import numpy as np

from pandas.compat import zip
from pandas.core.common import (isnull, notnull, _values_from_object,
                                is_bool_dtype,
                                is_list_like, is_categorical_dtype,
                                is_object_dtype)
from pandas.core.algorithms import take_1d
import pandas.compat as compat
from pandas.core.base import AccessorProperty, NoNewAttributesMixin
from pandas.util.decorators import Appender, deprecate_kwarg
import re
import pandas.lib as lib
import warnings
import textwrap
import codecs

_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 _get_array_list(arr, others):
    from pandas.core.series import Series

    if len(others) and isinstance(_values_from_object(others)[0],
                                  (list, np.ndarray, Series)):
        arrays = [arr] + list(others)
    else:
        arrays = [arr, others]

    return [np.asarray(x, dtype=object) for x in arrays]


def str_cat(arr, others=None, sep=None, na_rep=None):
    """
    Concatenate strings in the Series/Index with given separator.

    Parameters
    ----------
    others : list-like, or list of list-likes
      If None, returns str concatenating strings of the Series
    sep : string or None, default None
    na_rep : string or None, default None
        If None, NA in the series are ignored.

    Returns
    -------
    concat : Series/Index of objects or str

    Examples
    --------
    When ``na_rep`` is `None` (default behavior), NaN value(s)
    in the Series are ignored.

    >>> Series(['a','b',np.nan,'c']).str.cat(sep=' ')
    'a b c'

    >>> Series(['a','b',np.nan,'c']).str.cat(sep=' ', na_rep='?')
    'a b ? c'

    If ``others`` is specified, corresponding values are
    concatenated with the separator. Result will be a Series of strings.

    >>> Series(['a', 'b', 'c']).str.cat(['A', 'B', 'C'], sep=',')
    0    a,A
    1    b,B
    2    c,C
    dtype: object

    Otherwise, strings in the Series are concatenated. Result will be a string.

    >>> Series(['a', 'b', 'c']).str.cat(sep=',')
    'a,b,c'

    Also, you can pass a list of list-likes.

    >>> Series(['a', 'b']).str.cat([['x', 'y'], ['1', '2']], sep=',')
    0    a,x,1
    1    b,y,2
    dtype: object
    """
    if sep is None:
        sep = ''

    if others is not None:
        arrays = _get_array_list(arr, others)

        n = _length_check(arrays)
        masks = np.array([isnull(x) for x in arrays])
        cats = None

        if na_rep is None:
            na_mask = np.logical_or.reduce(masks, axis=0)

            result = np.empty(n, dtype=object)
            np.putmask(result, na_mask, np.nan)

            notmask = ~na_mask

            tuples = zip(*[x[notmask] for x in arrays])
            cats = [sep.join(tup) for tup in tuples]

            result[notmask] = cats
        else:
            for i, x in enumerate(arrays):
                x = np.where(masks[i], na_rep, x)
                if cats is None:
                    cats = x
                else:
                    cats = cats + sep + x

            result = cats

        return result
    else:
        arr = np.asarray(arr, dtype=object)
        mask = isnull(arr)
        if na_rep is None and mask.any():
            if sep == '':
                na_rep = ''
            else:
                return sep.join(arr[notnull(arr)])
        return sep.join(np.where(mask, na_rep, arr))


def _length_check(others):
    n = None
    for x in others:
        try:
            if n is None:
                n = len(x)
            elif len(x) != n:
                raise ValueError('All arrays must be same length')
        except TypeError:
            raise ValueError("Did you mean to supply a `sep` keyword?")
    return n


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):
    from pandas.core.series import Series

    if not len(arr):
        return np.ndarray(0, dtype=dtype)

    if isinstance(arr, Series):
        arr = arr.values
    if not isinstance(arr, np.ndarray):
        arr = np.asarray(arr, dtype=object)
    if na_mask:
        mask = isnull(arr)
        try:
            result = lib.map_infer_mask(arr, f, mask.view(np.uint8))
        except (TypeError, AttributeError):

            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.

    Parameters
    ----------
    pat : string, valid regular expression
    flags : int, default 0 (no flags)
        re module flags, e.g. re.IGNORECASE

    Returns
    -------
    counts : Series/Index of integer values
    """
    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):
    """
    Return boolean Series/``array`` whether given pattern/regex is
    contained in each string in the Series/Index.

    Parameters
    ----------
    pat : string
        Character sequence or regular expression
    case : boolean, 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.
    regex : bool, default True
        If True use re.search, otherwise use Python in operator

    Returns
    -------
    contained : Series/array of boolean values

    See Also
    --------
    match : analogous, but stricter, relying on re.match instead of re.search

    """
    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):
    """
    Return boolean Series/``array`` indicating whether each string in the
    Series/Index starts with passed pattern. Equivalent to
    :meth:`str.startswith`.

