code: from datetime import datetime, timedelta
  import warnings
  import operator
  from textwrap import dedent
  
  import numpy as np
  from pandas._libs import (lib, index as libindex, tslib as libts,
                            algos as libalgos, join as libjoin,
                            Timedelta)
  from pandas._libs.lib import is_datetime_array
  
  from pandas.compat import range, u, set_function_name
  from pandas.compat.numpy import function as nv
  from pandas import compat
  
  from pandas.core.accessor import CachedAccessor
  from pandas.core.arrays import ExtensionArray
  from pandas.core.dtypes.generic import (
      ABCSeries, ABCDataFrame,
      ABCMultiIndex,
      ABCPeriodIndex, ABCTimedeltaIndex,
      ABCDateOffset)
  from pandas.core.dtypes.missing import isna, array_equivalent
  from pandas.core.dtypes.common import (
      _ensure_int64,
      _ensure_object,
      _ensure_categorical,
      _ensure_platform_int,
      is_integer,
      is_float,
      is_dtype_equal,
      is_dtype_union_equal,
      is_object_dtype,
      is_categorical_dtype,
      is_interval_dtype,
      is_period_dtype,
      is_bool,
      is_bool_dtype,
      is_signed_integer_dtype,
      is_unsigned_integer_dtype,
      is_integer_dtype, is_float_dtype,
      is_datetime64_any_dtype,
      is_datetime64tz_dtype,
      is_timedelta64_dtype,
      is_hashable,
      needs_i8_conversion,
      is_iterator, is_list_like,
      is_scalar)
  
  from pandas.core.base import PandasObject, IndexOpsMixin
  import pandas.core.common as com
  from pandas.core import ops
  from pandas.util._decorators import (
      Appender, Substitution, cache_readonly, deprecate_kwarg)
  from pandas.core.indexes.frozen import FrozenList
  import pandas.core.dtypes.concat as _concat
  import pandas.core.missing as missing
  import pandas.core.algorithms as algos
  import pandas.core.sorting as sorting
  from pandas.io.formats.printing import pprint_thing
  from pandas.core.ops import make_invalid_op
  from pandas.core.config import get_option
  from pandas.core.strings import StringMethods
  
  
  # simplify
  default_pprint = lambda x, max_seq_items=None: \
      pprint_thing(x, escape_chars=('\t', '\r', '\n'), quote_strings=True,
                   max_seq_items=max_seq_items)
  
  __all__ = ['Index']
  
  _unsortable_types = frozenset(('mixed', 'mixed-integer'))
  
  _index_doc_kwargs = dict(klass='Index', inplace='',
                           target_klass='Index',
                           unique='Index', duplicated='np.ndarray')
  _index_shared_docs = dict()
  
  
  def _try_get_item(x):
      try:
          return x.item()
      except AttributeError:
          return x
  
  
  def _make_comparison_op(op, cls):
      def cmp_method(self, other):
          if isinstance(other, (np.ndarray, Index, ABCSeries)):
              if other.ndim > 0 and len(self) != len(other):
                  raise ValueError('Lengths must match to compare')
  
          # we may need to directly compare underlying
          # representations
          if needs_i8_conversion(self) and needs_i8_conversion(other):
              return self._evaluate_compare(other, op)
  
          if is_object_dtype(self) and self.nlevels == 1:
              # don't pass MultiIndex
              with np.errstate(all='ignore'):
                  result = ops._comp_method_OBJECT_ARRAY(op, self.values, other)
  
          else:
  
              # numpy will show a DeprecationWarning on invalid elementwise
              # comparisons, this will raise in the future
              with warnings.catch_warnings(record=True):
                  with np.errstate(all='ignore'):
                      result = op(self.values, np.asarray(other))
  
          # technically we could support bool dtyped Index
          # for now just return the indexing array directly
          if is_bool_dtype(result):
              return result
          try:
              return Index(result)
          except TypeError:
              return result
  
      name = '__{name}__'.format(name=op.__name__)
      # TODO: docstring?
      return set_function_name(cmp_method, name, cls)
  
  
  def _make_arithmetic_op(op, cls):
      def index_arithmetic_method(self, other):
          if isinstance(other, (ABCSeries, ABCDataFrame)):
              return NotImplemented
          elif isinstance(other, ABCTimedeltaIndex):
              # Defer to subclass implementation
              return NotImplemented
  
          other = self._validate_for_numeric_binop(other, op)
  
          # handle time-based others
          if isinstance(other, (ABCDateOffset, np.timedelta64, timedelta)):
              return self._evaluate_with_timedelta_like(other, op)
          elif isinstance(other, (datetime, np.datetime64)):
              return self._evaluate_with_datetime_like(other, op)
  
          values = self.values
          with np.errstate(all='ignore'):
              result = op(values, other)
  
          result = missing.dispatch_missing(op, values, other, result)
  
          attrs = self._get_attributes_dict()
          attrs = self._maybe_update_attributes(attrs)
          if op is divmod:
              result = (Index(result[0], **attrs), Index(result[1], **attrs))
          else:
              result = Index(result, **attrs)
          return result
  
      name = '__{name}__'.format(name=op.__name__)
      # TODO: docstring?
      return set_function_name(index_arithmetic_method, name, cls)
  
  
  class InvalidIndexError(Exception):
      pass
  
  
  _o_dtype = np.dtype(object)
  _Identity = object
  
  
  def _new_Index(cls, d):
      """ This is called upon unpickling, rather than the default which doesn't
      have arguments and breaks __new__
      """
      # required for backward compat, because PI can't be instantiated with
      # ordinals through __new__ GH #13277
      if issubclass(cls, ABCPeriodIndex):
          from pandas.core.indexes.period import _new_PeriodIndex
          return _new_PeriodIndex(cls, **d)
      return cls.__new__(cls, **d)
  
  
  class Index(IndexOpsMixin, PandasObject):
      """
      Immutable ndarray implementing an ordered, sliceable set. The basic object
      storing axis labels for all pandas objects
  
      Parameters
      ----------
      data : array-like (1-dimensional)
      dtype : NumPy dtype (default: object)
      copy : bool
          Make a copy of input ndarray
      name : object
          Name to be stored in the index
      tupleize_cols : bool (default: True)
          When True, attempt to create a MultiIndex if possible
  
      Notes
      -----
      An Index instance can **only** contain hashable objects
  
      Examples
      --------
      >>> pd.Index([1, 2, 3])
      Int64Index([1, 2, 3], dtype='int64')
  
      >>> pd.Index(list('abc'))
      Index(['a', 'b', 'c'], dtype='object')
  
      See Also
      ---------
      RangeIndex : Index implementing a monotonic integer range
      CategoricalIndex : Index of :class:`Categorical` s.
      MultiIndex : A multi-level, or hierarchical, Index
      IntervalIndex : an Index of :class:`Interval` s.
      DatetimeIndex, TimedeltaIndex, PeriodIndex
      Int64Index, UInt64Index,  Float64Index
      """
      # To hand over control to subclasses
      _join_precedence = 1
  
      # Cython methods
      _left_indexer_unique = libjoin.left_join_indexer_unique_object
      _left_indexer = libjoin.left_join_indexer_object
      _inner_indexer = libjoin.inner_join_indexer_object
      _outer_indexer = libjoin.outer_join_indexer_object
  
      _typ = 'index'
      _data = None
      _id = None
      name = None
      asi8 = None
      _comparables = ['name']
      _attributes = ['name']
      _is_numeric_dtype = False
      _can_hold_na = True
  
      # would we like our indexing holder to defer to us
      _defer_to_indexing = False
  
      # prioritize current class for _shallow_copy_with_infer,
      # used to infer integers as datetime-likes
      _infer_as_myclass = False
  
      _engine_type = libindex.ObjectEngine
  
      _accessors = set(['str'])
  
      str = CachedAccessor("str", StringMethods)
  
      def __new__(cls, data=None, dtype=None, copy=False, name=None,
                  fastpath=False, tupleize_cols=True, **kwargs):
  
          if name is None and hasattr(data, 'name'):
              name = data.name
  
          if fastpath:
              return cls._simple_new(data, name)
  
          from .range import RangeIndex
  
          # range
          if isinstance(data, RangeIndex):
              return RangeIndex(start=data, copy=copy, dtype=dtype, name=name)
          elif isinstance(data, range):
              return RangeIndex.from_range(data, copy=copy, dtype=dtype,
                                           name=name)
  
          # categorical
          if is_categorical_dtype(data) or is_categorical_dtype(dtype):
              from .category import CategoricalIndex
              return CategoricalIndex(data, dtype=dtype, copy=copy, name=name,
                                      **kwargs)
  
          # interval
          if is_interval_dtype(data) or is_interval_dtype(dtype):
              from .interval import IntervalIndex
              closed = kwargs.get('closed', None)
              return IntervalIndex(data, dtype=dtype, name=name, copy=copy,
                                   closed=closed)
  
          # index-like
          elif isinstance(data, (np.ndarray, Index, ABCSeries)):
  
              if (is_datetime64_any_dtype(data) or
                  (dtype is not None and is_datetime64_any_dtype(dtype)) or
                      'tz' in kwargs):
                  from pandas.core.indexes.datetimes import DatetimeIndex
                  result = DatetimeIndex(data, copy=copy, name=name,
                                         dtype=dtype, **kwargs)
                  if dtype is not None and is_dtype_equal(_o_dtype, dtype):
                      return Index(result.to_pydatetime(), dtype=_o_dtype)
                  else:
                      return result
  
              elif (is_timedelta64_dtype(data) or
                    (dtype is not None and is_timedelta64_dtype(dtype))):
                  from pandas.core.indexes.timedeltas import TimedeltaIndex
                  result = TimedeltaIndex(data, copy=copy, name=name, **kwargs)
                  if dtype is not None and _o_dtype == dtype:
                      return Index(result.to_pytimedelta(), dtype=_o_dtype)
                  else:
                      return result
  
              if dtype is not None:
                  try:
  
                      # we need to avoid having numpy coerce
                      # things that look like ints/floats to ints unless
                      # they are actually ints, e.g. '0' and 0.0
                      # should not be coerced
                      # GH 11836
                      if is_integer_dtype(dtype):
                          inferred = lib.infer_dtype(data)
                          if inferred == 'integer':
                              data = np.array(data, copy=copy, dtype=dtype)
                          elif inferred in ['floating', 'mixed-integer-float']:
                              if isna(data).any():
                                  raise ValueError('cannot convert float '
                                                   'NaN to integer')
  
                              # If we are actually all equal to integers,
                              # then coerce to integer.
                              try:
                                  return cls._try_convert_to_int_index(
                                      data, copy, name, dtype)
                              except ValueError:
                                  pass
  
                              # Return an actual float index.
                              from .numeric import Float64Index
                              return Float64Index(data, copy=copy, dtype=dtype,
                                                  name=name)
  
                          elif inferred == 'string':
                              pass
                          else:
                              data = data.astype(dtype)
                      elif is_float_dtype(dtype):
                          inferred = lib.infer_dtype(data)
                          if inferred == 'string':
                              pass
                          else:
                              data = data.astype(dtype)
                      else:
                          data = np.array(data, dtype=dtype, copy=copy)
  
                  except (TypeError, ValueError) as e:
                      msg = str(e)
                      if 'cannot convert float' in msg:
                          raise
  
              # maybe coerce to a sub-class
              from pandas.core.indexes.period import (
                  PeriodIndex, IncompatibleFrequency)
              if isinstance(data, PeriodIndex):
                  return PeriodIndex(data, copy=copy, name=name, **kwargs)
              if is_signed_integer_dtype(data.dtype):
                  from .numeric import Int64Index
                  return Int64Index(data, copy=copy, dtype=dtype, name=name)
              elif is_unsigned_integer_dtype(data.dtype):
                  from .numeric import UInt64Index
                  return UInt64Index(data, copy=copy, dtype=dtype, name=name)
              elif is_float_dtype(data.dtype):
                  from .numeric import Float64Index
                  return Float64Index(data, copy=copy, dtype=dtype, name=name)
              elif issubclass(data.dtype.type, np.bool) or is_bool_dtype(data):
                  subarr = data.astype('object')
              else:
                  subarr = com._asarray_tuplesafe(data, dtype=object)
  
              # _asarray_tuplesafe does not always copy underlying data,
              # so need to make sure that this happens
              if copy:
                  subarr = subarr.copy()
  
              if dtype is None:
                  inferred = lib.infer_dtype(subarr)
                  if inferred == 'integer':
                      try:
                          return cls._try_convert_to_int_index(
                              subarr, copy, name, dtype)
                      except ValueError:
                          pass
  
                      return Index(subarr, copy=copy,
                                   dtype=object, name=name)
                  elif inferred in ['floating', 'mixed-integer-float']:
                      from .numeric import Float64Index
                      return Float64Index(subarr, copy=copy, name=name)
                  elif inferred == 'interval':
                      from .interval import IntervalIndex
                      return IntervalIndex(subarr, name=name, copy=copy)
                  elif inferred == 'boolean':
                      # don't support boolean explicitly ATM
                      pass
                  elif inferred != 'string':
                      if inferred.startswith('datetime'):
                          if (lib.is_datetime_with_singletz_array(subarr) or
                                  'tz' in kwargs):
                              # only when subarr has the same tz
                              from pandas.core.indexes.datetimes import (
                                  DatetimeIndex)
                              try:
                                  return DatetimeIndex(subarr, copy=copy,
                                                       name=name, **kwargs)
                              except libts.OutOfBoundsDatetime:
                                  pass
  
                      elif inferred.startswith('timedelta'):
                          from pandas.core.indexes.timedeltas import (
                              TimedeltaIndex)
                          return TimedeltaIndex(subarr, copy=copy, name=name,
                                                **kwargs)
                      elif inferred == 'period':
                          try:
                              return PeriodIndex(subarr, name=name, **kwargs)
                          except IncompatibleFrequency:
                              pass
              return cls._simple_new(subarr, name)
  
          elif hasattr(data, '__array__'):
              return Index(np.asarray(data), dtype=dtype, copy=copy, name=name,
                           **kwargs)
          elif data is None or is_scalar(data):
              cls._scalar_data_error(data)
          else:
              if tupleize_cols and is_list_like(data) and data:
                  if is_iterator(data):
                      data = list(data)
                  # we must be all tuples, otherwise don't construct
                  # 10697
                  if all(isinstance(e, tuple) for e in data):
                      from .multi import MultiIndex
                      return MultiIndex.from_tuples(
                          data, names=name or kwargs.get('names'))
              # other iterable of some kind
              subarr = com._asarray_tuplesafe(data, dtype=object)
              return Index(subarr, dtype=dtype, copy=copy, name=name, **kwargs)
  
      """
      NOTE for new Index creation:
  
      - _simple_new: It returns new Index with the same type as the caller.
        All metadata (such as name) must be provided by caller's responsibility.
        Using _shallow_copy is recommended because it fills these metadata
        otherwise specified.
  
      - _shallow_copy: It returns new Index with the same type (using
        _simple_new), but fills caller's metadata otherwise specified. Passed
        kwargs will overwrite corresponding metadata.
  
      - _shallow_copy_with_infer: It returns new Index inferring its type
        from passed values. It fills caller's metadata otherwise specified as the
        same as _shallow_copy.
  
      See each method's docstring.
      """
  
      @classmethod
      def _simple_new(cls, values, name=None, dtype=None, **kwargs):
          """
          we require the we have a dtype compat for the values
          if we are passed a non-dtype compat, then coerce using the constructor
  
          Must be careful not to recurse.
          """
          if not hasattr(values, 'dtype'):
              if (values is None or not len(values)) and dtype is not None:
                  values = np.empty(0, dtype=dtype)
              else:
                  values = np.array(values, copy=False)
                  if is_object_dtype(values):
                      values = cls(values, name=name, dtype=dtype,
                                   **kwargs)._ndarray_values
  
          result = object.__new__(cls)
          result._data = values
          result.name = name
          for k, v in compat.iteritems(kwargs):
              setattr(result, k, v)
          return result._reset_identity()
  
      _index_shared_docs['_shallow_copy'] = """
          create a new Index with the same class as the caller, don't copy the
          data, use the same object attributes with passed in attributes taking
          precedence
  
          *this is an internal non-public method*
  
          Parameters
          ----------
          values : the values to create the new Index, optional
          kwargs : updates the default attributes for this Index
          """
  
      @Appender(_index_shared_docs['_shallow_copy'])
      def _shallow_copy(self, values=None, **kwargs):
          if values is None:
              values = self.values
          attributes = self._get_attributes_dict()
          attributes.update(kwargs)
          if not len(values) and 'dtype' not in kwargs:
              attributes['dtype'] = self.dtype
          return self._simple_new(values, **attributes)
  
      def _shallow_copy_with_infer(self, values=None, **kwargs):
          """
          create a new Index inferring the class with passed value, don't copy
          the data, use the same object attributes with passed in attributes
          taking precedence
  
          *this is an internal non-public method*
  
          Parameters
          ----------
          values : the values to create the new Index, optional
          kwargs : updates the default attributes for this Index
          """
          if values is None:
              values = self.values
          attributes = self._get_attributes_dict()
          attributes.update(kwargs)
          attributes['copy'] = False
          if not len(values) and 'dtype' not in kwargs:
              attributes['dtype'] = self.dtype
          if self._infer_as_myclass:
              try:
                  return self._constructor(values, **attributes)
              except (TypeError, ValueError):
                  pass
          return Index(values, **attributes)
  
      def _deepcopy_if_needed(self, orig, copy=False):
          """
          .. versionadded:: 0.19.0
  
          Make a copy of self if data coincides (in memory) with orig.
          Subclasses should override this if self._base is not an ndarray.
  
          Parameters
          ----------
          orig : ndarray
              other ndarray to compare self._data against
          copy : boolean, default False
              when False, do not run any check, just return self
  
          Returns
          -------
          A copy of self if needed, otherwise self : Index
          """
          if copy:
              # Retrieve the "base objects", i.e. the original memory allocations
              if not isinstance(orig, np.ndarray):
                  # orig is a DatetimeIndex
                  orig = orig.values
              orig = orig if orig.base is None else orig.base
              new = self._data if self._data.base is None else self._data.base
              if orig is new:
                  return self.copy(deep=True)
  
          return self
  
      def _update_inplace(self, result, **kwargs):
          # guard when called from IndexOpsMixin
          raise TypeError("Index can't be updated inplace")
  
      def _sort_levels_monotonic(self):
          """ compat with MultiIndex """
          return self
  
      _index_shared_docs['_get_grouper_for_level'] = """
          Get index grouper corresponding to an index level
  
          Parameters
          ----------
          mapper: Group mapping function or None
              Function mapping index values to groups
          level : int or None
              Index level
  
          Returns
          -------
          grouper : Index
              Index of values to group on
          labels : ndarray of int or None
              Array of locations in level_index
          uniques : Index or None
              Index of unique values for level
          """
  
      @Appender(_index_shared_docs['_get_grouper_for_level'])
      def _get_grouper_for_level(self, mapper, level=None):
          assert level is None or level == 0
          if mapper is None:
              grouper = self
          else:
              grouper = self.map(mapper)
  
          return grouper, None, None
  
      def is_(self, other):
          """
          More flexible, faster check like ``is`` but that works through views
  
          Note: this is *not* the same as ``Index.identical()``, which checks
          that metadata is also the same.
  
