如何转动DataFrame？

时间：2015-05-14 18:42:42

标签： scala apache-spark dataframe apache-spark-sql pivot

我开始使用Spark DataFrames，我需要能够透过数据来创建包含多行的1列中的多列。在Scalding中有内置的功能，我相信Python中的Pandas，但我无法为新的Spark Dataframe找到任何东西。

我认为我可以编写某种类型的自定义函数，但我甚至不确定如何启动，特别是因为我是Spark的新手。我有人知道如何使用内置功能或如何在Scala中编写内容的建议，非常感谢。

10 个答案:

答案 0 :(得分：54)

As mentioned David Anderson自版本1.6起，Spark提供pivot功能。一般语法如下所示：

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

使用nycflights13和csv格式的使用示例：

<强>的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

<强> Scala的：

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"))

<强>爪哇：

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)

<强> SQL ：

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)
 );

示例数据：

"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

效果考虑因素：

一般来说，旋转是一项昂贵的操作。

相关问题：

答案 1 :(得分：14)

我通过编写for循环来动态创建SQL查询来克服这个问题。说我有：

urlencoded

我希望：

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

我可以使用我想要透视的值创建一个列表，然后创建一个包含我需要的SQL查询的字符串。

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

我可以创建类似的查询然后进行聚合。这不是一个非常优雅的解决方案，但它适用于任何值列表，并且在调用代码时也可以作为参数传递。

答案 2 :(得分：9)

已将一个数据透视运算符添加到Spark数据帧API中，它是Spark 1.6的一部分。

有关详细信息，请参阅https://github.com/apache/spark/pull/7841。

答案 3 :(得分：7)

有一种 SIMPLE 数据透视方法：

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

  import sparkSession.implicits._

  val data = Seq(
    (1,"US",50),
    (1,"UK",100),
    (1,"Can",125),
    (2,"US",75),
    (2,"UK",150),
    (2,"Can",175),
  )

  val dataFrame = data.toDF("id","tag","value")

  val df2 = dataFrame
                    .groupBy("id")
                    .pivot("tag")
                    .max("value")
  df2.show()

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

答案 4 :(得分：5)

我通过以下步骤使用数据框解决了类似的问题：

为您的所有国家/地区创建列，其值为＆＃39;＆＃39;作为价值：

import org.apache.spark.sql.functions._
val countries = List("US", "UK", "Can")
val countryValue = udf{(countryToCheck: String, countryInRow: String, value: Long) =>
  if(countryToCheck == countryInRow) value else 0
}
val countryFuncs = countries.map{country => (dataFrame: DataFrame) => dataFrame.withColumn(country, countryValue(lit(country), df("tag"), df("value"))) }
val dfWithCountries = Function.chain(countryFuncs)(df).drop("tag").drop("value")

您的数据框＆dfWithCountries＆＃39;会是这样的：

+--+--+---+---+
|id|US| UK|Can|
+--+--+---+---+
| 1|50|  0|  0|
| 1| 0|100|  0|
| 1| 0|  0|125|
| 2|75|  0|  0|
| 2| 0|150|  0|
| 2| 0|  0|175|
+--+--+---+---+

现在，您可以将所需结果的所有值相加：

dfWithCountries.groupBy("id").sum(countries: _*).show

结果：

+--+-------+-------+--------+
|id|SUM(US)|SUM(UK)|SUM(Can)|
+--+-------+-------+--------+
| 1|     50|    100|     125|
| 2|     75|    150|     175|
+--+-------+-------+--------+

但这不是一个非常优雅的解决方案。我必须创建一系列函数来添加所有列。此外，如果我有很多国家/地区，我会将我的临时数据集扩展为一个非常广泛的数据集。

答案 5 :(得分：1)

有简单而优雅的解决方案。 

scala> spark.sql("select * from k_tags limit 10").show()
+---------------+-------------+------+
|           imsi|         name| value|
+---------------+-------------+------+
|246021000000000|          age|    37|
|246021000000000|       gender|Female|
|246021000000000|         arpu|    22|
|246021000000000|   DeviceType| Phone|
|246021000000000|DataAllowance|   6GB|
+---------------+-------------+------+

scala> spark.sql("select * from k_tags limit 10").groupBy($"imsi").pivot("name").agg(min($"value")).show()
+---------------+-------------+----------+---+----+------+
|           imsi|DataAllowance|DeviceType|age|arpu|gender|
+---------------+-------------+----------+---+----+------+
|246021000000000|          6GB|     Phone| 37|  22|Female|
|246021000000001|          1GB|     Phone| 72|  10|  Male|
+---------------+-------------+----------+---+----+------+

答案 6 :(得分：0)

最初我采用了Al M的解决方案。后来采取了同样的想法，并将此功能重写为转置功能。<​​/ p>

此方法使用键和值列

将任何df行转换为任何数据格式的列

输入csv 

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

<强>输出中 

+--+---+---+---+
|id| UK| US|Can|
+--+---+---+---+
| 2|150| 75|175|
| 1|100|50a|125|
+--+---+---+---+

