text: # -*- coding: utf-8 -*-
  import codecs
  import re
  import textwrap
  import warnings
  
  import numpy as np
  
  import pandas._libs.lib as lib
  import pandas._libs.ops as libops
  import pandas.compat as compat
  from pandas.util._decorators import Appender, deprecate_kwarg
  
  from pandas.core.dtypes.common import (
      ensure_object, is_bool_dtype, is_categorical_dtype, is_integer,
      is_list_like, is_object_dtype, is_re, is_scalar, is_string_like)
  from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
  from pandas.core.dtypes.missing import isna
  
  from pandas.core.algorithms import take_1d
  from pandas.core.base import NoNewAttributesMixin
  import pandas.core.common as com
  
  _cpython_optimized_encoders = (
      "utf-8", "utf8", "latin-1", "latin1", "iso-8859-1", "mbcs", "ascii"
  )
  _cpython_optimized_decoders = _cpython_optimized_encoders + (
      "utf-16", "utf-32"
  )
  
  _shared_docs = dict()
  
  
  def cat_core(list_of_columns, sep):
      """
      Auxiliary function for :meth:`str.cat`
  
      Parameters
      ----------
      list_of_columns : list of numpy arrays
          List of arrays to be concatenated with sep;
          these arrays may not contain NaNs!
      sep : string
          The separator string for concatenating the columns
  
      Returns
      -------
      nd.array
          The concatenation of list_of_columns with sep
      """
      list_with_sep = [sep] * (2 * len(list_of_columns) - 1)
      list_with_sep[::2] = list_of_columns
      return np.sum(list_with_sep, axis=0)
  
  
  def _na_map(f, arr, na_result=np.nan, dtype=object):
      # should really _check_ for NA
      return _map(f, arr, na_mask=True, na_value=na_result, dtype=dtype)
  
  
  def _map(f, arr, na_mask=False, na_value=np.nan, dtype=object):
      if not len(arr):
          return np.ndarray(0, dtype=dtype)
  
      if isinstance(arr, ABCSeries):
          arr = arr.values
      if not isinstance(arr, np.ndarray):
          arr = np.asarray(arr, dtype=object)
      if na_mask:
          mask = isna(arr)
          try:
              convert = not all(mask)
              result = lib.map_infer_mask(arr, f, mask.view(np.uint8), convert)
          except (TypeError, AttributeError) as e:
              # Reraise the exception if callable `f` got wrong number of args.
              # The user may want to be warned by this, instead of getting NaN
              if compat.PY2:
                  p_err = r'takes (no|(exactly|at (least|most)) ?\d+) arguments?'
              else:
                  p_err = (r'((takes)|(missing)) (?(2)from \d+ to )?\d+ '
                           r'(?(3)required )positional arguments?')
  
              if len(e.args) >= 1 and re.search(p_err, e.args[0]):
                  raise e
  
              def g(x):
                  try:
                      return f(x)
                  except (TypeError, AttributeError):
                      return na_value
  
              return _map(g, arr, dtype=dtype)
          if na_value is not np.nan:
              np.putmask(result, mask, na_value)
              if result.dtype == object:
                  result = lib.maybe_convert_objects(result)
          return result
      else:
          return lib.map_infer(arr, f)
  
  
  def str_count(arr, pat, flags=0):
      """
      Count occurrences of pattern in each string of the Series/Index.
  
      This function is used to count the number of times a particular regex
      pattern is repeated in each of the string elements of the
      :class:`~pandas.Series`.
  
      Parameters
      ----------
      pat : str
          Valid regular expression.
      flags : int, default 0, meaning no flags
          Flags for the `re` module. For a complete list, `see here
          <https://docs.python.org/3/howto/regex.html#compilation-flags>`_.
      **kwargs
          For compatibility with other string methods. Not used.
  
      Returns
      -------
      Series or Index
          Same type as the calling object containing the integer counts.
  
      See Also
      --------
      re : Standard library module for regular expressions.
      str.count : Standard library version, without regular expression support.
  
      Notes
      -----
      Some characters need to be escaped when passing in `pat`.
      eg. ``'$'`` has a special meaning in regex and must be escaped when
      finding this literal character.
  
      Examples
      --------
      >>> s = pd.Series(['A', 'B', 'Aaba', 'Baca', np.nan, 'CABA', 'cat'])
      >>> s.str.count('a')
      0    0.0
      1    0.0
      2    2.0
      3    2.0
      4    NaN
      5    0.0
      6    1.0
      dtype: float64
  
      Escape ``'$'`` to find the literal dollar sign.
  
      >>> s = pd.Series(['$', 'B', 'Aab$', '$$ca', 'C$B$', 'cat'])
      >>> s.str.count('\\$')
      0    1
      1    0
      2    1
      3    2
      4    2
      5    0
      dtype: int64
  
      This is also available on Index
  
      >>> pd.Index(['A', 'A', 'Aaba', 'cat']).str.count('a')
      Int64Index([0, 0, 2, 1], dtype='int64')
      """
      regex = re.compile(pat, flags=flags)
      f = lambda x: len(regex.findall(x))
      return _na_map(f, arr, dtype=int)
  
  
  def str_contains(arr, pat, case=True, flags=0, na=np.nan, regex=True):
      """
      Test if pattern or regex is contained within a string of a Series or Index.
  
      Return boolean Series or Index based on whether a given pattern or regex is
      contained within a string of a Series or Index.
  
      Parameters
      ----------
      pat : str
          Character sequence or regular expression.
      case : bool, default True
          If True, case sensitive.
      flags : int, default 0 (no flags)
          Flags to pass through to the re module, e.g. re.IGNORECASE.
      na : default NaN
          Fill value for missing values.
      regex : bool, default True
          If True, assumes the pat is a regular expression.
  
          If False, treats the pat as a literal string.
  
      Returns
      -------
      Series or Index of boolean values
          A Series or Index of boolean values indicating whether the
          given pattern is contained within the string of each element
          of the Series or Index.
  
      See Also
      --------
      match : Analogous, but stricter, relying on re.match instead of re.search.
      Series.str.startswith : Test if the start of each string element matches a
          pattern.
      Series.str.endswith : Same as startswith, but tests the end of string.
  
      Examples
      --------
  
      Returning a Series of booleans using only a literal pattern.
  
      >>> s1 = pd.Series(['Mouse', 'dog', 'house and parrot', '23', np.NaN])
      >>> s1.str.contains('og', regex=False)
      0    False
      1     True
      2    False
      3    False
      4      NaN
      dtype: object
  
      Returning an Index of booleans using only a literal pattern.
  
      >>> ind = pd.Index(['Mouse', 'dog', 'house and parrot', '23.0', np.NaN])
      >>> ind.str.contains('23', regex=False)
      Index([False, False, False, True, nan], dtype='object')
  
      Specifying case sensitivity using `case`.
  
      >>> s1.str.contains('oG', case=True, regex=True)
      0    False
      1    False
      2    False
      3    False
      4      NaN
      dtype: object
  
      Specifying `na` to be `False` instead of `NaN` replaces NaN values
      with `False`. If Series or Index does not contain NaN values
      the resultant dtype will be `bool`, otherwise, an `object` dtype.
  
      >>> s1.str.contains('og', na=False, regex=True)
      0    False
      1     True
      2    False
      3    False
      4    False
      dtype: bool
  
      Returning 'house' or 'dog' when either expression occurs in a string.
  
      >>> s1.str.contains('house|dog', regex=True)
      0    False
      1     True
      2     True
      3    False
      4      NaN
      dtype: object
  
      Ignoring case sensitivity using `flags` with regex.
  
      >>> import re
      >>> s1.str.contains('PARROT', flags=re.IGNORECASE, regex=True)
      0    False
      1    False
      2     True
      3    False
      4      NaN
      dtype: object
  
      Returning any digit using regular expression.
  
      >>> s1.str.contains('\\d', regex=True)
      0    False
      1    False
      2    False
      3     True
      4      NaN
      dtype: object
  
      Ensure `pat` is a not a literal pattern when `regex` is set to True.
      Note in the following example one might expect only `s2[1]` and `s2[3]` to
      return `True`. However, '.0' as a regex matches any character
      followed by a 0.
  
      >>> s2 = pd.Series(['40', '40.0', '41', '41.0', '35'])
      >>> s2.str.contains('.0', regex=True)
      0     True
      1     True
      2    False
      3     True
      4    False
      dtype: bool
      """
      if regex:
          if not case:
              flags |= re.IGNORECASE
  
          regex = re.compile(pat, flags=flags)
  
          if regex.groups > 0:
              warnings.warn("This pattern has match groups. To actually get the"
                            " groups, use str.extract.", UserWarning,
                            stacklevel=3)
  
          f = lambda x: bool(regex.search(x))
      else:
          if case:
              f = lambda x: pat in x
          else:
              upper_pat = pat.upper()
              f = lambda x: upper_pat in x
              uppered = _na_map(lambda x: x.upper(), arr)
              return _na_map(f, uppered, na, dtype=bool)
      return _na_map(f, arr, na, dtype=bool)
  
  
  def str_startswith(arr, pat, na=np.nan):
      """
      Test if the start of each string element matches a pattern.
  
      Equivalent to :meth:`str.startswith`.
  
      Parameters
      ----------
      pat : str
          Character sequence. Regular expressions are not accepted.
      na : object, default NaN
          Object shown if element tested is not a string.
  
      Returns
      -------
      Series or Index of bool
          A Series of booleans indicating whether the given pattern matches
          the start of each string element.
  
      See Also
      --------
      str.startswith : Python standard library string method.
      Series.str.endswith : Same as startswith, but tests the end of string.
      Series.str.contains : Tests if string element contains a pattern.
  
      Examples
      --------
      >>> s = pd.Series(['bat', 'Bear', 'cat', np.nan])
      >>> s
      0     bat
      1    Bear
      2     cat
      3     NaN
      dtype: object
  
      >>> s.str.startswith('b')
      0     True
      1    False
      2    False
      3      NaN
      dtype: object
  
      Specifying `na` to be `False` instead of `NaN`.
  
      >>> s.str.startswith('b', na=False)
      0     True
      1    False
      2    False
      3    False
      dtype: bool
      """
      f = lambda x: x.startswith(pat)
      return _na_map(f, arr, na, dtype=bool)
  
  
  def str_endswith(arr, pat, na=np.nan):
      """
      Test if the end of each string element matches a pattern.
  
      Equivalent to :meth:`str.endswith`.
  
      Parameters
      ----------
      pat : str
          Character sequence. Regular expressions are not accepted.
      na : object, default NaN
          Object shown if element tested is not a string.
  
      Returns
      -------
      Series or Index of bool
          A Series of booleans indicating whether the given pattern matches
          the end of each string element.
  
      See Also
      --------
      str.endswith : Python standard library string method.
      Series.str.startswith : Same as endswith, but tests the start of string.
      Series.str.contains : Tests if string element contains a pattern.
  
      Examples
      --------
      >>> s = pd.Series(['bat', 'bear', 'caT', np.nan])
      >>> s
      0     bat
      1    bear
      2     caT
      3     NaN
      dtype: object
  
      >>> s.str.endswith('t')
      0     True
      1    False
      2    False
      3      NaN
      dtype: object
  
      Specifying `na` to be `False` instead of `NaN`.
  
