Filter ufunc related warnings in pytests (#14652)

This PR ignores ufunc runtime warnings that show up in eval API and setitem deprecation warnings.

On pandas_2.0_feature_branch:

= 260 failed, 101179 passed, 2091 skipped, 954 xfailed, 312 xpassed in 1104.58s (0:18:24) =
This PR:

= 211 failed, 101228 passed, 2091 skipped, 954 xfailed, 312 xpassed in 1095.49s (0:18:15) =

Author: galipremsagar

python/cudf/cudf/tests/test_array_ufunc.py

@@ -10,8 +10,12 @@
 import pytest
 
 import cudf
-from cudf.core._compat import PANDAS_GE_150, PANDAS_GE_200
-from cudf.testing._utils import assert_eq, set_random_null_mask_inplace
+from cudf.core._compat import PANDAS_GE_150, PANDAS_GE_200, PANDAS_GE_210
+from cudf.testing._utils import (
+    assert_eq,
+    set_random_null_mask_inplace,
+    expect_warning_if,
+)
 
 _UFUNCS = [
     obj
@@ -47,6 +51,21 @@ def _hide_ufunc_warnings(ufunc):
                 category=RuntimeWarning,
             )
             yield
+    elif name in {
+        "bitwise_and",
+        "bitwise_or",
+        "bitwise_xor",
+    }:
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                "Operation between non boolean Series with different "
+                "indexes will no longer return a boolean result in "
+                "a future version. Cast both Series to object type "
+                "to maintain the prior behavior.",
+                category=FutureWarning,
+            )
+            yield
     else:
         yield
 
@@ -217,7 +236,27 @@ def test_ufunc_series(request, ufunc, has_nulls, indexed):
             assert_eq(g, e, check_exact=False)
     else:
         if has_nulls:
-            expect[mask] = np.nan
+            with expect_warning_if(
+                PANDAS_GE_210
+                and fname
+                in (
+                    "isfinite",
+                    "isinf",
+                    "isnan",
+                    "logical_and",
+                    "logical_not",
+                    "logical_or",
+                    "logical_xor",
+                    "signbit",
+                    "equal",
+                    "greater",
+                    "greater_equal",
+                    "less",
+                    "less_equal",
+                    "not_equal",
+                )
+            ):
+                expect[mask] = np.nan
             assert_eq(got, expect, check_exact=False)
 
 
@@ -443,5 +482,25 @@ def test_ufunc_dataframe(request, ufunc, has_nulls, indexed):
             assert_eq(g, e, check_exact=False)
     else:
         if has_nulls:
-            expect[mask] = np.nan
+            with expect_warning_if(
+                PANDAS_GE_210
+                and fname
+                in (
+                    "isfinite",
+                    "isinf",
+                    "isnan",
+                    "logical_and",
+                    "logical_not",
+                    "logical_or",
+                    "logical_xor",
+                    "signbit",
+                    "equal",
+                    "greater",
+                    "greater_equal",
+                    "less",
+                    "less_equal",
+                    "not_equal",
+                )
+            ):
+                expect[mask] = np.nan
         assert_eq(got, expect, check_exact=False)

python/cudf/cudf/tests/test_dataframe.py

@@ -9,6 +9,8 @@
 import re
 import string
 import textwrap
+import warnings
+from contextlib import contextmanager
 from collections import OrderedDict, defaultdict, namedtuple
 from copy import copy
 
@@ -65,6 +67,32 @@
     pytest_xfail = pytest.mark.skipif
 
 
+@contextmanager
+def _hide_ufunc_warnings(eval_str):
+    # pandas raises warnings for some inputs to the following ufuncs:
+    if any(
+        x in eval_str
+        for x in {
+            "arctanh",
+            "log",
+        }
+    ):
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                "invalid value encountered in",
+                category=RuntimeWarning,
+            )
+            warnings.filterwarnings(
+                "ignore",
+                "divide by zero encountered in",
+                category=RuntimeWarning,
+            )
+            yield
+    else:
+        yield
+
+
 def test_init_via_list_of_tuples():
     data = [
         (5, "cats", "jump", np.nan),
@@ -10071,7 +10099,8 @@ def df_eval(request):
 )
 def test_dataframe_eval(df_eval, expr, dtype):
     df_eval = df_eval.astype(dtype)
-    expect = df_eval.to_pandas().eval(expr)
+    with _hide_ufunc_warnings(expr):
+        expect = df_eval.to_pandas().eval(expr)
     got = df_eval.eval(expr)
     # In the specific case where the evaluated expression is a unary function
     # of a single column with no nesting, pandas will retain the name. This
@@ -10081,7 +10110,8 @@ def test_dataframe_eval(df_eval, expr, dtype):
     # Test inplace
     if re.search("[^=><]=[^=]", expr) is not None:
         pdf_eval = df_eval.to_pandas()
-        pdf_eval.eval(expr, inplace=True)
+        with _hide_ufunc_warnings(expr):
+            pdf_eval.eval(expr, inplace=True)
         df_eval.eval(expr, inplace=True)
         assert_eq(pdf_eval, df_eval)