groupby操作涉及分割对象、应用函数和组合结果的组合，这可以用于对大量数据进行分组，并对这些组进行计算操作。

参数说明：

• by：mapping, function, label, pd.Grouper or list of such

　　Used to determine the groups for the groupby. If by is a function, it’s called on each value of the object’s index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series’ values are first aligned; see .align()method). If a list or ndarray of length equal to the selected axis is passed, the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.

• axis：{0 or ‘index’, 1 or ‘columns’}, default 0

　　Split along rows (0) or columns (1). For Series this parameter is unused and defaults to 0.

• level：int, level name, or sequence of such, default None

　　If the axis is a MultiIndex (hierarchical), group by a particular level or levels. Do not specify both by and level.

• as_index：bool, default True

　　Return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output. This argument has no effect on filtrations, such as head(), tail(), nth()and in transformations.

• sort：bool, default True

　　Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. If False, the groups will appear in the same order as they did in the original DataFrame. This argument has no effect on filtrations, such as head(), tail(), nth() and in transformations.

• group_keys：bool, default True

　　When calling apply and the by argument produces a like-indexed (i.e. a transform) result, add group keys to index to identify pieces. By default group keys are not included when the result’s index (and column) labels match the inputs, and are included otherwise.

• observed：bool, default False

　　This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers.

• dropna：bool, default True

　　If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups.

代码示例：

 1 df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
 2                               'Parrot', 'Parrot'],
 3                    'Max Speed': [380., 370., 24., 26.]})
 4 print(df)
 5 
 6 ### 结果
 7 #    Animal  Max Speed
 8 # 0  Falcon      380.0
 9 # 1  Falcon      370.0
10 # 2  Parrot       24.0
11 # 3  Parrot       26.0

1 df1 = df.groupby(['Animal']).mean()
2 print(df1)
3 
4 ### 结果
5 #         Max Speed
6 # Animal           
7 # Falcon      375.0
8 # Parrot       25.0

 1 arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
 2           ['Captive', 'Wild', 'Captive', 'Wild']]
 3 index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
 4 df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
 5                   index=index)
 6 print(df)
 7 
 8 ### 结果
 9 #                 Max Speed
10 # Animal Type              
11 # Falcon Captive      390.0
12 #        Wild         350.0
13 # Parrot Captive       30.0
14 #        Wild          20.0

 1 df1 = df.groupby(level=0).mean()
 2 print(df1)
 3 
 4 ### 结果
 5 #         Max Speed
 6 # Animal           
 7 # Falcon      370.0
 8 # Parrot       25.0
 9 
10 df1 = df.groupby(level="Type").mean()
11 print(df1)
12 
13 ### 结果
14 #          Max Speed
15 # Type              
16 # Captive      210.0
17 # Wild         185.0

 1 list1 = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
 2 df = pd.DataFrame(list1, columns=["a", "b", "c"])
 3 print(df)
 4 
 5 ### 结果
 6 #    a    b  c
 7 # 0  1  2.0  3
 8 # 1  1  NaN  4
 9 # 2  2  1.0  3
10 # 3  1  2.0  2

1 df1 = df.groupby(by=["b"]).sum()
2 print(df1)
3 
4 ### 结果
5 #      a  c
6 # b        
7 # 1.0  2  3
8 # 2.0  2  5

1 df1 = df.groupby(by=["b"], dropna=False).sum()
2 print(df1)
3 
4 ### 结果
5 #      a  c
6 # b        
7 # 1.0  2  3
8 # 2.0  2  5
9 # NaN  1  4

 1 df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
 2                               'Parrot', 'Parrot'],
 3                    'Max Speed': [380., 370., 24., 26.]})
 4 print(df)
 5 
 6 ### 结果
 7 #    Animal  Max Speed
 8 # 0  Falcon      380.0
 9 # 1  Falcon      370.0
10 # 2  Parrot       24.0
11 # 3  Parrot       26.0

 1 df1 = df.groupby("Animal", group_keys=True)[['Max Speed']].apply(lambda x: x)
 2 print(df1)
 3 
 4 ### 结果
 5 #           Max Speed
 6 # Animal             
 7 # Falcon 0      380.0
 8 #        1      370.0
 9 # Parrot 2       24.0
10 #        3       26.0

1 df1 = df.groupby("Animal", group_keys=False)[['Max Speed']].apply(lambda x: x)
2 print(df1)
3 
4 ### 结果
5 #    Max Speed
6 # 0      380.0
7 # 1      370.0
8 # 2       24.0
9 # 3       26.0