          Parameters
          ----------
          other : object
              other object to compare against.
  
          Returns
          -------
          True if both have same underlying data, False otherwise : bool
          """
          # use something other than None to be clearer
          return self._id is getattr(
              other, '_id', Ellipsis) and self._id is not None
  
      def _reset_identity(self):
          """Initializes or resets ``_id`` attribute with new object"""
          self._id = _Identity()
          return self
  
      # ndarray compat
      def __len__(self):
          """
          return the length of the Index
          """
          return len(self._data)
  
      def __array__(self, dtype=None):
          """ the array interface, return my values """
          return self._data.view(np.ndarray)
  
      def __array_wrap__(self, result, context=None):
          """
          Gets called after a ufunc
          """
          if is_bool_dtype(result):
              return result
  
          attrs = self._get_attributes_dict()
          attrs = self._maybe_update_attributes(attrs)
          return Index(result, **attrs)
  
      @cache_readonly
      def dtype(self):
          """ return the dtype object of the underlying data """
          return self._data.dtype
  
      @cache_readonly
      def dtype_str(self):
          """ return the dtype str of the underlying data """
          return str(self.dtype)
  
      @property
      def values(self):
          """ return the underlying data as an ndarray """
          return self._data.view(np.ndarray)
  
      @property
      def _values(self):
          # type: () -> Union[ExtensionArray, Index]
          # TODO(EA): remove index types as they become extension arrays
          """The best array representation.
  
          This is an ndarray, ExtensionArray, or Index subclass. This differs
          from ``_ndarray_values``, which always returns an ndarray.
  
          Both ``_values`` and ``_ndarray_values`` are consistent between
          ``Series`` and ``Index``.
  
          It may differ from the public '.values' method.
  
          index             | values          | _values     | _ndarray_values |
          ----------------- | -------------- -| ----------- | --------------- |
          CategoricalIndex  | Categorical     | Categorical | codes           |
          DatetimeIndex[tz] | ndarray[M8ns]   | DTI[tz]     | ndarray[M8ns]   |
  
          For the following, the ``._values`` is currently ``ndarray[object]``,
          but will soon be an ``ExtensionArray``
  
          index             | values          | _values      | _ndarray_values |
          ----------------- | --------------- | ------------ | --------------- |
          PeriodIndex       | ndarray[object] | ndarray[obj] | ndarray[int]    |
          IntervalIndex     | ndarray[object] | ndarray[obj] | ndarray[object] |
  
          See Also
          --------
          values
          _ndarray_values
          """
          return self.values
  
      def get_values(self):
          """
          Return `Index` data as an `numpy.ndarray`.
  
          Returns
          -------
          numpy.ndarray
              A one-dimensional numpy array of the `Index` values.
  
          See Also
          --------
          Index.values : The attribute that get_values wraps.
  
          Examples
          --------
          Getting the `Index` values of a `DataFrame`:
  
          >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]],
          ...                    index=['a', 'b', 'c'], columns=['A', 'B', 'C'])
          >>> df
             A  B  C
          a  1  2  3
          b  4  5  6
          c  7  8  9
          >>> df.index.get_values()
          array(['a', 'b', 'c'], dtype=object)
  
          Standalone `Index` values:
  
          >>> idx = pd.Index(['1', '2', '3'])
          >>> idx.get_values()
          array(['1', '2', '3'], dtype=object)
  
          `MultiIndex` arrays also have only one dimension:
  
          >>> midx = pd.MultiIndex.from_arrays([[1, 2, 3], ['a', 'b', 'c']],
          ...                                  names=('number', 'letter'))
          >>> midx.get_values()
          array([(1, 'a'), (2, 'b'), (3, 'c')], dtype=object)
          >>> midx.get_values().ndim
          1
          """
          return self.values
  
      @Appender(IndexOpsMixin.memory_usage.__doc__)
      def memory_usage(self, deep=False):
          result = super(Index, self).memory_usage(deep=deep)
  
          # include our engine hashtable
          result += self._engine.sizeof(deep=deep)
          return result
  
      # ops compat
      @deprecate_kwarg(old_arg_name='n', new_arg_name='repeats')
      def repeat(self, repeats, *args, **kwargs):
          """
          Repeat elements of an Index.
  
          Returns a new index where each element of the current index
          is repeated consecutively a given number of times.
  
          Parameters
          ----------
          repeats : int
              The number of repetitions for each element.
          **kwargs
              Additional keywords have no effect but might be accepted for
              compatibility with numpy.
  
          Returns
          -------
          pandas.Index
              Newly created Index with repeated elements.
  
          See Also
          --------
          Series.repeat : Equivalent function for Series
          numpy.repeat : Underlying implementation
  
          Examples
          --------
          >>> idx = pd.Index([1, 2, 3])
          >>> idx
          Int64Index([1, 2, 3], dtype='int64')
          >>> idx.repeat(2)
          Int64Index([1, 1, 2, 2, 3, 3], dtype='int64')
          >>> idx.repeat(3)
          Int64Index([1, 1, 1, 2, 2, 2, 3, 3, 3], dtype='int64')
          """
          nv.validate_repeat(args, kwargs)
          return self._shallow_copy(self._values.repeat(repeats))
  
      _index_shared_docs['where'] = """
          .. versionadded:: 0.19.0
  
          Return an Index of same shape as self and whose corresponding
          entries are from self where cond is True and otherwise are from
          other.
  
          Parameters
          ----------
          cond : boolean array-like with the same length as self
          other : scalar, or array-like
          """
  
      @Appender(_index_shared_docs['where'])
      def where(self, cond, other=None):
          if other is None:
              other = self._na_value
  
          dtype = self.dtype
          values = self.values
  
          if is_bool(other) or is_bool_dtype(other):
  
              # bools force casting
              values = values.astype(object)
              dtype = None
  
          values = np.where(cond, values, other)
  
          if self._is_numeric_dtype and np.any(isna(values)):
              # We can't coerce to the numeric dtype of "self" (unless
              # it's float) if there are NaN values in our output.
              dtype = None
  
          return self._shallow_copy_with_infer(values, dtype=dtype)
  
      def ravel(self, order='C'):
          """
          return an ndarray of the flattened values of the underlying data
  
          See also
          --------
          numpy.ndarray.ravel
          """
          return self._ndarray_values.ravel(order=order)
  
      # construction helpers
      @classmethod
      def _try_convert_to_int_index(cls, data, copy, name, dtype):
          """
          Attempt to convert an array of data into an integer index.
  
          Parameters
          ----------
          data : The data to convert.
          copy : Whether to copy the data or not.
          name : The name of the index returned.
  
          Returns
          -------
          int_index : data converted to either an Int64Index or a
                      UInt64Index
  
          Raises
          ------
          ValueError if the conversion was not successful.
          """
  
          from .numeric import Int64Index, UInt64Index
          if not is_unsigned_integer_dtype(dtype):
              # skip int64 conversion attempt if uint-like dtype is passed, as
              # this could return Int64Index when UInt64Index is what's desrired
              try:
                  res = data.astype('i8', copy=False)
                  if (res == data).all():
                      return Int64Index(res, copy=copy, name=name)
              except (OverflowError, TypeError, ValueError):
                  pass
  
          # Conversion to int64 failed (possibly due to overflow) or was skipped,
          # so let's try now with uint64.
          try:
              res = data.astype('u8', copy=False)
              if (res == data).all():
                  return UInt64Index(res, copy=copy, name=name)
          except (OverflowError, TypeError, ValueError):
              pass
  
          raise ValueError
  
      @classmethod
      def _scalar_data_error(cls, data):
          raise TypeError('{0}(...) must be called with a collection of some '
                          'kind, {1} was passed'.format(cls.__name__,
                                                        repr(data)))
  
      @classmethod
      def _string_data_error(cls, data):
          raise TypeError('String dtype not supported, you may need '
                          'to explicitly cast to a numeric type')
  
      @classmethod
      def _coerce_to_ndarray(cls, data):
          """coerces data to ndarray, raises on scalar data. Converts other
          iterables to list first and then to array. Does not touch ndarrays.
          """
  
          if not isinstance(data, (np.ndarray, Index)):
              if data is None or is_scalar(data):
                  cls._scalar_data_error(data)
  
              # other iterable of some kind
              if not isinstance(data, (ABCSeries, list, tuple)):
                  data = list(data)
              data = np.asarray(data)
          return data
  
      def _get_attributes_dict(self):
          """ return an attributes dict for my class """
          return {k: getattr(self, k, None) for k in self._attributes}
  
      def view(self, cls=None):
  
          # we need to see if we are subclassing an
          # index type here
          if cls is not None and not hasattr(cls, '_typ'):
              result = self._data.view(cls)
          else:
              result = self._shallow_copy()
          if isinstance(result, Index):
              result._id = self._id
          return result
  
      def _coerce_scalar_to_index(self, item):
          """
          we need to coerce a scalar to a compat for our index type
  
          Parameters
          ----------
          item : scalar item to coerce
          """
          dtype = self.dtype
  
          if self._is_numeric_dtype and isna(item):
              # We can't coerce to the numeric dtype of "self" (unless
              # it's float) if there are NaN values in our output.
              dtype = None
  
          return Index([item], dtype=dtype, **self._get_attributes_dict())
  
      _index_shared_docs['copy'] = """
          Make a copy of this object.  Name and dtype sets those attributes on
          the new object.
  
          Parameters
          ----------
          name : string, optional
          deep : boolean, default False
          dtype : numpy dtype or pandas type
  
          Returns
          -------
          copy : Index
  
          Notes
          -----
          In most cases, there should be no functional difference from using
          ``deep``, but if ``deep`` is passed it will attempt to deepcopy.
          """
  
      @Appender(_index_shared_docs['copy'])
      def copy(self, name=None, deep=False, dtype=None, **kwargs):
          if deep:
              new_index = self._shallow_copy(self._data.copy())
          else:
              new_index = self._shallow_copy()
  
          names = kwargs.get('names')
          names = self._validate_names(name=name, names=names, deep=deep)
          new_index = new_index.set_names(names)
  
          if dtype:
              new_index = new_index.astype(dtype)
          return new_index
  
      def __copy__(self, **kwargs):
          return self.copy(**kwargs)
  
      def __deepcopy__(self, memo=None):
          if memo is None:
              memo = {}
          return self.copy(deep=True)
  
      def _validate_names(self, name=None, names=None, deep=False):
          """
          Handles the quirks of having a singular 'name' parameter for general
          Index and plural 'names' parameter for MultiIndex.
          """
          from copy import deepcopy
          if names is not None and name is not None:
              raise TypeError("Can only provide one of `names` and `name`")
          elif names is None and name is None:
              return deepcopy(self.names) if deep else self.names
          elif names is not None:
              if not is_list_like(names):
                  raise TypeError("Must pass list-like as `names`.")
              return names
          else:
              if not is_list_like(name):
                  return [name]
              return name
  
      def __unicode__(self):
          """
          Return a string representation for this object.
  
          Invoked by unicode(df) in py2 only. Yields a Unicode String in both
          py2/py3.
          """
          klass = self.__class__.__name__
          data = self._format_data()
          attrs = self._format_attrs()
          space = self._format_space()
  
          prepr = (u(",%s") %
                   space).join(u("%s=%s") % (k, v) for k, v in attrs)
  
          # no data provided, just attributes
          if data is None:
              data = ''
  
          res = u("%s(%s%s)") % (klass, data, prepr)
  
          return res
  
      def _format_space(self):
  
          # using space here controls if the attributes
          # are line separated or not (the default)
  
          # max_seq_items = get_option('display.max_seq_items')
          # if len(self) > max_seq_items:
          #    space = "\n%s" % (' ' * (len(klass) + 1))
          return " "
  
      @property
      def _formatter_func(self):
          """
          Return the formatted data as a unicode string
          """
          return default_pprint
  
      def _format_data(self, name=None):
          """
          Return the formatted data as a unicode string
          """
          from pandas.io.formats.console import get_console_size
          from pandas.io.formats.format import _get_adjustment
          display_width, _ = get_console_size()
          if display_width is None:
              display_width = get_option('display.width') or 80
          if name is None:
              name = self.__class__.__name__
  
          space1 = "\n%s" % (' ' * (len(name) + 1))
          space2 = "\n%s" % (' ' * (len(name) + 2))
  
          n = len(self)
          sep = ','
          max_seq_items = get_option('display.max_seq_items') or n
          formatter = self._formatter_func
  
          # do we want to justify (only do so for non-objects)
          is_justify = not (self.inferred_type in ('string', 'unicode') or
                            (self.inferred_type == 'categorical' and
                             is_object_dtype(self.categories)))
  
          # are we a truncated display
          is_truncated = n > max_seq_items
  
          # adj can optionally handle unicode eastern asian width
          adj = _get_adjustment()
  
          def _extend_line(s, line, value, display_width, next_line_prefix):
  
              if (adj.len(line.rstrip()) + adj.len(value.rstrip()) >=
                      display_width):
                  s += line.rstrip()
                  line = next_line_prefix
              line += value
              return s, line
  
          def best_len(values):
              if values:
                  return max(adj.len(x) for x in values)
              else:
                  return 0
  
          if n == 0:
              summary = '[], '
          elif n == 1:
              first = formatter(self[0])
              summary = '[%s], ' % first
          elif n == 2:
              first = formatter(self[0])
              last = formatter(self[-1])
              summary = '[%s, %s], ' % (first, last)
          else:
  
              if n > max_seq_items:
                  n = min(max_seq_items // 2, 10)
                  head = [formatter(x) for x in self[:n]]
                  tail = [formatter(x) for x in self[-n:]]
              else:
                  head = []
                  tail = [formatter(x) for x in self]
  
              # adjust all values to max length if needed
              if is_justify:
  
                  # however, if we are not truncated and we are only a single
                  # line, then don't justify
                  if (is_truncated or
                          not (len(', '.join(head)) < display_width and
                               len(', '.join(tail)) < display_width)):
                      max_len = max(best_len(head), best_len(tail))
                      head = [x.rjust(max_len) for x in head]
                      tail = [x.rjust(max_len) for x in tail]
  
              summary = ""
              line = space2
  
              for i in range(len(head)):
                  word = head[i] + sep + ' '
                  summary, line = _extend_line(summary, line, word,
                                               display_width, space2)
  
              if is_truncated:
                  # remove trailing space of last line
                  summary += line.rstrip() + space2 + '...'
                  line = space2
  
              for i in range(len(tail) - 1):
                  word = tail[i] + sep + ' '
                  summary, line = _extend_line(summary, line, word,
                                               display_width, space2)
  
              # last value: no sep added + 1 space of width used for trailing ','
              summary, line = _extend_line(summary, line, tail[-1],
                                           display_width - 2, space2)
              summary += line
              summary += '],'
  
              if len(summary) > (display_width):
                  summary += space1
              else:  # one row
                  summary += ' '
  
              # remove initial space
              summary = '[' + summary[len(space2):]
  
          return summary
  
      def _format_attrs(self):
          """
          Return a list of tuples of the (attr,formatted_value)
          """
          attrs = []
          attrs.append(('dtype', "'%s'" % self.dtype))
          if self.name is not None:
              attrs.append(('name', default_pprint(self.name)))
          max_seq_items = get_option('display.max_seq_items') or len(self)
          if len(self) > max_seq_items:
              attrs.append(('length', len(self)))
          return attrs
  
      def to_series(self, 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
          ----------
          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 : dtype will be based on the type of the Index values.
          """
  
          from pandas import Series
  
          if index is None:
              index = self._shallow_copy()
          if name is None:
              name = self.name
  
          return Series(self._to_embed(), index=index, name=name)
  
      def to_frame(self, index=True):
          """
          Create a DataFrame with a column containing the Index.
  
          .. versionadded:: 0.21.0
  
          Parameters
          ----------
          index : boolean, default True
              Set the index of the returned DataFrame as the original Index.
  
          Returns
          -------
          DataFrame
              DataFrame containing the original Index data.
  
          See Also
          --------
          Index.to_series : Convert an Index to a Series.
          Series.to_frame : Convert Series to DataFrame.
  
          Examples
          --------
          >>> idx = pd.Index(['Ant', 'Bear', 'Cow'], name='animal')
          >>> idx.to_frame()
                 animal
          animal
          Ant       Ant
          Bear     Bear
          Cow       Cow
  
          By default, the original Index is reused. To enforce a new Index:
  
          >>> idx.to_frame(index=False)
              animal
          0   Ant
          1  Bear
          2   Cow
          """
  
          from pandas import DataFrame
          result = DataFrame(self._shallow_copy(), columns=[self.name or 0])
  
          if index:
              result.index = self
          return result
  
      def _to_embed(self, keep_tz=False, dtype=None):
          """
          *this is an internal non-public method*
  
          return an array repr of this object, potentially casting to object
  
          """
          if dtype is not None:
              return self.astype(dtype)._to_embed(keep_tz=keep_tz)
  
          return self.values.copy()
  
      _index_shared_docs['astype'] = """
          Create an Index with values cast to dtypes. The class of a new Index
          is determined by dtype. When conversion is impossible, a ValueError
          exception is raised.
  
          Parameters
          ----------
          dtype : numpy dtype or pandas type
          copy : bool, default True
              By default, astype always returns a newly allocated object.
              If copy is set to False and internal requirements on dtype are
              satisfied, the original data is used to create a new Index
              or the original Index is returned.
  
