Nick Coghlan writes:
One of the things we do as part of the Python core development process is to look at features we appreciate having available in other languages we have experience with, and see whether or not there is a way to adapt them to be useful in making Python code easier to both read and write. This means that learning another programming language that focuses more specifically on a given style of software development can help improve anyone’s understanding of that style of programming in the context of Python.
To aid in such efforts, I’ve provided a list below of some possible areas for exploration, and other languages which may provide additional insight into those areas.
The languages and areas are:
- Procedural programming: C, Rust, Cython
- Object-oriented data modelling: Java, C#, Eiffel
- Object-oriented C derivatives: C++, D
- Array-oriented data processing: MATLAB/Octave, Julia
- Statistical data analysis: R
- Computational pipeline modelling: Haskell, Scala, Clojure, F#
- Gradual typing: TypeScript
- Dynamic metaprogramming: Hy, Ruby
- Pragmatic problem solving: Lua, PHP, Perl
- Computational thinking: Scratch, Logo