What is the workflow of the Scrapy framework in Python?

Scrapy is a Python framework used for data scraping, with a workflow that can be divided into the following steps:

1. create a new project in Scrapy
2. Create a Spider in the project, specifying the URL of the website to be crawled and how to parse the pages.
3. Write an Item Pipeline: Write an Item Pipeline as needed to process the scraped data, such as data cleaning and storage.
4. Set up Settings: Customize the Settings file according to the project’s requirements, including specifying the Spider, enabling middleware, etc.
5. Operate a spider using Scrapy
6. Fetch pages: Scrapy automatically sends requests to fetch page content, then passes the response to the Spider for parsing.
7. Parse page: The parsing method defined in Spider will extract the necessary data from the page and can also follow other links.
8. Process extracted data: The data extracted from the webpage can be processed using an Item Pipeline for tasks like data cleansing and storage.
9. Store data: Save processed data to a specified location, such as a database or file.
10. Follow links: During the parsing of a webpage, if there are other links that need to be followed, Scrapy will automatically send requests and proceed with the next round of crawling.

This is the workflow of the Scrapy framework. The process involves defining a Spider to specify the target to scrape and parsing methods, utilizing Item Pipeline to handle the data, configuring through Settings, and ultimately starting the crawling process by running the spider.