numpy.unique

numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False)

Find the unique elements of an array.

Returns the sorted unique elements of an array. There are two optional outputs in addition to the unique elements: the indices of the input array that give the unique values, and the indices of the unique array that reconstruct the input array.

Parameters:

ar : array_like

Input array. This will be flattened if it is not already 1-D.

return_index : bool, optional

If True, also return the indices of ar that result in the unique array.

return_inverse : bool, optional

If True, also return the indices of the unique array that can be used to reconstruct ar.

return_counts : bool, optional

New in version 1.9.0.

If True, also return the number of times each unique value comes up in ar.

Returns:

unique : ndarray

The sorted unique values.

unique_indices : ndarray, optional

The indices of the first occurrences of the unique values in the (flattened) original array. Only provided if return_index is True.

unique_inverse : ndarray, optional

The indices to reconstruct the (flattened) original array from the unique array. Only provided if return_inverse is True.

unique_counts : ndarray, optional

New in version 1.9.0.

The number of times each of the unique values comes up in the original array. Only provided if return_counts is True.

See also

numpy.lib.arraysetops

Module with a number of other functions for performing set operations on arrays.

Examples

>>> np.unique([1, 1, 2, 2, 3, 3])
array([1, 2, 3])
>>> a = np.array([[1, 1], [2, 3]])
>>> np.unique(a)
array([1, 2, 3])

Return the indices of the original array that give the unique values:

>>> a = np.array(['a', 'b', 'b', 'c', 'a'])
>>> u, indices = np.unique(a, return_index=True)
>>> u
array(['a', 'b', 'c'],
       dtype='|S1')
>>> indices
array([0, 1, 3])
>>> a[indices]
array(['a', 'b', 'c'],
       dtype='|S1')

Reconstruct the input array from the unique values:

>>> a = np.array([1, 2, 6, 4, 2, 3, 2])
>>> u, indices = np.unique(a, return_inverse=True)
>>> u
array([1, 2, 3, 4, 6])
>>> indices
array([0, 1, 4, 3, 1, 2, 1])
>>> u[indices]
array([1, 2, 6, 4, 2, 3, 2])