TST/CLN: Reuse more existing fixtures

pandas/tests/arrays/test_timedeltas.py

@@ -194,18 +194,17 @@ def test_add_timedeltaarraylike(self, tda):
 
 
 class TestTimedeltaArray:
-    @pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
-    def test_astype_int(self, dtype):
+    def test_astype_int(self, any_int_numpy_dtype):
         arr = TimedeltaArray._from_sequence(
             [Timedelta("1h"), Timedelta("2h")], dtype="m8[ns]"
         )
 
-        if np.dtype(dtype) != np.int64:
+        if np.dtype(any_int_numpy_dtype) != np.int64:
             with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"):
-                arr.astype(dtype)
+                arr.astype(any_int_numpy_dtype)
             return
 
-        result = arr.astype(dtype)
+        result = arr.astype(any_int_numpy_dtype)
         expected = arr._ndarray.view("i8")
         tm.assert_numpy_array_equal(result, expected)
 

pandas/tests/computation/test_eval.py

@@ -606,11 +606,10 @@ def test_unary_in_array(self):
         )
         tm.assert_numpy_array_equal(result, expected)
 
-    @pytest.mark.parametrize("dtype", [np.float32, np.float64])
     @pytest.mark.parametrize("expr", ["x < -0.1", "-5 > x"])
-    def test_float_comparison_bin_op(self, dtype, expr):
+    def test_float_comparison_bin_op(self, float_numpy_dtype, expr):
         # GH 16363
-        df = DataFrame({"x": np.array([0], dtype=dtype)})
+        df = DataFrame({"x": np.array([0], dtype=float_numpy_dtype)})
         res = df.eval(expr)
         assert res.values == np.array([False])
 
@@ -747,15 +746,16 @@ class TestTypeCasting:
     @pytest.mark.parametrize("op", ["+", "-", "*", "**", "/"])
     # maybe someday... numexpr has too many upcasting rules now
     # chain(*(np.core.sctypes[x] for x in ['uint', 'int', 'float']))
-    @pytest.mark.parametrize("dt", [np.float32, np.float64])
     @pytest.mark.parametrize("left_right", [("df", "3"), ("3", "df")])
-    def test_binop_typecasting(self, engine, parser, op, dt, left_right):
-        df = DataFrame(np.random.default_rng(2).standard_normal((5, 3)), dtype=dt)
+    def test_binop_typecasting(self, engine, parser, op, float_numpy_dtype, left_right):
+        df = DataFrame(
+            np.random.default_rng(2).standard_normal((5, 3)), dtype=float_numpy_dtype
+        )
         left, right = left_right
         s = f"{left} {op} {right}"
         res = pd.eval(s, engine=engine, parser=parser)
-        assert df.values.dtype == dt
-        assert res.values.dtype == dt
+        assert df.values.dtype == float_numpy_dtype
+        assert res.values.dtype == float_numpy_dtype
         tm.assert_frame_equal(res, eval(s))
 
 

pandas/tests/frame/indexing/test_indexing.py

@@ -1682,16 +1682,15 @@ def exp_single_cats_value(self):
         )
         return exp_single_cats_value
 
-    @pytest.mark.parametrize("indexer", [tm.loc, tm.iloc])
-    def test_loc_iloc_setitem_list_of_lists(self, orig, indexer):
+    def test_loc_iloc_setitem_list_of_lists(self, orig, indexer_li):
         #   - assign multiple rows (mixed values) -> exp_multi_row
         df = orig.copy()
 
         key = slice(2, 4)
-        if indexer is tm.loc:
+        if indexer_li is tm.loc:
             key = slice("j", "k")
 