      >>> s.str.endswith('t', na=False)
      0     True
      1    False
      2    False
      3    False
      dtype: bool
      """
      f = lambda x: x.endswith(pat)
      return _na_map(f, arr, na, dtype=bool)
  
  
  def str_replace(arr, pat, repl, n=-1, case=None, flags=0, regex=True):
      r"""
      Replace occurrences of pattern/regex in the Series/Index with
      some other string. Equivalent to :meth:`str.replace` or
      :func:`re.sub`.
  
      Parameters
      ----------
      pat : str or compiled regex
          String can be a character sequence or regular expression.
  
          .. versionadded:: 0.20.0
              `pat` also accepts a compiled regex.
  
      repl : str or callable
          Replacement string or a callable. The callable is passed the regex
          match object and must return a replacement string to be used.
          See :func:`re.sub`.
  
          .. versionadded:: 0.20.0
              `repl` also accepts a callable.
  
      n : int, default -1 (all)
          Number of replacements to make from start.
      case : bool, default None
          - If True, case sensitive (the default if `pat` is a string)
          - Set to False for case insensitive
          - Cannot be set if `pat` is a compiled regex
      flags : int, default 0 (no flags)
          - re module flags, e.g. re.IGNORECASE
          - Cannot be set if `pat` is a compiled regex
      regex : bool, default True
          - If True, assumes the passed-in pattern is a regular expression.
          - If False, treats the pattern as a literal string
          - Cannot be set to False if `pat` is a compiled regex or `repl` is
            a callable.
  
          .. versionadded:: 0.23.0
  
      Returns
      -------
      Series or Index of object
          A copy of the object with all matching occurrences of `pat` replaced by
          `repl`.
  
      Raises
      ------
      ValueError
          * if `regex` is False and `repl` is a callable or `pat` is a compiled
            regex
          * if `pat` is a compiled regex and `case` or `flags` is set
  
      Notes
      -----
      When `pat` is a compiled regex, all flags should be included in the
      compiled regex. Use of `case`, `flags`, or `regex=False` with a compiled
      regex will raise an error.
  
      Examples
      --------
      When `pat` is a string and `regex` is True (the default), the given `pat`
      is compiled as a regex. When `repl` is a string, it replaces matching
      regex patterns as with :meth:`re.sub`. NaN value(s) in the Series are
      left as is:
  
      >>> pd.Series(['foo', 'fuz', np.nan]).str.replace('f.', 'ba', regex=True)
      0    bao
      1    baz
      2    NaN
      dtype: object
  
      When `pat` is a string and `regex` is False, every `pat` is replaced with
      `repl` as with :meth:`str.replace`:
  
      >>> pd.Series(['f.o', 'fuz', np.nan]).str.replace('f.', 'ba', regex=False)
      0    bao
      1    fuz
      2    NaN
      dtype: object
  
      When `repl` is a callable, it is called on every `pat` using
      :func:`re.sub`. The callable should expect one positional argument
      (a regex object) and return a string.
  
      To get the idea:
  
      >>> pd.Series(['foo', 'fuz', np.nan]).str.replace('f', repr)
      0    <_sre.SRE_Match object; span=(0, 1), match='f'>oo
      1    <_sre.SRE_Match object; span=(0, 1), match='f'>uz
      2                                                  NaN
      dtype: object
  
      Reverse every lowercase alphabetic word:
  
      >>> repl = lambda m: m.group(0)[::-1]
      >>> pd.Series(['foo 123', 'bar baz', np.nan]).str.replace(r'[a-z]+', repl)
      0    oof 123
      1    rab zab
      2        NaN
      dtype: object
  
      Using regex groups (extract second group and swap case):
  
      >>> pat = r"(?P<one>\w+) (?P<two>\w+) (?P<three>\w+)"
      >>> repl = lambda m: m.group('two').swapcase()
      >>> pd.Series(['One Two Three', 'Foo Bar Baz']).str.replace(pat, repl)
      0    tWO
      1    bAR
      dtype: object
  
      Using a compiled regex with flags
  
      >>> import re
      >>> regex_pat = re.compile(r'FUZ', flags=re.IGNORECASE)
      >>> pd.Series(['foo', 'fuz', np.nan]).str.replace(regex_pat, 'bar')
      0    foo
      1    bar
      2    NaN
      dtype: object
      """
  
      # Check whether repl is valid (GH 13438, GH 15055)
      if not (is_string_like(repl) or callable(repl)):
          raise TypeError("repl must be a string or callable")
  
      is_compiled_re = is_re(pat)
      if regex:
          if is_compiled_re:
              if (case is not None) or (flags != 0):
                  raise ValueError("case and flags cannot be set"
                                   " when pat is a compiled regex")
          else:
              # not a compiled regex
              # set default case
              if case is None:
                  case = True
  
              # add case flag, if provided
              if case is False:
                  flags |= re.IGNORECASE
          if is_compiled_re or len(pat) > 1 or flags or callable(repl):
              n = n if n >= 0 else 0
              compiled = re.compile(pat, flags=flags)
              f = lambda x: compiled.sub(repl=repl, string=x, count=n)
          else:
              f = lambda x: x.replace(pat, repl, n)
      else:
          if is_compiled_re:
              raise ValueError("Cannot use a compiled regex as replacement "
                               "pattern with regex=False")
          if callable(repl):
              raise ValueError("Cannot use a callable replacement when "
                               "regex=False")
          f = lambda x: x.replace(pat, repl, n)
  
      return _na_map(f, arr)
  
  
  def str_repeat(arr, repeats):
      """
      Duplicate each string in the Series or Index.
  
      Parameters
      ----------
      repeats : int or sequence of int
          Same value for all (int) or different value per (sequence).
  
      Returns
      -------
      Series or Index of object
          Series or Index of repeated string objects specified by
          input parameter repeats.
  
      Examples
      --------
      >>> s = pd.Series(['a', 'b', 'c'])
      >>> s
      0    a
      1    b
      2    c
      dtype: object
  
      Single int repeats string in Series
  
      >>> s.str.repeat(repeats=2)
      0    aa
      1    bb
      2    cc
      dtype: object
  
      Sequence of int repeats corresponding string in Series
  
      >>> s.str.repeat(repeats=[1, 2, 3])
      0      a
      1     bb
      2    ccc
      dtype: object
      """
      if is_scalar(repeats):
          def rep(x):
              try:
                  return compat.binary_type.__mul__(x, repeats)
              except TypeError:
                  return compat.text_type.__mul__(x, repeats)
  
          return _na_map(rep, arr)
      else:
  
          def rep(x, r):
              try:
                  return compat.binary_type.__mul__(x, r)
              except TypeError:
                  return compat.text_type.__mul__(x, r)
  
          repeats = np.asarray(repeats, dtype=object)
          result = libops.vec_binop(com.values_from_object(arr), repeats, rep)
          return result
  
  
  def str_match(arr, pat, case=True, flags=0, na=np.nan):
      """
      Determine if each string matches a regular expression.
  
      Parameters
      ----------
      pat : str
          Character sequence or regular expression.
      case : bool, default True
          If True, case sensitive.
      flags : int, default 0 (no flags)
          re module flags, e.g. re.IGNORECASE.
      na : default NaN
          Fill value for missing values.
  
      Returns
      -------
      Series/array of boolean values
  
      See Also
      --------
      contains : Analogous, but less strict, relying on re.search instead of
          re.match.
      extract : Extract matched groups.
      """
      if not case:
          flags |= re.IGNORECASE
  
      regex = re.compile(pat, flags=flags)
  
      dtype = bool
      f = lambda x: bool(regex.match(x))
  
      return _na_map(f, arr, na, dtype=dtype)
  
  
  def _get_single_group_name(rx):
      try:
          return list(rx.groupindex.keys()).pop()
      except IndexError:
          return None
  
  
  def _groups_or_na_fun(regex):
      """Used in both extract_noexpand and extract_frame"""
      if regex.groups == 0:
          raise ValueError("pattern contains no capture groups")
      empty_row = [np.nan] * regex.groups
  
      def f(x):
          if not isinstance(x, compat.string_types):
              return empty_row
          m = regex.search(x)
          if m:
              return [np.nan if item is None else item for item in m.groups()]
          else:
              return empty_row
      return f
  
  
  def _str_extract_noexpand(arr, pat, flags=0):
      """
      Find groups in each string in the Series using passed regular
      expression. This function is called from
      str_extract(expand=False), and can return Series, DataFrame, or
      Index.
  
      """
      from pandas import DataFrame, Index
  
      regex = re.compile(pat, flags=flags)
      groups_or_na = _groups_or_na_fun(regex)
  
      if regex.groups == 1:
          result = np.array([groups_or_na(val)[0] for val in arr], dtype=object)
          name = _get_single_group_name(regex)
      else:
          if isinstance(arr, Index):
              raise ValueError("only one regex group is supported with Index")
          name = None
          names = dict(zip(regex.groupindex.values(), regex.groupindex.keys()))
          columns = [names.get(1 + i, i) for i in range(regex.groups)]
          if arr.empty:
              result = DataFrame(columns=columns, dtype=object)
          else:
              result = DataFrame(
                  [groups_or_na(val) for val in arr],
                  columns=columns,
                  index=arr.index,
                  dtype=object)
      return result, name
  
  
  def _str_extract_frame(arr, pat, flags=0):
      """
      For each subject string in the Series, extract groups from the
      first match of regular expression pat. This function is called from
      str_extract(expand=True), and always returns a DataFrame.
  
      """
      from pandas import DataFrame
  
      regex = re.compile(pat, flags=flags)
      groups_or_na = _groups_or_na_fun(regex)
      names = dict(zip(regex.groupindex.values(), regex.groupindex.keys()))
      columns = [names.get(1 + i, i) for i in range(regex.groups)]
  
      if len(arr) == 0:
          return DataFrame(columns=columns, dtype=object)
      try:
          result_index = arr.index
      except AttributeError:
          result_index = None
      return DataFrame(
          [groups_or_na(val) for val in arr],
          columns=columns,
          index=result_index,
          dtype=object)
  
  
  def str_extract(arr, pat, flags=0, expand=True):
      r"""
      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 :mod:`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.
  
          .. versionadded:: 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
      """
      if not isinstance(expand, bool):
          raise ValueError("expand must be True or False")
      if expand:
          return _str_extract_frame(arr._orig, pat, flags=flags)
      else:
          result, name = _str_extract_noexpand(arr._parent, pat, flags=flags)
          return arr._wrap_result(result, name=name, expand=expand)
  
  
  def str_extractall(arr, pat, flags=0):
      r"""
      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).
  
      .. versionadded:: 0.18.0
  
      Parameters
      ----------
      pat : str
          Regular expression pattern with capturing groups.
      flags : int, default 0 (no flags)
          A ``re`` module 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``.
  
      Returns
      -------
      DataFrame
          A ``DataFrame`` with one row for each match, and one column for each
          group. Its rows have a ``MultiIndex`` with 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.
  
      See Also
      --------
      extract : Returns first match only (not all matches).
  
