How to get the range of valid Numpy data types?

I'm interested in finding for a particular Numpy type (e.g. np.int64, np.uint32, np.float32, etc.) what the range of all possible valid values is (e.g. np.int32 can store numbers up to 2**31-1). Of course, I guess one can theoretically figure this out for each type, but is there a way to do this at run time to ensure more po开发者_如何学编程rtable code?

Quoting from a numpy discussion list:

That information is available via numpy.finfo() and numpy.iinfo():

In [12]: finfo('d').max
Out[12]: 1.7976931348623157e+308

In [13]: iinfo('i').max
Out[13]: 2147483647

In [14]: iinfo('uint8').max
Out[14]: 255

Link here.

You can use numpy.iinfo(arg).max to find the max value for integer types of arg, and numpy.finfo(arg).max to find the max value for float types of arg.

>>> numpy.iinfo(numpy.uint64).min
0
>>> numpy.iinfo(numpy.uint64).max
18446744073709551615L
>>> numpy.finfo(numpy.float64).max
1.7976931348623157e+308
>>> numpy.finfo(numpy.float64).min
-1.7976931348623157e+308

iinfo only offers min and max, but finfo also offers useful values such as eps (the smallest number > 0 representable) and resolution (the approximate decimal number resolution of the type of arg).