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# pandas.DataFrame.duplicated

DataFrame.duplicated(subset=None, keep='first')

  Return boolean Series denoting duplicate rows.

  Considering certain columns is optional.

  Parameters:
    subset column label or sequence of labels, optional

      Only consider certain columns for identifying duplicates, by default use all of the columns.

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

      Determines which duplicates (if any) to mark.

      - `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:
    Series

      Boolean series for each duplicated rows.

  See also

  `Index.duplicated`

    Equivalent method on index.

  `Series.duplicated`

    Equivalent method on Series.

  `Series.drop_duplicates`

    Remove duplicate values from Series.

  `DataFrame.drop_duplicates`

    Remove duplicate values from DataFrame.

  Examples

  Consider dataset containing ramen rating.

      >>> df = pd.DataFrame({
      ...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
      ...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
      ...     'rating': [4, 4, 3.5, 15, 5]
      ... })
      >>> df
          brand style  rating
      0  Yum Yum   cup     4.0
      1  Yum Yum   cup     4.0
      2  Indomie   cup     3.5
      3  Indomie  pack    15.0
      4  Indomie  pack     5.0

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

      >>> df.duplicated()
      0    False
      1     True
      2    False
      3    False
      4    False
      dtype: bool

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

      >>> df.duplicated(keep='last')
      0     True
      1    False
      2    False
      3    False
      4    False
      dtype: bool

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

      >>> df.duplicated(keep=False)
      0     True
      1     True
      2    False
      3    False
      4    False
      dtype: bool

  To find duplicates on specific column(s), use `subset`.

      >>> df.duplicated(subset=['brand'])
      0    False
      1     True
      2    False
      3     True
      4     True
      dtype: bool