Hello, usually, a masked array's .fill_value attribute has ndim=0 and the same dtype as the data attribute: In [27]: ar = array((0, [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], 0.0), dtype="int, (2,3)float, float") In [28]: arm = ma.masked_array(ar) In [29]: arm.fill_value.ndim Out[29]: 0 In [31]: arm.fill_value.dtype Out[31]: dtype([('f0', '<i8'), ('f1', '<f8', (2, 3)), ('f2', '<f8')]) What would be the optimal approach for .fill_value if I address the member "f1" in this case? The current behaviour is: In [32]: f = arm["f1"] In [36]: f.fill_value Out[36]: array([[ 1.00000000e+20, 1.00000000e+20, 1.00000000e+20], [ 1.00000000e+20, 1.00000000e+20, 1.00000000e+20]]) This breaks the usual behaviour that .fill_value has ndim=0, which can cause bugs such as reported in issue #6723: https://github.com/numpy/numpy/issues/6723 What should numpy do instead? In pull request 6728, I propose to change the behaviour so that arm["f1"].fill_value is set to arm.fill_value["f1"].flat[0]. This is an arbitrary and somewhat ad-hoc solution. If I have chosen to set arm.fill_value["f1"] to something else, such as array([[1., 2., 3.], [4., 5., 6.]]), then the rest of my fill_value is lost. I don't know if this might lead to problems. Does it matter? See also http://stackoverflow.com/questions/33921579/what-practical-impact-if-any-doe... . regards, Gerrit.