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How to count and getting the sum of value for unique Ids in a Spark Dataframe?
I have the following Dataframe and i am looking to aggregate by ids and also sum the 'value' column for each unique id:
import org.apache.spark.sql.functions._
import spark.implicits._
// some data...
val df = Seq(
(1, 2),
(1, 4),
(1, 1),
(2, 2),
(2, 2),
(3, 2),
(3, 1),
(3, 1)
).toDF("id","value")
df.show()
gives the following:
+---+-----+
| id|value|
+---+-----+
| 1| 2|
| 1| 4|
| 1| 1|
| 2| 2|
| 2| 2|
| 3| 2|
| 3| 1|
| 3| 1|
+---+-----+
Using the count function I know I can count the unique ids:
df.select("id").groupBy($"id").count.orderBy($"id".asc).show()
+---+-----+
| id|count|
+---+-----+
| 1| 3|
| 2| 2|
| 3| 3|
+---+-----+
but I also want to sum (or get the average of) the values for each of the unique ids. So the resulting table should be as follows:
+---+-----+----------+
| id|count|valueCount|
+---+-----+----------+
| 1| 3| 7|
| 2| 2| 4|
| 3| 3| 4|
+---+-----+----------+
Is there a way to do this programatically?
apache-spark dataframe
edited Nov 20 '18 at 16:11
user6910411
33.4k97499
asked Nov 20 '18 at 13:14
Eoin LaneEoin Lane
1791214
add a comment |
I have the following Dataframe and i am looking to aggregate by ids and also sum the 'value' column for each unique id:
import org.apache.spark.sql.functions._
import spark.implicits._
// some data...
val df = Seq(
(1, 2),
(1, 4),
(1, 1),
(2, 2),
(2, 2),
(3, 2),
(3, 1),
(3, 1)
).toDF("id","value")
df.show()
gives the following:
+---+-----+
| id|value|
+---+-----+
| 1| 2|
| 1| 4|
| 1| 1|
| 2| 2|
| 2| 2|
| 3| 2|
| 3| 1|
| 3| 1|
+---+-----+
Using the count function I know I can count the unique ids:
df.select("id").groupBy($"id").count.orderBy($"id".asc).show()
+---+-----+
| id|count|
+---+-----+
| 1| 3|
| 2| 2|
| 3| 3|
+---+-----+
but I also want to sum (or get the average of) the values for each of the unique ids. So the resulting table should be as follows:
+---+-----+----------+
| id|count|valueCount|
+---+-----+----------+
| 1| 3| 7|
| 2| 2| 4|
| 3| 3| 4|
+---+-----+----------+
Is there a way to do this programatically?
apache-spark dataframe
edited Nov 20 '18 at 16:11
user6910411
33.4k97499
asked Nov 20 '18 at 13:14
Eoin LaneEoin Lane
1791214
add a comment |
I have the following Dataframe and i am looking to aggregate by ids and also sum the 'value' column for each unique id:
import org.apache.spark.sql.functions._
import spark.implicits._
// some data...
val df = Seq(
(1, 2),
(1, 4),
(1, 1),
(2, 2),
(2, 2),
(3, 2),
(3, 1),
(3, 1)
).toDF("id","value")
df.show()
gives the following:
+---+-----+
| id|value|
+---+-----+
| 1| 2|
| 1| 4|
| 1| 1|
| 2| 2|
| 2| 2|
| 3| 2|
| 3| 1|
| 3| 1|
+---+-----+
Using the count function I know I can count the unique ids:
df.select("id").groupBy($"id").count.orderBy($"id".asc).show()
+---+-----+
| id|count|
+---+-----+
| 1| 3|
| 2| 2|
| 3| 3|
+---+-----+
but I also want to sum (or get the average of) the values for each of the unique ids. So the resulting table should be as follows:
+---+-----+----------+
| id|count|valueCount|
+---+-----+----------+
| 1| 3| 7|
| 2| 2| 4|
| 3| 3| 4|
+---+-----+----------+
Is there a way to do this programatically?
apache-spark dataframe
edited Nov 20 '18 at 16:11
user6910411
33.4k97499
asked Nov 20 '18 at 13:14
Eoin LaneEoin Lane
1791214
I have the following Dataframe and i am looking to aggregate by ids and also sum the 'value' column for each unique id:
import org.apache.spark.sql.functions._
import spark.implicits._
// some data...
val df = Seq(
(1, 2),
(1, 4),
(1, 1),
(2, 2),
(2, 2),
(3, 2),
(3, 1),
(3, 1)
).toDF("id","value")
df.show()
gives the following:
+---+-----+
| id|value|
+---+-----+
| 1| 2|
| 1| 4|
| 1| 1|
| 2| 2|
| 2| 2|
| 3| 2|
| 3| 1|
| 3| 1|
+---+-----+
Using the count function I know I can count the unique ids:
df.select("id").groupBy($"id").count.orderBy($"id".asc).show()
+---+-----+
| id|count|
+---+-----+
| 1| 3|
| 2| 2|
| 3| 3|
+---+-----+
but I also want to sum (or get the average of) the values for each of the unique ids. So the resulting table should be as follows:
+---+-----+----------+
| id|count|valueCount|
+---+-----+----------+
| 1| 3| 7|
| 2| 2| 4|
| 3| 3| 4|
+---+-----+----------+
Is there a way to do this programatically?
