CODEWITHSUNDEEP
pandashashanonymize
Erfan
Erfan

Reputation: 42946

Anonymizing data / replacing names

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

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

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

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'.

Upvotes: 3

Views: 5459

Answers (3)

L. Astola
L. Astola

Reputation: 55

labels, uniques =  pd.factorize(df['name'])
labels = ['person_'+str(l) for l in labels]
df['contributor_anonymized'] = labels

Upvotes: 1

jezrael
jezrael

Reputation: 863701

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

Upvotes: 8

Cem
Cem

Reputation: 3

Maybe try to create a data frame called "index" for this operation and keep unique name values inside it?

Then produce masks with unique name indexes and merge the resulting data frame indexwith data. 

index = pd.DataFrame()
index['name'] = df['name'].unique()
index['mask'] = index['name'].apply(lambda x : 'person' + 
str(index[index.name == x].index[0] + 1))

data.merge(index, how='left')[['mask', 'amount']]

Upvotes: 0

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