text: from datetime import datetime, time, timedelta
  import operator
  import warnings
  
  import numpy as np
  
  from pandas._libs import Timestamp, index as libindex, lib, tslib as libts
  import pandas._libs.join as libjoin
  from pandas._libs.tslibs import ccalendar, fields, parsing, timezones
  from pandas.util._decorators import Appender, Substitution, cache_readonly
  
  from pandas.core.dtypes.common import (
      _NS_DTYPE, ensure_int64, is_float, is_integer, is_list_like, is_scalar,
      is_string_like)
  import pandas.core.dtypes.concat as _concat
  from pandas.core.dtypes.dtypes import DatetimeTZDtype
  from pandas.core.dtypes.missing import isna
  
  from pandas.core.accessor import delegate_names
  from pandas.core.arrays.datetimes import (
      DatetimeArray, _to_M8, tz_to_dtype, validate_tz_from_dtype)
  from pandas.core.base import _shared_docs
  import pandas.core.common as com
  from pandas.core.indexes.base import Index
  from pandas.core.indexes.datetimelike import (
      DatetimeIndexOpsMixin, DatetimelikeDelegateMixin, ea_passthrough)
  from pandas.core.indexes.numeric import Int64Index
  from pandas.core.ops import get_op_result_name
  import pandas.core.tools.datetimes as tools
  
  from pandas.tseries.frequencies import Resolution, to_offset
  from pandas.tseries.offsets import CDay, Nano, prefix_mapping
  
  
  def _new_DatetimeIndex(cls, d):
      """ This is called upon unpickling, rather than the default which doesn't
      have arguments and breaks __new__ """
  
      if "data" in d and not isinstance(d["data"], DatetimeIndex):
          # Avoid need to verify integrity by calling simple_new directly
          data = d.pop("data")
          result = cls._simple_new(data, **d)
      else:
          with warnings.catch_warnings():
              # we ignore warnings from passing verify_integrity=False
              # TODO: If we knew what was going in to **d, we might be able to
              #  go through _simple_new instead
              warnings.simplefilter("ignore")
              result = cls.__new__(cls, verify_integrity=False, **d)
  
      return result
  
  
  class DatetimeDelegateMixin(DatetimelikeDelegateMixin):
      # Most attrs are dispatched via datetimelike_{ops,methods}
      # Some are "raw" methods, the result is not not re-boxed in an Index
      # We also have a few "extra" attrs, which may or may not be raw,
      # which we we dont' want to expose in the .dt accessor.
      _extra_methods = [
          'to_period',
          'to_perioddelta',
          'to_julian_date',
      ]
      _extra_raw_methods = [
          'to_pydatetime',
          '_local_timestamps',
          '_has_same_tz',
      ]
      _extra_raw_properties = [
          '_box_func',
          'tz', 'tzinfo',
      ]
      _delegated_properties = (
          DatetimeArray._datetimelike_ops + _extra_raw_properties
      )
      _delegated_methods = (
          DatetimeArray._datetimelike_methods + _extra_methods +
          _extra_raw_methods
      )
      _raw_properties = {
          'date',
          'time',
          'timetz',
      } | set(DatetimeArray._bool_ops) | set(_extra_raw_properties)
      _raw_methods = set(_extra_raw_methods)
      _delegate_class = DatetimeArray
  
  
  @delegate_names(DatetimeArray,
                  DatetimeDelegateMixin._delegated_properties,
                  typ="property")
  @delegate_names(DatetimeArray,
                  DatetimeDelegateMixin._delegated_methods,
                  typ="method", overwrite=False)
  class DatetimeIndex(DatetimeIndexOpsMixin, Int64Index, DatetimeDelegateMixin):
      """
      Immutable ndarray of datetime64 data, represented internally as int64, and
      which can be boxed to Timestamp objects that are subclasses of datetime and
      carry metadata such as frequency information.
  
      Parameters
      ----------
      data  : array-like (1-dimensional), optional
          Optional datetime-like data to construct index with
      copy  : bool
          Make a copy of input ndarray
      freq : string or pandas offset object, optional
          One of pandas date offset strings or corresponding objects. The string
          'infer' can be passed in order to set the frequency of the index as the
          inferred frequency upon creation
  
      start : starting value, datetime-like, optional
          If data is None, start is used as the start point in generating regular
          timestamp data.
  
          .. deprecated:: 0.24.0
  
      periods  : int, optional, > 0
          Number of periods to generate, if generating index. Takes precedence
          over end argument
  
          .. deprecated:: 0.24.0
  
      end : end time, datetime-like, optional
          If periods is none, generated index will extend to first conforming
          time on or just past end argument
  
          .. deprecated:: 0.24.0
  
      closed : string or None, default None
          Make the interval closed with respect to the given frequency to
          the 'left', 'right', or both sides (None)
  
          .. deprecated:: 0.24. 0
  
      tz : pytz.timezone or dateutil.tz.tzfile
      ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
          When clocks moved backward due to DST, ambiguous times may arise.
          For example in Central European Time (UTC+01), when going from 03:00
          DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC
          and at 01:30:00 UTC. In such a situation, the `ambiguous` parameter
          dictates how ambiguous times should be handled.
  
          - 'infer' will attempt to infer fall dst-transition hours based on
            order
          - bool-ndarray where True signifies a DST time, False signifies a
            non-DST time (note that this flag is only applicable for ambiguous
            times)
          - 'NaT' will return NaT where there are ambiguous times
          - 'raise' will raise an AmbiguousTimeError if there are ambiguous times
      name : object
          Name to be stored in the index
      dayfirst : bool, default False
          If True, parse dates in `data` with the day first order
      yearfirst : bool, default False
          If True parse dates in `data` with the year first order
  
      Attributes
      ----------
      year
      month
      day
      hour
      minute
      second
      microsecond
      nanosecond
      date
      time
      timetz
      dayofyear
      weekofyear
      week
      dayofweek
      weekday
      quarter
      tz
      freq
      freqstr
      is_month_start
      is_month_end
      is_quarter_start
      is_quarter_end
      is_year_start
      is_year_end
      is_leap_year
      inferred_freq
  
      Methods
      -------
      normalize
      strftime
      snap
      tz_convert
      tz_localize
      round
      floor
      ceil
      to_period
      to_perioddelta
      to_pydatetime
      to_series
      to_frame
      month_name
      day_name
  
      See Also
      --------
      Index : The base pandas Index type.
      TimedeltaIndex : Index of timedelta64 data.
      PeriodIndex : Index of Period data.
      to_datetime : Convert argument to datetime.
      date_range : Create a fixed-frequency DatetimeIndex.
  