      Examples
      --------
      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
      """
  
      regex = re.compile(pat, flags=flags)
      # the regex must contain capture groups.
      if regex.groups == 0:
          raise ValueError("pattern contains no capture groups")
  
      if isinstance(arr, ABCIndexClass):
          arr = arr.to_series().reset_index(drop=True)
  
      names = dict(zip(regex.groupindex.values(), regex.groupindex.keys()))
      columns = [names.get(1 + i, i) for i in range(regex.groups)]
      match_list = []
      index_list = []
      is_mi = arr.index.nlevels > 1
  
      for subject_key, subject in arr.iteritems():
          if isinstance(subject, compat.string_types):
  
              if not is_mi:
                  subject_key = (subject_key, )
  
              for match_i, match_tuple in enumerate(regex.findall(subject)):
                  if isinstance(match_tuple, compat.string_types):
                      match_tuple = (match_tuple,)
                  na_tuple = [np.NaN if group == "" else group
                              for group in match_tuple]
                  match_list.append(na_tuple)
                  result_key = tuple(subject_key + (match_i, ))
                  index_list.append(result_key)
  
      from pandas import MultiIndex
      index = MultiIndex.from_tuples(
          index_list, names=arr.index.names + ["match"])
  
      result = arr._constructor_expanddim(match_list, index=index,
                                          columns=columns)
      return result
  
  
  def str_get_dummies(arr, sep='|'):
      """
      Split each string in the Series by sep and return a DataFrame
      of dummy/indicator variables.
  
      Parameters
      ----------
      sep : str, default "|"
          String to split on.
  
      Returns
      -------
      DataFrame
          Dummy variables corresponding to values of the Series.
  
      See Also
      --------
      get_dummies : Convert categorical variable into dummy/indicator
          variables.
  
      Examples
      --------
      >>> pd.Series(['a|b', 'a', 'a|c']).str.get_dummies()
         a  b  c
      0  1  1  0
      1  1  0  0
      2  1  0  1
  
      >>> pd.Series(['a|b', np.nan, 'a|c']).str.get_dummies()
         a  b  c
      0  1  1  0
      1  0  0  0
      2  1  0  1
      """
      arr = arr.fillna('')
      try:
          arr = sep + arr + sep
      except TypeError:
          arr = sep + arr.astype(str) + sep
  
      tags = set()
      for ts in arr.str.split(sep):
          tags.update(ts)
      tags = sorted(tags - {""})
  
      dummies = np.empty((len(arr), len(tags)), dtype=np.int64)
  
      for i, t in enumerate(tags):
          pat = sep + t + sep
          dummies[:, i] = lib.map_infer(arr.values, lambda x: pat in x)
      return dummies, tags
  
  
  def str_join(arr, sep):
      """
      Join lists contained as elements in the Series/Index with passed delimiter.
  
      If the elements of a Series are lists themselves, join the content of these
      lists using the delimiter passed to the function.
      This function is an equivalent to :meth:`str.join`.
  
      Parameters
      ----------
      sep : str
          Delimiter to use between list entries.
  
      Returns
      -------
      Series/Index: object
          The list entries concatenated by intervening occurrences of the
          delimiter.
  
      Raises
      -------
      AttributeError
          If the supplied Series contains neither strings nor lists.
  
      See Also
      --------
      str.join : Standard library version of this method.
      Series.str.split : Split strings around given separator/delimiter.
  
      Notes
      -----
      If any of the list items is not a string object, the result of the join
      will be `NaN`.
  
      Examples
      --------
      Example with a list that contains non-string elements.
  
      >>> s = pd.Series([['lion', 'elephant', 'zebra'],
      ...                [1.1, 2.2, 3.3],
      ...                ['cat', np.nan, 'dog'],
      ...                ['cow', 4.5, 'goat'],
      ...                ['duck', ['swan', 'fish'], 'guppy']])
      >>> s
      0        [lion, elephant, zebra]
      1                [1.1, 2.2, 3.3]
      2                [cat, nan, dog]
      3               [cow, 4.5, goat]
      4    [duck, [swan, fish], guppy]
      dtype: object
  
      Join all lists using a '-'. The lists containing object(s) of types other
      than str will produce a NaN.
  
      >>> s.str.join('-')
      0    lion-elephant-zebra
      1                    NaN
      2                    NaN
      3                    NaN
      4                    NaN
      dtype: object
      """
      return _na_map(sep.join, arr)
  
  
  def str_findall(arr, pat, flags=0):
      """
      Find all occurrences of pattern or regular expression in the Series/Index.
  
      Equivalent to applying :func:`re.findall` to all the elements in the
      Series/Index.
  
      Parameters
      ----------
      pat : str
          Pattern or regular expression.
      flags : int, default 0
          Flags from ``re`` module, e.g. `re.IGNORECASE` (default is 0, which
          means no flags).
  
      Returns
      -------
      Series/Index of lists of strings
          All non-overlapping matches of pattern or regular expression in each
          string of this Series/Index.
  
      See Also
      --------
      count : Count occurrences of pattern or regular expression in each string
          of the Series/Index.
      extractall : For each string in the Series, extract groups from all matches
          of regular expression and return a DataFrame with one row for each
          match and one column for each group.
      re.findall : The equivalent ``re`` function to all non-overlapping matches
          of pattern or regular expression in string, as a list of strings.
  
      Examples
      --------
  
      >>> s = pd.Series(['Lion', 'Monkey', 'Rabbit'])
  
      The search for the pattern 'Monkey' returns one match:
  
      >>> s.str.findall('Monkey')
      0          []
      1    [Monkey]
      2          []
      dtype: object
  
      On the other hand, the search for the pattern 'MONKEY' doesn't return any
      match:
  
      >>> s.str.findall('MONKEY')
      0    []
      1    []
      2    []
      dtype: object
  
      Flags can be added to the pattern or regular expression. For instance,
      to find the pattern 'MONKEY' ignoring the case:
  
      >>> import re
      >>> s.str.findall('MONKEY', flags=re.IGNORECASE)
      0          []
      1    [Monkey]
      2          []
      dtype: object
  
      When the pattern matches more than one string in the Series, all matches
      are returned:
  
      >>> s.str.findall('on')
      0    [on]
      1    [on]
      2      []
      dtype: object
  
      Regular expressions are supported too. For instance, the search for all the
      strings ending with the word 'on' is shown next:
  
      >>> s.str.findall('on$')
      0    [on]
      1      []
      2      []
      dtype: object
  
      If the pattern is found more than once in the same string, then a list of
      multiple strings is returned:
  
      >>> s.str.findall('b')
      0        []
      1        []
      2    [b, b]
      dtype: object
      """
      regex = re.compile(pat, flags=flags)
      return _na_map(regex.findall, arr)
  
  
  def str_find(arr, sub, start=0, end=None, side='left'):
      """
      Return indexes in each strings in the Series/Index where the
      substring is fully contained between [start:end]. Return -1 on failure.
  
      Parameters
      ----------
      sub : str
          Substring being searched.
      start : int
          Left edge index.
      end : int
          Right edge index.
      side : {'left', 'right'}, default 'left'
          Specifies a starting side, equivalent to ``find`` or ``rfind``.
  
      Returns
      -------
      Series or Index
          Indexes where substring is found.
      """
  
      if not isinstance(sub, compat.string_types):
          msg = 'expected a string object, not {0}'
          raise TypeError(msg.format(type(sub).__name__))
  
      if side == 'left':
          method = 'find'
      elif side == 'right':
          method = 'rfind'
      else:  # pragma: no cover
          raise ValueError('Invalid side')
  
      if end is None:
          f = lambda x: getattr(x, method)(sub, start)
      else:
          f = lambda x: getattr(x, method)(sub, start, end)
  
      return _na_map(f, arr, dtype=int)
  
  
  def str_index(arr, sub, start=0, end=None, side='left'):
      if not isinstance(sub, compat.string_types):
          msg = 'expected a string object, not {0}'
          raise TypeError(msg.format(type(sub).__name__))
  
      if side == 'left':
          method = 'index'
      elif side == 'right':
          method = 'rindex'
      else:  # pragma: no cover
          raise ValueError('Invalid side')
  
      if end is None:
          f = lambda x: getattr(x, method)(sub, start)
      else:
          f = lambda x: getattr(x, method)(sub, start, end)
  
      return _na_map(f, arr, dtype=int)
  
  
  def str_pad(arr, width, side='left', fillchar=' '):
      """
      Pad strings in the Series/Index up to width.
  
      Parameters
      ----------
      width : int
          Minimum width of resulting string; additional characters will be filled
          with character defined in `fillchar`.
      side : {'left', 'right', 'both'}, default 'left'
          Side from which to fill resulting string.
      fillchar : str, default ' '
          Additional character for filling, default is whitespace.
  
      Returns
      -------
      Series or Index of object
          Returns Series or Index with minimum number of char in object.
  
      See Also
      --------
      Series.str.rjust : Fills the left side of strings with an arbitrary
          character. Equivalent to ``Series.str.pad(side='left')``.
      Series.str.ljust : Fills the right side of strings with an arbitrary
          character. Equivalent to ``Series.str.pad(side='right')``.
      Series.str.center : Fills boths sides of strings with an arbitrary
          character. Equivalent to ``Series.str.pad(side='both')``.
      Series.str.zfill :  Pad strings in the Series/Index by prepending '0'
          character. Equivalent to ``Series.str.pad(side='left', fillchar='0')``.
  
      Examples
      --------
      >>> s = pd.Series(["caribou", "tiger"])
      >>> s
      0    caribou
      1      tiger
      dtype: object
  
      >>> s.str.pad(width=10)
      0       caribou
      1         tiger
      dtype: object
  
      >>> s.str.pad(width=10, side='right', fillchar='-')
      0    caribou---
      1    tiger-----
      dtype: object
  
      >>> s.str.pad(width=10, side='both', fillchar='-')
      0    -caribou--
      1    --tiger---
      dtype: object
      """
      if not isinstance(fillchar, compat.string_types):
          msg = 'fillchar must be a character, not {0}'
          raise TypeError(msg.format(type(fillchar).__name__))
  
      if len(fillchar) != 1:
          raise TypeError('fillchar must be a character, not str')
  
      if not is_integer(width):
          msg = 'width must be of integer type, not {0}'
          raise TypeError(msg.format(type(width).__name__))
  
      if side == 'left':
          f = lambda x: x.rjust(width, fillchar)
      elif side == 'right':
          f = lambda x: x.ljust(width, fillchar)
      elif side == 'both':
          f = lambda x: x.center(width, fillchar)
      else:  # pragma: no cover
          raise ValueError('Invalid side')
  
      return _na_map(f, arr)
  
  
  def str_split(arr, pat=None, n=None):
  
      if pat is None:
          if n is None or n == 0:
              n = -1
          f = lambda x: x.split(pat, n)
      else:
          if len(pat) == 1:
              if n is None or n == 0:
                  n = -1
              f = lambda x: x.split(pat, n)
          else:
              if n is None or n == -1:
                  n = 0
              regex = re.compile(pat)
              f = lambda x: regex.split(x, maxsplit=n)
      res = _na_map(f, arr)
      return res
  
  
  def str_rsplit(arr, pat=None, n=None):
  
      if n is None or n == 0:
          n = -1
      f = lambda x: x.rsplit(pat, n)
      res = _na_map(f, arr)
      return res
  
  
  def str_slice(arr, start=None, stop=None, step=None):
      """
      Slice substrings from each element in the Series or Index.
  
