Split Pyspark data column by column in another Pyspark framework when IDs match
I have a PySpark DataFrame data file, df1, that looks like this:
CustomerID CustomerValue
12 .17
14 .15
14 .25
17 .50
17 .01
17 .35
I have a second PySpark DataFrame, df2, which df1 is grouped by CustomerID and aggregated with a sum function. It looks like this:
CustomerID CustomerValueSum
12 .17
14 .40
17 .86
I want to add a third column to df1 which is df1 ['CustomerValue'] split by df2 ['CustomerValueSum'] for the same customer IDs. It will look like this:
CustomerID CustomerValue NormalizedCustomerValue
12 .17 1.00
14 .15 .38
14 .25 .62
17 .50 .58
17 .01 .01
17 .35 .41
In other words, I am trying to convert this Python / Pandas code to PySpark:
normalized_list = []
for idx, row in df1.iterrows():
(
normalized_list
.append(
row.CustomerValue / df2[df2.CustomerID == row.CustomerID].CustomerValueSum
)
)
df1['NormalizedCustomerValue'] = [val.values[0] for val in normalized_list]
How can i do this?
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2 answers
code:
import pyspark.sql.functions as F
df1 = df1\
.join(df2, "CustomerID")\
.withColumn("NormalizedCustomerValue", (F.col("CustomerValue") / F.col("CustomerValueSum")))\
.drop("CustomerValueSum")
Output:
df1.show()
+----------+-------------+-----------------------+
|CustomerID|CustomerValue|NormalizedCustomerValue|
+----------+-------------+-----------------------+
| 17| 0.5| 0.5813953488372093|
| 17| 0.01| 0.011627906976744186|
| 17| 0.35| 0.4069767441860465|
| 12| 0.17| 1.0|
| 14| 0.15| 0.37499999999999994|
| 14| 0.25| 0.625|
+----------+-------------+-----------------------+
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This can also be achieved with the Spark Window function where you don't need to create a separate framework with aggregated values (df2):
Creating data for the input data block:
from pyspark.sql import HiveContext
sqlContext = HiveContext(sc)
data =[(12, 0.17), (14, 0.15), (14, 0.25), (17, 0.5), (17, 0.01), (17, 0.35)]
df1 = sqlContext.createDataFrame(data, ['CustomerID', 'CustomerValue'])
df1.show()
+----------+-------------+
|CustomerID|CustomerValue|
+----------+-------------+
| 12| 0.17|
| 14| 0.15|
| 14| 0.25|
| 17| 0.5|
| 17| 0.01|
| 17| 0.35|
+----------+-------------+
Defining a window split by CustomerID:
from pyspark.sql import Window
from pyspark.sql.functions import sum
w = Window.partitionBy('CustomerID')
df2 = df1.withColumn('NormalizedCustomerValue', df1.CustomerValue/sum(df1.CustomerValue).over(w)).orderBy('CustomerID')
df2.show()
+----------+-------------+-----------------------+
|CustomerID|CustomerValue|NormalizedCustomerValue|
+----------+-------------+-----------------------+
| 12| 0.17| 1.0|
| 14| 0.15| 0.37499999999999994|
| 14| 0.25| 0.625|
| 17| 0.5| 0.5813953488372093|
| 17| 0.01| 0.011627906976744186|
| 17| 0.35| 0.4069767441860465|
+----------+-------------+-----------------------+
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