I'm trying to add new columns of first values of the day for location and weight.
For instance, the original data format is:
id dttm location weight
--------------------------------------------
1 1/1/20 11:10:00 A 40
1 1/1/20 19:07:00 B 41.1
2 1/1/20 08:01:00 B 73.2
2 1/1/20 21:00:00 B 73.2
2 1/2/20 10:03:00 C 74
I want each id to have only one day record, such as:
id dttm location weight
--------------------------------------------
1 1/1/20 11:10:00 A 40
2 1/1/20 08:01:00 B 73.2
2 1/2/20 10:03:00 C 74
I have other columns in my data set that I'm using location and weight to create, so I don't think I can just filter for 'first' records of the day.. Is it possible to write query to recognize first record of the day for those two columns and create new column with those values?
You can use row_number():
select t.*
from (select t.*,
row_number() over (partition by id, ddtm::date order by dttm) as seqnum
from t
) t
where seqnum = 1;
Related
I have a table called Inventory with the below columns
item warehouse date sequence number value
111 100 2019-09-25 12:29:41.000 1 10
111 100 2019-09-26 12:29:41.000 1 20
222 200 2019-09-21 16:07:10.000 1 5
222 200 2019-09-21 16:07:10.000 2 10
333 300 2020-01-19 12:05:23.000 1 4
333 300 2020-01-20 12:05:23.000 1 5
Expected Output:
item warehouse date sequence number value
111 100 2019-09-26 12:29:41.000 1 20
222 200 2019-09-21 16:07:10.000 2 10
333 300 2020-01-20 12:05:23.000 1 5
Based on item and warehouse, i need to pick latest date and latest sequence number of value.
I tried with below code
select item,warehouse,sequencenumber,sum(value),max(date) as date1
from Inventory t1
where
t1.date IN (select max(date) from Inventory t2
where t1.warehouse=t2.warehouse
and t1.item = t2.item
group by t2.item,t2.warehouse)
group by t1.item,t1.warehouse,t1.sequencenumber
Its working for latest date but not for latest sequence number.
Can you please suggest how to write a query to get my expected output.
You can use row_number() for this:
select *
from (
select
t.*,
row_number() over(
partition by item, warehouse
order by date desc, sequence_number desc, value desc
) rn
from mytable t
) t
where rn = 1
I have the below table where I will need to compute the rolling average and standard deviation based on the dates. I have listed below the tables and expected results. I am trying to compute the rolling average for an id based on date. rollAvgA is computed based on metricA. For example, for the first occurrence of id for a particular date the result should return zero as it does not have any preceding values. Please let me know how this can be accomplished?
Current Table :
Date id metricA
8/1/2019 100 2
8/2/2019 100 3
8/3/2019 100 2
8/1/2019 101 2
8/2/2019 101 3
8/3/2019 101 2
8/4/2019 101 2
Expected Table :
Date id metricA rollAvgA
8/1/2019 100 2 0
8/2/2019 100 3 2.5
8/3/2019 100 2 2.3
8/1/2019 101 2 0
8/2/2019 101 3 2.5
8/3/2019 101 2 2.3
8/4/2019 101 2 2.25
You seem to want a cumulative average. This is basically:
select t.*,
avg(metricA * 1.0) over (partition by id order by date) as rollingavg
from t;
The only caveat is that the first value is an average of one value. To handle this, use a case expression:
select t.*,
(case when row_number() over (partition by id order by date) > 1
then avg(metricA * 1.0) over (partition by id order by date)
else 0
end) as rollingavg
from t;
I am trying to write a sql query (in amazon redshift) that counts the number of times that customer goes from not meeting criteria to meeting criteria, so when a 1 occurs the date after a 0.
I'm stuggling to figure out the logic to do this
ID Snapshot_date Meets Criteria
55 1/1/2018 0
55 1/5/2018 1
55 1/10/2018 1
55 1/15/2018 1
55 1/20/2018 0
55 1/25/2018 1
Use lag to get the previous value,check for the conditions and count.
select id,count(*)
from (select id,snapshot_date
,lag(meets_critetria,1) over(partition by id order by snapshot_date) as prev_m_c
from tbl
) t
where prev_m_c = 0 and meets_criteria = 1
group by id
I have a table which contain _id, underSubheadId, wefDate, price.
Whenever a product is created or price is edited an entry is made in this table also.
