`functools`

The Python functools module provides higher-order functions and tools for working with callable objects.

This module allows you to work with functions in a functional programming style, offering utilities that modify or extend the behavior of functions and methods in Python.

Here’s an example:

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>>> import functools

>>> double = functools.partial(lambda x, y: x * y, x=2)
>>> double(5)
10

Key Features

Provides tools for creating partial functions
Supports function memoization with lru_cache
Offers decorators for method caching
Includes utilities for comparing and ordering

Frequently Used Classes and Functions

Object	Type	Description
`functools.partial`	Function	Returns a new callable object that behaves like the original function with specified arguments
`@functools.lru_cache`	Decorator	Caches function calls with a Least Recently Used (LRU) cache strategy
`functools.reduce()`	Function	Performs a cumulative computation on a sequence of elements
`@functools.singledispatch`	Decorator	Transforms a function into a single-dispatch generic function.

Examples

Create a partial function that computes powers of 3:

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>>> from functools import partial

>>> cube = partial(lambda x, y: x**y, y=3)
>>> cube(4)
64

Use reduce() to compute the product of a list:

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>>> from functools import reduce
>>> reduce(lambda x, y: x * y, [1, 2, 3, 4])
24

Common Use Cases

Creating functions with fixed arguments for repeated use
Optimizing recursive functions with caching
Transforming comparison functions for sorting
Performing cumulative operations on data collections

Real-World Example

Suppose you have a recursive function that calculates Fibonacci numbers, and you want to optimize it by using caching to avoid redundant calculations:

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>>> from functools import lru_cache

>>> @lru_cache(maxsize=128)
... def fibonacci(n):
...     if n < 2:
...         return n
...     return fibonacci(n-1) + fibonacci(n-2)
...

>>> fibonacci(100)
354224848179261915075

Here, the @lru_cache decorator significantly improves the performance of the Fibonacci function by storing the results of previously computed values, avoiding the need to recompute them.

Tutorial

Functional Programming in Python: When and How to Use It

In this tutorial, you'll learn about functional programming in Python. You'll see what functional programming is, how it's supported in Python, and how you can use it in your Python code.

intermediate python

For additional information on related topics, take a look at the following resources: