# -*- coding: utf-8 -*-
import numpy as np

from pandas.compat import zip
from pandas.core.dtypes.generic import ABCSeries, ABCIndex
from pandas.core.dtypes.missing import isna
from pandas.core.dtypes.common import (
    ensure_object,
    is_bool_dtype,
    is_categorical_dtype,
    is_object_dtype,
    is_string_like,
    is_list_like,
    is_scalar,
    is_integer,
    is_re)

import pandas.core.common as com
from pandas.core.algorithms import take_1d
import pandas.compat as compat
from pandas.core.base import NoNewAttributesMixin
from pandas.util._decorators import Appender
import re
import pandas._libs.lib as lib
import pandas._libs.ops as libops
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 cat_core(list_of_columns, sep):
    """
    Auxiliary function for :meth:`str.cat`

    Parameters
    ----------
    list_of_columns : list of numpy arrays
        List of arrays to be concatenated with sep;
        these arrays may not contain NaNs!
    sep : string
        The separator string for concatenating the columns

    Returns
    -------
    nd.array
        The concatenation of list_of_columns with sep
    """
    list_with_sep = [sep] * (2 * len(list_of_columns) - 1)
    list_with_sep[::2] = list_of_columns
    return np.sum(list_with_sep, axis=0)


def _na_map(f, arr, na_result=np.nan, dtype=object):
    # should really _check_ for NA
    return _map(f, arr, na_mask=True, na_value=na_result, dtype=dtype)


def _map(f, arr, na_mask=False, na_value=np.nan, dtype=object):
    if not len(arr):
        return np.ndarray(0, dtype=dtype)

    if isinstance(arr, ABCSeries):
        arr = arr.values
    if not isinstance(arr, np.ndarray):
        arr = np.asarray(arr, dtype=object)
    if na_mask:
        mask = isna(arr)
        try:
            convert = not all(mask)
            result = lib.map_infer_mask(arr, f, mask.view(np.uint8), convert)
        except (TypeError, AttributeError) as e:
            # Reraise the exception if callable `f` got wrong number of args.
            # The user may want to be warned by this, instead of getting NaN
            if compat.PY2:
                p_err = r'takes (no|(exactly|at (least|most)) ?\d+) arguments?'
            else:
                p_err = (r'((takes)|(missing)) (?(2)from \d+ to )?\d+ '
                         r'(?(3)required )positional arguments?')

            if len(e.args) >= 1 and re.search(p_err, e.args[0]):
                raise e

            def g(x):
       
         try:
                    return f(x)
                except (TypeError, AttributeError):
                    return na_value

            return _map(g, arr, dtype=dtype)
        if na_value is not np.nan:
            np.putmask(result, mask, na_value)
            if result.dtype == object:
       
         result = lib.maybe_convert_objects(result)
        return result
    else:
        return lib.map_infer(arr, f)


def str_count(arr, pat, flags=0):
    """
    Count occurrences of pattern in each string of the Series/Index.

    This function is used to count the number of times a particular regex
    pattern is repeated in each of the string elements of the
    :class:`~pandas.Series`.

    Parameters
    ----------
    pat : str
        Valid regular expression.
    flags : int, default 0, meaning no flags
        Flags for the `re` module. For a complete list, `see here
        <https://docs.python.org/3/howto/regex.html#compilation-flags>`_.
    **kwargs
        For compatibility with other string methods. Not used.

    Returns
    -------
    counts : Series or Index
        Same type as the calling object containing the integer counts.

    Notes
    -----
    Some characters need to be escaped when passing in `pat`.
    eg. ``'$'`` has a special meaning in regex and must be escaped when
    finding this literal character.

    See Also
    --------
    re : Standard library module for regular expressions.
    str.count : Standard library version, without regular expression support.

    Examples
    --------
    >>> s = pd.Series(['A', 'B', 'Aaba', 'Baca', np.nan, 'CABA', 'cat'])
    >>> s.str.count('a')
    0    0.0
    1    0.0
    2    2.0
    3    2.0
    4    NaN
    5    0.0
    6    1.0
    dtype: float64

    Escape ``'$'`` to find the literal dollar sign.

    >>> s = pd.Series(['$', 'B', 'Aab$', '$$ca', 'C$B$', 'cat'])
    >>> s.str.count('\\$')
    0    1
    1    0
    2    1
    3    2
    4    2
    5    0
    dtype: int64

    This is also available on Index

    >>> pd.Index(['A', 'A', 'Aaba', 'cat']).str.count('a')
    Int64Index([0, 0, 2, 1], dtype='int64')
    """
    regex = re.compile(pat, flags=flags)
    f = lambda x: len(regex.findall(x))
    return _na_map(f, arr, dtype=int)


def str_contains(arr, pat, case=True, flags=0, na=np.nan, regex=True):
    """
    Test if pattern or regex is contained within a string of a Series or Index.

    Return boolean Series or Index based on whether a given pattern or regex is
    contained within a string of a Series or Index.

    Parameters
    ----------
    pat : str
        Character sequence or regular expression.
    case : bool, default True
        If True, case sensitive.
    flags : int, default 0 (no flags)
        Flags to pass through to the re module, e.g. re.IGNORECASE.
    na : default NaN
        Fill value for missing values.
    regex : bool, default True
        If True, assumes the pat is a regular expression.

        If False, treats the pat as a literal string.

    Returns
    -------
    Series or Index of boolean values
        A Series or Index of boolean values indicating whether the
        given pattern is contained within the string of each element
        of the Series or Index.

    See Also
    --------
    match : analogous, but stricter, relying on re.match instead of re.search
    Series.str.startswith : Test if the start of each string element matches a
        pattern.
    Series.str.endswith : Same as startswith, but tests the end of string.

    Examples
    --------

    Returning a Series of booleans using only a literal pattern.

    >>> s1 = pd.Series(['Mouse', 'dog', 'house and parrot', '23', np.NaN])
    >>> s1.str.contains('og', regex=False)
    0    False
    1     True
    2    False
    3    False
    4      NaN
    dtype: object

    Returning an Index of booleans using only a literal pattern.

    >>> ind = pd.Index(['Mouse', 'dog', 'house and parrot', '23.0', np.NaN])
    >>> ind.str.contains('23', regex=False)
    Index([False, False, False, True, nan], dtype='object')

    Specifying case sensitivity using `case`.

    >>> s1.str.contains('oG', case=True, regex=True)
    0    False
    1    False
    2    False
    3    False
    4      NaN
    dtype: object

    Specifying `na` to be `False` instead of `NaN` replaces NaN values
    with `False`. If Series or Index does not contain NaN values
    the resultant dtype will be `bool`, otherwise, an `object` dtype.

    >>> s1.str.contains('og', na=False, regex=True)
    0    False
    1     True
    2    False
    3    False
    4    False
    dtype: bool

    Returning 'house' or 'dog' when either expression occurs in a string.

    >>> s1.str.contains('house|dog', regex=True)
    0    False
    1     True
    2     True
    3    False
    4      NaN
    dtype: object

    Ignoring case sensitivity using `flags` with regex.

    >>> import re
    >>> s1.str.contains('PARROT', flags=re.IGNORECASE, regex=True)
    0    False
    1    False
    2     True
    3    False
    4      NaN
    dtype: object

    Returning any digit using regular expression.

    >>> s1.str.contains('\\d', regex=True)
    0    False
    1    False
    2    False
    3     True
    4      NaN
    dtype: object

    Ensure `pat` is a not a literal pattern when `regex` is set to True.
    Note in the following example one might expect only `s2[1]` and `s2[3]` to
    return `True`. However, '.0' as a regex matches any character
    f