pandas groupby aggregate multiple columns

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Simona
11 Jun 2018
1grouped_multiple = df.groupby(['Team', 'Pos']).agg({'Age': ['mean', 'min', 'max']})
2grouped_multiple.columns = ['age_mean', 'age_min', 'age_max']
3grouped_multiple = grouped_multiple.reset_index()
4print(grouped_multiple)
5
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Chrissy
12 Aug 2016
1df.groupby(['A','C'], as_index=False)['B'].sum()
2
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Silvia
16 Jan 2019
1df[['col1', 'col2', 'col3', 'col4']].groupby(['col1', 'col2']).agg(['mean', 'count'])
2
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Carolina
05 Aug 2020
1def f(x):
2    d = {}
3    d['a_sum'] = x['a'].sum()
4    d['a_max'] = x['a'].max()
5    d['b_mean'] = x['b'].mean()
6    d['c_d_prodsum'] = (x['c'] * x['d']).sum()
7    return pd.Series(d, index=['a_sum', 'a_max', 'b_mean', 'c_d_prodsum'])
8
9df.groupby('group').apply(f)
10
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