Web Scraping with Selenium and Python (2024)

Imagine that you can easily get all the data you need from the Internet without having to manually browse the web or copy and paste. That's the beauty of web scraping. Whether you are a data analyst, market researcher, or developer, web scraping opens up a whole new world of automated data collection.

In this data-driven era, information is power. However, manually extracting information from hundreds or even thousands of web pages is not only time-consuming, but also error-prone. Fortunately, Web scraping provides an efficient and accurate solution that allows you to automate the process of extracting the data you need from the Internet, thus greatly improving efficiency and data quality.

Table of Content

1. What is Web Scraping?
   • The process of Web scraping usually includes the following steps:
2. Getting Started with Selenium
   • Preparation
   • Import Libraries
   • Accessing a Page
   • Startup Parameters
   • Example: Running in Headless Mode
   • Locating Page Elements
   • Element Interaction
   • Data Extraction
   • Waiting for Elements to Load
3. Get Around Anti-Scraping Protections
4. Conclusion

What is Web Scraping?

Web scraping is a technique for automatically extracting information from web pages by writing programmes. This technology has a wide range of applications in many fields, including data analysis, market research, competitive intelligence, content aggregation, and more. With Web scraping, you can collect and consolidate data from a large number of web pages in a short period of time, rather than relying on manual operations.

The process of Web scraping usually includes the following steps:

• Send HTTP request: Programmatically send a request to the target website to get the HTML source code of the web page. Commonly used tools such as Python's requests library can do this easily.
• Parsing HTML content: After obtaining the HTML source code, it needs to be parsed in order to extract the required data. HTML parsing libraries such as BeautifulSoup or lxml can be used to process the HTML structure.
• Extracting data: Based on the parsed HTML structure, locate and extract specific content, such as article title, price information, image links, etc. Common methods include using XPath or CSS selectors.
• Store data: Save the extracted data to a suitable storage medium, such as a database, CSV file or JSON file, for subsequent analysis and processing.

And which, by using tools such as Selenium, can simulate the operation of the user's browser, bypassing some of the anti-crawler mechanisms, so as to complete the Web scraping task more efficiently.

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Getting Started with Selenium

Let's take ScrapingClub as an example and use selenium to complete exercise one.

Preparation

First, you need to ensure that Python is installed on your local machine. You can check the Python version by entering the following command in your terminal:

python --version

Make sure the Python version is greater than 3. If it is not installed or the version is too low, please download the latest version from the Python official website. Next, you need to install the selenium library using the following command:

pip install selenium

Import Libraries

from selenium import webdriver

Accessing a Page

Using Selenium to drive Google Chrome to access a page is not complicated. After initializing the Chrome Options object, you can use the get() method to access the target page:

import timefrom selenium import webdriverchrome_options = webdriver.ChromeOptions()driver = webdriver.Chrome(options=chrome_options)driver.get('https://scrapingclub.com/')time.sleep(5)driver.quit()

Startup Parameters

Chrome Options can add many startup parameters that help improve the efficiency of data retrieval. You can view the complete list of parameters on the official website: List of Chromium Command Line Switches. Some commonly used parameters are listed in the table below:

Example: Running in Headless Mode

import timefrom selenium import webdriverchrome_options = webdriver.ChromeOptions()chrome_options.add_argument('--headless')driver = webdriver.Chrome(options=chrome_options)driver.get('https://scrapingclub.com/')time.sleep(5)driver.quit()

Locating Page Elements

A necessary step in scraping data is to find the corresponding HTML elements in the DOM. Selenium provides two main methods to locate elements on the page:

• find_element: Finds a single element that meets the criteria.
• find_elements: Finds all elements that meet the criteria.

Both methods support eight different ways to locate HTML elements:

Let's return to the ScrapingClub page and write the following code to find the "Get Started" button element for exercise one:

import timefrom selenium import webdriverfrom selenium.webdriver.common.by import Bychrome_options = webdriver.ChromeOptions()driver = webdriver.Chrome(options=chrome_options)driver.get('https://scrapingclub.com/')get_started_button = driver.find_element(By.XPATH, "//div[@class='w-full rounded border'][1]/div[3]")time.sleep(5)driver.quit()

Element Interaction

Once we have found the "Get Started" button element, we need to click the button to enter the next page. This involves element interaction. Selenium provides several methods to simulate actions:

• click(): Click the element;
• clear(): Clear the content of the element;
• send_keys(*value: str): Simulate keyboard input;
• submit(): Submit a form;
• screenshot(filename): Save a screenshot of the page.

