Web Scraping Process

Web scraping is the process of automatically extracting data from websites using software or scripts. It involves fetching the HTML content of a web page and parsing it to retrieve specific information, such as text, images, links, or structured data. Web scraping enables users to collect large amounts of data quickly and efficiently, which can be used for various purposes, including research, analysis, and decision-making. Web scraping is about extracting data from websites by parsing its HTML. On some sites, data is available to download in CSV or JSON format, but in some cases, that’s not possible, and for that, we need web scraping.

Web scraping can be done using different tools and techniques, ranging from simple manual copying and pasting to sophisticated automated scripts. Some common methods include using HTTP requests to fetch web pages, parsing HTML or XML to extract relevant data, and utilizing APIs provided by websites. However, web scraping should be done responsibly and in compliance with websites’ terms of service, robot.txt files, and applicable laws to avoid legal issues and maintain ethical standards.

What Sorts of Web Data Can be Collected Through Web Scraping?

Large datasets can be scooped up from websites using web scraping techniques. Here are some of the common types of web data that can be extracted:

➡️ Product Information: This includes details like prices, descriptions, specifications, availability, and reviews from e-commerce platforms or product listing websites.

➡️ News Articles: Web scraping can be used to gather news stories and articles from various online publications.

➡️ Real Estate Listings: Websites with property listings can be scraped to collect data on houses, apartments, or commercial spaces for sale or rent.

➡️ Social Media Data: Public social media posts, profiles, and comments can be scraped, though terms of service restrictions apply to some platforms.

➡️ Financial Data: Stock quotes, currency exchange rates, and other financial information can be extracted from financial websites.

➡️ Scientific Data: Research publications, datasets, and other scientific information can sometimes be scraped from publicly accessible online repositories.

It’s important to remember that ethical considerations and website terms of service should always be respected when web scraping.

How do Web Scrapers Work?

Web scrapers are powerful tools that extract specific data points from websites, ensuring users collect only the most relevant information efficiently. By focusing on targeted data extraction, web scrapers save time and provide clean, concise datasets for further analysis.

The process begins with providing the scraper with the necessary URLs, followed by parsing the HTML, CSS, and JavaScript elements to navigate the web pages. The scraper then identifies and extracts the desired data points based on the user’s requirements, outputting the data in a structured format such as Excel, CSV, or JSON. This targeted approach results in a focused dataset ready for integration and decision-making.

Different Types of Web Scrapers

Web scrapers can be categorized based on various criteria, such as their build type, deployment method, and installation location. These categories include self-built or pre-built scrapers, browser extensions or software applications, and cloud-based or local installations.

For those with advanced programming skills, building a web scraper from scratch is an option. This approach allows for complete customization and control over the scraper’s functionality. However, creating a feature-rich scraper requires a significant amount of coding expertise.

Alternatively, pre-built web scrapers are readily available for download and use. These scrapers often come with various advanced features that can be easily customized to suit specific needs, making them a more convenient choice for those with limited programming knowledge.

When it comes to deployment, web scrapers can be divided into browser extensions and standalone software applications. Browser extension scrapers are easily integrated into web browsers, making them simple to use. However, their functionality is limited to the browser capabilities, restricting the inclusion of advanced features.

On the other hand, software web scrapers are separate applications that can be installed directly on a computer. Although they may be more complex than browser extensions, software scrapers offer a wider range of advanced features that are not constrained by browser limitations.

Another important consideration is the location where the web scraper runs. Cloud-based web scrapers operate on remote servers, typically provided by the company offering the scraper. This setup allows users to offload the resource-intensive task of web scraping to the cloud, freeing up their local computer for other tasks.

In contrast, local web scrapers run directly on the user’s computer, utilizing its CPU and RAM. While this approach provides more control over the scraping process, it can lead to slower computer performance, especially if the scraper demands significant resources.

In summary, web scrapers can be classified based on their build type (self-built or pre-built), deployment method (browser extension or software application), and installation location (cloud-based or local). Each category has advantages and disadvantages, and the choice ultimately depends on the user’s technical expertise, desired features, and available resources. By understanding these distinctions, users can select the most appropriate web scraper for their specific needs, ensuring efficient and effective data extraction from websites.

