converty numpy array of arrays to 2d array

In response your comment question, let's compare 2 ways of creating an array

First make an array from a list of arrays (all same length):

In [302]: arr = np.array([np.arange(3), np.arange(1,4), np.arange(10,13)])In [303]: arrOut[303]: array([[ 0,  1,  2],       [ 1,  2,  3],       [10, 11, 12]])

The result is a 2d array of numbers.

If instead we make an object dtype array, and fill it with arrays:

In [304]: arr = np.empty(3,object)In [305]: arr[:] = [np.arange(3), np.arange(1,4), np.arange(10,13)]In [306]: arrOut[306]: array([array([0, 1, 2]), array([1, 2, 3]), array([10, 11, 12])],      dtype=object)

Notice that this display is like yours. This is, by design a 1d array. Like a list it contains pointers to arrays elsewhere in memory. Notice that it requires an extra construction step. The default behavior of np.array is to create a multidimensional array where it can.

It takes extra effort to get around that. Likewise it takes some extra effort to undo that - to create the 2d numeric array.

Simply calling np.array on it does not change the structure.

In [307]: np.array(arr)Out[307]: array([array([0, 1, 2]), array([1, 2, 3]), array([10, 11, 12])],      dtype=object)

stack does change it to 2d. stack treats it as a list of arrays, which it joins on a new axis.

In [308]: np.stack(arr)Out[308]: array([[ 0,  1,  2],       [ 1,  2,  3],       [10, 11, 12]])