BUG: remove_unused_categories dtype coerces to int64

Author: sinhrks <sinhrks@gmail.com> Closes pandas-dev#13261 from sinhrks/categorical_remove_dtype and squashes the following commits: b5cbe2c [sinhrks] BUG: remove_unused_categories dtype coerces to int64

Author: sinhrks

doc/source/whatsnew/v0.18.2.txt

@@ -248,3 +248,5 @@ Bug Fixes
 
 
 - Bug in ``groupby`` where ``apply`` returns different result depending on whether first result is ``None`` or not (:issue:`12824`)
+
+- Bug in ``Categorical.remove_unused_categories()`` changes ``.codes`` dtype to platform int (:issue:`13261`)

pandas/core/categorical.py

@@ -898,8 +898,8 @@ def remove_unused_categories(self, inplace=False):
  if idx.size != 0 and idx[0] == -1: # na sentinel
  idx, inv = idx[1:], inv - 1
 
- cat._codes = inv
  cat._categories = cat.categories.take(idx)
+ cat._codes = _coerce_indexer_dtype(inv, self._categories)
 
  if not inplace:
  return cat

pandas/tests/test_categorical.py

@@ -1022,14 +1022,14 @@ def f():
  def test_remove_unused_categories(self):
  c = Categorical(["a", "b", "c", "d", "a"],
  categories=["a", "b", "c", "d", "e"])
- exp_categories_all = np.array(["a", "b", "c", "d", "e"])
- exp_categories_dropped = np.array(["a", "b", "c", "d"])
+ exp_categories_all = Index(["a", "b", "c", "d", "e"])
+ exp_categories_dropped = Index(["a", "b", "c", "d"])
 
  self.assert_numpy_array_equal(c.categories, exp_categories_all)
 
  res = c.remove_unused_categories()
- self.assert_numpy_array_equal(res.categories, exp_categories_dropped)
- self.assert_numpy_array_equal(c.categories, exp_categories_all)
+ self.assert_index_equal(res.categories, exp_categories_dropped)
+ self.assert_index_equal(c.categories, exp_categories_all)
 
  res = c.remove_unused_categories(inplace=True)
  self.assert_numpy_array_equal(c.categories, exp_categories_dropped)
@@ -1039,15 +1039,18 @@ def test_remove_unused_categories(self):
  c = Categorical(["a", "b", "c", np.nan],
  categories=["a", "b", "c", "d", "e"])
  res = c.remove_unused_categories()
- self.assert_numpy_array_equal(res.categories,
- np.array(["a", "b", "c"]))
- self.assert_numpy_array_equal(c.categories, exp_categories_all)
+ self.assert_index_equal(res.categories,
+ Index(np.array(["a", "b", "c"])))
+ exp_codes = np.array([0, 1, 2, -1], dtype=np.int8)
+ self.assert_numpy_array_equal(res.codes, exp_codes)
+ self.assert_index_equal(c.categories, exp_categories_all)
 
  val = ['F', np.nan, 'D', 'B', 'D', 'F', np.nan]
  cat = pd.Categorical(values=val, categories=list('ABCDEFG'))
  out = cat.remove_unused_categories()
- self.assert_numpy_array_equal(out.categories, ['B', 'D', 'F'])
- self.assert_numpy_array_equal(out.codes, [2, -1, 1, 0, 1, 2, -1])
+ self.assert_index_equal(out.categories, Index(['B', 'D', 'F']))
+ exp_codes = np.array([2, -1, 1, 0, 1, 2, -1], dtype=np.int8)
+ self.assert_numpy_array_equal(out.codes, exp_codes)
  self.assertEqual(out.get_values().tolist(), val)
 
  alpha = list('abcdefghijklmnopqrstuvwxyz')