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Easy peasy Python decorators

Project description

Easy-peasy Python decorators!


Decorators are great, but they’re hard to write, especially if you want to include arguments to your decorators, or use your decorators on class methods as well as functions. I know that, no matter how many I write, I still find myself looking up the syntax every time. And that’s just for simple function decorators. Getting decorators to work consistently at the class and method level is a whole ‘nother barrel of worms.

PyDecor aims to make function decoration easy and straightforward, so that developers can stop worrying about closures and syntax in triply nested functions and instead get down to decorating!

IMPORTANT: Upcoming Backwards Incompatible Changes

Version 2.0.0 will make some changes to the call signatures for functions passed to @before, @after, @instead, @decorate, and construct_decorator, as well as to the call signatures to the decorators themselves.

Specifically, rather than defaulting the call signature to some subset of decorated function args, kwargs, result, and the decorated function itself and allowing overrides with keyword arguments to the decorator like pass_params, all functions passed to @before, @after, and @instead will receive an immutable Decorated object, which will have args, kwargs, wrapped, and result attributes, and which will support direct calls as though it were the decorated function/method/class. The aim of this is to make writing functions to pass to the decorators more intuitive, but it will require some minor re-writing of passed functions.

You can experiment with this syntax and prepare for the cut-over right away by passing _use_future_syntax=True to any of your generic decorators (@after, @before, etc.) or to construct_decorator. See the below snippet to illustrate basic use of the new Decorated object:

from pydecor import after, Decorated

def after_func(decorated: Decorated, extra_kwarg=None):
    """A function to be called after the decorated function"""
    assert decorated.args == ('foo', )
    assert decorated.kwargs == {'bar': 'bar'}
    assert decorated.result == 'baz'
    assert extra_kwarg == 'extra_kwarg'

@after(after_func, extra_kwarg='extra_kwarg')
def some_function('foo', bar='bar'):
  """A function that returns 'baz'"""
  return 'baz'

All of the builtin non-generic decorators (@memoize, @intercept, and @log_call) are already using the future syntax, so feel free to look at those for more examples.

See the API docs for more information.


Install pydecor:

pip install pydecor

Use one of the ready-to-wear decorators:

# Memoize a function

from pydecor import memoize

def fibonacci(n):
    """Compute the given number of the fibonacci sequence"""
    if n < 2:
        return n
    return fibonacci(n - 2) + fibonacci(n - 1)

# Intercept an error and raise a different one

from flask import Flask
from pydecor import intercept
from werkzeug.exceptions import InternalServerError

app = Flask(__name__)

@intercept(catch=Exception, reraise=InternalServerError,
           err_msg='The server encountered an error rendering "some_view"')
def some_view():
    """The root view"""
    assert False
    return 'Asserted False successfully!'

client = app.test_client()
response = client.get('/')

assert response.status_code == 500
assert 'some_view'.encode() in

Use a generic decorator to run your own functions @before, @after, or @instead of another function, like in the following example, which sets a User-Agent header on a Flask response:

from flask import Flask, make_response
from pydecor import after

app = Flask(__name__)

def set_user_agent(view_result):
    """Sets the user-agent header on a result from a view"""
    resp = make_response(view_result)
    resp.headers.set('User-Agent', 'my_applicatoin')
    return resp

def index_view():
    return 'Hello, world!'

client = app.test_client()
response = client.get('/')
assert response.headers.get('User-Agent') == 'my_application'

Or make your own decorator with construct_decorator

from flask import request
from pydecor import construct_decorator
from werkzeug.exceptions import Unauthorized

def check_auth(request):
    """Theoretically checks auth

    It goes without saying, but this is example code. You should
    not actually check auth this way!
    if != 'localhost':
        raise Unauthorized('locals only!')

authed = construct_decorator(before=check_auth)

app = Flask(__name__)

def some_view():
    """An authenticated view"""
    return 'This is sensitive data!'

Why PyDecor?

  • It’s easy!

    With PyDecor, you can go from this:

    from functools import wraps
    from flask import request
    from werkzeug.exceptions import Unauthorized
    from my_pkg.auth import authorize_request
    def auth_decorator(request=None):
        """Check the passed request for authentication"""
        def decorator(decorated):
            def wrapper(*args, **kwargs):
                if not authorize_request(request):
                  raise Unauthorized('Not authorized!')
                return decorated(*args, **kwargs)
            return wrapper
        return decorated
    def some_view():
        return 'Hello, World!'

    to this:

    from flask import request
    from pydecor import before
    from werkzeug.exceptions import Unauthorized
    from my_pkg.auth import authorize_request
    def check_auth(request=request):
        """Ensure the request is authorized"""
        if not authorize_request(request):
          raise Unauthorized('Not authorized!')
    @before(check_auth, request=request)
    def some_view():
        return 'Hello, world!'

