text: # pylint: disable=W0223
  import textwrap
  import warnings
  import numpy as np
  from pandas.compat import range, zip
  import pandas.compat as compat
  from pandas.core.dtypes.generic import ABCDataFrame, ABCPanel, ABCSeries
  from pandas.core.dtypes.common import (
      is_integer_dtype,
      is_integer, is_float,
      is_list_like,
      is_sequence,
      is_iterator,
      is_scalar,
      is_sparse,
      _is_unorderable_exception,
      _ensure_platform_int)
  from pandas.core.dtypes.missing import isna, _infer_fill_value
  
  from pandas.core.index import Index, MultiIndex
  
  import pandas.core.common as com
  from pandas._libs.indexing import _NDFrameIndexerBase
  
  
  # the supported indexers
  def get_indexers_list():
  
      return [
          ('ix', _IXIndexer),
          ('iloc', _iLocIndexer),
          ('loc', _LocIndexer),
          ('at', _AtIndexer),
          ('iat', _iAtIndexer),
      ]
  
  
  # "null slice"
  _NS = slice(None, None)
  
  
  # the public IndexSlicerMaker
  class _IndexSlice(object):
      """
      Create an object to more easily perform multi-index slicing
  
      Examples
      --------
  
      >>> midx = pd.MultiIndex.from_product([['A0','A1'], ['B0','B1','B2','B3']])
      >>> columns = ['foo', 'bar']
      >>> dfmi = pd.DataFrame(np.arange(16).reshape((len(midx), len(columns))),
                              index=midx, columns=columns)
  
      Using the default slice command:
  
      >>> dfmi.loc[(slice(None), slice('B0', 'B1')), :]
                 foo  bar
          A0 B0    0    1
             B1    2    3
          A1 B0    8    9
             B1   10   11
  
      Using the IndexSlice class for a more intuitive command:
  
      >>> idx = pd.IndexSlice
      >>> dfmi.loc[idx[:, 'B0':'B1'], :]
                 foo  bar
          A0 B0    0    1
             B1    2    3
          A1 B0    8    9
             B1   10   11
      """
  
      def __getitem__(self, arg):
          return arg
  
  
  IndexSlice = _IndexSlice()
  
  
  class IndexingError(Exception):
      pass
  
  
  class _NDFrameIndexer(_NDFrameIndexerBase):
      _valid_types = None
      _exception = KeyError
      axis = None
  
      def __call__(self, axis=None):
          # we need to return a copy of ourselves
          new_self = self.__class__(self.name, self.obj)
  
          if axis is not None:
              axis = self.obj._get_axis_number(axis)
          new_self.axis = axis
          return new_self
  
      def __iter__(self):
          raise NotImplementedError('ix is not iterable')
  
      def __getitem__(self, key):
          if type(key) is tuple:
              key = tuple(com._apply_if_callable(x, self.obj)
                          for x in key)
              try:
                  values = self.obj._get_value(*key)
                  if is_scalar(values):
                      return values
              except Exception:
                  pass
  
              return self._getitem_tuple(key)
          else:
              # we by definition only have the 0th axis
              axis = self.axis or 0
  
              key = com._apply_if_callable(key, self.obj)
              return self._getitem_axis(key, axis=axis)
  
      def _get_label(self, label, axis=None):
          if axis is None:
              axis = self.axis or 0
  
          if self.ndim == 1:
              # for perf reasons we want to try _xs first
              # as its basically direct indexing
              # but will fail when the index is not present
              # see GH5667
              try:
                  return self.obj._xs(label, axis=axis)
              except:
                  return self.obj[label]
          elif isinstance(label, tuple) and isinstance(label[axis], slice):
              raise IndexingError('no slices here, handle elsewhere')
  
          return self.obj._xs(label, axis=axis)
  
      def _get_loc(self, key, axis=None):
          if axis is None:
              axis = self.axis
          return self.obj._ixs(key, axis=axis)
  
      def _slice(self, obj, axis=None, kind=None):
          if axis is None:
              axis = self.axis
          return self.obj._slice(obj, axis=axis, kind=kind)
  
      def _get_setitem_indexer(self, key):
          if self.axis is not None:
              return self._convert_tuple(key, is_setter=True)
  
          axis = self.obj._get_axis(0)
  
          if isinstance(axis, MultiIndex) and self.name != 'iloc':
              try:
                  return axis.get_loc(key)
              except Exception:
                  pass
  
          if isinstance(key, tuple):
              try:
                  return self._convert_tuple(key, is_setter=True)
              except IndexingError:
                  pass
  
          if isinstance(key, range):
              return self._convert_range(key, is_setter=True)
  
          try:
              return self._convert_to_indexer(key, is_setter=True)
          except TypeError as e:
  
              # invalid indexer type vs 'other' indexing errors
              if 'cannot do' in str(e):
                  raise
              raise IndexingError(key)
  
      def __setitem__(self, key, value):
          if isinstance(key, tuple):
              key = tuple(com._apply_if_callable(x, self.obj)
                          for x in key)
          else:
              key = com._apply_if_callable(key, self.obj)
          indexer = self._get_setitem_indexer(key)
          self._setitem_with_indexer(indexer, value)
  
      def _has_valid_type(self, k, axis):
          raise NotImplementedError()
  
      def _has_valid_tuple(self, key):
          """ check the key for valid keys across my indexer """
          for i, k in enumerate(key):
              if i >= self.obj.ndim:
                  raise IndexingError('Too many indexers')
              if not self._has_valid_type(k, i):
                  raise ValueError("Location based indexing can only have "
                                   "[{types}] types"
                                   .format(types=self._valid_types))
  
      def _should_validate_iterable(self, axis=None):
          """ return a boolean whether this axes needs validation for a passed
          iterable
          """
          if axis is None:
              axis = self.axis or 0
          ax = self.obj._get_axis(axis)
          if isinstance(ax, MultiIndex):
              return False
          elif ax.is_floating():
              return False
  
          return True
  
      def _is_nested_tuple_indexer(self, tup):
          if any(isinstance(ax, MultiIndex) for ax in self.obj.axes):
              return any(is_nested_tuple(tup, ax) for ax in self.obj.axes)
          return False
  
      def _convert_tuple(self, key, is_setter=False):
          keyidx = []
          if self.axis is not None:
              axis = self.obj._get_axis_number(self.axis)
              for i in range(self.ndim):
                  if i == axis:
                      keyidx.append(self._convert_to_indexer(
                          key, axis=axis, is_setter=is_setter))
                  else:
                      keyidx.append(slice(None))
          else:
              for i, k in enumerate(key):
                  if i >= self.obj.ndim:
                      raise IndexingError('Too many indexers')
                  idx = self._convert_to_indexer(k, axis=i, is_setter=is_setter)
                  keyidx.append(idx)
          return tuple(keyidx)
  
      def _convert_range(self, key, is_setter=False):
          """ convert a range argument """
          return list(key)
  
      def _convert_scalar_indexer(self, key, axis):
          # if we are accessing via lowered dim, use the last dim
          if axis is None:
              axis = 0
          ax = self.obj._get_axis(min(axis, self.ndim - 1))
          # a scalar
          return ax._convert_scalar_indexer(key, kind=self.name)
  
      def _convert_slice_indexer(self, key, axis):
          # if we are accessing via lowered dim, use the last dim
          ax = self.obj._get_axis(min(axis, self.ndim - 1))
          return ax._convert_slice_indexer(key, kind=self.name)
  
      def _has_valid_setitem_indexer(self, indexer):
          return True
  
      def _has_valid_positional_setitem_indexer(self, indexer):
          """ validate that an positional indexer cannot enlarge its target
          will raise if needed, does not modify the indexer externally
          """
          if isinstance(indexer, dict):
              raise IndexError("{0} cannot enlarge its target object"
                               .format(self.name))
          else:
              if not isinstance(indexer, tuple):
                  indexer = self._tuplify(indexer)
              for ax, i in zip(self.obj.axes, indexer):
                  if isinstance(i, slice):
                      # should check the stop slice?
                      pass
                  elif is_list_like_indexer(i):
                      # should check the elements?
                      pass
                  elif is_integer(i):
                      if i >= len(ax):
                          raise IndexError("{name} cannot enlarge its target "
                                           "object".format(name=self.name))
                  elif isinstance(i, dict):
                      raise IndexError("{name} cannot enlarge its target object"
                                       .format(name=self.name))
  
          return True
  
      def _setitem_with_indexer(self, indexer, value):
          self._has_valid_setitem_indexer(indexer)
  
          # also has the side effect of consolidating in-place
          # TODO: Panel, DataFrame are not imported, remove?
          from pandas import Panel, DataFrame, Series  # noqa
          info_axis = self.obj._info_axis_number
  
          # maybe partial set
          take_split_path = self.obj._is_mixed_type
  
          # if there is only one block/type, still have to take split path
          # unless the block is one-dimensional or it can hold the value
          if not take_split_path and self.obj._data.blocks:
              blk, = self.obj._data.blocks
              if 1 < blk.ndim:  # in case of dict, keys are indices
                  val = list(value.values()) if isinstance(value,
                                                           dict) else value
                  take_split_path = not blk._can_hold_element(val)
  
          if isinstance(indexer, tuple) and len(indexer) == len(self.obj.axes):
  
              for i, ax in zip(indexer, self.obj.axes):
  
