I have a dataframe where i need to first apply dataframe and then get weighted average as shown in the output calculation below. What is an efficient way in pyspark to do that?
data = sc.parallelize([
[111,3,0.4],
[111,4,0.3],
[222,2,0.2],
[222,3,0.2],
[222,4,0.5]]
).toDF(['id', 'val','weight'])
data.show()
+---+---+------+
| id|val|weight|
+---+---+------+
|111| 3| 0.4|
|111| 4| 0.3|
|222| 2| 0.2|
|222| 3| 0.2|
|222| 4| 0.5|
+---+---+------+
Output:
id weigthed_val
111 (3*0.4 + 4*0.3)/(0.4 + 0.3)
222 (2*0.2 + 3*0.2+4*0.5)/(0.2+0.2+0.5)
You can multiply columns weight and val, then aggregate:
import pyspark.sql.functions as F
data.groupBy("id").agg((F.sum(data.val * data.weight)/F.sum(data.weight)).alias("weighted_val")).show()
+---+------------------+
| id| weighted_val|
+---+------------------+
|222|3.3333333333333335|
|111|3.4285714285714293|
+---+------------------+
Related
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 would like to build a moving average on each row in a window. Let's say -10 rows. BUT if there are less than 10 rows available I would like to insert a 0 in the resulting row -> new column.
So what I would try to achieve is using a UDF in an aggregate window with input paramter List() (or whatever superclass) which has the values of all rows available.
Here's a code example that doesn't work:
val w = Window.partitionBy("id").rowsBetween(-10, +0)
dfRetail2.withColumn("test", udftestf(dfRetail2("salesMth")).over(w))
Expected output: List( 1,2,3,4) if no more rows are available and take this as input paramter for the udf function. udf function should return a calculated value or 0 if less than 10 rows available.
the above code terminates: Expression 'UDF(salesMth#152L)' not supported within a window function.;;
You can use Spark's built-in Window functions along with when/otherwise for your specific condition without the need of UDF/UDAF. For simplicity, the sliding-window size is reduced to 4 in the following example with dummy data:
import org.apache.spark.sql.functions._
import org.apache.spark.sql.expressions.Window
import spark.implicits._
val df = (1 to 2).flatMap(i => Seq.tabulate(8)(j => (i, i * 10.0 + j))).
toDF("id", "amount")
val slidingWin = 4
val winSpec = Window.partitionBy($"id").rowsBetween(-(slidingWin - 1), 0)
df.
withColumn("slidingCount", count($"amount").over(winSpec)).
withColumn("slidingAvg", when($"slidingCount" < slidingWin, 0.0).
otherwise(avg($"amount").over(winSpec))
).show
// +---+------+------------+----------+
// | id|amount|slidingCount|slidingAvg|
// +---+------+------------+----------+
// | 1| 10.0| 1| 0.0|
// | 1| 11.0| 2| 0.0|
// | 1| 12.0| 3| 0.0|
// | 1| 13.0| 4| 11.5|
// | 1| 14.0| 4| 12.5|
// | 1| 15.0| 4| 13.5|
// | 1| 16.0| 4| 14.5|
// | 1| 17.0| 4| 15.5|
// | 2| 20.0| 1| 0.0|
// | 2| 21.0| 2| 0.0|
// | 2| 22.0| 3| 0.0|
// | 2| 23.0| 4| 21.5|
// | 2| 24.0| 4| 22.5|
// | 2| 25.0| 4| 23.5|
// | 2| 26.0| 4| 24.5|
// | 2| 27.0| 4| 25.5|
// +---+------+------------+----------+
Per remark in the comments section, I'm including a solution via UDF below as an alternative:
def movingAvg(n: Int) = udf{ (ls: Seq[Double]) =>
val (avg, count) = ls.takeRight(n).foldLeft((0.0, 1)){
case ((a, i), next) => (a + (next-a)/i, i + 1)
}
if (count <= n) 0.0 else avg // Expand/Modify this for specific requirement
}
// To apply the UDF:
df.
withColumn("average", movingAvg(slidingWin)(collect_list($"amount").over(winSpec))).
show
Note that unlike sum or count, collect_list ignores rowsBetween() and generates partitioned data that can potentially be very large to be passed to the UDF (hence the need for takeRight()). If the computed Window sum and count are sufficient for what's needed for your specific requirement, consider passing them to the UDF instead.