For more interactions, refer to the official documentation: WebDriver API. Let's continue to improve the ScrapingClub exercise code by adding click interaction:

import timefrom selenium import webdriverfrom selenium.webdriver.common.by import Bychrome_options = webdriver.ChromeOptions()driver = webdriver.Chrome(options=chrome_options)driver.get('https://scrapingclub.com/')get_started_button = driver.find_element(By.XPATH, "//div[@class='w-full rounded border'][1]/div[3]")get_started_button.click()time.sleep(5)driver.quit()

Data Extraction

When we arrive at the first exercise page, we need to collect the product's image, name, price, and description information. We can use different methods to find these elements and extract them:

from selenium import webdriverfrom selenium.webdriver.common.by import Bychrome_options = webdriver.ChromeOptions()driver = webdriver.Chrome(options=chrome_options)driver.get('https://scrapingclub.com/')get_started_button = driver.find_element(By.XPATH, "//div[@class='w-full rounded border'][1]/div[3]")get_started_button.click()product_name = driver.find_element(By.CLASS_NAME, 'card-title').textproduct_image = driver.find_element(By.CSS_SELECTOR, '.card-img-top').get_attribute('src')product_price = driver.find_element(By.XPATH, '//h4').textproduct_description = driver.find_element(By.CSS_SELECTOR, '.card-description').textprint(f'Product name: {product_name}')print(f'Product image: {product_image}')print(f'Product price: {product_price}')print(f'Product description: {product_description}')driver.quit()

The code will output the following content:

Product name: Long-sleeved Jersey TopProduct image: https://scrapingclub.com/static/img/73840-Q.jpgProduct price: $12.99Product description: CONSCIOUS. Fitted, long-sleeved top in stretch jersey made from organic cotton with a round neckline. 92% cotton, 3% spandex, 3% rayon, 2% polyester.

Waiting for Elements to Load

Sometimes, due to network issues or other reasons, elements may not have loaded yet when Selenium finishes running, which can cause some data collection to fail. To solve this problem, we can set it to wait until a certain element is fully loaded before proceeding with data extraction. Here is an example code:

from selenium import webdriverfrom selenium.webdriver.common.by import Byfrom selenium.webdriver.support.ui import WebDriverWaitfrom selenium.webdriver.support import expected_conditions as ECchrome_options = webdriver.ChromeOptions()driver = webdriver.Chrome(options=chrome_options)driver.get('https://scrapingclub.com/')get_started_button = driver.find_element(By.XPATH, "//div[@class='w-full rounded border'][1]/div[3]")get_started_button.click()# waiting for the product image elements to load completelywait = WebDriverWait(driver, 10)wait.until(EC.presence_of_element_located((By.CSS_SELECTOR, '.card-img-top')))product_name = driver.find_element(By.CLASS_NAME, 'card-title').textproduct_image = driver.find_element(By.CSS_SELECTOR, '.card-img-top').get_attribute('src')product_price = driver.find_element(By.XPATH, '//h4').textproduct_description = driver.find_element(By.CSS_SELECTOR, '.card-description').textprint(f'Product name: {product_name}')print(f'Product image: {product_image}')print(f'Product price: {product_price}')print(f'Product description: {product_description}')driver.quit()

Get Around Anti-Scraping Protections

The ScrapingClub exercise is easy to complete. However, in actual data collection scenarios, obtaining data is not so easy because some websites employ anti-scraping techniques that may detect your script as a bot and block the collection. The most common situation is captcha challenges, such as funcaptcha, datadome, recaptcha, hcaptcha, geetest, etc.

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Conclusion

From extracting product details to navigating through complex anti-scraping measures, web scraping with Selenium opens doors to a vast realm of automated data collection. As we navigate the web's ever-evolving landscape, tools like CapSolver pave the way for smoother data extraction, making once-formidable challenges a thing of the past. So, whether you're a data enthusiast or a seasoned developer, harnessing these technologies not only enhances efficiency but also unlocks a world where data-driven insights are just a scrape away