              .. versionadded:: 0.19.0
  
          """
  
      @Appender(_index_shared_docs['astype'])
      def astype(self, dtype, copy=True):
          if is_dtype_equal(self.dtype, dtype):
              return self.copy() if copy else self
          elif is_categorical_dtype(dtype):
              from .category import CategoricalIndex
              return CategoricalIndex(self.values, name=self.name, dtype=dtype,
                                      copy=copy)
          try:
              return Index(self.values.astype(dtype, copy=copy), name=self.name,
                           dtype=dtype)
          except (TypeError, ValueError):
              msg = 'Cannot cast {name} to dtype {dtype}'
              raise TypeError(msg.format(name=type(self).__name__, dtype=dtype))
  
      def _to_safe_for_reshape(self):
          """ convert to object if we are a categorical """
          return self
  
      def _assert_can_do_setop(self, other):
          if not is_list_like(other):
              raise TypeError('Input must be Index or array-like')
          return True
  
      def _convert_can_do_setop(self, other):
          if not isinstance(other, Index):
              other = Index(other, name=self.name)
              result_name = self.name
          else:
              result_name = self.name if self.name == other.name else None
          return other, result_name
  
      def _convert_for_op(self, value):
          """ Convert value to be insertable to ndarray """
          return value
  
      def _assert_can_do_op(self, value):
          """ Check value is valid for scalar op """
          if not is_scalar(value):
              msg = "'value' must be a scalar, passed: {0}"
              raise TypeError(msg.format(type(value).__name__))
  
      @property
      def nlevels(self):
          return 1
  
      def _get_names(self):
          return FrozenList((self.name, ))
  
      def _set_names(self, values, level=None):
          """
          Set new names on index. Each name has to be a hashable type.
  
          Parameters
          ----------
          values : str or sequence
              name(s) to set
          level : int, level name, or sequence of int/level names (default None)
              If the index is a MultiIndex (hierarchical), level(s) to set (None
              for all levels).  Otherwise level must be None
  
          Raises
          ------
          TypeError if each name is not hashable.
          """
          if not is_list_like(values):
              raise ValueError('Names must be a list-like')
          if len(values) != 1:
              raise ValueError('Length of new names must be 1, got %d' %
                               len(values))
  
          # GH 20527
          # All items in 'name' need to be hashable:
          for name in values:
              if not is_hashable(name):
                  raise TypeError('{}.name must be a hashable type'
                                  .format(self.__class__.__name__))
          self.name = values[0]
  
      names = property(fset=_set_names, fget=_get_names)
  
      def set_names(self, names, level=None, inplace=False):
          """
          Set new names on index. Defaults to returning new index.
  
          Parameters
          ----------
          names : str or sequence
              name(s) to set
          level : int, level name, or sequence of int/level names (default None)
              If the index is a MultiIndex (hierarchical), level(s) to set (None
              for all levels).  Otherwise level must be None
          inplace : bool
              if True, mutates in place
  
          Returns
          -------
          new index (of same type and class...etc) [if inplace, returns None]
  
          Examples
          --------
          >>> Index([1, 2, 3, 4]).set_names('foo')
          Int64Index([1, 2, 3, 4], dtype='int64', name='foo')
          >>> Index([1, 2, 3, 4]).set_names(['foo'])
          Int64Index([1, 2, 3, 4], dtype='int64', name='foo')
          >>> idx = MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
                                            (2, u'one'), (2, u'two')],
                                            names=['foo', 'bar'])
          >>> idx.set_names(['baz', 'quz'])
          MultiIndex(levels=[[1, 2], [u'one', u'two']],
                     labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
                     names=[u'baz', u'quz'])
          >>> idx.set_names('baz', level=0)
          MultiIndex(levels=[[1, 2], [u'one', u'two']],
                     labels=[[0, 0, 1, 1], [0, 1, 0, 1]],
                     names=[u'baz', u'bar'])
          """
  
          if level is not None and self.nlevels == 1:
              raise ValueError('Level must be None for non-MultiIndex')
  
          if level is not None and not is_list_like(level) and is_list_like(
                  names):
              raise TypeError("Names must be a string")
  
          if not is_list_like(names) and level is None and self.nlevels > 1:
              raise TypeError("Must pass list-like as `names`.")
  
          if not is_list_like(names):
              names = [names]
          if level is not None and not is_list_like(level):
              level = [level]
  
          if inplace:
              idx = self
          else:
              idx = self._shallow_copy()
          idx._set_names(names, level=level)
          if not inplace:
              return idx
  
      def rename(self, name, inplace=False):
          """
          Set new names on index. Defaults to returning new index.
  
          Parameters
          ----------
          name : str or list
              name to set
          inplace : bool
              if True, mutates in place
  
          Returns
          -------
          new index (of same type and class...etc) [if inplace, returns None]
          """
          return self.set_names([name], inplace=inplace)
  
      @property
      def _has_complex_internals(self):
          # to disable groupby tricks in MultiIndex
          return False
  
      def _summary(self, name=None):
          """
          Return a summarized representation
  
          Parameters
          ----------
          name : str
              name to use in the summary representation
  
          Returns
          -------
          String with a summarized representation of the index
          """
          if len(self) > 0:
              head = self[0]
              if (hasattr(head, 'format') and
                      not isinstance(head, compat.string_types)):
                  head = head.format()
              tail = self[-1]
              if (hasattr(tail, 'format') and
                      not isinstance(tail, compat.string_types)):
                  tail = tail.format()
              index_summary = ', %s to %s' % (pprint_thing(head),
                                              pprint_thing(tail))
          else:
              index_summary = ''
  
          if name is None:
              name = type(self).__name__
          return '%s: %s entries%s' % (name, len(self), index_summary)
  
      def summary(self, name=None):
          """
          Return a summarized representation
          .. deprecated:: 0.23.0
          """
          warnings.warn("'summary' is deprecated and will be removed in a "
                        "future version.", FutureWarning, stacklevel=2)
          return self._summary(name)
  
      def _mpl_repr(self):
          # how to represent ourselves to matplotlib
          return self.values
  
      _na_value = np.nan
      """The expected NA value to use with this index."""
  
      # introspection
      @property
      def is_monotonic(self):
          """ alias for is_monotonic_increasing (deprecated) """
          return self.is_monotonic_increasing
  
      @property
      def is_monotonic_increasing(self):
          """
          return if the index is monotonic increasing (only equal or
          increasing) values.
  
          Examples
          --------
          >>> Index([1, 2, 3]).is_monotonic_increasing
          True
          >>> Index([1, 2, 2]).is_monotonic_increasing
          True
          >>> Index([1, 3, 2]).is_monotonic_increasing
          False
          """
          return self._engine.is_monotonic_increasing
  
      @property
      def is_monotonic_decreasing(self):
          """
          return if the index is monotonic decreasing (only equal or
          decreasing) values.
  
          Examples
          --------
          >>> Index([3, 2, 1]).is_monotonic_decreasing
          True
          >>> Index([3, 2, 2]).is_monotonic_decreasing
          True
          >>> Index([3, 1, 2]).is_monotonic_decreasing
          False
          """
          return self._engine.is_monotonic_decreasing
  
      @property
      def _is_strictly_monotonic_increasing(self):
          """return if the index is strictly monotonic increasing
          (only increasing) values
  
          Examples
          --------
          >>> Index([1, 2, 3])._is_strictly_monotonic_increasing
          True
          >>> Index([1, 2, 2])._is_strictly_monotonic_increasing
          False
          >>> Index([1, 3, 2])._is_strictly_monotonic_increasing
          False
          """
          return self.is_unique and self.is_monotonic_increasing
  
      @property
      def _is_strictly_monotonic_decreasing(self):
          """return if the index is strictly monotonic decreasing
          (only decreasing) values
  
          Examples
          --------
          >>> Index([3, 2, 1])._is_strictly_monotonic_decreasing
          True
          >>> Index([3, 2, 2])._is_strictly_monotonic_decreasing
          False
          >>> Index([3, 1, 2])._is_strictly_monotonic_decreasing
          False
          """
          return self.is_unique and self.is_monotonic_decreasing
  
      def is_lexsorted_for_tuple(self, tup):
          return True
  
      @cache_readonly
      def is_unique(self):
          """ return if the index has unique values """
          return self._engine.is_unique
  
      @property
      def has_duplicates(self):
          return not self.is_unique
  
      def is_boolean(self):
          return self.inferred_type in ['boolean']
  
      def is_integer(self):
          return self.inferred_type in ['integer']
  
      def is_floating(self):
          return self.inferred_type in ['floating', 'mixed-integer-float']
  
      def is_numeric(self):
          return self.inferred_type in ['integer', 'floating']
  
      def is_object(self):
          return is_object_dtype(self.dtype)
  
      def is_categorical(self):
          """
          Check if the Index holds categorical data.
  
          Returns
          -------
          boolean
              True if the Index is categorical.
  
          See Also
          --------
          CategoricalIndex : Index for categorical data.
  
          Examples
          --------
          >>> idx = pd.Index(["Watermelon", "Orange", "Apple",
          ...                 "Watermelon"]).astype("category")
          >>> idx.is_categorical()
          True
  
          >>> idx = pd.Index([1, 3, 5, 7])
          >>> idx.is_categorical()
          False
  
          >>> s = pd.Series(["Peter", "Victor", "Elisabeth", "Mar"])
          >>> s
          0        Peter
          1       Victor
          2    Elisabeth
          3          Mar
          dtype: object
          >>> s.index.is_categorical()
          False
          """
          return self.inferred_type in ['categorical']
  
      def is_interval(self):
          return self.inferred_type in ['interval']
  
      def is_mixed(self):
          return self.inferred_type in ['mixed']
  
      def holds_integer(self):
          return self.inferred_type in ['integer', 'mixed-integer']
  
      _index_shared_docs['_convert_scalar_indexer'] = """
          Convert a scalar indexer.
  
          Parameters
          ----------
          key : label of the slice bound
          kind : {'ix', 'loc', 'getitem', 'iloc'} or None
      """
  
      @Appender(_index_shared_docs['_convert_scalar_indexer'])
      def _convert_scalar_indexer(self, key, kind=None):
          assert kind in ['ix', 'loc', 'getitem', 'iloc', None]
  
          if kind == 'iloc':
              return self._validate_indexer('positional', key, kind)
  
          if len(self) and not isinstance(self, ABCMultiIndex,):
  
              # we can raise here if we are definitive that this
              # is positional indexing (eg. .ix on with a float)
              # or label indexing if we are using a type able
              # to be represented in the index
  
              if kind in ['getitem', 'ix'] and is_float(key):
                  if not self.is_floating():
                      return self._invalid_indexer('label', key)
  
              elif kind in ['loc'] and is_float(key):
  
                  # we want to raise KeyError on string/mixed here
                  # technically we *could* raise a TypeError
                  # on anything but mixed though
                  if self.inferred_type not in ['floating',
                                                'mixed-integer-float',
                                                'string',
                                                'unicode',
                                                'mixed']:
                      return self._invalid_indexer('label', key)
  
              elif kind in ['loc'] and is_integer(key):
                  if not self.holds_integer():
                      return self._invalid_indexer('label', key)
  
          return key
  
      _index_shared_docs['_convert_slice_indexer'] = """
          Convert a slice indexer.
  
          By definition, these are labels unless 'iloc' is passed in.
          Floats are not allowed as the start, step, or stop of the slice.
  
          Parameters
          ----------
          key : label of the slice bound
          kind : {'ix', 'loc', 'getitem', 'iloc'} or None
      """
  
      @Appender(_index_shared_docs['_convert_slice_indexer'])
      def _convert_slice_indexer(self, key, kind=None):
          assert kind in ['ix', 'loc', 'getitem', 'iloc', None]
  
          # if we are not a slice, then we are done
          if not isinstance(key, slice):
              return key
  
          # validate iloc
          if kind == 'iloc':
              return slice(self._validate_indexer('slice', key.start, kind),
                           self._validate_indexer('slice', key.stop, kind),
                           self._validate_indexer('slice', key.step, kind))
  
          # potentially cast the bounds to integers
          start, stop, step = key.start, key.stop, key.step
  
          # figure out if this is a positional indexer
          def is_int(v):
              return v is None or is_integer(v)
  
          is_null_slicer = start is None and stop is None
          is_index_slice = is_int(start) and is_int(stop)
          is_positional = is_index_slice and not self.is_integer()
  
          if kind == 'getitem':
              """
              called from the getitem slicers, validate that we are in fact
              integers
              """
              if self.is_integer() or is_index_slice:
                  return slice(self._validate_indexer('slice', key.start, kind),
                               self._validate_indexer('slice', key.stop, kind),
                               self._validate_indexer('slice', key.step, kind))
  
          # convert the slice to an indexer here
  
          # if we are mixed and have integers
          try:
              if is_positional and self.is_mixed():
                  # TODO: i, j are not used anywhere
                  if start is not None:
                      i = self.get_loc(start)  # noqa
                  if stop is not None:
                      j = self.get_loc(stop)  # noqa
                  is_positional = False
          except KeyError:
              if self.inferred_type == 'mixed-integer-float':
                  raise
  
          if is_null_slicer:
              indexer = key
          elif is_positional:
              indexer = key
          else:
              try:
                  indexer = self.slice_indexer(start, stop, step, kind=kind)
              except Exception:
                  if is_index_slice:
                      if self.is_integer():
                          raise
                      else:
                          indexer = key
                  else:
                      raise
  
          return indexer
  
      def _convert_listlike_indexer(self, keyarr, kind=None):
          """
          Parameters
          ----------
          keyarr : list-like
              Indexer to convert.
  
          Returns
          -------
          tuple (indexer, keyarr)
              indexer is an ndarray or None if cannot convert
              keyarr are tuple-safe keys
          """
          if isinstance(keyarr, Index):
              keyarr = self._convert_index_indexer(keyarr)
          else:
              keyarr = self._convert_arr_indexer(keyarr)
  
          indexer = self._convert_list_indexer(keyarr, kind=kind)
          return indexer, keyarr
  
      _index_shared_docs['_convert_arr_indexer'] = """
          Convert an array-like indexer to the appropriate dtype.
  
          Parameters
          ----------
          keyarr : array-like
              Indexer to convert.
  
          Returns
          -------
          converted_keyarr : array-like
      """
  
      @Appender(_index_shared_docs['_convert_arr_indexer'])
      def _convert_arr_indexer(self, keyarr):
          keyarr = com._asarray_tuplesafe(keyarr)
          return keyarr
  
      _index_shared_docs['_convert_index_indexer'] = """
          Convert an Index indexer to the appropriate dtype.
  
          Parameters
          ----------
          keyarr : Index (or sub-class)
              Indexer to convert.
  
          Returns
          -------
          converted_keyarr : Index (or sub-class)
      """
  
      @Appender(_index_shared_docs['_convert_index_indexer'])
      def _convert_index_indexer(self, keyarr):
          return keyarr
  
      _index_shared_docs['_convert_list_indexer'] = """
          Convert a list-like indexer to the appropriate dtype.
  
          Parameters
          ----------
          keyarr : Index (or sub-class)
              Indexer to convert.
          kind : iloc, ix, loc, optional
  
          Returns
          -------
          positional indexer or None
      """
  
      @Appender(_index_shared_docs['_convert_list_indexer'])
      def _convert_list_indexer(self, keyarr, kind=None):
          if (kind in [None, 'iloc', 'ix'] and
                  is_integer_dtype(keyarr) and not self.is_floating() and
                  not isinstance(keyarr, ABCPeriodIndex)):
  
              if self.inferred_type == 'mixed-integer':
                  indexer = self.get_indexer(keyarr)
                  if (indexer >= 0).all():
                      return indexer
                  # missing values are flagged as -1 by get_indexer and negative
                  # indices are already converted to positive indices in the
                  # above if-statement, so the negative flags are changed to
                  # values outside the range of indices so as to trigger an
                  # IndexError in maybe_convert_indices
                  indexer[indexer < 0] = len(self)
                  from pandas.core.indexing import maybe_convert_indices
                  return maybe_convert_indices(indexer, len(self))
  
              elif not self.inferred_type == 'integer':
                  keyarr = np.where(keyarr < 0, len(self) + keyarr, keyarr)
                  return keyarr
  
          return None
  
      def _invalid_indexer(self, form, key):
          """ consistent invalid indexer message """
          raise TypeError("cannot do {form} indexing on {klass} with these "
                          "indexers [{key}] of {kind}".format(
                              form=form, klass=type(self), key=key,
                              kind=type(key)))
  
      def get_duplicates(self):
          """
          Extract duplicated index elements.
  
          Returns a sorted list of index elements which appear more than once in
          the index.
  
          .. deprecated:: 0.23.0
              Use idx[idx.duplicated()].unique() instead
  
          Returns
          -------
          array-like
              List of duplicated indexes.
  
          See Also
          --------
          Index.duplicated : Return boolean array denoting duplicates.
          Index.drop_duplicates : Return Index with duplicates removed.
  
          Examples
          --------
  
          Works on different Index of types.
  
          >>> pd.Index([1, 2, 2, 3, 3, 3, 4]).get_duplicates()
          [2, 3]
          >>> pd.Index([1., 2., 2., 3., 3., 3., 4.]).get_duplicates()
          [2.0, 3.0]
          >>> pd.Index(['a', 'b', 'b', 'c', 'c', 'c', 'd']).get_duplicates()
          ['b', 'c']
  
          Note that for a DatetimeIndex, it does not return a list but a new
          DatetimeIndex:
  
          >>> dates = pd.to_datetime(['2018-01-01', '2018-01-02', '2018-01-03',
          ...                         '2018-01-03', '2018-01-04', '2018-01-04'],
          ...                        format='%Y-%m-%d')
          >>> pd.Index(dates).get_duplicates()
          DatetimeIndex(['2018-01-03', '2018-01-04'],
                        dtype='datetime64[ns]', freq=None)
  
          Sorts duplicated elements even when indexes are unordered.
  
          >>> pd.Index([1, 2, 3, 2, 3, 4, 3]).get_duplicates()
          [2, 3]
  
          Return empty array-like structure when all elements are unique.
  
          >>> pd.Index([1, 2, 3, 4]).get_duplicates()
          []
          >>> dates = pd.to_datetime(['2018-01-01', '2018-01-02', '2018-01-03'],
          ...                        format='%Y-%m-%d')
          >>> pd.Index(dates).get_duplicates()
          DatetimeIndex([], dtype='datetime64[ns]', freq=None)
          """
          warnings.warn("'get_duplicates' is deprecated and will be removed in "
                        "a future release. You can use "
                        "idx[idx.duplicated()].unique() instead",
                        FutureWarning, stacklevel=2)
  
          return self[self.duplicated()].unique()
  
      def _cleanup(self):
          self._engine.clear_mapping()
  
      @cache_readonly
      def _constructor(self):
          return type(self)
  
      @cache_readonly
      def _engine(self):
          # property, for now, slow to look up
          return self._engine_type(lambda: self._ndarray_values, len(self))
  
      def _validate_index_level(self, level):
          """
          Validate index level.
  
          For single-level Index getting level number is a no-op, but some
          verification must be done like in MultiIndex.
  
