REF: make copy keyword in recode_for_categories keyword only (#62019)

Author: mroeschke

pandas/_libs/index.pyx

@@ -803,7 +803,7 @@ cdef class BaseMultiIndexCodesEngine:
  int_keys : 1-dimensional array of dtype uint64 or object
  Integers representing one combination each
  """
- level_codes = list(target._recode_for_new_levels(self.levels))
+ level_codes = list(target._recode_for_new_levels(self.levels, copy=True))
  for i, codes in enumerate(level_codes):
  if self.levels[i].hasnans:
  na_index = self.levels[i].isna().nonzero()[0][0]

pandas/core/arrays/categorical.py

@@ -670,13 +670,15 @@ def _from_inferred_categories(
  if known_categories:
  # Recode from observation order to dtype.categories order.
  categories = dtype.categories
- codes = recode_for_categories(inferred_codes, cats, categories)
+ codes = recode_for_categories(inferred_codes, cats, categories, copy=False)
  elif not cats.is_monotonic_increasing:
  # Sort categories and recode for unknown categories.
  unsorted = cats.copy()
  categories = cats.sort_values()
 
- codes = recode_for_categories(inferred_codes, unsorted, categories)
+ codes = recode_for_categories(
+ inferred_codes, unsorted, categories, copy=False
+ )
  dtype = CategoricalDtype(categories, ordered=False)
  else:
  dtype = CategoricalDtype(cats, ordered=False)
@@ -945,7 +947,7 @@ def _set_categories(self, categories, fastpath: bool = False) -> None:
 
  super().__init__(self._ndarray, new_dtype)
 
- def _set_dtype(self, dtype: CategoricalDtype, copy: bool = True) -> Self:
+ def _set_dtype(self, dtype: CategoricalDtype, *, copy: bool) -> Self:
  """
  Internal method for directly updating the CategoricalDtype
 
@@ -959,7 +961,7 @@ def _set_dtype(self, dtype: CategoricalDtype, copy: bool = True) -> Self:
  a (valid) instance of `CategoricalDtype`.
  """
  codes = recode_for_categories(
- self.codes, self.categories, dtype.categories, copy
+ self.codes, self.categories, dtype.categories, copy=copy
  )
  return type(self)._simple_new(codes, dtype=dtype)
 
@@ -1154,7 +1156,7 @@ def set_categories(
  codes = cat._codes
  else:
  codes = recode_for_categories(
- cat.codes, cat.categories, new_dtype.categories
+ cat.codes, cat.categories, new_dtype.categories, copy=False
  )
  NDArrayBacked.__init__(cat, codes, new_dtype)
  return cat
@@ -3006,7 +3008,7 @@ def _get_codes_for_values(
 
 
 def recode_for_categories(
- codes: np.ndarray, old_categories, new_categories, copy: bool = True
+ codes: np.ndarray, old_categories, new_categories, *, copy: bool
 ) -> np.ndarray:
  """
  Convert a set of codes for to a new set of categories
@@ -3027,7 +3029,7 @@ def recode_for_categories(
  >>> old_cat = pd.Index(["b", "a", "c"])
  >>> new_cat = pd.Index(["a", "b"])
  >>> codes = np.array([0, 1, 1, 2])
- >>> recode_for_categories(codes, old_cat, new_cat)
+ >>> recode_for_categories(codes, old_cat, new_cat, copy=True)
  array([ 1, 0, 0, -1], dtype=int8)
  """
  if len(old_categories) == 0:

pandas/core/dtypes/concat.py

@@ -318,7 +318,8 @@ def _maybe_unwrap(x):
  categories = categories.sort_values()
 
  new_codes = [
- recode_for_categories(c.codes, c.categories, categories) for c in to_union
+ recode_for_categories(c.codes, c.categories, categories, copy=False)
+ for c in to_union
  ]
  new_codes = np.concatenate(new_codes)
  else:

pandas/core/groupby/categorical.py

@@ -53,7 +53,7 @@ def recode_for_groupby(c: Categorical, sort: bool, observed: bool) -> Categorica
 
