TST: Parameterize tests (pandas-dev#45862)

Author: mroeschke

pandas/tests/arithmetic/test_timedelta64.py

@@ -538,6 +538,7 @@ def test_timedelta(self, freq):
         expected = index + timedelta(-1)
         tm.assert_index_equal(result, expected)
 
+    def test_timedelta_tick_arithmetic(self):
         # GH#4134, buggy with timedeltas
         rng = pd.date_range("2013", "2014")
         s = Series(rng)

pandas/tests/arrays/sparse/test_indexing.py

@@ -14,12 +14,10 @@
 
 class TestGetitem:
     def test_getitem(self):
-        def _checkit(i):
-            tm.assert_almost_equal(arr[i], arr.to_dense()[i])
-
+        dense = arr.to_dense()
         for i in range(len(arr)):
-            _checkit(i)
-            _checkit(-i)
+            tm.assert_almost_equal(arr[i], dense[i])
+            tm.assert_almost_equal(arr[-i], dense[-i])
 
     def test_getitem_arraylike_mask(self):
         arr = SparseArray([0, 1, 2])

pandas/tests/frame/indexing/test_indexing.py

@@ -195,17 +195,14 @@ def test_getitem_boolean_casting(self, datetime_frame):
         )
         tm.assert_series_equal(result, expected)
 
-    def test_getitem_boolean_list(self):
+    @pytest.mark.parametrize(
+        "lst", [[True, False, True], [True, True, True], [False, False, False]]
+    )
+    def test_getitem_boolean_list(self, lst):
         df = DataFrame(np.arange(12).reshape(3, 4))
-
-        def _checkit(lst):
-            result = df[lst]
-            expected = df.loc[df.index[lst]]
-            tm.assert_frame_equal(result, expected)
-
-        _checkit([True, False, True])
-        _checkit([True, True, True])
-        _checkit([False, False, False])
+        result = df[lst]
+        expected = df.loc[df.index[lst]]
+        tm.assert_frame_equal(result, expected)
 
     def test_getitem_boolean_iadd(self):
         arr = np.random.randn(5, 5)

pandas/tests/frame/test_arithmetic.py

@@ -847,36 +847,12 @@ def test_arith_mixed(self):
         expected = DataFrame({"A": ["aa", "bb", "cc"], "B": [2, 4, 6]})
         tm.assert_frame_equal(result, expected)
 
-    def test_arith_getitem_commute(self):
+    @pytest.mark.parametrize("col", ["A", "B"])
+    def test_arith_getitem_commute(self, all_arithmetic_functions, col):
         df = DataFrame({"A": [1.1, 3.3], "B": [2.5, -3.9]})
-
-        def _test_op(df, op):
-            result = op(df, 1)
-
-            if not df.columns.is_unique:
-                raise ValueError("Only unique columns supported by this test")
-
-            for col in result.columns:
-                tm.assert_series_equal(result[col], op(df[col], 1))
-
-        _test_op(df, operator.add)
-        _test_op(df, operator.sub)
-        _test_op(df, operator.mul)
-        _test_op(df, operator.truediv)
-        _test_op(df, operator.floordiv)
-        _test_op(df, operator.pow)
-
-        _test_op(df, lambda x, y: y + x)
-        _test_op(df, lambda x, y: y - x)
-        _test_op(df, lambda x, y: y * x)
-        _test_op(df, lambda x, y: y / x)
-        _test_op(df, lambda x, y: y**x)
-
-        _test_op(df, lambda x, y: x + y)
-        _test_op(df, lambda x, y: x - y)
-        _test_op(df, lambda x, y: x * y)
-        _test_op(df, lambda x, y: x / y)
-        _test_op(df, lambda x, y: x**y)
+        result = all_arithmetic_functions(df, 1)[col]
+        expected = all_arithmetic_functions(df[col], 1)
+        tm.assert_series_equal(result, expected)
 
     @pytest.mark.parametrize(
         "values", [[1, 2], (1, 2), np.array([1, 2]), range(1, 3), deque([1, 2])]

pandas/tests/frame/test_stack_unstack.py

@@ -992,40 +992,9 @@ def test_stack_datetime_column_multiIndex(self):
         expected = DataFrame([1, 2, 3, 4], index=eidx, columns=ecols)
         tm.assert_frame_equal(result, expected)
 
