Question

这正常工作：

import pandas as pd

def fnc(m):
    return m+4

df = pd.DataFrame({"m": [1,2,3,4,5,6], "c": [1,1,1,1,1,1], "x":[5,3,6,2,6,1]})
df
# apply a self created function to a single column in pandas
df["y"] = df['m'].apply(fnc)
df

我试图修改上面的代码。在这里，我需要将列m的值添加到列c的值并将结果分配给列y：

import pandas as pd

def fnc(m,c):
    return m+c

df = pd.DataFrame({"m": [1,2,3,4,5,6], "c": [1,1,1,1,1,1], "x":[5,3,6,2,6,1]})
df
# apply a self created function to a single column in pandas
df["y"] = df[['m','c']].apply(fnc)
df

给我错误：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
d:\del\asfasf.py in 
      8 df
      9 # apply a self created function to a single column in pandas
----> 10 df["y"] = df[['m','c']].apply(fnc)
     11 df

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py in apply(self, func, axis, raw, result_type, args, **kwds)
   6876             kwds=kwds,
   6877         )
-> 6878         return op.get_result()
   6879 
   6880     def applymap(self, func) -> "DataFrame":

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\apply.py in get_result(self)
    184             return self.apply_raw()
    185 
--> 186         return self.apply_standard()
    187 
    188     def apply_empty_result(self):

C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\apply.py in apply_standard(self)
    294             try:
    295                 result = libreduction.compute_reduction(
--> 296                     values, self.f, axis=self.axis, dummy=dummy, labels=labels
    297                 )
    298             except ValueError as err:

pandas\_libs\reduction.pyx in pandas._libs.reduction.compute_reduction()

pandas\_libs\reduction.pyx in pandas._libs.reduction.Reducer.get_result()

TypeError: fnc() missing 1 required positional argument: 'c'

问题：如何更正我的第二个代码？如果可能，请提供答案标准函数语法（不是lambda函数）

Answer 1

添加要在数据帧的axis = 1中考虑的轴并访问函数中的每一列。

def fnc(m):
    return (m.m+m.c)

df = pd.DataFrame({"m": [1,2,3,4,5,6], "c": [1,1,1,1,1,1], "x":[5,3,6,2,6,1]})
df["y"] = df[['m',"c"]].apply(fnc,axis=1)

或者您可以申请df，而无需选择“ m”和“ c”列。

df["y"] = df.apply(fnc,axis=1)

输出

Answer 2

尝试此操作，将axis to 1设置为列级操作，将*x设置为解包参数。

df["y"] = df[['m','c']].apply(lambda x : fnc(*x), axis=1)

将功能应用于熊猫数据框的列

2 个答案: