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# numpy.unique

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

  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.

    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.

    equal_nan bool, optional

      If True, collapses multiple NaN values in the return array into one.

      New in version 1.24.

    sorted bool, optional

      If True, the unique elements are sorted. Elements may be sorted in practice even if `sorted=False`, but this could change without notice.

      New in version 2.3.

  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.

  See also

  `repeat`

    Repeat elements of an array.

  `sort`

    Return a sorted copy 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.

  Changed in version 1.21: Like np.sort, NaN will sort to the end of the values. For complex arrays all NaN values are considered equivalent (no matter whether the NaN is in the real or imaginary part). As the representant for the returned array the smallest one in the lexicographical order is chosen - see np.sort for how the lexicographical order is defined for complex arrays.

  Changed in version 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:

      >>> 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