python之根据某一行的某个值派生一个新的pandas列并应用直到下一个值再次出现

在 pandas dataframe 字符串列中，我想根据一行的值派生一个新列，直到下一个值再次出现。执行此操作的最有效方法是什么/实现此目的的最干净方法是什么？

输入数据框:

import pandas as pd 
 
df = pd.DataFrame({'neighborhood':['Chicago City', 'Wicker Park', 'Bucktown','Lincoln Park','West Loop','River North','Milwaukee City','Bay View','East Side','South Side','Bronzeville','North Side','New York City','Harlem','Midtown','Chinatown']}) 

我想要的数据框输出是:

      neighborhood city 
0     Chicago City Chicago 
1      Wicker Park Chicago 
2         Bucktown Chicago 
3     Lincoln Park Chicago 
4        West Loop Chicago 
5      River North Chicago 
6   Milwaukee City Milwaukee 
7         Bay View Milwaukee 
8        East Side Milwaukee 
9       South Side Milwaukee 
10     Bronzeville Milwaukee 
11      North Side Milwaukee 
12   New York City New York 
13          Harlem New York 
14         Midtown New York 
15       Chinatown New York 

请您参考如下方法:

1) 如果第一列包含'City'，则将其复制到第二列但切掉'City'部分

2) 用前向填充方法填充 NA

import numpy as np 
 
df['city'] = np.where( 
df.neighborhood.str.contains('City'), 
df.neighborhood.str.replace(' City', '', case = False), 
None) 

结果:

      neighborhood       city 
0     Chicago City    Chicago 
1      Wicker Park       None 
2         Bucktown       None 
3     Lincoln Park       None 
4        West Loop       None 
5      River North       None 
6   Milwaukee City  Milwaukee 
7         Bay View       None 
8        East Side       None 
9       South Side       None 
10     Bronzeville       None 
11      North Side       None 
12   New York City   New York 
13          Harlem       None 
14         Midtown       None 
15       Chinatown       None 

df['city'] = df['city'].fillna(method = 'ffill') 

结果:

      neighborhood       city 
0     Chicago City    Chicago 
1      Wicker Park    Chicago 
2         Bucktown    Chicago 
3     Lincoln Park    Chicago 
4        West Loop    Chicago 
5      River North    Chicago 
6   Milwaukee City  Milwaukee 
7         Bay View  Milwaukee 
8        East Side  Milwaukee 
9       South Side  Milwaukee 
10     Bronzeville  Milwaukee 
11      North Side  Milwaukee 
12   New York City   New York 
13          Harlem   New York 
14         Midtown   New York 
15       Chinatown   New York