otp.Operation.str.extract#

extract(pat, rewrite='\\0', caseless=False)#

Match the string against a regular expression specified by pat and return the first match. The rewrite parameter can optionally be used to arrange the matched substrings and embed them within the string specified in rewrite.

Parameters

  • pat (str or Column or Operation) – Pattern to search for specified via the POSIX extended regular expression syntax.

  • rewrite (str or Column or Operation) – A string that specifies how to arrange the matched text. \\0 refers to the entire matched text. \\1 to \\9 refer to the text matched by the corresponding parenthesized group in pat. \\u and \\l modifiers within the rewrite string convert the case of the text that matches the corresponding parenthesized group (e.g., \\u1 converts \\1 to uppercase).

  • caseless (bool) – If the caseless flag is set to True, matching is case-insensitive.

Returns

String matched by pat with format specified in rewrite.

Return type

Operation

Examples

>>> data = otp.Ticks(X=["Mr. Smith: +1348 +4781", "Ms. Smith: +8971"])
>>> data["TEL"] = data["X"].str.extract(r"\+\d{4}")
>>> otp.run(data)["TEL"]
0    +1348
1    +8971
Name: TEL, dtype: object

You can specify the group to extract in the rewrite param

>>> data = otp.Ticks(X=["Mr. Smith: 1992/12/22", "Ms. Smith: 1989/10/15"])
>>> data["BIRTH_YEAR"] = data["X"].str.extract(r"(\d{4})/(\d{2})/(\d{2})", rewrite="birth year: \\1")
>>> otp.run(data)["BIRTH_YEAR"]
0    birth year: 1992
1    birth year: 1989
Name: BIRTH_YEAR, dtype: object

You can use a column as a rewrite format orand pattern

>>> data = otp.Ticks(X=["Kelly, Mr. James", "Wilkes, Mrs. James", "Connolly, Miss. Kate"],
...                  PAT=["(Mrs?)\.", "(Mrs?)\.", "(Miss)\."],
...                  REWRITE=["Title 1:   \\1", "Title 2:  \\1", "Title 3: \\1"])
>>> data["TITLE"] = data["X"].str.extract(data["PAT"], rewrite=data["REWRITE"])
>>> otp.run(data)["TITLE"]
0    Title 1:   Mr
1    Title 2:  Mrs
2    Title 3: Miss
Name: TITLE, dtype: object

Case of the extracted string can be changed by adding l and u to extract group

>>> data = otp.Ticks(NAME=["mr. BroWn", "Ms. smITh"])
>>> data["NAME"] = data["NAME"].str.extract(r"(m)([rs]\. )([a-z])([a-z]*)", r"\u1\l2\u3\l4", caseless=True)
>>> otp.run(data)["NAME"]
0    Mr. Brown
1    Ms. Smith
Name: NAME, dtype: object

See also

regex_replace