Assuming this is the numpy array, with shape (3,3):

ar = np.array([1, 2, 3, 4, np.nan, 5, np.nan, 6, np.inf]).reshape((3,3))
print (ar)
[[ 1.  2.  3.]
 [ 4. nan  5.]
 [nan  6. inf]]

To check for NaN, positive infinity, negative infinity, or different combinations of those, we can use:

numpy.isnan(ar)     # True wherever nan
numpy.isposinf(ar)  # True wherever pos-inf
numpy.isneginf(ar)  # True wherever neg-inf
numpy.isinf(ar)     # True wherever pos-inf or neg-inf
~numpy.isfinite(ar) # True wherever pos-inf or neg-inf or nan

respectively. Each of these returns a bool array, and passing the bool array to numpy.where() gives us two index arrays (one index array per dimension of ar):

ar_nan = np.where(np.isnan(ar))
print (ar_nan)

(array([1, 2], dtype=int64), array([1, 0], dtype=int64)) # Means, nans at (1,1) and (2,0)

and

ar_inf = np.where(np.isinf(ar))
print (ar_inf)

(array([2], dtype=int64), array([2], dtype=int64)) # Means, inf is at (2,2)

Also, to see the limits of float64:

np.finfo(np.float64)

finfo(resolution=1e-15, min=-1.7976931348623157e+308, max=1.7976931348623157e+308, dtype=float64)