REF: de-duplicate extension tests (#54340)

* REF: unnecessary override in Period test, de-duplicate in arrow tess

* REF: de-duplicate extension tests

* typo fixup

* De-duplicate

* xfail for json

* fix on pyarrow-less builds

Author: jbrockmendel

pandas/tests/extension/base/methods.py

@@ -65,7 +65,13 @@ def test_value_counts_with_normalize(self, data):
         else:
             expected = pd.Series(0.0, index=result.index, name="proportion")
             expected[result > 0] = 1 / len(values)
-        if na_value_for_dtype(data.dtype) is pd.NA:
+
+        if getattr(data.dtype, "storage", "") == "pyarrow" or isinstance(
+            data.dtype, pd.ArrowDtype
+        ):
+            # TODO: avoid special-casing
+            expected = expected.astype("double[pyarrow]")
+        elif na_value_for_dtype(data.dtype) is pd.NA:
             # TODO(GH#44692): avoid special-casing
             expected = expected.astype("Float64")
 
@@ -678,3 +684,7 @@ def test_equals(self, data, na_value, as_series, box):
         # other types
         assert data.equals(None) is False
         assert data[[0]].equals(data[0]) is False
+
+    def test_equals_same_data_different_object(self, data):
+        # https://github.com/pandas-dev/pandas/issues/34660
+        assert pd.Series(data).equals(pd.Series(data))

pandas/tests/extension/json/test_json.py

@@ -241,6 +241,14 @@ def test_equals(self, data, na_value, as_series):
     def test_fillna_copy_frame(self, data_missing):
         super().test_fillna_copy_frame(data_missing)
 
+    def test_equals_same_data_different_object(
+        self, data, using_copy_on_write, request
+    ):
+        if using_copy_on_write:
+            mark = pytest.mark.xfail(reason="Fails with CoW")
+            request.node.add_marker(mark)
+        super().test_equals_same_data_different_object(data)
+
 
 class TestCasting(BaseJSON, base.BaseCastingTests):
     @pytest.mark.xfail(reason="failing on np.array(self, dtype=str)")

pandas/tests/extension/test_arrow.py

@@ -65,6 +65,18 @@
 from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
 
 
+def _require_timezone_database(request):
+    if is_platform_windows() and is_ci_environment():
+        mark = pytest.mark.xfail(
+            raises=pa.ArrowInvalid,
+            reason=(
+                "TODO: Set ARROW_TIMEZONE_DATABASE environment variable "
+                "on CI to path to the tzdata for pyarrow."
+            ),
+        )
+        request.node.add_marker(mark)
+
+
 @pytest.fixture(params=tm.ALL_PYARROW_DTYPES, ids=str)
 def dtype(request):
     return ArrowDtype(pyarrow_dtype=request.param)
@@ -314,16 +326,8 @@ def test_from_sequence_of_strings_pa_array(self, data, request):
                 )
             )
         elif pa.types.is_timestamp(pa_dtype) and pa_dtype.tz is not None:
-            if is_platform_windows() and is_ci_environment():
-                request.node.add_marker(
-                    pytest.mark.xfail(
-                        raises=pa.ArrowInvalid,
-                        reason=(
-                            "TODO: Set ARROW_TIMEZONE_DATABASE environment variable "
-                            "on CI to path to the tzdata for pyarrow."
-                        ),
-                    )
-                )
+            _require_timezone_database(request)
+
         pa_array = data._pa_array.cast(pa.string())
         result = type(data)._from_sequence_of_strings(pa_array, dtype=data.dtype)
         tm.assert_extension_array_equal(result, data)
@@ -795,20 +799,6 @@ def test_value_counts_returns_pyarrow_int64(self, data):
         result = data.value_counts()
         assert result.dtype == ArrowDtype(pa.int64())
 
-    def test_value_counts_with_normalize(self, data, request):
-        data = data[:10].unique()
-        values = np.array(data[~data.isna()])
-        ser = pd.Series(data, dtype=data.dtype)
-
-        result = ser.value_counts(normalize=True).sort_index()
-
-        expected = pd.Series(
-            [1 / len(values)] * len(values), index=result.index, name="proportion"
-        )
-        expected = expected.astype("double[pyarrow]")
-
-        self.assert_series_equal(result, expected)
-
     def test_argmin_argmax(
         self, data_for_sorting, data_missing_for_sorting, na_value, request
     ):
@@ -865,10 +855,6 @@ def test_combine_add(self, data_repeated, request):
         else:
             super().test_combine_add(data_repeated)
 
-    def test_basic_equals(self, data):
-        # https://github.com/pandas-dev/pandas/issues/34660
-        assert pd.Series(data).equals(pd.Series(data))
-
 
 class TestBaseArithmeticOps(base.BaseArithmeticOpsTests):
     divmod_exc = NotImplementedError
@@ -2563,33 +2549,17 @@ def test_dt_isocalendar():
 )
 def test_dt_day_month_name(method, exp, request):
     # GH 52388
-    if is_platform_windows() and is_ci_environment():
-        request.node.add_marker(
-            pytest.mark.xfail(
-                raises=pa.ArrowInvalid,
-                reason=(
-                    "TODO: Set ARROW_TIMEZONE_DATABASE environment variable "
-                    "on CI to path to the tzdata for pyarrow."
-                ),
-            )
-        )
+    _require_timezone_database(request)
+
     ser = pd.Series([datetime(2023, 1, 1), None], dtype=ArrowDtype(pa.timestamp("ms")))
     result = getattr(ser.dt, method)()
     expected = pd.Series([exp, None], dtype=ArrowDtype(pa.string()))
     tm.assert_series_equal(result, expected)
 