      Notes
      -----
      To learn more about the frequency strings, please see `this link
      <http://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
  
      Creating a DatetimeIndex based on `start`, `periods`, and `end` has
      been deprecated in favor of :func:`date_range`.
      """
      _typ = 'datetimeindex'
      _join_precedence = 10
  
      def _join_i8_wrapper(joinf, **kwargs):
          return DatetimeIndexOpsMixin._join_i8_wrapper(joinf, dtype='M8[ns]',
                                                        **kwargs)
  
      _inner_indexer = _join_i8_wrapper(libjoin.inner_join_indexer_int64)
      _outer_indexer = _join_i8_wrapper(libjoin.outer_join_indexer_int64)
      _left_indexer = _join_i8_wrapper(libjoin.left_join_indexer_int64)
      _left_indexer_unique = _join_i8_wrapper(
          libjoin.left_join_indexer_unique_int64, with_indexers=False)
  
      _engine_type = libindex.DatetimeEngine
  
      _tz = None
      _freq = None
      _comparables = ['name', 'freqstr', 'tz']
      _attributes = ['name', 'tz', 'freq']
  
      _is_numeric_dtype = False
      _infer_as_myclass = True
  
      # some things like freq inference make use of these attributes.
      _bool_ops = DatetimeArray._bool_ops
      _object_ops = DatetimeArray._object_ops
      _field_ops = DatetimeArray._field_ops
      _datetimelike_ops = DatetimeArray._datetimelike_ops
      _datetimelike_methods = DatetimeArray._datetimelike_methods
  
      # --------------------------------------------------------------------
      # Constructors
  
      def __new__(cls, data=None,
                  freq=None, start=None, end=None, periods=None, tz=None,
                  normalize=False, closed=None, ambiguous='raise',
                  dayfirst=False, yearfirst=False, dtype=None,
                  copy=False, name=None, verify_integrity=None):
  
          if verify_integrity is not None:
              warnings.warn("The 'verify_integrity' argument is deprecated, "
                            "will be removed in a future version.",
                            FutureWarning, stacklevel=2)
          else:
              verify_integrity = True
  
          if data is None:
              dtarr = DatetimeArray._generate_range(
                  start, end, periods,
                  freq=freq, tz=tz, normalize=normalize,
                  closed=closed, ambiguous=ambiguous)
              warnings.warn("Creating a DatetimeIndex by passing range "
                            "endpoints is deprecated.  Use "
                            "`pandas.date_range` instead.",
                            FutureWarning, stacklevel=2)
              return cls._simple_new(
                  dtarr._data, freq=dtarr.freq, tz=dtarr.tz, name=name)
  
          if is_scalar(data):
              raise TypeError("{cls}() must be called with a "
                              "collection of some kind, {data} was passed"
                              .format(cls=cls.__name__, data=repr(data)))
  
          # - Cases checked above all return/raise before reaching here - #
  
          if name is None and hasattr(data, 'name'):
              name = data.name
  
          dtarr = DatetimeArray._from_sequence(
              data, dtype=dtype, copy=copy, tz=tz, freq=freq,
              dayfirst=dayfirst, yearfirst=yearfirst, ambiguous=ambiguous,
              int_as_wall_time=True)
  
          subarr = cls._simple_new(dtarr, name=name,
                                   freq=dtarr.freq, tz=dtarr.tz)
          return subarr
  
      @classmethod
      def _simple_new(cls, values, name=None, freq=None, tz=None, dtype=None):
          """
          we require the we have a dtype compat for the values
          if we are passed a non-dtype compat, then coerce using the constructor
          """
          if isinstance(values, DatetimeArray):
              if tz:
                  tz = validate_tz_from_dtype(dtype, tz)
                  dtype = DatetimeTZDtype(tz=tz)
              elif dtype is None:
                  dtype = _NS_DTYPE
  
              values = DatetimeArray(values, freq=freq, dtype=dtype)
              tz = values.tz
              freq = values.freq
              values = values._data
  
          # DatetimeArray._simple_new will accept either i8 or M8[ns] dtypes
          if isinstance(values, DatetimeIndex):
              values = values._data
  
          dtype = tz_to_dtype(tz)
          dtarr = DatetimeArray._simple_new(values, freq=freq, dtype=dtype)
          assert isinstance(dtarr, DatetimeArray)
  
          result = object.__new__(cls)
          result._data = dtarr
          result.name = name
          # For groupby perf. See note in indexes/base about _index_data
          result._index_data = dtarr._data
          result._reset_identity()
          return result
  
      # --------------------------------------------------------------------
  
      def __array__(self, dtype=None):
          if (dtype is None and isinstance(self._data, DatetimeArray)
                  and getattr(self.dtype, 'tz', None)):
              msg = (
                  "Converting timezone-aware DatetimeArray to timezone-naive "
                  "ndarray with 'datetime64[ns]' dtype. In the future, this "
                  "will return an ndarray with 'object' dtype where each "
                  "element is a 'pandas.Timestamp' with the correct 'tz'.\n\t"
                  "To accept the future behavior, pass 'dtype=object'.\n\t"
                  "To keep the old behavior, pass 'dtype=\"datetime64[ns]\"'."
              )
              warnings.warn(msg, FutureWarning, stacklevel=3)
              dtype = 'M8[ns]'
          return np.asarray(self._data, dtype=dtype)
  
      @property
      def dtype(self):
          return self._data.dtype
  
      @property
      def tz(self):
          # GH 18595
          return self._data.tz
  
      @tz.setter
      def tz(self, value):
          # GH 3746: Prevent localizing or converting the index by setting tz
          raise AttributeError("Cannot directly set timezone. Use tz_localize() "
                               "or tz_convert() as appropriate")
  
      tzinfo = tz
  
      @cache_readonly
      def _is_dates_only(self):
          """Return a boolean if we are only dates (and don't have a timezone)"""
          from pandas.io.formats.format import _is_dates_only
          return _is_dates_only(self.values) and self.tz is None
  
      def __reduce__(self):
  
          # we use a special reudce here because we need
          # to simply set the .tz (and not reinterpret it)
  
          d = dict(data=self._data)
          d.update(self._get_attributes_dict())
          return _new_DatetimeIndex, (self.__class__, d), None
  
      def __setstate__(self, state):
          """Necessary for making this object picklable"""
          if isinstance(state, dict):
              super().__setstate__(state)
  
          elif isinstance(state, tuple):
  
