As a simplified example, I have a dataframe "df" with columns "col1,col2" and I want to compute a row-wise maximum after applying a function to each column :
def f(x):
return (x+1)
max_udf=udf(lambda x,y: max(x,y), IntegerType())
f_udf=udf(f, IntegerType())
df2=df.withColumn("result", max_udf(f_udf(df.col1),f_udf(df.col2)))
So if df:
col1 col2
1 2
3 0
Then
df2:
col1 col2 result
1 2 3
3 0 4
The above doesn't seem to work and produces "Cannot evaluate expression: PythonUDF#f..."
I'm absolutely positive "f_udf" works just fine on my table, and the main issue is with the max_udf.
Without creating extra columns or using basic map/reduce, is there a way to do the above entirely using dataframes and udfs? How should I modify "max_udf"?
I've also tried:
max_udf=udf(max, IntegerType())
which produces the same error.
I've also confirmed that the following works:
df2=(df.withColumn("temp1", f_udf(df.col1))
.withColumn("temp2", f_udf(df.col2))
df2=df2.withColumn("result", max_udf(df2.temp1,df2.temp2))
Why is it that I can't do these in one go?
I would like to see an answer that generalizes to any function "f_udf" and "max_udf."
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