You can use apply() method:

import numpy as np
import pandas as pl
np.random.seed(0)

people2 = pd.DataFrame(np.random.randn(5, 5), 
                      columns=['a', 'b', 'c', 'd', 'e'], 
                      index=['Joe', 'Steve', 'Wes', 'Jim', 'Travis'])
key = ['one', 'two', 'one', 'two', 'one']

Grouped = people2.groupby(key)

def f(df):
    df["f"] = (df.a.mean() - df.b.mean())*df.c
    return df

people2 = Grouped.apply(f)
print people2

If you want some generalize method:

Grouped = people2.groupby(key)

def f(a, b, c, **kw):
    return (a.mean() - b.mean())*c

people2["f"] = Grouped.apply(lambda df:f(**df))
print people2

for this particular case you could do:

g = df.groupby(['c', 'd'])

df['e'] = g.a.transform('sum') + g.b.transform('sum')

df
# outputs

   a  b  c  d   e
0  1  1  q  z  12
1  2  2  q  z  12
2  3  3  q  z  12
3  4  4  q  o   8
4  5  5  w  o  22
5  6  6  w  o  22

if you can construct the final result by a linear combination of the independent transforms on the same groupby, this method would work.

otherwise, you'd use a groupby-apply and then merge back to the original df.

example:

_ = df.groupby(['c','d']).apply(lambda x: sum(x.a+x.b)).rename('e').reset_index()
df.merge(_, on=['c','d'])
# same output as above.

Supposed you have a dataframe named df

You can first make a list of possible numeric types, then just do a loop

numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
for c in [c for c in df.columns if df[c].dtype in numerics]:
    df[c] = np.log10(df[c])

Or, a one-liner solution with lambda operator and np.dtype.kind

numeric_df = df.apply(lambda x: np.log10(x) if np.issubdtype(x.dtype, np.number) else x)