In general, especially if the data at hand is already in DataFrame format, it'd perform and scale better by using built-in DataFrame API to take advantage of Spark's execution engine optimization than using user-defined UDF/UDAF. You might be interested in reading this article re: advantages of DataFrame/Dataset API over UDF/UDAF.
Scala 2.12 and Spark 2.2.1 here. I have the following code:
myDf.show(5)
myDf.withColumn("rank", myDf("rank") * 10)
myDf.withColumn("lastRanOn", current_date())
println("And now:")
myDf.show(5)
When I run this, in the logs I see:
+---------+-----------+----+
|fizz|buzz|rizzrankrid|rank|
+---------+-----------+----+
| 2| 5| 1440370637| 128|
| 2| 5| 2114144780|1352|
| 2| 8| 199559784|3233|
| 2| 5| 1522258372| 895|
| 2| 9| 918480276| 882|
+---------+-----------+----+
And now:
+---------+-----------+-----+
|fizz|buzz|rizzrankrid| rank|
+---------+-----------+-----+
| 2| 5| 1440370637| 1280|
| 2| 5| 2114144780|13520|
| 2| 8| 199559784|32330|
| 2| 5| 1522258372| 8950|
| 2| 9| 918480276| 8820|
+---------+-----------+-----+
So, interesting:
The first withColumn works, transforming each row's rank value by multiplying itself by 10
However the second withColumn fails, which is just adding the current date/time to all rows as a new lastRanOn column
What do I need to do to get the lastRanOn column addition working?
Your example is probably too simple, because modifying rank should also not work.
withColumn does not update DataFrame, it's create a new DataFrame.
So you must do:
// if myDf is a var
myDf.show(5)
myDf = myDf.withColumn("rank", myDf("rank") * 10)
myDf = myDf.withColumn("lastRanOn", current_date())
println("And now:")
myDf.show(5)
or for example:
myDf.withColumn("rank", myDf("rank") * 10).withColumn("lastRanOn", current_date()).show(5)
Only then you will have new column added - after reassigning new DataFrame reference
I'm having a hard time framing the following Pyspark dataframe manipulation.
Essentially I am trying to group by category and then pivot/unmelt the subcategories and add new columns.
I've tried a number of ways, but they are very slow and and are not leveraging Spark's parallelism.
Here is my existing (slow, verbose) code:
from pyspark.sql.functions import lit
df = sqlContext.table('Table')
#loop over category
listids = [x.asDict().values()[0] for x in df.select("category").distinct().collect()]
dfArray = [df.where(df.category == x) for x in listids]
for d in dfArray:
#loop over subcategory
listids_sub = [x.asDict().values()[0] for x in d.select("sub_category").distinct().collect()]
dfArraySub = [d.where(d.sub_category == x) for x in listids_sub]
num = 1
for b in dfArraySub:
#renames all columns to append a number
for c in b.columns:
if c not in ['category','sub_category','date']:
column_name = str(c)+'_'+str(num)
b = b.withColumnRenamed(str(c), str(c)+'_'+str(num))
b = b.drop('sub_category')
num += 1
#if no df exists, create one and continually join new columns
try:
all_subs = all_subs.drop('sub_category').join(b.drop('sub_category'), on=['cateogry','date'], how='left')
except:
all_subs = b
#Fixes missing columns on union
try:
try:
diff_columns = list(set(all_cats.columns) - set(all_subs.columns))
for d in diff_columns:
all_subs = all_subs.withColumn(d, lit(None))
all_cats = all_cats.union(all_subs)
except:
diff_columns = list(set(all_subs.columns) - set(all_cats.columns))
for d in diff_columns:
all_cats = all_cats.withColumn(d, lit(None))
all_cats = all_cats.union(all_subs)
except Exception as e:
print e
all_cats = all_subs
But this is very slow. Any guidance would be greatly appreciated!
