Find unique rows in a NumPy array

In this article we will discuss how to find unique rows in a NumPy array. To find unique rows in a NumPy array we are using numpy.unique() function of NumPy library.

Syntax : numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None)

Now, let’s see an example:

Example 1:

Python3

# import library
import numpy as np

# Create a 2D numpy array
arr2D = np.array([[11, 11, 12, 11],
                     [13, 11, 12, 11],
                     [16, 11, 12, 11],
                     [11, 11, 12, 11]])

print('Original Array :' ,
      arr2D, sep = '\n')

# Get unique rows from
# complete 2D-array by
# passing axis = 0 in
# unique function along
# with 2D-array
uniqueRows = np.unique(arr2D,
                       axis = 0)

# print the output result
print('Unique Rows:',
      uniqueRows, sep = '\n')

Output:

Original Array :
[[11 11 12 11]
[13 11 12 11]
[16 11 12 11]
[11 11 12 11]]
Unique Rows:
[[11 11 12 11]
[13 11 12 11]
[16 11 12 11]]

Example 2:

Python3

# import library
import numpy as np

# create 2d numpy array
array = np.array([[1, 2, 3, 4],
                  [3, 2, 4, 1],
                  [6, 8, 1, 2]])

print("Original array: \n",
      array)

# Get unique rows from
# complete 2D-array by
# passing axis = 0 in
# unique function along
# with 2D-array
uniqueRows = np.unique(array,
                       axis = 0)

# print the output result
print('Unique Rows :',
      uniqueRows,
      sep = '\n')

Output:

Original array: 
 [[1 2 3 4]
 [3 2 4 1]
 [6 8 1 2]]
Unique Rows :
[[1 2 3 4]
 [3 2 4 1]
 [6 8 1 2]]