TST/CI: xfail 32-bit tests pandas-dev#36579, pandas-dev#32709

Author: fangchenli

pandas/tests/arrays/floating/test_function.py

@@ -73,6 +73,7 @@ def test_ufunc_reduce_raises(values):
         np.add.reduce(a)
 
 
+@pytest.mark.xfail(not IS64, reason="GH 36579: fail on 32-bit system")
 @pytest.mark.parametrize(
     "pandasmethname, kwargs",
     [
@@ -84,15 +85,10 @@ def test_ufunc_reduce_raises(values):
     ],
 )
 def test_stat_method(pandasmethname, kwargs):
-    s = pd.Series(
-        data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, np.nan, np.nan],
-        dtype="Float64" if IS64 else "Float32",
-    )
+    s = pd.Series(data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, np.nan, np.nan], dtype="Float64")
     pandasmeth = getattr(s, pandasmethname)
     result = pandasmeth(**kwargs)
-    s2 = pd.Series(
-        data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6], dtype="float64" if IS64 else "float32"
-    )
+    s2 = pd.Series(data=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6], dtype="float64")
     pandasmeth = getattr(s2, pandasmethname)
     expected = pandasmeth(**kwargs)
     assert expected == result

pandas/tests/io/formats/test_info.py

@@ -7,7 +7,7 @@
 import numpy as np
 import pytest
 
-from pandas.compat import PYPY
+from pandas.compat import IS64, PYPY
 
 from pandas import (
     CategoricalIndex,
@@ -475,6 +475,7 @@ def test_info_categorical():
     df.info(buf=buf)
 
 
+@pytest.mark.xfail(not IS64, reason="GH 36579: fail on 32-bit system")
 def test_info_int_columns():
     # GH#37245
     df = DataFrame({1: [1, 2], 2: [2, 3]}, index=["A", "B"])

pandas/tests/reshape/test_pivot.py

@@ -4,6 +4,8 @@
 import numpy as np
 import pytest
 
+from pandas.compat import IS64
+
 import pandas as pd
 from pandas import (
     Categorical,
@@ -2104,6 +2106,7 @@ def test_pivot_duplicates(self):
         with pytest.raises(ValueError, match="duplicate entries"):
             data.pivot("a", "b", "c")
 
+    @pytest.mark.xfail(not IS64, reason="GH 36579: fail on 32-bit system")
     def test_pivot_empty(self):
         df = DataFrame(columns=["a", "b", "c"])
         result = df.pivot("a", "b", "c")