fixup! Move .unstack() logic onto BlockManager and Block

Author: kernc

pandas/core/categorical.py

@@ -127,6 +127,8 @@ def maybe_to_categorical(array):
     """ coerce to a categorical if a series is given """
     if isinstance(array, (ABCSeries, ABCCategoricalIndex)):
         return array._values
+    elif isinstance(array, np.ndarray):
+        return Categorical(array)
     return array
 
 

pandas/core/internals.py

@@ -4082,20 +4082,19 @@ def unstack(self, unstacker):
         new_columns = dummy.get_new_columns()
         new_index = dummy.get_new_index()
         new_blocks = []
-        mask_blocks = np.zeros_like(new_columns, dtype=bool)
+        mask_columns = np.zeros_like(new_columns, dtype=bool)
 
         for blk in self.blocks:
             bunstacker = unstacker(
                 blk.values.T, value_columns=self.items[blk.mgr_locs.indexer])
             new_items = bunstacker.get_new_columns()
             new_values, mask = bunstacker.get_new_values()
             new_placement = new_columns.get_indexer(new_items)
-            mask_blocks[new_placement] = mask.any(0)
+            mask_columns[new_placement] = mask.any(0)
             new_blocks.extend(blk._unstack(new_values.T, new_placement))
 
-        new_blocks = [b for b, keep in zip(new_blocks, mask_blocks) if keep]
-        new_axes = [new_columns[mask_blocks], new_index]
-        bm = BlockManager(new_blocks, new_axes)
+        bm = BlockManager(new_blocks, [new_columns, new_index])
+        bm = bm.take(mask_columns.nonzero()[0], axis=0)
         return bm