31–40 of 44 posts
- einops (probably by far the most useful Python dsl for me as of late, esp. if you deal a lot with tensors) - numpy __array__ interface standardization (easy compatibility across a lot of numerical libraries, e.g. torch, awkward-array, zarr, jax.numpy, dask, xarray) - the various jit compilers (e.g. numba, jax.jit, torch.compile) - hw-accelerated functional programming style (e.g. functorch.vmap) - pybind11 for easy…
plot.ly now has an API:
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That is my point. I don't have any tools to prevent it from happening. I just don't do it when I write the code. If I have and my code is tested it comes up very quickly. Us older people tend to keep the whole workflow as simple as possible. That is why I love python. There is very little overhead to just using it. If you tend to make this mistake a lot it might make sense to add some tooling. I don't need it. If I w…
I think you're either misrepresenting reality or stretching the truth. For example, if you're redefining bug as "bug that made it past testing" that's one thing. But I don't buy anyone writing Python daily for 15 years hasn't seen the term "TypeError" which is what your previous two comments were implying.
Testing. Over that long time I have used many test frameworks. My normal workflow is to try and exercise all the code I write as I go.
On the other hand I am 100% pedantic about using 'schema' (https://pypi.org/project/schema/) to validate all the types, structure and domains when I read json.
- einops (probably by far the most useful Python dsl for me as of late, esp. if you deal a lot with tensors) - numpy __array__ interface standardization (easy compatibility across a lot of numerical libraries, e.g. torch, awkward-array, zarr, jax.numpy, dask, xarray) - the various jit compilers (e.g. numba, jax.jit, torch.compile) - hw-accelerated functional programming style (e.g. functorch.vmap) - pybind11 for easy…
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Why use Pydantic instead of dataclasses?
Pydantic can do validation. There’s also the related Typer, which builds command line interfaces from the type annotations on your entry point
The `click` library would be a nice replacement to `argparse`
The lack of boilerplate. Low ceremony. Just write the code, save the file and run it. That in itself is a great feature.
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I guess I meant what do you do to prevent that from happening.
That is my point. I don't have any tools to prevent it from happening. I just don't do it when I write the code. If I have and my code is tested it comes up very quickly. Us older people tend to keep the whole workflow as simple as possible. That is why I love python. There is very little overhead to just using it. If you tend to make this mistake a lot it might make sense to add some tooling. I don't need it. If I w…
Earlier quoted context omitted.
I think you're either misrepresenting reality or stretching the truth. For example, if you're redefining bug as "bug that made it past testing" that's one thing. But I don't buy anyone writing Python daily for 15 years hasn't seen the term "TypeError" which is what your previous two comments were implying.
OK, it's 100% likely I saw it, just so rarely I can't remember. Testing. Over that long time I have used many test frameworks. My normal workflow is to try and exercise all the code I write as I go. On the other hand I am 100% pedantic about using 'schema' ( https://pypi.org/project/schema/ ) to validate all the types, structure and domains when I read json.
But I made way more type errors when programming in C/++.
Why do you prefer schema to (fast)jsonschema?
If I ever could wrap my brain around asyncio, it would be an appreciated third.
I never used the walrus operator, IIRC, and I find type hints not so useful. Somewhere in the future I will try to use them in APIs, they may have their benefit there.
Edit: I forgot enums. Not so new, but a must.