问题
I have a following sample pyspark dataframe and after groupby I want to calculate mean, and first of multiple columns, In real case I have 100s of columns, so I cant do it individually
sp = spark.createDataFrame([['a',2,4,'cc','anc'], ['a',4,7,'cd','abc'], ['b',6,0,'as','asd'], ['b', 2, 4, 'ad','acb'],
['c', 4, 4, 'sd','acc']], ['id', 'col1', 'col2','col3', 'col4'])
+---+----+----+----+----+
| id|col1|col2|col3|col4|
+---+----+----+----+----+
| a| 2| 4| cc| anc|
| a| 4| 7| cd| abc|
| b| 6| 0| as| asd|
| b| 2| 4| ad| acb|
| c| 4| 4| sd| acc|
+---+----+----+----+----+
This is what I am trying
mean_cols = ['col1', 'col2']
first_cols = ['col3', 'col4']
sc.groupby('id').agg(*[ f.mean for col in mean_cols], *[f.first for col in first_cols])
but it's not working. How can I do it like this with pyspark
回答1:
The best way for multiple functions on multiple columns is to use the .agg(*expr) format.
import pyspark.sql.functions as F
from pyspark.sql.functions import udf
from pyspark.sql.types import *
import numpy as np
#Test data
tst = sqlContext.createDataFrame([(1,2,3,4),(3,4,5,1),(5,6,7,8),(7,8,9,2)],schema=['col1','col2','col3','col4'])
fn_l = [F.min,F.max,F.mean,F.first]
col_l=['col1','col2','col3']
expr = [fn(coln).alias(str(fn.__name__)+'_'+str(coln)) for fn in fn_l for coln in col_l]
tst_r = tst.groupby('col4').agg(*expr)
The result will be
tst_r.show()
+----+--------+--------+--------+--------+--------+--------+---------+---------+---------+----------+----------+----------+
|col4|min_col1|min_col2|min_col3|max_col1|max_col2|max_col3|mean_col1|mean_col2|mean_col3|first_col1|first_col2|first_col3|
+----+--------+--------+--------+--------+--------+--------+---------+---------+---------+----------+----------+----------+
| 5| 5| 6| 7| 7| 8| 9| 6.0| 7.0| 8.0| 5| 6| 7|
| 4| 1| 2| 3| 3| 4| 5| 2.0| 3.0| 4.0| 1| 2| 3|
+----+--------+--------+--------+--------+--------+--------+---------+---------+---------+----------+----------+----------+
For selectively applying functions on columns, you can have multiple expression arrays and concatenate them in aggregation.
fn_l = [F.min,F.max]
fn_2=[F.mean,F.first]
col_l=['col1','col2']
col_2=['col1','col3','col4']
expr1 = [fn(coln).alias(str(fn.__name__)+'_'+str(coln)) for fn in fn_l for coln in col_l]
expr2 = [fn(coln).alias(str(fn.__name__)+'_'+str(coln)) for fn in fn_2 for coln in col_2]
tst_r = tst.groupby('col4').agg(*(expr1+expr2))
来源：https://stackoverflow.com/questions/62620453/pyspark-groupby-and-aggregate-avg-and-first-on-multiple-columns