pandas.Series.str.extract

Series.str.extract(self, pat, flags=0, expand=True) [source]

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.

Parameters:

pat : str Regular expression pattern with capturing groups. flags : int, default 0 (no flags) 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. expand : bool, default True 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. New in version 0.18.0.

Returns:

DataFrame or Series or Index 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).

See also

extractall

Returns all matches (not just the first match).

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  1
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
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