          """
          if isinstance(level, int):
              if level < 0 and level != -1:
                  raise IndexError("Too many levels: Index has only 1 level,"
                                   " %d is not a valid level number" % (level, ))
              elif level > 0:
                  raise IndexError("Too many levels:"
                                   " Index has only 1 level, not %d" %
                                   (level + 1))
          elif level != self.name:
              raise KeyError('Level %s must be same as name (%s)' %
                             (level, self.name))
  
      def _get_level_number(self, level):
          self._validate_index_level(level)
          return 0
  
      @cache_readonly
      def inferred_type(self):
          """ return a string of the type inferred from the values """
          return lib.infer_dtype(self)
  
      def _is_memory_usage_qualified(self):
          """ return a boolean if we need a qualified .info display """
          return self.is_object()
  
      def is_type_compatible(self, kind):
          return kind == self.inferred_type
  
      @cache_readonly
      def is_all_dates(self):
          if self._data is None:
              return False
          return is_datetime_array(_ensure_object(self.values))
  
      def __reduce__(self):
          d = dict(data=self._data)
          d.update(self._get_attributes_dict())
          return _new_Index, (self.__class__, d), None
  
      def __setstate__(self, state):
          """Necessary for making this object picklable"""
  
          if isinstance(state, dict):
              self._data = state.pop('data')
              for k, v in compat.iteritems(state):
                  setattr(self, k, v)
  
          elif isinstance(state, tuple):
  
              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)
                  self.name = own_state[0]
  
              else:  # pragma: no cover
                  data = np.empty(state)
                  np.ndarray.__setstate__(data, state)
  
              self._data = data
              self._reset_identity()
          else:
              raise Exception("invalid pickle state")
  
      _unpickle_compat = __setstate__
  
      def __nonzero__(self):
          raise ValueError("The truth value of a {0} is ambiguous. "
                           "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
                           .format(self.__class__.__name__))
  
      __bool__ = __nonzero__
  
      _index_shared_docs['__contains__'] = """
          return a boolean if this key is IN the index
  
          Parameters
          ----------
          key : object
  
          Returns
          -------
          boolean
          """
  
      @Appender(_index_shared_docs['__contains__'] % _index_doc_kwargs)
      def __contains__(self, key):
          hash(key)
          try:
              return key in self._engine
          except (OverflowError, TypeError, ValueError):
              return False
  
      _index_shared_docs['contains'] = """
          return a boolean if this key is IN the index
  
          Parameters
          ----------
          key : object
  
          Returns
          -------
          boolean
          """
  
      @Appender(_index_shared_docs['contains'] % _index_doc_kwargs)
      def contains(self, key):
          hash(key)
          try:
              return key in self._engine
          except (TypeError, ValueError):
              return False
  
      def __hash__(self):
          raise TypeError("unhashable type: %r" % type(self).__name__)
  
      def __setitem__(self, key, value):
          raise TypeError("Index does not support mutable operations")
  
      def __getitem__(self, key):
          """
          Override numpy.ndarray's __getitem__ method to work as desired.
  
          This function adds lists and Series as valid boolean indexers
          (ndarrays only supports ndarray with dtype=bool).
  
          If resulting ndim != 1, plain ndarray is returned instead of
          corresponding `Index` subclass.
  
          """
          # There's no custom logic to be implemented in __getslice__, so it's
          # not overloaded intentionally.
          getitem = self._data.__getitem__
          promote = self._shallow_copy
  
          if is_scalar(key):
              return getitem(key)
  
          if isinstance(key, slice):
              # This case is separated from the conditional above to avoid
              # pessimization of basic indexing.
              return promote(getitem(key))
  
          if com.is_bool_indexer(key):
              key = np.asarray(key)
  
          key = com._values_from_object(key)
          result = getitem(key)
          if not is_scalar(result):
              return promote(result)
          else:
              return result
  
      def _can_hold_identifiers_and_holds_name(self, name):
          """
          Faster check for ``name in self`` when we know `name` is a Python
          identifier (e.g. in NDFrame.__getattr__, which hits this to support
          . key lookup). For indexes that can't hold identifiers (everything
          but object & categorical) we just return False.
  
          https://github.com/pandas-dev/pandas/issues/19764
          """
          if self.is_object() or self.is_categorical():
              return name in self
          return False
  
      def append(self, other):
          """
          Append a collection of Index options together
  
          Parameters
          ----------
          other : Index or list/tuple of indices
  
          Returns
          -------
          appended : Index
          """
  
          to_concat = [self]
  
          if isinstance(other, (list, tuple)):
              to_concat = to_concat + list(other)
          else:
              to_concat.append(other)
  
          for obj in to_concat:
              if not isinstance(obj, Index):
                  raise TypeError('all inputs must be Index')
  
          names = {obj.name for obj in to_concat}
          name = None if len(names) > 1 else self.name
  
          return self._concat(to_concat, name)
  
      def _concat(self, to_concat, name):
  
          typs = _concat.get_dtype_kinds(to_concat)
  
          if len(typs) == 1:
              return self._concat_same_dtype(to_concat, name=name)
          return _concat._concat_index_asobject(to_concat, name=name)
  
      def _concat_same_dtype(self, to_concat, name):
          """
          Concatenate to_concat which has the same class
          """
          # must be overridden in specific classes
          return _concat._concat_index_asobject(to_concat, name)
  
      _index_shared_docs['take'] = """
          return a new %(klass)s of the values selected by the indices
  
          For internal compatibility with numpy arrays.
  
          Parameters
          ----------
          indices : list
              Indices to be taken
          axis : int, optional
              The axis over which to select values, always 0.
          allow_fill : bool, default True
          fill_value : bool, default None
              If allow_fill=True and fill_value is not None, indices specified by
              -1 is regarded as NA. If Index doesn't hold NA, raise ValueError
  
          See also
          --------
          numpy.ndarray.take
          """
  
      @Appender(_index_shared_docs['take'] % _index_doc_kwargs)
      def take(self, indices, axis=0, allow_fill=True,
               fill_value=None, **kwargs):
          if kwargs:
              nv.validate_take(tuple(), kwargs)
          indices = _ensure_platform_int(indices)
          if self._can_hold_na:
              taken = self._assert_take_fillable(self.values, indices,
                                                 allow_fill=allow_fill,
                                                 fill_value=fill_value,
                                                 na_value=self._na_value)
          else:
              if allow_fill and fill_value is not None:
                  msg = 'Unable to fill values because {0} cannot contain NA'
                  raise ValueError(msg.format(self.__class__.__name__))
              taken = self.values.take(indices)
          return self._shallow_copy(taken)
  
      def _assert_take_fillable(self, values, indices, allow_fill=True,
                                fill_value=None, na_value=np.nan):
          """ Internal method to handle NA filling of take """
          indices = _ensure_platform_int(indices)
  
          # only fill if we are passing a non-None fill_value
          if allow_fill and fill_value is not None:
              if (indices < -1).any():
                  msg = ('When allow_fill=True and fill_value is not None, '
                         'all indices must be >= -1')
                  raise ValueError(msg)
              taken = algos.take(values,
                                 indices,
                                 allow_fill=allow_fill,
                                 fill_value=na_value)
          else:
              taken = values.take(indices)
          return taken
  
      @cache_readonly
      def _isnan(self):
          """ return if each value is nan"""
          if self._can_hold_na:
              return isna(self)
          else:
              # shouldn't reach to this condition by checking hasnans beforehand
              values = np.empty(len(self), dtype=np.bool_)
              values.fill(False)
              return values
  
      @cache_readonly
      def _nan_idxs(self):
          if self._can_hold_na:
              w, = self._isnan.nonzero()
              return w
          else:
              return np.array([], dtype=np.int64)
  
      @cache_readonly
      def hasnans(self):
          """ return if I have any nans; enables various perf speedups """
          if self._can_hold_na:
              return self._isnan.any()
          else:
              return False
  
      def isna(self):
          """
          Detect missing values.
  
          Return a boolean same-sized object indicating if the values are NA.
          NA values, such as ``None``, :attr:`numpy.NaN` or :attr:`pd.NaT`, get
          mapped to ``True`` values.
          Everything else get mapped to ``False`` values. Characters such as
          empty strings `''` or :attr:`numpy.inf` are not considered NA values
          (unless you set ``pandas.options.mode.use_inf_as_na = True``).
  
          .. versionadded:: 0.20.0
  
          Returns
          -------
          numpy.ndarray
              A boolean array of whether my values are NA
  
          See Also
          --------
          pandas.Index.notna : boolean inverse of isna.
          pandas.Index.dropna : omit entries with missing values.
          pandas.isna : top-level isna.
          Series.isna : detect missing values in Series object.
  
          Examples
          --------
          Show which entries in a pandas.Index are NA. The result is an
          array.
  
          >>> idx = pd.Index([5.2, 6.0, np.NaN])
          >>> idx
          Float64Index([5.2, 6.0, nan], dtype='float64')
          >>> idx.isna()
          array([False, False,  True], dtype=bool)
  
          Empty strings are not considered NA values. None is considered an NA
          value.
  
          >>> idx = pd.Index(['black', '', 'red', None])
          >>> idx
          Index(['black', '', 'red', None], dtype='object')
          >>> idx.isna()
          array([False, False, False,  True], dtype=bool)
  
          For datetimes, `NaT` (Not a Time) is considered as an NA value.
  
          >>> idx = pd.DatetimeIndex([pd.Timestamp('1940-04-25'),
          ...                         pd.Timestamp(''), None, pd.NaT])
          >>> idx
          DatetimeIndex(['1940-04-25', 'NaT', 'NaT', 'NaT'],
                        dtype='datetime64[ns]', freq=None)
          >>> idx.isna()
          array([False,  True,  True,  True], dtype=bool)
          """
          return self._isnan
      isnull = isna
  
      def notna(self):
          """
          Detect existing (non-missing) values.
  
          Return a boolean same-sized object indicating if the values are not NA.
          Non-missing values get mapped to ``True``. Characters such as empty
          strings ``''`` or :attr:`numpy.inf` are not considered NA values
          (unless you set ``pandas.options.mode.use_inf_as_na = True``).
          NA values, such as None or :attr:`numpy.NaN`, get mapped to ``False``
          values.
  
          .. versionadded:: 0.20.0
  
          Returns
          -------
          numpy.ndarray
              Boolean array to indicate which entries are not NA.
  
          See also
          --------
          Index.notnull : alias of notna
          Index.isna: inverse of notna
          pandas.notna : top-level notna
  
          Examples
          --------
          Show which entries in an Index are not NA. The result is an
          array.
  
          >>> idx = pd.Index([5.2, 6.0, np.NaN])
          >>> idx
          Float64Index([5.2, 6.0, nan], dtype='float64')
          >>> idx.notna()
          array([ True,  True, False])
  
          Empty strings are not considered NA values. None is considered a NA
          value.
  
          >>> idx = pd.Index(['black', '', 'red', None])
          >>> idx
          Index(['black', '', 'red', None], dtype='object')
          >>> idx.notna()
          array([ True,  True,  True, False])
          """
          return ~self.isna()
      notnull = notna
  
      def putmask(self, mask, value):
          """
          return a new Index of the values set with the mask
  
          See also
          --------
          numpy.ndarray.putmask
          """
          values = self.values.copy()
          try:
              np.putmask(values, mask, self._convert_for_op(value))
              return self._shallow_copy(values)
          except (ValueError, TypeError) as err:
              if is_object_dtype(self):
                  raise err
  
              # coerces to object
              return self.astype(object).putmask(mask, value)
  
      def format(self, name=False, formatter=None, **kwargs):
          """
          Render a string representation of the Index
          """
          header = []
          if name:
              header.append(pprint_thing(self.name,
                                         escape_chars=('\t', '\r', '\n')) if
                            self.name is not None else '')
  
          if formatter is not None:
              return header + list(self.map(formatter))
  
          return self._format_with_header(header, **kwargs)
  
      def _format_with_header(self, header, na_rep='NaN', **kwargs):
          values = self.values
  
          from pandas.io.formats.format import format_array
  
          if is_categorical_dtype(values.dtype):
              values = np.array(values)
  
          elif is_object_dtype(values.dtype):
              values = lib.maybe_convert_objects(values, safe=1)
  
          if is_object_dtype(values.dtype):
              result = [pprint_thing(x, escape_chars=('\t', '\r', '\n'))
                        for x in values]
  
              # could have nans
              mask = isna(values)
              if mask.any():
                  result = np.array(result)
                  result[mask] = na_rep
                  result = result.tolist()
  
          else:
              result = _trim_front(format_array(values, None, justify='left'))
          return header + result
  
      def to_native_types(self, slicer=None, **kwargs):
          """
          Format specified values of `self` and return them.
  
          Parameters
          ----------
          slicer : int, array-like
              An indexer into `self` that specifies which values
              are used in the formatting process.
          kwargs : dict
              Options for specifying how the values should be formatted.
              These options include the following:
  
              1) na_rep : str
                  The value that serves as a placeholder for NULL values
              2) quoting : bool or None
                  Whether or not there are quoted values in `self`
              3) date_format : str
                  The format used to represent date-like values
          """
  
          values = self
          if slicer is not None:
              values = values[slicer]
          return values._format_native_types(**kwargs)
  
      def _format_native_types(self, na_rep='', quoting=None, **kwargs):
          """ actually format my specific types """
          mask = isna(self)
          if not self.is_object() and not quoting:
              values = np.asarray(self).astype(str)
          else:
              values = np.array(self, dtype=object, copy=True)
  
          values[mask] = na_rep
          return values
  
      def equals(self, other):
          """
          Determines if two Index objects contain the same elements.
          """
          if self.is_(other):
              return True
  
          if not isinstance(other, Index):
              return False
  
          if is_object_dtype(self) and not is_object_dtype(other):
              # if other is not object, use other's logic for coercion
              return other.equals(self)
  
          try:
              return array_equivalent(com._values_from_object(self),
                                      com._values_from_object(other))
          except Exception:
              return False
  
      def identical(self, other):
          """Similar to equals, but check that other comparable attributes are
          also equal
          """
          return (self.equals(other) and
                  all((getattr(self, c, None) == getattr(other, c, None)
                       for c in self._comparables)) and
                  type(self) == type(other))
  
      def asof(self, label):
          """
          For a sorted index, return the most recent label up to and including
          the passed label. Return NaN if not found.
  
          See also
          --------
          get_loc : asof is a thin wrapper around get_loc with method='pad'
          """
          try:
              loc = self.get_loc(label, method='pad')
          except KeyError:
              return self._na_value
          else:
              if isinstance(loc, slice):
                  loc = loc.indices(len(self))[-1]
              return self[loc]
  
      def asof_locs(self, where, mask):
          """
          where : array of timestamps
          mask : array of booleans where data is not NA
  
          """
          locs = self.values[mask].searchsorted(where.values, side='right')
  
          locs = np.where(locs > 0, locs - 1, 0)
          result = np.arange(len(self))[mask].take(locs)
  
          first = mask.argmax()
          result[(locs == 0) & (where < self.values[first])] = -1
  
          return result
  
      def sort_values(self, return_indexer=False, ascending=True):
          """
          Return a sorted copy of the index.
  
          Return a sorted copy of the index, and optionally return the indices
          that sorted the index itself.
  
          Parameters
          ----------
          return_indexer : bool, default False
              Should the indices that would sort the index be returned.
          ascending : bool, default True
              Should the index values be sorted in an ascending order.
  
          Returns
          -------
          sorted_index : pandas.Index
              Sorted copy of the index.
          indexer : numpy.ndarray, optional
              The indices that the index itself was sorted by.
  
          See Also
          --------
          pandas.Series.sort_values : Sort values of a Series.
          pandas.DataFrame.sort_values : Sort values in a DataFrame.
  
          Examples
          --------
          >>> idx = pd.Index([10, 100, 1, 1000])
          >>> idx
          Int64Index([10, 100, 1, 1000], dtype='int64')
  
          Sort values in ascending order (default behavior).
  
          >>> idx.sort_values()
          Int64Index([1, 10, 100, 1000], dtype='int64')
  
          Sort values in descending order, and also get the indices `idx` was
          sorted by.
  
          >>> idx.sort_values(ascending=False, return_indexer=True)
          (Int64Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2]))
          """
          _as = self.argsort()
          if not ascending:
              _as = _as[::-1]
  
          sorted_index = self.take(_as)
  
          if return_indexer:
              return sorted_index, _as
          else:
              return sorted_index
  
      def sort(self, *args, **kwargs):
          raise TypeError("cannot sort an Index object in-place, use "
                          "sort_values instead")
  
      def sortlevel(self, level=None, ascending=True, sort_remaining=None):
          """
  
          For internal compatibility with with the Index API
  
          Sort the Index. This is for compat with MultiIndex
  
          Parameters
          ----------
          ascending : boolean, default True
              False to sort in descending order
  
          level, sort_remaining are compat parameters
  
          Returns
          -------
          sorted_index : Index
          """
          return self.sort_values(return_indexer=True, ascending=ascending)
  
      def shift(self, periods=1, freq=None):
          """
          Shift index by desired number of time frequency increments.
  
          This method is for shifting the values of datetime-like indexes
          by a specified time increment a given number of times.
  
          Parameters
          ----------
          periods : int, default 1
              Number of periods (or increments) to shift by,
              can be positive or negative.
          freq : pandas.DateOffset, pandas.Timedelta or string, optional
              Frequency increment to shift by.
              If None, the index is shifted by its own `freq` attribute.
              Offset aliases are valid strings, e.g., 'D', 'W', 'M' etc.
  
          Returns
          -------
          pandas.Index
              shifted index
  
          See Also
          --------
          Series.shift : Shift values of Series.
  
          Examples
          --------
          Put the first 5 month starts of 2011 into an index.
  
          >>> month_starts = pd.date_range('1/1/2011', periods=5, freq='MS')
          >>> month_starts
          DatetimeIndex(['2011-01-01', '2011-02-01', '2011-03-01', '2011-04-01',
                         '2011-05-01'],
                        dtype='datetime64[ns]', freq='MS')
  
          Shift the index by 10 days.
  
          >>> month_starts.shift(10, freq='D')
          DatetimeIndex(['2011-01-11', '2011-02-11', '2011-03-11', '2011-04-11',
                         '2011-05-11'],
                        dtype='datetime64[ns]', freq=None)
  
          The default value of `freq` is the `freq` attribute of the index,
          which is 'MS' (month start) in this example.
  
          >>> month_starts.shift(10)
          DatetimeIndex(['2011-11-01', '2011-12-01', '2012-01-01', '2012-02-01',
                         '2012-03-01'],
                        dtype='datetime64[ns]', freq='MS')
  
          Notes
          -----
          This method is only implemented for datetime-like index classes,
          i.e., DatetimeIndex, PeriodIndex and TimedeltaIndex.
          """
          raise NotImplementedError("Not supported for type %s" %
                                    type(self).__name__)
  
      def argsort(self, *args, **kwargs):
          """
          Return the integer indicies that would sort the index.
  