              # < 0.15 compat
              if len(state) == 2:
                  nd_state, own_state = state
                  data = np.empty(nd_state[1], dtype=nd_state[2])
                  np.ndarray.__setstate__(data, nd_state)
  
                  freq = own_state[1]
                  tz = timezones.tz_standardize(own_state[2])
                  dtype = tz_to_dtype(tz)
                  dtarr = DatetimeArray._simple_new(data, freq=freq, dtype=dtype)
  
                  self.name = own_state[0]
  
              else:  # pragma: no cover
                  data = np.empty(state)
                  np.ndarray.__setstate__(data, state)
                  dtarr = DatetimeArray(data)
  
              self._data = dtarr
              self._reset_identity()
  
          else:
              raise Exception("invalid pickle state")
      _unpickle_compat = __setstate__
  
      def _convert_for_op(self, value):
          """ Convert value to be insertable to ndarray """
          if self._has_same_tz(value):
              return _to_M8(value)
          raise ValueError('Passed item and index have different timezone')
  
      def _maybe_update_attributes(self, attrs):
          """ Update Index attributes (e.g. freq) depending on op """
          freq = attrs.get('freq', None)
          if freq is not None:
              # no need to infer if freq is None
              attrs['freq'] = 'infer'
          return attrs
  
      # --------------------------------------------------------------------
      # Rendering Methods
  
      def _mpl_repr(self):
          # how to represent ourselves to matplotlib
          return libts.ints_to_pydatetime(self.asi8, self.tz)
  
      def _format_native_types(self, na_rep='NaT', date_format=None, **kwargs):
          from pandas.io.formats.format import _get_format_datetime64_from_values
          fmt = _get_format_datetime64_from_values(self, date_format)
  
          return libts.format_array_from_datetime(self.asi8,
                                                  tz=self.tz,
                                                  format=fmt,
                                                  na_rep=na_rep)
  
      @property
      def _formatter_func(self):
          from pandas.io.formats.format import _get_format_datetime64
          formatter = _get_format_datetime64(is_dates_only=self._is_dates_only)
          return lambda x: "'%s'" % formatter(x, tz=self.tz)
  
      # --------------------------------------------------------------------
      # Set Operation Methods
  
      def _union(self, other, sort):
          if not len(other) or self.equals(other) or not len(self):
              return super()._union(other, sort=sort)
  
          if len(other) == 0 or self.equals(other) or len(self) == 0:
              return super().union(other, sort=sort)
  
          if not isinstance(other, DatetimeIndex):
              try:
                  other = DatetimeIndex(other)
              except TypeError:
                  pass
  
          this, other = self._maybe_utc_convert(other)
  
          if this._can_fast_union(other):
              return this._fast_union(other, sort=sort)
          else:
              result = Index._union(this, other, sort=sort)
              if isinstance(result, DatetimeIndex):
                  # TODO: we shouldn't be setting attributes like this;
                  #  in all the tests this equality already holds
                  result._data._dtype = this.dtype
                  if (result.freq is None and
                          (this.freq is not None or other.freq is not None)):
                      result.freq = to_offset(result.inferred_freq)
              return result
  
      def union_many(self, others):
          """
          A bit of a hack to accelerate unioning a collection of indexes
          """
          this = self
  
          for other in others:
              if not isinstance(this, DatetimeIndex):
                  this = Index.union(this, other)
                  continue
  
              if not isinstance(other, DatetimeIndex):
                  try:
                      other = DatetimeIndex(other)
                  except TypeError:
                      pass
  
              this, other = this._maybe_utc_convert(other)
  
              if this._can_fast_union(other):
                  this = this._fast_union(other)
              else:
                  dtype = this.dtype
                  this = Index.union(this, other)
                  if isinstance(this, DatetimeIndex):
                      # TODO: we shouldn't be setting attributes like this;
                      #  in all the tests this equality already holds
                      this._data._dtype = dtype
          return this
  
      def _can_fast_union(self, other):
          if not isinstance(other, DatetimeIndex):
              return False
  
          freq = self.freq
  
          if freq is None or freq != other.freq:
              return False
  
          if not self.is_monotonic or not other.is_monotonic:
              return False
  
          if len(self) == 0 or len(other) == 0:
              return True
  
          # to make our life easier, "sort" the two ranges
          if self[0] <= other[0]:
              left, right = self, other
          else:
              left, right = other, self
  
          right_start = right[0]
          left_end = left[-1]
  
          # Only need to "adjoin", not overlap
          try:
              return (right_start == left_end + freq) or right_start in left
          except (ValueError):
  
              # if we are comparing a freq that does not propagate timezones
              # this will raise
              return False
  
      def _fast_union(self, other, sort=None):
          if len(other) == 0:
              return self.view(type(self))
  
          if len(self) == 0:
              return other.view(type(self))
  
          # Both DTIs are monotonic. Check if they are already
          # in the "correct" order
          if self[0] <= other[0]:
              left, right = self, other
          # DTIs are not in the "correct" order and we don't want
          # to sort but want to remove overlaps
          elif sort is False:
              left, right = self, other
              left_start = left[0]
              loc = right.searchsorted(left_start, side='left')
              right_chunk = right.values[:loc]
              dates = _concat._concat_compat((left.values, right_chunk))
              return self._shallow_copy(dates)
          # DTIs are not in the "correct" order and we want
          # to sort
          else:
              left, right = other, self
  
          left_end = left[-1]
          right_end = right[-1]
  
          # TODO: consider re-implementing freq._should_cache for fastpath
  
          # concatenate dates
          if left_end < right_end:
              loc = right.searchsorted(left_end, side='right')
              right_chunk = right.values[loc:]
              dates = _concat._concat_compat((left.values, right_chunk))
              return self._shallow_copy(dates)
          else:
              return left
  
      def intersection(self, other, sort=False):
          """
          Specialized intersection for DatetimeIndex objects.
          May be much faster than Index.intersection
  
          Parameters
          ----------
          other : DatetimeIndex or array-like
          sort : False or None, default False
              Sort the resulting index if possible.
  
              .. versionadded:: 0.24.0
  
              .. versionchanged:: 0.24.1
  
                 Changed the default to ``False`` to match the behaviour
                 from before 0.24.0.
  
          Returns
          -------
          y : Index or DatetimeIndex or TimedeltaIndex
          """
          return super().intersection(other, sort=sort)
  
      def _wrap_setop_result(self, other, result):
          name = get_op_result_name(self, other)
          return self._shallow_copy(result, name=name, freq=None, tz=self.tz)
  
      # --------------------------------------------------------------------
  
      def _get_time_micros(self):
          values = self.asi8
          if self.tz is not None and not timezones.is_utc(self.tz):
              values = self._data._local_timestamps()
          return fields.get_time_micros(values)
  
      def to_series(self, keep_tz=None, index=None, name=None):
          """
          Create a Series with both index and values equal to the index keys
          useful with map for returning an indexer based on an index
  
          Parameters
          ----------
          keep_tz : optional, defaults False
              Return the data keeping the timezone.
  