    Parameters
    ----------
    pat : string
        Character sequence
    na : bool, default NaN

    Returns
    -------
    startswith : Series/array of boolean values
    """
    f = lambda x: x.startswith(pat)
    return _na_map(f, arr, na, dtype=bool)


def str_endswith(arr, pat, na=np.nan):
    """
    Return boolean Series indicating whether each string in the
    Series/Index ends with passed pattern. Equivalent to
    :meth:`str.endswith`.

    Parameters
    ----------
    pat : string
        Character sequence
    na : bool, default NaN

    Returns
    -------
    endswith : Series/array of boolean values
    """
    f = lambda x: x.endswith(pat)
    return _na_map(f, arr, na, dtype=bool)


def str_replace(arr, pat, repl, n=-1, case=True, flags=0):
    """
    Replace occurrences of pattern/regex in the Series/Index with
    some other string. Equivalent to :meth:`str.replace` or
    :func:`re.sub`.

    Parameters
    ----------
    pat : string
        Character sequence or regular expression
    repl : string
        Replacement sequence
    n : int, default -1 (all)
        Number of replacements to make from start
    case : boolean, default True
        If True, case sensitive
    flags : int, default 0 (no flags)
        re module flags, e.g. re.IGNORECASE

    Returns
    -------
    replaced : Series/Index of objects
    """
    use_re = not case or len(pat) > 1 or flags

    if use_re:
        if not case:
            flags |= re.IGNORECASE
        regex = re.compile(pat, flags=flags)
        n = n if n >= 0 else 0

        def f(x):
            return regex.sub(repl, x, count=n)
    else:
        f = lambda x: x.replace(pat, repl, n)

    return _na_map(f, arr)


def str_repeat(arr, repeats):
    """
    Duplicate each string in the Series/Index by indicated number
    of times.

    Parameters
    ----------
    repeats : int or array
        Same value for all (int) or different value per (array)

    Returns
    -------
    repeated : Series/Index of objects
    """
    if lib.isscalar(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 = lib.vec_binop(_values_from_object(arr), repeats, rep)
        return result


def str_match(arr, pat, case=True, flags=0, na=np.nan, as_indexer=False):
    """
    Deprecated: Find groups in each string in the Series/Index
    using passed regular expression.
    If as_indexer=True, determine if each string matches a regular expression.

    Parameters
    ----------
    pat : string
        Character sequence or regular expression
    case : boolean, 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.
    as_indexer : False, by default, gives deprecated behavior better achieved
        using str_extract. True return boolean indexer.

    Returns
    -------
    Series/array of boolean values
        if as_indexer=True
    Series/Index of tuples
        if as_indexer=False, default but deprecated

    See Also
    --------
    contains : analogous, but less strict, relying on re.search instead of
        re.match
    extract : now preferred to the deprecated usage of match (as_indexer=False)

    Notes
    -----
    To extract matched groups, which is the deprecated behavior of match, use
    str.extract.
    """

    if not case:
        flags |= re.IGNORECASE

    regex = re.compile(pat, flags=flags)

    if (not as_indexer) and regex.groups > 0:
        # Do this first, to make sure it happens even if the re.compile
        # raises below.
        warnings.warn("In future versions of pandas, match will change to"
                      " always return a bool indexer.", FutureWarning,
                      stacklevel=3)

    if as_indexer and regex.groups > 0:
        warnings.warn("This pattern has match groups. To actually get the"
                      " groups, use str.extract.", UserWarning, stacklevel=3)

    # If not as_indexer and regex.groups == 0, this returns empty lists
    # and is basically useless, so we will not warn.

    if (not as_indexer) and regex.groups > 0:
        dtype = object

        def f(x):
            m = regex.match(x)
            if m:
                return m.groups()
            else:
                return []
    else:
        # This is the new behavior of str_match.
        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=None):
    """
    For each subject string in the Series, extract groups from the
    first match of regular expression pat.

    .. versionadded:: 0.13.0

    Parameters
    ----------
    pat : string
        Regular expression pattern with capturing groups
    flags : int, default 0 (no flags)
        re module flags, e.g. re.IGNORECASE

    .. versionadded:: 0.18.0
    expand : bool, default False
        * If True, return DataFrame.
        * If False, return Series/Index/DataFrame.

    Returns
    -------
    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=True 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 = Series(['a1', 'b2', 'c3'])
    >>> s.str.extract('([ab])(\d)')
         0    1
    0    a    1
    1    b    2
    2  NaN  NaN

    A pattern may contain optional groups.