-    def test_stack_partial_multiIndex(self):
-        # GH 8844
-        def _test_stack_with_multiindex(multiindex):
-            df = DataFrame(
-                np.arange(3 * len(multiindex)).reshape(3, len(multiindex)),
-                columns=multiindex,
-            )
-            for level in (-1, 0, 1, [0, 1], [1, 0]):
-                result = df.stack(level=level, dropna=False)
-
-                if isinstance(level, int):
-                    # Stacking a single level should not make any all-NaN rows,
-                    # so df.stack(level=level, dropna=False) should be the same
-                    # as df.stack(level=level, dropna=True).
-                    expected = df.stack(level=level, dropna=True)
-                    if isinstance(expected, Series):
-                        tm.assert_series_equal(result, expected)
-                    else:
-                        tm.assert_frame_equal(result, expected)
-
-                df.columns = MultiIndex.from_tuples(
-                    df.columns.to_numpy(), names=df.columns.names
-                )
-                expected = df.stack(level=level, dropna=False)
-                if isinstance(expected, Series):
-                    tm.assert_series_equal(result, expected)
-                else:
-                    tm.assert_frame_equal(result, expected)
-
-        full_multiindex = MultiIndex.from_tuples(
-            [("B", "x"), ("B", "z"), ("A", "y"), ("C", "x"), ("C", "u")],
-            names=["Upper", "Lower"],
-        )
-        for multiindex_columns in (
+    @pytest.mark.parametrize(
+        "multiindex_columns",
+        [
             [0, 1, 2, 3, 4],
             [0, 1, 2, 3],
             [0, 1, 2, 4],
@@ -1038,12 +1007,56 @@ def _test_stack_with_multiindex(multiindex):
             [0],
             [2],
             [4],
-        ):
-            _test_stack_with_multiindex(full_multiindex[multiindex_columns])
-            if len(multiindex_columns) > 1:
-                multiindex_columns.reverse()
-                _test_stack_with_multiindex(full_multiindex[multiindex_columns])
+            [4, 3, 2, 1, 0],
+            [3, 2, 1, 0],
+            [4, 2, 1, 0],
+            [2, 1, 0],
+            [3, 2, 1],
+            [4, 3, 2],
+            [1, 0],
+            [2, 0],
+            [3, 0],
+        ],
+    )
+    @pytest.mark.parametrize("level", (-1, 0, 1, [0, 1], [1, 0]))
+    def test_stack_partial_multiIndex(self, multiindex_columns, level):
+        # GH 8844
+        full_multiindex = MultiIndex.from_tuples(
+            [("B", "x"), ("B", "z"), ("A", "y"), ("C", "x"), ("C", "u")],
+            names=["Upper", "Lower"],
+        )
+        multiindex = full_multiindex[multiindex_columns]
+        df = DataFrame(
+            np.arange(3 * len(multiindex)).reshape(3, len(multiindex)),
+            columns=multiindex,
+        )
+        result = df.stack(level=level, dropna=False)
+
+        if isinstance(level, int):
+            # Stacking a single level should not make any all-NaN rows,
+            # so df.stack(level=level, dropna=False) should be the same
+            # as df.stack(level=level, dropna=True).
+            expected = df.stack(level=level, dropna=True)
+            if isinstance(expected, Series):
+                tm.assert_series_equal(result, expected)
+            else:
+                tm.assert_frame_equal(result, expected)
+
+        df.columns = MultiIndex.from_tuples(
+            df.columns.to_numpy(), names=df.columns.names
+        )
+        expected = df.stack(level=level, dropna=False)
+        if isinstance(expected, Series):
+            tm.assert_series_equal(result, expected)
+        else:
+            tm.assert_frame_equal(result, expected)
 
+    def test_stack_full_multiIndex(self):
+        # GH 8844
+        full_multiindex = MultiIndex.from_tuples(
+            [("B", "x"), ("B", "z"), ("A", "y"), ("C", "x"), ("C", "u")],
+            names=["Upper", "Lower"],
+        )
         df = DataFrame(np.arange(6).reshape(2, 3), columns=full_multiindex[[0, 1, 3]])
         result = df.stack(dropna=False)
         expected = DataFrame(

pandas/tests/indexes/period/test_tools.py

@@ -14,22 +14,28 @@ class TestPeriodRepresentation:
     Wish to match NumPy units
     """
 