Your output is not really logical, but we can achieve this result using the pivot function. You need to precise your rules otherwise I can see a lot of cases it may fails.
from pyspark.sql import functions as F
from pyspark.sql.window import Window
df.show()
+----------+---------+------------+------------+------------+
| date| category|sub_category|metric_sales|metric_trans|
+----------+---------+------------+------------+------------+
|2018-01-01|furniture| bed| 100| 75|
|2018-01-01|furniture| chair| 110| 85|
|2018-01-01|furniture| shelf| 35| 30|
|2018-02-01|furniture| bed| 55| 50|
|2018-02-01|furniture| chair| 45| 40|
|2018-02-01|furniture| shelf| 10| 15|
|2018-01-01| rug| circle| 2| 5|
|2018-01-01| rug| square| 3| 6|
|2018-02-01| rug| circle| 3| 3|
|2018-02-01| rug| square| 4| 5|
+----------+---------+------------+------------+------------+
df.withColumn("fg", F.row_number().over(Window().partitionBy('date', 'category').orderBy("sub_category"))).groupBy('date', 'category', ).pivot('fg').sum('metric_sales', 'metric_trans').show()
+----------+---------+-------------------------------------+-------------------------------------+-------------------------------------+-------------------------------------+-------------------------------------+-------------------------------------+
| date| category|1_sum(CAST(`metric_sales` AS BIGINT))|1_sum(CAST(`metric_trans` AS BIGINT))|2_sum(CAST(`metric_sales` AS BIGINT))|2_sum(CAST(`metric_trans` AS BIGINT))|3_sum(CAST(`metric_sales` AS BIGINT))|3_sum(CAST(`metric_trans` AS BIGINT))|
+----------+---------+-------------------------------------+-------------------------------------+-------------------------------------+-------------------------------------+-------------------------------------+-------------------------------------+
|2018-02-01| rug| 3| 3| 4| 5| null| null|
|2018-02-01|furniture| 55| 50| 45| 40| 10| 15|
|2018-01-01|furniture| 100| 75| 110| 85| 35| 30|
|2018-01-01| rug| 2| 5| 3| 6| null| null|
+----------+---------+-------------------------------------+-------------------------------------+-------------------------------------+-------------------------------------+-------------------------------------+-------------------------------------+
Say I have two PySpark DataFrames df1 and df2.
df1= 'a'
1
2
5
df2= 'b'
3
6
And I want to find the closest df2['b'] value for each df1['a'], and add the closest values as a new column in df1.
In other words, for each value x in df1['a'], I want to find a y that achieves min(abx(x-y)) for all y in df2['b'](note: can assume that there is only one y that can achieve the minimum distance), and the result would be
'a' 'b'
1 3
2 3
5 6
I tried the following code to create a distance matrix first (before finding the values achieving the minimum distance):
from pyspark.sql.types import IntegerType
from pyspark.sql.functions import udf
def dict(x,y):
return abs(x-y)
udf_dict = udf(dict, IntegerType())
sql_sc = SQLContext(sc)
udf_dict(df1.a, df2.b)
which gives
Column<PythonUDF#dist(a,b)>
Then I tried
sql_sc.CreateDataFrame(udf_dict(df1.a, df2.b))
which runs forever without giving error/output.
My questions are:
As I'm new to Spark, is my way to construct the output DataFrame efficient? (My way would be creating a distance matrix for all the a and b values first, and then find the min one)
What's wrong with the last line of my code and how to fix it?
Starting with your second question - you can apply udf only to existing dataframe, I think you were thinking for something like this:
>>> df1.join(df2).withColumn('distance', udf_dict(df1.a, df2.b)).show()
+---+---+--------+
| a| b|distance|
+---+---+--------+
| 1| 3| 2|
| 1| 6| 5|
| 2| 3| 1|
| 2| 6| 4|
| 5| 3| 2|
| 5| 6| 1|
+---+---+--------+
But there is a more efficient way to apply this distance, by using internal abs:
>>> from pyspark.sql.functions import abs
>>> df1.join(df2).withColumn('distance', abs(df1.a -df2.b))
Then you can find matching numbers by calculating:
>>> distances = df1.join(df2).withColumn('distance', abs(df1.a -df2.b))
>>> min_distances = distances.groupBy('a').agg(min('distance').alias('distance'))
>>> distances.join(min_distances, ['a', 'distance']).select('a', 'b').show()
+---+---+
| a| b|
+---+---+
| 5| 6|
| 1| 3|
| 2| 3|
+---+---+