How to do web scraping using selenium and google colab?

Selenium is used for testing, web automation tasks, web scraping tasks etc. Its WebDriver component allows user actions to perform tasks in the web browser, while its headless mode performs automation tasks in the background. Google Colaboratory in short Google Colab is a cloud-based platform provided by Google to perform Python tasks, in an environment similar to Jupyter Notebook. It is a great way to work with Selenium as it provides free access to computing resources and flexible frameworks. This integration enables web automation, testing, and data extraction services. This allows users with high RAM (i.e. 12gb+) and great disk storage. In this article, we’ll use Selenium in Google Colab for Web Scraping.

What is Web Scraping?

Web scraping is the process of extracting data from websites using automated tools or scripts. It involves retrieving information from web pages and saving it in a structured format for further analysis or use. Web scraping is a powerful technique that allows users to gather large amounts of data from various sources on the internet ranging from market research to academic studies.

The process of web scraping typically involves sending HTTP requests to a website and then parsing the HTML or XML content of the response to extract the desired data.

Use cases of Web Scraping

1. Market Research: Businesses can scrape competitor websites to gather market intelligence, monitor pricing strategies, analyze product features, and identify trends. This information can help companies make informed decisions and stay competitive in the market.

2. Price Comparison: E-commerce platforms can scrape prices from different websites to provide users with accurate and up-to-date price comparisons. This allows consumers to find the best deals and make informed purchasing decisions.

3. Sentiment Analysis: Researchers and analysts can scrape data from social media platforms to analyze public sentiment towards a particular product, brand, or event. This information can be valuable for understanding customer preferences and improving marketing strategies.

4. Content Aggregation: News organizations and content aggregators can scrape data from various sources to curate and present relevant information to their audience. This helps in providing comprehensive coverage and diverse perspectives on a particular topic.

5. Lead Generation: Sales and marketing teams can scrape contact information from websites, directories, or social media platforms to generate leads for their products or services. This allows them to target potential customers more effectively.

6. Academic Research: Researchers can scrape data from scientific journals, research papers, or academic databases to gather information for their studies. This helps in analyzing trends, conducting literature reviews, and advancing scientific knowledge.

7. Investigative Journalism: Journalists can use web scraping to gather data for investigative reporting. They can scrape public records, government websites, or online databases to uncover hidden information, expose corruption, or track public spending.

Ethical and Legal considerations in Web Scraping

it is important to note that web scraping should be done ethically and responsibly. Websites have terms of service and may prohibit scraping or impose restrictions on the frequency and volume of requests. It is crucial to respect these guidelines and not overload servers or disrupt the normal functioning of websites.

Moreover, web scraping may raise legal and ethical concerns, especially when it involves personal data or copyrighted content. It is essential to ensure compliance with applicable laws and regulations, such as data protection and copyright laws. Additionally, it is advisablе to obtain permission or inform website owners about the scraping activities, especially if the data will be used for commercial purposes.

To mitigatе these challenges, web scraping tools often provide features like rate limiting, proxy support, and CAPTCHA solving to handle anti-scraping measures implemented by websites. These tools help ensure that scraping is done in a responsible and efficient manner.

Web Scraping using Selenium and Google Colab

Install necessary packages

To begin web scraping using selenium and google colab, we have to first start with installing necessary packages and modules in our google colab environment. Since this are not pre-installed in google colab.

Advanced Package Tool (APT) check for an updates to the list of available software packages and their versions.

Chromium web driver is an essential step as it will allows our program to interact with our chrome browser.

!pip install selenium
!apt update
!apt install chromium-chromedriver

Note : This may take some time as it tries to connect to a server. After it connects to a server ,then its a piece of cake. You can see all the necessary libraries starts to install. Take a look at below image for better understanding.

Step 1: Import Libraries

Now in next step we have to import necessary modules in our program.

Python

from selenium import webdriver
from selenium.webdriver.common.by import By

By class provides us a set of methods that we can further use to locate web elements.

Step 2: Configure Chrome Options

Now we need to configure our chrome options.

• “–headless” will allow chrome to operate without a graphic user interface (GUI) .
• “–no-sandbox” it will come in handy when we are running in certain environments where sandboxing might cause an issue. ( sandboxing is isolating software processes or “sandbox” to prevent security breach.)
• “–disable-dev-shm-usage” will disable /dev/shm/ file which can help with our resource management.

Python

options = webdriver.ChromeOptions()
options.add_argument('--headless')
options.add_argument('--no-sandbox')
options.add_argument('--disable-dev-shm-usage')
dr = webdriver.Chrome(options=options)

Now we are good to go and can preform web scraping using selenium and google colab with ease. Below we have shown a code snippet demonstrating web scraping with google colab.

Import the website for Scraping

Python3

dr.get("https://www.geeksforgeeks.org/") # Website used for scraping

#Displaying the title of the website in this case I had used GFG's Website
print(dr.title,"\n")

#Displaying some GFG's Articles
c=1
for i in dr.find_elements(By.CLASS_NAME,'gfg_home_page_article_meta'):
  print(str(c)+". ",i.text)
  c += 1

#quitting the browser
dr.quit()

Output:

GeeksforGeeks | A computer science portal for geeks 
1.  Roles and Responsibilities of an Automation Test Engineer
2.  Top 15 AI Website Builders For UI UX Designers
3.  10 Best UPI Apps for Cashback in 2023
4.  POTD Solutions | 31 Oct’ 23 | Move all zeroes to end of array
5.  Create Aspect Ratio Calculator using HTML CSS and JavaScript
6.  Design HEX To RGB Converter using ReactJS
7.  Create a Password Generator using HTML CSS and jQuery
8.  Waterfall vs Agile Software Development Model
9.  Top 8 Software Development Models used in Industry
10.  Create a Random User Generator using jQuery
11.  Multiple linear regression analysis of Boston Housing Dataset using R
12.  Outlier detection with Local Outlier Factor (LOF) using R
13.  NTG Full Form
14.  R Program to Check Prime Number
15.  A Complete Overview of Android Software Development for Beginners
16.  Difference Between Ethics and Morals
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18.  Difference Between Vapor and Gas

Conclusion

In this article we have seen the use of Google Colab in web scraping along with selenium. Google colab is a cloud-based and cost effective platform where we can perform our web-related tasks such web scraping, web automation with python with ease. In order to perform such tasks, our first step should be installing necessary packages and libraries in our environment. Since some of the libraries/packages are not pre-installed in our google colab environment. In this article we have demonstrated how we can install those libraries/packages. We have seen how to perform our web related tasks with selenium and google colab with concise examples for better understanding.