-    def _check_freq(self, freq, base_date):
+    @pytest.mark.parametrize(
+        "freq, base_date",
+        [
+            ("W-THU", "1970-01-01"),
+            ("D", "1970-01-01"),
+            ("B", "1970-01-01"),
+            ("H", "1970-01-01"),
+            ("T", "1970-01-01"),
+            ("S", "1970-01-01"),
+            ("L", "1970-01-01"),
+            ("U", "1970-01-01"),
+            ("N", "1970-01-01"),
+            ("M", "1970-01"),
+            ("A", 1970),
+        ],
+    )
+    def test_freq(self, freq, base_date):
         rng = period_range(start=base_date, periods=10, freq=freq)
         exp = np.arange(10, dtype=np.int64)
 
         tm.assert_numpy_array_equal(rng.asi8, exp)
 
-    def test_annual(self):
-        self._check_freq("A", 1970)
-
-    def test_monthly(self):
-        self._check_freq("M", "1970-01")
-
-    @pytest.mark.parametrize("freq", ["W-THU", "D", "B", "H", "T", "S", "L", "U", "N"])
-    def test_freq(self, freq):
-        self._check_freq(freq, "1970-01-01")
-
 
 class TestPeriodIndexConversion:
     def test_tolist(self):

pandas/tests/io/parser/test_textreader.py

@@ -261,34 +261,32 @@ def _make_reader(**kwds):
         assert (result[1] == exp[1]).all()
         assert (result[2] == exp[2]).all()
 
-    def test_cr_delimited(self):
-        def _test(text, **kwargs):
-            nice_text = text.replace("\r", "\r\n")
-            result = TextReader(StringIO(text), **kwargs).read()
-            expected = TextReader(StringIO(nice_text), **kwargs).read()
-            assert_array_dicts_equal(result, expected)
-
-        data = "a,b,c\r1,2,3\r4,5,6\r7,8,9\r10,11,12"
-        _test(data, delimiter=",")
-
-        data = "a  b  c\r1  2  3\r4  5  6\r7  8  9\r10  11  12"
-        _test(data, delim_whitespace=True)
-
-        data = "a,b,c\r1,2,3\r4,5,6\r,88,9\r10,11,12"
-        _test(data, delimiter=",")
-
-        sample = (
-            "A,B,C,D,E,F,G,H,I,J,K,L,M,N,O\r"
-            "AAAAA,BBBBB,0,0,0,0,0,0,0,0,0,0,0,0,0\r"
-            ",BBBBB,0,0,0,0,0,0,0,0,0,0,0,0,0"
-        )
-        _test(sample, delimiter=",")
-
-        data = "A  B  C\r  2  3\r4  5  6"
-        _test(data, delim_whitespace=True)
-
-        data = "A B C\r2 3\r4 5 6"
-        _test(data, delim_whitespace=True)
+    @pytest.mark.parametrize(
+        "text, kwargs",
+        [
+            ("a,b,c\r1,2,3\r4,5,6\r7,8,9\r10,11,12", {"delimiter": ","}),
+            (
+                "a  b  c\r1  2  3\r4  5  6\r7  8  9\r10  11  12",
+                {"delim_whitespace": True},
+            ),
+            ("a,b,c\r1,2,3\r4,5,6\r,88,9\r10,11,12", {"delimiter": ","}),
+            (
+                (
+                    "A,B,C,D,E,F,G,H,I,J,K,L,M,N,O\r"
+                    "AAAAA,BBBBB,0,0,0,0,0,0,0,0,0,0,0,0,0\r"
+                    ",BBBBB,0,0,0,0,0,0,0,0,0,0,0,0,0"
+                ),
+                {"delimiter": ","},
+            ),
+            ("A  B  C\r  2  3\r4  5  6", {"delim_whitespace": True}),
+            ("A B C\r2 3\r4 5 6", {"delim_whitespace": True}),
+        ],
+    )
+    def test_cr_delimited(self, text, kwargs):
+        nice_text = text.replace("\r", "\r\n")
+        result = TextReader(StringIO(text), **kwargs).read()
+        expected = TextReader(StringIO(nice_text), **kwargs).read()
+        assert_array_dicts_equal(result, expected)
 
     def test_empty_field_eof(self):
         data = "a,b,c\n1,2,3\n4,,"