How Can I Efficiently Transpose Columns and Rows in SQL?
SQL Column and Row Transposition: A Practical Guide
SQL often requires transposing data – converting table orientation from vertical (columns) to horizontal (rows) or the reverse. While the PIVOT
command exists, it can be cumbersome. This guide explores simpler alternatives.
Method 1: UNION ALL, Aggregate, and CASE Statement
This approach uses UNION ALL
to unpivot, then an aggregate function (here, SUM
) and a CASE
statement to repivot:
select name, sum(case when color = 'Red' then value else 0 end) Red, sum(case when color = 'Green' then value else 0 end) Green, sum(case when color = 'Blue' then value else 0 end) Blue from ( select color, Paul value, 'Paul' name from yourTable union all select color, John value, 'John' name from yourTable union all select color, Tim value, 'Tim' name from yourTable union all select color, Eric value, 'Eric' name from yourTable ) src group by name
Method 2: Static UNPIVOT and PIVOT
Knowing the number of columns to transform allows for a static UNPIVOT
and PIVOT
solution:
select name, [Red], [Green], [Blue] from ( select color, name, value from yourtable unpivot ( value for name in (Paul, John, Tim, Eric) ) unpiv ) src pivot ( sum(value) for color in ([Red], [Green], [Blue]) ) piv
Method 3: Dynamic Pivot for Variable Columns
When dealing with a dynamic number of columns and colors, dynamic SQL offers a solution:
DECLARE @colsUnpivot AS NVARCHAR(MAX), @query AS NVARCHAR(MAX), @colsPivot as NVARCHAR(MAX) select @colsUnpivot = stuff((select ','+quotename(C.name) from sys.columns as C where C.object_id = object_id('yourtable') and C.name <> 'color' for xml path('')), 1, 1, '') select @colsPivot = STUFF((SELECT ',' + quotename(color) from yourtable t FOR XML PATH(''), TYPE ).value('.', 'NVARCHAR(MAX)') ,1,1,'') set @query = 'select name, '+@colsPivot+' from ( select color, name, value from yourtable unpivot ( value for name in ('+@colsUnpivot+') ) unpiv ) src pivot ( sum(value) for color in ('+@colsPivot+') ) piv' exec(@query)
These methods provide versatile approaches to data transposition in SQL, adapting to various data manipulation needs.
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