    Not only is it less code, but you don’t have to remember decorator syntax or mess with nested functions. Full disclosure, I had to look up a decorator sample to be sure I got the first example’s syntax right, and I just spent two weeks writing a decorator library.

  • It’s fast!

    PyDecor aims to make your life easier, not slower. The decoration machinery is designed to be as efficient as is reasonable, and contributions to speed things up are always welcome.

  • Implicit Method Decoration!

    Getting a decorator to “roll down” to methods when applied to a class is a complicated business, but all of PyDecor’s decorators provide it for free, so rather than writing:

    from pydecor import log_call
    class FullyLoggedClass(object):
        def some_function(self, *args, **kwargs):
            return args, kwargs
        def another_function(self, *args, **kwargs):
            return None

    You can just write:

    from pydecor import log_call
    class FullyLoggedClass(object):
        def some_function(self, *args, **kwargs):
            return args, kwargs
        def another_function(self, *args, **kwargs):
            return None

    PyDecor ignores special methods (like __init__) so as not to interfere with deep Python magic. By default, it works on any methods of a class, including instance, class and static methods. It also ensures that class attributes are preserved after decoration, so your class references continue to behave as expected.

  • Consistent Method Decoration!

    Whether you’re decorating a class, an instance method, a class method, or a static method, you can use the same passed function. self and cls variables are stripped out of the method parameters passed to the provided callable, so your functions don’t need to care about where they’re used.

  • Lots of Tests!

    Seriously. Don’t believe me? Just look. We’ve got the best tests. Just phenomenal.


Supported Python versions are 2.7 and 3.4+

To install pydecor, simply run:

pip install -U pydecor

To install the current development release:

pip install --pre -U pydecor

You can also install from source to get the absolute most recent code, which may or may not be functional:

git clone
pip install ./pydecor


Provided Decorators

This package provides generic decorators, which can be used with any function to provide extra utility to decorated resources, as well as prête-à-porter (ready-to-wear) decorators for immediate use.

While the information below is enough to get you started, I highly recommend checking out the decorator module docs to see all the options and details for the various decorators!


  • @before - run a callable before the decorated function executes

    • by default called with no arguments other than extras

  • @after - run a callable after the decorated function executes

    • by default called with the result of the decorated function and any extras

  • @instead - run a callable in place of the decorated function

    • by default called with the args and kwargs to the decorated function, along with a reference to the function itself

  • @decorate - specify multiple callables to be run before, after, and/or instead of the decorated function

    • callables passed to decorate’s before, after, or instead keyword arguments will be called with the same default function signature as described for the individual decorators, above. Extras will be passed to all provided callables

  • construct_decorator - specify functions to be run before, after, or instead. Returns a reusable generator.

    • in addition to before, after, and instead, which receive callables, before_opts, after_opts, and instead_opts dicts may be passed to construct_decorator, and they will apply in the same way as their respective decorator parameters

Every generic decorator takes any number of keyword arguments, which will be passed directly into the provided callable, unless unpack_extras is False (see below), so, running the code below prints “red”:

from pydecor import before

def before_func(label=None):

@before(before_func, label='red')
def red_function():


Every generic decorator takes the following keyword arguments:

  • pass_params - if True, passes the args and kwargs, as a tuple and a dict, respectively, from the decorated function to the provided callable

  • pass_decorated - if True, passes a reference to the decorated function to the provided callable

  • implicit_method_decoration - if True, decorating a class implies decorating all of its methods. Caution: you should probably leave this on unless you know what you are doing.

  • instance_methods_only - if True, only instance methods (not class or static methods) will be automatically decorated when implicit_method_decoration is True

  • unpack_extras - if True, extras are unpacked into the provided callable. If False, extras are placed into a dictionary on extras_key, which is passed into the provided callable.

  • extras_key - the keyword to use when passing extras into the provided callable if unpack_extras is False

  • _use_future_syntax - See the note at the top on backwards incompatible changes in version 2.0.0.

