[tomnicholas1]

> The future of Python's main open source data science ecosystem, numfocus, does not seem bright. Despite performance improvements, Python will always be a glue language.

Your first sentence is a scorching hot take, but I don't see how it's justified by your second sentence.

The community always understood that python is a glue language, which is why the bottleneck interfaces (with IO or between array types) are implemented in lower-level languages or ABIs. The former was originally C but often is now Rust, and Apache Arrow is a great example of the latter.

The strength of using Python is when you want to do anything beyond pure computation (e.g. networking) the rest of the world already built a package for that.

[Kalanos]

So without the two-lang problem, I think all of these low-level optimization efforts across dataframes, tensors, and distributed computing would be part of a unified ecosystem based on shared compatibility.

For example, the reason why numfocus is so great is that everything was designed to work with numpy as its underlying data structure.