refactor

pandas/core/frame.py

@@ -115,6 +115,7 @@
 from pandas.core.dtypes.dtypes import (
  ArrowDtype,
  BaseMaskedDtype,
+ CategoricalDtype,
  ExtensionDtype,
 )
 from pandas.core.dtypes.generic import (
@@ -12015,15 +12016,29 @@ def _transform_ord_cat_cols_to_coded_cols(self) -> DataFrame:
  if len(categ.columns) == 0:
  return self
 
- cols_convert = categ.loc[:, categ.agg(lambda x: x.cat.ordered)].columns
+ cols_convert = categ.loc[:, categ.agg(lambda x: x.cat.ordered)].columns.unique()
+ single_cols = [col for col in cols_convert if isinstance(categ[col], Series)]
+ duplicated_cols = [
+ col for col in cols_convert if isinstance(categ[col], DataFrame)
+ ]
 
- if len(cols_convert) > 0:
- data = self.copy(deep=False)
- data[cols_convert] = data[cols_convert].transform(
+ if not single_cols and not duplicated_cols:
+ return self
+
+ data = self.copy(deep=False)
+ if single_cols:
+ data[single_cols] = data[single_cols].transform(
  lambda x: x.cat.codes.replace(-1, np.nan)
  )
- return data
- return self
+
+ if duplicated_cols:
+ data[duplicated_cols] = data[duplicated_cols].apply(
+ lambda x: x.cat.codes.replace(-1, np.nan)
+ if isinstance(x, CategoricalDtype) and bool(x.ordered)
+ else x
+ )
+
+ return data
 
  # ----------------------------------------------------------------------
  # ndarray-like stats methods

pandas/tests/frame/methods/test_cov_corr.py

@@ -255,11 +255,11 @@ def test_corr_numeric_only(self, meth, numeric_only):
  df.corr(meth, numeric_only=numeric_only)
 
  @pytest.mark.parametrize("method", ["kendall", "spearman"])
+ @td.skip_if_no("scipy")
  def test_corr_rank_ordered_categorical(
  self,
  method,
  ):
- pytest.importorskip("scipy")
  df = DataFrame(
  {
  "ord_cat": Series(