[Numpy-discussion] Request for a bit more info on structured arrays in the "basics" page

[Ralf Gommers]

On Sun, Mar 6, 2011 at 1:10 AM, Skipper Seabold <jsseabold at gmail.com> wrote:
> On Sat, Mar 5, 2011 at 9:28 AM, Ralf Gommers
> <ralf.gommers at googlemail.com> wrote:
>> On Sat, Mar 5, 2011 at 8:09 AM, Russell E. Owen <rowen at uw.edu> wrote:
>>> The page <http://docs.scipy.org/doc/numpy/user/basics.rec.html>
>>>
>>> gives a good introduction to structured arrays. However, it says nothing
>>> about how to set a particular element (all fields at once) from a
>>> collection of data.
>>>
>>> For instance:
>>>
>>> stArr = numpy.zeros([4,5], dtype=[("pos", float, (2,)), ("rot", float)])
>>>
>>> The question is how to set stArr[0]?
>>>
>>> >From experimentation it appears that you can provide a tuple, but not a
>>> list. Hence the following works just fine (and that the tuple can
>>> contain a list):
>>> strArr[0,0] = ([1.0, 1.1], 2.0)
>>>
>>> but the following fails:
>>> strArr[0,0] = [[1.0, 1.1], 2.0]
>>> with an error:
>>> TypeError: expected a readable buffer object
>>>
>>> This is useful information if one is trying to initialize a structured
>>> array from a collection of data, such as that returned from a database
>>> query.
>>>
>
> I added a bit at the end here, though it is mentioned briefly above.
> Feel free to expand. It's a wiki. You just need edit rights.
>
> http://docs.scipy.org/numpy/docs/numpy.doc.structured_arrays/

Thanks, I'll make sure that goes in for 1.6.0.

>> I'm wondering if that's not a bug? If it's intentional then it is
>> certainly counterintuitive.
>>
>
> This comes up from time to time.
>
> http://thread.gmane.org/gmane.comp.python.numeric.general/30793/focus=30793
>
> Perhaps an enhancement ticket could be filed? It doesn't sound trivial
> to implement.

I filed #1758.

You can also assign with an array which fails silently, certainly a bug:

>>> arr = np.zeros((5,), dtype=[('var1','f8'),('var2','f8')])
>>> arr['var1'] = np.arange(5)
>>> arr[0] = (10,20)
>>> arr[0]
(10.0, 20.0)

>>> arr[0] = np.array([10,20])  # no exception, but garbage out
>>> arr[0]
(4.2439915824246103e-313, 0.0)

Ralf