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# pandas.Index.drop_duplicates

Index.drop_duplicates(*, keep='first')

  Return Index with duplicate values removed.

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

      - ‘first’ : Drop duplicates except for the first occurrence.

      - ‘last’ : Drop duplicates except for the last occurrence.

      - `False` : Drop all duplicates.

  Returns:
    Index

      A new Index object with the duplicate values removed.

  See also

  `Series.drop_duplicates`

    Equivalent method on Series.

  `DataFrame.drop_duplicates`

    Equivalent method on DataFrame.

  `Index.duplicated`

    Related method on Index, indicating duplicate Index values.

  Examples

  Generate a pandas.Index with duplicate values.

      >>> idx = pd.Index(["llama", "cow", "llama", "beetle", "llama", "hippo"])

  The keep parameter controls which duplicate values are removed. The value ‘first’ keeps the first occurrence for each set of duplicated entries. The default value of keep is ‘first’.

      >>> idx.drop_duplicates(keep="first")
      Index(['llama', 'cow', 'beetle', 'hippo'], dtype='str')

  The value ‘last’ keeps the last occurrence for each set of duplicated entries.

      >>> idx.drop_duplicates(keep="last")
      Index(['cow', 'beetle', 'llama', 'hippo'], dtype='str')

  The value `False` discards all sets of duplicated entries.

      >>> idx.drop_duplicates(keep=False)
      Index(['cow', 'beetle', 'hippo'], dtype='str')