-        indexer(df)[key, :] = [["b", 2], ["b", 2]]
+        indexer_li(df)[key, :] = [["b", 2], ["b", 2]]
 
         cats2 = Categorical(["a", "a", "b", "b", "a", "a", "a"], categories=["a", "b"])
         idx2 = Index(["h", "i", "j", "k", "l", "m", "n"])
@@ -1701,7 +1700,7 @@ def test_loc_iloc_setitem_list_of_lists(self, orig, indexer):
 
         df = orig.copy()
         with pytest.raises(TypeError, match=msg1):
-            indexer(df)[key, :] = [["c", 2], ["c", 2]]
+            indexer_li(df)[key, :] = [["c", 2], ["c", 2]]
 
     @pytest.mark.parametrize("indexer", [tm.loc, tm.iloc, tm.at, tm.iat])
     def test_loc_iloc_at_iat_setitem_single_value_in_categories(
@@ -1722,32 +1721,30 @@ def test_loc_iloc_at_iat_setitem_single_value_in_categories(
         with pytest.raises(TypeError, match=msg1):
             indexer(df)[key] = "c"
 
-    @pytest.mark.parametrize("indexer", [tm.loc, tm.iloc])
     def test_loc_iloc_setitem_mask_single_value_in_categories(
-        self, orig, exp_single_cats_value, indexer
+        self, orig, exp_single_cats_value, indexer_li
     ):
         # mask with single True
         df = orig.copy()
 
         mask = df.index == "j"
         key = 0
-        if indexer is tm.loc:
+        if indexer_li is tm.loc:
             key = df.columns[key]
 
-        indexer(df)[mask, key] = "b"
+        indexer_li(df)[mask, key] = "b"
         tm.assert_frame_equal(df, exp_single_cats_value)
 
-    @pytest.mark.parametrize("indexer", [tm.loc, tm.iloc])
-    def test_loc_iloc_setitem_full_row_non_categorical_rhs(self, orig, indexer):
+    def test_loc_iloc_setitem_full_row_non_categorical_rhs(self, orig, indexer_li):
         #   - assign a complete row (mixed values) -> exp_single_row
         df = orig.copy()
 
         key = 2
-        if indexer is tm.loc:
+        if indexer_li is tm.loc:
             key = df.index[2]
 
         # not categorical dtype, but "b" _is_ among the categories for df["cat"]
-        indexer(df)[key, :] = ["b", 2]
+        indexer_li(df)[key, :] = ["b", 2]
         cats1 = Categorical(["a", "a", "b", "a", "a", "a", "a"], categories=["a", "b"])
         idx1 = Index(["h", "i", "j", "k", "l", "m", "n"])
         values1 = [1, 1, 2, 1, 1, 1, 1]
@@ -1756,56 +1753,54 @@ def test_loc_iloc_setitem_full_row_non_categorical_rhs(self, orig, indexer):
 
         # "c" is not among the categories for df["cat"]
         with pytest.raises(TypeError, match=msg1):
-            indexer(df)[key, :] = ["c", 2]
+            indexer_li(df)[key, :] = ["c", 2]
 
-    @pytest.mark.parametrize("indexer", [tm.loc, tm.iloc])
     def test_loc_iloc_setitem_partial_col_categorical_rhs(
-        self, orig, exp_parts_cats_col, indexer
+        self, orig, exp_parts_cats_col, indexer_li
     ):
         # assign a part of a column with dtype == categorical ->
         # exp_parts_cats_col
         df = orig.copy()
 
         key = (slice(2, 4), 0)
-        if indexer is tm.loc:
+        if indexer_li is tm.loc:
             key = (slice("j", "k"), df.columns[0])
 
         # same categories as we currently have in df["cats"]
         compat = Categorical(["b", "b"], categories=["a", "b"])
-        indexer(df)[key] = compat
+        indexer_li(df)[key] = compat
         tm.assert_frame_equal(df, exp_parts_cats_col)
 
         # categories do not match df["cat"]'s, but "b" is among them
         semi_compat = Categorical(list("bb"), categories=list("abc"))
         with pytest.raises(TypeError, match=msg2):
             # different categories but holdable values
             #  -> not sure if this should fail or pass
-            indexer(df)[key] = semi_compat
+            indexer_li(df)[key] = semi_compat
 
