pandas.Series.str.extract#

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

Extract capture groups in the regexpat as columns in a DataFrame.

For each subject string in the Series, extract groups from thefirst match of regular expressionpat.

Parameters:

patstr

Regular expression pattern with capturing groups.

flagsint, default 0 (no flags)

Flags from there module, e.g.re.IGNORECASE, thatmodify regular expression matching for things like case,spaces, etc. For more details, seere.

expandbool, default True

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

Returns:

DataFrame or Series or Index

A DataFrame with one row for each subject string, and onecolumn for each group. Any capture group names in regularexpression pat will be used for column names; otherwisecapture group numbers will be used. The dtype of each resultcolumn is always object, even when no match is found. Ifexpand=False and pat has only one capture group, thenreturn a Series (if subject is a Series) or Index (if subjectis 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    10    a    11    b    22  NaN  NaN

A pattern may contain optional groups.

>>>s.str.extract(r'([ab])?(\d)')    0  10    a  11    b  22  NaN  3

Named groups will become column names in the result.

>>>s.str.extract(r'(?P<letter>[ab])(?P<digit>\d)')letter digit0      a     11      b     22    NaN   NaN

A pattern with one group will return a DataFrame with one columnif expand=True.

>>>s.str.extract(r'[ab](\d)',expand=True)    00    11    22  NaN

A pattern with one group will return a Series if expand=False.

>>>s.str.extract(r'[ab](\d)',expand=False)0      11      22    NaNdtype: object