If by group by you mean adding values with same keys together you could try: let a = [‘A’, ‘B’, ‘C’, ‘D’, ‘E’, ‘F’, ‘G’, ‘A’, ‘F’, ‘C’, ‘A’, ‘E’]; let b = [‘5’, ‘7’, ‘4’, ‘3’, ‘8’, ‘1’, ‘9’,…
you should aggregate by column and then sum the results, try this: Notice that, the patient name should should have a numerical counter for keeping track. import pandas as pd import datetime import numpy as np # this a dummy…
You want to examine all the objects in your input array, then push it into an array identified by the first letter of the name in your object. var input = [{ name: “John”, id: “123” }, { name: “Josh”,…
Try this: uniq_accounts = differenc[‘AccountID’].unique() grped = differenc.groupby(‘AccountID’) ## You can get the grouped data for a certain AccountId like this and store in a dictionary: d = {} for account in uniq_accounts: d[account] = grped.get_group(account) ##Now, dictionary has all…
The ORDER BY clause should be the last, but you have to specify fields to be aggregated. Such as: SELECT surname, count(*) FROM people WHERE user_token=’$user_token’ GROUP BY surname ORDER BY surname 1 solved SELECT * FROM people WHERE user_id=’$user_id’…
SELECT SUM(how_many) AS per_day, `date` FROM cigaretes GROUP BY DATE(`date`) 0 solved How to sum and group values using MySQL ?
try this, create table #tmp (MR_ID varchar(50),DR_ID int) insert into #tmp VALUES (‘MR_123’, 1),(‘MR_123’, 3),(‘MR_124’, 4),(‘MR_124’, 5) ,(‘MR_124’, 6),(‘MR_125′, 0) declare @DRCol varchar(50) declare @Prefix varchar(20)=’DR_’ ;With CTE as ( select * ,ROW_NUMBER()over(partition by MR_ID order by DR_ID)rn from #tmp…
I understand that you want, for each day: the sum of order_items.energy_used for all orders created that day the created_at and order_sum that correspond to the latest order created on that day Your sample data is not very representative of…
In general ORDER BY in a sub-query makes no sense. (It only does when combined with FETCH FIRST/LIMIT/TOP etc.) The solution is to use a correlated sub-query to find the heaviest fish for the “main query”‘s current row’s username, location,…
var results = from kvp in source group kvp by kvp.Key.ToUpper() into g select new { Key= g.Key, Value= g.Average(kvp => kvp.Value) } or var results = source.GroupBy(c=>c.Name) .Select(c => new (c.Key, c.Average(d=>d.Value))); 3 solved Remove duplicate items and calculate…
I suggest you to use the dplyr package. You can try the following code: if df is your dataframe: library(dplyr) summarised_df <- group_by(df, Ticker) %>% summarise( max(tradeDate), sum(SharesBought), max(HighestPxPaid), min(LowestPxPaid) ) 3 solved How to summarise a dataframe in R…
Try this DECLARE @temp TABLE(col1 varchar(20),col2 int, col3 varchar(20)) insert into @temp values (‘data1′, 123 , ’12/03/2009’),(‘data1′, 124 , ’15/09/2009’), (‘data2 ‘,333 ,’02/09/2010’),(‘data2 ‘,323 , ’02/11/2010’), (‘data2 ‘,673 , ’02/09/2014’),(‘data2′,444 , ’05/01/2010’) SELECT (CASE rno WHEN 1 THEN col1 ELSE…
You can try like following using GROUP BY and UNION ALL. select count(*) CT,Mark from TableName group by Mark union all select Count(*), ‘A+B’ as mark from TableName where mark in(‘A’,’B’) UNION ALL select Count(*), ‘A+C’ as mark from TableName…
Try this using LINQ in C# var result = from tab in dt.AsEnumerable() group tab by tab[“ApplicationNmae”] into groupDt select new { ApplicationNmae = groupDt.Key, Sum = groupDt.Sum((r) => decimal.Parse(r[“Count”].ToString())) }; DataTable dt1 = new DataTable(); dt1 = dt.Clone(); foreach…
Do you mean like this? SELECT * FROM ( SELECT a.id, a.name, c.code, a.active_dt, a.inactive_dt, b.category, COUNT(1) OVER(PARTITION BY a.id, a.name, c.code) AS CNT FROM student a, class b, descrip c WHERE a.id=b.id AND a.id=c.id ) WHERE CNT > 1…