Extract capture groups in the regex pat as columns in DataFrame.
For each subject string in the Series, extract groups from all matches of regular expression pat. When each subject string in the Series has exactly one match, extractall(pat).xs(0, level=’match’) is the same as extract(pat).
Regular expression pattern with capturing groups.
- flagsint, default 0 (no flags)
remodule flag, for example
re.IGNORECASE. These allow to modify regular expression matching for things like case, spaces, etc. Multiple flags can be combined with the bitwise OR operator, for example
re.IGNORECASE | re.MULTILINE.
DataFramewith one row for each match, and one column for each group. Its rows have a
MultiIndexwith first levels that come from the subject
Series. The last level is named ‘match’ and indexes the matches in each item of the
Series. Any capture group names in regular expression pat will be used for column names; otherwise capture group numbers will be used.
Returns first match only (not all matches).
A pattern with one group will return a DataFrame with one column. Indices with no matches will not appear in the result.
>>> s = pd.Series(["a1a2", "b1", "c1"], index=["A", "B", "C"]) >>> s.str.extractall(r"[ab](\d)") 0 match A 0 1 1 2 B 0 1
Capture group names are used for column names of the result.
>>> s.str.extractall(r"[ab](?P<digit>\d)") digit match A 0 1 1 2 B 0 1
A pattern with two groups will return a DataFrame with two columns.
>>> s.str.extractall(r"(?P<letter>[ab])(?P<digit>\d)") letter digit match A 0 a 1 1 a 2 B 0 b 1
Optional groups that do not match are NaN in the result.
>>> s.str.extractall(r"(?P<letter>[ab])?(?P<digit>\d)") letter digit match A 0 a 1 1 a 2 B 0 b 1 C 0 NaN 1