Array joining in NumPy means combining two or more arrays into a single array.

Joining Arrays Using np.concatenate( ) :
concatenate( ) joins arrays along an existing axis.

syntax :
Python 
np.concatenate((array1, array2), axis=0)

Example :
Python 
import numpy as np arr1 = np.array([1, 2, 3]) arr2 = np.array([4, 5, 6]) result = np.concatenate((arr1, arr2)) print(result) #output [1 2 3 4 5 6]
Python 
import numpy as np arr1 = np.array([[1, 2], [3, 4]]) arr2 = np.array([[5, 6], [7, 8]]) result = np.concatenate((arr1, arr2), axis=0) print(result) #output [[1 2] [3 4] [5 6] [7 8]]


Joining Arrays Using np.stack( ) :
stack( ) joins arrays along a new axis .

Example :
Python 
import numpy as np arr1 = np.array([1, 2, 3]) arr2 = np.array([4, 5, 6]) result = np.stack((arr1, arr2)) print(result) #output [[1 2 3] [4 5 6]]


Joining Arrays Using np.hstack( ) :
Python 
import numpy as np arr1 = np.array([1, 2, 3]) arr2 = np.array([4, 5, 6]) result = np.hstack((arr1, arr2)) print(result) #output [1 2 3 4 5 6]


Joining Arrays Using np.vstack( ) :
Python 
import numpy as np arr1 = np.array([1, 2, 3]) arr2 = np.array([4, 5, 6]) result = np.vstack((arr1, arr2)) print(result) #output [[1 2 3] [4 5 6]]