On 9/26/07, Robert Kern wrote:

Here is the straightforward way:

In [15]: import numpy as np

In [16]: dt = np.dtype([('foo', int), ('bar', float)])

In [17]: r = np.zeros((3,3), dtype=dt)

Here is a (hopefully) simple question. If I create an array like this, how can I efficiently convert it to a record array which lets me do r.attr in addition to r['attr']. I'm pretty addicted to the former syntax. In [114]: dt = np.dtype([('foo', int), ('bar', float)]) In [115]: r = np.zeros((3,3), dtype=dt) In [116]: r.dtype Out[116]: dtype([('foo', '

John Hunter wrote:

On 9/26/07, Robert Kern wrote:

...

Here is the straightforward way:

In [15]: import numpy as np

In [16]: dt = np.dtype([('foo', int), ('bar', float)])

In [17]: r = np.zeros((3,3), dtype=dt)

Here is a (hopefully) simple question. If I create an array like this, how can I efficiently convert it to a record array which lets me do r.attr in addition to r['attr']. I'm pretty addicted to the former syntax.

In [32]: r2 = r.view(np.recarray) In [33]: r2.foo Out[33]: array([[0, 0, 0], [0, 0, 0], [0, 0, 0]]) -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco