I'm also a long time conda user and have recently switched to pixi (https://pixi.sh/), which gives a very similar experience for conda packages (and uses uv under the hood if you want to mix dependencies from pypi). It's been great and also has a `pixi global` similar to `pipx`, etc the makes it easy to grab general tools like ripgrep, ruff etc and make them widely available, but still managed.
I use numba quite a bit at work and it's fantastic. I recently, however, did a comparison between numba, cython, pythran and rust (ndarray) for a toy problem, and it yielded some interesting results:
Most surprising among them was how fast pythran was with little more effort than is required of numba (still required an aot compilation step with a setup.py, but minimal changes in the code). All of the usual caveats should be applied to a simple benchmark like this.
I didn't see your comment until I wrote mine, yes I did the same comparison and pythran really is amazing. One of the great things is that it also runs as reasonably fast regular python code (as you can just leave most numpy functions in place), which makes debugging and prototyping so much easier.
Another relatively new addition to the python statistical sampler space is Austin[1], that has a lot of similar features to py-spy. I haven't made a direct comparison yet between the two.
I'm actively looking for alternatives since I found this to be a super useful feature in terms of seeing the state of a repository and the relationship between active branches. I've seen https://gitup.co/, but it doesn't have a view that is organized by commit date.
First class multi-dimensional arrays with natural indexing syntax would be a huge draw. You get that in Numpy, Julia, Fortran and it is extremely powerful in terms of expressing the types of operations that are common across many scientific domains.
Conda is 100% free and open source, but is developed by Continuum, which is a company (but one that contributes heavily to many of the key components of the open-source pydata/scipy ecosystem, and whose CEO basically created numpy/scipy for the community at the expense of his academic career). Personally I have had nothing but good interactions with people from Continuum and the software they develop. They (as a company and as the individuals in it) do an incredible amount for the open source community and we all benefit from that. They seem to have worked out a sustainable model for running a company and working in/contributing to open source.
Well, this probably isn't the right place to complain about startups that are trying to make money, and I don't have any experience with NumPy, SciPy, or Conda.