The construct_decorator function can be used to combine @before, @after, and @instead calls into one decorator, without having to worry about unintended stacking effects. Let’s make a decorator that announces when we’re starting an exiting a function:

from pydecor import construct_decorator

def before_func(decorated_func):
    print('Starting decorated function '

def after_func(decorated_result, decorated_func):
    print('"{}" gave result "{}"'.format(
        decorated_func.__name__, decorated_result

my_decorator = construct_decorator(
    before_opts={'pass_decorated': True},
    after_opts={'pass_decorated': True},

def this_function_returns_nothing():
    return 'nothing'

And the output?

Starting decorated function "this_function_returns_nothing"
"this_function_returns_nothing" gave result "nothing"

Maybe a more realistic example would be useful. Let’s say we want to add headers to a Flask response.

from flask import Flask, Response, make_response
from pydecor import construct_decorator

def _set_app_json_header(response):
    # Ensure the response is a Response object, even if a tuple was
    # returned by the view function.
    response = make_response(response)
    response.headers.set('Content-Type', 'application/json')
    return response

application_json = construct_decorator(after=_set_app_json_header)

# Now you can decorate any Flask view, and your headers will be set.

app = Flask(__name__)

# Note that you must decorate "before" (closer to) the function than the
# app.route() decoration, because the route decorator must be called on
# the "finalized" version of your function

def root_view():
    return 'Hello, world!'

client = app.test_client()
response = app.get('/')


The output?


Content-Type: application/json
Content-Length: 13

Prête-à-porter (ready-to-wear)

  • intercept - catch the specified exception and optionally re-raise and/or call a provided callback to handle the exception

  • log_call - automatically log the decorated function’s call signature and results

  • memoize - memoize a function’s call and return values for re-use. Can use any cache in pydecor.caches, which all have options for automatic pruning to keep the memoization cache from growing too large.

More to come!! See Roadmap for more details on upcoming features


Three caches are provided with pydecor. These are designed to be passed to the @memoization decorator if you want to use something other than the default LRUCache, but they are perfectly functional for use elesewhere.

All caches implement the standard dictionary interface.


A least-recently-used cache. Both getting and setting of key/value pairs results in their having been considered most-recently-used. When the cache reaches the specified max_size, least-recently-used items are discarded.


A first-in, first-out cache. When the cache reaches the specified max_size, the first item that was inserted is discarded, then the second, and so on.


A cache whose entries expire. If a max_age is specified, any entries older than the max_age (in seconds) will be considered invalid, and will be removed upon access.


Generic and convenience decorators may be stacked! You can stack multiple of the same decorator, or you can mix and match. Some gotchas are listed below.

Generally, staciking works just as you might expect, but some care must be taken when using the @instead decorator, or @intercept, which uses @instead under the hood.

Just remember that @instead replaces everything that comes before. So, if long as @instead calls the decorated function, it’s okay to stack it. In these cases, it will be called before any decorators specified below it, and those decorators will be executed when it calls the decorated function. @intercept behaves this way.

If an @instead decorator does not call the decorated function and instead replaces it entirely, it must be specified first (at the bottom of the stacked decorator pile), otherwise the decorators below it will not execute.

For @before and @after, it doesn’t matter in what order the decorators are specified. @before is always called first, and then @after.

Class Decoration

Class decoration is difficult, but PyDecor aims to make it as easy and intuitive as possible!

By default, decorating a class applies that decorator to all of that class’ methods (instance, class, and static). The decoration applies to class and static methods whether they are referenced via an instance or via a class reference. “Extras” specified at the class level persist across calls to different methods, allowing for things like a class level memoization dictionary (there’s a very basic test in the test suite that demonstrates this pattern, and a convenient memoization decorator is scheduled for the next release!).

If you’d prefer that the decorator not apply to class and static methods, set the instance_methods_only=True when decorating the class.

If you want to decorate the class itself, and not its methods, keep in mind that the decorator will be triggered when the class is instantiated, and that, if the decorator replaces or alters the return, that return will replace the instantiated class. With those caveats in mind, setting implicit_method_decoration=False when decorating a class enables that funcitonality.

Method Decoration

Decorators can be applied to static, class, or instance methods directly, as well. If combined with @staticmethod or @classmethod decorators, those decorators should always be at the “top” of the decorator stack (furthest from the function).

When decorating instance methods, self is removed from the parameters passed to the provided callable.

When decorating class methods, cls is removed from the parameters passed to the provided callable.

Currently, the class and instance references do not have to be named "cls" and "self", respectively, in order to be removed. However, this is not guaranteed for future releases, so try to keep your naming standard if you can (just FYI, "self" is the more likely of the two to wind up being required).


