# 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