What is Web Scraping in Python?

How Web Scrapers Work?

Web scrapers in Python use libraries like requests to fetch webpage content and BeautifulSoup or lxml to parse HTML. For dynamic content, tools like Selenium or Playwright simulate browser behavior. The scraped data is extracted using tags, classes, attributes, or JavaScript rendering and stored in structured formats such as CSV, JSON, or databases.

Types of Web Scrapers

1. Static Web Scrapers:
   Use libraries like requests and BeautifulSoup to scrape data from static HTML pages.
2. Dynamic Web Scrapers:
   Employ tools like Selenium or Playwright to handle JavaScript-rendered content on webpages.
3. API Scrapers:
   Use Python libraries like requests to fetch structured data from APIs.
4. Headless Browser Scrapers:
   Utilize headless browsers (e.g., with Puppeteer or Selenium) to interact with and scrape content from websites without a visible UI.
5. Custom Framework Scrapers:
   Frameworks like Scrapy are used to build scalable, high-performance web scrapers tailored to specific projects.

Why is Python a Popular Programming Language for Web Scraping?

1. User-friendly Syntax: Python’s readability simplifies writing and debugging scraping scripts.
2. Rich Ecosystem: Libraries like requests, BeautifulSoup, Scrapy, and Selenium support a variety of scraping needs.
3. Flexibility: Python integrates with databases, cloud services, and data processing tools like Pandas, making it ideal for end-to-end solutions.
4. Community Support: Extensive resources, tutorials, and forums help address challenges effectively.
5. Cross-platform Availability: Python’s compatibility with multiple operating systems enhances its usability for diverse scraping projects.

Getting Started With Scrapy

Creating a project

First of all, if scrapy is not installed on your system, use the following command to install it. 

pip install scrapy

You must first create a new Scrapy project before you can begin scrapping. Navigate to a suitable directory and execute the following command in the terminal:

scrapy startproject scraperProject

This will create a folder named scraperProject. 

Using the tree command in the terminal, we can see the folder structure. 

Creating a Spider

Scrapy utilizes spiders, which are classes we create, to scrape information from a webpage (or a group of websites).

We have to create a Spider subclass and define the first requests and how to follow links in the sites and parse the downloaded page content for data extraction. The spider must first be given a name by using the name variable, and then the spider must be given a starting URL through which it will begin crawling. Define strategies for crawling far deeper into the webpage.

Navigate to the spider folder and create a new file called fetch.py. Always build one class with a unique name and define requirements when constructing a spider. The spider must first be given a name by using the name variable, and then the spider must be given a starting URL through which it will begin crawling.

Here are some essential things to be aware of

• The name variable identifies the spider. It must be unique within a project; distinct Spiders cannot have the same name.
• The Spider must return an iterable of Requests from which to start crawling. Following these first requests, more requests will be generated in sequential order.

Extracting Data using Scrapy Shell

Scrapy is similar to a Python interpreter, but it can scrape data from a specific URL.

In a nutshell, it's a Python interpreter with Scrapy support.

Syntax:

scrapy shell url

Use the following command to do the same for our website. 

scrapy shell 'http://quotes.toscrape.com'

We now get access to the response object with which we can query details about the webpage. 

Use selectors to get data from the specified page now. These selectors can be from either CSS or Xpath.

In[1]:response.css('title') 

When response.css(‘title’) is executed, SelectorList is returned. It represents a list of Selector objects that wrap around XML/HTML elements and allow you to run further queries to fine-grain the selection or extract the data.

For extracting the text of the title, use the following command

response.css('title::text').getall()
['Quotes to Scrape']

To get the first result of the list, use the following command.

response.css('title::text').get()
'Quotes to Scrape'

Now, for our spider, we need to extract the quotes and their respective authors. 

The quote and the author are contained in a div of class quote. 

response.css("div.quote")

It returns a selectorList on which we can further query to get the desired text. 

Let us further query on the first element of the result list. 

>>> firstQuote = response.css("div.quote")[0]
>>>text = firstQuote.css("span.text::text").get()
>>>text
'“The world as we have created it is a process of our thinking. It cannot be changed without changing our thinking.”'

>>>author = firstQuote.css("small.author::text").get()

>>>author
'Albert Einstein'

Now we have understood how to fetch the data using the scraper, let’s implement it in the code. 

import scrapy
class extractQuotes(scrapy.Spider):
    
   name = "fetchQuotes" #enter a unique name             
   
   def start_requests(self):
        
       # enter a list of  URL here
       urls = ['http://quotes.toscrape.com/']
       for url in urls:          
           yield scrapy.Request(url = url, callback = self.parse)

   def parse(self, response):

       container = 'div.quote'
       result = response.css(container )

       for quote in result:
           author = quote.css('small.author::text').get()
           text = quote.css('span.text::text').get()
           yield {
               'author': author,
               'text': text
           }

So now our fetch.py looks like above. 

Let's run the code in the terminal using the following command. 

scrapy crawl SPIDER_NAME

In our case, the spider name is fetchQuotes, so execute the following command. 

scrapy crawl fetchQuotes

You will see a lot of text in the terminal. In between, there will be our crawled data. Currently, the data is in a very messy format. So to output the data in a JSON file, use the following command. 

scrapy crawl fetchQuotes -o data.json

Output:

What is Web Scraping Used For?

Web scraping is used to extract data from websites for various applications, such as:

1. Market Research: Collect competitor pricing, customer reviews, and product details to inform business strategies.
2. Data Aggregation: Compile information from multiple sources, like news articles or job listings, into a single platform.
3. SEO and Digital Marketing: Analyze search engine rankings, keywords, and backlinks to optimize website performance.
4. Academic Research: Gather datasets for analysis in fields like social science, economics, and technology.
5. E-commerce and Retail: Track product availability, pricing trends, and customer sentiment.
6. Real Estate: Collect property listings, rental data, and market trends for analysis or display.
7. Social Media Insights: Monitor trends, hashtags, and engagement metrics for brand visibility and audience analysis.

Frequently Asked Questions

What is scrapy in Python?

Scrapy is a Python-based web crawling platform that is free and open-source. It was created with web scraping in mind, but it may also be used to collect data via APIs or as a general-purpose web crawler.

What is the advantage of using scrapy over other tools?

Scrapy has a lot of advantages, one of which is speed. Scrapy spiders don't have to wait to make requests one at a time because they're asynchronous; instead, they can make requests in parallel. This improves Scrapy's efficiency, making it more memory and CPU-efficient than the other web scraping solutions we looked at.

Which command is to be used to store the data in a JSON file?

The following command may be used to store the data in a JSON file. 

scrapy crawl SPIDER_NAME -o filename.json

Conclusion

We hope you have gained some insights on Web Scraping on Python through this article. Web scraping is the act of gathering and processing raw data from the internet, and the Python community has developed several special web scraping tools.