REF/INT: preserve block type in joining when only having a single block

pandas/core/internals.py

@@ -5182,7 +5182,15 @@ def concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy):
 
  for placement, join_units in concat_plan:
 
- if is_uniform_join_units(join_units):
+ if len(join_units) == 1 and not join_units[0].indexers:
+ b = join_units[0].block
+ values = b.values
+ if copy:
+ values = values.copy()
+ elif not copy:
+ values = values.view()
+ b = b.make_block_same_class(values, placement=placement)
+ elif is_uniform_join_units(join_units):
  b = join_units[0].block.concat_same_type(
  [ju.block for ju in join_units], placement=placement)
  else:

pandas/tests/internals/test_external_block.py

@@ -7,6 +7,8 @@
 from pandas.core.internals import (
  Block, BlockManager, SingleBlockManager, NonConsolidatableMixIn)
 
+import pytest
+
 
 class CustomBlock(NonConsolidatableMixIn, Block):
 
@@ -25,6 +27,17 @@ def concat_same_type(self, to_concat, placement=None):
  values, placement=placement or slice(0, len(values), 1))
 
 
+@pytest.fixture
+def df():
+ df1 = pd.DataFrame({'a': [1, 2, 3]})
+ blocks = df1._data.blocks
+ values = np.arange(3, dtype='int64')
+ custom_block = CustomBlock(values, placement=slice(1, 2))
+ blocks = blocks + (custom_block,)
+ block_manager = BlockManager(blocks, [pd.Index(['a', 'b']), df1.index])
+ return pd.DataFrame(block_manager)
+
+
 def test_custom_repr():
  values = np.arange(3, dtype='int64')
 
@@ -51,14 +64,14 @@ def test_concat_series():
  assert isinstance(res._data.blocks[0], CustomBlock)
 
 
-def test_concat_dataframe():
+def test_concat_dataframe(df):
  # GH17728
- df = pd.DataFrame({'a': [1, 2, 3]})
- blocks = df._data.blocks
- values = np.arange(3, dtype='int64')
- custom_block = CustomBlock(values, placement=slice(1, 2))
- blocks = blocks + (custom_block, )
- block_manager = BlockManager(blocks, [pd.Index(['a', 'b']), df.index])
- df = pd.DataFrame(block_manager)
  res = pd.concat([df, df])
  assert isinstance(res._data.blocks[1], CustomBlock)
+
+
+def test_concat_axis1(df):
+ # GH17954
+ df2 = pd.DataFrame({'c': [.1, .2, .3]})
+ res = pd.concat([df, df2], axis=1)
+ assert isinstance(res._data.blocks[1], CustomBlock)