                  # if we have any multi-indexes that have non-trivial slices
                  # (not null slices) then we must take the split path, xref
                  # GH 10360
                  if (isinstance(ax, MultiIndex) and
                          not (is_integer(i) or com.is_null_slice(i))):
                      take_split_path = True
                      break
  
          if isinstance(indexer, tuple):
              nindexer = []
              for i, idx in enumerate(indexer):
                  if isinstance(idx, dict):
  
                      # reindex the axis to the new value
                      # and set inplace
                      key, _ = convert_missing_indexer(idx)
  
                      # if this is the items axes, then take the main missing
                      # path first
                      # this correctly sets the dtype and avoids cache issues
                      # essentially this separates out the block that is needed
                      # to possibly be modified
                      if self.ndim > 1 and i == self.obj._info_axis_number:
  
                          # add the new item, and set the value
                          # must have all defined axes if we have a scalar
                          # or a list-like on the non-info axes if we have a
                          # list-like
                          len_non_info_axes = [
                              len(_ax) for _i, _ax in enumerate(self.obj.axes)
                              if _i != i
                          ]
                          if any(not l for l in len_non_info_axes):
                              if not is_list_like_indexer(value):
                                  raise ValueError("cannot set a frame with no "
                                                   "defined index and a scalar")
                              self.obj[key] = value
                              return self.obj
  
                          # add a new item with the dtype setup
                          self.obj[key] = _infer_fill_value(value)
  
                          new_indexer = convert_from_missing_indexer_tuple(
                              indexer, self.obj.axes)
                          self._setitem_with_indexer(new_indexer, value)
  
                          return self.obj
  
                      # reindex the axis
                      # make sure to clear the cache because we are
                      # just replacing the block manager here
                      # so the object is the same
                      index = self.obj._get_axis(i)
                      labels = index.insert(len(index), key)
                      self.obj._data = self.obj.reindex(labels, axis=i)._data
                      self.obj._maybe_update_cacher(clear=True)
                      self.obj._is_copy = None
  
                      nindexer.append(labels.get_loc(key))
  
                  else:
                      nindexer.append(idx)
  
              indexer = tuple(nindexer)
          else:
  
              indexer, missing = convert_missing_indexer(indexer)
  
              if missing:
  
                  # reindex the axis to the new value
                  # and set inplace
                  if self.ndim == 1:
                      index = self.obj.index
                      new_index = index.insert(len(index), indexer)
  
                      # we have a coerced indexer, e.g. a float
                      # that matches in an Int64Index, so
                      # we will not create a duplicate index, rather
                      # index to that element
                      # e.g. 0.0 -> 0
                      # GH12246
                      if index.is_unique:
                          new_indexer = index.get_indexer([new_index[-1]])
                          if (new_indexer != -1).any():
                              return self._setitem_with_indexer(new_indexer,
                                                                value)
  
                      # this preserves dtype of the value
                      new_values = Series([value])._values
                      if len(self.obj._values):
                          try:
                              new_values = np.concatenate([self.obj._values,
                                                           new_values])
                          except TypeError:
                              as_obj = self.obj.astype(object)
                              new_values = np.concatenate([as_obj,
                                                           new_values])
                      self.obj._data = self.obj._constructor(
                          new_values, index=new_index, name=self.obj.name)._data
                      self.obj._maybe_update_cacher(clear=True)
                      return self.obj
  
                  elif self.ndim == 2:
  
                      # no columns and scalar
                      if not len(self.obj.columns):
                          raise ValueError("cannot set a frame with no defined "
                                           "columns")
  
                      # append a Series
                      if isinstance(value, Series):
  
                          value = value.reindex(index=self.obj.columns,
                                                copy=True)
                          value.name = indexer
  
                      # a list-list
                      else:
  
                          # must have conforming columns
                          if is_list_like_indexer(value):
                              if len(value) != len(self.obj.columns):
                                  raise ValueError("cannot set a row with "
                                                   "mismatched columns")
  
                          value = Series(value, index=self.obj.columns,
                                         name=indexer)
  
                      self.obj._data = self.obj.append(value)._data
                      self.obj._maybe_update_cacher(clear=True)
                      return self.obj
  
                  # set using setitem (Panel and > dims)
                  elif self.ndim >= 3:
                      return self.obj.__setitem__(indexer, value)
  
          # set
          item_labels = self.obj._get_axis(info_axis)
  
          # align and set the values
          if take_split_path:
  
              if not isinstance(indexer, tuple):
                  indexer = self._tuplify(indexer)
  
              if isinstance(value, ABCSeries):
                  value = self._align_series(indexer, value)
  
              info_idx = indexer[info_axis]
              if is_integer(info_idx):
                  info_idx = [info_idx]
              labels = item_labels[info_idx]
  
              # if we have a partial multiindex, then need to adjust the plane
              # indexer here
              if (len(labels) == 1 and
                      isinstance(self.obj[labels[0]].axes[0], MultiIndex)):
                  item = labels[0]
                  obj = self.obj[item]
                  index = obj.index
                  idx = indexer[:info_axis][0]
  
                  plane_indexer = tuple([idx]) + indexer[info_axis + 1:]
                  lplane_indexer = length_of_indexer(plane_indexer[0], index)
  
                  # require that we are setting the right number of values that
                  # we are indexing
                  if is_list_like_indexer(value) and np.iterable(
                          value) and lplane_indexer != len(value):
  
                      if len(obj[idx]) != len(value):
                          raise ValueError("cannot set using a multi-index "
                                           "selection indexer with a different "
                                           "length than the value")
  
                      # make sure we have an ndarray
                      value = getattr(value, 'values', value).ravel()
  
                      # we can directly set the series here
                      # as we select a slice indexer on the mi
                      idx = index._convert_slice_indexer(idx)
                      obj._consolidate_inplace()
                      obj = obj.copy()
                      obj._data = obj._data.setitem(indexer=tuple([idx]),
                                                    value=value)
                      self.obj[item] = obj
                      return
  
              # non-mi
              else:
                  plane_indexer = indexer[:info_axis] + indexer[info_axis + 1:]
                  if info_axis > 0:
                      plane_axis = self.obj.axes[:info_axis][0]
                      lplane_indexer = length_of_indexer(plane_indexer[0],
                                                         plane_axis)
                  else:
                      lplane_indexer = 0
  
              def setter(item, v):
                  s = self.obj[item]
                  pi = plane_indexer[0] if lplane_indexer == 1 else plane_indexer
  
                  # perform the equivalent of a setitem on the info axis
                  # as we have a null slice or a slice with full bounds
                  # which means essentially reassign to the columns of a
                  # multi-dim object
                  # GH6149 (null slice), GH10408 (full bounds)
                  if (isinstance(pi, tuple) and
                          all(com.is_null_slice(idx) or
                              com.is_full_slice(idx, len(self.obj))
                              for idx in pi)):
                      s = v
                  else:
                      # set the item, possibly having a dtype change
                      s._consolidate_inplace()
                      s = s.copy()
                      s._data = s._data.setitem(indexer=pi, value=v)
                      s._maybe_update_cacher(clear=True)
  
                  # reset the sliced object if unique
                  self.obj[item] = s
  
              def can_do_equal_len():
                  """ return True if we have an equal len settable """
                  if (not len(labels) == 1 or not np.iterable(value) or
                          is_scalar(plane_indexer[0])):
                      return False
  
                  l = len(value)
                  item = labels[0]
                  index = self.obj[item].index
  
                  # equal len list/ndarray
                  if len(index) == l:
                      return True
                  elif lplane_indexer == l:
                      return True
  
                  return False
  
              # we need an iterable, with a ndim of at least 1
              # eg. don't pass through np.array(0)
              if is_list_like_indexer(value) and getattr(value, 'ndim', 1) > 0:
  
                  # we have an equal len Frame
                  if isinstance(value, ABCDataFrame) and value.ndim > 1:
                      sub_indexer = list(indexer)
                      multiindex_indexer = isinstance(labels, MultiIndex)
  
                      for item in labels:
                          if item in value:
                              sub_indexer[info_axis] = item
                              v = self._align_series(
                                  tuple(sub_indexer), value[item],
                                  multiindex_indexer)
                          else:
                              v = np.nan
  
                          setter(item, v)
  
                  # we have an equal len ndarray/convertible to our labels
                  elif np.array(value).ndim == 2:
  
                      # note that this coerces the dtype if we are mixed
                      # GH 7551
                      value = np.array(value, dtype=object)
                      if len(labels) != value.shape[1]:
                          raise ValueError('Must have equal len keys and value '
                                           'when setting with an ndarray')
  
                      for i, item in enumerate(labels):
  
                          # setting with a list, recoerces
                          setter(item, value[:, i].tolist())
  
                  # we have an equal len list/ndarray
                  elif can_do_equal_len():
                      setter(labels[0], value)
  
                  # per label values
                  else:
  
                      if len(labels) != len(value):
                          raise ValueError('Must have equal len keys and value '
                                           'when setting with an iterable')
  
                      for item, v in zip(labels, value):
                          setter(item, v)
              else:
  
                  # scalar
                  for item in labels:
                      setter(item, value)
  
          else:
              if isinstance(indexer, tuple):
                  indexer = maybe_convert_ix(*indexer)
  
