numpy.unique

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

Find the unique elements of an array.

Returns the sorted unique elements of an array. There are three optional outputs in addition to the unique elements: the indices of the input array that give the unique values, the indices of the unique array that reconstruct the input array, and the number of times each unique value comes up in the input array.

Parameters:

ar : array_like Input array. Unless axis is specified, this will be flattened if it is not already 1-D. return_index : bool, optional If True, also return the indices of ar (along the specified axis, if provided, or in the flattened array) that result in the unique array. return_inverse : bool, optional If True, also return the indices of the unique array (for the specified axis, if provided) that can be used to reconstruct ar. return_counts : bool, optional If True, also return the number of times each unique item appears in ar. .. versionadded:: 1.9.0 axis : int or None, optional The axis to operate on. If None, ar will be flattened beforehand. Otherwise, duplicate items will be removed along the provided axis, with all the other axes belonging to the each of the unique elements. Object arrays or structured arrays that contain objects are not supported if the axis kwarg is used. .. versionadded:: 1.13.0

Returns:

unique : ndarray The sorted unique values. unique_indices : ndarray, optional The indices of the first occurrences of the unique values in the original array. Only provided if return_index is True. unique_inverse : ndarray, optional The indices to reconstruct the original array from the unique array. Only provided if return_inverse is True. unique_counts : ndarray, optional The number of times each of the unique values comes up in the original array. Only provided if return_counts is True. .. versionadded:: 1.9.0

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 unique rows of a 2D array

>>> a = np.array([[1, 0, 0], [1, 0, 0], [2, 3, 4]])
>>> np.unique(a, axis=0)
array([[1, 0, 0], [2, 3, 4]])

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])