I divided the month into four weeks and printed the amount for each week. How do I set this up with a loop for 12 months?
declare
cursor c is
select varis_tar, tutar
from muhasebe.doviz_takip
where trunc(varis_tar) BETWEEN TO_DATE('01/10/2021', 'DD/MM/YYYY') AND
TO_DATE('31/10/2021', 'DD/MM/YYYY')
group by varis_tar,tutar;
tutar1 number(13,2):=0;
tutar2 number(13,2):=0;
tutar3 number(13,2):=0;
tutar4 number(13,2):=0;
begin
for r in c loop
if r.varis_tar between TO_DATE('01/10/2021', 'DD/MM/YYYY') AND
TO_DATE('07/10/2021', 'DD/MM/YYYY') then
tutar1:=(r.tutar)+tutar1;
--message(r.tutar);
elsif r.varis_tar between TO_DATE('07/10/2021', 'DD/MM/YYYY') AND
TO_DATE('14/10/2021', 'DD/MM/YYYY') then
tutar2:=(r.tutar)+tutar2;
--message(r.tutar);
elsif r.varis_tar between TO_DATE('14/10/2021', 'DD/MM/YYYY') AND
TO_DATE('21/10/2021', 'DD/MM/YYYY') then
tutar3:=(r.tutar)+tutar3;
--message(r.tutar);
elsif r.varis_tar between TO_DATE('21/10/2021', 'DD/MM/YYYY') AND
TO_DATE('31/10/2021', 'DD/MM/YYYY') then
tutar4:=(r.tutar)+tutar4;
--message(r.tutar);
end if;
end loop;
I tried to get the dates the same way for all the months. I tried that, but it worked wrong.
where trunc(varis_tar) BETWEEN TO_DATE('1', 'DD') AND
TO_DATE('31', 'DD')
if r.varis_tar between TO_DATE('1', 'DD') AND
TO_DATE('07', 'DD') then
elsif r.varis_tar between TO_DATE('7', 'DD') AND
TO_DATE('14', 'DD') then
elsif r.varis_tar between TO_DATE('14', 'DD') AND
TO_DATE('21', 'DD') then
elsif r.varis_tar between TO_DATE('21', 'DD') AND
TO_DATE('31', 'DD') then
I don't know if I'am understanding it correctly but:
try if extract(day from varis_tar) between 1 and 7
or more complex
l_week := to_char(varis_tar,'W'); --week number
if l_week = 1 then --first week
elsif l_week = 2 etc...
Your code has several issues:
date in Oracle is actually a datetime, so between will not count any time after the midnight of the upper boundary.
you count the midnight of the week's end twice: in current week and in the next week (between includes both boundaries).
you do not need any PL/SQL and especially a cursor loop, because it occupy resources during calculation outside of SQL context.
Use datetime format to calculate weeks, because it is easy to read and understand. Then group by corresponding components.
with a as (
select
date '2021-01-01' - 1 + level as dt
, level as val
from dual
connect by level < 400
)
, b as (
select
dt
, val
/*Map 29, 30 and 31 to 28*/
, to_char(
least(dt, trunc(dt, 'mm') + 27)
, 'yyyymmw'
) as w
from a
)
select
substr(w, 1, 4) as y
, substr(w, 5, 2) as m
, substr(w, -1) as w
, sum(val) as val
, min(dt) as dt_from
, max(dt) as dt_to
from b
group by
w
Y | M | W | VAL | DT_FROM | DT_TO
:--- | :- | :- | ---: | :--------- | :---------
2021 | 01 | 1 | 28 | 2021-01-01 | 2021-01-07
