匿名化数据/替换名称 [英] Anonymizing data / replacing names

问题描述

通常，我通过使用hashlib和.apply(hash)函数来对数据进行匿名处理.

Normally I anonymize my data by using hashlib and using the .apply(hash) function.

现在我正在尝试一种新方法，假设我必须遵循称为'data'的df:

Now im trying a new approach, imagine I have to following df called 'data':

df = pd.DataFrame({'contributor':['eric', 'frank', 'john', 'frank', 'barbara'],
                   'amount payed':[10,28,49,77,31]})

  contributor  amount payed
0        eric            10
1       frank            28
2        john            49
3       frank            77
4     barbara            31

我想通过将名称全部转换为person1，person2等来匿名化，就像这样:

Which I want to anonymize by turning the names all into person1, person2 etc, like this:

output = pd.DataFrame({'contributor':['person1', 'person2', 'person3', 'person2', 'person4'],
                       'amount payed':[10,28,49,77,31]})

  contributor  amount payed
0     person1            10
1     person2            28
2     person3            49
3     person2            77
4     person4            31

所以我的第一个操作是对 name 列进行汇总，以便将名称附加到唯一索引，并且我可以将该索引用作'person'之后的数字.

So my first though was summarizing the name column so the names are attached to a unique index and I can use that index for the number after 'person'.

推荐答案

我认为更快的解决方案是使用

I think faster solution is use factorize for unique values, add 1, convert to Series and strings and prepend Person string:

df['contributor'] = 'Person' + pd.Series(pd.factorize(df['contributor'])[0] + 1).astype(str)
print (df)
  contributor  amount payed
0     Person1            10
1     Person2            28
2     Person3            49
3     Person2            77
4     Person4            31

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