Select First Row in Each GROUP BY Group

Select first row in each GROUP BY group?

On databases that support CTE and windowing functions:

WITH summary AS (
    SELECT p.id, 
           p.customer, 
           p.total, 
           ROW_NUMBER() OVER(PARTITION BY p.customer 
                                 ORDER BY p.total DESC) AS rank
      FROM PURCHASES p)
 SELECT *
   FROM summary
 WHERE rank = 1

Supported by any database:

But you need to add logic to break ties:

  SELECT MIN(x.id),  -- change to MAX if you want the highest
         x.customer, 
         x.total
    FROM PURCHASES x
    JOIN (SELECT p.customer,
                 MAX(total) AS max_total
            FROM PURCHASES p
        GROUP BY p.customer) y ON y.customer = x.customer
                              AND y.max_total = x.total
GROUP BY x.customer, x.total

Get top 1 row of each group

;WITH cte AS
(
   SELECT *,
         ROW_NUMBER() OVER (PARTITION BY DocumentID ORDER BY DateCreated DESC) AS rn
   FROM DocumentStatusLogs
)
SELECT *
FROM cte
WHERE rn = 1

If you expect 2 entries per day, then this will arbitrarily pick one. To get both entries for a day, use DENSE_RANK instead

As for normalised or not, it depends if you want to:

• maintain status in 2 places
• preserve status history
• ...

As it stands, you preserve status history. If you want latest status in the parent table too (which is denormalisation) you'd need a trigger to maintain "status" in the parent. or drop this status history table.

Selecting first row per group

SELECT  a, b, c
FROM    (
        SELECT  *, ROW_NUMBER() OVER (PARTITION BY a ORDER BY b, c) rn
        FROM    mytable
        ) q
WHERE   rn = 1
ORDER BY
        a

or

SELECT  mi.*
FROM    (
        SELECT  DISTINCT  a
        FROM    mytable
        ) md
CROSS APPLY
        (
        SELECT  TOP 1 *
        FROM    mytable mi
        WHERE   mi.a = md.a
        ORDER BY
                b, c
        ) mi
ORDER BY
        a

Create a composite index on (a, b, c) for the queries to work faster.

Which one is more efficient depends on your data distribution.

If you have few distinct values of a but lots of records within each a, the second query would be better.

You could improve it even more by creating an indexed view:

CREATE VIEW v_mytable_da
WITH   SCHEMABINDING
AS
       SELECT  a, COUNT_BIG(*) cnt
       FROM    dbo.mytable
       GROUP BY
               a

GO

CREATE UNIQUE CLUSTERED INDEX
       pk_vmytableda_a
ON     v_mytable_da (a)

GO

SELECT  mi.*
FROM    v_mytable_da md
CROSS APPLY
        (
        SELECT  TOP 1 *
        FROM    mytable mi
        WHERE   mi.a = md.a
        ORDER BY
                b, c
        ) mi
ORDER BY
        a

sql server select first row from a group

If as you indicated, order doesn't matter, any aggregate function on b would be sufficient.

Example Using MIN

SELECT a, b = MIN(b)
FROM   YourTable
GROUP BY
       a

Select first row in each group in sql

You can use distinct on directly with group by:

select distinct on ("Country") Sum("Price"), "In-app Product", "Country"
from cleandatase
group by "Country", "In-app Product"
order by "Country", Sum("Price") desc;

Note: As Thorsten points out, if there are ties and you want all the ties, then distinct on is not the simplest solution.

get first row in a group and assign values

use df.groupby(...).cumcount() to get a counter of rows within the group which you can then manipulate.

