BUG/ENH: groupby.quantile support non-nano (#50978)

Author: jbrockmendel

pandas/core/groupby/groupby.py

@@ -3188,10 +3188,10 @@ def pre_processor(vals: ArrayLike) -> tuple[np.ndarray, Dtype | None]:
             elif is_bool_dtype(vals.dtype) and isinstance(vals, ExtensionArray):
                 out = vals.to_numpy(dtype=float, na_value=np.nan)
             elif is_datetime64_dtype(vals.dtype):
-                inference = np.dtype("datetime64[ns]")
+                inference = vals.dtype
                 out = np.asarray(vals).astype(float)
             elif is_timedelta64_dtype(vals.dtype):
-                inference = np.dtype("timedelta64[ns]")
+                inference = vals.dtype
                 out = np.asarray(vals).astype(float)
             elif isinstance(vals, ExtensionArray) and is_float_dtype(vals):
                 inference = np.dtype(np.float64)

pandas/tests/groupby/test_quantile.py

@@ -26,33 +26,45 @@
         ([np.nan, 4.0, np.nan, 2.0, np.nan], [np.nan, 4.0, np.nan, 2.0, np.nan]),
         # Timestamps
         (
-            list(pd.date_range("1/1/18", freq="D", periods=5)),
-            list(pd.date_range("1/1/18", freq="D", periods=5))[::-1],
+            pd.date_range("1/1/18", freq="D", periods=5),
+            pd.date_range("1/1/18", freq="D", periods=5)[::-1],
+        ),
+        (
+            pd.date_range("1/1/18", freq="D", periods=5).as_unit("s"),
+            pd.date_range("1/1/18", freq="D", periods=5)[::-1].as_unit("s"),
         ),
         # All NA
         ([np.nan] * 5, [np.nan] * 5),
     ],
 )
 @pytest.mark.parametrize("q", [0, 0.25, 0.5, 0.75, 1])
 def test_quantile(interpolation, a_vals, b_vals, q, request):
-    if interpolation == "nearest" and q == 0.5 and b_vals == [4, 3, 2, 1]:
+    if (
+        interpolation == "nearest"
+        and q == 0.5
+        and isinstance(b_vals, list)
+        and b_vals == [4, 3, 2, 1]
+    ):
         request.node.add_marker(
             pytest.mark.xfail(
                 reason="Unclear numpy expectation for nearest "
                 "result with equidistant data"
             )
         )
+    all_vals = pd.concat([pd.Series(a_vals), pd.Series(b_vals)])
 
     a_expected = pd.Series(a_vals).quantile(q, interpolation=interpolation)
     b_expected = pd.Series(b_vals).quantile(q, interpolation=interpolation)
 
-    df = DataFrame(
-        {"key": ["a"] * len(a_vals) + ["b"] * len(b_vals), "val": a_vals + b_vals}
-    )
+    df = DataFrame({"key": ["a"] * len(a_vals) + ["b"] * len(b_vals), "val": all_vals})
 
     expected = DataFrame(
         [a_expected, b_expected], columns=["val"], index=Index(["a", "b"], name="key")
     )
+    if all_vals.dtype.kind == "M" and expected.dtypes.values[0].kind == "M":
+        # TODO(non-nano): this should be unnecessary once array_to_datetime
+        #  correctly infers non-nano from Timestamp.unit
+        expected = expected.astype(all_vals.dtype)
     result = df.groupby("key").quantile(q, interpolation=interpolation)
 
     tm.assert_frame_equal(result, expected)

pandas/tests/resample/test_timedelta.py

@@ -174,10 +174,12 @@ def test_resample_with_timedelta_yields_no_empty_groups(duplicates):
     tm.assert_frame_equal(result, expected)
 
 
-def test_resample_quantile_timedelta():
+@pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+def test_resample_quantile_timedelta(unit):
     # GH: 29485
+    dtype = np.dtype(f"m8[{unit}]")
     df = DataFrame(
-        {"value": pd.to_timedelta(np.arange(4), unit="s")},
+        {"value": pd.to_timedelta(np.arange(4), unit="s").astype(dtype)},
         index=pd.date_range("20200101", periods=4, tz="UTC"),
     )
     result = df.resample("2D").quantile(0.99)
@@ -189,7 +191,7 @@ def test_resample_quantile_timedelta():
             ]
         },
         index=pd.date_range("20200101", periods=2, tz="UTC", freq="2D"),
-    )
+    ).astype(dtype)
     tm.assert_frame_equal(result, expected)