alternative implementation

Author: justinessert

pandas/core/construction.py

@@ -472,7 +472,12 @@ def sanitize_array(
 
             # figure out the dtype from the value (upcast if necessary)
             if dtype is None:
-                dtype, value = infer_dtype_from_scalar(value, pandas_dtype=True)
+                dtype, new_value = infer_dtype_from_scalar(value, pandas_dtype=True)
+
+                # infer_dtype_from_scalar converts periods to their ordinal values
+                # which we do not want here
+                if not lib.is_period(value):
+                    value = new_value
             else:
                 # need to possibly convert the value here
                 value = maybe_cast_to_datetime(value, dtype)

pandas/core/dtypes/cast.py

@@ -709,6 +709,7 @@ def infer_dtype_from_scalar(val, pandas_dtype: bool = False) -> Tuple[DtypeObj,
     elif pandas_dtype:
         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)

pandas/core/indexes/interval.py

@@ -612,8 +612,6 @@ def _maybe_convert_i8(self, key):
         if scalar:
             # Timestamp/Timedelta
             key_dtype, key_i8 = infer_dtype_from_scalar(key, pandas_dtype=True)
-            if lib.is_period(key):
-                key_i8 = key.ordinal
         else:
             # DatetimeIndex/TimedeltaIndex
             key_dtype, key_i8 = key.dtype, Index(key.asi8)

pandas/tests/dtypes/cast/test_infer_dtype.py

@@ -84,11 +84,13 @@ def test_infer_dtype_from_period(freq, pandas_dtype):
 
     if pandas_dtype:
         exp_dtype = f"period[{freq}]"
+        exp_val = p.ordinal
     else:
         exp_dtype = np.object_
+        exp_val = p
 
     assert dtype == exp_dtype
-    assert val == p
+    assert val == exp_val
 
 
 @pytest.mark.parametrize(