tools like BeautifulSoup and Selenium are built for it.
• Active community: Tons of support and examples online.
• Flexible: Can combine with data analysis, ML or APIs.

Web Scrapping Using Python

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