import numpy as np

from pandas.compat import zip
from pandas.core.common import (isnull, _values_from_object, is_bool_dtype, is_list_like,
                                is_categorical_dtype, is_object_dtype, 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


_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, an NA in any array will propagate

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

    Examples
    --------
    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():
            return np.nan
        return sep.join(np.where(mask, na_rep, arr))


def _length_check(others):
    n = None
    for x in others:
        if n is None:
            n = len(x)
        elif len(x) != n:
            raise ValueError('All arrays must be same length')

    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 np.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 : analagous, 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 str_extract(arr, pat, flags=0):
    """
    Find groups in each string in the Series using passed regular
    expression.

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

    Returns
    -------
    extracted groups : Series (one group) or DataFrame (multiple groups)
        Note that dtype of the result is always object, even when no match is
        found and the result is a Series or DataFrame containing only NaN
        values.

    Examples
    --------
    A pattern with one group will return a Series. Non-matches will be NaN.

    >>> Series(['a1', 'b2', 'c3']).str.extract('[ab](\d)')
    0      1
    1      2
    2    NaN
    dtype: object

    A pattern with more than one group will return a DataFrame.

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

    A pattern may contain optional groups.

    >>> Series(['a1', 'b2', 'c3']).str.extract('([ab])?(\d)')
         0  1
    0    a  1
    1    b  2
    2  NaN  3

    Named groups will become column names in the result.

    >>> Series(['a1', 'b2', 'c3']).str.extract('(?P<letter>[ab])(?P<digit>\d)')
      letter digit
    0      a     1
    1      b     2
    2    NaN   NaN

    """
    from pandas.core.series import Series
    from pandas.core.frame import DataFrame
    from pandas.core.index import Index

    regex = re.compile(pat, flags=flags)
    # just to be safe, check this
    if regex.groups == 0:
        raise ValueError("This pattern contains no groups to capture.")
    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

    if regex.groups == 1:
        result = np.array([f(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([f(val) for val in arr],
                               columns=columns,
                               index=arr.index,
                               dtype=object)
    return result, name


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
    """
    from pandas.core.frame import DataFrame
    from pandas.core.index import Index

    # GH9980, Index.str does not support get_dummies() as it returns a frame
    if isinstance(arr, Index):
        raise TypeError("get_dummies is not supported for string methods on Index")

    # TODO remove this hack?
    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 DataFrame(dummies, arr.index, 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
    """
    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 to unicode
    using indicated encoding. Equivalent to :meth:`str.decode`.

    Parameters
    ----------
    encoding : string
    errors : string

    Returns
    -------
    decoded : Series/Index of objects
    """
    f = lambda x: x.decode(encoding, errors)
    return _na_map(f, arr)


def str_encode(arr, encoding, errors="strict"):
    """
    Encode character string in the Series/Index to some other encoding
    using indicated encoding. Equivalent to :meth:`str.encode`.

    Parameters
    ----------
    encoding : string
    errors : string

    Returns
    -------
    encoded : Series/Index of objects
    """
    f = lambda x: x.encode(encoding, errors)
    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):

        # 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)

        # leave as it is to keep extract and get_dummies results
        # can be merged to _wrap_result_expand in v0.17
        from pandas.core.series import Series
        from pandas.core.frame import DataFrame
        from pandas.core.index import Index

        if not hasattr(result, 'ndim'):
            return result
        name = name or getattr(result, 'name', None) or self._orig.name

        if result.ndim == 1:
            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
                return Index(result, name=name)
            return Series(result, index=self._orig.index, name=name)
        else:
            assert result.ndim < 3
            return DataFrame(result, index=self._orig.index)

    def _wrap_result_expand(self, result, expand=False):
        if not isinstance(expand, bool):
            raise ValueError("expand must be True or False")

        # for category, we do the stuff on the categories, so blow it up
        # to the full series again
        if self._is_categorical:
            result = take_1d(result, self._orig.cat.codes)

        from pandas.core.index import Index, MultiIndex
        if not hasattr(result, 'ndim'):
            return result

        if isinstance(self._orig, Index):
            name = getattr(result, 'name', None)
            # if result is a boolean np.array, return the np.array
            # instead of wrapping it into a boolean Index (GH 8875)
            if hasattr(result, 'dtype') and 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:
                def cons_row(x):
                    if is_list_like(x):
                        return x
                    else:
                        return [ x ]
                cons = self._orig._constructor_expanddim
                data = [cons_row(x) for x in result]
                return cons(data, index=index)
            else:
                name = getattr(result, 'name', None)
                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_expand(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_expand(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_expand(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_expand(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 = str_get_dummies(data, sep)
        return self._wrap_result(result, use_codes=(not self._is_categorical))

    @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):
        result, name = str_extract(self._data, pat, flags=flags)
        return self._wrap_result(result, name=name)

    _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()
