I have the following columns in DataFrame df:
c_id p_id type values
278230 57371100 11 1
278230 57371100 12 1
...
I execute the following code and expect to see columns 11_total and 12_total:
df
.groupBy($"c_id",$"p_id")
.pivot("type")
.agg(sum("values") as "total")
.na.fill(0)
.show()
Instead, I get columns 11 and 12:
+-----------+----------+---+---+
| c_id| p_id| 11| 12|
+-----------+----------+---+---+
| 278230| 57371100| 0| 1|
| 337790| 72031970| 3| 0|
| 320710| 71904400| 0| 1|
Why?
That's because Spark appends aliases to the pivot column values only when there are multiple aggregations for clarity:
val df = Seq(
(278230, 57371100, 11, 1),
(278230, 57371100, 12, 2),
(337790, 72031970, 11, 1),
(337790, 72031970, 11, 2),
(337790, 72031970, 12, 3)
)toDF("c_id", "p_id", "type", "values")
df.groupBy($"c_id", $"p_id").pivot("type").
agg(sum("values").as("total")).
show
// +------+--------+---+---+
// | c_id| p_id| 11| 12|
// +------+--------+---+---+
// |337790|72031970| 3| 3|
// |278230|57371100| 1| 2|
// +------+--------+---+---+
df.groupBy($"c_id", $"p_id").pivot("type").
agg(sum("values").as("total"), max("values").as("max")).
show
// +------+--------+--------+------+--------+------+
// | c_id| p_id|11_total|11_max|12_total|12_max|
// +------+--------+--------+------+--------+------+
// |337790|72031970| 3| 2| 3| 3|
// |278230|57371100| 1| 1| 2| 2|
// +------+--------+--------+------+--------+------+
Related
I have 2 dataframes
val df1 = Seq((1, "1","6"), (2, "10","8"), (3, "6","4")).toDF("id", "value1","value2")
val df2 = Seq((1, "1","6"), (2, "5","4"), (4, "3","1")).toDF("id", "value1","value2")
and i want to find the difference of column level
output should look like
id,value1_df1,value1_df2,diff_value1,value2_df1,value_df2,diff_value2
1, 1 ,1 , 0 , 6 ,6 ,0
2, 10 ,5 , 5 , 8 ,4 ,4
3, 6 ,3 , 1 , 4 ,1 ,3
like wise i have 100's of column and want to compute difference between same column in 2 dataframes columns are dynamic
Maybe this will help:
val spark = SparkSession.builder.appName("Test").master("local[*]").getOrCreate();
import spark.implicits._
var df1 = Seq((1, "1", "6"), (2, "10", "8"), (3, "6", "4")).toDF("id", "value1", "value2")
var df2 = Seq((1, "1", "6"), (2, "5", "4"), (3, "3", "1")).toDF("id", "value1", "value2")
df1.columns.foreach(column => {
df1 = df1.withColumn(column, df1.col(column).cast(IntegerType))
})
df2.columns.foreach(column => {
df2 = df2.withColumn(column, df2.col(column).cast(IntegerType))
})
df1 = df1.withColumnRenamed("id", "df1_id")
df2 = df2.withColumnRenamed("id", "df2_id")
df1.show()
df2.show()
so till now you have two dataframes with value_x,value_y,value_z and going on ...
df1:
+------+------+------+
|df1_id|value1|value2|
+------+------+------+
| 1| 1| 6|
| 2| 10| 8|
| 3| 6| 4|
+------+------+------+
df2:
+------+------+------+
|df2_id|value1|value2|
+------+------+------+
| 1| 1| 6|
| 2| 5| 4|
| 3| 3| 1|
+------+------+------+
Now we are gonna join them base on id:
var df3 = df1.alias("df1").join(df2.alias("df2"), $"df1.df1_id" === $"df2.df2_id")
and last, we gonna take all columns on df1/df2 (* Its important that they will have the same columns) - without the id, and create a new column of the diff:
df1.columns.tail.foreach(col => {
val new_col_name = s"${col}-diff"
val df_a_col = s"df1.${col}"
val df_b_col = s"df2.${col}"
df3 = df3.withColumn(new_col_name, df3.col(df_a_col) - df3.col(df_b_col))
})
df3.show()
Result:
+------+------+------+------+------+------+-----------+-----------+
|df1_id|value1|value2|df2_id|value1|value2|value1-diff|value2-diff|
+------+------+------+------+------+------+-----------+-----------+
| 1| 1| 6| 1| 1| 6| 0| 0|
| 2| 10| 8| 2| 5| 4| 5| 4|
| 3| 6| 4| 3| 3| 1| 3| 3|
+------+------+------+------+------+------+-----------+-----------+
This is the result, and it`s dynamic so you can add valueX you want.