                  # if we are setting on the info axis ONLY
                  # set using those methods to avoid block-splitting
                  # logic here
                  if (len(indexer) > info_axis and
                          is_integer(indexer[info_axis]) and
                          all(com.is_null_slice(idx)
                              for i, idx in enumerate(indexer)
                              if i != info_axis) and
                          item_labels.is_unique):
                      self.obj[item_labels[indexer[info_axis]]] = value
                      return
  
              if isinstance(value, (ABCSeries, dict)):
                  # TODO(EA): ExtensionBlock.setitem this causes issues with
                  # setting for extensionarrays that store dicts. Need to decide
                  # if it's worth supporting that.
                  value = self._align_series(indexer, Series(value))
  
              elif isinstance(value, ABCDataFrame):
                  value = self._align_frame(indexer, value)
  
              if isinstance(value, ABCPanel):
                  value = self._align_panel(indexer, value)
  
              # check for chained assignment
              self.obj._check_is_chained_assignment_possible()
  
              # actually do the set
              self.obj._consolidate_inplace()
              self.obj._data = self.obj._data.setitem(indexer=indexer,
                                                      value=value)
              self.obj._maybe_update_cacher(clear=True)
  
      def _align_series(self, indexer, ser, multiindex_indexer=False):
          """
          Parameters
          ----------
          indexer : tuple, slice, scalar
              The indexer used to get the locations that will be set to
              `ser`
  
          ser : pd.Series
              The values to assign to the locations specified by `indexer`
  
          multiindex_indexer : boolean, optional
              Defaults to False. Should be set to True if `indexer` was from
              a `pd.MultiIndex`, to avoid unnecessary broadcasting.
  
  
          Returns:
          --------
          `np.array` of `ser` broadcast to the appropriate shape for assignment
          to the locations selected by `indexer`
  
          """
          if isinstance(indexer, (slice, np.ndarray, list, Index)):
              indexer = tuple([indexer])
  
          if isinstance(indexer, tuple):
  
              # flatten np.ndarray indexers
              ravel = lambda i: i.ravel() if isinstance(i, np.ndarray) else i
              indexer = tuple(map(ravel, indexer))
  
              aligners = [not com.is_null_slice(idx) for idx in indexer]
              sum_aligners = sum(aligners)
              single_aligner = sum_aligners == 1
              is_frame = self.obj.ndim == 2
              is_panel = self.obj.ndim >= 3
              obj = self.obj
  
              # are we a single alignable value on a non-primary
              # dim (e.g. panel: 1,2, or frame: 0) ?
              # hence need to align to a single axis dimension
              # rather that find all valid dims
  
              # frame
              if is_frame:
                  single_aligner = single_aligner and aligners[0]
  
              # panel
              elif is_panel:
                  single_aligner = (single_aligner and
                                    (aligners[1] or aligners[2]))
  
              # we have a frame, with multiple indexers on both axes; and a
              # series, so need to broadcast (see GH5206)
              if (sum_aligners == self.ndim and
                      all(is_sequence(_) for _ in indexer)):
                  ser = ser.reindex(obj.axes[0][indexer[0]], copy=True)._values
  
                  # single indexer
                  if len(indexer) > 1 and not multiindex_indexer:
                      l = len(indexer[1])
                      ser = np.tile(ser, l).reshape(l, -1).T
  
                  return ser
  
              for i, idx in enumerate(indexer):
                  ax = obj.axes[i]
  
                  # multiple aligners (or null slices)
                  if is_sequence(idx) or isinstance(idx, slice):
                      if single_aligner and com.is_null_slice(idx):
                          continue
                      new_ix = ax[idx]
                      if not is_list_like_indexer(new_ix):
                          new_ix = Index([new_ix])
                      else:
                          new_ix = Index(new_ix)
                      if ser.index.equals(new_ix) or not len(new_ix):
                          return ser._values.copy()
  
                      return ser.reindex(new_ix)._values
  
                  # 2 dims
                  elif single_aligner and is_frame:
  
                      # reindex along index
                      ax = self.obj.axes[1]
                      if ser.index.equals(ax) or not len(ax):
                          return ser._values.copy()
                      return ser.reindex(ax)._values
  
                  # >2 dims
                  elif single_aligner:
  
                      broadcast = []
                      for n, labels in enumerate(self.obj._get_plane_axes(i)):
  
                          # reindex along the matching dimensions
                          if len(labels & ser.index):
                              ser = ser.reindex(labels)
                          else:
                              broadcast.append((n, len(labels)))
  
                      # broadcast along other dims
                      ser = ser._values.copy()
                      for (axis, l) in broadcast:
                          shape = [-1] * (len(broadcast) + 1)
                          shape[axis] = l
                          ser = np.tile(ser, l).reshape(shape)
  
                      if self.obj.ndim == 3:
                          ser = ser.T
  
                      return ser
  
          elif is_scalar(indexer):
              ax = self.obj._get_axis(1)
  
              if ser.index.equals(ax):
                  return ser._values.copy()
  
              return ser.reindex(ax)._values
  
          raise ValueError('Incompatible indexer with Series')
  
      def _align_frame(self, indexer, df):
          is_frame = self.obj.ndim == 2
          is_panel = self.obj.ndim >= 3
  
          if isinstance(indexer, tuple):
  
              aligners = [not com.is_null_slice(idx) for idx in indexer]
              sum_aligners = sum(aligners)
              # TODO: single_aligner is not used
              single_aligner = sum_aligners == 1  # noqa
  
              idx, cols = None, None
              sindexers = []
              for i, ix in enumerate(indexer):
                  ax = self.obj.axes[i]
                  if is_sequence(ix) or isinstance(ix, slice):
                      if isinstance(ix, np.ndarray):
                          ix = ix.ravel()
                      if idx is None:
                          idx = ax[ix]
                      elif cols is None:
                          cols = ax[ix]
                      else:
                          break
                  else:
                      sindexers.append(i)
  
              # panel
              if is_panel:
  
                  # need to conform to the convention
                  # as we are not selecting on the items axis
                  # and we have a single indexer
                  # GH 7763
                  if len(sindexers) == 1 and sindexers[0] != 0:
                      df = df.T
  
                  if idx is None:
                      idx = df.index
                  if cols is None:
                      cols = df.columns
  
              if idx is not None and cols is not None:
  
                  if df.index.equals(idx) and df.columns.equals(cols):
                      val = df.copy()._values
                  else:
                      val = df.reindex(idx, columns=cols)._values
                  return val
  
          elif ((isinstance(indexer, slice) or is_list_like_indexer(indexer)) and
                is_frame):
              ax = self.obj.index[indexer]
              if df.index.equals(ax):
                  val = df.copy()._values
              else:
  
                  # we have a multi-index and are trying to align
                  # with a particular, level GH3738
                  if (isinstance(ax, MultiIndex) and
                          isinstance(df.index, MultiIndex) and
                          ax.nlevels != df.index.nlevels):
                      raise TypeError("cannot align on a multi-index with out "
                                      "specifying the join levels")
  
                  val = df.reindex(index=ax)._values
              return val
  
          elif is_scalar(indexer) and is_panel:
              idx = self.obj.axes[1]
              cols = self.obj.axes[2]
  
              # by definition we are indexing on the 0th axis
              # a passed in dataframe which is actually a transpose
              # of what is needed
              if idx.equals(df.index) and cols.equals(df.columns):
                  return df.copy()._values
  
              return df.reindex(idx, columns=cols)._values
  
          raise ValueError('Incompatible indexer with DataFrame')
  
      def _align_panel(self, indexer, df):
          # TODO: is_frame, is_panel are unused
          is_frame = self.obj.ndim == 2  # noqa
          is_panel = self.obj.ndim >= 3  # noqa
          raise NotImplementedError("cannot set using an indexer with a Panel "
                                    "yet!")
  
      def _getitem_tuple(self, tup):
          try:
              return self._getitem_lowerdim(tup)
          except IndexingError:
              pass
  
          # no multi-index, so validate all of the indexers
          self._has_valid_tuple(tup)
  
          # ugly hack for GH #836
          if self._multi_take_opportunity(tup):
              return self._multi_take(tup)
  
          # no shortcut needed
          retval = self.obj
          for i, key in enumerate(tup):
              if i >= self.obj.ndim:
                  raise IndexingError('Too many indexers')
  
              if com.is_null_slice(key):
                  continue
  
              retval = getattr(retval, self.name)._getitem_axis(key, axis=i)
  
          return retval
  
      def _multi_take_opportunity(self, tup):
          from pandas.core.generic import NDFrame
  
