TST: Move groupby tests out of test_function (pandas-dev#55967)

* TST: Move groupby tests out of test_function

* one more

Author: rhshadrach

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -356,6 +356,12 @@ def test_agg_multiple_functions_maintain_order(df):
     tm.assert_index_equal(result.columns, exp_cols)
 
 
+def test_series_index_name(df):
+    grouped = df.loc[:, ["C"]].groupby(df["A"])
+    result = grouped.agg(lambda x: x.mean())
+    assert result.index.name == "A"
+
+
 def test_agg_multiple_functions_same_name():
     # GH 30880
     df = DataFrame(

pandas/tests/groupby/methods/test_describe.py

@@ -219,3 +219,73 @@ def test_describe_duplicate_columns():
     )
     expected.index.names = [1]
     tm.assert_frame_equal(result, expected)
+
+
+class TestGroupByNonCythonPaths:
+    # GH#5610 non-cython calls should not include the grouper
+    # Tests for code not expected to go through cython paths.
+
+    @pytest.fixture
+    def df(self):
+        df = DataFrame(
+            [[1, 2, "foo"], [1, np.nan, "bar"], [3, np.nan, "baz"]],
+            columns=["A", "B", "C"],
+        )
+        return df
+
+    @pytest.fixture
+    def gb(self, df):
+        gb = df.groupby("A")
+        return gb
+
+    @pytest.fixture
+    def gni(self, df):
+        gni = df.groupby("A", as_index=False)
+        return gni
+
+    def test_describe(self, df, gb, gni):
+        # describe
+        expected_index = Index([1, 3], name="A")
+        expected_col = MultiIndex(
+            levels=[["B"], ["count", "mean", "std", "min", "25%", "50%", "75%", "max"]],
+            codes=[[0] * 8, list(range(8))],
+        )
+        expected = DataFrame(
+            [
+                [1.0, 2.0, np.nan, 2.0, 2.0, 2.0, 2.0, 2.0],
+                [0.0, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
+            ],
+            index=expected_index,
+            columns=expected_col,
+        )
+        result = gb.describe()
+        tm.assert_frame_equal(result, expected)
+
+        expected = expected.reset_index()
+        result = gni.describe()
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", [int, float, object])
+@pytest.mark.parametrize(
+    "kwargs",
+    [
+        {"percentiles": [0.10, 0.20, 0.30], "include": "all", "exclude": None},
+        {"percentiles": [0.10, 0.20, 0.30], "include": None, "exclude": ["int"]},
+        {"percentiles": [0.10, 0.20, 0.30], "include": ["int"], "exclude": None},
+    ],
+)
+def test_groupby_empty_dataset(dtype, kwargs):
+    # GH#41575
+    df = DataFrame([[1, 2, 3]], columns=["A", "B", "C"], dtype=dtype)
+    df["B"] = df["B"].astype(int)
+    df["C"] = df["C"].astype(float)
+
+    result = df.iloc[:0].groupby("A").describe(**kwargs)
+    expected = df.groupby("A").describe(**kwargs).reset_index(drop=True).iloc[:0]
+    tm.assert_frame_equal(result, expected)
+
+    result = df.iloc[:0].groupby("A").B.describe(**kwargs)
+    expected = df.groupby("A").B.describe(**kwargs).reset_index(drop=True).iloc[:0]
+    expected.index = Index([])
+    tm.assert_frame_equal(result, expected)

pandas/tests/groupby/test_apply.py

@@ -1559,3 +1559,45 @@ def test_include_groups(include_groups):
     if not include_groups:
         expected = expected[["b"]]
     tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("f", [max, min, sum])
+@pytest.mark.parametrize("keys", ["jim", ["jim", "joe"]])  # Single key  # Multi-key
+def test_builtins_apply(keys, f):
+    # see gh-8155
+    rs = np.random.default_rng(2)
+    df = DataFrame(rs.integers(1, 7, (10, 2)), columns=["jim", "joe"])
+    df["jolie"] = rs.standard_normal(10)
+
+    gb = df.groupby(keys)
+
+    fname = f.__name__
+
+    warn = None if f is not sum else FutureWarning
+    msg = "The behavior of DataFrame.sum with axis=None is deprecated"
+    with tm.assert_produces_warning(
+        warn, match=msg, check_stacklevel=False, raise_on_extra_warnings=False
+    ):
+        # Also warns on deprecation GH#53425
+        result = gb.apply(f)
+    ngroups = len(df.drop_duplicates(subset=keys))
+
+    assert_msg = f"invalid frame shape: {result.shape} (expected ({ngroups}, 3))"
+    assert result.shape == (ngroups, 3), assert_msg
+
+    npfunc = lambda x: getattr(np, fname)(x, axis=0)  # numpy's equivalent function
+    msg = "DataFrameGroupBy.apply operated on the grouping columns"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        expected = gb.apply(npfunc)
+    tm.assert_frame_equal(result, expected)
+
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        expected2 = gb.apply(lambda x: npfunc(x))
+    tm.assert_frame_equal(result, expected2)
+
+    if f != sum:
+        expected = gb.agg(fname).reset_index()
+        expected.set_index(keys, inplace=True, drop=False)
+        tm.assert_frame_equal(result, expected, check_dtype=False)
+
+    tm.assert_series_equal(getattr(result, fname)(axis=0), getattr(df, fname)(axis=0))

pandas/tests/groupby/test_cumulative.py

@@ -289,3 +289,30 @@ def test_nullable_int_not_cast_as_float(method, dtype, val):
     expected = DataFrame({"b": data}, dtype=dtype)
 
     tm.assert_frame_equal(result, expected)
+
+
+def test_cython_api2():
+    # this takes the fast apply path
+
+    # cumsum (GH5614)
+    df = DataFrame([[1, 2, np.nan], [1, np.nan, 9], [3, 4, 9]], columns=["A", "B", "C"])
+    expected = DataFrame([[2, np.nan], [np.nan, 9], [4, 9]], columns=["B", "C"])
+    result = df.groupby("A").cumsum()
+    tm.assert_frame_equal(result, expected)
+
+    # GH 5755 - cumsum is a transformer and should ignore as_index
+    result = df.groupby("A", as_index=False).cumsum()
+    tm.assert_frame_equal(result, expected)
+
+    # GH 13994
+    msg = "DataFrameGroupBy.cumsum with axis=1 is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = df.groupby("A").cumsum(axis=1)
+    expected = df.cumsum(axis=1)
+    tm.assert_frame_equal(result, expected)
+
+    msg = "DataFrameGroupBy.cumprod with axis=1 is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        result = df.groupby("A").cumprod(axis=1)
+    expected = df.cumprod(axis=1)
+    tm.assert_frame_equal(result, expected)