         # categories do not match df["cat"]'s, and "c" is not among them
         incompat = Categorical(list("cc"), categories=list("abc"))
         with pytest.raises(TypeError, match=msg2):
             # different values
-            indexer(df)[key] = incompat
+            indexer_li(df)[key] = incompat
 
-    @pytest.mark.parametrize("indexer", [tm.loc, tm.iloc])
     def test_loc_iloc_setitem_non_categorical_rhs(
-        self, orig, exp_parts_cats_col, indexer
+        self, orig, exp_parts_cats_col, indexer_li
     ):
         # assign a part of a column with dtype != categorical -> exp_parts_cats_col
         df = orig.copy()
 
         key = (slice(2, 4), 0)
-        if indexer is tm.loc:
+        if indexer_li is tm.loc:
             key = (slice("j", "k"), df.columns[0])
 
         # "b" is among the categories for df["cat"]
-        indexer(df)[key] = ["b", "b"]
+        indexer_li(df)[key] = ["b", "b"]
         tm.assert_frame_equal(df, exp_parts_cats_col)
 
         # "c" not part of the categories
         with pytest.raises(TypeError, match=msg1):
-            indexer(df)[key] = ["c", "c"]
+            indexer_li(df)[key] = ["c", "c"]
 
     @pytest.mark.parametrize("indexer", [tm.getitem, tm.loc, tm.iloc])
     def test_getitem_preserve_object_index_with_dates(self, indexer):

pandas/tests/frame/indexing/test_setitem.py

@@ -1000,13 +1000,12 @@ def test_setitem_slice_position(self):
         expected = DataFrame(arr)
         tm.assert_frame_equal(df, expected)
 
-    @pytest.mark.parametrize("indexer", [tm.setitem, tm.iloc])
     @pytest.mark.parametrize("box", [Series, np.array, list, pd.array])
     @pytest.mark.parametrize("n", [1, 2, 3])
-    def test_setitem_slice_indexer_broadcasting_rhs(self, n, box, indexer):
+    def test_setitem_slice_indexer_broadcasting_rhs(self, n, box, indexer_si):
         # GH#40440
         df = DataFrame([[1, 3, 5]] + [[2, 4, 6]] * n, columns=["a", "b", "c"])
-        indexer(df)[1:] = box([10, 11, 12])
+        indexer_si(df)[1:] = box([10, 11, 12])
         expected = DataFrame([[1, 3, 5]] + [[10, 11, 12]] * n, columns=["a", "b", "c"])
         tm.assert_frame_equal(df, expected)
 
@@ -1019,15 +1018,14 @@ def test_setitem_list_indexer_broadcasting_rhs(self, n, box):
         expected = DataFrame([[1, 3, 5]] + [[10, 11, 12]] * n, columns=["a", "b", "c"])
         tm.assert_frame_equal(df, expected)
 
-    @pytest.mark.parametrize("indexer", [tm.setitem, tm.iloc])
     @pytest.mark.parametrize("box", [Series, np.array, list, pd.array])
     @pytest.mark.parametrize("n", [1, 2, 3])
-    def test_setitem_slice_broadcasting_rhs_mixed_dtypes(self, n, box, indexer):
+    def test_setitem_slice_broadcasting_rhs_mixed_dtypes(self, n, box, indexer_si):
         # GH#40440
         df = DataFrame(
             [[1, 3, 5], ["x", "y", "z"]] + [[2, 4, 6]] * n, columns=["a", "b", "c"]
         )
-        indexer(df)[1:] = box([10, 11, 12])
+        indexer_si(df)[1:] = box([10, 11, 12])
         expected = DataFrame(
             [[1, 3, 5]] + [[10, 11, 12]] * (n + 1),
             columns=["a", "b", "c"],
@@ -1105,13 +1103,12 @@ def test_setitem_loc_only_false_indexer_dtype_changed(self, box):
         df.loc[indexer, ["b"]] = 9
         tm.assert_frame_equal(df, expected)
 
