4.14. Series Normalization

  • 80% of machine learning and data science is cleaning data

  • Using str methods for cleaning user input

4.14.1. Normalization

Comparing not normalized strings will yield invalid or at least unexpected results:

>>> 'MacGyver' == 'Macgyver'
False

Normalize strings before comparing:

>>> 'MacGyver'.casefold() == 'Macgyver'.casefold()
True

4.14.2. Addresses

Address prefix (street, road, court, place, etc.):

>>> prefix = 'ul'
>>> prefix = 'Ul'
>>> prefix = 'UL'
>>> prefix = 'ul.'
>>> prefix = 'Ul.'
>>> prefix = 'UL.'
>>> prefix = 'ulica'
>>> prefix = 'Ulica'
>>> prefix = 'os'
>>> prefix = 'Os'
>>> prefix = 'os.'
>>> prefix = 'Os.'
>>> prefix = 'oś'
>>> prefix = 'oś.'
>>> prefix = 'Oś.'
>>> prefix = 'osiedle'
>>> prefix = 'ośedle'
>>> prefix = 'pl'
>>> prefix = 'pl.'
>>> prefix = 'Pl.'
>>> prefix = 'plac'
>>> prefix = 'al'
>>> prefix = 'al.'
>>> prefix = 'Al.'
>>> prefix = 'aleja'
>>> prefix = 'aleia'
>>> prefix = 'alei'
>>> prefix = 'aleii'
>>> prefix = 'aleji'

House and apartment number:

>>> address = 'Ćwiartki 3/4'
>>> address = 'Ćwiartki 3 / 4'
>>> address = 'Ćwiartki 3 m. 4'
>>> address = 'Ćwiartki 3 m 4'
>>> address = 'Brighton Beach 1st apt 2'
>>> address = 'Brighton Beach 1st apt. 2'
>>> address = 'Górczewska 180f/8f'
>>> address = 'Górczewska 180f/8'
>>> address = 'Górczewska 180/8f'
>>> address = 'Myśliwiecka 3/5/7'
>>> address = 'Jana Pawła II 1 m. 5'
>>> address = 'Powstańców 13d bud. A piętro II sala 3'

4.14.3. Numbers

When comparing age, height, temperature etc, the following numbers has the same meaning. Therefore after converting to float() it will be exactly the same.

>>> age = 42
>>> age = 42.0
>>> age = 42.00
>>> age = '42'
>>> age = '42.0'
>>> age = '42.00'

However, when those values indicates for example a version of a program to find in text their meaning will be different. Version 21 and '21.00' will be a completely different object, so it should not be treated exactly the same.

>>> version = 42
>>> version = 42.0
>>> version = 42.00
>>> version = '42'
>>> version = '42.0'
>>> version = '42.00'

4.14.4. Phone Numbers

Which one is mobile, and which is landline?

>>> phone = '+48 (12) 355-5678'
>>> phone = '+48 123 555 678'

Note, the numbers. They are completely the same. Your brain can easily recognize that those are the same phone numbers.

>>> phone = '123 555 1337'
>>> phone = '1235551337'
>>> phone = '+11235551337'
>>> phone = '+1 12 3555 1337'
>>> phone = '+1 123 555 1337'
>>> phone = '+1 (123) 555 1337'
>>> phone = '+1 (123) 555-1337'
>>> phone = '+1 (123)-555-1337'
>>> phone = '+1 (123).555.1337'
>>>
>>> phone = '+1 800-python'
>>> phone = '+1 800-798466'
>>>
>>> phone = '+48 123555133,1'
>>> phone = '+48 123555133,1,,2'
>>> phone = '+48 123 555 133 wew. 7'

4.14.5. Dates and Time

>>> date = '2000-01-02'
>>> date = '02-01-2000'
>>> date = '02.01.2000'
>>> date = '2.1.2000'
>>> date = '01/02/2000'
>>> date = '1/2/2000'
>>> date = '01/02/00'
>>> date = '1/2/00'
>>> date = 'Jan 2, 2000'
>>> date = 'Jan 2nd, 2000'
>>> date = 'January 2, 2000'
>>> date = 'January 2nd, 2000'
>>> time = '03:00:00'
>>> time = '03:00'
>>> time = '03:00 am'
>>> duration = '04:50:00'
>>> duration = '4h 50m'
>>> duration = '4 hours 50 minutes'

