move private functions to the one place they are used

pandas/core/dtypes/concat.py

@@ -25,7 +25,6 @@
     ABCIndexClass,
     ABCPeriodIndex,
     ABCRangeIndex,
-    ABCSparseDataFrame,
     ABCTimedeltaIndex,
 )
 
@@ -71,40 +70,6 @@ def get_dtype_kinds(l):
     return typs
 
 
-def _get_series_result_type(result, objs=None):
-    """
-    return appropriate class of Series concat
-    input is either dict or array-like
-    """
-    from pandas import SparseSeries, SparseDataFrame, DataFrame
-
-    # concat Series with axis 1
-    if isinstance(result, dict):
-        # concat Series with axis 1
-        if all(isinstance(c, (SparseSeries, SparseDataFrame)) for c in result.values()):
-            return SparseDataFrame
-        else:
-            return DataFrame
-
-    # otherwise it is a SingleBlockManager (axis = 0)
-    return objs[0]._constructor
-
-
-def _get_frame_result_type(result, objs):
-    """
-    return appropriate class of DataFrame-like concat
-    if all blocks are sparse, return SparseDataFrame
-    otherwise, return 1st obj
-    """
-
-    if result.blocks and (any(isinstance(obj, ABCSparseDataFrame) for obj in objs)):
-        from pandas.core.sparse.api import SparseDataFrame
-
-        return SparseDataFrame
-    else:
-        return next(obj for obj in objs if not isinstance(obj, ABCSparseDataFrame))
-
-
 def concat_compat(to_concat, axis=0):
     """
     provide concatenation of an array of arrays each of which is a single

pandas/core/reshape/concat.py

@@ -6,7 +6,7 @@
 
 import numpy as np
 
-import pandas.core.dtypes.concat as _concat
+from pandas.core.dtypes.generic import ABCSparseDataFrame
 
 from pandas import DataFrame, Index, MultiIndex, Series
 from pandas.core import common as com
@@ -439,13 +439,13 @@ def get_result(self):
                 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)
+                cons = _get_series_result_type(mgr, self.objs)
                 return cons(mgr, name=name).__finalize__(self, method="concat")
 
             # combine as columns in a frame
             else:
                 data = dict(zip(range(len(self.objs)), self.objs))
-                cons = _concat._get_series_result_type(data)
+                cons = _get_series_result_type(data)
 
                 index, columns = self.new_axes
                 df = cons(data, index=index)
@@ -475,7 +475,7 @@ def get_result(self):
             if not self.copy:
                 new_data._consolidate_inplace()
 
-            cons = _concat._get_frame_result_type(new_data, self.objs)
+            cons = _get_frame_result_type(new_data, self.objs)
             return cons._from_axes(new_data, self.new_axes).__finalize__(
                 self, method="concat"
             )
@@ -708,3 +708,37 @@ def _make_concat_multiindex(indexes, keys, levels=None, names=None):
     return MultiIndex(
         levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
     )
+
+
+def _get_series_result_type(result, objs=None):
+    """
+    return appropriate class of Series concat
+    input is either dict or array-like
+    """
+    from pandas import SparseSeries, SparseDataFrame, DataFrame
+
+    # concat Series with axis 1
+    if isinstance(result, dict):
+        # concat Series with axis 1
+        if all(isinstance(c, (SparseSeries, SparseDataFrame)) for c in result.values()):
+            return SparseDataFrame
+        else:
+            return DataFrame
+
+    # otherwise it is a SingleBlockManager (axis = 0)
+    return objs[0]._constructor
+
+
+def _get_frame_result_type(result, objs):
+    """
+    return appropriate class of DataFrame-like concat
+    if all blocks are sparse, return SparseDataFrame
+    otherwise, return 1st obj
+    """
+
+    if result.blocks and (any(isinstance(obj, ABCSparseDataFrame) for obj in objs)):
+        from pandas.core.sparse.api import SparseDataFrame
+
+        return SparseDataFrame
+    else:
+        return next(obj for obj in objs if not isinstance(obj, ABCSparseDataFrame))