Python transform如何处理分裂的组

不是这样的，但是transform会运行一些判断来查看输出的类型.通常，您不会将transform用于副作用(您应该使用apply，如后面所示)，而是返回与输入形状相同的内容.

使用自定义函数可能会更明确地说明究竟发生了什么:

def f(group):
    print('---')
    print(group.name)  # with `transform` this shouldn't give the group name
    print(group)
    print('===')
    
df.groupby('subject').transform(f)

输出:

---                           # first group
level
0    hard
1    None
Name: level, dtype: object
===
---                           # internal pandas check (not a real group)
a
  level
0  hard
1  None
===
---                           # second group
level
2    None
3    easy
Name: level, dtype: object
===
---                           # third group
level
4    None
Name: level, dtype: object
===
---                           # fourth group
level
5    medium
Name: level, dtype: object
===

相比之下，使用apply会给出组名，您可以将其用于此类操作:

df.groupby('subject').apply(f)

---
a
  subject level
0       a  hard
1       a  None
===
---
b
  subject level
2       b  None
3       b  easy
===
---
c
  subject level
4       c  None
===
---
d
  subject   level
5       d  medium
===

don't use transform to manually work on groups.

这是另一个例子.在transform中，group.name返回当前的Series名称，请查看多个列的情况:

df = pd.DataFrame({'subject': ['a', 'a', 'b', 'b', 'c', 'd'],
                   'level': ['hard', None, None, 'easy', None, 'medium'],
                   'level2': ['hard', None, None, 'easy', None, 'medium']
                  })
df.groupby('subject').transform(lambda g: print(g.name))

print输出:

level    # first group, column "level"
level2   # first group, column "level2"
a        # some internal check run only once
level    # second group, column "level"
level2   # second group, column "level2"
level    # etc.
level2
level
level2

相比之下，apply会将每个组作为DataFrame返回:

df.groupby('subject').apply(lambda g: print(g.name))

a
b
c
d