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@@ -1266,3 +1266,6 @@ videochat2/lib/python3.10/site-packages/pandas/_libs/tslibs/timedeltas.cpython-3
 videochat2/lib/python3.10/site-packages/pandas/_libs/tslibs/nattype.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 videochat2/lib/python3.10/site-packages/pandas/_libs/tslibs/timestamps.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
 videochat2/lib/python3.10/site-packages/pandas/_libs/tslibs/period.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+videochat2/lib/python3.10/site-packages/pandas/_libs/window/aggregations.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+videochat2/lib/python3.10/site-packages/pandas/_libs/tslibs/offsets.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+videochat2/lib/python3.10/site-packages/pandas/_libs/tslibs/vectorized.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text

videochat2/lib/python3.10/site-packages/pandas/_libs/tslibs/offsets.cpython-310-x86_64-linux-gnu.so

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videochat2/lib/python3.10/site-packages/pandas/_libs/tslibs/vectorized.cpython-310-x86_64-linux-gnu.so

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videochat2/lib/python3.10/site-packages/pandas/_libs/window/aggregations.cpython-310-x86_64-linux-gnu.so

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videochat2/lib/python3.10/site-packages/pandas/tests/groupby/__init__.py

@@ -0,0 +1,25 @@
+def get_groupby_method_args(name, obj):
+    """
+    Get required arguments for a groupby method.
+
+    When parametrizing a test over groupby methods (e.g. "sum", "mean", "fillna"),
+    it is often the case that arguments are required for certain methods.
+
+    Parameters
+    ----------
+    name: str
+        Name of the method.
+    obj: Series or DataFrame
+        pandas object that is being grouped.
+
+    Returns
+    -------
+    A tuple of required arguments for the method.
+    """
+    if name in ("nth", "fillna", "take"):
+        return (0,)
+    if name == "quantile":
+        return (0.5,)
+    if name == "corrwith":
+        return (obj,)
+    return ()

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/aggregate/test_aggregate.py

@@ -0,0 +1,1523 @@
+"""
+test .agg behavior / note that .apply is tested generally in test_groupby.py
+"""
+import datetime
+import functools
+from functools import partial
+import re
+
+import numpy as np
+import pytest
+
+from pandas.errors import SpecificationError
+
+from pandas.core.dtypes.common import is_integer_dtype
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    concat,
+    to_datetime,
+)
+import pandas._testing as tm
+from pandas.core.groupby.grouper import Grouping
+
+
+def test_groupby_agg_no_extra_calls():
+    # GH#31760
+    df = DataFrame({"key": ["a", "b", "c", "c"], "value": [1, 2, 3, 4]})
+    gb = df.groupby("key")["value"]
+
+    def dummy_func(x):
+        assert len(x) != 0
+        return x.sum()
+
+    gb.agg(dummy_func)
+
+
+def test_agg_regression1(tsframe):
+    grouped = tsframe.groupby([lambda x: x.year, lambda x: x.month])
+    result = grouped.agg(np.mean)
+    expected = grouped.mean()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_must_agg(df):
+    grouped = df.groupby("A")["C"]
+
+    msg = "Must produce aggregated value"
+    with pytest.raises(Exception, match=msg):
+        grouped.agg(lambda x: x.describe())
+    with pytest.raises(Exception, match=msg):
+        grouped.agg(lambda x: x.index[:2])
+
+
+def test_agg_ser_multi_key(df):
+    f = lambda x: x.sum()
+    results = df.C.groupby([df.A, df.B]).aggregate(f)
+    expected = df.groupby(["A", "B"]).sum()["C"]
+    tm.assert_series_equal(results, expected)
+
+
+def test_groupby_aggregation_mixed_dtype():
+    # GH 6212
+    expected = DataFrame(
+        {
+            "v1": [5, 5, 7, np.nan, 3, 3, 4, 1],
+            "v2": [55, 55, 77, np.nan, 33, 33, 44, 11],
+        },
+        index=MultiIndex.from_tuples(
+            [
+                (1, 95),
+                (1, 99),
+                (2, 95),
+                (2, 99),
+                ("big", "damp"),
+                ("blue", "dry"),
+                ("red", "red"),
+                ("red", "wet"),
+            ],
+            names=["by1", "by2"],
+        ),
+    )
+
+    df = DataFrame(
+        {
+            "v1": [1, 3, 5, 7, 8, 3, 5, np.nan, 4, 5, 7, 9],
+            "v2": [11, 33, 55, 77, 88, 33, 55, np.nan, 44, 55, 77, 99],
+            "by1": ["red", "blue", 1, 2, np.nan, "big", 1, 2, "red", 1, np.nan, 12],
+            "by2": [
+                "wet",
+                "dry",
+                99,
+                95,
+                np.nan,
+                "damp",
+                95,
+                99,
+                "red",
+                99,
+                np.nan,
+                np.nan,
+            ],
+        }
+    )
+
+    g = df.groupby(["by1", "by2"])
+    result = g[["v1", "v2"]].mean()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_aggregation_multi_level_column():
+    # GH 29772
+    lst = [
+        [True, True, True, False],
+        [True, False, np.nan, False],
+        [True, True, np.nan, False],
+        [True, True, np.nan, False],
+    ]
+    df = DataFrame(
+        data=lst,
+        columns=MultiIndex.from_tuples([("A", 0), ("A", 1), ("B", 0), ("B", 1)]),
+    )
+
+    gb = df.groupby(level=1, axis=1)
+    result = gb.sum(numeric_only=False)
+    expected = DataFrame({0: [2.0, True, True, True], 1: [1, 0, 1, 1]})
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_apply_corner(ts, tsframe):
+    # nothing to group, all NA
+    grouped = ts.groupby(ts * np.nan, group_keys=False)
+    assert ts.dtype == np.float64
+
+    # groupby float64 values results in a float64 Index
+    exp = Series([], dtype=np.float64, index=Index([], dtype=np.float64))
+    tm.assert_series_equal(grouped.sum(), exp)
+    tm.assert_series_equal(grouped.agg(np.sum), exp)
+    tm.assert_series_equal(grouped.apply(np.sum), exp, check_index_type=False)
+
+    # DataFrame
+    grouped = tsframe.groupby(tsframe["A"] * np.nan, group_keys=False)
+    exp_df = DataFrame(
+        columns=tsframe.columns,
+        dtype=float,
+        index=Index([], name="A", dtype=np.float64),
+    )
+    tm.assert_frame_equal(grouped.sum(), exp_df)
+    tm.assert_frame_equal(grouped.agg(np.sum), exp_df)
+    tm.assert_frame_equal(grouped.apply(np.sum), exp_df)
+
+
+def test_agg_grouping_is_list_tuple(ts):
+    df = tm.makeTimeDataFrame()
+
+    grouped = df.groupby(lambda x: x.year)
+    grouper = grouped.grouper.groupings[0].grouping_vector
+    grouped.grouper.groupings[0] = Grouping(ts.index, list(grouper))
+
+    result = grouped.agg(np.mean)
+    expected = grouped.mean()
+    tm.assert_frame_equal(result, expected)
+
+    grouped.grouper.groupings[0] = Grouping(ts.index, tuple(grouper))
+
+    result = grouped.agg(np.mean)
+    expected = grouped.mean()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_python_multiindex(mframe):
+    grouped = mframe.groupby(["A", "B"])
+
+    result = grouped.agg(np.mean)
+    expected = grouped.mean()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "groupbyfunc", [lambda x: x.weekday(), [lambda x: x.month, lambda x: x.weekday()]]
+)
+def test_aggregate_str_func(tsframe, groupbyfunc):
+    grouped = tsframe.groupby(groupbyfunc)
+
+    # single series
+    result = grouped["A"].agg("std")
+    expected = grouped["A"].std()
+    tm.assert_series_equal(result, expected)
+
+    # group frame by function name
+    result = grouped.aggregate("var")
+    expected = grouped.var()
+    tm.assert_frame_equal(result, expected)
+
+    # group frame by function dict
+    result = grouped.agg({"A": "var", "B": "std", "C": "mean", "D": "sem"})
+    expected = DataFrame(
+        {
+            "A": grouped["A"].var(),
+            "B": grouped["B"].std(),
+            "C": grouped["C"].mean(),
+            "D": grouped["D"].sem(),
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_std_masked_dtype(any_numeric_ea_dtype):
+    # GH#35516
+    df = DataFrame(
+        {
+            "a": [2, 1, 1, 1, 2, 2, 1],
+            "b": Series([pd.NA, 1, 2, 1, 1, 1, 2], dtype="Float64"),
+        }
+    )
+    result = df.groupby("a").std()
+    expected = DataFrame(
+        {"b": [0.57735, 0]}, index=Index([1, 2], name="a"), dtype="Float64"
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_str_with_kwarg_axis_1_raises(df, reduction_func):
+    gb = df.groupby(level=0)
+    if reduction_func in ("idxmax", "idxmin"):
+        error = TypeError
+        msg = "reduction operation '.*' not allowed for this dtype"
+    else:
+        error = ValueError
+        msg = f"Operation {reduction_func} does not support axis=1"
+    with pytest.raises(error, match=msg):
+        gb.agg(reduction_func, axis=1)
+
+
+@pytest.mark.parametrize(
+    "func, expected, dtype, result_dtype_dict",
+    [
+        ("sum", [5, 7, 9], "int64", {}),
+        ("std", [4.5**0.5] * 3, int, {"i": float, "j": float, "k": float}),
+        ("var", [4.5] * 3, int, {"i": float, "j": float, "k": float}),
+        ("sum", [5, 7, 9], "Int64", {"j": "int64"}),
+        ("std", [4.5**0.5] * 3, "Int64", {"i": float, "j": float, "k": float}),
+        ("var", [4.5] * 3, "Int64", {"i": "float64", "j": "float64", "k": "float64"}),
+    ],
+)
+def test_multiindex_groupby_mixed_cols_axis1(func, expected, dtype, result_dtype_dict):
+    # GH#43209
+    df = DataFrame(
+        [[1, 2, 3, 4, 5, 6]] * 3,
+        columns=MultiIndex.from_product([["a", "b"], ["i", "j", "k"]]),
+    ).astype({("a", "j"): dtype, ("b", "j"): dtype})
+    result = df.groupby(level=1, axis=1).agg(func)
+    expected = DataFrame([expected] * 3, columns=["i", "j", "k"]).astype(
+        result_dtype_dict
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "func, expected_data, result_dtype_dict",
+    [
+        ("sum", [[2, 4], [10, 12], [18, 20]], {10: "int64", 20: "int64"}),
+        # std should ideally return Int64 / Float64 #43330
+        ("std", [[2**0.5] * 2] * 3, "float64"),
+        ("var", [[2] * 2] * 3, {10: "float64", 20: "float64"}),
+    ],
+)
+def test_groupby_mixed_cols_axis1(func, expected_data, result_dtype_dict):
+    # GH#43209
+    df = DataFrame(
+        np.arange(12).reshape(3, 4),
+        index=Index([0, 1, 0], name="y"),
+        columns=Index([10, 20, 10, 20], name="x"),
+        dtype="int64",
+    ).astype({10: "Int64"})
+    result = df.groupby("x", axis=1).agg(func)
+    expected = DataFrame(
+        data=expected_data,
+        index=Index([0, 1, 0], name="y"),
+        columns=Index([10, 20], name="x"),
+    ).astype(result_dtype_dict)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_aggregate_item_by_item(df):
+    grouped = df.groupby("A")
+
+    aggfun_0 = lambda ser: ser.size
+    result = grouped.agg(aggfun_0)
+    foosum = (df.A == "foo").sum()
+    barsum = (df.A == "bar").sum()
+    K = len(result.columns)
+
+    # GH5782
+    exp = Series(np.array([foosum] * K), index=list("BCD"), name="foo")
+    tm.assert_series_equal(result.xs("foo"), exp)
+
+    exp = Series(np.array([barsum] * K), index=list("BCD"), name="bar")
+    tm.assert_almost_equal(result.xs("bar"), exp)
+
+    def aggfun_1(ser):
+        return ser.size
+
+    result = DataFrame().groupby(df.A).agg(aggfun_1)
+    assert isinstance(result, DataFrame)
+    assert len(result) == 0
+
+
+def test_wrap_agg_out(three_group):
+    grouped = three_group.groupby(["A", "B"])
+
+    def func(ser):
+        if ser.dtype == object:
+            raise TypeError("Test error message")
+        return ser.sum()
+
+    with pytest.raises(TypeError, match="Test error message"):
+        grouped.aggregate(func)
+    result = grouped[[c for c in three_group if c != "C"]].aggregate(func)
+    exp_grouped = three_group.loc[:, three_group.columns != "C"]
+    expected = exp_grouped.groupby(["A", "B"]).aggregate(func)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_multiple_functions_maintain_order(df):
+    # GH #610
+    funcs = [("mean", np.mean), ("max", np.max), ("min", np.min)]
+    result = df.groupby("A")["C"].agg(funcs)
+    exp_cols = Index(["mean", "max", "min"])
+
+    tm.assert_index_equal(result.columns, exp_cols)
+
+
+def test_agg_multiple_functions_same_name():
+    # GH 30880
+    df = DataFrame(
+        np.random.randn(1000, 3),
+        index=pd.date_range("1/1/2012", freq="S", periods=1000),
+        columns=["A", "B", "C"],
+    )
+    result = df.resample("3T").agg(
+        {"A": [partial(np.quantile, q=0.9999), partial(np.quantile, q=0.1111)]}
+    )
+    expected_index = pd.date_range("1/1/2012", freq="3T", periods=6)
+    expected_columns = MultiIndex.from_tuples([("A", "quantile"), ("A", "quantile")])
+    expected_values = np.array(
+        [df.resample("3T").A.quantile(q=q).values for q in [0.9999, 0.1111]]
+    ).T
+    expected = DataFrame(
+        expected_values, columns=expected_columns, index=expected_index
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_multiple_functions_same_name_with_ohlc_present():
+    # GH 30880
+    # ohlc expands dimensions, so different test to the above is required.
+    df = DataFrame(
+        np.random.randn(1000, 3),
+        index=pd.date_range("1/1/2012", freq="S", periods=1000, name="dti"),
+        columns=Index(["A", "B", "C"], name="alpha"),
+    )
+    result = df.resample("3T").agg(
+        {"A": ["ohlc", partial(np.quantile, q=0.9999), partial(np.quantile, q=0.1111)]}
+    )
+    expected_index = pd.date_range("1/1/2012", freq="3T", periods=6, name="dti")
+    expected_columns = MultiIndex.from_tuples(
+        [
+            ("A", "ohlc", "open"),
+            ("A", "ohlc", "high"),
+            ("A", "ohlc", "low"),
+            ("A", "ohlc", "close"),
+            ("A", "quantile", "A"),
+            ("A", "quantile", "A"),
+        ],
+        names=["alpha", None, None],
+    )
+    non_ohlc_expected_values = np.array(
+        [df.resample("3T").A.quantile(q=q).values for q in [0.9999, 0.1111]]
+    ).T
+    expected_values = np.hstack([df.resample("3T").A.ohlc(), non_ohlc_expected_values])
+    expected = DataFrame(
+        expected_values, columns=expected_columns, index=expected_index
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_multiple_functions_tuples_and_non_tuples(df):
+    # #1359
+    # Columns B and C would cause partial failure
+    df = df.drop(columns=["B", "C"])
+
+    funcs = [("foo", "mean"), "std"]
+    ex_funcs = [("foo", "mean"), ("std", "std")]
+
+    result = df.groupby("A")["D"].agg(funcs)
+    expected = df.groupby("A")["D"].agg(ex_funcs)
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby("A").agg(funcs)
+    expected = df.groupby("A").agg(ex_funcs)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_more_flexible_frame_multi_function(df):
+    grouped = df.groupby("A")
+
+    exmean = grouped.agg({"C": np.mean, "D": np.mean})
+    exstd = grouped.agg({"C": np.std, "D": np.std})
+
+    expected = concat([exmean, exstd], keys=["mean", "std"], axis=1)
+    expected = expected.swaplevel(0, 1, axis=1).sort_index(level=0, axis=1)
+
+    d = {"C": [np.mean, np.std], "D": [np.mean, np.std]}
+    result = grouped.aggregate(d)
+
+    tm.assert_frame_equal(result, expected)
+
+    # be careful
+    result = grouped.aggregate({"C": np.mean, "D": [np.mean, np.std]})
+    expected = grouped.aggregate({"C": np.mean, "D": [np.mean, np.std]})
+    tm.assert_frame_equal(result, expected)
+
+    def numpymean(x):
+        return np.mean(x)
+
+    def numpystd(x):
+        return np.std(x, ddof=1)
+
+    # this uses column selection & renaming
+    msg = r"nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=msg):
+        d = {"C": np.mean, "D": {"foo": np.mean, "bar": np.std}}
+        grouped.aggregate(d)
+
+    # But without renaming, these functions are OK
+    d = {"C": [np.mean], "D": [numpymean, numpystd]}
+    grouped.aggregate(d)
+
+
+def test_multi_function_flexible_mix(df):
+    # GH #1268
+    grouped = df.groupby("A")
+
+    # Expected
+    d = {"C": {"foo": "mean", "bar": "std"}, "D": {"sum": "sum"}}
+    # this uses column selection & renaming
+    msg = r"nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=msg):
+        grouped.aggregate(d)
+
+    # Test 1
+    d = {"C": {"foo": "mean", "bar": "std"}, "D": "sum"}
+    # this uses column selection & renaming
+    with pytest.raises(SpecificationError, match=msg):
+        grouped.aggregate(d)
+
+    # Test 2
+    d = {"C": {"foo": "mean", "bar": "std"}, "D": "sum"}
+    # this uses column selection & renaming
+    with pytest.raises(SpecificationError, match=msg):
+        grouped.aggregate(d)
+
+
+def test_groupby_agg_coercing_bools():
+    # issue 14873
+    dat = DataFrame({"a": [1, 1, 2, 2], "b": [0, 1, 2, 3], "c": [None, None, 1, 1]})
+    gp = dat.groupby("a")
+
+    index = Index([1, 2], name="a")
+
+    result = gp["b"].aggregate(lambda x: (x != 0).all())
+    expected = Series([False, True], index=index, name="b")
+    tm.assert_series_equal(result, expected)
+
+    result = gp["c"].aggregate(lambda x: x.isnull().all())
+    expected = Series([True, False], index=index, name="c")
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_agg_dict_with_getitem():
+    # issue 25471
+    dat = DataFrame({"A": ["A", "A", "B", "B", "B"], "B": [1, 2, 1, 1, 2]})
+    result = dat.groupby("A")[["B"]].agg({"B": "sum"})
+
+    expected = DataFrame({"B": [3, 4]}, index=["A", "B"]).rename_axis("A", axis=0)
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "op",
+    [
+        lambda x: x.sum(),
+        lambda x: x.cumsum(),
+        lambda x: x.transform("sum"),
+        lambda x: x.transform("cumsum"),
+        lambda x: x.agg("sum"),
+        lambda x: x.agg("cumsum"),
+    ],
+)
+def test_bool_agg_dtype(op):
+    # GH 7001
+    # Bool sum aggregations result in int
+    df = DataFrame({"a": [1, 1], "b": [False, True]})
+    s = df.set_index("a")["b"]
+
+    result = op(df.groupby("a"))["b"].dtype
+    assert is_integer_dtype(result)
+
+    result = op(s.groupby("a")).dtype
+    assert is_integer_dtype(result)
+
+
+@pytest.mark.parametrize(
+    "keys, agg_index",
+    [
+        (["a"], Index([1], name="a")),
+        (["a", "b"], MultiIndex([[1], [2]], [[0], [0]], names=["a", "b"])),
+    ],
+)
+@pytest.mark.parametrize(
+    "input_dtype", ["bool", "int32", "int64", "float32", "float64"]
+)
+@pytest.mark.parametrize(
+    "result_dtype", ["bool", "int32", "int64", "float32", "float64"]
+)
+@pytest.mark.parametrize("method", ["apply", "aggregate", "transform"])
+def test_callable_result_dtype_frame(
+    keys, agg_index, input_dtype, result_dtype, method
+):
+    # GH 21240
+    df = DataFrame({"a": [1], "b": [2], "c": [True]})
+    df["c"] = df["c"].astype(input_dtype)
+    op = getattr(df.groupby(keys)[["c"]], method)
+    result = op(lambda x: x.astype(result_dtype).iloc[0])
+    expected_index = pd.RangeIndex(0, 1) if method == "transform" else agg_index
+    expected = DataFrame({"c": [df["c"].iloc[0]]}, index=expected_index).astype(
+        result_dtype
+    )
+    if method == "apply":
+        expected.columns.names = [0]
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "keys, agg_index",
+    [
+        (["a"], Index([1], name="a")),
+        (["a", "b"], MultiIndex([[1], [2]], [[0], [0]], names=["a", "b"])),
+    ],
+)
+@pytest.mark.parametrize("input", [True, 1, 1.0])
+@pytest.mark.parametrize("dtype", [bool, int, float])
+@pytest.mark.parametrize("method", ["apply", "aggregate", "transform"])
+def test_callable_result_dtype_series(keys, agg_index, input, dtype, method):
+    # GH 21240
+    df = DataFrame({"a": [1], "b": [2], "c": [input]})
+    op = getattr(df.groupby(keys)["c"], method)
+    result = op(lambda x: x.astype(dtype).iloc[0])
+    expected_index = pd.RangeIndex(0, 1) if method == "transform" else agg_index
+    expected = Series([df["c"].iloc[0]], index=expected_index, name="c").astype(dtype)
+    tm.assert_series_equal(result, expected)
+
+
+def test_order_aggregate_multiple_funcs():
+    # GH 25692
+    df = DataFrame({"A": [1, 1, 2, 2], "B": [1, 2, 3, 4]})
+
+    res = df.groupby("A").agg(["sum", "max", "mean", "ohlc", "min"])
+    result = res.columns.levels[1]
+
+    expected = Index(["sum", "max", "mean", "ohlc", "min"])
+
+    tm.assert_index_equal(result, expected)
+
+
+def test_ohlc_ea_dtypes(any_numeric_ea_dtype):
+    # GH#37493
+    df = DataFrame(
+        {"a": [1, 1, 2, 3, 4, 4], "b": [22, 11, pd.NA, 10, 20, pd.NA]},
+        dtype=any_numeric_ea_dtype,
+    )
+    gb = df.groupby("a")
+    result = gb.ohlc()
+    expected = DataFrame(
+        [[22, 22, 11, 11], [pd.NA] * 4, [10] * 4, [20] * 4],
+        columns=MultiIndex.from_product([["b"], ["open", "high", "low", "close"]]),
+        index=Index([1, 2, 3, 4], dtype=any_numeric_ea_dtype, name="a"),
+        dtype=any_numeric_ea_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+    gb2 = df.groupby("a", as_index=False)
+    result2 = gb2.ohlc()
+    expected2 = expected.reset_index()
+    tm.assert_frame_equal(result2, expected2)
+
+
+@pytest.mark.parametrize("dtype", [np.int64, np.uint64])
+@pytest.mark.parametrize("how", ["first", "last", "min", "max", "mean", "median"])
+def test_uint64_type_handling(dtype, how):
+    # GH 26310
+    df = DataFrame({"x": 6903052872240755750, "y": [1, 2]})
+    expected = df.groupby("y").agg({"x": how})
+    df.x = df.x.astype(dtype)
+    result = df.groupby("y").agg({"x": how})
+    if how not in ("mean", "median"):
+        # mean and median always result in floats
+        result.x = result.x.astype(np.int64)
+    tm.assert_frame_equal(result, expected, check_exact=True)
+
+
+def test_func_duplicates_raises():
+    # GH28426
+    msg = "Function names"
+    df = DataFrame({"A": [0, 0, 1, 1], "B": [1, 2, 3, 4]})
+    with pytest.raises(SpecificationError, match=msg):
+        df.groupby("A").agg(["min", "min"])
+
+
+@pytest.mark.parametrize(
+    "index",
+    [
+        pd.CategoricalIndex(list("abc")),
+        pd.interval_range(0, 3),
+        pd.period_range("2020", periods=3, freq="D"),
+        MultiIndex.from_tuples([("a", 0), ("a", 1), ("b", 0)]),
+    ],
+)
+def test_agg_index_has_complex_internals(index):
+    # GH 31223
+    df = DataFrame({"group": [1, 1, 2], "value": [0, 1, 0]}, index=index)
+    result = df.groupby("group").agg({"value": Series.nunique})
+    expected = DataFrame({"group": [1, 2], "value": [2, 1]}).set_index("group")
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_split_block():
+    # https://github.com/pandas-dev/pandas/issues/31522
+    df = DataFrame(
+        {
+            "key1": ["a", "a", "b", "b", "a"],
+            "key2": ["one", "two", "one", "two", "one"],
+            "key3": ["three", "three", "three", "six", "six"],
+        }
+    )
+    result = df.groupby("key1").min()
+    expected = DataFrame(
+        {"key2": ["one", "one"], "key3": ["six", "six"]},
+        index=Index(["a", "b"], name="key1"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_split_object_part_datetime():
+    # https://github.com/pandas-dev/pandas/pull/31616
+    df = DataFrame(
+        {
+            "A": pd.date_range("2000", periods=4),
+            "B": ["a", "b", "c", "d"],
+            "C": [1, 2, 3, 4],
+            "D": ["b", "c", "d", "e"],
+            "E": pd.date_range("2000", periods=4),
+            "F": [1, 2, 3, 4],
+        }
+    ).astype(object)
+    result = df.groupby([0, 0, 0, 0]).min()
+    expected = DataFrame(
+        {
+            "A": [pd.Timestamp("2000")],
+            "B": ["a"],
+            "C": [1],
+            "D": ["b"],
+            "E": [pd.Timestamp("2000")],
+            "F": [1],
+        },
+        index=np.array([0]),
+        dtype=object,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+class TestNamedAggregationSeries:
+    def test_series_named_agg(self):
+        df = Series([1, 2, 3, 4])
+        gr = df.groupby([0, 0, 1, 1])
+        result = gr.agg(a="sum", b="min")
+        expected = DataFrame(
+            {"a": [3, 7], "b": [1, 3]}, columns=["a", "b"], index=np.array([0, 1])
+        )
+        tm.assert_frame_equal(result, expected)
+
+        result = gr.agg(b="min", a="sum")
+        expected = expected[["b", "a"]]
+        tm.assert_frame_equal(result, expected)
+
+    def test_no_args_raises(self):
+        gr = Series([1, 2]).groupby([0, 1])
+        with pytest.raises(TypeError, match="Must provide"):
+            gr.agg()
+
+        # but we do allow this
+        result = gr.agg([])
+        expected = DataFrame(columns=[])
+        tm.assert_frame_equal(result, expected)
+
+    def test_series_named_agg_duplicates_no_raises(self):
+        # GH28426
+        gr = Series([1, 2, 3]).groupby([0, 0, 1])
+        grouped = gr.agg(a="sum", b="sum")
+        expected = DataFrame({"a": [3, 3], "b": [3, 3]}, index=np.array([0, 1]))
+        tm.assert_frame_equal(expected, grouped)
+
+    def test_mangled(self):
+        gr = Series([1, 2, 3]).groupby([0, 0, 1])
+        result = gr.agg(a=lambda x: 0, b=lambda x: 1)
+        expected = DataFrame({"a": [0, 0], "b": [1, 1]}, index=np.array([0, 1]))
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "inp",
+        [
+            pd.NamedAgg(column="anything", aggfunc="min"),
+            ("anything", "min"),
+            ["anything", "min"],
+        ],
+    )
+    def test_named_agg_nametuple(self, inp):
+        # GH34422
+        s = Series([1, 1, 2, 2, 3, 3, 4, 5])
+        msg = f"func is expected but received {type(inp).__name__}"
+        with pytest.raises(TypeError, match=msg):
+            s.groupby(s.values).agg(a=inp)
+
+
+class TestNamedAggregationDataFrame:
+    def test_agg_relabel(self):
+        df = DataFrame(
+            {"group": ["a", "a", "b", "b"], "A": [0, 1, 2, 3], "B": [5, 6, 7, 8]}
+        )
+        result = df.groupby("group").agg(a_max=("A", "max"), b_max=("B", "max"))
+        expected = DataFrame(
+            {"a_max": [1, 3], "b_max": [6, 8]},
+            index=Index(["a", "b"], name="group"),
+            columns=["a_max", "b_max"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # order invariance
+        p98 = functools.partial(np.percentile, q=98)
+        result = df.groupby("group").agg(
+            b_min=("B", "min"),
+            a_min=("A", min),
+            a_mean=("A", np.mean),
+            a_max=("A", "max"),
+            b_max=("B", "max"),
+            a_98=("A", p98),
+        )
+        expected = DataFrame(
+            {
+                "b_min": [5, 7],
+                "a_min": [0, 2],
+                "a_mean": [0.5, 2.5],
+                "a_max": [1, 3],
+                "b_max": [6, 8],
+                "a_98": [0.98, 2.98],
+            },
+            index=Index(["a", "b"], name="group"),
+            columns=["b_min", "a_min", "a_mean", "a_max", "b_max", "a_98"],
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_agg_relabel_non_identifier(self):
+        df = DataFrame(
+            {"group": ["a", "a", "b", "b"], "A": [0, 1, 2, 3], "B": [5, 6, 7, 8]}
+        )
+
+        result = df.groupby("group").agg(**{"my col": ("A", "max")})
+        expected = DataFrame({"my col": [1, 3]}, index=Index(["a", "b"], name="group"))
+        tm.assert_frame_equal(result, expected)
+
+    def test_duplicate_no_raises(self):
+        # GH 28426, if use same input function on same column,
+        # no error should raise
+        df = DataFrame({"A": [0, 0, 1, 1], "B": [1, 2, 3, 4]})
+
+        grouped = df.groupby("A").agg(a=("B", "min"), b=("B", "min"))
+        expected = DataFrame({"a": [1, 3], "b": [1, 3]}, index=Index([0, 1], name="A"))
+        tm.assert_frame_equal(grouped, expected)
+
+        quant50 = functools.partial(np.percentile, q=50)
+        quant70 = functools.partial(np.percentile, q=70)
+        quant50.__name__ = "quant50"
+        quant70.__name__ = "quant70"
+
+        test = DataFrame({"col1": ["a", "a", "b", "b", "b"], "col2": [1, 2, 3, 4, 5]})
+
+        grouped = test.groupby("col1").agg(
+            quantile_50=("col2", quant50), quantile_70=("col2", quant70)
+        )
+        expected = DataFrame(
+            {"quantile_50": [1.5, 4.0], "quantile_70": [1.7, 4.4]},
+            index=Index(["a", "b"], name="col1"),
+        )
+        tm.assert_frame_equal(grouped, expected)
+
+    def test_agg_relabel_with_level(self):
+        df = DataFrame(
+            {"A": [0, 0, 1, 1], "B": [1, 2, 3, 4]},
+            index=MultiIndex.from_product([["A", "B"], ["a", "b"]]),
+        )
+        result = df.groupby(level=0).agg(
+            aa=("A", "max"), bb=("A", "min"), cc=("B", "mean")
+        )
+        expected = DataFrame(
+            {"aa": [0, 1], "bb": [0, 1], "cc": [1.5, 3.5]}, index=["A", "B"]
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_agg_relabel_other_raises(self):
+        df = DataFrame({"A": [0, 0, 1], "B": [1, 2, 3]})
+        grouped = df.groupby("A")
+        match = "Must provide"
+        with pytest.raises(TypeError, match=match):
+            grouped.agg(foo=1)
+
+        with pytest.raises(TypeError, match=match):
+            grouped.agg()
+
+        with pytest.raises(TypeError, match=match):
+            grouped.agg(a=("B", "max"), b=(1, 2, 3))
+
+    def test_missing_raises(self):
+        df = DataFrame({"A": [0, 1], "B": [1, 2]})
+        match = re.escape("Column(s) ['C'] do not exist")
+        with pytest.raises(KeyError, match=match):
+            df.groupby("A").agg(c=("C", "sum"))
+
+    def test_agg_namedtuple(self):
+        df = DataFrame({"A": [0, 1], "B": [1, 2]})
+        result = df.groupby("A").agg(
+            b=pd.NamedAgg("B", "sum"), c=pd.NamedAgg(column="B", aggfunc="count")
+        )
+        expected = df.groupby("A").agg(b=("B", "sum"), c=("B", "count"))
+        tm.assert_frame_equal(result, expected)
+
+    def test_mangled(self):
+        df = DataFrame({"A": [0, 1], "B": [1, 2], "C": [3, 4]})
+        result = df.groupby("A").agg(b=("B", lambda x: 0), c=("C", lambda x: 1))
+        expected = DataFrame({"b": [0, 0], "c": [1, 1]}, index=Index([0, 1], name="A"))
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "agg_col1, agg_col2, agg_col3, agg_result1, agg_result2, agg_result3",
+    [
+        (
+            (("y", "A"), "max"),
+            (("y", "A"), np.min),
+            (("y", "B"), "mean"),
+            [1, 3],
+            [0, 2],
+            [5.5, 7.5],
+        ),
+        (
+            (("y", "A"), lambda x: max(x)),
+            (("y", "A"), lambda x: 1),
+            (("y", "B"), "mean"),
+            [1, 3],
+            [1, 1],
+            [5.5, 7.5],
+        ),
+        (
+            pd.NamedAgg(("y", "A"), "max"),
+            pd.NamedAgg(("y", "B"), np.mean),
+            pd.NamedAgg(("y", "A"), lambda x: 1),
+            [1, 3],
+            [5.5, 7.5],
+            [1, 1],
+        ),
+    ],
+)
+def test_agg_relabel_multiindex_column(
+    agg_col1, agg_col2, agg_col3, agg_result1, agg_result2, agg_result3
+):
+    # GH 29422, add tests for multiindex column cases
+    df = DataFrame(
+        {"group": ["a", "a", "b", "b"], "A": [0, 1, 2, 3], "B": [5, 6, 7, 8]}
+    )
+    df.columns = MultiIndex.from_tuples([("x", "group"), ("y", "A"), ("y", "B")])
+    idx = Index(["a", "b"], name=("x", "group"))
+
+    result = df.groupby(("x", "group")).agg(a_max=(("y", "A"), "max"))
+    expected = DataFrame({"a_max": [1, 3]}, index=idx)
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby(("x", "group")).agg(
+        col_1=agg_col1, col_2=agg_col2, col_3=agg_col3
+    )
+    expected = DataFrame(
+        {"col_1": agg_result1, "col_2": agg_result2, "col_3": agg_result3}, index=idx
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_relabel_multiindex_raises_not_exist():
+    # GH 29422, add test for raises scenario when aggregate column does not exist
+    df = DataFrame(
+        {"group": ["a", "a", "b", "b"], "A": [0, 1, 2, 3], "B": [5, 6, 7, 8]}
+    )
+    df.columns = MultiIndex.from_tuples([("x", "group"), ("y", "A"), ("y", "B")])
+
+    with pytest.raises(KeyError, match="do not exist"):
+        df.groupby(("x", "group")).agg(a=(("Y", "a"), "max"))
+
+
+def test_agg_relabel_multiindex_duplicates():
+    # GH29422, add test for raises scenario when getting duplicates
+    # GH28426, after this change, duplicates should also work if the relabelling is
+    # different
+    df = DataFrame(
+        {"group": ["a", "a", "b", "b"], "A": [0, 1, 2, 3], "B": [5, 6, 7, 8]}
+    )
+    df.columns = MultiIndex.from_tuples([("x", "group"), ("y", "A"), ("y", "B")])
+
+    result = df.groupby(("x", "group")).agg(
+        a=(("y", "A"), "min"), b=(("y", "A"), "min")
+    )
+    idx = Index(["a", "b"], name=("x", "group"))
+    expected = DataFrame({"a": [0, 2], "b": [0, 2]}, index=idx)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("kwargs", [{"c": ["min"]}, {"b": [], "c": ["min"]}])
+def test_groupby_aggregate_empty_key(kwargs):
+    # GH: 32580
+    df = DataFrame({"a": [1, 1, 2], "b": [1, 2, 3], "c": [1, 2, 4]})
+    result = df.groupby("a").agg(kwargs)
+    expected = DataFrame(
+        [1, 4],
+        index=Index([1, 2], dtype="int64", name="a"),
+        columns=MultiIndex.from_tuples([["c", "min"]]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_aggregate_empty_key_empty_return():
+    # GH: 32580 Check if everything works, when return is empty
+    df = DataFrame({"a": [1, 1, 2], "b": [1, 2, 3], "c": [1, 2, 4]})
+    result = df.groupby("a").agg({"b": []})
+    expected = DataFrame(columns=MultiIndex(levels=[["b"], []], codes=[[], []]))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_aggregate_empty_with_multiindex_frame():
+    # GH 39178
+    df = DataFrame(columns=["a", "b", "c"])
+    result = df.groupby(["a", "b"], group_keys=False).agg(d=("c", list))
+    expected = DataFrame(
+        columns=["d"], index=MultiIndex([[], []], [[], []], names=["a", "b"])
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_grouby_agg_loses_results_with_as_index_false_relabel():
+    # GH 32240: When the aggregate function relabels column names and
+    # as_index=False is specified, the results are dropped.
+
+    df = DataFrame(
+        {"key": ["x", "y", "z", "x", "y", "z"], "val": [1.0, 0.8, 2.0, 3.0, 3.6, 0.75]}
+    )
+
+    grouped = df.groupby("key", as_index=False)
+    result = grouped.agg(min_val=pd.NamedAgg(column="val", aggfunc="min"))
+    expected = DataFrame({"key": ["x", "y", "z"], "min_val": [1.0, 0.8, 0.75]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_grouby_agg_loses_results_with_as_index_false_relabel_multiindex():
+    # GH 32240: When the aggregate function relabels column names and
+    # as_index=False is specified, the results are dropped. Check if
+    # multiindex is returned in the right order
+
+    df = DataFrame(
+        {
+            "key": ["x", "y", "x", "y", "x", "x"],
+            "key1": ["a", "b", "c", "b", "a", "c"],
+            "val": [1.0, 0.8, 2.0, 3.0, 3.6, 0.75],
+        }
+    )
+
+    grouped = df.groupby(["key", "key1"], as_index=False)
+    result = grouped.agg(min_val=pd.NamedAgg(column="val", aggfunc="min"))
+    expected = DataFrame(
+        {"key": ["x", "x", "y"], "key1": ["a", "c", "b"], "min_val": [1.0, 0.75, 0.8]}
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "func", [lambda s: s.mean(), lambda s: np.mean(s), lambda s: np.nanmean(s)]
+)
+def test_multiindex_custom_func(func):
+    # GH 31777
+    data = [[1, 4, 2], [5, 7, 1]]
+    df = DataFrame(
+        data,
+        columns=MultiIndex.from_arrays(
+            [[1, 1, 2], [3, 4, 3]], names=["Sisko", "Janeway"]
+        ),
+    )
+    result = df.groupby(np.array([0, 1])).agg(func)
+    expected_dict = {
+        (1, 3): {0: 1.0, 1: 5.0},
+        (1, 4): {0: 4.0, 1: 7.0},
+        (2, 3): {0: 2.0, 1: 1.0},
+    }
+    expected = DataFrame(expected_dict, index=np.array([0, 1]), columns=df.columns)
+    tm.assert_frame_equal(result, expected)
+
+
+def myfunc(s):
+    return np.percentile(s, q=0.90)
+
+
+@pytest.mark.parametrize("func", [lambda s: np.percentile(s, q=0.90), myfunc])
+def test_lambda_named_agg(func):
+    # see gh-28467
+    animals = DataFrame(
+        {
+            "kind": ["cat", "dog", "cat", "dog"],
+            "height": [9.1, 6.0, 9.5, 34.0],
+            "weight": [7.9, 7.5, 9.9, 198.0],
+        }
+    )
+
+    result = animals.groupby("kind").agg(
+        mean_height=("height", "mean"), perc90=("height", func)
+    )
+    expected = DataFrame(
+        [[9.3, 9.1036], [20.0, 6.252]],
+        columns=["mean_height", "perc90"],
+        index=Index(["cat", "dog"], name="kind"),
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_aggregate_mixed_types():
+    # GH 16916
+    df = DataFrame(
+        data=np.array([0] * 9).reshape(3, 3), columns=list("XYZ"), index=list("abc")
+    )
+    df["grouping"] = ["group 1", "group 1", 2]
+    result = df.groupby("grouping").aggregate(lambda x: x.tolist())
+    expected_data = [[[0], [0], [0]], [[0, 0], [0, 0], [0, 0]]]
+    expected = DataFrame(
+        expected_data,
+        index=Index([2, "group 1"], dtype="object", name="grouping"),
+        columns=Index(["X", "Y", "Z"], dtype="object"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.xfail(reason="Not implemented;see GH 31256")
+def test_aggregate_udf_na_extension_type():
+    # https://github.com/pandas-dev/pandas/pull/31359
+    # This is currently failing to cast back to Int64Dtype.
+    # The presence of the NA causes two problems
+    # 1. NA is not an instance of Int64Dtype.type (numpy.int64)
+    # 2. The presence of an NA forces object type, so the non-NA values is
+    #    a Python int rather than a NumPy int64. Python ints aren't
+    #    instances of numpy.int64.
+    def aggfunc(x):
+        if all(x > 2):
+            return 1
+        else:
+            return pd.NA
+
+    df = DataFrame({"A": pd.array([1, 2, 3])})
+    result = df.groupby([1, 1, 2]).agg(aggfunc)
+    expected = DataFrame({"A": pd.array([1, pd.NA], dtype="Int64")}, index=[1, 2])
+    tm.assert_frame_equal(result, expected)
+
+
+class TestLambdaMangling:
+    def test_basic(self):
+        df = DataFrame({"A": [0, 0, 1, 1], "B": [1, 2, 3, 4]})
+        result = df.groupby("A").agg({"B": [lambda x: 0, lambda x: 1]})
+
+        expected = DataFrame(
+            {("B", "<lambda_0>"): [0, 0], ("B", "<lambda_1>"): [1, 1]},
+            index=Index([0, 1], name="A"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_mangle_series_groupby(self):
+        gr = Series([1, 2, 3, 4]).groupby([0, 0, 1, 1])
+        result = gr.agg([lambda x: 0, lambda x: 1])
+        exp_data = {"<lambda_0>": [0, 0], "<lambda_1>": [1, 1]}
+        expected = DataFrame(exp_data, index=np.array([0, 1]))
+        tm.assert_frame_equal(result, expected)
+
+    @pytest.mark.xfail(reason="GH-26611. kwargs for multi-agg.")
+    def test_with_kwargs(self):
+        f1 = lambda x, y, b=1: x.sum() + y + b
+        f2 = lambda x, y, b=2: x.sum() + y * b
+        result = Series([1, 2]).groupby([0, 0]).agg([f1, f2], 0)
+        expected = DataFrame({"<lambda_0>": [4], "<lambda_1>": [6]})
+        tm.assert_frame_equal(result, expected)
+
+        result = Series([1, 2]).groupby([0, 0]).agg([f1, f2], 0, b=10)
+        expected = DataFrame({"<lambda_0>": [13], "<lambda_1>": [30]})
+        tm.assert_frame_equal(result, expected)
+
+    def test_agg_with_one_lambda(self):
+        # GH 25719, write tests for DataFrameGroupby.agg with only one lambda
+        df = DataFrame(
+            {
+                "kind": ["cat", "dog", "cat", "dog"],
+                "height": [9.1, 6.0, 9.5, 34.0],
+                "weight": [7.9, 7.5, 9.9, 198.0],
+            }
+        )
+
+        columns = ["height_sqr_min", "height_max", "weight_max"]
+        expected = DataFrame(
+            {
+                "height_sqr_min": [82.81, 36.00],
+                "height_max": [9.5, 34.0],
+                "weight_max": [9.9, 198.0],
+            },
+            index=Index(["cat", "dog"], name="kind"),
+            columns=columns,
+        )
+
+        # check pd.NameAgg case
+        result1 = df.groupby(by="kind").agg(
+            height_sqr_min=pd.NamedAgg(
+                column="height", aggfunc=lambda x: np.min(x**2)
+            ),
+            height_max=pd.NamedAgg(column="height", aggfunc="max"),
+            weight_max=pd.NamedAgg(column="weight", aggfunc="max"),
+        )
+        tm.assert_frame_equal(result1, expected)
+
+        # check agg(key=(col, aggfunc)) case
+        result2 = df.groupby(by="kind").agg(
+            height_sqr_min=("height", lambda x: np.min(x**2)),
+            height_max=("height", "max"),
+            weight_max=("weight", "max"),
+        )
+        tm.assert_frame_equal(result2, expected)
+
+    def test_agg_multiple_lambda(self):
+        # GH25719, test for DataFrameGroupby.agg with multiple lambdas
+        # with mixed aggfunc
+        df = DataFrame(
+            {
+                "kind": ["cat", "dog", "cat", "dog"],
+                "height": [9.1, 6.0, 9.5, 34.0],
+                "weight": [7.9, 7.5, 9.9, 198.0],
+            }
+        )
+        columns = [
+            "height_sqr_min",
+            "height_max",
+            "weight_max",
+            "height_max_2",
+            "weight_min",
+        ]
+        expected = DataFrame(
+            {
+                "height_sqr_min": [82.81, 36.00],
+                "height_max": [9.5, 34.0],
+                "weight_max": [9.9, 198.0],
+                "height_max_2": [9.5, 34.0],
+                "weight_min": [7.9, 7.5],
+            },
+            index=Index(["cat", "dog"], name="kind"),
+            columns=columns,
+        )
+
+        # check agg(key=(col, aggfunc)) case
+        result1 = df.groupby(by="kind").agg(
+            height_sqr_min=("height", lambda x: np.min(x**2)),
+            height_max=("height", "max"),
+            weight_max=("weight", "max"),
+            height_max_2=("height", lambda x: np.max(x)),
+            weight_min=("weight", lambda x: np.min(x)),
+        )
+        tm.assert_frame_equal(result1, expected)
+
+        # check pd.NamedAgg case
+        result2 = df.groupby(by="kind").agg(
+            height_sqr_min=pd.NamedAgg(
+                column="height", aggfunc=lambda x: np.min(x**2)
+            ),
+            height_max=pd.NamedAgg(column="height", aggfunc="max"),
+            weight_max=pd.NamedAgg(column="weight", aggfunc="max"),
+            height_max_2=pd.NamedAgg(column="height", aggfunc=lambda x: np.max(x)),
+            weight_min=pd.NamedAgg(column="weight", aggfunc=lambda x: np.min(x)),
+        )
+        tm.assert_frame_equal(result2, expected)
+
+
+def test_groupby_get_by_index():
+    # GH 33439
+    df = DataFrame({"A": ["S", "W", "W"], "B": [1.0, 1.0, 2.0]})
+    res = df.groupby("A").agg({"B": lambda x: x.get(x.index[-1])})
+    expected = DataFrame({"A": ["S", "W"], "B": [1.0, 2.0]}).set_index("A")
+    tm.assert_frame_equal(res, expected)
+
+
+@pytest.mark.parametrize(
+    "grp_col_dict, exp_data",
+    [
+        ({"nr": "min", "cat_ord": "min"}, {"nr": [1, 5], "cat_ord": ["a", "c"]}),
+        ({"cat_ord": "min"}, {"cat_ord": ["a", "c"]}),
+        ({"nr": "min"}, {"nr": [1, 5]}),
+    ],
+)
+def test_groupby_single_agg_cat_cols(grp_col_dict, exp_data):
+    # test single aggregations on ordered categorical cols GHGH27800
+
+    # create the result dataframe
+    input_df = DataFrame(
+        {
+            "nr": [1, 2, 3, 4, 5, 6, 7, 8],
+            "cat_ord": list("aabbccdd"),
+            "cat": list("aaaabbbb"),
+        }
+    )
+
+    input_df = input_df.astype({"cat": "category", "cat_ord": "category"})
+    input_df["cat_ord"] = input_df["cat_ord"].cat.as_ordered()
+    result_df = input_df.groupby("cat").agg(grp_col_dict)
+
+    # create expected dataframe
+    cat_index = pd.CategoricalIndex(
+        ["a", "b"], categories=["a", "b"], ordered=False, name="cat", dtype="category"
+    )
+
+    expected_df = DataFrame(data=exp_data, index=cat_index)
+
+    if "cat_ord" in expected_df:
+        # ordered categorical columns should be preserved
+        dtype = input_df["cat_ord"].dtype
+        expected_df["cat_ord"] = expected_df["cat_ord"].astype(dtype)
+
+    tm.assert_frame_equal(result_df, expected_df)
+
+
+@pytest.mark.parametrize(
+    "grp_col_dict, exp_data",
+    [
+        ({"nr": ["min", "max"], "cat_ord": "min"}, [(1, 4, "a"), (5, 8, "c")]),
+        ({"nr": "min", "cat_ord": ["min", "max"]}, [(1, "a", "b"), (5, "c", "d")]),
+        ({"cat_ord": ["min", "max"]}, [("a", "b"), ("c", "d")]),
+    ],
+)
+def test_groupby_combined_aggs_cat_cols(grp_col_dict, exp_data):
+    # test combined aggregations on ordered categorical cols GH27800
+
+    # create the result dataframe
+    input_df = DataFrame(
+        {
+            "nr": [1, 2, 3, 4, 5, 6, 7, 8],
+            "cat_ord": list("aabbccdd"),
+            "cat": list("aaaabbbb"),
+        }
+    )
+
+    input_df = input_df.astype({"cat": "category", "cat_ord": "category"})
+    input_df["cat_ord"] = input_df["cat_ord"].cat.as_ordered()
+    result_df = input_df.groupby("cat").agg(grp_col_dict)
+
+    # create expected dataframe
+    cat_index = pd.CategoricalIndex(
+        ["a", "b"], categories=["a", "b"], ordered=False, name="cat", dtype="category"
+    )
+
+    # unpack the grp_col_dict to create the multi-index tuple
+    # this tuple will be used to create the expected dataframe index
+    multi_index_list = []
+    for k, v in grp_col_dict.items():
+        if isinstance(v, list):
+            for value in v:
+                multi_index_list.append([k, value])
+        else:
+            multi_index_list.append([k, v])
+    multi_index = MultiIndex.from_tuples(tuple(multi_index_list))
+
+    expected_df = DataFrame(data=exp_data, columns=multi_index, index=cat_index)
+    for col in expected_df.columns:
+        if isinstance(col, tuple) and "cat_ord" in col:
+            # ordered categorical should be preserved
+            expected_df[col] = expected_df[col].astype(input_df["cat_ord"].dtype)
+
+    tm.assert_frame_equal(result_df, expected_df)
+
+
+def test_nonagg_agg():
+    # GH 35490 - Single/Multiple agg of non-agg function give same results
+    # TODO: agg should raise for functions that don't aggregate
+    df = DataFrame({"a": [1, 1, 2, 2], "b": [1, 2, 2, 1]})
+    g = df.groupby("a")
+
+    result = g.agg(["cumsum"])
+    result.columns = result.columns.droplevel(-1)
+    expected = g.agg("cumsum")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_aggregate_datetime_objects():
+    # https://github.com/pandas-dev/pandas/issues/36003
+    # ensure we don't raise an error but keep object dtype for out-of-bounds
+    # datetimes
+    df = DataFrame(
+        {
+            "A": ["X", "Y"],
+            "B": [
+                datetime.datetime(2005, 1, 1, 10, 30, 23, 540000),
+                datetime.datetime(3005, 1, 1, 10, 30, 23, 540000),
+            ],
+        }
+    )
+    result = df.groupby("A").B.max()
+    expected = df.set_index("A")["B"]
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_index_object_dtype():
+    # GH 40014
+    df = DataFrame({"c0": ["x", "x", "x"], "c1": ["x", "x", "y"], "p": [0, 1, 2]})
+    df.index = df.index.astype("O")
+    grouped = df.groupby(["c0", "c1"])
+    res = grouped.p.agg(lambda x: all(x > 0))
+    # Check that providing a user-defined function in agg()
+    # produces the correct index shape when using an object-typed index.
+    expected_index = MultiIndex.from_tuples(
+        [("x", "x"), ("x", "y")], names=("c0", "c1")
+    )
+    expected = Series([False, True], index=expected_index, name="p")
+    tm.assert_series_equal(res, expected)
+
+
+def test_timeseries_groupby_agg():
+    # GH#43290
+
+    def func(ser):
+        if ser.isna().all():
+            return None
+        return np.sum(ser)
+
+    df = DataFrame([1.0], index=[pd.Timestamp("2018-01-16 00:00:00+00:00")])
+    res = df.groupby(lambda x: 1).agg(func)
+
+    expected = DataFrame([[1.0]], index=[1])
+    tm.assert_frame_equal(res, expected)
+
+
+def test_groupby_aggregate_directory(reduction_func):
+    # GH#32793
+    if reduction_func in ["corrwith", "nth"]:
+        return None
+
+    obj = DataFrame([[0, 1], [0, np.nan]])
+
+    result_reduced_series = obj.groupby(0).agg(reduction_func)
+    result_reduced_frame = obj.groupby(0).agg({1: reduction_func})
+
+    if reduction_func in ["size", "ngroup"]:
+        # names are different: None / 1
+        tm.assert_series_equal(
+            result_reduced_series, result_reduced_frame[1], check_names=False
+        )
+    else:
+        tm.assert_frame_equal(result_reduced_series, result_reduced_frame)
+        tm.assert_series_equal(
+            result_reduced_series.dtypes, result_reduced_frame.dtypes
+        )
+
+
+def test_group_mean_timedelta_nat():
+    # GH43132
+    data = Series(["1 day", "3 days", "NaT"], dtype="timedelta64[ns]")
+    expected = Series(["2 days"], dtype="timedelta64[ns]", index=np.array([0]))
+
+    result = data.groupby([0, 0, 0]).mean()
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "input_data, expected_output",
+    [
+        (  # no timezone
+            ["2021-01-01T00:00", "NaT", "2021-01-01T02:00"],
+            ["2021-01-01T01:00"],
+        ),
+        (  # timezone
+            ["2021-01-01T00:00-0100", "NaT", "2021-01-01T02:00-0100"],
+            ["2021-01-01T01:00-0100"],
+        ),
+    ],
+)
+def test_group_mean_datetime64_nat(input_data, expected_output):
+    # GH43132
+    data = to_datetime(Series(input_data))
+    expected = to_datetime(Series(expected_output, index=np.array([0])))
+
+    result = data.groupby([0, 0, 0]).mean()
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "func, output", [("mean", [8 + 18j, 10 + 22j]), ("sum", [40 + 90j, 50 + 110j])]
+)
+def test_groupby_complex(func, output):
+    # GH#43701
+    data = Series(np.arange(20).reshape(10, 2).dot([1, 2j]))
+    result = data.groupby(data.index % 2).agg(func)
+    expected = Series(output)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("func", ["min", "max", "var"])
+def test_groupby_complex_raises(func):
+    # GH#43701
+    data = Series(np.arange(20).reshape(10, 2).dot([1, 2j]))
+    msg = "No matching signature found"
+    with pytest.raises(TypeError, match=msg):
+        data.groupby(data.index % 2).agg(func)
+
+
+@pytest.mark.parametrize(
+    "func", [["min"], ["mean", "max"], {"b": "sum"}, {"b": "prod", "c": "median"}]
+)
+def test_multi_axis_1_raises(func):
+    # GH#46995
+    df = DataFrame({"a": [1, 1, 2], "b": [3, 4, 5], "c": [6, 7, 8]})
+    gb = df.groupby("a", axis=1)
+    with pytest.raises(NotImplementedError, match="axis other than 0 is not supported"):
+        gb.agg(func)
+
+
+@pytest.mark.parametrize(
+    "test, constant",
+    [
+        ([[20, "A"], [20, "B"], [10, "C"]], {0: [10, 20], 1: ["C", ["A", "B"]]}),
+        ([[20, "A"], [20, "B"], [30, "C"]], {0: [20, 30], 1: [["A", "B"], "C"]}),
+        ([["a", 1], ["a", 1], ["b", 2], ["b", 3]], {0: ["a", "b"], 1: [1, [2, 3]]}),
+        pytest.param(
+            [["a", 1], ["a", 2], ["b", 3], ["b", 3]],
+            {0: ["a", "b"], 1: [[1, 2], 3]},
+            marks=pytest.mark.xfail,
+        ),
+    ],
+)
+def test_agg_of_mode_list(test, constant):
+    # GH#25581
+    df1 = DataFrame(test)
+    result = df1.groupby(0).agg(Series.mode)
+    # Mode usually only returns 1 value, but can return a list in the case of a tie.
+
+    expected = DataFrame(constant)
+    expected = expected.set_index(0)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test__dataframe_groupy_agg_list_like_func_with_args():
+    # GH 50624
+    df = DataFrame({"x": [1, 2, 3], "y": ["a", "b", "c"]})
+    gb = df.groupby("y")
+
+    def foo1(x, a=1, c=0):
+        return x.sum() + a + c
+
+    def foo2(x, b=2, c=0):
+        return x.sum() + b + c
+
+    msg = r"foo1\(\) got an unexpected keyword argument 'b'"
+    with pytest.raises(TypeError, match=msg):
+        gb.agg([foo1, foo2], 3, b=3, c=4)
+
+    result = gb.agg([foo1, foo2], 3, c=4)
+    expected = DataFrame(
+        [[8, 8], [9, 9], [10, 10]],
+        index=Index(["a", "b", "c"], name="y"),
+        columns=MultiIndex.from_tuples([("x", "foo1"), ("x", "foo2")]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test__series_groupy_agg_list_like_func_with_args():
+    # GH 50624
+    s = Series([1, 2, 3])
+    sgb = s.groupby(s)
+
+    def foo1(x, a=1, c=0):
+        return x.sum() + a + c
+
+    def foo2(x, b=2, c=0):
+        return x.sum() + b + c
+
+    msg = r"foo1\(\) got an unexpected keyword argument 'b'"
+    with pytest.raises(TypeError, match=msg):
+        sgb.agg([foo1, foo2], 3, b=3, c=4)
+
+    result = sgb.agg([foo1, foo2], 3, c=4)
+    expected = DataFrame(
+        [[8, 8], [9, 9], [10, 10]], index=Index([1, 2, 3]), columns=["foo1", "foo2"]
+    )
+    tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/aggregate/test_cython.py

