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- import re
- import pandas as pd
- # To find and replace all sensitive numbers (phone numbers, ssn, latitude and longitude coordinates, some zipcodes)
- # in the data frame by xxx we can create a function such as below:
- def sensitive_numerics(number):
- number = re.sub(r'[\+\(]?\d[\d .\-\(\)]{6,}', r'xxx', number)
- # [0-9] matches a single digit in the range 0 through 9 (inclusive), {4} indicates that four such digits should occur in a row,
- # - means a hyphen, and | means an OR and separates the two patterns you mention.
- # '123-4-5648' '1-234-5-6789'
- number = re.sub('[0-9]-[0-9]{3}-[0-9]-[0-9]{4}|[0-9]{3}-[0-9]-[0-9]{4}', 'xxx', number)
- return number
- def numerics(data):
- return pd.Series(data.apply(sensitive_numerics))
- ############################################# Test
- ##################################################
- # Texts = ['1231451469', '42.2', '123 145 1469', '123.145.1469', '(123) 145.1469', '(123) 145 1469',
- # '(123) 145–1469', '123–145–1469', '+1(123) 145–1469 ', '1234567890999111', '123HELLO56',
- # '-123', '04/04/1998', 'it’s015–96–0342 you know my number call me', '+123–145–1469',
- # '48236–123', 'I live close to (42.293564, -83.638916)', '123-4-5648', '1-234-5-6789']
- # data = pd.Series(Texts)
- # print(numerics(data))
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