[python] ValueError: all the input arrays must have same number of dimensions

I'm having a problem with np.append.

I'm trying to duplicate the last column of 20x361 matrix n_list_converted by using the code below:

n_last = []
n_last = n_list_converted[:, -1]
n_lists = np.append(n_list_converted, n_last, axis=1)

But I get error:

ValueError: all the input arrays must have same number of dimensions

However, I've checked the matrix dimensions by doing

 print(n_last.shape, type(n_last), n_list_converted.shape, type(n_list_converted))

and I get

(20L,) (20L, 361L)

so the dimensions match? Where is the mistake?

The reason why you get your error is because a "1 by n" matrix is different from an array of length n.

I recommend using hstack() and vstack() instead. Like this:

import numpy as np
a = np.arange(32).reshape(4,8) # 4 rows 8 columns matrix.
b = a[:,-1:]                    # last column of that matrix.

result = np.hstack((a,b))       # stack them horizontally like this:
#array([[ 0,  1,  2,  3,  4,  5,  6,  7,  7],
#       [ 8,  9, 10, 11, 12, 13, 14, 15, 15],
#       [16, 17, 18, 19, 20, 21, 22, 23, 23],
#       [24, 25, 26, 27, 28, 29, 30, 31, 31]])

Notice the repeated "7, 15, 23, 31" column. Also, notice that I used a[:,-1:] instead of a[:,-1]. My version generates a column:

array([[7],
       [15],
       [23],
       [31]])

Instead of a row array([7,15,23,31])

Edit: append() is much slower. Read this answer.

(n,) and (n,1) are not the same shape. Try casting the vector to an array by using the [:, None] notation:

n_lists = np.append(n_list_converted, n_last[:, None], axis=1)

Alternatively, when extracting n_last you can use

n_last = n_list_converted[:, -1:]

to get a (20, 1) array.

You can also cast (n,) to (n,1) by enclosing within brackets [ ].

e.g. Instead of np.append(b,a,axis=0) use np.append(b,[a],axis=0)

a=[1,2]
b=[[5,6],[7,8]]
np.append(b,[a],axis=0)

returns

array([[5, 6],
       [7, 8],
       [1, 2]])