          # ugly hack for GH #836
          if not isinstance(self.obj, NDFrame):
              return False
  
          if not all(is_list_like_indexer(x) for x in tup):
              return False
  
          # just too complicated
          for indexer, ax in zip(tup, self.obj._data.axes):
              if isinstance(ax, MultiIndex):
                  return False
              elif com.is_bool_indexer(indexer):
                  return False
              elif not ax.is_unique:
                  return False
  
          return True
  
      def _multi_take(self, tup):
          """ create the reindex map for our objects, raise the _exception if we
          can't create the indexer
          """
          try:
              o = self.obj
              d = {a: self._convert_for_reindex(t, axis=o._get_axis_number(a))
                   for t, a in zip(tup, o._AXIS_ORDERS)}
              return o.reindex(**d)
          except(KeyError, IndexingError):
              raise self._exception
  
      def _convert_for_reindex(self, key, axis=None):
          if axis is None:
              axis = self.axis or 0
          labels = self.obj._get_axis(axis)
  
          if com.is_bool_indexer(key):
              key = check_bool_indexer(labels, key)
              return labels[key]
          else:
              if isinstance(key, Index):
                  keyarr = labels._convert_index_indexer(key)
              else:
                  # asarray can be unsafe, NumPy strings are weird
                  keyarr = com._asarray_tuplesafe(key)
  
              if is_integer_dtype(keyarr):
                  # Cast the indexer to uint64 if possible so
                  # that the values returned from indexing are
                  # also uint64.
                  keyarr = labels._convert_arr_indexer(keyarr)
  
                  if not labels.is_integer():
                      keyarr = _ensure_platform_int(keyarr)
                      return labels.take(keyarr)
  
              return keyarr
  
      def _handle_lowerdim_multi_index_axis0(self, tup):
          # we have an axis0 multi-index, handle or raise
  
          try:
              # fast path for series or for tup devoid of slices
              return self._get_label(tup, axis=self.axis)
          except TypeError:
              # slices are unhashable
              pass
          except Exception as e1:
              if isinstance(tup[0], (slice, Index)):
                  raise IndexingError("Handle elsewhere")
  
              # raise the error if we are not sorted
              ax0 = self.obj._get_axis(0)
              if not ax0.is_lexsorted_for_tuple(tup):
                  raise e1
  
          return None
  
      def _getitem_lowerdim(self, tup):
  
          # we can directly get the axis result since the axis is specified
          if self.axis is not None:
              axis = self.obj._get_axis_number(self.axis)
              return self._getitem_axis(tup, axis=axis)
  
          # we may have a nested tuples indexer here
          if self._is_nested_tuple_indexer(tup):
              return self._getitem_nested_tuple(tup)
  
          # we maybe be using a tuple to represent multiple dimensions here
          ax0 = self.obj._get_axis(0)
          # ...but iloc should handle the tuple as simple integer-location
          # instead of checking it as multiindex representation (GH 13797)
          if isinstance(ax0, MultiIndex) and self.name != 'iloc':
              result = self._handle_lowerdim_multi_index_axis0(tup)
              if result is not None:
                  return result
  
          if len(tup) > self.obj.ndim:
              raise IndexingError("Too many indexers. handle elsewhere")
  
          # to avoid wasted computation
          # df.ix[d1:d2, 0] -> columns first (True)
          # df.ix[0, ['C', 'B', A']] -> rows first (False)
          for i, key in enumerate(tup):
              if is_label_like(key) or isinstance(key, tuple):
                  section = self._getitem_axis(key, axis=i)
  
                  # we have yielded a scalar ?
                  if not is_list_like_indexer(section):
                      return section
  
                  elif section.ndim == self.ndim:
                      # we're in the middle of slicing through a MultiIndex
                      # revise the key wrt to `section` by inserting an _NS
                      new_key = tup[:i] + (_NS,) + tup[i + 1:]
  
                  else:
                      new_key = tup[:i] + tup[i + 1:]
  
                      # unfortunately need an odious kludge here because of
                      # DataFrame transposing convention
                      if (isinstance(section, ABCDataFrame) and i > 0 and
                              len(new_key) == 2):
                          a, b = new_key
                          new_key = b, a
  
                      if len(new_key) == 1:
                          new_key, = new_key
  
                  # Slices should return views, but calling iloc/loc with a null
                  # slice returns a new object.
                  if com.is_null_slice(new_key):
                      return section
                  # This is an elided recursive call to iloc/loc/etc'
                  return getattr(section, self.name)[new_key]
  
          raise IndexingError('not applicable')
  
      def _getitem_nested_tuple(self, tup):
          # we have a nested tuple so have at least 1 multi-index level
          # we should be able to match up the dimensionaility here
  
          # we have too many indexers for our dim, but have at least 1
          # multi-index dimension, try to see if we have something like
          # a tuple passed to a series with a multi-index
          if len(tup) > self.ndim:
              result = self._handle_lowerdim_multi_index_axis0(tup)
              if result is not None:
                  return result
  
              # this is a series with a multi-index specified a tuple of
              # selectors
              return self._getitem_axis(tup, axis=self.axis)
  
          # handle the multi-axis by taking sections and reducing
          # this is iterative
          obj = self.obj
          axis = 0
          for i, key in enumerate(tup):
  
              if com.is_null_slice(key):
                  axis += 1
                  continue
  
              current_ndim = obj.ndim
              obj = getattr(obj, self.name)._getitem_axis(key, axis=axis)
              axis += 1
  
              # if we have a scalar, we are done
              if is_scalar(obj) or not hasattr(obj, 'ndim'):
                  break
  
              # has the dim of the obj changed?
              # GH 7199
              if obj.ndim < current_ndim:
  
                  # GH 7516
                  # if had a 3 dim and are going to a 2d
                  # axes are reversed on a DataFrame
                  if i >= 1 and current_ndim == 3 and obj.ndim == 2:
                      obj = obj.T
  
                  axis -= 1
  
          return obj
  
      def _getitem_axis(self, key, axis=None):
  
          if axis is None:
              axis = self.axis or 0
  
          if self._should_validate_iterable(axis):
              self._has_valid_type(key, axis)
  
          labels = self.obj._get_axis(axis)
          if isinstance(key, slice):
              return self._get_slice_axis(key, axis=axis)
          elif (is_list_like_indexer(key) and
                not (isinstance(key, tuple) and
                     isinstance(labels, MultiIndex))):
  
              if hasattr(key, 'ndim') and key.ndim > 1:
                  raise ValueError('Cannot index with multidimensional key')
  
              return self._getitem_iterable(key, axis=axis)
          else:
  
              # maybe coerce a float scalar to integer
              key = labels._maybe_cast_indexer(key)
  
              if is_integer(key):
                  if axis == 0 and isinstance(labels, MultiIndex):
                      try:
                          return self._get_label(key, axis=axis)
                      except (KeyError, TypeError):
                          if self.obj.index.levels[0].is_integer():
                              raise
  
                  # this is the fallback! (for a non-float, non-integer index)
                  if not labels.is_floating() and not labels.is_integer():
                      return self._get_loc(key, axis=axis)
  
              return self._get_label(key, axis=axis)
  
      def _getitem_iterable(self, key, axis=None):
          if axis is None:
              axis = self.axis or 0
  
          if self._should_validate_iterable(axis):
              self._has_valid_type(key, axis)
  
          labels = self.obj._get_axis(axis)
  
          if com.is_bool_indexer(key):
              key = check_bool_indexer(labels, key)
              inds, = key.nonzero()
              return self.obj._take(inds, axis=axis, convert=False)
          else:
              # Have the index compute an indexer or return None
              # if it cannot handle; we only act on all found values
              indexer, keyarr = labels._convert_listlike_indexer(
                  key, kind=self.name)
              if indexer is not None and (indexer != -1).all():
                  return self.obj.take(indexer, axis=axis)
  
              # existing labels are unique and indexer are unique
              if labels.is_unique and Index(keyarr).is_unique:
  
                  try:
                      return self.obj.reindex(keyarr, axis=axis)
                  except AttributeError:
  
                      # Series
                      if axis != 0:
                          raise AssertionError('axis must be 0')
                      return self.obj.reindex(keyarr)
  
              # existing labels are non-unique
              else:
  
                  # reindex with the specified axis
                  if axis + 1 > self.obj.ndim:
                      raise AssertionError("invalid indexing error with "
                                           "non-unique index")
  
                  new_target, indexer, new_indexer = labels._reindex_non_unique(
                      keyarr)
  
                  if new_indexer is not None:
                      result = self.obj._take(indexer[indexer != -1], axis=axis,
                                              convert=False)
  
                      result = result._reindex_with_indexers(
                          {axis: [new_target, new_indexer]},
                          copy=True, allow_dups=True)
  
                  else:
                      result = self.obj._take(indexer, axis=axis)
  
                  return result
  
      def _convert_to_indexer(self, obj, axis=None, is_setter=False):
          """
          Convert indexing key into something we can use to do actual fancy
          indexing on an ndarray
  
          Examples
          ix[:5] -> slice(0, 5)
          ix[[1,2,3]] -> [1,2,3]
          ix[['foo', 'bar', 'baz']] -> [i, j, k] (indices of foo, bar, baz)
  
          Going by Zen of Python?
          'In the face of ambiguity, refuse the temptation to guess.'
          raise AmbiguousIndexError with integer labels?
          - No, prefer label-based indexing
          """
          if axis is None:
              axis = self.axis or 0
  
          labels = self.obj._get_axis(axis)
  
          if isinstance(obj, slice):
              return self._convert_slice_indexer(obj, axis)
  
          # try to find out correct indexer, if not type correct raise
          try:
              obj = self._convert_scalar_indexer(obj, axis)
          except TypeError:
  
              # but we will allow setting
              if is_setter:
                  pass
  
          # see if we are positional in nature
          is_int_index = labels.is_integer()
          is_int_positional = is_integer(obj) and not is_int_index
  
          # if we are a label return me
          try:
              return labels.get_loc(obj)
          except LookupError:
              if isinstance(obj, tuple) and isinstance(labels, MultiIndex):
                  if is_setter and len(obj) == labels.nlevels:
                      return {'key': obj}
                  raise
          except TypeError:
              pass
          except (ValueError):
              if not is_int_positional:
                  raise
  