-    @pytest.mark.parametrize("indexer", [tm.setitem, tm.loc])
-    def test_setitem_boolean_mask_aligning(self, indexer):
+    def test_setitem_boolean_mask_aligning(self, indexer_sl):
         # GH#39931
         df = DataFrame({"a": [1, 4, 2, 3], "b": [5, 6, 7, 8]})
         expected = df.copy()
         mask = df["a"] >= 3
-        indexer(df)[mask] = indexer(df)[mask].sort_values("a")
+        indexer_sl(df)[mask] = indexer_sl(df)[mask].sort_values("a")
         tm.assert_frame_equal(df, expected)
 
     def test_setitem_mask_categorical(self):

pandas/tests/frame/methods/test_astype.py

@@ -134,17 +134,16 @@ def test_astype_with_view_mixed_float(self, mixed_float_frame):
         tf.astype(np.int64)
         tf.astype(np.float32)
 
-    @pytest.mark.parametrize("dtype", [np.int32, np.int64])
     @pytest.mark.parametrize("val", [np.nan, np.inf])
-    def test_astype_cast_nan_inf_int(self, val, dtype):
+    def test_astype_cast_nan_inf_int(self, val, any_int_numpy_dtype):
         # see GH#14265
         #
         # Check NaN and inf --> raise error when converting to int.
         msg = "Cannot convert non-finite values \\(NA or inf\\) to integer"
         df = DataFrame([val])
 
         with pytest.raises(ValueError, match=msg):
-            df.astype(dtype)
+            df.astype(any_int_numpy_dtype)
 
     def test_astype_str(self):
         # see GH#9757
@@ -323,9 +322,9 @@ def test_astype_categoricaldtype_class_raises(self, cls):
         with pytest.raises(TypeError, match=xpr):
             df["A"].astype(cls)
 
-    @pytest.mark.parametrize("dtype", ["Int64", "Int32", "Int16"])
-    def test_astype_extension_dtypes(self, dtype):
+    def test_astype_extension_dtypes(self, any_int_ea_dtype):
         # GH#22578
+        dtype = any_int_ea_dtype
         df = DataFrame([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], columns=["a", "b"])
 
         expected1 = DataFrame(
@@ -348,9 +347,9 @@ def test_astype_extension_dtypes(self, dtype):
         tm.assert_frame_equal(df.astype(dtype), expected1)
         tm.assert_frame_equal(df.astype("int64").astype(dtype), expected1)
 
-    @pytest.mark.parametrize("dtype", ["Int64", "Int32", "Int16"])
-    def test_astype_extension_dtypes_1d(self, dtype):
+    def test_astype_extension_dtypes_1d(self, any_int_ea_dtype):
         # GH#22578
+        dtype = any_int_ea_dtype
         df = DataFrame({"a": [1.0, 2.0, 3.0]})
 
         expected1 = DataFrame({"a": pd.array([1, 2, 3], dtype=dtype)})
@@ -433,14 +432,13 @@ def test_astype_from_datetimelike_to_object(self, dtype, unit):
         else:
             assert result.iloc[0, 0] == Timedelta(1, unit=unit)
 
-    @pytest.mark.parametrize("arr_dtype", [np.int64, np.float64])
     @pytest.mark.parametrize("dtype", ["M8", "m8"])
     @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"])
-    def test_astype_to_datetimelike_unit(self, arr_dtype, dtype, unit):
+    def test_astype_to_datetimelike_unit(self, any_real_numpy_dtype, dtype, unit):
         # tests all units from numeric origination
         # GH#19223 / GH#12425
         dtype = f"{dtype}[{unit}]"
-        arr = np.array([[1, 2, 3]], dtype=arr_dtype)
+        arr = np.array([[1, 2, 3]], dtype=any_real_numpy_dtype)
         df = DataFrame(arr)
         result = df.astype(dtype)
         expected = DataFrame(arr.astype(dtype))