I am using spark with Scala to transform a Dataframe , where I would like to compute a new variable which calculates the rank of one variable per row within many variables.
Example -
Input DF-
+---+---+---+
|c_0|c_1|c_2|
+---+---+---+
| 11| 11| 35|
| 22| 12| 66|
| 44| 22| 12|
+---+---+---+
Expected DF-
+---+---+---+--------+--------+--------+
|c_0|c_1|c_2|c_0_rank|c_1_rank|c_2_rank|
+---+---+---+--------+--------+--------+
| 11| 11| 35| 2| 3| 1|
| 22| 12| 66| 2| 3| 1|
| 44| 22| 12| 1| 2| 3|
+---+---+---+--------+--------+--------+
This has aleady been answered using R - Rank per row over multiple columns in R,
but I need to do the same in spark-sql using scala. Thanks for the Help!
Edit- 4/1 . Encountered one scenario where if the values are same the ranks should be different. Editing first row for replicating the situation.
If I understand correctly, you want to have the rank of each column, within each row.
Let's first define the data, and the columns to "rank".
val df = Seq((11, 21, 35),(22, 12, 66),(44, 22 , 12))
.toDF("c_0", "c_1", "c_2")
val cols = df.columns
Then we define a UDF that finds the index of an element in an array.
val pos = udf((a : Seq[Int], elt : Int) => a.indexOf(elt)+1)
We finally create a sorted array (in descending order) and use the UDF to find the rank of each column.
val ranks = cols.map(c => pos(col("array"), col(c)).as(c+"_rank"))
df.withColumn("array", sort_array(array(cols.map(col) : _*), false))
.select((cols.map(col)++ranks) :_*).show
+---+---+---+--------+--------+--------+
|c_0|c_1|c_2|c_0_rank|c_1_rank|c_2_rank|
+---+---+---+--------+--------+--------+
| 11| 12| 35| 3| 2| 1|
| 22| 12| 66| 2| 3| 1|
| 44| 22| 12| 1| 2| 3|
+---+---+---+--------+--------+--------+
EDIT:
As of Spark 2.4, the pos UDF that I defined can be replaced by the built in function array_position(column: Column, value: Any) that works exactly the same way (the first index is 1). This avoids using UDFs that can be slightly less efficient.
EDIT2:
The code above will generate duplicated indices in case you have duplidated keys. If you want to avoid it, you can create the array, zip it to remember which column is which, sort it and zip it again to get the final rank. It would look like this:
val colMap = df.columns.zipWithIndex.map(_.swap).toMap
val zip = udf((s: Seq[Int]) => s
.zipWithIndex
.sortBy(-_._1)
.map(_._2)
.zipWithIndex
.toMap
.mapValues(_+1))
val ranks = (0 until cols.size)
.map(i => 'zip.getItem(i) as colMap(i) + "_rank")
val result = df
.withColumn("zip", zip(array(cols.map(col) : _*)))
.select(cols.map(col) ++ ranks :_*)
One way to go about this would be to use windows.
val df = Seq((11, 21, 35),(22, 12, 66),(44, 22 , 12))
.toDF("c_0", "c_1", "c_2")
(0 to 2)
.map("c_"+_)
.foldLeft(df)((d, column) =>
d.withColumn(column+"_rank", rank() over Window.orderBy(desc(column))))
.show
+---+---+---+--------+--------+--------+
|c_0|c_1|c_2|c_0_rank|c_1_rank|c_2_rank|
+---+---+---+--------+--------+--------+
| 22| 12| 66| 2| 3| 1|
| 11| 21| 35| 3| 2| 2|
| 44| 22| 12| 1| 1| 3|
+---+---+---+--------+--------+--------+
But this is not a good idea. All the data will end up in one partition which will cause an OOM error if all the data does not fit inside one executor.
