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This is not the standard behavior of UNION vs UNION ALL, as documented in this SO question. My code here, borrowing from the Spark SQL documentation, has the two functions returning
SELECT [Client_List_Mar_2018].Client_First_Name, [Client_List_Mar_2018].Client_Last_Name, [Client_List_Mar_2018].Client_Country_of_Residence FROM [Client_List_Mar_2018]. And the result of your query would look like this: Apply a UNION across more than 2 tables.
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pandas.concat(objs: Union[Iterable[FrameOrSeries], Mapping[Label, FrameOrSeries]], axis='0', join: str = "'outer'", ignore_index: bool = 'False', keys='None'