@@ -0,0 +1,399 @@
+"""
+test cython .agg behavior
+"""
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import (
+    is_float_dtype,
+    is_integer_dtype,
+)
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    NaT,
+    Series,
+    Timedelta,
+    Timestamp,
+    bdate_range,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "op_name",
+    [
+        "count",
+        "sum",
+        "std",
+        "var",
+        "sem",
+        "mean",
+        pytest.param(
+            "median",
+            # ignore mean of empty slice
+            # and all-NaN
+            marks=[pytest.mark.filterwarnings("ignore::RuntimeWarning")],
+        ),
+        "prod",
+        "min",
+        "max",
+    ],
+)
+def test_cythonized_aggers(op_name):
+    data = {
+        "A": [0, 0, 0, 0, 1, 1, 1, 1, 1, 1.0, np.nan, np.nan],
+        "B": ["A", "B"] * 6,
+        "C": np.random.randn(12),
+    }
+    df = DataFrame(data)
+    df.loc[2:10:2, "C"] = np.nan
+
+    op = lambda x: getattr(x, op_name)()
+
+    # single column
+    grouped = df.drop(["B"], axis=1).groupby("A")
+    exp = {cat: op(group["C"]) for cat, group in grouped}
+    exp = DataFrame({"C": exp})
+    exp.index.name = "A"
+    result = op(grouped)
+    tm.assert_frame_equal(result, exp)
+
+    # multiple columns
+    grouped = df.groupby(["A", "B"])
+    expd = {}
+    for (cat1, cat2), group in grouped:
+        expd.setdefault(cat1, {})[cat2] = op(group["C"])
+    exp = DataFrame(expd).T.stack(dropna=False)
+    exp.index.names = ["A", "B"]
+    exp.name = "C"
+
+    result = op(grouped)["C"]
+    if op_name in ["sum", "prod"]:
+        tm.assert_series_equal(result, exp)
+
+
+def test_cython_agg_boolean():
+    frame = DataFrame(
+        {
+            "a": np.random.randint(0, 5, 50),
+            "b": np.random.randint(0, 2, 50).astype("bool"),
+        }
+    )
+    result = frame.groupby("a")["b"].mean()
+    expected = frame.groupby("a")["b"].agg(np.mean)
+
+    tm.assert_series_equal(result, expected)
+
+
+def test_cython_agg_nothing_to_agg():
+    frame = DataFrame({"a": np.random.randint(0, 5, 50), "b": ["foo", "bar"] * 25})
+
+    msg = "Cannot use numeric_only=True with SeriesGroupBy.mean and non-numeric dtypes"
+    with pytest.raises(TypeError, match=msg):
+        frame.groupby("a")["b"].mean(numeric_only=True)
+
+    frame = DataFrame({"a": np.random.randint(0, 5, 50), "b": ["foo", "bar"] * 25})
+
+    result = frame[["b"]].groupby(frame["a"]).mean(numeric_only=True)
+    expected = DataFrame(
+        [], index=frame["a"].sort_values().drop_duplicates(), columns=[]
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_cython_agg_nothing_to_agg_with_dates():
+    frame = DataFrame(
+        {
+            "a": np.random.randint(0, 5, 50),
+            "b": ["foo", "bar"] * 25,
+            "dates": pd.date_range("now", periods=50, freq="T"),
+        }
+    )
+    msg = "Cannot use numeric_only=True with SeriesGroupBy.mean and non-numeric dtypes"
+    with pytest.raises(TypeError, match=msg):
+        frame.groupby("b").dates.mean(numeric_only=True)
+
+
+def test_cython_agg_frame_columns():
+    # #2113
+    df = DataFrame({"x": [1, 2, 3], "y": [3, 4, 5]})
+
+    df.groupby(level=0, axis="columns").mean()
+    df.groupby(level=0, axis="columns").mean()
+    df.groupby(level=0, axis="columns").mean()
+    df.groupby(level=0, axis="columns").mean()
+
+
+def test_cython_agg_return_dict():
+    # GH 16741
+    df = DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+            "C": np.random.randn(8),
+            "D": np.random.randn(8),
+        }
+    )
+
+    ts = df.groupby("A")["B"].agg(lambda x: x.value_counts().to_dict())
+    expected = Series(
+        [{"two": 1, "one": 1, "three": 1}, {"two": 2, "one": 2, "three": 1}],
+        index=Index(["bar", "foo"], name="A"),
+        name="B",
+    )
+    tm.assert_series_equal(ts, expected)
+
+
+def test_cython_fail_agg():
+    dr = bdate_range("1/1/2000", periods=50)
+    ts = Series(["A", "B", "C", "D", "E"] * 10, index=dr)
+
+    grouped = ts.groupby(lambda x: x.month)
+    summed = grouped.sum()
+    expected = grouped.agg(np.sum)
+    tm.assert_series_equal(summed, expected)
+
+
+@pytest.mark.parametrize(
+    "op, targop",
+    [
+        ("mean", np.mean),
+        ("median", np.median),
+        ("var", np.var),
+        ("sum", np.sum),
+        ("prod", np.prod),
+        ("min", np.min),
+        ("max", np.max),
+        ("first", lambda x: x.iloc[0]),
+        ("last", lambda x: x.iloc[-1]),
+    ],
+)
+def test__cython_agg_general(op, targop):
+    df = DataFrame(np.random.randn(1000))
+    labels = np.random.randint(0, 50, size=1000).astype(float)
+
+    result = df.groupby(labels)._cython_agg_general(op, alt=None, numeric_only=True)
+    expected = df.groupby(labels).agg(targop)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "op, targop",
+    [
+        ("mean", np.mean),
+        ("median", lambda x: np.median(x) if len(x) > 0 else np.nan),
+        ("var", lambda x: np.var(x, ddof=1)),
+        ("min", np.min),
+        ("max", np.max),
+    ],
+)
+def test_cython_agg_empty_buckets(op, targop, observed):
+    df = DataFrame([11, 12, 13])
+    grps = range(0, 55, 5)
+
+    # calling _cython_agg_general directly, instead of via the user API
+    # which sets different values for min_count, so do that here.
+    g = df.groupby(pd.cut(df[0], grps), observed=observed)
+    result = g._cython_agg_general(op, alt=None, numeric_only=True)
+
+    g = df.groupby(pd.cut(df[0], grps), observed=observed)
+    expected = g.agg(lambda x: targop(x))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_cython_agg_empty_buckets_nanops(observed):
+    # GH-18869 can't call nanops on empty groups, so hardcode expected
+    # for these
+    df = DataFrame([11, 12, 13], columns=["a"])
+    grps = np.arange(0, 25, 5, dtype=np.int_)
+    # add / sum
+    result = df.groupby(pd.cut(df["a"], grps), observed=observed)._cython_agg_general(
+        "sum", alt=None, numeric_only=True
+    )
+    intervals = pd.interval_range(0, 20, freq=5)
+    expected = DataFrame(
+        {"a": [0, 0, 36, 0]},
+        index=pd.CategoricalIndex(intervals, name="a", ordered=True),
+    )
+    if observed:
+        expected = expected[expected.a != 0]
+
+    tm.assert_frame_equal(result, expected)
+
+    # prod
+    result = df.groupby(pd.cut(df["a"], grps), observed=observed)._cython_agg_general(
+        "prod", alt=None, numeric_only=True
+    )
+    expected = DataFrame(
+        {"a": [1, 1, 1716, 1]},
+        index=pd.CategoricalIndex(intervals, name="a", ordered=True),
+    )
+    if observed:
+        expected = expected[expected.a != 1]
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("op", ["first", "last", "max", "min"])
+@pytest.mark.parametrize(
+    "data", [Timestamp("2016-10-14 21:00:44.557"), Timedelta("17088 days 21:00:44.557")]
+)
+def test_cython_with_timestamp_and_nat(op, data):
+    # https://github.com/pandas-dev/pandas/issues/19526
+    df = DataFrame({"a": [0, 1], "b": [data, NaT]})
+    index = Index([0, 1], name="a")
+
+    # We will group by a and test the cython aggregations
+    expected = DataFrame({"b": [data, NaT]}, index=index)
+
+    result = df.groupby("a").aggregate(op)
+    tm.assert_frame_equal(expected, result)
+
+
+@pytest.mark.parametrize(
+    "agg",
+    [
+        "min",
+        "max",
+        "count",
+        "sum",
+        "prod",
+        "var",
+        "mean",
+        "median",
+        "ohlc",
+        "cumprod",
+        "cumsum",
+        "shift",
+        "any",
+        "all",
+        "quantile",
+        "first",
+        "last",
+        "rank",
+        "cummin",
+        "cummax",
+    ],
+)
+def test_read_only_buffer_source_agg(agg):
+    # https://github.com/pandas-dev/pandas/issues/36014
+    df = DataFrame(
+        {
+            "sepal_length": [5.1, 4.9, 4.7, 4.6, 5.0],
+            "species": ["setosa", "setosa", "setosa", "setosa", "setosa"],
+        }
+    )
+    df._mgr.arrays[0].flags.writeable = False
+
+    result = df.groupby(["species"]).agg({"sepal_length": agg})
+    expected = df.copy().groupby(["species"]).agg({"sepal_length": agg})
+
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "op_name",
+    [
+        "count",
+        "sum",
+        "std",
+        "var",
+        "sem",
+        "mean",
+        "median",
+        "prod",
+        "min",
+        "max",
+    ],
+)
+def test_cython_agg_nullable_int(op_name):
+    # ensure that the cython-based aggregations don't fail for nullable dtype
+    # (eg https://github.com/pandas-dev/pandas/issues/37415)
+    df = DataFrame(
+        {
+            "A": ["A", "B"] * 5,
+            "B": pd.array([1, 2, 3, 4, 5, 6, 7, 8, 9, pd.NA], dtype="Int64"),
+        }
+    )
+    result = getattr(df.groupby("A")["B"], op_name)()
+    df2 = df.assign(B=df["B"].astype("float64"))
+    expected = getattr(df2.groupby("A")["B"], op_name)()
+
+    if op_name != "count":
+        # the result is not yet consistently using Int64/Float64 dtype,
+        # so for now just checking the values by casting to float
+        result = result.astype("float64")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("with_na", [True, False])
+@pytest.mark.parametrize(
+    "op_name, action",
+    [
+        # ("count", "always_int"),
+        ("sum", "large_int"),
+        # ("std", "always_float"),
+        ("var", "always_float"),
+        # ("sem", "always_float"),
+        ("mean", "always_float"),
+        ("median", "always_float"),
+        ("prod", "large_int"),
+        ("min", "preserve"),
+        ("max", "preserve"),
+        ("first", "preserve"),
+        ("last", "preserve"),
+    ],
+)
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.array([1, 2, 3, 4], dtype="Int64"),
+        pd.array([1, 2, 3, 4], dtype="Int8"),
+        pd.array([0.1, 0.2, 0.3, 0.4], dtype="Float32"),
+        pd.array([0.1, 0.2, 0.3, 0.4], dtype="Float64"),
+        pd.array([True, True, False, False], dtype="boolean"),
+    ],
+)
+def test_cython_agg_EA_known_dtypes(data, op_name, action, with_na):
+    if with_na:
+        data[3] = pd.NA
+
+    df = DataFrame({"key": ["a", "a", "b", "b"], "col": data})
+    grouped = df.groupby("key")
+
+    if action == "always_int":
+        # always Int64
+        expected_dtype = pd.Int64Dtype()
+    elif action == "large_int":
+        # for any int/bool use Int64, for float preserve dtype
+        if is_float_dtype(data.dtype):
+            expected_dtype = data.dtype
+        elif is_integer_dtype(data.dtype):
+            # match the numpy dtype we'd get with the non-nullable analogue
+            expected_dtype = data.dtype
+        else:
+            expected_dtype = pd.Int64Dtype()
+    elif action == "always_float":
+        # for any int/bool use Float64, for float preserve dtype
+        if is_float_dtype(data.dtype):
+            expected_dtype = data.dtype
+        else:
+            expected_dtype = pd.Float64Dtype()
+    elif action == "preserve":
+        expected_dtype = data.dtype
+
+    result = getattr(grouped, op_name)()
+    assert result["col"].dtype == expected_dtype
+
+    result = grouped.aggregate(op_name)
+    assert result["col"].dtype == expected_dtype
+
+    result = getattr(grouped["col"], op_name)()
+    assert result.dtype == expected_dtype
+
+    result = grouped["col"].aggregate(op_name)
+    assert result.dtype == expected_dtype

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/aggregate/test_numba.py

@@ -0,0 +1,242 @@
+import numpy as np
+import pytest
+
+from pandas.errors import NumbaUtilError
+import pandas.util._test_decorators as td
+
+from pandas import (
+    DataFrame,
+    Index,
+    NamedAgg,
+    Series,
+    option_context,
+)
+import pandas._testing as tm
+
+
+@td.skip_if_no("numba")
+def test_correct_function_signature():
+    def incorrect_function(x):
+        return sum(x) * 2.7
+
+    data = DataFrame(
+        {"key": ["a", "a", "b", "b", "a"], "data": [1.0, 2.0, 3.0, 4.0, 5.0]},
+        columns=["key", "data"],
+    )
+    with pytest.raises(NumbaUtilError, match="The first 2"):
+        data.groupby("key").agg(incorrect_function, engine="numba")
+
+    with pytest.raises(NumbaUtilError, match="The first 2"):
+        data.groupby("key")["data"].agg(incorrect_function, engine="numba")
+
+
+@td.skip_if_no("numba")
+def test_check_nopython_kwargs():
+    def incorrect_function(values, index):
+        return sum(values) * 2.7
+
+    data = DataFrame(
+        {"key": ["a", "a", "b", "b", "a"], "data": [1.0, 2.0, 3.0, 4.0, 5.0]},
+        columns=["key", "data"],
+    )
+    with pytest.raises(NumbaUtilError, match="numba does not support"):
+        data.groupby("key").agg(incorrect_function, engine="numba", a=1)
+
+    with pytest.raises(NumbaUtilError, match="numba does not support"):
+        data.groupby("key")["data"].agg(incorrect_function, engine="numba", a=1)
+
+
+@td.skip_if_no("numba")
+@pytest.mark.filterwarnings("ignore")
+# Filter warnings when parallel=True and the function can't be parallelized by Numba
+@pytest.mark.parametrize("jit", [True, False])
+@pytest.mark.parametrize("pandas_obj", ["Series", "DataFrame"])
+@pytest.mark.parametrize("as_index", [True, False])
+def test_numba_vs_cython(jit, pandas_obj, nogil, parallel, nopython, as_index):
+    def func_numba(values, index):
+        return np.mean(values) * 2.7
+
+    if jit:
+        # Test accepted jitted functions
+        import numba
+
+        func_numba = numba.jit(func_numba)
+
+    data = DataFrame(
+        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
+    )
+    engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
+    grouped = data.groupby(0, as_index=as_index)
+    if pandas_obj == "Series":
+        grouped = grouped[1]
+
+    result = grouped.agg(func_numba, engine="numba", engine_kwargs=engine_kwargs)
+    expected = grouped.agg(lambda x: np.mean(x) * 2.7, engine="cython")
+
+    tm.assert_equal(result, expected)
+
+
+@td.skip_if_no("numba")
+@pytest.mark.filterwarnings("ignore")
+# Filter warnings when parallel=True and the function can't be parallelized by Numba
+@pytest.mark.parametrize("jit", [True, False])
+@pytest.mark.parametrize("pandas_obj", ["Series", "DataFrame"])
+def test_cache(jit, pandas_obj, nogil, parallel, nopython):
+    # Test that the functions are cached correctly if we switch functions
+    def func_1(values, index):
+        return np.mean(values) - 3.4
+
+    def func_2(values, index):
+        return np.mean(values) * 2.7
+
+    if jit:
+        import numba
+
+        func_1 = numba.jit(func_1)
+        func_2 = numba.jit(func_2)
+
+    data = DataFrame(
+        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
+    )
+    engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
+    grouped = data.groupby(0)
+    if pandas_obj == "Series":
+        grouped = grouped[1]
+
+    result = grouped.agg(func_1, engine="numba", engine_kwargs=engine_kwargs)
+    expected = grouped.agg(lambda x: np.mean(x) - 3.4, engine="cython")
+    tm.assert_equal(result, expected)
+
+    # Add func_2 to the cache
+    result = grouped.agg(func_2, engine="numba", engine_kwargs=engine_kwargs)
+    expected = grouped.agg(lambda x: np.mean(x) * 2.7, engine="cython")
+    tm.assert_equal(result, expected)
+
+    # Retest func_1 which should use the cache
+    result = grouped.agg(func_1, engine="numba", engine_kwargs=engine_kwargs)
+    expected = grouped.agg(lambda x: np.mean(x) - 3.4, engine="cython")
+    tm.assert_equal(result, expected)
+
+
+@td.skip_if_no("numba")
+def test_use_global_config():
+    def func_1(values, index):
+        return np.mean(values) - 3.4
+
+    data = DataFrame(
+        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
+    )
+    grouped = data.groupby(0)
+    expected = grouped.agg(func_1, engine="numba")
+    with option_context("compute.use_numba", True):
+        result = grouped.agg(func_1, engine=None)
+    tm.assert_frame_equal(expected, result)
+
+
+@td.skip_if_no("numba")
+@pytest.mark.parametrize(
+    "agg_func",
+    [
+        ["min", "max"],
+        "min",
+        {"B": ["min", "max"], "C": "sum"},
+        NamedAgg(column="B", aggfunc="min"),
+    ],
+)
+def test_multifunc_notimplimented(agg_func):
+    data = DataFrame(
+        {0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
+    )
+    grouped = data.groupby(0)
+    with pytest.raises(NotImplementedError, match="Numba engine can"):
+        grouped.agg(agg_func, engine="numba")
+
+    with pytest.raises(NotImplementedError, match="Numba engine can"):
+        grouped[1].agg(agg_func, engine="numba")
+
+
+@td.skip_if_no("numba")
+def test_args_not_cached():
+    # GH 41647
+    def sum_last(values, index, n):
+        return values[-n:].sum()
+
+    df = DataFrame({"id": [0, 0, 1, 1], "x": [1, 1, 1, 1]})
+    grouped_x = df.groupby("id")["x"]
+    result = grouped_x.agg(sum_last, 1, engine="numba")
+    expected = Series([1.0] * 2, name="x", index=Index([0, 1], name="id"))
+    tm.assert_series_equal(result, expected)
+
+    result = grouped_x.agg(sum_last, 2, engine="numba")
+    expected = Series([2.0] * 2, name="x", index=Index([0, 1], name="id"))
+    tm.assert_series_equal(result, expected)
+
+
+@td.skip_if_no("numba")
+def test_index_data_correctly_passed():
+    # GH 43133
+    def f(values, index):
+        return np.mean(index)
+
+    df = DataFrame({"group": ["A", "A", "B"], "v": [4, 5, 6]}, index=[-1, -2, -3])
+    result = df.groupby("group").aggregate(f, engine="numba")
+    expected = DataFrame(
+        [-1.5, -3.0], columns=["v"], index=Index(["A", "B"], name="group")
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@td.skip_if_no("numba")
+def test_engine_kwargs_not_cached():
+    # If the user passes a different set of engine_kwargs don't return the same
+    # jitted function
+    nogil = True
+    parallel = False
+    nopython = True
+
+    def func_kwargs(values, index):
+        return nogil + parallel + nopython
+
+    engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
+    df = DataFrame({"value": [0, 0, 0]})
+    result = df.groupby(level=0).aggregate(
+        func_kwargs, engine="numba", engine_kwargs=engine_kwargs
+    )
+    expected = DataFrame({"value": [2.0, 2.0, 2.0]})
+    tm.assert_frame_equal(result, expected)
+
+    nogil = False
+    engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
+    result = df.groupby(level=0).aggregate(
+        func_kwargs, engine="numba", engine_kwargs=engine_kwargs
+    )
+    expected = DataFrame({"value": [1.0, 1.0, 1.0]})
+    tm.assert_frame_equal(result, expected)
+
+
+@td.skip_if_no("numba")
+@pytest.mark.filterwarnings("ignore")
+def test_multiindex_one_key(nogil, parallel, nopython):
+    def numba_func(values, index):
+        return 1
+
+    df = DataFrame([{"A": 1, "B": 2, "C": 3}]).set_index(["A", "B"])
+    engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
+    result = df.groupby("A").agg(
+        numba_func, engine="numba", engine_kwargs=engine_kwargs
+    )
+    expected = DataFrame([1.0], index=Index([1], name="A"), columns=["C"])
+    tm.assert_frame_equal(result, expected)
+
+
+@td.skip_if_no("numba")
+def test_multiindex_multi_key_not_supported(nogil, parallel, nopython):
+    def numba_func(values, index):
+        return 1
+
+    df = DataFrame([{"A": 1, "B": 2, "C": 3}]).set_index(["A", "B"])
+    engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
+    with pytest.raises(NotImplementedError, match="More than 1 grouping labels"):
+        df.groupby(["A", "B"]).agg(
+            numba_func, engine="numba", engine_kwargs=engine_kwargs
+        )

