Map, filter and reduce functions in Python work precisely in this manner - they create and work on new sets of data, leaving the original data intact.įunctions are first-class citizens in functional programming. Because the values of immutable data types cannot be altered, we only work on the copies of the original data structure. Instead of changing the values of variables, functional programming works only on immutable data types. In contrast, functional programming eliminates the notion of state.
For example, a for loop may repeatedly execute a statement, each time changing the value of a variable, as below:Īs the value of counter increases each iteration of the loop by 1, the computation’s state experiences a corresponding change, each time advancing closer towards the final state. These consist of commands whose execution changes a variable’s value, and therefore the state of the computation. In imperative programming - the more common programming paradigm you’re probably already familiar with, computation is carried out through statements. One of functional programming’s important distinguishing features is the concept of an unchangeable state. Functional Programmingįunctional programming is a programming paradigm that defines computation through functions. This article covers the usage of Python’s map, filter and reduce functions and how they conform to the ideas of functional programming. While Python isn’t a purely functional programming language, it boasts many features that allow it to be used as such. Map, filter and reduce functions are the pillars of functional programming.
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