          Parameters
          ----------
          *args
              Passed to `numpy.ndarray.argsort`.
          **kwargs
              Passed to `numpy.ndarray.argsort`.
  
          Returns
          -------
          numpy.ndarray
              Integer indicies that would sort the index if used as
              an indexer.
  
          See also
          --------
          numpy.argsort : Similar method for NumPy arrays.
          Index.sort_values : Return sorted copy of Index.
  
          Examples
          --------
          >>> idx = pd.Index(['b', 'a', 'd', 'c'])
          >>> idx
          Index(['b', 'a', 'd', 'c'], dtype='object')
  
          >>> order = idx.argsort()
          >>> order
          array([1, 0, 3, 2])
  
          >>> idx[order]
          Index(['a', 'b', 'c', 'd'], dtype='object')
          """
          result = self.asi8
          if result is None:
              result = np.array(self)
          return result.argsort(*args, **kwargs)
  
      def __add__(self, other):
          return Index(np.array(self) + other)
  
      def __radd__(self, other):
          return Index(other + np.array(self))
  
      def __iadd__(self, other):
          # alias for __add__
          return self + other
  
      def __sub__(self, other):
          raise TypeError("cannot perform __sub__ with this index type: "
                          "{typ}".format(typ=type(self).__name__))
  
      def __and__(self, other):
          return self.intersection(other)
  
      def __or__(self, other):
          return self.union(other)
  
      def __xor__(self, other):
          return self.symmetric_difference(other)
  
      def _get_consensus_name(self, other):
          """
          Given 2 indexes, give a consensus name meaning
          we take the not None one, or None if the names differ.
          Return a new object if we are resetting the name
          """
          if self.name != other.name:
              if self.name is None or other.name is None:
                  name = self.name or other.name
              else:
                  name = None
              if self.name != name:
                  return self._shallow_copy(name=name)
          return self
  
      def union(self, other):
          """
          Form the union of two Index objects and sorts if possible.
  
          Parameters
          ----------
          other : Index or array-like
  
          Returns
          -------
          union : Index
  
          Examples
          --------
  
          >>> idx1 = pd.Index([1, 2, 3, 4])
          >>> idx2 = pd.Index([3, 4, 5, 6])
          >>> idx1.union(idx2)
          Int64Index([1, 2, 3, 4, 5, 6], dtype='int64')
  
          """
          self._assert_can_do_setop(other)
          other = _ensure_index(other)
  
          if len(other) == 0 or self.equals(other):
              return self._get_consensus_name(other)
  
          if len(self) == 0:
              return other._get_consensus_name(self)
  
          # TODO: is_dtype_union_equal is a hack around
          # 1. buggy set ops with duplicates (GH #13432)
          # 2. CategoricalIndex lacking setops (GH #10186)
          # Once those are fixed, this workaround can be removed
          if not is_dtype_union_equal(self.dtype, other.dtype):
              this = self.astype('O')
              other = other.astype('O')
              return this.union(other)
  
          # TODO(EA): setops-refactor, clean all this up
          if is_period_dtype(self) or is_datetime64tz_dtype(self):
              lvals = self._ndarray_values
          else:
              lvals = self._values
          if is_period_dtype(other) or is_datetime64tz_dtype(other):
              rvals = other._ndarray_values
          else:
              rvals = other._values
  
          if self.is_monotonic and other.is_monotonic:
              try:
                  result = self._outer_indexer(lvals, rvals)[0]
              except TypeError:
                  # incomparable objects
                  result = list(lvals)
  
                  # worth making this faster? a very unusual case
                  value_set = set(lvals)
                  result.extend([x for x in rvals if x not in value_set])
          else:
              indexer = self.get_indexer(other)
              indexer, = (indexer == -1).nonzero()
  
              if len(indexer) > 0:
                  other_diff = algos.take_nd(rvals, indexer,
                                             allow_fill=False)
                  result = _concat._concat_compat((lvals, other_diff))
  
                  try:
                      lvals[0] < other_diff[0]
                  except TypeError as e:
                      warnings.warn("%s, sort order is undefined for "
                                    "incomparable objects" % e, RuntimeWarning,
                                    stacklevel=3)
                  else:
                      types = frozenset((self.inferred_type,
                                         other.inferred_type))
                      if not types & _unsortable_types:
                          result.sort()
  
              else:
                  result = lvals
  
                  try:
                      result = np.sort(result)
                  except TypeError as e:
                      warnings.warn("%s, sort order is undefined for "
                                    "incomparable objects" % e, RuntimeWarning,
                                    stacklevel=3)
  
          # for subclasses
          return self._wrap_union_result(other, result)
  
      def _wrap_union_result(self, other, result):
          name = self.name if self.name == other.name else None
          return self.__class__(result, name=name)
  
      def intersection(self, other):
          """
          Form the intersection of two Index objects.
  
          This returns a new Index with elements common to the index and `other`,
          preserving the order of the calling index.
  
          Parameters
          ----------
          other : Index or array-like
  
          Returns
          -------
          intersection : Index
  
          Examples
          --------
  
          >>> idx1 = pd.Index([1, 2, 3, 4])
          >>> idx2 = pd.Index([3, 4, 5, 6])
          >>> idx1.intersection(idx2)
          Int64Index([3, 4], dtype='int64')
  
          """
          self._assert_can_do_setop(other)
          other = _ensure_index(other)
  
          if self.equals(other):
              return self._get_consensus_name(other)
  
          if not is_dtype_equal(self.dtype, other.dtype):
              this = self.astype('O')
              other = other.astype('O')
              return this.intersection(other)
  
          # TODO(EA): setops-refactor, clean all this up
          if is_period_dtype(self):
              lvals = self._ndarray_values
          else:
              lvals = self._values
          if is_period_dtype(other):
              rvals = other._ndarray_values
          else:
              rvals = other._values
  
          if self.is_monotonic and other.is_monotonic:
              try:
                  result = self._inner_indexer(lvals, rvals)[0]
                  return self._wrap_union_result(other, result)
              except TypeError:
                  pass
  
          try:
              indexer = Index(rvals).get_indexer(lvals)
              indexer = indexer.take((indexer != -1).nonzero()[0])
          except Exception:
              # duplicates
              indexer = algos.unique1d(
                  Index(rvals).get_indexer_non_unique(lvals)[0])
              indexer = indexer[indexer != -1]
  
          taken = other.take(indexer)
          if self.name != other.name:
              taken.name = None
          return taken
  
      def difference(self, other):
          """
          Return a new Index with elements from the index that are not in
          `other`.
  
          This is the set difference of two Index objects.
          It's sorted if sorting is possible.
  
          Parameters
          ----------
          other : Index or array-like
  
          Returns
          -------
          difference : Index
  
          Examples
          --------
  
          >>> idx1 = pd.Index([1, 2, 3, 4])
          >>> idx2 = pd.Index([3, 4, 5, 6])
          >>> idx1.difference(idx2)
          Int64Index([1, 2], dtype='int64')
  
          """
          self._assert_can_do_setop(other)
  
          if self.equals(other):
              return self._shallow_copy([])
  
          other, result_name = self._convert_can_do_setop(other)
  
          this = self._get_unique_index()
  
          indexer = this.get_indexer(other)
          indexer = indexer.take((indexer != -1).nonzero()[0])
  
          label_diff = np.setdiff1d(np.arange(this.size), indexer,
                                    assume_unique=True)
          the_diff = this.values.take(label_diff)
          try:
              the_diff = sorting.safe_sort(the_diff)
          except TypeError:
              pass
  
          return this._shallow_copy(the_diff, name=result_name, freq=None)
  
      def symmetric_difference(self, other, result_name=None):
          """
          Compute the symmetric difference of two Index objects.
          It's sorted if sorting is possible.
  
          Parameters
          ----------
          other : Index or array-like
          result_name : str
  
          Returns
          -------
          symmetric_difference : Index
  
          Notes
          -----
          ``symmetric_difference`` contains elements that appear in either
          ``idx1`` or ``idx2`` but not both. Equivalent to the Index created by
          ``idx1.difference(idx2) | idx2.difference(idx1)`` with duplicates
          dropped.
  
          Examples
          --------
          >>> idx1 = Index([1, 2, 3, 4])
          >>> idx2 = Index([2, 3, 4, 5])
          >>> idx1.symmetric_difference(idx2)
          Int64Index([1, 5], dtype='int64')
  
          You can also use the ``^`` operator:
  
          >>> idx1 ^ idx2
          Int64Index([1, 5], dtype='int64')
          """
          self._assert_can_do_setop(other)
          other, result_name_update = self._convert_can_do_setop(other)
          if result_name is None:
              result_name = result_name_update
  
          this = self._get_unique_index()
          other = other._get_unique_index()
          indexer = this.get_indexer(other)
  
          # {this} minus {other}
          common_indexer = indexer.take((indexer != -1).nonzero()[0])
          left_indexer = np.setdiff1d(np.arange(this.size), common_indexer,
                                      assume_unique=True)
          left_diff = this.values.take(left_indexer)
  
          # {other} minus {this}
          right_indexer = (indexer == -1).nonzero()[0]
          right_diff = other.values.take(right_indexer)
  
          the_diff = _concat._concat_compat([left_diff, right_diff])
          try:
              the_diff = sorting.safe_sort(the_diff)
          except TypeError:
              pass
  
          attribs = self._get_attributes_dict()
          attribs['name'] = result_name
          if 'freq' in attribs:
              attribs['freq'] = None
          return self._shallow_copy_with_infer(the_diff, **attribs)
  
      def _get_unique_index(self, dropna=False):
          """
          Returns an index containing unique values.
  
          Parameters
          ----------
          dropna : bool
              If True, NaN values are dropped.
  
          Returns
          -------
          uniques : index
          """
          if self.is_unique and not dropna:
              return self
  
          values = self.values
  
          if not self.is_unique:
              values = self.unique()
  
          if dropna:
              try:
                  if self.hasnans:
                      values = values[~isna(values)]
              except NotImplementedError:
                  pass
  
          return self._shallow_copy(values)
  
      _index_shared_docs['get_loc'] = """
          Get integer location, slice or boolean mask for requested label.
  
          Parameters
          ----------
          key : label
          method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional
              * default: exact matches only.
              * pad / ffill: find the PREVIOUS index value if no exact match.
              * backfill / bfill: use NEXT index value if no exact match
              * nearest: use the NEAREST index value if no exact match. Tied
                distances are broken by preferring the larger index value.
          tolerance : optional
              Maximum distance from index value for inexact matches. The value of
              the index at the matching location most satisfy the equation
              ``abs(index[loc] - key) <= tolerance``.
  
              Tolerance may be a scalar
              value, which applies the same tolerance to all values, or
              list-like, which applies variable tolerance per element. List-like
              includes list, tuple, array, Series, and must be the same size as
              the index and its dtype must exactly match the index's type.
  
              .. versionadded:: 0.21.0 (list-like tolerance)
  
          Returns
          -------
          loc : int if unique index, slice if monotonic index, else mask
  
          Examples
          ---------
          >>> unique_index = pd.Index(list('abc'))
          >>> unique_index.get_loc('b')
          1
  
          >>> monotonic_index = pd.Index(list('abbc'))
          >>> monotonic_index.get_loc('b')
          slice(1, 3, None)
  
          >>> non_monotonic_index = pd.Index(list('abcb'))
          >>> non_monotonic_index.get_loc('b')
          array([False,  True, False,  True], dtype=bool)
          """
  
      @Appender(_index_shared_docs['get_loc'])
      def get_loc(self, key, method=None, tolerance=None):
          if method is None:
              if tolerance is not None:
                  raise ValueError('tolerance argument only valid if using pad, '
                                   'backfill or nearest lookups')
              try:
                  return self._engine.get_loc(key)
              except KeyError:
                  return self._engine.get_loc(self._maybe_cast_indexer(key))
  
          indexer = self.get_indexer([key], method=method, tolerance=tolerance)
          if indexer.ndim > 1 or indexer.size > 1:
              raise TypeError('get_loc requires scalar valued input')
          loc = indexer.item()
          if loc == -1:
              raise KeyError(key)
          return loc
  
      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 we have something that is Index-like, then
          # use this, e.g. DatetimeIndex
          s = getattr(series, '_values', None)
          if isinstance(s, (ExtensionArray, Index)) and is_scalar(key):
              # GH 20825
              # Unify Index and ExtensionArray treatment
              # First try to convert the key to a location
              # If that fails, see if key is an integer, and
              # try that
              try:
                  iloc = self.get_loc(key)
                  return s[iloc]
              except KeyError:
                  if is_integer(key):
                      return s[key]
  
          s = com._values_from_object(series)
          k = com._values_from_object(key)
  
          k = self._convert_scalar_indexer(k, kind='getitem')
          try:
              return self._engine.get_value(s, k,
                                            tz=getattr(series.dtype, 'tz', None))
          except KeyError as e1:
              if len(self) > 0 and self.inferred_type in ['integer', 'boolean']:
                  raise
  
              try:
                  return libindex.get_value_box(s, key)
              except IndexError:
                  raise
              except TypeError:
                  # generator/iterator-like
                  if is_iterator(key):
                      raise InvalidIndexError(key)
                  else:
                      raise e1
              except Exception:  # pragma: no cover
                  raise e1
          except TypeError:
              # python 3
              if is_scalar(key):  # pragma: no cover
                  raise IndexError(key)
              raise InvalidIndexError(key)
  
      def set_value(self, arr, key, value):
          """
          Fast lookup of value from 1-dimensional ndarray. Only use this if you
          know what you're doing
          """
          self._engine.set_value(com._values_from_object(arr),
                                 com._values_from_object(key), value)
  
      def _get_level_values(self, level):
          """
          Return an Index of values for requested level, equal to the length
          of the index.
  
          Parameters
          ----------
          level : int or str
              ``level`` is either the integer position of the level in the
              MultiIndex, or the name of the level.
  
          Returns
          -------
          values : Index
              ``self``, as there is only one level in the Index.
  
          See also
          ---------
          pandas.MultiIndex.get_level_values : get values for a level of a
                                               MultiIndex
          """
  
          self._validate_index_level(level)
          return self
  
      get_level_values = _get_level_values
  
      def droplevel(self, level=0):
          """
          Return index with requested level(s) removed. If resulting index has
          only 1 level left, the result will be of Index type, not MultiIndex.
  
          .. versionadded:: 0.23.1 (support for non-MultiIndex)
  
          Parameters
          ----------
          level : int, str, or list-like, default 0
              If a string is given, must be the name of a level
              If list-like, elements must be names or indexes of levels.
  
          Returns
          -------
          index : Index or MultiIndex
          """
          if not isinstance(level, (tuple, list)):
              level = [level]
  
          levnums = sorted(self._get_level_number(lev) for lev in level)[::-1]
  
          if len(level) == 0:
              return self
          if len(level) >= self.nlevels:
              raise ValueError("Cannot remove {} levels from an index with {} "
                               "levels: at least one level must be "
                               "left.".format(len(level), self.nlevels))
          # The two checks above guarantee that here self is a MultiIndex
  
          new_levels = list(self.levels)
          new_labels = list(self.labels)
          new_names = list(self.names)
  
          for i in levnums:
              new_levels.pop(i)
              new_labels.pop(i)
              new_names.pop(i)
  
          if len(new_levels) == 1:
  
              # set nan if needed
              mask = new_labels[0] == -1
              result = new_levels[0].take(new_labels[0])
              if mask.any():
                  result = result.putmask(mask, np.nan)
  
              result.name = new_names[0]
              return result
          else:
              from .multi import MultiIndex
              return MultiIndex(levels=new_levels, labels=new_labels,
                                names=new_names, verify_integrity=False)
  
      _index_shared_docs['get_indexer'] = """
          Compute indexer and mask for new index given the current index. The
          indexer should be then used as an input to ndarray.take to align the
          current data to the new index.
  
          Parameters
          ----------
          target : %(target_klass)s
          method : {None, 'pad'/'ffill', 'backfill'/'bfill', 'nearest'}, optional
              * default: exact matches only.
              * pad / ffill: find the PREVIOUS index value if no exact match.
              * backfill / bfill: use NEXT index value if no exact match
              * nearest: use the NEAREST index value if no exact match. Tied
                distances are broken by preferring the larger index value.
          limit : int, optional
              Maximum number of consecutive labels in ``target`` to match for
              inexact matches.
          tolerance : optional
              Maximum distance between original and new labels for inexact
              matches. The values of the index at the matching locations most
              satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
  
              Tolerance may be a scalar value, which applies the same tolerance
              to all values, or list-like, which applies variable tolerance per
              element. List-like includes list, tuple, array, Series, and must be
              the same size as the index and its dtype must exactly match the
              index's type.
  