2021 | 01 | 2 | 77 | 2021-01-08 | 2021-01-14
2021 | 01 | 3 | 126 | 2021-01-15 | 2021-01-21
2021 | 01 | 4 | 265 | 2021-01-22 | 2021-01-31
2021 | 02 | 1 | 245 | 2021-02-01 | 2021-02-07
2021 | 02 | 2 | 294 | 2021-02-08 | 2021-02-14
2021 | 02 | 3 | 343 | 2021-02-15 | 2021-02-21
2021 | 02 | 4 | 392 | 2021-02-22 | 2021-02-28
2021 | 03 | 1 | 441 | 2021-03-01 | 2021-03-07
2021 | 03 | 2 | 490 | 2021-03-08 | 2021-03-14
2021 | 03 | 3 | 539 | 2021-03-15 | 2021-03-21
2021 | 03 | 4 | 855 | 2021-03-22 | 2021-03-31
2021 | 04 | 1 | 658 | 2021-04-01 | 2021-04-07
2021 | 04 | 2 | 707 | 2021-04-08 | 2021-04-14
2021 | 04 | 3 | 756 | 2021-04-15 | 2021-04-21
2021 | 04 | 4 | 1044 | 2021-04-22 | 2021-04-30
2021 | 05 | 1 | 868 | 2021-05-01 | 2021-05-07
2021 | 05 | 2 | 917 | 2021-05-08 | 2021-05-14
2021 | 05 | 3 | 966 | 2021-05-15 | 2021-05-21
2021 | 05 | 4 | 1465 | 2021-05-22 | 2021-05-31
2021 | 06 | 1 | 1085 | 2021-06-01 | 2021-06-07
2021 | 06 | 2 | 1134 | 2021-06-08 | 2021-06-14
2021 | 06 | 3 | 1183 | 2021-06-15 | 2021-06-21
2021 | 06 | 4 | 1593 | 2021-06-22 | 2021-06-30
2021 | 07 | 1 | 1295 | 2021-07-01 | 2021-07-07
2021 | 07 | 2 | 1344 | 2021-07-08 | 2021-07-14
2021 | 07 | 3 | 1393 | 2021-07-15 | 2021-07-21
2021 | 07 | 4 | 2075 | 2021-07-22 | 2021-07-31
2021 | 08 | 1 | 1512 | 2021-08-01 | 2021-08-07
2021 | 08 | 2 | 1561 | 2021-08-08 | 2021-08-14
2021 | 08 | 3 | 1610 | 2021-08-15 | 2021-08-21
2021 | 08 | 4 | 2385 | 2021-08-22 | 2021-08-31
2021 | 09 | 1 | 1729 | 2021-09-01 | 2021-09-07
2021 | 09 | 2 | 1778 | 2021-09-08 | 2021-09-14
2021 | 09 | 3 | 1827 | 2021-09-15 | 2021-09-21
2021 | 09 | 4 | 2421 | 2021-09-22 | 2021-09-30
2021 | 10 | 1 | 1939 | 2021-10-01 | 2021-10-07
2021 | 10 | 2 | 1988 | 2021-10-08 | 2021-10-14
2021 | 10 | 3 | 2037 | 2021-10-15 | 2021-10-21
2021 | 10 | 4 | 2995 | 2021-10-22 | 2021-10-31
2021 | 11 | 1 | 2156 | 2021-11-01 | 2021-11-07
2021 | 11 | 2 | 2205 | 2021-11-08 | 2021-11-14
2021 | 11 | 3 | 2254 | 2021-11-15 | 2021-11-21
2021 | 11 | 4 | 2970 | 2021-11-22 | 2021-11-30
2021 | 12 | 1 | 2366 | 2021-12-01 | 2021-12-07
2021 | 12 | 2 | 2415 | 2021-12-08 | 2021-12-14
2021 | 12 | 3 | 2464 | 2021-12-15 | 2021-12-21
2021 | 12 | 4 | 3605 | 2021-12-22 | 2021-12-31
2022 | 01 | 1 | 2583 | 2022-01-01 | 2022-01-07
2022 | 01 | 2 | 2632 | 2022-01-08 | 2022-01-14
2022 | 01 | 3 | 2681 | 2022-01-15 | 2022-01-21
2022 | 01 | 4 | 3915 | 2022-01-22 | 2022-01-31
2022 | 02 | 1 | 1194 | 2022-02-01 | 2022-02-03
db<>fiddle here
Or the same in columns:
with a as (
select
date '2021-01-01' - 1 + level as dt
, level as val
from dual
connect by level < 400
)
, b as (
select
val
/*Map 29, 30 and 31 to 28*/
, to_char(dt, 'yyyymm') as m
, to_char(
least(dt, trunc(dt, 'mm') + 27)
, 'w'
) as w
from a
)
select
substr(m, 1, 4) as y
, substr(m, 5, 2) as m
, tutar1
, tutar2
, tutar3
, tutar4
