[Numpy-discussion] BOF notes: Fernando's proposal: NumPy ndarray with named axes
Mon Jul 12 12:52:28 CDT 2010
It's not just about the rows: a 2-D datarray can also index by
columns, an operation that has no equivalent in a 1-D array of records
like your example.
In the movie example, arr.col_named(305) (or, in datarray syntax,
arr.named[:,305], or arr.user.named) contains the movie ratings
for the user with ID 305, still indexed by movie titles. You can't do
that at all with a record array of the form you described, except by
using a list comprehension over the whole array that turns it into
2-D datarrays and 1-D record arrays may look similar, but they are
very different data structures. In fact, they're probably orthogonal
to each other -- I see no reason one couldn't make a datarray of
records, except for the fact that I wouldn't want to write the __str__
for such a beast.
(Speaking of which, I'm working on a 2-D datarray __str__ based on the
Divisi one. I have to make it support datatypes besides floats,
On Sun, Jul 11, 2010 at 2:09 PM, Neil Crighton <firstname.lastname@example.org> wrote:
> Robert Kern <robert.kern <at> gmail.com> writes:
>> On Sun, Jul 11, 2010 at 11:36, Rob Speer <rspeer <at> mit.edu> wrote:
>> >> But the utility of named indices is not so clear
>> >> to me. As I understand it, these new arrays will still only be
>> >> able to have a single type of data (one of float, str, int and so
>> >> on). This seems to be pretty limiting.
>> Having ticks on *every* axis is the primary feature there.
> I see, thanks.
> So for Rob's example slide you could use a record array:
> rec = np.rec.fromrecords(data, names='name,305,6,234')
> (Here data is a list of tuples, each tuple giving the movie name + it's data.)
> In this case it's easy to index by field name (rec['205']), but a trickier to
> choose the row using the movie name:
> ind = dict((n,i) for i,n in enumerate(rec.name))
> rec[ind['Wrong Trousers, The (1993)']]
> So datarrays would make this easier.
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