    >>> s.str.extract('([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('(?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('[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('[ab](\d)', expand=False)
    0      1
    1      2
    2    NaN
    dtype: object

    """
    if expand is None:
        warnings.warn(
            "currently extract(expand=None) " +
            "means expand=False (return Index/Series/DataFrame) " +
            "but in a future version of pandas this will be changed " +
            "to expand=True (return DataFrame)",
            FutureWarning,
            stacklevel=3)
        expand = False
    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._data, pat, flags=flags)
        return arr._wrap_result(result, name=name, expand=expand)


def str_extractall(arr, pat, flags=0):
    """
    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 : string
        Regular expression pattern with capturing groups
    flags : int, default 0 (no flags)
        re module flags, e.g. re.IGNORECASE

    Returns
    -------
    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 indicates
    the order in the subject. 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 = Series(["a1a2", "b1", "c1"], index=["A", "B", "C"])
    >>> s.str.extractall("[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("[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("(?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("(?P<letter>[ab])?(?P<digit>\d)")
            letter digit
      match
    A 0          a     1
      1          a     2
    B 0          b     1
    C 0        NaN     1

    """
    from pandas import DataFrame, MultiIndex
    regex = re.compile(pat, flags=flags)
    # the regex must contain capture groups.
    if regex.groups == 0:
        raise ValueError("pattern contains no capture groups")
    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 = []
    for subject_key, subject in arr.iteritems():
        if isinstance(subject, compat.string_types):
            try:
                key_list = list(subject_key)
            except TypeError:
                key_list = [subject_key]
            for match_i, match_tuple in enumerate(regex.findall(subject)):
                na_tuple = [
                    np.NaN if group == "" else group for group in match_tuple]
                match_list.append(na_tuple)
                result_key = tuple(key_list + [match_i])
                index_list.append(result_key)
    if 0 < len(index_list):
        index = MultiIndex.from_tuples(
            index_list, names=arr.index.names + ["match"])
    else:
        index = None
    result = DataFrame(match_list, index, columns)
    return result


def str_get_dummies(arr, sep='|'):
    """
    Split each string in the Series by sep and return a frame of
    dummy/indicator variables.

    Parameters
    ----------
    sep : string, default "|"
        String to split on.

    Returns
    -------
    dummies : DataFrame

    Examples
    --------
    >>> Series(['a|b', 'a', 'a|c']).str.get_dummies()
       a  b  c
    0  1  1  0
    1  1  0  0
    2  1  0  1

    >>> 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

    See Also
    --------
    pandas.get_dummies
    """
    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 - set([""]))

    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. Equivalent to :meth:`str.join`.

    Parameters
    ----------
    sep : string
        Delimiter

    Returns
    -------
    joined : Series/Index of objects
    """
    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 :func:`re.findall`.

    Parameters
    ----------
    pat : string
        Pattern or regular expression
    flags : int, default 0 (no flags)
        re module flags, e.g. re.IGNORECASE

    Returns
    -------
    matches : Series/Index of lists

    See Also
    --------
    extractall : returns DataFrame with one column per capture group
    """
    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
    -------
    found : Series/Index of integer values
    """

    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 with an additional character to
    specified side.

    Parameters
    ----------
    width : int
        Minimum width of resulting string; additional characters will be filled
        with spaces
    side : {'left', 'right', 'both'}, default 'left'
    fillchar : str
        Additional character for filling, default is whitespace

    Returns
    -------
    padded : Series/Index of objects
    """

    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 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):
    """
    Split each string (a la re.split) in the Series/Index by given
    pattern, propagating NA values. Equivalent to :meth:`str.split`.

    Parameters
    ----------
    pat : string, default None
        String or regular expression to split on. If None, splits on whitespace
    n : int, default -1 (all)
        None, 0 and -1 will be interpreted as return all splits
    expand : bool, default False
        * If True, return DataFrame/MultiIndex expanding dimensionality.
        * If False, return Series/Index.

        .. versionadded:: 0.16.1
    return_type : deprecated, use `expand`

    Returns
    -------
    split : Series/Index or DataFrame/MultiIndex of objects
    """
    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):
    """
    Split each string in the Series/Index by the given delimiter
    string, starting at the end of the string and working to the front.
    Equivalent to :meth:`str.rsplit`.

    .. versionadded:: 0.16.2

    Parameters
    ----------
    pat : string, default None
        Separator to split on. If None, splits on whitespace
    n : int, default -1 (all)
        None, 0 and -1 will be interpreted as return all splits
    expand : bool, default False
        * If True, return DataFrame/MultiIndex expanding dimensionality.
        * If False, return Series/Index.

