reader VS Scrapy

I like this pattern so much, I wrote an article about it; if you want to see what it looks like in real life, check this out.

It can also be used to build bookmarking / read later functionality similar to that of Tiny Tiny RSS; extracting content from arbitrary pages would be pretty helpful here.

To find out more, check out the GitHub repo and the docs, or give the tutorial a try.

[1]: https://death.andgravity.com/query-builder-how#more-init

[2]: https://github.com/lemon24/reader/blob/10ccabb9186f531da04db...

Looking now through the Python API docs, I found the Quoting characters for use in search thing, which may come in handy for something I'm doing for my feed reader library (deduplicating articles). Not sure if I'll vendor it or add sqlite-utils as a new dependency, but it's good to know someone already solved this problem (I have a prototype, but I haven't really tested it).

I'm happy to announce version 2.0 of reader, a Python RSS / Atom / JSON feed reader library.

I'm also working on the 2.0 version of my feed reader library. The backwards compatibility break allows me to clean up a bunch of unnecessary code and fix some bad design decisions; I've deleted 250 lines of code until now, and it's very satisfying.

The exact same problems None has when it's not a valid value. None is different from the variable type, that's why you have Optional[VarType], which is an alias for Union[VarType, None]; you can model this in exactly the same way: Union[VarType, MissingType]; here's an example.

reader is a Python feed reader library – it offers all the high-level things you'd want when making a feed reader app/website, except the actual web app.

BeautifulSoup4 Scrapy

While there is no specific library for SERP, there are some web scraping libraries that can do the Google Search Page Ranking. One of them which is quite famous is Scrapy - It is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It offers rich developer community support and has been used by more than 50+ projects.

If you're looking for a turn-key solution, I'd have to dig a little. I generally write a scraper in python that dumps into a database or flat file (depending on number of records I'm hunting).

Scraping is a separate subject, but once you write one you can generally reuse relevant portions for many others. If you can get adept at a scraping framework like Scrapy you can do it fairly quickly, but there aren't many tools that work out of the box for every site you'll encounter.

Once you've written the spider, it's generally able to be rerun for updates unless the site code is dramatically altered. It really comes down to how brittle the spider is coded (i.e. hunting for specific heading sizes or fonts or something) instead of grabbing the underlying JSON/XHR that doesn't usually change frequently.

1. https://scrapy.org

Scrapy capitalizes headers for request

Scrapy is a web scraping toolkit

I know this might not be a good answer, as it's not .NET, but we use https://scrapy.org/ (Python).