text: """
  Provide classes to perform the groupby aggregate operations.
  
  These are not exposed to the user and provide implementations of the grouping
  operations, primarily in cython. These classes (BaseGrouper and BinGrouper)
  are contained *in* the SeriesGroupBy and DataFrameGroupBy objects.
  """
  
  import collections
  from typing import List, Optional, Sequence, Tuple, Type
  
  import numpy as np
  
  from pandas._libs import NaT, iNaT, lib
  import pandas._libs.groupby as libgroupby
  import pandas._libs.reduction as libreduction
  from pandas._typing import FrameOrSeries
  from pandas.errors import AbstractMethodError
  from pandas.util._decorators import cache_readonly
  
  from pandas.core.dtypes.common import (
      ensure_float64,
      ensure_int64,
      ensure_int_or_float,
      ensure_platform_int,
      is_bool_dtype,
      is_categorical_dtype,
      is_complex_dtype,
      is_datetime64_any_dtype,
      is_datetime64tz_dtype,
      is_extension_array_dtype,
      is_integer_dtype,
      is_numeric_dtype,
      is_sparse,
      is_timedelta64_dtype,
      needs_i8_conversion,
  )
  from pandas.core.dtypes.missing import _maybe_fill, isna
  
  import pandas.core.algorithms as algorithms
  from pandas.core.base import SelectionMixin
  import pandas.core.common as com
  from pandas.core.frame import DataFrame
  from pandas.core.generic import NDFrame
  from pandas.core.groupby import base, grouper
  from pandas.core.indexes.api import Index, MultiIndex, ensure_index
  from pandas.core.series import Series
  from pandas.core.sorting import (
      compress_group_index,
      decons_obs_group_ids,
      get_flattened_iterator,
      get_group_index,
      get_group_index_sorter,
      get_indexer_dict,
  )
  
  
  class BaseGrouper:
      """
      This is an internal Grouper class, which actually holds
      the generated groups
  
      Parameters
      ----------
      axis : Index
      groupings : Sequence[Grouping]
          all the grouping instances to handle in this grouper
          for example for grouper list to groupby, need to pass the list
      sort : bool, default True
          whether this grouper will give sorted result or not
      group_keys : bool, default True
      mutated : bool, default False
      indexer : intp array, optional
          the indexer created by Grouper
          some groupers (TimeGrouper) will sort its axis and its
          group_info is also sorted, so need the indexer to reorder
  
      """
  
      def __init__(
          self,
          axis: Index,
          groupings: "Sequence[grouper.Grouping]",
          sort: bool = True,
          group_keys: bool = True,
          mutated: bool = False,
          indexer: Optional[np.ndarray] = None,
      ):
          assert isinstance(axis, Index), axis
  
          self._filter_empty_groups = self.compressed = len(groupings) != 1
          self.axis = axis
          self._groupings: List[grouper.Grouping] = list(groupings)
          self.sort = sort
          self.group_keys = group_keys
          self.mutated = mutated
          self.indexer = indexer
  
      @property
      def groupings(self) -> List["grouper.Grouping"]:
          return self._groupings
  
      @property
      def shape(self):
          return tuple(ping.ngroups for ping in self.groupings)
  
      def __iter__(self):
          return iter(self.indices)
  
      @property
      def nkeys(self) -> int:
          return len(self.groupings)
  
      def get_iterator(self, data: FrameOrSeries, axis: int = 0):
          """
          Groupby iterator
  
          Returns
          -------
          Generator yielding sequence of (name, subsetted object)
          for each group
          """
          splitter = self._get_splitter(data, axis=axis)
          keys = self._get_group_keys()
          for key, (i, group) in zip(keys, splitter):
              yield key, group
  
      def _get_splitter(self, data: FrameOrSeries, axis: int = 0) -> "DataSplitter":
          comp_ids, _, ngroups = self.group_info
          return get_splitter(data, comp_ids, ngroups, axis=axis)
  
      def _get_grouper(self):
          """
          We are a grouper as part of another's groupings.
  
