Python Selenium Web Scraping: How-To Guide With Examples

May 30, 2024Ankit Shrivastava
Mastering Python Selenium Web Scraping: A detailed guide to perform Web Scraping

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In the last decade or so, data has become the most valuable resource in the world. The influence of data has become so prominent in our society and industry that experts have now coined the term “data economy” to emphasize it. Undoubtedly, data has also made a huge impact on how the software industry and businesses operate. To stay competitive, most companies/marketers need to gather data and analyze it to come up with trends, predictions, customer behavior segmentation, classification, etc. and take action based on them.

But data, in its raw form, isn’t of much help for most of their use cases. Data needs to be structured, cleansed, refined, arranged and then connected or measured with other data to become a valuable resource. The first and foremost step is to collect this data quickly, reliably, and correctly from different sources. And what can be the best source other than the internet that hosts a giant pool of easily accessible data?

To leverage this huge amount of available data from the internet, we need tools that can go inside it, launch websites, and collect, process and store those data for us in an automated way. Sometimes in a scheduled manner, but always as per our expectations. Though we can collect data manually, the process can be error-prone and time-consuming. Imagine thousands and millions of data.

Surely it will be a cumbersome task for any human to obtain data manually at this scale. Here, automation comes to the rescue. One of the most efficient tools to do such automated task is the combination of Python (a versatile scripting language) and Selenium (a popular browser automation tool). This article will discuss the importance of this combination for web scraping.

What is Web Scraping?

Firstly, let’s understand what web scraping is. It is a generic term used for the various methods one can apply to extract data from websites in an automated way. Sometimes the companies behind those websites make it easier for people to collect data by providing Web APIs (Application Programming Interfaces). You can call those APIs using a tool or code to collect the data. 

But, in most cases, you will need dedicated APIs to hit and collect the needed data. For that, you have to take the help of the web scraping process from the UI (User Interface) itself, which is far slower than the API route but can be as effective as the latter if you know how to optimize and effectively do it. 

The data received from the scraping process in UI is mainly in an unstructured format. With the help of various tools, we can structure it and store it in data repositories like databases or spreadsheets for further analysis. The analysis will depend on what we try to achieve with the gathered data:

  • To keep it as general information purpose (as records)
  • To use it for complex tasks: feeding machine learning models for prediction or feeding recommendation engines to recommend products to its end customers.

Web scraping methods also act as enablers to facilitate other vital activities like researching market trends, monitoring e-commerce applications/prices/consumer sentiments, gathering social media feeds, extracting new research in particular fields, finding investment opportunities, generating sales leads, etc.

What are Web Scraping Applications?

Web scraping finds diverse applications across various domains and industries. Here are some key areas where web scraping is commonly utilized:

1. Market Research and Competitive Analysis: Web scraping enables companies to gather data on market trends, competitor strategies, and customer sentiments. By extracting and analyzing data from different sources, businesses can make informed decisions, identify new opportunities, and stay ahead in the market.

2. E-commerce and Price Comparison: Web scraping plays a crucial role in e-commerce by collecting product information, prices, and customer reviews from multiple online platforms. This data can be used to create price comparison websites, analyze pricing strategies, and enhance competitive intelligence.

3. Content Aggregation and News Monitoring: Web scraping allows aggregating news articles, blog posts, and social media feeds from various sources. This helps monitor trends, track industry-specific information, and stay updated with the latest news in a specific domain.

4. Lead Generation and Sales Intelligence: Web scraping assists in lead generation by extracting contact information from websites, directories, or social media platforms. This data can be utilized to build targeted marketing campaigns, identify potential customers, and generate quality sales leads.

5. Financial Data and Investment Research: Web scraping is utilized in the finance industry to extract financial data, stock prices, economic indicators, and other relevant information. It aids in conducting investment research, analyzing market trends, and making data-driven investment decisions.

6. Real Estate and Property Listings: Web scraping enables the collection of real estate data, property listings, rental prices, and market trends. This information assists individuals and businesses in making informed decisions when buying, renting, or investing in properties.

7. Academic and Scientific Research: Researchers and academics use web scraping to collect data for their studies, analyze scientific papers, and extract relevant information from research databases. This facilitates efficient data gathering and supports research efforts in various fields.

8. Government and Public Data: Web scraping extracts public data, government records, census information, and other publicly available datasets. This data can be utilized for analysis, policy-making, urban planning, and other public sector initiatives.

These are just a few examples of the wide range of applications for web scraping. Its versatility and effectiveness in gathering data from the web make it an invaluable tool in various industries and domains. However, ensuring compliance with legal and ethical guidelines and respecting the websites’ terms of service being scraped is essential.

What is Selenium Web Scraping?

Selenium is a popular open-source umbrella project consisting of several tools, servers and libraries that help to automate different browsers. The main remote control interface- “Selenium WebDriver” –  enables the control of user agents and making connections with browsers to mimic the other actions humans can perform on them. The tool is quite popular in the web application testing world. But it can go beyond the testing realm and help people scrape websites. 

