# numpy.unique

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

  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

  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.

      New in version 1.9.0.

    axis int or 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. Object arrays or structured arrays that contain objects are not supported if the axis kwarg is used. The default is None.

      New in version 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.

      New in version 1.9.0.

  See also

  `numpy.lib.arraysetops`

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

  `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. The flattened subarrays are then viewed as a structured type with each element given a label, with the effect that we end up with a 1-D array of structured types that can be treated in the same way as any other 1-D array. The result is that the flattened subarrays are sorted in lexicographic order starting with the first element.

  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='<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:

      >>> 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

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