REF: Use default_index or preserve original Index type for empty-like results (#59035)

* Use more default_index for empty cases

* fix tests

* Update number

* Address typing

Author: mroeschke

doc/source/whatsnew/v3.0.0.rst

@@ -503,8 +503,8 @@ Timezones
 
 Numeric
 ^^^^^^^
+- Bug in :meth:`DataFrame.quantile` where the column type was not preserved when ``numeric_only=True`` with a list-like ``q`` produced an empty result (:issue:`59035`)
 - Bug in ``np.matmul`` with :class:`Index` inputs raising a ``TypeError`` (:issue:`57079`)
--
 
 Conversion
 ^^^^^^^^^^

pandas/core/frame.py

@@ -13078,7 +13078,7 @@ def quantile(
 
         if len(data.columns) == 0:
             # GH#23925 _get_numeric_data may have dropped all columns
-            cols = Index([], name=self.columns.name)
+            cols = self.columns[:0]
 
             dtype = np.float64
             if axis == 1:

pandas/core/generic.py

@@ -158,7 +158,6 @@
     Index,
     MultiIndex,
     PeriodIndex,
-    RangeIndex,
     default_index,
     ensure_index,
 )
@@ -1852,7 +1851,7 @@ def _drop_labels_or_levels(self, keys, axis: AxisInt = 0):
                 else:
                     # Drop the last level of Index by replacing with
                     # a RangeIndex
-                    dropped.columns = RangeIndex(dropped.columns.size)
+                    dropped.columns = default_index(dropped.columns.size)
 
             # Handle dropping index labels
             if labels_to_drop:

pandas/core/groupby/groupby.py

@@ -128,7 +128,6 @@ class providing the base-class of operations.
 from pandas.core.indexes.api import (
     Index,
     MultiIndex,
-    RangeIndex,
     default_index,
 )
 from pandas.core.internals.blocks import ensure_block_shape
@@ -1264,7 +1263,7 @@ def _set_result_index_ordered(
         if self._grouper.has_dropped_na:
             # Add back in any missing rows due to dropna - index here is integral
             # with values referring to the row of the input so can use RangeIndex
-            result = result.reindex(RangeIndex(len(index)), axis=0)
+            result = result.reindex(default_index(len(index)), axis=0)
         result = result.set_axis(index, axis=0)
 
         return result
@@ -1334,7 +1333,7 @@ def _wrap_aggregated_output(
             #   enforced in __init__
             result = self._insert_inaxis_grouper(result, qs=qs)
             result = result._consolidate()
-            result.index = RangeIndex(len(result))
+            result.index = default_index(len(result))
 
         else:
             index = self._grouper.result_index

pandas/core/groupby/grouper.py

@@ -34,6 +34,7 @@
 from pandas.core.indexes.api import (
     Index,
     MultiIndex,
+    default_index,
 )
 from pandas.core.series import Series
 
@@ -901,7 +902,7 @@ def is_in_obj(gpr) -> bool:
     if len(groupings) == 0 and len(obj):
         raise ValueError("No group keys passed!")
     if len(groupings) == 0:
-        groupings.append(Grouping(Index([], dtype="int"), np.array([], dtype=np.intp)))
+        groupings.append(Grouping(default_index(0), np.array([], dtype=np.intp)))
 
     # create the internals grouper
     grouper = ops.BaseGrouper(group_axis, groupings, sort=sort, dropna=dropna)

pandas/core/indexes/api.py

@@ -130,7 +130,7 @@ def _get_combined_index(
     # TODO: handle index names!
     indexes = _get_distinct_objs(indexes)
     if len(indexes) == 0:
-        index = Index([])
+        index: Index = default_index(0)
     elif len(indexes) == 1:
         index = indexes[0]
     elif intersect:

pandas/core/internals/managers.py

@@ -249,7 +249,7 @@ def blklocs(self) -> npt.NDArray[np.intp]:
     def make_empty(self, axes=None) -> Self:
         """return an empty BlockManager with the items axis of len 0"""
         if axes is None:
-            axes = [Index([])] + self.axes[1:]
+            axes = [default_index(0)] + self.axes[1:]
 
         # preserve dtype if possible
         if self.ndim == 1:

pandas/core/methods/selectn.py

@@ -29,6 +29,8 @@
 )
 from pandas.core.dtypes.dtypes import BaseMaskedDtype
 
+from pandas.core.indexes.api import default_index
+
 if TYPE_CHECKING:
     from pandas._typing import (
         DtypeObj,
@@ -38,6 +40,7 @@
 
     from pandas import (
         DataFrame,
+        Index,
         Series,
     )
 else:
@@ -199,8 +202,6 @@ def __init__(self, obj: DataFrame, n: int, keep: str, columns: IndexLabel) -> No
         self.columns = columns
 
     def compute(self, method: str) -> DataFrame:
-        from pandas.core.api import Index
-
         n = self.n
         frame = self.obj
         columns = self.columns
@@ -227,7 +228,7 @@ def get_indexer(current_indexer: Index, other_indexer: Index) -> Index:
         original_index = frame.index
         cur_frame = frame = frame.reset_index(drop=True)
         cur_n = n
-        indexer = Index([], dtype=np.int64)
+        indexer: Index = default_index(0)
 
         for i, column in enumerate(columns):
             # For each column we apply method to cur_frame[column].

pandas/core/reshape/reshape.py

@@ -42,7 +42,7 @@
 from pandas.core.indexes.api import (
     Index,
     MultiIndex,
-    RangeIndex,
+    default_index,
 )
 from pandas.core.reshape.concat import concat
 from pandas.core.series import Series
@@ -1047,7 +1047,7 @@ def stack_reshape(
             if data.ndim == 1:
                 data.name = 0
             else:
-                data.columns = RangeIndex(len(data.columns))
+                data.columns = default_index(len(data.columns))
         buf.append(data)
 
     if len(buf) > 0 and not frame.empty:

pandas/tests/frame/methods/test_quantile.py

@@ -710,14 +710,14 @@ def test_quantile_empty_no_columns(self, interp_method):
         result = df.quantile(
             0.5, numeric_only=True, interpolation=interpolation, method=method
         )
-        expected = Series([], index=[], name=0.5, dtype=np.float64)
+        expected = Series([], name=0.5, dtype=np.float64)
         expected.index.name = "captain tightpants"
         tm.assert_series_equal(result, expected)
 
         result = df.quantile(
             [0.5], numeric_only=True, interpolation=interpolation, method=method
         )
-        expected = DataFrame([], index=[0.5], columns=[])
+        expected = DataFrame([], index=[0.5])
         expected.columns.name = "captain tightpants"
         tm.assert_frame_equal(result, expected)
 
@@ -926,3 +926,12 @@ def test_datelike_numeric_only(self, expected_data, expected_index, axis):
             expected_data, name=0.5, index=Index(expected_index), dtype=np.float64
         )
         tm.assert_series_equal(result, expected)
+
+
+def test_multi_quantile_numeric_only_retains_columns():
+    df = DataFrame(list("abc"))
+    result = df.quantile([0.5, 0.7], numeric_only=True)
+    expected = DataFrame(index=[0.5, 0.7])
+    tm.assert_frame_equal(
+        result, expected, check_index_type=True, check_column_type=True
+    )