Functional Programming in Python with Marimo

🚧 This collection is a work in progress.

This series of marimo notebooks introduces the powerful paradigm of functional programming through Python. Taking inspiration from Haskell and Category Theory, we'll build a strong foundation in FP concepts that can transform how you approach software development.

What You'll Learn

Using only Python's standard library, we'll construct functional programming concepts from first principles.

Topics include:

Recursion and higher-order functions
Category theory fundamentals
Functors, applicatives, and monads
Composable abstractions for robust code

Timeline & Collaboration

I'm currently studying functional programming and Haskell, estimating about 2 months to complete this series. The structure may evolve as the project develops.

If you're interested in collaborating or have questions, please reach out to me on Discord (@eugene.hs). I welcome contributors who share an interest in bringing functional programming concepts to the Python ecosystem.

Running notebooks. To run a notebook locally, use

uvx marimo edit <URL>

For example, run the Functor tutorial with

uvx marimo edit https://github.com/marimo-team/learn/blob/main/Functional_programming/05_functors.py

You can also open notebooks in our online playground by appending marimo.app/ to a notebook's URL: marimo.app/github.com/marimo-team/learn/blob/main/functional_programming/05_functors.py.

Current series structure

Notebook	Description	Status
Functional Programming Fundamentals	Core FP principles in Python, comparison with imperative programming, and Haskell-inspired thinking patterns	🚧
Higher-Order Functions and Currying	Functions as first-class values, composition patterns, and implementing Haskell-style currying in Python	🚧
Python's Functional Toolkit: functools, itertools and operator	Leveraging Python's built-in functional programming utilities, advanced iterator operations, and function transformations	🚧
Recursion and Tail Recursion	Recursive problem solving, implementing tail-call optimization in Python, and trampoline techniques to avoid stack overflow	🚧
Category Theory and Functors	Introduction to categories, morphisms, functor patterns, and implementing the functor interface and practical use cases	🚧
Applicatives and Effectful Programming	Combining independent computations with effects, implementing the applicative interface and practical use cases	🚧
Kleisli Category and Monads	Understanding monadic computation, composing impure functions, and implementing basic monads	🚧
Monad Fail, Transformers and Fix	Error handling with MonadFail, combining monads with transformers, and handling recursive structures	🚧
Monadic Parsing	Building a parser combinator library, implementing recursive descent parsers, and practical text processing	🚧
Semigroups and Monoids	Composable operations, algebraic structures, laws, and practical applications for data aggregation	🚧
Foldables and Traversables	Abstract folding beyond lists, traversing with effects, and implementing these interfaces for custom data types	🚧
Bifunctors	Working with two-parameter type constructors, implementing the bifunctor interface, and practical examples	🚧
Arrows	Arrow abstractions beyond monads, implementing the Arrow interface, and creating arrow-based computations	🚧
Comonads	Understanding dual concepts to monads, implementing Store and Stream comonads, and practical applications	🚧
Design Patterns in Functional Python	Applying FP concepts to solve real-world problems, functional architecture, and testing strategies	🚧

Description of notebooks

05. Category and Functors

In this notebook, you would learn:

Why len is the Functor from the category of list concatentation to the category of integer addition
How to lift an ordinary function to a specific computation context
How to write an adpter between two categories

References

Authors.

Thanks to all our notebook authors!

eugene.hs