      Parameters
      ----------
      start : int, optional
          Start position for slice operation.
      stop : int, optional
          Stop position for slice operation.
      step : int, optional
          Step size for slice operation.
  
      Returns
      -------
      Series or Index of object
          Series or Index from sliced substring from original string object.
  
      See Also
      --------
      Series.str.slice_replace : Replace a slice with a string.
      Series.str.get : Return element at position.
          Equivalent to `Series.str.slice(start=i, stop=i+1)` with `i`
          being the position.
  
      Examples
      --------
      >>> s = pd.Series(["koala", "fox", "chameleon"])
      >>> s
      0        koala
      1          fox
      2    chameleon
      dtype: object
  
      >>> s.str.slice(start=1)
      0        oala
      1          ox
      2    hameleon
      dtype: object
  
      >>> s.str.slice(stop=2)
      0    ko
      1    fo
      2    ch
      dtype: object
  
      >>> s.str.slice(step=2)
      0      kaa
      1       fx
      2    caeen
      dtype: object
  
      >>> s.str.slice(start=0, stop=5, step=3)
      0    kl
      1     f
      2    cm
      dtype: object
  
      Equivalent behaviour to:
  
      >>> s.str[0:5:3]
      0    kl
      1     f
      2    cm
      dtype: object
      """
      obj = slice(start, stop, step)
      f = lambda x: x[obj]
      return _na_map(f, arr)
  
  
  def str_slice_replace(arr, start=None, stop=None, repl=None):
      """
      Replace a positional slice of a string with another value.
  
      Parameters
      ----------
      start : int, optional
          Left index position to use for the slice. If not specified (None),
          the slice is unbounded on the left, i.e. slice from the start
          of the string.
      stop : int, optional
          Right index position to use for the slice. If not specified (None),
          the slice is unbounded on the right, i.e. slice until the
          end of the string.
      repl : str, optional
          String for replacement. If not specified (None), the sliced region
          is replaced with an empty string.
  
      Returns
      -------
      Series or Index
          Same type as the original object.
  
      See Also
      --------
      Series.str.slice : Just slicing without replacement.
  
      Examples
      --------
      >>> s = pd.Series(['a', 'ab', 'abc', 'abdc', 'abcde'])
      >>> s
      0        a
      1       ab
      2      abc
      3     abdc
      4    abcde
      dtype: object
  
      Specify just `start`, meaning replace `start` until the end of the
      string with `repl`.
  
      >>> s.str.slice_replace(1, repl='X')
      0    aX
      1    aX
      2    aX
      3    aX
      4    aX
      dtype: object
  
      Specify just `stop`, meaning the start of the string to `stop` is replaced
      with `repl`, and the rest of the string is included.
  
      >>> s.str.slice_replace(stop=2, repl='X')
      0       X
      1       X
      2      Xc
      3     Xdc
      4    Xcde
      dtype: object
  
      Specify `start` and `stop`, meaning the slice from `start` to `stop` is
      replaced with `repl`. Everything before or after `start` and `stop` is
      included as is.
  
      >>> s.str.slice_replace(start=1, stop=3, repl='X')
      0      aX
      1      aX
      2      aX
      3     aXc
      4    aXde
      dtype: object
      """
      if repl is None:
          repl = ''
  
      def f(x):
          if x[start:stop] == '':
              local_stop = start
          else:
              local_stop = stop
          y = ''
          if start is not None:
              y += x[:start]
          y += repl
          if stop is not None:
              y += x[local_stop:]
          return y
  
      return _na_map(f, arr)
  
  
  def str_strip(arr, to_strip=None, side='both'):
      """
      Strip whitespace (including newlines) from each string in the
      Series/Index.
  
      Parameters
      ----------
      to_strip : str or unicode
      side : {'left', 'right', 'both'}, default 'both'
  
      Returns
      -------
      Series or Index
      """
      if side == 'both':
          f = lambda x: x.strip(to_strip)
      elif side == 'left':
          f = lambda x: x.lstrip(to_strip)
      elif side == 'right':
          f = lambda x: x.rstrip(to_strip)
      else:  # pragma: no cover
          raise ValueError('Invalid side')
      return _na_map(f, arr)
  
  
  def str_wrap(arr, width, **kwargs):
      r"""
      Wrap long strings in the Series/Index to be formatted in
      paragraphs with length less than a given width.
  
      This method has the same keyword parameters and defaults as
      :class:`textwrap.TextWrapper`.
  
      Parameters
      ----------
      width : int
          Maximum line width.
      expand_tabs : bool, optional
          If True, tab characters will be expanded to spaces (default: True).
      replace_whitespace : bool, optional
          If True, each whitespace character (as defined by string.whitespace)
          remaining after tab expansion will be replaced by a single space
          (default: True).
      drop_whitespace : bool, optional
          If True, whitespace that, after wrapping, happens to end up at the
          beginning or end of a line is dropped (default: True).
      break_long_words : bool, optional
          If True, then words longer than width will be broken in order to ensure
          that no lines are longer than width. If it is false, long words will
          not be broken, and some lines may be longer than width (default: True).
      break_on_hyphens : bool, optional
          If True, wrapping will occur preferably on whitespace and right after
          hyphens in compound words, as it is customary in English. If false,
          only whitespaces will be considered as potentially good places for line
          breaks, but you need to set break_long_words to false if you want truly
          insecable words (default: True).
  
      Returns
      -------
      Series or Index
  
      Notes
      -----
      Internally, this method uses a :class:`textwrap.TextWrapper` instance with
      default settings. To achieve behavior matching R's stringr library str_wrap
      function, use the arguments:
  
      - expand_tabs = False
      - replace_whitespace = True
      - drop_whitespace = True
      - break_long_words = False
      - break_on_hyphens = False
  
      Examples
      --------
  
      >>> s = pd.Series(['line to be wrapped', 'another line to be wrapped'])
      >>> s.str.wrap(12)
      0             line to be\nwrapped
      1    another line\nto be\nwrapped
      dtype: object
      """
      kwargs['width'] = width
  
      tw = textwrap.TextWrapper(**kwargs)
  
      return _na_map(lambda s: '\n'.join(tw.wrap(s)), arr)
  
  
  def str_translate(arr, table, deletechars=None):
      """
      Map all characters in the string through the given mapping table.
      Equivalent to standard :meth:`str.translate`. Note that the optional
      argument deletechars is only valid if you are using python 2. For python 3,
      character deletion should be specified via the table argument.
  
      Parameters
      ----------
      table : dict (python 3), str or None (python 2)
          In python 3, table is a mapping of Unicode ordinals to Unicode
          ordinals, strings, or None. Unmapped characters are left untouched.
          Characters mapped to None are deleted. :meth:`str.maketrans` is a
          helper function for making translation tables.
          In python 2, table is either a string of length 256 or None. If the
          table argument is None, no translation is applied and the operation
          simply removes the characters in deletechars. :func:`string.maketrans`
          is a helper function for making translation tables.
      deletechars : str, optional (python 2)
          A string of characters to delete. This argument is only valid
          in python 2.
  
      Returns
      -------
      Series or Index
      """
      if deletechars is None:
          f = lambda x: x.translate(table)
      else:
          if compat.PY3:
              raise ValueError("deletechars is not a valid argument for "
                               "str.translate in python 3. You should simply "
                               "specify character deletions in the table "
                               "argument")
          f = lambda x: x.translate(table, deletechars)
      return _na_map(f, arr)
  
  
  def str_get(arr, i):
      """
      Extract element from each component at specified position.
  
      Extract element from lists, tuples, or strings in each element in the
      Series/Index.
  
      Parameters
      ----------
      i : int
          Position of element to extract.
  
      Returns
      -------
      Series or Index
  
      Examples
      --------
      >>> s = pd.Series(["String",
      ...               (1, 2, 3),
      ...               ["a", "b", "c"],
      ...               123,
      ...               -456,
      ...               {1: "Hello", "2": "World"}])
      >>> s
      0                        String
      1                     (1, 2, 3)
      2                     [a, b, c]
      3                           123
      4                          -456
      5    {1: 'Hello', '2': 'World'}
      dtype: object
  
      >>> s.str.get(1)
      0        t
      1        2
      2        b
      3      NaN
      4      NaN
      5    Hello
      dtype: object
  
      >>> s.str.get(-1)
      0      g
      1      3
      2      c
      3    NaN
      4    NaN
      5    None
      dtype: object
      """
      def f(x):
          if isinstance(x, dict):
              return x.get(i)
          elif len(x) > i >= -len(x):
              return x[i]
          return np.nan
      return _na_map(f, arr)
  
  
  def str_decode(arr, encoding, errors="strict"):
      """
      Decode character string in the Series/Index using indicated encoding.
      Equivalent to :meth:`str.decode` in python2 and :meth:`bytes.decode` in
      python3.
  
      Parameters
      ----------
      encoding : str
      errors : str, optional
  
      Returns
      -------
      Series or Index
      """
      if encoding in _cpython_optimized_decoders:
          # CPython optimized implementation
          f = lambda x: x.decode(encoding, errors)
      else:
          decoder = codecs.getdecoder(encoding)
          f = lambda x: decoder(x, errors)[0]
      return _na_map(f, arr)
  
  
  def str_encode(arr, encoding, errors="strict"):
      """
      Encode character string in the Series/Index using indicated encoding.
      Equivalent to :meth:`str.encode`.
  
      Parameters
      ----------
      encoding : str
      errors : str, optional
  
      Returns
      -------
      encoded : Series/Index of objects
      """
      if encoding in _cpython_optimized_encoders:
          # CPython optimized implementation
          f = lambda x: x.encode(encoding, errors)
      else:
          encoder = codecs.getencoder(encoding)
          f = lambda x: encoder(x, errors)[0]
      return _na_map(f, arr)
  
  
  def _noarg_wrapper(f, docstring=None, **kargs):
      def wrapper(self):
          result = _na_map(f, self._parent, **kargs)
          return self._wrap_result(result)
  
      wrapper.__name__ = f.__name__
      if docstring is not None:
          wrapper.__doc__ = docstring
      else:
          raise ValueError('Provide docstring')
  
      return wrapper
  
  
  def _pat_wrapper(f, flags=False, na=False, **kwargs):
      def wrapper1(self, pat):
          result = f(self._parent, pat)
          return self._wrap_result(result)
  
      def wrapper2(self, pat, flags=0, **kwargs):
          result = f(self._parent, pat, flags=flags, **kwargs)
          return self._wrap_result(result)
  
      def wrapper3(self, pat, na=np.nan):
          result = f(self._parent, pat, na=na)
          return self._wrap_result(result)
  
      wrapper = wrapper3 if na else wrapper2 if flags else wrapper1
  
      wrapper.__name__ = f.__name__
      if f.__doc__:
          wrapper.__doc__ = f.__doc__
  
      return wrapper
  
  
  def copy(source):
      "Copy a docstring from another source function (if present)"
  
      def do_copy(target):
          if source.__doc__:
              target.__doc__ = source.__doc__
          return target
  
      return do_copy
  
  
  class StringMethods(NoNewAttributesMixin):
      """
      Vectorized string functions for Series and Index. NAs stay NA unless
      handled otherwise by a particular method. Patterned after Python's string
      methods, with some inspiration from R's stringr package.
  