from b
pivot(
sum(val)
for w in (
1 as tutar1, 2 as tutar2
, 3 as tutar3, 4 as tutar4
)
)
Y | M | TUTAR1 | TUTAR2 | TUTAR3 | TUTAR4
:--- | :- | -----: | -----: | -----: | -----:
2021 | 01 | 28 | 77 | 126 | 265
2021 | 02 | 245 | 294 | 343 | 392
2021 | 03 | 441 | 490 | 539 | 855
2021 | 04 | 658 | 707 | 756 | 1044
2021 | 05 | 868 | 917 | 966 | 1465
2021 | 06 | 1085 | 1134 | 1183 | 1593
2021 | 07 | 1295 | 1344 | 1393 | 2075
2021 | 08 | 1512 | 1561 | 1610 | 2385
2021 | 09 | 1729 | 1778 | 1827 | 2421
2021 | 10 | 1939 | 1988 | 2037 | 2995
2021 | 11 | 2156 | 2205 | 2254 | 2970
2021 | 12 | 2366 | 2415 | 2464 | 3605
2022 | 01 | 2583 | 2632 | 2681 | 3915
2022 | 02 | 1194 | null | null | null
db<>fiddle here
I'd like to analyze some daily data by hydrologic year: From 1 September to 31 August. I've created a synthetic data set with:
import pandas as pd
t = pd.date_range(start='2015-01-01', freq='D', end='2021-09-03')
df = pd.DataFrame(index = t)
df['hydro_year'] = df.index.year
df['hydro_year'].loc[df.index.month >= 9] += 1
df['id'] = df['hydro_year'] - df.index.year[0]
df['count'] = 1
Note that in reality I do not have a hydro_year column so I do not use groupby. I would expect the following to resample by hydrologic year:
print(df['2015-09-01':].resample('12M').agg({'hydro_year':'mean','id':'mean','count':'sum'}))
But the output does not align:
| | hydro_year | id | count |
|---------------------+------------+---------+-------|
| 2015-09-30 00:00:00 | 2016 | 1 | 30 |
| 2016-09-30 00:00:00 | 2016.08 | 1.08197 | 366 |
| 2017-09-30 00:00:00 | 2017.08 | 2.08219 | 365 |
| 2018-09-30 00:00:00 | 2018.08 | 3.08219 | 365 |
| 2019-09-30 00:00:00 | 2019.08 | 4.08219 | 365 |
| 2020-09-30 00:00:00 | 2020.08 | 5.08197 | 366 |
| 2021-09-30 00:00:00 | 2021.01 | 6.00888 | 338 |
However, if I start a day earlier, then things do align, except the first day is 'early' and dangling alone...
| | hydro_year | id | count |
|---------------------+------------+----+-------|
| 2015-08-31 00:00:00 | 2015 | 0 | 1 |
| 2016-08-31 00:00:00 | 2016 | 1 | 366 |
| 2017-08-31 00:00:00 | 2017 | 2 | 365 |
| 2018-08-31 00:00:00 | 2018 | 3 | 365 |
| 2019-08-31 00:00:00 | 2019 | 4 | 365 |
| 2020-08-31 00:00:00 | 2020 | 5 | 366 |
| 2021-08-31 00:00:00 | 2021 | 6 | 365 |
| 2022-08-31 00:00:00 | 2022 | 7 | 3 |
IIUC, you can use 12MS (Start) instead of 12M:
>>> df['2015-09-01':].resample('12MS') \
.agg({'hydro_year':'mean','id':'mean','count':'sum'})
hydro_year id count
2015-09-01 2016.0 1.0 366
2016-09-01 2017.0 2.0 365
2017-09-01 2018.0 3.0 365
2018-09-01 2019.0 4.0 365
2019-09-01 2020.0 5.0 366
2020-09-01 2021.0 6.0 365
2021-09-01 2022.0 7.0 3
We can try with Anchored Offsets annually starting with SEP:
resampled_df = df['2015-09-01':].resample('AS-SEP').agg({
'hydro_year': 'mean', 'id': 'mean', 'count': 'sum'
