You could use df.loc to select the sub-DataFrame:
import pandas as pd
import pandas.rpy.common as com
import rpy2.robjects as ro
r = ro.r
statedata = r('''cbind(data.frame(state.x77), state.abb, state.area, state.center,
state.division, state.name, state.region)''')
df = com.convert_robj(statedata)
df.columns = df.columns.to_series().str.replace('state.', '')
subdf = df.loc[df['region']=='Northeast', 'Murder']
print(subdf)
# Connecticut 3.1
# Maine 2.7
# Massachusetts 3.3
# New Hampshire 3.3
# New Jersey 5.2
# New York 10.9
# Pennsylvania 6.1
# Rhode Island 2.4
# Vermont 5.5
# Name: Murder, dtype: float64
print(subdf.idxmax())
prints
New York
To select the state with the highest murder rate (as of 1976) for each region:
In [24]: df.groupby('region')['Murder'].idxmax()
Out[24]:
region
North Central Michigan
Northeast New York
South Alabama
West Nevada
Name: Murder, dtype: object