A poly1d object is iterable
In [1]: np.poly1d((3,2))
Out[1]: poly1d([3, 2])
In [2]: list(_)
Out[2]: [3, 2]
np.array tries to makes a multidimensional numeric array from its inputs, iterating where possible. That's why making an array from these poly1d object ends up looking like you did np.array([[3,2],[3,2]]).
The most reliable way to create an object dtype array is to initialize a 'blank' one and fill it.
In [12]: arr = np.empty(2, object)
In [13]: arr[:] = [np.poly1d((3,2)), np.poly1d((4,2))]
In [14]: arr
Out[14]: array([poly1d([3, 2]), poly1d([4, 2])], dtype=object)
But do you really need an object dtype array? Why not stick with a list of the poly1d objects?
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We've observed in other SO that attempting to create a object dtype array can go several different ways. Some times it works, sometimes you get a numeric array, and sometimes an error.
In [17]: np.array([np.poly1d((3,2)), np.poly1d((3,2,1))])
Out[17]: array([poly1d([3, 2]), poly1d([3, 2, 1])], dtype=object)
In [18]: np.array([np.poly1d((3,2,1)), np.poly1d((3,2,1))])
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
in
----> 1 np.array([np.poly1d((3,2,1)), np.poly1d((3,2,1))])
ValueError: cannot copy sequence with size 2 to array axis with dimension 3
