将具有多个参数的函数传递给DataFrame.apply [英] Passing a function with multiple arguments to DataFrame.apply

问题描述

假设我有一个像这样的数据框:

Suppose I have a dataframe like this:

df = pd.DataFrame([['foo', 'x'], ['bar', 'y']], columns=['A', 'B'])


       A    B
0    foo    x
1    bar    y

对于数据帧，我知道如何在Apply中使用单个参数函数，如下所示:

I know how to use a single argument function with Apply when it comes to dataframes, like this:

def some_func(row):
    return '{0}-{1}'.format(row['A'], row['B'])

df['C'] = df.apply(some_func, axis=1)

df


       A    B        C
0    foo    x    foo-x
1    bar    y    bar-y

当数据框包含多个输入参数时，如何使用Apply?这是我想要的示例:

How can I use apply on dataframes when they involve multiple input arguments? Here's an example of what I want:

def some_func(row, var1):
    return '{0}-{1}-{2}'.format(row['A'], row['B'], var1)

df['C'] = df.apply(some_func(row, var1='DOG'), axis=1)

df


       A    B            C
0    foo    x    foo-x-DOG
1    bar    y    bar-y-DOG

我不是在寻找解决此特定示例的方法，而只是在一般情况下如何执行此操作.任何建议将不胜感激，谢谢.

I'm not looking for work-arounds to solve this one particular example, just how to do something like this in general. Any advice would be well appreciated, thanks.

推荐答案

这就是您的想法， apply 接受 args 和 kwargs 并将它们直接传递到 some_func .

It's just the way you think it would be, apply accepts args and kwargs and passes them directly to some_func.

df.apply(some_func, var1='DOG', axis=1)

或者，

df.apply(some_func, args=('DOG', ), axis=1)

0    foo-x-DOG
1    bar-y-DOG
dtype: object

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