Decorators - Python Wiki

See also PythonDecoratorLibrary. And apart from decorators in the standard library, there are also lots of PyPI packages with convenient, interesting or novel use cases:

Category	Decorator	Summary
builtin	@property	Turn function into property accessor, also: `@property.setter`
builtin	@classsmethod	Use `cls` instead of `self`
builtin	@staticmethod	Defines methods without self handle
stdlib	@functools.wraps	Retain modicum of meta infos (`__doc__`) of wrapped function
stdlib	@functools.singledispatch	Allows for divergent implementations per @f.register()
stdlib/backports	@functools.lru_cache	Wrap computationally expensive functions
stdlib	@functools.cmp_to_key	Transform an old-style comparison function to a key function.
stdlib	@functools.total_ordering	Adds any of the missing lt,le,eq,ge,gt comparison dunder methods
stdlib	@functools.partial	Prepopulate a couple of function arguments
stdlib	@typing.overload	Singledispatch based on function parameter or return types
stdlib	@contextlib.contextmanager	Uses generator protocol `yield` to turn function into `with`-able context mananger
stdlib	@dataclasses.dataclass	Adds various “dunder” methods to the class to streamline property access
stdlib	@atexit.register	Run as shutdown function
stdlib	@abc.ABCMeta.register	Turn into abstract base class
stdlib	@enum.unique	Guarantee unique members in enum class
stdlib	@xmlrpc.register_function	...
stdlib	@sys.settrace	Register function as debugger callback
asyncio	@coroutine	Outdated scheme for `async def`, deprecated in 3.8
asyncio	@throttle(2,1)	Reduce invocations per timeframe
asyncio	@exceptionCatcher…	Log exceptions more instantly
asyncio	@timeit	Basic benchmarking
async	@async_process	make func async and execute in other process
threading	@threaded, @debug, @skip_if_env	Convenient threading wrapper, and testing wrappers
threading	@lock_or_wait	Aquire and release locks for functions
testing	@unittest.mock.patch	Override function w/ signature
testing	@wrapt.patch_function_wrapper	Override function w/ signature
debugging	@trycatch	Capture exceptions to the log
debugging	@log_calls	Record invocations with arguments
decorator	@decorator	Assists declaring signature-preserving and single dispatch decorators
decorator	@decorator_args	Ease parameterizing decorators themselvess
decorator	@named_decorator	Retain callees' function name to simplify tracebacks
decorator	@dec.decorator(before=cb,after=cb)	Craft trivial enter/exit decorator
decorator	@log_call, @intercept, @instead, @before	Simplifies decorator construnction/interactions, parameter handling
decorator	undecorated(fn)	reversible decoration
collect	@click.option("-d")	Argparse alternative
collect	@config_decorator.setting	Declarative dict structure for application settings
collect	@regex_decorator.listen	Register function callbacks for pattern matching
collect	@expose, @register	Plugin interface/registration
typing	@contract({"param":gt(3)})	Exhaustive function parameter constraints
typing	@pedantic, @trace, @dirty, …	Encourages cleaner and better documented code
typing	@Decorator	OO-style class/method parameters
typing	@mutable, @final, @auto_obj	decorators for str/repr, equality, immutability, and more
typing	@to_string, @data, @repeat	Reduces some common boilerplate for methods
typing	@DataAtrribute("desc")	.NET-like attributes
parameter	@curried	Convenient decorator-alternative to partial()
parameter	@attrs	object declarations
parameter	@arg.validate	simplifies parameter handling/assertion
flow handling	@__main__	Automatically run a function if invocation script
flow handling	@retry(times=2)	Reinvokes function a few times when encountering (temporary) exceptions, alt: pypi:retry-decorator
flow handling	@retry	Rerun function in case of exceptions
flow handling	@self	method chaining (fluent interfaces)
monkeypatching	@inject(modules…)	Shorthand assignment to modules/objects
monkeypatching	@monkeybiz.patch(fn)	Swap out function in object, retain reference to original

(Note: Not sure this is going anywhere. Relisting builtins is somewhat redundant, but on topic here. The focus is novel/interesting decorators in the wild. The categorization probably won't hold up; and probably going to split this up into sections.)

main

This decorator does not alter a function, but causes it to be executed if __name__ == '__main__'. This provides an experimental cleaner syntax to the traditional way of bootstrapping a python script. This should not be used on class methods.

The function gets a copied list of sys.argv arguments.

   1 def __main__(func):
   2 
   3     if __name__ == "__main__":
   4         import sys, os
   5         args = sys.argv[:]
   6         args[0] = os.path.abspath(args[0])
   7         func(args)

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