Vincent Davis wrote:
> I have a list of a list of integers. The lists are long so i cant really
> show an actual example of on of the lists, but I know that they contain
> only the integers 1,2,3,4. so for example.
> s2 = [[1,2,2,3,2,1,4,4],[2,4,3,2,3,1]]
>
> I am calculating the product, sum, max, min.... of each list in s2 but I
> get negative or 0 for the product for a lot of the lists. (I am doing this
> in ipython)
>
> for x in s2:
> print('len = ', len(x), 'sum = ', sum(x), 'prod = ', prod(x), 'max =
> ',
> max(x), 'min = ', min(x))
>
> ...
>
> ('len = ', 100, 'sum = ', 247, 'prod = ', 0, 'max = ', 4, 'min = ', 1)
> ('len = ', 100, 'sum = ', 230, 'prod = ', -4611686018427387904, 'max =
> ', 4, 'min = ', 1)
> ('len = ', 100, 'sum = ', 261, 'prod = ', 0, 'max = ', 4, 'min = ', 1)
>
> .....
>
> ('prod =', 0, 'max =', 4, 'min =', 1)
> ('prod =', 1729382256910270464, 'max =', 4, 'min =', 1)
> ('prod =', 0, 'max =', 4, 'min =', 1)
>
> ....
>
>
> Whats going on?
Instead of Python's integer type (int/long) numpy uses the faster machine
integers (typically 32 or 64 bit) that overflow:
>>> a = numpy.array([0xffffffff, 0xffffffff])
>>> numpy.prod(a)
-8589934591
>>> a.dtype
dtype('int64')
As a workaround you can use Python's ints (slower)
>>> a = numpy.array([0xffffffff, 0xffffffff], dtype=object)
>>> numpy.prod(a)
18446744065119617025L
or float (loss of precision)
>>> a = numpy.array([0xffffffff, 0xffffffff], dtype=float)
>>> numpy.prod(a)
1.8446744065119617e+19
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