BUG: instantiation using a dict with a period scalar (#35966)

Author: justinessert

doc/source/whatsnew/v1.2.0.rst

@@ -334,7 +334,7 @@ Sparse
 ExtensionArray
 ^^^^^^^^^^^^^^
 
--
+- Fixed Bug where :class:`DataFrame` column set to scalar extension type via a dict instantion was considered an object type rather than the extension type (:issue:`35965`)
 -
 
 

pandas/core/construction.py

@@ -472,7 +472,7 @@ def sanitize_array(
 
             # figure out the dtype from the value (upcast if necessary)
             if dtype is None:
-                dtype, value = infer_dtype_from_scalar(value)
+                dtype, value = infer_dtype_from_scalar(value, pandas_dtype=True)
             else:
                 # need to possibly convert the value here
                 value = maybe_cast_to_datetime(value, dtype)

pandas/core/dtypes/cast.py

@@ -709,7 +709,6 @@ 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

@@ -589,6 +589,8 @@ 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

@@ -84,13 +84,11 @@ 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 == exp_val
+    assert val == p
 
 
 @pytest.mark.parametrize(

pandas/tests/frame/test_constructors.py

@@ -717,6 +717,24 @@ def test_constructor_period_dict(self):
         assert df["a"].dtype == a.dtype
         assert df["b"].dtype == b.dtype
 
+    @pytest.mark.parametrize(
+        "data,dtype",
+        [
+            (pd.Period("2012-01", freq="M"), "period[M]"),
+            (pd.Period("2012-02-01", freq="D"), "period[D]"),
+            (Interval(left=0, right=5), IntervalDtype("int64")),
+            (Interval(left=0.1, right=0.5), IntervalDtype("float64")),
+        ],
+    )
+    def test_constructor_period_dict_scalar(self, data, dtype):
+        # scalar periods
+        df = DataFrame({"a": data}, index=[0])
+        assert df["a"].dtype == dtype
+
+        expected = DataFrame(index=[0], columns=["a"], data=data)
+
+        tm.assert_frame_equal(df, expected)
+
     @pytest.mark.parametrize(
         "data,dtype",
         [

pandas/tests/series/test_constructors.py

@@ -8,16 +8,23 @@
 from pandas._libs import iNaT, lib
 
 from pandas.core.dtypes.common import is_categorical_dtype, is_datetime64tz_dtype
-from pandas.core.dtypes.dtypes import CategoricalDtype
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
 
 import pandas as pd
 from pandas import (
     Categorical,
     DataFrame,
     Index,
+    Interval,
     IntervalIndex,
     MultiIndex,
     NaT,
+    Period,
     Series,
     Timestamp,
     date_range,
@@ -1075,6 +1082,26 @@ def test_constructor_dict_order(self):
         expected = Series([1, 0, 2], index=list("bac"))
         tm.assert_series_equal(result, expected)
 
+    @pytest.mark.parametrize(
+        "data,dtype",
+        [
+            (Period("2020-01"), PeriodDtype("M")),
+            (Interval(left=0, right=5), IntervalDtype("int64")),
+            (
+                Timestamp("2011-01-01", tz="US/Eastern"),
+                DatetimeTZDtype(tz="US/Eastern"),
+            ),
+        ],
+    )
+    def test_constructor_dict_extension(self, data, dtype):
+        d = {"a": data}
+        result = Series(d, index=["a"])
+        expected = Series(data, index=["a"], dtype=dtype)
+
+        assert result.dtype == dtype
+
+        tm.assert_series_equal(result, expected)
+
     @pytest.mark.parametrize("value", [2, np.nan, None, float("nan")])
     def test_constructor_dict_nan_key(self, value):
         # GH 18480