REF/INT: concat blocks of same type with preserving block type (panda…

…s-dev#17728)

pandas/core/dtypes/concat.py

@@ -63,11 +63,12 @@ def get_dtype_kinds(l):
     return typs
 
 
-def _get_series_result_type(result):
+def _get_series_result_type(result, objs=None):
     """
     return appropriate class of Series concat
     input is either dict or array-like
     """
+    # concat Series with axis 1
     if isinstance(result, dict):
         # concat Series with axis 1
         if all(is_sparse(c) for c in compat.itervalues(result)):
@@ -77,13 +78,12 @@ def _get_series_result_type(result):
             from pandas.core.frame import DataFrame
             return DataFrame
 
-    elif is_sparse(result):
-        # concat Series with axis 1
+    # otherwise it is a SingleBlockManager (axis = 0)
+    if result._block.is_sparse:
         from pandas.core.sparse.api import SparseSeries
         return SparseSeries
     else:
-        from pandas.core.series import Series
-        return Series
+        return objs[0]._constructor
 
 
 def _get_frame_result_type(result, objs):

pandas/core/internals.py

@@ -102,6 +102,7 @@ class Block(PandasObject):
     _validate_ndim = True
     _ftype = 'dense'
     _holder = None
+    _concatenator = staticmethod(np.concatenate)
 
     def __init__(self, values, placement, ndim=None, fastpath=False):
         if ndim is None:
@@ -314,6 +315,15 @@ def ftype(self):
     def merge(self, other):
         return _merge_blocks([self, other])
 
+    def concat_same_type(self, to_concat, placement=None):
+        """
+        Concatenate list of single blocks of the same type.
+        """
+        values = self._concatenator([blk.values for blk in to_concat],
+                                    axis=self.ndim - 1)
+        return self.make_block_same_class(
+            values, placement=placement or slice(0, len(values), 1))
+
     def reindex_axis(self, indexer, method=None, axis=1, fill_value=None,
                      limit=None, mask_info=None):
         """
@@ -2309,6 +2319,7 @@ class CategoricalBlock(NonConsolidatableMixIn, ObjectBlock):
     _verify_integrity = True
     _can_hold_na = True
     _holder = Categorical
+    _concatenator = staticmethod(_concat._concat_categorical)
 
     def __init__(self, values, placement, fastpath=False, **kwargs):
 
@@ -2432,6 +2443,17 @@ def to_native_types(self, slicer=None, na_rep='', quoting=None, **kwargs):
         # we are expected to return a 2-d ndarray
         return values.reshape(1, len(values))
 
+    def concat_same_type(self, to_concat, placement=None):
+        """
+        Concatenate list of single blocks of the same type.
+        """
+        values = self._concatenator([blk.values for blk in to_concat],
+                                    axis=self.ndim - 1)
+        # not using self.make_block_same_class as values can be object dtype
+        return make_block(
+            values, placement=placement or slice(0, len(values), 1),
+            ndim=self.ndim)
+
 
 class DatetimeBlock(DatetimeLikeBlockMixin, Block):
     __slots__ = ()
@@ -2571,6 +2593,7 @@ class DatetimeTZBlock(NonConsolidatableMixIn, DatetimeBlock):
     """ implement a datetime64 block with a tz attribute """
     __slots__ = ()
     _holder = DatetimeIndex
+    _concatenator = staticmethod(_concat._concat_datetime)
     is_datetimetz = True
 
     def __init__(self, values, placement, ndim=2, **kwargs):
@@ -2711,6 +2734,16 @@ def shift(self, periods, axis=0, mgr=None):
         return [self.make_block_same_class(new_values,
                                            placement=self.mgr_locs)]
 
+    def concat_same_type(self, to_concat, placement=None):
+        """
+        Concatenate list of single blocks of the same type.
+        """
+        values = self._concatenator([blk.values for blk in to_concat],
+                                    axis=self.ndim - 1)
+        # not using self.make_block_same_class as values can be non-tz dtype
+        return make_block(
+            values, placement=placement or slice(0, len(values), 1))
+
 
 class SparseBlock(NonConsolidatableMixIn, Block):
     """ implement as a list of sparse arrays of the same dtype """
@@ -2721,6 +2754,7 @@ class SparseBlock(NonConsolidatableMixIn, Block):
     _can_hold_na = True
     _ftype = 'sparse'
     _holder = SparseArray
+    _concatenator = staticmethod(_concat._concat_sparse)
 
     @property
     def shape(self):
@@ -4517,6 +4551,45 @@ def fast_xs(self, loc):
         """
         return self._block.values[loc]
 
