dpnp.unique

dpnp.unique(ar, return_index=False, return_inverse=False, return_counts=False, axis=None, *, equal_nan=True)[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

  • the number of times each unique value comes up in the input array

For full documentation refer to numpy.unique.

Parameters:

  • ar ({dpnp.ndarray, usm_ndarray}) -- 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. Default: False.

  • 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. Default: False.

  • return_counts (bool, optional) -- If True, also return the number of times each unique item appears in ar. Default: False.

  • axis ({int, None}, optional) -- The axis to operate on. If None, ar will be flattened. If an integer, the subarrays indexed by the given axis will be flattened and treated as the elements of a 1-D array with the dimension of the given axis, see the notes for more details. Default: None.

  • equal_nan (bool, optional) -- If True, collapses multiple NaN values in the return array into one. Default: True.

Returns:

  • unique (dpnp.ndarray) -- The sorted unique values.

  • unique_indices (dpnp.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 (dpnp.ndarray, optional) -- The indices to reconstruct the original array from the unique array. Only provided if return_inverse is True.

  • unique_counts (dpnp.ndarray, optional) -- The number of times each of the unique values comes up in the original array. Only provided if return_counts is True.

See also

dpnp.repeat

Repeat elements of an array.

Notes

When an axis is specified the subarrays indexed by the axis are sorted. This is done by making the specified axis the first dimension of the array (move the axis to the first dimension to keep the order of the other axes) and then flattening the subarrays in C order. For complex arrays all NaN values are considered equivalent (no matter whether the NaN is in the real or imaginary part). As the representative for the returned array the smallest one in the lexicographical order is chosen. For multi-dimensional inputs, unique_inverse is reshaped such that the input can be reconstructed using dpnp.take(unique, unique_inverse, axis=axis).

Examples

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

Reconstruct the input array from the unique values and inverse:

>>> 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])

Reconstruct the input values from the unique values and counts:

>>> a = np.array([1, 2, 6, 4, 2, 3, 2])
>>> values, counts = np.unique(a, return_counts=True)
>>> values
array([1, 2, 3, 4, 6])
>>> counts
array([1, 3, 1, 1, 1])
>>> np.repeat(values, counts)
array([1, 2, 2, 2, 3, 4, 6])    # original order not preserved