              If keep_tz is True:
  
                If the timezone is not set, the resulting
                Series will have a datetime64[ns] dtype.
  
                Otherwise the Series will have an datetime64[ns, tz] dtype; the
                tz will be preserved.
  
              If keep_tz is False:
  
                Series will have a datetime64[ns] dtype. TZ aware
                objects will have the tz removed.
  
              .. versionchanged:: 0.24
                  The default value will change to True in a future release.
                  You can set ``keep_tz=True`` to already obtain the future
                  behaviour and silence the warning.
  
          index : Index, optional
              index of resulting Series. If None, defaults to original index
          name : string, optional
              name of resulting Series. If None, defaults to name of original
              index
  
          Returns
          -------
          Series
          """
          from pandas import Series
  
          if index is None:
              index = self._shallow_copy()
          if name is None:
              name = self.name
  
          if keep_tz is None and self.tz is not None:
              warnings.warn("The default of the 'keep_tz' keyword in "
                            "DatetimeIndex.to_series will change "
                            "to True in a future release. You can set "
                            "'keep_tz=True' to obtain the future behaviour and "
                            "silence this warning.", FutureWarning, stacklevel=2)
              keep_tz = False
          elif keep_tz is False:
              warnings.warn("Specifying 'keep_tz=False' is deprecated and this "
                            "option will be removed in a future release. If "
                            "you want to remove the timezone information, you "
                            "can do 'idx.tz_convert(None)' before calling "
                            "'to_series'.", FutureWarning, stacklevel=2)
  
          if keep_tz and self.tz is not None:
              # preserve the tz & copy
              values = self.copy(deep=True)
          else:
              values = self.values.copy()
  
          return Series(values, index=index, name=name)
  
      def snap(self, freq='S'):
          """
          Snap time stamps to nearest occurring frequency
  
          Returns
          -------
          DatetimeIndex
          """
          # Superdumb, punting on any optimizing
          freq = to_offset(freq)
  
          snapped = np.empty(len(self), dtype=_NS_DTYPE)
  
          for i, v in enumerate(self):
              s = v
              if not freq.onOffset(s):
                  t0 = freq.rollback(s)
                  t1 = freq.rollforward(s)
                  if abs(s - t0) < abs(t1 - s):
                      s = t0
                  else:
                      s = t1
              snapped[i] = s
  
          # we know it conforms; skip check
          return DatetimeIndex._simple_new(snapped, name=self.name, tz=self.tz,
                                           freq=freq)
  
      def join(self, other, how='left', level=None, return_indexers=False,
               sort=False):
          """
          See Index.join
          """
          if (not isinstance(other, DatetimeIndex) and len(other) > 0 and
              other.inferred_type not in ('floating', 'integer', 'mixed-integer',
                                          'mixed-integer-float', 'mixed')):
              try:
                  other = DatetimeIndex(other)
              except (TypeError, ValueError):
                  pass
  
          this, other = self._maybe_utc_convert(other)
          return Index.join(this, other, how=how, level=level,
                            return_indexers=return_indexers, sort=sort)
  
      def _maybe_utc_convert(self, other):
          this = self
          if isinstance(other, DatetimeIndex):
              if self.tz is not None:
                  if other.tz is None:
                      raise TypeError('Cannot join tz-naive with tz-aware '
                                      'DatetimeIndex')
              elif other.tz is not None:
                  raise TypeError('Cannot join tz-naive with tz-aware '
                                  'DatetimeIndex')
  
              if not timezones.tz_compare(self.tz, other.tz):
                  this = self.tz_convert('UTC')
                  other = other.tz_convert('UTC')
          return this, other
  
      def _wrap_joined_index(self, joined, other):
          name = get_op_result_name(self, other)
          if (isinstance(other, DatetimeIndex) and
                  self.freq == other.freq and
                  self._can_fast_union(other)):
              joined = self._shallow_copy(joined)
              joined.name = name
              return joined
          else:
              tz = getattr(other, 'tz', None)
              return self._simple_new(joined, name, tz=tz)
  
      def _parsed_string_to_bounds(self, reso, parsed):
          """
          Calculate datetime bounds for parsed time string and its resolution.
  
          Parameters
          ----------
          reso : Resolution
              Resolution provided by parsed string.
          parsed : datetime
              Datetime from parsed string.
  
          Returns
          -------
          lower, upper: pd.Timestamp
  
          """
          valid_resos = {'year', 'month', 'quarter', 'day', 'hour', 'minute',
                         'second', 'minute', 'second', 'microsecond'}
          if reso not in valid_resos:
              raise KeyError
          if reso == 'year':
              start = Timestamp(parsed.year, 1, 1)
              end = Timestamp(parsed.year, 12, 31, 23, 59, 59, 999999)
          elif reso == 'month':
              d = ccalendar.get_days_in_month(parsed.year, parsed.month)
              start = Timestamp(parsed.year, parsed.month, 1)
              end = Timestamp(parsed.year, parsed.month, d, 23, 59, 59, 999999)
          elif reso == 'quarter':
              qe = (((parsed.month - 1) + 2) % 12) + 1  # two months ahead
              d = ccalendar.get_days_in_month(parsed.year, qe)  # at end of month
              start = Timestamp(parsed.year, parsed.month, 1)
              end = Timestamp(parsed.year, qe, d, 23, 59, 59, 999999)
          elif reso == 'day':
              start = Timestamp(parsed.year, parsed.month, parsed.day)
              end = start + timedelta(days=1) - Nano(1)
          elif reso == 'hour':
              start = Timestamp(parsed.year, parsed.month, parsed.day,
                                parsed.hour)
              end = start + timedelta(hours=1) - Nano(1)
          elif reso == 'minute':
              start = Timestamp(parsed.year, parsed.month, parsed.day,
                                parsed.hour, parsed.minute)
              end = start + timedelta(minutes=1) - Nano(1)
          elif reso == 'second':
              start = Timestamp(parsed.year, parsed.month, parsed.day,
                                parsed.hour, parsed.minute, parsed.second)
              end = start + timedelta(seconds=1) - Nano(1)
          elif reso == 'microsecond':
              start = Timestamp(parsed.year, parsed.month, parsed.day,
                                parsed.hour, parsed.minute, parsed.second,
                                parsed.microsecond)
              end = start + timedelta(microseconds=1) - Nano(1)
          # GH 24076
          # If an incoming date string contained a UTC offset, need to localize
          # the parsed date to this offset first before aligning with the index's
          # timezone
          if parsed.tzinfo is not None:
              if self.tz is None:
                  raise ValueError("The index must be timezone aware "
                                   "when indexing with a date string with a "
                                   "UTC offset")
              start = start.tz_localize(parsed.tzinfo).tz_convert(self.tz)
              end = end.tz_localize(parsed.tzinfo).tz_convert(self.tz)
          elif self.tz is not None:
              start = start.tz_localize(self.tz)
              end = end.tz_localize(self.tz)
          return start, end
  
      def _partial_date_slice(self, reso, parsed, use_lhs=True, use_rhs=True):
          is_monotonic = self.is_monotonic
          if (is_monotonic and reso in ['day', 'hour', 'minute', 'second'] and
                  self._resolution >= Resolution.get_reso(reso)):
              # These resolution/monotonicity validations came from GH3931,
              # GH3452 and GH2369.
  
