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You could use repr (repr works well with things that have a __repr__() method):

>>> OPTIONS=('ON', 'OFF', )">>> "OPTIONS="+repr(OPTIONS)""OPTIONS=('ON', 'OFF')"

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Ooh, this is tricky (and fun). SWIG doesn't recognize this as an opportunity to generate @property: I imagine it'd be all too easy to slip up and recognize lots of false positives if it weren't done really carefully. However, since SWIG won't do it in generating C++, it's still entirely possible to do this in Python using a small metaclass.

So, below, let's say we have a Math class that lets us set and get an integer variable named "pi". Then we can use this code:

#ifndef EXAMPLE_H"#define EXAMPLE_H""class Math {" public:"    int pi() const {"        return this->_pi;"    }""    void pi(int pi) {"        this->_pi = pi;"    }"" private:"    int _pi;"};""#endif

%module example""%{"    #define SWIG_FILE_WITH_INIT"    #include "example.h""%}""[essentially example.h repeated again]

#include "example.h"

class PropertyVoodoo(type):"    """A metaclass. Initializes when the *class* is initialized, not"    the object. Therefore, we are free to muck around the class"    methods and, specifically, descriptors."""""    def __init__(cls, *a):"        # OK, so the list of C++ properties using the style described"        # in the OP is stored in a __properties__ magic variable on"        # the class."        for prop in cls.__properties__:""            # Get accessor."            def fget(self):"                # Get the SWIG class using super. We have to use super"                # because the only information we're working off of is"                # the class object itself (cls). This is not the most"                # robust way of doing things but works when the SWIG"                # class is the only superclass."                s = super(cls, self)""                # Now get the C++ method and call its operator()."                return getattr(s, prop)()""            # Set accessor."            def fset(self, value):"                # Same as above."                s = super(cls, self)""                # Call its overloaded operator(int value) to set it."                return getattr(s, prop)(value)""            # Properties in Python are descriptors, which are in turn"            # static variables on the class. So, here we create the"            # static variable and set it to the property."            setattr(cls, prop, property(fget=fget, fset=fset))""        # type() needs the additional arguments we didn't use to do"        # inheritance. (Parent classes are passed in as arguments as"        # part of the metaclass protocol.) Usually a = [] right now."        super(PropertyVoodoo, cls).__init__(*a)""        # One more piece of work: SWIG selfishly overrides"        # __setattr__. Normal Python classes use object.__setattr__,"        # so that's what we use here. It's not really important whose"        # __setattr__ we use as long as we skip the SWIG class in the"        # inheritance chain because SWIG's __setattr__ will skip the"        # property we just created."        def __setattr__(self, name, value):"            # Only do this for the properties listed."            if name in cls.__properties__:"                object.__setattr__(self, name, value)"            else:"                # Same as above."                s = super(cls, self)""                s.__setattr__(name, value)""        # Note that __setattr__ is supposed to be an instance method,"        # hence the self. Simply assigning it to the class attribute"        # will ensure it's an instance method; that is, it will *not*"        # turn into a static/classmethod magically."        cls.__setattr__ = __setattr__

import example"from util import PropertyVoodoo""class Math(example.Math):"    __properties__ = ['pi']"    __metaclass__  = PropertyVoodoo""m = Math()"print m.pi"m.pi = 1024"print m.pi"m.pi = 10000"print m.pi

So the end result is just that you have to create a wrapper class for every SWIG Python class and then type two lines: one to mark which methods should be converted in properties and one to bring in the metaclass.

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Update:

In Python 3.8, the prod function was added to the math module. See: math.prod().

Older info: Python 3.7 and prior

The function you're looking for would be called prod() or product() but Python doesn't have that function. So, you need to write your own (which is easy).

Pronouncement on prod()

Yes, that's right. Guido rejected the idea for a built-in prod() function because he thought it was rarely needed.

Alternative with reduce()

As you suggested, it is not hard to make your own using reduce() and operator.mul():

from functools import reduce  # Required in Python 3"import operator"def prod(iterable):"    return reduce(operator.mul, iterable, 1)"">>> prod(range(1, 5))"24"

Note, in Python 3, the reduce() function was moved to the functools module.

Specific case: Factorials

As a side note, the primary motivating use case for prod() is to compute factorials. We already have support for that in the math module:

>>> import math"">>> math.factorial(10)"3628800"

Alternative with logarithms

If your data consists of floats, you can compute a product using sum() with exponents and logarithms:

>>> from math import log, exp"">>> data = [1.2, 1.5, 2.5, 0.9, 14.2, 3.8]">>> exp(sum(map(log, data)))"218.53799999999993"">>> 1.2 * 1.5 * 2.5 * 0.9 * 14.2 * 3.8"218.53799999999998"

Note, the use of log() requires that all the inputs are positive.

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