如何在plsql中进行数据透视

Question

我有这样的数据

Col_A     Col_B      Col_C    Value    Col_E
Id_1    String_a   type1      null     Flat
Id_1    String_a   type2      30       Not Flat
Id_1    String_b   type1      null     Flat
Id_1    String_b   type2      30       Not Flat
Id_2    String_c   type1      35       Flat
Id_2    String_c   type2      55       Flat

我希望输出如下

Col_A    type1_Col_E  type1  type1_count type2_Col_E  type_2 type2_count
Id_1     Flat         null   0           Not Flat     30     2
Id_2     Flat         35     1           Flat         35     1

我需要将所有类型都作为列，并且需要将每种类型的计数都视为另一列。 因此，如果我有2个与一个ID相关联的字符串，则其类型应为2。 我需要将所有类型都作为列，并且需要计数以指示有多少个字符串与每种类型相关联。 如果Value为null，则应将计数视为零。

使用PL SQL可以实现吗？

Answer 1

您不需要PL/SQL ，但具有以下相关子查询的SQL Select语句最适合您的情况，如下所示：

with tab( Col_A, Col_B, Col_C, Value, Col_E ) as
(
 select 'Id_1','String_a','type1',null,'Flat'     from dual union all
 select 'Id_1','String_a','type2',  30,'Not Flat' from dual union all
 select 'Id_1','String_b','type1',null,'Flat'     from dual union all
 select 'Id_1','String_b','type2',  30,'Not Flat' from dual union all
 select 'Id_2','String_c','type1',  35,'Flat'     from dual union all
 select 'Id_2','String_c','type2',  55,'Flat'     from dual 
)
select Col_A,
       ( select distinct Col_E from tab where Col_A = t.Col_A and Col_C = 'type1' ) 
         as type1_Col_E,
       ( select distinct value from tab where Col_A = t.Col_A and Col_C = 'type1' )   
         as type1,
       ( select sum(nvl2(value,1,0)) from tab where Col_A = t.Col_A and Col_C = 'type1' )   
         as type1_count,
       ( select distinct Col_E from tab where Col_A = t.Col_A and Col_C = 'type2' ) 
         as type2_Col_E,
       ( select distinct value from tab where Col_A = t.Col_A and Col_C = 'type2' )   
         as type2,
       ( select sum(nvl2(value,1,0)) from tab where Col_A = t.Col_A and Col_C = 'type2' )   
         as type2_count                        
      from tab t
     group by t.Col_A;

COL_A   TYPE1_COL_E  TYPE1  TYPE1_COUNT TYPE2_COL_E  TYPE2  TYPE2_COUNT
-----  -----------  ------- ----------- -----------  ------ -----------
Id_1     Flat         null      0        Not Flat      30        2
Id_2     Flat         35        1        Flat          35        1