Renaming columns for PySpark DataFrame aggregates

Question

I am analysing some data with PySpark DataFrames. Suppose I have a DataFrame df that I am aggregating:

(df.groupBy("group")    .agg({"money":"sum"})    .show(100) ) 

This will give me:

group                SUM(money#2L) A                    137461285853 B                    172185566943 C                    271179590646 

The aggregation works just fine but I dislike the new column name SUM(money#2L). Is there a way to rename this column into something human readable from the .agg method? Maybe something more similar to what one would do in dplyr:

df %>% group_by(group) %>% summarise(sum_money = sum(money)) 

Answer 1

Although I still prefer dplyr syntax, this code snippet will do:

import pyspark.sql.functions as sf  (df.groupBy("group")    .agg(sf.sum('money').alias('money'))    .show(100)) 

It gets verbose.