Is there a way to apply an aggregate function to all (or a list of) columns of a dataframe, when doing a groupBy? In other words, is there a way to avoid doing this for every column:
df.groupBy("col1")
.agg(sum("col2").alias("col2"), sum("col3").alias("col3"), ...)
There are multiple ways of applying aggregate functions to multiple columns.
GroupedData class provides a number of methods for the most common functions, including count, max, min, mean and sum, which can be used directly as follows:
Python:
df = sqlContext.createDataFrame(
[(1.0, 0.3, 1.0), (1.0, 0.5, 0.0), (-1.0, 0.6, 0.5), (-1.0, 5.6, 0.2)],
("col1", "col2", "col3"))
df.groupBy("col1").sum()
## +----+---------+-----------------+---------+
## |col1|sum(col1)| sum(col2)|sum(col3)|
## +----+---------+-----------------+---------+
## | 1.0| 2.0| 0.8| 1.0|
## |-1.0| -2.0|6.199999999999999| 0.7|
## +----+---------+-----------------+---------+
Scala
val df = sc.parallelize(Seq(
(1.0, 0.3, 1.0), (1.0, 0.5, 0.0),
(-1.0, 0.6, 0.5), (-1.0, 5.6, 0.2))
).toDF("col1", "col2", "col3")
df.groupBy($"col1").min().show
// +----+---------+---------+---------+
// |col1|min(col1)|min(col2)|min(col3)|
// +----+---------+---------+---------+
// | 1.0| 1.0| 0.3| 0.0|
// |-1.0| -1.0| 0.6| 0.2|
// +----+---------+---------+---------+
Optionally you can pass a list of columns which should be aggregated
df.groupBy("col1").sum("col2", "col3")
You can also pass dictionary / map with columns a the keys and functions as the values:
Python
exprs = {x: "sum" for x in df.columns}
df.groupBy("col1").agg(exprs).show()
## +----+---------+
## |col1|avg(col3)|
## +----+---------+
## | 1.0| 0.5|
## |-1.0| 0.35|
## +----+---------+
Scala
val exprs = df.columns.map((_ -> "mean")).toMap
df.groupBy($"col1").agg(exprs).show()
// +----+---------+------------------+---------+
// |col1|avg(col1)| avg(col2)|avg(col3)|
// +----+---------+------------------+---------+
// | 1.0| 1.0| 0.4| 0.5|
// |-1.0| -1.0|3.0999999999999996| 0.35|
// +----+---------+------------------+---------+
Finally you can use varargs:
Python
from pyspark.sql.functions import min
exprs = [min(x) for x in df.columns]
df.groupBy("col1").agg(*exprs).show()
Scala
import org.apache.spark.sql.functions.sum
val exprs = df.columns.map(sum(_))
df.groupBy($"col1").agg(exprs.head, exprs.tail: _*)
There are some other way to achieve a similar effect but these should more than enough most of the time.
See also:
Another example of the same concept - but say - you have 2 different columns - and you want to apply different agg functions to each of them i.e
f.groupBy("col1").agg(sum("col2").alias("col2"), avg("col3").alias("col3"), ...)
Here is the way to achieve it - though I do not yet know how to add the alias in this case
See the example below - Using Maps
val Claim1 = StructType(Seq(StructField("pid", StringType, true),StructField("diag1", StringType, true),StructField("diag2", StringType, true), StructField("allowed", IntegerType, true), StructField("allowed1", IntegerType, true)))
val claimsData1 = Seq(("PID1", "diag1", "diag2", 100, 200), ("PID1", "diag2", "diag3", 300, 600), ("PID1", "diag1", "diag5", 340, 680), ("PID2", "diag3", "diag4", 245, 490), ("PID2", "diag2", "diag1", 124, 248))
val claimRDD1 = sc.parallelize(claimsData1)
val claimRDDRow1 = claimRDD1.map(p => Row(p._1, p._2, p._3, p._4, p._5))
val claimRDD2DF1 = sqlContext.createDataFrame(claimRDDRow1, Claim1)
val l = List("allowed", "allowed1")
val exprs = l.map((_ -> "sum")).toMap
claimRDD2DF1.groupBy("pid").agg(exprs) show false
val exprs = Map("allowed" -> "sum", "allowed1" -> "avg")
claimRDD2DF1.groupBy("pid").agg(exprs) show false
Current answers are perfectly correct on how to create the aggregations, but none actually address the column alias/renaming that is also requested in the question.
Typically, this is how I handle this case:
val dimensionFields = List("col1")
val metrics = List("col2", "col3", "col4")
val columnOfInterests = dimensions ++ metrics
val df = spark.read.table("some_table").
.select(columnOfInterests.map(c => col(c)):_*)
.groupBy(dimensions.map(d => col(d)): _*)
.agg(metrics.map( m => m -> "sum").toMap)
.toDF(columnOfInterests:_*) // that's the interesting part
The last line essentially renames every columns of the aggregated dataframe to the original fields, essentially changing sum(col2) and sum(col3) to simply col2 and col3.
