如何在熊猫中使用多个列映射一个函数?

I've checked out map, apply, mapapply, and combine, but can't seem to find a simple way of doing the following:

我已经检查了map、apply、mapapply和combine，但似乎找不到一种简单的方法来完成以下操作:

I have a dataframe with 10 columns. I need to pass three of them into a function that takes scalars and returns a scalar ...

我有一个包含10列的dataframe。我需要将它们中的三个传递给一个函数，该函数接受标量并返回标量……

some_func(int a, int b, int c) returns int d

I want to apply this and create a new column in the dataframe with the result.

我想应用它，并在dataframe中创建一个包含结果的新列。

df['d'] = some_func(a = df['a'], b = df['b'], c = df['c'])

All the solutions that I've found seem to suggest to rewrite some_func to work with Series instead of scalars, but this is not possible as it is part of another package. How do I elegantly do the above?

我发现的所有解决方案似乎都建议重写some_func以使用级数而不是标量，但这是不可能的，因为它是另一个包的一部分。我如何优雅地完成上面的工作?

2 个解决方案

#1

Use pd.DataFrame.apply(), as below:

使用pd.DataFrame.apply(),如下:

df['d'] = df.apply(lambda x: some_func(a = x['a'], b = x['b'], c = x['c']), axis=1)

NOTE: As @ashishsingal asked about columns, the axis argument should be provided with a value of 1, as the default is 0 (as in the documentation and copied below).

注意:当@ashishsingal询问列时，axis参数应该为1，因为默认值是0(如文档中所示，并在下面复制)。

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

axis:{0或' index '， 1或' columns '}，默认为0

0 or ‘index’: apply function to each column

0或“索引”:将函数应用于每一列

or ‘columns’: apply function to each row

或“列”:对每一行应用函数

#2

I'm using the following:

我使用下面的:

df['d'] = df.apply(lambda x: some_func(a = x['a'], b = x['b'], c = x['c']))

Seems to be working well, but if anyone else has a better solution, please let me know.

看起来工作得不错，但是如果有人有更好的解决办法，请告诉我。

#1