BUG: Fix infer_dtype_from_scalar to infer IntervalDtype

doc/source/whatsnew/v1.0.0.rst

@@ -758,7 +758,7 @@ Interval
 ^^^^^^^^
 
 - Bug in :meth:`IntervalIndex.get_indexer` where a :class:`Categorical` or :class:`CategoricalIndex` ``target`` would incorrectly raise a ``TypeError`` (:issue:`30063`)
--
+- Bug in ``pandas.core.dtypes.cast.infer_dtype_from_scalar`` where passing ``pandas_dtype=True`` did not infer :class:`IntervalDtype` (:issue:`30337`)
 
 Indexing
 ^^^^^^^^

pandas/core/dtypes/cast.py

@@ -41,7 +41,7 @@
     is_unsigned_integer_dtype,
     pandas_dtype,
 )
-from .dtypes import DatetimeTZDtype, ExtensionDtype, PeriodDtype
+from .dtypes import DatetimeTZDtype, ExtensionDtype, IntervalDtype, PeriodDtype
 from .generic import (
     ABCDataFrame,
     ABCDatetimeArray,
@@ -601,6 +601,9 @@ def infer_dtype_from_scalar(val, pandas_dtype: bool = False):
         if lib.is_period(val):
             dtype = PeriodDtype(freq=val.freq)
             val = val.ordinal
+        elif lib.is_interval(val):
+            subtype = infer_dtype_from_scalar(val.left, pandas_dtype=True)[0]
+            dtype = IntervalDtype(subtype=subtype)
 
     return dtype, val
 

pandas/tests/dtypes/cast/test_infer_dtype.py

@@ -10,7 +10,15 @@
 )
 from pandas.core.dtypes.common import is_dtype_equal
 
-from pandas import Categorical, Period, Series, Timedelta, Timestamp, date_range
+from pandas import (
+    Categorical,
+    Interval,
+    Period,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+)
 import pandas.util.testing as tm
 
 
@@ -107,6 +115,25 @@ def test_infer_from_scalar_tz(tz, pandas_dtype):
     assert val == exp_val
 
 
+@pytest.mark.parametrize(
+    "left, right, subtype",
+    [
+        (0, 1, "int64"),
+        (0.0, 1.0, "float64"),
+        (Timestamp(0), Timestamp(1), "datetime64[ns]"),
+        (Timestamp(0, tz="UTC"), Timestamp(1, tz="UTC"), "datetime64[ns, UTC]"),
+        (Timedelta(0), Timedelta(1), "timedelta64[ns]"),
+    ],
+)
+def test_infer_from_interval(left, right, subtype, closed, pandas_dtype):
+    # GH 30337
+    interval = Interval(left, right, closed)
+    result_dtype, result_value = infer_dtype_from_scalar(interval, pandas_dtype)
+    expected_dtype = f"interval[{subtype}]" if pandas_dtype else np.object_
+    assert result_dtype == expected_dtype
+    assert result_value == interval
+
+
 def test_infer_dtype_from_scalar_errors():
     msg = "invalid ndarray passed to infer_dtype_from_scalar"