转置方法： 

def transpose(hc : HiveContext , df: DataFrame,compositeId: List[String], key: String, value: String) = {

val distinctCols =   df.select(key).distinct.map { r => r(0) }.collect().toList

val rdd = df.map { row =>
(compositeId.collect { case id => row.getAs(id).asInstanceOf[Any] },
scala.collection.mutable.Map(row.getAs(key).asInstanceOf[Any] -> row.getAs(value).asInstanceOf[Any]))
}
val pairRdd = rdd.reduceByKey(_ ++ _)
val rowRdd = pairRdd.map(r => dynamicRow(r, distinctCols))
hc.createDataFrame(rowRdd, getSchema(df.schema, compositeId, (key, distinctCols)))

}

private def dynamicRow(r: (List[Any], scala.collection.mutable.Map[Any, Any]), colNames: List[Any]) = {
val cols = colNames.collect { case col => r._2.getOrElse(col.toString(), null) }
val array = r._1 ++ cols
Row(array: _*)
}

private  def getSchema(srcSchema: StructType, idCols: List[String], distinctCols: (String, List[Any])): StructType = {
val idSchema = idCols.map { idCol => srcSchema.apply(idCol) }
val colSchema = srcSchema.apply(distinctCols._1)
val colsSchema = distinctCols._2.map { col => StructField(col.asInstanceOf[String], colSchema.dataType, colSchema.nullable) }
StructType(idSchema ++ colsSchema)
}

主要摘要 

import java.util.Date
import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.sql.Row
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.sql.types.StructField


...
...
def main(args: Array[String]): Unit = {

    val sc = new SparkContext(conf)
    val sqlContext = new org.apache.spark.sql.SQLContext(sc)
    val dfdata1 = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").option("inferSchema", "true")
    .load("data.csv")
    dfdata1.show()  
    val dfOutput = transpose(new HiveContext(sc), dfdata1, List("id"), "tag", "value")
    dfOutput.show

}

答案 7 :(得分：0)

关于数据集/数据框的数据透视运算的例子很多，但是我找不到很多使用SQL的例子。这是一个对我有用的例子。

create or replace temporary view faang 
as SELECT stock.date AS `Date`,
    stock.adj_close AS `Price`,
    stock.symbol as `Symbol` 
FROM stock  
WHERE (stock.symbol rlike '^(FB|AAPL|GOOG|AMZN)$') and year(date) > 2010;


SELECT * from faang 

PIVOT (max(price) for symbol in ('AAPL', 'FB', 'GOOG', 'AMZN')) order by date;

答案 8 :(得分：-1)

内置的火花枢轴功能效率低下。波纹管实现在spark 2.4+上工作-想法是聚合一个映射并将值提取为列。唯一的限制是它不处理枢轴列中的聚合函数，仅处理列。

在8M表上，这些功能在 3秒上适用，而在内置spark版本中则为 40分钟：

# pass an optional list of string to avoid computation of columns
def pivot(df, group_by, key, aggFunction, levels=[]):
    if not levels:
        levels = [row[key] for row in df.filter(col(key).isNotNull()).groupBy(col(key)).agg(count(key)).select(key).collect()]
    return df.filter(col(key).isin(*levels) == True).groupBy(group_by).agg(map_from_entries(collect_list(struct(key, expr(aggFunction)))).alias("group_map")).select([group_by] + ["group_map." + l for l in levels])

# Usage
pivot(df, "id", "key", "value")
pivot(df, "id", "key", "array(value)")

// pass an optional list of string to avoid computation of columns
  def pivot(df: DataFrame, groupBy: Column, key: Column, aggFunct: String, _levels: List[String] = Nil): DataFrame = {
    val levels =
      if (_levels.isEmpty) df.filter(key.isNotNull).select(key).distinct().collect().map(row => row.getString(0)).toList
      else _levels

    df
      .filter(key.isInCollection(levels))
      .groupBy(groupBy)
      .agg(map_from_entries(collect_list(struct(key, expr(aggFunct)))).alias("group_map"))
      .select(groupBy.toString, levels.map(f => "group_map." + f): _*)
  }

// Usage:
pivot(df, col("id"), col("key"), "value")
pivot(df, col("id"), col("key"), "array(value)")

答案 9 :(得分：-1)

Spark一直在改进“透视Spark DataFrame”。枢纽功能已添加到Spark DataFrame API到Spark 1.6版本中，并且存在性能问题，在Spark 2.0中已得到纠正

但是，如果您使用的是较低版本；请注意，pivot是非常昂贵的操作，因此，建议提供列数据（如果已知）作为函数的参数，如下所示。

val countries = Seq("USA","China","Canada","Mexico")
val pivotDF = df.groupBy("Product").pivot("Country", countries).sum("Amount")
pivotDF.show()

对此已在Pivoting and Unpivoting Spark DataFrame

中进行了详细说明。

学习愉快！

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