[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!