          # a positional
          if is_int_positional:
  
              # if we are setting and its not a valid location
              # its an insert which fails by definition
              if is_setter:
  
                  # always valid
                  if self.name == 'loc':
                      return {'key': obj}
  
                  # a positional
                  if (obj >= self.obj.shape[axis] and
                          not isinstance(labels, MultiIndex)):
                      raise ValueError("cannot set by positional indexing with "
                                       "enlargement")
  
              return obj
  
          if is_nested_tuple(obj, labels):
              return labels.get_locs(obj)
  
          elif is_list_like_indexer(obj):
  
              if com.is_bool_indexer(obj):
                  obj = check_bool_indexer(labels, obj)
                  inds, = obj.nonzero()
                  return inds
              else:
  
                  # Have the index compute an indexer or return None
                  # if it cannot handle
                  indexer, objarr = labels._convert_listlike_indexer(
                      obj, kind=self.name)
                  if indexer is not None:
                      return indexer
  
                  # unique index
                  if labels.is_unique:
                      indexer = check = labels.get_indexer(objarr)
  
                  # non-unique (dups)
                  else:
                      (indexer,
                       missing) = labels.get_indexer_non_unique(objarr)
                      # 'indexer' has dupes, create 'check' using 'missing'
                      check = np.zeros_like(objarr)
                      check[missing] = -1
  
                  mask = check == -1
                  if mask.any():
                      raise KeyError('{mask} not in index'
                                     .format(mask=objarr[mask]))
  
                  return com._values_from_object(indexer)
  
          else:
              try:
                  return labels.get_loc(obj)
              except LookupError:
                  # allow a not found key only if we are a setter
                  if not is_list_like_indexer(obj) and is_setter:
                      return {'key': obj}
                  raise
  
      def _tuplify(self, loc):
          tup = [slice(None, None) for _ in range(self.ndim)]
          tup[0] = loc
          return tuple(tup)
  
      def _get_slice_axis(self, slice_obj, axis=None):
          obj = self.obj
  
          if axis is None:
              axis = self.axis or 0
  
          if not need_slice(slice_obj):
              return obj.copy(deep=False)
          indexer = self._convert_slice_indexer(slice_obj, axis)
  
          if isinstance(indexer, slice):
              return self._slice(indexer, axis=axis, kind='iloc')
          else:
              return self.obj._take(indexer, axis=axis, convert=False)
  
  
  class _IXIndexer(_NDFrameIndexer):
      """A primarily label-location based indexer, with integer position
      fallback.
  
      Warning: Starting in 0.20.0, the .ix indexer is deprecated, in
      favor of the more strict .iloc and .loc indexers.
  
      ``.ix[]`` supports mixed integer and label based access. It is
      primarily label based, but will fall back to integer positional
      access unless the corresponding axis is of integer type.
  
      ``.ix`` is the most general indexer and will support any of the
      inputs in ``.loc`` and ``.iloc``. ``.ix`` also supports floating
      point label schemes. ``.ix`` is exceptionally useful when dealing
      with mixed positional and label based hierarchical indexes.
  
      However, when an axis is integer based, ONLY label based access
      and not positional access is supported. Thus, in such cases, it's
      usually better to be explicit and use ``.iloc`` or ``.loc``.
  
      See more at :ref:`Advanced Indexing <advanced>`.
  
      """
  
      def __init__(self, name, obj):
  
          _ix_deprecation_warning = textwrap.dedent("""
              .ix is deprecated. Please use
              .loc for label based indexing or
              .iloc for positional indexing
  
              See the documentation here:
              http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated""")  # noqa
  
          warnings.warn(_ix_deprecation_warning,
                        DeprecationWarning, stacklevel=2)
          super(_IXIndexer, self).__init__(name, obj)
  
      def _has_valid_type(self, key, axis):
          if isinstance(key, slice):
              return True
  
          elif com.is_bool_indexer(key):
              return True
  
          elif is_list_like_indexer(key):
              return True
  
          else:
  
              self._convert_scalar_indexer(key, axis)
  
          return True
  
  
  class _LocationIndexer(_NDFrameIndexer):
      _exception = Exception
  
      def __getitem__(self, key):
          if type(key) is tuple:
              key = tuple(com._apply_if_callable(x, self.obj)
                          for x in key)
              try:
                  if self._is_scalar_access(key):
                      return self._getitem_scalar(key)
              except (KeyError, IndexError):
                  pass
              return self._getitem_tuple(key)
          else:
              # we by definition only have the 0th axis
              axis = self.axis or 0
  
              maybe_callable = com._apply_if_callable(key, self.obj)
              return self._getitem_axis(maybe_callable, axis=axis)
  
      def _is_scalar_access(self, key):
          raise NotImplementedError()
  
      def _getitem_scalar(self, key):
          raise NotImplementedError()
  
      def _getitem_axis(self, key, axis=None):
          raise NotImplementedError()
  
      def _getbool_axis(self, key, axis=None):
          if axis is None:
              axis = self.axis or 0
          labels = self.obj._get_axis(axis)
          key = check_bool_indexer(labels, key)
          inds, = key.nonzero()
          try:
              return self.obj._take(inds, axis=axis, convert=False)
          except Exception as detail:
              raise self._exception(detail)
  
      def _get_slice_axis(self, slice_obj, axis=None):
          """ this is pretty simple as we just have to deal with labels """
          if axis is None:
              axis = self.axis or 0
  
          obj = self.obj
          if not need_slice(slice_obj):
              return obj.copy(deep=False)
  
          labels = obj._get_axis(axis)
          indexer = labels.slice_indexer(slice_obj.start, slice_obj.stop,
                                         slice_obj.step, kind=self.name)
  
          if isinstance(indexer, slice):
              return self._slice(indexer, axis=axis, kind='iloc')
          else:
              return self.obj._take(indexer, axis=axis, convert=False)
  
  
  class _LocIndexer(_LocationIndexer):
      """
      Access a group of rows and columns by label(s) or a boolean array.
  
      ``.loc[]`` is primarily label based, but may also be used with a
      boolean array.
  
      Allowed inputs are:
  
      - A single label, e.g. ``5`` or ``'a'``, (note that ``5`` is
        interpreted as a *label* of the index, and **never** as an
        integer position along the index).
      - A list or array of labels, e.g. ``['a', 'b', 'c']``.
      - A slice object with labels, e.g. ``'a':'f'``.
  
        .. warning:: Note that contrary to usual python slices, **both** the
            start and the stop are included
  
      - A boolean array of the same length as the axis being sliced,
        e.g. ``[True, False, True]``.
      - A ``callable`` function with one argument (the calling Series, DataFrame
        or Panel) and that returns valid output for indexing (one of the above)
  
      See more at :ref:`Selection by Label <indexing.label>`
  
      See Also
      --------
      DataFrame.at : Access a single value for a row/column label pair
      DataFrame.iloc : Access group of rows and columns by integer position(s)
      DataFrame.xs : Returns a cross-section (row(s) or column(s)) from the
          Series/DataFrame.
      Series.loc : Access group of values using labels
  
      Examples
      --------
      **Getting values**
  
      >>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
      ...      index=['cobra', 'viper', 'sidewinder'],
      ...      columns=['max_speed', 'shield'])
      >>> df
                  max_speed  shield
      cobra               1       2
      viper               4       5
      sidewinder          7       8
  
      Single label. Note this returns the row as a Series.
  
      >>> df.loc['viper']
      max_speed    4
      shield       5
      Name: viper, dtype: int64
  
      List of labels. Note using ``[[]]`` returns a DataFrame.
  
      >>> df.loc[['viper', 'sidewinder']]
                  max_speed  shield
      viper               4       5
      sidewinder          7       8
  
      Single label for row and column
  
      >>> df.loc['cobra', 'shield']
      2
  
      Slice with labels for row and single label for column. As mentioned
      above, note that both the start and stop of the slice are included.
  
      >>> df.loc['cobra':'viper', 'max_speed']
      cobra    1
      viper    4
      Name: max_speed, dtype: int64
  
      Boolean list with the same length as the row axis
  
      >>> df.loc[[False, False, True]]
                  max_speed  shield
      sidewinder          7       8
  
      Conditional that returns a boolean Series
  
      >>> df.loc[df['shield'] > 6]
                  max_speed  shield
      sidewinder          7       8
  
      Conditional that returns a boolean Series with column labels specified
  
      >>> df.loc[df['shield'] > 6, ['max_speed']]
                  max_speed
      sidewinder          7
  
      Callable that returns a boolean Series
  
      >>> df.loc[lambda df: df['shield'] == 8]
                  max_speed  shield
      sidewinder          7       8
  
      **Setting values**
  
      Set value for all items matching the list of labels
  
      >>> df.loc[['viper', 'sidewinder'], ['shield']] = 50
      >>> df
                  max_speed  shield
      cobra               1       2
      viper               4      50
      sidewinder          7      50
  
      Set value for an entire row
  
      >>> df.loc['cobra'] = 10
      >>> df
                  max_speed  shield
      cobra              10      10
      viper               4      50
      sidewinder          7      50
  
      Set value for an entire column
  
      >>> df.loc[:, 'max_speed'] = 30
      >>> df
                  max_speed  shield
      cobra              30      10
      viper              30      50
      sidewinder         30      50
  
      Set value for rows matching callable condition
  
      >>> df.loc[df['shield'] > 35] = 0
      >>> df
                  max_speed  shield
      cobra              30      10
      viper               0       0
      sidewinder          0       0
  
      **Getting values on a DataFrame with an index that has integer labels**
  
      Another example using integers for the index
  
      >>> df = pd.DataFrame([[1, 2], [4, 5], [7, 8]],
      ...      index=[7, 8, 9], columns=['max_speed', 'shield'])
      >>> df
         max_speed  shield
      7          1       2
      8          4       5
      9          7       8
  
      Slice with integer labels for rows. As mentioned above, note that both
      the start and stop of the slice are included.
  
