TST/CLN: Reuse top level fixtures instead of parametrizations

pandas/conftest.py

@@ -281,17 +281,6 @@ def axis(request):
     return request.param
 
 
-axis_frame = axis
-
-
-@pytest.fixture(params=[1, "columns"], ids=lambda x: f"axis={repr(x)}")
-def axis_1(request):
-    """
-    Fixture for returning aliases of axis 1 of a DataFrame.
-    """
-    return request.param
-
-
 @pytest.fixture(params=[True, False, None])
 def observed(request):
     """
@@ -313,6 +302,22 @@ def ordered(request):
     return request.param
 
 
+@pytest.fixture(params=[True, False])
+def dropna(request):
+    """
+    Boolean 'dropna' parameter.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def sort(request):
+    """
+    Boolean 'sort' parameter.
+    """
+    return request.param
+
+
 @pytest.fixture(params=[True, False])
 def skipna(request):
     """
@@ -414,6 +419,74 @@ def nselect_method(request):
     return request.param
 
 
+@pytest.fixture(params=[None, "ignore"])
+def na_action(request):
+    """
+    Fixture for 'na_action' argument in map.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def ascending(request):
+    """
+    Fixture for 'na_action' argument in sort_values/sort_index/rank.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["average", "min", "max", "first", "dense"])
+def rank_method(request):
+    """
+    Fixture for 'rank' argument in rank.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def as_index(request):
+    """
+    Fixture for 'as_index' argument in groupby.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def cache(request):
+    """
+    Fixture for 'cache' argument in to_datetime.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def parallel(request):
+    """
+    Fixture for parallel keyword argument for numba.jit.
+    """
+    return request.param
+
+
+# Can parameterize nogil & nopython over True | False, but limiting per
+# https://github.com/pandas-dev/pandas/pull/41971#issuecomment-860607472
+
+
+@pytest.fixture(params=[False])
+def nogil(request):
+    """
+    Fixture for nogil keyword argument for numba.jit.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True])
+def nopython(request):
+    """
+    Fixture for nopython keyword argument for numba.jit.
+    """
+    return request.param
+
+
 # ----------------------------------------------------------------
 # Missing values & co.
 # ----------------------------------------------------------------
@@ -478,10 +551,6 @@ def index_or_series(request):
     return request.param
 
 
-# Generate cartesian product of index_or_series fixture:
-index_or_series2 = index_or_series
-
-
 @pytest.fixture(params=[Index, Series, pd.array], ids=["index", "series", "array"])
 def index_or_series_or_array(request):
     """
@@ -674,10 +743,6 @@ def index(request):
     return indices_dict[request.param].copy()
 
 
-# Needed to generate cartesian product of indices
-index_fixture2 = index
-
-
 @pytest.fixture(
     params=[
         key for key, value in indices_dict.items() if not isinstance(value, MultiIndex)
@@ -691,10 +756,6 @@ def index_flat(request):
     return indices_dict[key].copy()
 
 
-# Alias so we can test with cartesian product of index_flat
-index_flat2 = index_flat
-
-
 @pytest.fixture(
     params=[
         key

pandas/tests/apply/test_str.py

@@ -43,10 +43,9 @@ def test_apply_with_string_funcs(request, float_frame, func, args, kwds, how):
     tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.parametrize("arg", ["sum", "mean", "min", "max", "std"])
-def test_with_string_args(datetime_series, arg):
-    result = datetime_series.apply(arg)
-    expected = getattr(datetime_series, arg)()
+def test_with_string_args(datetime_series, all_numeric_reductions):
+    result = datetime_series.apply(all_numeric_reductions)
+    expected = getattr(datetime_series, all_numeric_reductions)()
     assert result == expected
 
 

pandas/tests/arithmetic/test_datetime64.py

@@ -1221,7 +1221,6 @@ class TestDatetime64DateOffsetArithmetic:
     # Tick DateOffsets
 
     # TODO: parametrize over timezone?
-    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
     def test_dt64arr_series_add_tick_DateOffset(self, box_with_array, unit):
         # GH#4532
         # operate with pd.offsets
@@ -1311,7 +1310,6 @@ def test_dti_add_tick_tzaware(self, tz_aware_fixture, box_with_array):
     # -------------------------------------------------------------
     # RelativeDelta DateOffsets
 
-    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
     def test_dt64arr_add_sub_relativedelta_offsets(self, box_with_array, unit):
         # GH#10699
         vec = DatetimeIndex(
@@ -1422,7 +1420,6 @@ def test_dt64arr_add_sub_relativedelta_offsets(self, box_with_array, unit):
     )
     @pytest.mark.parametrize("normalize", [True, False])
     @pytest.mark.parametrize("n", [0, 5])
-    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
     @pytest.mark.parametrize("tz", [None, "US/Central"])
     def test_dt64arr_add_sub_DateOffsets(
         self, box_with_array, n, normalize, cls_and_kwargs, unit, tz
@@ -2356,7 +2353,6 @@ def test_dti_addsub_object_arraylike(
 
