NumPy concatenate()

import numpy as np

array1 = np.array([[0, 1], [2, 3]])
array2 = np.array([[4, 5], [6, 7]])

# join the arrays 
concatenatedArray = np.concatenate((array1, array2))

print(concatenatedArray)

'''
Output: 
[[0 1]
 [2 3]
 [4 5]
 [6 7]]
'''

concatenate() Syntax

The syntax of concatenate() is:

numpy.concatenate((array1, array2, …), axis, out)

concatenate() Arguments

The concatenate() method takes the following arguments:

• (array1, array2, …) - the sequence of arrays to be joined
• axis (optional)- defines the dimension in which the arrays are joined
• out (optional) - destination to place the result.
• dtype (optional) - datatype of the resultant array

concatenate() Return Value

The concatenate() method returns the joined array.

Example 1: Concatenate Two Arrays

import numpy as np

array1 = np.array( [ [1, 2], [3, 4] ] )
array2 = np.array( [[5, 6]] )

# concatenate the arrays along axis 0
concatenatedArray = np.concatenate((array1, array2))

print(concatenatedArray)

# concatenate the arrays along axis 0
concatenatedArray = np.concatenate((array1, array2), axis = 0)

print(concatenatedArray)

Output

[[1 2]
 [3 4]
 [5 6]]

[[1 2]
 [3 4]
 [5 6]]

If we do not pass the axis argument, the value of axis will be 0 by default.

Example 2: Concatenate Two Arrays in Different Dimensions

import numpy as np

array1 = np.array([[[0, 1], [2, 3]], [[4, 5], [6, 7]]])
array2 = np.array([[[10, 11], [12, 13]], [[14, 15], [16, 17]]])

print('Joining the array when axis = 0')

# join the arrays at axis equal to 0 
concatenatedArray = np.concatenate((array1, array2), 0)
print(concatenatedArray)

print('Joining the array when axis = 1')

# join the array at axis equal to 1
concatenatedArray = np.concatenate((array1, array2), 1)
print(concatenatedArray)

print('Joining the array when axis = 2')

# join the array at axis equal to 2
concatenatedArray = np.concatenate((array1, array2), 2)
print(concatenatedArray)

Output

Joining the array when axis = 0
[[[ 0  1]
  [ 2  3]]

 [[ 4  5]
  [ 6  7]]

 [[10 11]
  [12 13]]

 [[14 15]
  [16 17]]]

Joining the array when axis = 1
[[[ 0  1]
  [ 2  3]
  [10 11]
  [12 13]]

 [[ 4  5]
  [ 6  7]
  [14 15]
  [16 17]]]

Joining the array when axis = 2
[[[ 0  1 10 11]
  [ 2  3 12 13]]

 [[ 4  5 14 15]
  [ 6  7 16 17]]]

Example 3: Concatenate Flattened Arrays

If we pass None as the axis argument, concatenate() flattens the arrays and concatenates them.

import numpy as np

array1 = np.array([[0, 1], [2, 3]])
array2 = np.array([[10, 11], [12, 13]])

# join the flat arrays 
concatenatedArray = np.concatenate((array1, array2), None)

print(concatenatedArray)

Output

Output: [ 0 1 2 3 10 11 12 13]

Note: We can also use numpy.append() to concatenate arrays. However, unlike numpy.concatenate, numpy.append creates a new copy with appended values, making it less efficient.

Example 4: Return an Existing Array as Concatenated Array

In our previous examples, concatenate() created a new array as a result.

However, passing an existing array as the argument stores the resultant array as the given array.

import numpy as np

array1 = np.array([[0, 1], [2, 3]])
array2 = np.array([[10, 11], [12, 13]])

# create an array of shape (4, 2)
# and initialize all elements to 0
array3 = np.zeros((4, 2)) 

# join the arrays and store result in array3 
np.concatenate((array1, array2), out = array3)

print(array3)

Output

[[ 0.  1.]
 [ 2.  3.]
 [10. 11.]
 [12. 13.]]

Example 5: Specify the Datatype of a Concatenated Array

We can change the data type of concatenated array by passing the dtype argument.

import numpy as np

array1 = np.array([[0, 1], [2, 3]])
array2 = np.array([[10, 11], [12, 13]])

# change elements of the concatenated array to string 
concatenatedArray = np.concatenate((array1, array2), dtype = str)

print(concatenatedArray)

Output

[['0' '1']
 ['2' '3']
 ['10' '11']
 ['12' '13']]

Related NumPy Methods

• numpy.vstack() concatenates arrays along the axis 0.
• numpy.hstack() concatenates arrays along the axis 1.
• numpy.dstack() concatenates arrays along the axis 2.