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()
andnumpy.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
).
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