      >>> df.loc[7:9]
         max_speed  shield
      7          1       2
      8          4       5
      9          7       8
  
      **Getting values with a MultiIndex**
  
      A number of examples using a DataFrame with a MultiIndex
  
      >>> tuples = [
      ...    ('cobra', 'mark i'), ('cobra', 'mark ii'),
      ...    ('sidewinder', 'mark i'), ('sidewinder', 'mark ii'),
      ...    ('viper', 'mark ii'), ('viper', 'mark iii')
      ... ]
      >>> index = pd.MultiIndex.from_tuples(tuples)
      >>> values = [[12, 2], [0, 4], [10, 20],
      ...         [1, 4], [7, 1], [16, 36]]
      >>> df = pd.DataFrame(values, columns=['max_speed', 'shield'], index=index)
      >>> df
                           max_speed  shield
      cobra      mark i           12       2
                 mark ii           0       4
      sidewinder mark i           10      20
                 mark ii           1       4
      viper      mark ii           7       1
                 mark iii         16      36
  
      Single label. Note this returns a DataFrame with a single index.
  
      >>> df.loc['cobra']
               max_speed  shield
      mark i          12       2
      mark ii          0       4
  
      Single index tuple. Note this returns a Series.
  
      >>> df.loc[('cobra', 'mark ii')]
      max_speed    0
      shield       4
      Name: (cobra, mark ii), dtype: int64
  
      Single label for row and column. Similar to passing in a tuple, this
      returns a Series.
  
      >>> df.loc['cobra', 'mark i']
      max_speed    12
      shield        2
      Name: (cobra, mark i), dtype: int64
  
      Single tuple. Note using ``[[]]`` returns a DataFrame.
  
      >>> df.loc[[('cobra', 'mark ii')]]
                     max_speed  shield
      cobra mark ii          0       4
  
      Single tuple for the index with a single label for the column
  
      >>> df.loc[('cobra', 'mark i'), 'shield']
      2
  
      Slice from index tuple to single label
  
      >>> df.loc[('cobra', 'mark i'):'viper']
                           max_speed  shield
      cobra      mark i           12       2
                 mark ii           0       4
      sidewinder mark i           10      20
                 mark ii           1       4
      viper      mark ii           7       1
                 mark iii         16      36
  
      Slice from index tuple to index tuple
  
      >>> df.loc[('cobra', 'mark i'):('viper', 'mark ii')]
                          max_speed  shield
      cobra      mark i          12       2
                 mark ii          0       4
      sidewinder mark i          10      20
                 mark ii          1       4
      viper      mark ii          7       1
  
      Raises
      ------
      KeyError:
          when any items are not found
      """
  
      _valid_types = ("labels (MUST BE IN THE INDEX), slices of labels (BOTH "
                      "endpoints included! Can be slices of integers if the "
                      "index is integers), listlike of labels, boolean")
      _exception = KeyError
  
      def _has_valid_type(self, key, axis):
          ax = self.obj._get_axis(axis)
  
          # valid for a label where all labels are in the index
          # slice of labels (where start-end in labels)
          # slice of integers (only if in the labels)
          # boolean
  
          if isinstance(key, slice):
              return True
  
          elif com.is_bool_indexer(key):
              return True
  
          elif is_list_like_indexer(key):
  
              # mi is just a passthru
              if isinstance(key, tuple) and isinstance(ax, MultiIndex):
                  return True
  
              if not is_iterator(key) and len(key):
  
                  # True indicates missing values
                  missing = ax.get_indexer_for(key) < 0
  
                  if np.any(missing):
                      if len(key) == 1 or np.all(missing):
                          raise KeyError(
                              u"None of [{key}] are in the [{axis}]".format(
                                  key=key, axis=self.obj._get_axis_name(axis)))
                      else:
  
                          # we skip the warning on Categorical/Interval
                          # as this check is actually done (check for
                          # non-missing values), but a bit later in the
                          # code, so we want to avoid warning & then
                          # just raising
                          _missing_key_warning = textwrap.dedent("""
                          Passing list-likes to .loc or [] with any missing label will raise
                          KeyError in the future, you can use .reindex() as an alternative.
  
                          See the documentation here:
                          https://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate-loc-reindex-listlike""")  # noqa
  
                          if not (ax.is_categorical() or ax.is_interval()):
                              warnings.warn(_missing_key_warning,
                                            FutureWarning, stacklevel=5)
  
              return True
  
          else:
  
              def error():
                  if isna(key):
                      raise TypeError("cannot use label indexing with a null "
                                      "key")
                  raise KeyError(u"the label [{key}] is not in the [{axis}]"
                                 .format(key=key,
                                         axis=self.obj._get_axis_name(axis)))
  
              try:
                  key = self._convert_scalar_indexer(key, axis)
                  if not ax.contains(key):
                      error()
              except TypeError as e:
  
                  # python 3 type errors should be raised
                  if _is_unorderable_exception(e):
                      error()
                  raise
              except:
                  error()
  
          return True
  
      def _is_scalar_access(self, key):
          # this is a shortcut accessor to both .loc and .iloc
          # that provide the equivalent access of .at and .iat
          # a) avoid getting things via sections and (to minimize dtype changes)
          # b) provide a performant path
          if not hasattr(key, '__len__'):
              return False
  
          if len(key) != self.ndim:
              return False
  
          for i, k in enumerate(key):
              if not is_scalar(k):
                  return False
  
              ax = self.obj.axes[i]
              if isinstance(ax, MultiIndex):
                  return False
  
              if not ax.is_unique:
                  return False
  
          return True
  
      def _getitem_scalar(self, key):
          # a fast-path to scalar access
          # if not, raise
          values = self.obj._get_value(*key)
          return values
  
      def _get_partial_string_timestamp_match_key(self, key, labels):
          """Translate any partial string timestamp matches in key, returning the
          new key (GH 10331)"""
          if isinstance(labels, MultiIndex):
              if isinstance(key, compat.string_types) and \
                      labels.levels[0].is_all_dates:
                  # Convert key '2016-01-01' to
                  # ('2016-01-01'[, slice(None, None, None)]+)
                  key = tuple([key] + [slice(None)] * (len(labels.levels) - 1))
  
              if isinstance(key, tuple):
                  # Convert (..., '2016-01-01', ...) in tuple to
                  # (..., slice('2016-01-01', '2016-01-01', None), ...)
                  new_key = []
                  for i, component in enumerate(key):
                      if isinstance(component, compat.string_types) and \
                              labels.levels[i].is_all_dates:
                          new_key.append(slice(component, component, None))
                      else:
                          new_key.append(component)
                  key = tuple(new_key)
  
          return key
  
      def _getitem_axis(self, key, axis=None):
          if axis is None:
              axis = self.axis or 0
  
          labels = self.obj._get_axis(axis)
          key = self._get_partial_string_timestamp_match_key(key, labels)
  
          if isinstance(key, slice):
              self._has_valid_type(key, axis)
              return self._get_slice_axis(key, axis=axis)
          elif com.is_bool_indexer(key):
              return self._getbool_axis(key, axis=axis)
          elif is_list_like_indexer(key):
  
              # convert various list-like indexers
              # to a list of keys
              # we will use the *values* of the object
              # and NOT the index if its a PandasObject
              if isinstance(labels, MultiIndex):
  
                  if isinstance(key, (ABCSeries, np.ndarray)) and key.ndim <= 1:
                      # Series, or 0,1 ndim ndarray
                      # GH 14730
                      key = list(key)
                  elif isinstance(key, ABCDataFrame):
                      # GH 15438
                      raise NotImplementedError("Indexing a MultiIndex with a "
                                                "DataFrame key is not "
                                                "implemented")
                  elif hasattr(key, 'ndim') and key.ndim > 1:
                      raise NotImplementedError("Indexing a MultiIndex with a "
                                                "multidimensional key is not "
                                                "implemented")
  
                  if (not isinstance(key, tuple) and len(key) > 1 and
                          not isinstance(key[0], tuple)):
                      key = tuple([key])
  
              # an iterable multi-selection
              if not (isinstance(key, tuple) and isinstance(labels, MultiIndex)):
  
                  if hasattr(key, 'ndim') and key.ndim > 1:
                      raise ValueError('Cannot index with multidimensional key')
  
                  return self._getitem_iterable(key, axis=axis)
  
              # nested tuple slicing
              if is_nested_tuple(key, labels):
                  locs = labels.get_locs(key)
                  indexer = [slice(None)] * self.ndim
                  indexer[axis] = locs
                  return self.obj.iloc[tuple(indexer)]
  
          # fall thru to straight lookup
          self._has_valid_type(key, axis)
          return self._get_label(key, axis=axis)
  
  
  class _iLocIndexer(_LocationIndexer):
      """Purely integer-location based indexing for selection by position.
  