    Returns
    -------
    split : Series/Index or DataFrame/MultiIndex of objects
    """
    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/Index

    Parameters
    ----------
    start : int or None
    stop : int or None
    step : int or None

    Returns
    -------
    sliced : Series/Index of objects
    """
    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 slice of each string in the Series/Index with another
    string.

    Parameters
    ----------
    start : int or None
    stop : int or None
    repl : str or None
        String for replacement

    Returns
    -------
    replaced : Series/Index of objects
    """
    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
    -------
    stripped : Series/Index of objects
    """
    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
    -------
    wrapped : Series/Index of objects

    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
    """
    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
    -------
    translated : Series/Index of objects
    """
    if deletechars is None:
        f = lambda x: x.translate(table)
    else:
        from pandas import compat
        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 lists, tuples, or strings in each element in the
    Series/Index.

    Parameters
    ----------
    i : int
        Integer index (location)

    Returns
    -------
    items : Series/Index of objects
    """
    f = lambda x: x[i] if len(x) > i else 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
    -------
    decoded : Series/Index of objects
    """
    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._data, **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._data, pat)
        return self._wrap_result(result)

    def wrapper2(self, pat, flags=0, **kwargs):
        result = f(self._data, pat, flags=flags, **kwargs)
        return self._wrap_result(result)

    def wrapper3(self, pat, na=np.nan):
        result = f(self._data, 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._is_categorical = is_categorical_dtype(data)
        self._data = data.cat.categories if self._is_categorical else data
        # save orig to blow up categoricals to the right type
        self._orig = data
        self._freeze()

    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.notnull().any():
            yield g
            i += 1
            g = self.get(i)

    def _wrap_result(self, result, use_codes=True,
                     name=None, expand=None):

        from pandas.core.index import Index, 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:
            result = take_1d(result, self._orig.cat.codes)

        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 = False if result.ndim == 1 else True

        elif expand is True and not isinstance(self._orig, Index):
            # required when expand=True is explicitly specified
            # not needed when infered

            def cons_row(x):
                if is_list_like(x):
                    return x
                else:
                    return [x]

            result = [cons_row(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)
                return MultiIndex.from_tuples(result, names=name)
            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)

    @copy(str_cat)
    def cat(self, others=None, sep=None, na_rep=None):
        data = self._orig if self._is_categorical else self._data
        result = str_cat(data, others=others, sep=sep, na_rep=na_rep)
        return self._wrap_result(result, use_codes=(not self._is_categorical))

    @deprecate_kwarg('return_type', 'expand', mapping={'series': False,
                                                       'frame': True})
    @copy(str_split)
    def split(self, pat=None, n=-1, expand=False):
        result = str_split(self._data, pat, n=n)
        return self._wrap_result(result, expand=expand)

    @copy(str_rsplit)
    def rsplit(self, pat=None, n=-1, expand=False):
        result = str_rsplit(self._data, pat, n=n)
        return self._wrap_result(result, expand=expand)

    _shared_docs['str_partition'] = ("""
    Split the string at the %(side)s occurrence of `sep`, and return 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
    ----------
    pat : string, default whitespace
        String to split on.
    expand : bool, default True
        * If True, return DataFrame/MultiIndex expanding dimensionality.
        * If False, return Series/Index.

    Returns
    -------
    split : DataFrame/MultiIndex or Series/Index of objects

    See Also
    --------
    %(also)s

    Examples
    --------

    >>> s = Series(['A_B_C', 'D_E_F', 'X'])
    0    A_B_C
    1    D_E_F
    2        X
    dtype: object

    >>> s.str.partition('_')
       0  1    2
    0  A  _  B_C
    1  D  _  E_F
    2  X

    >>> s.str.rpartition('_')
         0  1  2
    0  A_B  _  C
    1  D_E  _  F
    2          X
    """)

    @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`'
    })
    def partition(self, pat=' ', expand=True):
        f = lambda x: x.partition(pat)
        result = _na_map(f, self._data)
        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`'
    })
    def rpartition(self, pat=' ', expand=True):
        f = lambda x: x.rpartition(pat)
        result = _na_map(f, self._data)
        return self._wrap_result(result, expand=expand)

    @copy(str_get)
    def get(self, i):
        result = str_get(self._data, i)
        return self._wrap_result(result)

    @copy(str_join)
    def join(self, sep):
        result = str_join(self._data, 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._data, pat, case=case, flags=flags, na=na,
                              regex=regex)
        return self._wrap_result(result)

