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# pandas.DataFrame.drop_duplicates

DataFrame.drop_duplicates(subset=None, *, keep='first', inplace=False, ignore_index=False)

  Return DataFrame with duplicate rows removed.

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

  Parameters:
    subset column label or iterable 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 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`

      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.

  Notes

  This method requires columns specified by `subset` to be of hashable type. Passing unhashable columns will raise a `TypeError`.

  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