TST: Inline once used variables & parameterize (#45976)

* TST: Inline once used variables

* Fix more tests

Author: mroeschke

pandas/tests/apply/test_str.py

@@ -45,12 +45,11 @@ def test_apply_with_string_funcs(request, float_frame, func, args, kwds, how):
     tm.assert_series_equal(result, expected)
 
 
-def test_with_string_args(datetime_series):
-
-    for arg in ["sum", "mean", "min", "max", "std"]:
-        result = datetime_series.apply(arg)
-        expected = getattr(datetime_series, arg)()
-        assert 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)()
+    assert result == expected
 
 
 @pytest.mark.parametrize("op", ["mean", "median", "std", "var"])

pandas/tests/arithmetic/test_numeric.py

@@ -5,7 +5,6 @@
 
 from collections import abc
 from decimal import Decimal
-from itertools import combinations
 import operator
 from typing import Any
 
@@ -928,7 +927,6 @@ def test_datetime64_with_index(self):
     # TODO: taken from tests.frame.test_operators, needs cleanup
     def test_frame_operators(self, float_frame):
         frame = float_frame
-        frame2 = pd.DataFrame(float_frame, columns=["D", "C", "B", "A"])
 
         garbage = np.random.random(4)
         colSeries = Series(garbage, index=np.array(frame.columns))
@@ -952,23 +950,27 @@ def test_frame_operators(self, float_frame):
                 else:
                     assert np.isnan(origVal)
 
+    def test_frame_operators_col_align(self, float_frame):
+        frame2 = pd.DataFrame(float_frame, columns=["D", "C", "B", "A"])
         added = frame2 + frame2
         expected = frame2 * 2
         tm.assert_frame_equal(added, expected)
 
+    def test_frame_operators_none_to_nan(self):
         df = pd.DataFrame({"a": ["a", None, "b"]})
         tm.assert_frame_equal(df + df, pd.DataFrame({"a": ["aa", np.nan, "bb"]}))
 
+    @pytest.mark.parametrize("dtype", ("float", "int64"))
+    def test_frame_operators_empty_like(self, dtype):
         # Test for issue #10181
-        for dtype in ("float", "int64"):
-            frames = [
-                pd.DataFrame(dtype=dtype),
-                pd.DataFrame(columns=["A"], dtype=dtype),
-                pd.DataFrame(index=[0], dtype=dtype),
-            ]
-            for df in frames:
-                assert (df + df).equals(df)
-                tm.assert_frame_equal(df + df, df)
+        frames = [
+            pd.DataFrame(dtype=dtype),
+            pd.DataFrame(columns=["A"], dtype=dtype),
+            pd.DataFrame(index=[0], dtype=dtype),
+        ]
+        for df in frames:
+            assert (df + df).equals(df)
+            tm.assert_frame_equal(df + df, df)
 
     @pytest.mark.parametrize(
         "func",
@@ -1164,45 +1166,85 @@ def test_operators_reverse_object(self, op):
 class TestNumericArithmeticUnsorted:
     # Tests in this class have been moved from type-specific test modules
     #  but not yet sorted, parametrized, and de-duplicated
-
-    def check_binop(self, ops, scalars, idxs):
-        for op in ops:
-            for a, b in combinations(idxs, 2):
-                a = a._rename("foo")
-                b = b._rename("bar")
-                result = op(a, b)
-                expected = op(Int64Index(a), Int64Index(b))
-                tm.assert_index_equal(result, expected, exact="equiv")
-            for idx in idxs:
-                for scalar in scalars:
-                    result = op(idx, scalar)
-                    expected = op(Int64Index(idx), scalar)
-                    tm.assert_index_equal(result, expected, exact="equiv")
-
-    def test_binops(self):
-        ops = [
+    @pytest.mark.parametrize(
+        "op",
+        [
             operator.add,
             operator.sub,
             operator.mul,
             operator.floordiv,
             operator.truediv,
-        ]
-        scalars = [-1, 1, 2]
-        idxs = [
+        ],
+    )
+    @pytest.mark.parametrize(
+        "idx1",
+        [
             RangeIndex(0, 10, 1),
             RangeIndex(0, 20, 2),
             RangeIndex(-10, 10, 2),
             RangeIndex(5, -5, -1),
-        ]
-        self.check_binop(ops, scalars, idxs)
+        ],
+    )
+    @pytest.mark.parametrize(
+        "idx2",
+        [
+            RangeIndex(0, 10, 1),
+            RangeIndex(0, 20, 2),
+            RangeIndex(-10, 10, 2),
+            RangeIndex(5, -5, -1),
+        ],
+    )
+    def test_binops_index(self, op, idx1, idx2):
+        idx1 = idx1._rename("foo")
+        idx2 = idx2._rename("bar")
+        result = op(idx1, idx2)
+        expected = op(Int64Index(idx1), Int64Index(idx2))
+        tm.assert_index_equal(result, expected, exact="equiv")
 
