# dask.dataframe.Series.drop_duplicates

Series.drop_duplicates(subset=None, split_every=None, split_out=1, shuffle_method=None, ignore_index=False, **kwargs)

  Return DataFrame with duplicate rows removed.

  This docstring was copied from pandas.core.frame.DataFrame.drop_duplicates.

  Some inconsistencies with the Dask version may exist.

  Known inconsistencies:

    keep=False will raise a `NotImplementedError`

  Considering certain columns is optional. Indexes, including time indexes are ignored.

  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’ (Not supported in Dask)

      Determines which duplicates (if any) to keep.

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

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

      - `False` : Drop all duplicates.

    inplace bool, default `False` (Not supported in Dask)

      Whether to modify the DataFrame rather than creating a new one.

    ignore_index bool, default `False`

      If `True`, the resulting axis will be labeled 0, 1, …, n - 1.

  Returns:
    DataFrame or None

      DataFrame with duplicates removed or None if `inplace=True`.

  See also

  `DataFrame.value_counts`

    Count unique combinations of columns.

  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, it removes duplicate rows based on all columns.

      >>> df.drop_duplicates()
          brand style  rating
      0  Yum Yum   cup     4.0
      2  Indomie   cup     3.5
      3  Indomie  pack    15.0
      4  Indomie  pack     5.0

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

      >>> df.drop_duplicates(subset=['brand'])
          brand style  rating
      0  Yum Yum   cup     4.0
      2  Indomie   cup     3.5

  To remove duplicates and keep last occurrences, use `keep`.

      >>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
          brand style  rating
      1  Yum Yum   cup     4.0
      2  Indomie   cup     3.5
      4  Indomie  pack     5.0