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/aggregate/test_other.py

@@ -0,0 +1,664 @@
+"""
+test all other .agg behavior
+"""
+
+import datetime as dt
+from functools import partial
+
+import numpy as np
+import pytest
+
+from pandas.errors import SpecificationError
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    PeriodIndex,
+    Series,
+    date_range,
+    period_range,
+)
+import pandas._testing as tm
+
+from pandas.io.formats.printing import pprint_thing
+
+
+def test_agg_partial_failure_raises():
+    # GH#43741
+
+    df = DataFrame(
+        {
+            "data1": np.random.randn(5),
+            "data2": np.random.randn(5),
+            "key1": ["a", "a", "b", "b", "a"],
+            "key2": ["one", "two", "one", "two", "one"],
+        }
+    )
+    grouped = df.groupby("key1")
+
+    def peak_to_peak(arr):
+        return arr.max() - arr.min()
+
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        grouped.agg([peak_to_peak])
+
+    with pytest.raises(TypeError, match="unsupported operand type"):
+        grouped.agg(peak_to_peak)
+
+
+def test_agg_datetimes_mixed():
+    data = [[1, "2012-01-01", 1.0], [2, "2012-01-02", 2.0], [3, None, 3.0]]
+
+    df1 = DataFrame(
+        {
+            "key": [x[0] for x in data],
+            "date": [x[1] for x in data],
+            "value": [x[2] for x in data],
+        }
+    )
+
+    data = [
+        [
+            row[0],
+            (dt.datetime.strptime(row[1], "%Y-%m-%d").date() if row[1] else None),
+            row[2],
+        ]
+        for row in data
+    ]
+
+    df2 = DataFrame(
+        {
+            "key": [x[0] for x in data],
+            "date": [x[1] for x in data],
+            "value": [x[2] for x in data],
+        }
+    )
+
+    df1["weights"] = df1["value"] / df1["value"].sum()
+    gb1 = df1.groupby("date").aggregate(np.sum)
+
+    df2["weights"] = df1["value"] / df1["value"].sum()
+    gb2 = df2.groupby("date").aggregate(np.sum)
+
+    assert len(gb1) == len(gb2)
+
+
+def test_agg_period_index():
+    prng = period_range("2012-1-1", freq="M", periods=3)
+    df = DataFrame(np.random.randn(3, 2), index=prng)
+    rs = df.groupby(level=0).sum()
+    assert isinstance(rs.index, PeriodIndex)
+
+    # GH 3579
+    index = period_range(start="1999-01", periods=5, freq="M")
+    s1 = Series(np.random.rand(len(index)), index=index)
+    s2 = Series(np.random.rand(len(index)), index=index)
+    df = DataFrame.from_dict({"s1": s1, "s2": s2})
+    grouped = df.groupby(df.index.month)
+    list(grouped)
+
+
+def test_agg_dict_parameter_cast_result_dtypes():
+    # GH 12821
+
+    df = DataFrame(
+        {
+            "class": ["A", "A", "B", "B", "C", "C", "D", "D"],
+            "time": date_range("1/1/2011", periods=8, freq="H"),
+        }
+    )
+    df.loc[[0, 1, 2, 5], "time"] = None
+
+    # test for `first` function
+    exp = df.loc[[0, 3, 4, 6]].set_index("class")
+    grouped = df.groupby("class")
+    tm.assert_frame_equal(grouped.first(), exp)
+    tm.assert_frame_equal(grouped.agg("first"), exp)
+    tm.assert_frame_equal(grouped.agg({"time": "first"}), exp)
+    tm.assert_series_equal(grouped.time.first(), exp["time"])
+    tm.assert_series_equal(grouped.time.agg("first"), exp["time"])
+
+    # test for `last` function
+    exp = df.loc[[0, 3, 4, 7]].set_index("class")
+    grouped = df.groupby("class")
+    tm.assert_frame_equal(grouped.last(), exp)
+    tm.assert_frame_equal(grouped.agg("last"), exp)
+    tm.assert_frame_equal(grouped.agg({"time": "last"}), exp)
+    tm.assert_series_equal(grouped.time.last(), exp["time"])
+    tm.assert_series_equal(grouped.time.agg("last"), exp["time"])
+
+    # count
+    exp = Series([2, 2, 2, 2], index=Index(list("ABCD"), name="class"), name="time")
+    tm.assert_series_equal(grouped.time.agg(len), exp)
+    tm.assert_series_equal(grouped.time.size(), exp)
+
+    exp = Series([0, 1, 1, 2], index=Index(list("ABCD"), name="class"), name="time")
+    tm.assert_series_equal(grouped.time.count(), exp)
+
+
+def test_agg_cast_results_dtypes():
+    # similar to GH12821
+    # xref #11444
+    u = [dt.datetime(2015, x + 1, 1) for x in range(12)]
+    v = list("aaabbbbbbccd")
+    df = DataFrame({"X": v, "Y": u})
+
+    result = df.groupby("X")["Y"].agg(len)
+    expected = df.groupby("X")["Y"].count()
+    tm.assert_series_equal(result, expected)
+
+
+def test_aggregate_float64_no_int64():
+    # see gh-11199
+    df = DataFrame({"a": [1, 2, 3, 4, 5], "b": [1, 2, 2, 4, 5], "c": [1, 2, 3, 4, 5]})
+
+    expected = DataFrame({"a": [1, 2.5, 4, 5]}, index=[1, 2, 4, 5])
+    expected.index.name = "b"
+
+    result = df.groupby("b")[["a"]].mean()
+    tm.assert_frame_equal(result, expected)
+
+    expected = DataFrame({"a": [1, 2.5, 4, 5], "c": [1, 2.5, 4, 5]}, index=[1, 2, 4, 5])
+    expected.index.name = "b"
+
+    result = df.groupby("b")[["a", "c"]].mean()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_aggregate_api_consistency():
+    # GH 9052
+    # make sure that the aggregates via dict
+    # are consistent
+    df = DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
+            "C": np.random.randn(8) + 1.0,
+            "D": np.arange(8),
+        }
+    )
+
+    grouped = df.groupby(["A", "B"])
+    c_mean = grouped["C"].mean()
+    c_sum = grouped["C"].sum()
+    d_mean = grouped["D"].mean()
+    d_sum = grouped["D"].sum()
+
+    result = grouped["D"].agg(["sum", "mean"])
+    expected = pd.concat([d_sum, d_mean], axis=1)
+    expected.columns = ["sum", "mean"]
+    tm.assert_frame_equal(result, expected, check_like=True)
+
+    result = grouped.agg([np.sum, np.mean])
+    expected = pd.concat([c_sum, c_mean, d_sum, d_mean], axis=1)
+    expected.columns = MultiIndex.from_product([["C", "D"], ["sum", "mean"]])
+    tm.assert_frame_equal(result, expected, check_like=True)
+
+    result = grouped[["D", "C"]].agg([np.sum, np.mean])
+    expected = pd.concat([d_sum, d_mean, c_sum, c_mean], axis=1)
+    expected.columns = MultiIndex.from_product([["D", "C"], ["sum", "mean"]])
+    tm.assert_frame_equal(result, expected, check_like=True)
+
+    result = grouped.agg({"C": "mean", "D": "sum"})
+    expected = pd.concat([d_sum, c_mean], axis=1)
+    tm.assert_frame_equal(result, expected, check_like=True)
+
+    result = grouped.agg({"C": ["mean", "sum"], "D": ["mean", "sum"]})
+    expected = pd.concat([c_mean, c_sum, d_mean, d_sum], axis=1)
+    expected.columns = MultiIndex.from_product([["C", "D"], ["mean", "sum"]])
+
+    msg = r"Column\(s\) \['r', 'r2'\] do not exist"
+    with pytest.raises(KeyError, match=msg):
+        grouped[["D", "C"]].agg({"r": np.sum, "r2": np.mean})
+
+
+def test_agg_dict_renaming_deprecation():
+    # 15931
+    df = DataFrame({"A": [1, 1, 1, 2, 2], "B": range(5), "C": range(5)})
+
+    msg = r"nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=msg):
+        df.groupby("A").agg(
+            {"B": {"foo": ["sum", "max"]}, "C": {"bar": ["count", "min"]}}
+        )
+
+    msg = r"Column\(s\) \['ma'\] do not exist"
+    with pytest.raises(KeyError, match=msg):
+        df.groupby("A")[["B", "C"]].agg({"ma": "max"})
+
+    msg = r"nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=msg):
+        df.groupby("A").B.agg({"foo": "count"})
+
+
+def test_agg_compat():
+    # GH 12334
+    df = DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
+            "C": np.random.randn(8) + 1.0,
+            "D": np.arange(8),
+        }
+    )
+
+    g = df.groupby(["A", "B"])
+
+    msg = r"nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=msg):
+        g["D"].agg({"C": ["sum", "std"]})
+
+    with pytest.raises(SpecificationError, match=msg):
+        g["D"].agg({"C": "sum", "D": "std"})
+
+
+def test_agg_nested_dicts():
+    # API change for disallowing these types of nested dicts
+    df = DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
+            "C": np.random.randn(8) + 1.0,
+            "D": np.arange(8),
+        }
+    )
+
+    g = df.groupby(["A", "B"])
+
+    msg = r"nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=msg):
+        g.aggregate({"r1": {"C": ["mean", "sum"]}, "r2": {"D": ["mean", "sum"]}})
+
+    with pytest.raises(SpecificationError, match=msg):
+        g.agg({"C": {"ra": ["mean", "std"]}, "D": {"rb": ["mean", "std"]}})
+
+    # same name as the original column
+    # GH9052
+    with pytest.raises(SpecificationError, match=msg):
+        g["D"].agg({"result1": np.sum, "result2": np.mean})
+
+    with pytest.raises(SpecificationError, match=msg):
+        g["D"].agg({"D": np.sum, "result2": np.mean})
+
+
+def test_agg_item_by_item_raise_typeerror():
+    df = DataFrame(np.random.randint(10, size=(20, 10)))
+
+    def raiseException(df):
+        pprint_thing("----------------------------------------")
+        pprint_thing(df.to_string())
+        raise TypeError("test")
+
+    with pytest.raises(TypeError, match="test"):
+        df.groupby(0).agg(raiseException)
+
+
+def test_series_agg_multikey():
+    ts = tm.makeTimeSeries()
+    grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
+
+    result = grouped.agg(np.sum)
+    expected = grouped.sum()
+    tm.assert_series_equal(result, expected)
+
+
+def test_series_agg_multi_pure_python():
+    data = DataFrame(
+        {
+            "A": [
+                "foo",
+                "foo",
+                "foo",
+                "foo",
+                "bar",
+                "bar",
+                "bar",
+                "bar",
+                "foo",
+                "foo",
+                "foo",
+            ],
+            "B": [
+                "one",
+                "one",
+                "one",
+                "two",
+                "one",
+                "one",
+                "one",
+                "two",
+                "two",
+                "two",
+                "one",
+            ],
+            "C": [
+                "dull",
+                "dull",
+                "shiny",
+                "dull",
+                "dull",
+                "shiny",
+                "shiny",
+                "dull",
+                "shiny",
+                "shiny",
+                "shiny",
+            ],
+            "D": np.random.randn(11),
+            "E": np.random.randn(11),
+            "F": np.random.randn(11),
+        }
+    )
+
+    def bad(x):
+        assert len(x.values.base) > 0
+        return "foo"
+
+    result = data.groupby(["A", "B"]).agg(bad)
+    expected = data.groupby(["A", "B"]).agg(lambda x: "foo")
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_consistency():
+    # agg with ([]) and () not consistent
+    # GH 6715
+    def P1(a):
+        return np.percentile(a.dropna(), q=1)
+
+    df = DataFrame(
+        {
+            "col1": [1, 2, 3, 4],
+            "col2": [10, 25, 26, 31],
+            "date": [
+                dt.date(2013, 2, 10),
+                dt.date(2013, 2, 10),
+                dt.date(2013, 2, 11),
+                dt.date(2013, 2, 11),
+            ],
+        }
+    )
+
+    g = df.groupby("date")
+
+    expected = g.agg([P1])
+    expected.columns = expected.columns.levels[0]
+
+    result = g.agg(P1)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_callables():
+    # GH 7929
+    df = DataFrame({"foo": [1, 2], "bar": [3, 4]}).astype(np.int64)
+
+    class fn_class:
+        def __call__(self, x):
+            return sum(x)
+
+    equiv_callables = [
+        sum,
+        np.sum,
+        lambda x: sum(x),
+        lambda x: x.sum(),
+        partial(sum),
+        fn_class(),
+    ]
+
+    expected = df.groupby("foo").agg(sum)
+    for ecall in equiv_callables:
+        result = df.groupby("foo").agg(ecall)
+        tm.assert_frame_equal(result, expected)
+
+
+def test_agg_over_numpy_arrays():
+    # GH 3788
+    df = DataFrame(
+        [
+            [1, np.array([10, 20, 30])],
+            [1, np.array([40, 50, 60])],
+            [2, np.array([20, 30, 40])],
+        ],
+        columns=["category", "arraydata"],
+    )
+    gb = df.groupby("category")
+
+    expected_data = [[np.array([50, 70, 90])], [np.array([20, 30, 40])]]
+    expected_index = Index([1, 2], name="category")
+    expected_column = ["arraydata"]
+    expected = DataFrame(expected_data, index=expected_index, columns=expected_column)
+
+    alt = gb.sum(numeric_only=False)
+    tm.assert_frame_equal(alt, expected)
+
+    result = gb.agg("sum", numeric_only=False)
+    tm.assert_frame_equal(result, expected)
+
+    # FIXME: the original version of this test called `gb.agg(sum)`
+    #  and that raises TypeError if `numeric_only=False` is passed
+
+
+@pytest.mark.parametrize("as_period", [True, False])
+def test_agg_tzaware_non_datetime_result(as_period):
+    # discussed in GH#29589, fixed in GH#29641, operating on tzaware values
+    #  with function that is not dtype-preserving
+    dti = date_range("2012-01-01", periods=4, tz="UTC")
+    if as_period:
+        dti = dti.tz_localize(None).to_period("D")
+
+    df = DataFrame({"a": [0, 0, 1, 1], "b": dti})
+    gb = df.groupby("a")
+
+    # Case that _does_ preserve the dtype
+    result = gb["b"].agg(lambda x: x.iloc[0])
+    expected = Series(dti[::2], name="b")
+    expected.index.name = "a"
+    tm.assert_series_equal(result, expected)
+
+    # Cases that do _not_ preserve the dtype
+    result = gb["b"].agg(lambda x: x.iloc[0].year)
+    expected = Series([2012, 2012], name="b")
+    expected.index.name = "a"
+    tm.assert_series_equal(result, expected)
+
+    result = gb["b"].agg(lambda x: x.iloc[-1] - x.iloc[0])
+    expected = Series([pd.Timedelta(days=1), pd.Timedelta(days=1)], name="b")
+    expected.index.name = "a"
+    if as_period:
+        expected = Series([pd.offsets.Day(1), pd.offsets.Day(1)], name="b")
+        expected.index.name = "a"
+    tm.assert_series_equal(result, expected)
+
+
+def test_agg_timezone_round_trip():
+    # GH 15426
+    ts = pd.Timestamp("2016-01-01 12:00:00", tz="US/Pacific")
+    df = DataFrame({"a": 1, "b": [ts + dt.timedelta(minutes=nn) for nn in range(10)]})
+
+    result1 = df.groupby("a")["b"].agg(np.min).iloc[0]
+    result2 = df.groupby("a")["b"].agg(lambda x: np.min(x)).iloc[0]
+    result3 = df.groupby("a")["b"].min().iloc[0]
+
+    assert result1 == ts
+    assert result2 == ts
+    assert result3 == ts
+
+    dates = [
+        pd.Timestamp(f"2016-01-0{i:d} 12:00:00", tz="US/Pacific") for i in range(1, 5)
+    ]
+    df = DataFrame({"A": ["a", "b"] * 2, "B": dates})
+    grouped = df.groupby("A")
+
+    ts = df["B"].iloc[0]
+    assert ts == grouped.nth(0)["B"].iloc[0]
+    assert ts == grouped.head(1)["B"].iloc[0]
+    assert ts == grouped.first()["B"].iloc[0]
+
+    # GH#27110 applying iloc should return a DataFrame
+    assert ts == grouped.apply(lambda x: x.iloc[0]).iloc[0, 1]
+
+    ts = df["B"].iloc[2]
+    assert ts == grouped.last()["B"].iloc[0]
+
+    # GH#27110 applying iloc should return a DataFrame
+    assert ts == grouped.apply(lambda x: x.iloc[-1]).iloc[0, 1]
+
+
+def test_sum_uint64_overflow():
+    # see gh-14758
+    # Convert to uint64 and don't overflow
+    df = DataFrame([[1, 2], [3, 4], [5, 6]], dtype=object)
+    df = df + 9223372036854775807
+
+    index = Index(
+        [9223372036854775808, 9223372036854775810, 9223372036854775812], dtype=np.uint64
+    )
+    expected = DataFrame(
+        {1: [9223372036854775809, 9223372036854775811, 9223372036854775813]},
+        index=index,
+        dtype=object,
+    )
+
+    expected.index.name = 0
+    result = df.groupby(0).sum(numeric_only=False)
+    tm.assert_frame_equal(result, expected)
+
+    # out column is non-numeric, so with numeric_only=True it is dropped
+    result2 = df.groupby(0).sum(numeric_only=True)
+    expected2 = expected[[]]
+    tm.assert_frame_equal(result2, expected2)
+
+
+@pytest.mark.parametrize(
+    "structure, expected",
+    [
+        (tuple, DataFrame({"C": {(1, 1): (1, 1, 1), (3, 4): (3, 4, 4)}})),
+        (list, DataFrame({"C": {(1, 1): [1, 1, 1], (3, 4): [3, 4, 4]}})),
+        (
+            lambda x: tuple(x),
+            DataFrame({"C": {(1, 1): (1, 1, 1), (3, 4): (3, 4, 4)}}),
+        ),
+        (
+            lambda x: list(x),
+            DataFrame({"C": {(1, 1): [1, 1, 1], (3, 4): [3, 4, 4]}}),
+        ),
+    ],
+)
+def test_agg_structs_dataframe(structure, expected):
+    df = DataFrame(
+        {"A": [1, 1, 1, 3, 3, 3], "B": [1, 1, 1, 4, 4, 4], "C": [1, 1, 1, 3, 4, 4]}
+    )
+
+    result = df.groupby(["A", "B"]).aggregate(structure)
+    expected.index.names = ["A", "B"]
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "structure, expected",
+    [
+        (tuple, Series([(1, 1, 1), (3, 4, 4)], index=[1, 3], name="C")),
+        (list, Series([[1, 1, 1], [3, 4, 4]], index=[1, 3], name="C")),
+        (lambda x: tuple(x), Series([(1, 1, 1), (3, 4, 4)], index=[1, 3], name="C")),
+        (lambda x: list(x), Series([[1, 1, 1], [3, 4, 4]], index=[1, 3], name="C")),
+    ],
+)
+def test_agg_structs_series(structure, expected):
+    # Issue #18079
+    df = DataFrame(
+        {"A": [1, 1, 1, 3, 3, 3], "B": [1, 1, 1, 4, 4, 4], "C": [1, 1, 1, 3, 4, 4]}
+    )
+
+    result = df.groupby("A")["C"].aggregate(structure)
+    expected.index.name = "A"
+    tm.assert_series_equal(result, expected)
+
+
+def test_agg_category_nansum(observed):
+    categories = ["a", "b", "c"]
+    df = DataFrame(
+        {"A": pd.Categorical(["a", "a", "b"], categories=categories), "B": [1, 2, 3]}
+    )
+    result = df.groupby("A", observed=observed).B.agg(np.nansum)
+    expected = Series(
+        [3, 3, 0],
+        index=pd.CategoricalIndex(["a", "b", "c"], categories=categories, name="A"),
+        name="B",
+    )
+    if observed:
+        expected = expected[expected != 0]
+    tm.assert_series_equal(result, expected)
+
+
+def test_agg_list_like_func():
+    # GH 18473
+    df = DataFrame({"A": [str(x) for x in range(3)], "B": [str(x) for x in range(3)]})
+    grouped = df.groupby("A", as_index=False, sort=False)
+    result = grouped.agg({"B": lambda x: list(x)})
+    expected = DataFrame(
+        {"A": [str(x) for x in range(3)], "B": [[str(x)] for x in range(3)]}
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_lambda_with_timezone():
+    # GH 23683
+    df = DataFrame(
+        {
+            "tag": [1, 1],
+            "date": [
+                pd.Timestamp("2018-01-01", tz="UTC"),
+                pd.Timestamp("2018-01-02", tz="UTC"),
+            ],
+        }
+    )
+    result = df.groupby("tag").agg({"date": lambda e: e.head(1)})
+    expected = DataFrame(
+        [pd.Timestamp("2018-01-01", tz="UTC")],
+        index=Index([1], name="tag"),
+        columns=["date"],
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "err_cls",
+    [
+        NotImplementedError,
+        RuntimeError,
+        KeyError,
+        IndexError,
+        OSError,
+        ValueError,
+        ArithmeticError,
+        AttributeError,
+    ],
+)
+def test_groupby_agg_err_catching(err_cls):
+    # make sure we suppress anything other than TypeError or AssertionError
+    #  in _python_agg_general
+
+    # Use a non-standard EA to make sure we don't go down ndarray paths
+    from pandas.tests.extension.decimal.array import (
+        DecimalArray,
+        make_data,
+        to_decimal,
+    )
+
+    data = make_data()[:5]
+    df = DataFrame(
+        {"id1": [0, 0, 0, 1, 1], "id2": [0, 1, 0, 1, 1], "decimals": DecimalArray(data)}
+    )
+
+    expected = Series(to_decimal([data[0], data[3]]))
+
+    def weird_func(x):
+        # weird function that raise something other than TypeError or IndexError
+        #  in _python_agg_general
+        if len(x) == 0:
+            raise err_cls
+        return x.iloc[0]
+
+    result = df["decimals"].groupby(df["id1"]).agg(weird_func)
+    tm.assert_series_equal(result, expected, check_names=False)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/conftest.py

@@ -0,0 +1,204 @@
+import numpy as np
+import pytest
+
+from pandas import DataFrame
+import pandas._testing as tm
+from pandas.core.groupby.base import (
+    reduction_kernels,
+    transformation_kernels,
+)
+
+
+@pytest.fixture(params=[True, False])
+def sort(request):
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def as_index(request):
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def dropna(request):
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def observed(request):
+    return request.param
+
+
+@pytest.fixture
+def mframe(multiindex_dataframe_random_data):
+    return multiindex_dataframe_random_data
+
+
+@pytest.fixture
+def df():
+    return DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+            "C": np.random.randn(8),
+            "D": np.random.randn(8),
+        }
+    )
+
+
+@pytest.fixture
+def ts():
+    return tm.makeTimeSeries()
+
+
+@pytest.fixture
+def tsd():
+    return tm.getTimeSeriesData()
+
+
+@pytest.fixture
+def tsframe(tsd):
+    return DataFrame(tsd)
+
+
+@pytest.fixture
+def df_mixed_floats():
+    return DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+            "C": np.random.randn(8),
+            "D": np.array(np.random.randn(8), dtype="float32"),
+        }
+    )
+
+
+@pytest.fixture
+def three_group():
+    return DataFrame(
+        {
+            "A": [
+                "foo",
+                "foo",
+                "foo",
+                "foo",
+                "bar",
+                "bar",
+                "bar",
+                "bar",
+                "foo",
+                "foo",
+                "foo",
+            ],
+            "B": [
+                "one",
+                "one",
+                "one",
+                "two",
+                "one",
+                "one",
+                "one",
+                "two",
+                "two",
+                "two",
+                "one",
+            ],
+            "C": [
+                "dull",
+                "dull",
+                "shiny",
+                "dull",
+                "dull",
+                "shiny",
+                "shiny",
+                "dull",
+                "shiny",
+                "shiny",
+                "shiny",
+            ],
+            "D": np.random.randn(11),
+            "E": np.random.randn(11),
+            "F": np.random.randn(11),
+        }
+    )
+
+
+@pytest.fixture()
+def slice_test_df():
+    data = [
+        [0, "a", "a0_at_0"],
+        [1, "b", "b0_at_1"],
+        [2, "a", "a1_at_2"],
+        [3, "b", "b1_at_3"],
+        [4, "c", "c0_at_4"],
+        [5, "a", "a2_at_5"],
+        [6, "a", "a3_at_6"],
+        [7, "a", "a4_at_7"],
+    ]
+    df = DataFrame(data, columns=["Index", "Group", "Value"])
+    return df.set_index("Index")
+
+
+@pytest.fixture()
+def slice_test_grouped(slice_test_df):
+    return slice_test_df.groupby("Group", as_index=False)
+
+
+@pytest.fixture(params=sorted(reduction_kernels))
+def reduction_func(request):
+    """
+    yields the string names of all groupby reduction functions, one at a time.
+    """
+    return request.param
+
+
+@pytest.fixture(params=sorted(transformation_kernels))
+def transformation_func(request):
+    """yields the string names of all groupby transformation functions."""
+    return request.param
+
+
+@pytest.fixture(params=sorted(reduction_kernels) + sorted(transformation_kernels))
+def groupby_func(request):
+    """yields both aggregation and transformation functions."""
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def parallel(request):
+    """parallel keyword argument for numba.jit"""
+    return request.param
+
+
+# Can parameterize nogil & nopython over True | False, but limiting per
+# https://github.com/pandas-dev/pandas/pull/41971#issuecomment-860607472
+
+
+@pytest.fixture(params=[False])
+def nogil(request):
+    """nogil keyword argument for numba.jit"""
+    return request.param
+
+
+@pytest.fixture(params=[True])
+def nopython(request):
+    """nopython keyword argument for numba.jit"""
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        ("mean", {}),
+        ("var", {"ddof": 1}),
+        ("var", {"ddof": 0}),
+        ("std", {"ddof": 1}),
+        ("std", {"ddof": 0}),
+        ("sum", {}),
+        ("min", {}),
+        ("max", {}),
+    ],
+    ids=["mean", "var_1", "var_0", "std_1", "std_0", "sum", "min", "max"],
+)
+def numba_supported_reductions(request):
+    """reductions supported with engine='numba'"""
+    return request.param

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_allowlist.py

@@ -0,0 +1,326 @@
+"""
+test methods relating to generic function evaluation
+the so-called white/black lists
+"""
+
+from string import ascii_lowercase
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Series,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core.groupby.base import (
+    groupby_other_methods,
+    reduction_kernels,
+    transformation_kernels,
+)
+
+AGG_FUNCTIONS = [
+    "sum",
+    "prod",
+    "min",
+    "max",
+    "median",
+    "mean",
+    "skew",
+    "std",
+    "var",
+    "sem",
+]
+AGG_FUNCTIONS_WITH_SKIPNA = ["skew"]
+
+
+@pytest.fixture
+def df():
+    return DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+            "C": np.random.randn(8),
+            "D": np.random.randn(8),
+        }
+    )
+
+
+@pytest.fixture
+def df_letters():
+    letters = np.array(list(ascii_lowercase))
+    N = 10
+    random_letters = letters.take(np.random.randint(0, 26, N))
+    df = DataFrame(
+        {
+            "floats": N / 10 * Series(np.random.random(N)),
+            "letters": Series(random_letters),
+        }
+    )
+    return df
+
+
+@pytest.fixture
+def raw_frame():
+    return DataFrame([0])
+
+
+@pytest.mark.parametrize("op", AGG_FUNCTIONS)
+@pytest.mark.parametrize("axis", [0, 1])
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+def test_regression_allowlist_methods(raw_frame, op, axis, skipna, sort):
+    # GH6944
+    # GH 17537
+    # explicitly test the allowlist methods
+    if axis == 0:
+        frame = raw_frame
+    else:
+        frame = raw_frame.T
+
+    if op in AGG_FUNCTIONS_WITH_SKIPNA:
+        grouped = frame.groupby(level=0, axis=axis, sort=sort)
+        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:
+        grouped = frame.groupby(level=0, axis=axis, sort=sort)
+        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)
+
+
+def test_groupby_blocklist(df_letters):
+    df = df_letters
+    s = df_letters.floats
+
+    blocklist = [
+        "eval",
+        "query",
+        "abs",
+        "where",
+        "mask",
+        "align",
+        "groupby",
+        "clip",
+        "astype",
+        "at",
+        "combine",
+        "consolidate",
+        "convert_objects",
+    ]
+    to_methods = [method for method in dir(df) if method.startswith("to_")]
+
+    blocklist.extend(to_methods)
+
+    for bl in blocklist:
+        for obj in (df, s):
+            gb = obj.groupby(df.letters)
+
+            # e.g., to_csv
+            defined_but_not_allowed = (
+                f"(?:^Cannot.+{repr(bl)}.+'{type(gb).__name__}'.+try "
+                f"using the 'apply' method$)"
+            )
+
+            # e.g., query, eval
+            not_defined = (
+                f"(?:^'{type(gb).__name__}' object has no attribute {repr(bl)}$)"
+            )
+
+            msg = f"{defined_but_not_allowed}|{not_defined}"
+
+            with pytest.raises(AttributeError, match=msg):
+                getattr(gb, bl)
+
+
+def test_tab_completion(mframe):
+    grp = mframe.groupby(level="second")
+    results = {v for v in dir(grp) if not v.startswith("_")}
+    expected = {
+        "A",
+        "B",
+        "C",
+        "agg",
+        "aggregate",
+        "apply",
+        "boxplot",
+        "filter",
+        "first",
+        "get_group",
+        "groups",
+        "hist",
+        "indices",
+        "last",
+        "max",
+        "mean",
+        "median",
+        "min",
+        "ngroups",
+        "nth",
+        "ohlc",
+        "plot",
+        "prod",
+        "size",
+        "std",
+        "sum",
+        "transform",
+        "var",
+        "sem",
+        "count",
+        "nunique",
+        "head",
+        "describe",
+        "cummax",
+        "quantile",
+        "rank",
+        "cumprod",
+        "tail",
+        "resample",
+        "cummin",
+        "fillna",
+        "cumsum",
+        "cumcount",
+        "ngroup",
+        "all",
+        "shift",
+        "skew",
+        "take",
+        "pct_change",
+        "any",
+        "corr",
+        "corrwith",
+        "cov",
+        "dtypes",
+        "ndim",
+        "diff",
+        "idxmax",
+        "idxmin",
+        "ffill",
+        "bfill",
+        "rolling",
+        "expanding",
+        "pipe",
+        "sample",
+        "ewm",
+        "value_counts",
+    }
+    assert results == expected
+
+
+def test_groupby_function_rename(mframe):
+    grp = mframe.groupby(level="second")
+    for name in ["sum", "prod", "min", "max", "first", "last"]:
+        f = getattr(grp, name)
+        assert f.__name__ == name
+
+
+@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))
+    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)
+    )
+
+
+def test_all_methods_categorized(mframe):
+    grp = mframe.groupby(mframe.iloc[:, 0])
+    names = {_ for _ in dir(grp) if not _.startswith("_")} - set(mframe.columns)
+    new_names = set(names)
+    new_names -= reduction_kernels
+    new_names -= transformation_kernels
+    new_names -= groupby_other_methods
+
+    assert not reduction_kernels & transformation_kernels
+    assert not reduction_kernels & groupby_other_methods
+    assert not transformation_kernels & groupby_other_methods
+
+    # new public method?
+    if new_names:
+        msg = f"""
+There are uncategorized methods defined on the Grouper class:
+{new_names}.
+
+Was a new method recently added?
+
+Every public method On Grouper must appear in exactly one the
+following three lists defined in pandas.core.groupby.base:
+- `reduction_kernels`
+- `transformation_kernels`
+- `groupby_other_methods`
+see the comments in pandas/core/groupby/base.py for guidance on
+how to fix this test.
+        """
+        raise AssertionError(msg)
+
+    # removed a public method?
+    all_categorized = reduction_kernels | transformation_kernels | groupby_other_methods
+    if names != all_categorized:
+        msg = f"""
+Some methods which are supposed to be on the Grouper class
+are missing:
+{all_categorized - names}.
+
+They're still defined in one of the lists that live in pandas/core/groupby/base.py.
+If you removed a method, you should update them
+"""
+        raise AssertionError(msg)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_any_all.py

@@ -0,0 +1,189 @@
+import builtins
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+    isna,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("agg_func", ["any", "all"])
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize(
+    "vals",
+    [
+        ["foo", "bar", "baz"],
+        ["foo", "", ""],
+        ["", "", ""],
+        [1, 2, 3],
+        [1, 0, 0],
+        [0, 0, 0],
+        [1.0, 2.0, 3.0],
+        [1.0, 0.0, 0.0],
+        [0.0, 0.0, 0.0],
+        [True, True, True],
+        [True, False, False],
+        [False, False, False],
+        [np.nan, np.nan, np.nan],
+    ],
+)
+def test_groupby_bool_aggs(agg_func, skipna, vals):
+    df = DataFrame({"key": ["a"] * 3 + ["b"] * 3, "val": vals * 2})
+
+    # Figure out expectation using Python builtin
+    exp = getattr(builtins, agg_func)(vals)
+
+    # edge case for missing data with skipna and 'any'
+    if skipna and all(isna(vals)) and agg_func == "any":
+        exp = False
+
+    exp_df = DataFrame([exp] * 2, columns=["val"], index=Index(["a", "b"], name="key"))
+    result = getattr(df.groupby("key"), agg_func)(skipna=skipna)
+    tm.assert_frame_equal(result, exp_df)
+
+
+def test_any():
+    df = DataFrame(
+        [[1, 2, "foo"], [1, np.nan, "bar"], [3, np.nan, "baz"]],
+        columns=["A", "B", "C"],
+    )
+    expected = DataFrame(
+        [[True, True], [False, True]], columns=["B", "C"], index=[1, 3]
+    )
+    expected.index.name = "A"
+    result = df.groupby("A").any()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
+def test_bool_aggs_dup_column_labels(bool_agg_func):
+    # 21668
+    df = DataFrame([[True, True]], columns=["a", "a"])
+    grp_by = df.groupby([0])
+    result = getattr(grp_by, bool_agg_func)()
+
+    expected = df.set_axis(np.array([0]))
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
+@pytest.mark.parametrize("skipna", [True, False])
+@pytest.mark.parametrize(
+    "data",
+    [
+        [False, False, False],
+        [True, True, True],
+        [pd.NA, pd.NA, pd.NA],
+        [False, pd.NA, False],
+        [True, pd.NA, True],
+        [True, pd.NA, False],
+    ],
+)
+def test_masked_kleene_logic(bool_agg_func, skipna, data):
+    # GH#37506
+    ser = Series(data, dtype="boolean")
+
+    # The result should match aggregating on the whole series. Correctness
+    # there is verified in test_reductions.py::test_any_all_boolean_kleene_logic
+    expected_data = getattr(ser, bool_agg_func)(skipna=skipna)
+    expected = Series(expected_data, index=np.array([0]), dtype="boolean")
+
+    result = ser.groupby([0, 0, 0]).agg(bool_agg_func, skipna=skipna)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "dtype1,dtype2,exp_col1,exp_col2",
+    [
+        (
+            "float",
+            "Float64",
+            np.array([True], dtype=bool),
+            pd.array([pd.NA], dtype="boolean"),
+        ),
+        (
+            "Int64",
+            "float",
+            pd.array([pd.NA], dtype="boolean"),
+            np.array([True], dtype=bool),
+        ),
+        (
+            "Int64",
+            "Int64",
+            pd.array([pd.NA], dtype="boolean"),
+            pd.array([pd.NA], dtype="boolean"),
+        ),
+        (
+            "Float64",
+            "boolean",
+            pd.array([pd.NA], dtype="boolean"),
+            pd.array([pd.NA], dtype="boolean"),
+        ),
+    ],
+)
+def test_masked_mixed_types(dtype1, dtype2, exp_col1, exp_col2):
+    # GH#37506
+    data = [1.0, np.nan]
+    df = DataFrame(
+        {"col1": pd.array(data, dtype=dtype1), "col2": pd.array(data, dtype=dtype2)}
+    )
+    result = df.groupby([1, 1]).agg("all", skipna=False)
+
+    expected = DataFrame({"col1": exp_col1, "col2": exp_col2}, index=np.array([1]))
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
+@pytest.mark.parametrize("dtype", ["Int64", "Float64", "boolean"])
+@pytest.mark.parametrize("skipna", [True, False])
+def test_masked_bool_aggs_skipna(bool_agg_func, dtype, skipna, frame_or_series):
+    # GH#40585
+    obj = frame_or_series([pd.NA, 1], dtype=dtype)
+    expected_res = True
+    if not skipna and bool_agg_func == "all":
+        expected_res = pd.NA
+    expected = frame_or_series([expected_res], index=np.array([1]), dtype="boolean")
+
+    result = obj.groupby([1, 1]).agg(bool_agg_func, skipna=skipna)
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "bool_agg_func,data,expected_res",
+    [
+        ("any", [pd.NA, np.nan], False),
+        ("any", [pd.NA, 1, np.nan], True),
+        ("all", [pd.NA, pd.NaT], True),
+        ("all", [pd.NA, False, pd.NaT], False),
+    ],
+)
+def test_object_type_missing_vals(bool_agg_func, data, expected_res, frame_or_series):
+    # GH#37501
+    obj = frame_or_series(data, dtype=object)
+    result = obj.groupby([1] * len(data)).agg(bool_agg_func)
+    expected = frame_or_series([expected_res], index=np.array([1]), dtype="bool")
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
+def test_object_NA_raises_with_skipna_false(bool_agg_func):
+    # GH#37501
+    ser = Series([pd.NA], dtype=object)
+    with pytest.raises(TypeError, match="boolean value of NA is ambiguous"):
+        ser.groupby([1]).agg(bool_agg_func, skipna=False)
+
+
+@pytest.mark.parametrize("bool_agg_func", ["any", "all"])
+def test_empty(frame_or_series, bool_agg_func):
+    # GH 45231
+    kwargs = {"columns": ["a"]} if frame_or_series is DataFrame else {"name": "a"}
+    obj = frame_or_series(**kwargs, dtype=object)
+    result = getattr(obj.groupby(obj.index), bool_agg_func)()
+    expected = frame_or_series(**kwargs, dtype=bool)
+    tm.assert_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_api_consistency.py

@@ -0,0 +1,142 @@
+"""
+Test the consistency of the groupby API, both internally and with other pandas objects.
+"""
+
+import inspect
+
+import pytest
+
+from pandas import (
+    DataFrame,
+    Series,
+)
+from pandas.core.groupby.generic import (
+    DataFrameGroupBy,
+    SeriesGroupBy,
+)
+
+
+def test_frame_consistency(request, groupby_func):
+    # GH#48028
+    if groupby_func in ("first", "last"):
+        msg = "first and last are entirely different between frame and groupby"
+        request.node.add_marker(pytest.mark.xfail(reason=msg))
+    if groupby_func in ("cumcount",):
+        msg = "DataFrame has no such method"
+        request.node.add_marker(pytest.mark.xfail(reason=msg))
+
+    if groupby_func == "ngroup":
+        assert not hasattr(DataFrame, groupby_func)
+        return
+
+    frame_method = getattr(DataFrame, groupby_func)
+    gb_method = getattr(DataFrameGroupBy, groupby_func)
+    result = set(inspect.signature(gb_method).parameters)
+    if groupby_func == "size":
+        # "size" is a method on GroupBy but property on DataFrame:
+        expected = {"self"}
+    else:
+        expected = set(inspect.signature(frame_method).parameters)
+
+    # Exclude certain arguments from result and expected depending on the operation
+    # Some of these may be purposeful inconsistencies between the APIs
+    exclude_expected, exclude_result = set(), set()
+    if groupby_func in ("any", "all"):
+        exclude_expected = {"kwargs", "bool_only", "axis"}
+    elif groupby_func in ("count",):
+        exclude_expected = {"numeric_only", "axis"}
+    elif groupby_func in ("nunique",):
+        exclude_expected = {"axis"}
+    elif groupby_func in ("max", "min"):
+        exclude_expected = {"axis", "kwargs", "skipna"}
+        exclude_result = {"min_count", "engine", "engine_kwargs"}
+    elif groupby_func in ("mean", "std", "sum", "var"):
+        exclude_expected = {"axis", "kwargs", "skipna"}
+        exclude_result = {"engine", "engine_kwargs"}
+    elif groupby_func in ("median", "prod", "sem"):
+        exclude_expected = {"axis", "kwargs", "skipna"}
+    elif groupby_func in ("backfill", "bfill", "ffill", "pad"):
+        exclude_expected = {"downcast", "inplace", "axis"}
+    elif groupby_func in ("cummax", "cummin"):
+        exclude_expected = {"skipna", "args"}
+        exclude_result = {"numeric_only"}
+    elif groupby_func in ("cumprod", "cumsum"):
+        exclude_expected = {"skipna"}
+    elif groupby_func in ("pct_change",):
+        exclude_expected = {"kwargs"}
+        exclude_result = {"axis"}
+    elif groupby_func in ("rank",):
+        exclude_expected = {"numeric_only"}
+    elif groupby_func in ("quantile",):
+        exclude_expected = {"method", "axis"}
+
+    # Ensure excluded arguments are actually in the signatures
+    assert result & exclude_result == exclude_result
+    assert expected & exclude_expected == exclude_expected
+
+    result -= exclude_result
+    expected -= exclude_expected
+    assert result == expected
+
+
+def test_series_consistency(request, groupby_func):
+    # GH#48028
+    if groupby_func in ("first", "last"):
+        msg = "first and last are entirely different between Series and groupby"
+        request.node.add_marker(pytest.mark.xfail(reason=msg))
+    if groupby_func in ("cumcount", "corrwith"):
+        msg = "Series has no such method"
+        request.node.add_marker(pytest.mark.xfail(reason=msg))
+
+    if groupby_func == "ngroup":
+        assert not hasattr(Series, groupby_func)
+        return
+
+    series_method = getattr(Series, groupby_func)
+    gb_method = getattr(SeriesGroupBy, groupby_func)
+    result = set(inspect.signature(gb_method).parameters)
+    if groupby_func == "size":
+        # "size" is a method on GroupBy but property on Series
+        expected = {"self"}
+    else:
+        expected = set(inspect.signature(series_method).parameters)
+
+    # Exclude certain arguments from result and expected depending on the operation
+    # Some of these may be purposeful inconsistencies between the APIs
+    exclude_expected, exclude_result = set(), set()
+    if groupby_func in ("any", "all"):
+        exclude_expected = {"kwargs", "bool_only", "axis"}
+    elif groupby_func in ("diff",):
+        exclude_result = {"axis"}
+    elif groupby_func in ("max", "min"):
+        exclude_expected = {"axis", "kwargs", "skipna"}
+        exclude_result = {"min_count", "engine", "engine_kwargs"}
+    elif groupby_func in ("mean", "std", "sum", "var"):
+        exclude_expected = {"axis", "kwargs", "skipna"}
+        exclude_result = {"engine", "engine_kwargs"}
+    elif groupby_func in ("median", "prod", "sem"):
+        exclude_expected = {"axis", "kwargs", "skipna"}
+    elif groupby_func in ("backfill", "bfill", "ffill", "pad"):
+        exclude_expected = {"downcast", "inplace", "axis"}
+    elif groupby_func in ("cummax", "cummin"):
+        exclude_expected = {"skipna", "args"}
+        exclude_result = {"numeric_only"}
+    elif groupby_func in ("cumprod", "cumsum"):
+        exclude_expected = {"skipna"}
+    elif groupby_func in ("pct_change",):
+        exclude_expected = {"kwargs"}
+        exclude_result = {"axis"}
+    elif groupby_func in ("rank",):
+        exclude_expected = {"numeric_only"}
+    elif groupby_func in ("idxmin", "idxmax"):
+        exclude_expected = {"args", "kwargs"}
+    elif groupby_func in ("quantile",):
+        exclude_result = {"numeric_only"}
+
+    # Ensure excluded arguments are actually in the signatures
+    assert result & exclude_result == exclude_result
+    assert expected & exclude_expected == exclude_expected
+
+    result -= exclude_result
+    expected -= exclude_expected
+    assert result == expected

