Repository: numpy/numpy
File: numpy/lib/_arraysetops_impl.py (Python)

Code before:
        order is defined for complex arrays.

    .. versionchanged:: 2.0
        For multi-dimensional inputs, ``unique_inverse`` is reshaped
        such that the input can be reconstructed using
        ``np.take(unique, unique_inverse, axis=axis)``. The result is
        now not 1-dimensional when ``axis=None``.

        Note that in NumPy 2.0.0 a higher dimensional array was returned also
        when ``axis`` was not ``None``.  This was reverted, but
        ``inverse.reshape(-1)`` can be used to ensure compatibility with both
        versions.

    Examples
    --------
    >>> import numpy as np
    >>> 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])
    >>> a = np.array([np.nan,0,0,np.nan])
    >>> u = np.unique(a, axis=0, equal_nan=True)
    >>> u
    array([ 0., nan])
    # 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='<U1')
    >>> indices
    array([0, 1, 3])
    >>> a[indices]
    array(['a', 'b', 'c'], dtype='<U1')

    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]

Review comment (suggestion):
```suggestion

    Return the unique rows of a 2D array
```

Code after:
        order is defined for complex arrays.

    .. versionchanged:: 2.0
        For multi-dimensional inputs, ``unique_inverse`` is reshaped
        such that the input can be reconstructed using
        ``np.take(unique, unique_inverse, axis=axis)``. The result is
        now not 1-dimensional when ``axis=None``.

        Note that in NumPy 2.0.0 a higher dimensional array was returned also
        when ``axis`` was not ``None``.  This was reverted, but
        ``inverse.reshape(-1)`` can be used to ensure compatibility with both
        versions.

    Examples
    --------
    >>> import numpy as np
    >>> 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='<U1')
    >>> indices
    array([0, 1, 3])
    >>> a[indices]
    array(['a', 'b', 'c'], dtype='<U1')

    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: