[Numpy-discussion] problem with assigning to recarrays

Robert Kern robert.kern@gmail....
Fri Feb 27 18:30:24 CST 2009


On Fri, Feb 27, 2009 at 18:26, Brian Gerke <bgerke@slac.stanford.edu> wrote:
>
> Hi-
>
> I'm quite new to numpy and to python in general, so I apologize if I'm
> missing something obvious, but I've come across some seemingly nasty
> behavior when trying to assign values to the fields of an indexed
> subarray of a numpy record array.   Perhaps an example would explain
> it best.
>
> First, I make a boring record array:
>
>        In [124]: r = rec.fromarrays([zeros(5), zeros(5)],
> names='field1,field2')
>
> This has five elements with two fields, all values are zero.
> Now I can change the values for field1 for a few of the array elements:
>
>        In [125]: r[1].field1=1
>
>        In [126]: r[3].field1=1
>
> Let's check and make sure that worked:
>
>        In [127]: print r.field1
>        [ 0. 1. 0. 1. 0.]
>
> So far, so good.
> Now I want to change the value of field2 for those same elements:
>
>        In [128]: r[where(r.field1 == 1.)].field2 = 1
>
> Ok, so now the values of field 2 have been changed, for those elements
> right?
>
>        In [129]: r.field2
>
>        Out[129]: array([ 0.,  0.,  0.,  0.,  0.])
>
> Wait.  What?
> That can't be right.  Let's check again:
>
>        In [130]: print r[where(r.field1 == 1.)].field2
>        [ 0. 0.]
>
> Ok, so it appears that I can *access* fields in this array with an
> array of indices, but I can't assign new values to fields so
> accessed.  However, I *can* change the values if I use a scalar
> index.  This is different from the behavior of ordinary arrays, for
> which I can reassign elements' values either way.
>
> Moreover, when I try to reassign record array fields by indexing with
> an array of indices, it would appear that nothing at all happens.
> This syntax is equivalent to the pass command.
>
> So, my question is this:  is there some reason for this behavior in
> record arrays, which is unexpectedly different from the behavior of
> normal arrays, and rather confusing.   If so, why does the attempt to
> assign values to fields of an indexed subarray not raise some kind of
> error, rather than doing nothing?  I think it's unlikely that I've
> actually found a bug in numpy, but this behavior does not make sense
> to me.

r[where(r.field1 == 1.)] make a copy. There is no way for us to
construct a view onto the original memory for this circumstance given
numpy's memory model.

r[where(r.field1 == 1.)].field2 = 0.0 assigns to the copy.

-- 
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


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