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_apply.py

@@ -0,0 +1,1341 @@
+from datetime import (
+    date,
+    datetime,
+)
+from io import StringIO
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    bdate_range,
+)
+import pandas._testing as tm
+from pandas.tests.groupby import get_groupby_method_args
+
+
+def test_apply_issues():
+    # GH 5788
+
+    s = """2011.05.16,00:00,1.40893
+2011.05.16,01:00,1.40760
+2011.05.16,02:00,1.40750
+2011.05.16,03:00,1.40649
+2011.05.17,02:00,1.40893
+2011.05.17,03:00,1.40760
+2011.05.17,04:00,1.40750
+2011.05.17,05:00,1.40649
+2011.05.18,02:00,1.40893
+2011.05.18,03:00,1.40760
+2011.05.18,04:00,1.40750
+2011.05.18,05:00,1.40649"""
+
+    df = pd.read_csv(
+        StringIO(s),
+        header=None,
+        names=["date", "time", "value"],
+        parse_dates=[["date", "time"]],
+    )
+    df = df.set_index("date_time")
+
+    expected = df.groupby(df.index.date).idxmax()
+    result = df.groupby(df.index.date).apply(lambda x: x.idxmax())
+    tm.assert_frame_equal(result, expected)
+
+    # GH 5789
+    # don't auto coerce dates
+    df = pd.read_csv(StringIO(s), header=None, names=["date", "time", "value"])
+    exp_idx = Index(
+        ["2011.05.16", "2011.05.17", "2011.05.18"], dtype=object, name="date"
+    )
+    expected = Series(["00:00", "02:00", "02:00"], index=exp_idx)
+    result = df.groupby("date", group_keys=False).apply(
+        lambda x: x["time"][x["value"].idxmax()]
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_trivial():
+    # GH 20066
+    # trivial apply: ignore input and return a constant dataframe.
+    df = DataFrame(
+        {"key": ["a", "a", "b", "b", "a"], "data": [1.0, 2.0, 3.0, 4.0, 5.0]},
+        columns=["key", "data"],
+    )
+    expected = pd.concat([df.iloc[1:], df.iloc[1:]], axis=1, keys=["float64", "object"])
+    result = df.groupby([str(x) for x in df.dtypes], axis=1).apply(
+        lambda x: df.iloc[1:]
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_trivial_fail():
+    # GH 20066
+    df = DataFrame(
+        {"key": ["a", "a", "b", "b", "a"], "data": [1.0, 2.0, 3.0, 4.0, 5.0]},
+        columns=["key", "data"],
+    )
+    expected = pd.concat([df, df], axis=1, keys=["float64", "object"])
+    result = df.groupby([str(x) for x in df.dtypes], axis=1, group_keys=True).apply(
+        lambda x: df
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "df, group_names",
+    [
+        (DataFrame({"a": [1, 1, 1, 2, 3], "b": ["a", "a", "a", "b", "c"]}), [1, 2, 3]),
+        (DataFrame({"a": [0, 0, 1, 1], "b": [0, 1, 0, 1]}), [0, 1]),
+        (DataFrame({"a": [1]}), [1]),
+        (DataFrame({"a": [1, 1, 1, 2, 2, 1, 1, 2], "b": range(8)}), [1, 2]),
+        (DataFrame({"a": [1, 2, 3, 1, 2, 3], "two": [4, 5, 6, 7, 8, 9]}), [1, 2, 3]),
+        (
+            DataFrame(
+                {
+                    "a": list("aaabbbcccc"),
+                    "B": [3, 4, 3, 6, 5, 2, 1, 9, 5, 4],
+                    "C": [4, 0, 2, 2, 2, 7, 8, 6, 2, 8],
+                }
+            ),
+            ["a", "b", "c"],
+        ),
+        (DataFrame([[1, 2, 3], [2, 2, 3]], columns=["a", "b", "c"]), [1, 2]),
+    ],
+    ids=[
+        "GH2936",
+        "GH7739 & GH10519",
+        "GH10519",
+        "GH2656",
+        "GH12155",
+        "GH20084",
+        "GH21417",
+    ],
+)
+def test_group_apply_once_per_group(df, group_names):
+    # GH2936, GH7739, GH10519, GH2656, GH12155, GH20084, GH21417
+
+    # This test should ensure that a function is only evaluated
+    # once per group. Previously the function has been evaluated twice
+    # on the first group to check if the Cython index slider is safe to use
+    # This test ensures that the side effect (append to list) is only triggered
+    # once per group
+
+    names = []
+    # cannot parameterize over the functions since they need external
+    # `names` to detect side effects
+
+    def f_copy(group):
+        # this takes the fast apply path
+        names.append(group.name)
+        return group.copy()
+
+    def f_nocopy(group):
+        # this takes the slow apply path
+        names.append(group.name)
+        return group
+
+    def f_scalar(group):
+        # GH7739, GH2656
+        names.append(group.name)
+        return 0
+
+    def f_none(group):
+        # GH10519, GH12155, GH21417
+        names.append(group.name)
+
+    def f_constant_df(group):
+        # GH2936, GH20084
+        names.append(group.name)
+        return DataFrame({"a": [1], "b": [1]})
+
+    for func in [f_copy, f_nocopy, f_scalar, f_none, f_constant_df]:
+        del names[:]
+
+        df.groupby("a", group_keys=False).apply(func)
+        assert names == group_names
+
+
+def test_group_apply_once_per_group2(capsys):
+    # GH: 31111
+    # groupby-apply need to execute len(set(group_by_columns)) times
+
+    expected = 2  # Number of times `apply` should call a function for the current test
+
+    df = DataFrame(
+        {
+            "group_by_column": [0, 0, 0, 0, 1, 1, 1, 1],
+            "test_column": ["0", "2", "4", "6", "8", "10", "12", "14"],
+        },
+        index=["0", "2", "4", "6", "8", "10", "12", "14"],
+    )
+
+    df.groupby("group_by_column", group_keys=False).apply(
+        lambda df: print("function_called")
+    )
+
+    result = capsys.readouterr().out.count("function_called")
+    # If `groupby` behaves unexpectedly, this test will break
+    assert result == expected
+
+
+def test_apply_fast_slow_identical():
+    # GH 31613
+
+    df = DataFrame({"A": [0, 0, 1], "b": range(3)})
+
+    # For simple index structures we check for fast/slow apply using
+    # an identity check on in/output
+    def slow(group):
+        return group
+
+    def fast(group):
+        return group.copy()
+
+    fast_df = df.groupby("A", group_keys=False).apply(fast)
+    slow_df = df.groupby("A", group_keys=False).apply(slow)
+
+    tm.assert_frame_equal(fast_df, slow_df)
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        lambda x: x,
+        lambda x: x[:],
+        lambda x: x.copy(deep=False),
+        lambda x: x.copy(deep=True),
+    ],
+)
+def test_groupby_apply_identity_maybecopy_index_identical(func):
+    # GH 14927
+    # Whether the function returns a copy of the input data or not should not
+    # have an impact on the index structure of the result since this is not
+    # transparent to the user
+
+    df = DataFrame({"g": [1, 2, 2, 2], "a": [1, 2, 3, 4], "b": [5, 6, 7, 8]})
+
+    result = df.groupby("g", group_keys=False).apply(func)
+    tm.assert_frame_equal(result, df)
+
+
+def test_apply_with_mixed_dtype():
+    # GH3480, apply with mixed dtype on axis=1 breaks in 0.11
+    df = DataFrame(
+        {
+            "foo1": np.random.randn(6),
+            "foo2": ["one", "two", "two", "three", "one", "two"],
+        }
+    )
+    result = df.apply(lambda x: x, axis=1).dtypes
+    expected = df.dtypes
+    tm.assert_series_equal(result, expected)
+
+    # GH 3610 incorrect dtype conversion with as_index=False
+    df = DataFrame({"c1": [1, 2, 6, 6, 8]})
+    df["c2"] = df.c1 / 2.0
+    result1 = df.groupby("c2").mean().reset_index().c2
+    result2 = df.groupby("c2", as_index=False).mean().c2
+    tm.assert_series_equal(result1, result2)
+
+
+def test_groupby_as_index_apply():
+    # GH #4648 and #3417
+    df = DataFrame(
+        {
+            "item_id": ["b", "b", "a", "c", "a", "b"],
+            "user_id": [1, 2, 1, 1, 3, 1],
+            "time": range(6),
+        }
+    )
+
+    g_as = df.groupby("user_id", as_index=True)
+    g_not_as = df.groupby("user_id", as_index=False)
+
+    res_as = g_as.head(2).index
+    res_not_as = g_not_as.head(2).index
+    exp = Index([0, 1, 2, 4])
+    tm.assert_index_equal(res_as, exp)
+    tm.assert_index_equal(res_not_as, exp)
+
+    res_as_apply = g_as.apply(lambda x: x.head(2)).index
+    res_not_as_apply = g_not_as.apply(lambda x: x.head(2)).index
+
+    # apply doesn't maintain the original ordering
+    # changed in GH5610 as the as_index=False returns a MI here
+    exp_not_as_apply = MultiIndex.from_tuples([(0, 0), (0, 2), (1, 1), (2, 4)])
+    tp = [(1, 0), (1, 2), (2, 1), (3, 4)]
+    exp_as_apply = MultiIndex.from_tuples(tp, names=["user_id", None])
+
+    tm.assert_index_equal(res_as_apply, exp_as_apply)
+    tm.assert_index_equal(res_not_as_apply, exp_not_as_apply)
+
+    ind = Index(list("abcde"))
+    df = DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]], index=ind)
+    res = df.groupby(0, as_index=False, group_keys=False).apply(lambda x: x).index
+    tm.assert_index_equal(res, ind)
+
+
+def test_apply_concat_preserve_names(three_group):
+    grouped = three_group.groupby(["A", "B"])
+
+    def desc(group):
+        result = group.describe()
+        result.index.name = "stat"
+        return result
+
+    def desc2(group):
+        result = group.describe()
+        result.index.name = "stat"
+        result = result[: len(group)]
+        # weirdo
+        return result
+
+    def desc3(group):
+        result = group.describe()
+
+        # names are different
+        result.index.name = f"stat_{len(group):d}"
+
+        result = result[: len(group)]
+        # weirdo
+        return result
+
+    result = grouped.apply(desc)
+    assert result.index.names == ("A", "B", "stat")
+
+    result2 = grouped.apply(desc2)
+    assert result2.index.names == ("A", "B", "stat")
+
+    result3 = grouped.apply(desc3)
+    assert result3.index.names == ("A", "B", None)
+
+
+def test_apply_series_to_frame():
+    def f(piece):
+        with np.errstate(invalid="ignore"):
+            logged = np.log(piece)
+        return DataFrame(
+            {"value": piece, "demeaned": piece - piece.mean(), "logged": logged}
+        )
+
+    dr = bdate_range("1/1/2000", periods=100)
+    ts = Series(np.random.randn(100), index=dr)
+
+    grouped = ts.groupby(lambda x: x.month, group_keys=False)
+    result = grouped.apply(f)
+
+    assert isinstance(result, DataFrame)
+    assert not hasattr(result, "name")  # GH49907
+    tm.assert_index_equal(result.index, ts.index)
+
+
+def test_apply_series_yield_constant(df):
+    result = df.groupby(["A", "B"])["C"].apply(len)
+    assert result.index.names[:2] == ("A", "B")
+
+
+def test_apply_frame_yield_constant(df):
+    # GH13568
+    result = df.groupby(["A", "B"]).apply(len)
+    assert isinstance(result, Series)
+    assert result.name is None
+
+    result = df.groupby(["A", "B"])[["C", "D"]].apply(len)
+    assert isinstance(result, Series)
+    assert result.name is None
+
+
+def test_apply_frame_to_series(df):
+    grouped = df.groupby(["A", "B"])
+    result = grouped.apply(len)
+    expected = grouped.count()["C"]
+    tm.assert_index_equal(result.index, expected.index)
+    tm.assert_numpy_array_equal(result.values, expected.values)
+
+
+def test_apply_frame_not_as_index_column_name(df):
+    # GH 35964 - path within _wrap_applied_output not hit by a test
+    grouped = df.groupby(["A", "B"], as_index=False)
+    result = grouped.apply(len)
+    expected = grouped.count().rename(columns={"C": np.nan}).drop(columns="D")
+    # TODO(GH#34306): Use assert_frame_equal when column name is not np.nan
+    tm.assert_index_equal(result.index, expected.index)
+    tm.assert_numpy_array_equal(result.values, expected.values)
+
+
+def test_apply_frame_concat_series():
+    def trans(group):
+        return group.groupby("B")["C"].sum().sort_values().iloc[:2]
+
+    def trans2(group):
+        grouped = group.groupby(df.reindex(group.index)["B"])
+        return grouped.sum().sort_values().iloc[:2]
+
+    df = DataFrame(
+        {
+            "A": np.random.randint(0, 5, 1000),
+            "B": np.random.randint(0, 5, 1000),
+            "C": np.random.randn(1000),
+        }
+    )
+
+    result = df.groupby("A").apply(trans)
+    exp = df.groupby("A")["C"].apply(trans2)
+    tm.assert_series_equal(result, exp, check_names=False)
+    assert result.name == "C"
+
+
+def test_apply_transform(ts):
+    grouped = ts.groupby(lambda x: x.month, group_keys=False)
+    result = grouped.apply(lambda x: x * 2)
+    expected = grouped.transform(lambda x: x * 2)
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_multikey_corner(tsframe):
+    grouped = tsframe.groupby([lambda x: x.year, lambda x: x.month])
+
+    def f(group):
+        return group.sort_values("A")[-5:]
+
+    result = grouped.apply(f)
+    for key, group in grouped:
+        tm.assert_frame_equal(result.loc[key], f(group))
+
+
+@pytest.mark.parametrize("group_keys", [True, False])
+def test_apply_chunk_view(group_keys):
+    # Low level tinkering could be unsafe, make sure not
+    df = DataFrame({"key": [1, 1, 1, 2, 2, 2, 3, 3, 3], "value": range(9)})
+
+    result = df.groupby("key", group_keys=group_keys).apply(lambda x: x.iloc[:2])
+    expected = df.take([0, 1, 3, 4, 6, 7])
+    if group_keys:
+        expected.index = MultiIndex.from_arrays(
+            [[1, 1, 2, 2, 3, 3], expected.index], names=["key", None]
+        )
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_no_name_column_conflict():
+    df = DataFrame(
+        {
+            "name": [1, 1, 1, 1, 1, 1, 2, 2, 2, 2],
+            "name2": [0, 0, 0, 1, 1, 1, 0, 0, 1, 1],
+            "value": range(9, -1, -1),
+        }
+    )
+
+    # it works! #2605
+    grouped = df.groupby(["name", "name2"])
+    grouped.apply(lambda x: x.sort_values("value", inplace=True))
+
+
+def test_apply_typecast_fail():
+    df = DataFrame(
+        {
+            "d": [1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
+            "c": np.tile(["a", "b", "c"], 2),
+            "v": np.arange(1.0, 7.0),
+        }
+    )
+
+    def f(group):
+        v = group["v"]
+        group["v2"] = (v - v.min()) / (v.max() - v.min())
+        return group
+
+    result = df.groupby("d", group_keys=False).apply(f)
+
+    expected = df.copy()
+    expected["v2"] = np.tile([0.0, 0.5, 1], 2)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_multiindex_fail():
+    index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3]])
+    df = DataFrame(
+        {
+            "d": [1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
+            "c": np.tile(["a", "b", "c"], 2),
+            "v": np.arange(1.0, 7.0),
+        },
+        index=index,
+    )
+
+    def f(group):
+        v = group["v"]
+        group["v2"] = (v - v.min()) / (v.max() - v.min())
+        return group
+
+    result = df.groupby("d", group_keys=False).apply(f)
+
+    expected = df.copy()
+    expected["v2"] = np.tile([0.0, 0.5, 1], 2)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_corner(tsframe):
+    result = tsframe.groupby(lambda x: x.year, group_keys=False).apply(lambda x: x * 2)
+    expected = tsframe * 2
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_without_copy():
+    # GH 5545
+    # returning a non-copy in an applied function fails
+
+    data = DataFrame(
+        {
+            "id_field": [100, 100, 200, 300],
+            "category": ["a", "b", "c", "c"],
+            "value": [1, 2, 3, 4],
+        }
+    )
+
+    def filt1(x):
+        if x.shape[0] == 1:
+            return x.copy()
+        else:
+            return x[x.category == "c"]
+
+    def filt2(x):
+        if x.shape[0] == 1:
+            return x
+        else:
+            return x[x.category == "c"]
+
+    expected = data.groupby("id_field").apply(filt1)
+    result = data.groupby("id_field").apply(filt2)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("test_series", [True, False])
+def test_apply_with_duplicated_non_sorted_axis(test_series):
+    # GH 30667
+    df = DataFrame(
+        [["x", "p"], ["x", "p"], ["x", "o"]], columns=["X", "Y"], index=[1, 2, 2]
+    )
+    if test_series:
+        ser = df.set_index("Y")["X"]
+        result = ser.groupby(level=0, group_keys=False).apply(lambda x: x)
+
+        # not expecting the order to remain the same for duplicated axis
+        result = result.sort_index()
+        expected = ser.sort_index()
+        tm.assert_series_equal(result, expected)
+    else:
+        result = df.groupby("Y", group_keys=False).apply(lambda x: x)
+
+        # not expecting the order to remain the same for duplicated axis
+        result = result.sort_values("Y")
+        expected = df.sort_values("Y")
+        tm.assert_frame_equal(result, expected)
+
+
+def test_apply_reindex_values():
+    # GH: 26209
+    # reindexing from a single column of a groupby object with duplicate indices caused
+    # a ValueError (cannot reindex from duplicate axis) in 0.24.2, the problem was
+    # solved in #30679
+    values = [1, 2, 3, 4]
+    indices = [1, 1, 2, 2]
+    df = DataFrame({"group": ["Group1", "Group2"] * 2, "value": values}, index=indices)
+    expected = Series(values, index=indices, name="value")
+
+    def reindex_helper(x):
+        return x.reindex(np.arange(x.index.min(), x.index.max() + 1))
+
+    # the following group by raised a ValueError
+    result = df.groupby("group", group_keys=False).value.apply(reindex_helper)
+    tm.assert_series_equal(expected, result)
+
+
+def test_apply_corner_cases():
+    # #535, can't use sliding iterator
+
+    N = 1000
+    labels = np.random.randint(0, 100, size=N)
+    df = DataFrame(
+        {
+            "key": labels,
+            "value1": np.random.randn(N),
+            "value2": ["foo", "bar", "baz", "qux"] * (N // 4),
+        }
+    )
+
+    grouped = df.groupby("key", group_keys=False)
+
+    def f(g):
+        g["value3"] = g["value1"] * 2
+        return g
+
+    result = grouped.apply(f)
+    assert "value3" in result
+
+
+def test_apply_numeric_coercion_when_datetime():
+    # In the past, group-by/apply operations have been over-eager
+    # in converting dtypes to numeric, in the presence of datetime
+    # columns.  Various GH issues were filed, the reproductions
+    # for which are here.
+
+    # GH 15670
+    df = DataFrame(
+        {"Number": [1, 2], "Date": ["2017-03-02"] * 2, "Str": ["foo", "inf"]}
+    )
+    expected = df.groupby(["Number"]).apply(lambda x: x.iloc[0])
+    df.Date = pd.to_datetime(df.Date)
+    result = df.groupby(["Number"]).apply(lambda x: x.iloc[0])
+    tm.assert_series_equal(result["Str"], expected["Str"])
+
+    # GH 15421
+    df = DataFrame(
+        {"A": [10, 20, 30], "B": ["foo", "3", "4"], "T": [pd.Timestamp("12:31:22")] * 3}
+    )
+
+    def get_B(g):
+        return g.iloc[0][["B"]]
+
+    result = df.groupby("A").apply(get_B)["B"]
+    expected = df.B
+    expected.index = df.A
+    tm.assert_series_equal(result, expected)
+
+    # GH 14423
+    def predictions(tool):
+        out = Series(index=["p1", "p2", "useTime"], dtype=object)
+        if "step1" in list(tool.State):
+            out["p1"] = str(tool[tool.State == "step1"].Machine.values[0])
+        if "step2" in list(tool.State):
+            out["p2"] = str(tool[tool.State == "step2"].Machine.values[0])
+            out["useTime"] = str(tool[tool.State == "step2"].oTime.values[0])
+        return out
+
+    df1 = DataFrame(
+        {
+            "Key": ["B", "B", "A", "A"],
+            "State": ["step1", "step2", "step1", "step2"],
+            "oTime": ["", "2016-09-19 05:24:33", "", "2016-09-19 23:59:04"],
+            "Machine": ["23", "36L", "36R", "36R"],
+        }
+    )
+    df2 = df1.copy()
+    df2.oTime = pd.to_datetime(df2.oTime)
+    expected = df1.groupby("Key").apply(predictions).p1
+    result = df2.groupby("Key").apply(predictions).p1
+    tm.assert_series_equal(expected, result)
+
+
+def test_apply_aggregating_timedelta_and_datetime():
+    # Regression test for GH 15562
+    # The following groupby caused ValueErrors and IndexErrors pre 0.20.0
+
+    df = DataFrame(
+        {
+            "clientid": ["A", "B", "C"],
+            "datetime": [np.datetime64("2017-02-01 00:00:00")] * 3,
+        }
+    )
+    df["time_delta_zero"] = df.datetime - df.datetime
+    result = df.groupby("clientid").apply(
+        lambda ddf: Series(
+            {"clientid_age": ddf.time_delta_zero.min(), "date": ddf.datetime.min()}
+        )
+    )
+    expected = DataFrame(
+        {
+            "clientid": ["A", "B", "C"],
+            "clientid_age": [np.timedelta64(0, "D")] * 3,
+            "date": [np.datetime64("2017-02-01 00:00:00")] * 3,
+        }
+    ).set_index("clientid")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_groupby_datetimeindex():
+    # GH 26182
+    # groupby apply failed on dataframe with DatetimeIndex
+
+    data = [["A", 10], ["B", 20], ["B", 30], ["C", 40], ["C", 50]]
+    df = DataFrame(
+        data, columns=["Name", "Value"], index=pd.date_range("2020-09-01", "2020-09-05")
+    )
+
+    result = df.groupby("Name").sum()
+
+    expected = DataFrame({"Name": ["A", "B", "C"], "Value": [10, 50, 90]})
+    expected.set_index("Name", inplace=True)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_time_field_bug():
+    # Test a fix for the following error related to GH issue 11324 When
+    # non-key fields in a group-by dataframe contained time-based fields
+    # that were not returned by the apply function, an exception would be
+    # raised.
+
+    df = DataFrame({"a": 1, "b": [datetime.now() for nn in range(10)]})
+
+    def func_with_no_date(batch):
+        return Series({"c": 2})
+
+    def func_with_date(batch):
+        return Series({"b": datetime(2015, 1, 1), "c": 2})
+
+    dfg_no_conversion = df.groupby(by=["a"]).apply(func_with_no_date)
+    dfg_no_conversion_expected = DataFrame({"c": 2}, index=[1])
+    dfg_no_conversion_expected.index.name = "a"
+
+    dfg_conversion = df.groupby(by=["a"]).apply(func_with_date)
+    dfg_conversion_expected = DataFrame({"b": datetime(2015, 1, 1), "c": 2}, index=[1])
+    dfg_conversion_expected.index.name = "a"
+
+    tm.assert_frame_equal(dfg_no_conversion, dfg_no_conversion_expected)
+    tm.assert_frame_equal(dfg_conversion, dfg_conversion_expected)
+
+
+def test_gb_apply_list_of_unequal_len_arrays():
+    # GH1738
+    df = DataFrame(
+        {
+            "group1": ["a", "a", "a", "b", "b", "b", "a", "a", "a", "b", "b", "b"],
+            "group2": ["c", "c", "d", "d", "d", "e", "c", "c", "d", "d", "d", "e"],
+            "weight": [1.1, 2, 3, 4, 5, 6, 2, 4, 6, 8, 1, 2],
+            "value": [7.1, 8, 9, 10, 11, 12, 8, 7, 6, 5, 4, 3],
+        }
+    )
+    df = df.set_index(["group1", "group2"])
+    df_grouped = df.groupby(level=["group1", "group2"], sort=True)
+
+    def noddy(value, weight):
+        out = np.array(value * weight).repeat(3)
+        return out
+
+    # the kernel function returns arrays of unequal length
+    # pandas sniffs the first one, sees it's an array and not
+    # a list, and assumed the rest are of equal length
+    # and so tries a vstack
+
+    # don't die
+    df_grouped.apply(lambda x: noddy(x.value, x.weight))
+
+
+def test_groupby_apply_all_none():
+    # Tests to make sure no errors if apply function returns all None
+    # values. Issue 9684.
+    test_df = DataFrame({"groups": [0, 0, 1, 1], "random_vars": [8, 7, 4, 5]})
+
+    def test_func(x):
+        pass
+
+    result = test_df.groupby("groups").apply(test_func)
+    expected = DataFrame()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_apply_none_first():
+    # GH 12824. Tests if apply returns None first.
+    test_df1 = DataFrame({"groups": [1, 1, 1, 2], "vars": [0, 1, 2, 3]})
+    test_df2 = DataFrame({"groups": [1, 2, 2, 2], "vars": [0, 1, 2, 3]})
+
+    def test_func(x):
+        if x.shape[0] < 2:
+            return None
+        return x.iloc[[0, -1]]
+
+    result1 = test_df1.groupby("groups").apply(test_func)
+    result2 = test_df2.groupby("groups").apply(test_func)
+    index1 = MultiIndex.from_arrays([[1, 1], [0, 2]], names=["groups", None])
+    index2 = MultiIndex.from_arrays([[2, 2], [1, 3]], names=["groups", None])
+    expected1 = DataFrame({"groups": [1, 1], "vars": [0, 2]}, index=index1)
+    expected2 = DataFrame({"groups": [2, 2], "vars": [1, 3]}, index=index2)
+    tm.assert_frame_equal(result1, expected1)
+    tm.assert_frame_equal(result2, expected2)
+
+
+def test_groupby_apply_return_empty_chunk():
+    # GH 22221: apply filter which returns some empty groups
+    df = DataFrame({"value": [0, 1], "group": ["filled", "empty"]})
+    groups = df.groupby("group")
+    result = groups.apply(lambda group: group[group.value != 1]["value"])
+    expected = Series(
+        [0],
+        name="value",
+        index=MultiIndex.from_product(
+            [["empty", "filled"], [0]], names=["group", None]
+        ).drop("empty"),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_with_mixed_types():
+    # gh-20949
+    df = DataFrame({"A": "a a b".split(), "B": [1, 2, 3], "C": [4, 6, 5]})
+    g = df.groupby("A", group_keys=False)
+
+    result = g.transform(lambda x: x / x.sum())
+    expected = DataFrame({"B": [1 / 3.0, 2 / 3.0, 1], "C": [0.4, 0.6, 1.0]})
+    tm.assert_frame_equal(result, expected)
+
+    result = g.apply(lambda x: x / x.sum())
+    tm.assert_frame_equal(result, expected)
+
+
+def test_func_returns_object():
+    # GH 28652
+    df = DataFrame({"a": [1, 2]}, index=Index([1, 2]))
+    result = df.groupby("a").apply(lambda g: g.index)
+    expected = Series([Index([1]), Index([2])], index=Index([1, 2], name="a"))
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "group_column_dtlike",
+    [datetime.today(), datetime.today().date(), datetime.today().time()],
+)
+def test_apply_datetime_issue(group_column_dtlike):
+    # GH-28247
+    # groupby-apply throws an error if one of the columns in the DataFrame
+    #   is a datetime object and the column labels are different from
+    #   standard int values in range(len(num_columns))
+
+    df = DataFrame({"a": ["foo"], "b": [group_column_dtlike]})
+    result = df.groupby("a").apply(lambda x: Series(["spam"], index=[42]))
+
+    expected = DataFrame(
+        ["spam"], Index(["foo"], dtype="object", name="a"), columns=[42]
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_series_return_dataframe_groups():
+    # GH 10078
+    tdf = DataFrame(
+        {
+            "day": {
+                0: pd.Timestamp("2015-02-24 00:00:00"),
+                1: pd.Timestamp("2015-02-24 00:00:00"),
+                2: pd.Timestamp("2015-02-24 00:00:00"),
+                3: pd.Timestamp("2015-02-24 00:00:00"),
+                4: pd.Timestamp("2015-02-24 00:00:00"),
+            },
+            "userAgent": {
+                0: "some UA string",
+                1: "some UA string",
+                2: "some UA string",
+                3: "another UA string",
+                4: "some UA string",
+            },
+            "userId": {
+                0: "17661101",
+                1: "17661101",
+                2: "17661101",
+                3: "17661101",
+                4: "17661101",
+            },
+        }
+    )
+
+    def most_common_values(df):
+        return Series({c: s.value_counts().index[0] for c, s in df.items()})
+
+    result = tdf.groupby("day").apply(most_common_values)["userId"]
+    expected = Series(
+        ["17661101"], index=pd.DatetimeIndex(["2015-02-24"], name="day"), name="userId"
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("category", [False, True])
+def test_apply_multi_level_name(category):
+    # https://github.com/pandas-dev/pandas/issues/31068
+    b = [1, 2] * 5
+    if category:
+        b = pd.Categorical(b, categories=[1, 2, 3])
+        expected_index = pd.CategoricalIndex([1, 2, 3], categories=[1, 2, 3], name="B")
+        # GH#40669 - summing an empty frame gives float dtype
+        expected_values = [20.0, 25.0, 0.0]
+    else:
+        expected_index = Index([1, 2], name="B")
+        expected_values = [20, 25]
+    expected = DataFrame(
+        {"C": expected_values, "D": expected_values}, index=expected_index
+    )
+
+    df = DataFrame(
+        {"A": np.arange(10), "B": b, "C": list(range(10)), "D": list(range(10))}
+    ).set_index(["A", "B"])
+    result = df.groupby("B").apply(lambda x: x.sum())
+    tm.assert_frame_equal(result, expected)
+    assert df.index.names == ["A", "B"]
+
+
+def test_groupby_apply_datetime_result_dtypes():
+    # GH 14849
+    data = DataFrame.from_records(
+        [
+            (pd.Timestamp(2016, 1, 1), "red", "dark", 1, "8"),
+            (pd.Timestamp(2015, 1, 1), "green", "stormy", 2, "9"),
+            (pd.Timestamp(2014, 1, 1), "blue", "bright", 3, "10"),
+            (pd.Timestamp(2013, 1, 1), "blue", "calm", 4, "potato"),
+        ],
+        columns=["observation", "color", "mood", "intensity", "score"],
+    )
+    result = data.groupby("color").apply(lambda g: g.iloc[0]).dtypes
+    expected = Series(
+        [np.dtype("datetime64[ns]"), object, object, np.int64, object],
+        index=["observation", "color", "mood", "intensity", "score"],
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "index",
+    [
+        pd.CategoricalIndex(list("abc")),
+        pd.interval_range(0, 3),
+        pd.period_range("2020", periods=3, freq="D"),
+        MultiIndex.from_tuples([("a", 0), ("a", 1), ("b", 0)]),
+    ],
+)
+def test_apply_index_has_complex_internals(index):
+    # GH 31248
+    df = DataFrame({"group": [1, 1, 2], "value": [0, 1, 0]}, index=index)
+    result = df.groupby("group", group_keys=False).apply(lambda x: x)
+    tm.assert_frame_equal(result, df)
+
+
+@pytest.mark.parametrize(
+    "function, expected_values",
+    [
+        (lambda x: x.index.to_list(), [[0, 1], [2, 3]]),
+        (lambda x: set(x.index.to_list()), [{0, 1}, {2, 3}]),
+        (lambda x: tuple(x.index.to_list()), [(0, 1), (2, 3)]),
+        (
+            lambda x: dict(enumerate(x.index.to_list())),
+            [{0: 0, 1: 1}, {0: 2, 1: 3}],
+        ),
+        (
+            lambda x: [{n: i} for (n, i) in enumerate(x.index.to_list())],
+            [[{0: 0}, {1: 1}], [{0: 2}, {1: 3}]],
+        ),
+    ],
+)
+def test_apply_function_returns_non_pandas_non_scalar(function, expected_values):
+    # GH 31441
+    df = DataFrame(["A", "A", "B", "B"], columns=["groups"])
+    result = df.groupby("groups").apply(function)
+    expected = Series(expected_values, index=Index(["A", "B"], name="groups"))
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_function_returns_numpy_array():
+    # GH 31605
+    def fct(group):
+        return group["B"].values.flatten()
+
+    df = DataFrame({"A": ["a", "a", "b", "none"], "B": [1, 2, 3, np.nan]})
+
+    result = df.groupby("A").apply(fct)
+    expected = Series(
+        [[1.0, 2.0], [3.0], [np.nan]], index=Index(["a", "b", "none"], name="A")
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("function", [lambda gr: gr.index, lambda gr: gr.index + 1 - 1])
+def test_apply_function_index_return(function):
+    # GH: 22541
+    df = DataFrame([1, 2, 2, 2, 1, 2, 3, 1, 3, 1], columns=["id"])
+    result = df.groupby("id").apply(function)
+    expected = Series(
+        [Index([0, 4, 7, 9]), Index([1, 2, 3, 5]), Index([6, 8])],
+        index=Index([1, 2, 3], name="id"),
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_function_with_indexing_return_column():
+    # GH#7002, GH#41480, GH#49256
+    df = DataFrame(
+        {
+            "foo1": ["one", "two", "two", "three", "one", "two"],
+            "foo2": [1, 2, 4, 4, 5, 6],
+        }
+    )
+    result = df.groupby("foo1", as_index=False).apply(lambda x: x.mean())
+    expected = DataFrame(
+        {
+            "foo1": ["one", "three", "two"],
+            "foo2": [3.0, 4.0, 4.0],
+        }
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "udf",
+    [(lambda x: x.copy()), (lambda x: x.copy().rename(lambda y: y + 1))],
+)
+@pytest.mark.parametrize("group_keys", [True, False])
+def test_apply_result_type(group_keys, udf):
+    # https://github.com/pandas-dev/pandas/issues/34809
+    # We'd like to control whether the group keys end up in the index
+    # regardless of whether the UDF happens to be a transform.
+    df = DataFrame({"A": ["a", "b"], "B": [1, 2]})
+    df_result = df.groupby("A", group_keys=group_keys).apply(udf)
+    series_result = df.B.groupby(df.A, group_keys=group_keys).apply(udf)
+
+    if group_keys:
+        assert df_result.index.nlevels == 2
+        assert series_result.index.nlevels == 2
+    else:
+        assert df_result.index.nlevels == 1
+        assert series_result.index.nlevels == 1
+
+
+def test_result_order_group_keys_false():
+    # GH 34998
+    # apply result order should not depend on whether index is the same or just equal
+    df = DataFrame({"A": [2, 1, 2], "B": [1, 2, 3]})
+    result = df.groupby("A", group_keys=False).apply(lambda x: x)
+    expected = df.groupby("A", group_keys=False).apply(lambda x: x.copy())
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_with_timezones_aware():
+    # GH: 27212
+    dates = ["2001-01-01"] * 2 + ["2001-01-02"] * 2 + ["2001-01-03"] * 2
+    index_no_tz = pd.DatetimeIndex(dates)
+    index_tz = pd.DatetimeIndex(dates, tz="UTC")
+    df1 = DataFrame({"x": list(range(2)) * 3, "y": range(6), "t": index_no_tz})
+    df2 = DataFrame({"x": list(range(2)) * 3, "y": range(6), "t": index_tz})
+
+    result1 = df1.groupby("x", group_keys=False).apply(lambda df: df[["x", "y"]].copy())
+    result2 = df2.groupby("x", group_keys=False).apply(lambda df: df[["x", "y"]].copy())
+
+    tm.assert_frame_equal(result1, result2)
+
+
+def test_apply_is_unchanged_when_other_methods_are_called_first(reduction_func):
+    # GH #34656
+    # GH #34271
+    df = DataFrame(
+        {
+            "a": [99, 99, 99, 88, 88, 88],
+            "b": [1, 2, 3, 4, 5, 6],
+            "c": [10, 20, 30, 40, 50, 60],
+        }
+    )
+
+    expected = DataFrame(
+        {"a": [264, 297], "b": [15, 6], "c": [150, 60]},
+        index=Index([88, 99], name="a"),
+    )
+
+    # Check output when no other methods are called before .apply()
+    grp = df.groupby(by="a")
+    result = grp.apply(sum)
+    tm.assert_frame_equal(result, expected)
+
+    # Check output when another method is called before .apply()
+    grp = df.groupby(by="a")
+    args = get_groupby_method_args(reduction_func, df)
+    _ = getattr(grp, reduction_func)(*args)
+    result = grp.apply(sum)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_with_date_in_multiindex_does_not_convert_to_timestamp():
+    # GH 29617
+
+    df = DataFrame(
+        {
+            "A": ["a", "a", "a", "b"],
+            "B": [
+                date(2020, 1, 10),
+                date(2020, 1, 10),
+                date(2020, 2, 10),
+                date(2020, 2, 10),
+            ],
+            "C": [1, 2, 3, 4],
+        },
+        index=Index([100, 101, 102, 103], name="idx"),
+    )
+
+    grp = df.groupby(["A", "B"])
+    result = grp.apply(lambda x: x.head(1))
+
+    expected = df.iloc[[0, 2, 3]]
+    expected = expected.reset_index()
+    expected.index = MultiIndex.from_frame(expected[["A", "B", "idx"]])
+    expected = expected.drop(columns="idx")
+
+    tm.assert_frame_equal(result, expected)
+    for val in result.index.levels[1]:
+        assert type(val) is date
+
+
+def test_apply_by_cols_equals_apply_by_rows_transposed():
+    # GH 16646
+    # Operating on the columns, or transposing and operating on the rows
+    # should give the same result. There was previously a bug where the
+    # by_rows operation would work fine, but by_cols would throw a ValueError
+
+    df = DataFrame(
+        np.random.random([6, 4]),
+        columns=MultiIndex.from_product([["A", "B"], [1, 2]]),
+    )
+
+    by_rows = df.T.groupby(axis=0, level=0).apply(
+        lambda x: x.droplevel(axis=0, level=0)
+    )
+    by_cols = df.groupby(axis=1, level=0).apply(lambda x: x.droplevel(axis=1, level=0))
+
+    tm.assert_frame_equal(by_cols, by_rows.T)
+    tm.assert_frame_equal(by_cols, df)
+
+
+@pytest.mark.parametrize("dropna", [True, False])
+def test_apply_dropna_with_indexed_same(dropna):
+    # GH 38227
+    # GH#43205
+    df = DataFrame(
+        {
+            "col": [1, 2, 3, 4, 5],
+            "group": ["a", np.nan, np.nan, "b", "b"],
+        },
+        index=list("xxyxz"),
+    )
+    result = df.groupby("group", dropna=dropna, group_keys=False).apply(lambda x: x)
+    expected = df.dropna() if dropna else df.iloc[[0, 3, 1, 2, 4]]
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "as_index, expected",
+    [
+        [
+            False,
+            DataFrame(
+                [[1, 1, 1], [2, 2, 1]], columns=Index(["a", "b", None], dtype=object)
+            ),
+        ],
+        [
+            True,
+            Series(
+                [1, 1], index=MultiIndex.from_tuples([(1, 1), (2, 2)], names=["a", "b"])
+            ),
+        ],
+    ],
+)
+def test_apply_as_index_constant_lambda(as_index, expected):
+    # GH 13217
+    df = DataFrame({"a": [1, 1, 2, 2], "b": [1, 1, 2, 2], "c": [1, 1, 1, 1]})
+    result = df.groupby(["a", "b"], as_index=as_index).apply(lambda x: 1)
+    tm.assert_equal(result, expected)
+
+
+def test_sort_index_groups():
+    # GH 20420
+    df = DataFrame(
+        {"A": [1, 2, 3, 4, 5], "B": [6, 7, 8, 9, 0], "C": [1, 1, 1, 2, 2]},
+        index=range(5),
+    )
+    result = df.groupby("C").apply(lambda x: x.A.sort_index())
+    expected = Series(
+        range(1, 6),
+        index=MultiIndex.from_tuples(
+            [(1, 0), (1, 1), (1, 2), (2, 3), (2, 4)], names=["C", None]
+        ),
+        name="A",
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_positional_slice_groups_datetimelike():
+    # GH 21651
+    expected = DataFrame(
+        {
+            "date": pd.date_range("2010-01-01", freq="12H", periods=5),
+            "vals": range(5),
+            "let": list("abcde"),
+        }
+    )
+    result = expected.groupby(
+        [expected.let, expected.date.dt.date], group_keys=False
+    ).apply(lambda x: x.iloc[0:])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_apply_shape_cache_safety():
+    # GH#42702 this fails if we cache_readonly Block.shape
+    df = DataFrame({"A": ["a", "a", "b"], "B": [1, 2, 3], "C": [4, 6, 5]})
+    gb = df.groupby("A")
+    result = gb[["B", "C"]].apply(lambda x: x.astype(float).max() - x.min())
+
+    expected = DataFrame(
+        {"B": [1.0, 0.0], "C": [2.0, 0.0]}, index=Index(["a", "b"], name="A")
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dropna", [True, False])
+def test_apply_na(dropna):
+    # GH#28984
+    df = DataFrame(
+        {"grp": [1, 1, 2, 2], "y": [1, 0, 2, 5], "z": [1, 2, np.nan, np.nan]}
+    )
+    dfgrp = df.groupby("grp", dropna=dropna)
+    result = dfgrp.apply(lambda grp_df: grp_df.nlargest(1, "z"))
+    expected = dfgrp.apply(lambda x: x.sort_values("z", ascending=False).head(1))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_apply_empty_string_nan_coerce_bug():
+    # GH#24903
+    result = (
+        DataFrame(
+            {
+                "a": [1, 1, 2, 2],
+                "b": ["", "", "", ""],
+                "c": pd.to_datetime([1, 2, 3, 4], unit="s"),
+            }
+        )
+        .groupby(["a", "b"])
+        .apply(lambda df: df.iloc[-1])
+    )
+    expected = DataFrame(
+        [[1, "", pd.to_datetime(2, unit="s")], [2, "", pd.to_datetime(4, unit="s")]],
+        columns=["a", "b", "c"],
+        index=MultiIndex.from_tuples([(1, ""), (2, "")], names=["a", "b"]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("index_values", [[1, 2, 3], [1.0, 2.0, 3.0]])
+def test_apply_index_key_error_bug(index_values):
+    # GH 44310
+    result = DataFrame(
+        {
+            "a": ["aa", "a2", "a3"],
+            "b": [1, 2, 3],
+        },
+        index=Index(index_values),
+    )
+    expected = DataFrame(
+        {
+            "b_mean": [2.0, 3.0, 1.0],
+        },
+        index=Index(["a2", "a3", "aa"], name="a"),
+    )
+    result = result.groupby("a").apply(
+        lambda df: Series([df["b"].mean()], index=["b_mean"])
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "arg,idx",
+    [
+        [
+            [
+                1,
+                2,
+                3,
+            ],
+            [
+                0.1,
+                0.3,
+                0.2,
+            ],
+        ],
+        [
+            [
+                1,
+                2,
+                3,
+            ],
+            [
+                0.1,
+                0.2,
+                0.3,
+            ],
+        ],
+        [
+            [
+                1,
+                4,
+                3,
+            ],
+            [
+                0.1,
+                0.4,
+                0.2,
+            ],
+        ],
+    ],
+)
+def test_apply_nonmonotonic_float_index(arg, idx):
+    # GH 34455
+    expected = DataFrame({"col": arg}, index=idx)
+    result = expected.groupby("col", group_keys=False).apply(lambda x: x)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("args, kwargs", [([True], {}), ([], {"numeric_only": True})])
+def test_apply_str_with_args(df, args, kwargs):
+    # GH#46479
+    gb = df.groupby("A")
+    result = gb.apply("sum", *args, **kwargs)
+    expected = gb.sum(numeric_only=True)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("name", ["some_name", None])
+def test_result_name_when_one_group(name):
+    # GH 46369
+    ser = Series([1, 2], name=name)
+    result = ser.groupby(["a", "a"], group_keys=False).apply(lambda x: x)
+    expected = Series([1, 2], name=name)
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "method, op",
+    [
+        ("apply", lambda gb: gb.values[-1]),
+        ("apply", lambda gb: gb["b"].iloc[0]),
+        ("agg", "skew"),
+        ("agg", "prod"),
+        ("agg", "sum"),
+    ],
+)
+def test_empty_df(method, op):
+    # GH 47985
+    empty_df = DataFrame({"a": [], "b": []})
+    gb = empty_df.groupby("a", group_keys=True)
+    group = getattr(gb, "b")
+
+    result = getattr(group, method)(op)
+    expected = Series(
+        [], name="b", dtype="float64", index=Index([], dtype="float64", name="a")
+    )
+
+    tm.assert_series_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_apply_mutate.py

@@ -0,0 +1,144 @@
+import numpy as np
+
+import pandas as pd
+import pandas._testing as tm
+
+
+def test_group_by_copy():
+    # GH#44803
+    df = pd.DataFrame(
+        {
+            "name": ["Alice", "Bob", "Carl"],
+            "age": [20, 21, 20],
+        }
+    ).set_index("name")
+
+    grp_by_same_value = df.groupby(["age"], group_keys=False).apply(lambda group: group)
+    grp_by_copy = df.groupby(["age"], group_keys=False).apply(
+        lambda group: group.copy()
+    )
+    tm.assert_frame_equal(grp_by_same_value, grp_by_copy)
+
+
+def test_mutate_groups():
+    # GH3380
+
+    df = pd.DataFrame(
+        {
+            "cat1": ["a"] * 8 + ["b"] * 6,
+            "cat2": ["c"] * 2
+            + ["d"] * 2
+            + ["e"] * 2
+            + ["f"] * 2
+            + ["c"] * 2
+            + ["d"] * 2
+            + ["e"] * 2,
+            "cat3": [f"g{x}" for x in range(1, 15)],
+            "val": np.random.randint(100, size=14),
+        }
+    )
+
+    def f_copy(x):
+        x = x.copy()
+        x["rank"] = x.val.rank(method="min")
+        return x.groupby("cat2")["rank"].min()
+
+    def f_no_copy(x):
+        x["rank"] = x.val.rank(method="min")
+        return x.groupby("cat2")["rank"].min()
+
+    grpby_copy = df.groupby("cat1").apply(f_copy)
+    grpby_no_copy = df.groupby("cat1").apply(f_no_copy)
+    tm.assert_series_equal(grpby_copy, grpby_no_copy)
+
+
+def test_no_mutate_but_looks_like():
+    # GH 8467
+    # first show's mutation indicator
+    # second does not, but should yield the same results
+    df = pd.DataFrame({"key": [1, 1, 1, 2, 2, 2, 3, 3, 3], "value": range(9)})
+
+    result1 = df.groupby("key", group_keys=True).apply(lambda x: x[:].key)
+    result2 = df.groupby("key", group_keys=True).apply(lambda x: x.key)
+    tm.assert_series_equal(result1, result2)
+
+
+def test_apply_function_with_indexing():
+    # GH: 33058
+    df = pd.DataFrame(
+        {"col1": ["A", "A", "A", "B", "B", "B"], "col2": [1, 2, 3, 4, 5, 6]}
+    )
+
+    def fn(x):
+        x.loc[x.index[-1], "col2"] = 0
+        return x.col2
+
+    result = df.groupby(["col1"], as_index=False).apply(fn)
+    expected = pd.Series(
+        [1, 2, 0, 4, 5, 0],
+        index=pd.MultiIndex.from_tuples(
+            [(0, 0), (0, 1), (0, 2), (1, 3), (1, 4), (1, 5)]
+        ),
+        name="col2",
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_apply_mutate_columns_multiindex():
+    # GH 12652
+    df = pd.DataFrame(
+        {
+            ("C", "julian"): [1, 2, 3],
+            ("B", "geoffrey"): [1, 2, 3],
+            ("A", "julian"): [1, 2, 3],
+            ("B", "julian"): [1, 2, 3],
+            ("A", "geoffrey"): [1, 2, 3],
+            ("C", "geoffrey"): [1, 2, 3],
+        },
+        columns=pd.MultiIndex.from_tuples(
+            [
+                ("A", "julian"),
+                ("A", "geoffrey"),
+                ("B", "julian"),
+                ("B", "geoffrey"),
+                ("C", "julian"),
+                ("C", "geoffrey"),
+            ]
+        ),
+    )
+
+    def add_column(grouped):
+        name = grouped.columns[0][1]
+        grouped["sum", name] = grouped.sum(axis=1)
+        return grouped
+
+    result = df.groupby(level=1, axis=1).apply(add_column)
+    expected = pd.DataFrame(
+        [
+            [1, 1, 1, 3, 1, 1, 1, 3],
+            [2, 2, 2, 6, 2, 2, 2, 6],
+            [
+                3,
+                3,
+                3,
+                9,
+                3,
+                3,
+                3,
+                9,
+            ],
+        ],
+        columns=pd.MultiIndex.from_tuples(
+            [
+                ("geoffrey", "A", "geoffrey"),
+                ("geoffrey", "B", "geoffrey"),
+                ("geoffrey", "C", "geoffrey"),
+                ("geoffrey", "sum", "geoffrey"),
+                ("julian", "A", "julian"),
+                ("julian", "B", "julian"),
+                ("julian", "C", "julian"),
+                ("julian", "sum", "julian"),
+            ]
+        ),
+    )
+    tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_bin_groupby.py

@@ -0,0 +1,65 @@
+import numpy as np
+import pytest
+
+from pandas._libs import lib
+import pandas.util._test_decorators as td
+
+import pandas as pd
+import pandas._testing as tm
+
+
+def assert_block_lengths(x):
+    assert len(x) == len(x._mgr.blocks[0].mgr_locs)
+    return 0
+
+
+def cumsum_max(x):
+    x.cumsum().max()
+    return 0
+
+
+@pytest.mark.parametrize(
+    "func",
+    [
+        cumsum_max,
+        pytest.param(assert_block_lengths, marks=td.skip_array_manager_invalid_test),
+    ],
+)
+def test_mgr_locs_updated(func):
+    # https://github.com/pandas-dev/pandas/issues/31802
+    # Some operations may require creating new blocks, which requires
+    # valid mgr_locs
+    df = pd.DataFrame({"A": ["a", "a", "a"], "B": ["a", "b", "b"], "C": [1, 1, 1]})
+    result = df.groupby(["A", "B"]).agg(func)
+    expected = pd.DataFrame(
+        {"C": [0, 0]},
+        index=pd.MultiIndex.from_product([["a"], ["a", "b"]], names=["A", "B"]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "binner,closed,expected",
+    [
+        (
+            np.array([0, 3, 6, 9], dtype=np.int64),
+            "left",
+            np.array([2, 5, 6], dtype=np.int64),
+        ),
+        (
+            np.array([0, 3, 6, 9], dtype=np.int64),
+            "right",
+            np.array([3, 6, 6], dtype=np.int64),
+        ),
+        (np.array([0, 3, 6], dtype=np.int64), "left", np.array([2, 5], dtype=np.int64)),
+        (
+            np.array([0, 3, 6], dtype=np.int64),
+            "right",
+            np.array([3, 6], dtype=np.int64),
+        ),
+    ],
+)
+def test_generate_bins(binner, closed, expected):
+    values = np.array([1, 2, 3, 4, 5, 6], dtype=np.int64)
+    result = lib.generate_bins_dt64(values, binner, closed=closed)
+    tm.assert_numpy_array_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_categorical.py

