[lynndotpy]

I have to agree. I have a lot of gripes with Python, but the documentation and the standard library are generally great.

The author favors Javascript while deriding Python for its syntax, type-hints, and standard library, and favors Go while deriding a Python Docker image's size and documentation. I feel like the author must use Python in a very different way than I do for Javascript and Go to be the winners in these categories.

I do agree without reservation that package management and dependencies are horrible with Python.

[Silhouette]

I have a lot of gripes with Python, but the documentation and the standard library are generally great.

It depends what you're comparing them against. Python's style of documentation - for the language itself and for many of the popular libraries that follow the same style - is mostly reference material and often incomplete. It's very lacking in examples of usage. It almost completely ignores types. It often doesn't appear in search engine results for relevant keywords leading to spending several minutes brute force searching the official docs site to find something that should have been a 10 second search. Perhaps the most obvious comparison is with the JavaScript/TypeScript world, which has embraced both types and different kinds of documentation and as a result gives a much better developer experience in those areas today.

The standard library in Python is strange because it has a lot of content but much of that content just isn't very good. Entire packages in the standard library are largely ignored in favour of some de facto standard package from PyPI that does the same job much better. Some of the packages for working with different protocols and file formats are useful in the right circumstances but they're so slow that they're not suitable for many applications and again you end up pulling in a better alternative. Meanwhile common data structures and algorithms that you might look for in any modern language's toolbox are scarce to non-existent and the ones that do exist don't always compose easily.

[lynndotpy]

It definitely varies from library to library. The urllib.request example provided is pretty horrible, and it's simply just been in a blind spot in my eye.

I also agree with you on typing, searchability, and the problem of standards vs defacto standards. (The page for `urllib.request` points you to `requests`, which is good! But the page for `array` has a link to `numpy` only at the very bottom.) I also don't have the JS/TS experience to compare against, but I believe you there. (The MDN alone is excellent.)

I think my experience is biased from spending a lot of time in deep learning. (Keras and Pytorch have a bit of a competition for having good docs). The adjacent libraries, like Numpy or Pandas or stdlib like `socket`, have also been good in my experience. (Perhaps these benefit from having relatively 'obvious' types for most functions. One might infer the types and dimensions of `numpy.matmul(x1, x2)` easily, whereas I have no idea what the types of the args in `Request.set_proxy(host, type)` are.)

It's a shame this post was flagged, because I've had a lot of blind spots uncovered in this thread!