CLN/TST: Remove redundant/unnecessary windows/moments tests

ci/run_tests.sh

@@ -22,9 +22,7 @@ fi
 PYTEST_CMD="${XVFB}pytest -m \"$PATTERN\" -n $PYTEST_WORKERS --dist=loadfile $TEST_ARGS $COVERAGE $PYTEST_TARGET"
 
 if [[ $(uname) != "Linux"  && $(uname) != "Darwin" ]]; then
-    # GH#37455 windows py38 build appears to be running out of memory
-    #  skip collection of window tests
-    PYTEST_CMD="$PYTEST_CMD --ignore=pandas/tests/window/moments --ignore=pandas/tests/plotting/"
+    PYTEST_CMD="$PYTEST_CMD --ignore=pandas/tests/plotting/"
 fi
 
 echo $PYTEST_CMD

pandas/tests/window/moments/conftest.py

@@ -1,3 +1,5 @@
+import itertools
+
 import numpy as np
 import pytest
 
@@ -12,133 +14,20 @@
 def _create_consistency_data():
     def create_series():
         return [
-            Series(dtype=object),
-            Series([np.nan]),
-            Series([np.nan, np.nan]),
-            Series([3.0]),
-            Series([np.nan, 3.0]),
-            Series([3.0, np.nan]),
-            Series([1.0, 3.0]),
-            Series([2.0, 2.0]),
-            Series([3.0, 1.0]),
-            Series(
-                [5.0, 5.0, 5.0, 5.0, np.nan, np.nan, np.nan, 5.0, 5.0, np.nan, np.nan]
-            ),
-            Series(
-                [
-                    np.nan,
-                    5.0,
-                    5.0,
-                    5.0,
-                    np.nan,
-                    np.nan,
-                    np.nan,
-                    5.0,
-                    5.0,
-                    np.nan,
-                    np.nan,
-                ]
-            ),
-            Series(
-                [
-                    np.nan,
-                    np.nan,
-                    5.0,
-                    5.0,
-                    np.nan,
-                    np.nan,
-                    np.nan,
-                    5.0,
-                    5.0,
-                    np.nan,
-                    np.nan,
-                ]
-            ),
-            Series(
-                [
-                    np.nan,
-                    3.0,
-                    np.nan,
-                    3.0,
-                    4.0,
-                    5.0,
-                    6.0,
-                    np.nan,
-                    np.nan,
-                    7.0,
-                    12.0,
-                    13.0,
-                    14.0,
-                    15.0,
-                ]
-            ),
-            Series(
-                [
-                    np.nan,
-                    5.0,
-                    np.nan,
-                    2.0,
-                    4.0,
-                    0.0,
-                    9.0,
-                    np.nan,
-                    np.nan,
-                    3.0,
-                    12.0,
-                    13.0,
-                    14.0,
-                    15.0,
-                ]
-            ),
-            Series(
-                [
-                    2.0,
-                    3.0,
-                    np.nan,
-                    3.0,
-                    4.0,
-                    5.0,
-                    6.0,
-                    np.nan,
-                    np.nan,
-                    7.0,
-                    12.0,
-                    13.0,
-                    14.0,
-                    15.0,
-                ]
-            ),
-            Series(
-                [
-                    2.0,
-                    5.0,
-                    np.nan,
-                    2.0,
-                    4.0,
-                    0.0,
-                    9.0,
-                    np.nan,
-                    np.nan,
-                    3.0,
-                    12.0,
-                    13.0,
-                    14.0,
-                    15.0,
-                ]
-            ),
-            Series(range(10)),
-            Series(range(20, 0, -2)),
+            Series(dtype=np.float64, name="a"),
+            Series([np.nan] * 5),
+            Series([1.0] * 5),
+            Series(range(5, 0, -1)),
+            Series(range(5)),
+            Series([np.nan, 1.0, np.nan, 1.0, 1.0]),
+            Series([np.nan, 1.0, np.nan, 2.0, 3.0]),
+            Series([np.nan, 1.0, np.nan, 3.0, 2.0]),
         ]
 
     def create_dataframes():
         return [
-            DataFrame(),
-            DataFrame(columns=["a"]),
             DataFrame(columns=["a", "a"]),
-            DataFrame(columns=["a", "b"]),
-            DataFrame(np.arange(10).reshape((5, 2))),
-            DataFrame(np.arange(25).reshape((5, 5))),
-            DataFrame(np.arange(25).reshape((5, 5)), columns=["a", "b", 99, "d", "d"]),
+            DataFrame(np.arange(15).reshape((5, 3)), columns=["a", "a", 99]),
         ] + [DataFrame(s) for s in create_series()]
 
     def is_constant(x):
@@ -148,13 +37,34 @@ def is_constant(x):
     def no_nans(x):
         return x.notna().all().all()
 