          We have a specific method of grouping, so cannot
          convert to a Index for our grouper.
          """
          return self.groupings[0].grouper
  
      def _get_group_keys(self):
          if len(self.groupings) == 1:
              return self.levels[0]
          else:
              comp_ids, _, ngroups = self.group_info
  
              # provide "flattened" iterator for multi-group setting
              return get_flattened_iterator(comp_ids, ngroups, self.levels, self.codes)
  
      def apply(self, f, data: FrameOrSeries, axis: int = 0):
          mutated = self.mutated
          splitter = self._get_splitter(data, axis=axis)
          group_keys = self._get_group_keys()
          result_values = None
  
          sdata: FrameOrSeries = splitter._get_sorted_data()
          if sdata.ndim == 2 and np.any(sdata.dtypes.apply(is_extension_array_dtype)):
              # calling splitter.fast_apply will raise TypeError via apply_frame_axis0
              #  if we pass EA instead of ndarray
              #  TODO: can we have a workaround for EAs backed by ndarray?
              pass
  
          elif (
              com.get_callable_name(f) not in base.plotting_methods
              and isinstance(splitter, FrameSplitter)
              and axis == 0
              # fast_apply/libreduction doesn't allow non-numpy backed indexes
              and not sdata.index._has_complex_internals
          ):
              try:
                  result_values, mutated = splitter.fast_apply(f, group_keys)
  
              except libreduction.InvalidApply as err:
                  # This Exception is raised if `f` triggers an exception
                  # but it is preferable to raise the exception in Python.
                  if "Let this error raise above us" not in str(err):
                      # TODO: can we infer anything about whether this is
                      #  worth-retrying in pure-python?
                      raise
  
              else:
                  # If the fast apply path could be used we can return here.
                  # Otherwise we need to fall back to the slow implementation.
                  if len(result_values) == len(group_keys):
                      return group_keys, result_values, mutated
  
          for key, (i, group) in zip(group_keys, splitter):
              object.__setattr__(group, "name", key)
  
              # result_values is None if fast apply path wasn't taken
              # or fast apply aborted with an unexpected exception.
              # In either case, initialize the result list and perform
              # the slow iteration.
              if result_values is None:
                  result_values = []
  
              # If result_values is not None we're in the case that the
              # fast apply loop was broken prematurely but we have
              # already the result for the first group which we can reuse.
              elif i == 0:
                  continue
  
              # group might be modified
              group_axes = group.axes
              res = f(group)
              if not _is_indexed_like(res, group_axes):
                  mutated = True
              result_values.append(res)
  
          return group_keys, result_values, mutated
  
      @cache_readonly
      def indices(self):
          """ dict {group name -> group indices} """
          if len(self.groupings) == 1:
              return self.groupings[0].indices
          else:
              codes_list = [ping.codes for ping in self.groupings]
              keys = [com.values_from_object(ping.group_index) for ping in self.groupings]
              return get_indexer_dict(codes_list, keys)
  
      @property
      def codes(self) -> List[np.ndarray]:
          return [ping.codes for ping in self.groupings]
  
      @property
      def levels(self) -> List[Index]:
          return [ping.group_index for ping in self.groupings]
  
      @property
      def names(self):
          return [ping.name for ping in self.groupings]
  
      def size(self) -> Series:
          """
          Compute group sizes.
          """
          ids, _, ngroup = self.group_info
          ids = ensure_platform_int(ids)
          if ngroup:
              out = np.bincount(ids[ids != -1], minlength=ngroup)
          else:
              out = []
          return Series(out, index=self.result_index, dtype="int64")
  
      @cache_readonly
      def groups(self):
          """ dict {group name -> group labels} """
          if len(self.groupings) == 1:
              return self.groupings[0].groups
          else:
              to_groupby = zip(*(ping.grouper for ping in self.groupings))
              to_groupby = Index(to_groupby)
              return self.axis.groupby(to_groupby)
  
      @cache_readonly
      def is_monotonic(self) -> bool:
          # return if my group orderings are monotonic
          return Index(self.group_info[0]).is_monotonic
  
      @cache_readonly
      def group_info(self):
          comp_ids, obs_group_ids = self._get_compressed_codes()
  
          ngroups = len(obs_group_ids)
          comp_ids = ensure_int64(comp_ids)
          return comp_ids, obs_group_ids, ngroups
  