      ``.iloc[]`` is primarily integer position based (from ``0`` to
      ``length-1`` of the axis), but may also be used with a boolean
      array.
  
      Allowed inputs are:
  
      - An integer, e.g. ``5``.
      - A list or array of integers, e.g. ``[4, 3, 0]``.
      - A slice object with ints, e.g. ``1:7``.
      - A boolean array.
      - A ``callable`` function with one argument (the calling Series, DataFrame
        or Panel) and that returns valid output for indexing (one of the above)
  
      ``.iloc`` will raise ``IndexError`` if a requested indexer is
      out-of-bounds, except *slice* indexers which allow out-of-bounds
      indexing (this conforms with python/numpy *slice* semantics).
  
      See more at :ref:`Selection by Position <indexing.integer>`
  
      """
  
      _valid_types = ("integer, integer slice (START point is INCLUDED, END "
                      "point is EXCLUDED), listlike of integers, boolean array")
      _exception = IndexError
  
      def _has_valid_type(self, key, axis):
          if com.is_bool_indexer(key):
              if hasattr(key, 'index') and isinstance(key.index, Index):
                  if key.index.inferred_type == 'integer':
                      raise NotImplementedError("iLocation based boolean "
                                                "indexing on an integer type "
                                                "is not available")
                  raise ValueError("iLocation based boolean indexing cannot use "
                                   "an indexable as a mask")
              return True
  
          if isinstance(key, slice):
              return True
          elif is_integer(key):
              return self._is_valid_integer(key, axis)
          elif is_list_like_indexer(key):
              return self._is_valid_list_like(key, axis)
          return False
  
      def _has_valid_setitem_indexer(self, indexer):
          self._has_valid_positional_setitem_indexer(indexer)
  
      def _is_scalar_access(self, key):
          # this is a shortcut accessor to both .loc and .iloc
          # that provide the equivalent access of .at and .iat
          # a) avoid getting things via sections and (to minimize dtype changes)
          # b) provide a performant path
          if not hasattr(key, '__len__'):
              return False
  
          if len(key) != self.ndim:
              return False
  
          for i, k in enumerate(key):
              if not is_integer(k):
                  return False
  
              ax = self.obj.axes[i]
              if not ax.is_unique:
                  return False
  
          return True
  
      def _getitem_scalar(self, key):
          # a fast-path to scalar access
          # if not, raise
          values = self.obj._get_value(*key, takeable=True)
          return values
  
      def _is_valid_integer(self, key, axis):
          # return a boolean if we have a valid integer indexer
  
          ax = self.obj._get_axis(axis)
          l = len(ax)
          if key >= l or key < -l:
              raise IndexError("single positional indexer is out-of-bounds")
          return True
  
      def _is_valid_list_like(self, key, axis):
          # return a boolean if we are a valid list-like (e.g. that we don't
          # have out-of-bounds values)
  
          # a tuple should already have been caught by this point
          # so don't treat a tuple as a valid indexer
          if isinstance(key, tuple):
              raise IndexingError('Too many indexers')
  
          # coerce the key to not exceed the maximum size of the index
          arr = np.array(key)
          ax = self.obj._get_axis(axis)
          l = len(ax)
          if (hasattr(arr, '__len__') and len(arr) and
                  (arr.max() >= l or arr.min() < -l)):
              raise IndexError("positional indexers are out-of-bounds")
  
          return True
  
      def _getitem_tuple(self, tup):
  
          self._has_valid_tuple(tup)
          try:
              return self._getitem_lowerdim(tup)
          except:
              pass
  
          retval = self.obj
          axis = 0
          for i, key in enumerate(tup):
              if i >= self.obj.ndim:
                  raise IndexingError('Too many indexers')
  
              if com.is_null_slice(key):
                  axis += 1
                  continue
  
              retval = getattr(retval, self.name)._getitem_axis(key, axis=axis)
  
              # if the dim was reduced, then pass a lower-dim the next time
              if retval.ndim < self.ndim:
                  axis -= 1
  
              # try to get for the next axis
              axis += 1
  
          return retval
  
      def _get_slice_axis(self, slice_obj, axis=None):
          if axis is None:
              axis = self.axis or 0
          obj = self.obj
  
          if not need_slice(slice_obj):
              return obj.copy(deep=False)
  
          slice_obj = self._convert_slice_indexer(slice_obj, axis)
          if isinstance(slice_obj, slice):
              return self._slice(slice_obj, axis=axis, kind='iloc')
          else:
              return self.obj._take(slice_obj, axis=axis, convert=False)
  
      def _get_list_axis(self, key, axis=None):
          """
          Return Series values by list or array of integers
  
          Parameters
          ----------
          key : list-like positional indexer
          axis : int (can only be zero)
  
          Returns
          -------
          Series object
          """
          if axis is None:
              axis = self.axis or 0
          try:
              return self.obj._take(key, axis=axis, convert=False)
          except IndexError:
              # re-raise with different error message
              raise IndexError("positional indexers are out-of-bounds")
  
      def _getitem_axis(self, key, axis=None):
          if axis is None:
              axis = self.axis or 0
  
          if isinstance(key, slice):
              self._has_valid_type(key, axis)
              return self._get_slice_axis(key, axis=axis)
  
          if isinstance(key, list):
              try:
                  key = np.asarray(key)
              except TypeError:  # pragma: no cover
                  pass
  
          if com.is_bool_indexer(key):
              self._has_valid_type(key, axis)
              return self._getbool_axis(key, axis=axis)
  
          # a list of integers
          elif is_list_like_indexer(key):
              return self._get_list_axis(key, axis=axis)
  
          # a single integer
          else:
              key = self._convert_scalar_indexer(key, axis)
  
              if not is_integer(key):
                  raise TypeError("Cannot index by location index with a "
                                  "non-integer key")
  
              # validate the location
              self._is_valid_integer(key, axis)
  
              return self._get_loc(key, axis=axis)
  
      def _convert_to_indexer(self, obj, axis=None, is_setter=False):
          """ much simpler as we only have to deal with our valid types """
          if axis is None:
              axis = self.axis or 0
  
          # make need to convert a float key
          if isinstance(obj, slice):
              return self._convert_slice_indexer(obj, axis)
  
          elif is_float(obj):
              return self._convert_scalar_indexer(obj, axis)
  
          elif self._has_valid_type(obj, axis):
              return obj
  
          raise ValueError("Can only index by location with "
                           "a [{types}]".format(types=self._valid_types))
  
  
  class _ScalarAccessIndexer(_NDFrameIndexer):
      """ access scalars quickly """
  
      def _convert_key(self, key, is_setter=False):
          return list(key)
  
      def __getitem__(self, key):
          if not isinstance(key, tuple):
  
              # we could have a convertible item here (e.g. Timestamp)
              if not is_list_like_indexer(key):
                  key = tuple([key])
              else:
                  raise ValueError('Invalid call for scalar access (getting)!')
  
          key = self._convert_key(key)
          return self.obj._get_value(*key, takeable=self._takeable)
  
      def __setitem__(self, key, value):
          if isinstance(key, tuple):
              key = tuple(com._apply_if_callable(x, self.obj)
                          for x in key)
          else:
              # scalar callable may return tuple
              key = com._apply_if_callable(key, self.obj)
  
          if not isinstance(key, tuple):
              key = self._tuplify(key)
          if len(key) != self.obj.ndim:
              raise ValueError('Not enough indexers for scalar access '
                               '(setting)!')
          key = list(self._convert_key(key, is_setter=True))
          key.append(value)
          self.obj._set_value(*key, takeable=self._takeable)
  
  
  class _AtIndexer(_ScalarAccessIndexer):
      """
      Access a single value for a row/column label pair.
  
      Similar to ``loc``, in that both provide label-based lookups. Use
      ``at`` if you only need to get or set a single value in a DataFrame
      or Series.
  
      See Also
      --------
      DataFrame.iat : Access a single value for a row/column pair by integer
          position
      DataFrame.loc : Access a group of rows and columns by label(s)
      Series.at : Access a single value using a label
  
      Examples
      --------
      >>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
      ...                   index=[4, 5, 6], columns=['A', 'B', 'C'])
      >>> df
          A   B   C
      4   0   2   3
      5   0   4   1
      6  10  20  30
  
      Get value at specified row/column pair
  
      >>> df.at[4, 'B']
      2
  
      Set value at specified row/column pair
  
      >>> df.at[4, 'B'] = 10
      >>> df.at[4, 'B']
      10
  
      Get value within a Series
  
      >>> df.loc[5].at['B']
      4
  
      Raises
      ------
      KeyError
          When label does not exist in DataFrame
      """
  
      _takeable = False
  
      def _convert_key(self, key, is_setter=False):
          """ require they keys to be the same type as the index (so we don't
          fallback)
          """
  
          # allow arbitrary setting
          if is_setter:
              return list(key)
  
          for ax, i in zip(self.obj.axes, key):
              if ax.is_integer():
                  if not is_integer(i):
                      raise ValueError("At based indexing on an integer index "
                                       "can only have integer indexers")
              else:
                  if is_integer(i):
                      raise ValueError("At based indexing on an non-integer "
                                       "index can only have non-integer "
                                       "indexers")
          return key
  
  
  class _iAtIndexer(_ScalarAccessIndexer):
      """
      Access a single value for a row/column pair by integer position.
  
