Iterating through rows to create custom formula structure in PySpark - apache-spark

Question

I have a dataframe with variable names and numerator and denominator.
Each variable is a ratio, eg below:
And another dataset with actual data to compute the attributes:
Goal is to create these attributes with formulas in 1st and compute with 2nd.
Currently my approach is very naive:
df = df.withColumn("var1", col('a')/col('b'))./
.
.
.
Desired Output:
Since I have >500 variables, any suggestions for a smarter way to get around this are welcome!

Answer 1

This can be achieved by cross join , unpivot and pivot function in PySpark.
import pyspark.sql.functions as f
from pyspark.sql.functions import *
from pyspark.sql.types import *
data = [
("var1", "a","c"),
("var2", "b","d"),
("var3", "b","a"),
("var4", "d","c")
]
schema = StructType([
StructField('name', StringType(),True), \
StructField('numerator', StringType(),True), \
StructField('denonminator', StringType(),True)
])
data2 = [
("ID1", 6,4,3,7),
("ID2", 1,2,3,9)
]
schema2 = StructType([
StructField('ID', StringType(),True), \
StructField('a', IntegerType(),True), \
StructField('b', IntegerType(),True),\
StructField('c', IntegerType(),True), \
StructField('d', IntegerType(),True)
])
df = spark.createDataFrame(data=data, schema=schema)
df2 = spark.createDataFrame(data=data2, schema=schema2)
df.createOrReplaceTempView("table1")
df2.createOrReplaceTempView("table2")
df.createOrReplaceTempView("table1")
df2.createOrReplaceTempView("table2")
""" CRoss Join for Duplicating the values """
df3=spark.sql("select * from table1 cross join table2")
df3.createOrReplaceTempView("table3")
""" Unpivoting the values and joining to fecth the value of numerator and denominator"""
cols = df2.columns[1:]
df4=df2.selectExpr('ID', "stack({}, {})".format(len(cols), ', '.join(("'{}', {}".format(i, i) for i in cols))))
df4.createOrReplaceTempView("table4")
df5=spark.sql("select name,B.ID,round(B.col1/C.col1,2) as value from table3 A left outer join table4 B on A.ID=B.ID and a.numerator=b.col0 left outer join table4 C on A.ID=C.ID and a.denonminator=C.col0 order by name,ID")
""" Pivot for fetching the results """
df_final=df5.groupBy("ID").pivot("name").max("value")
The results of all intermediate and final dataframes
>>> df.show()
+----+---------+------------+
|name|numerator|denonminator|
+----+---------+------------+
|var1| a| c|
|var2| b| d|
|var3| b| a|
|var4| d| c|
+----+---------+------------+
>>> df2.show()
+---+---+---+---+---+
| ID| a| b| c| d|
+---+---+---+---+---+
|ID1| 6| 4| 3| 7|
|ID2| 1| 2| 3| 9|
+---+---+---+---+---+
>>> df3.show()
+----+---------+------------+---+---+---+---+---+
|name|numerator|denonminator| ID| a| b| c| d|
+----+---------+------------+---+---+---+---+---+
|var1| a| c|ID1| 6| 4| 3| 7|
|var2| b| d|ID1| 6| 4| 3| 7|
|var1| a| c|ID2| 1| 2| 3| 9|
|var2| b| d|ID2| 1| 2| 3| 9|
|var3| b| a|ID1| 6| 4| 3| 7|
|var4| d| c|ID1| 6| 4| 3| 7|
|var3| b| a|ID2| 1| 2| 3| 9|
|var4| d| c|ID2| 1| 2| 3| 9|
+----+---------+------------+---+---+---+---+---+
>>> df4.show()
+---+----+----+
| ID|col0|col1|
+---+----+----+
|ID1| a| 6|
|ID1| b| 4|
|ID1| c| 3|
|ID1| d| 7|
|ID2| a| 1|
|ID2| b| 2|
|ID2| c| 3|
|ID2| d| 9|
+---+----+----+
>>> df5.show()
+----+---+-----+
|name| ID|value|
+----+---+-----+
|var1|ID1| 2.0|
|var1|ID2| 0.33|
|var2|ID1| 0.57|
|var2|ID2| 0.22|
|var3|ID1| 0.67|
|var3|ID2| 2.0|
|var4|ID1| 2.33|
|var4|ID2| 3.0|
+----+---+-----+
>>> df_final.show() final
+---+----+----+----+----+
| ID|var1|var2|var3|var4|
+---+----+----+----+----+
|ID2|0.33|0.22| 2.0| 3.0|
|ID1| 2.0|0.57|0.67|2.33|
+---+----+----+----+----+