test = elder_with_time_nonan_test
test['recommended'] = test['rating'].map(lambda value: 1 if value == "Recommended" else 0)

def balanced_stratified_sample(df, col="recommended"):

   group_df = df.groupby(col)

   minimum_sample_size = group_df.size().min()

   new_data = pd.DataFrame()

   for group_name, g_df in group_df:
       sample = g_df.sample(minimum_sample_size)
       new_data = pd.concat([new_data, sample])

   return pd.DataFrame(new_data)


stratified_df = balanced_stratified_sample(test)
print "stratified sample: ", stratified_df.groupby("recommended").size()
print "original sample:", test.groupby("recommended").size()