TST/CLN: Remove groupby.test_allowlist (pandas-dev#52880)

Author: rhshadrach

pandas/tests/groupby/test_allowlist.py

@@ -1662,3 +1662,53 @@ def test_duplicate_columns(request, groupby_func, as_index):
     if groupby_func not in ("size", "ngroup", "cumcount"):
         expected = expected.rename(columns={"c": "b"})
     tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "op",
+    [
+        "sum",
+        "prod",
+        "min",
+        "max",
+        "median",
+        "mean",
+        "skew",
+        "std",
+        "var",
+        "sem",
+    ],
+)
+@pytest.mark.parametrize("axis", [0, 1])
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+def test_regression_allowlist_methods(op, axis, skipna, sort):
+    # GH6944
+    # GH 17537
+    # explicitly test the allowlist methods
+    raw_frame = DataFrame([0])
+    if axis == 0:
+        frame = raw_frame
+        msg = "The 'axis' keyword in DataFrame.groupby is deprecated and will be"
+    else:
+        frame = raw_frame.T
+        msg = "DataFrame.groupby with axis=1 is deprecated"
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        grouped = frame.groupby(level=0, axis=axis, sort=sort)
+
+    if op == "skew":
+        # skew has skipna
+        result = getattr(grouped, op)(skipna=skipna)
+        expected = frame.groupby(level=0).apply(
+            lambda h: getattr(h, op)(axis=axis, skipna=skipna)
+        )
+        if sort:
+            expected = expected.sort_index(axis=axis)
+        tm.assert_frame_equal(result, expected)
+    else:
+        result = getattr(grouped, op)()
+        expected = frame.groupby(level=0).apply(lambda h: getattr(h, op)(axis=axis))
+        if sort:
+            expected = expected.sort_index(axis=axis)
+        tm.assert_frame_equal(result, expected)

pandas/tests/groupby/test_function.py

@@ -2981,3 +2981,65 @@ def test_groupby_sum_on_nan_should_return_nan(bug_var):
 
     expected_df = DataFrame([bug_var, bug_var, bug_var, np.nan], columns=["A"])
     tm.assert_frame_equal(result, expected_df)
+
+
+@pytest.mark.parametrize(
+    "method",
+    [
+        "count",
+        "corr",
+        "cummax",
+        "cummin",
+        "cumprod",
+        "describe",
+        "rank",
+        "quantile",
+        "diff",
+        "shift",
+        "all",
+        "any",
+        "idxmin",
+        "idxmax",
+        "ffill",
+        "bfill",
+        "pct_change",
+    ],
+)
+def test_groupby_selection_with_methods(df, method):
+    # some methods which require DatetimeIndex
+    rng = date_range("2014", periods=len(df))
+    df.index = rng
+
+    g = df.groupby(["A"])[["C"]]
+    g_exp = df[["C"]].groupby(df["A"])
+    # TODO check groupby with > 1 col ?
+
+    res = getattr(g, method)()
+    exp = getattr(g_exp, method)()
+
+    # should always be frames!
+    tm.assert_frame_equal(res, exp)
+
+
+def test_groupby_selection_other_methods(df):
+    # some methods which require DatetimeIndex
+    rng = date_range("2014", periods=len(df))
+    df.columns.name = "foo"
+    df.index = rng
+
+    g = df.groupby(["A"])[["C"]]
+    g_exp = df[["C"]].groupby(df["A"])
+
+    # methods which aren't just .foo()
+    tm.assert_frame_equal(g.fillna(0), g_exp.fillna(0))
+    msg = "DataFrameGroupBy.dtypes is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        tm.assert_frame_equal(g.dtypes, g_exp.dtypes)
+    tm.assert_frame_equal(g.apply(lambda x: x.sum()), g_exp.apply(lambda x: x.sum()))
+
+    tm.assert_frame_equal(g.resample("D").mean(), g_exp.resample("D").mean())
+    tm.assert_frame_equal(g.resample("D").ohlc(), g_exp.resample("D").ohlc())
+
+    tm.assert_frame_equal(
+        g.filter(lambda x: len(x) == 3), g_exp.filter(lambda x: len(x) == 3)
+    )