      Similar to ``iloc``, in that both provide integer-based lookups. Use
      ``iat`` if you only need to get or set a single value in a DataFrame
      or Series.
  
      See Also
      --------
      DataFrame.at : Access a single value for a row/column label pair
      DataFrame.loc : Access a group of rows and columns by label(s)
      DataFrame.iloc : Access a group of rows and columns by integer position(s)
  
      Examples
      --------
      >>> df = pd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
      ...                   columns=['A', 'B', 'C'])
      >>> df
          A   B   C
      0   0   2   3
      1   0   4   1
      2  10  20  30
  
      Get value at specified row/column pair
  
      >>> df.iat[1, 2]
      1
  
      Set value at specified row/column pair
  
      >>> df.iat[1, 2] = 10
      >>> df.iat[1, 2]
      10
  
      Get value within a series
  
      >>> df.loc[0].iat[1]
      2
  
      Raises
      ------
      IndexError
          When integer position is out of bounds
      """
  
      _takeable = True
  
      def _has_valid_setitem_indexer(self, indexer):
          self._has_valid_positional_setitem_indexer(indexer)
  
      def _convert_key(self, key, is_setter=False):
          """ require integer args (and convert to label arguments) """
          for a, i in zip(self.obj.axes, key):
              if not is_integer(i):
                  raise ValueError("iAt based indexing can only have integer "
                                   "indexers")
          return key
  
  
  def length_of_indexer(indexer, target=None):
      """return the length of a single non-tuple indexer which could be a slice
      """
      if target is not None and isinstance(indexer, slice):
          l = len(target)
          start = indexer.start
          stop = indexer.stop
          step = indexer.step
          if start is None:
              start = 0
          elif start < 0:
              start += l
          if stop is None or stop > l:
              stop = l
          elif stop < 0:
              stop += l
          if step is None:
              step = 1
          elif step < 0:
              step = -step
          return (stop - start + step - 1) // step
      elif isinstance(indexer, (ABCSeries, Index, np.ndarray, list)):
          return len(indexer)
      elif not is_list_like_indexer(indexer):
          return 1
      raise AssertionError("cannot find the length of the indexer")
  
  
  def convert_to_index_sliceable(obj, key):
      """if we are index sliceable, then return my slicer, otherwise return None
      """
      idx = obj.index
      if isinstance(key, slice):
          return idx._convert_slice_indexer(key, kind='getitem')
  
      elif isinstance(key, compat.string_types):
  
          # we are an actual column
          if obj._data.items.contains(key):
              return None
  
          # We might have a datetimelike string that we can translate to a
          # slice here via partial string indexing
          if idx.is_all_dates:
              try:
                  return idx._get_string_slice(key)
              except (KeyError, ValueError, NotImplementedError):
                  return None
  
      return None
  
  
  def check_bool_indexer(ax, key):
      # boolean indexing, need to check that the data are aligned, otherwise
      # disallowed
  
      # this function assumes that is_bool_indexer(key) == True
  
      result = key
      if isinstance(key, ABCSeries) and not key.index.equals(ax):
          result = result.reindex(ax)
          mask = isna(result._values)
          if mask.any():
              raise IndexingError('Unalignable boolean Series provided as '
                                  'indexer (index of the boolean Series and of '
                                  'the indexed object do not match')
          result = result.astype(bool)._values
      elif is_sparse(result):
          result = result.to_dense()
          result = np.asarray(result, dtype=bool)
      else:
          # is_bool_indexer has already checked for nulls in the case of an
          # object array key, so no check needed here
          result = np.asarray(result, dtype=bool)
  
      return result
  
  
  def check_setitem_lengths(indexer, value, values):
      """Validate that value and indexer are the same length.
  
      An special-case is allowed for when the indexer is a boolean array
      and the number of true values equals the length of ``value``. In
      this case, no exception is raised.
  
      Parameters
      ----------
      indexer : sequence
          The key for the setitem
      value : array-like
          The value for the setitem
      values : array-like
          The values being set into
  
      Returns
      -------
      None
  
      Raises
      ------
      ValueError
          When the indexer is an ndarray or list and the lengths don't
          match.
      """
      # boolean with truth values == len of the value is ok too
      if isinstance(indexer, (np.ndarray, list)):
          if is_list_like(value) and len(indexer) != len(value):
              if not (isinstance(indexer, np.ndarray) and
                      indexer.dtype == np.bool_ and
                      len(indexer[indexer]) == len(value)):
                  raise ValueError("cannot set using a list-like indexer "
                                   "with a different length than the value")
      # slice
      elif isinstance(indexer, slice):
  
          if is_list_like(value) and len(values):
              if len(value) != length_of_indexer(indexer, values):
                  raise ValueError("cannot set using a slice indexer with a "
                                   "different length than the value")
  
  
  def convert_missing_indexer(indexer):
      """ reverse convert a missing indexer, which is a dict
      return the scalar indexer and a boolean indicating if we converted
      """
  
      if isinstance(indexer, dict):
  
          # a missing key (but not a tuple indexer)
          indexer = indexer['key']
  
          if isinstance(indexer, bool):
              raise KeyError("cannot use a single bool to index into setitem")
          return indexer, True
  
      return indexer, False
  
  
  def convert_from_missing_indexer_tuple(indexer, axes):
      """ create a filtered indexer that doesn't have any missing indexers """
  
      def get_indexer(_i, _idx):
          return (axes[_i].get_loc(_idx['key']) if isinstance(_idx, dict) else
                  _idx)
  
      return tuple(get_indexer(_i, _idx) for _i, _idx in enumerate(indexer))
  
  
  def maybe_convert_indices(indices, n):
      """
      Attempt to convert indices into valid, positive indices.
  
      If we have negative indices, translate to positive here.
      If we have indices that are out-of-bounds, raise an IndexError.
  
      Parameters
      ----------
      indices : array-like
          The array of indices that we are to convert.
      n : int
          The number of elements in the array that we are indexing.
  
      Returns
      -------
      valid_indices : array-like
          An array-like of positive indices that correspond to the ones
          that were passed in initially to this function.
  
      Raises
      ------
      IndexError : one of the converted indices either exceeded the number
          of elements (specified by `n`) OR was still negative.
      """
  
      if isinstance(indices, list):
          indices = np.array(indices)
          if len(indices) == 0:
              # If list is empty, np.array will return float and cause indexing
              # errors.
              return np.empty(0, dtype=np.int_)
  
      mask = indices < 0
      if mask.any():
          indices[mask] += n
      mask = (indices >= n) | (indices < 0)
      if mask.any():
          raise IndexError("indices are out-of-bounds")
      return indices
  
  
  def maybe_convert_ix(*args):
      """
      We likely want to take the cross-product
      """
  
      ixify = True
      for arg in args:
          if not isinstance(arg, (np.ndarray, list, ABCSeries, Index)):
              ixify = False
  
      if ixify:
          return np.ix_(*args)
      else:
          return args
  
  
  def is_nested_tuple(tup, labels):
      # check for a compatible nested tuple and multiindexes among the axes
      if not isinstance(tup, tuple):
          return False
  
      for i, k in enumerate(tup):
  
          if is_list_like(k) or isinstance(k, slice):
              return isinstance(labels, MultiIndex)
  
      return False
  
  
  def is_list_like_indexer(key):
      # allow a list_like, but exclude NamedTuples which can be indexers
      return is_list_like(key) and not (isinstance(key, tuple) and
                                        type(key) is not tuple)
  
  
  def is_label_like(key):
      # select a label or row
      return not isinstance(key, slice) and not is_list_like_indexer(key)
  
  
  def need_slice(obj):
      return (obj.start is not None or obj.stop is not None or
              (obj.step is not None and obj.step != 1))
  
  
  def maybe_droplevels(index, key):
      # drop levels
      original_index = index
      if isinstance(key, tuple):
          for _ in key:
              try:
                  index = index.droplevel(0)
              except:
                  # we have dropped too much, so back out
                  return original_index
      else:
          try:
              index = index.droplevel(0)
          except:
              pass
  
      return index
  
  
  def _non_reducing_slice(slice_):
      """
      Ensurse that a slice doesn't reduce to a Series or Scalar.
  
      Any user-paseed `subset` should have this called on it
      to make sure we're always working with DataFrames.
      """
      # default to column slice, like DataFrame
      # ['A', 'B'] -> IndexSlices[:, ['A', 'B']]
      kinds = tuple(list(compat.string_types) + [ABCSeries, np.ndarray, Index,
                                                 list])
      if isinstance(slice_, kinds):
          slice_ = IndexSlice[:, slice_]
  
      def pred(part):
          # true when slice does *not* reduce
          return isinstance(part, slice) or is_list_like(part)
  
      if not is_list_like(slice_):
          if not isinstance(slice_, slice):
              # a 1-d slice, like df.loc[1]
              slice_ = [[slice_]]
          else:
              # slice(a, b, c)
              slice_ = [slice_]  # to tuplize later
      else:
          slice_ = [part if pred(part) else [part] for part in slice_]
      return tuple(slice_)
  
  
  def _maybe_numeric_slice(df, slice_, include_bool=False):
      """
      want nice defaults for background_gradient that don't break
      with non-numeric data. But if slice_ is passed go with that.
      """
      if slice_ is None:
          dtypes = [np.number]
          if include_bool:
              dtypes.append(bool)
          slice_ = IndexSlice[:, df.select_dtypes(include=dtypes).columns]
      return slice_
  