What does -> mean in Python function definitions?

I've recently noticed something interesting when looking at Python 3.3 grammar specification:

funcdef: 'def' NAME parameters ['->' test] ':' suite

The optional 'arrow' block was absent in Python 2 and I couldn't find any information regarding its meaning in Python 3. It turns out this is correct Python and it's accepted by the interpreter:

def f(x) -> 123:
    return x

I thought that this might be some kind of a precondition syntax, but:

• I cannot test x here, as it is still undefined,
• No matter what I put after the arrow (e.g. 2 < 1), it doesn't affect the function behavior.

Could anyone familiar with this syntax style explain it?

Solution 1:

It's a function annotation.

In more detail, Python 2.x has docstrings, which allow you to attach a metadata string to various types of object. This is amazingly handy, so Python 3 extends the feature by allowing you to attach metadata to functions describing their parameters and return values.

There's no preconceived use case, but the PEP suggests several. One very handy one is to allow you to annotate parameters with their expected types; it would then be easy to write a decorator that verifies the annotations or coerces the arguments to the right type. Another is to allow parameter-specific documentation instead of encoding it into the docstring.

Solution 2:

These are function annotations covered in PEP 3107. Specifically, the -> marks the return function annotation.

Examples:

def kinetic_energy(m:'in KG', v:'in M/S')->'Joules': 
    return 1/2*m*v**2
 
>>> kinetic_energy.__annotations__
{'return': 'Joules', 'v': 'in M/S', 'm': 'in KG'}

Annotations are dictionaries, so you can do this:

>>> '{:,} {}'.format(kinetic_energy(12,30),
      kinetic_energy.__annotations__['return'])
'5,400.0 Joules'

You can also have a python data structure rather than just a string:

rd={'type':float,'units':'Joules',
    'docstring':'Given mass and velocity returns kinetic energy in Joules'}
def f()->rd:
    pass

>>> f.__annotations__['return']['type']
<class 'float'>
>>> f.__annotations__['return']['units']
'Joules'
>>> f.__annotations__['return']['docstring']
'Given mass and velocity returns kinetic energy in Joules'

Or, you can use function attributes to validate called values:

def validate(func, locals):
    for var, test in func.__annotations__.items():
        value = locals[var]
        try: 
            pr=test.__name__+': '+test.__docstring__
        except AttributeError:
            pr=test.__name__   
        msg = '{}=={}; Test: {}'.format(var, value, pr)
        assert test(value), msg

def between(lo, hi):
    def _between(x):
            return lo <= x <= hi
    _between.__docstring__='must be between {} and {}'.format(lo,hi)       
    return _between

def f(x: between(3,10), y:lambda _y: isinstance(_y,int)):
    validate(f, locals())
    print(x,y)

Prints

>>> f(2,2) 
AssertionError: x==2; Test: _between: must be between 3 and 10
>>> f(3,2.1)
AssertionError: y==2.1; Test: <lambda>