finding a string after identifying group
re.match(r'(?:TEL)?:? ?([0-9 ]{9-12})').group(1)
(?:...) makes it a non-capturing group
([0-9 ]{9-12}) captures that part as the group(1)
I am iterating through a few thousand lines of some really messy data from a csv file using pandas.  I'm iterating through one of the dataframe columns which contains generally fairly short strings of disparate, concatenated customer information (name, location, customer numbers, telephone numbers, etc).
There's not a lot of identifiable difference between customer numbers and telephone numbers, though most of the rows in the df column contain a TEL identifier within the string text for a telephone number, as demonstrated below (where 0123456 is a customer number, and 55555 5555 is the TEL number):
JERRY 0123456 TEL: 55555 5555 LOCATION CITY
I can clear the whitespace from the digits following the TEL: indicator, but can't seem to formulate a regular expression that only pulls the text following the TEL: indicator.  My ideal output in my new df["TEL"] column could be 555555555.
So far the regular expression I have is (note, some of the phone numbers are different lengths to deal with international callers, some of which include country code, and some of which do not):
re.match(r'(TEL)?:? ?[0-9 ]{9-12}').group()
However, the above regular expression still pulls the TEL piece of the string I am matching against.  How do I fix this error?