BUG: reset_index is passing a bad dtype to NumPy (#35111)

Author: simonjayhawkins

pandas/core/frame.py

@@ -75,6 +75,7 @@
 from pandas.core.dtypes.cast import (
     cast_scalar_to_array,
     coerce_to_dtypes,
+    construct_1d_arraylike_from_scalar,
     find_common_type,
     infer_dtype_from_scalar,
     invalidate_string_dtypes,
@@ -109,7 +110,7 @@
     needs_i8_conversion,
     pandas_dtype,
 )
-from pandas.core.dtypes.missing import isna, notna
+from pandas.core.dtypes.missing import isna, na_value_for_dtype, notna
 
 from pandas.core import algorithms, common as com, nanops, ops
 from pandas.core.accessor import CachedAccessor
@@ -4731,8 +4732,11 @@ def _maybe_casted_values(index, labels=None):
                 # we can have situations where the whole mask is -1,
                 # meaning there is nothing found in labels, so make all nan's
                 if mask.all():
-                    values = np.empty(len(mask), dtype=index.dtype)
-                    values.fill(np.nan)
+                    dtype = index.dtype
+                    fill_value = na_value_for_dtype(dtype)
+                    values = construct_1d_arraylike_from_scalar(
+                        fill_value, len(mask), dtype
+                    )
                 else:
                     values = values.take(labels)
 

pandas/tests/frame/methods/test_reset_index.py

@@ -3,6 +3,7 @@
 import numpy as np
 import pytest
 
+import pandas as pd
 from pandas import (
     DataFrame,
     Index,
@@ -299,9 +300,19 @@ def test_reset_index_range(self):
         tm.assert_frame_equal(result, expected)
 
 
-def test_reset_index_dtypes_on_empty_frame_with_multiindex():
+@pytest.mark.parametrize(
+    "array, dtype",
+    [
+        (["a", "b"], object),
+        (
+            pd.period_range("12-1-2000", periods=2, freq="Q-DEC"),
+            pd.PeriodDtype(freq="Q-DEC"),
+        ),
+    ],
+)
+def test_reset_index_dtypes_on_empty_frame_with_multiindex(array, dtype):
     # GH 19602 - Preserve dtype on empty DataFrame with MultiIndex
-    idx = MultiIndex.from_product([[0, 1], [0.5, 1.0], ["a", "b"]])
+    idx = MultiIndex.from_product([[0, 1], [0.5, 1.0], array])
     result = DataFrame(index=idx)[:0].reset_index().dtypes
-    expected = Series({"level_0": np.int64, "level_1": np.float64, "level_2": object})
+    expected = Series({"level_0": np.int64, "level_1": np.float64, "level_2": dtype})
     tm.assert_series_equal(result, expected)

pandas/tests/series/methods/test_reset_index.py

@@ -1,6 +1,7 @@
 import numpy as np
 import pytest
 
+import pandas as pd
 from pandas import DataFrame, Index, MultiIndex, RangeIndex, Series
 import pandas._testing as tm
 
@@ -110,11 +111,21 @@ def test_reset_index_drop_errors(self):
             s.reset_index("wrong", drop=True)
 
 
-def test_reset_index_dtypes_on_empty_series_with_multiindex():
+@pytest.mark.parametrize(
+    "array, dtype",
+    [
+        (["a", "b"], object),
+        (
+            pd.period_range("12-1-2000", periods=2, freq="Q-DEC"),
+            pd.PeriodDtype(freq="Q-DEC"),
+        ),
+    ],
+)
+def test_reset_index_dtypes_on_empty_series_with_multiindex(array, dtype):
     # GH 19602 - Preserve dtype on empty Series with MultiIndex
-    idx = MultiIndex.from_product([[0, 1], [0.5, 1.0], ["a", "b"]])
+    idx = MultiIndex.from_product([[0, 1], [0.5, 1.0], array])
     result = Series(dtype=object, index=idx)[:0].reset_index().dtypes
     expected = Series(
-        {"level_0": np.int64, "level_1": np.float64, "level_2": object, 0: object}
+        {"level_0": np.int64, "level_1": np.float64, "level_2": dtype, 0: object}
     )
     tm.assert_series_equal(result, expected)