groupby in pandas

1>>> n_by_state = df.groupby("state")["state"].count()
2>>> n_by_state.head(10)
3state
4AK     16
5AL    206
6AR    117
7AS      2
8AZ     48
9CA    361
10CO     90
11CT    240
12DC      2
13DE     97
14Name: last_name, dtype: int64
15

1>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
2...                               'Parrot', 'Parrot'],
3...                    'Max Speed': [380., 370., 24., 26.]})
4>>> df
5   Animal  Max Speed
60  Falcon      380.0
71  Falcon      370.0
82  Parrot       24.0
93  Parrot       26.0
10>>> df.groupby(['Animal']).mean()
11        Max Speed
12Animal
13Falcon      375.0
14Parrot       25.0
15

1# Groups the DataFrame using the specified columns
2
3df.groupBy().avg().collect()
4# [Row(avg(age)=3.5)]
5sorted(df.groupBy('name').agg({'age': 'mean'}).collect())
6# [Row(name='Alice', avg(age)=2.0), Row(name='Bob', avg(age)=5.0)]
7sorted(df.groupBy(df.name).avg().collect())
8# [Row(name='Alice', avg(age)=2.0), Row(name='Bob', avg(age)=5.0)]
9sorted(df.groupBy(['name', df.age]).count().collect())
10# [Row(name='Alice', age=2, count=1), Row(name='Bob', age=5, count=1)]

1In [1]: df = pd.DataFrame( {'a':['A','A','B','B','B','C'], 'b':[1,2,5,5,4,6]})
2        df
3
4Out[1]: 
5   a  b
60  A  1
71  A  2
82  B  5
93  B  5
104  B  4
115  C  6
12
13In [2]: df.groupby('a')['b'].apply(list)
14Out[2]: 
15a
16A       [1, 2]
17B    [5, 5, 4]
18C          [6]
19Name: b, dtype: object
20
21In [3]: df1 = df.groupby('a')['b'].apply(list).reset_index(name='new')
22        df1
23Out[3]: 
24   a        new
250  A     [1, 2]
261  B  [5, 5, 4]
272  C        [6]
28

1data.groupby('month', as_index=False).agg({"duration": "sum"})

1>>> emp.groupby(['dept', 'gender']).agg({'salary':'mean'}).round(-3)