也许这是有帮助的-

val spark_df = Seq((1, 7, "foo"),
      (2, 6, "bar"),
      (3, 4, "foo"),
      (4, 8, "bar"),
      (5, 1, "bar")
    ).toDF("v1", "v2", "id")
    spark_df.show(false)
    spark_df.printSchema()
    spark_df.summary() // default= "count", "mean", "stddev", "min", "25%", "50%", "75%", "max"
      .show(false)

    /**
      * +---+---+---+
      * |v1 |v2 |id |
      * +---+---+---+
      * |1  |7  |foo|
      * |2  |6  |bar|
      * |3  |4  |foo|
      * |4  |8  |bar|
      * |5  |1  |bar|
      * +---+---+---+
      *
      * root
      * |-- v1: integer (nullable = false)
      * |-- v2: integer (nullable = false)
      * |-- id: string (nullable = true)
      *
      * +-------+------------------+------------------+----+
      * |summary|v1                |v2                |id  |
      * +-------+------------------+------------------+----+
      * |count  |5                 |5                 |5   |
      * |mean   |3.0               |5.2               |null|
      * |stddev |1.5811388300841898|2.7748873851023217|null|
      * |min    |1                 |1                 |bar |
      * |25%    |2                 |4                 |null|
      * |50%    |3                 |6                 |null|
      * |75%    |4                 |7                 |null|
      * |max    |5                 |8                 |foo |
      * +-------+------------------+------------------+----+
      */

如果您需要在格式，然后使用下面的答案。

方法DESCRIPE计算Dataframe中数字列的统计信息，如平均值、最小值、最大值等。
df.descripe（）.show（）

我认为这样的语法就是你想要的：

spark.createOrRegisterTempTable("spark_table")    
spark.sql("SELECT id, AVG(v1) AS avg_v1, AVG(v2) AS avg_v2, \
 MIN(v1) AS min_v1, MIN(v2) AS min_v2, \
 percentile_approx(v1, 0.25) AS p25_v1, percentile_approx(v2, 0.25) AS p25_v2, \
 percentile_approx(v1, 0.5)AS p50_v1, percentile_approx(v2, 0.5) AS p50_v2 \
 FROM spark_table GROUP BY id").show(5)

它有助于创建别名，因为未格式化的列名很难处理。