python - Pandas Data Frame function application to a specific row -

i'm trying apply 1 function f1 rows ['utah,'texas'] , f2 other rows. don't want create separate df each function.

example adjusted wes mckinney's python data analysis:

mwe:

import pandas pd import numpy np  frame = pd.dataframe(np.random.randn(4, 3),                    columns=list('bde'),                    index=['utah', 'ohio', 'texas', 'oregon'])  f1 = lambda x: (x-x.min())/(x.max() - x.min()) f2 = lambda x: (x-x.max())/(x.min() - x.max()) 

i've tried selecting row label: frame.loc['utah'].apply(f1,axis=1).

i can feel small i'm missing but...

this creates 2d numpy.array each row application of 1 of 2 functions dataframe according rules specified row:

np.where(     np.array([frame.index.isin(['utah', 'texas']) _ in frame.columns]).t,     frame.apply(f1, axis=1),     frame.apply(f2, axis=1)) 

since didn't specify output fully, it's hard guess want further.