Our Web Scraping Process

Web Scraping Process | Mindbowser

Check Out Our Video to Learn More About Scraping Websites Like Yelp!

How Is Web Scraping Done?

We can do web scraping with Python.


Scrapy is a fast high-level web crawling and web scraping framework used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing. It is developed & maintained by Scrapinghub and many other contributors.

Scrapy is best out of the two because in it we have to focus mostly on parsing the webpage HTML structure and not on sending requests and getting HTML content from the response, in Scrapy that part is done by Scrapy we have to only mention the website URL.

A Scrapy project can also be hosted on Scrapinghub, we can set a schedule for when to run a scraper.

Beautiful Soup

Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work.
To scrape a website with Beautiful Soup we also need to use requests library to send requests to the website and get the response and then get HTML content from that response and pass it to the Beautiful Soup object for parsing.


Selenium Python bindings provide a simple API to write functional/acceptance tests using Selenium WebDriver. Through Selenium Python API you can access all functionalities of Selenium WebDriver in an intuitive way.

Selenium is used to scrape websites that load content dynamically like Facebook, Twitter, etc. or if we have to perform a click or scroll page action to log in or signup to get to the page that has to be scrapped.

Selenium can be used with Scrapy and Beautiful Soup after the site has loaded the dynamically generated content we can get access to the HTML of that site through selenium and pass it to Scrapy or beautiful soup and perform the same operations.

Meet Our Tech Expert

Sandeep Natoo

Sandeep is a highly experienced Python Developer with 15+ years of work experience developing heterogeneous systems in the IT sector. He is an expert in building integrated web applications using Java and Python. With a background in data analytics. Sandeep has a knack for translating complex datasets into meaningful insights, and his passion lies in interpreting the data and providing valuable predictions with a good eye for detail.

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Step-By-Step Data Scraping Example

For this example, we will be scraping Yelp for restaurant reviews in San Francisco, California with Scrapy.

Step 1 => Since we are only fetching restaurant reviews in San Francisco, the scraping URL will redirect us to the page below.

Data Scraping Example | Mindbowser

Step 2 => We will now create a Scrapy project with the command below.

Scrapy startproject restaurant_reviews
Scrapy project structure

Automate Data Collection With Our Advanced Web Scraping Services

Step 3 => Now we will create 2 items (Restaurant and Review) in items.py to store and output the extracted data in a structured format.

Web Scraping Steps | Mindbowser

Step 4 => Now we will create a custom pipeline, in Scrapy to output data in 2 separate CSV files (Restaurants.csv & Reviews.csv). After creating the custom pipeline we will add it in ITEM_PIPELINES of Scrapy settings.py file.

Web Scraping Process Steps | Mindbowser


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Step 5 => Now we will inspect the Yelp page we are going to scrape and find the and will find the URL for each restaurant’s review page from where we will fetch the reviews. In the below image, we can see that all the search results are in <li> tags with the same CSS classes. In the same manner, we will inspect the review pages of some restaurants to understand their structure.

Web Scraping Process Steps | Mindbowser

Step 6 => Now we will create a scraper to fetch the information.


1. Restaurants.csv
Here we can see all the restaurants fetched.

Web Scraping Output Screenshot | Mindbowser

2. Reviews.csv
Here we can see the reviews with their restaurant references.

Web Scraping Output Screenshot | Mindbowser

Why Mindbowser For Web Scraping?

When you appoint data scraping experts from Mindbowser, we dedicatedly provide end-to-end support to accomplish your organizational objectives quickly.

Mindbowser has been delivering high-quality web scraping services to all-size businesses across the world for more than 10 years. At Mindbowser, you will receive comprehensive support from our web data scraping experts, who have immense knowledge in the latest website scraping tools, technologies, and methodologies.
Why choose mindbowser for web scraping


The above example shows us the web scraping process and how with the help of some tools, we can extract information from a website for several purposes. It only shows a basic use case of Scrapy, it can do much more.

We can do a lot of things with the output of the above example, like:

  • Topic Modelling: It can help us get in-depth information about the review topics.
  • Sentiment Analysis: It can help us get the sentiments from each review for a more in-depth analysis.

We can also extract reviews from other review sites.

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