})
hydro_year id count
2015-09-01 2016.0 1.0 366
2016-09-01 2017.0 2.0 365
2017-09-01 2018.0 3.0 365
2018-09-01 2019.0 4.0 365
2019-09-01 2020.0 5.0 366
2020-09-01 2021.0 6.0 365
2021-09-01 2022.0 7.0 3
This is my source table
Reference ModifiedDate Teachers Students SchoolID ETC
-------------------------------------------------------------------------
1023175 2017-03-03 16:02:01.723 10 25 5
1023175 2017-03-07 07:59:49.283 15 50 15
1023175 2017-03-12 11:14:40.230 25 6 5
1023176 2017-03-04 16:02:01.723 11 35 8
1023176 2017-03-08 07:59:49.283 16 60 25
1023177 2017-03-15 11:14:40.230 15 7 2
I need the following output
Reference StartDate EndDate
---------------------------------------------
1023175 2017-03-03 16:02:01.723 2017-03-07 07:59:49.283
1023175 2017-03-07 07:59:49.283 2017-03-12 11:14:40.230
1023175 2017-03-12 11:14:40.230 9999-12-31 00:00:00.000
1023176 2017-03-04 16:02:01.723 2017-03-08 07:59:49.283
1023176 2017-03-08 07:59:49.283 9999-12-31 00:00:00.000
1023177 2017-03-15 11:14:40.230 9999-12-31 00:00:00.000 (last record should have this value)
Teachers Students SchoolID
10 25 5
15 50 15
25 6 5
11 35 8
16 60 25
15 7 2
All other columns like Teachers,Students and SchoolId etc also have to be in the output along with each record.
Any suggestions on how this can be achieved?
Using Sql Server 2008
using outer apply():
select
Reference
, StartDate = t.ModifiedDate
, EndDate = coalesce(x.ModifiedDate, convert(datetime,'9999-12-31 00:00:00.000'))
, Teachers
, Students
, SchoolID
from t
outer apply (
select top 1 i.ModifiedDate
from t as i
where i.Reference = t.Reference
and i.ModifiedDate > t.ModifiedDate
order by i.ModifiedDate asc
) x
rextester demo: http://rextester.com/RFTD32624
returns:
+-----------+-------------------------+-------------------------+----------+----------+----------+
| Reference | StartDate | EndDate | Teachers | Students | SchoolID |
+-----------+-------------------------+-------------------------+----------+----------+----------+
| 1023175 | 2017-03-03 16:02:01.723 | 2017-03-07 07:59:49.283 | 10 | 25 | 5 |
| 1023175 | 2017-03-07 07:59:49.283 | 2017-03-12 11:14:40.230 | 15 | 50 | 15 |
| 1023175 | 2017-03-12 11:14:40.230 | 9999-12-31 00:00:00.000 | 25 | 6 | 5 |
| 1023176 | 2017-03-04 16:02:01.723 | 2017-03-08 07:59:49.283 | 11 | 35 | 8 |
| 1023176 | 2017-03-08 07:59:49.283 | 9999-12-31 00:00:00.000 | 16 | 60 | 25 |
| 1023177 | 2017-03-15 11:14:40.230 | 9999-12-31 00:00:00.000 | 15 | 7 | 2 |
+-----------+-------------------------+-------------------------+----------+----------+----------+
Reference:
apply() - msdn
The power of T-SQL's APPLY operator - Rob Farley
APPLY: It Slices! It Dices! It Does It All! - Brad Shulz
I need a query to group an aggregate in one table by date ranges in another table.