    @copy(str_match)
    def match(self, pat, case=True, flags=0, na=np.nan, as_indexer=False):
        result = str_match(self._data, pat, case=case, flags=flags, na=na,
                           as_indexer=as_indexer)
        return self._wrap_result(result)

    @copy(str_replace)
    def replace(self, pat, repl, n=-1, case=True, flags=0):
        result = str_replace(self._data, pat, repl, n=n, case=case,
                             flags=flags)
        return self._wrap_result(result)

    @copy(str_repeat)
    def repeat(self, repeats):
        result = str_repeat(self._data, repeats)
        return self._wrap_result(result)

    @copy(str_pad)
    def pad(self, width, side='left', fillchar=' '):
        result = str_pad(self._data, 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):
        """"
        Filling left side of strings in the Series/Index with 0.
        Equivalent to :meth:`str.zfill`.

        Parameters
        ----------
        width : int
            Minimum width of resulting string; additional characters will be
            filled with 0

        Returns
        -------
        filled : Series/Index of objects
        """
        result = str_pad(self._data, 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._data, 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._data, start, stop, repl)
        return self._wrap_result(result)

    @copy(str_decode)
    def decode(self, encoding, errors="strict"):
        result = str_decode(self._data, encoding, errors)
        return self._wrap_result(result)

    @copy(str_encode)
    def encode(self, encoding, errors="strict"):
        result = str_encode(self._data, encoding, errors)
        return self._wrap_result(result)

    _shared_docs['str_strip'] = ("""
    Strip whitespace (including newlines) from each string in the
    Series/Index from %(side)s. Equivalent to :meth:`str.%(method)s`.

    Returns
    -------
    stripped : Series/Index of objects
    """)

    @Appender(_shared_docs['str_strip'] % dict(side='left and right sides',
                                               method='strip'))
    def strip(self, to_strip=None):
        result = str_strip(self._data, 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._data, 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._data, to_strip, side='right')
        return self._wrap_result(result)

    @copy(str_wrap)
    def wrap(self, width, **kwargs):
        result = str_wrap(self._data, 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._data
        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._data, 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=None):
        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._data, 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._data, 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, compat.u_safe(x))
        result = _na_map(f, self._data)
        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._data, 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._data, sub, start=start, end=end, side='right')
        return self._wrap_result(result)

    _shared_docs['len'] = ("""
    Compute length of each string in the Series/Index.

    Returns
    -------
    lengths : Series/Index of integer values
    """)
    len = _noarg_wrapper(len, docstring=_shared_docs['len'], dtype=int)

    _shared_docs['casemethods'] = ("""
    Convert strings in the Series/Index to %(type)s.
    Equivalent to :meth:`str.%(method)s`.

    Returns
    -------
    converted : Series/Index of objects
    """)
    _shared_docs['lower'] = dict(type='lowercase', method='lower')
    _shared_docs['upper'] = dict(type='uppercase', method='upper')
    _shared_docs['title'] = dict(type='titlecase', method='title')
    _shared_docs['capitalize'] = dict(type='be capitalized',
                                      method='capitalize')
    _shared_docs['swapcase'] = dict(type='be swapcased', method='swapcase')
    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'])

    _shared_docs['ismethods'] = ("""
    Check whether all characters in each string in the Series/Index
    are %(type)s. Equivalent to :meth:`str.%(method)s`.

    Returns
    -------
    is : Series/array of boolean values
    """)
    _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: compat.u_safe(x).isnumeric(),
                               docstring=_shared_docs['ismethods'] %
                               _shared_docs['isnumeric'])
    isdecimal = _noarg_wrapper(lambda x: compat.u_safe(x).isdecimal(),
                               docstring=_shared_docs['ismethods'] %
                               _shared_docs['isdecimal'])


class StringAccessorMixin(object):
    """ Mixin to add a `.str` acessor to the class."""

    # string methods
    def _make_str_accessor(self):
        from pandas.core.series import Series
        from pandas.core.index import Index
        if (isinstance(self, Series) and
                not ((is_categorical_dtype(self.dtype) and
                      is_object_dtype(self.values.categories)) or
                     (is_object_dtype(self.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(self, Index):
            # see scc/inferrence.pyx which can contain string values
            allowed_types = ('string', 'unicode', 'mixed', 'mixed-integer')
            if self.inferred_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 self.nlevels > 1:
                message = ("Can only use .str accessor with Index, not "
                           "MultiIndex")
                raise AttributeError(message)
        return StringMethods(self)

    str = AccessorProperty(StringMethods, _make_str_accessor)

    def _dir_additions(self):
        return set()

    def _dir_deletions(self):
        try:
            getattr(self, 'str')
        except AttributeError:
            return set(['str'])
        return set()