-    def test_binops_pow(self):
+    @pytest.mark.parametrize(
+        "op",
+        [
+            operator.add,
+            operator.sub,
+            operator.mul,
+            operator.floordiv,
+            operator.truediv,
+        ],
+    )
+    @pytest.mark.parametrize(
+        "idx",
+        [
+            RangeIndex(0, 10, 1),
+            RangeIndex(0, 20, 2),
+            RangeIndex(-10, 10, 2),
+            RangeIndex(5, -5, -1),
+        ],
+    )
+    @pytest.mark.parametrize("scalar", [-1, 1, 2])
+    def test_binops_index_scalar(self, op, idx, scalar):
+        result = op(idx, scalar)
+        expected = op(Int64Index(idx), scalar)
+        tm.assert_index_equal(result, expected, exact="equiv")
+
+    @pytest.mark.parametrize("idx1", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)])
+    @pytest.mark.parametrize("idx2", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)])
+    def test_binops_index_pow(self, idx1, idx2):
         # numpy does not allow powers of negative integers so test separately
         # https://github.com/numpy/numpy/pull/8127
-        ops = [pow]
-        scalars = [1, 2]
-        idxs = [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)]
-        self.check_binop(ops, scalars, idxs)
+        idx1 = idx1._rename("foo")
+        idx2 = idx2._rename("bar")
+        result = pow(idx1, idx2)
+        expected = pow(Int64Index(idx1), Int64Index(idx2))
+        tm.assert_index_equal(result, expected, exact="equiv")
+
+    @pytest.mark.parametrize("idx", [RangeIndex(0, 10, 1), RangeIndex(0, 20, 2)])
+    @pytest.mark.parametrize("scalar", [1, 2])
+    def test_binops_index_scalar_pow(self, idx, scalar):
+        # numpy does not allow powers of negative integers so test separately
+        # https://github.com/numpy/numpy/pull/8127
+        result = pow(idx, scalar)
+        expected = pow(Int64Index(idx), scalar)
+        tm.assert_index_equal(result, expected, exact="equiv")
 
     # TODO: divmod?
     @pytest.mark.parametrize(
@@ -1273,8 +1315,9 @@ def test_numeric_compat2(self):
         expected = Int64Index(idx._values) ** 2
         tm.assert_index_equal(Index(result.values), expected, exact=True)
 
-        # __floordiv__
-        cases_exact = [
+    @pytest.mark.parametrize(
+        "idx, div, expected",
+        [
             (RangeIndex(0, 1000, 2), 2, RangeIndex(0, 500, 1)),
             (RangeIndex(-99, -201, -3), -3, RangeIndex(33, 67, 1)),
             (
@@ -1291,9 +1334,11 @@ def test_numeric_compat2(self):
             (RangeIndex(2, 4, 2), 3, RangeIndex(0, 1, 1)),
             (RangeIndex(-5, -10, -6), 4, RangeIndex(-2, -1, 1)),
             (RangeIndex(-100, -200, 3), 2, RangeIndex(0)),
-        ]
-        for idx, div, expected in cases_exact:
-            tm.assert_index_equal(idx // div, expected, exact=True)
+        ],
+    )
+    def test_numeric_compat2_floordiv(self, idx, div, expected):
+        # __floordiv__
+        tm.assert_index_equal(idx // div, expected, exact=True)
 
