Dear reader, if you are a Python programmer or a data scientist looking for an efficient way to extract data from the web, chances are you already heard about Scrapy.
Scrapy is one of the most popular web scraping frameworks in Python. If you want to develop a complex project where you have to extract data from hundreds of web pages, this Python module is the best to do this job.
In this article, we will introduce you to Scrapy and its major components behind the scenes. We will explore how Scrapy works, its features, and the benefits of using it for web scraping.
What Exactly is Scrapy in Python
If you have come to this page with the intention of gathering information about the Scrapy module, chances are high that you already have at least a basic understanding of Python.
Python is the most popular programing language used for web scraping, and if you are using Python for that purpose you can’t create a complex Web Scraping project without learning Scrapy.
Suggested: Install Scrapy on Python Virtual Environment
So to be more precise, Web Scraping is general data extraction from websites. Any data that is available on the internet can be extracted with the right tool.
If you choose to achieve that task with Python, Scrapy is one of the most popular Web scraping frameworks of python which you can.
Similar to Python’s built-in request library and BeutifulSoup this is also used with data from the websites.
But unlike those tools, this can be used to build complex and big Scraping projects.
When we talk about scrapping hundreds and hundreds of pages we really need to use scrappy, because this was built for this job.
It is able to scrape thousands of pages per minute.
Main Component of Scrapy
To fully understand this framework, we must understand its major component.
Scrapy has 5 main components, which are the following –
Here we are going to see each of these components in more detail.
The spider component is where we define from where and what we want to extract. Here we have 5 kinds of Spiders classes available for us
Pipelines perform the related data operations such as :
- Cleaning the data.
- Remove the duplication.
- Storing the data.
Middleware is performed when we request and receive a response from the web pages. We can use it in injecting custom headers and Proxying.
The engine is responsible for coordinating all of the other components. It ensures the consistency that all the operation that happens.
The scheduler is a simple data instructor, it is responsible for preserving the order of operation.
Almost all of the sites include Robots.txt in the root directory. This file indicates which part of the site we are allowed to scrape and which part we are not.
This file is generally created by the site owner to instruct the search engine bots which part of the site they want to index on search engines.
This file basically contains 3 major elements:
- User-Agent – Refers to Users, Bots, that crawl the site.
- Allow – Specify the mentioned URL is allowed to index.
- Disallow – Specify the mentioned URL is not allowed to index.
You can check a real-world example of robots.txt at https://Www.Facebook.Com/robots.txt.
Benefits of using Scrapy for Web Scraping in Python
Benefits of Scrapy do include – Easy to use features, easily customizable, and Suitable for complex projects.
Here are more details on these benefits –
Easy to use
Scrapy is easy to use, easy to manage, and easy to set up.
For starters to install this library on your computer, you just have to run a PIP command and it will install this framework on your computer.
Once you Install it, you can easily set up your project in a few simple steps.
Whether you want to build a simple project or a complex project, you can easily customize it to fit your needs.
Whether you want to extract just product prices or create a complex project for your AI can be done using this library.
Efficient and Fast
While this is not fast as BS4, still it is optimized for speed and efficiency.
This makes it an ideal tool if you want to not only retrieve but save a large number of data frequently.
Suitable for complex Project
Whether you are developing a small program that will give a small amount of data or a complex project to extract a high amount of data this library suits you in both cases.
For example, you can use this to gather data in order to train your AI.
In fact, Most AIs (for example ChatGPT) do depend on data gathered from the web in order to learn more.
So overall, Scrapy is one of the best frameworks that you can use for data extraction from the web.
But still, it is not the only framework for this job. There are other options are available as well.
For example, you can use Requests, Selenium, and BeautiafulSoup. Furthermore, there are things that these frameworks do better compared to Scrapy.
Scrapy is a complex web scraping framework and this could be a little bit hard to learn, especially for new programmers.
You should use it if you want to develop a complex project.
If you rather have just a simple 1-page script you should go with BS4, which is much easier to set up and use.