Find top n results for multiple fields in Spark dataframe - apache-spark

Question

I have a dataframe like this one:
name field1 field2 field3
a 4 10 8
b 5 0 11
c 10 7 4
d 0 1 5
I need to find top 3 names for each field.
Expected output:
top3-field1 top3-field2 top3-field3
c a b
b c a
a d d
So, I tried to sort field(n) column values, limit top 3 results and generate new columns using withColumn method, like this:
df1 = df.orderBy(f.col("field1").desc(), "name") \
.limit(3) \
.withColumn("top3-field1", df["name"]) \
.select("top3-field1", "field1")
With this approach I have to create different dataframes for each field(n), and then join them to get the result as described above. I feel that there must be better solution for this problem. Hope someone can give me suggestions

Answer 1

You can first stack the df, then get the rank descending, then filter out rank less than or equal to 3, finally pivot the names:
Note that I am using this function in my code to make stacking a little easier in typing per se:
from pyspark.sql import functions as F, Window as W #imports
w = W.partitionBy("col").orderBy(F.desc("values"))
out = (df.selectExpr("name",stack_multiple_col(df,df.columns[1:]))
.withColumn("Rnk",F.dense_rank().over(w))
.where("Rnk<=3").groupBy("Rnk").pivot("col").agg(F.first("name")))
out.show()
+---+------+------+------+
|Rnk|field1|field2|field3|
+---+------+------+------+
| 1| c| a| b|
| 2| b| c| a|
| 3| a| d| d|
+---+------+------+------+
If you are not willing to use the function, you can write the same as :
w = W.partitionBy("col").orderBy(F.desc("values"))
out = (df.selectExpr("name",
'stack(3,"field1",field1,"field2",field2,"field3",field3) as (col,values)')
.withColumn("Rnk",F.dense_rank().over(w))
.where("Rnk<=3").groupBy("Rnk").pivot("col").agg(F.first("name")))
Full code:
def stack_multiple_col(df,cols=df.columns,output_columns=["col","values"]):
"""stacks multiple columns in a dataframe,
takes all columns by default unless passed a list of values"""
return (f"""stack({len(cols)},{','.join(map(','.join,
(zip([f'"{i}"' for i in cols],cols))))}) as ({','.join(output_columns)})""")
w = W.partitionBy("col").orderBy(F.desc("values"))
out = (df.selectExpr("name",stack_multiple_col(df,df.columns[1:]))
.withColumn("Rnk",F.dense_rank().over(w))
.where("Rnk<=3").groupBy("Rnk").pivot("col").agg(F.first("name")))
out.show()