[SciPy-User] numpy.array of mixed type.
Charles R Harris
charlesr.harris@gmail....
Sun Apr 25 11:41:55 CDT 2010
On Sun, Apr 25, 2010 at 9:00 AM, <josef.pktd@gmail.com> wrote:
> On Sun, Apr 25, 2010 at 10:52 AM, Robert Kern <robert.kern@gmail.com>
> wrote:
> > On Thu, Apr 22, 2010 at 10:16, Éric Depagne <edepagne@lcogt.net> wrote:
> >> Le mercredi 21 avril 2010 17:27:14, Charles R Harris a écrit :
> >>> On Wed, Apr 21, 2010 at 9:49 AM, Éric Depagne <edepagne@lcogt.net>
> wrote:
> >>> > Hi.
> >>> >
> >>> > I'd like to create an array that would contain data of two different
> >>> > types: str and float64.
> >>> >
> >>> > I've created a dtype accordingly :
> >>> > dt = dtype({'names': ['Type', 'Chisquare'], 'formats': ['S8',
> float64]})
> >>> > then, I initialise my array as follow:
> >>> > temp = zeros ((1,1), dtype = dt)
> >>> >
> >>> > that gives me the following:
> >>> > array([[('', 0.0)]],
> >>> > dtype=[('Type', '|S8'), ('Chisquare', '<f8')])
> >>> >
> >>> > which is almost good.
> >>> >
> >>> > I'd like to know if it is possible instead of having an array with
> one
> >>> > column
> >>> > that will contain a tuple, to create an array with two columns, the
> >>> > first column being a str and the second a float.
> >>>
> >>> It's not a tuple, it is just displayed that way, it is more like a
> packed c
> >>> structure. But no, you can't have *actual* ndarray columns of different
> >>> types. What problem do you have with using the dtype?
> >>
> >> I tried to sort my array along the Chisquare column and could not.
> >> temp.sort (axis = 0) sorts along the column named Type, but I can't find
> a way
> >> to sort along Chisquare. Is it possible?
> >
> > Use numpy.lexsort()
> >
> > i = numpy.lexsort([myarray['Chisquare']])
> > sorted_array = myarray[i]
>
> I don't know if I'm missing something but the docstring or numpy.sort
> looks pretty good:
>
> order : list, optional
> When a is a structured array, this argument specifies which fields to
> compare first, second, and so on. This list does not need to include
> all of the fields.
> <...>
> Use the order keyword to specify a field to use when sorting a structured
> array:
>
> >>> dtype = [('name', 'S10'), ('height', float), ('age', int)]
> >>> values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),
> ... ('Galahad', 1.7, 38)]
> >>> a = np.array(values, dtype=dtype) # create a structured array
> >>> np.sort(a, order='height')
> array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),
> ('Lancelot', 1.8999999999999999, 38)],
> dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])
>
>
>
I'll bet lexsort is faster, though.
Chuck
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