@@ -0,0 +1,2057 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    CategoricalIndex,
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    qcut,
+)
+import pandas._testing as tm
+from pandas.core.groupby.generic import SeriesGroupBy
+from pandas.tests.groupby import get_groupby_method_args
+
+
+def cartesian_product_for_groupers(result, args, names, fill_value=np.NaN):
+    """Reindex to a cartesian production for the groupers,
+    preserving the nature (Categorical) of each grouper
+    """
+
+    def f(a):
+        if isinstance(a, (CategoricalIndex, Categorical)):
+            categories = a.categories
+            a = Categorical.from_codes(
+                np.arange(len(categories)), categories=categories, ordered=a.ordered
+            )
+        return a
+
+    index = MultiIndex.from_product(map(f, args), names=names)
+    return result.reindex(index, fill_value=fill_value).sort_index()
+
+
+_results_for_groupbys_with_missing_categories = {
+    # This maps the builtin groupby functions to their expected outputs for
+    # missing categories when they are called on a categorical grouper with
+    # observed=False. Some functions are expected to return NaN, some zero.
+    # These expected values can be used across several tests (i.e. they are
+    # the same for SeriesGroupBy and DataFrameGroupBy) but they should only be
+    # hardcoded in one place.
+    "all": np.NaN,
+    "any": np.NaN,
+    "count": 0,
+    "corrwith": np.NaN,
+    "first": np.NaN,
+    "idxmax": np.NaN,
+    "idxmin": np.NaN,
+    "last": np.NaN,
+    "max": np.NaN,
+    "mean": np.NaN,
+    "median": np.NaN,
+    "min": np.NaN,
+    "nth": np.NaN,
+    "nunique": 0,
+    "prod": np.NaN,
+    "quantile": np.NaN,
+    "sem": np.NaN,
+    "size": 0,
+    "skew": np.NaN,
+    "std": np.NaN,
+    "sum": 0,
+    "var": np.NaN,
+}
+
+
+def test_apply_use_categorical_name(df):
+    cats = qcut(df.C, 4)
+
+    def get_stats(group):
+        return {
+            "min": group.min(),
+            "max": group.max(),
+            "count": group.count(),
+            "mean": group.mean(),
+        }
+
+    result = df.groupby(cats, observed=False).D.apply(get_stats)
+    assert result.index.names[0] == "C"
+
+
+def test_basic():  # TODO: split this test
+    cats = Categorical(
+        ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
+        categories=["a", "b", "c", "d"],
+        ordered=True,
+    )
+    data = DataFrame({"a": [1, 1, 1, 2, 2, 2, 3, 4, 5], "b": cats})
+
+    exp_index = CategoricalIndex(list("abcd"), name="b", ordered=True)
+    expected = DataFrame({"a": [1, 2, 4, np.nan]}, index=exp_index)
+    result = data.groupby("b", observed=False).mean()
+    tm.assert_frame_equal(result, expected)
+
+    cat1 = Categorical(["a", "a", "b", "b"], categories=["a", "b", "z"], ordered=True)
+    cat2 = Categorical(["c", "d", "c", "d"], categories=["c", "d", "y"], ordered=True)
+    df = DataFrame({"A": cat1, "B": cat2, "values": [1, 2, 3, 4]})
+
+    # single grouper
+    gb = df.groupby("A", observed=False)
+    exp_idx = CategoricalIndex(["a", "b", "z"], name="A", ordered=True)
+    expected = DataFrame({"values": Series([3, 7, 0], index=exp_idx)})
+    result = gb.sum(numeric_only=True)
+    tm.assert_frame_equal(result, expected)
+
+    # GH 8623
+    x = DataFrame(
+        [[1, "John P. Doe"], [2, "Jane Dove"], [1, "John P. Doe"]],
+        columns=["person_id", "person_name"],
+    )
+    x["person_name"] = Categorical(x.person_name)
+
+    g = x.groupby(["person_id"], observed=False)
+    result = g.transform(lambda x: x)
+    tm.assert_frame_equal(result, x[["person_name"]])
+
+    result = x.drop_duplicates("person_name")
+    expected = x.iloc[[0, 1]]
+    tm.assert_frame_equal(result, expected)
+
+    def f(x):
+        return x.drop_duplicates("person_name").iloc[0]
+
+    result = g.apply(f)
+    expected = x.iloc[[0, 1]].copy()
+    expected.index = Index([1, 2], name="person_id")
+    expected["person_name"] = expected["person_name"].astype("object")
+    tm.assert_frame_equal(result, expected)
+
+    # GH 9921
+    # Monotonic
+    df = DataFrame({"a": [5, 15, 25]})
+    c = pd.cut(df.a, bins=[0, 10, 20, 30, 40])
+
+    result = df.a.groupby(c, observed=False).transform(sum)
+    tm.assert_series_equal(result, df["a"])
+
+    tm.assert_series_equal(
+        df.a.groupby(c, observed=False).transform(lambda xs: np.sum(xs)), df["a"]
+    )
+    tm.assert_frame_equal(df.groupby(c, observed=False).transform(sum), df[["a"]])
+
+    gbc = df.groupby(c, observed=False)
+    result = gbc.transform(lambda xs: np.max(xs, axis=0))
+    tm.assert_frame_equal(result, df[["a"]])
+
+    with tm.assert_produces_warning(None):
+        result2 = gbc.transform(lambda xs: np.max(xs, axis=0))
+        result3 = gbc.transform(max)
+        result4 = gbc.transform(np.maximum.reduce)
+        result5 = gbc.transform(lambda xs: np.maximum.reduce(xs))
+    tm.assert_frame_equal(result2, df[["a"]], check_dtype=False)
+    tm.assert_frame_equal(result3, df[["a"]], check_dtype=False)
+    tm.assert_frame_equal(result4, df[["a"]])
+    tm.assert_frame_equal(result5, df[["a"]])
+
+    # Filter
+    tm.assert_series_equal(df.a.groupby(c, observed=False).filter(np.all), df["a"])
+    tm.assert_frame_equal(df.groupby(c, observed=False).filter(np.all), df)
+
+    # Non-monotonic
+    df = DataFrame({"a": [5, 15, 25, -5]})
+    c = pd.cut(df.a, bins=[-10, 0, 10, 20, 30, 40])
+
+    result = df.a.groupby(c, observed=False).transform(sum)
+    tm.assert_series_equal(result, df["a"])
+
+    tm.assert_series_equal(
+        df.a.groupby(c, observed=False).transform(lambda xs: np.sum(xs)), df["a"]
+    )
+    tm.assert_frame_equal(df.groupby(c, observed=False).transform(sum), df[["a"]])
+    tm.assert_frame_equal(
+        df.groupby(c, observed=False).transform(lambda xs: np.sum(xs)), df[["a"]]
+    )
+
+    # GH 9603
+    df = DataFrame({"a": [1, 0, 0, 0]})
+    c = pd.cut(df.a, [0, 1, 2, 3, 4], labels=Categorical(list("abcd")))
+    result = df.groupby(c, observed=False).apply(len)
+
+    exp_index = CategoricalIndex(c.values.categories, ordered=c.values.ordered)
+    expected = Series([1, 0, 0, 0], index=exp_index)
+    expected.index.name = "a"
+    tm.assert_series_equal(result, expected)
+
+    # more basic
+    levels = ["foo", "bar", "baz", "qux"]
+    codes = np.random.randint(0, 4, size=100)
+
+    cats = Categorical.from_codes(codes, levels, ordered=True)
+
+    data = DataFrame(np.random.randn(100, 4))
+
+    result = data.groupby(cats, observed=False).mean()
+
+    expected = data.groupby(np.asarray(cats), observed=False).mean()
+    exp_idx = CategoricalIndex(levels, categories=cats.categories, ordered=True)
+    expected = expected.reindex(exp_idx)
+
+    tm.assert_frame_equal(result, expected)
+
+    grouped = data.groupby(cats, observed=False)
+    desc_result = grouped.describe()
+
+    idx = cats.codes.argsort()
+    ord_labels = np.asarray(cats).take(idx)
+    ord_data = data.take(idx)
+
+    exp_cats = Categorical(
+        ord_labels, ordered=True, categories=["foo", "bar", "baz", "qux"]
+    )
+    expected = ord_data.groupby(exp_cats, sort=False, observed=False).describe()
+    tm.assert_frame_equal(desc_result, expected)
+
+    # GH 10460
+    expc = Categorical.from_codes(np.arange(4).repeat(8), levels, ordered=True)
+    exp = CategoricalIndex(expc)
+    tm.assert_index_equal((desc_result.stack().index.get_level_values(0)), exp)
+    exp = Index(["count", "mean", "std", "min", "25%", "50%", "75%", "max"] * 4)
+    tm.assert_index_equal((desc_result.stack().index.get_level_values(1)), exp)
+
+
+def test_level_get_group(observed):
+    # GH15155
+    df = DataFrame(
+        data=np.arange(2, 22, 2),
+        index=MultiIndex(
+            levels=[CategoricalIndex(["a", "b"]), range(10)],
+            codes=[[0] * 5 + [1] * 5, range(10)],
+            names=["Index1", "Index2"],
+        ),
+    )
+    g = df.groupby(level=["Index1"], observed=observed)
+
+    # expected should equal test.loc[["a"]]
+    # GH15166
+    expected = DataFrame(
+        data=np.arange(2, 12, 2),
+        index=MultiIndex(
+            levels=[CategoricalIndex(["a", "b"]), range(5)],
+            codes=[[0] * 5, range(5)],
+            names=["Index1", "Index2"],
+        ),
+    )
+    result = g.get_group("a")
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_sorting_with_different_categoricals():
+    # GH 24271
+    df = DataFrame(
+        {
+            "group": ["A"] * 6 + ["B"] * 6,
+            "dose": ["high", "med", "low"] * 4,
+            "outcomes": np.arange(12.0),
+        }
+    )
+
+    df.dose = Categorical(df.dose, categories=["low", "med", "high"], ordered=True)
+
+    result = df.groupby("group")["dose"].value_counts()
+    result = result.sort_index(level=0, sort_remaining=True)
+    index = ["low", "med", "high", "low", "med", "high"]
+    index = Categorical(index, categories=["low", "med", "high"], ordered=True)
+    index = [["A", "A", "A", "B", "B", "B"], CategoricalIndex(index)]
+    index = MultiIndex.from_arrays(index, names=["group", "dose"])
+    expected = Series([2] * 6, index=index, name="count")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("ordered", [True, False])
+def test_apply(ordered):
+    # GH 10138
+
+    dense = Categorical(list("abc"), ordered=ordered)
+
+    # 'b' is in the categories but not in the list
+    missing = Categorical(list("aaa"), categories=["a", "b"], ordered=ordered)
+    values = np.arange(len(dense))
+    df = DataFrame({"missing": missing, "dense": dense, "values": values})
+    grouped = df.groupby(["missing", "dense"], observed=True)
+
+    # missing category 'b' should still exist in the output index
+    idx = MultiIndex.from_arrays([missing, dense], names=["missing", "dense"])
+    expected = DataFrame([0, 1, 2.0], index=idx, columns=["values"])
+
+    result = grouped.apply(lambda x: np.mean(x, axis=0))
+    tm.assert_frame_equal(result, expected)
+
+    result = grouped.mean()
+    tm.assert_frame_equal(result, expected)
+
+    result = grouped.agg(np.mean)
+    tm.assert_frame_equal(result, expected)
+
+    # but for transform we should still get back the original index
+    idx = MultiIndex.from_arrays([missing, dense], names=["missing", "dense"])
+    expected = Series(1, index=idx)
+    result = grouped.apply(lambda x: 1)
+    tm.assert_series_equal(result, expected)
+
+
+def test_observed(observed):
+    # multiple groupers, don't re-expand the output space
+    # of the grouper
+    # gh-14942 (implement)
+    # gh-10132 (back-compat)
+    # gh-8138 (back-compat)
+    # gh-8869
+
+    cat1 = Categorical(["a", "a", "b", "b"], categories=["a", "b", "z"], ordered=True)
+    cat2 = Categorical(["c", "d", "c", "d"], categories=["c", "d", "y"], ordered=True)
+    df = DataFrame({"A": cat1, "B": cat2, "values": [1, 2, 3, 4]})
+    df["C"] = ["foo", "bar"] * 2
+
+    # multiple groupers with a non-cat
+    gb = df.groupby(["A", "B", "C"], observed=observed)
+    exp_index = MultiIndex.from_arrays(
+        [cat1, cat2, ["foo", "bar"] * 2], names=["A", "B", "C"]
+    )
+    expected = DataFrame({"values": Series([1, 2, 3, 4], index=exp_index)}).sort_index()
+    result = gb.sum()
+    if not observed:
+        expected = cartesian_product_for_groupers(
+            expected, [cat1, cat2, ["foo", "bar"]], list("ABC"), fill_value=0
+        )
+
+    tm.assert_frame_equal(result, expected)
+
+    gb = df.groupby(["A", "B"], observed=observed)
+    exp_index = MultiIndex.from_arrays([cat1, cat2], names=["A", "B"])
+    expected = DataFrame(
+        {"values": [1, 2, 3, 4], "C": ["foo", "bar", "foo", "bar"]}, index=exp_index
+    )
+    result = gb.sum()
+    if not observed:
+        expected = cartesian_product_for_groupers(
+            expected, [cat1, cat2], list("AB"), fill_value=0
+        )
+
+    tm.assert_frame_equal(result, expected)
+
+    # https://github.com/pandas-dev/pandas/issues/8138
+    d = {
+        "cat": Categorical(
+            ["a", "b", "a", "b"], categories=["a", "b", "c"], ordered=True
+        ),
+        "ints": [1, 1, 2, 2],
+        "val": [10, 20, 30, 40],
+    }
+    df = DataFrame(d)
+
+    # Grouping on a single column
+    groups_single_key = df.groupby("cat", observed=observed)
+    result = groups_single_key.mean()
+
+    exp_index = CategoricalIndex(
+        list("ab"), name="cat", categories=list("abc"), ordered=True
+    )
+    expected = DataFrame({"ints": [1.5, 1.5], "val": [20.0, 30]}, index=exp_index)
+    if not observed:
+        index = CategoricalIndex(
+            list("abc"), name="cat", categories=list("abc"), ordered=True
+        )
+        expected = expected.reindex(index)
+
+    tm.assert_frame_equal(result, expected)
+
+    # Grouping on two columns
+    groups_double_key = df.groupby(["cat", "ints"], observed=observed)
+    result = groups_double_key.agg("mean")
+    expected = DataFrame(
+        {
+            "val": [10.0, 30.0, 20.0, 40.0],
+            "cat": Categorical(
+                ["a", "a", "b", "b"], categories=["a", "b", "c"], ordered=True
+            ),
+            "ints": [1, 2, 1, 2],
+        }
+    ).set_index(["cat", "ints"])
+    if not observed:
+        expected = cartesian_product_for_groupers(
+            expected, [df.cat.values, [1, 2]], ["cat", "ints"]
+        )
+
+    tm.assert_frame_equal(result, expected)
+
+    # GH 10132
+    for key in [("a", 1), ("b", 2), ("b", 1), ("a", 2)]:
+        c, i = key
+        result = groups_double_key.get_group(key)
+        expected = df[(df.cat == c) & (df.ints == i)]
+        tm.assert_frame_equal(result, expected)
+
+    # gh-8869
+    # with as_index
+    d = {
+        "foo": [10, 8, 4, 8, 4, 1, 1],
+        "bar": [10, 20, 30, 40, 50, 60, 70],
+        "baz": ["d", "c", "e", "a", "a", "d", "c"],
+    }
+    df = DataFrame(d)
+    cat = pd.cut(df["foo"], np.linspace(0, 10, 3))
+    df["range"] = cat
+    groups = df.groupby(["range", "baz"], as_index=False, observed=observed)
+    result = groups.agg("mean")
+
+    groups2 = df.groupby(["range", "baz"], as_index=True, observed=observed)
+    expected = groups2.agg("mean").reset_index()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_observed_codes_remap(observed):
+    d = {"C1": [3, 3, 4, 5], "C2": [1, 2, 3, 4], "C3": [10, 100, 200, 34]}
+    df = DataFrame(d)
+    values = pd.cut(df["C1"], [1, 2, 3, 6])
+    values.name = "cat"
+    groups_double_key = df.groupby([values, "C2"], observed=observed)
+
+    idx = MultiIndex.from_arrays([values, [1, 2, 3, 4]], names=["cat", "C2"])
+    expected = DataFrame(
+        {"C1": [3.0, 3.0, 4.0, 5.0], "C3": [10.0, 100.0, 200.0, 34.0]}, index=idx
+    )
+    if not observed:
+        expected = cartesian_product_for_groupers(
+            expected, [values.values, [1, 2, 3, 4]], ["cat", "C2"]
+        )
+
+    result = groups_double_key.agg("mean")
+    tm.assert_frame_equal(result, expected)
+
+
+def test_observed_perf():
+    # we create a cartesian product, so this is
+    # non-performant if we don't use observed values
+    # gh-14942
+    df = DataFrame(
+        {
+            "cat": np.random.randint(0, 255, size=30000),
+            "int_id": np.random.randint(0, 255, size=30000),
+            "other_id": np.random.randint(0, 10000, size=30000),
+            "foo": 0,
+        }
+    )
+    df["cat"] = df.cat.astype(str).astype("category")
+
+    grouped = df.groupby(["cat", "int_id", "other_id"], observed=True)
+    result = grouped.count()
+    assert result.index.levels[0].nunique() == df.cat.nunique()
+    assert result.index.levels[1].nunique() == df.int_id.nunique()
+    assert result.index.levels[2].nunique() == df.other_id.nunique()
+
+
+def test_observed_groups(observed):
+    # gh-20583
+    # test that we have the appropriate groups
+
+    cat = Categorical(["a", "c", "a"], categories=["a", "b", "c"])
+    df = DataFrame({"cat": cat, "vals": [1, 2, 3]})
+    g = df.groupby("cat", observed=observed)
+
+    result = g.groups
+    if observed:
+        expected = {"a": Index([0, 2], dtype="int64"), "c": Index([1], dtype="int64")}
+    else:
+        expected = {
+            "a": Index([0, 2], dtype="int64"),
+            "b": Index([], dtype="int64"),
+            "c": Index([1], dtype="int64"),
+        }
+
+    tm.assert_dict_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "keys, expected_values, expected_index_levels",
+    [
+        ("a", [15, 9, 0], CategoricalIndex([1, 2, 3], name="a")),
+        (
+            ["a", "b"],
+            [7, 8, 0, 0, 0, 9, 0, 0, 0],
+            [CategoricalIndex([1, 2, 3], name="a"), Index([4, 5, 6])],
+        ),
+        (
+            ["a", "a2"],
+            [15, 0, 0, 0, 9, 0, 0, 0, 0],
+            [
+                CategoricalIndex([1, 2, 3], name="a"),
+                CategoricalIndex([1, 2, 3], name="a"),
+            ],
+        ),
+    ],
+)
+@pytest.mark.parametrize("test_series", [True, False])
+def test_unobserved_in_index(keys, expected_values, expected_index_levels, test_series):
+    # GH#49354 - ensure unobserved cats occur when grouping by index levels
+    df = DataFrame(
+        {
+            "a": Categorical([1, 1, 2], categories=[1, 2, 3]),
+            "a2": Categorical([1, 1, 2], categories=[1, 2, 3]),
+            "b": [4, 5, 6],
+            "c": [7, 8, 9],
+        }
+    ).set_index(["a", "a2"])
+    if "b" not in keys:
+        # Only keep b when it is used for grouping for consistent columns in the result
+        df = df.drop(columns="b")
+
+    gb = df.groupby(keys, observed=False)
+    if test_series:
+        gb = gb["c"]
+    result = gb.sum()
+
+    if len(keys) == 1:
+        index = expected_index_levels
+    else:
+        codes = [[0, 0, 0, 1, 1, 1, 2, 2, 2], 3 * [0, 1, 2]]
+        index = MultiIndex(
+            expected_index_levels,
+            codes=codes,
+            names=keys,
+        )
+    expected = DataFrame({"c": expected_values}, index=index)
+    if test_series:
+        expected = expected["c"]
+    tm.assert_equal(result, expected)
+
+
+def test_observed_groups_with_nan(observed):
+    # GH 24740
+    df = DataFrame(
+        {
+            "cat": Categorical(["a", np.nan, "a"], categories=["a", "b", "d"]),
+            "vals": [1, 2, 3],
+        }
+    )
+    g = df.groupby("cat", observed=observed)
+    result = g.groups
+    if observed:
+        expected = {"a": Index([0, 2], dtype="int64")}
+    else:
+        expected = {
+            "a": Index([0, 2], dtype="int64"),
+            "b": Index([], dtype="int64"),
+            "d": Index([], dtype="int64"),
+        }
+    tm.assert_dict_equal(result, expected)
+
+
+def test_observed_nth():
+    # GH 26385
+    cat = Categorical(["a", np.nan, np.nan], categories=["a", "b", "c"])
+    ser = Series([1, 2, 3])
+    df = DataFrame({"cat": cat, "ser": ser})
+
+    result = df.groupby("cat", observed=False)["ser"].nth(0)
+    expected = df["ser"].iloc[[0]]
+    tm.assert_series_equal(result, expected)
+
+
+def test_dataframe_categorical_with_nan(observed):
+    # GH 21151
+    s1 = Categorical([np.nan, "a", np.nan, "a"], categories=["a", "b", "c"])
+    s2 = Series([1, 2, 3, 4])
+    df = DataFrame({"s1": s1, "s2": s2})
+    result = df.groupby("s1", observed=observed).first().reset_index()
+    if observed:
+        expected = DataFrame(
+            {"s1": Categorical(["a"], categories=["a", "b", "c"]), "s2": [2]}
+        )
+    else:
+        expected = DataFrame(
+            {
+                "s1": Categorical(["a", "b", "c"], categories=["a", "b", "c"]),
+                "s2": [2, np.nan, np.nan],
+            }
+        )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("ordered", [True, False])
+@pytest.mark.parametrize("observed", [True, False])
+@pytest.mark.parametrize("sort", [True, False])
+def test_dataframe_categorical_ordered_observed_sort(ordered, observed, sort):
+    # GH 25871: Fix groupby sorting on ordered Categoricals
+    # GH 25167: Groupby with observed=True doesn't sort
+
+    # Build a dataframe with cat having one unobserved category ('missing'),
+    # and a Series with identical values
+    label = Categorical(
+        ["d", "a", "b", "a", "d", "b"],
+        categories=["a", "b", "missing", "d"],
+        ordered=ordered,
+    )
+    val = Series(["d", "a", "b", "a", "d", "b"])
+    df = DataFrame({"label": label, "val": val})
+
+    # aggregate on the Categorical
+    result = df.groupby("label", observed=observed, sort=sort)["val"].aggregate("first")
+
+    # If ordering works, we expect index labels equal to aggregation results,
+    # except for 'observed=False': label 'missing' has aggregation None
+    label = Series(result.index.array, dtype="object")
+    aggr = Series(result.array)
+    if not observed:
+        aggr[aggr.isna()] = "missing"
+    if not all(label == aggr):
+        msg = (
+            "Labels and aggregation results not consistently sorted\n"
+            f"for (ordered={ordered}, observed={observed}, sort={sort})\n"
+            f"Result:\n{result}"
+        )
+        assert False, msg
+
+
+def test_datetime():
+    # GH9049: ensure backward compatibility
+    levels = pd.date_range("2014-01-01", periods=4)
+    codes = np.random.randint(0, 4, size=100)
+
+    cats = Categorical.from_codes(codes, levels, ordered=True)
+
+    data = DataFrame(np.random.randn(100, 4))
+    result = data.groupby(cats, observed=False).mean()
+
+    expected = data.groupby(np.asarray(cats), observed=False).mean()
+    expected = expected.reindex(levels)
+    expected.index = CategoricalIndex(
+        expected.index, categories=expected.index, ordered=True
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+    grouped = data.groupby(cats, observed=False)
+    desc_result = grouped.describe()
+
+    idx = cats.codes.argsort()
+    ord_labels = cats.take(idx)
+    ord_data = data.take(idx)
+    expected = ord_data.groupby(ord_labels, observed=False).describe()
+    tm.assert_frame_equal(desc_result, expected)
+    tm.assert_index_equal(desc_result.index, expected.index)
+    tm.assert_index_equal(
+        desc_result.index.get_level_values(0), expected.index.get_level_values(0)
+    )
+
+    # GH 10460
+    expc = Categorical.from_codes(np.arange(4).repeat(8), levels, ordered=True)
+    exp = CategoricalIndex(expc)
+    tm.assert_index_equal((desc_result.stack().index.get_level_values(0)), exp)
+    exp = Index(["count", "mean", "std", "min", "25%", "50%", "75%", "max"] * 4)
+    tm.assert_index_equal((desc_result.stack().index.get_level_values(1)), exp)
+
+
+def test_categorical_index():
+    s = np.random.RandomState(12345)
+    levels = ["foo", "bar", "baz", "qux"]
+    codes = s.randint(0, 4, size=20)
+    cats = Categorical.from_codes(codes, levels, ordered=True)
+    df = DataFrame(np.repeat(np.arange(20), 4).reshape(-1, 4), columns=list("abcd"))
+    df["cats"] = cats
+
+    # with a cat index
+    result = df.set_index("cats").groupby(level=0, observed=False).sum()
+    expected = df[list("abcd")].groupby(cats.codes, observed=False).sum()
+    expected.index = CategoricalIndex(
+        Categorical.from_codes([0, 1, 2, 3], levels, ordered=True), name="cats"
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # with a cat column, should produce a cat index
+    result = df.groupby("cats", observed=False).sum()
+    expected = df[list("abcd")].groupby(cats.codes, observed=False).sum()
+    expected.index = CategoricalIndex(
+        Categorical.from_codes([0, 1, 2, 3], levels, ordered=True), name="cats"
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_describe_categorical_columns():
+    # GH 11558
+    cats = CategoricalIndex(
+        ["qux", "foo", "baz", "bar"],
+        categories=["foo", "bar", "baz", "qux"],
+        ordered=True,
+    )
+    df = DataFrame(np.random.randn(20, 4), columns=cats)
+    result = df.groupby([1, 2, 3, 4] * 5).describe()
+
+    tm.assert_index_equal(result.stack().columns, cats)
+    tm.assert_categorical_equal(result.stack().columns.values, cats.values)
+
+
+def test_unstack_categorical():
+    # GH11558 (example is taken from the original issue)
+    df = DataFrame(
+        {"a": range(10), "medium": ["A", "B"] * 5, "artist": list("XYXXY") * 2}
+    )
+    df["medium"] = df["medium"].astype("category")
+
+    gcat = df.groupby(["artist", "medium"], observed=False)["a"].count().unstack()
+    result = gcat.describe()
+
+    exp_columns = CategoricalIndex(["A", "B"], ordered=False, name="medium")
+    tm.assert_index_equal(result.columns, exp_columns)
+    tm.assert_categorical_equal(result.columns.values, exp_columns.values)
+
+    result = gcat["A"] + gcat["B"]
+    expected = Series([6, 4], index=Index(["X", "Y"], name="artist"))
+    tm.assert_series_equal(result, expected)
+
+
+def test_bins_unequal_len():
+    # GH3011
+    series = Series([np.nan, np.nan, 1, 1, 2, 2, 3, 3, 4, 4])
+    bins = pd.cut(series.dropna().values, 4)
+
+    # len(bins) != len(series) here
+    with pytest.raises(ValueError, match="Grouper and axis must be same length"):
+        series.groupby(bins).mean()
+
+
+@pytest.mark.parametrize(
+    ["series", "data"],
+    [
+        # Group a series with length and index equal to those of the grouper.
+        (Series(range(4)), {"A": [0, 3], "B": [1, 2]}),
+        # Group a series with length equal to that of the grouper and index unequal to
+        # that of the grouper.
+        (Series(range(4)).rename(lambda idx: idx + 1), {"A": [2], "B": [0, 1]}),
+        # GH44179: Group a series with length unequal to that of the grouper.
+        (Series(range(7)), {"A": [0, 3], "B": [1, 2]}),
+    ],
+)
+def test_categorical_series(series, data):
+    # Group the given series by a series with categorical data type such that group A
+    # takes indices 0 and 3 and group B indices 1 and 2, obtaining the values mapped in
+    # the given data.
+    groupby = series.groupby(Series(list("ABBA"), dtype="category"))
+    result = groupby.aggregate(list)
+    expected = Series(data, index=CategoricalIndex(data.keys()))
+    tm.assert_series_equal(result, expected)
+
+
+def test_as_index():
+    # GH13204
+    df = DataFrame(
+        {
+            "cat": Categorical([1, 2, 2], [1, 2, 3]),
+            "A": [10, 11, 11],
+            "B": [101, 102, 103],
+        }
+    )
+    result = df.groupby(["cat", "A"], as_index=False, observed=True).sum()
+    expected = DataFrame(
+        {
+            "cat": Categorical([1, 2], categories=df.cat.cat.categories),
+            "A": [10, 11],
+            "B": [101, 205],
+        },
+        columns=["cat", "A", "B"],
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # function grouper
+    f = lambda r: df.loc[r, "A"]
+    result = df.groupby(["cat", f], as_index=False, observed=True).sum()
+    expected = DataFrame(
+        {
+            "cat": Categorical([1, 2], categories=df.cat.cat.categories),
+            "A": [10, 22],
+            "B": [101, 205],
+        },
+        columns=["cat", "A", "B"],
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # another not in-axis grouper (conflicting names in index)
+    s = Series(["a", "b", "b"], name="cat")
+    result = df.groupby(["cat", s], as_index=False, observed=True).sum()
+    tm.assert_frame_equal(result, expected)
+
+    # is original index dropped?
+    group_columns = ["cat", "A"]
+    expected = DataFrame(
+        {
+            "cat": Categorical([1, 2], categories=df.cat.cat.categories),
+            "A": [10, 11],
+            "B": [101, 205],
+        },
+        columns=["cat", "A", "B"],
+    )
+
+    for name in [None, "X", "B"]:
+        df.index = Index(list("abc"), name=name)
+        result = df.groupby(group_columns, as_index=False, observed=True).sum()
+
+        tm.assert_frame_equal(result, expected)
+
+
+def test_preserve_categories():
+    # GH-13179
+    categories = list("abc")
+
+    # ordered=True
+    df = DataFrame({"A": Categorical(list("ba"), categories=categories, ordered=True)})
+    sort_index = CategoricalIndex(categories, categories, ordered=True, name="A")
+    nosort_index = CategoricalIndex(list("bac"), categories, ordered=True, name="A")
+    tm.assert_index_equal(
+        df.groupby("A", sort=True, observed=False).first().index, sort_index
+    )
+    # GH#42482 - don't sort result when sort=False, even when ordered=True
+    tm.assert_index_equal(
+        df.groupby("A", sort=False, observed=False).first().index, nosort_index
+    )
+
+    # ordered=False
+    df = DataFrame({"A": Categorical(list("ba"), categories=categories, ordered=False)})
+    sort_index = CategoricalIndex(categories, categories, ordered=False, name="A")
+    # GH#48749 - don't change order of categories
+    # GH#42482 - don't sort result when sort=False, even when ordered=True
+    nosort_index = CategoricalIndex(list("bac"), list("abc"), ordered=False, name="A")
+    tm.assert_index_equal(
+        df.groupby("A", sort=True, observed=False).first().index, sort_index
+    )
+    tm.assert_index_equal(
+        df.groupby("A", sort=False, observed=False).first().index, nosort_index
+    )
+
+
+def test_preserve_categorical_dtype():
+    # GH13743, GH13854
+    df = DataFrame(
+        {
+            "A": [1, 2, 1, 1, 2],
+            "B": [10, 16, 22, 28, 34],
+            "C1": Categorical(list("abaab"), categories=list("bac"), ordered=False),
+            "C2": Categorical(list("abaab"), categories=list("bac"), ordered=True),
+        }
+    )
+    # single grouper
+    exp_full = DataFrame(
+        {
+            "A": [2.0, 1.0, np.nan],
+            "B": [25.0, 20.0, np.nan],
+            "C1": Categorical(list("bac"), categories=list("bac"), ordered=False),
+            "C2": Categorical(list("bac"), categories=list("bac"), ordered=True),
+        }
+    )
+    for col in ["C1", "C2"]:
+        result1 = df.groupby(by=col, as_index=False, observed=False).mean(
+            numeric_only=True
+        )
+        result2 = (
+            df.groupby(by=col, as_index=True, observed=False)
+            .mean(numeric_only=True)
+            .reset_index()
+        )
+        expected = exp_full.reindex(columns=result1.columns)
+        tm.assert_frame_equal(result1, expected)
+        tm.assert_frame_equal(result2, expected)
+
+
+@pytest.mark.parametrize(
+    "func, values",
+    [
+        ("first", ["second", "first"]),
+        ("last", ["fourth", "third"]),
+        ("min", ["fourth", "first"]),
+        ("max", ["second", "third"]),
+    ],
+)
+def test_preserve_on_ordered_ops(func, values):
+    # gh-18502
+    # preserve the categoricals on ops
+    c = Categorical(["first", "second", "third", "fourth"], ordered=True)
+    df = DataFrame({"payload": [-1, -2, -1, -2], "col": c})
+    g = df.groupby("payload")
+    result = getattr(g, func)()
+    expected = DataFrame(
+        {"payload": [-2, -1], "col": Series(values, dtype=c.dtype)}
+    ).set_index("payload")
+    tm.assert_frame_equal(result, expected)
+
+    # we should also preserve categorical for SeriesGroupBy
+    sgb = df.groupby("payload")["col"]
+    result = getattr(sgb, func)()
+    expected = expected["col"]
+    tm.assert_series_equal(result, expected)
+
+
+def test_categorical_no_compress():
+    data = Series(np.random.randn(9))
+
+    codes = np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])
+    cats = Categorical.from_codes(codes, [0, 1, 2], ordered=True)
+
+    result = data.groupby(cats, observed=False).mean()
+    exp = data.groupby(codes, observed=False).mean()
+
+    exp.index = CategoricalIndex(
+        exp.index, categories=cats.categories, ordered=cats.ordered
+    )
+    tm.assert_series_equal(result, exp)
+
+    codes = np.array([0, 0, 0, 1, 1, 1, 3, 3, 3])
+    cats = Categorical.from_codes(codes, [0, 1, 2, 3], ordered=True)
+
+    result = data.groupby(cats, observed=False).mean()
+    exp = data.groupby(codes, observed=False).mean().reindex(cats.categories)
+    exp.index = CategoricalIndex(
+        exp.index, categories=cats.categories, ordered=cats.ordered
+    )
+    tm.assert_series_equal(result, exp)
+
+    cats = Categorical(
+        ["a", "a", "a", "b", "b", "b", "c", "c", "c"],
+        categories=["a", "b", "c", "d"],
+        ordered=True,
+    )
+    data = DataFrame({"a": [1, 1, 1, 2, 2, 2, 3, 4, 5], "b": cats})
+
+    result = data.groupby("b", observed=False).mean()
+    result = result["a"].values
+    exp = np.array([1, 2, 4, np.nan])
+    tm.assert_numpy_array_equal(result, exp)
+
+
+def test_groupby_empty_with_category():
+    # GH-9614
+    # test fix for when group by on None resulted in
+    # coercion of dtype categorical -> float
+    df = DataFrame({"A": [None] * 3, "B": Categorical(["train", "train", "test"])})
+    result = df.groupby("A").first()["B"]
+    expected = Series(
+        Categorical([], categories=["test", "train"]),
+        index=Series([], dtype="object", name="A"),
+        name="B",
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_sort():
+    # https://stackoverflow.com/questions/23814368/sorting-pandas-
+    #        categorical-labels-after-groupby
+    # This should result in a properly sorted Series so that the plot
+    # has a sorted x axis
+    # self.cat.groupby(['value_group'])['value_group'].count().plot(kind='bar')
+
+    df = DataFrame({"value": np.random.randint(0, 10000, 100)})
+    labels = [f"{i} - {i+499}" for i in range(0, 10000, 500)]
+    cat_labels = Categorical(labels, labels)
+
+    df = df.sort_values(by=["value"], ascending=True)
+    df["value_group"] = pd.cut(
+        df.value, range(0, 10500, 500), right=False, labels=cat_labels
+    )
+
+    res = df.groupby(["value_group"], observed=False)["value_group"].count()
+    exp = res[sorted(res.index, key=lambda x: float(x.split()[0]))]
+    exp.index = CategoricalIndex(exp.index, name=exp.index.name)
+    tm.assert_series_equal(res, exp)
+
+
+@pytest.mark.parametrize("ordered", [True, False])
+def test_sort2(sort, ordered):
+    # dataframe groupby sort was being ignored # GH 8868
+    # GH#48749 - don't change order of categories
+    # GH#42482 - don't sort result when sort=False, even when ordered=True
+    df = DataFrame(
+        [
+            ["(7.5, 10]", 10, 10],
+            ["(7.5, 10]", 8, 20],
+            ["(2.5, 5]", 5, 30],
+            ["(5, 7.5]", 6, 40],
+            ["(2.5, 5]", 4, 50],
+            ["(0, 2.5]", 1, 60],
+            ["(5, 7.5]", 7, 70],
+        ],
+        columns=["range", "foo", "bar"],
+    )
+    df["range"] = Categorical(df["range"], ordered=ordered)
+    result = df.groupby("range", sort=sort, observed=False).first()
+
+    if sort:
+        data_values = [[1, 60], [5, 30], [6, 40], [10, 10]]
+        index_values = ["(0, 2.5]", "(2.5, 5]", "(5, 7.5]", "(7.5, 10]"]
+    else:
+        data_values = [[10, 10], [5, 30], [6, 40], [1, 60]]
+        index_values = ["(7.5, 10]", "(2.5, 5]", "(5, 7.5]", "(0, 2.5]"]
+    expected = DataFrame(
+        data_values,
+        columns=["foo", "bar"],
+        index=CategoricalIndex(index_values, name="range", ordered=ordered),
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("ordered", [True, False])
+def test_sort_datetimelike(sort, ordered):
+    # GH10505
+    # GH#42482 - don't sort result when sort=False, even when ordered=True
+
+    # use same data as test_groupby_sort_categorical, which category is
+    # corresponding to datetime.month
+    df = DataFrame(
+        {
+            "dt": [
+                datetime(2011, 7, 1),
+                datetime(2011, 7, 1),
+                datetime(2011, 2, 1),
+                datetime(2011, 5, 1),
+                datetime(2011, 2, 1),
+                datetime(2011, 1, 1),
+                datetime(2011, 5, 1),
+            ],
+            "foo": [10, 8, 5, 6, 4, 1, 7],
+            "bar": [10, 20, 30, 40, 50, 60, 70],
+        },
+        columns=["dt", "foo", "bar"],
+    )
+
+    # ordered=True
+    df["dt"] = Categorical(df["dt"], ordered=ordered)
+    if sort:
+        data_values = [[1, 60], [5, 30], [6, 40], [10, 10]]
+        index_values = [
+            datetime(2011, 1, 1),
+            datetime(2011, 2, 1),
+            datetime(2011, 5, 1),
+            datetime(2011, 7, 1),
+        ]
+    else:
+        data_values = [[10, 10], [5, 30], [6, 40], [1, 60]]
+        index_values = [
+            datetime(2011, 7, 1),
+            datetime(2011, 2, 1),
+            datetime(2011, 5, 1),
+            datetime(2011, 1, 1),
+        ]
+    expected = DataFrame(
+        data_values,
+        columns=["foo", "bar"],
+        index=CategoricalIndex(index_values, name="dt", ordered=ordered),
+    )
+    result = df.groupby("dt", sort=sort, observed=False).first()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_empty_sum():
+    # https://github.com/pandas-dev/pandas/issues/18678
+    df = DataFrame(
+        {"A": Categorical(["a", "a", "b"], categories=["a", "b", "c"]), "B": [1, 2, 1]}
+    )
+    expected_idx = CategoricalIndex(["a", "b", "c"], name="A")
+
+    # 0 by default
+    result = df.groupby("A", observed=False).B.sum()
+    expected = Series([3, 1, 0], expected_idx, name="B")
+    tm.assert_series_equal(result, expected)
+
+    # min_count=0
+    result = df.groupby("A", observed=False).B.sum(min_count=0)
+    expected = Series([3, 1, 0], expected_idx, name="B")
+    tm.assert_series_equal(result, expected)
+
+    # min_count=1
+    result = df.groupby("A", observed=False).B.sum(min_count=1)
+    expected = Series([3, 1, np.nan], expected_idx, name="B")
+    tm.assert_series_equal(result, expected)
+
+    # min_count>1
+    result = df.groupby("A", observed=False).B.sum(min_count=2)
+    expected = Series([3, np.nan, np.nan], expected_idx, name="B")
+    tm.assert_series_equal(result, expected)
+
+
+def test_empty_prod():
+    # https://github.com/pandas-dev/pandas/issues/18678
+    df = DataFrame(
+        {"A": Categorical(["a", "a", "b"], categories=["a", "b", "c"]), "B": [1, 2, 1]}
+    )
+
+    expected_idx = CategoricalIndex(["a", "b", "c"], name="A")
+
+    # 1 by default
+    result = df.groupby("A", observed=False).B.prod()
+    expected = Series([2, 1, 1], expected_idx, name="B")
+    tm.assert_series_equal(result, expected)
+
+    # min_count=0
+    result = df.groupby("A", observed=False).B.prod(min_count=0)
+    expected = Series([2, 1, 1], expected_idx, name="B")
+    tm.assert_series_equal(result, expected)
+
+    # min_count=1
+    result = df.groupby("A", observed=False).B.prod(min_count=1)
+    expected = Series([2, 1, np.nan], expected_idx, name="B")
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_multiindex_categorical_datetime():
+    # https://github.com/pandas-dev/pandas/issues/21390
+
+    df = DataFrame(
+        {
+            "key1": Categorical(list("abcbabcba")),
+            "key2": Categorical(
+                list(pd.date_range("2018-06-01 00", freq="1T", periods=3)) * 3
+            ),
+            "values": np.arange(9),
+        }
+    )
+    result = df.groupby(["key1", "key2"]).mean()
+
+    idx = MultiIndex.from_product(
+        [
+            Categorical(["a", "b", "c"]),
+            Categorical(pd.date_range("2018-06-01 00", freq="1T", periods=3)),
+        ],
+        names=["key1", "key2"],
+    )
+    expected = DataFrame({"values": [0, 4, 8, 3, 4, 5, 6, np.nan, 2]}, index=idx)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "as_index, expected",
+    [
+        (
+            True,
+            Series(
+                index=MultiIndex.from_arrays(
+                    [Series([1, 1, 2], dtype="category"), [1, 2, 2]], names=["a", "b"]
+                ),
+                data=[1, 2, 3],
+                name="x",
+            ),
+        ),
+        (
+            False,
+            DataFrame(
+                {
+                    "a": Series([1, 1, 2], dtype="category"),
+                    "b": [1, 2, 2],
+                    "x": [1, 2, 3],
+                }
+            ),
+        ),
+    ],
+)
+def test_groupby_agg_observed_true_single_column(as_index, expected):
+    # GH-23970
+    df = DataFrame(
+        {"a": Series([1, 1, 2], dtype="category"), "b": [1, 2, 2], "x": [1, 2, 3]}
+    )
+
+    result = df.groupby(["a", "b"], as_index=as_index, observed=True)["x"].sum()
+
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize("fill_value", [None, np.nan, pd.NaT])
+def test_shift(fill_value):
+    ct = Categorical(
+        ["a", "b", "c", "d"], categories=["a", "b", "c", "d"], ordered=False
+    )
+    expected = Categorical(
+        [None, "a", "b", "c"], categories=["a", "b", "c", "d"], ordered=False
+    )
+    res = ct.shift(1, fill_value=fill_value)
+    tm.assert_equal(res, expected)
+
+
+@pytest.fixture
+def df_cat(df):
+    """
+    DataFrame with multiple categorical columns and a column of integers.
+    Shortened so as not to contain all possible combinations of categories.
+    Useful for testing `observed` kwarg functionality on GroupBy objects.
+
+    Parameters
+    ----------
+    df: DataFrame
+        Non-categorical, longer DataFrame from another fixture, used to derive
+        this one
+
+    Returns
+    -------
+    df_cat: DataFrame
+    """
+    df_cat = df.copy()[:4]  # leave out some groups
+    df_cat["A"] = df_cat["A"].astype("category")
+    df_cat["B"] = df_cat["B"].astype("category")
+    df_cat["C"] = Series([1, 2, 3, 4])
+    df_cat = df_cat.drop(["D"], axis=1)
+    return df_cat
+
+
+@pytest.mark.parametrize("operation", ["agg", "apply"])
+def test_seriesgroupby_observed_true(df_cat, operation):
+    # GH#24880
+    # GH#49223 - order of results was wrong when grouping by index levels
+    lev_a = Index(["bar", "bar", "foo", "foo"], dtype=df_cat["A"].dtype, name="A")
+    lev_b = Index(["one", "three", "one", "two"], dtype=df_cat["B"].dtype, name="B")
+    index = MultiIndex.from_arrays([lev_a, lev_b])
+    expected = Series(data=[2, 4, 1, 3], index=index, name="C").sort_index()
+
+    grouped = df_cat.groupby(["A", "B"], observed=True)["C"]
+    result = getattr(grouped, operation)(sum)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("operation", ["agg", "apply"])
+@pytest.mark.parametrize("observed", [False, None])
+def test_seriesgroupby_observed_false_or_none(df_cat, observed, operation):
+    # GH 24880
+    # GH#49223 - order of results was wrong when grouping by index levels
+    index, _ = MultiIndex.from_product(
+        [
+            CategoricalIndex(["bar", "foo"], ordered=False),
+            CategoricalIndex(["one", "three", "two"], ordered=False),
+        ],
+        names=["A", "B"],
+    ).sortlevel()
+
+    expected = Series(data=[2, 4, np.nan, 1, np.nan, 3], index=index, name="C")
+    if operation == "agg":
+        expected = expected.fillna(0, downcast="infer")
+    grouped = df_cat.groupby(["A", "B"], observed=observed)["C"]
+    result = getattr(grouped, operation)(sum)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "observed, index, data",
+    [
+        (
+            True,
+            MultiIndex.from_arrays(
+                [
+                    Index(["bar"] * 4 + ["foo"] * 4, dtype="category", name="A"),
+                    Index(
+                        ["one", "one", "three", "three", "one", "one", "two", "two"],
+                        dtype="category",
+                        name="B",
+                    ),
+                    Index(["min", "max"] * 4),
+                ]
+            ),
+            [2, 2, 4, 4, 1, 1, 3, 3],
+        ),
+        (
+            False,
+            MultiIndex.from_product(
+                [
+                    CategoricalIndex(["bar", "foo"], ordered=False),
+                    CategoricalIndex(["one", "three", "two"], ordered=False),
+                    Index(["min", "max"]),
+                ],
+                names=["A", "B", None],
+            ),
+            [2, 2, 4, 4, np.nan, np.nan, 1, 1, np.nan, np.nan, 3, 3],
+        ),
+        (
+            None,
+            MultiIndex.from_product(
+                [
+                    CategoricalIndex(["bar", "foo"], ordered=False),
+                    CategoricalIndex(["one", "three", "two"], ordered=False),
+                    Index(["min", "max"]),
+                ],
+                names=["A", "B", None],
+            ),
+            [2, 2, 4, 4, np.nan, np.nan, 1, 1, np.nan, np.nan, 3, 3],
+        ),
+    ],
+)
+def test_seriesgroupby_observed_apply_dict(df_cat, observed, index, data):
+    # GH 24880
+    expected = Series(data=data, index=index, name="C")
+    result = df_cat.groupby(["A", "B"], observed=observed)["C"].apply(
+        lambda x: {"min": x.min(), "max": x.max()}
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_categorical_series_dataframe_consistent(df_cat):
+    # GH 20416
+    expected = df_cat.groupby(["A", "B"])["C"].mean()
+    result = df_cat.groupby(["A", "B"]).mean()["C"]
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("code", [([1, 0, 0]), ([0, 0, 0])])
+def test_groupby_categorical_axis_1(code):
+    # GH 13420
+    df = DataFrame({"a": [1, 2, 3, 4], "b": [-1, -2, -3, -4], "c": [5, 6, 7, 8]})
+    cat = Categorical.from_codes(code, categories=list("abc"))
+    result = df.groupby(cat, axis=1).mean()
+    expected = df.T.groupby(cat, axis=0).mean().T
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_cat_preserves_structure(observed, ordered):
+    # GH 28787
+    df = DataFrame(
+        {"Name": Categorical(["Bob", "Greg"], ordered=ordered), "Item": [1, 2]},
+        columns=["Name", "Item"],
+    )
+    expected = df.copy()
+
+    result = (
+        df.groupby("Name", observed=observed)
+        .agg(DataFrame.sum, skipna=True)
+        .reset_index()
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_get_nonexistent_category():
+    # Accessing a Category that is not in the dataframe
+    df = DataFrame({"var": ["a", "a", "b", "b"], "val": range(4)})
+    with pytest.raises(KeyError, match="'vau'"):
+        df.groupby("var").apply(
+            lambda rows: DataFrame(
+                {"var": [rows.iloc[-1]["var"]], "val": [rows.iloc[-1]["vau"]]}
+            )
+        )
+
+
+def test_series_groupby_on_2_categoricals_unobserved(reduction_func, observed, request):
+    # GH 17605
+    if reduction_func == "ngroup":
+        pytest.skip("ngroup is not truly a reduction")
+
+    if reduction_func == "corrwith":  # GH 32293
+        mark = pytest.mark.xfail(
+            reason="TODO: implemented SeriesGroupBy.corrwith. See GH 32293"
+        )
+        request.node.add_marker(mark)
+
+    df = DataFrame(
+        {
+            "cat_1": Categorical(list("AABB"), categories=list("ABCD")),
+            "cat_2": Categorical(list("AB") * 2, categories=list("ABCD")),
+            "value": [0.1] * 4,
+        }
+    )
+    args = get_groupby_method_args(reduction_func, df)
+
+    expected_length = 4 if observed else 16
+
+    series_groupby = df.groupby(["cat_1", "cat_2"], observed=observed)["value"]
+    agg = getattr(series_groupby, reduction_func)
+    result = agg(*args)
+
+    assert len(result) == expected_length
+
+
+def test_series_groupby_on_2_categoricals_unobserved_zeroes_or_nans(
+    reduction_func, request
+):
+    # GH 17605
+    # Tests whether the unobserved categories in the result contain 0 or NaN
+
+    if reduction_func == "ngroup":
+        pytest.skip("ngroup is not truly a reduction")
+
+    if reduction_func == "corrwith":  # GH 32293
+        mark = pytest.mark.xfail(
+            reason="TODO: implemented SeriesGroupBy.corrwith. See GH 32293"
+        )
+        request.node.add_marker(mark)
+
+    df = DataFrame(
+        {
+            "cat_1": Categorical(list("AABB"), categories=list("ABC")),
+            "cat_2": Categorical(list("AB") * 2, categories=list("ABC")),
+            "value": [0.1] * 4,
+        }
+    )
+    unobserved = [tuple("AC"), tuple("BC"), tuple("CA"), tuple("CB"), tuple("CC")]
+    args = get_groupby_method_args(reduction_func, df)
+
+    series_groupby = df.groupby(["cat_1", "cat_2"], observed=False)["value"]
+    agg = getattr(series_groupby, reduction_func)
+    result = agg(*args)
+
+    zero_or_nan = _results_for_groupbys_with_missing_categories[reduction_func]
+
+    for idx in unobserved:
+        val = result.loc[idx]
+        assert (pd.isna(zero_or_nan) and pd.isna(val)) or (val == zero_or_nan)
+
+    # If we expect unobserved values to be zero, we also expect the dtype to be int.
+    # Except for .sum(). If the observed categories sum to dtype=float (i.e. their
+    # sums have decimals), then the zeros for the missing categories should also be
+    # floats.
+    if zero_or_nan == 0 and reduction_func != "sum":
+        assert np.issubdtype(result.dtype, np.integer)
+
+
+def test_dataframe_groupby_on_2_categoricals_when_observed_is_true(reduction_func):
+    # GH 23865
+    # GH 27075
+    # Ensure that df.groupby, when 'by' is two Categorical variables,
+    # does not return the categories that are not in df when observed=True
+    if reduction_func == "ngroup":
+        pytest.skip("ngroup does not return the Categories on the index")
+
+    df = DataFrame(
+        {
+            "cat_1": Categorical(list("AABB"), categories=list("ABC")),
+            "cat_2": Categorical(list("1111"), categories=list("12")),
+            "value": [0.1, 0.1, 0.1, 0.1],
+        }
+    )
+    unobserved_cats = [("A", "2"), ("B", "2"), ("C", "1"), ("C", "2")]
+
+    df_grp = df.groupby(["cat_1", "cat_2"], observed=True)
+
+    args = get_groupby_method_args(reduction_func, df)
+    res = getattr(df_grp, reduction_func)(*args)
+
+    for cat in unobserved_cats:
+        assert cat not in res.index
+
+
+@pytest.mark.parametrize("observed", [False, None])
+def test_dataframe_groupby_on_2_categoricals_when_observed_is_false(
+    reduction_func, observed
+):
+    # GH 23865
+    # GH 27075
+    # Ensure that df.groupby, when 'by' is two Categorical variables,
+    # returns the categories that are not in df when observed=False/None
+
+    if reduction_func == "ngroup":
+        pytest.skip("ngroup does not return the Categories on the index")
+
+    df = DataFrame(
+        {
+            "cat_1": Categorical(list("AABB"), categories=list("ABC")),
+            "cat_2": Categorical(list("1111"), categories=list("12")),
+            "value": [0.1, 0.1, 0.1, 0.1],
+        }
+    )
+    unobserved_cats = [("A", "2"), ("B", "2"), ("C", "1"), ("C", "2")]
+
+    df_grp = df.groupby(["cat_1", "cat_2"], observed=observed)
+
+    args = get_groupby_method_args(reduction_func, df)
+    res = getattr(df_grp, reduction_func)(*args)
+
+    expected = _results_for_groupbys_with_missing_categories[reduction_func]
+
+    if expected is np.nan:
+        assert res.loc[unobserved_cats].isnull().all().all()
+    else:
+        assert (res.loc[unobserved_cats] == expected).all().all()
+
+
+def test_series_groupby_categorical_aggregation_getitem():
+    # GH 8870
+    d = {"foo": [10, 8, 4, 1], "bar": [10, 20, 30, 40], "baz": ["d", "c", "d", "c"]}
+    df = DataFrame(d)
+    cat = pd.cut(df["foo"], np.linspace(0, 20, 5))
+    df["range"] = cat
+    groups = df.groupby(["range", "baz"], as_index=True, sort=True)
+    result = groups["foo"].agg("mean")
+    expected = groups.agg("mean")["foo"]
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "func, expected_values",
+    [(Series.nunique, [1, 1, 2]), (Series.count, [1, 2, 2])],
+)
+def test_groupby_agg_categorical_columns(func, expected_values):
+    # 31256
+    df = DataFrame(
+        {
+            "id": [0, 1, 2, 3, 4],
+            "groups": [0, 1, 1, 2, 2],
+            "value": Categorical([0, 0, 0, 0, 1]),
+        }
+    ).set_index("id")
+    result = df.groupby("groups").agg(func)
+
+    expected = DataFrame(
+        {"value": expected_values}, index=Index([0, 1, 2], name="groups")
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_agg_non_numeric():
+    df = DataFrame({"A": Categorical(["a", "a", "b"], categories=["a", "b", "c"])})
+    expected = DataFrame({"A": [2, 1]}, index=np.array([1, 2]))
+
+    result = df.groupby([1, 2, 1]).agg(Series.nunique)
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby([1, 2, 1]).nunique()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("func", ["first", "last"])
+def test_groupby_first_returned_categorical_instead_of_dataframe(func):
+    # GH 28641: groupby drops index, when grouping over categorical column with
+    # first/last. Renamed Categorical instead of DataFrame previously.
+    df = DataFrame({"A": [1997], "B": Series(["b"], dtype="category").cat.as_ordered()})
+    df_grouped = df.groupby("A")["B"]
+    result = getattr(df_grouped, func)()
+
+    # ordered categorical dtype should be preserved
+    expected = Series(
+        ["b"], index=Index([1997], name="A"), name="B", dtype=df["B"].dtype
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_read_only_category_no_sort():
+    # GH33410
+    cats = np.array([1, 2])
+    cats.flags.writeable = False
+    df = DataFrame(
+        {"a": [1, 3, 5, 7], "b": Categorical([1, 1, 2, 2], categories=Index(cats))}
+    )
+    expected = DataFrame(data={"a": [2.0, 6.0]}, index=CategoricalIndex(cats, name="b"))
+    result = df.groupby("b", sort=False).mean()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_sorted_missing_category_values():
+    # GH 28597
+    df = DataFrame(
+        {
+            "foo": [
+                "small",
+                "large",
+                "large",
+                "large",
+                "medium",
+                "large",
+                "large",
+                "medium",
+            ],
+            "bar": ["C", "A", "A", "C", "A", "C", "A", "C"],
+        }
+    )
+    df["foo"] = (
+        df["foo"]
+        .astype("category")
+        .cat.set_categories(["tiny", "small", "medium", "large"], ordered=True)
+    )
+
+    expected = DataFrame(
+        {
+            "tiny": {"A": 0, "C": 0},
+            "small": {"A": 0, "C": 1},
+            "medium": {"A": 1, "C": 1},
+            "large": {"A": 3, "C": 2},
+        }
+    )
+    expected = expected.rename_axis("bar", axis="index")
+    expected.columns = CategoricalIndex(
+        ["tiny", "small", "medium", "large"],
+        categories=["tiny", "small", "medium", "large"],
+        ordered=True,
+        name="foo",
+        dtype="category",
+    )
+
+    result = df.groupby(["bar", "foo"]).size().unstack()
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_agg_cython_category_not_implemented_fallback():
+    # https://github.com/pandas-dev/pandas/issues/31450
+    df = DataFrame({"col_num": [1, 1, 2, 3]})
+    df["col_cat"] = df["col_num"].astype("category")
+
+    result = df.groupby("col_num").col_cat.first()
+
+    # ordered categorical dtype should definitely be preserved;
+    #  this is unordered, so is less-clear case (if anything, it should raise)
+    expected = Series(
+        [1, 2, 3],
+        index=Index([1, 2, 3], name="col_num"),
+        name="col_cat",
+        dtype=df["col_cat"].dtype,
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = df.groupby("col_num").agg({"col_cat": "first"})
+    expected = expected.to_frame()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_aggregate_categorical_with_isnan():
+    # GH 29837
+    df = DataFrame(
+        {
+            "A": [1, 1, 1, 1],
+            "B": [1, 2, 1, 2],
+            "numerical_col": [0.1, 0.2, np.nan, 0.3],
+            "object_col": ["foo", "bar", "foo", "fee"],
+            "categorical_col": ["foo", "bar", "foo", "fee"],
+        }
+    )
+
+    df = df.astype({"categorical_col": "category"})
+
+    result = df.groupby(["A", "B"]).agg(lambda df: df.isna().sum())
+    index = MultiIndex.from_arrays([[1, 1], [1, 2]], names=("A", "B"))
+    expected = DataFrame(
+        data={
+            "numerical_col": [1, 0],
+            "object_col": [0, 0],
+            "categorical_col": [0, 0],
+        },
+        index=index,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_categorical_transform():
+    # GH 29037
+    df = DataFrame(
+        {
+            "package_id": [1, 1, 1, 2, 2, 3],
+            "status": [
+                "Waiting",
+                "OnTheWay",
+                "Delivered",
+                "Waiting",
+                "OnTheWay",
+                "Waiting",
+            ],
+        }
+    )
+
+    delivery_status_type = pd.CategoricalDtype(
+        categories=["Waiting", "OnTheWay", "Delivered"], ordered=True
+    )
+    df["status"] = df["status"].astype(delivery_status_type)
+    df["last_status"] = df.groupby("package_id")["status"].transform(max)
+    result = df.copy()
+
+    expected = DataFrame(
+        {
+            "package_id": [1, 1, 1, 2, 2, 3],
+            "status": [
+                "Waiting",
+                "OnTheWay",
+                "Delivered",
+                "Waiting",
+                "OnTheWay",
+                "Waiting",
+            ],
+            "last_status": [
+                "Delivered",
+                "Delivered",
+                "Delivered",
+                "OnTheWay",
+                "OnTheWay",
+                "Waiting",
+            ],
+        }
+    )
+
+    expected["status"] = expected["status"].astype(delivery_status_type)
+
+    # .transform(max) should preserve ordered categoricals
+    expected["last_status"] = expected["last_status"].astype(delivery_status_type)
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("func", ["first", "last"])
+def test_series_groupby_first_on_categorical_col_grouped_on_2_categoricals(
+    func: str, observed: bool
+):
+    # GH 34951
+    cat = Categorical([0, 0, 1, 1])
+    val = [0, 1, 1, 0]
+    df = DataFrame({"a": cat, "b": cat, "c": val})
+
+    cat2 = Categorical([0, 1])
+    idx = MultiIndex.from_product([cat2, cat2], names=["a", "b"])
+    expected_dict = {
+        "first": Series([0, np.NaN, np.NaN, 1], idx, name="c"),
+        "last": Series([1, np.NaN, np.NaN, 0], idx, name="c"),
+    }
+
+    expected = expected_dict[func]
+    if observed:
+        expected = expected.dropna().astype(np.int64)
+
+    srs_grp = df.groupby(["a", "b"], observed=observed)["c"]
+    result = getattr(srs_grp, func)()
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("func", ["first", "last"])
+def test_df_groupby_first_on_categorical_col_grouped_on_2_categoricals(
+    func: str, observed: bool
+):
+    # GH 34951
+    cat = Categorical([0, 0, 1, 1])
+    val = [0, 1, 1, 0]
+    df = DataFrame({"a": cat, "b": cat, "c": val})
+
+    cat2 = Categorical([0, 1])
+    idx = MultiIndex.from_product([cat2, cat2], names=["a", "b"])
+    expected_dict = {
+        "first": Series([0, np.NaN, np.NaN, 1], idx, name="c"),
+        "last": Series([1, np.NaN, np.NaN, 0], idx, name="c"),
+    }
+
+    expected = expected_dict[func].to_frame()
+    if observed:
+        expected = expected.dropna().astype(np.int64)
+
+    df_grp = df.groupby(["a", "b"], observed=observed)
+    result = getattr(df_grp, func)()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_categorical_indices_unused_categories():
+    # GH#38642
+    df = DataFrame(
+        {
+            "key": Categorical(["b", "b", "a"], categories=["a", "b", "c"]),
+            "col": range(3),
+        }
+    )
+    grouped = df.groupby("key", sort=False)
+    result = grouped.indices
+    expected = {
+        "b": np.array([0, 1], dtype="intp"),
+        "a": np.array([2], dtype="intp"),
+        "c": np.array([], dtype="intp"),
+    }
+    assert result.keys() == expected.keys()
+    for key in result.keys():
+        tm.assert_numpy_array_equal(result[key], expected[key])
+
+
+@pytest.mark.parametrize("func", ["first", "last"])
+def test_groupby_last_first_preserve_categoricaldtype(func):
+    # GH#33090
+    df = DataFrame({"a": [1, 2, 3]})
+    df["b"] = df["a"].astype("category")
+    result = getattr(df.groupby("a")["b"], func)()
+    expected = Series(
+        Categorical([1, 2, 3]), name="b", index=Index([1, 2, 3], name="a")
+    )
+    tm.assert_series_equal(expected, result)
+
+
+def test_groupby_categorical_observed_nunique():
+    # GH#45128
+    df = DataFrame({"a": [1, 2], "b": [1, 2], "c": [10, 11]})
+    df = df.astype(dtype={"a": "category", "b": "category"})
+    result = df.groupby(["a", "b"], observed=True).nunique()["c"]
+    expected = Series(
+        [1, 1],
+        index=MultiIndex.from_arrays(
+            [CategoricalIndex([1, 2], name="a"), CategoricalIndex([1, 2], name="b")]
+        ),
+        name="c",
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_categorical_aggregate_functions():
+    # GH#37275
+    dtype = pd.CategoricalDtype(categories=["small", "big"], ordered=True)
+    df = DataFrame(
+        [[1, "small"], [1, "big"], [2, "small"]], columns=["grp", "description"]
+    ).astype({"description": dtype})
+
+    result = df.groupby("grp")["description"].max()
+    expected = Series(
+        ["big", "small"],
+        index=Index([1, 2], name="grp"),
+        name="description",
+        dtype=pd.CategoricalDtype(categories=["small", "big"], ordered=True),
+    )
+
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_categorical_dropna(observed, dropna):
+    # GH#48645 - dropna should have no impact on the result when there are no NA values
+    cat = Categorical([1, 2], categories=[1, 2, 3])
+    df = DataFrame({"x": Categorical([1, 2], categories=[1, 2, 3]), "y": [3, 4]})
+    gb = df.groupby("x", observed=observed, dropna=dropna)
+    result = gb.sum()
+
+    if observed:
+        expected = DataFrame({"y": [3, 4]}, index=cat)
+    else:
+        index = CategoricalIndex([1, 2, 3], [1, 2, 3])
+        expected = DataFrame({"y": [3, 4, 0]}, index=index)
+    expected.index.name = "x"
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("index_kind", ["range", "single", "multi"])
+@pytest.mark.parametrize("ordered", [True, False])
+def test_category_order_reducer(
+    request, as_index, sort, observed, reduction_func, index_kind, ordered
+):
+    # GH#48749
+    if (
+        reduction_func in ("idxmax", "idxmin")
+        and not observed
+        and index_kind != "multi"
+    ):
+        msg = "GH#10694 - idxmax/min fail with unused categories"
+        request.node.add_marker(pytest.mark.xfail(reason=msg))
+    elif reduction_func == "corrwith" and not as_index:
+        msg = "GH#49950 - corrwith with as_index=False may not have grouping column"
+        request.node.add_marker(pytest.mark.xfail(reason=msg))
+    elif index_kind != "range" and not as_index:
+        pytest.skip(reason="Result doesn't have categories, nothing to test")
+    df = DataFrame(
+        {
+            "a": Categorical([2, 1, 2, 3], categories=[1, 4, 3, 2], ordered=ordered),
+            "b": range(4),
+        }
+    )
+    if index_kind == "range":
+        keys = ["a"]
+    elif index_kind == "single":
+        keys = ["a"]
+        df = df.set_index(keys)
+    elif index_kind == "multi":
+        keys = ["a", "a2"]
+        df["a2"] = df["a"]
+        df = df.set_index(keys)
+    args = get_groupby_method_args(reduction_func, df)
+    gb = df.groupby(keys, as_index=as_index, sort=sort, observed=observed)
+    op_result = getattr(gb, reduction_func)(*args)
+    if as_index:
+        result = op_result.index.get_level_values("a").categories
+    else:
+        result = op_result["a"].cat.categories
+    expected = Index([1, 4, 3, 2])
+    tm.assert_index_equal(result, expected)
+
+    if index_kind == "multi":
+        result = op_result.index.get_level_values("a2").categories
+        tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("index_kind", ["single", "multi"])
+@pytest.mark.parametrize("ordered", [True, False])
+def test_category_order_transformer(
+    as_index, sort, observed, transformation_func, index_kind, ordered
+):
+    # GH#48749
+    df = DataFrame(
+        {
+            "a": Categorical([2, 1, 2, 3], categories=[1, 4, 3, 2], ordered=ordered),
+            "b": range(4),
+        }
+    )
+    if index_kind == "single":
+        keys = ["a"]
+        df = df.set_index(keys)
+    elif index_kind == "multi":
+        keys = ["a", "a2"]
+        df["a2"] = df["a"]
+        df = df.set_index(keys)
+    args = get_groupby_method_args(transformation_func, df)
+    gb = df.groupby(keys, as_index=as_index, sort=sort, observed=observed)
+    op_result = getattr(gb, transformation_func)(*args)
+    result = op_result.index.get_level_values("a").categories
+    expected = Index([1, 4, 3, 2])
+    tm.assert_index_equal(result, expected)
+
+    if index_kind == "multi":
+        result = op_result.index.get_level_values("a2").categories
+        tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("index_kind", ["range", "single", "multi"])
+@pytest.mark.parametrize("method", ["head", "tail"])
+@pytest.mark.parametrize("ordered", [True, False])
+def test_category_order_head_tail(
+    as_index, sort, observed, method, index_kind, ordered
+):
+    # GH#48749
+    df = DataFrame(
+        {
+            "a": Categorical([2, 1, 2, 3], categories=[1, 4, 3, 2], ordered=ordered),
+            "b": range(4),
+        }
+    )
+    if index_kind == "range":
+        keys = ["a"]
+    elif index_kind == "single":
+        keys = ["a"]
+        df = df.set_index(keys)
+    elif index_kind == "multi":
+        keys = ["a", "a2"]
+        df["a2"] = df["a"]
+        df = df.set_index(keys)
+    gb = df.groupby(keys, as_index=as_index, sort=sort, observed=observed)
+    op_result = getattr(gb, method)()
+    if index_kind == "range":
+        result = op_result["a"].cat.categories
+    else:
+        result = op_result.index.get_level_values("a").categories
+    expected = Index([1, 4, 3, 2])
+    tm.assert_index_equal(result, expected)
+
+    if index_kind == "multi":
+        result = op_result.index.get_level_values("a2").categories
+        tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("index_kind", ["range", "single", "multi"])
+@pytest.mark.parametrize("method", ["apply", "agg", "transform"])
+@pytest.mark.parametrize("ordered", [True, False])
+def test_category_order_apply(as_index, sort, observed, method, index_kind, ordered):
+    # GH#48749
+    if (method == "transform" and index_kind == "range") or (
+        not as_index and index_kind != "range"
+    ):
+        pytest.skip("No categories in result, nothing to test")
+    df = DataFrame(
+        {
+            "a": Categorical([2, 1, 2, 3], categories=[1, 4, 3, 2], ordered=ordered),
+            "b": range(4),
+        }
+    )
+    if index_kind == "range":
+        keys = ["a"]
+    elif index_kind == "single":
+        keys = ["a"]
+        df = df.set_index(keys)
+    elif index_kind == "multi":
+        keys = ["a", "a2"]
+        df["a2"] = df["a"]
+        df = df.set_index(keys)
+    gb = df.groupby(keys, as_index=as_index, sort=sort, observed=observed)
+    op_result = getattr(gb, method)(lambda x: x.sum(numeric_only=True))
+    if (method == "transform" or not as_index) and index_kind == "range":
+        result = op_result["a"].cat.categories
+    else:
+        result = op_result.index.get_level_values("a").categories
+    expected = Index([1, 4, 3, 2])
+    tm.assert_index_equal(result, expected)
+
+    if index_kind == "multi":
+        result = op_result.index.get_level_values("a2").categories
+        tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("index_kind", ["range", "single", "multi"])
+def test_many_categories(as_index, sort, index_kind, ordered):
+    # GH#48749 - Test when the grouper has many categories
+    if index_kind != "range" and not as_index:
+        pytest.skip(reason="Result doesn't have categories, nothing to test")
+    categories = np.arange(9999, -1, -1)
+    grouper = Categorical([2, 1, 2, 3], categories=categories, ordered=ordered)
+    df = DataFrame({"a": grouper, "b": range(4)})
+    if index_kind == "range":
+        keys = ["a"]
+    elif index_kind == "single":
+        keys = ["a"]
+        df = df.set_index(keys)
+    elif index_kind == "multi":
+        keys = ["a", "a2"]
+        df["a2"] = df["a"]
+        df = df.set_index(keys)
+    gb = df.groupby(keys, as_index=as_index, sort=sort, observed=True)
+    result = gb.sum()
+
+    # Test is setup so that data and index are the same values
+    data = [3, 2, 1] if sort else [2, 1, 3]
+
+    index = CategoricalIndex(
+        data, categories=grouper.categories, ordered=ordered, name="a"
+    )
+    if as_index:
+        expected = DataFrame({"b": data})
+        if index_kind == "multi":
+            expected.index = MultiIndex.from_frame(DataFrame({"a": index, "a2": index}))
+        else:
+            expected.index = index
+    elif index_kind == "multi":
+        expected = DataFrame({"a": Series(index), "a2": Series(index), "b": data})
+    else:
+        expected = DataFrame({"a": Series(index), "b": data})
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("test_series", [True, False])
+@pytest.mark.parametrize("keys", [["a1"], ["a1", "a2"]])
+def test_agg_list(request, as_index, observed, reduction_func, test_series, keys):
+    # GH#52760
+    if test_series and reduction_func == "corrwith":
+        assert not hasattr(SeriesGroupBy, "corrwith")
+        pytest.skip("corrwith not implemented for SeriesGroupBy")
+    elif reduction_func == "corrwith":
+        msg = "GH#32293: attempts to call SeriesGroupBy.corrwith"
+        request.node.add_marker(pytest.mark.xfail(reason=msg))
+    elif (
+        reduction_func == "nunique"
+        and not test_series
+        and len(keys) != 1
+        and not observed
+        and not as_index
+    ):
+        msg = "GH#52848 - raises a ValueError"
+        request.node.add_marker(pytest.mark.xfail(reason=msg))
+
+    df = DataFrame({"a1": [0, 0, 1], "a2": [2, 3, 3], "b": [4, 5, 6]})
+    df = df.astype({"a1": "category", "a2": "category"})
+    if "a2" not in keys:
+        df = df.drop(columns="a2")
+    gb = df.groupby(by=keys, as_index=as_index, observed=observed)
+    if test_series:
+        gb = gb["b"]
+    args = get_groupby_method_args(reduction_func, df)
+
+    result = gb.agg([reduction_func], *args)
+    expected = getattr(gb, reduction_func)(*args)
+
+    if as_index and (test_series or reduction_func == "size"):
+        expected = expected.to_frame(reduction_func)
+    if not test_series:
+        if not as_index:
+            # TODO: GH#52849 - as_index=False is not respected
+            expected = expected.set_index(keys)
+        expected.columns = MultiIndex(
+            levels=[["b"], [reduction_func]], codes=[[0], [0]]
+        )
+    elif not as_index:
+        expected.columns = keys + [reduction_func]
+
+    tm.assert_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_counting.py

