Scrapy is a Python framework that lets you scrape web pages. It allows you to write a script called a “Spider” that tells it where to crawl, how to make requests, and how to parse the data it finds.
You can use it to write spiders that crawl hundreds of pages in a single run. It also makes it easy to write spiders that are smart enough to adjust their crawl rate based on load.
There’s also a lot of other stuff you can do with it, like sending email notifications and logging to a database. You can also build custom components and middlewares that extend the functionality of your spiders.
The main thing to remember about using Scrapy is that it requires a working Python installation (Python 2.7 and higher – it should work in both Python 2 and 3), and a series of libraries. It’s also best to read the documentation before starting your first Scrapy project.
One thing to watch out for is memory leaks in your scraper. This can be caused by loading this article dynamically-generated page data or downloading files and images associated with your scraped items.
This can be an expensive operation and is a good reason to only process one webpage at a time.
It’s also a good idea to test your scraper on real-world websites before you start dumping data into the internet. This will help you identify the most efficient way to process your data.
XPath, CSS selectors and Regular Expressions are all ways to extract data from web pages.
The Scrapy library provides convenient classes for representing HTTP requests and responses.
A special item type called an “Item” provides a simple interface to store and retrieve data from the web. It’s akin to a Python dictionary in that each item can contain multiple fields to store data elements.