      @cache_readonly
      def codes_info(self) -> np.ndarray:
          # return the codes of items in original grouped axis
          codes, _, _ = self.group_info
          if self.indexer is not None:
              sorter = np.lexsort((codes, self.indexer))
              codes = codes[sorter]
          return codes
  
      def _get_compressed_codes(self) -> Tuple[np.ndarray, np.ndarray]:
          all_codes = self.codes
          if len(all_codes) > 1:
              group_index = get_group_index(all_codes, self.shape, sort=True, xnull=True)
              return compress_group_index(group_index, sort=self.sort)
  
          ping = self.groupings[0]
          return ping.codes, np.arange(len(ping.group_index))
  
      @cache_readonly
      def ngroups(self) -> int:
          return len(self.result_index)
  
      @property
      def reconstructed_codes(self) -> List[np.ndarray]:
          codes = self.codes
          comp_ids, obs_ids, _ = self.group_info
          return decons_obs_group_ids(comp_ids, obs_ids, self.shape, codes, xnull=True)
  
      @cache_readonly
      def result_index(self) -> Index:
          if not self.compressed and len(self.groupings) == 1:
              return self.groupings[0].result_index.rename(self.names[0])
  
          codes = self.reconstructed_codes
          levels = [ping.result_index for ping in self.groupings]
          result = MultiIndex(
              levels=levels, codes=codes, verify_integrity=False, names=self.names
          )
          return result
  
      def get_group_levels(self):
          if not self.compressed and len(self.groupings) == 1:
              return [self.groupings[0].result_index]
  
          name_list = []
          for ping, codes in zip(self.groupings, self.reconstructed_codes):
              codes = ensure_platform_int(codes)
              levels = ping.result_index.take(codes)
  
              name_list.append(levels)
  
          return name_list
  
      # ------------------------------------------------------------
      # Aggregation functions
  
      _cython_functions = {
          "aggregate": {
              "add": "group_add",
              "prod": "group_prod",
              "min": "group_min",
              "max": "group_max",
              "mean": "group_mean",
              "median": "group_median",
              "var": "group_var",
              "first": "group_nth",
              "last": "group_last",
              "ohlc": "group_ohlc",
          },
          "transform": {
              "cumprod": "group_cumprod",
              "cumsum": "group_cumsum",
              "cummin": "group_cummin",
              "cummax": "group_cummax",
              "rank": "group_rank",
          },
      }
  
      _cython_arity = {"ohlc": 4}  # OHLC
  
      _name_functions = {"ohlc": ["open", "high", "low", "close"]}
  
      def _is_builtin_func(self, arg):
          """
          if we define an builtin function for this argument, return it,
          otherwise return the arg
          """
          return SelectionMixin._builtin_table.get(arg, arg)
  
      def _get_cython_function(self, kind: str, how: str, values, is_numeric: bool):
  
          dtype_str = values.dtype.name
          ftype = self._cython_functions[kind][how]
  
          # see if there is a fused-type version of function
          # only valid for numeric
          f = getattr(libgroupby, ftype, None)
          if f is not None and is_numeric:
              return f
  
          # otherwise find dtype-specific version, falling back to object
          for dt in [dtype_str, "object"]:
              f2 = getattr(libgroupby, f"{ftype}_{dt}", None)
              if f2 is not None:
                  return f2
  
          if hasattr(f, "__signatures__"):
              # inspect what fused types are implemented
              if dtype_str == "object" and "object" not in f.__signatures__:
                  # disallow this function so we get a NotImplementedError below
                  #  instead of a TypeError at runtime
                  f = None
  
          func = f
  
          if func is None:
              raise NotImplementedError(
                  f"function is not implemented for this dtype: "
                  f"[how->{how},dtype->{dtype_str}]"
              )
  
          return func
  
      def _get_cython_func_and_vals(
          self, kind: str, how: str, values: np.ndarray, is_numeric: bool
      ):
          """
          Find the appropriate cython function, casting if necessary.
  