-    # data is a tuple(object, is_constant, no_nans)
-    data = create_series() + create_dataframes()
-
-    return [(x, is_constant(x), no_nans(x)) for x in data]
+    return [
+        (x, is_constant(x), no_nans(x))
+        for x in itertools.chain(create_dataframes(), create_dataframes())
+    ]
 
 
 @pytest.fixture(params=_create_consistency_data())
 def consistency_data(request):
-    """Create consistency data"""
+    """
+    Test:
+        - Empty Series / DataFrame
+        - All NaN
+        - All consistent value
+        - Monotonically decreasing
+        - Monotonically increasing
+        - Monotonically consistent with NaNs
+        - Monotonically increasing with NaNs
+        - Monotonically decreasing with NaNs
+    """
+    return request.param
+
+
+@pytest.fixture(params=[(1, 0), (5, 1)])
+def rolling_consistency_cases(request):
+    """window, min_periods"""
+    return request.param
+
+
+@pytest.fixture(params=[0, 2])
+def min_periods(request):
     return request.param

pandas/tests/window/moments/test_moments_consistency_ewm.py

@@ -18,7 +18,7 @@ def create_mock_weights(obj, com, adjust, ignore_na):
                 create_mock_series_weights(
                     obj.iloc[:, i], com=com, adjust=adjust, ignore_na=ignore_na
                 )
-                for i, _ in enumerate(obj.columns)
+                for i in range(len(obj.columns))
             ],
             axis=1,
         )
@@ -58,7 +58,6 @@ def create_mock_series_weights(s, com, adjust, ignore_na):
     return w
 
 
-@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
 def test_ewm_consistency_mean(consistency_data, adjust, ignore_na, min_periods):
     x, is_constant, no_nans = consistency_data
     com = 3.0
@@ -76,7 +75,6 @@ def test_ewm_consistency_mean(consistency_data, adjust, ignore_na, min_periods):
     tm.assert_equal(result, expected.astype("float64"))
 
 
-@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
 def test_ewm_consistency_consistent(consistency_data, adjust, ignore_na, min_periods):
     x, is_constant, no_nans = consistency_data
     com = 3.0
@@ -102,7 +100,6 @@ def test_ewm_consistency_consistent(consistency_data, adjust, ignore_na, min_per
         tm.assert_equal(corr_x_x, expected)
 
 
-@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
 def test_ewm_consistency_var_debiasing_factors(
     consistency_data, adjust, ignore_na, min_periods
 ):
@@ -128,7 +125,6 @@ def test_ewm_consistency_var_debiasing_factors(
     tm.assert_equal(var_unbiased_x, var_biased_x * var_debiasing_factors_x)
 
 
-@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
 @pytest.mark.parametrize("bias", [True, False])
 def test_moments_consistency_var(
     consistency_data, adjust, ignore_na, min_periods, bias
@@ -154,7 +150,6 @@ def test_moments_consistency_var(
         tm.assert_equal(var_x, mean_x2 - (mean_x * mean_x))
 
 
-@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
 @pytest.mark.parametrize("bias", [True, False])
 def test_moments_consistency_var_constant(
     consistency_data, adjust, ignore_na, min_periods, bias
@@ -176,34 +171,23 @@ def test_moments_consistency_var_constant(
         tm.assert_equal(var_x, expected)
 
 
-@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
 @pytest.mark.parametrize("bias", [True, False])
 def test_ewm_consistency_std(consistency_data, adjust, ignore_na, min_periods, bias):
     x, is_constant, no_nans = consistency_data
     com = 3.0
     var_x = x.ewm(
         com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
     ).var(bias=bias)
+    assert not (var_x < 0).any().any()
+
     std_x = x.ewm(
         com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
     ).std(bias=bias)
-    assert not (var_x < 0).any().any()
     assert not (std_x < 0).any().any()
 
     # check that var(x) == std(x)^2
     tm.assert_equal(var_x, std_x * std_x)
 
-
-@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
-@pytest.mark.parametrize("bias", [True, False])
-def test_ewm_consistency_cov(consistency_data, adjust, ignore_na, min_periods, bias):
-    x, is_constant, no_nans = consistency_data
-    com = 3.0
-    var_x = x.ewm(
-        com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
-    ).var(bias=bias)
-    assert not (var_x < 0).any().any()
-
     cov_x_x = x.ewm(
         com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
     ).cov(x, bias=bias)
@@ -213,7 +197,6 @@ def test_ewm_consistency_cov(consistency_data, adjust, ignore_na, min_periods, b
     tm.assert_equal(var_x, cov_x_x)
 
 
-@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
 @pytest.mark.parametrize("bias", [True, False])
 def test_ewm_consistency_series_cov_corr(
     consistency_data, adjust, ignore_na, min_periods, bias