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numpy.unique
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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