pandas/tests/frame/test_arithmetic.py

@@ -304,8 +304,7 @@ def test_df_string_comparison(self):
 
 class TestFrameFlexComparisons:
     # TODO: test_bool_flex_frame needs a better name
-    @pytest.mark.parametrize("op", ["eq", "ne", "gt", "lt", "ge", "le"])
-    def test_bool_flex_frame(self, op):
+    def test_bool_flex_frame(self, comparison_op):
         data = np.random.default_rng(2).standard_normal((5, 3))
         other_data = np.random.default_rng(2).standard_normal((5, 3))
         df = DataFrame(data)
@@ -315,8 +314,8 @@ def test_bool_flex_frame(self, op):
         # DataFrame
         assert df.eq(df).values.all()
         assert not df.ne(df).values.any()
-        f = getattr(df, op)
-        o = getattr(operator, op)
+        f = getattr(df, comparison_op.__name__)
+        o = comparison_op
         # No NAs
         tm.assert_frame_equal(f(other), o(df, other))
         # Unaligned
@@ -459,25 +458,23 @@ def test_flex_comparison_nat(self):
         result = df.ne(pd.NaT)
         assert result.iloc[0, 0].item() is True
 
-    @pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"])
-    def test_df_flex_cmp_constant_return_types(self, opname):
+    def test_df_flex_cmp_constant_return_types(self, comparison_op):
         # GH 15077, non-empty DataFrame
         df = DataFrame({"x": [1, 2, 3], "y": [1.0, 2.0, 3.0]})
         const = 2
 
-        result = getattr(df, opname)(const).dtypes.value_counts()
+        result = getattr(df, comparison_op.__name__)(const).dtypes.value_counts()
         tm.assert_series_equal(
             result, Series([2], index=[np.dtype(bool)], name="count")
         )
 
-    @pytest.mark.parametrize("opname", ["eq", "ne", "gt", "lt", "ge", "le"])
-    def test_df_flex_cmp_constant_return_types_empty(self, opname):
+    def test_df_flex_cmp_constant_return_types_empty(self, comparison_op):
         # GH 15077 empty DataFrame
         df = DataFrame({"x": [1, 2, 3], "y": [1.0, 2.0, 3.0]})
         const = 2
 
         empty = df.iloc[:0]
-        result = getattr(empty, opname)(const).dtypes.value_counts()
+        result = getattr(empty, comparison_op.__name__)(const).dtypes.value_counts()
         tm.assert_series_equal(
             result, Series([2], index=[np.dtype(bool)], name="count")
         )
@@ -664,11 +661,12 @@ def test_arith_flex_series(self, simple_frame):
         tm.assert_frame_equal(df.div(row), df / row)
         tm.assert_frame_equal(df.div(col, axis=0), (df.T / col).T)
 
-    @pytest.mark.parametrize("dtype", ["int64", "float64"])
-    def test_arith_flex_series_broadcasting(self, dtype):
+    def test_arith_flex_series_broadcasting(self, any_real_numpy_dtype):
         # broadcasting issue in GH 7325
-        df = DataFrame(np.arange(3 * 2).reshape((3, 2)), dtype=dtype)
+        df = DataFrame(np.arange(3 * 2).reshape((3, 2)), dtype=any_real_numpy_dtype)
         expected = DataFrame([[np.nan, np.inf], [1.0, 1.5], [1.0, 1.25]])
+        if any_real_numpy_dtype == "float32":
+            expected = expected.astype(any_real_numpy_dtype)
         result = df.div(df[0], axis="index")
         tm.assert_frame_equal(result, expected)
 

pandas/tests/groupby/transform/test_transform.py

@@ -706,7 +706,6 @@ def test_cython_transform_series(op, args, targop):
 
 
 @pytest.mark.parametrize("op", ["cumprod", "cumsum"])
-@pytest.mark.parametrize("skipna", [False, True])
 @pytest.mark.parametrize(
     "input, exp",
     [