It turns out that when comparing dtypes, the subarray is currently ignored. This means you can get things like this:
import numpy as np np.dtype(('f4',(6))) == np.dtype(('f4',(2,3))) True np.dtype(([('a','i4')],2)) == np.dtype(([('b','u2'),('c','i2')],2)) True
which pretty clearly should be False in both cases. I've implemented a patch to fix this, for which a pull request is here: http://github.com/numpy/numpy/pull/6 The main points included in this patch are: * If there's a subarray shape, it has to match exactly. * Currently, sometimes the subarray shape was an integer, other times a tuple. The output formatting code checks for this and prints a tuple in both cases. I changed the code to turn the integer into a tuple on construction instead. This didn't cause any tests to fail, so I think it's a safe change. * The current code converts (type, 1) and (type, tuple()) to just type, so this means (type, 1) != (type, (1,)) but (type, 2) == (type, (2,)) I put in some tests of these edge cases. http://github.com/numpy/numpy/blob/master/numpy/core/src/multiarray/descript... Cheers, Mark