REF: de-privatize dtypes.concat functions

pandas/core/arrays/categorical.py

@@ -2481,9 +2481,9 @@ def _can_hold_na(self):
 
     @classmethod
     def _concat_same_type(self, to_concat):
-        from pandas.core.dtypes.concat import _concat_categorical
+        from pandas.core.dtypes.concat import concat_categorical
 
-        return _concat_categorical(to_concat)
+        return concat_categorical(to_concat)
 
     def isin(self, values):
         """

pandas/core/dtypes/concat.py

@@ -25,7 +25,6 @@
     ABCIndexClass,
     ABCPeriodIndex,
     ABCRangeIndex,
-    ABCSparseDataFrame,
     ABCTimedeltaIndex,
 )
 
@@ -71,41 +70,7 @@ 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):
+def concat_compat(to_concat, axis=0):
     """
     provide concatenation of an array of arrays each of which is a single
     'normalized' dtypes (in that for example, if it's object, then it is a
@@ -142,12 +107,12 @@ def is_nonempty(x):
     _contains_period = any(typ.startswith("period") for typ in typs)
 
     if "category" in typs:
-        # this must be prior to _concat_datetime,
+        # this must be prior to concat_datetime,
         # to support Categorical + datetime-like
-        return _concat_categorical(to_concat, axis=axis)
+        return concat_categorical(to_concat, axis=axis)
 
     elif _contains_datetime or "timedelta" in typs or _contains_period:
-        return _concat_datetime(to_concat, axis=axis, typs=typs)
+        return concat_datetime(to_concat, axis=axis, typs=typs)
 
     # these are mandated to handle empties as well
     elif "sparse" in typs:
@@ -174,7 +139,7 @@ def is_nonempty(x):
     return np.concatenate(to_concat, axis=axis)
 
 
-def _concat_categorical(to_concat, axis=0):
+def concat_categorical(to_concat, axis=0):
     """Concatenate an object/categorical array of arrays, each of which is a
     single dtype
 
@@ -214,7 +179,7 @@ def _concat_categorical(to_concat, axis=0):
         else np.asarray(x.astype(object))
         for x in to_concat
     ]
-    result = _concat_compat(to_concat)
+    result = concat_compat(to_concat)
     if axis == 1:
         result = result.reshape(1, len(result))
     return result
@@ -404,7 +369,7 @@ def _concatenate_2d(to_concat, axis):
     return np.concatenate(to_concat, axis=axis)
 
 
-def _concat_datetime(to_concat, axis=0, typs=None):
+def concat_datetime(to_concat, axis=0, typs=None):
     """
     provide concatenation of an datetimelike array of arrays each of which is a
     single M8[ns], datetimet64[ns, tz] or m8[ns] dtype

pandas/core/indexes/base.py

@@ -15,6 +15,7 @@
 from pandas.compat.numpy import function as nv
 from pandas.util._decorators import Appender, Substitution, cache_readonly
 
+from pandas.core.dtypes import concat as _concat
 from pandas.core.dtypes.cast import maybe_cast_to_integer_array
 from pandas.core.dtypes.common import (
     ensure_categorical,
@@ -45,7 +46,7 @@
     is_unsigned_integer_dtype,
     pandas_dtype,
 )
-import pandas.core.dtypes.concat as _concat
+from pandas.core.dtypes.concat import concat_compat
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
     ABCDateOffset,
@@ -2542,7 +2543,7 @@ def _union(self, other, sort):
 
             if len(indexer) > 0:
                 other_diff = algos.take_nd(rvals, indexer, allow_fill=False)
-                result = _concat._concat_compat((lvals, other_diff))
+                result = concat_compat((lvals, other_diff))
 
             else:
                 result = lvals
@@ -2788,7 +2789,7 @@ def symmetric_difference(self, other, result_name=None, sort=None):
         right_indexer = (indexer == -1).nonzero()[0]
         right_diff = other.values.take(right_indexer)
 
-        the_diff = _concat._concat_compat([left_diff, right_diff])
+        the_diff = concat_compat([left_diff, right_diff])
         if sort is None:
             try:
                 the_diff = sorting.safe_sort(the_diff)

pandas/core/indexes/datetimes.py

@@ -18,7 +18,7 @@
     is_scalar,
     is_string_like,
 )
-import pandas.core.dtypes.concat as _concat
+from pandas.core.dtypes.concat import concat_compat
 from pandas.core.dtypes.dtypes import DatetimeTZDtype
 from pandas.core.dtypes.missing import isna
 
@@ -608,7 +608,7 @@ def _fast_union(self, other, sort=None):
             left_start = left[0]
             loc = right.searchsorted(left_start, side="left")
             right_chunk = right.values[:loc]
-            dates = _concat._concat_compat((left.values, right_chunk))
+            dates = concat_compat((left.values, right_chunk))
             return self._shallow_copy(dates)
         # DTIs are not in the "correct" order and we want
         # to sort
@@ -624,7 +624,7 @@ def _fast_union(self, other, sort=None):
         if left_end < right_end:
             loc = right.searchsorted(left_end, side="right")
             right_chunk = right.values[loc:]
-            dates = _concat._concat_compat((left.values, right_chunk))
+            dates = concat_compat((left.values, right_chunk))
             return self._shallow_copy(dates)
         else:
             return left

pandas/core/indexes/timedeltas.py

@@ -18,7 +18,7 @@
     is_timedelta64_ns_dtype,
     pandas_dtype,
 )
-import pandas.core.dtypes.concat as _concat
+from pandas.core.dtypes.concat import concat_compat
 from pandas.core.dtypes.missing import isna
 
