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

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
• Beautiful Soup
• Selenium

Scrapy

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.

Step-By-Step Data Scraping Example

Scrapy startproject restaurant_reviews
Scrapy project structure

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.

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.

settings.py