Select corresponding to row from the same table SQL Server

I’m not really smart from your description, however, the result can be achieved using the following query select your_table.* from your_table join ( select BlilShortName, max(billversion) bmax from your_table group by BlilShortName ) t on your_table.billversion = t.bmax and your_table.BlilShortName = t.BlilShortName From my experience it can be faster in some cases when compared to … Read more

Selecting all corresponding fields using MAX and GROUP BY

without a single primary key field, I think your best bet is: select * from deal_status inner join (select deal_id as did, max(timestamp) as ts from deal_status group by deal_id) as ds on deal_status.deal_id = ds.did and deal_status.timestamp = ds.ts this still won’t work if you allow having two different statuses for the same product … Read more

How to select the most recent set of dated records from a mysql table

This solution was updated recently. Comments below may be outdated This can query may perform well, because there are no joins. SELECT * FROM ( SELECT *,if(@last_method=method,0,1) as new_method_group,@last_method:=method FROM rpc_responses ORDER BY method,timestamp DESC ) as t1 WHERE new_method_group=1; Given that you want one resulting row per method this solution should work, using mysql … Read more

Get most common value for each value of another column in SQL

It is now even simpler: PostgreSQL 9.4 introduced the mode() function: select mode() within group (order by food_id) from munch group by country returns (like user2247323’s example): country | mode ————– GB | 3 US | 1 See documentation here: https://wiki.postgresql.org/wiki/Aggregate_Mode https://www.postgresql.org/docs/current/static/functions-aggregate.html#FUNCTIONS-ORDEREDSET-TABLE

Select row by max value in group in a pandas dataframe

A standard approach is to use groupby(keys)[column].idxmax(). However, to select the desired rows using idxmax you need idxmax to return unique index values. One way to obtain a unique index is to call reset_index. Once you obtain the index values from groupby(keys)[column].idxmax() you can then select the entire row using df.loc: In [20]: df.loc[df.reset_index().groupby([‘F_Type’])[‘to_date’].idxmax()] Out[20]: … Read more

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