numpy.unique


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This function returns an array of unique elements in the input array. The function can be able to return a tuple of array of unique vales and an array of associated indices. Nature of the indices depend upon the type of return parameter in the function call.

numpy.unique(arr, return_index, return_inverse, return_counts)

Where,

Sr.No. Parameter & Description
1

arr

The input array. Will be flattened if not 1-D array

2

return_index

If True, returns the indices of elements in the input array

3

return_inverse

If True, returns the indices of unique array, which can be used to reconstruct the input array

4

return_counts

If True, returns the number of times the element in unique array appears in the original array

Example

import numpy as np 
a = np.array([5,2,6,2,7,5,6,8,2,9]) 

print 'First array:' 
print a 
print '\n'  

print 'Unique values of first array:' 
u = np.unique(a) 
print u 
print '\n'  

print 'Unique array and Indices array:' 
u,indices = np.unique(a, return_index = True) 
print indices 
print '\n'  

print 'We can see each number corresponds to index in original array:' 
print a 
print '\n'  

print 'Indices of unique array:' 
u,indices = np.unique(a,return_inverse = True) 
print u 
print '\n' 

print 'Indices are:' 
print indices 
print '\n'  

print 'Reconstruct the original array using indices:' 
print u[indices] 
print '\n'  

print 'Return the count of repetitions of unique elements:' 
u,indices = np.unique(a,return_counts = True) 
print u 
print indices

Its output is as follows −

First array:
[5 2 6 2 7 5 6 8 2 9]

Unique values of first array:
[2 5 6 7 8 9]

Unique array and Indices array:
[1 0 2 4 7 9]

We can see each number corresponds to index in original array:
[5 2 6 2 7 5 6 8 2 9]

Indices of unique array:
[2 5 6 7 8 9]

Indices are:
[1 0 2 0 3 1 2 4 0 5]

Reconstruct the original array using indices:
[5 2 6 2 7 5 6 8 2 9]

Return the count of repetitions of unique elements:
[2 5 6 7 8 9]
 [3 2 2 1 1 1]
numpy_array_manipulation.htm
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