      Examples
      --------
      >>> s.str.split('_')
      >>> s.str.replace('_', '')
      """
  
      def __init__(self, data):
          self._validate(data)
          self._is_categorical = is_categorical_dtype(data)
  
          # .values.categories works for both Series/Index
          self._parent = data.values.categories if self._is_categorical else data
          # save orig to blow up categoricals to the right type
          self._orig = data
          self._freeze()
  
      @staticmethod
      def _validate(data):
          from pandas.core.index import Index
  
          if (isinstance(data, ABCSeries) and
                  not ((is_categorical_dtype(data.dtype) and
                        is_object_dtype(data.values.categories)) or
                       (is_object_dtype(data.dtype)))):
              # it's neither a string series not a categorical series with
              # strings inside the categories.
              # this really should exclude all series with any non-string values
              # (instead of test for object dtype), but that isn't practical for
              # performance reasons until we have a str dtype (GH 9343)
              raise AttributeError("Can only use .str accessor with string "
                                   "values, which use np.object_ dtype in "
                                   "pandas")
          elif isinstance(data, Index):
              # can't use ABCIndex to exclude non-str
  
              # see src/inference.pyx which can contain string values
              allowed_types = ('string', 'unicode', 'mixed', 'mixed-integer')
              if is_categorical_dtype(data.dtype):
                  inf_type = data.categories.inferred_type
              else:
                  inf_type = data.inferred_type
              if inf_type not in allowed_types:
                  message = ("Can only use .str accessor with string values "
                             "(i.e. inferred_type is 'string', 'unicode' or "
                             "'mixed')")
                  raise AttributeError(message)
              if data.nlevels > 1:
                  message = ("Can only use .str accessor with Index, not "
                             "MultiIndex")
                  raise AttributeError(message)
  
      def __getitem__(self, key):
          if isinstance(key, slice):
              return self.slice(start=key.start, stop=key.stop, step=key.step)
          else:
              return self.get(key)
  
      def __iter__(self):
          i = 0
          g = self.get(i)
          while g.notna().any():
              yield g
              i += 1
              g = self.get(i)
  
      def _wrap_result(self, result, use_codes=True,
                       name=None, expand=None, fill_value=np.nan):
  
          from pandas import Index, Series, MultiIndex
  
          # for category, we do the stuff on the categories, so blow it up
          # to the full series again
          # But for some operations, we have to do the stuff on the full values,
          # so make it possible to skip this step as the method already did this
          # before the transformation...
          if use_codes and self._is_categorical:
              # if self._orig is a CategoricalIndex, there is no .cat-accessor
              result = take_1d(result, Series(self._orig, copy=False).cat.codes,
                               fill_value=fill_value)
  
          if not hasattr(result, 'ndim') or not hasattr(result, 'dtype'):
              return result
          assert result.ndim < 3
  
          if expand is None:
              # infer from ndim if expand is not specified
              expand = result.ndim != 1
  
          elif expand is True and not isinstance(self._orig, Index):
              # required when expand=True is explicitly specified
              # not needed when inferred
  
              def cons_row(x):
                  if is_list_like(x):
                      return x
                  else:
                      return [x]
  
              result = [cons_row(x) for x in result]
              if result:
                  # propagate nan values to match longest sequence (GH 18450)
                  max_len = max(len(x) for x in result)
                  result = [x * max_len if len(x) == 0 or x[0] is np.nan
                            else x for x in result]
  
          if not isinstance(expand, bool):
              raise ValueError("expand must be True or False")
  
          if expand is False:
              # if expand is False, result should have the same name
              # as the original otherwise specified
              if name is None:
                  name = getattr(result, 'name', None)
              if name is None:
                  # do not use logical or, _orig may be a DataFrame
                  # which has "name" column
                  name = self._orig.name
  
          # Wait until we are sure result is a Series or Index before
          # checking attributes (GH 12180)
          if isinstance(self._orig, Index):
              # if result is a boolean np.array, return the np.array
              # instead of wrapping it into a boolean Index (GH 8875)
              if is_bool_dtype(result):
                  return result
  
              if expand:
                  result = list(result)
                  out = MultiIndex.from_tuples(result, names=name)
                  if out.nlevels == 1:
                      # We had all tuples of length-one, which are
                      # better represented as a regular Index.
                      out = out.get_level_values(0)
                  return out
              else:
                  return Index(result, name=name)
          else:
              index = self._orig.index
              if expand:
                  cons = self._orig._constructor_expanddim
                  return cons(result, columns=name, index=index)
              else:
                  # Must be a Series
                  cons = self._orig._constructor
                  return cons(result, name=name, index=index)
  
      def _get_series_list(self, others, ignore_index=False):
          """
          Auxiliary function for :meth:`str.cat`. Turn potentially mixed input
          into a list of Series (elements without an index must match the length
          of the calling Series/Index).
  
          Parameters
          ----------
          others : Series, Index, DataFrame, np.ndarray, list-like or list-like
              of objects that are Series, Index or np.ndarray (1-dim)
          ignore_index : boolean, default False
              Determines whether to forcefully align others with index of caller
  
          Returns
          -------
          tuple : (others transformed into list of Series,
                   boolean whether FutureWarning should be raised)
          """
  
          # Once str.cat defaults to alignment, this function can be simplified;
          # will not need `ignore_index` and the second boolean output anymore
  
          from pandas import Index, Series, DataFrame
  
          # self._orig is either Series or Index
          idx = self._orig if isinstance(self._orig, Index) else self._orig.index
  
          err_msg = ('others must be Series, Index, DataFrame, np.ndarrary or '
                     'list-like (either containing only strings or containing '
                     'only objects of type Series/Index/list-like/np.ndarray)')
  
          # Generally speaking, all objects without an index inherit the index
          # `idx` of the calling Series/Index - i.e. must have matching length.
          # Objects with an index (i.e. Series/Index/DataFrame) keep their own
          # index, *unless* ignore_index is set to True.
          if isinstance(others, Series):
              warn = not others.index.equals(idx)
              # only reconstruct Series when absolutely necessary
              los = [Series(others.values, index=idx)
                     if ignore_index and warn else others]
              return (los, warn)
          elif isinstance(others, Index):
              warn = not others.equals(idx)
              los = [Series(others.values,
                            index=(idx if ignore_index else others))]
              return (los, warn)
          elif isinstance(others, DataFrame):
              warn = not others.index.equals(idx)
              if ignore_index and warn:
                  # without copy, this could change "others"
                  # that was passed to str.cat
                  others = others.copy()
                  others.index = idx
              return ([others[x] for x in others], warn)
          elif isinstance(others, np.ndarray) and others.ndim == 2:
              others = DataFrame(others, index=idx)
              return ([others[x] for x in others], False)
          elif is_list_like(others, allow_sets=False):
              others = list(others)  # ensure iterators do not get read twice etc
  
              # in case of list-like `others`, all elements must be
              # either one-dimensional list-likes or scalars
              if all(is_list_like(x, allow_sets=False) for x in others):
                  los = []
                  join_warn = False
                  depr_warn = False
                  # iterate through list and append list of series for each
                  # element (which we check to be one-dimensional and non-nested)
                  while others:
                      nxt = others.pop(0)  # nxt is guaranteed list-like by above
  
                      # GH 21950 - DeprecationWarning
                      # only allowing Series/Index/np.ndarray[1-dim] will greatly
                      # simply this function post-deprecation.
                      if not (isinstance(nxt, (Series, Index)) or
                              (isinstance(nxt, np.ndarray) and nxt.ndim == 1)):
                          depr_warn = True
  
                      if not isinstance(nxt, (DataFrame, Series,
                                              Index, np.ndarray)):
                          # safety for non-persistent list-likes (e.g. iterators)
                          # do not map indexed/typed objects; info needed below
                          nxt = list(nxt)
  
                      # known types for which we can avoid deep inspection
                      no_deep = ((isinstance(nxt, np.ndarray) and nxt.ndim == 1)
                                 or isinstance(nxt, (Series, Index)))
                      # nested list-likes are forbidden:
                      # -> elements of nxt must not be list-like
                      is_legal = ((no_deep and nxt.dtype == object)
                                  or all(not is_list_like(x) for x in nxt))
  
                      # DataFrame is false positive of is_legal
                      # because "x in df" returns column names
                      if not is_legal or isinstance(nxt, DataFrame):
                          raise TypeError(err_msg)
  
                      nxt, wnx = self._get_series_list(nxt,
                                                       ignore_index=ignore_index)
                      los = los + nxt
                      join_warn = join_warn or wnx
  
                  if depr_warn:
                      warnings.warn('list-likes other than Series, Index, or '
                                    'np.ndarray WITHIN another list-like are '
                                    'deprecated and will be removed in a future '
                                    'version.', FutureWarning, stacklevel=3)
                  return (los, join_warn)
              elif all(not is_list_like(x) for x in others):
                  return ([Series(others, index=idx)], False)
          raise TypeError(err_msg)
  
      def cat(self, others=None, sep=None, na_rep=None, join=None):
          """
          Concatenate strings in the Series/Index with given separator.
  
          If `others` is specified, this function concatenates the Series/Index
          and elements of `others` element-wise.
          If `others` is not passed, then all values in the Series/Index are
          concatenated into a single string with a given `sep`.
  
          Parameters
          ----------
          others : Series, Index, DataFrame, np.ndarrary or list-like
              Series, Index, DataFrame, np.ndarray (one- or two-dimensional) and
              other list-likes of strings must have the same length as the
              calling Series/Index, with the exception of indexed objects (i.e.
              Series/Index/DataFrame) if `join` is not None.
  
              If others is a list-like that contains a combination of Series,
              Index or np.ndarray (1-dim), then all elements will be unpacked and
              must satisfy the above criteria individually.
  
              If others is None, the method returns the concatenation of all
              strings in the calling Series/Index.
          sep : str, default ''
              The separator between the different elements/columns. By default
              the empty string `''` is used.
          na_rep : str or None, default None
              Representation that is inserted for all missing values:
  
              - If `na_rep` is None, and `others` is None, missing values in the
                Series/Index are omitted from the result.
              - If `na_rep` is None, and `others` is not None, a row containing a
                missing value in any of the columns (before concatenation) will
                have a missing value in the result.
          join : {'left', 'right', 'outer', 'inner'}, default None
              Determines the join-style between the calling Series/Index and any
              Series/Index/DataFrame in `others` (objects without an index need
              to match the length of the calling Series/Index). If None,
              alignment is disabled, but this option will be removed in a future
              version of pandas and replaced with a default of `'left'`. To
              disable alignment, use `.values` on any Series/Index/DataFrame in
              `others`.
  
              .. versionadded:: 0.23.0
  
          Returns
          -------
          str, Series or Index
              If `others` is None, `str` is returned, otherwise a `Series/Index`
              (same type as caller) of objects is returned.
  
          See Also
          --------
          split : Split each string in the Series/Index.
          join : Join lists contained as elements in the Series/Index.
  
          Examples
          --------
          When not passing `others`, all values are concatenated into a single
          string:
  
          >>> s = pd.Series(['a', 'b', np.nan, 'd'])
          >>> s.str.cat(sep=' ')
          'a b d'
  
          By default, NA values in the Series are ignored. Using `na_rep`, they
          can be given a representation:
  
          >>> s.str.cat(sep=' ', na_rep='?')
          'a b ? d'
  
          If `others` is specified, corresponding values are concatenated with
          the separator. Result will be a Series of strings.
  
