class_docstr: Holds the grouping information for a single key
  
  Parameters
  ----------
  index : Index
  grouper :
  obj : DataFrame or Series
  name : Label
  level :
  observed : bool, default False
      If we are a Categorical, use the observed values
  in_axis : if the Grouping is a column in self.obj and hence among
      Groupby.exclusions list
  dropna : bool, default True
      Whether to drop NA groups.
  uniques : Array-like, optional
      When specified, will be used for unique values. Enables including empty groups
      in the result for a BinGrouper. Must not contain duplicates.
  
  Attributes
  -------
  indices : dict
      Mapping of {group -> index_list}
  codes : ndarray
      Group codes
  group_index : Index or None
      unique groups
  groups : dict
      Mapping of {group -> label_list}
code: def _codes_and_uniques(
      self,
  ) -> tuple[npt.NDArray[np.signedinteger], ArrayLike]:
      uniques: ArrayLike
      if self._passed_categorical:
          # we make a CategoricalIndex out of the cat grouper
          # preserving the categories / ordered attributes;
          # doesn't (yet - GH#46909) handle dropna=False
          cat = self.grouping_vector
          categories = cat.categories
  
          if self._observed:
              ucodes = algorithms.unique1d(cat.codes)
              ucodes = ucodes[ucodes != -1]
              if self._sort:
                  ucodes = np.sort(ucodes)
          else:
              ucodes = np.arange(len(categories))
  
          uniques = Categorical.from_codes(
              codes=ucodes,
              categories=categories,
              ordered=cat.ordered,
              validate=False,
          )
  
          codes = cat.codes
          if not self._dropna:
              na_mask = codes < 0
              if np.any(na_mask):
                  if self._sort:
                      # Replace NA codes with `largest code + 1`
                      na_code = len(categories)
                      codes = np.where(na_mask, na_code, codes)
                  else:
                      # Insert NA code into the codes based on first appearance
                      # A negative code must exist, no need to check codes[na_idx] < 0
                      na_idx = na_mask.argmax()
                      # count number of unique codes that comes before the nan value
                      na_code = algorithms.nunique_ints(codes[:na_idx])
                      codes = np.where(
                          codes >= na_code, codes + 1, codes
                      )
                      codes = np.where(na_mask, na_code, codes)
  
          if not self._observed:
              uniques = uniques.reorder_categories(self._orig_cats)
  
          return codes, uniques
  
      elif isinstance(self.grouping_vector, ops.BaseGrouper):
          # we have a list of groupers
          codes = self.grouping_vector.codes_info
          uniques = self.grouping_vector.result_index._values
      elif self._uniques is not None:
          # GH#50486 Code grouping_vector using _uniques; allows
          # including uniques that are not present in grouping_vector.
          cat = Categorical(
              self.grouping_vector, categories=self._uniques
          )
          codes = cat.codes
          uniques = self._uniques
      else:
          # GH35667, replace dropna=False with use_na_sentinel=False
          # error: Incompatible types in assignment (expression has type "Union[
          # ndarray[Any, Any], Index]", variable has type "Categorical")
          codes, uniques = algorithms.factorize(  # type: ignore[assignment]
              self.grouping_vector,
              sort=self._sort,
              use_na_sentinel=self._dropna,
          )
      return codes, uniques
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, final
  import warnings
  import numpy
  from pandas._config import using_copy_on_write, warn_copy_on_write
  from pandas._libs import lib
  from pandas._libs.tslibs import OutOfBoundsDatetime
  from pandas.errors import InvalidIndexError
  from pandas.util._decorators import cache_readonly
  from pandas.util._exceptions import find_stack_level
  from pandas.core.dtypes.common import is_list_like, is_scalar
  from pandas.core.dtypes.dtypes import CategoricalDtype
  from pandas.core import algorithms
  from pandas.core.arrays import Categorical, ExtensionArray
  import pandas.core.common
  from pandas.core.frame import DataFrame
  from pandas.core.groupby import ops
  from pandas.core.groupby.categorical import recode_for_groupby
  from pandas.core.indexes.api import CategoricalIndex, Index, MultiIndex
  from pandas.core.series import Series
  from pandas.io.formats.printing import pprint_thing
label_desc: Use image node_id 16 for calling the Grouping obj's underlying member method code with example usage: obj._codes_and_uniques() and returns: codes,  uniques, codes,  uniques