Why is Selenium Web Scraping Used?

Selenium has bindings for different programming languages (like Python, Java, C#, JavaScript, etc.). Since it supports multiple languages, people can easily use it to scrape websites by writing code of their choice. Also, it has a vibrant community surrounding it; if any problem occurs, the user can quickly get an efficient solution. 

Before we move on to understand how to perform scraping using Selenium, know that scraping can be of two types:

Types of Web Scraping 

Traditional Data Scraping

In traditional data scraping, the user does not necessarily need to connect to the internet or open websites to collect data. The data can be collected from different sources, such as

  • databases (relational or non-relational) e.g., MongoDB, SQL, etc.,
  • spreadsheets (e.g., Excel, CSV)
  • various reports/charts (e.g., HTML reports, Email reports, bar charts, pie charts, etc.)

This data is aggregated to form a common source of informational records. 

Browser-based Scraping

In browser-based scraping, the user needs to open/launch a website inside a browser (e.g. Chrome, Safari, Firefox, etc.), utilize an internet connection, and copy data manually or use an automated tool like Selenium to scrape data from it. 

In this article, we will be talking about browser-based scraping.

Before you start scraping websites for your needs, it would be crucial to know whether it is legal to scrape any website. In general, scraping websites is not an illegal activity, but its legality also depends on various other factors:-

  • What are you going to use the data for?
  • How are you going to use the data?
  • Has any “Terms and Conditions” been violated?
  • Is there any rule imposed by the hosting website on data scraping?
  • For how long are you going to hold the data?
  • Is the data publicly available on the internet?

Suggestions would be to have reasonable answers to all the questions mentioned above and to stay away from scraping any data that holds personal information or is intellectual property.

Use Cases for Python Selenium Web Scraping

Let’s explore some of the authorized and valuable use cases where web scraping comes in handy:

1. Search Engines: You know those fantastic search engines that crawl through countless websites to deliver relevant search results? They’re powered by web scraping! Often called “spidering,” these search bots download web pages and follow links to discover new pages. By analyzing the content and considering factors like keywords, freshness, and user engagement, they rank the pages to provide you with the best search experience.

2. E-commerce (Price Comparison): Have you ever wondered how price comparison websites fetch accurate price rates from different e-commerce platforms? Well, they rely on web scraping! Python web scraping is a reliable and efficient method for extracting product data from various e-commerce websites based on your specific requirements. Price comparison sites either develop web scraping techniques or partner with Data as a Service (DaaS) providers to gather the necessary data.

3. Sentiment Analysis: Python web scraping also plays a vital role in sentiment analysis for market research companies. By scraping relevant online content, businesses gain valuable insights into customer sentiments, allowing them to understand how people feel about specific brands and products. This analysis helps companies make informed decisions and shape their strategies accordingly.

4. Job Postings: Searching for job openings can be time-consuming, but web scraping makes it more accessible. Job listing websites scrape information from multiple sources and aggregate it in one place. This way, users can access all the details about job openings and interviews from a single platform. Thanks to web scraping, finding your dream job becomes a breeze!

So, as you can see, web scraping has a wide range of valid and authorized applications. From enhancing search engine capabilities to empowering e-commerce and market research, it’s a powerful tool in the digital landscape. Remember that when engaging in web scraping, it’s important to respect website policies and ensure compliance with legal and ethical guidelines.

General Process Followed When Performing Web Scraping

Here’s a list of the general process followed when performing web scraping:

1. Identify the Target Website: Determine the website from which you want to extract data. Ensure you have permission to scrape the site and review any relevant terms of service or legal considerations.

2. Choose the Right Tools: Select the appropriate web scraping tools based on the specific requirements of your scraping project. Python offers popular libraries such as Beautiful Soup, Scrapy, and Selenium, which provide different capabilities and functionalities.

3. Inspect the Website Structure: Use your web browser’s developer tools to inspect the HTML structure of the target website. Identify the elements that contain the data you want to extract, such as tags, classes, or IDs.

4. Develop the Scraping Code: Write your web scraping code using the chosen tools and programming language. Use techniques like parsing HTML, submitting forms, clicking buttons, or navigating through pages to extract the desired data.

5. Handle Captchas and Authentication: Some websites may have security measures, such as captchas or user authentication, to prevent scraping. Implement mechanisms to handle these challenges, such as using captcha-solving services or providing login credentials.

6. Implement Data Extraction: Use the identified HTML elements and CSS selectors to extract the relevant data from the web page. Apply parsing techniques, regular expressions, or specific library functions to extract and format the data in a usable format.

7. Handle Pagination and Iteration: If the target website has multiple pages or requires iterative actions to access all the desired data, implement techniques to handle pagination, such as iterating through URLs or clicking the “Next” buttons.