Below are some examples for the generic and standard decorators. Please check out the API Docs for more information, and also check out the convenience decorators, which are all implemented using the before, after, and instead decorators from this library.

Update a Function’s Args or Kwargs

Functions passed to @before can either return None, in which case nothing happens to the decorated functions parameters, or they can return a tuple of args (as a tuple) and kwargs (as a dict), in which case those parameters are used in the decorated function. In this example, we sillify a very serious function.

from pydecor import before

def spamify_func(args, kwargs):
    """Mess with the function arguments"""
    args = tuple(['spam' for _ in args])
    kwargs = {k: 'spam' for k in kwargs}
    return args, kwargs

@before(spamify_func, pass_params=True)
def serious_function(serious_string, serious_kwarg='serious'):
    """A very serious function"""
    print('A serious arg: {}'.format(serious_string))
    print('A serious kwarg: {}'.format(serious_kwarg))

serious_function('Politics', serious_kwarg='Religion')

The output?

A serious arg: spam
A serious kwarg: spam

Do Something with a Function’s Return Value

Functions passed to @after receive the decorated function’s return value by default. If @after returns None, the return value is sent back unchanged. However, if @after returns something, its return value is sent back as the return value of the function.

In this example, we ensure that a function’s return value has been thoroughly spammified.

from pydecor import after

def spamify_return(result):
    """Spamify the result of a function"""
    return 'spam-spam-spam-spam-{}-spam-spam-spam-spam'.format(result)

def unspammed_function():
    """Return a non-spammy value"""
    return 'beef'


The output?


Do Something Instead of a Function

Functions passed to @instead by default receive the args and kwargs of the decorated function, along with a reference to that function. But, they don’t have to receive anything. Maybe you want to skip a function when a certain condition is True, but you don’t want to use pytest.skipif, because pytest can’t be a dependency of your production code for whatever reason.

from pydecor import instead

def skip(args, kwargs, decorated, when=False):
    if when:
        return decorated(*args, **kwargs)

@instead(skip, when=True)
def uncalled_function():
    print("You won't see me (you won't see me)")


The output?

(There is no output, because the function was skipped)

Automatically Log Function Calls and Results

Maybe you want to make sure your functions get logged without having to bother with the logging boilerplate each time. @log_call tries to automatically get a logging instance corresponding to the module in which the decoration occurs (in the same way as if you made a call to logging.getLogger(__name__), or you can pass it your own, fancy, custom, spoiler-bedecked logger instance.

from logging import getLogger, StreamHandler
from sys import stdout

from pydecor import log_call

# We're just getting a logger here so we can see the output. This isn't
# actually necessary for @log_call to work!
log = getLogger(__name__)

def get_schwifty(*args, **kwargs):
    """Get schwifty in heeeeere"""
    return "Gettin' Schwifty"

get_schwifty('wubba', 'lubba', dub='dub')

And the output?

get_schwifty(*('wubba', 'lubba'), **{'dub': 'dub'}) -> Gettin' Schwifty

Intercept an Exception and Re-raise a Custom One

Are you a put-upon library developer tired of constantly having to re-raise custom exceptions so that users of your library can have one nice try/except looking for your base exception? Let’s make that easier:

from pydecor import intercept

class BetterException(Exception):
    """Much better than all those other exceptions"""

@intercept(catch=RuntimeError, reraise=BetterException)
def sometimes_i_error(val):
    """Sometimes, this function raises an exception"""
    if val > 5:
        raise RuntimeError('This value is too big!')

for i in range(7):

The output?

Traceback (most recent call last):
  File "/Users/Nautilus/Library/Preferences/PyCharm2017.1/scratches/", line 88, in <module>
  File "/Users/Nautilus/Documents/Programming/pydecor/pydecor/", line 389, in wrapper
    return fn(**fkwargs)
  File "/Users/Nautilus/Documents/Programming/pydecor/pydecor/", line 58, in intercept
    raise_from(new_exc, context)
  File "<string>", line 2, in raise_from
__main__.BetterException: This value is too big!

