Numpy array with numpy arrays as objects

Question

I'd like to create a numpy ndarray with entries of type ndarray itself. I was able to wrap ndarrays into another type to get it work but I want to do this without wrapping. With wrapping a ndarray x into e.g. the dictionary {1:x} I can do

F = np.vectorize(lambda x: {1:np.repeat(x,3)})
F(np.arange(9).reshape(3,3))

and get (3,3) ndarray with entries {1:[0,0,0]} ... {1:[8,8,8]} (with ndarrays). When change F to F = np.vectorize(lambda x: np.repeat(x,3)) numpy complains ValueError: setting an array element with a sequence. I guess it detects that the entries as arrays themselves and doesn't threat them as objects anymore.

How can I avoid this and do the same thing without wrapping the entries from ndarray into something different?

Thanks a lot in advance for hints :)

Using otypes=[object] might get vectorize to work. vectorize uses frompyfunc, but changes the returned dtype. — hpaulj
– hpaulj, Commented Mar 1, 2018 at 12:18

Paul Panzer · Accepted Answer · 2018-03-01 11:26:50Z

3

You can (ab-)use numpy.frompyfunc:

>>> F = np.arange(9).reshape(3, 3)
>>> np.frompyfunc(F.__getitem__, 1, 1)(range(3))
array([array([0, 1, 2]), array([3, 4, 5]), array([6, 7, 8])], dtype=object)

answered Mar 1, 2018 at 11:26

Paul Panzer

53.3k3 gold badges59 silver badges103 bronze badges

Sign up to request clarification or add additional context in comments.

2 Comments

hpaulj Over a year ago

Looks like we've found a new tool for creating object arrays of arbitrary shape and complexity. :)

Paul Panzer Over a year ago

@hpaulj ;-) I'd still prefer the numpy chaps to add something like ndmax to the array factory. But for the time being this method may well be the least awkward.

Collectives™ on Stack Overflow

Numpy array with numpy arrays as objects

1 Answer 1

2 Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

2 Comments

Your Answer

Sign up or log in

Post as a guest

Related