 
 def test_dt_strftime(request):
-    if is_platform_windows() and is_ci_environment():
-        request.node.add_marker(
-            pytest.mark.xfail(
-                raises=pa.ArrowInvalid,
-                reason=(
-                    "TODO: Set ARROW_TIMEZONE_DATABASE environment variable "
-                    "on CI to path to the tzdata for pyarrow."
-                ),
-            )
-        )
+    _require_timezone_database(request)
+
     ser = pd.Series(
         [datetime(year=2023, month=1, day=2, hour=3), None],
         dtype=ArrowDtype(pa.timestamp("ns")),
@@ -2700,16 +2670,8 @@ def test_dt_tz_localize_none():
 
 @pytest.mark.parametrize("unit", ["us", "ns"])
 def test_dt_tz_localize(unit, request):
-    if is_platform_windows() and is_ci_environment():
-        request.node.add_marker(
-            pytest.mark.xfail(
-                raises=pa.ArrowInvalid,
-                reason=(
-                    "TODO: Set ARROW_TIMEZONE_DATABASE environment variable "
-                    "on CI to path to the tzdata for pyarrow."
-                ),
-            )
-        )
+    _require_timezone_database(request)
+
     ser = pd.Series(
         [datetime(year=2023, month=1, day=2, hour=3), None],
         dtype=ArrowDtype(pa.timestamp(unit)),
@@ -2731,16 +2693,8 @@ def test_dt_tz_localize(unit, request):
     ],
 )
 def test_dt_tz_localize_nonexistent(nonexistent, exp_date, request):
-    if is_platform_windows() and is_ci_environment():
-        request.node.add_marker(
-            pytest.mark.xfail(
-                raises=pa.ArrowInvalid,
-                reason=(
-                    "TODO: Set ARROW_TIMEZONE_DATABASE environment variable "
-                    "on CI to path to the tzdata for pyarrow."
-                ),
-            )
-        )
+    _require_timezone_database(request)
+
     ser = pd.Series(
         [datetime(year=2023, month=3, day=12, hour=2, minute=30), None],
         dtype=ArrowDtype(pa.timestamp("ns")),

pandas/tests/extension/test_period.py

@@ -198,9 +198,7 @@ class TestPrinting(BasePeriodTests, base.BasePrintingTests):
 
 
 class TestParsing(BasePeriodTests, base.BaseParsingTests):
-    @pytest.mark.parametrize("engine", ["c", "python"])
-    def test_EA_types(self, engine, data):
-        super().test_EA_types(engine, data)
+    pass
 
 
 class Test2DCompat(BasePeriodTests, base.NDArrayBacked2DTests):

pandas/tests/extension/test_sparse.py

@@ -319,9 +319,6 @@ def test_where_series(self, data, na_value):
         expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype))
         self.assert_series_equal(result, expected)
 
-    def test_combine_first(self, data, request):
-        super().test_combine_first(data)
-
     def test_searchsorted(self, data_for_sorting, as_series):
         with tm.assert_produces_warning(PerformanceWarning, check_stacklevel=False):
             super().test_searchsorted(data_for_sorting, as_series)

pandas/tests/extension/test_string.py

@@ -182,22 +182,7 @@ def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna):
 
 
 class TestMethods(base.BaseMethodsTests):
-    def test_value_counts_with_normalize(self, data):
-        data = data[:10].unique()
-        values = np.array(data[~data.isna()])
-        ser = pd.Series(data, dtype=data.dtype)
-
-        result = ser.value_counts(normalize=True).sort_index()
-
-        expected = pd.Series(
-            [1 / len(values)] * len(values), index=result.index, name="proportion"
-        )
-        if getattr(data.dtype, "storage", "") == "pyarrow":
-            expected = expected.astype("double[pyarrow]")
-        else:
-            expected = expected.astype("Float64")
-
-        self.assert_series_equal(result, expected)
+    pass
 
 
 class TestCasting(base.BaseCastingTests):
@@ -226,20 +211,6 @@ class TestPrinting(base.BasePrintingTests):
 
 
 class TestGroupBy(base.BaseGroupbyTests):
-    @pytest.mark.parametrize("as_index", [True, False])
-    def test_groupby_extension_agg(self, as_index, data_for_grouping):
-        df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
-        result = df.groupby("B", as_index=as_index).A.mean()
-        _, uniques = pd.factorize(data_for_grouping, sort=True)
-
-        if as_index:
-            index = pd.Index(uniques, name="B")
-            expected = pd.Series([3.0, 1.0, 4.0], index=index, name="A")
-            self.assert_series_equal(result, expected)
-        else:
-            expected = pd.DataFrame({"B": uniques, "A": [3.0, 1.0, 4.0]})
-            self.assert_frame_equal(result, expected)
-
     @pytest.mark.filterwarnings("ignore:Falling back:pandas.errors.PerformanceWarning")
     def test_groupby_extension_apply(self, data_for_grouping, groupby_apply_op):
         super().test_groupby_extension_apply(data_for_grouping, groupby_apply_op)