BUG: Don't raise when constructing Series from ordered set

doc/source/whatsnew/v1.1.2.rst

@@ -32,6 +32,8 @@ Bug fixes
 - Bug in :meth:`DataFrame.apply` with ``result_type="reduce"`` returning with incorrect index (:issue:`35683`)
 - Bug in :meth:`DateTimeIndex.format` and :meth:`PeriodIndex.format` with ``name=True`` setting the first item to ``"None"`` where it should bw ``""`` (:issue:`35712`)
 - Bug in :meth:`Float64Index.__contains__` incorrectly raising ``TypeError`` instead of returning ``False`` (:issue:`35788`)
+- Bug in :class:`Series` constructor incorrectly raising a ``TypeError`` when passed an ordered set (:issue:`36044`)
+-
 
 .. ---------------------------------------------------------------------------
 

pandas/core/construction.py

@@ -437,7 +437,11 @@ def sanitize_array(
             subarr = subarr.copy()
         return subarr
 
-    elif isinstance(data, (list, tuple)) and len(data) > 0:
+    elif isinstance(data, (list, tuple, abc.Set)) and len(data) > 0:
+        if isinstance(data, set):
+            raise TypeError("Set type is unordered")
+        data = list(data)
+
         if dtype is not None:
             subarr = _try_cast(data, dtype, copy, raise_cast_failure)
         else:
@@ -449,8 +453,6 @@ def sanitize_array(
         # GH#16804
         arr = np.arange(data.start, data.stop, data.step, dtype="int64")
         subarr = _try_cast(arr, dtype, copy, raise_cast_failure)
-    elif isinstance(data, abc.Set):
-        raise TypeError("Set type is unordered")
     elif lib.is_scalar(data) and index is not None and dtype is not None:
         data = maybe_cast_to_datetime(data, dtype)
         if not lib.is_scalar(data):

pandas/tests/series/test_constructors.py

@@ -1464,3 +1464,9 @@ def test_constructor_sparse_datetime64(self, values):
         arr = pd.arrays.SparseArray(values, dtype=dtype)
         expected = pd.Series(arr)
         tm.assert_series_equal(result, expected)
+
+    def test_construction_from_ordered_set(self):
+        # https://github.com/pandas-dev/pandas/issues/36044
+        result = Series({"a": 1, "b": 2}.keys())
+        expected = Series(["a", "b"])
+        tm.assert_series_equal(result, expected)