In all modern databases all these constructs are optimized to the same plan. Some databases (like SQL Server) require an ON condition after the INNER JOIN, so your third query just won’t parse there. Visibility scope of the tables is in the JOIN order, so this query: SELECT * FROM s1 JOIN s2 ON s1.id … Read more
in post-pandemic new world we can solve this with: =INDEX(FLATTEN(A2:A3&” “&TRANSPOSE(B2:B4))) to account for future expansion we can do: =INDEX(FLATTEN(FILTER(A2:A; A2:A<>””)&” “&TRANSPOSE(FILTER(B2:B; B2:B<>””)))) for 3 columns: =INDEX(FLATTEN(FLATTEN( FILTER(A2:A; A2:A<>””)&” “&TRANSPOSE( FILTER(B2:B; B2:B<>””)))&” “&TRANSPOSE( FILTER(C2:C; C2:C<>””)))) 4 columns: =INDEX(FLATTEN(FLATTEN(FLATTEN( FILTER(A2:A; A2:A<>””)&” “&TRANSPOSE( FILTER(B2:B; B2:B<>””)))&” “&TRANSPOSE( FILTER(C2:C; C2:C<>””)))&” “&TRANSPOSE( FILTER(D2:D; D2:D<>””)))) for more see: https://stackoverflow.com/a/74160711/5632629
A cross-join is simply the Cartesian product of two sets. There’s no explicit join operator for it. var combo = from p in people from c in cars select new { p.Name, c.Make, c.Model, c.Colour };
In recent versions of Pandas (>= 1.2) this is built into merge so you can do: from pandas import DataFrame df1 = DataFrame({‘col1′:[1,2],’col2’:[3,4]}) df2 = DataFrame({‘col3′:[5,6]}) df1.merge(df2, how=’cross’) This is equivalent to the previous pandas < 1.2 answer but is easier to read. For pandas < 1.2: If you have a key that is repeated … Read more
Here is the best example of Cross Join and Inner Join. Consider the following tables TABLE : Teacher x————————x | TchrId | TeacherName | x———-|————-x | T1 | Mary | | T2 | Jim | x————————x TABLE : Student x————————————–x | StudId | TchrId | StudentName | x———-|————-|————-x | S1 | T1 | Vineeth | … Read more
A cross join produces a cartesian product between the two tables, returning all possible combinations of all rows. It has no on clause because you’re just joining everything to everything. A full outer join is a combination of a left outer and right outer join. It returns all rows in both tables that match the … Read more