          Parameters
          ----------
          kind : sttr
          how : srt
          values : np.ndarray
          is_numeric : bool
  
          Returns
          -------
          func : callable
          values : np.ndarray
          """
          try:
              func = self._get_cython_function(kind, how, values, is_numeric)
          except NotImplementedError:
              if is_numeric:
                  try:
                      values = ensure_float64(values)
                  except TypeError:
                      if lib.infer_dtype(values, skipna=False) == "complex":
                          values = values.astype(complex)
                      else:
                          raise
                  func = self._get_cython_function(kind, how, values, is_numeric)
              else:
                  raise
          return func, values
  
      def _cython_operation(
          self, kind: str, values, how: str, axis, min_count: int = -1, **kwargs
      ) -> Tuple[np.ndarray, Optional[List[str]]]:
          """
          Returns the values of a cython operation as a Tuple of [data, names].
  
          Names is only useful when dealing with 2D results, like ohlc
          (see self._name_functions).
          """
  
          assert kind in ["transform", "aggregate"]
          orig_values = values
  
          if values.ndim > 2:
              raise NotImplementedError("number of dimensions is currently limited to 2")
          elif values.ndim == 2:
              # Note: it is *not* the case that axis is always 0 for 1-dim values,
              #  as we can have 1D ExtensionArrays that we need to treat as 2D
              assert axis == 1, axis
  
          # can we do this operation with our cython functions
          # if not raise NotImplementedError
  
          # we raise NotImplemented if this is an invalid operation
          # entirely, e.g. adding datetimes
  
          # categoricals are only 1d, so we
          # are not setup for dim transforming
          if is_categorical_dtype(values) or is_sparse(values):
              raise NotImplementedError(f"{values.dtype} dtype not supported")
          elif is_datetime64_any_dtype(values):
              if how in ["add", "prod", "cumsum", "cumprod"]:
                  raise NotImplementedError(
                      f"datetime64 type does not support {how} operations"
                  )
          elif is_timedelta64_dtype(values):
              if how in ["prod", "cumprod"]:
                  raise NotImplementedError(
                      f"timedelta64 type does not support {how} operations"
                  )
  
          if is_datetime64tz_dtype(values.dtype):
              # Cast to naive; we'll cast back at the end of the function
              # TODO: possible need to reshape?  kludge can be avoided when
              #  2D EA is allowed.
              values = values.view("M8[ns]")
  
          is_datetimelike = needs_i8_conversion(values.dtype)
          is_numeric = is_numeric_dtype(values.dtype)
  
          if is_datetimelike:
              values = values.view("int64")
              is_numeric = True
          elif is_bool_dtype(values.dtype):
              values = ensure_float64(values)
          elif is_integer_dtype(values):
              # we use iNaT for the missing value on ints
              # so pre-convert to guard this condition
              if (values == iNaT).any():
                  values = ensure_float64(values)
              else:
                  values = ensure_int_or_float(values)
          elif is_numeric and not is_complex_dtype(values):
              values = ensure_float64(values)
          else:
              values = values.astype(object)
  
          arity = self._cython_arity.get(how, 1)
  
          vdim = values.ndim
          swapped = False
          if vdim == 1:
              values = values[:, None]
              out_shape = (self.ngroups, arity)
          else:
              if axis > 0:
                  swapped = True
                  assert axis == 1, axis
                  values = values.T
              if arity > 1:
                  raise NotImplementedError(
                      "arity of more than 1 is not supported for the 'how' argument"
                  )
              out_shape = (self.ngroups,) + values.shape[1:]
  
          func, values = self._get_cython_func_and_vals(kind, how, values, is_numeric)
  
          if how == "rank":
              out_dtype = "float"
          else:
              if is_numeric:
                  out_dtype = f"{values.dtype.kind}{values.dtype.itemsize}"
              else:
                  out_dtype = "object"
  
          codes, _, _ = self.group_info
  
          if kind == "aggregate":
              result = _maybe_fill(
                  np.empty(out_shape, dtype=out_dtype), fill_value=np.nan
              )
              counts = np.zeros(self.ngroups, dtype=np.int64)
              result = self._aggregate(
                  result, counts, values, codes, func, is_datetimelike, min_count
              )
          elif kind == "transform":
              result = _maybe_fill(
                  np.empty_like(values, dtype=out_dtype), fill_value=np.nan
              )
  
