Pandas soma várias colunas do grupo
df.groupby(['col1','col2']).agg({'col3':'sum','col4':'sum'}).reset_index()
Comfortable Cardinal
df.groupby(['col1','col2']).agg({'col3':'sum','col4':'sum'}).reset_index()
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)
In [11]: df.groupby(['col5', 'col2']).size()
Out[11]:
col5 col2
1 A 1
D 3
2 B 2
3 A 3
C 1
4 B 1
5 B 2
6 B 1
dtype: int64
In [8]: grouped = df.groupby('A')
In [9]: grouped = df.groupby(['A', 'B'])
SELECT country, state, MIN(age) as min_age
FROM Persons
GROUP BY country, state;Group By X means put all those with the same value for X in the one group.
Group By X, Y means put all those with the same values for both X and Y in the one group.