+    def concat(self, to_concat, new_axis):
+        """
+        Concatenate a list of SingleBlockManagers into a single
+        SingleBlockManager.
+
+        Used for pd.concat of Series objects with axis=0.
+
+        Parameters
+        ----------
+        to_concat : list of SingleBlockManagers
+        new_axis : Index of the result
+
+        Returns
+        -------
+        SingleBlockManager
+
+        """
+        non_empties = [x for x in to_concat if len(x) > 0]
+
+        # check if all series are of the same block type:
+        if len(non_empties) > 0:
+            blocks = [obj.blocks[0] for obj in non_empties]
+
+            if all([type(b) is type(blocks[0]) for b in blocks[1:]]):  # noqa
+                new_block = blocks[0].concat_same_type(blocks)
+            else:
+                values = [x.values for x in blocks]
+                values = _concat._concat_compat(values)
+                new_block = make_block(
+                    values, placement=slice(0, len(values), 1))
+        else:
+            values = [x._block.values for x in to_concat]
+            values = _concat._concat_compat(values)
+            new_block = make_block(
+                values, placement=slice(0, len(values), 1))
+
+        mgr = SingleBlockManager(new_block, new_axis)
+        return mgr
+
 
 def construction_error(tot_items, block_shape, axes, e=None):
     """ raise a helpful message about our construction """
@@ -5105,13 +5178,42 @@ def concatenate_block_managers(mgrs_indexers, axes, concat_axis, copy):
         [get_mgr_concatenation_plan(mgr, indexers)
          for mgr, indexers in mgrs_indexers], concat_axis)
 
-    blocks = [make_block(
-        concatenate_join_units(join_units, concat_axis, copy=copy),
-        placement=placement) for placement, join_units in concat_plan]
+    blocks = []
+
+    for placement, join_units in concat_plan:
+
+        if is_uniform_join_units(join_units):
+            b = join_units[0].block.concat_same_type(
+                [ju.block for ju in join_units], placement=placement)
+        else:
+            b = make_block(
+                concatenate_join_units(join_units, concat_axis, copy=copy),
+                placement=placement)
+        blocks.append(b)
 
     return BlockManager(blocks, axes)
 
 
+def is_uniform_join_units(join_units):
+    """
+    Check if the join units consist of blocks of uniform type that can
+    be concatenated using Block.concat_same_type instead of the generic
+    concatenate_join_units (which uses `_concat._concat_compat`).
+
+    """
+    return (
+        # all blocks need to have the same type
+        all([type(ju.block) is type(join_units[0].block) for ju in join_units]) and  # noqa
+        # no blocks that would get missing values (can lead to type upcasts)
+        all([not ju.is_na for ju in join_units]) and
+        # no blocks with indexers (as then the dimensions do not fit)
+        all([not ju.indexers for ju in join_units]) and
+        # disregard Panels
+        all([ju.block.ndim <= 2 for ju in join_units]) and
+        # only use this path when there is something to concatenate
+        len(join_units) > 1)
+
+
 def get_empty_dtype_and_na(join_units):
     """
     Return dtype and N/A values to use when concatenating specified units.

pandas/core/reshape/concat.py

@@ -362,20 +362,12 @@ def get_result(self):
 
             # stack blocks
             if self.axis == 0:
-                # concat Series with length to keep dtype as much
-                non_empties = [x for x in self.objs if len(x) > 0]
-                if len(non_empties) > 0:
-                    values = [x._values for x in non_empties]
-                else:
-                    values = [x._values for x in self.objs]
-                new_data = _concat._concat_compat(values)
-
                 name = com._consensus_name_attr(self.objs)
-                cons = _concat._get_series_result_type(new_data)
 
-                return (cons(new_data, index=self.new_axes[0],
-                             name=name, dtype=new_data.dtype)
-                        .__finalize__(self, method='concat'))
+                mgr = self.objs[0]._data.concat([x._data for x in self.objs],
+                                                self.new_axes)
+                cons = _concat._get_series_result_type(mgr, self.objs)
+                return cons(mgr, name=name).__finalize__(self, method='concat')
 
             # combine as columns in a frame
             else:

pandas/tests/internals/test_external_block.py

@@ -4,14 +4,26 @@
 import numpy as np
 
 import pandas as pd
-from pandas.core.internals import Block, BlockManager, SingleBlockManager
+from pandas.core.internals import (
+    Block, BlockManager, SingleBlockManager, NonConsolidatableMixIn)
 
 
-class CustomBlock(Block):
+class CustomBlock(NonConsolidatableMixIn, Block):
+
+    _holder = np.ndarray
 
     def formatting_values(self):
         return np.array(["Val: {}".format(i) for i in self.values])
 
+    def concat_same_type(self, to_concat, placement=None):
+        """
+        Always concatenate disregarding self.ndim as the values are
+        always 1D in this custom Block
+        """
+        values = np.concatenate([blk.values for blk in to_concat])
+        return self.make_block_same_class(
+            values, placement=placement or slice(0, len(values), 1))
+
 
 def test_custom_repr():
     values = np.arange(3, dtype='int64')
@@ -23,7 +35,30 @@ def test_custom_repr():
     assert repr(s) == '0    Val: 0\n1    Val: 1\n2    Val: 2\ndtype: int64'
 
     # dataframe
-    block = CustomBlock(values.reshape(1, -1), placement=slice(0, 1))
+    block = CustomBlock(values, placement=slice(0, 1))
     blk_mgr = BlockManager([block], [['col'], range(3)])
     df = pd.DataFrame(blk_mgr)
     assert repr(df) == '      col\n0  Val: 0\n1  Val: 1\n2  Val: 2'
+
+
+def test_concat_series():
+    # GH17728
+    values = np.arange(3, dtype='int64')
+    block = CustomBlock(values, placement=slice(0, 3))
+    s = pd.Series(block, pd.RangeIndex(3), fastpath=True)
+
+    res = pd.concat([s, s])
+    assert isinstance(res._data.blocks[0], CustomBlock)
+
+
+def test_concat_dataframe():
+    # 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)