Another way would require to sort the dataframe three times, but at least that would scale to any size of data.
Let's define a function that zips a dataframe with consecutive indices (it exists for RDDs but not for dataframes)
def zipWithIndex(df : DataFrame, name : String) : DataFrame = {
val rdd = df.rdd.zipWithIndex
.map{ case (row, i) => Row.fromSeq(row.toSeq :+ (i+1)) }
val newSchema = df.schema.add(StructField(name, LongType, false))
df.sparkSession.createDataFrame(rdd, newSchema)
}
And let's use it on the same dataframe df:
(0 to 2)
.map("c_"+_)
.foldLeft(df)((d, column) =>
zipWithIndex(d.orderBy(desc(column)), column+"_rank"))
.show
which provides the exact same result as above.
You could probably create a window function. Do note that this is susceptible to OOM if you have too much data. But, I just wanted to introduce to the concept of window functions here.
inputDF.createOrReplaceTempView("my_df")
val expectedDF = spark.sql("""
select
c_0
, c_1
, c_2
, rank(c_0) over (order by c_0 desc) c_0_rank
, rank(c_1) over (order by c_1 desc) c_1_rank
, rank(c_2) over (order by c_2 desc) c_2_rank
from my_df""")
expectedDF.show()
+---+---+---+--------+--------+--------+
|c_0|c_1|c_2|c_0_rank|c_1_rank|c_2_rank|
+---+---+---+--------+--------+--------+
| 44| 22| 12| 3| 3| 1|
| 11| 21| 35| 1| 2| 2|
| 22| 12| 66| 2| 1| 3|
+---+---+---+--------+--------+--------+
I have a hashmap in which I stored the values
Map(862304021470656 -> List(0.0, 0.0, 0.0, 0.0, 1.540980096E9, 74.365111, 22.302669, 0.0),866561010400483 -> List(0.0, 1.0, 1.0, 2.0, 1.543622306E9, 78.0204, 10.005262, 56.0))
This is the dataframe
| id| lt| ln| evt| lstevt| s| d|agl|chg| d1| d2| d3| d4|ebt|ibt|port| a1| a2| a3| a4|nos|dfrmd|
+---------------+---------+---------+----------+----------+---+---+---+---+---+---+---+---+---+---+----+---+---+---+---+---+-----+
|862304021470656|25.284158|82.435973|1540980095|1540980095| 0| 39|298| 0| 0| 1| 1| 2| 0| 5| 97| 12| -1| -1| 22| 0| 0|
|862304021470656|25.284158|82.435973|1540980105|1540980105| 0| 0|298| 0| 0| 1| 1| 2| 0| 5| 97| 12| -1| -1| 22| 0| 0|
|862304021470656|25.284724|82.434222|1540980155|1540980155| 14| 47|289| 0| 0| 1| 1| 2| 0| 5| 97| 11| -1| -1| 22| 0| 0|
|866561010400483|25.284858|82.433831|1544980165|1540980165| 12| 42|295| 0| 0| 1| 1| 2| 0| 5| 97| 12| -1| -1| 22| 0| 0|
I want to just filter those value from dataframe, comparing the 4th index of list from the evt column,picking only the rows whose evt value is greater than that 4th index value of list,key in the map is id column of dataframe.
