[SciPy-User] numpy.array of mixed type.

[josef.pktd at gmail.com]

On Sun, Apr 25, 2010 at 10:52 AM, Robert Kern <robert.kern at gmail.com> wrote:
> On Thu, Apr 22, 2010 at 10:16, Éric Depagne <edepagne at 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 at 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')])


Josef


>
> --
> Robert Kern
>
> "I have come to believe that the whole world is an enigma, a harmless
> enigma that is made terrible by our own mad attempt to interpret it as
> though it had an underlying truth."
>  -- Umberto Eco
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