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Introduction
The functools module is for higher-order functions: functions that act on or return other functions. It provides functions for working with other functions and callable objects to use or extend them without completely rewriting them.
Memoizing the function calls
Memoizing: Storing the result of some expensive function, which is called with the same input again and again. So, we don't have to run the function repeatedly.
@functools.lru_cache(maxsize=128, typed=False)
@functools.lru_cache(maxsize=128, typed=False) Decorator to wrap a function with a memoizing callable that saves up to the maxsize most recent calls. It can save time when an expensive or I/O bound function is periodically called with the same arguments.
Since a dictionary is used to cache results, the positional and keyword arguments to the function must be hashable.
Here maxsize = 128 means that it is going to memoize latest 128 function calls at max.
@functools.cache(user_function)
@functools.cache(user_function) the same as lru_cache(maxsize=None), creating a thin wrapper around a dictionary lookup for the function arguments. Because it never needs to evict old values, this is smaller and faster than lru_cache() with a size limit.
Generic functions
Generic functions are those which preform the operation based on the argument given to them.
In statically typed languages it can be done by function overloading, In python functools provides the singledispatch(func) decorator to register a set of generic functions for automatic switching based on the type of the first argument to a function.
For class methods we can use singledispatchmethod(func) to register a set of generic methods for automatic switching based on the type of the first non-self or non-class argument to a function.
Partial
functools.partial(func, /, *args, **keywords) return a new partial object which when called will behave like func called with the positional arguments args and keyword arguments keywords. If more arguments are supplied to the call, they are appended to args.The partial is used for partial function application which “freezes” some portion of a function’s arguments and/or keywords resulting in a new object with a simplified signature.
functools.partialmethod(func, /, *args, **keywords) Return a new partialmethod descriptor which behaves like partial except that it is designed to be used as a method definition rather than being directly callable.
Wraps
functools.update_wrapper(wrapper, wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)
functools.update_wrapper(wrapper, wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES) Update a wrapper function to look like the wrapped function. The optional arguments are tuples to specify which attributes of the original function are assigned directly to the matching attributes on the wrapper function and which attributes of the wrapper function are updated with the corresponding attributes from the original function.
functools.wraps(wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)
functools.wraps(wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES) is a convenience function for invoking update_wrapper() as a function decorator when defining a wrapper function. It is equivalent to partial(update_wrapper, wrapped=wrapped, assigned=assigned, updated=updated).