@@ -0,0 +1,377 @@
+from itertools import product
+from string import ascii_lowercase
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Period,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+class TestCounting:
+    def test_cumcount(self):
+        df = DataFrame([["a"], ["a"], ["a"], ["b"], ["a"]], columns=["A"])
+        g = df.groupby("A")
+        sg = g.A
+
+        expected = Series([0, 1, 2, 0, 3])
+
+        tm.assert_series_equal(expected, g.cumcount())
+        tm.assert_series_equal(expected, sg.cumcount())
+
+    def test_cumcount_empty(self):
+        ge = DataFrame().groupby(level=0)
+        se = Series(dtype=object).groupby(level=0)
+
+        # edge case, as this is usually considered float
+        e = Series(dtype="int64")
+
+        tm.assert_series_equal(e, ge.cumcount())
+        tm.assert_series_equal(e, se.cumcount())
+
+    def test_cumcount_dupe_index(self):
+        df = DataFrame(
+            [["a"], ["a"], ["a"], ["b"], ["a"]], columns=["A"], index=[0] * 5
+        )
+        g = df.groupby("A")
+        sg = g.A
+
+        expected = Series([0, 1, 2, 0, 3], index=[0] * 5)
+
+        tm.assert_series_equal(expected, g.cumcount())
+        tm.assert_series_equal(expected, sg.cumcount())
+
+    def test_cumcount_mi(self):
+        mi = MultiIndex.from_tuples([[0, 1], [1, 2], [2, 2], [2, 2], [1, 0]])
+        df = DataFrame([["a"], ["a"], ["a"], ["b"], ["a"]], columns=["A"], index=mi)
+        g = df.groupby("A")
+        sg = g.A
+
+        expected = Series([0, 1, 2, 0, 3], index=mi)
+
+        tm.assert_series_equal(expected, g.cumcount())
+        tm.assert_series_equal(expected, sg.cumcount())
+
+    def test_cumcount_groupby_not_col(self):
+        df = DataFrame(
+            [["a"], ["a"], ["a"], ["b"], ["a"]], columns=["A"], index=[0] * 5
+        )
+        g = df.groupby([0, 0, 0, 1, 0])
+        sg = g.A
+
+        expected = Series([0, 1, 2, 0, 3], index=[0] * 5)
+
+        tm.assert_series_equal(expected, g.cumcount())
+        tm.assert_series_equal(expected, sg.cumcount())
+
+    def test_ngroup(self):
+        df = DataFrame({"A": list("aaaba")})
+        g = df.groupby("A")
+        sg = g.A
+
+        expected = Series([0, 0, 0, 1, 0])
+
+        tm.assert_series_equal(expected, g.ngroup())
+        tm.assert_series_equal(expected, sg.ngroup())
+
+    def test_ngroup_distinct(self):
+        df = DataFrame({"A": list("abcde")})
+        g = df.groupby("A")
+        sg = g.A
+
+        expected = Series(range(5), dtype="int64")
+
+        tm.assert_series_equal(expected, g.ngroup())
+        tm.assert_series_equal(expected, sg.ngroup())
+
+    def test_ngroup_one_group(self):
+        df = DataFrame({"A": [0] * 5})
+        g = df.groupby("A")
+        sg = g.A
+
+        expected = Series([0] * 5)
+
+        tm.assert_series_equal(expected, g.ngroup())
+        tm.assert_series_equal(expected, sg.ngroup())
+
+    def test_ngroup_empty(self):
+        ge = DataFrame().groupby(level=0)
+        se = Series(dtype=object).groupby(level=0)
+
+        # edge case, as this is usually considered float
+        e = Series(dtype="int64")
+
+        tm.assert_series_equal(e, ge.ngroup())
+        tm.assert_series_equal(e, se.ngroup())
+
+    def test_ngroup_series_matches_frame(self):
+        df = DataFrame({"A": list("aaaba")})
+        s = Series(list("aaaba"))
+
+        tm.assert_series_equal(df.groupby(s).ngroup(), s.groupby(s).ngroup())
+
+    def test_ngroup_dupe_index(self):
+        df = DataFrame({"A": list("aaaba")}, index=[0] * 5)
+        g = df.groupby("A")
+        sg = g.A
+
+        expected = Series([0, 0, 0, 1, 0], index=[0] * 5)
+
+        tm.assert_series_equal(expected, g.ngroup())
+        tm.assert_series_equal(expected, sg.ngroup())
+
+    def test_ngroup_mi(self):
+        mi = MultiIndex.from_tuples([[0, 1], [1, 2], [2, 2], [2, 2], [1, 0]])
+        df = DataFrame({"A": list("aaaba")}, index=mi)
+        g = df.groupby("A")
+        sg = g.A
+        expected = Series([0, 0, 0, 1, 0], index=mi)
+
+        tm.assert_series_equal(expected, g.ngroup())
+        tm.assert_series_equal(expected, sg.ngroup())
+
+    def test_ngroup_groupby_not_col(self):
+        df = DataFrame({"A": list("aaaba")}, index=[0] * 5)
+        g = df.groupby([0, 0, 0, 1, 0])
+        sg = g.A
+
+        expected = Series([0, 0, 0, 1, 0], index=[0] * 5)
+
+        tm.assert_series_equal(expected, g.ngroup())
+        tm.assert_series_equal(expected, sg.ngroup())
+
+    def test_ngroup_descending(self):
+        df = DataFrame(["a", "a", "b", "a", "b"], columns=["A"])
+        g = df.groupby(["A"])
+
+        ascending = Series([0, 0, 1, 0, 1])
+        descending = Series([1, 1, 0, 1, 0])
+
+        tm.assert_series_equal(descending, (g.ngroups - 1) - ascending)
+        tm.assert_series_equal(ascending, g.ngroup(ascending=True))
+        tm.assert_series_equal(descending, g.ngroup(ascending=False))
+
+    def test_ngroup_matches_cumcount(self):
+        # verify one manually-worked out case works
+        df = DataFrame(
+            [["a", "x"], ["a", "y"], ["b", "x"], ["a", "x"], ["b", "y"]],
+            columns=["A", "X"],
+        )
+        g = df.groupby(["A", "X"])
+        g_ngroup = g.ngroup()
+        g_cumcount = g.cumcount()
+        expected_ngroup = Series([0, 1, 2, 0, 3])
+        expected_cumcount = Series([0, 0, 0, 1, 0])
+
+        tm.assert_series_equal(g_ngroup, expected_ngroup)
+        tm.assert_series_equal(g_cumcount, expected_cumcount)
+
+    def test_ngroup_cumcount_pair(self):
+        # brute force comparison for all small series
+        for p in product(range(3), repeat=4):
+            df = DataFrame({"a": p})
+            g = df.groupby(["a"])
+
+            order = sorted(set(p))
+            ngroupd = [order.index(val) for val in p]
+            cumcounted = [p[:i].count(val) for i, val in enumerate(p)]
+
+            tm.assert_series_equal(g.ngroup(), Series(ngroupd))
+            tm.assert_series_equal(g.cumcount(), Series(cumcounted))
+
+    def test_ngroup_respects_groupby_order(self, sort):
+        np.random.seed(0)
+        df = DataFrame({"a": np.random.choice(list("abcdef"), 100)})
+        g = df.groupby("a", sort=sort)
+        df["group_id"] = -1
+        df["group_index"] = -1
+
+        for i, (_, group) in enumerate(g):
+            df.loc[group.index, "group_id"] = i
+            for j, ind in enumerate(group.index):
+                df.loc[ind, "group_index"] = j
+
+        tm.assert_series_equal(Series(df["group_id"].values), g.ngroup())
+        tm.assert_series_equal(Series(df["group_index"].values), g.cumcount())
+
+    @pytest.mark.parametrize(
+        "datetimelike",
+        [
+            [Timestamp(f"2016-05-{i:02d} 20:09:25+00:00") for i in range(1, 4)],
+            [Timestamp(f"2016-05-{i:02d} 20:09:25") for i in range(1, 4)],
+            [Timestamp(f"2016-05-{i:02d} 20:09:25", tz="UTC") for i in range(1, 4)],
+            [Timedelta(x, unit="h") for x in range(1, 4)],
+            [Period(freq="2W", year=2017, month=x) for x in range(1, 4)],
+        ],
+    )
+    def test_count_with_datetimelike(self, datetimelike):
+        # test for #13393, where DataframeGroupBy.count() fails
+        # when counting a datetimelike column.
+
+        df = DataFrame({"x": ["a", "a", "b"], "y": datetimelike})
+        res = df.groupby("x").count()
+        expected = DataFrame({"y": [2, 1]}, index=["a", "b"])
+        expected.index.name = "x"
+        tm.assert_frame_equal(expected, res)
+
+    def test_count_with_only_nans_in_first_group(self):
+        # GH21956
+        df = DataFrame({"A": [np.nan, np.nan], "B": ["a", "b"], "C": [1, 2]})
+        result = df.groupby(["A", "B"]).C.count()
+        mi = MultiIndex(levels=[[], ["a", "b"]], codes=[[], []], names=["A", "B"])
+        expected = Series([], index=mi, dtype=np.int64, name="C")
+        tm.assert_series_equal(result, expected, check_index_type=False)
+
+    def test_count_groupby_column_with_nan_in_groupby_column(self):
+        # https://github.com/pandas-dev/pandas/issues/32841
+        df = DataFrame({"A": [1, 1, 1, 1, 1], "B": [5, 4, np.NaN, 3, 0]})
+        res = df.groupby(["B"]).count()
+        expected = DataFrame(
+            index=Index([0.0, 3.0, 4.0, 5.0], name="B"), data={"A": [1, 1, 1, 1]}
+        )
+        tm.assert_frame_equal(expected, res)
+
+    def test_groupby_count_dateparseerror(self):
+        dr = date_range(start="1/1/2012", freq="5min", periods=10)
+
+        # BAD Example, datetimes first
+        ser = Series(np.arange(10), index=[dr, np.arange(10)])
+        grouped = ser.groupby(lambda x: x[1] % 2 == 0)
+        result = grouped.count()
+
+        ser = Series(np.arange(10), index=[np.arange(10), dr])
+        grouped = ser.groupby(lambda x: x[0] % 2 == 0)
+        expected = grouped.count()
+
+        tm.assert_series_equal(result, expected)
+
+
+def test_groupby_timedelta_cython_count():
+    df = DataFrame(
+        {"g": list("ab" * 2), "delt": np.arange(4).astype("timedelta64[ns]")}
+    )
+    expected = Series([2, 2], index=Index(["a", "b"], name="g"), name="delt")
+    result = df.groupby("g").delt.count()
+    tm.assert_series_equal(expected, result)
+
+
+def test_count():
+    n = 1 << 15
+    dr = date_range("2015-08-30", periods=n // 10, freq="T")
+
+    df = DataFrame(
+        {
+            "1st": np.random.choice(list(ascii_lowercase), n),
+            "2nd": np.random.randint(0, 5, n),
+            "3rd": np.random.randn(n).round(3),
+            "4th": np.random.randint(-10, 10, n),
+            "5th": np.random.choice(dr, n),
+            "6th": np.random.randn(n).round(3),
+            "7th": np.random.randn(n).round(3),
+            "8th": np.random.choice(dr, n) - np.random.choice(dr, 1),
+            "9th": np.random.choice(list(ascii_lowercase), n),
+        }
+    )
+
+    for col in df.columns.drop(["1st", "2nd", "4th"]):
+        df.loc[np.random.choice(n, n // 10), col] = np.nan
+
+    df["9th"] = df["9th"].astype("category")
+
+    for key in ["1st", "2nd", ["1st", "2nd"]]:
+        left = df.groupby(key).count()
+        right = df.groupby(key).apply(DataFrame.count).drop(key, axis=1)
+        tm.assert_frame_equal(left, right)
+
+
+def test_count_non_nulls():
+    # GH#5610
+    # count counts non-nulls
+    df = DataFrame(
+        [[1, 2, "foo"], [1, np.nan, "bar"], [3, np.nan, np.nan]],
+        columns=["A", "B", "C"],
+    )
+
+    count_as = df.groupby("A").count()
+    count_not_as = df.groupby("A", as_index=False).count()
+
+    expected = DataFrame([[1, 2], [0, 0]], columns=["B", "C"], index=[1, 3])
+    expected.index.name = "A"
+    tm.assert_frame_equal(count_not_as, expected.reset_index())
+    tm.assert_frame_equal(count_as, expected)
+
+    count_B = df.groupby("A")["B"].count()
+    tm.assert_series_equal(count_B, expected["B"])
+
+
+def test_count_object():
+    df = DataFrame({"a": ["a"] * 3 + ["b"] * 3, "c": [2] * 3 + [3] * 3})
+    result = df.groupby("c").a.count()
+    expected = Series([3, 3], index=Index([2, 3], name="c"), name="a")
+    tm.assert_series_equal(result, expected)
+
+    df = DataFrame({"a": ["a", np.nan, np.nan] + ["b"] * 3, "c": [2] * 3 + [3] * 3})
+    result = df.groupby("c").a.count()
+    expected = Series([1, 3], index=Index([2, 3], name="c"), name="a")
+    tm.assert_series_equal(result, expected)
+
+
+def test_count_cross_type():
+    # GH8169
+    vals = np.hstack(
+        (np.random.randint(0, 5, (100, 2)), np.random.randint(0, 2, (100, 2)))
+    )
+
+    df = DataFrame(vals, columns=["a", "b", "c", "d"])
+    df[df == 2] = np.nan
+    expected = df.groupby(["c", "d"]).count()
+
+    for t in ["float32", "object"]:
+        df["a"] = df["a"].astype(t)
+        df["b"] = df["b"].astype(t)
+        result = df.groupby(["c", "d"]).count()
+        tm.assert_frame_equal(result, expected)
+
+
+def test_lower_int_prec_count():
+    df = DataFrame(
+        {
+            "a": np.array([0, 1, 2, 100], np.int8),
+            "b": np.array([1, 2, 3, 6], np.uint32),
+            "c": np.array([4, 5, 6, 8], np.int16),
+            "grp": list("ab" * 2),
+        }
+    )
+    result = df.groupby("grp").count()
+    expected = DataFrame(
+        {"a": [2, 2], "b": [2, 2], "c": [2, 2]}, index=Index(list("ab"), name="grp")
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_count_uses_size_on_exception():
+    class RaisingObjectException(Exception):
+        pass
+
+    class RaisingObject:
+        def __init__(self, msg="I will raise inside Cython") -> None:
+            super().__init__()
+            self.msg = msg
+
+        def __eq__(self, other):
+            # gets called in Cython to check that raising calls the method
+            raise RaisingObjectException(self.msg)
+
+    df = DataFrame({"a": [RaisingObject() for _ in range(4)], "grp": list("ab" * 2)})
+    result = df.groupby("grp").count()
+    expected = DataFrame({"a": [2, 2]}, index=Index(list("ab"), name="grp"))
+    tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_filters.py