              # TODO: min_count
              result = self._transform(
                  result, values, codes, func, is_datetimelike, **kwargs
              )
  
          if is_integer_dtype(result) and not is_datetimelike:
              mask = result == iNaT
              if mask.any():
                  result = result.astype("float64")
                  result[mask] = np.nan
          elif (
              how == "add"
              and is_integer_dtype(orig_values.dtype)
              and is_extension_array_dtype(orig_values.dtype)
          ):
              # We need this to ensure that Series[Int64Dtype].resample().sum()
              # remains int64 dtype.
              # Two options for avoiding this special case
              # 1. mask-aware ops and avoid casting to float with NaN above
              # 2. specify the result dtype when calling this method
              result = result.astype("int64")
  
          if kind == "aggregate" and self._filter_empty_groups and not counts.all():
              assert result.ndim != 2
              result = result[counts > 0]
  
          if vdim == 1 and arity == 1:
              result = result[:, 0]
  
          names: Optional[List[str]] = self._name_functions.get(how, None)
  
          if swapped:
              result = result.swapaxes(0, axis)
  
          if is_datetime64tz_dtype(orig_values.dtype):
              result = type(orig_values)(result.astype(np.int64), dtype=orig_values.dtype)
          elif is_datetimelike and kind == "aggregate":
              result = result.astype(orig_values.dtype)
  
          return result, names
  
      def aggregate(
          self, values, how: str, axis: int = 0, min_count: int = -1
      ) -> Tuple[np.ndarray, Optional[List[str]]]:
          return self._cython_operation(
              "aggregate", values, how, axis, min_count=min_count
          )
  
      def transform(self, values, how: str, axis: int = 0, **kwargs):
          return self._cython_operation("transform", values, how, axis, **kwargs)
  
      def _aggregate(
          self,
          result,
          counts,
          values,
          comp_ids,
          agg_func,
          is_datetimelike: bool,
          min_count: int = -1,
      ):
          if agg_func is libgroupby.group_nth:
              # different signature from the others
              # TODO: should we be using min_count instead of hard-coding it?
              agg_func(result, counts, values, comp_ids, rank=1, min_count=-1)
          else:
              agg_func(result, counts, values, comp_ids, min_count)
  
          return result
  
      def _transform(
          self, result, values, comp_ids, transform_func, is_datetimelike: bool, **kwargs
      ):
  
          comp_ids, _, ngroups = self.group_info
          transform_func(result, values, comp_ids, ngroups, is_datetimelike, **kwargs)
  
          return result
  
      def agg_series(self, obj: Series, func):
          # Caller is responsible for checking ngroups != 0
          assert self.ngroups != 0
  
          if len(obj) == 0:
              # SeriesGrouper would raise if we were to call _aggregate_series_fast
              return self._aggregate_series_pure_python(obj, func)
  
          elif is_extension_array_dtype(obj.dtype):
              # _aggregate_series_fast would raise TypeError when
              #  calling libreduction.Slider
              # In the datetime64tz case it would incorrectly cast to tz-naive
              # TODO: can we get a performant workaround for EAs backed by ndarray?
              return self._aggregate_series_pure_python(obj, func)
  
          elif obj.index._has_complex_internals:
              # Pre-empt TypeError in _aggregate_series_fast
              return self._aggregate_series_pure_python(obj, func)
  
          try:
              return self._aggregate_series_fast(obj, func)
          except ValueError as err:
              if "Function does not reduce" in str(err):
                  # raised in libreduction
                  pass
              else:
                  raise
          return self._aggregate_series_pure_python(obj, func)
  
      def _aggregate_series_fast(self, obj: Series, func):
          # At this point we have already checked that
          #  - obj.index is not a MultiIndex
          #  - obj is backed by an ndarray, not ExtensionArray
          #  - len(obj) > 0
          #  - ngroups != 0
          func = self._is_builtin_func(func)
  
          group_index, _, ngroups = self.group_info
  
          # avoids object / Series creation overhead
          dummy = obj._get_values(slice(None, 0))
          indexer = get_group_index_sorter(group_index, ngroups)
          obj = obj.take(indexer)
          group_index = algorithms.take_nd(group_index, indexer, allow_fill=False)
          grouper = libreduction.SeriesGrouper(obj, func, group_index, ngroups, dummy)
          result, counts = grouper.get_result()
          return result, counts
  
