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I can see several columns ( fields ) at once in a numpy structured array by indexing with a list of the field names, for example import numpy as np a = np.array([(1.5, 2.5, (1.0,2.0)), (3.,4.,(4.,5.)), (1.,3.,(2.,6.))], dtype=[('x',float), ('y',float), ('value',float,(2,2))]) print a[['x','y']] #[(1.5, 2.5) (3.0, 4.0) (1.0, 3.0)] print a[['x','y']].dtype #[('x', '<f4') ('y', '<f4')]) But the problem is that it seems to be a copy rather than a view: b = a[['x','y']] b[0] = (9.,9.) print b #[(9.0, 9.0) (3.0, 4.0) (1.0, 3.0)] print a[['x','y']] #[(1.5, 2.5) (3.0, 4.0) (1.0, 3.0)] If I only select one column, it's a view: c = x['y'] c[0] = 99. print c #[ 99. 4. 3. ] print a['y'] #[ 99. 4. 3. ] Is there any way I can get the view behavior for more than one column at once? I have two workarounds, one is to just loop through the columns, the other is to create a hierarchical dtype , so that the one column actually returns a structured array with the two (or more) fields that I want. Unfortunately, zip also returns a copy, so I can't do: x = a['x']; y = a['y'] z = zip(x,y) z[0] = (9.,9.)

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