I have Three tables as shown below..
I need output as shown in output table
for this i need to join three tables and order output in month order
tbl_MonthList
MonthID MonthList
1 January
2 February
3 March
4 April
5 May
6 June
7 July
8 August
9 September
10 October
11 November
12 December
tbl_Amount:
Month_id Amount_Received Customer_id
3 500 aaa
3 1000 bbb
4 700 jjj
5 300 aaa
5 400 jjj
5 500 ppp
7 1000 aaa
10 1500 bbb
12 700 jjj
tbl_Month_Target
MonthID MonthF_L
1 10000
2 150000
3 1000
4 50000
5 5000
6 3000
7 20000
8 12000
9 34000
10 85000
11 34000
12 45000
I need output as shown below
Month Total_amount MonthF_L
January 0 10000
February 0 150000
March 2000 1000
April 700 50000
May 1200 5000
June 0 3000
July 1000 20000
August 0 12000
September 0 34000
October 1500 85000
November 0 34000
December 700 45000
SELECT ML.MonthList AS Month,
Sum(A.Amount_Received) AS Total_amount,
First(MT.MonthF_L) AS MonthF_L
FROM (tbl_MonthList AS ML
INNER JOIN tbl_Month_Target AS MT ON ML.MonthID = MT.MonthID)
LEFT JOIN tbl_Amount AS A ON ML.MonthID = A.Month_id
GROUP BY ML.MonthList, ML.MonthID
ORDER BY ML.MonthID
Note: In MS Access, multiple joins must be explicitly nested within parentheses
Try this:
select ml.MonthList, sum(a.Amount_Received), mt.MonthF_L from tbl_MonthList ml
left join tbl_Month_Target mt on mt.MonthID = ml.MonthID
left join tbl_Amount a on ml.Month_id = ml.MonthID
group by ml.MonthList, mt.MonthF_L
Related
I have a table that is layed out as follows :
Code,Year, Wk1Val, Wk2Val, Wk3Val, Wk4Val etc to to Wk52Val.
Is there SQL to show the data as follows, without having to do 52 Case statements :
Code, Year, Week, Value
You can use UNPIVOT with REPLACE functions. Be sure to add all 52 week columns to the UNPIVOT IN portion of the query.
SELECT
u.Code,
u.Year,
REPLACE(REPLACE(u.Weeks, 'Wk', ''), 'Val', '') AS Weeks,
u.Value
FROM test
UNPIVOT
(
Value FOR Weeks IN (Wk1Val, Wk2Val, Wk3Val, Wk4Val, Wk52Val)
) AS u;
Fiddle here.
Input:
Code
Year
Wk1Val
Wk2Val
Wk3Val
Wk4Val
Wk52Val
1
2022
5000
15000
9000
4000
1000
2
2022
900
3200
7000
8500
9500
3
2022
3500
7800
10000
6200
5600
Output:
Code
Year
Weeks
Value
1
2022
1
5000
1
2022
2
15000
1
2022
3
9000
1
2022
4
4000
1
2022
52
1000
2
2022
1
900
2
2022
2
3200
2
2022
3
7000
2
2022
4
8500
2
2022
52
9500
3
2022
1
3500
3
2022
2
7800
3
2022
3
10000
3
2022
4
6200
3
2022
52
5600
I am trying to use common table expression to split an yearly record into 12 monthly records. I have to do it for next 20 years records . That means 20 rows into 600 rows (20*12=600 records).
What is the best way to do it. Can anyone help with an efficient way to do it.
