code: def recode_for_groupby(
      c: Categorical, sort: bool, observed: bool
  ) -> tuple[Categorical, Categorical | None]:
      """
      Code the categories to ensure we can groupby for categoricals.
  
      If observed=True, we return a new Categorical with the observed
      categories only.
  
      If sort=False, return a copy of self, coded with categories as
      returned by .unique(), followed by any categories not appearing in
      the data. If sort=True, return self.
  
      This method is needed solely to ensure the categorical index of the
      GroupBy result has categories in the order of appearance in the data
      (GH-8868).
  
      Parameters
      ----------
      c : Categorical
      sort : bool
          The value of the sort parameter groupby was called with.
      observed : bool
          Account only for the observed values
  
      Returns
      -------
      Categorical
          If sort=False, the new categories are set to the order of
          appearance in codes (unless ordered=True, in which case the
          original order is preserved), followed by any unrepresented
          categories in the original order.
      Categorical or None
          If we are observed, return the original categorical, otherwise None
      """
      # we only care about observed values
      if observed:
          # In cases with c.ordered, this is equivalent to
          #  return c.remove_unused_categories(), c
  
          unique_codes = unique1d(c.codes)
  
          take_codes = unique_codes[unique_codes != -1]
          if sort:
              take_codes = np.sort(take_codes)
  
          # we recode according to the uniques
          categories = c.categories.take(take_codes)
          codes = recode_for_categories(
              c.codes, c.categories, categories
          )
  
          # return a new categorical that maps our new codes
          # and categories
          dtype = CategoricalDtype(categories, ordered=c.ordered)
          return Categorical._simple_new(codes, dtype=dtype), c
  
      # Already sorted according to c.categories; all is fine
      if sort:
          return c, None
  
      # sort=False should order groups in as-encountered order (GH-8868)
  
      # xref GH:46909: Re-ordering codes faster than using (set|add|reorder)_categories
      all_codes = np.arange(c.categories.nunique())
      # GH 38140: exclude nan from indexer for categories
      unique_notnan_codes = unique1d(c.codes[c.codes != -1])
      if sort:
          unique_notnan_codes = np.sort(unique_notnan_codes)
      if len(all_codes) > len(unique_notnan_codes):
          # GH 13179: All categories need to be present, even if missing from the data
          missing_codes = np.setdiff1d(
              all_codes, unique_notnan_codes, assume_unique=True
          )
          take_codes = np.concatenate(
              (unique_notnan_codes, missing_codes)
          )
      else:
          take_codes = unique_notnan_codes
  
      return (
          Categorical(c, c.unique().categories.take(take_codes)),
          None,
      )
  
imports: from __future__ import annotations
  import numpy
  from pandas.core.algorithms import unique1d
  from pandas.core.arrays.categorical import Categorical, CategoricalDtype, recode_for_categories
label_desc: Use image node_id 1 for calling a global function with example usage: recode_for_groupby(c, sort, observed) and returns: Categorical,  None, Categorical,  c, c,  None