BUG: Fix potential segfault after pd.Categorical(pd.Series(...), categories=...)

Author: batterseapower

doc/source/whatsnew/v0.24.2.rst

@@ -30,6 +30,8 @@ Fixed Regressions
 - Fixed regression in :class:`TimedeltaIndex` where `np.sum(index)` incorrectly returned a zero-dimensional object instead of a scalar (:issue:`25282`)
 - Fixed regression in ``IntervalDtype`` construction where passing an incorrect string with 'Interval' as a prefix could result in a ``RecursionError``. (:issue:`25338`)
 
+- Fixed regression in ``Categorical``, where constructing it from a categorical ``Series`` and an explicit ``categories=`` that differed from that in the ``Series`` created an invalid object which could trigger segfaults. (:issue:`25318`)
+
 .. _whatsnew_0242.enhancements:
 
 Enhancements

pandas/core/arrays/categorical.py

@@ -323,14 +323,6 @@ def __init__(self, values, categories=None, ordered=None, dtype=None,
  # we may have dtype.categories be None, and we need to
  # infer categories in a factorization step futher below
 
- if is_categorical(values):
- # GH23814, for perf, if values._values already an instance of
- # Categorical, set values to codes, and run fastpath
- if (isinstance(values, (ABCSeries, ABCIndexClass)) and
- isinstance(values._values, type(self))):
- values = values._values.codes.copy()
- fastpath = True
-
  if fastpath:
  self._codes = coerce_indexer_dtype(values, dtype.categories)
  self._dtype = self._dtype.update_dtype(dtype)
@@ -2625,6 +2617,9 @@ def _recode_for_categories(codes, old_categories, new_categories):
  if len(old_categories) == 0:
  # All null anyway, so just retain the nulls
  return codes.copy()
+ elif new_categories.equals(old_categories):
+ # Same categories, so no need to actually recode
+ return codes.copy()
  indexer = coerce_indexer_dtype(new_categories.get_indexer(old_categories),
  new_categories)
  new_codes = take_1d(indexer, codes.copy(), fill_value=-1)

pandas/tests/arrays/categorical/test_constructors.py

@@ -149,6 +149,17 @@ def test_constructor(self):
  categories=["a", "b", "c", "d"])
  tm.assert_categorical_equal(c1, c2)
 
+ # GH25318: constructing with pd.Series used to bogusly skip recoding
+ # categories
+ c0 = Categorical(["a", "b", "c", "a"])
+ c1 = Categorical(["a", "b", "c", "a"], categories=["b", "c"])
+
+ c2 = Categorical(c0, categories=c1.categories)
+ tm.assert_categorical_equal(c1, c2)
+
+ c3 = Categorical(Series(c0), categories=c1.categories)
+ tm.assert_categorical_equal(c1, c3)
+
  # This should result in integer categories, not float!
  cat = Categorical([1, 2, 3, np.nan], categories=[1, 2, 3])
  assert is_integer_dtype(cat.categories)