Table 1
weeknumber | weekyear | weekstart | weekend
------------+----------+------------+------------
18 | 2016 | 2016-02-01 | 2016-02-08
19 | 2016 | 2016-02-08 | 2016-02-15
20 | 2016 | 2016-02-15 | 2016-02-22
21 | 2016 | 2016-02-22 | 2016-02-29
22 | 2016 | 2016-02-29 | 2016-03-07
23 | 2016 | 2016-03-07 | 2016-03-14
24 | 2016 | 2016-03-14 | 2016-03-21
25 | 2016 | 2016-03-21 | 2016-03-28
26 | 2016 | 2016-03-28 | 2016-04-04
27 | 2016 | 2016-04-04 | 2016-04-11
28 | 2016 | 2016-04-11 | 2016-04-18
29 | 2016 | 2016-04-18 | 2016-04-25
30 | 2016 | 2016-04-25 | 2016-05-02
31 | 2016 | 2016-05-02 | 2016-05-09
32 | 2016 | 2016-05-09 | 2016-05-16
33 | 2016 | 2016-05-16 | 2016-05-23
34 | 2016 | 2016-05-23 | 2016-05-30
35 | 2016 | 2016-05-30 | 2016-06-06
36 | 2016 | 2016-06-06 | 2016-06-13
37 | 2016 | 2016-06-13 | 2016-06-20
38 | 2016 | 2016-06-20 | 2016-06-27
39 | 2016 | 2016-06-27 | 2016-07-04
40 | 2016 | 2016-07-04 | 2016-07-11
41 | 2016 | 2016-07-11 | 2016-07-18
42 | 2016 | 2016-07-18 | 2016-07-25
43 | 2016 | 2016-07-25 | 2016-08-01
44 | 2016 | 2016-08-01 | 2016-08-08
45 | 2016 | 2016-08-08 | 2016-08-15
46 | 2016 | 2016-08-15 | 2016-08-22
47 | 2016 | 2016-08-22 | 2016-08-29
48 | 2016 | 2016-08-29 | 2016-09-05
49 | 2016 | 2016-09-05 | 2016-09-12
Table 2
accountid | rdate | fee1 | fee2 | fee3 | fee4
-----------+------------+------+------+------+------
481164 | 2015-12-22 | 8 | 1 | 5 | 1
481164 | 2002-12-22 | 1 | 0 | 0 | 0
481166 | 2015-12-22 | 1 | 0 | 0 | 0
481166 | 2016-10-20 | 14 | 0 | 0 | 0
481166 | 2016-10-02 | 5 | 0 | 0 | 0
481166 | 2016-01-06 | 18 | 4 | 0 | 5
482136 | 2016-07-04 | 18 | 0 | 0 | 0
481164 | 2016-07-04 | 2 | 3 | 4 | 5
481164 | 2016-06-28 | 34 | 0 | 0 | 0
481166 | 2016-07-20 | 50 | 0 | 0 | 69
481166 | 2016-07-13 | 16 | 0 | 0 | 5
481166 | 2016-09-15 | 8 | 0 | 0 | 2
481166 | 2016-10-03 | 8 | 0 | 0 | 0
I need to aggregate fee1+fee2+fee3+fee4 for rdates in each date range(weekstart,weekend) in table 1 and then group by accountid. Something like this:
accountid | weekstart | weekend | SUM
-----------+------------+------------+------
481164 | 2016-02-01 | 2016-02-08 | 69
481166 | 2016-02-01 | 2016-02-08 | 44
481164 | 2016-02-08 | 2016-02-15 | 22
481166 | 2016-02-08 | 2016-02-15 | 12
select accountid, weekstart, weekend,
sum(fee1 + fee2 + fee3 + fee4) as total_fee
from table2
inner join table1 on table2.rdate >= table1.weekstart and table2.rdate < table1.weekend
group by accountid, weekstart, weekend;
Just a thing:
weeknumber | weekyear | weekstart | weekend
------------+----------+------------+------------
18 | 2016 | 2016-02-01 | 2016-02-08
19 | 2016 | 2016-02-08 | 2016-02-15
weekend for week 18 should be 2016-02-07, because 2016-02-08 is weekstart for week 19.
weeknumber | weekyear | weekstart | weekend
------------+----------+------------+------------
18 | 2016 | 2016-02-01 | 2016-02-07
19 | 2016 | 2016-02-08 | 2016-02-14
Check it here: http://rextester.com/NCBO56250
I have got some rows of results like below, if each sum (row(i)) same, I can suppose the results are correct. How can I write a SQL clause to calculate sum of each row? thanks.
27 | 29 | 27 | 36 | 33 | 29 | 16 | 17 | 35 | 28 | 34 | 15
27 | 29 | 27 | 29 | 33 | 29 | 16 | 17 | 35 | 28 | 34 | 15
27 | 29 | 27 | 14 | 33 | 29 | 16 | 17 | 35 | 28 | 34 | 15
27 | 29 | 16 | 37 | 33 | 29 | 16 | 17 | 35 | 28 | 34 | 15
27 | 29 | 16 | 36 | 33 | 29 | 16 | 17 | 35 | 28 | 34 | 15