Numpy np.unique() method-Python
                                        
                                                                                    
                                                
                                                    Last Updated : 
                                                    21 Jun, 2025
                                                
                                                 
                                                 
                                             
                                                                             
                                                             
                            
                            
                                                                                    
                numpy.unique() finds the unique elements of an array. It is often used in data analysis to eliminate duplicate values and return only the distinct values in sorted order. Example:
            Python
    import numpy as np
a = np.array([1, 2, 2, 3, 4, 4, 4])
res = np.unique(a)
print(res)
Explanation: numpy.unique() removes duplicates and returns only the unique sorted values from the array.
Syntax
numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None)
Parameter:
| Parameter | Description | 
|---|
| ar	 | Input array flattened if not 1-D unless axis is specified. | 
|---|
| return_index | If True, returns indices of first occurrences of unique values. | 
|---|
| return_inverse | If True, returns indices to reconstruct the original array. | 
|---|
| return_counts | If True, returns the count of each unique value. | 
|---|
| axis	 | Operates along the given axis and if None, the array is flattened. | 
|---|
Returns:
- A sorted 1-D array of unique values.
- Optional arrays depending on return_index, return_inverse, and return_counts.
Examples
Example 1: In this example, we use the return_counts=True parameter to get both the unique elements and how many times each value appears in the array.
            Python
    import numpy as np
a = np.array([1, 2, 2, 3, 3, 3])
res, counts = np.unique(a, return_counts=True)
print("Unique:", res)
print("Counts:", counts)
OutputUnique: [1 2 3]
Counts: [1 2 3]
 Explanation: np.unique(a, return_counts=True) returns both unique values and how many times each occurs.
Example 2: In this example, we use the return_inverse=True parameter to get an array that can be used to reconstruct the original array using the unique values.
            Python
    import numpy as np
a = np.array([3, 1, 2, 1])
unique, inverse = np.unique(a, return_inverse=True)
print("Unique:", unique)
print("Inverse:", inverse)
OutputUnique: [1 2 3]
Inverse: [2 0 1 0]
 Explanation: Inverse array contains indices such that unique[inverse] reconstructs the original array.
Example 3: In this example, we use the return_index=True parameter to find the indices of the first occurrences of the unique values in the original array.
            Python
    import numpy as np
a = np.array([4, 3, 3, 2, 1, 2])
unique, indices = np.unique(a, return_index=True)
print("Unique:", unique)
print("Indices:", indices)
OutputUnique: [1 2 3 4]
Indices: [4 3 1 0]
 Explanation: Indices array tells the index in the original array where each unique element first appeared.
                                
                                
                            
                                                                                
                                                            
                                                    
                                                
                                                        
                            
                        
                                                
                        
                                                                                    
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