Fix pad/backfill 2d

pandas/_libs/algos.pyx

@@ -629,7 +629,7 @@ def pad_inplace(algos_t[:] values, uint8_t[:] mask, limit=None):
 
 @cython.boundscheck(False)
 @cython.wraparound(False)
-def pad_2d_inplace(algos_t[:, :] values, const uint8_t[:, :] mask, limit=None):
+def pad_2d_inplace(algos_t[:, :] values, uint8_t[:, :] mask, limit=None):
     cdef:
         Py_ssize_t i, j, N, K
         algos_t val
@@ -648,10 +648,11 @@ def pad_2d_inplace(algos_t[:, :] values, const uint8_t[:, :] mask, limit=None):
         val = values[j, 0]
         for i in range(N):
             if mask[j, i]:
-                if fill_count >= lim:
+                if fill_count >= lim or i == 0:
                     continue
                 fill_count += 1
                 values[j, i] = val
+                mask[j, i] = False
             else:
                 fill_count = 0
                 val = values[j, i]
@@ -776,7 +777,7 @@ def backfill_inplace(algos_t[:] values, uint8_t[:] mask, limit=None):
 @cython.boundscheck(False)
 @cython.wraparound(False)
 def backfill_2d_inplace(algos_t[:, :] values,
-                        const uint8_t[:, :] mask,
+                        uint8_t[:, :] mask,
                         limit=None):
     cdef:
         Py_ssize_t i, j, N, K
@@ -800,6 +801,7 @@ def backfill_2d_inplace(algos_t[:, :] values,
                     continue
                 fill_count += 1
                 values[j, i] = val
+                mask[j, i] = False
             else:
                 fill_count = 0
                 val = values[j, i]

pandas/core/arrays/categorical.py

@@ -104,7 +104,6 @@
     extract_array,
     sanitize_array,
 )
-from pandas.core.missing import interpolate_2d
 from pandas.core.ops.common import unpack_zerodim_and_defer
 from pandas.core.sorting import nargsort
 from pandas.core.strings.object_array import ObjectStringArrayMixin
@@ -1776,13 +1775,9 @@ def fillna(self, value=None, method=None, limit=None):
 
         if method is not None:
             # pad / bfill
-
-            # TODO: dispatch when self.categories is EA-dtype
-            values = np.asarray(self).reshape(-1, len(self))
-            values = interpolate_2d(values, method, 0, None).astype(
-                self.categories.dtype
-            )[0]
-            codes = _get_codes_for_values(values, self.categories)
+            return NDArrayBackedExtensionArray.fillna(
+                self, None, method=method, limit=limit
+            )
 
         else:
             # We copy even if there is nothing to fill
@@ -2604,6 +2599,11 @@ def _get_codes_for_values(values, categories: Index) -> np.ndarray:
     """
     dtype_equal = is_dtype_equal(values.dtype, categories.dtype)
 
+    if values.ndim > 1:
+        flat = values.ravel()
+        codes = _get_codes_for_values(flat, categories)
+        return codes.reshape(values.shape)
+
     if is_extension_array_dtype(categories.dtype) and is_object_dtype(values):
         # Support inferring the correct extension dtype from an array of
         # scalar objects. e.g.

pandas/core/arrays/masked.py

@@ -170,13 +170,13 @@ def fillna(
 
         if mask.any():
             if method is not None:
-                func = missing.get_fill_func(method)
+                func = missing.get_fill_func(method, ndim=self.ndim)
                 new_values, new_mask = func(
-                    self._data.copy(),
+                    self._data.copy().T,
                     limit=limit,
-                    mask=mask.copy(),
+                    mask=mask.copy().T,
                 )
-                return type(self)(new_values, new_mask.view(np.bool_))
+                return type(self)(new_values.T, new_mask.view(np.bool_).T)
             else:
                 # fill with value
                 new_values = self.copy()