          >>> s.str.cat(['A', 'B', 'C', 'D'], sep=',')
          0    a,A
          1    b,B
          2    NaN
          3    d,D
          dtype: object
  
          Missing values will remain missing in the result, but can again be
          represented using `na_rep`
  
          >>> s.str.cat(['A', 'B', 'C', 'D'], sep=',', na_rep='-')
          0    a,A
          1    b,B
          2    -,C
          3    d,D
          dtype: object
  
          If `sep` is not specified, the values are concatenated without
          separation.
  
          >>> s.str.cat(['A', 'B', 'C', 'D'], na_rep='-')
          0    aA
          1    bB
          2    -C
          3    dD
          dtype: object
  
          Series with different indexes can be aligned before concatenation. The
          `join`-keyword works as in other methods.
  
          >>> t = pd.Series(['d', 'a', 'e', 'c'], index=[3, 0, 4, 2])
          >>> s.str.cat(t, join='left', na_rep='-')
          0    aa
          1    b-
          2    -c
          3    dd
          dtype: object
          >>>
          >>> s.str.cat(t, join='outer', na_rep='-')
          0    aa
          1    b-
          2    -c
          3    dd
          4    -e
          dtype: object
          >>>
          >>> s.str.cat(t, join='inner', na_rep='-')
          0    aa
          2    -c
          3    dd
          dtype: object
          >>>
          >>> s.str.cat(t, join='right', na_rep='-')
          3    dd
          0    aa
          4    -e
          2    -c
          dtype: object
  
          For more examples, see :ref:`here <text.concatenate>`.
          """
          from pandas import Index, Series, concat
  
          if isinstance(others, compat.string_types):
              raise ValueError("Did you mean to supply a `sep` keyword?")
          if sep is None:
              sep = ''
  
          if isinstance(self._orig, Index):
              data = Series(self._orig, index=self._orig)
          else:  # Series
              data = self._orig
  
          # concatenate Series/Index with itself if no "others"
          if others is None:
              data = ensure_object(data)
              na_mask = isna(data)
              if na_rep is None and na_mask.any():
                  data = data[~na_mask]
              elif na_rep is not None and na_mask.any():
                  data = np.where(na_mask, na_rep, data)
              return sep.join(data)
  
          try:
              # turn anything in "others" into lists of Series
              others, warn = self._get_series_list(others,
                                                   ignore_index=(join is None))
          except ValueError:  # do not catch TypeError raised by _get_series_list
              if join is None:
                  raise ValueError('All arrays must be same length, except '
                                   'those having an index if `join` is not None')
              else:
                  raise ValueError('If `others` contains arrays or lists (or '
                                   'other list-likes without an index), these '
                                   'must all be of the same length as the '
                                   'calling Series/Index.')
  
          if join is None and warn:
              warnings.warn("A future version of pandas will perform index "
                            "alignment when `others` is a Series/Index/"
                            "DataFrame (or a list-like containing one). To "
                            "disable alignment (the behavior before v.0.23) and "
                            "silence this warning, use `.values` on any Series/"
                            "Index/DataFrame in `others`. To enable alignment "
                            "and silence this warning, pass `join='left'|"
                            "'outer'|'inner'|'right'`. The future default will "
                            "be `join='left'`.", FutureWarning, stacklevel=2)
  
          # if join is None, _get_series_list already force-aligned indexes
          join = 'left' if join is None else join
  
          # align if required
          if any(not data.index.equals(x.index) for x in others):
              # Need to add keys for uniqueness in case of duplicate columns
              others = concat(others, axis=1,
                              join=(join if join == 'inner' else 'outer'),
                              keys=range(len(others)), sort=False, copy=False)
              data, others = data.align(others, join=join)
              others = [others[x] for x in others]  # again list of Series
  
          all_cols = [ensure_object(x) for x in [data] + others]
          na_masks = np.array([isna(x) for x in all_cols])
          union_mask = np.logical_or.reduce(na_masks, axis=0)
  
          if na_rep is None and union_mask.any():
              # no na_rep means NaNs for all rows where any column has a NaN
              # only necessary if there are actually any NaNs
              result = np.empty(len(data), dtype=object)
              np.putmask(result, union_mask, np.nan)
  
              not_masked = ~union_mask
              result[not_masked] = cat_core([x[not_masked] for x in all_cols],
                                            sep)
          elif na_rep is not None and union_mask.any():
              # fill NaNs with na_rep in case there are actually any NaNs
              all_cols = [np.where(nm, na_rep, col)
                          for nm, col in zip(na_masks, all_cols)]
              result = cat_core(all_cols, sep)
          else:
              # no NaNs - can just concatenate
              result = cat_core(all_cols, sep)
  
          if isinstance(self._orig, Index):
              # add dtype for case that result is all-NA
              result = Index(result, dtype=object, name=self._orig.name)
          else:  # Series
              result = Series(result, dtype=object, index=data.index,
                              name=self._orig.name)
          return result
  
      _shared_docs['str_split'] = ("""
      Split strings around given separator/delimiter.
  
      Splits the string in the Series/Index from the %(side)s,
      at the specified delimiter string. Equivalent to :meth:`str.%(method)s`.
  
      Parameters
      ----------
      pat : str, optional
          String or regular expression to split on.
          If not specified, split on whitespace.
      n : int, default -1 (all)
          Limit number of splits in output.
          ``None``, 0 and -1 will be interpreted as return all splits.
      expand : bool, default False
          Expand the splitted strings into separate columns.
  
          * If ``True``, return DataFrame/MultiIndex expanding dimensionality.
          * If ``False``, return Series/Index, containing lists of strings.
  
      Returns
      -------
      Series, Index, DataFrame or MultiIndex
          Type matches caller unless ``expand=True`` (see Notes).
  
      See Also
      --------
       Series.str.split : Split strings around given separator/delimiter.
       Series.str.rsplit : Splits string around given separator/delimiter,
       starting from the right.
       Series.str.join : Join lists contained as elements in the Series/Index
       with passed delimiter.
       str.split : Standard library version for split.
       str.rsplit : Standard library version for rsplit.
  
      Notes
      -----
      The handling of the `n` keyword depends on the number of found splits:
  
      - If found splits > `n`,  make first `n` splits only
      - If found splits <= `n`, make all splits
      - If for a certain row the number of found splits < `n`,
        append `None` for padding up to `n` if ``expand=True``
  
      If using ``expand=True``, Series and Index callers return DataFrame and
      MultiIndex objects, respectively.
  
      Examples
      --------
      >>> s = pd.Series(["this is a regular sentence",
      "https://docs.python.org/3/tutorial/index.html", np.nan])
  
      In the default setting, the string is split by whitespace.
  
      >>> s.str.split()
      0                   [this, is, a, regular, sentence]
      1    [https://docs.python.org/3/tutorial/index.html]
      2                                                NaN
      dtype: object
  
      Without the `n` parameter, the outputs of `rsplit` and `split`
      are identical.
  
      >>> s.str.rsplit()
      0                   [this, is, a, regular, sentence]
      1    [https://docs.python.org/3/tutorial/index.html]
      2                                                NaN
      dtype: object
  
      The `n` parameter can be used to limit the number of splits on the
      delimiter. The outputs of `split` and `rsplit` are different.
  
      >>> s.str.split(n=2)
      0                     [this, is, a regular sentence]
      1    [https://docs.python.org/3/tutorial/index.html]
      2                                                NaN
      dtype: object
  
      >>> s.str.rsplit(n=2)
      0                     [this is a, regular, sentence]
      1    [https://docs.python.org/3/tutorial/index.html]
      2                                                NaN
      dtype: object
  
      The `pat` parameter can be used to split by other characters.
  
      >>> s.str.split(pat = "/")
      0                         [this is a regular sentence]
      1    [https:, , docs.python.org, 3, tutorial, index...
      2                                                  NaN
      dtype: object
  
      When using ``expand=True``, the split elements will expand out into
      separate columns. If NaN is present, it is propagated throughout
      the columns during the split.
  
      >>> s.str.split(expand=True)
                                                     0     1     2        3
      0                                           this    is     a  regular
      1  https://docs.python.org/3/tutorial/index.html  None  None     None
      2                                            NaN   NaN   NaN      NaN \
  
                   4
      0     sentence
      1         None
      2          NaN
  
      For slightly more complex use cases like splitting the html document name
      from a url, a combination of parameter settings can be used.
  
      >>> s.str.rsplit("/", n=1, expand=True)
                                          0           1
      0          this is a regular sentence        None
      1  https://docs.python.org/3/tutorial  index.html
      2                                 NaN         NaN
      """)
  
      @Appender(_shared_docs['str_split'] % {
          'side': 'beginning',
          'method': 'split'})
      def split(self, pat=None, n=-1, expand=False):
          result = str_split(self._parent, pat, n=n)
          return self._wrap_result(result, expand=expand)
  
      @Appender(_shared_docs['str_split'] % {
          'side': 'end',
          'method': 'rsplit'})
      def rsplit(self, pat=None, n=-1, expand=False):
          result = str_rsplit(self._parent, pat, n=n)
          return self._wrap_result(result, expand=expand)
  
      _shared_docs['str_partition'] = ("""
      Split the string at the %(side)s occurrence of `sep`.
  
      This method splits the string at the %(side)s occurrence of `sep`,
      and returns 3 elements containing the part before the separator,
      the separator itself, and the part after the separator.
      If the separator is not found, return %(return)s.
  
      Parameters
      ----------
      sep : str, default whitespace
          String to split on.
      pat : str, default whitespace
          .. deprecated:: 0.24.0
             Use ``sep`` instead
      expand : bool, default True
          If True, return DataFrame/MultiIndex expanding dimensionality.
          If False, return Series/Index.
  
      Returns
      -------
      DataFrame/MultiIndex or Series/Index of objects
  
      See Also
      --------
      %(also)s
      Series.str.split : Split strings around given separators.
      str.partition : Standard library version.
  