              .. versionadded:: 0.21.0 (list-like tolerance)
  
          Examples
          --------
          >>> indexer = index.get_indexer(new_index)
          >>> new_values = cur_values.take(indexer)
  
          Returns
          -------
          indexer : ndarray of int
              Integers from 0 to n - 1 indicating that the index at these
              positions matches the corresponding target values. Missing values
              in the target are marked by -1.
          """
  
      @Appender(_index_shared_docs['get_indexer'] % _index_doc_kwargs)
      def get_indexer(self, target, method=None, limit=None, tolerance=None):
          method = missing.clean_reindex_fill_method(method)
          target = _ensure_index(target)
          if tolerance is not None:
              tolerance = self._convert_tolerance(tolerance, target)
  
          # Treat boolean labels passed to a numeric index as not found. Without
          # this fix False and True would be treated as 0 and 1 respectively.
          # (GH #16877)
          if target.is_boolean() and self.is_numeric():
              return _ensure_platform_int(np.repeat(-1, target.size))
  
          pself, ptarget = self._maybe_promote(target)
          if pself is not self or ptarget is not target:
              return pself.get_indexer(ptarget, method=method, limit=limit,
                                       tolerance=tolerance)
  
          if not is_dtype_equal(self.dtype, target.dtype):
              this = self.astype(object)
              target = target.astype(object)
              return this.get_indexer(target, method=method, limit=limit,
                                      tolerance=tolerance)
  
          if not self.is_unique:
              raise InvalidIndexError('Reindexing only valid with uniquely'
                                      ' valued Index objects')
  
          if method == 'pad' or method == 'backfill':
              indexer = self._get_fill_indexer(target, method, limit, tolerance)
          elif method == 'nearest':
              indexer = self._get_nearest_indexer(target, limit, tolerance)
          else:
              if tolerance is not None:
                  raise ValueError('tolerance argument only valid if doing pad, '
                                   'backfill or nearest reindexing')
              if limit is not None:
                  raise ValueError('limit argument only valid if doing pad, '
                                   'backfill or nearest reindexing')
  
              indexer = self._engine.get_indexer(target._ndarray_values)
  
          return _ensure_platform_int(indexer)
  
      def _convert_tolerance(self, tolerance, target):
          # override this method on subclasses
          tolerance = np.asarray(tolerance)
          if target.size != tolerance.size and tolerance.size > 1:
              raise ValueError('list-like tolerance size must match '
                               'target index size')
          return tolerance
  
      def _get_fill_indexer(self, target, method, limit=None, tolerance=None):
          if self.is_monotonic_increasing and target.is_monotonic_increasing:
              method = (self._engine.get_pad_indexer if method == 'pad' else
                        self._engine.get_backfill_indexer)
              indexer = method(target._ndarray_values, limit)
          else:
              indexer = self._get_fill_indexer_searchsorted(target, method,
                                                            limit)
          if tolerance is not None:
              indexer = self._filter_indexer_tolerance(target._ndarray_values,
                                                       indexer,
                                                       tolerance)
          return indexer
  
      def _get_fill_indexer_searchsorted(self, target, method, limit=None):
          """
          Fallback pad/backfill get_indexer that works for monotonic decreasing
          indexes and non-monotonic targets
          """
          if limit is not None:
              raise ValueError('limit argument for %r method only well-defined '
                               'if index and target are monotonic' % method)
  
          side = 'left' if method == 'pad' else 'right'
  
          # find exact matches first (this simplifies the algorithm)
          indexer = self.get_indexer(target)
          nonexact = (indexer == -1)
          indexer[nonexact] = self._searchsorted_monotonic(target[nonexact],
                                                           side)
          if side == 'left':
              # searchsorted returns "indices into a sorted array such that,
              # if the corresponding elements in v were inserted before the
              # indices, the order of a would be preserved".
              # Thus, we need to subtract 1 to find values to the left.
              indexer[nonexact] -= 1
              # This also mapped not found values (values of 0 from
              # np.searchsorted) to -1, which conveniently is also our
              # sentinel for missing values
          else:
              # Mark indices to the right of the largest value as not found
              indexer[indexer == len(self)] = -1
          return indexer
  
      def _get_nearest_indexer(self, target, limit, tolerance):
          """
          Get the indexer for the nearest index labels; requires an index with
          values that can be subtracted from each other (e.g., not strings or
          tuples).
          """
          left_indexer = self.get_indexer(target, 'pad', limit=limit)
          right_indexer = self.get_indexer(target, 'backfill', limit=limit)
  
          target = np.asarray(target)
          left_distances = abs(self.values[left_indexer] - target)
          right_distances = abs(self.values[right_indexer] - target)
  
          op = operator.lt if self.is_monotonic_increasing else operator.le
          indexer = np.where(op(left_distances, right_distances) |
                             (right_indexer == -1), left_indexer, right_indexer)
          if tolerance is not None:
              indexer = self._filter_indexer_tolerance(target, indexer,
                                                       tolerance)
          return indexer
  
      def _filter_indexer_tolerance(self, target, indexer, tolerance):
          distance = abs(self.values[indexer] - target)
          indexer = np.where(distance <= tolerance, indexer, -1)
          return indexer
  
      _index_shared_docs['get_indexer_non_unique'] = """
          Compute indexer and mask for new index given the current index. The
          indexer should be then used as an input to ndarray.take to align the
          current data to the new index.
  
          Parameters
          ----------
          target : %(target_klass)s
  
          Returns
          -------
          indexer : ndarray of int
              Integers from 0 to n - 1 indicating that the index at these
              positions matches the corresponding target values. Missing values
              in the target are marked by -1.
          missing : ndarray of int
              An indexer into the target of the values not found.
              These correspond to the -1 in the indexer array
          """
  
      @Appender(_index_shared_docs['get_indexer_non_unique'] % _index_doc_kwargs)
      def get_indexer_non_unique(self, target):
          target = _ensure_index(target)
          pself, ptarget = self._maybe_promote(target)
          if pself is not self or ptarget is not target:
              return pself.get_indexer_non_unique(ptarget)
  
          if self.is_all_dates:
              self = Index(self.asi8)
              tgt_values = target.asi8
          else:
              tgt_values = target._ndarray_values
  
          indexer, missing = self._engine.get_indexer_non_unique(tgt_values)
          return _ensure_platform_int(indexer), missing
  
      def get_indexer_for(self, target, **kwargs):
          """
          guaranteed return of an indexer even when non-unique
          This dispatches to get_indexer or get_indexer_nonunique as appropriate
          """
          if self.is_unique:
              return self.get_indexer(target, **kwargs)
          indexer, _ = self.get_indexer_non_unique(target, **kwargs)
          return indexer
  
      def _maybe_promote(self, other):
          # A hack, but it works
          from pandas.core.indexes.datetimes import DatetimeIndex
          if self.inferred_type == 'date' and isinstance(other, DatetimeIndex):
              return DatetimeIndex(self), other
          elif self.inferred_type == 'boolean':
              if not is_object_dtype(self.dtype):
                  return self.astype('object'), other.astype('object')
          return self, other
  
      def groupby(self, values):
          """
          Group the index labels by a given array of values.
  
          Parameters
          ----------
          values : array
              Values used to determine the groups.
  
          Returns
          -------
          groups : dict
              {group name -> group labels}
          """
  
          # TODO: if we are a MultiIndex, we can do better
          # that converting to tuples
          from .multi import MultiIndex
          if isinstance(values, MultiIndex):
              values = values.values
          values = _ensure_categorical(values)
          result = values._reverse_indexer()
  
          # map to the label
          result = {k: self.take(v) for k, v in compat.iteritems(result)}
  
          return result
  
      def map(self, mapper, na_action=None):
          """
          Map values using input correspondence (a dict, Series, or function).
  
          Parameters
          ----------
          mapper : function, dict, or Series
              Mapping correspondence.
          na_action : {None, 'ignore'}
              If 'ignore', propagate NA values, without passing them to the
              mapping correspondence.
  
          Returns
          -------
          applied : Union[Index, MultiIndex], inferred
              The output of the mapping function applied to the index.
              If the function returns a tuple with more than one element
              a MultiIndex will be returned.
          """
  
          from .multi import MultiIndex
          new_values = super(Index, self)._map_values(
              mapper, na_action=na_action)
  
          attributes = self._get_attributes_dict()
  
          # we can return a MultiIndex
          if new_values.size and isinstance(new_values[0], tuple):
              if isinstance(self, MultiIndex):
                  names = self.names
              elif attributes.get('name'):
                  names = [attributes.get('name')] * len(new_values[0])
              else:
                  names = None
              return MultiIndex.from_tuples(new_values,
                                            names=names)
  
          attributes['copy'] = False
          if not new_values.size:
              # empty
              attributes['dtype'] = self.dtype
  
          return Index(new_values, **attributes)
  
      def isin(self, values, level=None):
          """
          Return a boolean array where the index values are in `values`.
  
          Compute boolean array of whether each index value is found in the
          passed set of values. The length of the returned boolean array matches
          the length of the index.
  
          Parameters
          ----------
          values : set or list-like
              Sought values.
  
              .. versionadded:: 0.18.1
  
                 Support for values as a set.
  
          level : str or int, optional
              Name or position of the index level to use (if the index is a
              `MultiIndex`).
  
          Returns
          -------
          is_contained : ndarray
              NumPy array of boolean values.
  
          See also
          --------
          Series.isin : Same for Series.
          DataFrame.isin : Same method for DataFrames.
  
          Notes
          -----
          In the case of `MultiIndex` you must either specify `values` as a
          list-like object containing tuples that are the same length as the
          number of levels, or specify `level`. Otherwise it will raise a
          ``ValueError``.
  
          If `level` is specified:
  
          - if it is the name of one *and only one* index level, use that level;
          - otherwise it should be a number indicating level position.
  
          Examples
          --------
          >>> idx = pd.Index([1,2,3])
          >>> idx
          Int64Index([1, 2, 3], dtype='int64')
  
          Check whether each index value in a list of values.
          >>> idx.isin([1, 4])
          array([ True, False, False])
  
          >>> midx = pd.MultiIndex.from_arrays([[1,2,3],
          ...                                  ['red', 'blue', 'green']],
          ...                                  names=('number', 'color'))
          >>> midx
          MultiIndex(levels=[[1, 2, 3], ['blue', 'green', 'red']],
                     labels=[[0, 1, 2], [2, 0, 1]],
                     names=['number', 'color'])
  
          Check whether the strings in the 'color' level of the MultiIndex
          are in a list of colors.
  
          >>> midx.isin(['red', 'orange', 'yellow'], level='color')
          array([ True, False, False])
  
          To check across the levels of a MultiIndex, pass a list of tuples:
  
          >>> midx.isin([(1, 'red'), (3, 'red')])
          array([ True, False, False])
  
          For a DatetimeIndex, string values in `values` are converted to
          Timestamps.
  
          >>> dates = ['2000-03-11', '2000-03-12', '2000-03-13']
          >>> dti = pd.to_datetime(dates)
          >>> dti
          DatetimeIndex(['2000-03-11', '2000-03-12', '2000-03-13'],
          dtype='datetime64[ns]', freq=None)
  
          >>> dti.isin(['2000-03-11'])
          array([ True, False, False])
          """
          if level is not None:
              self._validate_index_level(level)
          return algos.isin(self, values)
  
      def _can_reindex(self, indexer):
          """
          *this is an internal non-public method*
  
          Check if we are allowing reindexing with this particular indexer
  
          Parameters
          ----------
          indexer : an integer indexer
  
          Raises
          ------
          ValueError if its a duplicate axis
          """
  
          # trying to reindex on an axis with duplicates
          if not self.is_unique and len(indexer):
              raise ValueError("cannot reindex from a duplicate axis")
  
      def reindex(self, target, method=None, level=None, limit=None,
                  tolerance=None):
          """
          Create index with target's values (move/add/delete values as necessary)
  
          Parameters
          ----------
          target : an iterable
  
          Returns
          -------
          new_index : pd.Index
              Resulting index
          indexer : np.ndarray or None
              Indices of output values in original index
  
          """
          # GH6552: preserve names when reindexing to non-named target
          # (i.e. neither Index nor Series).
          preserve_names = not hasattr(target, 'name')
  
          # GH7774: preserve dtype/tz if target is empty and not an Index.
          target = _ensure_has_len(target)  # target may be an iterator
  
          if not isinstance(target, Index) and len(target) == 0:
              attrs = self._get_attributes_dict()
              attrs.pop('freq', None)  # don't preserve freq
              target = self._simple_new(None, dtype=self.dtype, **attrs)
          else:
              target = _ensure_index(target)
  
          if level is not None:
              if method is not None:
                  raise TypeError('Fill method not supported if level passed')
              _, indexer, _ = self._join_level(target, level, how='right',
                                               return_indexers=True)
          else:
              if self.equals(target):
                  indexer = None
              else:
  
                  if self.is_unique:
                      indexer = self.get_indexer(target, method=method,
                                                 limit=limit,
                                                 tolerance=tolerance)
                  else:
                      if method is not None or limit is not None:
                          raise ValueError("cannot reindex a non-unique index "
                                           "with a method or limit")
                      indexer, missing = self.get_indexer_non_unique(target)
  
          if preserve_names and target.nlevels == 1 and target.name != self.name:
              target = target.copy()
              target.name = self.name
  
          return target, indexer
  
      def _reindex_non_unique(self, target):
          """
          *this is an internal non-public method*
  
          Create a new index with target's values (move/add/delete values as
          necessary) use with non-unique Index and a possibly non-unique target
  
          Parameters
          ----------
          target : an iterable
  
          Returns
          -------
          new_index : pd.Index
              Resulting index
          indexer : np.ndarray or None
              Indices of output values in original index
  
          """
  
          target = _ensure_index(target)
          indexer, missing = self.get_indexer_non_unique(target)
          check = indexer != -1
          new_labels = self.take(indexer[check])
          new_indexer = None
  
          if len(missing):
              length = np.arange(len(indexer))
  
              missing = _ensure_platform_int(missing)
              missing_labels = target.take(missing)
              missing_indexer = _ensure_int64(length[~check])
              cur_labels = self.take(indexer[check]).values
              cur_indexer = _ensure_int64(length[check])
  
              new_labels = np.empty(tuple([len(indexer)]), dtype=object)
              new_labels[cur_indexer] = cur_labels
              new_labels[missing_indexer] = missing_labels
  
              # a unique indexer
              if target.is_unique:
  
                  # see GH5553, make sure we use the right indexer
                  new_indexer = np.arange(len(indexer))
                  new_indexer[cur_indexer] = np.arange(len(cur_labels))
                  new_indexer[missing_indexer] = -1
  
              # we have a non_unique selector, need to use the original
              # indexer here
              else:
  
                  # need to retake to have the same size as the indexer
                  indexer[~check] = 0
  
                  # reset the new indexer to account for the new size
                  new_indexer = np.arange(len(self.take(indexer)))
                  new_indexer[~check] = -1
  
          new_index = self._shallow_copy_with_infer(new_labels, freq=None)
          return new_index, indexer, new_indexer
  
      _index_shared_docs['join'] = """
          *this is an internal non-public method*
  
          Compute join_index and indexers to conform data
          structures to the new index.
  
          Parameters
          ----------
          other : Index
          how : {'left', 'right', 'inner', 'outer'}
          level : int or level name, default None
          return_indexers : boolean, default False
          sort : boolean, default False
              Sort the join keys lexicographically in the result Index. If False,
              the order of the join keys depends on the join type (how keyword)
  
              .. versionadded:: 0.20.0
  
          Returns
          -------
          join_index, (left_indexer, right_indexer)
          """
  
      @Appender(_index_shared_docs['join'])
      def join(self, other, how='left', level=None, return_indexers=False,
               sort=False):
          from .multi import MultiIndex
          self_is_mi = isinstance(self, MultiIndex)
          other_is_mi = isinstance(other, MultiIndex)
  
          # try to figure out the join level
          # GH3662
          if level is None and (self_is_mi or other_is_mi):
  
              # have the same levels/names so a simple join
              if self.names == other.names:
                  pass
              else:
                  return self._join_multi(other, how=how,
                                          return_indexers=return_indexers)
  
          # join on the level
          if level is not None and (self_is_mi or other_is_mi):
              return self._join_level(other, level, how=how,
                                      return_indexers=return_indexers)
  
          other = _ensure_index(other)
  
          if len(other) == 0 and how in ('left', 'outer'):
              join_index = self._shallow_copy()
              if return_indexers:
                  rindexer = np.repeat(-1, len(join_index))
                  return join_index, None, rindexer
              else:
                  return join_index
  
          if len(self) == 0 and how in ('right', 'outer'):
              join_index = other._shallow_copy()
              if return_indexers:
                  lindexer = np.repeat(-1, len(join_index))
                  return join_index, lindexer, None
              else:
                  return join_index
  
          if self._join_precedence < other._join_precedence:
              how = {'right': 'left', 'left': 'right'}.get(how, how)
              result = other.join(self, how=how, level=level,
                                  return_indexers=return_indexers)
              if return_indexers:
                  x, y, z = result
                  result = x, z, y
              return result
  
          if not is_dtype_equal(self.dtype, other.dtype):
              this = self.astype('O')
              other = other.astype('O')
              return this.join(other, how=how, return_indexers=return_indexers)
  
          _validate_join_method(how)
  
          if not self.is_unique and not other.is_unique:
              return self._join_non_unique(other, how=how,
                                           return_indexers=return_indexers)
          elif not self.is_unique or not other.is_unique:
              if self.is_monotonic and other.is_monotonic:
                  return self._join_monotonic(other, how=how,
                                              return_indexers=return_indexers)
              else:
                  return self._join_non_unique(other, how=how,
                                               return_indexers=return_indexers)
          elif self.is_monotonic and other.is_monotonic:
              try:
                  return self._join_monotonic(other, how=how,
                                              return_indexers=return_indexers)
              except TypeError:
                  pass
  
          if how == 'left':
              join_index = self
          elif how == 'right':
              join_index = other
          elif how == 'inner':
              join_index = self.intersection(other)
          elif how == 'outer':
              join_index = self.union(other)
  
          if sort:
              join_index = join_index.sort_values()
  
          if return_indexers:
              if join_index is self:
                  lindexer = None
              else:
                  lindexer = self.get_indexer(join_index)
              if join_index is other:
                  rindexer = None
              else:
                  rindexer = other.get_indexer(join_index)
              return join_index, lindexer, rindexer
          else:
              return join_index
  
      def _join_multi(self, other, how, return_indexers=True):
          from .multi import MultiIndex
          self_is_mi = isinstance(self, MultiIndex)
          other_is_mi = isinstance(other, MultiIndex)
  
          # figure out join names
          self_names = com._not_none(*self.names)
          other_names = com._not_none(*other.names)
          overlap = list(set(self_names) & set(other_names))
  
          # need at least 1 in common, but not more than 1
          if not len(overlap):
              raise ValueError("cannot join with no level specified and no "
                               "overlapping names")
          if len(overlap) > 1:
              raise NotImplementedError("merging with more than one level "
                                        "overlap on a multi-index is not "
                                        "implemented")
          jl = overlap[0]
  
          # make the indices into mi's that match
          if not (self_is_mi and other_is_mi):
  
              flip_order = False
              if self_is_mi:
                  self, other = other, self
                  flip_order = True
                  # flip if join method is right or left
                  how = {'right': 'left', 'left': 'right'}.get(how, how)
  
              level = other.names.index(jl)
              result = self._join_level(other, level, how=how,
                                        return_indexers=return_indexers)
  
              if flip_order:
                  if isinstance(result, tuple):
                      return result[0], result[2], result[1]
              return result
  
          # 2 multi-indexes
          raise NotImplementedError("merging with both multi-indexes is not "
                                    "implemented")
  
      def _join_non_unique(self, other, how='left', return_indexers=False):
          from pandas.core.reshape.merge import _get_join_indexers
  
          left_idx, right_idx = _get_join_indexers([self._ndarray_values],
                                                   [other._ndarray_values],
                                                   how=how,
                                                   sort=True)
  
          left_idx = _ensure_platform_int(left_idx)
          right_idx = _ensure_platform_int(right_idx)
  
          join_index = np.asarray(self._ndarray_values.take(left_idx))
          mask = left_idx == -1
          np.putmask(join_index, mask, other._ndarray_values.take(right_idx))
  
          join_index = self._wrap_joined_index(join_index, other)
  
          if return_indexers:
              return join_index, left_idx, right_idx
          else:
              return join_index
  
      def _join_level(self, other, level, how='left', return_indexers=False,
                      keep_order=True):
          """
          The join method *only* affects the level of the resulting
          MultiIndex. Otherwise it just exactly aligns the Index data to the
          labels of the level in the MultiIndex. If `keep_order` == True, the
          order of the data indexed by the MultiIndex will not be changed;
          otherwise, it will tie out with `other`.
          """
          from .multi import MultiIndex
  
          def _get_leaf_sorter(labels):
              """
              returns sorter for the inner most level while preserving the
              order of higher levels
              """
              if labels[0].size == 0:
                  return np.empty(0, dtype='int64')
  
              if len(labels) == 1:
                  lab = _ensure_int64(labels[0])
                  sorter, _ = libalgos.groupsort_indexer(lab, 1 + lab.max())
                  return sorter
  
