Functional-Programming-Techniques-In-Python VS Pyrsistent

https://github.com/mpkocher/Functional-Programming-Technique...

I covered a few core concepts (e.g., functions as first-class citizens, closures, partial application, etc...) and added a few real world examples of using a functional centric design. The text/format has some rough edges, but overall I think the text is useful for internalizing how to leverage a functional-ish approach.

Other resources.

https://github.com/sfermigier/awesome-functional-python

Perhaps a custom compose function can help with these use cases? This series has a few examples of composing computation in Python that might be useful.

https://github.com/mpkocher/Functional-Programming-Technique...

And there's a general list of resources here.

https://github.com/sfermigier/awesome-functional-python

There are python libraries that implement Clojure style functional data types. Have you tried pyrsistent - https://github.com/tobgu/pyrsistent

Hissp is really interesting. Read through the docs and you'll understand Python more deeply. It works well with Toolz and Pyrsistent.

This is normal for some functional languages, since by definition they should prohibit assignment and hence mutation. But you can also achieve a similar (not the same) effect in python, using libraries like pyrsistent (https://github.com/tobgu/pyrsistent/)

It still is, and Cython is great for accelerating critical Python code.

A C extension is far preferable when you want to code in C, either to write a new data type[1], or write a Python frontend to a C library[2] that is too complex to be well supported by simple FFI.

I think people use Cython more internally when they value the maintainability of "mostly Python" over the fact that it's slower than what native C would get them.

[1]: https://github.com/tobgu/pyrsistent

[2]: https://github.com/libgit2/pygit2

There's Pyrsistent[1], which provides persistent data structures.

[1] https://github.com/tobgu/pyrsistent