NumPy VS Scrapy

Unmatched integration with ML/AI ecosystems through NumPy, TensorFlow, and PyTorch

> Let’s take a pause here for a second - the ‘CanIndex’ and ‘SupportsIndex’ from the looks are just “int”.

The PR for the change is https://github.com/numpy/numpy/pull/28913 - The details of files changed[0] shows the change was made in 'numpy/__init__.pyi'. Looking at the whole file[1] shows SupportsIndex is being imported from the standard library's typing module[2].

Where are you seeing SupportsIndex being defined as an int?

> I have a hard time dealing with these custom types because they are so obscure.

SupportsIndex is obscure, I agree, but it's not a custom type. It's defined in stdlib's typing module[2], and was added in Python 3.8.

[0]: https://github.com/numpy/numpy/pull/28913/files

[1]: https://github.com/charris/numpy/blob/c906f847f8ebfe0adec896...

[2]: https://docs.python.org/3/library/typing.html#typing.Support...

Know (or check) tells of older versions, such as the python sdk of OpenAI changing from a client with global state in v0.x.x, to a declared instance in v1.x.x, or numpy's change in how random generators are declared.

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As is the case with most Python libraries, it is open-source and free-to-use, making it easily accessible by anyone willing to learn machine learning, and it is built upon other open-source libraries within Python, like SciPy for advanced scientific operations, NumPy for efficient numerical computations, Matplotlib for data visualization, and Cython for increased efficiency and speed, similar to that of C/C++.

User-Agent: Scrapy/2.11.0 (+https://scrapy.org)

Last week, I worked on code refactoring in Scrapy, which is an essential practice in larger and more complex projects. Refactoring not only improves code maintainability but also makes it easier for other contributors to understand and extend the project. This task was a good starting point for me to verify that I had the Scrapy project correctly set up locally, as refactoring of codes should not break existing functionalities.

In the past three months, I worked on various open source projects, including my own project Repo Context Packager, Math Worksheet Generator and Open Web Calendar. This month, I want to challenge myself to work on a larger and more widely used project - Scrapy, a Python module for web crawling.

What I have seen it is hard to tell what "serious scrapers" use. They use many things. Some use this, some not. This is what I have learned reading webscraping on reddit. Nobody speaks things like that out loud.

There are many tools, see links below

Personally I think that running selenium can be a bottle neck, as it does not play nice, sometimes processes break, even system sometimes requires restart because of things blocked, can be memory hog, etc. etc. That is my experience.

To be able to scale I think you have to have your own implementation. Serious scrapers complain about people using selenium, or derivatives as noobs, who will come back asking why page X does not work in scraping mechanisms.

https://github.com/lexiforest/curl_cffi

https://github.com/encode/httpx

https://github.com/scrapy/scrapy

https://github.com/apify/crawlee

Scrapy is a robust and scalable open-source web crawling framework. It is highly efficient for large-scale projects and supports asynchronous scraping.

Language: Python | GitHub: 52.9k stars | link

One might ask, what about Scrapy? I'll be honest: I don't really keep up with their updates. But I haven't heard about Zyte doing anything to bypass TLS fingerprinting. So out of the box Scrapy will also be blocked, but nothing is stopping you from using curl_cffi in your Scrapy Spider.

Install scrapy (Offical website) either using pip or conda (Follow for detailed instructions):