 from pandas.core.accessor import delegate_names
@@ -462,7 +462,7 @@ def _fast_union(self, other):
         if left_end < right_end:
             loc = right.searchsorted(left_end, side="right")
             right_chunk = right.values[loc:]
-            dates = _concat._concat_compat((left.values, right_chunk))
+            dates = concat_compat((left.values, right_chunk))
             return self._shallow_copy(dates)
         else:
             return left

pandas/core/indexing.py

@@ -21,7 +21,7 @@
     is_sequence,
     is_sparse,
 )
-from pandas.core.dtypes.concat import _concat_compat
+from pandas.core.dtypes.concat import concat_compat
 from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
 from pandas.core.dtypes.missing import _infer_fill_value, isna
 
@@ -607,7 +607,7 @@ def _setitem_with_indexer_missing(self, indexer, value):
             if len(self.obj._values):
                 # GH#22717 handle casting compatibility that np.concatenate
                 #  does incorrectly
-                new_values = _concat_compat([self.obj._values, new_values])
+                new_values = concat_compat([self.obj._values, new_values])
             self.obj._data = self.obj._constructor(
                 new_values, index=new_index, name=self.obj.name
             )._data

pandas/core/internals/blocks.py

@@ -49,7 +49,7 @@
     is_timedelta64_dtype,
     pandas_dtype,
 )
-import pandas.core.dtypes.concat as _concat
+from pandas.core.dtypes.concat import concat_categorical, concat_datetime
 from pandas.core.dtypes.dtypes import CategoricalDtype, ExtensionDtype
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
@@ -2563,7 +2563,7 @@ def concat_same_type(self, to_concat, placement=None):
         # Instead of placing the condition here, it could also go into the
         # is_uniform_join_units check, but I'm not sure what is better.
         if len({x.dtype for x in to_concat}) > 1:
-            values = _concat._concat_datetime([x.values for x in to_concat])
+            values = concat_datetime([x.values for x in to_concat])
             placement = placement or slice(0, len(values), 1)
 
             if self.ndim > 1:
@@ -3088,7 +3088,7 @@ class CategoricalBlock(ExtensionBlock):
     is_categorical = True
     _verify_integrity = True
     _can_hold_na = True
-    _concatenator = staticmethod(_concat._concat_categorical)
+    _concatenator = staticmethod(concat_categorical)
 
     def __init__(self, values, placement, ndim=None):
         from pandas.core.arrays.categorical import _maybe_to_categorical

pandas/core/internals/concat.py

@@ -19,7 +19,7 @@
     is_sparse,
     is_timedelta64_dtype,
 )
-import pandas.core.dtypes.concat as _concat
+from pandas.core.dtypes.concat import concat_compat
 from pandas.core.dtypes.missing import isna
 
 import pandas.core.algorithms as algos
@@ -211,7 +211,7 @@ def get_reindexed_values(self, empty_dtype, upcasted_na):
 
             if not self.indexers:
                 if not self.block._can_consolidate:
-                    # preserve these for validation in _concat_compat
+                    # preserve these for validation in concat_compat
                     return self.block.values
 
             if self.block.is_bool and not self.block.is_categorical:
@@ -265,7 +265,7 @@ def concatenate_join_units(join_units, concat_axis, copy):
             else:
                 concat_values = concat_values.copy()
     else:
-        concat_values = _concat._concat_compat(to_concat, axis=concat_axis)
+        concat_values = concat_compat(to_concat, axis=concat_axis)
 
     return concat_values
 
@@ -380,7 +380,7 @@ 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`).
+    concatenate_join_units (which uses `concat_compat`).
 
     """
     return (

pandas/core/internals/managers.py

@@ -26,7 +26,7 @@
     is_scalar,
     is_sparse,
 )
-import pandas.core.dtypes.concat as _concat
+from pandas.core.dtypes.concat import concat_compat
 from pandas.core.dtypes.dtypes import ExtensionDtype
 from pandas.core.dtypes.generic import ABCExtensionArray, ABCSeries
 from pandas.core.dtypes.missing import isna
@@ -532,7 +532,7 @@ def get_axe(block, qs, axes):
             return self.__class__(blocks, new_axes)
 
         # single block, i.e. ndim == {1}
-        values = _concat._concat_compat([b.values for b in blocks])
+        values = concat_compat([b.values for b in blocks])
 
         # compute the orderings of our original data
         if len(self.blocks) > 1:
@@ -1647,11 +1647,11 @@ def concat(self, to_concat, new_axis):
                 new_block = blocks[0].concat_same_type(blocks)
             else:
                 values = [x.values for x in blocks]
-                values = _concat._concat_compat(values)
+                values = 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)
+            values = concat_compat(values)
             new_block = make_block(values, placement=slice(0, len(values), 1))
 
         mgr = SingleBlockManager(new_block, new_axis)

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))

pandas/core/reshape/melt.py

@@ -171,9 +171,9 @@ def lreshape(data, groups, dropna=True, label=None):
     for target, names in zip(keys, values):
         to_concat = [data[col].values for col in names]
 
-        import pandas.core.dtypes.concat as _concat
+        from pandas.core.dtypes.concat import concat_compat
 
-        mdata[target] = _concat._concat_compat(to_concat)
+        mdata[target] = concat_compat(to_concat)
         pivot_cols.append(target)
 
     for col in id_cols: