>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
[3, 4, 6]])
numpy.concatenate([arr1, arr2]) # Joining arr1 and arr2# concatenate 2 numpy arrays: row-wise
>np.concatenate((array2D_1, array2D_2))
array([[ 0, 1, 2],
[ 3, 4, 5],
[ 6, 7, 8],
[10, 11, 12],
[13, 14, 15],
[16, 17, 18]])
a = np.array([1, 2, 3, 4])
b = np.array([5, 6, 7, 8])
np.concatenate((a, b))
#// required library
import numpy as npy
#// define 3 (1D) numpy arrays
arr1 = npy.array([10, 20, 30])
arr2 = npy.array([40, 50, 60])
arr3 = npy.array([70, 80, 90])
arrCon = npy.concatenate([arr1, arr2, arr3])
print(arrCon)
#// concatenation can also happen with 2D arrays
arr1_2d = npy.array([
[10, 20, 30],
[40, 50, 60]
])
arr2_2d = npy.array([
[11, 22, 33],
[44, 55, 66]
])
arr_2dCon = npy.concatenate([arr1_2d, arr2_2d])
print(arr_2dCon)