Helper function for to_datetime. Performs the conversions of 1D listlike
    of dates

    Parameters
    ----------
    arg : list, tuple, ndarray, Series, Index
        date to be parced
    box : boolean
        True boxes result as an Index-like, False returns an ndarray
    name : object
        None or string for the Index name
    tz : object
        None or 'utc'
    unit : string
        None or string of the frequency of the passed data
    errors : string
        error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
    infer_datetime_format : boolean
        inferring format behavior from to_datetime
    dayfirst : boolean
        dayfirst parsing behavior from to_datetime
    yearfirst : boolean
        yearfirst parsing behavior from to_datetime
    exact : boolean
        exact format matching behavior from to_datetime

    Returns
    -------
    ndarray of parsed dates
        Returns:

        - Index-like if box=True
        - ndarray of Timestamps if box=False

def _convert_listlike_datetimes(arg, box, format, name=None, tz=None,
                                unit=None, errors=None,
                                infer_datetime_format=None, dayfirst=None,
                                yearfirst=None, exact=None):
    """
    Helper function for to_datetime. Performs the conversions of 1D listlike
    of dates

    Parameters
    ----------
    arg : list, tuple, ndarray, Series, Index
        date to be parced
    box : boolean
        True boxes result as an Index-like, False returns an ndarray
    name : object
        None or string for the Index name
    tz : object
        None or 'utc'
    unit : string
        None or string of the frequency of the passed data
    errors : string
        error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
    infer_datetime_format : boolean
        inferring format behavior from to_datetime
    dayfirst : boolean
        dayfirst parsing behavior from to_datetime
    yearfirst : boolean
        yearfirst parsing behavior from to_datetime
    exact : boolean
        exact format matching behavior from to_datetime

    Returns
    -------
    ndarray of parsed dates
        Returns:

        - Index-like if box=True
        - ndarray of Timestamps if box=False
    """
    from pandas import DatetimeIndex
    from pandas.core.arrays import DatetimeArray
    from pandas.core.arrays.datetimes import (
        maybe_convert_dtype, objects_to_datetime64ns)

    if isinstance(arg, (list, tuple)):
        arg = np.array(arg, dtype='O')

    # these are shortcutable
    if is_datetime64tz_dtype(arg):
        if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
            return DatetimeIndex(arg, tz=tz, name=name)
        if tz == 'utc':
            arg = arg.tz_convert(None).tz_localize(tz)
        return arg

    elif is_datetime64_ns_dtype(arg):
        if box and not isinstance(arg, (DatetimeArray, DatetimeIndex)):
            try:
                return DatetimeIndex(arg, tz=tz, name=name)
            except ValueError:
                pass

        return arg

    elif unit is not None:
        if format is not None:
            raise ValueError("cannot specify both format and unit")
        arg = getattr(arg, 'values', arg)
        result = tslib.array_with_unit_to_datetime(arg, unit,
                                                   errors=errors)
        if box:
            if errors == 'ignore':
                from pandas import Index
                result = Index(result, name=name)
                # GH 23758: We may still need to localize the result with tz
                try:
                    return result.tz_localize(tz)
                except AttributeError:
                    return result

            return DatetimeIndex(result, tz=tz, name=name)
        return result
    elif getattr(arg, 'ndim', 1) > 1:
        raise TypeError('arg must be a string, datetime, list, tuple, '
                        '1-d array, or Series')

    # warn if passing timedelta64, raise for PeriodDtype
    # NB: this must come after unit transformation
    orig_arg = arg
    arg, _ = maybe_convert_dtype(arg, copy=False)

    arg = ensure_object(arg)
    require_iso8601 = False

    if infer_datetime_format and format is None:
        format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)

    if format is not None:
        # There is a special fast-path for iso8601 formatted
        # datetime strings, so in those cases don't use the inferred
        # format because this path makes process slower in this
        # special case
        format_is_iso8601 = _format_is_iso(format)
        if format_is_iso8601:
            require_iso8601 = not infer_datetime_format
            format = None

    tz_parsed = None
    result = None

    if format is not None:
        try:
            # shortcut formatting here
            if format == '%Y%m%d':
                try:
                    # pass orig_arg as float-dtype may have been converted to
                    # datetime64[ns]
                    orig_arg = ensure_object(orig_arg)
                    result = _attempt_YYYYMMDD(orig_arg, errors=errors)
                except (ValueError, TypeError, tslibs.OutOfBoundsDatetime):
                    raise ValueError("cannot convert the input to "
                                     "'%Y%m%d' date format")

            # fallback
            if result is None:
                try:
                    result, timezones = array_strptime(
                        arg, format, exact=exact, errors=errors)
                    if '%Z' in format or '%z' in format:
                        return _return_parsed_timezone_results(
                            result, timezones, box, tz, name)
                except tslibs.OutOfBoundsDatetime:
                    if errors == 'raise':
                        raise
                    elif errors == 'coerce':
                        result = np.empty(arg.shape, dtype='M8[ns]')
                        iresult = result.view('i8')
                        iresult.fill(tslibs.iNaT)
                    else:
                        result = arg
                except ValueError:
                    # if format was inferred, try falling back
                    # to array_to_datetime - terminate here
                    # for specified formats
                    if not infer_datetime_format:
                        if errors == 'raise':
                            raise
                        elif errors == 'coerce':
                            result = np.empty(arg.shape, dtype='M8[ns]')
                            iresult = result.view('i8')
                            iresult.fill(tslibs.iNaT)
                        else:
                            result = arg
        except ValueError as e:
            # Fallback to try to convert datetime objects if timezone-aware
            #  datetime objects are found without passing `utc=True`
            try:
                values, tz = conversion.datetime_to_datetime64(arg)
                return DatetimeIndex._simple_new(values, name=name, tz=tz)
            except (ValueError, TypeError):
                raise e

    if result is None:
        assert format is None or infer_datetime_format
        utc = tz == 'utc'
        result, tz_parsed = objects_to_datetime64ns(
            arg, dayfirst=dayfirst, yearfirst=yearfirst,
            utc=utc, errors=errors, require_iso8601=require_iso8601,
            allow_object=True)

    if tz_parsed is not None:
        if box:
            # We can take a shortcut since the datetime64 numpy array
            # is in UTC
            return DatetimeIndex._simple_new(result, name=name,
                                             tz=tz_parsed)
        else:
            # Convert the datetime64 numpy array to an numpy array
            # of datetime objects
            result = [Timestamp(ts, tz=tz_parsed).to_pydatetime()
                      for ts in result]
            return np.array(result, dtype=object)

    if box:
        # Ensure we return an Index in all cases where box=True
        if is_datetime64_dtype(result):
            return DatetimeIndex(result, tz=tz, name=name)
        elif is_object_dtype(result):
            # e.g. an Index of datetime objects
            from pandas import Index
            return Index(result, name=name)
    return result