Web Scraping Using Scrapy Framework with Python | Belayet Hossain

Web Scrapping: Web scraping is a method of automatically extracting information from websites.

Most Usage Web scraping methods:

1). Requests. 2). Selenium. 3). Scrapy.

Scope of scrapy: It’s used in which website Java Script is enabled.

THIS ARTICLE ABOUT SCRAPY FRAMEWORK:

Scrapy is a fast, efficient, and highly customizable web crawling framework for Python. It is designed to make the process of web scraping easier and quicker, providing a complete solution for extracting data from websites. With built-in features, open-source availability, and compatibility with other data science libraries and tools, Scrapy is an ideal choice for data scientists and data analysts who need to collect and extract data from the internet.

HERE ARE THE STEPS TO USE SCRAPY FOR WEB SCRAPING IN DATA SCIENCE:

1. Install Scrapy: You can install Scrapy using pip or conda by running the following command: "pip install scrapy" or "conda install -c conda-forge scrapy".

2. Create a new Scrapy project: Open your terminal or command prompt and run the following command to create a new Scrapy project: "scrapy startproject project_name".

3. Define the items to scrape: In the newly created project, you need to define the items you want to scrape using an Item class. This class will define the data fields that will be extracted from the website.

4. Create a spider: A spider is a script that defines how Scrapy should follow links and extract data from a website. You can create a spider using the following command: "scrapy genspider spider_name website_name.com".

5. Define the parsing logic: In the spider, you need to define the parsing logic, which is responsible for extracting the data from the website. You can use the response object to extract data using CSS selectors or XPATH.

6. Start the crawl: Once you have defined the parsing logic, you can start the crawl by running the following command: "scrapy crawl spider_name".

7. Store the data: Finally, you can store the extracted data in a structured format, such as CSV or JSON, using the Feed Exporters available in Scrapy.

THE ADVANTAGES OF USING SCRAPY FOR WEB SCRAPING IN DATA SCIENCE ARE AS FOLLOWS:-

1. Speed: Scrapy is designed to be fast and efficient, allowing you to scrape large amounts of data quickly.

2. Customizability: Scrapy is highly customizable and can be tailored to meet the specific needs of your data science project.

3. Built-in functionality: Scrapy has a number of built-in features, such as handling requests and responses, crawling and extracting data, and storing data in a structured format, making it a complete web scraping solution.

4. Open-source: Scrapy is an open-source framework, so it is free to use and can be easily modified to meet your specific needs.

5. Robustness: Scrapy is designed to handle complex and challenging web scraping projects. It can handle problems such as broken links, errors, and changing website structure with ease.

6. Python compatibility: Scrapy is built on Python, making it easy to integrate with other data science libraries and tools, such as NumPy, Pandas, and Matplotlib.

7. Large community: Scrapy has a large community of users, so you can easily find support and resources to help you with your web scraping project.

Scrapped Data Ref.