In [51]: df
Out[51]:
     a  b  c
0  def  1  0
1  abc  0  1
2  def  1  0
3  abc  0  1

In [52]: df2 = df.sort_values(['a','b','c'])

In [53]: df2['result'] = df2.groupby(['a', 'b', 'c']).cumcount()

In [54]: df2['result'] = np.where(df2['result'] == 0, 1, 0)

In [55]: df2
Out[55]:
     a  b  c  result
1  abc  0  1       1
3  abc  0  1       0
0  def  1  0       1
2  def  1  0       0

How to get the first row per group?

if your MySQL version support ROW_NUMBER + window function, you can try to use ROW_NUMBER to get the biggest num by category_id

Query #1

SELECT num,business_id,category_id
FROM (
    SELECT *,ROW_NUMBER() OVER(PARTITION BY category_id ORDER BY num desc) rn
    FROM (
        select count(1) num, business_id, category_id
        from mytable
        group by business_id, category_id
    ) t1
) t1
WHERE rn = 1

import org.apache.spark.sql.functions.{row_number, max, broadcast}
import org.apache.spark.sql.expressions.Window

val df = sc.parallelize(Seq(
  (0,"cat26",30.9), (0,"cat13",22.1), (0,"cat95",19.6), (0,"cat105",1.3),
  (1,"cat67",28.5), (1,"cat4",26.8), (1,"cat13",12.6), (1,"cat23",5.3),
  (2,"cat56",39.6), (2,"cat40",29.7), (2,"cat187",27.9), (2,"cat68",9.8),
  (3,"cat8",35.6))).toDF("Hour", "Category", "TotalValue")

val w = Window.partitionBy($"hour").orderBy($"TotalValue".desc)

val dfTop = df.withColumn("rn", row_number.over(w)).where($"rn" === 1).drop("rn")

dfTop.show
// +----+--------+----------+
// |Hour|Category|TotalValue|
// +----+--------+----------+
// |   0|   cat26|      30.9|
// |   1|   cat67|      28.5|
// |   2|   cat56|      39.6|
// |   3|    cat8|      35.6|
// +----+--------+----------+

val dfMax = df.groupBy($"hour".as("max_hour")).agg(max($"TotalValue").as("max_value"))

val dfTopByJoin = df.join(broadcast(dfMax),
    ($"hour" === $"max_hour") && ($"TotalValue" === $"max_value"))
  .drop("max_hour")
  .drop("max_value")

dfTopByJoin.show

// +----+--------+----------+
// |Hour|Category|TotalValue|
// +----+--------+----------+
// |   0|   cat26|      30.9|
// |   1|   cat67|      28.5|
// |   2|   cat56|      39.6|
// |   3|    cat8|      35.6|
// +----+--------+----------+

dfTopByJoin
  .groupBy($"hour")
  .agg(
    first("category").alias("category"),
    first("TotalValue").alias("TotalValue"))

val dfTop = df.select($"Hour", struct($"TotalValue", $"Category").alias("vs"))
  .groupBy($"hour")
  .agg(max("vs").alias("vs"))
  .select($"Hour", $"vs.Category", $"vs.TotalValue")

dfTop.show
// +----+--------+----------+
// |Hour|Category|TotalValue|
// +----+--------+----------+
// |   0|   cat26|      30.9|
// |   1|   cat67|      28.5|
// |   2|   cat56|      39.6|
// |   3|    cat8|      35.6|
// +----+--------+----------+

case class Record(Hour: Integer, Category: String, TotalValue: Double)

df.as[Record]
  .groupBy($"hour")
  .reduce((x, y) => if (x.TotalValue > y.TotalValue) x else y)
  .show

// +---+--------------+
// | _1|            _2|
// +---+--------------+
// |[0]|[0,cat26,30.9]|
// |[1]|[1,cat67,28.5]|
// |[2]|[2,cat56,39.6]|
// |[3]| [3,cat8,35.6]|
// +---+--------------+

df.as[Record]
  .groupByKey(_.Hour)
  .reduceGroups((x, y) => if (x.TotalValue > y.TotalValue) x else y)

df.orderBy(...).groupBy(...).agg(first(...), ...)

df.orderBy(...).dropDuplicates(...)