不要将lambda函数应用于缺少的值

Question

我有一个列中包含患者诊断的数据框架，我想使用Panda对诊断进行二分===>ISM，非ISM。我试过这个

df["initial_diagnosis"] = df["initial_diagnosis"].apply(lambda x: x if x=="ISM" else "non ISM")

但它正在分配"；非ISM"；也适用于缺失的值。有没有办法做到这一点，并保持缺失的值不变？

我试图编写的专栏如下：

initial_diagnosis    I
ISM 
ISM
WDSM
NaN
ISM
SSM
CM
ASM
ISM

Answer 1

我认为它应该可以工作。可能缺少的值是空字符串或只有None，我只能猜测

missing_values = {...} # Set of values you want to keep
df["initial_diagnosis"] = df["initial_diagnosis"].apply(lambda x: x if x=="ISM" or x in missing_values else "non ISM")

编辑：

import pandas as pd
from numpy import nan
data = pd.read_csv("test.csv")
print(data['initial_diagnosis']) 
#0    ISM
#1     ISM
#2    WDSM
#3     NaN
#4     ISM
#5     SSM
#6      CM
#7     ASM
#8     ISM 
#Name: initial_diagnosis, dtype: object
missing_values = {nan}
data["initial_diagnosis"] = data["initial_diagnosis"].apply(lambda x: x if x =="ISM" or x in missing_values else "non ISM")
print(data['initial_diagnosis'])
#0    non ISM
#1        ISM
#2    non ISM
#3        NaN
#4        ISM
#5    non ISM
#6    non ISM
#7    non ISM
#8        ISM