Using a single table as shown below. Year 0 means current year so it should split into remaining months and year=1 means next year onward it should split into 12 (months) records
id year value
1 0 3155174.87
1 1 30423037.3
1 2 35339631.25
expected result should look like this:
Id Year Month Value Calender year
1 0 5 150 2022
1 0 6 150 2022
1 0 7 150 2022
1 0 8 150 2022
1 0 9 150 2022
1 0 10 150 2022
1 0 11 150 2022
1 0 12 150 2022
1 0 1 150 2023
1 0 2 150 2023
1 0 3 150 2023
1 0 4 150 2023
1 1 5 100 2023
1 1 6 100 2023
1 1 7 100 2023
1 1 8 100 2023
1 1 9 100 2023
1 1 10 100 2023
1 1 11 100 2023
1 1 12 100 2023
1 1 1 100 2024
1 1 2 100 2024
1 1 3 100 2024
1 1 4 100 2024
You can simply join onto a list of months, and then use a bit of arithmetic to split the Value
SELECT
t.Id,
t.Year,
v.Month,
Value = t.Value / CASE WHEN t.Year = 0 THEN 13 - MONTH(GETDATE()) ELSE 12 END
FROM YourTable t
JOIN (VALUES
(1),(2),(3),(4),(5),(6),(7),(8),(9),(10),(11),(12)
) v(Month) ON t.year > 0 OR v.Month >= MONTH(GETDATE());
db<>fiddle
While transposing single columns is pretty straight forward I need to transpose a large amount of data with 3 sets of , 10+ related columns needed to be transposed.
create table test
(month int,year int,po1 int,po2 int,ro1 int,ro2 int,mo1 int,mo2 int, mo3 int);
insert into test
values
(5,2013,100,20,10,1,3,4,5),(4,2014,200,30,20,2,4,5,6),(6,2015,200,80,30,3,5,6,7) ;
select * FROM test;
gives
month
year
po1
po2
ro1
ro2
mo1
mo2
mo3
5
2013
100
20
10
1
3
4
5
4
2014
200
30
20
2
4
5
6
6
2015
200
80
30
3
5
6
7
Transposing using UNPIVOT
select
month, year,
PO, RO, MO
from ( SELECT * from test) src
unpivot
( PO for Description in (po1, po2))unpiv1
unpivot
(RO for Description1 in (ro1, ro2)) unpiv2
unpivot
(MO for Description2 in (mo1, mo2, mo3)) unpiv3
order by year
Gives me this
month
year
PO
RO
MO
5
2013
100
10
3
5
2013
100
10
4
5
2013
100
10
5
5
2013
100
1
3
5
2013
100
1
4
5
2013
100
1
5
5
2013
20
10
3
5
2013
20
10
4
5
2013
20
10
5
5
2013
20
1
3
5
2013
20
1
4
5
2013
20
1
5
4
2014
200
20
4
4
2014
200
20
5
4
2014
200
20
6
4
2014
200
2
4
4
2014
200
2
5
4
2014
200
2
6
4
2014
30
20
4
4
2014
30
20
5
4
2014
30
20
6
4
2014
30
2
4
4
2014
30
2
5
4
2014
30
2
6
6
2015
200
30
5
6
2015
200
30
6
6
2015
200
30
7
6
2015
200
3
5
6
2015
200
3
6
6
2015
200
3
7
6
2015
80
30
5
6
2015
80
30
6
6
2015
80
30
7
6
2015
80
3
5
6
2015
80
3
6
6
2015
80
3
7
I will like to turn it to something like this. Is that possible?
month
year
PO
RO
MO
5
2013
100
10
3
5
2013
20
1
4
5
2013
0
0
5
4
2014
200
20
4
4
2014
30
2
5
4
2014
0
0
6
6
2015
200
30
5
6
2015
80
3
6
6
2015
0
0
7
Maybe use a query like below which creates rows as per your design using CROSS APPLY
select month,year,po,ro,mo from
test cross apply
(values (po1,ro1,mo1), (po2,ro2,mo2),(0,0,mo3))v(po,ro,mo)
see demo here
Unpivot acts similar as union,Use union all in your case
SELECT month,
year,
po1 AS PO,
ro1 AS RO,
mo1 AS MO
FROM test
UNION ALL
SELECT month,
year,
po2,
ro2,
mo2
FROM test
UNION ALL
SELECT month,
year,
0,
0,
mo2
FROM test
I have a data frame as shown below. which is a sales data of two health care product starting from December 2016 to November 2018.
