Skip to main content

Function Caching in Python

 


Learning Sections          show

Function Caching in Python

Function caching is a technique used to store the results of expensive function calls and reuse those results when the same inputs occur again. Python's functools module provides a built-in way to cache function results using the lru_cache decorator.

Using lru_cache from functools

The lru_cache decorator caches the results of a function based on its inputs. It uses a Least Recently Used (LRU) caching strategy to manage the cache size:


from functools import lru_cache

# Apply the lru_cache decorator
@lru_cache(maxsize=128)
def fib(n):
    if n == 0:
        return 0
    elif n == 1:
        return 1
    else:
        return fib(n - 1) + fib(n - 2)

# Call the cached function
print(fib(10))

In this example, the Fibonacci function results are cached, so repeated calls with the same input are faster.

Parameters of lru_cache
  • maxsize: Defines the maximum size of the cache. If the cache exceeds this size, the least recently used items are discarded. Setting it to None disables the LRU feature and allows the cache to grow without bound.
  • typed: If set to True, arguments of different types will be cached separately. For example, f(3) and f(3.0) will be treated as distinct calls with separate cache entries.
Benefits of Function Caching
  • Performance Improvement: Function caching can significantly improve the performance of your program by reducing the need to recompute results for the same inputs.
  • Resource Efficiency: Reduces the consumption of computational resources for functions that are called multiple times with the same arguments.
  • Ease of Use: The lru_cache decorator is easy to apply and requires minimal code changes to implement caching.
Labels:

Popular posts from this blog

Introduction to OOPs in Python

  Learning Sections          show Introduction to Object-Oriented Programming (OOP) Object-Oriented Programming (OOP) is a programming paradigm that organizes software design around objects rather than actions and data rather than logic. It revolves around the concept of "objects", which are instances of classes. These objects encapsulate data, in the form of attributes or properties, and behaviors, in the form of methods or functions. OOP promotes modularity, reusability, and extensibility in software development. Key Concepts of OOP: Class: A class is a blueprint or template for creating objects. It defines the attributes (data) and methods (functions) that will characterize any object instantiated from that class. Object: An object is an instance of a class. It is a concrete realization of the class blueprint, containing actual values instead of placeholders for attributes. Encapsulation: Encapsulation is ...
Read more

Inheritance in Python

  Learning Sections          show Inheritance in Python Inheritance is a fundamental concept in object-oriented programming (OOP) that allows a class to inherit attributes and methods from another class. The class that inherits is called the child class or subclass, and the class being inherited from is called the parent class or superclass. Basic Inheritance In Python, a child class inherits from a parent class by specifying the parent class in parentheses after the child class name. Example: class Animal : def __init__ ( self , name ): self . name = name def speak ( self ): raise NotImplementedError ( "Subclass must implement this method" ) class Dog ( Animal ): def speak ( self ): return "Woof!" class Cat ( Animal ): def speak ( self ): return "Meow!" # Create instances of Dog and Cat dog = Dog ( "Buddy" ) cat = Cat ( "Whiskers" ...
Read more

AsyncIO in Python

  Learning Sections          show AsyncIO in Python AsyncIO is a library to write concurrent code using the async/await syntax. It allows you to manage asynchronous tasks and I/O operations in Python. Basics of AsyncIO AsyncIO provides an event loop, coroutines, and tasks: Event loop: The core of every asyncio application. It runs asynchronous tasks and callbacks. Coroutines: Special functions defined with async def . They use await to pause their execution and wait for other coroutines. Tasks: Used to schedule coroutines to run concurrently in the event loop. Creating a Simple Coroutine import asyncio # Define a coroutine async def say_hello (): print ( "Hello" ) await asyncio.sleep ( 1 ) print ( "World" ) # Create an event loop and run the coroutine asyncio.run ( say_hello ()) Running Multiple Coroutines import asyncio async de...
Read more