Convert 2D array to 3D numpy array

Question

I have a created a numpy array, each element of the array contains an array of the same shape (9,5). What I want is a 3D array.

I've tried using np.stack.

data = list(map(lambda x: getKmers(x, 9), data)) # getKmers creates a       
                                                 # list of list from a pandas dataframe
data1D = np.array(data)                          # shape (350,)
data2D = np.stack(data1D)

---

data1D:
array([list([      pdbID  AtomNo Type      Eta    Theta
0  1a9l.pdb     2.0    G  169.225  212.838
1  1a9l.pdb     3.0    G  168.439  206.785
2  1a9l.pdb     4.0    U  170.892  205.845
3  1a9l.pdb     5.0    G  164.726  225.982
4  1a9l.pdb     6.0    A  308.788  144.370
5  1a9l.pdb     7.0    C  185.211  209.363
6  1a9l.pdb     8.0    U  167.612  216.614
7  1a9l.pdb     9.0    C  168.741  219.239
8  1a9l.pdb    10.0    C  163.639  207.044,       pdbID  AtomNo Type          Eta    Theta
1  1a9l.pdb     3.0    G  168.439  206.785
2  1a9l.pdb     4.0    U  170.892  205.845
3  1a9l.pdb     5.0    G  164.726  225.982
4  1a9l.pdb     6.0    A  308.788  144.370
5  1a9l.pdb     7.0    C  185.211  209.363
6  1a9l.pdb     8.0    U  167.612  216.614
7  1a9l.pdb     9.0    C  168.741  219.239
8  1a9l.pdb    10.0    C  163.639  207.044

I get this error: cannot copy sequence with size 9 to array axis with dimension 5

I want to create a 3D Matrix, where every subarray is in the new 3D dimension. I gues the new shape would be (9,5,350)

Answer 1

You need to use

 data.reshape((data.shape[0], data.shape[1], 1))

Example

from numpy import array
data = [[11, 22],
    [33, 44],
    [55, 66]]
data = array(data)
print(data.shape)
data = data.reshape((data.shape[0], data.shape[1], 1))
print(data.shape)

Running the example first prints the size of each dimension in the 2D array, reshapes the array, then summarizes the shape of the new 3D array.

Result

(3,2)
(3,2,1)

Source :https://machinelearningmastery.com/index-slice-reshape-numpy-arrays-machine-learning-python/