How do I merge two tables together?
Table 1:
LastName Age Weight Smoker
__________ ___ ______ ______
'Smith' 38 176 true
'Johnson' 43 163 false
'Williams' 38 131 false
Table 2:
LastName Age Weight Smoker
__________ ___ ______ ______
'Jones' 40 133 false
'Brown' 49 119 false
into:
LastName Age Weight Smoker
__________ ___ ______ ______
'Smith' 38 176 true
'Johnson' 43 163 false
'Williams' 38 131 false
'Jones' 40 133 false
'Brown' 49 119 false
You can simply do this using:
Complete_Table=[Table1;
Table2]
Complete Code:-
LastName = {'Smith';'Johnson';'Williams'};
Age = [38;43;38];
Weight = [176;163;131];
Smoker=logical([1;0;0]);
Table1 = table(LastName,Age,Weight,Smoker)
%Overwriting
LastName = {'Jones';'Brown'};
Age = [40;49];
Weight = [133;119];
Smoker=logical([0;0]);
Table2 = table(LastName,Age,Weight,Smoker)
Complete_Table=[Table1;
Table2]
Output:-
Table1 =
LastName Age Weight Smoker
__________ ___ ______ ______
'Smith' 38 176 true
'Johnson' 43 163 false
'Williams' 38 131 false
Table2 =
LastName Age Weight Smoker
________ ___ ______ ______
'Jones' 40 133 false
'Brown' 49 119 false
Complete_Table =
LastName Age Weight Smoker
__________ ___ ______ ______
'Smith' 38 176 true
'Johnson' 43 163 false
'Williams' 38 131 false
'Jones' 40 133 false
'Brown' 49 119 false
Related
Input Table:
prod
acct
acctno
newcinsfx
John
A01
1
89
John
A01
2
90
John
A01
2
92
Mary
A02
1
92
Mary
A02
3
81
Desired output table:
prod
acct
newcinsfx1
newcinsfx2
John
A01
89
John
A01
90
92
Mary
A02
92
Mary
A02
81
I tried to do it by distinct function.
df.select('prod',"acctno").distinct()
df.show()
I have a table which looks like this:
Entry number
Timestamp
Value1
Value2
Value3
Value4
5758
28-06-2018 16:30
34
63
34.2
60.9
5759
28-06-2018 17:00
33.5
58
34.9
58.4
5758
28-06-2018 16:30
34
63
34.2
60.9
5759
28-06-2018 17:00
33.5
58
34.9
58.4
5760
28-06-2018 17:30
33
53
35.2
58.5
5761
28-06-2018 18:00
33
63
35
57.9
5762
28-06-2018 18:30
33
61
34.6
58.9
5763
28-06-2018 19:00
33
59
34.1
59.4
5764
28-06-2018 19:30
28
89
33.5
64.2
5765
28-06-2018 20:00
28
89
33
66.1
5766
28-06-2018 20:30
28
83
32.5
67
5767
28-06-2018 21:00
29
89
32.2
68.4
Where '28-06-2018 16:30' is under one column. So I have 6 columns:
Entry number, Timestamp, Value1, Value2, Value3, Value4
I want to extract all rows that belong to '28-06-2018', i.e all data pertaining to that day. Since my table is too large I couldn't fit more data, however, the entries under the timestamp range for a couple of months.
t=table([5758;5759],["28-06-2018 16:30";"29-06-2018 16:30"],[34;33.5],'VariableNames',{'Entry number','Timestamp','Value1'})
t =
2×3 table
Entry number Timestamp Value1
____________ __________________ ______
5758 "28-06-2018 16:30" 34
5759 "29-06-2018 16:30" 33.5
t(contains(t.('Timestamp'),"28-06"),:)
ans =
1×3 table
Entry number Timestamp Value1
____________ __________________ ______
5758 "28-06-2018 16:30" 34
Hi Can anyone help me please to get unique group number?
I need to give unique rows for each group even when same group repeats after some groups.
