Python numpy.unique()

  1. Syntax of numpy.unique():
  2. Example Codes: numpy.unique() Method
  3. Example Codes: Set return_index=True in numpy.unique() Method
  4. Example Codes: Set return_counts=True in numpy.unique() Method
  5. Example Codes: Set return_inverse=True in numpy.unique() Method
  6. Example Codes: Set axis Parameter in the numpy.unique() Method

Python Numpy numpy.unique() function retrieves all the unique values in the given NumPy array and sorts these unique values.

Syntax of numpy.unique():

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

Parameters

ar Array or Object which could be converted into an array
return_index Boolean. If True, return an array of indices of the first occurrence of each unique value.
return_inverse Boolean. If True, return the indices of a unique array, which can be used to reconstruct the input array.
return_counts Boolean. If True, return an array of the count of each unique value.
axis find unique rows (axis=0) or columns (axis=1). By default, unique elements are retrieved from the flattened array.

Return

It returns sorted unique values of the array.

If return_index=True, it returns an array of indices of the first occurrence of each unique value.

If return_counts=True, it returns an array of the count of each unique value the input array.

If return_inverse=True, it returns the indices of a unique array, which can be used to reconstruct the input array.

Example Codes: numpy.unique() Method

import numpy as np

a=np.array([[2,3,4],
            [5,4,7],
           [4,2,3]])

unique_array=np.unique(a)

print(unique_array)

Output:

[2 3 4 5 7]

It returns sorted unique values of the flattened input array.

By flattening the array, we mean placing all the rows one after another to convert the given array to a 1-D array.

Example Codes: Set return_index=True in numpy.unique() Method

import numpy as np

a=np.array([[2,3,4],
            [5,4,7],
           [4,2,3]])

unique_array=np.unique(a,return_index=True)

print(unique_array)

Output:

(array([2, 3, 4, 5, 7]), array([0, 1, 2, 3, 5]))

It gives a tuple of an array of sorted unique values in the given flattened input array and an array of indices of the first occurrence of each unique value.

Example Codes: Set return_counts=True in numpy.unique() Method

import numpy as np

a=np.array([[2,3,4],
            [5,4,7],
           [4,2,3]])

unique_array=np.unique(a,return_counts=True)

print(unique_array)

Output:

(array([2, 3, 4, 5, 7]), array([2, 2, 3, 1, 1]))

It gives a tuple of an array of sorted unique values in the given flattened input array and an array of the count of each unique value the input array.

Example Codes: Set return_inverse=True in numpy.unique() Method

import numpy as np

a=np.array([[2,3,4],
            [5,4,7],
           [4,2,3]])

unique_array=np.unique(a,return_inverse=True)

print(unique_array)

Output:

(array([2, 3, 4, 5, 7]), array([0, 1, 2, 3, 2, 4, 2, 0, 1]))

It gives a tuple of an array of sorted unique values in the given flattened input array and an array of the indices of a unique array.

Here, 2 occurs at the first position and the second last position of the flattened array. Similarly, we can find which value occurs at which position.

Example Codes: Set axis Parameter in the numpy.unique() Method

Find Unique Rows

import numpy as np

a=np.array([[2,3,2],
            [2,3,2],
           [4,2,3]])

unique_array=np.unique(a,axis=0)

print(unique_array)

Output:

[[2 3 2]
 [4 2 3]]

It gives all the unique rows in the input array.

Find Unique Columns

import numpy as np

a=np.array([[2,3,2],
            [2,3,2],
           [3,2,3]])

unique_array=np.unique(a,axis=1)

print(unique_array)

Output:

[[2 3]
 [2 3]
 [3 2]]

It gives all the unique columns in the input array.

comments powered by Disqus