使用字典中的值替换列中的值 [英] use values in dictionary to replace values in column

问题描述

import pandas as pd
df= pd.DataFrame({'Data':['Hey this is 123456 Jonny B Good',
                              'This is Jonny B Good at 511-233-1137',
                                  'Wow that is Alice N Wonderland A999b',
                                  'Yes hi: Mick E Mouse 1A25629Q88 or ',
                            'Bye Mick E Mouse A13B ok was seen on '], 
                          'E_ID': ['E11','E11', 'E22', 'E33', 'E33'],
                           'N_ID' : ['111', '112', '211', '311', '312'],
                           'Name' : ['JONNY B GOOD', 'JONNY B GOOD', 
                                      'ALICE N WONDERLAND',
                                      'MICK E MOUSE', 'MICK E MOUSE'],        
                          })

df
                      Data                 E_ID N_ID    Name
0   Hey this is 123456 Jonny B Good         E11 111 JONNY B GOOD
1   This is Jonny B Good at 511-233-1137    E11 112 JONNY B GOOD
2   Wow that is Alice N Wonderland A999b    E22 211 ALICE N WONDERLAND
3   Yes hi: Mick E Mouse 1A25629Q88 or      E33 311 MICK E MOUSE
4   Bye Mick E Mouse A13B ok was seen on    E33 312 MICK E MOUSE

我有一个样本df，如上所述.我也有示例字典d，如下所示

I have a sample df as seen above. I also have sample dictionary d as seen below

d = {'E11': ['Jonny',
  'B',
  'Good',
   'Jonny',
   'B',
  'Good',
   '123456',
    '511-233-1137'],

'E22': ['Alice',
  'N',
  'Wonderland',
  'A999b'],

'E33': ['Mick', 
        'E' , 
        'Mouse',
        'Mick', 
        'E' , 
        'Mouse',
        '1A25629Q88',
  'A13B',]} 

我想使用d中的值，例如Jonny更改Data中的相应值.所以0行中的Jonny将变为@@@.

I would like use the values from d e.g. Jonny to change the corresponding value in Data. So e.g. Jonny in row 0 will become @@@.

为此，我查看了 dict 和如何替换列值用熊猫中的字典键，但它们没有太多帮助.我想我需要使用类似的

To do so, I have looked Remap values in pandas column with a dict and how to replace column values with dictionary keys in pandas but they arent much help. I think I need to use something like this

 df['New'] = df['Data'].str.replace(d[value], '@@@')

我希望我的输出看起来像这样

I would like my output to look like this

     Data   E_ID N_ID Name  New
0                           Hey this is @@@ @@@ @@@ @@@             
1                           This is @@@  @@@  @@@  at @@@   
2                           Wow that is @@@  @@@  @@@  @@@  
3                           Yes hi: @@@  @@@  @@@  @@@  or      
4                           Bye @@@  @@@  @@@  @@@  ok was seen on

我需要怎么做才能获得此输出?

What do I need to do to get this output?

推荐答案

您可以生成和使用正则表达式，如下所示:

You could generate and use regular expressions, like this:

df['New']= df['Data']
for key, value in d.items():   
    regex='({alternatives})'.format(alternatives='|'.join(value))
    df.loc[df['E_ID']==key, 'New']= df.loc[df['E_ID']==key, 'New'].str.replace(regex, '@@@')

结果如下:

Out[115]: 
                                    Data E_ID N_ID                Name                                  New
0        Hey this is 123456 Jonny B Good  E11  111        JONNY B GOOD          Hey this is @@@ @@@ @@@ @@@
1   This is Jonny B Good at 511-233-1137  E11  112        JONNY B GOOD           This is @@@ @@@ @@@ at @@@
2   Wow that is Alice N Wonderland A999b  E22  211  ALICE N WONDERLAND          Wow that is @@@ @@@ @@@ @@@
3    Yes hi: Mick E Mouse 1A25629Q88 or   E33  311        MICK E MOUSE          Yes hi: @@@ @@@ @@@ @@@ or 
4  Bye Mick E Mouse A13B ok was seen on   E33  312        MICK E MOUSE  Bye @@@ @@@ @@@ @@@ ok was seen on 

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