parameters: (
          self, keep="first", nulls_are_equal=True,
      )
return_statement: return self._from_columns_like_self(
              drop_duplicates(
                  list(self._columns),
                  keys=range(len(self._data)),
                  keep=keep,
                  nulls_are_equal=nulls_are_equal,
              ),
              self._column_names,
          )
docstring: Drop duplicate rows in index.
  
          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.
          nulls_are_equal: bool, default True
              Null elements are considered equal to other null elements.
docstring_summary: Drop duplicate rows in index.
docstring_tokens: Drop duplicate rows in index .
function: def drop_duplicates(
          self, keep="first", nulls_are_equal=True,
      ):
          """
          Drop duplicate rows in index.
  
          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.
          nulls_are_equal: bool, default True
              Null elements are considered equal to other null elements.
          """
  
          # This utilizes the fact that all `Index` is also a `Frame`.
          # Except RangeIndex.
          return self._from_columns_like_self(
              drop_duplicates(
                  list(self._columns),
                  keys=range(len(self._data)),
                  keep=keep,
                  nulls_are_equal=nulls_are_equal,
              ),
              self._column_names,
          )
function_tokens: def drop_duplicates ( self , keep = "first" , nulls_are_equal = True , ) : # This utilizes the fact that all `Index` is also a `Frame`. # Except RangeIndex. return self . _from_columns_like_self ( drop_duplicates ( list ( self . _columns ) , keys = range ( len ( self . _data ) ) , keep = keep , nulls_are_equal = nulls_are_equal , ) , self . _column_names , )