              # See also GH14826
              raise KeyError
  
          if reso == 'microsecond':
              # _partial_date_slice doesn't allow microsecond resolution, but
              # _parsed_string_to_bounds allows it.
              raise KeyError
  
          t1, t2 = self._parsed_string_to_bounds(reso, parsed)
          stamps = self.asi8
  
          if is_monotonic:
  
              # we are out of range
              if (len(stamps) and ((use_lhs and t1.value < stamps[0] and
                                    t2.value < stamps[0]) or
                                   ((use_rhs and t1.value > stamps[-1] and
                                     t2.value > stamps[-1])))):
                  raise KeyError
  
              # a monotonic (sorted) series can be sliced
              left = stamps.searchsorted(
                  t1.value, side='left') if use_lhs else None
              right = stamps.searchsorted(
                  t2.value, side='right') if use_rhs else None
  
              return slice(left, right)
  
          lhs_mask = (stamps >= t1.value) if use_lhs else True
          rhs_mask = (stamps <= t2.value) if use_rhs else True
  
          # try to find a the dates
          return (lhs_mask & rhs_mask).nonzero()[0]
  
      def _maybe_promote(self, other):
          if other.inferred_type == 'date':
              other = DatetimeIndex(other)
          return self, other
  
      def get_value(self, series, key):
          """
          Fast lookup of value from 1-dimensional ndarray. Only use this if you
          know what you're doing
          """
  
          if isinstance(key, datetime):
  
              # needed to localize naive datetimes
              if self.tz is not None:
                  if key.tzinfo is not None:
                      key = Timestamp(key).tz_convert(self.tz)
                  else:
                      key = Timestamp(key).tz_localize(self.tz)
  
              return self.get_value_maybe_box(series, key)
  
          if isinstance(key, time):
              locs = self.indexer_at_time(key)
              return series.take(locs)
  
          try:
              return com.maybe_box(self, Index.get_value(self, series, key),
                                   series, key)
          except KeyError:
              try:
                  loc = self._get_string_slice(key)
                  return series[loc]
              except (TypeError, ValueError, KeyError):
                  pass
  
              try:
                  return self.get_value_maybe_box(series, key)
              except (TypeError, ValueError, KeyError):
                  raise KeyError(key)
  
      def get_value_maybe_box(self, series, key):
          # needed to localize naive datetimes
          if self.tz is not None:
              key = Timestamp(key)
              if key.tzinfo is not None:
                  key = key.tz_convert(self.tz)
              else:
                  key = key.tz_localize(self.tz)
          elif not isinstance(key, Timestamp):
              key = Timestamp(key)
          values = self._engine.get_value(com.values_from_object(series),
                                          key, tz=self.tz)
          return com.maybe_box(self, values, series, key)
  
      def get_loc(self, key, method=None, tolerance=None):
          """
          Get integer location for requested label
  
          Returns
          -------
          loc : int
          """
  
          if tolerance is not None:
              # try converting tolerance now, so errors don't get swallowed by
              # the try/except clauses below
              tolerance = self._convert_tolerance(tolerance, np.asarray(key))
  
          if isinstance(key, datetime):
              # needed to localize naive datetimes
              if key.tzinfo is None:
                  key = Timestamp(key, tz=self.tz)
              else:
                  key = Timestamp(key).tz_convert(self.tz)
              return Index.get_loc(self, key, method, tolerance)
  
          elif isinstance(key, timedelta):
              # GH#20464
              raise TypeError("Cannot index {cls} with {other}"
                              .format(cls=type(self).__name__,
                                      other=type(key).__name__))
  
          if isinstance(key, time):
              if method is not None:
                  raise NotImplementedError('cannot yet lookup inexact labels '
                                            'when key is a time object')
              return self.indexer_at_time(key)
  
          try:
              return Index.get_loc(self, key, method, tolerance)
          except (KeyError, ValueError, TypeError):
              try:
                  return self._get_string_slice(key)
              except (TypeError, KeyError, ValueError, OverflowError):
                  pass
  
              try:
                  stamp = Timestamp(key)
                  if stamp.tzinfo is not None and self.tz is not None:
                      stamp = stamp.tz_convert(self.tz)
                  else:
                      stamp = stamp.tz_localize(self.tz)
                  return Index.get_loc(self, stamp, method, tolerance)
              except KeyError:
                  raise KeyError(key)
              except ValueError as e:
                  # list-like tolerance size must match target index size
                  if 'list-like' in str(e):
                      raise e
                  raise KeyError(key)
  
      def _maybe_cast_slice_bound(self, label, side, kind):
          """
          If label is a string, cast it to datetime according to resolution.
  
          Parameters
          ----------
          label : object
          side : {'left', 'right'}
          kind : {'ix', 'loc', 'getitem'}
  
          Returns
          -------
          label :  object
  
          Notes
          -----
          Value of `side` parameter should be validated in caller.
  
          """
          assert kind in ['ix', 'loc', 'getitem', None]
  
          if is_float(label) or isinstance(label, time) or is_integer(label):
              self._invalid_indexer('slice', label)
  
          if isinstance(label, str):
              freq = getattr(self, 'freqstr',
                             getattr(self, 'inferred_freq', None))
              _, parsed, reso = parsing.parse_time_string(label, freq)
              lower, upper = self._parsed_string_to_bounds(reso, parsed)
              # lower, upper form the half-open interval:
              #   [parsed, parsed + 1 freq)
              # because label may be passed to searchsorted
              # the bounds need swapped if index is reverse sorted and has a
              # length > 1 (is_monotonic_decreasing gives True for empty
              # and length 1 index)
              if self._is_strictly_monotonic_decreasing and len(self) > 1:
                  return upper if side == 'left' else lower
              return lower if side == 'left' else upper
          else:
              return label
  
      def _get_string_slice(self, key, use_lhs=True, use_rhs=True):
          freq = getattr(self, 'freqstr',
                         getattr(self, 'inferred_freq', None))
          _, parsed, reso = parsing.parse_time_string(key, freq)
          loc = self._partial_date_slice(reso, parsed, use_lhs=use_lhs,
                                         use_rhs=use_rhs)
          return loc
  
      def slice_indexer(self, start=None, end=None, step=None, kind=None):
          """
          Return indexer for specified label slice.
          Index.slice_indexer, customized to handle time slicing.
  