4.14.6. Conversion

>>> LETTERS_EN = 'abcdefghijklmnopqrstuvwxyz'
>>> LETTERS_PL = 'aąbcćdeęfghijklłmnńoóprsśtuwyzżź'
>>>
>>> LETTERS_PLEN = {'ą': 'a', 'ć': 'c', 'ę': 'e',
...                 'ł': 'l', 'ń': 'n', 'ó': 'o',
...                 'ś': 's', 'ż': 'z', 'ź': 'z'}
>>> MONTHS_EN = ['January', 'February', 'March', 'April',
...              'May', 'June', 'July', 'August', 'September',
...              'October', 'November', 'December']
>>>
>>> MONTHS_PL = ['styczeń', 'luty', 'marzec', 'kwiecień',
...              'maj', 'czerwiec', 'lipiec', 'sierpień',
...              'wrzesień', 'październik', 'listopad', 'grudzień']
>>>
>>> MONTHS_PLEN = {'styczeń': 'January',
...                'luty': 'February',
...                'marzec': 'March',
...                'kwiecień': 'April',
...                'maj': 'May',
...                'czerwiec': 'June',
...                'lipiec': 'July',
...                'sierpień': 'August',
...                'wrzesień': 'September',
...                'październik': 'October',
...                'listopad': 'November',
...                'grudzień': 'December'}
>>>
>>> MONTHS_ENPL = {'January': 'styczeń',
...                'February': 'luty',
...                'March': 'marzec',
...                'April': 'kwiecień',
...                'May': 'maj',
...                'June': 'czerwiec',
...                'July': 'lipiec',
...                'August': 'sierpień',
...                'September': 'wrzesień',
...                'October': 'październik',
...                'November': 'listopad',
...                'December': 'grudzień'}

4.14.7. Case Study

  • Is This the Same Address?

  • This is a dump of distinct records of a single address

  • Which one of the below is a true address?

The following code is an output from real customer relationship management (CRM) system, that I wrote in 2000s for a swimming pool in Poznan, Poland. The output is a result of a SELECT DISTINCT(address) result in SQL.

Note to english speaking users:

  • os. - stands for osiedle, which means blocks of flats

  • ul. - stands for ulica, which means street

Is this the same address?

>>> street = 'os. Jana III Sobieskiego'
>>> street = 'osiedle Jana III Sobieskiego'
>>> street = 'ul Jana III Sobieskiego'
>>> street = 'ul. Jana III Sobieskiego'
>>> street = 'Ul. Jana III Sobieskiego'
>>> street = 'UL. Jana III Sobieskiego'
>>> street = 'ulica Jana III Sobieskiego'
>>> street = 'Jana 3 Sobieskiego'
>>> street = 'Jana 3ego Sobieskiego'
>>> street = 'Jana III Sobieskiego'
>>> street = 'Jana IIi Sobieskiego'
>>> street = 'Jana Iii Sobieskiego'
>>> street = 'Jana lll Sobieskiego'  # three small letters 'L'
>>> street = '3ego Sobieskiego'
>>> street = 'Trzeciego Sobieskiego'

Yes, this is the same address. Despite having information about two different geographical entities (osiedle and ulica), this is the same address. Why? It is just a simple mistake from people who entered data.

SELECT DISTINCT(address) won't show you the number of occurrences for each result. What seems to be a high error rate at the first glance, in further analysis happens to be a superbly few mistakes. How come? Number of results for os. Jana III Sobieskiego was around 50 thousands. The other results was one or two at most. So, few mistakes from 50k results. That's really good result.

Why we had those errors? Browser autocomplete. User error while imputing data. And simple shortcuts during conversation: Where do you live?, at Sobieskiego. There is only one place in Poznan, Poland with that name, so it was precise during the conversation. But, receiving party put that incorrectly to the database assuming that it was ulica which is far more common then osiedle addresses.