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# pandas.Index.duplicated

Index.duplicated(keep='first')

  Indicate duplicate index values.

  Duplicated values are indicated as `True` values in the resulting array. Either all duplicates, all except the first, or all except the last occurrence of duplicates can be indicated.

  Parameters:
    keep{‘first’, ‘last’, False}, default ‘first’

      The value or values in a set of duplicates to mark as missing.

      - ‘first’ : Mark duplicates as `True` except for the first occurrence.

      - ‘last’ : Mark duplicates as `True` except for the last occurrence.

      - `False` : Mark all duplicates as `True`.

  Returns:
    np.ndarray[bool]

      A numpy array of boolean values indicating duplicate index values.

  See also

  `Series.duplicated`

    Equivalent method on pandas.Series.

  `DataFrame.duplicated`

    Equivalent method on pandas.DataFrame.

  `Index.drop_duplicates`

    Remove duplicate values from Index.

  Examples

  By default, for each set of duplicated values, the first occurrence is set to False and all others to True:

      >>> idx = pd.Index(["llama", "cow", "llama", "beetle", "llama"])
      >>> idx.duplicated()
      array([False, False,  True, False,  True])

  which is equivalent to

      >>> idx.duplicated(keep="first")
      array([False, False,  True, False,  True])

  By using ‘last’, the last occurrence of each set of duplicated values is set on False and all others on True:

      >>> idx.duplicated(keep="last")
      array([ True, False,  True, False, False])

  By setting keep on `False`, all duplicates are True:

      >>> idx.duplicated(keep=False)
      array([ True, False,  True, False,  True])