How to calculate group-based quantiles?

Question

I'm using spark-sql-2.4.1v, and I'm trying to do find quantiles, i.e. percentile 0, percentile 25, etc, on each column of my given data.

My dataframe df:

+----+---------+-------------+----------+-----------+--------+
|  id|     date|      revenue|con_dist_1| con_dist_2| state  |
+----+---------+-------------+----------+-----------+--------+
|  10|1/15/2018|  0.010680705|         6|0.019875458|   TX   |
|  10|1/15/2018|  0.006628853|         4|0.816039063|   AZ   |
|  10|1/15/2018|   0.01378215|         4|0.082049528|   TX   |
|  10|1/15/2018|  0.010680705|         6|0.019875458|   TX   |
|  10|1/15/2018|  0.006628853|         4|0.816039063|   AZ   |
+----+---------+-------------+----------+-----------+--------+

How to find the quantile on the columns "con_dist_1" & "con_dist_2" for each state?

Related question: https://stackoverflow.com/questions/46845672/median-quantiles-within-pyspark-groupby — Shaido, Mar 09 '20 at 05:45

Anand Sai · Accepted Answer · 2020-03-06T14:17:26.830

The possible solution could be:

scala> input.show
+---+---------+-----------+----------+-----------+-----+
| id|     date|    revenue|con_dist_1| con_dist_2|state|
+---+---------+-----------+----------+-----------+-----+
| 10|1/15/2018|0.010680705|         6|0.019875458|   TX|
| 10|1/15/2018|0.006628853|         4|0.816039063|   AZ|
| 10|1/15/2018| 0.01378215|         4|0.082049528|   TX|
| 10|1/15/2018|0.010680705|         6|0.019875458|   TX|
| 10|1/15/2018|0.006628853|         4|0.816039063|   AZ|
+---+---------+-----------+----------+-----------+-----+

scala> val df1 = input.groupBy("state").agg(collect_list("con_dist_1").as("combined_1"), collect_list("con_dist_2").as("combined_2"))
df1: org.apache.spark.sql.DataFrame = [state: string, combined_1: array<int> ... 1 more field]

scala> df1.show
+-----+----------+--------------------+                                         
|state|combined_1|          combined_2|
+-----+----------+--------------------+
|   AZ|    [4, 4]|[0.816039063, 0.8...|
|   TX| [6, 4, 6]|[0.019875458, 0.0...|
+-----+----------+--------------------+

scala> df1.
     | withColumn("comb1_Q1", sort_array($"combined_1")(((size($"combined_1")-1)*0.25).cast("int"))).
     | withColumn("comb1_Q2", sort_array($"combined_1")(((size($"combined_1")-1)*0.5).cast("int"))).
     | withColumn("comb1_Q3", sort_array($"combined_1")(((size($"combined_1")-1)*0.75).cast("int"))).
     | withColumn("comb_2_Q1", sort_array($"combined_2")(((size($"combined_2")-1)*0.25).cast("int"))).
     | withColumn("comb_2_Q2", sort_array($"combined_2")(((size($"combined_2")-1)*0.5).cast("int"))).
     | withColumn("comb_2_Q3", sort_array($"combined_2")(((size($"combined_2")-1)*0.75).cast("int"))).
     | show
+-----+----------+--------------------+--------+--------+--------+-----------+-----------+-----------+
|state|combined_1|          combined_2|comb1_Q1|comb1_Q2|comb1_Q3|  comb_2_Q1|  comb_2_Q2|  comb_2_Q3|
+-----+----------+--------------------+--------+--------+--------+-----------+-----------+-----------+
|   AZ|    [4, 4]|[0.816039063, 0.8...|       4|       4|       4|0.816039063|0.816039063|0.816039063|
|   TX| [6, 4, 6]|[0.019875458, 0.0...|       4|       6|       6|0.019875458|0.019875458|0.019875458|
+-----+----------+--------------------+--------+--------+--------+-----------+-----------+-----------+

EDIT

I don't think we can achieve using approx quantile method as you want it for each state for which you will need to group by on state column and aggregate the con_dist columns and approx quantile expects a whole column of integers or float but not of array types.

The other solution is to use spark-sql as shown below:

scala> input.show
+---+---------+-----------+----------+-----------+-----+
| id|     date|    revenue|con_dist_1| con_dist_2|state|
+---+---------+-----------+----------+-----------+-----+
| 10|1/15/2018|0.010680705|         6|0.019875458|   TX|
| 10|1/15/2018|0.006628853|         4|0.816039063|   AZ|
| 10|1/15/2018| 0.01378215|         4|0.082049528|   TX|
| 10|1/15/2018|0.010680705|         6|0.019875458|   TX|
| 10|1/15/2018|0.006628853|         4|0.816039063|   AZ|
+---+---------+-----------+----------+-----------+-----+


scala> input.createOrReplaceTempView("input")

scala> :paste
// Entering paste mode (ctrl-D to finish)

val query = "select state, percentile_approx(con_dist_1,0.25) as col1_quantile_1, " +
  "percentile_approx(con_dist_1,0.5) as col1_quantile_2," +
  "percentile_approx(con_dist_1,0.75) as col1_quantile_3, " +
  "percentile_approx(con_dist_2,0.25) as col2_quantile_1,"+
  "percentile_approx(con_dist_2,0.5) as col2_quantile_2," +
  "percentile_approx(con_dist_2,0.75) as col2_quantile_3 " +
  "from input group by state"

// Exiting paste mode, now interpreting.

query: String = select state, percentile_approx(con_dist_1,0.25) as col1_quantile_1, percentile_approx(con_dist_1,0.5) as col1_quantile_2,percentile_approx(con_dist_1,0.75) as col1_quantile_3, percentile_approx(con_dist_2,0.25) as col2_quantile_1,percentile_approx(con_dist_2,0.5) as col2_quantile_2,percentile_approx(con_dist_2,0.75) as col2_quantile_3 from input group by state

scala> val df2 = spark.sql(query)
df2: org.apache.spark.sql.DataFrame = [state: string, col1_quantile_1: int ... 5 more fields]

scala> df2.show
+-----+---------------+---------------+---------------+---------------+---------------+---------------+
|state|col1_quantile_1|col1_quantile_2|col1_quantile_3|col2_quantile_1|col2_quantile_2|col2_quantile_3|
+-----+---------------+---------------+---------------+---------------+---------------+---------------+
|   AZ|              4|              4|              4|    0.816039063|    0.816039063|    0.816039063|
|   TX|              4|              6|              6|    0.019875458|    0.019875458|    0.082049528|
+-----+---------------+---------------+---------------+---------------+---------------+---------------+

Let me know if it helps!!

@BdEngineer Yeah you can keep it inside a loop but usually, you make temp view outside of loops. Since it is just a view, it can be replaced. — Anand Sai, Mar 06 '20 at 15:47
yeah, input view will be overwritten.It depends on the data frame from which you are making the view — Anand Sai, Mar 06 '20 at 16:34

How to calculate group-based quantiles?

1 Answers1