      Examples
      --------
  
      >>> s = pd.Series(['Linda van der Berg', 'George Pitt-Rivers'])
      >>> s
      0    Linda van der Berg
      1    George Pitt-Rivers
      dtype: object
  
      >>> s.str.partition()
              0  1             2
      0   Linda     van der Berg
      1  George      Pitt-Rivers
  
      To partition by the last space instead of the first one:
  
      >>> s.str.rpartition()
                     0  1            2
      0  Linda van der            Berg
      1         George     Pitt-Rivers
  
      To partition by something different than a space:
  
      >>> s.str.partition('-')
                          0  1       2
      0  Linda van der Berg
      1         George Pitt  -  Rivers
  
      To return a Series containining tuples instead of a DataFrame:
  
      >>> s.str.partition('-', expand=False)
      0    (Linda van der Berg, , )
      1    (George Pitt, -, Rivers)
      dtype: object
  
      Also available on indices:
  
      >>> idx = pd.Index(['X 123', 'Y 999'])
      >>> idx
      Index(['X 123', 'Y 999'], dtype='object')
  
      Which will create a MultiIndex:
  
      >>> idx.str.partition()
      MultiIndex(levels=[['X', 'Y'], [' '], ['123', '999']],
                 codes=[[0, 1], [0, 0], [0, 1]])
  
      Or an index with tuples with ``expand=False``:
  
      >>> idx.str.partition(expand=False)
      Index([('X', ' ', '123'), ('Y', ' ', '999')], dtype='object')
      """)
  
      @Appender(_shared_docs['str_partition'] % {
          'side': 'first',
          'return': '3 elements containing the string itself, followed by two '
                    'empty strings',
          'also': 'rpartition : Split the string at the last occurrence of '
                  '`sep`.'
      })
      @deprecate_kwarg(old_arg_name='pat', new_arg_name='sep')
      def partition(self, sep=' ', expand=True):
          f = lambda x: x.partition(sep)
          result = _na_map(f, self._parent)
          return self._wrap_result(result, expand=expand)
  
      @Appender(_shared_docs['str_partition'] % {
          'side': 'last',
          'return': '3 elements containing two empty strings, followed by the '
                    'string itself',
          'also': 'partition : Split the string at the first occurrence of '
                  '`sep`.'
      })
      @deprecate_kwarg(old_arg_name='pat', new_arg_name='sep')
      def rpartition(self, sep=' ', expand=True):
          f = lambda x: x.rpartition(sep)
          result = _na_map(f, self._parent)
          return self._wrap_result(result, expand=expand)
  
      @copy(str_get)
      def get(self, i):
          result = str_get(self._parent, i)
          return self._wrap_result(result)
  
      @copy(str_join)
      def join(self, sep):
          result = str_join(self._parent, sep)
          return self._wrap_result(result)
  
      @copy(str_contains)
      def contains(self, pat, case=True, flags=0, na=np.nan, regex=True):
          result = str_contains(self._parent, pat, case=case, flags=flags, na=na,
                                regex=regex)
          return self._wrap_result(result, fill_value=na)
  
      @copy(str_match)
      def match(self, pat, case=True, flags=0, na=np.nan):
          result = str_match(self._parent, pat, case=case, flags=flags, na=na)
          return self._wrap_result(result, fill_value=na)
  
      @copy(str_replace)
      def replace(self, pat, repl, n=-1, case=None, flags=0, regex=True):
          result = str_replace(self._parent, pat, repl, n=n, case=case,
                               flags=flags, regex=regex)
          return self._wrap_result(result)
  
      @copy(str_repeat)
      def repeat(self, repeats):
          result = str_repeat(self._parent, repeats)
          return self._wrap_result(result)
  
      @copy(str_pad)
      def pad(self, width, side='left', fillchar=' '):
          result = str_pad(self._parent, width, side=side, fillchar=fillchar)
          return self._wrap_result(result)
  
      _shared_docs['str_pad'] = ("""
      Filling %(side)s side of strings in the Series/Index with an
      additional character. Equivalent to :meth:`str.%(method)s`.
  
      Parameters
      ----------
      width : int
          Minimum width of resulting string; additional characters will be filled
          with ``fillchar``
      fillchar : str
          Additional character for filling, default is whitespace
  
      Returns
      -------
      filled : Series/Index of objects
      """)
  
      @Appender(_shared_docs['str_pad'] % dict(side='left and right',
                                               method='center'))
      def center(self, width, fillchar=' '):
          return self.pad(width, side='both', fillchar=fillchar)
  
      @Appender(_shared_docs['str_pad'] % dict(side='right', method='ljust'))
      def ljust(self, width, fillchar=' '):
          return self.pad(width, side='right', fillchar=fillchar)
  
      @Appender(_shared_docs['str_pad'] % dict(side='left', method='rjust'))
      def rjust(self, width, fillchar=' '):
          return self.pad(width, side='left', fillchar=fillchar)
  
      def zfill(self, width):
          """
          Pad strings in the Series/Index by prepending '0' characters.
  
          Strings in the Series/Index are padded with '0' characters on the
          left of the string to reach a total string length  `width`. Strings
          in the Series/Index with length greater or equal to `width` are
          unchanged.
  
          Parameters
          ----------
          width : int
              Minimum length of resulting string; strings with length less
              than `width` be prepended with '0' characters.
  
          Returns
          -------
          Series/Index of objects
  
          See Also
          --------
          Series.str.rjust : Fills the left side of strings with an arbitrary
              character.
          Series.str.ljust : Fills the right side of strings with an arbitrary
              character.
          Series.str.pad : Fills the specified sides of strings with an arbitrary
              character.
          Series.str.center : Fills boths sides of strings with an arbitrary
              character.
  
          Notes
          -----
          Differs from :meth:`str.zfill` which has special handling
          for '+'/'-' in the string.
  
          Examples
          --------
          >>> s = pd.Series(['-1', '1', '1000', 10, np.nan])
          >>> s
          0      -1
          1       1
          2    1000
          3      10
          4     NaN
          dtype: object
  
          Note that ``10`` and ``NaN`` are not strings, therefore they are
          converted to ``NaN``. The minus sign in ``'-1'`` is treated as a
          regular character and the zero is added to the left of it
          (:meth:`str.zfill` would have moved it to the left). ``1000``
          remains unchanged as it is longer than `width`.
  
          >>> s.str.zfill(3)
          0     0-1
          1     001
          2    1000
          3     NaN
          4     NaN
          dtype: object
          """
          result = str_pad(self._parent, width, side='left', fillchar='0')
          return self._wrap_result(result)
  
      @copy(str_slice)
      def slice(self, start=None, stop=None, step=None):
          result = str_slice(self._parent, start, stop, step)
          return self._wrap_result(result)
  
      @copy(str_slice_replace)
      def slice_replace(self, start=None, stop=None, repl=None):
          result = str_slice_replace(self._parent, start, stop, repl)
          return self._wrap_result(result)
  
      @copy(str_decode)
      def decode(self, encoding, errors="strict"):
          result = str_decode(self._parent, encoding, errors)
          return self._wrap_result(result)
  
      @copy(str_encode)
      def encode(self, encoding, errors="strict"):
          result = str_encode(self._parent, encoding, errors)
          return self._wrap_result(result)
  
      _shared_docs['str_strip'] = (r"""
      Remove leading and trailing characters.
  
      Strip whitespaces (including newlines) or a set of specified characters
      from each string in the Series/Index from %(side)s.
      Equivalent to :meth:`str.%(method)s`.
  
      Parameters
      ----------
      to_strip : str or None, default None
          Specifying the set of characters to be removed.
          All combinations of this set of characters will be stripped.
          If None then whitespaces are removed.
  
      Returns
      -------
      Series/Index of objects
  
      See Also
      --------
      Series.str.strip : Remove leading and trailing characters in Series/Index.
      Series.str.lstrip : Remove leading characters in Series/Index.
      Series.str.rstrip : Remove trailing characters in Series/Index.
  
      Examples
      --------
      >>> s = pd.Series(['1. Ant.  ', '2. Bee!\n', '3. Cat?\t', np.nan])
      >>> s
      0    1. Ant.
      1    2. Bee!\n
      2    3. Cat?\t
      3          NaN
      dtype: object
  
      >>> s.str.strip()
      0    1. Ant.
      1    2. Bee!
      2    3. Cat?
      3        NaN
      dtype: object
  
      >>> s.str.lstrip('123.')
      0    Ant.
      1    Bee!\n
      2    Cat?\t
      3       NaN
      dtype: object
  
      >>> s.str.rstrip('.!? \n\t')
      0    1. Ant
      1    2. Bee
      2    3. Cat
      3       NaN
      dtype: object
  
      >>> s.str.strip('123.!? \n\t')
      0    Ant
      1    Bee
      2    Cat
      3    NaN
      dtype: object
      """)
  
      @Appender(_shared_docs['str_strip'] % dict(side='left and right sides',
                                                 method='strip'))
      def strip(self, to_strip=None):
          result = str_strip(self._parent, to_strip, side='both')
          return self._wrap_result(result)
  
      @Appender(_shared_docs['str_strip'] % dict(side='left side',
                                                 method='lstrip'))
      def lstrip(self, to_strip=None):
          result = str_strip(self._parent, to_strip, side='left')
          return self._wrap_result(result)
  
      @Appender(_shared_docs['str_strip'] % dict(side='right side',
                                                 method='rstrip'))
      def rstrip(self, to_strip=None):
          result = str_strip(self._parent, to_strip, side='right')
          return self._wrap_result(result)
  
      @copy(str_wrap)
      def wrap(self, width, **kwargs):
          result = str_wrap(self._parent, width, **kwargs)
          return self._wrap_result(result)
  
      @copy(str_get_dummies)
      def get_dummies(self, sep='|'):
          # we need to cast to Series of strings as only that has all
          # methods available for making the dummies...
          data = self._orig.astype(str) if self._is_categorical else self._parent
          result, name = str_get_dummies(data, sep)
          return self._wrap_result(result, use_codes=(not self._is_categorical),
                                   name=name, expand=True)
  
      @copy(str_translate)
      def translate(self, table, deletechars=None):
          result = str_translate(self._parent, table, deletechars)
          return self._wrap_result(result)
  
      count = _pat_wrapper(str_count, flags=True)
      startswith = _pat_wrapper(str_startswith, na=True)
      endswith = _pat_wrapper(str_endswith, na=True)
      findall = _pat_wrapper(str_findall, flags=True)
  
      @copy(str_extract)
      def extract(self, pat, flags=0, expand=True):
          return str_extract(self, pat, flags=flags, expand=expand)
  
      @copy(str_extractall)
      def extractall(self, pat, flags=0):
          return str_extractall(self._orig, pat, flags=flags)
  
      _shared_docs['find'] = ("""
      Return %(side)s indexes in each strings in the Series/Index
      where the substring is fully contained between [start:end].
      Return -1 on failure. Equivalent to standard :meth:`str.%(method)s`.
  
      Parameters
      ----------
      sub : str
          Substring being searched
      start : int
          Left edge index
      end : int
          Right edge index
  
      Returns
      -------
      found : Series/Index of integer values
  
      See Also
      --------
      %(also)s
      """)
  
      @Appender(_shared_docs['find'] %
                dict(side='lowest', method='find',
                     also='rfind : Return highest indexes in each strings.'))
      def find(self, sub, start=0, end=None):
          result = str_find(self._parent, sub, start=start, end=end, side='left')
          return self._wrap_result(result)
  
      @Appender(_shared_docs['find'] %
                dict(side='highest', method='rfind',
                     also='find : Return lowest indexes in each strings.'))
      def rfind(self, sub, start=0, end=None):
          result = str_find(self._parent, sub,
                            start=start, end=end, side='right')
          return self._wrap_result(result)
  
      def normalize(self, form):
          """
          Return the Unicode normal form for the strings in the Series/Index.
          For more information on the forms, see the
          :func:`unicodedata.normalize`.
  
          Parameters
          ----------
          form : {'NFC', 'NFKC', 'NFD', 'NFKD'}
              Unicode form
  
          Returns
          -------
          normalized : Series/Index of objects
          """
          import unicodedata
          f = lambda x: unicodedata.normalize(form, x)
          result = _na_map(f, self._parent)
          return self._wrap_result(result)
  
      _shared_docs['index'] = ("""
      Return %(side)s indexes in each strings where the substring is
      fully contained between [start:end]. This is the same as
      ``str.%(similar)s`` except instead of returning -1, it raises a ValueError
      when the substring is not found. Equivalent to standard ``str.%(method)s``.
  
