Fix DataFrame/Series.rank for int and null data in mode.pandas_compat…

…ible (#17954)

closes #17948

Authors:
  - Matthew Roeschke (https://github.com/mroeschke)

Approvers:
  - Vyas Ramasubramani (https://github.com/vyasr)

URL: #17954

python/cudf/cudf/core/indexed_frame.py

@@ -5027,7 +5027,7 @@ def repeat(self, repeats, axis=None):
 
     def astype(
         self,
-        dtype: dict[Any, Dtype],
+        dtype: Dtype | dict[Any, Dtype],
         copy: bool = False,
         errors: Literal["raise", "ignore"] = "raise",
     ) -> Self:
@@ -6340,13 +6340,13 @@ def _preprocess_subset(self, subset) -> set[abc.Hashable]:
     @_performance_tracking
     def rank(
         self,
-        axis=0,
-        method="average",
-        numeric_only=False,
-        na_option="keep",
-        ascending=True,
-        pct=False,
-    ):
+        axis: Literal[0, "index"] = 0,
+        method: Literal["average", "min", "max", "first", "dense"] = "average",
+        numeric_only: bool = False,
+        na_option: Literal["keep", "top", "bottom"] = "keep",
+        ascending: bool = True,
+        pct: bool = False,
+    ) -> Self:
         """
         Compute numerical data ranks (1 through n) along axis.
 
@@ -6404,7 +6404,7 @@ def rank(
         if numeric_only:
             if isinstance(
                 source, cudf.Series
-            ) and not _is_non_decimal_numeric_dtype(self.dtype):
+            ) and not _is_non_decimal_numeric_dtype(self.dtype):  # type: ignore[attr-defined]
                 raise TypeError(
                     "Series.rank does not allow numeric_only=True with "
                     "non-numeric dtype."
@@ -6416,7 +6416,7 @@ def rank(
             )
             source = self._get_columns_by_label(numeric_cols)
             if source.empty:
-                return source.astype("float64")
+                return source.astype(np.dtype(np.float64))
             elif source._num_columns != num_cols:
                 dropped_cols = True
 
@@ -6449,6 +6449,8 @@ def rank(
             else plc.types.NullPolicy.INCLUDE
         )
 
+        if cudf.get_option("mode.pandas_compatible"):
+            source = source.nans_to_nulls()
         with acquire_spill_lock():
             result_columns = [
                 libcudf.column.Column.from_pylibcudf(

python/cudf/cudf/tests/test_rank.py

@@ -1,11 +1,12 @@
-# Copyright (c) 2020-2024, NVIDIA CORPORATION.
+# Copyright (c) 2020-2025, NVIDIA CORPORATION.
 
 from itertools import chain, combinations_with_replacement, product
 
 import numpy as np
 import pandas as pd
 import pytest
 
+import cudf
 from cudf import DataFrame
 from cudf.testing import assert_eq
 from cudf.testing._utils import assert_exceptions_equal
@@ -151,3 +152,14 @@ def test_series_rank_combinations(elem, dtype):
     ranked_ps = df["a"].rank(method="first")
     # Check
     assert_eq(ranked_ps, ranked_gs)
+
+
+@pytest.mark.parametrize("klass", ["Series", "DataFrame"])
+def test_int_nan_pandas_compatible(klass):
+    data = [3, 6, 1, 1, None, 6]
+    pd_obj = getattr(pd, klass)(data)
+    cudf_obj = getattr(cudf, klass)(data)
+    with cudf.option_context("mode.pandas_compatible", True):
+        result = cudf_obj.rank()
+    expected = pd_obj.rank()
+    assert_eq(result, expected)