Here's one way using a UDF to fetch the evt value for comparison:
import org.apache.spark.sql.functions._
val df = Seq(
(862304021470656L, 25.284158, 82.435973, 1540980095),
(862304021470656L, 25.284158, 82.435973, 1540980105),
(862304021470656L, 25.284724, 82.434222, 1540980155),
(866561010400483L, 25.284858, 82.433831, 1544980165)
).toDF("id", "lt", "ln", "evt")
val listMap = Map(
862304021470656L -> List(0.0, 0.0, 0.0, 0.0, 1.540980096E9, 74.365111, 22.302669, 0.0),
866561010400483L -> List(0.0, 1.0, 1.0, 2.0, 1.543622306E9, 78.0204, 10.005262, 56.0)
)
def evtLimit(m: Map[Long, List[Double]], evtIdx: Int) = udf(
(id: Long) => m.get(id) match {
case Some(ls) => if (evtIdx < ls.size) ls(evtIdx) else Double.MaxValue
case None => Double.MaxValue
}
)
df.where($"evt" > evtLimit(listMap, 4)($"id")).show
// +---------------+---------+---------+----------+
// | id| lt| ln| evt|
// +---------------+---------+---------+----------+
// |862304021470656|25.284158|82.435973|1540980105|
// |862304021470656|25.284724|82.434222|1540980155|
// |866561010400483|25.284858|82.433831|1544980165|
// +---------------+---------+---------+----------+
Note that the UDF returns Double.MaxValue in case of non-matching key or invalid value in the provided Map. That can certainly be revised for specific business requirement.
You can get this with a simple sql:
import spark.implicits._
import org.apache.spark.sql.functions._
val df = ... //your main Dataframe
val map = Map(..your data here..).toDF("id", "list")
val join = df.join(map, "id").filter(length($"list") >= 5 /* <-- just in case */)
val res = join.filter($"evt" > $"list"(4))
I have the following DataFrame df:
val df = Seq(
(1, 0, 1, 0, 0), (1, 4, 1, 0, 4), (2, 2, 1, 2, 2),
(4, 3, 1, 4, 4), (4, 5, 1, 4, 4)
).toDF("from", "to", "attr", "type_from", "type_to")
+-----+-----+----+---------------+---------------+
|from |to |attr|type_from |type_to |
+-----+-----+----+---------------+---------------+
| 1| 0| 1| 0| 0|
| 1| 4| 1| 0| 4|
| 2| 2| 1| 2| 2|
| 4| 3| 1| 4| 4|
| 4| 5| 1| 4| 4|
+-----+-----+----+---------------+---------------+
I want to count the number of ingoing and outgoing links for each node only when the type of from node is the same as the type of to node (i.e. the values of type_from and type_to).
The cases when to and from are equal should be excluded.
This is how I calculate the number of outgoing links based on this answer proposed by user8371915.
df
.where($"type_from" === $"type_to" && $"from" =!= $"to")
.groupBy($"from" as "nodeId", $"type_from" as "type")
.agg(count("*") as "numLinks")
.na.fill(0)
.show()
Of course, I can repeat the same calculation for the incoming links and then join the results. But is there any shorter solution?
df2
.where($"type_from" === $"type_to" && $"from" =!= $"to")
.groupBy($"to" as "nodeId", $"type_to" as "type")
.agg(count("*") as "numLinks")
.na.fill(0)
.show()
val df_result = df.join(df2, Seq("nodeId", "type"), "rightouter")
Newbie question ,
I am try to add columns to exist DataFrame , I am working with Spark 1.4.1
import sqlContext.implicits._
case class Test(rule: Int)
val test = sc.parallelize((1 to 2).map(i => Test(i-i))).toDF
test.registerTempTable("test")
test.show
+----+
|rule|
+----+
| 0|
| 0|
+----+
Then - add columns, one column - OK
import org.apache.spark.sql.functions.lit
val t1 = test.withColumn("1",lit(0) )
t1.show
+----+-+
|rule|1|
+----+-+
| 0|0|
| 0|0|
+----+-+
Problem appears when I try to add several columns:
val t1 = (1 to 5).map( i => test.withColumn(i,lit(i) ))
t1.show()
error: value show is not a member of scala.collection.immutable.IndexedSeq[org.apache.spark.sql.DataFrame]
You need a reduce process, so instead of using map, you can use foldLeft with test data frame as your initial parameter:
val t1 = (1 to 5).foldLeft(test){ case(df, i) => df.withColumn(i.toString, lit(i))}
t1.show
+----+---+---+---+---+---+
|rule| 1| 2| 3| 4| 5|
+----+---+---+---+---+---+
| 0| 1| 2| 3| 4| 5|
| 0| 1| 2| 3| 4| 5|
+----+---+---+---+---+---+