/Python/Pandas groupby aggregate multiple columns

Pandas groupby aggregate multiple columns

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)

Source: jamesrledoux.com

Python group by multiple aggregates

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)

Source: stackoverflow.com

Python groupby sum single columns

df.groupby(['A','C'], as_index=False)['B'].sum()

Source: stackoverflow.com

Group by, aggregate multiple column -pandas

df[['col1', 'col2', 'col3', 'col4']].groupby(['col1', 'col2']).agg(['mean', 'count'])

Source: stackoverflow.com

Pandas groupby multiple columns

df['COUNTER'] =1       #initially, set that counter to 1.
group_data = df.groupby(['Alphabet','Words'])['COUNTER'].sum() #sum function
print(group_data)

Source: stackoverflow.com

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