the order of keys is stable now (the same as in the input), fix test cases

Author: realead

pandas/tests/arrays/boolean/test_function.py

@@ -77,18 +77,18 @@ def test_ufunc_reduce_raises(values):
 def test_value_counts_na():
     arr = pd.array([True, False, pd.NA], dtype="boolean")
     result = arr.value_counts(dropna=False)
-    expected = pd.Series([1, 1, 1], index=[False, True, pd.NA], dtype="Int64")
+    expected = pd.Series([1, 1, 1], index=[True, False, pd.NA], dtype="Int64")
     tm.assert_series_equal(result, expected)
 
     result = arr.value_counts(dropna=True)
-    expected = pd.Series([1, 1], index=[False, True], dtype="Int64")
+    expected = pd.Series([1, 1], index=[True, False], dtype="Int64")
     tm.assert_series_equal(result, expected)
 
 
 def test_value_counts_with_normalize():
     s = pd.Series([True, False, pd.NA], dtype="boolean")
     result = s.value_counts(normalize=True)
-    expected = pd.Series([1, 1], index=[False, True], dtype="Float64") / 2
+    expected = pd.Series([1, 1], index=[True, False], dtype="Float64") / 2
     tm.assert_series_equal(result, expected)
 
 

pandas/tests/arrays/test_datetimes.py

@@ -281,7 +281,7 @@ def test_value_counts_preserves_tz(self):
 
         arr[-2] = pd.NaT
         result = arr.value_counts()
-        expected = pd.Series([1, 4, 2], index=[pd.NaT, dti[0], dti[1]])
+        expected = pd.Series([4, 2, 1], index=[dti[0], dti[1], pd.NaT])
         tm.assert_series_equal(result, expected)
 
     @pytest.mark.parametrize("method", ["pad", "backfill"])

pandas/tests/series/methods/test_value_counts.py

@@ -195,7 +195,7 @@ def test_value_counts_categorical_with_nan(self):
             (
                 Series(range(3), index=[True, False, np.nan]).index,
                 False,
-                Series([1, 1, 1], index=[pd.NA, False, True]),
+                Series([1, 1, 1], index=[np.nan, True, False]),
             ),
         ],
     )

pandas/tests/test_algos.py

@@ -6,7 +6,6 @@
 import pytest
 
 from pandas._libs import algos as libalgos, hashtable as ht
-from pandas.compat import IS64
 from pandas.compat.numpy import np_array_datetime64_compat
 import pandas.util._test_decorators as td
 
@@ -1273,12 +1272,10 @@ def test_value_counts_uint64(self):
         tm.assert_series_equal(result, expected)
 
         arr = np.array([-1, 2 ** 63], dtype=object)
-        expected = Series([1, 1], index=[2 ** 63, -1])
+        expected = Series([1, 1], index=[-1, 2 ** 63])
         result = algos.value_counts(arr)
 
-        # 32-bit linux has a different ordering
-        if IS64:
-            tm.assert_series_equal(result, expected)
+        tm.assert_series_equal(result, expected)
 
 
 class TestDuplicated: