Ah, yes, that's one of the corner cases I noticed too. There's a similar one where "unviewed" bytes get filled in with garbage. Here are demos of the one you mentioned, plus that one:

>>> a = np.array([( 1, 2),( 3, 4)], dtype=[('foo', 'i8'), ('bar', 'i8')])
>>> a[:] = np.array([(10,20),(30,40)], dtype=[('bar', 'i8'),('baz', 'i8')])
>>> a
array([(0, 10), (0, 30)], 
      dtype=[('foo', '<i8'), ('bar', '<i8')])

Other edge case:

>>> a = np.array([(1,2),(3,4)], dtype=[('foo', 'i8'), ('bar', 'i8')])
>>> b = a.view({'names': ['bar'], 'formats': ['i8'], 'offsets': [8]})
>>> b[:] = 4
>>> a
array([(139714726796880, 4), (139714726796880, 4)], 
      dtype=[('foo', '<i8'), ('bar', '<i8')])

Also, I don't like the current behavior that fields get assigned "by field name", and was hoping to be able to change it to assignment "by field position", if the back-compatibility break wasn't too bad (I suspect it might not be). That way assignment by structured scalars would behave the same as assignment using tuples (or equivalently, assignment using .item() on a structured scalar).

Probably a lot of my changes (which might turn out to be a bad idea in the end..) are othogonal to your PR for making the view writeable, so I'll keep thinking about them in the meantime.

Uh oh!

Implement structure subset views #5994

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