pandas.DataFrame.apply（）使用方括号过滤时会产生NaN

Question

import pandas as pd
df = pd.DataFrame({"First_Column": [-2,-1,1,2,3]})
df['Second_Column']='Good'
df.loc[:, 'Second_Column']=df[df.First_Column>0]['Second_Column'].apply(lambda x: 'Bad')

运行此命令时，我在Bad中得到NaN和Second_Column，而不是Good和Bad。为什么apply()用NaN覆盖不符合条件的值？

Answer 1

通过使用mask

df.Second_Column=df.Second_Column.mask(df.First_Column>0,'Bad')
df
Out[441]: 
   First_Column Second_Column
0            -2          Good
1            -1          Good
2             1           Bad
3             2           Bad
4             3           Bad

或

df.loc[df.First_Column>0,'Second_Column']='Bad'
df
Out[443]: 
   First_Column Second_Column
0            -2          Good
1            -1          Good
2             1           Bad
3             2           Bad
4             3           Bad

或者更简单地使用np.where

df['Second_Column']=np.where(df.First_Column>0,'Bad','Good')
df
Out[445]: 
   First_Column Second_Column
0            -2          Good
1            -1          Good
2             1           Bad
3             2           Bad
4             3           Bad