              # find indexers of beginning of each set of
              # same-key labels w.r.t all but last level
              tic = labels[0][:-1] != labels[0][1:]
              for lab in labels[1:-1]:
                  tic |= lab[:-1] != lab[1:]
  
              starts = np.hstack(([True], tic, [True])).nonzero()[0]
              lab = _ensure_int64(labels[-1])
              return lib.get_level_sorter(lab, _ensure_int64(starts))
  
          if isinstance(self, MultiIndex) and isinstance(other, MultiIndex):
              raise TypeError('Join on level between two MultiIndex objects '
                              'is ambiguous')
  
          left, right = self, other
  
          flip_order = not isinstance(self, MultiIndex)
          if flip_order:
              left, right = right, left
              how = {'right': 'left', 'left': 'right'}.get(how, how)
  
          level = left._get_level_number(level)
          old_level = left.levels[level]
  
          if not right.is_unique:
              raise NotImplementedError('Index._join_level on non-unique index '
                                        'is not implemented')
  
          new_level, left_lev_indexer, right_lev_indexer = \
              old_level.join(right, how=how, return_indexers=True)
  
          if left_lev_indexer is None:
              if keep_order or len(left) == 0:
                  left_indexer = None
                  join_index = left
              else:  # sort the leaves
                  left_indexer = _get_leaf_sorter(left.labels[:level + 1])
                  join_index = left[left_indexer]
  
          else:
              left_lev_indexer = _ensure_int64(left_lev_indexer)
              rev_indexer = lib.get_reverse_indexer(left_lev_indexer,
                                                    len(old_level))
  
              new_lev_labels = algos.take_nd(rev_indexer, left.labels[level],
                                             allow_fill=False)
  
              new_labels = list(left.labels)
              new_labels[level] = new_lev_labels
  
              new_levels = list(left.levels)
              new_levels[level] = new_level
  
              if keep_order:  # just drop missing values. o.w. keep order
                  left_indexer = np.arange(len(left), dtype=np.intp)
                  mask = new_lev_labels != -1
                  if not mask.all():
                      new_labels = [lab[mask] for lab in new_labels]
                      left_indexer = left_indexer[mask]
  
              else:  # tie out the order with other
                  if level == 0:  # outer most level, take the fast route
                      ngroups = 1 + new_lev_labels.max()
                      left_indexer, counts = libalgos.groupsort_indexer(
                          new_lev_labels, ngroups)
  
                      # missing values are placed first; drop them!
                      left_indexer = left_indexer[counts[0]:]
                      new_labels = [lab[left_indexer] for lab in new_labels]
  
                  else:  # sort the leaves
                      mask = new_lev_labels != -1
                      mask_all = mask.all()
                      if not mask_all:
                          new_labels = [lab[mask] for lab in new_labels]
  
                      left_indexer = _get_leaf_sorter(new_labels[:level + 1])
                      new_labels = [lab[left_indexer] for lab in new_labels]
  
                      # left_indexers are w.r.t masked frame.
                      # reverse to original frame!
                      if not mask_all:
                          left_indexer = mask.nonzero()[0][left_indexer]
  
              join_index = MultiIndex(levels=new_levels, labels=new_labels,
                                      names=left.names, verify_integrity=False)
  
          if right_lev_indexer is not None:
              right_indexer = algos.take_nd(right_lev_indexer,
                                            join_index.labels[level],
                                            allow_fill=False)
          else:
              right_indexer = join_index.labels[level]
  
          if flip_order:
              left_indexer, right_indexer = right_indexer, left_indexer
  
          if return_indexers:
              left_indexer = (None if left_indexer is None
                              else _ensure_platform_int(left_indexer))
              right_indexer = (None if right_indexer is None
                               else _ensure_platform_int(right_indexer))
              return join_index, left_indexer, right_indexer
          else:
              return join_index
  
      def _join_monotonic(self, other, how='left', return_indexers=False):
          if self.equals(other):
              ret_index = other if how == 'right' else self
              if return_indexers:
                  return ret_index, None, None
              else:
                  return ret_index
  
          sv = self._ndarray_values
          ov = other._ndarray_values
  
          if self.is_unique and other.is_unique:
              # We can perform much better than the general case
              if how == 'left':
                  join_index = self
                  lidx = None
                  ridx = self._left_indexer_unique(sv, ov)
              elif how == 'right':
                  join_index = other
                  lidx = self._left_indexer_unique(ov, sv)
                  ridx = None
              elif how == 'inner':
                  join_index, lidx, ridx = self._inner_indexer(sv, ov)
                  join_index = self._wrap_joined_index(join_index, other)
              elif how == 'outer':
                  join_index, lidx, ridx = self._outer_indexer(sv, ov)
                  join_index = self._wrap_joined_index(join_index, other)
          else:
              if how == 'left':
                  join_index, lidx, ridx = self._left_indexer(sv, ov)
              elif how == 'right':
                  join_index, ridx, lidx = self._left_indexer(ov, sv)
              elif how == 'inner':
                  join_index, lidx, ridx = self._inner_indexer(sv, ov)
              elif how == 'outer':
                  join_index, lidx, ridx = self._outer_indexer(sv, ov)
              join_index = self._wrap_joined_index(join_index, other)
  
          if return_indexers:
              lidx = None if lidx is None else _ensure_platform_int(lidx)
              ridx = None if ridx is None else _ensure_platform_int(ridx)
              return join_index, lidx, ridx
          else:
              return join_index
  
      def _wrap_joined_index(self, joined, other):
          name = self.name if self.name == other.name else None
          return Index(joined, name=name)
  
      def _get_string_slice(self, key, use_lhs=True, use_rhs=True):
          # this is for partial string indexing,
          # overridden in DatetimeIndex, TimedeltaIndex and PeriodIndex
          raise NotImplementedError
  
      def slice_indexer(self, start=None, end=None, step=None, kind=None):
          """
          For an ordered or unique index, compute the slice indexer for input
          labels and step.
  
          Parameters
          ----------
          start : label, default None
              If None, defaults to the beginning
          end : label, default None
              If None, defaults to the end
          step : int, default None
          kind : string, default None
  
          Returns
          -------
          indexer : slice
  
          Raises
          ------
          KeyError : If key does not exist, or key is not unique and index is
              not ordered.
  
          Notes
          -----
          This function assumes that the data is sorted, so use at your own peril
  
          Examples
          ---------
          This is a method on all index types. For example you can do:
  
          >>> idx = pd.Index(list('abcd'))
          >>> idx.slice_indexer(start='b', end='c')
          slice(1, 3)
  
          >>> idx = pd.MultiIndex.from_arrays([list('abcd'), list('efgh')])
          >>> idx.slice_indexer(start='b', end=('c', 'g'))
          slice(1, 3)
          """
          start_slice, end_slice = self.slice_locs(start, end, step=step,
                                                   kind=kind)
  
          # return a slice
          if not is_scalar(start_slice):
              raise AssertionError("Start slice bound is non-scalar")
          if not is_scalar(end_slice):
              raise AssertionError("End slice bound is non-scalar")
  
          return slice(start_slice, end_slice, step)
  
      def _maybe_cast_indexer(self, key):
          """
          If we have a float key and are not a floating index
          then try to cast to an int if equivalent
          """
  
          if is_float(key) and not self.is_floating():
              try:
                  ckey = int(key)
                  if ckey == key:
                      key = ckey
              except (OverflowError, ValueError, TypeError):
                  pass
          return key
  
      def _validate_indexer(self, form, key, kind):
          """
          if we are positional indexer
          validate that we have appropriate typed bounds
          must be an integer
          """
          assert kind in ['ix', 'loc', 'getitem', 'iloc']
  
          if key is None:
              pass
          elif is_integer(key):
              pass
          elif kind in ['iloc', 'getitem']:
              self._invalid_indexer(form, key)
          return key
  
      _index_shared_docs['_maybe_cast_slice_bound'] = """
          This function should be overloaded in subclasses that allow non-trivial
          casting on label-slice bounds, e.g. datetime-like indices allowing
          strings containing formatted datetimes.
  
          Parameters
          ----------
          label : object
          side : {'left', 'right'}
          kind : {'ix', 'loc', 'getitem'}
  
          Returns
          -------
          label :  object
  
          Notes
          -----
          Value of `side` parameter should be validated in caller.
  
          """
  
      @Appender(_index_shared_docs['_maybe_cast_slice_bound'])
      def _maybe_cast_slice_bound(self, label, side, kind):
          assert kind in ['ix', 'loc', 'getitem', None]
  
          # We are a plain index here (sub-class override this method if they
          # wish to have special treatment for floats/ints, e.g. Float64Index and
          # datetimelike Indexes
          # reject them
          if is_float(label):
              if not (kind in ['ix'] and (self.holds_integer() or
                                          self.is_floating())):
                  self._invalid_indexer('slice', label)
  
          # we are trying to find integer bounds on a non-integer based index
          # this is rejected (generally .loc gets you here)
          elif is_integer(label):
              self._invalid_indexer('slice', label)
  
          return label
  
      def _searchsorted_monotonic(self, label, side='left'):
          if self.is_monotonic_increasing:
              return self.searchsorted(label, side=side)
          elif self.is_monotonic_decreasing:
              # np.searchsorted expects ascending sort order, have to reverse
              # everything for it to work (element ordering, search side and
              # resulting value).
              pos = self[::-1].searchsorted(label, side='right' if side == 'left'
                                            else 'left')
              return len(self) - pos
  
          raise ValueError('index must be monotonic increasing or decreasing')
  
      def _get_loc_only_exact_matches(self, key):
          """
          This is overridden on subclasses (namely, IntervalIndex) to control
          get_slice_bound.
          """
          return self.get_loc(key)
  
      def get_slice_bound(self, label, side, kind):
          """
          Calculate slice bound that corresponds to given label.
  
          Returns leftmost (one-past-the-rightmost if ``side=='right'``) position
          of given label.
  
          Parameters
          ----------
          label : object
          side : {'left', 'right'}
          kind : {'ix', 'loc', 'getitem'}
  
          """
          assert kind in ['ix', 'loc', 'getitem', None]
  
          if side not in ('left', 'right'):
              raise ValueError("Invalid value for side kwarg,"
                               " must be either 'left' or 'right': %s" %
                               (side, ))
  
          original_label = label
  
          # For datetime indices label may be a string that has to be converted
          # to datetime boundary according to its resolution.
          label = self._maybe_cast_slice_bound(label, side, kind)
  
          # we need to look up the label
          try:
              slc = self._get_loc_only_exact_matches(label)
          except KeyError as err:
              try:
                  return self._searchsorted_monotonic(label, side)
              except ValueError:
                  # raise the original KeyError
                  raise err
  
          if isinstance(slc, np.ndarray):
              # get_loc may return a boolean array or an array of indices, which
              # is OK as long as they are representable by a slice.
              if is_bool_dtype(slc):
                  slc = lib.maybe_booleans_to_slice(slc.view('u1'))
              else:
                  slc = lib.maybe_indices_to_slice(slc.astype('i8'), len(self))
              if isinstance(slc, np.ndarray):
                  raise KeyError("Cannot get %s slice bound for non-unique "
                                 "label: %r" % (side, original_label))
  
          if isinstance(slc, slice):
              if side == 'left':
                  return slc.start
              else:
                  return slc.stop
          else:
              if side == 'right':
                  return slc + 1
              else:
                  return slc
  
      def slice_locs(self, start=None, end=None, step=None, kind=None):
          """
          Compute slice locations for input labels.
  
          Parameters
          ----------
          start : label, default None
              If None, defaults to the beginning
          end : label, default None
              If None, defaults to the end
          step : int, defaults None
              If None, defaults to 1
          kind : {'ix', 'loc', 'getitem'} or None
  
          Returns
          -------
          start, end : int
  
          Notes
          -----
          This method only works if the index is monotonic or unique.
  
          Examples
          ---------
          >>> idx = pd.Index(list('abcd'))
          >>> idx.slice_locs(start='b', end='c')
          (1, 3)
  
          See Also
          --------
          Index.get_loc : Get location for a single label
          """
          inc = (step is None or step >= 0)
  
          if not inc:
              # If it's a reverse slice, temporarily swap bounds.
              start, end = end, start
  
          start_slice = None
          if start is not None:
              start_slice = self.get_slice_bound(start, 'left', kind)
          if start_slice is None:
              start_slice = 0
  
          end_slice = None
          if end is not None:
              end_slice = self.get_slice_bound(end, 'right', kind)
          if end_slice is None:
              end_slice = len(self)
  
          if not inc:
              # Bounds at this moment are swapped, swap them back and shift by 1.
              #
              # slice_locs('B', 'A', step=-1): s='B', e='A'
              #
              #              s='A'                 e='B'
              # AFTER SWAP:    |                     |
              #                v ------------------> V
              #           -----------------------------------
              #           | | |A|A|A|A| | | | | |B|B| | | | |
              #           -----------------------------------
              #              ^ <------------------ ^
              # SHOULD BE:   |                     |
              #           end=s-1              start=e-1
              #
              end_slice, start_slice = start_slice - 1, end_slice - 1
  
              # i == -1 triggers ``len(self) + i`` selection that points to the
              # last element, not before-the-first one, subtracting len(self)
              # compensates that.
              if end_slice == -1:
                  end_slice -= len(self)
              if start_slice == -1:
                  start_slice -= len(self)
  
          return start_slice, end_slice
  
      def delete(self, loc):
          """
          Make new Index with passed location(-s) deleted
  
          Returns
          -------
          new_index : Index
          """
          return self._shallow_copy(np.delete(self._data, loc))
  
      def insert(self, loc, item):
          """
          Make new Index inserting new item at location. Follows
          Python list.append semantics for negative values
  
          Parameters
          ----------
          loc : int
          item : object
  
          Returns
          -------
          new_index : Index
          """
          if is_scalar(item) and isna(item):
              # GH 18295
              item = self._na_value
  
          _self = np.asarray(self)
          item = self._coerce_scalar_to_index(item)._ndarray_values
          idx = np.concatenate((_self[:loc], item, _self[loc:]))
          return self._shallow_copy_with_infer(idx)
  
      def drop(self, labels, errors='raise'):
          """
          Make new Index with passed list of labels deleted
  
          Parameters
          ----------
          labels : array-like
          errors : {'ignore', 'raise'}, default 'raise'
              If 'ignore', suppress error and existing labels are dropped.
  
          Returns
          -------
          dropped : Index
  
          Raises
          ------
          KeyError
              If none of the labels are found in the selected axis
          """
          arr_dtype = 'object' if self.dtype == 'object' else None
          labels = com._index_labels_to_array(labels, dtype=arr_dtype)
          indexer = self.get_indexer(labels)
          mask = indexer == -1
          if mask.any():
              if errors != 'ignore':
                  raise KeyError(
                      'labels %s not contained in axis' % labels[mask])
              indexer = indexer[~mask]
          return self.delete(indexer)
  
      _index_shared_docs['index_unique'] = (
          """
          Return unique values in the index. Uniques are returned in order
          of appearance, this does NOT sort.
  
          Parameters
          ----------
          level : int or str, optional, default None
              Only return values from specified level (for MultiIndex)
  
              .. versionadded:: 0.23.0
  
          Returns
          -------
          Index without duplicates
  
          See Also
          --------
          unique
          Series.unique
          """)
  
      @Appender(_index_shared_docs['index_unique'] % _index_doc_kwargs)
      def unique(self, level=None):
          if level is not None:
              self._validate_index_level(level)
          result = super(Index, self).unique()
          return self._shallow_copy(result)
  
      def drop_duplicates(self, keep='first'):
          """
          Return Index with duplicate values removed.
  
          Parameters
          ----------
          keep : {'first', 'last', ``False``}, default 'first'
              - 'first' : Drop duplicates except for the first occurrence.
              - 'last' : Drop duplicates except for the last occurrence.
              - ``False`` : Drop all duplicates.
  
          Returns
          -------
          deduplicated : Index
  
          See Also
          --------
          Series.drop_duplicates : equivalent method on Series
          DataFrame.drop_duplicates : equivalent method on DataFrame
          Index.duplicated : related method on Index, indicating duplicate
              Index values.
  
          Examples
          --------
          Generate an pandas.Index with duplicate values.
  
          >>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'])
  
          The `keep` parameter controls  which duplicate values are removed.
          The value 'first' keeps the first occurrence for each
          set of duplicated entries. The default value of keep is 'first'.
  
          >>> idx.drop_duplicates(keep='first')
          Index(['lama', 'cow', 'beetle', 'hippo'], dtype='object')
  
          The value 'last' keeps the last occurrence for each set of duplicated
          entries.
  
          >>> idx.drop_duplicates(keep='last')
          Index(['cow', 'beetle', 'lama', 'hippo'], dtype='object')
  
          The value ``False`` discards all sets of duplicated entries.
  
          >>> idx.drop_duplicates(keep=False)
          Index(['cow', 'beetle', 'hippo'], dtype='object')
          """
          return super(Index, self).drop_duplicates(keep=keep)
  
      def duplicated(self, keep='first'):
          """
          Indicate duplicate index values.
  
          Duplicated values are indicated as ``True`` values in the resulting
          array. Either all duplicates, all except the first, or all except the
          last occurrence of duplicates can be indicated.
  
          Parameters
          ----------
          keep : {'first', 'last', False}, default 'first'
              The value or values in a set of duplicates to mark as missing.
  
              - 'first' : Mark duplicates as ``True`` except for the first
                occurrence.
              - 'last' : Mark duplicates as ``True`` except for the last
                occurrence.
              - ``False`` : Mark all duplicates as ``True``.
  