I have following data:
id version product startdate enddate
123 0 2443 2010/09/01 2011/01/02
123 1 131 2011/01/03 2011/03/09
123 2 131 2011/08/10 2012/09/10
123 3 3009 2012/09/11 2014/03/31
123 4 668 2014/04/01 2014/04/30
123 5 668 2014/05/01 2016/01/01
123 6 668 2016/01/02 2017/09/08
123 7 131 2017/09/09 2017/10/10
123 8 131 2018/10/11 2019/01/01
123 9 550 2019/01/02 2099/01/01
select *,
dense_rank()over(partition by id order by id,product)
from table
Expected results:
id version product startdate enddate count
123 0 2443 2010/09/01 2011/01/02 1
123 1 131 2011/01/03 2011/03/09 2
123 2 131 2011/08/10 2012/09/10 2
123 3 3009 2012/09/11 2014/03/31 3
123 4 668 2014/04/01 2014/04/30 4
123 5 668 2014/05/01 2016/01/01 4
123 6 668 2016/01/02 2017/09/08 4
123 7 131 2017/09/09 2017/10/10 5
123 8 131 2018/10/11 2019/01/01 5
123 9 550 2019/01/02 2099/01/01 6
Try the following
SELECT
id,version,product,startdate,enddate,
1+SUM(v)OVER(PARTITION BY id ORDER BY version) n
FROM
(
SELECT
*,
IIF(LAG(product)OVER(PARTITION BY id ORDER BY version)<>product,1,0) v
FROM TestTable
) q
select distinct(msg_id),sub_id from programs where sub_id IN
(
select sub_id from programs group by sub_id having count(sub_id) = 2 limit 5
)
sub_id means subscriberID
Inner query will return those subscriberID which are exactly 2 times in the program table and main query will gives those subscriberID which having distinct msg_id.
This result will generated
msg_id sub_id
------|--------|
112 | 313
111 | 222
113 | 313
115 | 112
116 | 112
117 | 101
118 | 115
119 | 115
110 | 222
I want it should be
msg_id sub_id
------|--------|
112 | 313
111 | 222
113 | 313
115 | 112
116 | 112
118 | 115
119 | 115
110 | 222
117 | 101 (this result should not be in output because its only once)
I want only those record which are twice.
I'm not sure, but are you just missing the second field in your in-list?
select distinct msg_id, sub_id, <presumably other fields>
from programs
where (sub_id, msg_id) IN
(
select sub_id, msg_id
from programs
group by sub_id, msg_id
having count(sub_id) = 2
)
If so, you can also do this with a windowing function:
with cte as (
select
msg_id, sub_id, <presumably other fields>,
count (*) over (partition by msg_id, sub_id) as cnt
from programs
)
select distinct
msg_id, sub_id, <presumably other fields>
from cte
where cnt = 2
try this
SELECT msg_id, MAX(sub_id)
FROM programs
GROUP BY msg_id
HAVING COUNT(sub_id) = 2 -- COUNT(sub_id) > 1 if you want all those that repeat more than once
ORDER BY msg_id
I have a tableA (ID int, Match varchar, code char, status = char)
ID Match code Status
101 123 A
102 123 B
103 123 C
104 234 A
105 234 B
106 234 C
107 234 B
108 456 A
109 456 B
110 456 C
I want to populate status with 'FAIL' when:
For same match, there exists code different than (A,B or C)
or the code exists multiple times.
In other words, code can be only (A,B,C) and it should exists only one for same match, else fail. So, expected result would be:
ID Match code Status
101 123 A NULL
102 123 B NULL
103 123 C NULL
104 234 A NULL
105 234 B NULL
106 234 C NULL
107 234 B FAIL
108 456 A NULL
109 456 B NULL
110 456 C NULL
Thanks
No guarantees on efficiency here...
update tableA
set status = 'FAIL'
where ID not in (
select min(ID)
from tableA
group by Match, code)