product price sale_date discount
A 50 2016-12-01 5
A 50 2017-01-03 4
B 200 2016-12-24 10
A 50 2017-01-18 3
B 200 2017-01-28 15
A 50 2017-01-18 6
B 200 2017-01-28 20
A 50 2017-04-18 6
B 200 2017-12-08 25
A 50 2017-11-18 6
B 200 2017-08-21 20
B 200 2017-12-28 30
A 50 2018-03-18 10
B 300 2018-06-08 45
B 300 2018-09-20 50
A 50 2018-11-18 8
B 300 2018-11-28 35
From the above I would like to prepare below data frame
Expected Output:
product year number_of_months total_price total_discount number_of_sales
A 2016 1 50 5 1
B 2016 1 200 10 1
A 2017 12 250 25 5
B 2017 12 1000 110 5
A 2018 11 100 18 2
B 2018 11 900 130 3
Note: Please note that the data starts from Dec 2016 to Nov 2018.
So number of months in 2016 is 1, in 2017 we have full data so 12 months and 2018 we have 11 months.
First aggregate sum by years and product and then create new column for counts by months by DataFrame.insert and Series.map:
df1 =(df.groupby(['product',df['sale_date'].dt.year], sort=False).sum().add_prefix('total_')
.reset_index())
df1.insert(2,'number_of_months', df1['sale_date'].map({2016:1, 2017:12, 2018:11}))
print (df1)
product sale_date number_of_months total_price total_discount
0 A 2016 1 50 5
1 A 2017 12 250 25
2 B 2016 1 200 10
3 B 2017 12 1000 110
4 A 2018 11 100 18
5 B 2018 11 900 130
If want dynamic dictionary by minumal and maximal datetimes use:
s = pd.date_range(df['sale_date'].min(), df['sale_date'].max(), freq='MS')
d = s.year.value_counts().to_dict()
print (d)
{2017: 12, 2018: 11, 2016: 1}
df1 = (df.groupby(['product',df['sale_date'].dt.year], sort=False).sum().add_prefix('total_')
.reset_index())
df1.insert(2,'number_of_months', df1['sale_date'].map(d))
print (df1)
product sale_date number_of_months total_price total_discount
0 A 2016 1 50 5
1 A 2017 12 250 25
2 B 2016 1 200 10
3 B 2017 12 1000 110
4 A 2018 11 100 18
5 B 2018 11 900 130
For ploting is used DataFrame.set_index with DataFrame.unstack:
df2 = (df1.set_index(['sale_date','product'])[['total_price','total_discount']]
.unstack(fill_value=0))
df2.columns = df2.columns.map('_'.join)
print (df2)
total_price_A total_price_B total_discount_A total_discount_B
sale_date
2016 50 200 5 10
2017 250 1000 25 110
2018 100 900 18 130
df2.plot()
EDIT:
df1 = (df.groupby(['product',df['sale_date'].dt.year], sort=False)
.agg( total_price=('price','sum'),
total_discount=('discount','sum'),
number_of_sales=('discount','size'))
.reset_index())
df1.insert(2,'number_of_months', df1['sale_date'].map({2016:1, 2017:12, 2018:11}))
print (df1)
product sale_date number_of_months total_price total_discount \
0 A 2016 NaN 50 5
1 A 2017 NaN 250 25
2 B 2016 NaN 200 10
3 B 2017 NaN 1000 110
4 A 2018 NaN 100 18
5 B 2018 NaN 900 130
number_of_sales
0 1
1 5
2 1
3 5
4 2
5 3
Table 1:
ID Year Month
-----------------
1 2018 1
2 2018 1
3 2018 1
1 2018 2
2 2018 2
3 2018 2
Table 2:
ID Year Jan Feb Mar
------------------------
1 2018 100 200 300
2 2018 200 400 300
3 2018 200 500 700
How can I join these two tables even though they are laid out differently?
I was exploring a case join but that doesn't seem to be exactly what I need.
I'd like my output to be:
ID Year Month Data
1 2018 1 100
2 2018 1 200
3 2018 1 200
1 2018 2 200
2 2018 2 400
3 2018 2 500
1 2018 3 300
2 2018 3 300
3 2018 3 700
So, firstly we get TableB in the right format:
SELECT B.ID, B.Year, B.MonthValue
INTO TableB_New
FROM TableB T
UNPIVOT
(
MonthValue FOR Month IN (Jan, Feb, Mar)
) AS B
And then you do the join. Good Luck!