Fill NA/NaN values using the specified method.

        Args:
            value: Value to use to fill holes. This value cannot be a list.

            method: Method to use for filling holes in reindexed Series pad.
                ffill: propagate last valid observation forward to next valid
                backfill.
                bfill: use NEXT valid observation to fill gap.

            axis: 0 or 'index', 1 or 'columns'.

            inplace: If True, fill in place. Note: this will modify any other
                views on this object.

            limit: If method is specified, this is the maximum number of
                consecutive NaN values to forward/backward fill. In other
                words, if there is a gap with more than this number of
                consecutive NaNs, it will only be partially filled. If method
                is not specified, this is the maximum number of entries along
                the entire axis where NaNs will be filled. Must be greater
                than 0 if not None.

            downcast: A dict of item->dtype of what to downcast if possible,
                or the string 'infer' which will try to downcast to an
                appropriate equal type.

        Returns:
            filled: DataFrame

def fillna(
        self,
        value=None,
        method=None,
        axis=None,
        inplace=False,
        limit=None,
        downcast=None,
        **kwargs
    ):
        """Fill NA/NaN values using the specified method.

        Args:
            value: Value to use to fill holes. This value cannot be a list.

            method: Method to use for filling holes in reindexed Series pad.
                ffill: propagate last valid observation forward to next valid
                backfill.
                bfill: use NEXT valid observation to fill gap.

            axis: 0 or 'index', 1 or 'columns'.

            inplace: If True, fill in place. Note: this will modify any other
                views on this object.

            limit: If method is specified, this is the maximum number of
                consecutive NaN values to forward/backward fill. In other
                words, if there is a gap with more than this number of
                consecutive NaNs, it will only be partially filled. If method
                is not specified, this is the maximum number of entries along
                the entire axis where NaNs will be filled. Must be greater
                than 0 if not None.

            downcast: A dict of item->dtype of what to downcast if possible,
                or the string 'infer' which will try to downcast to an
                appropriate equal type.

        Returns:
            filled: DataFrame
        """
        # TODO implement value passed as DataFrame/Series
        if isinstance(value, BasePandasDataset):
            new_query_compiler = self._default_to_pandas(
                "fillna",
                value=value._to_pandas(),
                method=method,
                axis=axis,
                inplace=False,
                limit=limit,
                downcast=downcast,
                **kwargs
            )._query_compiler
            return self._create_or_update_from_compiler(new_query_compiler, inplace)
        inplace = validate_bool_kwarg(inplace, "inplace")
        axis = self._get_axis_number(axis) if axis is not None else 0
        if isinstance(value, (list, tuple)):
            raise TypeError(
                '"value" parameter must be a scalar or dict, but '
                'you passed a "{0}"'.format(type(value).__name__)
            )
        if value is None and method is None:
            raise ValueError("must specify a fill method or value")
        if value is not None and method is not None:
            raise ValueError("cannot specify both a fill method and value")
        if method is not None and method not in ["backfill", "bfill", "pad", "ffill"]:
            expecting = "pad (ffill) or backfill (bfill)"
            msg = "Invalid fill method. Expecting {expecting}. Got {method}".format(
                expecting=expecting, method=method
            )
            raise ValueError(msg)

        new_query_compiler = self._query_compiler.fillna(
            value=value,
            method=method,
            axis=axis,
            inplace=False,
            limit=limit,
            downcast=downcast,
            **kwargs
        )
        return self._create_or_update_from_compiler(new_query_compiler, inplace)