np.concatenate for variable dimension array-CodePudding

c = np.array([[2,2],[2]])
d = np.array([[3,3],[3]])
res=np.concatenate((c,d),axis=1)

I tried concatenating c and d using np.concatenate but it gives me an error due to variable dimensions.

numpy.AxisError: axis 1 is out of bounds for array of dimension 1

I want to concatenate c and d to give :

res=np.array([[2,3],[2,3]],[[2,3]])

How can I get this result using numpy library functions? Thanks in Advance :)

CodePudding user response：

Code:

[np.dstack((res[i], res[i 2]))[0] for i in range(len(np.concatenate((c,d))[:2]))]

Output:

 [array([[2, 3],
        [2, 3]]),
 array([[2, 3]])]

CodePudding user response：

Did you get the warning when you created the arrays? Did you look at the arrays?

In [183]: c = np.array([[2,2],[2]])
     ...: d = np.array([[3,3],[3]])
C:\Users\paul\AppData\Local\Temp\ipykernel_6304\2700245180.py:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  c = np.array([[2,2],[2]])
C:\Users\paul\AppData\Local\Temp\ipykernel_6304\2700245180.py:2: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
  d = np.array([[3,3],[3]])

In [184]: c
Out[184]: array([list([2, 2]), list([2])], dtype=object)

In [185]: c.shape
Out[185]: (2,)

That is 1d, so of course np.concatenate will complain if you specify axis 1! It can only use axis 0

In [186]: np.concatenate((c,d))
Out[186]: array([list([2, 2]), list([2]), list([3, 3]), list([3])], dtype=object)

making a (4,) shape array.

stack is a variant that can join arrays on a new axis:

In [188]: np.stack((c,d))
Out[188]: 
array([[list([2, 2]), list([2])],
       [list([3, 3]), list([3])]], dtype=object)

In [189]: np.stack((c,d),axis=1)
Out[189]: 
array([[list([2, 2]), list([3, 3])],
       [list([2]), list([3])]], dtype=object)

Look at your desired result

In [191]: res=np.array(([[2,3],[2,3]],[[2,3]]),object)
In [192]: res
Out[192]: array([list([[2, 3], [2, 3]]), list([[2, 3]])], dtype=object)

(this too is (2,) shape; note it handles the 1 element lists different from the 2 element ones).

Compare that to what we get with a plain list "transpose":

In [193]: list(zip(c,d))
Out[193]: [([2, 2], [3, 3]), ([2], [3])]

Wrapped in np.array, that makes a (2,2) object dtype.

Redefining c to contain a list and a number:

c1 = np.array([[2,2],2],object)

In [212]: np.stack((c1,d1),axis=1)
Out[212]: 
array([[list([2, 2]), list([3, 3])],
       [2, 3]], dtype=object)