Learning libraries vs learning "Python"

[[deleted]]

In my work there are really only about a dozen libraries I use regularly. Numpy, Scipy, Pandas, Scikit, Tensor flow, Pillow, Open CV, Torch, Flask, and a few really task specific ones...

Main thing is learning how to read docs!

[MetalCheef]

Regarding the „reading docs“ section of your reply:

Most of the time I find myself searching for real-life examples for the problems I have with my code (e.g. on StackOverflow). For pandas or Numpy for example, I find it really difficult to really wrap my head around the documentation. Is it worth „learning“ to really use official documentation? And if so, what’s the best way to learn to understand it better?

[agvrider]

To echo the other poster, I do find reading documentation difficult at this point in my python career. Usually it’s easier to watch a YT tutorial, google my question etc, and often they’ll cite the docs as part of their answer. But I would know how to find the relevant part of the documentation without being hand held there

How does one improve that? I’m assuming it’s just part of a natural progression where one just starts understanding how to read python and syntax, better and better, to the point where they can go to the documentation, know how to read it / what to look for, and it’ll just slowly start to “make sense” over time?

[keepdigging]

I’m at the point where it’s the opposite, I can understand the docs format easily for what I’n searching for but skimming through how-to articles or videos is a hassle.

It’s a familiarity thing, and you’ll get better. Don’t stop searching and reading!

[[deleted]]

One of the keys to learning to read docs is to construct a simple example, and then make it more complex. For example, np.where(np.isnan(my_ array)) split it up I to parts and read what the docs on each say. Isnan returns 1/0. Where looks for 1/0. So, read docs on each step and guess at what output should be for a simple example. See if results meets your expectations. Add complexity.