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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def numeric_function_clean_dataframe(self, axis):
 """Preprocesses numeric functions to clean dataframe and pick numeric indices. Args: axis: '0' if columns and '1' if rows. Returns: Tuple with return value(if any), indices to apply func to & cleaned Manager. """

        result = None
        query_compiler = self
        # If no numeric columns and over columns, then return empty Series
        if not axis and len(self.index) == 0:
            result = pandas.Series(dtype=np.int64)

        nonnumeric = [
            col
            for col, dtype in zip(self.columns, self.dtypes)
            if not is_numeric_dtype(dtype)
        ]
        if len(nonnumeric) == len(self.columns):
            # If over rows and no numeric columns, return this
            if axis:
                result = pandas.Series([np.nan for _ in self.index])
            else:
                result = pandas.Series([0 for _ in self.index])
        else:
            query_compiler = self.drop(columns=nonnumeric)
        return result, query_compiler