Preserve DataFrame(columns=).columns dtype during empty-like construc…

…tion (#14381)

`.column` used to always return `pd.Index([], dtype=object)` even if an empty-dtyped columns was passed into the DataFrame constructor e.g. `DatetimeIndex([])`. Needed to preserved some information about what column dtype was passed in so we can return a correctly type Index

Authors:
  - Matthew Roeschke (https://github.com/mroeschke)

Approvers:
  - Lawrence Mitchell (https://github.com/wence-)

URL: #14381

python/cudf/cudf/core/column_accessor.py

@@ -27,6 +27,7 @@
 from cudf.core import column
 
 if TYPE_CHECKING:
+    from cudf._typing import Dtype
     from cudf.core.column import ColumnBase
 
 
@@ -99,6 +100,9 @@ class ColumnAccessor(abc.MutableMapping):
     rangeindex : bool, optional
         Whether the keys should be returned as a RangeIndex
         in `to_pandas_index` (default=False).
+    label_dtype : Dtype, optional
+        What dtype should be returned in `to_pandas_index`
+        (default=None).
     """
 
     _data: "Dict[Any, ColumnBase]"
@@ -111,8 +115,10 @@ def __init__(
         multiindex: bool = False,
         level_names=None,
         rangeindex: bool = False,
+        label_dtype: Dtype | None = None,
     ):
         self.rangeindex = rangeindex
+        self.label_dtype = label_dtype
         if data is None:
             data = {}
         # TODO: we should validate the keys of `data`
@@ -123,6 +129,7 @@ def __init__(
             self.multiindex = multiindex
             self._level_names = level_names
             self.rangeindex = data.rangeindex
+            self.label_dtype = data.label_dtype
         else:
             # This code path is performance-critical for copies and should be
             # modified with care.
@@ -292,7 +299,12 @@ def to_pandas_index(self) -> pd.Index:
                             self.names[0], self.names[-1] + diff, diff
                         )
                         return pd.RangeIndex(new_range, name=self.name)
-            result = pd.Index(self.names, name=self.name, tupleize_cols=False)
+            result = pd.Index(
+                self.names,
+                name=self.name,
+                tupleize_cols=False,
+                dtype=self.label_dtype,
+            )
         return result
 
     def insert(

python/cudf/cudf/core/dataframe.py

@@ -734,6 +734,7 @@ def __init__(
                 rangeindex = isinstance(
                     columns, (range, pd.RangeIndex, cudf.RangeIndex)
                 )
+                label_dtype = getattr(columns, "dtype", None)
                 self._data = ColumnAccessor(
                     {
                         k: column.column_empty(
@@ -745,6 +746,7 @@ def __init__(
                     if isinstance(columns, pd.Index)
                     else None,
                     rangeindex=rangeindex,
+                    label_dtype=label_dtype,
                 )
         elif isinstance(data, ColumnAccessor):
             raise TypeError(
@@ -995,12 +997,15 @@ def _init_from_list_like(self, data, index=None, columns=None):
             self._data.rangeindex = isinstance(
                 columns, (range, pd.RangeIndex, cudf.RangeIndex)
             )
+            self._data.label_dtype = getattr(columns, "dtype", None)
 
     @_cudf_nvtx_annotate
     def _init_from_dict_like(
         self, data, index=None, columns=None, nan_as_null=None
     ):
+        label_dtype = None
         if columns is not None:
+            label_dtype = getattr(columns, "dtype", None)
             # remove all entries in data that are not in columns,
             # inserting new empty columns for entries in columns that
             # are not in data
@@ -1069,6 +1074,7 @@ def _init_from_dict_like(
             if isinstance(columns, pd.Index)
             else self._data._level_names
         )
+        self._data.label_dtype = label_dtype
 
     @classmethod
     def _from_data(

python/cudf/cudf/tests/test_dataframe.py

@@ -4566,6 +4566,17 @@ def test_dataframe_columns_returns_rangeindex_single_col():
     assert_eq(result, expected)
 
 
+@pytest.mark.parametrize("dtype", ["int64", "datetime64[ns]", "int8"])
+@pytest.mark.parametrize("idx_data", [[], [1, 2]])
+@pytest.mark.parametrize("data", [None, [], {}])
+def test_dataframe_columns_empty_data_preserves_dtype(dtype, idx_data, data):
+    result = cudf.DataFrame(
+        data, columns=cudf.Index(idx_data, dtype=dtype)
+    ).columns
+    expected = pd.Index(idx_data, dtype=dtype)
+    assert_eq(result, expected)
+
+
 @pytest.mark.parametrize(
     "data",
     [