Getting attributes from arrays of objects in NumPy

The closest thing to what you want is to use a recarray instead of an ndarray of Python objects:

num_stars = 10dtype = numpy.dtype([('x', float), ('y', float), ('colour', float)])a = numpy.recarray(num_stars, dtype=dtype)a.colour = numpy.arange(num_stars)print a.colour

prints

[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]

Using a NumPy array of Python objects usually is less efficient than using a plain list, while a recarray stores the data in a more efficient format.

You could use numpy.fromiter(s.color for s in stars) (note lack of square brackets). That will avoid creating the intermediate list, which I imagine you might care about if you are using numpy.

(Thanks to @SvenMarnach and @DSM for their corrections below).

In case star is a more complicated class, here is an approach to get and setthe attributes with a helper class on top.

import numpy as npclass star:    def __init__(self, mass=1, radius=1):        self.mass = mass        self.radius = radiusclass Stars(list):    __getattr__ = lambda self, attr: np.array([getattr(s, attr) for s in self])    def __setattr__(self, attr, vals):        if hasattr(vals, '__len__'):            [s.__setattr__(attr, val) for (s,val) in zip(self,vals)]        else:            [s.__setattr__(attr, vals) for s in self]s1 = star(1, 1.1)s2 = star(2, 3)S = Stars([s1, s2])print(S.mass)print(S.radius)S.density = S.mass / S.radius**3print(S.density)print(s1.density)

Of course, if the class can be reimplemented into a recarray, it should be more efficient. Yet, such a reimplementaion might be undesirable.

Note, outer computations, like the density calculation, are still vectorised. And often those could be bottleneck, rather than setting and getting attributes.