grouped_multiple = df.groupby(['Team', 'Pos']).agg({'Age': ['mean', 'min', 'max']})
grouped_multiple.columns = ['age_mean', 'age_min', 'age_max']
grouped_multiple = grouped_multiple.reset_index()
print(grouped_multiple)
df.groupby(['A','C'], as_index=False)['B'].sum()
def f(x):
d = {}
d['a_sum'] = x['a'].sum()
d['a_max'] = x['a'].max()
d['b_mean'] = x['b'].mean()
d['c_d_prodsum'] = (x['c'] * x['d']).sum()
return pd.Series(d, index=['a_sum', 'a_max', 'b_mean', 'c_d_prodsum'])
df.groupby('group').apply(f)
df[['col1', 'col2', 'col3', 'col4']].groupby(['col1', 'col2']).agg(['mean', 'count'])
#formatting
candidates_salary_by_month = candidates_df.groupby('month').agg(num_cand_month = \
('num_candidates', 'sum'), \
avg_sal = ('salary', 'mean')).style.format('{:.0f}')
print(candidates_salary_by_month)df['COUNTER'] =1 #initially, set that counter to 1.
group_data = df.groupby(['Alphabet','Words'])['COUNTER'].sum() #sum function
print(group_data)