Question

I have a recarray with a couple columns that I use for selecting a subset. Something like

>>> x
   array([ ('label1',True,3),
          ('label2',True,2),
          ('label1',False,4)],
         dtype=[('status', '|S16'), ('select', '|b1'), ('somedata', '<i4')])

Data is selected from this array using an approach similar to a previous SO question.

condit=(x['status']=='label1')&(x['select']==True)
x_subids=numpy.where(condit)[0]
x_sub=x[x_subids]

Then I do some work on the subset and update the original.

x[x_subids]=x_sub

I understand that x_sub is a copy rather than a view due to advanced indexing, and I was wondering if there was an elegant way of avoiding the array copy and just working with the original given the conditions that I need to subset the data.

Answer 1

The kind of modifications you mention in the comments to my first answer can be done with the numpy.place() function:

>>> import numpy
>>> x = numpy.array([("label1",True,3), ("label2",False,2), ("label1",True,4)],
...     dtype=[("status", "|S16"), ("select", "|b1"), ("somedata", ">> mask = x["select"]
>>> numpy.place(x["somedata"], mask, (5, 6))
>>> print x
[('label1', True, 5) ('label2', False, 2) ('label1', True, 6)]
>>> numpy.place(x["status"], mask, "label3")
>>> print x
[('label3', True, 5) ('label2', False, 2) ('label3', True, 6)]

Note that

1. I changed the values and conditions a bit for the sake of a pertinent example.

2. This time, the values where mask is True are selected again, not masked out as in my previous answer.

3. The ==True part in your mask condit is redundant, just leave it out 🙂