How to use regex capture groups in pandas replace function

Question

I've got a pandas DataFrame that uses "2Nd" instead of "2nd", "136Th" instead of "136th", etc. I want the letter immediately following the number to be lowercase.

Sample data:

data = pd.Series(['21St StNew York', 'Exampe BlvdSt Louis', '1St Rd'])

Desired output:

['21st StNew York', 'Exampe BlvdSt Louis', '1st Rd']

Tried using str.replace():

data = data.str.replace('\BSt', 'st', regex=True)
['21st StNew York', 'Exampe Blvdst Louis', '1st Rd']

Is it possible to use a capture group?

data = data.str.replace('[0-9]+(St)', 'st', regex=True)
['st StNew York', 'Exampe BlvdSt Louis', 'st Rd']

Andy L. · Accepted Answer · 2019-12-16 02:54:21Z

5

Use a callable for repl

new_data = data.str.replace('(\d+[A-Z])', lambda m: m.group(1).lower())

Out[49]:
0        21st StNew York
1    Exampe BlvdSt Louis
2                 1st Rd
dtype: object

answered Dec 16, 2019 at 2:54

Andy L.

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Comments

Tim Biegeleisen · Accepted Answer · 2019-12-16 03:24:38Z

0

We can try doing a regex replacement on the pattern (?<=\d)[A-Z], and then replacing with the lowercase version:

df['dat'] = df['data'].str.replace(r'(?<=\d)[A-Z]', lambda x: x.group(0).lower())

edited Dec 16, 2019 at 3:24

answered Dec 16, 2019 at 2:55

Tim Biegeleisen

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1 Comment

jmon Over a year ago

This is returning null values for me for some reason.

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