Regex 基于名称阻止列中的文本

背景

这个问题是另一个问题

我有以下的

df

，故意有各种问题

import pandas as pd
df = pd.DataFrame({'Text' : ['But now Smith,J J is Here from Smithsville', 
                                   'Maryland is HYDER,A MARY Found here ', 
                                   'hey here is Annual Doe,Jane Ann until ',
                                'The tuckered was Tucker,Tom is Not here but'], 

                      'P_ID': [1,2,3,4], 
                      'P_Name' : ['SMITH,J J', 'HYDER,A MARY', 'DOE,JANE ANN', 'TUCKER,TOM T'],
                      'N_ID' : ['A1', 'A2', 'A3', 'A4']

                     })

输出

   N_ID P_ID P_Name         Text
0   A1  1   SMITH,J J       But now Smith,J J is Here from Smithsville
1   A2  2   HYDER,A MARY    Maryland is HYDER,A MARY Found here
2   A3  3   DOE,JANE ANN    hey here is Annual Doe,Jane Ann until
3   A4  4   TUCKER,TOM T    The tuckered was Tucker,Tom is Not here but

    N_ID P_ID P_Name Text   New_Text
0                           But now **BLOCK** is Here from Smithsville
1                           Maryland is **BLOCK**  Found here
2                           hey here is Annual **BLOCK**  until
3                           The tuckered was **BLOCK** is Not here but

目标

1） 对于

p_Name

中的名称，例如

SMITH，J

块名，在相应的

Text

列中包含

**块**

2） 创建

新文本

列

所需输出

   N_ID P_ID P_Name         Text
0   A1  1   SMITH,J J       But now Smith,J J is Here from Smithsville
1   A2  2   HYDER,A MARY    Maryland is HYDER,A MARY Found here
2   A3  3   DOE,JANE ANN    hey here is Annual Doe,Jane Ann until
3   A4  4   TUCKER,TOM T    The tuckered was Tucker,Tom is Not here but

    N_ID P_ID P_Name Text   New_Text
0                           But now **BLOCK** is Here from Smithsville
1                           Maryland is **BLOCK**  Found here
2                           hey here is Annual **BLOCK**  until
3                           The tuckered was **BLOCK** is Not here but

问题

如何实现所需的输出？

这应该可以：

df['New_Text'] = df.apply(lambda x:x['Text'].lower().replace(x['P_Name'].lower(), '**BLOCK**'), axis=1)

您的示例存在一些空白问题，但它应该适用于正确构造的示例

输出（修改空白问题，最后一行没有完全匹配）

---

如果要删除空格，请使用

replace

函数

regex=True

# new data frame without the whitespace inconsistencies
df = pd.DataFrame({'Text' : ['But now Smith,J J is Here from Smithsville', 
                                   'Maryland is HYDER,A MARY Found here ', 
                                   'hey here is Annual Doe,Jane Ann until ',
                                'The tuckered was Tucker,Tom T is Not here but'], 

                      'P_ID': [1,2,3,4], 
                      'P_Name' : ['SMITH,J J', 'HYDER,A MARY', 'DOE,JANE ANN', 'TUCKER,TOM T'],
                      'N_ID' : ['A1', 'A2', 'A3', 'A4']

                     })

print(df.Text.str.lower().replace(df.P_Name.str.lower(), '**BLOCK**', regex=True))

0    but now **BLOCK** is here from smithsville
1             maryland is **BLOCK** found here 
2           hey here is annual **BLOCK** until 
3    the tuckered was **BLOCK** is not here but
Name: Text, dtype: object

---

空白区问题是故意的。我的实际数据与上面的数据非常相似，包括空白。上面的代码会彻底改变以解释空白吗？好吧，这不是原始问题的一部分。如果是这种情况，则需要模糊匹配。或者删除所有空白，并进行一些非常有创意的空白插入。但是你的新问题很难回答，所以要有耐心！是的，但我想我在最初的背景陈述中并不清楚。我可以调整上面的问题以消除空白问题。谢谢