I have a pandas series features that has the following values (features.values)

array([array([0, 0, 0, ..., 0, 0, 0]), array([0, 0, 0, ..., 0, 0, 0]),array([0, 0, 0, ..., 0, 0, 0]), ...,array([0, 0, 0, ..., 0, 0, 0]), array([0, 0, 0, ..., 0, 0, 0]),array([0, 0, 0, ..., 0, 0, 0])], dtype=object)

Now I really want this to be recognized as matrix, but if I do

>>> features.values.shape
(10000,)

rather than (10000, 3000) which is what I would expect.

How can I get this to be recognized as 2d rather than a 1d array with arrays as values. Also why does it not automatically detect it as a 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]: arr
Out[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]: arr
Out[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]])