Handle construction from scalar

Author: jbrockmendel

pandas/_libs/lib.pyx

@@ -2621,7 +2621,7 @@ def maybe_convert_objects(ndarray[object] objects,
                 seen.object_ = True
                 break
         elif PyTime_Check(val):
-            if convert_non_numeric:
+            if convert_non_numeric and val.tzinfo is None:
                 seen.time_ = True
             else:
                 seen.object_ = True
@@ -2692,6 +2692,7 @@ def maybe_convert_objects(ndarray[object] objects,
 
     elif seen.time_:
         if is_time_array(objects):
+            # FIXME: need to ensure this is not timetz
             opt = get_option("future.infer_time")
             if opt is True:
                 import pyarrow as pa

pandas/core/dtypes/cast.py

@@ -18,6 +18,8 @@
 
 import numpy as np
 
+from pandas._config import get_option
+
 from pandas._libs import lib
 from pandas._libs.missing import (
     NA,
@@ -38,6 +40,7 @@
     IntCastingNaNError,
     LossySetitemError,
 )
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import (
     ensure_int8,
@@ -822,6 +825,29 @@ def infer_dtype_from_scalar(val) -> tuple[DtypeObj, Any]:
                 val = val.asm8
             dtype = val.dtype
 
+    elif isinstance(val, dt.time):
+        if val.tzinfo is None:
+            # pyarrow doesn't have a dtype for timetz.
+            opt = get_option("future.infer_time")
+            if opt is None:
+                warnings.warn(
+                    "Pandas type inference with a `datetime.time` "
+                    "object is deprecated. In a future version, this will give "
+                    "time32[pyarrow] dtype, which will require pyarrow to be "
+                    "installed. To opt in to the new behavior immediately set "
+                    "`pd.set_option('future.infer_time', True)`. To keep the "
+                    "old behavior pass `dtype=object`.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
+                )
+            elif opt is True:
+                import pyarrow as pa
+
+                pa_dtype = pa.time64("us")
+                from pandas.core.arrays.arrow import ArrowDtype
+
+                dtype = ArrowDtype(pa_dtype)
+
     elif is_bool(val):
         dtype = np.dtype(np.bool_)
 

pandas/tests/frame/test_constructors.py

@@ -3168,6 +3168,40 @@ def test_tzaware_data_tznaive_dtype(self, constructor, box, frame_or_series):
         with pytest.raises(err, match=msg):
             constructor(ts, dtype="M8[ns]")
 
+    @pytest.mark.parametrize(
+        "future", [pytest.param(True, marks=td.skip_if_no("pyarrow")), False, None]
+    )
+    def test_from_pytime(self, constructor, box, frame_or_series, future):
+        item = Timestamp("2023-05-04 08:53").time()
+
+        warn = None
+        if box is list or (box is dict and frame_or_series is Series):
+            msg = (
+                "Pandas type inference with a sequence of `datetime.time` "
+                "objects is deprecated"
+            )
+        else:
+            msg = "Pandas type inference with a `datetime.time` object is deprecated"
+        exp_dtype = np.dtype(object)
+        if future is None:
+            warn = FutureWarning
+        elif future is True:
+            import pyarrow as pa
+
+            pa_type = pa.time64("us")
+            exp_dtype = pd.ArrowDtype(pa_type)
+
+        with pd.option_context("future.infer_time", future):
+            with tm.assert_produces_warning(warn, match=msg):
+                result = constructor(item)
+        dtype = tm.get_dtype(result)
+        assert dtype == exp_dtype
+
+        aware = Timestamp("2023-05-04 08:53", tz="US/Pacific").timetz()
+        result2 = constructor(aware)
+        dtype = tm.get_dtype(result2)
+        assert dtype == np.dtype(object)
+
 
 # TODO: better location for this test?
 class TestAllowNonNano: