Pandas Groupby 均值和多列中的第一个

Question

My Pandas df is like following and want to apply groupby and then want to calculate the average and first of many columns

index col1   col2   col3   col4   col5  col6
  0     a      c      1      2      f    5
  1     a      c      1      2      f    7
  2     a      d      1      2      g    9
  3     b      d      6      2      g    4
  4     b      e      1      2      g    8
  5     b      e      1      2      g    2

something like this I tried

df.groupby(['col1','col5').agg({['col6','col3']:'mean',['col4','col2']:'first'})

expecting output

col1  col5   col6  col3  col4  col2
  a     f     6     1     2     c
  a     g     9     1     2     d
  b     g     4     3     2     e

but it seems, list is not an option here, in my real dataset I have 100 of columns of different nature so I cant pass them individually. Any thoughts on passing them as list?

Answer 1

if you have lists depending on the aggregation, you can do:

l_mean = ['col6','col3']
l_first = ['col4','col2']
df.groupby(['col1','col5']).agg({**{col:'mean' for col in l_mean},
                                 **{col:'first' for col in l_first}})

the notation **{} is for unpacking dictionary, doing {**{}, **{}} create one dictionary from 2 dictionaries (it could be ore than two), it is like union of dictionaries. And doing {col:'mean' for col in l_mean} create a dictionary with each col of the list as a key and 'mean' as value, it is dictionary comprehension.

Or using concat :

gr = df.groupby(['col1','col5'])
pd.concat([gr[l_mean].mean(), 
           gr[l_first].first()], 
          axis=1)

and reset_index after to get the expected output

Answer 2

(
    df.groupby(['col1','col5'])
    .agg(col6=('col6', 'mean'),
        col3=('col3', 'mean'),
        col4=('col4', 'first'),
        col2=('col2', 'first'))
)

Answer 3

this is an extension of @Ben.T's solution, just wrapping it in a function and passing it via the pipe method:

#set the list1, list2 
def fil(grp,list1,list2):
    A = grp.mean().filter(list1)
    B = grp.first().filter(list2)
    C = A.join(B)
    return C

grp1 = ['col6','col3']
grp2 = ['col4','col2']
m = df.groupby(['col1','col5']).pipe(fil,grp1,grp2)
m