      def _aggregate_series_pure_python(self, obj: Series, func):
  
          group_index, _, ngroups = self.group_info
  
          counts = np.zeros(ngroups, dtype=int)
          result = None
  
          splitter = get_splitter(obj, group_index, ngroups, axis=0)
  
          for label, group in splitter:
              res = func(group)
              if result is None:
                  if isinstance(res, (Series, Index, np.ndarray)):
                      if len(res) == 1:
                          # e.g. test_agg_lambda_with_timezone lambda e: e.head(1)
                          # FIXME: are we potentially losing import res.index info?
                          res = res.item()
                      else:
                          raise ValueError("Function does not reduce")
                  result = np.empty(ngroups, dtype="O")
  
              counts[label] = group.shape[0]
              result[label] = res
  
          assert result is not None
          result = lib.maybe_convert_objects(result, try_float=0)
          # TODO: try_cast back to EA?
  
          return result, counts
  
  
  class BinGrouper(BaseGrouper):
      """
      This is an internal Grouper class
  
      Parameters
      ----------
      bins : the split index of binlabels to group the item of axis
      binlabels : the label list
      filter_empty : boolean, default False
      mutated : boolean, default False
      indexer : a intp array
  
      Examples
      --------
      bins: [2, 4, 6, 8, 10]
      binlabels: DatetimeIndex(['2005-01-01', '2005-01-03',
          '2005-01-05', '2005-01-07', '2005-01-09'],
          dtype='datetime64[ns]', freq='2D')
  
      the group_info, which contains the label of each item in grouped
      axis, the index of label in label list, group number, is
  
      (array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4]), array([0, 1, 2, 3, 4]), 5)
  
      means that, the grouped axis has 10 items, can be grouped into 5
      labels, the first and second items belong to the first label, the
      third and forth items belong to the second label, and so on
  
      """
  
      def __init__(
          self,
          bins,
          binlabels,
          filter_empty: bool = False,
          mutated: bool = False,
          indexer=None,
      ):
          self.bins = ensure_int64(bins)
          self.binlabels = ensure_index(binlabels)
          self._filter_empty_groups = filter_empty
          self.mutated = mutated
          self.indexer = indexer
  
          # These lengths must match, otherwise we could call agg_series
          #  with empty self.bins, which would raise in libreduction.
          assert len(self.binlabels) == len(self.bins)
  
      @cache_readonly
      def groups(self):
          """ dict {group name -> group labels} """
  
          # this is mainly for compat
          # GH 3881
          result = {
              key: value
              for key, value in zip(self.binlabels, self.bins)
              if key is not NaT
          }
          return result
  
      @property
      def nkeys(self) -> int:
          return 1
  
      def _get_grouper(self):
          """
          We are a grouper as part of another's groupings.
  
          We have a specific method of grouping, so cannot
          convert to a Index for our grouper.
          """
          return self
  
      def get_iterator(self, data: FrameOrSeries, axis: int = 0):
          """
          Groupby iterator
  
          Returns
          -------
          Generator yielding sequence of (name, subsetted object)
          for each group
          """
          slicer = lambda start, edge: data._slice(slice(start, edge), axis=axis)
          length = len(data.axes[axis])
  
          start = 0
          for edge, label in zip(self.bins, self.binlabels):
              if label is not NaT:
                  yield label, slicer(start, edge)
              start = edge
  
          if start < length:
              yield self.binlabels[-1], slicer(start, None)
  
      @cache_readonly
      def indices(self):
          indices = collections.defaultdict(list)
  
          i = 0
          for label, bin in zip(self.binlabels, self.bins):
              if i < bin:
                  if label is not NaT:
                      indices[label] = list(range(i, bin))
                  i = bin
          return indices
  
      @cache_readonly
      def group_info(self):
          ngroups = self.ngroups
          obs_group_ids = np.arange(ngroups)
          rep = np.diff(np.r_[0, self.bins])
  
          rep = ensure_platform_int(rep)
          if ngroups == len(self.bins):
              comp_ids = np.repeat(np.arange(ngroups), rep)
          else:
              comp_ids = np.repeat(np.r_[-1, np.arange(ngroups)], rep)
  
          return (
              comp_ids.astype("int64", copy=False),
              obs_group_ids.astype("int64", copy=False),
              ngroups,
          )
  
