code: def recode_for_groupby(c, sort, observed):
      """
      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 : boolean
          The value of the sort parameter groupby was called with.
      observed : boolean
          Account only for the observed values
  
      Returns
      -------
      New 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:
          unique_codes = unique1d(c.codes)
  
          take_codes = unique_codes[unique_codes != -1]
          if c.ordered:
              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(codes, dtype=dtype, fastpath=True), 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)
      cat = c.unique()
  
      # But for groupby to work, all categories should be present,
      # including those missing from the data (GH-13179), which .unique()
      # above dropped
      cat = cat.add_categories(
          c.categories[~c.categories.isin(cat.categories)]
      )
  
      return c.reorder_categories(cat.categories), None
  
imports: 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: c,  None, Categorical,  c, c,  None