     @pytest.mark.parametrize("dtype", [np.int64, np.float64])
     @pytest.mark.parametrize("delta", [1, 0, -1])

pandas/tests/arithmetic/test_timedelta64.py

@@ -204,8 +204,26 @@ def test_comp_nat(self, dtype):
         tm.assert_numpy_array_equal(lhs < NaT, expected)
         tm.assert_numpy_array_equal(NaT > lhs, expected)
 
-    def test_comparisons_nat(self):
-        tdidx1 = TimedeltaIndex(
+    @pytest.mark.parametrize(
+        "idx2",
+        [
+            TimedeltaIndex(
+                ["2 day", "2 day", NaT, NaT, "1 day 00:00:02", "5 days 00:00:03"]
+            ),
+            np.array(
+                [
+                    np.timedelta64(2, "D"),
+                    np.timedelta64(2, "D"),
+                    np.timedelta64("nat"),
+                    np.timedelta64("nat"),
+                    np.timedelta64(1, "D") + np.timedelta64(2, "s"),
+                    np.timedelta64(5, "D") + np.timedelta64(3, "s"),
+                ]
+            ),
+        ],
+    )
+    def test_comparisons_nat(self, idx2):
+        idx1 = TimedeltaIndex(
             [
                 "1 day",
                 NaT,
@@ -215,48 +233,30 @@ def test_comparisons_nat(self):
                 "5 day 00:00:03",
             ]
         )
-        tdidx2 = TimedeltaIndex(
-            ["2 day", "2 day", NaT, NaT, "1 day 00:00:02", "5 days 00:00:03"]
-        )
-        tdarr = np.array(
-            [
-                np.timedelta64(2, "D"),
-                np.timedelta64(2, "D"),
-                np.timedelta64("nat"),
-                np.timedelta64("nat"),
-                np.timedelta64(1, "D") + np.timedelta64(2, "s"),
-                np.timedelta64(5, "D") + np.timedelta64(3, "s"),
-            ]
-        )
-
-        cases = [(tdidx1, tdidx2), (tdidx1, tdarr)]
-
         # Check pd.NaT is handles as the same as np.nan
-        for idx1, idx2 in cases:
-
-            result = idx1 < idx2
-            expected = np.array([True, False, False, False, True, False])
-            tm.assert_numpy_array_equal(result, expected)
+        result = idx1 < idx2
+        expected = np.array([True, False, False, False, True, False])
+        tm.assert_numpy_array_equal(result, expected)
 
-            result = idx2 > idx1
-            expected = np.array([True, False, False, False, True, False])
-            tm.assert_numpy_array_equal(result, expected)
+        result = idx2 > idx1
+        expected = np.array([True, False, False, False, True, False])
+        tm.assert_numpy_array_equal(result, expected)
 
-            result = idx1 <= idx2
-            expected = np.array([True, False, False, False, True, True])
-            tm.assert_numpy_array_equal(result, expected)
+        result = idx1 <= idx2
+        expected = np.array([True, False, False, False, True, True])
+        tm.assert_numpy_array_equal(result, expected)
 
-            result = idx2 >= idx1
-            expected = np.array([True, False, False, False, True, True])
-            tm.assert_numpy_array_equal(result, expected)
+        result = idx2 >= idx1
+        expected = np.array([True, False, False, False, True, True])
+        tm.assert_numpy_array_equal(result, expected)
 
-            result = idx1 == idx2
-            expected = np.array([False, False, False, False, False, True])
-            tm.assert_numpy_array_equal(result, expected)
+        result = idx1 == idx2
+        expected = np.array([False, False, False, False, False, True])
+        tm.assert_numpy_array_equal(result, expected)
 
