Python Data Science NumPy Joining Array

Joining means putting contents of two or more arrays in a single array.

In SQL we join tables based on a key, whereas in NumPy we join arrays by axis.

We pass a sequence of arrays that we want to join to the concatenate() function, along with the axis. If axis is not explicitly passed,
it is taken as 0.

Example 1: Join two arrays

Code

import numpy as np

arr1 = np.array([11, 12, 13])

arr2 = np.array([14, 15, 16])

arr = np.concatenate((arr1, arr2))

print(arr)

the output will be

[11 12 13 14 15 16]

Example 2: Join two 2-D arrays along rows (axis=1).

Code

import numpy as np

arr1 = np.array([[11, 12], [13, 14]])

arr2 = np.array([[15, 16], [17, 18]])

arr = np.concatenate((arr1, arr2), axis=1)

print(arr)

the output will be

[[11 12 15 16]
 [13 14 17 18]]    

Joining Arrays Using Stack Functions

Stacking is same as concatenation, the only difference is that stacking is done along a new axis.

We can concatenate two 1-D arrays along the second axis which would result in putting them one over the other, ie. stacking.

We pass a sequence of arrays that we want to join to the stack() method along with the axis. If axis is not explicitly passed it is taken as 0.

Example 3: Concatenate two 1-D arrays along the second axis.

Code

import numpy as np

arr1 = np.array([11, 12, 13])

arr2 = np.array([14, 15, 16])

arr = np.stack((arr1, arr2), axis=1)

print(arr)

the output will be

[[11 14]
 [12 15]
 [13 16]]    

Stacking Along Rows

NumPy provides a helper function which is known as hstack() to stack along rows.

Example 4: Use hstack() to stack method to join array along rows.

Code

import numpy as np

arr1 = np.array([11, 12, 13])

arr2 = np.array([14, 15, 16])

arr = np.hstack((arr1, arr2))

print(arr)

the output will be

[11 12 13 14 15 16]

Stacking Along Columns

NumPy provides a helper function vstack() to stack along columns.

Example 5: Use vstack() to stack method to join array along columns.

Code

import numpy as np

arr1 = np.array([11, 12, 13])

arr2 = np.array([14, 15, 16])

arr = np.vstack((arr1, arr2))

print(arr)

the output will be

[[11 12 13]
 [14 15 16]]    

Stacking Along Height (depth)

NumPy provides a helper function dstack() to stack along height, which is the same as depth.

Example 6: Use vstack() to stack method to join array along height/depth.

Code

import numpy as np

arr1 = np.array([11, 12, 13])

arr2 = np.array([14, 15, 16])

arr = np.dstack((arr1, arr2))

print(arr)

the output will be

[[[11 14]
  [12 15]
  [13 16]]]