 @pytest.mark.parametrize("years", [-1, 0, 1])
 @pytest.mark.parametrize("months", [-2, 0, 2])
-@pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
 def test_shift_months(years, months, unit):
     dti = DatetimeIndex(
         [

pandas/tests/arrays/boolean/test_reduction.py

@@ -43,7 +43,6 @@ def test_any_all(values, exp_any, exp_all, exp_any_noskip, exp_all_noskip):
         assert np.all(a.all()) is exp_all
 
 
-@pytest.mark.parametrize("dropna", [True, False])
 def test_reductions_return_types(dropna, data, all_numeric_reductions):
     op = all_numeric_reductions
     s = pd.Series(data)

pandas/tests/arrays/categorical/test_algos.py

@@ -5,7 +5,6 @@
 import pandas._testing as tm
 
 
-@pytest.mark.parametrize("ordered", [True, False])
 @pytest.mark.parametrize("categories", [["b", "a", "c"], ["a", "b", "c", "d"]])
 def test_factorize(categories, ordered):
     cat = pd.Categorical(

pandas/tests/arrays/categorical/test_analytics.py

@@ -97,7 +97,6 @@ def test_min_max_ordered_empty(self, categories, expected, aggregation):
         "values, categories",
         [(["a", "b", "c", np.nan], list("cba")), ([1, 2, 3, np.nan], [3, 2, 1])],
     )
-    @pytest.mark.parametrize("skipna", [True, False])
     @pytest.mark.parametrize("function", ["min", "max"])
     def test_min_max_with_nan(self, values, categories, function, skipna):
         # GH 25303
@@ -111,7 +110,6 @@ def test_min_max_with_nan(self, values, categories, function, skipna):
             assert result == expected
 
     @pytest.mark.parametrize("function", ["min", "max"])
-    @pytest.mark.parametrize("skipna", [True, False])
     def test_min_max_only_nan(self, function, skipna):
         # https://github.com/pandas-dev/pandas/issues/33450
         cat = Categorical([np.nan], categories=[1, 2], ordered=True)

pandas/tests/arrays/categorical/test_api.py

@@ -301,7 +301,6 @@ def test_set_categories(self):
             (["a", "b", "c"], ["a", "b"], ["d", "e"]),
         ],
     )
-    @pytest.mark.parametrize("ordered", [True, False])
     def test_set_categories_many(self, values, categories, new_categories, ordered):
         c = Categorical(values, categories)
         expected = Categorical(values, new_categories, ordered)

pandas/tests/arrays/categorical/test_astype.py

@@ -81,7 +81,6 @@ def test_astype_str_int_categories_to_nullable_float(self):
         expected = array(codes, dtype="Float64") / 2
         tm.assert_extension_array_equal(res, expected)
 
-    @pytest.mark.parametrize("ordered", [True, False])
     def test_astype(self, ordered):
         # string
         cat = Categorical(list("abbaaccc"), ordered=ordered)
@@ -108,11 +107,10 @@ def test_astype(self, ordered):
         tm.assert_numpy_array_equal(result, expected)
 
     @pytest.mark.parametrize("dtype_ordered", [True, False])
-    @pytest.mark.parametrize("cat_ordered", [True, False])
-    def test_astype_category(self, dtype_ordered, cat_ordered):
+    def test_astype_category(self, dtype_ordered, ordered):
         # GH#10696/GH#18593
         data = list("abcaacbab")
-        cat = Categorical(data, categories=list("bac"), ordered=cat_ordered)
+        cat = Categorical(data, categories=list("bac"), ordered=ordered)
 
         # standard categories
         dtype = CategoricalDtype(ordered=dtype_ordered)

pandas/tests/arrays/categorical/test_constructors.py

@@ -437,7 +437,6 @@ def test_constructor_dtype_and_others_raises(self):
             Categorical(["a", "b"], ordered=False, dtype=dtype)
 
     @pytest.mark.parametrize("categories", [None, ["a", "b"], ["a", "c"]])
-    @pytest.mark.parametrize("ordered", [True, False])
     def test_constructor_str_category(self, categories, ordered):
         result = Categorical(
             ["a", "b"], categories=categories, ordered=ordered, dtype="category"
@@ -683,7 +682,6 @@ def test_from_inferred_categories_coerces(self):
         expected = Categorical([1, 1, 2, np.nan])
         tm.assert_categorical_equal(result, expected)
 
-    @pytest.mark.parametrize("ordered", [None, True, False])
     def test_construction_with_ordered(self, ordered):
         # GH 9347, 9190
         cat = Categorical([0, 1, 2], ordered=ordered)

pandas/tests/arrays/categorical/test_dtypes.py

@@ -83,7 +83,6 @@ def test_set_dtype_new_categories(self):
             (["a", "b", "c"], ["a", "b"], ["d", "e"]),
         ],
     )
-    @pytest.mark.parametrize("ordered", [True, False])
     def test_set_dtype_many(self, values, categories, new_categories, ordered):
         c = Categorical(values, categories)
         expected = Categorical(values, new_categories, ordered)

pandas/tests/arrays/categorical/test_map.py

@@ -10,11 +10,6 @@
 import pandas._testing as tm
 
 
-@pytest.fixture(params=[None, "ignore"])
-def na_action(request):
-    return request.param
-
-
 @pytest.mark.parametrize(
     "data, categories",
     [

pandas/tests/arrays/datetimes/test_constructors.py

@@ -165,7 +165,6 @@ def test_copy(self):
         arr = DatetimeArray._from_sequence(data, copy=True)
         assert arr._ndarray is not data
 
