How to identify numpy types in python?

That actually depends on what you're looking for.

• If you want to test whether a sequence is actually a ndarray, a isinstance(..., np.ndarray) is probably the easiest. Make sure you don't reload numpy in the background as the module may be different, but otherwise, you should be OK. MaskedArrays, matrix, recarray are all subclasses of ndarray, so you should be set.
• If you want to test whether a scalar is a numpy scalar, things get a bit more complicated. You could check whether it has a shape and a dtype attribute. You can compare its dtype to the basic dtypes, whose list you can find in np.core.numerictypes.genericTypeRank. Note that the elements of this list are strings, so you'd have to do a tested.dtype is np.dtype(an_element_of_the_list)...

The solution I've come up with is:

isinstance(y, (np.ndarray, np.generic) )

However, it's not 100% clear that all numpy types are guaranteed to be either np.ndarray or np.generic, and this probably isn't version robust.

Old question but I came up with a definitive answer with an example. Can't hurt to keep questions fresh as I had this same problem and didn't find a clear answer. The key is to make sure you have numpy imported, and then run the isinstance bool. While this may seem simple, if you are doing some computations across different data types, this small check can serve as a quick test before your start some numpy vectorized operation.

##################
# important part!
##################

import numpy as np

####################
# toy array for demo
####################

arr = np.asarray(range(1,100,2))

########################
# The instance check
######################## 

isinstance(arr,np.ndarray)

Use the builtin type function to get the type, then you can use the __module__ property to find out where it was defined:

>>> import numpy as np
a = np.array([1, 2, 3])
>>> type(a)
<type 'numpy.ndarray'>
>>> type(a).__module__
'numpy'
>>> type(a).__module__ == np.__name__
True