A Simple Guide to Scraping Static and Dynamic Websites with Python

thanks-web 14 Mar 2023

You may need to retrieve a vast amount of information from a website swiftly for various reasons. It might be for your business or for your clients who need such information from websites to market their services and products. In such cases, web scraping can be incredibly useful, as it streamlines the task and guarantees the data’s quality. Currently, web scraping bots can scrape static and dynamic websites and present the data in an organized format.

Nevertheless, one may wonder how these bots can extract data from a static website and what techniques are involved in dynamic website scraping using Python. Additionally, it’s worth exploring the advantages of scraping different types of websites.

Benefits of Static and Dynamic Websites

Static Websites

Here are some of the well-known benefits of a static website:

Faster Creation:

Static websites do not require links to organized databases and can be created and published more swiftly due to their more straightforward nature.

Faster Load Time:

Static websites prioritize load time to enhance browsing efficiency, resulting in a website that utilizes fewer server resources and loads quickly.

Dynamic Websites

The benefits of having a dynamic website are:

Easy to update:

Dynamic websites are capable of updating multiple pages’ content without altering their appearance when a single page’s content changes.

Efficiency:

Due to their interactive nature, dynamic pages provide efficient and high-quality services to end-users, resulting in an overall superior user experience.

Web Scraping Using Python

Web scraping with Python is an exciting technique that allows us to extract vital data from websites with ease and efficiency. By utilizing the structured nature of HTML, and Python programming, and its powerful libraries such as BeautifulSoup and Scrapy, developers can gain valuable insights and even automate tasks like data mining and analysis. What makes this process truly captivating is its versatility, enabling you to acquire information from e-commerce listings, market trends, social media interactions, or any other online source your imagination could conceive. Furthermore, Python’s user-friendly syntax and extensive online resources facilitate learning and growth for novices and experts alike. No doubt, mastering the art of scraping web pages with python will unlock a world of boundless opportunities and new possibilities.

Ways to Scrape Static Websites With Python

Before scraping the data from a static website, figure out what data you need to extract. Create a new folder on the desktop with the extension (.py) using the terminal command, and launch Visual Studio Code with the command $ code. Then, open the single empty file in the “scrapper.py” folder and import the entire library. Use HTTP requests and BeautifulSoup to parse and search for the necessary data. Save the data to empty arrays and extract the required data using loops. Next, create a CSV file data frame with the header as the key and value pairs, and use the DataFrame method to convert data into a data frame. Finally, export the data frame to a CSV file using .to_csv(“name.csv”).

Ways to Scrape Dynamic Website with Python

Scraping dynamic web pages requires the use of a headless web browser and a combination of BeautifulSoup and Selenium libraries. To extract the resulting page links, use a while loop to navigate through the search result pages and store each page URL in the list page_lst_link.

Next, extract the product links from the resultant pages by navigating to each page link, parsing the HTML with BeautifulSoup, and storing the product links in the list product_links. You will need to create a data frame to store the extracted data, scrape the relevant information, and store the requests from the URL in page_content using the Selenium web driver. Create a CSV file after extracting the required information to complete the dynamic website scraping process.

Also Read: How do I scrape data from Google Maps?

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