how to convert a Series of arrays into a single matrix in pandas/numpy?

Another way is to extract the values of your series and use numpy.stack on them.

np.stack(s.values)

PS. I've run into similar situations often.

For the pandas>=0.24, you can also np.stack(s.to_numpy()) or np.concatenate(s.to_numpy()), depending on your requirement.

If, for some reason, you have found yourself with that abomination of a Series, getting it back into the sort of matrix or array you want is straightforward:

In [16]: s
Out[16]:
0     [1, 2, 3]
1     [2, 3, 4]
2     [3, 4, 5]
3     [2, 3, 4]
4     [3, 4, 5]
5     [2, 3, 4]
6     [3, 4, 5]
7     [2, 3, 4]
8     [3, 4, 5]
9     [2, 3, 4]
10    [3, 4, 5]
dtype: object

In [17]: sm = np.array(s.tolist())

In [18]: sm
Out[18]:
array([[1, 2, 3],
       [2, 3, 4],
       [3, 4, 5],
       [2, 3, 4],
       [3, 4, 5],
       [2, 3, 4],
       [3, 4, 5],
       [2, 3, 4],
       [3, 4, 5],
       [2, 3, 4],
       [3, 4, 5]])

In [19]: sm.shape
Out[19]: (11, 3)

But unless it's something you can't change, having that Series makes little sense to begin with.

I tested above methods with 5793 of 100D vectors. The old method, converting to list first, is fastest.

%time print(np.stack(df.features.values).shape)
%time print(np.stack(df.features.to_numpy()).shape)
%time print(np.array(df.features.tolist()).shape)
%time print(np.array(list(df.features)).shape)

Result

(5793, 100)
CPU times: user 11.7 ms, sys: 3.42 ms, total: 15.1 ms
Wall time: 22.7 ms
(5793, 100)
CPU times: user 11.1 ms, sys: 137 µs, total: 11.3 ms
Wall time: 11.9 ms
(5793, 100)
CPU times: user 5.96 ms, sys: 0 ns, total: 5.96 ms
Wall time: 6.91 ms
(5793, 100)
CPU times: user 5.74 ms, sys: 0 ns, total: 5.74 ms
Wall time: 6.43 ms