Intercept an Exception, Do Something, and Re-raise the Original

Maybe you don’t want to raise a custom exception. Maybe the original one was just fine. All you want to do is print a special message before re-raising the original exception. PyDecor has you covered:

from pydecor import intercept

def print_exception(exc):
    """Make sure stdout knows about our exceptions"""
    print('Houston, we have a problem: {}'.format(exc))

@intercept(catch=Exception, handler=print_exception, reraise=True)
def assert_false():
    """All I do is assert that False is True"""
    assert False, 'Turns out, False is not True'


And the output:

Houston, we have a problem: Turns out, False is not True
Traceback (most recent call last):
  File "/Users/Nautilus/Library/Preferences/PyCharm2017.1/scratches/", line 105, in <module>
  File "/Users/Nautilus/Documents/Programming/pydecor/pydecor/", line 389, in wrapper
    return fn(**fkwargs)
  File "/Users/Nautilus/Documents/Programming/pydecor/pydecor/", line 49, in intercept
    return decorated(*decorated_args, **decorated_kwargs)
  File "/Users/Nautilus/Library/Preferences/PyCharm2017.1/scratches/", line 102, in assert_false
    assert False, 'Turns out, False is not True'
AssertionError: Turns out, False is not True

Intercept an Exception, Handle, and Be Done with It

Sometimes an exception isn’t the end of the world, and it doesn’t need to bubble up to the top of your application. In these cases, maybe just handle it and don’t re-raise:

from pydecor import intercept

def let_us_know_it_happened(exc):
    """Just let us know an exception happened (if we are reading stdout)"""
    print('This non-critical exception happened: {}'.format(exc))

@intercept(catch=ValueError, handler=let_us_know_it_happened)
def resilient_function(val):
    """I am so resilient!"""
    val = int(val)
    print('If I get here, I have an integer: {}'.format(val))



If I get here, I have an integer: 50
This non-critical exception happened: invalid literal for int() with base 10: 'foo'

Note that the function does not continue running after the exception is handled. Use this for short-circuiting under certain conditions rather than for instituting a try/except:pass block. Maybe one day I’ll figure out how to make this work like that, but as it stands, the decorator surrounds the entire function, so it does not provide that fine-grained level of control.



More Prête-à-porter Decorators

  • export - add the decorated item to __all__

  • skipif - similar to py.test’s decorator, skip the function if a provided condition is True

Let me know if you’ve got any idea for other decorators that would be nice to have!

Typing Stubfiles

Right now type hints are provided via rst-style docstring specification. Although this format is supported by PyCharm, it does not conform to the type-hinting standard defined in PEP 484.

In order to better conform with the new standard (and to remain compatible with Python 2.7), stubfiles will be added for the 1.1.0 release, and docstring hints will be removed so that contributors don’t have to adjust type specifications in two places.

Build-process Updates

A more automated build process, because remembering all the steps to push a new version is a pain. This is marked as scheduled for a patch release, because it does not affect users at all, so a minor version bump would lead people on to thinking that some new functionality had been added, when it hadn’t.


  • Use of immutable Decorated object to pass information about the deprecated function

  • Deprecation of pass_params, pass_kwargs, pass_decorated, pass_result, unapck_extras, and extras_key keyword arguments to all decorators.

  • Better organization of documentation


Contributions are welcome! If you find a bug or if something doesn’t work the way you think it should, please raise an issue. If you know how to fix the bug, please open a PR!

I absolutely welcome any level of contribution. If you think the docs could be better, or if you’ve found a typo, please open up a PR to improve and/or fix them.

Contributor Conduct

There is a file with details, based on one of GitHub’s templates, but the upshot is that I expect everyone who contributes to this project to do their best to be helpful, friendly, and patient. Discrimination of any kind will not be tolerated and will be promptly reported to GitHub.

On a personal note, Open Source survives because of people who are willing to contribute their time and effort for free. The least we can do is treat them with respect.


Tests are fairly easy to run, with few dependencies. You’ll need Python 2.7, 3.4, and 3.6 installed on your system to run the full suite, as well as tox in whatever environment or virtual environment you’re using. From there, you should just be able to run tox. The underlying test suite is py.test, and any extra arguments passed to tox get sent along. For example, to send stdout/stderr to the console and stop on the first failure, tox -- -sx. You can also run py.test directly. If you do, make sure the deps specified in tox.ini are installed to your virtualenv, and install the package in development mode with pip install -e ..

PRs that cause tests to fail will not be merged until tests pass.

Any new functionality is expected to come with appropriate tests. That being said, the test suite is fairly complex, with lots of mocking and parametrization. Don’t feel as though you have to follow this pattern when writing new tests! A bunch of simpler tests are just as good. If you have any questions, feel free to reach out to me via email at msplanchard @ gmail or on Twitter as @msplanchard.

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