          Examples
          --------
          By default, for each set of duplicated values, the first occurrence is
          set to False and all others to True:
  
          >>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama'])
          >>> idx.duplicated()
          array([False, False,  True, False,  True])
  
          which is equivalent to
  
          >>> idx.duplicated(keep='first')
          array([False, False,  True, False,  True])
  
          By using 'last', the last occurrence of each set of duplicated values
          is set on False and all others on True:
  
          >>> idx.duplicated(keep='last')
          array([ True, False,  True, False, False])
  
          By setting keep on ``False``, all duplicates are True:
  
          >>> idx.duplicated(keep=False)
          array([ True, False,  True, False,  True])
  
          Returns
          -------
          numpy.ndarray
  
          See Also
          --------
          pandas.Series.duplicated : Equivalent method on pandas.Series
          pandas.DataFrame.duplicated : Equivalent method on pandas.DataFrame
          pandas.Index.drop_duplicates : Remove duplicate values from Index
          """
          return super(Index, self).duplicated(keep=keep)
  
      _index_shared_docs['fillna'] = """
          Fill NA/NaN values with the specified value
  
          Parameters
          ----------
          value : scalar
              Scalar value to use to fill holes (e.g. 0).
              This value cannot be a list-likes.
          downcast : dict, default is None
              a dict of item->dtype of what to downcast if possible,
              or the string 'infer' which will try to downcast to an appropriate
              equal type (e.g. float64 to int64 if possible)
  
          Returns
          -------
          filled : %(klass)s
          """
  
      @Appender(_index_shared_docs['fillna'])
      def fillna(self, value=None, downcast=None):
          self._assert_can_do_op(value)
          if self.hasnans:
              result = self.putmask(self._isnan, value)
              if downcast is None:
                  # no need to care metadata other than name
                  # because it can't have freq if
                  return Index(result, name=self.name)
          return self._shallow_copy()
  
      _index_shared_docs['dropna'] = """
          Return Index without NA/NaN values
  
          Parameters
          ----------
          how :  {'any', 'all'}, default 'any'
              If the Index is a MultiIndex, drop the value when any or all levels
              are NaN.
  
          Returns
          -------
          valid : Index
          """
  
      @Appender(_index_shared_docs['dropna'])
      def dropna(self, how='any'):
          if how not in ('any', 'all'):
              raise ValueError("invalid how option: {0}".format(how))
  
          if self.hasnans:
              return self._shallow_copy(self.values[~self._isnan])
          return self._shallow_copy()
  
      def _evaluate_with_timedelta_like(self, other, op):
          # Timedelta knows how to operate with np.array, so dispatch to that
          # operation and then wrap the results
          other = Timedelta(other)
          values = self.values
  
          with np.errstate(all='ignore'):
              result = op(values, other)
  
          attrs = self._get_attributes_dict()
          attrs = self._maybe_update_attributes(attrs)
          if op == divmod:
              return Index(result[0], **attrs), Index(result[1], **attrs)
          return Index(result, **attrs)
  
      def _evaluate_with_datetime_like(self, other, op):
          raise TypeError("can only perform ops with datetime like values")
  
      def _evaluate_compare(self, other, op):
          raise com.AbstractMethodError(self)
  
      @classmethod
      def _add_comparison_methods(cls):
          """ add in comparison methods """
          cls.__eq__ = _make_comparison_op(operator.eq, cls)
          cls.__ne__ = _make_comparison_op(operator.ne, cls)
          cls.__lt__ = _make_comparison_op(operator.lt, cls)
          cls.__gt__ = _make_comparison_op(operator.gt, cls)
          cls.__le__ = _make_comparison_op(operator.le, cls)
          cls.__ge__ = _make_comparison_op(operator.ge, cls)
  
      @classmethod
      def _add_numeric_methods_add_sub_disabled(cls):
          """ add in the numeric add/sub methods to disable """
          cls.__add__ = make_invalid_op('__add__')
          cls.__radd__ = make_invalid_op('__radd__')
          cls.__iadd__ = make_invalid_op('__iadd__')
          cls.__sub__ = make_invalid_op('__sub__')
          cls.__rsub__ = make_invalid_op('__rsub__')
          cls.__isub__ = make_invalid_op('__isub__')
  
      @classmethod
      def _add_numeric_methods_disabled(cls):
          """ add in numeric methods to disable other than add/sub """
          cls.__pow__ = make_invalid_op('__pow__')
          cls.__rpow__ = make_invalid_op('__rpow__')
          cls.__mul__ = make_invalid_op('__mul__')
          cls.__rmul__ = make_invalid_op('__rmul__')
          cls.__floordiv__ = make_invalid_op('__floordiv__')
          cls.__rfloordiv__ = make_invalid_op('__rfloordiv__')
          cls.__truediv__ = make_invalid_op('__truediv__')
          cls.__rtruediv__ = make_invalid_op('__rtruediv__')
          if not compat.PY3:
              cls.__div__ = make_invalid_op('__div__')
              cls.__rdiv__ = make_invalid_op('__rdiv__')
          cls.__mod__ = make_invalid_op('__mod__')
          cls.__divmod__ = make_invalid_op('__divmod__')
          cls.__neg__ = make_invalid_op('__neg__')
          cls.__pos__ = make_invalid_op('__pos__')
          cls.__abs__ = make_invalid_op('__abs__')
          cls.__inv__ = make_invalid_op('__inv__')
  
      def _maybe_update_attributes(self, attrs):
          """ Update Index attributes (e.g. freq) depending on op """
          return attrs
  
      def _validate_for_numeric_unaryop(self, op, opstr):
          """ validate if we can perform a numeric unary operation """
  
          if not self._is_numeric_dtype:
              raise TypeError("cannot evaluate a numeric op "
                              "{opstr} for type: {typ}"
                              .format(opstr=opstr, typ=type(self).__name__))
  
      def _validate_for_numeric_binop(self, other, op):
          """
          return valid other, evaluate or raise TypeError
          if we are not of the appropriate type
  
          internal method called by ops
          """
          opstr = '__{opname}__'.format(opname=op.__name__)
          # if we are an inheritor of numeric,
          # but not actually numeric (e.g. DatetimeIndex/PeriodIndex)
          if not self._is_numeric_dtype:
              raise TypeError("cannot evaluate a numeric op {opstr} "
                              "for type: {typ}"
                              .format(opstr=opstr, typ=type(self).__name__))
  
          if isinstance(other, Index):
              if not other._is_numeric_dtype:
                  raise TypeError("cannot evaluate a numeric op "
                                  "{opstr} with type: {typ}"
                                  .format(opstr=opstr, typ=type(other)))
          elif isinstance(other, np.ndarray) and not other.ndim:
              other = other.item()
  
          if isinstance(other, (Index, ABCSeries, np.ndarray)):
              if len(self) != len(other):
                  raise ValueError("cannot evaluate a numeric op with "
                                   "unequal lengths")
              other = com._values_from_object(other)
              if other.dtype.kind not in ['f', 'i', 'u']:
                  raise TypeError("cannot evaluate a numeric op "
                                  "with a non-numeric dtype")
          elif isinstance(other, (ABCDateOffset, np.timedelta64, timedelta)):
              # higher up to handle
              pass
          elif isinstance(other, (datetime, np.datetime64)):
              # higher up to handle
              pass
          else:
              if not (is_float(other) or is_integer(other)):
                  raise TypeError("can only perform ops with scalar values")
  
          return other
  
      @classmethod
      def _add_numeric_methods_binary(cls):
          """ add in numeric methods """
          cls.__add__ = _make_arithmetic_op(operator.add, cls)
          cls.__radd__ = _make_arithmetic_op(ops.radd, cls)
          cls.__sub__ = _make_arithmetic_op(operator.sub, cls)
          cls.__rsub__ = _make_arithmetic_op(ops.rsub, cls)
          cls.__mul__ = _make_arithmetic_op(operator.mul, cls)
          cls.__rmul__ = _make_arithmetic_op(ops.rmul, cls)
          cls.__rpow__ = _make_arithmetic_op(ops.rpow, cls)
          cls.__pow__ = _make_arithmetic_op(operator.pow, cls)
          cls.__mod__ = _make_arithmetic_op(operator.mod, cls)
          cls.__floordiv__ = _make_arithmetic_op(operator.floordiv, cls)
          cls.__rfloordiv__ = _make_arithmetic_op(ops.rfloordiv, cls)
          cls.__truediv__ = _make_arithmetic_op(operator.truediv, cls)
          cls.__rtruediv__ = _make_arithmetic_op(ops.rtruediv, cls)
          if not compat.PY3:
              cls.__div__ = _make_arithmetic_op(operator.div, cls)
              cls.__rdiv__ = _make_arithmetic_op(ops.rdiv, cls)
  
          cls.__divmod__ = _make_arithmetic_op(divmod, cls)
  
      @classmethod
      def _add_numeric_methods_unary(cls):
          """ add in numeric unary methods """
  
          def _make_evaluate_unary(op, opstr):
  
              def _evaluate_numeric_unary(self):
  
                  self._validate_for_numeric_unaryop(op, opstr)
                  attrs = self._get_attributes_dict()
                  attrs = self._maybe_update_attributes(attrs)
                  return Index(op(self.values), **attrs)
  
              return _evaluate_numeric_unary
  
          cls.__neg__ = _make_evaluate_unary(operator.neg, '__neg__')
          cls.__pos__ = _make_evaluate_unary(operator.pos, '__pos__')
          cls.__abs__ = _make_evaluate_unary(np.abs, '__abs__')
          cls.__inv__ = _make_evaluate_unary(lambda x: -x, '__inv__')
  
      @classmethod
      def _add_numeric_methods(cls):
          cls._add_numeric_methods_unary()
          cls._add_numeric_methods_binary()
  
      @classmethod
      def _add_logical_methods(cls):
          """ add in logical methods """
  
          _doc = """
          %(desc)s
  
          Parameters
          ----------
          *args
              These parameters will be passed to numpy.%(outname)s.
          **kwargs
              These parameters will be passed to numpy.%(outname)s.
  
          Returns
          -------
          %(outname)s : bool or array_like (if axis is specified)
              A single element array_like may be converted to bool."""
  
          _index_shared_docs['index_all'] = dedent("""
  
          See Also
          --------
          pandas.Index.any : Return whether any element in an Index is True.
          pandas.Series.any : Return whether any element in a Series is True.
          pandas.Series.all : Return whether all elements in a Series are True.
  
          Notes
          -----
          Not a Number (NaN), positive infinity and negative infinity
          evaluate to True because these are not equal to zero.
  
          Examples
          --------
          **all**
  
          True, because nonzero integers are considered True.
  
          >>> pd.Index([1, 2, 3]).all()
          True
  
          False, because ``0`` is considered False.
  
          >>> pd.Index([0, 1, 2]).all()
          False
  
          **any**
  
          True, because ``1`` is considered True.
  
          >>> pd.Index([0, 0, 1]).any()
          True
  
          False, because ``0`` is considered False.
  
          >>> pd.Index([0, 0, 0]).any()
          False
          """)
  
          _index_shared_docs['index_any'] = dedent("""
  
          See Also
          --------
          pandas.Index.all : Return whether all elements are True.
          pandas.Series.all : Return whether all elements are True.
  
          Notes
          -----
          Not a Number (NaN), positive infinity and negative infinity
          evaluate to True because these are not equal to zero.
  
          Examples
          --------
          >>> index = pd.Index([0, 1, 2])
          >>> index.any()
          True
  
          >>> index = pd.Index([0, 0, 0])
          >>> index.any()
          False
          """)
  
          def _make_logical_function(name, desc, f):
              @Substitution(outname=name, desc=desc)
              @Appender(_index_shared_docs['index_' + name])
              @Appender(_doc)
              def logical_func(self, *args, **kwargs):
                  result = f(self.values)
                  if (isinstance(result, (np.ndarray, ABCSeries, Index)) and
                          result.ndim == 0):
                      # return NumPy type
                      return result.dtype.type(result.item())
                  else:  # pragma: no cover
                      return result
  
              logical_func.__name__ = name
              return logical_func
  
          cls.all = _make_logical_function('all', 'Return whether all elements '
                                                  'are True.',
                                           np.all)
          cls.any = _make_logical_function('any',
                                           'Return whether any element is True.',
                                           np.any)
  
      @classmethod
      def _add_logical_methods_disabled(cls):
          """ add in logical methods to disable """
          cls.all = make_invalid_op('all')
          cls.any = make_invalid_op('any')
  
  
  Index._add_numeric_methods_disabled()
  Index._add_logical_methods()
  Index._add_comparison_methods()
  
  
  def _ensure_index_from_sequences(sequences, names=None):
      """Construct an index from sequences of data.
  
      A single sequence returns an Index. Many sequences returns a
      MultiIndex.
  
      Parameters
      ----------
      sequences : sequence of sequences
      names : sequence of str
  
      Returns
      -------
      index : Index or MultiIndex
  
      Examples
      --------
      >>> _ensure_index_from_sequences([[1, 2, 3]], names=['name'])
      Int64Index([1, 2, 3], dtype='int64', name='name')
  
      >>> _ensure_index_from_sequences([['a', 'a'], ['a', 'b']],
                                       names=['L1', 'L2'])
      MultiIndex(levels=[['a'], ['a', 'b']],
                 labels=[[0, 0], [0, 1]],
                 names=['L1', 'L2'])
  
      See Also
      --------
      _ensure_index
      """
      from .multi import MultiIndex
  
      if len(sequences) == 1:
          if names is not None:
              names = names[0]
          return Index(sequences[0], name=names)
      else:
          return MultiIndex.from_arrays(sequences, names=names)
  
  
  def _ensure_index(index_like, copy=False):
      """
      Ensure that we have an index from some index-like object
  
      Parameters
      ----------
      index : sequence
          An Index or other sequence
      copy : bool
  
      Returns
      -------
      index : Index or MultiIndex
  
      Examples
      --------
      >>> _ensure_index(['a', 'b'])
      Index(['a', 'b'], dtype='object')
  
      >>> _ensure_index([('a', 'a'),  ('b', 'c')])
      Index([('a', 'a'), ('b', 'c')], dtype='object')
  
      >>> _ensure_index([['a', 'a'], ['b', 'c']])
      MultiIndex(levels=[['a'], ['b', 'c']],
                 labels=[[0, 0], [0, 1]])
  
      See Also
      --------
      _ensure_index_from_sequences
      """
      if isinstance(index_like, Index):
          if copy:
              index_like = index_like.copy()
          return index_like
      if hasattr(index_like, 'name'):
          return Index(index_like, name=index_like.name, copy=copy)
  
      if is_iterator(index_like):
          index_like = list(index_like)
  
      # must check for exactly list here because of strict type
      # check in clean_index_list
      if isinstance(index_like, list):
          if type(index_like) != list:
              index_like = list(index_like)
  
          converted, all_arrays = lib.clean_index_list(index_like)
  
          if len(converted) > 0 and all_arrays:
              from .multi import MultiIndex
              return MultiIndex.from_arrays(converted)
          else:
              index_like = converted
      else:
          # clean_index_list does the equivalent of copying
          # so only need to do this if not list instance
          if copy:
              from copy import copy
              index_like = copy(index_like)
  
      return Index(index_like)
  
  
  def _ensure_has_len(seq):
      """If seq is an iterator, put its values into a list."""
      try:
          len(seq)
      except TypeError:
          return list(seq)
      else:
          return seq
  
  
  def _trim_front(strings):
      """
      Trims zeros and decimal points
      """
      trimmed = strings
      while len(strings) > 0 and all(x[0] == ' ' for x in trimmed):
          trimmed = [x[1:] for x in trimmed]
      return trimmed
  
  
  def _validate_join_method(method):
      if method not in ['left', 'right', 'inner', 'outer']:
          raise ValueError('do not recognize join method %s' % method)
  
apis: pandas.core.indexes.frozen.FrozenList pandas.io.formats.format._get_adjustment numpy.asarray pandas.core.ops.make_invalid_op pandas.io.formats.format.format_array pandas.util._decorators.deprecate_kwarg pandas.core.dtypes.common.is_unsigned_integer_dtype pandas.core.dtypes.common.is_datetime64_any_dtype pandas._libs.Timedelta pandas.core.indexes.period._new_PeriodIndex pandas._libs.lib.infer_dtype pandas.core.dtypes.common.is_period_dtype numpy.repeat pandas.core.dtypes.common.is_scalar pandas.util._decorators.Appender numpy.errstate pandas.io.formats.console.get_console_size numpy.array pandas.core.dtypes.common.is_dtype_union_equal pandas.compat.iteritems pandas.core.dtypes.concat._concat_index_asobject pandas.core.dtypes.concat._concat_compat pandas.core.dtypes.common.is_integer numpy.ndarray.__setstate__ pandas.core.indexes.timedeltas.TimedeltaIndex pandas.core.dtypes.common.is_hashable pandas._libs.index.get_value_box pandas.io.formats.printing.pprint_thing numpy.delete pandas.core.dtypes.common.is_timedelta64_dtype numpy.hstack pandas.core.dtypes.missing.isna pandas.core.ops._comp_method_OBJECT_ARRAY pandas.core.common._index_labels_to_array pandas._libs.lib.clean_index_list pandas.core.dtypes.common.is_datetime64tz_dtype pandas.core.dtypes.common.is_object_dtype numpy.concatenate pandas.core.dtypes.common.is_integer_dtype pandas.core.common.AbstractMethodError pandas.core.dtypes.common.is_list_like pandas.core.dtypes.common.is_categorical_dtype pandas._libs.lib.is_datetime_with_singletz_array pandas.core.sorting.safe_sort pandas.core.dtypes.common._ensure_object pandas.core.dtypes.common._ensure_categorical numpy.arange numpy.sort pandas.core.dtypes.common.is_bool pandas.compat.u pandas.core.algorithms.take pandas.core.dtypes.common._ensure_platform_int pandas.core.indexes.period.PeriodIndex pandas.core.dtypes.common.is_signed_integer_dtype numpy.dtype pandas.core.accessor.CachedAccessor pandas.core.dtypes.common.is_float_dtype pandas.core.common._asarray_tuplesafe pandas._libs.lib.maybe_convert_objects pandas.compat.numpy.function.validate_repeat pandas.core.dtypes.common.is_float pandas.core.config.get_option pandas.core.dtypes.common.is_iterator pandas.core.common._values_from_object numpy.where pandas.core.dtypes.concat.get_dtype_kinds pandas.core.dtypes.common.needs_i8_conversion pandas.core.dtypes.common._ensure_int64 pandas.core.common._not_none pandas.compat.set_function_name pandas.core.dtypes.common.is_bool_dtype pandas.core.dtypes.common.is_interval_dtype pandas.core.algorithms.take_nd pandas._libs.algos.groupsort_indexer pandas.core.common.is_bool_indexer pandas.core.algorithms.isin pandas.core.indexes.datetimes.DatetimeIndex pandas.util._decorators.Substitution numpy.empty pandas.core.dtypes.common.is_dtype_equal pandas.core.reshape.merge._get_join_indexers