How to use Python decorators to check function arguments?

On Python 3.3, you can use function annotations and inspect:

import inspect def validate(f): def wrapper(*args): fname = f.__name__ fsig = inspect.signature(f) vars = ', '.join('{}={}'.format(*pair) for pair in zip(fsig.parameters, args)) params={k:v for k,v in zip(fsig.parameters, args)} print('wrapped call to {}({})'.format(fname, params)) for k, v in fsig.parameters.items(): p=params[k] msg='call to {}({}): {} failed {})'.format(fname, vars, k, v.annotation.__name__) assert v.annotation(params[k]), msg ret = f(*args) print(' returning {} with annotation: "{}"'.format(ret, fsig.return_annotation)) return ret return wrapper @validate def xXy(x: lambda _x: 10<_x<100, y: lambda _y: isinstance(_y,float)) -> ('x times y','in X and Y units'): return x*y xy = xXy(10,3) print(xy) 

If there is a validation error, prints:

AssertionError: call to xXy(x=12, y=3): y failed <lambda>) 

If there is not a validation error, prints:

wrapped call to xXy({'y': 3.0, 'x': 12}) returning 36.0 with annotation: "('x times y', 'in X and Y units')" 

You can use a function rather than a lambda to get a name in the assertion failure.

As you certainly know, it's not pythonic to reject an argument only based on its type.
Pythonic approach is rather "try to deal with it first"
That's why I would rather do a decorator to convert the arguments

def enforce(*types): def decorator(f): def new_f(*args, **kwds): #we need to convert args into something mutable newargs = [] for (a, t) in zip(args, types): newargs.append( t(a)) #feel free to have more elaborated convertion return f(*newargs, **kwds) return new_f return decorator 

This way, your function is fed with the type you expect But if the parameter can quack like a float, it is accepted

@enforce(int, float) def func(arg1, arg2): return arg1 * arg2 print (func(3, 2)) # -> 6.0 print (func('3', 2)) # -> 6.0 print (func('three', 2)) # -> ValueError: invalid literal for int() with base 10: 'three' 

I use this trick (with proper conversion method) to deal with vectors.
Many methods I write expect MyVector class as it has plenty of functionalities; but sometime you just want to write

transpose ((2,4)) 

The package typeguard provides a decorator for this, it reads the type information from type annotations, it requires Python >=3.5.2 though. I think the resulting code is quite nice.

@typeguard.typechecked def my_function(this_var_is_an_int: int, this_var_is_a_float: float) ''' Here my code ''' pass 

I think the Python 3.5 answer to this question is beartype. As explained in this post it comes with handy features. Your code would then look like this

from beartype import beartype @beartype def sprint(s: str) -> None: print(s) 

and results in

>>> sprint("s") s >>> sprint(3) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "<string>", line 13, in func_beartyped TypeError: sprint() parameter s=3 not of <class 'str'> 

To enforce string arguments to a parser that would throw cryptic errors when provided with non-string input, I wrote the following, which tries to avoid allocation and function calls:

from functools import wraps def argtype(**decls): """Decorator to check argument types. Usage: @argtype(name=str, text=str) def parse_rule(name, text): ... """ def decorator(func): code = func.func_code fname = func.func_name names = code.co_varnames[:code.co_argcount] @wraps(func) def decorated(*args,**kwargs): for argname, argtype in decls.iteritems(): try: argval = args[names.index(argname)] except ValueError: argval = kwargs.get(argname) if argval is None: raise TypeError("%s(...): arg '%s' is null" % (fname, argname)) if not isinstance(argval, argtype): raise TypeError("%s(...): arg '%s': type is %s, must be %s" % (fname, argname, type(argval), argtype)) return func(*args,**kwargs) return decorated return decorator 

I have a slightly improved version of @jbouwmans sollution, using python decorator module, which makes the decorator fully transparent and keeps not only signature but also docstrings in place and might be the most elegant way of using decorators

from decorator import decorator def check_args(**decls): """Decorator to check argument types. Usage: @check_args(name=str, text=str) def parse_rule(name, text): ... """ @decorator def wrapper(func, *args, **kwargs): code = func.func_code fname = func.func_name names = code.co_varnames[:code.co_argcount] for argname, argtype in decls.iteritems(): try: argval = args[names.index(argname)] except IndexError: argval = kwargs.get(argname) if argval is None: raise TypeError("%s(...): arg '%s' is null" % (fname, argname)) if not isinstance(argval, argtype): raise TypeError("%s(...): arg '%s': type is %s, must be %s" % (fname, argname, type(argval), argtype)) return func(*args, **kwargs) return wrapper 

All of these posts seem out of date - pint now provides this functionality built in. See here. Copied here for posterity:

Checking dimensionality When you want pint quantities to be used as inputs to your functions, pint provides a wrapper to ensure units are of correct type - or more precisely, they match the expected dimensionality of the physical quantity.

Similar to wraps(), you can pass None to skip checking of some parameters, but the return parameter type is not checked.

>>> mypp = ureg.check('[length]')(pendulum_period) 

In the decorator format:

>>> @ureg.check('[length]') ... def pendulum_period(length): ... return 2*math.pi*math.sqrt(length/G) 

you could try with the pydantic validation_decorator. from the documentation pydantic:

Data validation and settings management using python type annotations. pydantic enforces type hints at runtime, and provides user friendly errors when data is invalid. In benchmarks pydantic is faster than all other tested libraries.

from pydantic import validate_arguments, ValidationError @validate_arguments def repeat(s: str, count: int, *, separator: bytes = b'') -> bytes: b = s.encode() return separator.join(b for _ in range(count)) a = repeat('hello', 3) print(a) #> b'hellohellohello' b = repeat('x', '4', separator=' ') print(b) #> b'x x x x' try: c = repeat('hello', 'wrong') except ValidationError as exc: print(exc) """ 1 validation error for Repeat count value is not a valid integer (type=type_error.integer) """ 

For me, the codes shared above looks complicated. What I did for defining 'generic decorator' for type-check:

I used *args, **kwargs feature, little extra work when using function/method but easy to manage.

Appropriate example definition for test

argument_types = { 'name':str, 'count':int, 'value':float } 

Decoration Defination

//from functools import wraps def azure_type(func): @wraps(func) def type_decorator(*args, **kwargs): for key, value in kwargs.items(): if key in argument_types: if type(value) != argument_types[key]: #enter code here return 'Error Message or what ever you like to do' return func(*args, **kwargs) return type_decorator 

Simple sample in code

// all other definitions @azure_type def stt(name:str, value:float)->(int): #some calculation and creation of int output count_output = #something int return count_output // call the function: stt(name='ati', value=32.90) #can test from that