Python:如何删除字符串左侧第二个逗号后的所有文本

Question

我想删除包含“County, Texas”的数据框中字符串左侧第二个逗号后的所有文本。例如，

之前:

1. “德克萨斯州林肯县威尔伯银行的 jack ·史密斯”
2. “ jack ·史密斯，银行，信贷，银行，威尔伯，德克萨斯州林肯县”
3. “Jack Smith, Bank, Union, Credit, Bank, Wilber, Lincoln County, Texas, Branch, Landing, Services”
4. “ jack ·史密斯，银行，信贷，银行，威尔伯，分行，登陆，服务”

之后:

1. “ jack ·史密斯，银行”
2. “ jack ·史密斯，银行”
3. “ jack ·史密斯，银行，工会”
4. “ jack ·史密斯，银行，信贷，银行，威尔伯，分行，登陆，服务”

感谢您的帮助!

Answer 1

使用mask和str.contains()对具有指定条件的行进行操作，然后使用如下操作:.str.split (', ').str[0:2].agg(', '.join)):

df['Col'] = df['Col'].mask(df['Col'].str.contains('County, Texas'),
                           df['Col'].str.split(', ').str[0:2].agg(', '.join))

完整代码:

import pandas as pd
df = pd.DataFrame({'Col': {0: 'Jack Smith, Bank, Wilber, Lincoln County, Texas',
  1: 'Jack Smith, Union, Credit, Bank, Wilber, Lincoln County, Texas',
  2: 'Jack Smith, Union, Credit, Bank, Wilber, Lincoln County, Texas, Branch, Landing, Services',
  3: 'Jack Smith, Union, Credit, Bank, Wilber, Branch, Landing, Services'}})
df['Col'] = df['Col'].mask(df['Col'].str.contains('County, Texas'),
                           df['Col'].str.split(', ').str[0:2].agg(', '.join))                            
df
Out[1]: 
                                                 Col
0                                   Jack Smith, Bank
1                                  Jack Smith, Union
2                                  Jack Smith, Union
3  Jack Smith, Union, Credit, Bank, Wilber, Branc...

---

根据更新的问题，您可以使用 np.select:

import pandas as pd
df = pd.DataFrame({'Col': {0: 'Jack Smith, Bank, Wilber, Lincoln County, Texas',
  1: 'Jack Smith, Bank, Credit, Bank, Wilber, Lincoln County, Texas',
  2: 'Jack Smith, Bank, Union, Credit, Bank, Wilber, Lincoln County, Texas, Branch, Landing, Services',
  3: 'Jack Smith, Bank, Credit, Bank, Wilber, Branch, Landing, Services'}})
df['Col'] = np.select([df['Col'].str.contains('County, Texas') & ~df['Col'].str.contains('Union'),
                       df['Col'].str.contains('County, Texas') & df['Col'].str.contains('Union')],
                      [df['Col'].str.split(', ').str[0:2].agg(', '.join),
                       df['Col'].str.split(', ').str[0:3].agg(', '.join)],
                       df['Col'])                            
df
Out[2]: 
                                                 Col
0                                   Jack Smith, Bank
1                                   Jack Smith, Bank
2                            Jack Smith, Bank, Union
3  Jack Smith, Bank, Credit, Bank, Wilber, Branch...