Pandas GroupBy:应用带有两个参数的函数 [英] Pandas GroupBy: apply a function with two arguments

问题描述

通常在使用 .apply() 方法时，会传递一个只接受一个参数的函数.

Usually when using the .apply() method, one passes a function that takes exactly one argument.

def somefunction(group):
    group['ColumnC'] == group['ColumnC']**2
    return group

df.groupby(['ColumnA', 'ColumnB']).apply(somefunction)

这里 somefunction 应用于每个 group，然后返回.基本上我使用这个这里的例子.

Here somefunction is applied for each group, which is then returned. Basically I'm using this example here.

我希望能够不指定列名ColumnC.将它作为 somefunction 的参数传递会使代码更灵活.

I want to have the ability to not specify the column name ColumnC beforehand. Passing it along as an argument of somefunction would make the code more flexible.

def somefunction(group, column_name):
    group[column_name] == group[column_name]**2
    return group

df.groupby(['ColumnA', 'ColumnB']).apply(somefunction)

有什么办法可以使这个工作?我无法将 group 传递给 somefunction，因为这是由 .apply() 在后台神奇地完成的.

Is there any way to make this work? I can't pass group to somefunction, because that is magically done by .apply() in the background.

推荐答案

可以通过apply

df.groupby(['ColumnA', 'ColumnB']).apply(somefunction, column_name='col')

<小时>

MCVE

df = pd.DataFrame(dict(A=list(range(2)) * 5, B=range(10)[::-1]))

def f(df, arg1):
    return df * arg1

df.groupby('A').apply(f, arg1=3)

   A   B
0  0  27
1  3  24
2  0  21
3  3  18
4  0  15
5  3  12
6  0   9
7  3   6
8  0   3
9  3   0

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