REF: simplify fixtures

pandas/tests/extension/conftest.py

@@ -26,12 +26,17 @@ def data():
 
 
 @pytest.fixture
-def data_for_twos():
+def data_for_twos(dtype):
     """
     Length-100 array in which all the elements are two.
 
     Call pytest.skip in your fixture if the dtype does not support divmod.
     """
+    if not (dtype._is_numeric or dtype.kind == "m"):
+        # Object-dtypes may want to allow this, but for the most part
+        #  only numeric and timedelta-like dtypes will need to implement this.
+        pytest.skip("Not a numeric dtype")
+
     raise NotImplementedError
 
 
@@ -112,9 +117,9 @@ def na_cmp():
 
 
 @pytest.fixture
-def na_value():
-    """The scalar missing value for this type. Default 'None'"""
-    return None
+def na_value(dtype):
+    """The scalar missing value for this type. Default dtype.na_value"""
+    return dtype.na_value
 
 
 @pytest.fixture

pandas/tests/extension/decimal/test_decimal.py

@@ -56,11 +56,6 @@ def na_cmp():
     return lambda x, y: x.is_nan() and y.is_nan()
 
 
-@pytest.fixture
-def na_value():
-    return decimal.Decimal("NaN")
-
-
 @pytest.fixture
 def data_for_grouping():
     b = decimal.Decimal("1.0")

pandas/tests/extension/json/test_json.py

@@ -52,11 +52,6 @@ def data_missing_for_sorting():
     return JSONArray([{"b": 1}, {}, {"a": 4}])
 
 
-@pytest.fixture
-def na_value(dtype):
-    return dtype.na_value
-
-
 @pytest.fixture
 def na_cmp():
     return operator.eq
@@ -78,11 +73,6 @@ def data_for_grouping():
     )
 
 
-@pytest.fixture
-def data_for_twos(dtype):
-    pytest.skip("Not a numeric dtype")
-
-
 class BaseJSON:
     pass
 

pandas/tests/extension/test_arrow.py

@@ -265,12 +265,6 @@ def data_for_twos(data):
     # TODO: skip otherwise?
 
 
-@pytest.fixture
-def na_value():
-    """The scalar missing value for this type. Default 'None'"""
-    return pd.NA
-
-
 class TestBaseCasting(base.BaseCastingTests):
     def test_astype_str(self, data, request):
         pa_dtype = data.dtype.pyarrow_dtype

pandas/tests/extension/test_categorical.py

@@ -67,16 +67,6 @@ def data_missing_for_sorting():
     return Categorical(["A", None, "B"], categories=["B", "A"], ordered=True)
 
 
-@pytest.fixture
-def data_for_twos(dtype):
-    pytest.skip("Not a numeric dtype")
-
-
-@pytest.fixture
-def na_value():
-    return np.nan
-
-
 @pytest.fixture
 def data_for_grouping():
     return Categorical(["a", "a", None, None, "b", "b", "a", "c"])

pandas/tests/extension/test_datetime.py

@@ -73,11 +73,6 @@ def data_for_grouping(dtype):
     )
 
 
-@pytest.fixture
-def data_for_twos(dtype):
-    pytest.skip("Not a numeric dtype.")
-
-
 @pytest.fixture
 def na_cmp():
     def cmp(a, b):
@@ -86,11 +81,6 @@ def cmp(a, b):
     return cmp
 
 
-@pytest.fixture
-def na_value():
-    return pd.NaT
-
-
 # ----------------------------------------------------------------------------
 class BaseDatetimeTests:
     pass

pandas/tests/extension/test_interval.py

@@ -60,11 +60,6 @@ def data_missing_for_sorting():
     return IntervalArray.from_tuples([(1, 2), None, (0, 1)])
 
 
-@pytest.fixture
-def na_value():
-    return np.nan
-
-
 @pytest.fixture
 def data_for_grouping():
     a = (0, 1)

pandas/tests/extension/test_masked.py

@@ -140,11 +140,6 @@ def na_cmp():
     return lambda x, y: x is pd.NA and y is pd.NA
 
 
-@pytest.fixture
-def na_value():
-    return pd.NA
-
-
 @pytest.fixture
 def data_for_grouping(dtype):
     if dtype.kind == "f":

pandas/tests/extension/test_numpy.py

@@ -97,11 +97,6 @@ def data_missing(allow_in_pandas, dtype):
     return NumpyExtensionArray(np.array([np.nan, 1.0]))
 
 
-@pytest.fixture
-def na_value():
-    return np.nan
-
-
 @pytest.fixture
 def na_cmp():
     def cmp(a, b):

pandas/tests/extension/test_period.py

@@ -22,7 +22,6 @@
 
 from pandas.core.dtypes.dtypes import PeriodDtype
 
-import pandas as pd
 import pandas._testing as tm
 from pandas.core.arrays import PeriodArray
 from pandas.tests.extension import base
@@ -38,11 +37,6 @@ def data(dtype):
     return PeriodArray(np.arange(1970, 2070), dtype=dtype)
 
 
-@pytest.fixture
-def data_for_twos(dtype):
-    pytest.skip("Not a numeric dtype")
-
-
 @pytest.fixture
 def data_for_sorting(dtype):
     return PeriodArray([2018, 2019, 2017], dtype=dtype)
@@ -67,11 +61,6 @@ def data_for_grouping(dtype):
     return PeriodArray([B, B, NA, NA, A, A, B, C], dtype=dtype)
 
 
-@pytest.fixture
-def na_value():
-    return pd.NaT
-
-
 class BasePeriodTests:
     pass
 

pandas/tests/extension/test_sparse.py

@@ -83,11 +83,6 @@ def data_missing_for_sorting(request):
     return SparseArray([2, np.nan, 1], fill_value=request.param)
 
 
-@pytest.fixture
-def na_value():
-    return np.nan
-
-
 @pytest.fixture
 def na_cmp():
     return lambda left, right: pd.isna(left) and pd.isna(right)

pandas/tests/extension/test_string.py

@@ -83,11 +83,6 @@ def data_missing_for_sorting(dtype, chunked):
     return split_array(arr) if chunked else arr
 
 
-@pytest.fixture
-def na_value():
-    return pd.NA
-
-
 @pytest.fixture
 def data_for_grouping(dtype, chunked):
     arr = dtype.construct_array_type()._from_sequence(