Spark SQL: apply aggregate functions to a list of columns

Question

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"), ...)

Answer 1

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:

• Multiple Aggregate operations on the same column of a spark dataframe