Functional Python Programming in Introductory Computer Science Courses

TL;DR

The paper addresses how to introduce functional programming concepts to beginners using Python by enforcing a purely functional subset in CS1/CS2 courses. It defines a concrete best-practice, restricting assignment, control flow, and mutation while promoting immutability, higher-order functions, and comprehensions. The authors illustrate the approach with small examples (Caesar cipher, twin primes) and a challenging CS2 assignment involving translating Datalog atoms to Relational Algebra, implemented in a functional style. Anecdotal evidence from a CS2 course suggests the method increases engagement and may enhance long-term programming skill, supporting its curricular viability.

Abstract

The functional programming paradigm has a long and storied history, with its beginnings in the Lambda Calculus. In recent decades, pure functional languages such as Haskell have been shown to be highly effective in producing robust software due to immutable data structures, among other functional features. The advantages of programming with immutable data structures can also be had in non-functional languages such as Python. Over the years, non-functional languages have introduced immutable data structures as well as comprehension and lambda expressions, and it is possible to program in a purely functional style in them. In this paper, we present a ``best practice'' idea in introductory programming classes that forces students to learn and complete programming assignments in a purely functional subset of Python. By doing so, the student can learn functional ideas such as immutability, pure functions with no side effects, and stateless programming. We define a functional subset of Python and illustrate the best practice using small examples. We strongly feel that students in computing need familiarity with pure functional programming and argue that this can be taught in introductory programming courses that use Python.