On 8/31/06, Christopher Barker <Chris.Barker@noaa.gov> wrote:
Tom Denniston wrote:
> I would think one would want to throw an error when the data has
> inconsistent dimensions.

But it doesn't have inconsistent dimensions - they are perfectly
consistent with a (2,) array of objects. How is the code to know what
you intended?

Same as it produces a float array from array([1,2,3.0]). Array is a complicated function for precisely these sort of reasons, but the convenience makes it worthwhile. So, if a list contains something that can only be interpreted as an object, dtype should be set to object.

With numeric types, it is unambiguous to march down through the
sequences until you get a number. As a sequence is an object, there no
way to unambiguously do this automatically.

Perhaps the way to solve this is for the array constructor to take a
"shape" or "rank" argument, so you could specify what you intend. But
that's really just syntactic sugar to avoid for calling numpy.empty() first.

Perhaps a numpy.object_array() constructor would be useful, although as
I think about it, even specifying a shape or rank would not be unambiguous!

This is a useful discussion. If we ever get a nd-array into the standard
lib, I suspect that object arrays will get heavy use -- better to clean
up the semantics now.

Perhaps a Wiki page on building object arrays is called for.