e85cb30e6adb96462791fb07c084c87ac8f69c9f,pdpipe/col_generation.py,OneHotEncode,__init__,#OneHotEncode#Any#Any#Any#Any#Any#Any#,200

Before Change

        **kwargs
    ):
        if columns is None:
            self._columns = None
        else:
            self._columns = _interpret_columns_param(columns)
        self._dummy_na = dummy_na
        if exclude_columns is None:
            self._exclude_columns = []
        else:
            self._exclude_columns = _interpret_columns_param(exclude_columns)
        self._col_subset = col_subset
        self._drop_first = drop_first
        self._drop = drop
        self._dummy_col_map = {}
        self._encoder_map = {}
        col_str = _list_str(self._columns)
        super_kwargs = {
            "exmsg": OneHotEncode._DEF_1HENCODE_EXC_MSG.format(col_str),
            "appmsg": OneHotEncode._DEF_1HENCODE_APP_MSG.format(
                col_str or "all columns"
            ),
            "desc": "One-hot encode {}".format(
                col_str or "all categorical columns"
            ),
        }
        super_kwargs.update(**kwargs)
        super().__init__(**super_kwargs)

    def _prec(self, df):

After Change

        if columns is None:
            columns = OfDtypes(["object", "category"])
        else:
            columns = columns_to_qualifier(columns)
        if exclude_columns:
            exclude_columns = columns_to_qualifier(exclude_columns)
            columns = columns - exclude_columns
        self._dummy_na = dummy_na
        self._col_subset = col_subset
        self._drop_first = drop_first
        self._drop = drop
        self._dummy_col_map = {}
        self._encoder_map = {}
        super_kwargs = {
            "columns": columns,
            "desc_temp": "One-hot encode {}",
        }
        super_kwargs.update(**kwargs)
        super().__init__(**super_kwargs)

    def _prec(self, df):

Instances