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Functional Programming Principles

17. 11. 2020 Updated: 27. 03. 2026 1 min read intermediate
This article was published in 2020. Some information may be outdated.

Functional programming is not just an academic concept. Pure functions, immutability, and composition lead to cleaner code.

Key Principles

  • Pure functions: Same input → same output, no side effects
  • Immutability: Data is not mutated, new data is created
  • First-class functions: Functions as values
  • Composition: Composing small functions

Practical Examples

Functional Programming Principles

from functools import reduce numbers = [1, 2, 3, 4, 5] doubled = list(map(lambda x: x * 2, numbers)) evens = list(filter(lambda x: x % 2 == 0, numbers)) total = reduce(lambda a, b: a + b, numbers)

List comprehension (pythonic)

doubled = [x * 2 for x in numbers] evens = [x for x in numbers if x % 2 == 0]

Composition

from functools import reduce def compose(*fns): return reduce(lambda f, g: lambda x: f(g(x)), fns) process = compose(format_output, validate, parse_input) result = process(raw_data)

JavaScript FP

const users = [{ name: ‘Jan’, age: 30 }, { name: ‘Eva’, age: 25 }]; const names = users.map(u => u.name); const adults = users.filter(u => u.age >= 18); const totalAge = users.reduce((sum, u) => sum + u.age, 0); // Immutability const updated = { …user, age: 31 }; // Spread, not mutation const newList = […items, newItem];

Key Takeaway

Pure functions for testability, immutability for predictability, map/filter/reduce instead of for loops.

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We build core systems and AI agents that keep operations running. 15 years of experience with enterprise IT.