python - efficient function to find harmonic mean across different pandas dataframes -

i have several dataframes identical shape/types, different numeric values. can produce new dataframe mean of input dataframes via:

df = pd.concat([input_dataframes]) df = df.groupby(df.index).mean() 

i want same harmonic mean (probably scipy.stats.hmean function). have attempted using:

.groupby(df.index).apply(scipy.stats.hmean) 

but alters structure of dataframe. there better way this, or need use more lengthly/manual implementation?

to illustrate :

df_input1:    'a' 'b' 'c' 'x' 1   1   2  'y' 2   2   4  'z' 3   3   6  df_input2:    'a' 'b' 'c' 'x' 2   2   4  'y' 3   3   6  'z' 4   4   8  desired output (but w/ hmean):    'a'  'b'  'c' 'x' 1.5  1.5  3  'y' 2.5  2.5  5  'z' 3.5  3.5  7 

create pandas panel, , apply harmonic mean function on 'item' axis.

example dataframes df1 , df2:

import pandas pd scipy import stats  d = {'1':df1,'2':df2} pan = pd.panel(d) pan.apply(axis='items',func=stats.hmean) 

yields:

        'a'         'b'         'c' 'x'     1.333333    1.333333    2.666667 'y'     2.400000    2.400000    4.800000 'z'     3.428571    3.428571    6.857143