8. Store and Analyze the Extracted Data: Save the extracted data in a structured format, such as CSV, JSON, or a database. Perform any necessary data cleaning, transformation, or analysis to derive insights or integrate them into your application or system.

Remember to be mindful of the website’s terms of service, respect its resources and bandwidth, and ensure compliance with legal and ethical guidelines when performing web scraping. Happy scraping!

How to Perform Web Scraping Using Selenium and Python?

As mentioned earlier, Selenium WebDriver is a browser automation tool having bindings for programming languages like Python. So, Selenium can be used as a library in Python, along with many other open-sourced Python libraries that make extracting data from websites and arranging and storing them easier. 

Let’s see an example of how we can scrape data using Selenium and Python. To begin with, first, you need to set up a python environment using any IDE tool that supports Python e.g. PyCharm, Spyder, VS Code, etc.

For our example, before writing your first code, you need to install the required python packages using the below commands in the Python environment:

pip install selenium

pip install pandas

pip install openpyxl

You also need to download chromedriver from the below link (according to the chrome browser version you are using in your system): – Click here to get link

Then you need to create a python project in the IDE, create a “driver” folder inside it and place the downloaded chromedriver file within the “driver” folder.

Then, you can create a python file inside the project and give any name to the created module. I have given it the name “scraper_main.py”. Inside it, I have created a class name “Scraper” (with a function called “main”) and wrote the below code:


Code Explanation and Output

Let’s go through this code and see what each of its lines does:

First, I have imported the required packages and modules to drive the scraping process:

Then, inside the main method, I am launching the Testsigma blog website taking the help of Selenium WebDriver. There I am also checking whether the URL has successfully launched or not:

After that, I am listing down the total number of blogs getting displayed on that page:

Then, I am going through each of the displayed blogs and extract the blog title, blog author and blog published date information from them (as a python dictionary and using Xpath) and put them inside a python list.

After that, I am converting that list into a pandas dataframe before inserting the captured data into a newly created excel file named “Blog_Info.xlsx”

The excel file will look like below:

Output after scraping process

Static and Dynamic Selenium and Python Web Scraping

Sure! Here’s an explanation of static and dynamic web scraping using Python and Selenium in a conversational tone:

1. Static Web Scraping: 

Static web scraping is the extraction of data from fixed HTML content on online pages. Python and Selenium can automate exploring static web pages, identifying specific parts, and extracting data. This method is suitable for stable website structures, eliminating the need for complex interaction or JavaScript rendering.

2. Dynamic Web Scraping:

Dynamic web scraping is essential for online sites that use JavaScript or AJAX to load dynamic content. Python and Selenium can simulate user behaviors like button clicks, form submissions, and scrolling, enabling the retrieval of data only after JavaScript execution or AJAX queries. This opens up new scraping possibilities.

3. Selenium in Python:

Selenium is a powerful Python web scraping tool with a browser automation framework. It allows programmatic control of web browsers like Chrome or Firefox to interact with websites, perform actions, and extract data. Selenium’s versatility and compatibility make it a popular choice for scraping activities, particularly for complex JavaScript rendering websites.

Mastering static and dynamic web scraping approaches and using Python and Selenium can solve various scraping problems. These tools efficiently extract data from static HTML or dynamic JavaScript pages. Respect website regulations and follow legal and ethical norms while scraping. Enjoy the process!


In this article, we have seen step-by-step how to scrape a website using Python Selenium web scraping method. We have understood the different types of scraping processes and circumstances when you might need to check whether scraping is legal. We have also discussed how data is becoming more valuable to the world because of its applications in numerous fields and use cases. Hope this article has provided you with all the information you were looking for related to Web Scraping with Selenium and Python.

Frequently Asked Questions

Is Selenium good for web scraping?

Selenium is indeed a very good tool for performing web scraping. To take out the best from Selenium, you have to know a supported programming language (like Python) and utilize various useful techniques like synchronization, multi-threading, etc. Besides, you have a helpful community you can reach out to regarding any queries or suggestions.

How to use Selenium to scrape a website?

To use Selenium to scrape a website, you first need to know a supported programming language (e.g. Python). You need to then install the required packages and write code (using the IDE of your choice) to launch the website and scrape data from it.

Which Python IDE is best for web scraping?

Any known Python IDEs can be used for web scraping and the speed of execution will not depend on the IDE being used. This includes PyCharm, Spyder, VS Code, Jupyter Notebook etc. However, nowadays, most people’s preferred choice is PyCharm IDE or VS Code, since code development using these two IDEs is much faster and more convenient.

Is Scrapy better than Selenium?

There is no right or wrong answer to whether Scrapy is better than Python regarding web scraping. For some use cases where you would like to scrape data very quickly, Scrapy may perform better, but in cases wherein you have to deal with variations in Javascript in UI and AJAX calls, Selenium would do the tasks a lot better.

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