@@ -0,0 +1,622 @@
+from string import ascii_lowercase
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Series,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+def test_filter_series():
+    s = Series([1, 3, 20, 5, 22, 24, 7])
+    expected_odd = Series([1, 3, 5, 7], index=[0, 1, 3, 6])
+    expected_even = Series([20, 22, 24], index=[2, 4, 5])
+    grouper = s.apply(lambda x: x % 2)
+    grouped = s.groupby(grouper)
+    tm.assert_series_equal(grouped.filter(lambda x: x.mean() < 10), expected_odd)
+    tm.assert_series_equal(grouped.filter(lambda x: x.mean() > 10), expected_even)
+    # Test dropna=False.
+    tm.assert_series_equal(
+        grouped.filter(lambda x: x.mean() < 10, dropna=False),
+        expected_odd.reindex(s.index),
+    )
+    tm.assert_series_equal(
+        grouped.filter(lambda x: x.mean() > 10, dropna=False),
+        expected_even.reindex(s.index),
+    )
+
+
+def test_filter_single_column_df():
+    df = DataFrame([1, 3, 20, 5, 22, 24, 7])
+    expected_odd = DataFrame([1, 3, 5, 7], index=[0, 1, 3, 6])
+    expected_even = DataFrame([20, 22, 24], index=[2, 4, 5])
+    grouper = df[0].apply(lambda x: x % 2)
+    grouped = df.groupby(grouper)
+    tm.assert_frame_equal(grouped.filter(lambda x: x.mean() < 10), expected_odd)
+    tm.assert_frame_equal(grouped.filter(lambda x: x.mean() > 10), expected_even)
+    # Test dropna=False.
+    tm.assert_frame_equal(
+        grouped.filter(lambda x: x.mean() < 10, dropna=False),
+        expected_odd.reindex(df.index),
+    )
+    tm.assert_frame_equal(
+        grouped.filter(lambda x: x.mean() > 10, dropna=False),
+        expected_even.reindex(df.index),
+    )
+
+
+def test_filter_multi_column_df():
+    df = DataFrame({"A": [1, 12, 12, 1], "B": [1, 1, 1, 1]})
+    grouper = df["A"].apply(lambda x: x % 2)
+    grouped = df.groupby(grouper)
+    expected = DataFrame({"A": [12, 12], "B": [1, 1]}, index=[1, 2])
+    tm.assert_frame_equal(
+        grouped.filter(lambda x: x["A"].sum() - x["B"].sum() > 10), expected
+    )
+
+
+def test_filter_mixed_df():
+    df = DataFrame({"A": [1, 12, 12, 1], "B": "a b c d".split()})
+    grouper = df["A"].apply(lambda x: x % 2)
+    grouped = df.groupby(grouper)
+    expected = DataFrame({"A": [12, 12], "B": ["b", "c"]}, index=[1, 2])
+    tm.assert_frame_equal(grouped.filter(lambda x: x["A"].sum() > 10), expected)
+
+
+def test_filter_out_all_groups():
+    s = Series([1, 3, 20, 5, 22, 24, 7])
+    grouper = s.apply(lambda x: x % 2)
+    grouped = s.groupby(grouper)
+    tm.assert_series_equal(grouped.filter(lambda x: x.mean() > 1000), s[[]])
+    df = DataFrame({"A": [1, 12, 12, 1], "B": "a b c d".split()})
+    grouper = df["A"].apply(lambda x: x % 2)
+    grouped = df.groupby(grouper)
+    tm.assert_frame_equal(grouped.filter(lambda x: x["A"].sum() > 1000), df.loc[[]])
+
+
+def test_filter_out_no_groups():
+    s = Series([1, 3, 20, 5, 22, 24, 7])
+    grouper = s.apply(lambda x: x % 2)
+    grouped = s.groupby(grouper)
+    filtered = grouped.filter(lambda x: x.mean() > 0)
+    tm.assert_series_equal(filtered, s)
+    df = DataFrame({"A": [1, 12, 12, 1], "B": "a b c d".split()})
+    grouper = df["A"].apply(lambda x: x % 2)
+    grouped = df.groupby(grouper)
+    filtered = grouped.filter(lambda x: x["A"].mean() > 0)
+    tm.assert_frame_equal(filtered, df)
+
+
+def test_filter_out_all_groups_in_df():
+    # GH12768
+    df = DataFrame({"a": [1, 1, 2], "b": [1, 2, 0]})
+    res = df.groupby("a")
+    res = res.filter(lambda x: x["b"].sum() > 5, dropna=False)
+    expected = DataFrame({"a": [np.nan] * 3, "b": [np.nan] * 3})
+    tm.assert_frame_equal(expected, res)
+
+    df = DataFrame({"a": [1, 1, 2], "b": [1, 2, 0]})
+    res = df.groupby("a")
+    res = res.filter(lambda x: x["b"].sum() > 5, dropna=True)
+    expected = DataFrame({"a": [], "b": []}, dtype="int64")
+    tm.assert_frame_equal(expected, res)
+
+
+def test_filter_condition_raises():
+    def raise_if_sum_is_zero(x):
+        if x.sum() == 0:
+            raise ValueError
+        return x.sum() > 0
+
+    s = Series([-1, 0, 1, 2])
+    grouper = s.apply(lambda x: x % 2)
+    grouped = s.groupby(grouper)
+    msg = "the filter must return a boolean result"
+    with pytest.raises(TypeError, match=msg):
+        grouped.filter(raise_if_sum_is_zero)
+
+
+def test_filter_with_axis_in_groupby():
+    # issue 11041
+    index = pd.MultiIndex.from_product([range(10), [0, 1]])
+    data = DataFrame(np.arange(100).reshape(-1, 20), columns=index, dtype="int64")
+    result = data.groupby(level=0, axis=1).filter(lambda x: x.iloc[0, 0] > 10)
+    expected = data.iloc[:, 12:20]
+    tm.assert_frame_equal(result, expected)
+
+
+def test_filter_bad_shapes():
+    df = DataFrame({"A": np.arange(8), "B": list("aabbbbcc"), "C": np.arange(8)})
+    s = df["B"]
+    g_df = df.groupby("B")
+    g_s = s.groupby(s)
+
+    f = lambda x: x
+    msg = "filter function returned a DataFrame, but expected a scalar bool"
+    with pytest.raises(TypeError, match=msg):
+        g_df.filter(f)
+    msg = "the filter must return a boolean result"
+    with pytest.raises(TypeError, match=msg):
+        g_s.filter(f)
+
+    f = lambda x: x == 1
+    msg = "filter function returned a DataFrame, but expected a scalar bool"
+    with pytest.raises(TypeError, match=msg):
+        g_df.filter(f)
+    msg = "the filter must return a boolean result"
+    with pytest.raises(TypeError, match=msg):
+        g_s.filter(f)
+
+    f = lambda x: np.outer(x, x)
+    msg = "can't multiply sequence by non-int of type 'str'"
+    with pytest.raises(TypeError, match=msg):
+        g_df.filter(f)
+    msg = "the filter must return a boolean result"
+    with pytest.raises(TypeError, match=msg):
+        g_s.filter(f)
+
+
+def test_filter_nan_is_false():
+    df = DataFrame({"A": np.arange(8), "B": list("aabbbbcc"), "C": np.arange(8)})
+    s = df["B"]
+    g_df = df.groupby(df["B"])
+    g_s = s.groupby(s)
+
+    f = lambda x: np.nan
+    tm.assert_frame_equal(g_df.filter(f), df.loc[[]])
+    tm.assert_series_equal(g_s.filter(f), s[[]])
+
+
+def test_filter_pdna_is_false():
+    # in particular, dont raise in filter trying to call bool(pd.NA)
+    df = DataFrame({"A": np.arange(8), "B": list("aabbbbcc"), "C": np.arange(8)})
+    ser = df["B"]
+    g_df = df.groupby(df["B"])
+    g_s = ser.groupby(ser)
+
+    func = lambda x: pd.NA
+    res = g_df.filter(func)
+    tm.assert_frame_equal(res, df.loc[[]])
+    res = g_s.filter(func)
+    tm.assert_series_equal(res, ser[[]])
+
+
+def test_filter_against_workaround():
+    np.random.seed(0)
+    # Series of ints
+    s = Series(np.random.randint(0, 100, 1000))
+    grouper = s.apply(lambda x: np.round(x, -1))
+    grouped = s.groupby(grouper)
+    f = lambda x: x.mean() > 10
+
+    old_way = s[grouped.transform(f).astype("bool")]
+    new_way = grouped.filter(f)
+    tm.assert_series_equal(new_way.sort_values(), old_way.sort_values())
+
+    # Series of floats
+    s = 100 * Series(np.random.random(1000))
+    grouper = s.apply(lambda x: np.round(x, -1))
+    grouped = s.groupby(grouper)
+    f = lambda x: x.mean() > 10
+    old_way = s[grouped.transform(f).astype("bool")]
+    new_way = grouped.filter(f)
+    tm.assert_series_equal(new_way.sort_values(), old_way.sort_values())
+
+    # Set up DataFrame of ints, floats, strings.
+    letters = np.array(list(ascii_lowercase))
+    N = 1000
+    random_letters = letters.take(np.random.randint(0, 26, N))
+    df = DataFrame(
+        {
+            "ints": Series(np.random.randint(0, 100, N)),
+            "floats": N / 10 * Series(np.random.random(N)),
+            "letters": Series(random_letters),
+        }
+    )
+
+    # Group by ints; filter on floats.
+    grouped = df.groupby("ints")
+    old_way = df[grouped.floats.transform(lambda x: x.mean() > N / 20).astype("bool")]
+    new_way = grouped.filter(lambda x: x["floats"].mean() > N / 20)
+    tm.assert_frame_equal(new_way, old_way)
+
+    # Group by floats (rounded); filter on strings.
+    grouper = df.floats.apply(lambda x: np.round(x, -1))
+    grouped = df.groupby(grouper)
+    old_way = df[grouped.letters.transform(lambda x: len(x) < N / 10).astype("bool")]
+    new_way = grouped.filter(lambda x: len(x.letters) < N / 10)
+    tm.assert_frame_equal(new_way, old_way)
+
+    # Group by strings; filter on ints.
+    grouped = df.groupby("letters")
+    old_way = df[grouped.ints.transform(lambda x: x.mean() > N / 20).astype("bool")]
+    new_way = grouped.filter(lambda x: x["ints"].mean() > N / 20)
+    tm.assert_frame_equal(new_way, old_way)
+
+
+def test_filter_using_len():
+    # BUG GH4447
+    df = DataFrame({"A": np.arange(8), "B": list("aabbbbcc"), "C": np.arange(8)})
+    grouped = df.groupby("B")
+    actual = grouped.filter(lambda x: len(x) > 2)
+    expected = DataFrame(
+        {"A": np.arange(2, 6), "B": list("bbbb"), "C": np.arange(2, 6)},
+        index=np.arange(2, 6, dtype=np.int64),
+    )
+    tm.assert_frame_equal(actual, expected)
+
+    actual = grouped.filter(lambda x: len(x) > 4)
+    expected = df.loc[[]]
+    tm.assert_frame_equal(actual, expected)
+
+    # Series have always worked properly, but we'll test anyway.
+    s = df["B"]
+    grouped = s.groupby(s)
+    actual = grouped.filter(lambda x: len(x) > 2)
+    expected = Series(4 * ["b"], index=np.arange(2, 6, dtype=np.int64), name="B")
+    tm.assert_series_equal(actual, expected)
+
+    actual = grouped.filter(lambda x: len(x) > 4)
+    expected = s[[]]
+    tm.assert_series_equal(actual, expected)
+
+
+def test_filter_maintains_ordering():
+    # Simple case: index is sequential. #4621
+    df = DataFrame(
+        {"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]}
+    )
+    s = df["pid"]
+    grouped = df.groupby("tag")
+    actual = grouped.filter(lambda x: len(x) > 1)
+    expected = df.iloc[[1, 2, 4, 7]]
+    tm.assert_frame_equal(actual, expected)
+
+    grouped = s.groupby(df["tag"])
+    actual = grouped.filter(lambda x: len(x) > 1)
+    expected = s.iloc[[1, 2, 4, 7]]
+    tm.assert_series_equal(actual, expected)
+
+    # Now index is sequentially decreasing.
+    df.index = np.arange(len(df) - 1, -1, -1)
+    s = df["pid"]
+    grouped = df.groupby("tag")
+    actual = grouped.filter(lambda x: len(x) > 1)
+    expected = df.iloc[[1, 2, 4, 7]]
+    tm.assert_frame_equal(actual, expected)
+
+    grouped = s.groupby(df["tag"])
+    actual = grouped.filter(lambda x: len(x) > 1)
+    expected = s.iloc[[1, 2, 4, 7]]
+    tm.assert_series_equal(actual, expected)
+
+    # Index is shuffled.
+    SHUFFLED = [4, 6, 7, 2, 1, 0, 5, 3]
+    df.index = df.index[SHUFFLED]
+    s = df["pid"]
+    grouped = df.groupby("tag")
+    actual = grouped.filter(lambda x: len(x) > 1)
+    expected = df.iloc[[1, 2, 4, 7]]
+    tm.assert_frame_equal(actual, expected)
+
+    grouped = s.groupby(df["tag"])
+    actual = grouped.filter(lambda x: len(x) > 1)
+    expected = s.iloc[[1, 2, 4, 7]]
+    tm.assert_series_equal(actual, expected)
+
+
+def test_filter_multiple_timestamp():
+    # GH 10114
+    df = DataFrame(
+        {
+            "A": np.arange(5, dtype="int64"),
+            "B": ["foo", "bar", "foo", "bar", "bar"],
+            "C": Timestamp("20130101"),
+        }
+    )
+
+    grouped = df.groupby(["B", "C"])
+
+    result = grouped["A"].filter(lambda x: True)
+    tm.assert_series_equal(df["A"], result)
+
+    result = grouped["A"].transform(len)
+    expected = Series([2, 3, 2, 3, 3], name="A")
+    tm.assert_series_equal(result, expected)
+
+    result = grouped.filter(lambda x: True)
+    tm.assert_frame_equal(df, result)
+
+    result = grouped.transform("sum")
+    expected = DataFrame({"A": [2, 8, 2, 8, 8]})
+    tm.assert_frame_equal(result, expected)
+
+    result = grouped.transform(len)
+    expected = DataFrame({"A": [2, 3, 2, 3, 3]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_filter_and_transform_with_non_unique_int_index():
+    # GH4620
+    index = [1, 1, 1, 2, 1, 1, 0, 1]
+    df = DataFrame(
+        {"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
+        index=index,
+    )
+    grouped_df = df.groupby("tag")
+    ser = df["pid"]
+    grouped_ser = ser.groupby(df["tag"])
+    expected_indexes = [1, 2, 4, 7]
+
+    # Filter DataFrame
+    actual = grouped_df.filter(lambda x: len(x) > 1)
+    expected = df.iloc[expected_indexes]
+    tm.assert_frame_equal(actual, expected)
+
+    actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
+    expected = df.copy()
+    expected.iloc[[0, 3, 5, 6]] = np.nan
+    tm.assert_frame_equal(actual, expected)
+
+    # Filter Series
+    actual = grouped_ser.filter(lambda x: len(x) > 1)
+    expected = ser.take(expected_indexes)
+    tm.assert_series_equal(actual, expected)
+
+    actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
+    expected = Series([np.nan, 1, 1, np.nan, 2, np.nan, np.nan, 3], index, name="pid")
+    # ^ made manually because this can get confusing!
+    tm.assert_series_equal(actual, expected)
+
+    # Transform Series
+    actual = grouped_ser.transform(len)
+    expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
+    tm.assert_series_equal(actual, expected)
+
+    # Transform (a column from) DataFrameGroupBy
+    actual = grouped_df.pid.transform(len)
+    tm.assert_series_equal(actual, expected)
+
+
+def test_filter_and_transform_with_multiple_non_unique_int_index():
+    # GH4620
+    index = [1, 1, 1, 2, 0, 0, 0, 1]
+    df = DataFrame(
+        {"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
+        index=index,
+    )
+    grouped_df = df.groupby("tag")
+    ser = df["pid"]
+    grouped_ser = ser.groupby(df["tag"])
+    expected_indexes = [1, 2, 4, 7]
+
+    # Filter DataFrame
+    actual = grouped_df.filter(lambda x: len(x) > 1)
+    expected = df.iloc[expected_indexes]
+    tm.assert_frame_equal(actual, expected)
+
+    actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
+    expected = df.copy()
+    expected.iloc[[0, 3, 5, 6]] = np.nan
+    tm.assert_frame_equal(actual, expected)
+
+    # Filter Series
+    actual = grouped_ser.filter(lambda x: len(x) > 1)
+    expected = ser.take(expected_indexes)
+    tm.assert_series_equal(actual, expected)
+
+    actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
+    expected = Series([np.nan, 1, 1, np.nan, 2, np.nan, np.nan, 3], index, name="pid")
+    # ^ made manually because this can get confusing!
+    tm.assert_series_equal(actual, expected)
+
+    # Transform Series
+    actual = grouped_ser.transform(len)
+    expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
+    tm.assert_series_equal(actual, expected)
+
+    # Transform (a column from) DataFrameGroupBy
+    actual = grouped_df.pid.transform(len)
+    tm.assert_series_equal(actual, expected)
+
+
+def test_filter_and_transform_with_non_unique_float_index():
+    # GH4620
+    index = np.array([1, 1, 1, 2, 1, 1, 0, 1], dtype=float)
+    df = DataFrame(
+        {"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
+        index=index,
+    )
+    grouped_df = df.groupby("tag")
+    ser = df["pid"]
+    grouped_ser = ser.groupby(df["tag"])
+    expected_indexes = [1, 2, 4, 7]
+
+    # Filter DataFrame
+    actual = grouped_df.filter(lambda x: len(x) > 1)
+    expected = df.iloc[expected_indexes]
+    tm.assert_frame_equal(actual, expected)
+
+    actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
+    expected = df.copy()
+    expected.iloc[[0, 3, 5, 6]] = np.nan
+    tm.assert_frame_equal(actual, expected)
+
+    # Filter Series
+    actual = grouped_ser.filter(lambda x: len(x) > 1)
+    expected = ser.take(expected_indexes)
+    tm.assert_series_equal(actual, expected)
+
+    actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
+    expected = Series([np.nan, 1, 1, np.nan, 2, np.nan, np.nan, 3], index, name="pid")
+    # ^ made manually because this can get confusing!
+    tm.assert_series_equal(actual, expected)
+
+    # Transform Series
+    actual = grouped_ser.transform(len)
+    expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
+    tm.assert_series_equal(actual, expected)
+
+    # Transform (a column from) DataFrameGroupBy
+    actual = grouped_df.pid.transform(len)
+    tm.assert_series_equal(actual, expected)
+
+
+def test_filter_and_transform_with_non_unique_timestamp_index():
+    # GH4620
+    t0 = Timestamp("2013-09-30 00:05:00")
+    t1 = Timestamp("2013-10-30 00:05:00")
+    t2 = Timestamp("2013-11-30 00:05:00")
+    index = [t1, t1, t1, t2, t1, t1, t0, t1]
+    df = DataFrame(
+        {"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
+        index=index,
+    )
+    grouped_df = df.groupby("tag")
+    ser = df["pid"]
+    grouped_ser = ser.groupby(df["tag"])
+    expected_indexes = [1, 2, 4, 7]
+
+    # Filter DataFrame
+    actual = grouped_df.filter(lambda x: len(x) > 1)
+    expected = df.iloc[expected_indexes]
+    tm.assert_frame_equal(actual, expected)
+
+    actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
+    expected = df.copy()
+    expected.iloc[[0, 3, 5, 6]] = np.nan
+    tm.assert_frame_equal(actual, expected)
+
+    # Filter Series
+    actual = grouped_ser.filter(lambda x: len(x) > 1)
+    expected = ser.take(expected_indexes)
+    tm.assert_series_equal(actual, expected)
+
+    actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
+    expected = Series([np.nan, 1, 1, np.nan, 2, np.nan, np.nan, 3], index, name="pid")
+    # ^ made manually because this can get confusing!
+    tm.assert_series_equal(actual, expected)
+
+    # Transform Series
+    actual = grouped_ser.transform(len)
+    expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
+    tm.assert_series_equal(actual, expected)
+
+    # Transform (a column from) DataFrameGroupBy
+    actual = grouped_df.pid.transform(len)
+    tm.assert_series_equal(actual, expected)
+
+
+def test_filter_and_transform_with_non_unique_string_index():
+    # GH4620
+    index = list("bbbcbbab")
+    df = DataFrame(
+        {"pid": [1, 1, 1, 2, 2, 3, 3, 3], "tag": [23, 45, 62, 24, 45, 34, 25, 62]},
+        index=index,
+    )
+    grouped_df = df.groupby("tag")
+    ser = df["pid"]
+    grouped_ser = ser.groupby(df["tag"])
+    expected_indexes = [1, 2, 4, 7]
+
+    # Filter DataFrame
+    actual = grouped_df.filter(lambda x: len(x) > 1)
+    expected = df.iloc[expected_indexes]
+    tm.assert_frame_equal(actual, expected)
+
+    actual = grouped_df.filter(lambda x: len(x) > 1, dropna=False)
+    expected = df.copy()
+    expected.iloc[[0, 3, 5, 6]] = np.nan
+    tm.assert_frame_equal(actual, expected)
+
+    # Filter Series
+    actual = grouped_ser.filter(lambda x: len(x) > 1)
+    expected = ser.take(expected_indexes)
+    tm.assert_series_equal(actual, expected)
+
+    actual = grouped_ser.filter(lambda x: len(x) > 1, dropna=False)
+    expected = Series([np.nan, 1, 1, np.nan, 2, np.nan, np.nan, 3], index, name="pid")
+    # ^ made manually because this can get confusing!
+    tm.assert_series_equal(actual, expected)
+
+    # Transform Series
+    actual = grouped_ser.transform(len)
+    expected = Series([1, 2, 2, 1, 2, 1, 1, 2], index, name="pid")
+    tm.assert_series_equal(actual, expected)
+
+    # Transform (a column from) DataFrameGroupBy
+    actual = grouped_df.pid.transform(len)
+    tm.assert_series_equal(actual, expected)
+
+
+def test_filter_has_access_to_grouped_cols():
+    df = DataFrame([[1, 2], [1, 3], [5, 6]], columns=["A", "B"])
+    g = df.groupby("A")
+    # previously didn't have access to col A #????
+    filt = g.filter(lambda x: x["A"].sum() == 2)
+    tm.assert_frame_equal(filt, df.iloc[[0, 1]])
+
+
+def test_filter_enforces_scalarness():
+    df = DataFrame(
+        [
+            ["best", "a", "x"],
+            ["worst", "b", "y"],
+            ["best", "c", "x"],
+            ["best", "d", "y"],
+            ["worst", "d", "y"],
+            ["worst", "d", "y"],
+            ["best", "d", "z"],
+        ],
+        columns=["a", "b", "c"],
+    )
+    with pytest.raises(TypeError, match="filter function returned a.*"):
+        df.groupby("c").filter(lambda g: g["a"] == "best")
+
+
+def test_filter_non_bool_raises():
+    df = DataFrame(
+        [
+            ["best", "a", 1],
+            ["worst", "b", 1],
+            ["best", "c", 1],
+            ["best", "d", 1],
+            ["worst", "d", 1],
+            ["worst", "d", 1],
+            ["best", "d", 1],
+        ],
+        columns=["a", "b", "c"],
+    )
+    with pytest.raises(TypeError, match="filter function returned a.*"):
+        df.groupby("a").filter(lambda g: g.c.mean())
+
+
+def test_filter_dropna_with_empty_groups():
+    # GH 10780
+    data = Series(np.random.rand(9), index=np.repeat([1, 2, 3], 3))
+    groupped = data.groupby(level=0)
+    result_false = groupped.filter(lambda x: x.mean() > 1, dropna=False)
+    expected_false = Series([np.nan] * 9, index=np.repeat([1, 2, 3], 3))
+    tm.assert_series_equal(result_false, expected_false)
+
+    result_true = groupped.filter(lambda x: x.mean() > 1, dropna=True)
+    expected_true = Series(index=pd.Index([], dtype=int), dtype=np.float64)
+    tm.assert_series_equal(result_true, expected_true)
+
+
+def test_filter_consistent_result_before_after_agg_func():
+    # GH 17091
+    df = DataFrame({"data": range(6), "key": list("ABCABC")})
+    grouper = df.groupby("key")
+    result = grouper.filter(lambda x: True)
+    expected = DataFrame({"data": range(6), "key": list("ABCABC")})
+    tm.assert_frame_equal(result, expected)
+
+    grouper.sum()
+    result = grouper.filter(lambda x: True)
+    tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_function.py