      Parameters
      ----------
      sub : str
          Substring being searched
      start : int
          Left edge index
      end : int
          Right edge index
  
      Returns
      -------
      found : Series/Index of objects
  
      See Also
      --------
      %(also)s
      """)
  
      @Appender(_shared_docs['index'] %
                dict(side='lowest', similar='find', method='index',
                     also='rindex : Return highest indexes in each strings.'))
      def index(self, sub, start=0, end=None):
          result = str_index(self._parent, sub,
                             start=start, end=end, side='left')
          return self._wrap_result(result)
  
      @Appender(_shared_docs['index'] %
                dict(side='highest', similar='rfind', method='rindex',
                     also='index : Return lowest indexes in each strings.'))
      def rindex(self, sub, start=0, end=None):
          result = str_index(self._parent, sub,
                             start=start, end=end, side='right')
          return self._wrap_result(result)
  
      _shared_docs['len'] = ("""
      Compute the length of each element in the Series/Index. The element may be
      a sequence (such as a string, tuple or list) or a collection
      (such as a dictionary).
  
      Returns
      -------
      Series or Index of int
          A Series or Index of integer values indicating the length of each
          element in the Series or Index.
  
      See Also
      --------
      str.len : Python built-in function returning the length of an object.
      Series.size : Returns the length of the Series.
  
      Examples
      --------
      Returns the length (number of characters) in a string. Returns the
      number of entries for dictionaries, lists or tuples.
  
      >>> s = pd.Series(['dog',
      ...                 '',
      ...                 5,
      ...                 {'foo' : 'bar'},
      ...                 [2, 3, 5, 7],
      ...                 ('one', 'two', 'three')])
      >>> s
      0                  dog
      1
      2                    5
      3       {'foo': 'bar'}
      4         [2, 3, 5, 7]
      5    (one, two, three)
      dtype: object
      >>> s.str.len()
      0    3.0
      1    0.0
      2    NaN
      3    1.0
      4    4.0
      5    3.0
      dtype: float64
      """)
      len = _noarg_wrapper(len, docstring=_shared_docs['len'], dtype=int)
  
      _shared_docs['casemethods'] = ("""
      Convert strings in the Series/Index to %(type)s.
      %(version)s
      Equivalent to :meth:`str.%(method)s`.
  
      Returns
      -------
      Series/Index of objects
  
      See Also
      --------
      Series.str.lower : Converts all characters to lowercase.
      Series.str.upper : Converts all characters to uppercase.
      Series.str.title : Converts first character of each word to uppercase and
          remaining to lowercase.
      Series.str.capitalize : Converts first character to uppercase and
          remaining to lowercase.
      Series.str.swapcase : Converts uppercase to lowercase and lowercase to
          uppercase.
      Series.str.casefold: Removes all case distinctions in the string.
  
      Examples
      --------
      >>> s = pd.Series(['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe'])
      >>> s
      0                 lower
      1              CAPITALS
      2    this is a sentence
      3              SwApCaSe
      dtype: object
  
      >>> s.str.lower()
      0                 lower
      1              capitals
      2    this is a sentence
      3              swapcase
      dtype: object
  
      >>> s.str.upper()
      0                 LOWER
      1              CAPITALS
      2    THIS IS A SENTENCE
      3              SWAPCASE
      dtype: object
  
      >>> s.str.title()
      0                 Lower
      1              Capitals
      2    This Is A Sentence
      3              Swapcase
      dtype: object
  
      >>> s.str.capitalize()
      0                 Lower
      1              Capitals
      2    This is a sentence
      3              Swapcase
      dtype: object
  
      >>> s.str.swapcase()
      0                 LOWER
      1              capitals
      2    THIS IS A SENTENCE
      3              sWaPcAsE
      dtype: object
      """)
      _shared_docs['lower'] = dict(type='lowercase', method='lower', version='')
      _shared_docs['upper'] = dict(type='uppercase', method='upper', version='')
      _shared_docs['title'] = dict(type='titlecase', method='title', version='')
      _shared_docs['capitalize'] = dict(type='be capitalized',
                                        method='capitalize', version='')
      _shared_docs['swapcase'] = dict(type='be swapcased', method='swapcase',
                                      version='')
      _shared_docs['casefold'] = dict(type='be casefolded', method='casefold',
                                      version='\n    .. versionadded:: 0.25.0\n')
      lower = _noarg_wrapper(lambda x: x.lower(),
                             docstring=_shared_docs['casemethods'] %
                             _shared_docs['lower'])
      upper = _noarg_wrapper(lambda x: x.upper(),
                             docstring=_shared_docs['casemethods'] %
                             _shared_docs['upper'])
      title = _noarg_wrapper(lambda x: x.title(),
                             docstring=_shared_docs['casemethods'] %
                             _shared_docs['title'])
      capitalize = _noarg_wrapper(lambda x: x.capitalize(),
                                  docstring=_shared_docs['casemethods'] %
                                  _shared_docs['capitalize'])
      swapcase = _noarg_wrapper(lambda x: x.swapcase(),
                                docstring=_shared_docs['casemethods'] %
                                _shared_docs['swapcase'])
      casefold = _noarg_wrapper(lambda x: x.casefold(),
                                docstring=_shared_docs['casemethods'] %
                                _shared_docs['casefold'])
  
      _shared_docs['ismethods'] = ("""
      Check whether all characters in each string are %(type)s.
  
      This is equivalent to running the Python string method
      :meth:`str.%(method)s` for each element of the Series/Index. If a string
      has zero characters, ``False`` is returned for that check.
  
      Returns
      -------
      Series or Index of bool
          Series or Index of boolean values with the same length as the original
          Series/Index.
  
      See Also
      --------
      Series.str.isalpha : Check whether all characters are alphabetic.
      Series.str.isnumeric : Check whether all characters are numeric.
      Series.str.isalnum : Check whether all characters are alphanumeric.
      Series.str.isdigit : Check whether all characters are digits.
      Series.str.isdecimal : Check whether all characters are decimal.
      Series.str.isspace : Check whether all characters are whitespace.
      Series.str.islower : Check whether all characters are lowercase.
      Series.str.isupper : Check whether all characters are uppercase.
      Series.str.istitle : Check whether all characters are titlecase.
  
      Examples
      --------
      **Checks for Alphabetic and Numeric Characters**
  
      >>> s1 = pd.Series(['one', 'one1', '1', ''])
  
      >>> s1.str.isalpha()
      0     True
      1    False
      2    False
      3    False
      dtype: bool
  
      >>> s1.str.isnumeric()
      0    False
      1    False
      2     True
      3    False
      dtype: bool
  
      >>> s1.str.isalnum()
      0     True
      1     True
      2     True
      3    False
      dtype: bool
  
      Note that checks against characters mixed with any additional punctuation
      or whitespace will evaluate to false for an alphanumeric check.
  
      >>> s2 = pd.Series(['A B', '1.5', '3,000'])
      >>> s2.str.isalnum()
      0    False
      1    False
      2    False
      dtype: bool
  
      **More Detailed Checks for Numeric Characters**
  
      There are several different but overlapping sets of numeric characters that
      can be checked for.
  
      >>> s3 = pd.Series(['23', '³', '⅕', ''])
  
      The ``s3.str.isdecimal`` method checks for characters used to form numbers
      in base 10.
  
      >>> s3.str.isdecimal()
      0     True
      1    False
      2    False
      3    False
      dtype: bool
  
      The ``s.str.isdigit`` method is the same as ``s3.str.isdecimal`` but also
      includes special digits, like superscripted and subscripted digits in
      unicode.
  
      >>> s3.str.isdigit()
      0     True
      1     True
      2    False
      3    False
      dtype: bool
  
      The ``s.str.isnumeric`` method is the same as ``s3.str.isdigit`` but also
      includes other characters that can represent quantities such as unicode
      fractions.
  
      >>> s3.str.isnumeric()
      0     True
      1     True
      2     True
      3    False
      dtype: bool
  
      **Checks for Whitespace**
  
      >>> s4 = pd.Series([' ', '\\t\\r\\n ', ''])
      >>> s4.str.isspace()
      0     True
      1     True
      2    False
      dtype: bool
  
      **Checks for Character Case**
  
      >>> s5 = pd.Series(['leopard', 'Golden Eagle', 'SNAKE', ''])
  
      >>> s5.str.islower()
      0     True
      1    False
      2    False
      3    False
      dtype: bool
  
      >>> s5.str.isupper()
      0    False
      1    False
      2     True
      3    False
      dtype: bool
  
      The ``s5.str.istitle`` method checks for whether all words are in title
      case (whether only the first letter of each word is capitalized). Words are
      assumed to be as any sequence of non-numeric characters seperated by
      whitespace characters.
  
      >>> s5.str.istitle()
      0    False
      1     True
      2    False
      3    False
      dtype: bool
      """)
      _shared_docs['isalnum'] = dict(type='alphanumeric', method='isalnum')
      _shared_docs['isalpha'] = dict(type='alphabetic', method='isalpha')
      _shared_docs['isdigit'] = dict(type='digits', method='isdigit')
      _shared_docs['isspace'] = dict(type='whitespace', method='isspace')
      _shared_docs['islower'] = dict(type='lowercase', method='islower')
      _shared_docs['isupper'] = dict(type='uppercase', method='isupper')
      _shared_docs['istitle'] = dict(type='titlecase', method='istitle')
      _shared_docs['isnumeric'] = dict(type='numeric', method='isnumeric')
      _shared_docs['isdecimal'] = dict(type='decimal', method='isdecimal')
      isalnum = _noarg_wrapper(lambda x: x.isalnum(),
                               docstring=_shared_docs['ismethods'] %
                               _shared_docs['isalnum'])
      isalpha = _noarg_wrapper(lambda x: x.isalpha(),
                               docstring=_shared_docs['ismethods'] %
                               _shared_docs['isalpha'])
      isdigit = _noarg_wrapper(lambda x: x.isdigit(),
                               docstring=_shared_docs['ismethods'] %
                               _shared_docs['isdigit'])
      isspace = _noarg_wrapper(lambda x: x.isspace(),
                               docstring=_shared_docs['ismethods'] %
                               _shared_docs['isspace'])
      islower = _noarg_wrapper(lambda x: x.islower(),
                               docstring=_shared_docs['ismethods'] %
                               _shared_docs['islower'])
      isupper = _noarg_wrapper(lambda x: x.isupper(),
                               docstring=_shared_docs['ismethods'] %
                               _shared_docs['isupper'])
      istitle = _noarg_wrapper(lambda x: x.istitle(),
                               docstring=_shared_docs['ismethods'] %
                               _shared_docs['istitle'])
      isnumeric = _noarg_wrapper(lambda x: x.isnumeric(),
                                 docstring=_shared_docs['ismethods'] %
                                 _shared_docs['isnumeric'])
      isdecimal = _noarg_wrapper(lambda x: x.isdecimal(),
                                 docstring=_shared_docs['ismethods'] %
                                 _shared_docs['isdecimal'])
  
      @classmethod
      def _make_accessor(cls, data):
          cls._validate(data)
          return cls(data)
  