          In addition to functionality provided by Index.slice_indexer, does the
          following:
  
          - if both `start` and `end` are instances of `datetime.time`, it
            invokes `indexer_between_time`
          - if `start` and `end` are both either string or None perform
            value-based selection in non-monotonic cases.
  
          """
          # For historical reasons DatetimeIndex supports slices between two
          # instances of datetime.time as if it were applying a slice mask to
          # an array of (self.hour, self.minute, self.seconds, self.microsecond).
          if isinstance(start, time) and isinstance(end, time):
              if step is not None and step != 1:
                  raise ValueError('Must have step size of 1 with time slices')
              return self.indexer_between_time(start, end)
  
          if isinstance(start, time) or isinstance(end, time):
              raise KeyError('Cannot mix time and non-time slice keys')
  
          try:
              return Index.slice_indexer(self, start, end, step, kind=kind)
          except KeyError:
              # For historical reasons DatetimeIndex by default supports
              # value-based partial (aka string) slices on non-monotonic arrays,
              # let's try that.
              if ((start is None or isinstance(start, str)) and
                      (end is None or isinstance(end, str))):
                  mask = True
                  if start is not None:
                      start_casted = self._maybe_cast_slice_bound(
                          start, 'left', kind)
                      mask = start_casted <= self
  
                  if end is not None:
                      end_casted = self._maybe_cast_slice_bound(
                          end, 'right', kind)
                      mask = (self <= end_casted) & mask
  
                  indexer = mask.nonzero()[0][::step]
                  if len(indexer) == len(self):
                      return slice(None)
                  else:
                      return indexer
              else:
                  raise
  
      # --------------------------------------------------------------------
      # Wrapping DatetimeArray
  
      # Compat for frequency inference, see GH#23789
      _is_monotonic_increasing = Index.is_monotonic_increasing
      _is_monotonic_decreasing = Index.is_monotonic_decreasing
      _is_unique = Index.is_unique
  
      _timezone = cache_readonly(DatetimeArray._timezone.fget)  # type: ignore
      is_normalized = cache_readonly(
          DatetimeArray.is_normalized.fget)  # type: ignore
      _resolution = cache_readonly(
          DatetimeArray._resolution.fget)  # type: ignore
  
      strftime = ea_passthrough(DatetimeArray.strftime)
      _has_same_tz = ea_passthrough(DatetimeArray._has_same_tz)
  
      @property
      def offset(self):
          """
          get/set the frequency of the instance
          """
          msg = ('{cls}.offset has been deprecated and will be removed '
                 'in a future version; use {cls}.freq instead.'
                 .format(cls=type(self).__name__))
          warnings.warn(msg, FutureWarning, stacklevel=2)
          return self.freq
  
      @offset.setter
      def offset(self, value):
          """
          get/set the frequency of the instance
          """
          msg = ('{cls}.offset has been deprecated and will be removed '
                 'in a future version; use {cls}.freq instead.'
                 .format(cls=type(self).__name__))
          warnings.warn(msg, FutureWarning, stacklevel=2)
          self.freq = value
  
      def __getitem__(self, key):
          result = self._data.__getitem__(key)
          if is_scalar(result):
              return result
          elif result.ndim > 1:
              # To support MPL which performs slicing with 2 dim
              # even though it only has 1 dim by definition
              assert isinstance(result, np.ndarray), result
              return result
          return type(self)(result, name=self.name)
  
      @property
      def _box_func(self):
          return lambda x: Timestamp(x, tz=self.tz)
  
      # --------------------------------------------------------------------
  
      @Substitution(klass='DatetimeIndex')
      @Appender(_shared_docs['searchsorted'])
      def searchsorted(self, value, side='left', sorter=None):
          if isinstance(value, (np.ndarray, Index)):
              value = np.array(value, dtype=_NS_DTYPE, copy=False)
          else:
              value = _to_M8(value, tz=self.tz)
  
          return self.values.searchsorted(value, side=side)
  
      def is_type_compatible(self, typ):
          return typ == self.inferred_type or typ == 'datetime'
  
      @property
      def inferred_type(self):
          # b/c datetime is represented as microseconds since the epoch, make
          # sure we can't have ambiguous indexing
          return 'datetime64'
  
      @property
      def is_all_dates(self):
          return True
  
      def insert(self, loc, item):
          """
          Make new Index inserting new item at location
  
          Parameters
          ----------
          loc : int
          item : object
              if not either a Python datetime or a numpy integer-like, returned
              Index dtype will be object rather than datetime.
  
          Returns
          -------
          new_index : Index
          """
          if is_scalar(item) and isna(item):
              # GH 18295
              item = self._na_value
  
          freq = None
  
          if isinstance(item, (datetime, np.datetime64)):
              self._assert_can_do_op(item)
              if not self._has_same_tz(item) and not isna(item):
                  raise ValueError(
                      'Passed item and index have different timezone')
              # check freq can be preserved on edge cases
              if self.size and self.freq is not None:
                  if ((loc == 0 or loc == -len(self)) and
                          item + self.freq == self[0]):
                      freq = self.freq
                  elif (loc == len(self)) and item - self.freq == self[-1]:
                      freq = self.freq
              item = _to_M8(item, tz=self.tz)
  
          try:
              new_dates = np.concatenate((self[:loc].asi8, [item.view(np.int64)],
                                          self[loc:].asi8))
              return self._shallow_copy(new_dates, freq=freq)
          except (AttributeError, TypeError):
  
              # fall back to object index
              if isinstance(item, str):
                  return self.astype(object).insert(loc, item)
              raise TypeError(
                  "cannot insert DatetimeIndex with incompatible label")
  
      def delete(self, loc):
          """
          Make a new DatetimeIndex with passed location(s) deleted.
  
          Parameters
          ----------
          loc: int, slice or array of ints
              Indicate which sub-arrays to remove.
  
          Returns
          -------
          new_index : DatetimeIndex
          """
          new_dates = np.delete(self.asi8, loc)
  
          freq = None
          if is_integer(loc):
              if loc in (0, -len(self), -1, len(self) - 1):
                  freq = self.freq
          else:
              if is_list_like(loc):
                  loc = lib.maybe_indices_to_slice(
                      ensure_int64(np.array(loc)), len(self))
              if isinstance(loc, slice) and loc.step in (1, None):
                  if (loc.start in (0, None) or loc.stop in (len(self), None)):
                      freq = self.freq
  
          return self._shallow_copy(new_dates, freq=freq)
  
      def indexer_at_time(self, time, asof=False):
          """
          Return index locations of index values at particular time of day
          (e.g. 9:30AM).
  
