NumPy: Joining Arrays Using Stack Functions

stack() function is available in Numpy Package. It is used to join two or more arrays along a new axis. It works the same as concatenate.

Syntax:

numpy.stack((array1,array2......), axis=0)

Example:

import numpy as np
a=np.array([1,2,3])#(1,3)
b=np.array([4,5,6])#(1,3)
c=np.stack((a,b))
print(c)

Output:

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

Stacking along Rows:

We use hstack() to do stacking along a row.

Example:

For 1-D Arrays

import numpy as np
a=np.arange(6)
b=np.arange(5)
print(np.hstack((a,b)))#Here a(1,6) and b(1,5) along axis=0

Output:

[0 1 2 3 4 5 0 1 2 3 4]

For 2-D Arrays

import numpy as np
a=np.array([[1,2],[3,4]])#axis=(2,2)
b=np.array([[5,6],[7,8]])#axis(2,2)
c=np.hstack((a,b))
print(c)

Output:

[[1 2 5 6]

[3 4 7 8]]

Stacking along Columns:

We use vstack() to do stacking along a column.

Example:

For 1-D Array:

import numpy as np
a=np.arange(5)
b=np.arange(5)
print(np.vstack((a,b)))

Output:

[[0 1 2 3 4]
 [0 1 2 3 4]]

For 2-D Array:

import numpy as np
a=np.array([[1,2],[3,4]])#axis=(2,2)
b=np.array([[5,6]])#axis(1,2)
c=np.vstack((a,b))
print(c)

Output:

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

Stacking along Height:

We use a dstack() to perform stacking along height(same as depth).

Example:

For 1-D Array:

import numpy as np
a=np.arange(5)
b=np.arange(5)
print(np.dstack((a,b)))

Output:

[[[0 0]
  [1 1]
  [2 2]
  [3 3]
  [4 4]]]

For 2-D Array:

import numpy as np
a=np.array([[1,2],[3,4]])#axis=(2,2)
b=np.array([[5,6],[7,8]])#axis(1,2)
c=np.dstack((a,b))
print(c)

Output:

[[[1 5] 
[2 6]] 
[[3 7] 
[4 8]]]
Thu, 09/02/2021 - 17:08

Authored by

Devanshi, is working as a Data Scientist with iVagus. She has expertise in Python, NumPy, Pandas and other data science technologies.