Extract capture groups in the regex pat as columns in a DataFrame.

For each subject string in the Series, extract groups from the first match of regular expression pat .

Regular expression pattern with capturing groups.

Flags from the re module, e.g. re.IGNORECASE , that modify regular expression matching for things like case, spaces, etc. For more details, see re .

If True, return DataFrame with one column per capture group. If False, return a Series/Index if there is one capture group or DataFrame if there are multiple capture groups.

A DataFrame with one row for each subject string, and one column for each group. Any capture group names in regular expression pat will be used for column names; otherwise capture group numbers will be used. The dtype of each result column is always object, even when no match is found. If expand=False and pat has only one capture group, then return a Series (if subject is a Series) or Index (if subject is an Index).

Examples

A pattern with two groups will return a DataFrame with two columns. Non-matches will be NaN.

>>> s = pd.Series(['a1', 'b2', 'c3'])
>>> s.str.extract(r'([ab])(\d)')
    0    1
0    a    1
1    b    2
2  NaN  NaN
A pattern may contain optional groups.
>>> s.str.extract(r'([ab])?(\d)')
0    a  1
1    b  2
2  NaN  3
Named groups will become column names in the result.
>>> s.str.extract(r'(?P<letter>[ab])(?P<digit>\d)')
letter digit
0      a     1
1      b     2
2    NaN   NaN
A pattern with one group will return a DataFrame with one column
if expand=True.
>>> s.str.extract(r'[ab](\d)', expand=True)
0    1
1    2
2  NaN
A pattern with one group will return a Series if expand=False.
>>> s.str.extract(r'[ab](\d)', expand=False)
0      1
1      2
2    NaN
dtype: object