Python's reduce(): From Functional to Pythonic Style – Real Python

Python's reduce(): From Functional to Pythonic Style – Real Python

Python's reduce(): From Functional to Pythonic Style – Real Python

Exploring Functional Programming in Python

Functional programming is a programming paradigm based on breaking down a problem into a set of individual functions. Ideally, every function only takes a set of input arguments and produces an output.

In functional programming, functions don’t have any internal state that affects the output that they produce for a given input. This means that anytime you call a function with the same set of input arguments, you’ll get the same result or output.

In a functional program, input data flows through a set of functions. Each function operates on its input and produces some output. Functional programming tries to avoid mutable data types and state changes as much as possible. It works with the data that flow between functions.

Other core features of functional programming include the following:

  • The use of recursion rather than loops or other structures as a primary flow control structure
  • A focus on lists or arrays processing
  • A focus on what is to be computed rather than on how to compute it
  • The use of pure functions that avoid side effects
  • The use of higher-order functions

There are several important concepts in this list. Here’s a closer look to some of them:

  • Recursion is a technique in which functions call themselves, either directly or indirectly, in order to loop. It allows a program to loop over data structures that have unknown or unpredictable lengths.
  • Pure functions are functions that have no side effects at all. In other words, they’re functions that do not update or modify any global variable, object, or data structure in the program. These functions produce an output that depends only on the input, which is closer to the concept of a mathematical function.
  • Higher-order functions are functions that operate on other functions by taking functions as arguments, returning functions, or both, as with Python decorators.

Since Python is a multi-paradigm programming language, it provides some tools that support a functional programming style:

  • Functions as first-class objects
  • Recursion capabilities
  • Anonymous functions with [lambda](
  • Iterators and generators
  • Standard modules like [functools]( and [itertools](
  • Tools like [map()]([filter()]([reduce()]([sum()]([len()]([any()]([all()]([min()]([max()](, and so on

Even though Python isn’t heavily influenced by functional programming languages, back in 1993 there was a clear demand for some of the functional programming features listed above.

In response, several functional tools were added to the language. According to Guido van Rossum, they were contributed by a community member:

Python acquired lambdareduce()filter() and map(), courtesy of (I believe) a Lisp hacker who missed them and submitted working patches. (Source)

Over the years, new features such as list comprehensionsgenerator expressions, and built-in functions like sum()min()max()all(), and any() were viewed as Pythonic replacements for map()filter(), and reduce(). Guido planned to remove map()filter()reduce(), and even lambda from the language in Python 3.

Luckily, this removal didn’t take effect, mainly because the Python community didn’t want to let go of such popular features. They’re still around and still widely used among developers with a strong functional programming background.

In this tutorial, you’ll cover how to use Python’s reduce() to process iterables and reduce them to a single cumulative value without using a [for]( loop. You’ll also learn about some Python tools that you can use in place of reduce() to make your code more Pythonic, readable, and efficient.

python functional pythonic style

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