apache-spark dataframe
apache-spark dataframe
edited Nov 20 '18 at 16:11
user6910411
33.4k97499
asked Nov 20 '18 at 13:14
Eoin LaneEoin Lane
1791214
edited Nov 20 '18 at 16:11
user6910411
33.4k97499
asked Nov 20 '18 at 13:14
Eoin LaneEoin Lane
1791214
edited Nov 20 '18 at 16:11
user6910411
33.4k97499
edited Nov 20 '18 at 16:11
user6910411
33.4k97499
edited Nov 20 '18 at 16:11
user6910411
33.4k97499
33.4k97499
asked Nov 20 '18 at 13:14
Eoin LaneEoin Lane
1791214
asked Nov 20 '18 at 13:14
Eoin LaneEoin Lane
1791214
asked Nov 20 '18 at 13:14
Eoin LaneEoin Lane
1791214
1791214
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
The way to do it is to use aggregate functions. Sparks comes with a number of predefined ones (average, sum, count, first, collect list, collect set, min, max, ...), so you can always, on your example, do it like this :
df.groupBy("id").agg(
count("id").as("countOfIds"),
sum("id").as("sumOfIds"),
avg("id").as("avgOfIds")
).show
+---+----------+--------+--------+
| id|countOfIds|sumOfIds|avgOfIds|
+---+----------+--------+--------+
| 1| 3| 3| 1.0|
| 3| 3| 9| 3.0|
| 2| 2| 4| 2.0|
+---+----------+--------+--------+
You can view the defined functions inside the sql.function package documentation, by looking the ones defined as "Aggregate functions". All of those have a SQL syntax equivalent if you are using the SQL oriented syntax.
answered Nov 20 '18 at 13:23
GPIGPI
5,90112035
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
The way to do it is to use aggregate functions. Sparks comes with a number of predefined ones (average, sum, count, first, collect list, collect set, min, max, ...), so you can always, on your example, do it like this :
df.groupBy("id").agg(
count("id").as("countOfIds"),
sum("id").as("sumOfIds"),
avg("id").as("avgOfIds")
).show
+---+----------+--------+--------+
| id|countOfIds|sumOfIds|avgOfIds|
+---+----------+--------+--------+
| 1| 3| 3| 1.0|
| 3| 3| 9| 3.0|
| 2| 2| 4| 2.0|
+---+----------+--------+--------+
You can view the defined functions inside the sql.function package documentation, by looking the ones defined as "Aggregate functions". All of those have a SQL syntax equivalent if you are using the SQL oriented syntax.
answered Nov 20 '18 at 13:23
GPIGPI
5,90112035
add a comment |
The way to do it is to use aggregate functions. Sparks comes with a number of predefined ones (average, sum, count, first, collect list, collect set, min, max, ...), so you can always, on your example, do it like this :
df.groupBy("id").agg(
count("id").as("countOfIds"),
sum("id").as("sumOfIds"),
avg("id").as("avgOfIds")
).show
+---+----------+--------+--------+
| id|countOfIds|sumOfIds|avgOfIds|
+---+----------+--------+--------+
| 1| 3| 3| 1.0|
| 3| 3| 9| 3.0|
| 2| 2| 4| 2.0|
+---+----------+--------+--------+
You can view the defined functions inside the sql.function package documentation, by looking the ones defined as "Aggregate functions". All of those have a SQL syntax equivalent if you are using the SQL oriented syntax.
answered Nov 20 '18 at 13:23
GPIGPI
5,90112035
add a comment |
The way to do it is to use aggregate functions. Sparks comes with a number of predefined ones (average, sum, count, first, collect list, collect set, min, max, ...), so you can always, on your example, do it like this :
df.groupBy("id").agg(
count("id").as("countOfIds"),
sum("id").as("sumOfIds"),
avg("id").as("avgOfIds")
).show
+---+----------+--------+--------+
| id|countOfIds|sumOfIds|avgOfIds|
+---+----------+--------+--------+
| 1| 3| 3| 1.0|
| 3| 3| 9| 3.0|
| 2| 2| 4| 2.0|
+---+----------+--------+--------+
You can view the defined functions inside the sql.function package documentation, by looking the ones defined as "Aggregate functions". All of those have a SQL syntax equivalent if you are using the SQL oriented syntax.
answered Nov 20 '18 at 13:23
GPIGPI
5,90112035
The way to do it is to use aggregate functions. Sparks comes with a number of predefined ones (average, sum, count, first, collect list, collect set, min, max, ...), so you can always, on your example, do it like this :
df.groupBy("id").agg(
count("id").as("countOfIds"),
sum("id").as("sumOfIds"),
avg("id").as("avgOfIds")
).show
+---+----------+--------+--------+
| id|countOfIds|sumOfIds|avgOfIds|
+---+----------+--------+--------+
| 1| 3| 3| 1.0|
| 3| 3| 9| 3.0|
| 2| 2| 4| 2.0|
+---+----------+--------+--------+
You can view the defined functions inside the sql.function package documentation, by looking the ones defined as "Aggregate functions". All of those have a SQL syntax equivalent if you are using the SQL oriented syntax.
answered Nov 20 '18 at 13:23
GPIGPI
5,90112035
edited Nov 20 '18 at 14:01
answered Nov 20 '18 at 13:23
GPIGPI
5,90112035
answered Nov 20 '18 at 13:23
GPIGPI
5,90112035
answered Nov 20 '18 at 13:23
GPIGPI
5,90112035
5,90112035
add a comment |
add a comment |
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