-    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
     def test_numpy_datetime_unit(self, unit):
         data = np.array([1, 2, 3], dtype=f"M8[{unit}]")
         arr = DatetimeArray._from_sequence(data)

pandas/tests/arrays/datetimes/test_reductions.py

@@ -10,10 +10,6 @@
 
 
 class TestReductions:
-    @pytest.fixture(params=["s", "ms", "us", "ns"])
-    def unit(self, request):
-        return request.param
-
     @pytest.fixture
     def arr1d(self, tz_naive_fixture):
         """Fixture returning DatetimeArray with parametrized timezones"""
@@ -54,7 +50,6 @@ def test_min_max(self, arr1d, unit):
         assert result is NaT
 
     @pytest.mark.parametrize("tz", [None, "US/Central"])
-    @pytest.mark.parametrize("skipna", [True, False])
     def test_min_max_empty(self, skipna, tz):
         dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
         arr = DatetimeArray._from_sequence([], dtype=dtype)
@@ -65,7 +60,6 @@ def test_min_max_empty(self, skipna, tz):
         assert result is NaT
 
     @pytest.mark.parametrize("tz", [None, "US/Central"])
-    @pytest.mark.parametrize("skipna", [True, False])
     def test_median_empty(self, skipna, tz):
         dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
         arr = DatetimeArray._from_sequence([], dtype=dtype)
@@ -164,7 +158,6 @@ def test_mean_2d(self):
         expected = dti.mean()
         assert result == expected
 
-    @pytest.mark.parametrize("skipna", [True, False])
     def test_mean_empty(self, arr1d, skipna):
         arr = arr1d[:0]
 

pandas/tests/arrays/floating/test_function.py

@@ -127,7 +127,6 @@ def test_value_counts_with_normalize():
     tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.parametrize("skipna", [True, False])
 @pytest.mark.parametrize("min_count", [0, 4])
 def test_floating_array_sum(skipna, min_count, dtype):
     arr = pd.array([1, 2, 3, None], dtype=dtype)
@@ -171,7 +170,6 @@ def test_preserve_dtypes(op):
     tm.assert_frame_equal(result, expected)
 
 
-@pytest.mark.parametrize("skipna", [True, False])
 @pytest.mark.parametrize("method", ["min", "max"])
 def test_floating_array_min_max(skipna, method, dtype):
     arr = pd.array([0.0, 1.0, None], dtype=dtype)
@@ -183,7 +181,6 @@ def test_floating_array_min_max(skipna, method, dtype):
         assert result is pd.NA
 
 
-@pytest.mark.parametrize("skipna", [True, False])
 @pytest.mark.parametrize("min_count", [0, 9])
 def test_floating_array_prod(skipna, min_count, dtype):
     arr = pd.array([1.0, 2.0, None], dtype=dtype)

pandas/tests/arrays/integer/test_dtypes.py

@@ -59,7 +59,6 @@ def test_astype_nansafe():
         arr.astype("uint32")
 
 
-@pytest.mark.parametrize("dropna", [True, False])
 def test_construct_index(all_data, dropna):
     # ensure that we do not coerce to different Index dtype or non-index
 
@@ -76,7 +75,6 @@ def test_construct_index(all_data, dropna):
     tm.assert_index_equal(result, expected)
 
 
-@pytest.mark.parametrize("dropna", [True, False])
 def test_astype_index(all_data, dropna):
     # as an int/uint index to Index
 

pandas/tests/arrays/integer/test_function.py

@@ -141,7 +141,6 @@ def test_value_counts_with_normalize():
     tm.assert_series_equal(result, expected)
 
 
-@pytest.mark.parametrize("skipna", [True, False])
 @pytest.mark.parametrize("min_count", [0, 4])
 def test_integer_array_sum(skipna, min_count, any_int_ea_dtype):
     dtype = any_int_ea_dtype
@@ -153,7 +152,6 @@ def test_integer_array_sum(skipna, min_count, any_int_ea_dtype):
         assert result is pd.NA
 
 
-@pytest.mark.parametrize("skipna", [True, False])
 @pytest.mark.parametrize("method", ["min", "max"])
 def test_integer_array_min_max(skipna, method, any_int_ea_dtype):
     dtype = any_int_ea_dtype
@@ -166,7 +164,6 @@ def test_integer_array_min_max(skipna, method, any_int_ea_dtype):
         assert result is pd.NA
 
 
-@pytest.mark.parametrize("skipna", [True, False])
 @pytest.mark.parametrize("min_count", [0, 9])
 def test_integer_array_prod(skipna, min_count, any_int_ea_dtype):
     dtype = any_int_ea_dtype