  # we recode according to the uniques
  categories = c.categories.take(take_codes)
- codes = recode_for_categories(c.codes, c.categories, categories)
+ codes = recode_for_categories(c.codes, c.categories, categories, copy=False)
 
  # return a new categorical that maps our new codes
  # and categories

pandas/core/indexes/multi.py

@@ -2675,7 +2675,7 @@ def _reorder_ilevels(self, order) -> MultiIndex:
  )
 
  def _recode_for_new_levels(
- self, new_levels, copy: bool = True
+ self, new_levels, *, copy: bool
  ) -> Generator[np.ndarray]:
  if len(new_levels) > self.nlevels:
  raise AssertionError(

pandas/tests/arrays/categorical/test_api.py

@@ -480,7 +480,7 @@ def test_recode_to_categories(self, codes, old, new, expected):
  expected = np.asanyarray(expected, dtype=np.int8)
  old = Index(old)
  new = Index(new)
- result = recode_for_categories(codes, old, new)
+ result = recode_for_categories(codes, old, new, copy=True)
  tm.assert_numpy_array_equal(result, expected)
 
  def test_recode_to_categories_large(self):
@@ -489,5 +489,5 @@ def test_recode_to_categories_large(self):
  old = Index(codes)
  expected = np.arange(N - 1, -1, -1, dtype=np.int16)
  new = Index(expected)
- result = recode_for_categories(codes, old, new)
+ result = recode_for_categories(codes, old, new, copy=True)
  tm.assert_numpy_array_equal(result, expected)

pandas/tests/arrays/categorical/test_dtypes.py

@@ -49,12 +49,12 @@ def test_categories_match_up_to_permutation(self):
 
  def test_set_dtype_same(self):
  c = Categorical(["a", "b", "c"])
- result = c._set_dtype(CategoricalDtype(["a", "b", "c"]))
+ result = c._set_dtype(CategoricalDtype(["a", "b", "c"]), copy=True)
  tm.assert_categorical_equal(result, c)
 
  def test_set_dtype_new_categories(self):
  c = Categorical(["a", "b", "c"])
- result = c._set_dtype(CategoricalDtype(list("abcd")))
+ result = c._set_dtype(CategoricalDtype(list("abcd")), copy=True)
  tm.assert_numpy_array_equal(result.codes, c.codes)
  tm.assert_index_equal(result.dtype.categories, Index(list("abcd")))
 
@@ -86,12 +86,12 @@ def test_set_dtype_new_categories(self):
  def test_set_dtype_many(self, values, categories, new_categories, ordered):
  c = Categorical(values, categories)
  expected = Categorical(values, new_categories, ordered)
- result = c._set_dtype(expected.dtype)
+ result = c._set_dtype(expected.dtype, copy=True)
  tm.assert_categorical_equal(result, expected)
 
  def test_set_dtype_no_overlap(self):
  c = Categorical(["a", "b", "c"], ["d", "e"])
- result = c._set_dtype(CategoricalDtype(["a", "b"]))
+ result = c._set_dtype(CategoricalDtype(["a", "b"]), copy=True)
  expected = Categorical([None, None, None], categories=["a", "b"])
  tm.assert_categorical_equal(result, expected)
 

pandas/tests/arrays/categorical/test_missing.py

@@ -52,7 +52,7 @@ def test_nan_handling(self):
 
  def test_set_dtype_nans(self):
  c = Categorical(["a", "b", np.nan])
- result = c._set_dtype(CategoricalDtype(["a", "c"]))
+ result = c._set_dtype(CategoricalDtype(["a", "c"]), copy=True)
  tm.assert_numpy_array_equal(result.codes, np.array([0, -1, -1], dtype="int8"))
 
  def test_set_item_nan(self):