Backport PR #42936 on branch 1.3.x (BUG:Can't calculate quantiles from Int64Dtype Series when results are floats)

doc/source/whatsnew/v1.3.2.rst

@@ -23,6 +23,7 @@ Fixed regressions
 - Fixed regression where :meth:`pandas.read_csv` raised a ``ValueError`` when parameters ``names`` and ``prefix`` were both set to None (:issue:`42387`)
 - Fixed regression in comparisons between :class:`Timestamp` object and ``datetime64`` objects outside the implementation bounds for nanosecond ``datetime64`` (:issue:`42794`)
 - Fixed regression in :meth:`.Styler.highlight_min` and :meth:`.Styler.highlight_max` where ``pandas.NA`` was not successfully ignored (:issue:`42650`)
+- Fixed regression in :meth:`pandas.Series.quantile` with :class:`pandas.Int64Dtype` (:issue:`42626`)
 
 .. ---------------------------------------------------------------------------
 

pandas/core/array_algos/quantile.py

@@ -181,5 +181,10 @@ def _quantile_ea_fallback(
     assert res.ndim == 2
     assert res.shape[0] == 1
     res = res[0]
-    out = type(values)._from_sequence(res, dtype=values.dtype)
+    try:
+        out = type(values)._from_sequence(res, dtype=values.dtype)
+    except TypeError:
+        # GH#42626: not able to safely cast Int64
+        # for floating point output
+        out = np.atleast_2d(np.asarray(res, dtype=np.float64))
     return out

pandas/tests/series/methods/test_quantile.py

@@ -217,3 +217,9 @@ def test_quantile_empty(self):
         res = s.quantile([0.5])
         exp = Series([pd.NaT], index=[0.5])
         tm.assert_series_equal(res, exp)
+
+    @pytest.mark.parametrize("dtype", [int, float, "Int64"])
+    def test_quantile_dtypes(self, dtype):
+        result = Series([1, 2, 3], dtype=dtype).quantile(np.arange(0, 1, 0.25))
+        expected = Series(np.arange(1, 3, 0.5), index=np.arange(0, 1, 0.25))
+        tm.assert_series_equal(result, expected)