> 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.
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.
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.