What I want is if I enter a date, I get the latest price of all distinct UnderSubheadIds before the date (or on that date if no entry found)
_id underHeadId wefDate price
1 1 2016-11-01 5
2 2 2016-11-01 50
3 1 2016-11-25 500
4 3 2016-11-01 20
5 4 2016-11-11 30
6 5 2016-11-01 40
7 3 2016-11-20 25
8 5 2016-11-15 52
If I enter 2016-11-20 as date I should get
1 5
2 50
3 25
4 30
5 52
I have achieved the result using ROW NUMBER function in SQL SERVER, but I want this result in Sqlite which don't have such function.
Also if a date like 2016-10-25(which have no entries) is entered I want the price of the date which is first.
Like for 1 we will get price as 5 as the nearest and the 1st entry is 2016-11-01.
This is the query for SQL SERVER which is working fine. But I want it for Sqlite which don't have ROW_NUMBER function.
select underSubHeadId,price from(
select underSubHeadId,price, ROW_NUMBER() OVER (Partition By underSubHeadId order by wefDate desc) rn from rates
where wefDate<='2016-11-19') newTable
where newTable.rn=1
Thank You
This is a little tricky, but here is one way:
select t.*
from t
where t.wefDate = (select max(t2.wefDate)
from t t2
where t2.underSubHeadId = t.underSubHeadId and
t2.wefdate <= '2016-11-20'
);
select underHeadId, max(price)
from t
where wefDate <= "2016-11-20"
group by underHead;
I'm trying to find the min and max date within a subgroup of another group. Here's example 'data'
ID Type Date
1 A 7/1/2015
1 B 1/1/2015
1 A 8/5/2014
22 B 3/1/2015
22 B 9/1/2014
333 A 8/1/2015
333 B 4/1/2015
333 B 3/29/2014
333 B 2/28/2013
333 C 1/1/2013
What I'd like to identify is - within an ID, what is the min/max Date for each block of similar Type? So for ID # 333 I want the below info:
A: min & max = 8/1/2015
B: min = 2/28/2013
max = 4/1/2015
C: min & max = 1/1/2013
I'm having trouble figuring out how to identify only uninterrupted groupings of Type within a grouping of ID. For ID #1, I need to keep the two 'A' Types with separate min/max dates because they were split by a Type 'B', so I can't just pull the min date of all Type A's for ID #1, it has to be two separate instances.
What I've tried is something like the below two lines, but neither of these accurately captures the case mentioned above for ID #1 where Type B interrupts Type A.
Max(Date) OVER (Partition By ID, Type)
or this:
Row_Number() OVER (Partition By ID, Type ORDER BY Date DESC)
,then selecting Row #1 for max date, and date ASC w/ row #1 for min date
Thank you for any insight you can provide!
If I understand right, you want the min/max values for an id/type grouped using a descending date sort, but the catch is that you want them based on clusters within the id by time.
What you can do is use CONDITIONAL_CHANGE_EVENT to tag the rows on change of type, then use that in your GROUP BY on a standard min/max aggregation.
This would be the intermediate step towards getting to what you want:
select ID, Type, Date,
CONDITIONAL_CHANGE_EVENT(Type) OVER( PARTITION BY ID ORDER BY Date desc) cce
from mytable
group by ID, Type, Date
order by ID, Date desc, Type
ID Type Date cce
1 A 2015-07-01 00:00:00 0
1 B 2015-01-01 00:00:00 1
1 A 2014-08-05 00:00:00 2
22 B 2015-03-01 00:00:00 0
22 B 2014-09-01 00:00:00 0
333 A 2015-08-01 00:00:00 0
333 B 2015-04-01 00:00:00 1
333 B 2014-03-29 00:00:00 1
333 B 2013-02-28 00:00:00 1
333 C 2013-01-01 00:00:00 2
Once you have them grouped using CCE, you can do an aggregate on this to get the min/max you are looking for grouping on cce. You can play with the order by at the bottom, this ordering seem to make the most sense to me.
select id, type, min(date), max(date)
from (
select ID, Type, Date,
CONDITIONAL_CHANGE_EVENT(Type) OVER( PARTITION BY ID ORDER BY Date desc) cce
from mytable
group by ID, Type, Date
) x
group by id, type, cce
order by id, 3 desc, 4 desc;
id type min max
1 A 2015-07-01 00:00:00 2015-07-01 00:00:00
1 B 2015-01-01 00:00:00 2015-01-01 00:00:00
1 A 2014-08-05 00:00:00 2014-08-05 00:00:00
22 B 2014-09-01 00:00:00 2015-03-01 00:00:00
333 A 2015-08-01 00:00:00 2015-08-01 00:00:00
333 B 2013-02-28 00:00:00 2015-04-01 00:00:00
333 C 2013-01-01 00:00:00 2013-01-01 00:00:00