          Parameters
          ----------
          time : datetime.time or string
              datetime.time or string in appropriate format ("%H:%M", "%H%M",
              "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p",
              "%I%M%S%p").
  
          Returns
          -------
          values_at_time : array of integers
  
          See Also
          --------
          indexer_between_time, DataFrame.at_time
          """
          if asof:
              raise NotImplementedError("'asof' argument is not supported")
  
          if isinstance(time, str):
              from dateutil.parser import parse
              time = parse(time).time()
  
          if time.tzinfo:
              if self.tz is None:
                  raise ValueError("Index must be timezone aware.")
              time_micros = self.tz_convert(time.tzinfo)._get_time_micros()
          else:
              time_micros = self._get_time_micros()
          micros = _time_to_micros(time)
          return (micros == time_micros).nonzero()[0]
  
      def indexer_between_time(self, start_time, end_time, include_start=True,
                               include_end=True):
          """
          Return index locations of values between particular times of day
          (e.g., 9:00-9:30AM).
  
          Parameters
          ----------
          start_time, end_time : datetime.time, str
              datetime.time or string in appropriate format ("%H:%M", "%H%M",
              "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p",
              "%I%M%S%p").
          include_start : boolean, default True
          include_end : boolean, default True
  
          Returns
          -------
          values_between_time : array of integers
  
          See Also
          --------
          indexer_at_time, DataFrame.between_time
          """
          start_time = tools.to_time(start_time)
          end_time = tools.to_time(end_time)
          time_micros = self._get_time_micros()
          start_micros = _time_to_micros(start_time)
          end_micros = _time_to_micros(end_time)
  
          if include_start and include_end:
              lop = rop = operator.le
          elif include_start:
              lop = operator.le
              rop = operator.lt
          elif include_end:
              lop = operator.lt
              rop = operator.le
          else:
              lop = rop = operator.lt
  
          if start_time <= end_time:
              join_op = operator.and_
          else:
              join_op = operator.or_
  
          mask = join_op(lop(start_micros, time_micros),
                         rop(time_micros, end_micros))
  
          return mask.nonzero()[0]
  
  
  DatetimeIndex._add_comparison_ops()
  DatetimeIndex._add_numeric_methods_disabled()
  DatetimeIndex._add_logical_methods_disabled()
  DatetimeIndex._add_datetimelike_methods()
  
  
  def date_range(start=None, end=None, periods=None, freq=None, tz=None,
                 normalize=False, name=None, closed=None, **kwargs):
      """
      Return a fixed frequency DatetimeIndex.
  
      Parameters
      ----------
      start : str or datetime-like, optional
          Left bound for generating dates.
      end : str or datetime-like, optional
          Right bound for generating dates.
      periods : integer, optional
          Number of periods to generate.
      freq : str or DateOffset, default 'D'
          Frequency strings can have multiples, e.g. '5H'. See
          :ref:`here <timeseries.offset_aliases>` for a list of
          frequency aliases.
      tz : str or tzinfo, optional
          Time zone name for returning localized DatetimeIndex, for example
          'Asia/Hong_Kong'. By default, the resulting DatetimeIndex is
          timezone-naive.
      normalize : bool, default False
          Normalize start/end dates to midnight before generating date range.
      name : str, default None
          Name of the resulting DatetimeIndex.
      closed : {None, 'left', 'right'}, optional
          Make the interval closed with respect to the given frequency to
          the 'left', 'right', or both sides (None, the default).
      **kwargs
          For compatibility. Has no effect on the result.
  
      Returns
      -------
      rng : DatetimeIndex
  
      See Also
      --------
      DatetimeIndex : An immutable container for datetimes.
      timedelta_range : Return a fixed frequency TimedeltaIndex.
      period_range : Return a fixed frequency PeriodIndex.
      interval_range : Return a fixed frequency IntervalIndex.
  
      Notes
      -----
      Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
      exactly three must be specified. If ``freq`` is omitted, the resulting
      ``DatetimeIndex`` will have ``periods`` linearly spaced elements between
      ``start`` and ``end`` (closed on both sides).
  
      To learn more about the frequency strings, please see `this link
      <http://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
  
      Examples
      --------
      **Specifying the values**
  
      The next four examples generate the same `DatetimeIndex`, but vary
      the combination of `start`, `end` and `periods`.
  
      Specify `start` and `end`, with the default daily frequency.
  
      >>> pd.date_range(start='1/1/2018', end='1/08/2018')
      DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
                     '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
                    dtype='datetime64[ns]', freq='D')
  
      Specify `start` and `periods`, the number of periods (days).
  
      >>> pd.date_range(start='1/1/2018', periods=8)
      DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
                     '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'],
                    dtype='datetime64[ns]', freq='D')
  
      Specify `end` and `periods`, the number of periods (days).
  
      >>> pd.date_range(end='1/1/2018', periods=8)
      DatetimeIndex(['2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28',
                     '2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01'],
                    dtype='datetime64[ns]', freq='D')
  
      Specify `start`, `end`, and `periods`; the frequency is generated
      automatically (linearly spaced).
  
      >>> pd.date_range(start='2018-04-24', end='2018-04-27', periods=3)
      DatetimeIndex(['2018-04-24 00:00:00', '2018-04-25 12:00:00',
                     '2018-04-27 00:00:00'],
                    dtype='datetime64[ns]', freq=None)
  
      **Other Parameters**
  
      Changed the `freq` (frequency) to ``'M'`` (month end frequency).
  
      >>> pd.date_range(start='1/1/2018', periods=5, freq='M')
      DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30',
                     '2018-05-31'],
                    dtype='datetime64[ns]', freq='M')
  
      Multiples are allowed
  
      >>> pd.date_range(start='1/1/2018', periods=5, freq='3M')
      DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
                     '2019-01-31'],
                    dtype='datetime64[ns]', freq='3M')
  
      `freq` can also be specified as an Offset object.
  
      >>> pd.date_range(start='1/1/2018', periods=5, freq=pd.offsets.MonthEnd(3))
      DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31',
                     '2019-01-31'],
                    dtype='datetime64[ns]', freq='3M')
  
      Specify `tz` to set the timezone.
  
