python - 将 lambda 函数应用于具有 NaN 值的数据框？

Question

我正在尝试执行在具有保持一致变量的行之间更改的计算。当一行有不完整的数据时，如何使用这个 lambda 函数？

跟进这个问题:Create a new column based on calculations that change between rows?

#example
import pandas as pd
import numpy as np

conversion = [["a",5],["b",1],["c",10]]
conversion_table = pd.DataFrame(conversion,columns=['Variable','Cost'])

data1 = [[1,"2*a+b"],[2,"c"],[3,"2*c"],[4, np.NaN]]
to_solve = pd.DataFrame(data1,columns=['Day','Q1'])

#Desired dataframe: 

desired = [[1,11],[2,10],[3,20]]
desired_table=pd.DataFrame(desired,columns=['Day','desired output'])

#Using lambda to map values does not work when NaN is present.

#Map values
mapping = dict(zip(conversion_table['Variable'], conversion_table['Cost']))

desired_table["solved"]=to_solve['Q1'].map(lambda x: eval(''.join([str(mapping[i]) if i.isalpha() else str(i) for i in x])))

当我的列不包含 NaN 值时，此代码有效，但当我的数据不完整时，我需要此代码。我收到以下错误:'float' 对象不可迭代。我只想将 NaN 值留在原处并填写其余部分。

Answer 1

desired_table["solved"]=to_solve['Q1'].map(lambda x: ..., na_action='ignore')

应该做你想做的。

In [6]: to_solve['Q1'].map(lambda x: eval(''.join([str(mapping[i]) if i.isalpha() else str(i) for i in x])), na_action='ignore')                                                                            
Out[6]: 
0    11.0
1    10.0
2    20.0
3     NaN
Name: Q1, dtype: float64