Pandas groupby aggregate to new columns

Question

I have a DataFrame that looks something like this:

A    B    C   D
1    10   22  14
1    12   20  37
1    11   8   18
1    10   10  6
2    11   13  4
2    12   10  12
3    14   0   5

and a function that looks something like this ( NOTE: it's actually doing something more complex that can't be easily separated into three independent calls, but I'm simplifying for clarity ):

def myfunc(g):
    return min(g), mean(g), max(g)

I want to use groupby on A with myfunc to get an output on columns B and C (ignoring D ) something like this:

                B               C
   min  mean  max  min  mean  max
A
1  10  10.75  12     8  15.0   22
2  11  11.50  12    10  11.5   13
3  14  14.00  14     0   0.0    0

I can do the following:

df2.groupby('A')[['B','C']].agg(
    {
        'min':  lambda g: myfunc(g)[0],
        'mean': lambda g: myfunc(g)[1],
        'max':  lambda g: myfunc(g)[2]
    })

But then—aside from this being ugly and calling myfunc multiple times—I end up with

  max       mean       min
    B   C      B     C   B   C
A
1  12  22  10.75  15.0  10   8
2  12  13  11.50  11.5  11  10
3  14   0  14.00   0.0  14   0

I can use .swaplevel(axis=1) to swap the column levels, but even then B and C are in multiple duplicated columns, and with the multiple function calls it feels like barking up the wrong tree.

Answer 1

If you arrange for myfunc to return a DataFrame whose columns are ['A','B','C','D'] and whose rows index are ['min', 'mean', 'max'] , then you could use groupby/apply to call the function (once for each group) and concatenate the results as desired:

import numpy as np
import pandas as pd

def myfunc(g):
    result = pd.DataFrame({'min':np.min(g),
                           'mean':np.mean(g),
                           'max':np.max(g)}).T
    return result

df = pd.DataFrame({'A': [1, 1, 1, 1, 2, 2, 3],
 'B': [10, 12, 11, 10, 11, 12, 14],
 'C': [22, 20, 8, 10, 13, 10, 0],
 'D': [14, 37, 18, 6, 4, 12, 5]})

result = df.groupby('A')[['B','C']].apply(myfunc)
result = result.unstack(level=-1)
print(result)

prints

      B                  C            
    max   mean   min   max  mean   min
A                                     
1  12.0  10.75  10.0  22.0  15.0   8.0
2  12.0  11.50  11.0  13.0  11.5  10.0
3  14.0  14.00  14.0   0.0   0.0   0.0

---

For others who may run across this and who do not need a custom function, note that it behooves you to always use builtin aggregators (below, specified by the strings 'min' , 'mean' and 'max' ) if possible. They perform better than custom Python functions. Happily, in this toy problem, it produces the desired result:

In [99]: df.groupby('A')[['B','C']].agg(['min','mean','max'])
Out[99]: 
    B              C          
  min   mean max min  mean max
A                             
1  10  10.75  12   8  15.0  22
2  11  11.50  12  10  11.5  13
3  14  14.00  14   0   0.0   0

Answer 2

Something like this might work.

df2.groupby('A')[['B','C']]
aggregated = df2.agg(['min', 'mean', 'max'])

then you could use swap level to get the column order swapped around

aggregated.columns = aggregated.columns.swaplevel(0, 1)
aggregated.sortlevel(0, axis=1, inplace=True)