      >>> pd.date_range(start='1/1/2018', periods=5, tz='Asia/Tokyo')
      DatetimeIndex(['2018-01-01 00:00:00+09:00', '2018-01-02 00:00:00+09:00',
                     '2018-01-03 00:00:00+09:00', '2018-01-04 00:00:00+09:00',
                     '2018-01-05 00:00:00+09:00'],
                    dtype='datetime64[ns, Asia/Tokyo]', freq='D')
  
      `closed` controls whether to include `start` and `end` that are on the
      boundary. The default includes boundary points on either end.
  
      >>> pd.date_range(start='2017-01-01', end='2017-01-04', closed=None)
      DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'],
                    dtype='datetime64[ns]', freq='D')
  
      Use ``closed='left'`` to exclude `end` if it falls on the boundary.
  
      >>> pd.date_range(start='2017-01-01', end='2017-01-04', closed='left')
      DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'],
                    dtype='datetime64[ns]', freq='D')
  
      Use ``closed='right'`` to exclude `start` if it falls on the boundary.
  
      >>> pd.date_range(start='2017-01-01', end='2017-01-04', closed='right')
      DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'],
                    dtype='datetime64[ns]', freq='D')
      """
  
      if freq is None and com._any_none(periods, start, end):
          freq = 'D'
  
      dtarr = DatetimeArray._generate_range(
          start=start, end=end, periods=periods,
          freq=freq, tz=tz, normalize=normalize,
          closed=closed, **kwargs)
      return DatetimeIndex._simple_new(
          dtarr, tz=dtarr.tz, freq=dtarr.freq, name=name)
  
  
  def bdate_range(start=None, end=None, periods=None, freq='B', tz=None,
                  normalize=True, name=None, weekmask=None, holidays=None,
                  closed=None, **kwargs):
      """
      Return a fixed frequency DatetimeIndex, with business day as the default
      frequency
  
      Parameters
      ----------
      start : string or datetime-like, default None
          Left bound for generating dates.
      end : string or datetime-like, default None
          Right bound for generating dates.
      periods : integer, default None
          Number of periods to generate.
      freq : string or DateOffset, default 'B' (business daily)
          Frequency strings can have multiples, e.g. '5H'.
      tz : string or None
          Time zone name for returning localized DatetimeIndex, for example
          Asia/Beijing.
      normalize : bool, default False
          Normalize start/end dates to midnight before generating date range.
      name : string, default None
          Name of the resulting DatetimeIndex.
      weekmask : string or None, default None
          Weekmask of valid business days, passed to ``numpy.busdaycalendar``,
          only used when custom frequency strings are passed.  The default
          value None is equivalent to 'Mon Tue Wed Thu Fri'.
  
          .. versionadded:: 0.21.0
  
      holidays : list-like or None, default None
          Dates to exclude from the set of valid business days, passed to
          ``numpy.busdaycalendar``, only used when custom frequency strings
          are passed.
  
          .. versionadded:: 0.21.0
  
      closed : string, default None
          Make the interval closed with respect to the given frequency to
          the 'left', 'right', or both sides (None).
      **kwargs
          For compatibility. Has no effect on the result.
  
      Returns
      -------
      DatetimeIndex
  
      Notes
      -----
      Of the four parameters: ``start``, ``end``, ``periods``, and ``freq``,
      exactly three must be specified.  Specifying ``freq`` is a requirement
      for ``bdate_range``.  Use ``date_range`` if specifying ``freq`` is not
      desired.
  
      To learn more about the frequency strings, please see `this link
      <http://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
  
      Examples
      --------
      Note how the two weekend days are skipped in the result.
  
      >>> pd.bdate_range(start='1/1/2018', end='1/08/2018')
      DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
                 '2018-01-05', '2018-01-08'],
                dtype='datetime64[ns]', freq='B')
      """
      if freq is None:
          msg = 'freq must be specified for bdate_range; use date_range instead'
          raise TypeError(msg)
  
      if is_string_like(freq) and freq.startswith('C'):
          try:
              weekmask = weekmask or 'Mon Tue Wed Thu Fri'
              freq = prefix_mapping[freq](holidays=holidays, weekmask=weekmask)
          except (KeyError, TypeError):
              msg = 'invalid custom frequency string: {freq}'.format(freq=freq)
              raise ValueError(msg)
      elif holidays or weekmask:
          msg = ('a custom frequency string is required when holidays or '
                 'weekmask are passed, got frequency {freq}').format(freq=freq)
          raise ValueError(msg)
  
      return date_range(start=start, end=end, periods=periods,
                        freq=freq, tz=tz, normalize=normalize, name=name,
                        closed=closed, **kwargs)
  
  
  def cdate_range(start=None, end=None, periods=None, freq='C', tz=None,
                  normalize=True, name=None, closed=None, **kwargs):
      """
      Return a fixed frequency DatetimeIndex, with CustomBusinessDay as the
      default frequency
  
      .. deprecated:: 0.21.0
  
      Parameters
      ----------
      start : string or datetime-like, default None
          Left bound for generating dates
      end : string or datetime-like, default None
          Right bound for generating dates
      periods : integer, default None
          Number of periods to generate
      freq : string or DateOffset, default 'C' (CustomBusinessDay)
          Frequency strings can have multiples, e.g. '5H'
      tz : string, default None
          Time zone name for returning localized DatetimeIndex, for example
          Asia/Beijing
      normalize : bool, default False
          Normalize start/end dates to midnight before generating date range
      name : string, default None
          Name of the resulting DatetimeIndex
      weekmask : string, Default 'Mon Tue Wed Thu Fri'
          weekmask of valid business days, passed to ``numpy.busdaycalendar``
      holidays : list
          list/array of dates to exclude from the set of valid business days,
          passed to ``numpy.busdaycalendar``
      closed : string, default None
          Make the interval closed with respect to the given frequency to
          the 'left', 'right', or both sides (None)
  
      Notes
      -----
      Of the three parameters: ``start``, ``end``, and ``periods``, exactly two
      must be specified.
  
      To learn more about the frequency strings, please see `this link
      <http://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
  
      Returns
      -------
      rng : DatetimeIndex
      """
      warnings.warn("cdate_range is deprecated and will be removed in a future "
                    "version, instead use pd.bdate_range(..., freq='{freq}')"
                    .format(freq=freq), FutureWarning, stacklevel=2)
  
      if freq == 'C':
          holidays = kwargs.pop('holidays', [])
          weekmask = kwargs.pop('weekmask', 'Mon Tue Wed Thu Fri')
          freq = CDay(holidays=holidays, weekmask=weekmask)
  
      return date_range(start=start, end=end, periods=periods, freq=freq,
                        tz=tz, normalize=normalize, name=name,
                        closed=closed, **kwargs)
  
  
  def _time_to_micros(time):
      seconds = time.hour * 60 * 60 + 60 * time.minute + time.second
      return 1000000 * seconds + time.microsecond
  