# pandas.Series.str.extract

`Series.str.``extract`(pat, flags=0, expand=True)

  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

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