[Numpy-discussion] ragged array implimentation

John Salvatier jsalvati@u.washington....
Mon Mar 7 11:41:47 CST 2011


@Jeff
I need to work with ragged arrays too. Are object arrays of 1d numpy arrays
slower than lists of 1d numpy arrays?

@ Christopher
I'd be interested in hearing if you come up with any better solutions.

On Mon, Mar 7, 2011 at 9:37 AM, Jeff Whitaker <jswhit@fastmail.fm> wrote:

> On 3/7/11 10:28 AM, Christopher Barker wrote:
> > Hi folks,
> >
> > I'm setting out to write some code to access and work with ragged arrays
> > stored in netcdf files. It dawned on me that ragged arrays are not all
> > that uncommon, so I'm wondering if any of you have any code you've
> > developed that I could learn-from borrow from, etc.
> >
> > note that when I say a "ragged array", I mean a set of data where the
> > each row could be a different arbitrary length:
> >
> > 1, 2, 3, 4
> > 5, 6
> > 7, 8, 9, 10, 11, 12
> > 13, 14, 15
> > ...
> >
> > In my case, these will only be 2-d, though I suppose one could have a
> > n-d version where the last dimension was ragged (or any dimension, I
> > suppose, though I'm having trouble wrapping my brain around what that
> > would look like...
> >
> > I'm not getting more specific about what I think the API should look
> > like -- that is part of what I'm looking for suggestions, previous
> > implementations, etc for.
> >
> > Is there any "standard" way to work with such data?
> >
> > -Chris
> >
>
> Chris:  The netcdf4-python modules reads netcdf vlen arrays and returns
> numpy object arrays, where the elements of the object arrays are
> themselves 1d numpy arrays. I don't think there is any other way to do
> it.  In your example, the 'ragged' array would be a 1d numpy array with
> dtype='O', and the individual elements would be 1d numpy arrays with
> dtype=int.  Of course, these arrays are very awkward to deal with and
> operations will be slow.
>
> -Jeff
>
> --
> Jeffrey S. Whitaker         Phone  : (303)497-6313
> Meteorologist               FAX    : (303)497-6449
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