How to pivot Spark DataFrame?

I overcame this by writing a for loop to dynamically create a SQL query. Say I have:

id  tag  value
1   US    50
1   UK    100
1   Can   125
2   US    75
2   UK    150
2   Can   175

and I want:

id  US  UK   Can
1   50  100  125
2   75  150  175

I can create a list with the value I want to pivot and then create a string containing the SQL query I need.

val countries = List("US", "UK", "Can")
val numCountries = countries.length - 1

var query = "select *, "
for (i <- 0 to numCountries-1) {
  query += """case when tag = """" + countries(i) + """" then value else 0 end as """ + countries(i) + ", "
}
query += """case when tag = """" + countries.last + """" then value else 0 end as """ + countries.last + " from myTable"

myDataFrame.registerTempTable("myTable")
val myDF1 = sqlContext.sql(query)

I can create similar query to then do the aggregation. Not a very elegant solution but it works and is flexible for any list of values, which can also be passed in as an argument when your code is called.

As mentioned by David Anderson Spark provides pivot function since version 1.6. General syntax looks as follows:

df
  .groupBy(grouping_columns)
  .pivot(pivot_column, [values]) 
  .agg(aggregate_expressions)

Usage examples using nycflights13 and csv format:

Python:

from pyspark.sql.functions import avg

flights = (sqlContext
    .read
    .format("csv")
    .options(inferSchema="true", header="true")
    .load("flights.csv")
    .na.drop())

flights.registerTempTable("flights")
sqlContext.cacheTable("flights")

gexprs = ("origin", "dest", "carrier")
aggexpr = avg("arr_delay")

flights.count()
## 336776

%timeit -n10 flights.groupBy(*gexprs ).pivot("hour").agg(aggexpr).count()
## 10 loops, best of 3: 1.03 s per loop

Scala:

val flights = sqlContext
  .read
  .format("csv")
  .options(Map("inferSchema" -> "true", "header" -> "true"))
  .load("flights.csv")

flights
  .groupBy($"origin", $"dest", $"carrier")
  .pivot("hour")
  .agg(avg($"arr_delay"))

Java:

import static org.apache.spark.sql.functions.*;
import org.apache.spark.sql.*;

Dataset<Row> df = spark.read().format("csv")
        .option("inferSchema", "true")
        .option("header", "true")
        .load("flights.csv");

df.groupBy(col("origin"), col("dest"), col("carrier"))
        .pivot("hour")
        .agg(avg(col("arr_delay")));

R / SparkR:

library(magrittr)

flights <- read.df("flights.csv", source="csv", header=TRUE, inferSchema=TRUE)

flights %>% 
  groupBy("origin", "dest", "carrier") %>% 
  pivot("hour") %>% 
  agg(avg(column("arr_delay")))

R / sparklyr

library(dplyr)

flights <- spark_read_csv(sc, "flights", "flights.csv")

avg.arr.delay <- function(gdf) {
   expr <- invoke_static(
      sc,
      "org.apache.spark.sql.functions",
      "avg",
      "arr_delay"
    )
    gdf %>% invoke("agg", expr, list())
}

flights %>% 
  sdf_pivot(origin + dest + carrier ~  hour, fun.aggregate=avg.arr.delay)

SQL:

Note that PIVOT keyword in Spark SQL is supported starting from version 2.4.

CREATE TEMPORARY VIEW flights 
USING csv 
OPTIONS (header 'true', path 'flights.csv', inferSchema 'true') ;

 SELECT * FROM (
   SELECT origin, dest, carrier, arr_delay, hour FROM flights
 ) PIVOT (
   avg(arr_delay)
   FOR hour IN (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
                13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23)
 );

Example data:

"year","month","day","dep_time","sched_dep_time","dep_delay","arr_time","sched_arr_time","arr_delay","carrier","flight","tailnum","origin","dest","air_time","distance","hour","minute","time_hour"
2013,1,1,517,515,2,830,819,11,"UA",1545,"N14228","EWR","IAH",227,1400,5,15,2013-01-01 05:00:00
2013,1,1,533,529,4,850,830,20,"UA",1714,"N24211","LGA","IAH",227,1416,5,29,2013-01-01 05:00:00
2013,1,1,542,540,2,923,850,33,"AA",1141,"N619AA","JFK","MIA",160,1089,5,40,2013-01-01 05:00:00
2013,1,1,544,545,-1,1004,1022,-18,"B6",725,"N804JB","JFK","BQN",183,1576,5,45,2013-01-01 05:00:00
2013,1,1,554,600,-6,812,837,-25,"DL",461,"N668DN","LGA","ATL",116,762,6,0,2013-01-01 06:00:00
2013,1,1,554,558,-4,740,728,12,"UA",1696,"N39463","EWR","ORD",150,719,5,58,2013-01-01 05:00:00
2013,1,1,555,600,-5,913,854,19,"B6",507,"N516JB","EWR","FLL",158,1065,6,0,2013-01-01 06:00:00
2013,1,1,557,600,-3,709,723,-14,"EV",5708,"N829AS","LGA","IAD",53,229,6,0,2013-01-01 06:00:00
2013,1,1,557,600,-3,838,846,-8,"B6",79,"N593JB","JFK","MCO",140,944,6,0,2013-01-01 06:00:00
2013,1,1,558,600,-2,753,745,8,"AA",301,"N3ALAA","LGA","ORD",138,733,6,0,2013-01-01 06:00:00

Performance considerations:

Generally speaking pivoting is an expensive operation.

• if you can, try to provide values list, as this avoids an extra hit to compute the uniques:

  vs = list(range(25))
  %timeit -n10 flights.groupBy(*gexprs ).pivot("hour", vs).agg(aggexpr).count()
  ## 10 loops, best of 3: 392 ms per loop
  

• in some cases it proved to be beneficial (likely no longer worth the effort in 2.0 or later) to repartition and / or pre-aggregate the data

• for reshaping only, you can use first: Pivot String column on Pyspark Dataframe

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