[SciPy-User] best way to convert a structured array to a float view (again)

Skipper Seabold jsseabold@gmail....
Fri Jun 4 10:55:48 CDT 2010


Say I have the following arrays that I want to view as/cast to plain
ndarrays with float dtype

import numpy as np
arr = np.array([(24,),(24,),(24,),(24,),(24,)], dtype=[("var1",int)])

arr2 = np.array([(24,4.5),(24,4.5),(24,4.5),(24,4.5),(24,4.5)],
dtype=[("var1",int),("var2",float)])

What I really want to be able to do is something like

arr.view(float)

or

arr2.view((float,2))

But I realize that I can't do this because of how the structs are
defined in memory.  So my question is, is this the best (cheapest,
easiest) way to get arr or arr2 as all floats.

arr3 = np.zeros(len(arr), dtype=float)
arr3[:] = arr.view(int)

or

arr4 = np.zeros(len(arr2),
dtype=zip(arr2.dtype.names,['float']*len(arr2.dtype.names)))
arr4[:] = arr2[:]
arr5 = arr4.view((float,len(arr4.dtype.names)))

So now I have arr3 and arr5.  I need this to be rather general (can
ignore strings and object types for now), so that's the reason for the
approach I'm taking here.

Thanks,

Skipper


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