WIP

Author: rhshadrach

pandas/core/frame.py

@@ -13045,6 +13045,7 @@ def quantile(
         C        1 days 12:00:00
         Name: 0.5, dtype: object
         """
+        from pandas.core.dtypes.common import is_object_dtype
         validate_percentile(q)
         axis = self._get_axis_number(axis)
 
@@ -13064,7 +13065,7 @@ def quantile(
                 # GH#41544 try to get an appropriate dtype
                 dtype = "float64"
                 cdtype = find_common_type(list(self.dtypes))
-                if needs_i8_conversion(cdtype):
+                if needs_i8_conversion(cdtype) or is_object_dtype(cdtype):
                     dtype = cdtype
                 return res.astype(dtype)
             return res
@@ -13083,7 +13084,7 @@ def quantile(
             if axis == 1:
                 # GH#41544 try to get an appropriate dtype
                 cdtype = find_common_type(list(self.dtypes))
-                if needs_i8_conversion(cdtype):
+                if needs_i8_conversion(cdtype) or is_object_dtype(cdtype):
                     dtype = cdtype
 
             res = self._constructor([], index=q, columns=cols, dtype=dtype)
@@ -13094,6 +13095,18 @@ def quantile(
             raise ValueError(
                 f"Invalid method: {method}. Method must be in {valid_method}."
             )
+
+        # handle degenerate case
+        if len(data) == 0:
+            dtype = np.float64
+            if data.ndim == 2:
+                cdtype = find_common_type(list(self.dtypes))
+            else:
+                cdtype = self.dtype
+            if needs_i8_conversion(cdtype) or is_object_dtype(cdtype):
+                dtype = cdtype
+            return self._constructor([], index=q, columns=data.columns, dtype=dtype)
+
         if method == "single":
             res = data._mgr.quantile(qs=q, interpolation=interpolation)
         elif method == "table":
@@ -13103,13 +13116,6 @@ def quantile(
                     f"Invalid interpolation: {interpolation}. "
                     f"Interpolation must be in {valid_interpolation}"
                 )
-            # handle degenerate case
-            if len(data) == 0:
-                if data.ndim == 2:
-                    dtype = find_common_type(list(self.dtypes))
-                else:
-                    dtype = self.dtype
-                return self._constructor([], index=q, columns=data.columns, dtype=dtype)
 
             q_idx = np.quantile(np.arange(len(data)), q, method=interpolation)
 

pandas/tests/frame/methods/test_quantile.py

@@ -319,7 +319,8 @@ def test_quantile_multi_empty(self, interp_method):
         result = DataFrame({"x": [], "y": []}).quantile(
             [0.1, 0.9], axis=0, interpolation=interpolation, method=method
         )
-        dtype = "float64" if method == "single" else "object"
+        # dtype = "float64" if method == "single" else "object"
+        dtype = "object"
         expected = DataFrame(
             {"x": [np.nan, np.nan], "y": [np.nan, np.nan]},
             index=[0.1, 0.9],
@@ -692,7 +693,7 @@ def test_quantile_empty_no_rows_dt64(self, interp_method):
             0.5, numeric_only=False, interpolation=interpolation, method=method
         )
         exp = exp.astype(object)
-        if interpolation == "nearest":
+        if True or interpolation == "nearest":
             # GH#18463 TODO: would we prefer NaTs here?
             exp = exp.fillna(np.nan)
         tm.assert_series_equal(res, exp)
@@ -911,7 +912,7 @@ def test_empty_datelike(
     @pytest.mark.parametrize(
         "expected_data, expected_index, axis",
         [
-            [[np.nan, np.nan], range(2), 1],
+            [[pd.NaT, pd.NaT], range(2), 1],
             [[], [], 0],
         ],
     )
@@ -926,6 +927,6 @@ def test_datelike_numeric_only(self, expected_data, expected_index, axis):
         )
         result = df[["a", "c"]].quantile(0.5, axis=axis, numeric_only=True)
         expected = Series(
-            expected_data, name=0.5, index=Index(expected_index), dtype=np.float64
+            expected_data, name=0.5, index=Index(expected_index)
         )
         tm.assert_series_equal(result, expected)