Reputation: 43
My output from SQL is as follows:
------------------------------------
| Name | Identifier | Date | Value |
-------------------------------------
| A | Bid | XX/XX | 10 |
-------------------------------------
| A | Ask | XX/XX | 11 |
-------------------------------------
| B | Bid | YY/YY | 20 |
-------------------------------------
| B | Ask | YY/YY | 21 |
-------------------------------------
My desired output preferably directly from SQL or with the help of Python or Excel is as follows:
--------------------------------
| Name | Date | Bid | Ask |
--------------------------------
| A | XX/XX | 10 | 11 |
--------------------------------
| B | YY/YY | 20 | 21 |
--------------------------------
What is the best way to accomplish this in either SQL, Python or Excel? My problem is that the next step in which I wish to use this data only handles inputs that are in the form of the "desired output" table.
EDIT: The original query is as follows:
SELECT * FROM table where Name (LIKE 'A' or LIKE 'B') and Date between 'AA/AA' and 'ZZ/ZZ'
Upvotes: 0
Views: 553
Reputation: 385
You can achieve the desired output using pivot. It is a functionality which can be found in all data analysis framework like excel, SQL etc.
For Excel, you can follow this link to acheive the desired result : http://www.excel-easy.com/data-analysis/pivot-tables.html
SQL :
I have written a dynamic sql by using pivot function
create table tbl1 ( name varchar(100),Identifier varchar(100), Date_val varchar(100), Value int);
INSERT INTO tbl1 values ('A','Bid','XX/XX',10),('A','Ask','XX/XX',11),('b','Bid','YY/YY',20),
('b','Ask','YY/YY',21)
DECLARE @cols AS NVARCHAR(MAX), @query AS NVARCHAR(MAX)
select @cols = STUFF((SELECT ',' + QUOTENAME(Identifier)
from tbl1
group by Identifier
order by Identifier
FOR XML PATH(''), TYPE
).value('.', 'NVARCHAR(MAX)')
,1,1,'')
set @query = 'SELECT name,Date_val,' + @cols + ' from
(
select name,Identifier, Date_val, value
from tbl1
) x
pivot
(
sum(value)
for Identifier in (' + @cols + ')
) p '
execute (@query)
Upvotes: 1
Reputation: 2475
In Python Pandas you could use PD.melt and specify the columns you want to keep the same. The others will get pivoted.
For more info: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.melt.html
Upvotes: 0