BUG: groupby.rank for dt64tz, period dtypes (#38187)

doc/source/whatsnew/v1.2.0.rst

@@ -768,6 +768,7 @@ Groupby/resample/rolling
 - Bug in :meth:`DataFrame.groupby` dropped ``nan`` groups from result with ``dropna=False`` when grouping over a single column (:issue:`35646`, :issue:`35542`)
 - Bug in :meth:`.DataFrameGroupBy.head`, :meth:`.DataFrameGroupBy.tail`, :meth:`SeriesGroupBy.head`, and :meth:`SeriesGroupBy.tail` would raise when used with ``axis=1`` (:issue:`9772`)
 - Bug in :meth:`.DataFrameGroupBy.transform` would raise when used with ``axis=1`` and a transformation kernel (e.g. "shift") (:issue:`36308`)
+- Bug in :meth:`DataFrameGroupBy.rank` with ``datetime64tz`` or period dtype incorrectly casting results to those dtypes instead of returning ``float64`` dtype (:issue:`38187`)
 
 Reshaping
 ^^^^^^^^^

pandas/core/groupby/groupby.py

@@ -995,7 +995,7 @@ def _cython_transform(
 
             try:
                 result, _ = self.grouper._cython_operation(
-                    "transform", obj.values, how, axis, **kwargs
+                    "transform", obj._values, how, axis, **kwargs
                 )
             except NotImplementedError:
                 continue

pandas/core/groupby/ops.py

@@ -491,8 +491,11 @@ def _ea_wrap_cython_operation(
             res_values, names = self._cython_operation(
                 kind, values, how, axis, min_count, **kwargs
             )
+            if how in ["rank"]:
+                # preserve float64 dtype
+                return res_values, names
+
             res_values = res_values.astype("i8", copy=False)
-            # FIXME: this is wrong for rank, but not tested.
             result = type(orig_values)._simple_new(res_values, dtype=orig_values.dtype)
             return result, names
 

pandas/tests/groupby/test_rank.py

@@ -42,15 +42,40 @@ def test_rank_apply():
 @pytest.mark.parametrize(
     "vals",
     [
-        [2, 2, 8, 2, 6],
+        np.array([2, 2, 8, 2, 6], dtype=dtype)
+        for dtype in ["i8", "i4", "i2", "i1", "u8", "u4", "u2", "u1", "f8", "f4", "f2"]
+    ]
+    + [
         [
             pd.Timestamp("2018-01-02"),
             pd.Timestamp("2018-01-02"),
             pd.Timestamp("2018-01-08"),
             pd.Timestamp("2018-01-02"),
             pd.Timestamp("2018-01-06"),
         ],
+        [
+            pd.Timestamp("2018-01-02", tz="US/Pacific"),
+            pd.Timestamp("2018-01-02", tz="US/Pacific"),
+            pd.Timestamp("2018-01-08", tz="US/Pacific"),
+            pd.Timestamp("2018-01-02", tz="US/Pacific"),
+            pd.Timestamp("2018-01-06", tz="US/Pacific"),
+        ],
+        [
+            pd.Timestamp("2018-01-02") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-02") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-08") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-02") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-06") - pd.Timestamp(0),
+        ],
+        [
+            pd.Timestamp("2018-01-02").to_period("D"),
+            pd.Timestamp("2018-01-02").to_period("D"),
+            pd.Timestamp("2018-01-08").to_period("D"),
+            pd.Timestamp("2018-01-02").to_period("D"),
+            pd.Timestamp("2018-01-06").to_period("D"),
+        ],
     ],
+    ids=lambda x: type(x[0]),
 )
 @pytest.mark.parametrize(
     "ties_method,ascending,pct,exp",
@@ -79,7 +104,12 @@ def test_rank_apply():
 )
 def test_rank_args(grps, vals, ties_method, ascending, pct, exp):
     key = np.repeat(grps, len(vals))
-    vals = vals * len(grps)
+
+    orig_vals = vals
+    vals = list(vals) * len(grps)
+    if isinstance(orig_vals, np.ndarray):
+        vals = np.array(vals, dtype=orig_vals.dtype)
+
     df = DataFrame({"key": key, "val": vals})
     result = df.groupby("key").rank(method=ties_method, ascending=ascending, pct=pct)
 
@@ -142,7 +172,10 @@ def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
 @pytest.mark.parametrize(
     "vals",
     [
-        [2, 2, np.nan, 8, 2, 6, np.nan, np.nan],
+        np.array([2, 2, np.nan, 8, 2, 6, np.nan, np.nan], dtype=dtype)
+        for dtype in ["f8", "f4", "f2"]
+    ]
+    + [
         [
             pd.Timestamp("2018-01-02"),
             pd.Timestamp("2018-01-02"),
@@ -153,7 +186,38 @@ def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
             np.nan,
             np.nan,
         ],
+        [
+            pd.Timestamp("2018-01-02", tz="US/Pacific"),
+            pd.Timestamp("2018-01-02", tz="US/Pacific"),
+            np.nan,
+            pd.Timestamp("2018-01-08", tz="US/Pacific"),
+            pd.Timestamp("2018-01-02", tz="US/Pacific"),
+            pd.Timestamp("2018-01-06", tz="US/Pacific"),
+            np.nan,
+            np.nan,
+        ],
+        [
+            pd.Timestamp("2018-01-02") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-02") - pd.Timestamp(0),
+            np.nan,
+            pd.Timestamp("2018-01-08") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-02") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-06") - pd.Timestamp(0),
+            np.nan,
+            np.nan,
+        ],
+        [
+            pd.Timestamp("2018-01-02").to_period("D"),
+            pd.Timestamp("2018-01-02").to_period("D"),
+            np.nan,
+            pd.Timestamp("2018-01-08").to_period("D"),
+            pd.Timestamp("2018-01-02").to_period("D"),
+            pd.Timestamp("2018-01-06").to_period("D"),
+            np.nan,
+            np.nan,
+        ],
     ],
+    ids=lambda x: type(x[0]),
 )
 @pytest.mark.parametrize(
     "ties_method,ascending,na_option,pct,exp",
@@ -346,7 +410,12 @@ def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
 )
 def test_rank_args_missing(grps, vals, ties_method, ascending, na_option, pct, exp):
     key = np.repeat(grps, len(vals))
-    vals = vals * len(grps)
+
+    orig_vals = vals
+    vals = list(vals) * len(grps)
+    if isinstance(orig_vals, np.ndarray):
+        vals = np.array(vals, dtype=orig_vals.dtype)
+
     df = DataFrame({"key": key, "val": vals})
     result = df.groupby("key").rank(
         method=ties_method, ascending=ascending, na_option=na_option, pct=pct