      @cache_readonly
      def reconstructed_codes(self) -> List[np.ndarray]:
          # get unique result indices, and prepend 0 as groupby starts from the first
          return [np.r_[0, np.flatnonzero(self.bins[1:] != self.bins[:-1]) + 1]]
  
      @cache_readonly
      def result_index(self):
          if len(self.binlabels) != 0 and isna(self.binlabels[0]):
              return self.binlabels[1:]
  
          return self.binlabels
  
      @property
      def levels(self):
          return [self.binlabels]
  
      @property
      def names(self):
          return [self.binlabels.name]
  
      @property
      def groupings(self) -> "List[grouper.Grouping]":
          return [
              grouper.Grouping(lvl, lvl, in_axis=False, level=None, name=name)
              for lvl, name in zip(self.levels, self.names)
          ]
  
      def agg_series(self, obj: Series, func):
          # Caller is responsible for checking ngroups != 0
          assert self.ngroups != 0
          assert len(self.bins) > 0  # otherwise we'd get IndexError in get_result
  
          if is_extension_array_dtype(obj.dtype):
              # pre-empt SeriesBinGrouper from raising TypeError
              return self._aggregate_series_pure_python(obj, func)
  
          dummy = obj[:0]
          grouper = libreduction.SeriesBinGrouper(obj, func, self.bins, dummy)
          return grouper.get_result()
  
  
  def _is_indexed_like(obj, axes) -> bool:
      if isinstance(obj, Series):
          if len(axes) > 1:
              return False
          return obj.index.equals(axes[0])
      elif isinstance(obj, DataFrame):
          return obj.index.equals(axes[0])
  
      return False
  
  
  # ----------------------------------------------------------------------
  # Splitting / application
  
  
  class DataSplitter:
      def __init__(self, data: FrameOrSeries, labels, ngroups: int, axis: int = 0):
          self.data = data
          self.labels = ensure_int64(labels)
          self.ngroups = ngroups
  
          self.axis = axis
          assert isinstance(axis, int), axis
  
      @cache_readonly
      def slabels(self):
          # Sorted labels
          return algorithms.take_nd(self.labels, self.sort_idx, allow_fill=False)
  
      @cache_readonly
      def sort_idx(self):
          # Counting sort indexer
          return get_group_index_sorter(self.labels, self.ngroups)
  
      def __iter__(self):
          sdata = self._get_sorted_data()
  
          if self.ngroups == 0:
              # we are inside a generator, rather than raise StopIteration
              # we merely return signal the end
              return
  
          starts, ends = lib.generate_slices(self.slabels, self.ngroups)
  
          for i, (start, end) in enumerate(zip(starts, ends)):
              yield i, self._chop(sdata, slice(start, end))
  
      def _get_sorted_data(self) -> FrameOrSeries:
          return self.data.take(self.sort_idx, axis=self.axis)
  
      def _chop(self, sdata, slice_obj: slice) -> NDFrame:
          raise AbstractMethodError(self)
  
  
  class SeriesSplitter(DataSplitter):
      def _chop(self, sdata: Series, slice_obj: slice) -> Series:
          return sdata._get_values(slice_obj)
  
  
  class FrameSplitter(DataSplitter):
      def fast_apply(self, f, names):
          # must return keys::list, values::list, mutated::bool
          starts, ends = lib.generate_slices(self.slabels, self.ngroups)
  
          sdata = self._get_sorted_data()
          return libreduction.apply_frame_axis0(sdata, f, names, starts, ends)
  
      def _chop(self, sdata: DataFrame, slice_obj: slice) -> DataFrame:
          if self.axis == 0:
              return sdata.iloc[slice_obj]
          else:
              return sdata._slice(slice_obj, axis=1)
  
  
  def get_splitter(data: FrameOrSeries, *args, **kwargs) -> DataSplitter:
      if isinstance(data, Series):
          klass: Type[DataSplitter] = SeriesSplitter
      else:
          # i.e. DataFrame
          klass = FrameSplitter
  
      return klass(data, *args, **kwargs)
  