-            result = idx1 != idx2
-            expected = np.array([True, True, True, True, True, False])
-            tm.assert_numpy_array_equal(result, expected)
+        result = idx1 != idx2
+        expected = np.array([True, True, True, True, True, False])
+        tm.assert_numpy_array_equal(result, expected)
 
     # TODO: better name
     def test_comparisons_coverage(self):
@@ -665,28 +665,21 @@ def test_tdi_ops_attributes(self):
 class TestAddSubNaTMasking:
     # TODO: parametrize over boxes
 
-    def test_tdarr_add_timestamp_nat_masking(self, box_with_array):
+    @pytest.mark.parametrize("str_ts", ["1950-01-01", "1980-01-01"])
+    def test_tdarr_add_timestamp_nat_masking(self, box_with_array, str_ts):
         # GH#17991 checking for overflow-masking with NaT
         tdinat = pd.to_timedelta(["24658 days 11:15:00", "NaT"])
         tdobj = tm.box_expected(tdinat, box_with_array)
 
-        tsneg = Timestamp("1950-01-01")
-        ts_neg_variants = [
-            tsneg,
-            tsneg.to_pydatetime(),
-            tsneg.to_datetime64().astype("datetime64[ns]"),
-            tsneg.to_datetime64().astype("datetime64[D]"),
-        ]
-
-        tspos = Timestamp("1980-01-01")
-        ts_pos_variants = [
-            tspos,
-            tspos.to_pydatetime(),
-            tspos.to_datetime64().astype("datetime64[ns]"),
-            tspos.to_datetime64().astype("datetime64[D]"),
+        ts = Timestamp(str_ts)
+        ts_variants = [
+            ts,
+            ts.to_pydatetime(),
+            ts.to_datetime64().astype("datetime64[ns]"),
+            ts.to_datetime64().astype("datetime64[D]"),
         ]
 
-        for variant in ts_neg_variants + ts_pos_variants:
+        for variant in ts_variants:
             res = tdobj + variant
             if box_with_array is DataFrame:
                 assert res.iloc[1, 1] is NaT

pandas/tests/frame/test_query_eval.py

@@ -16,16 +16,15 @@
 import pandas._testing as tm
 from pandas.core.computation.check import NUMEXPR_INSTALLED
 
-PARSERS = "python", "pandas"
-ENGINES = "python", pytest.param("numexpr", marks=td.skip_if_no_ne)
 
-
-@pytest.fixture(params=PARSERS, ids=lambda x: x)
+@pytest.fixture(params=["python", "pandas"], ids=lambda x: x)
 def parser(request):
     return request.param
 
 
-@pytest.fixture(params=ENGINES, ids=lambda x: x)
+@pytest.fixture(
+    params=["python", pytest.param("numexpr", marks=td.skip_if_no_ne)], ids=lambda x: x
+)
 def engine(request):
     return request.param
 

pandas/tests/indexes/test_setops.py

@@ -32,11 +32,6 @@
     UInt64Index,
 )
 
-COMPATIBLE_INCONSISTENT_PAIRS = [
-    (np.float64, np.int64),
-    (np.float64, np.uint64),
-]
-
 
 def test_union_same_types(index):
     # Union with a non-unique, non-monotonic index raises error

pandas/tests/io/parser/test_parse_dates.py

@@ -45,9 +45,6 @@
 # cause a deadlock instead, so we skip these instead of xfailing
 skip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")
 
-# constant
-_DEFAULT_DATETIME = datetime(1, 1, 1)
-
 
 @xfail_pyarrow
 def test_read_csv_with_custom_date_parser(all_parsers):
@@ -1723,7 +1720,7 @@ def test_hypothesis_delimited_date(
     except_in_dateutil, expected = _helper_hypothesis_delimited_date(
         du_parse,
         date_string,
-        default=_DEFAULT_DATETIME,
+        default=datetime(1, 1, 1),
         dayfirst=dayfirst,
         yearfirst=False,
     )