Pandas 字符串切片返回 NaN

Question

I have a dataframe as below:我有一个如下的数据框：

Ref   Net   
C1    1- A:VCC 
C2    2- A:VDD 
C3    3- A:GND

I would like to remove 1- A: , 2- A: and 3- A: from the Net column:我想从Net列中删除1- A: 、 2- A:和3- A: ：

Ref   Net  
C1    VCC  
C2    VDD  
C3    GND

I tried this command: df.Net = df.Net.str.slice(df.Net.str.find('A:'))我试过这个命令： df.Net = df.Net.str.slice(df.Net.str.find('A:'))

But then the Net column became NaN :但随后Net列变成了NaN ：

print(df.Net)
---------
0  NaN
1  NaN
2  NaN

The slice command returns the proper values: slice 命令返回正确的值：

print(df.Net.str.find('A:'))
------
0  3
1  3
2  3

What did I miss here?我在这里错过了什么？

Answer 1

I assume, net Series has type str.我假设，net 系列的类型为 str。 Perhaps you should try something like :也许您应该尝试以下操作：

df['result'] = df['Net'].apply(str).apply(lambda x: x.split(':')[-1])

apply(str) is optional if type is str.如果 type 是 str， apply(str)是可选的。

Answer 2

You can do this:你可以这样做：

In [539]: df.Net = df.Net.str.split(':').str[-1]

In [540]: df
Out[540]: 
  Ref  Net
0  C1  VCC
1  C2  VDD
2  C3  GND

Answer 3

You can use regex for this您可以为此使用正则表达式

import pandas as pd

df = pd.DataFrame({'Ref':['C1', 'C2'], 'Net':['1- A:VCC', '2- A:VDD']})
df['Net'] = df['Net'].str.replace(r'\d- \w:', '')

Output:输出：

  Ref    Net
0  C1  VCC
1  C2  VDD