@@ -0,0 +1,1637 @@
+import builtins
+from io import StringIO
+
+import numpy as np
+import pytest
+
+from pandas._libs import lib
+from pandas.errors import UnsupportedFunctionCall
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core import nanops
+from pandas.tests.groupby import get_groupby_method_args
+from pandas.util import _test_decorators as td
+
+
+@pytest.fixture(
+    params=[np.int32, np.int64, np.float32, np.float64, "Int64", "Float64"],
+    ids=["np.int32", "np.int64", "np.float32", "np.float64", "Int64", "Float64"],
+)
+def dtypes_for_minmax(request):
+    """
+    Fixture of dtypes with min and max values used for testing
+    cummin and cummax
+    """
+    dtype = request.param
+
+    np_type = dtype
+    if dtype == "Int64":
+        np_type = np.int64
+    elif dtype == "Float64":
+        np_type = np.float64
+
+    min_val = (
+        np.iinfo(np_type).min
+        if np.dtype(np_type).kind == "i"
+        else np.finfo(np_type).min
+    )
+    max_val = (
+        np.iinfo(np_type).max
+        if np.dtype(np_type).kind == "i"
+        else np.finfo(np_type).max
+    )
+
+    return (dtype, min_val, max_val)
+
+
+def test_intercept_builtin_sum():
+    s = Series([1.0, 2.0, np.nan, 3.0])
+    grouped = s.groupby([0, 1, 2, 2])
+
+    result = grouped.agg(builtins.sum)
+    result2 = grouped.apply(builtins.sum)
+    expected = grouped.sum()
+    tm.assert_series_equal(result, expected)
+    tm.assert_series_equal(result2, 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
+    df = DataFrame(np.random.randint(1, 50, (1000, 2)), columns=["jim", "joe"])
+    df["jolie"] = np.random.randn(1000)
+
+    gb = df.groupby(keys)
+
+    fname = f.__name__
+    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
+    expected = gb.apply(npfunc)
+    tm.assert_frame_equal(result, expected)
+
+    with tm.assert_produces_warning(None):
+        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))
+
+
+class TestNumericOnly:
+    # make sure that we are passing thru kwargs to our agg functions
+
+    @pytest.fixture
+    def df(self):
+        # GH3668
+        # GH5724
+        df = DataFrame(
+            {
+                "group": [1, 1, 2],
+                "int": [1, 2, 3],
+                "float": [4.0, 5.0, 6.0],
+                "string": list("abc"),
+                "category_string": Series(list("abc")).astype("category"),
+                "category_int": [7, 8, 9],
+                "datetime": date_range("20130101", periods=3),
+                "datetimetz": date_range("20130101", periods=3, tz="US/Eastern"),
+                "timedelta": pd.timedelta_range("1 s", periods=3, freq="s"),
+            },
+            columns=[
+                "group",
+                "int",
+                "float",
+                "string",
+                "category_string",
+                "category_int",
+                "datetime",
+                "datetimetz",
+                "timedelta",
+            ],
+        )
+        return df
+
+    @pytest.mark.parametrize("method", ["mean", "median"])
+    def test_averages(self, df, method):
+        # mean / median
+        expected_columns_numeric = Index(["int", "float", "category_int"])
+
+        gb = df.groupby("group")
+        expected = DataFrame(
+            {
+                "category_int": [7.5, 9],
+                "float": [4.5, 6.0],
+                "timedelta": [pd.Timedelta("1.5s"), pd.Timedelta("3s")],
+                "int": [1.5, 3],
+                "datetime": [
+                    Timestamp("2013-01-01 12:00:00"),
+                    Timestamp("2013-01-03 00:00:00"),
+                ],
+                "datetimetz": [
+                    Timestamp("2013-01-01 12:00:00", tz="US/Eastern"),
+                    Timestamp("2013-01-03 00:00:00", tz="US/Eastern"),
+                ],
+            },
+            index=Index([1, 2], name="group"),
+            columns=[
+                "int",
+                "float",
+                "category_int",
+            ],
+        )
+
+        result = getattr(gb, method)(numeric_only=True)
+        tm.assert_frame_equal(result.reindex_like(expected), expected)
+
+        expected_columns = expected.columns
+
+        self._check(df, method, expected_columns, expected_columns_numeric)
+
+    @pytest.mark.parametrize("method", ["min", "max"])
+    def test_extrema(self, df, method):
+        # TODO: min, max *should* handle
+        # categorical (ordered) dtype
+
+        expected_columns = Index(
+            [
+                "int",
+                "float",
+                "string",
+                "category_int",
+                "datetime",
+                "datetimetz",
+                "timedelta",
+            ]
+        )
+        expected_columns_numeric = expected_columns
+
+        self._check(df, method, expected_columns, expected_columns_numeric)
+
+    @pytest.mark.parametrize("method", ["first", "last"])
+    def test_first_last(self, df, method):
+        expected_columns = Index(
+            [
+                "int",
+                "float",
+                "string",
+                "category_string",
+                "category_int",
+                "datetime",
+                "datetimetz",
+                "timedelta",
+            ]
+        )
+        expected_columns_numeric = expected_columns
+
+        self._check(df, method, expected_columns, expected_columns_numeric)
+
+    @pytest.mark.parametrize("method", ["sum", "cumsum"])
+    def test_sum_cumsum(self, df, method):
+        expected_columns_numeric = Index(["int", "float", "category_int"])
+        expected_columns = Index(
+            ["int", "float", "string", "category_int", "timedelta"]
+        )
+        if method == "cumsum":
+            # cumsum loses string
+            expected_columns = Index(["int", "float", "category_int", "timedelta"])
+
+        self._check(df, method, expected_columns, expected_columns_numeric)
+
+    @pytest.mark.parametrize("method", ["prod", "cumprod"])
+    def test_prod_cumprod(self, df, method):
+        expected_columns = Index(["int", "float", "category_int"])
+        expected_columns_numeric = expected_columns
+
+        self._check(df, method, expected_columns, expected_columns_numeric)
+
+    @pytest.mark.parametrize("method", ["cummin", "cummax"])
+    def test_cummin_cummax(self, df, method):
+        # like min, max, but don't include strings
+        expected_columns = Index(
+            ["int", "float", "category_int", "datetime", "datetimetz", "timedelta"]
+        )
+
+        # GH#15561: numeric_only=False set by default like min/max
+        expected_columns_numeric = expected_columns
+
+        self._check(df, method, expected_columns, expected_columns_numeric)
+
+    def _check(self, df, method, expected_columns, expected_columns_numeric):
+        gb = df.groupby("group")
+
+        # object dtypes for transformations are not implemented in Cython and
+        # have no Python fallback
+        exception = NotImplementedError if method.startswith("cum") else TypeError
+
+        if method in ("min", "max", "cummin", "cummax", "cumsum", "cumprod"):
+            # The methods default to numeric_only=False and raise TypeError
+            msg = "|".join(
+                [
+                    "Categorical is not ordered",
+                    "function is not implemented for this dtype",
+                    f"Cannot perform {method} with non-ordered Categorical",
+                ]
+            )
+            with pytest.raises(exception, match=msg):
+                getattr(gb, method)()
+        elif method in ("sum", "mean", "median", "prod"):
+            msg = "|".join(
+                [
+                    "category type does not support sum operations",
+                    "[Cc]ould not convert",
+                    "can't multiply sequence by non-int of type 'str'",
+                ]
+            )
+            with pytest.raises(exception, match=msg):
+                getattr(gb, method)()
+        else:
+            result = getattr(gb, method)()
+            tm.assert_index_equal(result.columns, expected_columns_numeric)
+
+        if method not in ("first", "last"):
+            msg = "|".join(
+                [
+                    "[Cc]ould not convert",
+                    "Categorical is not ordered",
+                    "category type does not support",
+                    "can't multiply sequence",
+                    "function is not implemented for this dtype",
+                    f"Cannot perform {method} with non-ordered Categorical",
+                ]
+            )
+            with pytest.raises(exception, match=msg):
+                getattr(gb, method)(numeric_only=False)
+        else:
+            result = getattr(gb, method)(numeric_only=False)
+            tm.assert_index_equal(result.columns, expected_columns)
+
+
+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)
+
+
+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
+    result = df.groupby("A").cumsum(axis=1)
+    expected = df.cumsum(axis=1)
+    tm.assert_frame_equal(result, expected)
+    result = df.groupby("A").cumprod(axis=1)
+    expected = df.cumprod(axis=1)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_cython_median():
+    arr = np.random.randn(1000)
+    arr[::2] = np.nan
+    df = DataFrame(arr)
+
+    labels = np.random.randint(0, 50, size=1000).astype(float)
+    labels[::17] = np.nan
+
+    result = df.groupby(labels).median()
+    exp = df.groupby(labels).agg(nanops.nanmedian)
+    tm.assert_frame_equal(result, exp)
+
+    df = DataFrame(np.random.randn(1000, 5))
+    rs = df.groupby(labels).agg(np.median)
+    xp = df.groupby(labels).median()
+    tm.assert_frame_equal(rs, xp)
+
+
+def test_median_empty_bins(observed):
+    df = DataFrame(np.random.randint(0, 44, 500))
+
+    grps = range(0, 55, 5)
+    bins = pd.cut(df[0], grps)
+
+    result = df.groupby(bins, observed=observed).median()
+    expected = df.groupby(bins, observed=observed).agg(lambda x: x.median())
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "dtype", ["int8", "int16", "int32", "int64", "float32", "float64", "uint64"]
+)
+@pytest.mark.parametrize(
+    "method,data",
+    [
+        ("first", {"df": [{"a": 1, "b": 1}, {"a": 2, "b": 3}]}),
+        ("last", {"df": [{"a": 1, "b": 2}, {"a": 2, "b": 4}]}),
+        ("min", {"df": [{"a": 1, "b": 1}, {"a": 2, "b": 3}]}),
+        ("max", {"df": [{"a": 1, "b": 2}, {"a": 2, "b": 4}]}),
+        ("count", {"df": [{"a": 1, "b": 2}, {"a": 2, "b": 2}], "out_type": "int64"}),
+    ],
+)
+def test_groupby_non_arithmetic_agg_types(dtype, method, data):
+    # GH9311, GH6620
+    df = DataFrame(
+        [{"a": 1, "b": 1}, {"a": 1, "b": 2}, {"a": 2, "b": 3}, {"a": 2, "b": 4}]
+    )
+
+    df["b"] = df.b.astype(dtype)
+
+    if "args" not in data:
+        data["args"] = []
+
+    if "out_type" in data:
+        out_type = data["out_type"]
+    else:
+        out_type = dtype
+
+    exp = data["df"]
+    df_out = DataFrame(exp)
+
+    df_out["b"] = df_out.b.astype(out_type)
+    df_out.set_index("a", inplace=True)
+
+    grpd = df.groupby("a")
+    t = getattr(grpd, method)(*data["args"])
+    tm.assert_frame_equal(t, df_out)
+
+
+@pytest.mark.parametrize(
+    "i",
+    [
+        (
+            Timestamp("2011-01-15 12:50:28.502376"),
+            Timestamp("2011-01-20 12:50:28.593448"),
+        ),
+        (24650000000000001, 24650000000000002),
+    ],
+)
+def test_groupby_non_arithmetic_agg_int_like_precision(i):
+    # see gh-6620, gh-9311
+    df = DataFrame([{"a": 1, "b": i[0]}, {"a": 1, "b": i[1]}])
+
+    grp_exp = {
+        "first": {"expected": i[0]},
+        "last": {"expected": i[1]},
+        "min": {"expected": i[0]},
+        "max": {"expected": i[1]},
+        "nth": {"expected": i[1], "args": [1]},
+        "count": {"expected": 2},
+    }
+
+    for method, data in grp_exp.items():
+        if "args" not in data:
+            data["args"] = []
+
+        grouped = df.groupby("a")
+        res = getattr(grouped, method)(*data["args"])
+
+        assert res.iloc[0].b == data["expected"]
+
+
+@pytest.mark.parametrize(
+    "func, values",
+    [
+        ("idxmin", {"c_int": [0, 2], "c_float": [1, 3], "c_date": [1, 2]}),
+        ("idxmax", {"c_int": [1, 3], "c_float": [0, 2], "c_date": [0, 3]}),
+    ],
+)
+@pytest.mark.parametrize("numeric_only", [True, False])
+def test_idxmin_idxmax_returns_int_types(func, values, numeric_only):
+    # GH 25444
+    df = DataFrame(
+        {
+            "name": ["A", "A", "B", "B"],
+            "c_int": [1, 2, 3, 4],
+            "c_float": [4.02, 3.03, 2.04, 1.05],
+            "c_date": ["2019", "2018", "2016", "2017"],
+        }
+    )
+    df["c_date"] = pd.to_datetime(df["c_date"])
+    df["c_date_tz"] = df["c_date"].dt.tz_localize("US/Pacific")
+    df["c_timedelta"] = df["c_date"] - df["c_date"].iloc[0]
+    df["c_period"] = df["c_date"].dt.to_period("W")
+    df["c_Integer"] = df["c_int"].astype("Int64")
+    df["c_Floating"] = df["c_float"].astype("Float64")
+
+    result = getattr(df.groupby("name"), func)(numeric_only=numeric_only)
+
+    expected = DataFrame(values, index=Index(["A", "B"], name="name"))
+    if numeric_only:
+        expected = expected.drop(columns=["c_date"])
+    else:
+        expected["c_date_tz"] = expected["c_date"]
+        expected["c_timedelta"] = expected["c_date"]
+        expected["c_period"] = expected["c_date"]
+    expected["c_Integer"] = expected["c_int"]
+    expected["c_Floating"] = expected["c_float"]
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_idxmin_idxmax_axis1():
+    df = DataFrame(np.random.randn(10, 4), columns=["A", "B", "C", "D"])
+    df["A"] = [1, 2, 3, 1, 2, 3, 1, 2, 3, 4]
+
+    gb = df.groupby("A")
+
+    res = gb.idxmax(axis=1)
+
+    alt = df.iloc[:, 1:].idxmax(axis=1)
+    indexer = res.index.get_level_values(1)
+
+    tm.assert_series_equal(alt[indexer], res.droplevel("A"))
+
+    df["E"] = date_range("2016-01-01", periods=10)
+    gb2 = df.groupby("A")
+
+    msg = "reduction operation 'argmax' not allowed for this dtype"
+    with pytest.raises(TypeError, match=msg):
+        gb2.idxmax(axis=1)
+
+
+@pytest.mark.parametrize("numeric_only", [True, False, None])
+def test_axis1_numeric_only(request, groupby_func, numeric_only):
+    if groupby_func in ("idxmax", "idxmin"):
+        pytest.skip("idxmax and idx_min tested in test_idxmin_idxmax_axis1")
+    if groupby_func in ("corrwith", "skew"):
+        msg = "GH#47723 groupby.corrwith and skew do not correctly implement axis=1"
+        request.node.add_marker(pytest.mark.xfail(reason=msg))
+
+    df = DataFrame(np.random.randn(10, 4), columns=["A", "B", "C", "D"])
+    df["E"] = "x"
+    groups = [1, 2, 3, 1, 2, 3, 1, 2, 3, 4]
+    gb = df.groupby(groups)
+    method = getattr(gb, groupby_func)
+    args = get_groupby_method_args(groupby_func, df)
+    kwargs = {"axis": 1}
+    if numeric_only is not None:
+        # when numeric_only is None we don't pass any argument
+        kwargs["numeric_only"] = numeric_only
+
+    # Functions without numeric_only and axis args
+    no_args = ("cumprod", "cumsum", "diff", "fillna", "pct_change", "rank", "shift")
+    # Functions with axis args
+    has_axis = (
+        "cumprod",
+        "cumsum",
+        "diff",
+        "pct_change",
+        "rank",
+        "shift",
+        "cummax",
+        "cummin",
+        "idxmin",
+        "idxmax",
+        "fillna",
+    )
+    if numeric_only is not None and groupby_func in no_args:
+        msg = "got an unexpected keyword argument 'numeric_only'"
+        with pytest.raises(TypeError, match=msg):
+            method(*args, **kwargs)
+    elif groupby_func not in has_axis:
+        msg = "got an unexpected keyword argument 'axis'"
+        with pytest.raises(TypeError, match=msg):
+            method(*args, **kwargs)
+    # fillna and shift are successful even on object dtypes
+    elif (numeric_only is None or not numeric_only) and groupby_func not in (
+        "fillna",
+        "shift",
+    ):
+        msgs = (
+            # cummax, cummin, rank
+            "not supported between instances of",
+            # cumprod
+            "can't multiply sequence by non-int of type 'float'",
+            # cumsum, diff, pct_change
+            "unsupported operand type",
+        )
+        with pytest.raises(TypeError, match=f"({'|'.join(msgs)})"):
+            method(*args, **kwargs)
+    else:
+        result = method(*args, **kwargs)
+
+        df_expected = df.drop(columns="E").T if numeric_only else df.T
+        expected = getattr(df_expected, groupby_func)(*args).T
+        if groupby_func == "shift" and not numeric_only:
+            # shift with axis=1 leaves the leftmost column as numeric
+            # but transposing for expected gives us object dtype
+            expected = expected.astype(float)
+
+        tm.assert_equal(result, expected)
+
+
+def test_groupby_cumprod():
+    # GH 4095
+    df = DataFrame({"key": ["b"] * 10, "value": 2})
+
+    actual = df.groupby("key")["value"].cumprod()
+    expected = df.groupby("key", group_keys=False)["value"].apply(lambda x: x.cumprod())
+    expected.name = "value"
+    tm.assert_series_equal(actual, expected)
+
+    df = DataFrame({"key": ["b"] * 100, "value": 2})
+    df["value"] = df["value"].astype(float)
+    actual = df.groupby("key")["value"].cumprod()
+    expected = df.groupby("key", group_keys=False)["value"].apply(lambda x: x.cumprod())
+    expected.name = "value"
+    tm.assert_series_equal(actual, expected)
+
+
+def test_groupby_cumprod_overflow():
+    # GH#37493 if we overflow we return garbage consistent with numpy
+    df = DataFrame({"key": ["b"] * 4, "value": 100_000})
+    actual = df.groupby("key")["value"].cumprod()
+    expected = Series(
+        [100_000, 10_000_000_000, 1_000_000_000_000_000, 7766279631452241920],
+        name="value",
+    )
+    tm.assert_series_equal(actual, expected)
+
+    numpy_result = df.groupby("key", group_keys=False)["value"].apply(
+        lambda x: x.cumprod()
+    )
+    numpy_result.name = "value"
+    tm.assert_series_equal(actual, numpy_result)
+
+
+def test_groupby_cumprod_nan_influences_other_columns():
+    # GH#48064
+    df = DataFrame(
+        {
+            "a": 1,
+            "b": [1, np.nan, 2],
+            "c": [1, 2, 3.0],
+        }
+    )
+    result = df.groupby("a").cumprod(numeric_only=True, skipna=False)
+    expected = DataFrame({"b": [1, np.nan, np.nan], "c": [1, 2, 6.0]})
+    tm.assert_frame_equal(result, expected)
+
+
+def scipy_sem(*args, **kwargs):
+    from scipy.stats import sem
+
+    return sem(*args, ddof=1, **kwargs)
+
+
+@pytest.mark.parametrize(
+    "op,targop",
+    [
+        ("mean", np.mean),
+        ("median", np.median),
+        ("std", np.std),
+        ("var", np.var),
+        ("sum", np.sum),
+        ("prod", np.prod),
+        ("min", np.min),
+        ("max", np.max),
+        ("first", lambda x: x.iloc[0]),
+        ("last", lambda x: x.iloc[-1]),
+        ("count", np.size),
+        pytest.param("sem", scipy_sem, marks=td.skip_if_no_scipy),
+    ],
+)
+def test_ops_general(op, targop):
+    df = DataFrame(np.random.randn(1000))
+    labels = np.random.randint(0, 50, size=1000).astype(float)
+
+    result = getattr(df.groupby(labels), op)()
+    expected = df.groupby(labels).agg(targop)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_max_nan_bug():
+    raw = """,Date,app,File
+-04-23,2013-04-23 00:00:00,,log080001.log
+-05-06,2013-05-06 00:00:00,,log.log
+-05-07,2013-05-07 00:00:00,OE,xlsx"""
+
+    with tm.assert_produces_warning(UserWarning, match="Could not infer format"):
+        df = pd.read_csv(StringIO(raw), parse_dates=[0])
+    gb = df.groupby("Date")
+    r = gb[["File"]].max()
+    e = gb["File"].max().to_frame()
+    tm.assert_frame_equal(r, e)
+    assert not r["File"].isna().any()
+
+
+def test_nlargest():
+    a = Series([1, 3, 5, 7, 2, 9, 0, 4, 6, 10])
+    b = Series(list("a" * 5 + "b" * 5))
+    gb = a.groupby(b)
+    r = gb.nlargest(3)
+    e = Series(
+        [7, 5, 3, 10, 9, 6],
+        index=MultiIndex.from_arrays([list("aaabbb"), [3, 2, 1, 9, 5, 8]]),
+    )
+    tm.assert_series_equal(r, e)
+
+    a = Series([1, 1, 3, 2, 0, 3, 3, 2, 1, 0])
+    gb = a.groupby(b)
+    e = Series(
+        [3, 2, 1, 3, 3, 2],
+        index=MultiIndex.from_arrays([list("aaabbb"), [2, 3, 1, 6, 5, 7]]),
+    )
+    tm.assert_series_equal(gb.nlargest(3, keep="last"), e)
+
+
+def test_nlargest_mi_grouper():
+    # see gh-21411
+    npr = np.random.RandomState(123456789)
+
+    dts = date_range("20180101", periods=10)
+    iterables = [dts, ["one", "two"]]
+
+    idx = MultiIndex.from_product(iterables, names=["first", "second"])
+    s = Series(npr.randn(20), index=idx)
+
+    result = s.groupby("first").nlargest(1)
+
+    exp_idx = MultiIndex.from_tuples(
+        [
+            (dts[0], dts[0], "one"),
+            (dts[1], dts[1], "one"),
+            (dts[2], dts[2], "one"),
+            (dts[3], dts[3], "two"),
+            (dts[4], dts[4], "one"),
+            (dts[5], dts[5], "one"),
+            (dts[6], dts[6], "one"),
+            (dts[7], dts[7], "one"),
+            (dts[8], dts[8], "two"),
+            (dts[9], dts[9], "one"),
+        ],
+        names=["first", "first", "second"],
+    )
+
+    exp_values = [
+        2.2129019979039612,
+        1.8417114045748335,
+        0.858963679564603,
+        1.3759151378258088,
+        0.9430284594687134,
+        0.5296914208183142,
+        0.8318045593815487,
+        -0.8476703342910327,
+        0.3804446884133735,
+        -0.8028845810770998,
+    ]
+
+    expected = Series(exp_values, index=exp_idx)
+    tm.assert_series_equal(result, expected, check_exact=False, rtol=1e-3)
+
+
+def test_nsmallest():
+    a = Series([1, 3, 5, 7, 2, 9, 0, 4, 6, 10])
+    b = Series(list("a" * 5 + "b" * 5))
+    gb = a.groupby(b)
+    r = gb.nsmallest(3)
+    e = Series(
+        [1, 2, 3, 0, 4, 6],
+        index=MultiIndex.from_arrays([list("aaabbb"), [0, 4, 1, 6, 7, 8]]),
+    )
+    tm.assert_series_equal(r, e)
+
+    a = Series([1, 1, 3, 2, 0, 3, 3, 2, 1, 0])
+    gb = a.groupby(b)
+    e = Series(
+        [0, 1, 1, 0, 1, 2],
+        index=MultiIndex.from_arrays([list("aaabbb"), [4, 1, 0, 9, 8, 7]]),
+    )
+    tm.assert_series_equal(gb.nsmallest(3, keep="last"), e)
+
+
+@pytest.mark.parametrize(
+    "data, groups",
+    [([0, 1, 2, 3], [0, 0, 1, 1]), ([0], [0])],
+)
+@pytest.mark.parametrize("dtype", [None, *tm.ALL_INT_NUMPY_DTYPES])
+@pytest.mark.parametrize("method", ["nlargest", "nsmallest"])
+def test_nlargest_and_smallest_noop(data, groups, dtype, method):
+    # GH 15272, GH 16345, GH 29129
+    # Test nlargest/smallest when it results in a noop,
+    # i.e. input is sorted and group size <= n
+    if dtype is not None:
+        data = np.array(data, dtype=dtype)
+    if method == "nlargest":
+        data = list(reversed(data))
+    ser = Series(data, name="a")
+    result = getattr(ser.groupby(groups), method)(n=2)
+    expidx = np.array(groups, dtype=np.int_) if isinstance(groups, list) else groups
+    expected = Series(data, index=MultiIndex.from_arrays([expidx, ser.index]), name="a")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("func", ["cumprod", "cumsum"])
+def test_numpy_compat(func):
+    # see gh-12811
+    df = DataFrame({"A": [1, 2, 1], "B": [1, 2, 3]})
+    g = df.groupby("A")
+
+    msg = "numpy operations are not valid with groupby"
+
+    with pytest.raises(UnsupportedFunctionCall, match=msg):
+        getattr(g, func)(1, 2, 3)
+    with pytest.raises(UnsupportedFunctionCall, match=msg):
+        getattr(g, func)(foo=1)
+
+
+def test_cummin(dtypes_for_minmax):
+    dtype = dtypes_for_minmax[0]
+    min_val = dtypes_for_minmax[1]
+
+    # GH 15048
+    base_df = DataFrame({"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [3, 4, 3, 2, 2, 3, 2, 1]})
+    expected_mins = [3, 3, 3, 2, 2, 2, 2, 1]
+
+    df = base_df.astype(dtype)
+
+    expected = DataFrame({"B": expected_mins}).astype(dtype)
+    result = df.groupby("A").cummin()
+    tm.assert_frame_equal(result, expected)
+    result = df.groupby("A", group_keys=False).B.apply(lambda x: x.cummin()).to_frame()
+    tm.assert_frame_equal(result, expected)
+
+    # Test w/ min value for dtype
+    df.loc[[2, 6], "B"] = min_val
+    df.loc[[1, 5], "B"] = min_val + 1
+    expected.loc[[2, 3, 6, 7], "B"] = min_val
+    expected.loc[[1, 5], "B"] = min_val + 1  # should not be rounded to min_val
+    result = df.groupby("A").cummin()
+    tm.assert_frame_equal(result, expected, check_exact=True)
+    expected = (
+        df.groupby("A", group_keys=False).B.apply(lambda x: x.cummin()).to_frame()
+    )
+    tm.assert_frame_equal(result, expected, check_exact=True)
+
+    # Test nan in some values
+    # Explicit cast to float to avoid implicit cast when setting nan
+    base_df = base_df.astype({"B": "float"})
+    base_df.loc[[0, 2, 4, 6], "B"] = np.nan
+    expected = DataFrame({"B": [np.nan, 4, np.nan, 2, np.nan, 3, np.nan, 1]})
+    result = base_df.groupby("A").cummin()
+    tm.assert_frame_equal(result, expected)
+    expected = (
+        base_df.groupby("A", group_keys=False).B.apply(lambda x: x.cummin()).to_frame()
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # GH 15561
+    df = DataFrame({"a": [1], "b": pd.to_datetime(["2001"])})
+    expected = Series(pd.to_datetime("2001"), index=[0], name="b")
+
+    result = df.groupby("a")["b"].cummin()
+    tm.assert_series_equal(expected, result)
+
+    # GH 15635
+    df = DataFrame({"a": [1, 2, 1], "b": [1, 2, 2]})
+    result = df.groupby("a").b.cummin()
+    expected = Series([1, 2, 1], name="b")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("method", ["cummin", "cummax"])
+@pytest.mark.parametrize("dtype", ["UInt64", "Int64", "Float64", "float", "boolean"])
+def test_cummin_max_all_nan_column(method, dtype):
+    base_df = DataFrame({"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [np.nan] * 8})
+    base_df["B"] = base_df["B"].astype(dtype)
+    grouped = base_df.groupby("A")
+
+    expected = DataFrame({"B": [np.nan] * 8}, dtype=dtype)
+    result = getattr(grouped, method)()
+    tm.assert_frame_equal(expected, result)
+
+    result = getattr(grouped["B"], method)().to_frame()
+    tm.assert_frame_equal(expected, result)
+
+
+def test_cummax(dtypes_for_minmax):
+    dtype = dtypes_for_minmax[0]
+    max_val = dtypes_for_minmax[2]
+
+    # GH 15048
+    base_df = DataFrame({"A": [1, 1, 1, 1, 2, 2, 2, 2], "B": [3, 4, 3, 2, 2, 3, 2, 1]})
+    expected_maxs = [3, 4, 4, 4, 2, 3, 3, 3]
+
+    df = base_df.astype(dtype)
+
+    expected = DataFrame({"B": expected_maxs}).astype(dtype)
+    result = df.groupby("A").cummax()
+    tm.assert_frame_equal(result, expected)
+    result = df.groupby("A", group_keys=False).B.apply(lambda x: x.cummax()).to_frame()
+    tm.assert_frame_equal(result, expected)
+
+    # Test w/ max value for dtype
+    df.loc[[2, 6], "B"] = max_val
+    expected.loc[[2, 3, 6, 7], "B"] = max_val
+    result = df.groupby("A").cummax()
+    tm.assert_frame_equal(result, expected)
+    expected = (
+        df.groupby("A", group_keys=False).B.apply(lambda x: x.cummax()).to_frame()
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # Test nan in some values
+    # Explicit cast to float to avoid implicit cast when setting nan
+    base_df = base_df.astype({"B": "float"})
+    base_df.loc[[0, 2, 4, 6], "B"] = np.nan
+    expected = DataFrame({"B": [np.nan, 4, np.nan, 4, np.nan, 3, np.nan, 3]})
+    result = base_df.groupby("A").cummax()
+    tm.assert_frame_equal(result, expected)
+    expected = (
+        base_df.groupby("A", group_keys=False).B.apply(lambda x: x.cummax()).to_frame()
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # GH 15561
+    df = DataFrame({"a": [1], "b": pd.to_datetime(["2001"])})
+    expected = Series(pd.to_datetime("2001"), index=[0], name="b")
+
+    result = df.groupby("a")["b"].cummax()
+    tm.assert_series_equal(expected, result)
+
+    # GH 15635
+    df = DataFrame({"a": [1, 2, 1], "b": [2, 1, 1]})
+    result = df.groupby("a").b.cummax()
+    expected = Series([2, 1, 2], name="b")
+    tm.assert_series_equal(result, expected)
+
+
+def test_cummax_i8_at_implementation_bound():
+    # the minimum value used to be treated as NPY_NAT+1 instead of NPY_NAT
+    #  for int64 dtype GH#46382
+    ser = Series([pd.NaT._value + n for n in range(5)])
+    df = DataFrame({"A": 1, "B": ser, "C": ser.view("M8[ns]")})
+    gb = df.groupby("A")
+
+    res = gb.cummax()
+    exp = df[["B", "C"]]
+    tm.assert_frame_equal(res, exp)
+
+
+@pytest.mark.parametrize("method", ["cummin", "cummax"])
+@pytest.mark.parametrize("dtype", ["float", "Int64", "Float64"])
+@pytest.mark.parametrize(
+    "groups,expected_data",
+    [
+        ([1, 1, 1], [1, None, None]),
+        ([1, 2, 3], [1, None, 2]),
+        ([1, 3, 3], [1, None, None]),
+    ],
+)
+def test_cummin_max_skipna(method, dtype, groups, expected_data):
+    # GH-34047
+    df = DataFrame({"a": Series([1, None, 2], dtype=dtype)})
+    orig = df.copy()
+    gb = df.groupby(groups)["a"]
+
+    result = getattr(gb, method)(skipna=False)
+    expected = Series(expected_data, dtype=dtype, name="a")
+
+    # check we didn't accidentally alter df
+    tm.assert_frame_equal(df, orig)
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("method", ["cummin", "cummax"])
+def test_cummin_max_skipna_multiple_cols(method):
+    # Ensure missing value in "a" doesn't cause "b" to be nan-filled
+    df = DataFrame({"a": [np.nan, 2.0, 2.0], "b": [2.0, 2.0, 2.0]})
+    gb = df.groupby([1, 1, 1])[["a", "b"]]
+
+    result = getattr(gb, method)(skipna=False)
+    expected = DataFrame({"a": [np.nan, np.nan, np.nan], "b": [2.0, 2.0, 2.0]})
+
+    tm.assert_frame_equal(result, expected)
+
+
+@td.skip_if_32bit
+@pytest.mark.parametrize("method", ["cummin", "cummax"])
+@pytest.mark.parametrize(
+    "dtype,val", [("UInt64", np.iinfo("uint64").max), ("Int64", 2**53 + 1)]
+)
+def test_nullable_int_not_cast_as_float(method, dtype, val):
+    data = [val, pd.NA]
+    df = DataFrame({"grp": [1, 1], "b": data}, dtype=dtype)
+    grouped = df.groupby("grp")
+
+    result = grouped.transform(method)
+    expected = DataFrame({"b": data}, dtype=dtype)
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "in_vals, out_vals",
+    [
+        # Basics: strictly increasing (T), strictly decreasing (F),
+        # abs val increasing (F), non-strictly increasing (T)
+        ([1, 2, 5, 3, 2, 0, 4, 5, -6, 1, 1], [True, False, False, True]),
+        # Test with inf vals
+        (
+            [1, 2.1, np.inf, 3, 2, np.inf, -np.inf, 5, 11, 1, -np.inf],
+            [True, False, True, False],
+        ),
+        # Test with nan vals; should always be False
+        (
+            [1, 2, np.nan, 3, 2, np.nan, np.nan, 5, -np.inf, 1, np.nan],
+            [False, False, False, False],
+        ),
+    ],
+)
+def test_is_monotonic_increasing(in_vals, out_vals):
+    # GH 17015
+    source_dict = {
+        "A": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11"],
+        "B": ["a", "a", "a", "b", "b", "b", "c", "c", "c", "d", "d"],
+        "C": in_vals,
+    }
+    df = DataFrame(source_dict)
+    result = df.groupby("B").C.is_monotonic_increasing
+    index = Index(list("abcd"), name="B")
+    expected = Series(index=index, data=out_vals, name="C")
+    tm.assert_series_equal(result, expected)
+
+    # Also check result equal to manually taking x.is_monotonic_increasing.
+    expected = df.groupby(["B"]).C.apply(lambda x: x.is_monotonic_increasing)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "in_vals, out_vals",
+    [
+        # Basics: strictly decreasing (T), strictly increasing (F),
+        # abs val decreasing (F), non-strictly increasing (T)
+        ([10, 9, 7, 3, 4, 5, -3, 2, 0, 1, 1], [True, False, False, True]),
+        # Test with inf vals
+        (
+            [np.inf, 1, -np.inf, np.inf, 2, -3, -np.inf, 5, -3, -np.inf, -np.inf],
+            [True, True, False, True],
+        ),
+        # Test with nan vals; should always be False
+        (
+            [1, 2, np.nan, 3, 2, np.nan, np.nan, 5, -np.inf, 1, np.nan],
+            [False, False, False, False],
+        ),
+    ],
+)
+def test_is_monotonic_decreasing(in_vals, out_vals):
+    # GH 17015
+    source_dict = {
+        "A": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11"],
+        "B": ["a", "a", "a", "b", "b", "b", "c", "c", "c", "d", "d"],
+        "C": in_vals,
+    }
+
+    df = DataFrame(source_dict)
+    result = df.groupby("B").C.is_monotonic_decreasing
+    index = Index(list("abcd"), name="B")
+    expected = Series(index=index, data=out_vals, name="C")
+    tm.assert_series_equal(result, expected)
+
+
+# describe
+# --------------------------------
+
+
+def test_apply_describe_bug(mframe):
+    grouped = mframe.groupby(level="first")
+    grouped.describe()  # it works!
+
+
+def test_series_describe_multikey():
+    ts = tm.makeTimeSeries()
+    grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
+    result = grouped.describe()
+    tm.assert_series_equal(result["mean"], grouped.mean(), check_names=False)
+    tm.assert_series_equal(result["std"], grouped.std(), check_names=False)
+    tm.assert_series_equal(result["min"], grouped.min(), check_names=False)
+
+
+def test_series_describe_single():
+    ts = tm.makeTimeSeries()
+    grouped = ts.groupby(lambda x: x.month)
+    result = grouped.apply(lambda x: x.describe())
+    expected = grouped.describe().stack()
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("keys", ["key1", ["key1", "key2"]])
+def test_series_describe_as_index(as_index, keys):
+    # GH#49256
+    df = DataFrame(
+        {
+            "key1": ["one", "two", "two", "three", "two"],
+            "key2": ["one", "two", "two", "three", "two"],
+            "foo2": [1, 2, 4, 4, 6],
+        }
+    )
+    gb = df.groupby(keys, as_index=as_index)["foo2"]
+    result = gb.describe()
+    expected = DataFrame(
+        {
+            "key1": ["one", "three", "two"],
+            "count": [1.0, 1.0, 3.0],
+            "mean": [1.0, 4.0, 4.0],
+            "std": [np.nan, np.nan, 2.0],
+            "min": [1.0, 4.0, 2.0],
+            "25%": [1.0, 4.0, 3.0],
+            "50%": [1.0, 4.0, 4.0],
+            "75%": [1.0, 4.0, 5.0],
+            "max": [1.0, 4.0, 6.0],
+        }
+    )
+    if len(keys) == 2:
+        expected.insert(1, "key2", expected["key1"])
+    if as_index:
+        expected = expected.set_index(keys)
+    tm.assert_frame_equal(result, expected)
+
+
+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_frame_describe_multikey(tsframe):
+    grouped = tsframe.groupby([lambda x: x.year, lambda x: x.month])
+    result = grouped.describe()
+    desc_groups = []
+    for col in tsframe:
+        group = grouped[col].describe()
+        # GH 17464 - Remove duplicate MultiIndex levels
+        group_col = MultiIndex(
+            levels=[[col], group.columns],
+            codes=[[0] * len(group.columns), range(len(group.columns))],
+        )
+        group = DataFrame(group.values, columns=group_col, index=group.index)
+        desc_groups.append(group)
+    expected = pd.concat(desc_groups, axis=1)
+    tm.assert_frame_equal(result, expected)
+
+    groupedT = tsframe.groupby({"A": 0, "B": 0, "C": 1, "D": 1}, axis=1)
+    result = groupedT.describe()
+    expected = tsframe.describe().T
+    # reverting the change from https://github.com/pandas-dev/pandas/pull/35441/
+    expected.index = MultiIndex(
+        levels=[[0, 1], expected.index],
+        codes=[[0, 0, 1, 1], range(len(expected.index))],
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_frame_describe_tupleindex():
+    # GH 14848 - regression from 0.19.0 to 0.19.1
+    df1 = DataFrame(
+        {
+            "x": [1, 2, 3, 4, 5] * 3,
+            "y": [10, 20, 30, 40, 50] * 3,
+            "z": [100, 200, 300, 400, 500] * 3,
+        }
+    )
+    df1["k"] = [(0, 0, 1), (0, 1, 0), (1, 0, 0)] * 5
+    df2 = df1.rename(columns={"k": "key"})
+    msg = "Names should be list-like for a MultiIndex"
+    with pytest.raises(ValueError, match=msg):
+        df1.groupby("k").describe()
+    with pytest.raises(ValueError, match=msg):
+        df2.groupby("key").describe()
+
+
+def test_frame_describe_unstacked_format():
+    # GH 4792
+    prices = {
+        Timestamp("2011-01-06 10:59:05", tz=None): 24990,
+        Timestamp("2011-01-06 12:43:33", tz=None): 25499,
+        Timestamp("2011-01-06 12:54:09", tz=None): 25499,
+    }
+    volumes = {
+        Timestamp("2011-01-06 10:59:05", tz=None): 1500000000,
+        Timestamp("2011-01-06 12:43:33", tz=None): 5000000000,
+        Timestamp("2011-01-06 12:54:09", tz=None): 100000000,
+    }
+    df = DataFrame({"PRICE": prices, "VOLUME": volumes})
+    result = df.groupby("PRICE").VOLUME.describe()
+    data = [
+        df[df.PRICE == 24990].VOLUME.describe().values.tolist(),
+        df[df.PRICE == 25499].VOLUME.describe().values.tolist(),
+    ]
+    expected = DataFrame(
+        data,
+        index=Index([24990, 25499], name="PRICE"),
+        columns=["count", "mean", "std", "min", "25%", "50%", "75%", "max"],
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.filterwarnings(
+    "ignore:"
+    "indexing past lexsort depth may impact performance:"
+    "pandas.errors.PerformanceWarning"
+)
+@pytest.mark.parametrize("as_index", [True, False])
+@pytest.mark.parametrize("keys", [["a1"], ["a1", "a2"]])
+def test_describe_with_duplicate_output_column_names(as_index, keys):
+    # GH 35314
+    df = DataFrame(
+        {
+            "a1": [99, 99, 99, 88, 88, 88],
+            "a2": [99, 99, 99, 88, 88, 88],
+            "b": [1, 2, 3, 4, 5, 6],
+            "c": [10, 20, 30, 40, 50, 60],
+        },
+        columns=["a1", "a2", "b", "b"],
+        copy=False,
+    )
+    if keys == ["a1"]:
+        df = df.drop(columns="a2")
+
+    expected = (
+        DataFrame.from_records(
+            [
+                ("b", "count", 3.0, 3.0),
+                ("b", "mean", 5.0, 2.0),
+                ("b", "std", 1.0, 1.0),
+                ("b", "min", 4.0, 1.0),
+                ("b", "25%", 4.5, 1.5),
+                ("b", "50%", 5.0, 2.0),
+                ("b", "75%", 5.5, 2.5),
+                ("b", "max", 6.0, 3.0),
+                ("b", "count", 3.0, 3.0),
+                ("b", "mean", 5.0, 2.0),
+                ("b", "std", 1.0, 1.0),
+                ("b", "min", 4.0, 1.0),
+                ("b", "25%", 4.5, 1.5),
+                ("b", "50%", 5.0, 2.0),
+                ("b", "75%", 5.5, 2.5),
+                ("b", "max", 6.0, 3.0),
+            ],
+        )
+        .set_index([0, 1])
+        .T
+    )
+    expected.columns.names = [None, None]
+    if len(keys) == 2:
+        expected.index = MultiIndex(
+            levels=[[88, 99], [88, 99]], codes=[[0, 1], [0, 1]], names=["a1", "a2"]
+        )
+    else:
+        expected.index = Index([88, 99], name="a1")
+
+    if not as_index:
+        expected = expected.reset_index()
+
+    result = df.groupby(keys, as_index=as_index).describe()
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_describe_duplicate_columns():
+    # GH#50806
+    df = DataFrame([[0, 1, 2, 3]])
+    df.columns = [0, 1, 2, 0]
+    gb = df.groupby(df[1])
+    result = gb.describe(percentiles=[])
+
+    columns = ["count", "mean", "std", "min", "50%", "max"]
+    frames = [
+        DataFrame([[1.0, val, np.nan, val, val, val]], index=[1], columns=columns)
+        for val in (0.0, 2.0, 3.0)
+    ]
+    expected = pd.concat(frames, axis=1)
+    expected.columns = MultiIndex(
+        levels=[[0, 2], columns],
+        codes=[6 * [0] + 6 * [1] + 6 * [0], 3 * list(range(6))],
+    )
+    expected.index.names = [1]
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_mean_no_overflow():
+    # Regression test for (#22487)
+    df = DataFrame(
+        {
+            "user": ["A", "A", "A", "A", "A"],
+            "connections": [4970, 4749, 4719, 4704, 18446744073699999744],
+        }
+    )
+    assert df.groupby("user")["connections"].mean()["A"] == 3689348814740003840
+
+
+@pytest.mark.parametrize(
+    "values",
+    [
+        {
+            "a": [1, 1, 1, 2, 2, 2, 3, 3, 3],
+            "b": [1, pd.NA, 2, 1, pd.NA, 2, 1, pd.NA, 2],
+        },
+        {"a": [1, 1, 2, 2, 3, 3], "b": [1, 2, 1, 2, 1, 2]},
+    ],
+)
+@pytest.mark.parametrize("function", ["mean", "median", "var"])
+def test_apply_to_nullable_integer_returns_float(values, function):
+    # https://github.com/pandas-dev/pandas/issues/32219
+    output = 0.5 if function == "var" else 1.5
+    arr = np.array([output] * 3, dtype=float)
+    idx = Index([1, 2, 3], name="a", dtype="Int64")
+    expected = DataFrame({"b": arr}, index=idx).astype("Float64")
+
+    groups = DataFrame(values, dtype="Int64").groupby("a")
+
+    result = getattr(groups, function)()
+    tm.assert_frame_equal(result, expected)
+
+    result = groups.agg(function)
+    tm.assert_frame_equal(result, expected)
+
+    result = groups.agg([function])
+    expected.columns = MultiIndex.from_tuples([("b", function)])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_sum_below_mincount_nullable_integer():
+    # https://github.com/pandas-dev/pandas/issues/32861
+    df = DataFrame({"a": [0, 1, 2], "b": [0, 1, 2], "c": [0, 1, 2]}, dtype="Int64")
+    grouped = df.groupby("a")
+    idx = Index([0, 1, 2], name="a", dtype="Int64")
+
+    result = grouped["b"].sum(min_count=2)
+    expected = Series([pd.NA] * 3, dtype="Int64", index=idx, name="b")
+    tm.assert_series_equal(result, expected)
+
+    result = grouped.sum(min_count=2)
+    expected = DataFrame({"b": [pd.NA] * 3, "c": [pd.NA] * 3}, dtype="Int64", index=idx)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_mean_on_timedelta():
+    # GH 17382
+    df = DataFrame({"time": pd.to_timedelta(range(10)), "cat": ["A", "B"] * 5})
+    result = df.groupby("cat")["time"].mean()
+    expected = Series(
+        pd.to_timedelta([4, 5]), name="time", index=Index(["A", "B"], name="cat")
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_sum_timedelta_with_nat():
+    # GH#42659
+    df = DataFrame(
+        {
+            "a": [1, 1, 2, 2],
+            "b": [pd.Timedelta("1d"), pd.Timedelta("2d"), pd.Timedelta("3d"), pd.NaT],
+        }
+    )
+    td3 = pd.Timedelta(days=3)
+
+    gb = df.groupby("a")
+
+    res = gb.sum()
+    expected = DataFrame({"b": [td3, td3]}, index=Index([1, 2], name="a"))
+    tm.assert_frame_equal(res, expected)
+
+    res = gb["b"].sum()
+    tm.assert_series_equal(res, expected["b"])
+
+    res = gb["b"].sum(min_count=2)
+    expected = Series([td3, pd.NaT], dtype="m8[ns]", name="b", index=expected.index)
+    tm.assert_series_equal(res, expected)
+
+
+@pytest.mark.parametrize(
+    "kernel, has_arg",
+    [
+        ("all", False),
+        ("any", False),
+        ("bfill", False),
+        ("corr", True),
+        ("corrwith", True),
+        ("cov", True),
+        ("cummax", True),
+        ("cummin", True),
+        ("cumprod", True),
+        ("cumsum", True),
+        ("diff", False),
+        ("ffill", False),
+        ("fillna", False),
+        ("first", True),
+        ("idxmax", True),
+        ("idxmin", True),
+        ("last", True),
+        ("max", True),
+        ("mean", True),
+        ("median", True),
+        ("min", True),
+        ("nth", False),
+        ("nunique", False),
+        ("pct_change", False),
+        ("prod", True),
+        ("quantile", True),
+        ("sem", True),
+        ("skew", True),
+        ("std", True),
+        ("sum", True),
+        ("var", True),
+    ],
+)
+@pytest.mark.parametrize("numeric_only", [True, False, lib.no_default])
+@pytest.mark.parametrize("keys", [["a1"], ["a1", "a2"]])
+def test_numeric_only(kernel, has_arg, numeric_only, keys):
+    # GH#46072
+    # drops_nuisance: Whether the op drops nuisance columns even when numeric_only=False
+    # has_arg: Whether the op has a numeric_only arg
+    df = DataFrame({"a1": [1, 1], "a2": [2, 2], "a3": [5, 6], "b": 2 * [object]})
+
+    args = get_groupby_method_args(kernel, df)
+    kwargs = {} if numeric_only is lib.no_default else {"numeric_only": numeric_only}
+
+    gb = df.groupby(keys)
+    method = getattr(gb, kernel)
+    if has_arg and numeric_only is True:
+        # Cases where b does not appear in the result
+        result = method(*args, **kwargs)
+        assert "b" not in result.columns
+    elif (
+        # kernels that work on any dtype and have numeric_only arg
+        kernel in ("first", "last")
+        or (
+            # kernels that work on any dtype and don't have numeric_only arg
+            kernel in ("any", "all", "bfill", "ffill", "fillna", "nth", "nunique")
+            and numeric_only is lib.no_default
+        )
+    ):
+        result = method(*args, **kwargs)
+        assert "b" in result.columns
+    elif has_arg or kernel in ("idxmax", "idxmin"):
+        assert numeric_only is not True
+        # kernels that are successful on any dtype were above; this will fail
+
+        # object dtypes for transformations are not implemented in Cython and
+        # have no Python fallback
+        exception = NotImplementedError if kernel.startswith("cum") else TypeError
+
+        msg = "|".join(
+            [
+                "not allowed for this dtype",
+                "must be a string or a number",
+                "cannot be performed against 'object' dtypes",
+                "must be a string or a real number",
+                "unsupported operand type",
+                "not supported between instances of",
+                "function is not implemented for this dtype",
+            ]
+        )
+        with pytest.raises(exception, match=msg):
+            method(*args, **kwargs)
+    elif not has_arg and numeric_only is not lib.no_default:
+        with pytest.raises(
+            TypeError, match="got an unexpected keyword argument 'numeric_only'"
+        ):
+            method(*args, **kwargs)
+    else:
+        assert kernel in ("diff", "pct_change")
+        assert numeric_only is lib.no_default
+        # Doesn't have numeric_only argument and fails on nuisance columns
+        with pytest.raises(TypeError, match=r"unsupported operand type"):
+            method(*args, **kwargs)
+
+
+@pytest.mark.parametrize("dtype", [bool, int, float, object])
+def test_deprecate_numeric_only_series(dtype, groupby_func, request):
+    # GH#46560
+    if groupby_func == "corrwith":
+        msg = "corrwith is not implemented on SeriesGroupBy"
+        request.node.add_marker(pytest.mark.xfail(reason=msg))
+
+    grouper = [0, 0, 1]
+
+    ser = Series([1, 0, 0], dtype=dtype)
+    gb = ser.groupby(grouper)
+    method = getattr(gb, groupby_func)
+
+    expected_ser = Series([1, 0, 0])
+    expected_gb = expected_ser.groupby(grouper)
+    expected_method = getattr(expected_gb, groupby_func)
+
+    args = get_groupby_method_args(groupby_func, ser)
+
+    fails_on_numeric_object = (
+        "corr",
+        "cov",
+        "cummax",
+        "cummin",
+        "cumprod",
+        "cumsum",
+        "idxmax",
+        "idxmin",
+        "quantile",
+    )
+    # ops that give an object result on object input
+    obj_result = (
+        "first",
+        "last",
+        "nth",
+        "bfill",
+        "ffill",
+        "shift",
+        "sum",
+        "diff",
+        "pct_change",
+        "var",
+        "mean",
+        "median",
+        "min",
+        "max",
+        "prod",
+    )
+
+    # Test default behavior; kernels that fail may be enabled in the future but kernels
+    # that succeed should not be allowed to fail (without deprecation, at least)
+    if groupby_func in fails_on_numeric_object and dtype is object:
+        if groupby_func in ("idxmax", "idxmin"):
+            msg = "not allowed for this dtype"
+        elif groupby_func == "quantile":
+            msg = "cannot be performed against 'object' dtypes"
+        else:
+            msg = "is not supported for object dtype"
+        with pytest.raises(TypeError, match=msg):
+            method(*args)
+    elif dtype is object:
+        result = method(*args)
+        expected = expected_method(*args)
+        if groupby_func in obj_result:
+            expected = expected.astype(object)
+        tm.assert_series_equal(result, expected)
+
+    has_numeric_only = (
+        "first",
+        "last",
+        "max",
+        "mean",
+        "median",
+        "min",
+        "prod",
+        "quantile",
+        "sem",
+        "skew",
+        "std",
+        "sum",
+        "var",
+        "cummax",
+        "cummin",
+        "cumprod",
+        "cumsum",
+    )
+    if groupby_func not in has_numeric_only:
+        msg = "got an unexpected keyword argument 'numeric_only'"
+        with pytest.raises(TypeError, match=msg):
+            method(*args, numeric_only=True)
+    elif dtype is object:
+        msg = "|".join(
+            [
+                "SeriesGroupBy.sem called with numeric_only=True and dtype object",
+                "Series.skew does not allow numeric_only=True with non-numeric",
+                "cum(sum|prod|min|max) is not supported for object dtype",
+                r"Cannot use numeric_only=True with SeriesGroupBy\..* and non-numeric",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            method(*args, numeric_only=True)
+    else:
+        result = method(*args, numeric_only=True)
+        expected = method(*args, numeric_only=False)
+        tm.assert_series_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)
+
+
+def test_corrwith_with_1_axis():
+    # GH 47723
+    df = DataFrame({"a": [1, 1, 2], "b": [3, 7, 4]})
+    result = df.groupby("a").corrwith(df, axis=1)
+    index = Index(
+        data=[(1, 0), (1, 1), (1, 2), (2, 2), (2, 0), (2, 1)],
+        name=("a", None),
+    )
+    expected = Series([np.nan] * 6, index=index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_multiindex_group_all_columns_when_empty(groupby_func):
+    # GH 32464
+    df = DataFrame({"a": [], "b": [], "c": []}).set_index(["a", "b", "c"])
+    gb = df.groupby(["a", "b", "c"], group_keys=False)
+    method = getattr(gb, groupby_func)
+    args = get_groupby_method_args(groupby_func, df)
+
+    result = method(*args).index
+    expected = df.index
+    tm.assert_index_equal(result, expected)
+
+
+def test_duplicate_columns(request, groupby_func, as_index):
+    # GH#50806
+    if groupby_func == "corrwith":
+        msg = "GH#50845 - corrwith fails when there are duplicate columns"
+        request.node.add_marker(pytest.mark.xfail(reason=msg))
+    df = DataFrame([[1, 3, 6], [1, 4, 7], [2, 5, 8]], columns=list("abb"))
+    args = get_groupby_method_args(groupby_func, df)
+    gb = df.groupby("a", as_index=as_index)
+    result = getattr(gb, groupby_func)(*args)
+
+    expected_df = df.set_axis(["a", "b", "c"], axis=1)
+    expected_args = get_groupby_method_args(groupby_func, expected_df)
+    expected_gb = expected_df.groupby("a", as_index=as_index)
+    expected = getattr(expected_gb, groupby_func)(*expected_args)
+    if groupby_func not in ("size", "ngroup", "cumcount"):
+        expected = expected.rename(columns={"c": "b"})
+    tm.assert_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_groupby.py

@@ -0,0 +1,2837 @@
+from datetime import datetime
+from decimal import Decimal
+
+import numpy as np
+import pytest
+
+from pandas.compat import IS64
+from pandas.errors import (
+    PerformanceWarning,
+    SpecificationError,
+)
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    Grouper,
+    Index,
+    MultiIndex,
+    RangeIndex,
+    Series,
+    Timedelta,
+    Timestamp,
+    date_range,
+    to_datetime,
+)
+import pandas._testing as tm
+from pandas.core.arrays import BooleanArray
+import pandas.core.common as com
+from pandas.tests.groupby import get_groupby_method_args
+
+
+def test_repr():
+    # GH18203
+    result = repr(Grouper(key="A", level="B"))
+    expected = "Grouper(key='A', level='B', axis=0, sort=False, dropna=True)"
+    assert result == expected
+
+
+def test_groupby_std_datetimelike():
+    # GH#48481
+    tdi = pd.timedelta_range("1 Day", periods=10000)
+    ser = Series(tdi)
+    ser[::5] *= 2  # get different std for different groups
+
+    df = ser.to_frame("A")
+
+    df["B"] = ser + Timestamp(0)
+    df["C"] = ser + Timestamp(0, tz="UTC")
+    df.iloc[-1] = pd.NaT  # last group includes NaTs
+
+    gb = df.groupby(list(range(5)) * 2000)
+
+    result = gb.std()
+
+    # Note: this does not _exactly_ match what we would get if we did
+    # [gb.get_group(i).std() for i in gb.groups]
+    #  but it _does_ match the floating point error we get doing the
+    #  same operation on int64 data xref GH#51332
+    td1 = Timedelta("2887 days 11:21:02.326710176")
+    td4 = Timedelta("2886 days 00:42:34.664668096")
+    exp_ser = Series([td1 * 2, td1, td1, td1, td4], index=np.arange(5))
+    expected = DataFrame({"A": exp_ser, "B": exp_ser, "C": exp_ser})
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["int64", "int32", "float64", "float32"])
+def test_basic(dtype):
+    data = Series(np.arange(9) // 3, index=np.arange(9), dtype=dtype)
+
+    index = np.arange(9)
+    np.random.shuffle(index)
+    data = data.reindex(index)
+
+    grouped = data.groupby(lambda x: x // 3, group_keys=False)
+
+    for k, v in grouped:
+        assert len(v) == 3
+
+    agged = grouped.aggregate(np.mean)
+    assert agged[1] == 1
+
+    tm.assert_series_equal(agged, grouped.agg(np.mean))  # shorthand
+    tm.assert_series_equal(agged, grouped.mean())
+    tm.assert_series_equal(grouped.agg(np.sum), grouped.sum())
+
+    expected = grouped.apply(lambda x: x * x.sum())
+    transformed = grouped.transform(lambda x: x * x.sum())
+    assert transformed[7] == 12
+    tm.assert_series_equal(transformed, expected)
+
+    value_grouped = data.groupby(data)
+    tm.assert_series_equal(
+        value_grouped.aggregate(np.mean), agged, check_index_type=False
+    )
+
+    # complex agg
+    agged = grouped.aggregate([np.mean, np.std])
+
+    msg = r"nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=msg):
+        grouped.aggregate({"one": np.mean, "two": np.std})
+
+    group_constants = {0: 10, 1: 20, 2: 30}
+    agged = grouped.agg(lambda x: group_constants[x.name] + x.mean())
+    assert agged[1] == 21
+
+    # corner cases
+    msg = "Must produce aggregated value"
+    # exception raised is type Exception
+    with pytest.raises(Exception, match=msg):
+        grouped.aggregate(lambda x: x * 2)
+
+
+def test_groupby_nonobject_dtype(mframe, df_mixed_floats):
+    key = mframe.index.codes[0]
+    grouped = mframe.groupby(key)
+    result = grouped.sum()
+
+    expected = mframe.groupby(key.astype("O")).sum()
+    assert result.index.dtype == np.int8
+    assert expected.index.dtype == np.int64
+    tm.assert_frame_equal(result, expected, check_index_type=False)
+
+    # GH 3911, mixed frame non-conversion
+    df = df_mixed_floats.copy()
+    df["value"] = range(len(df))
+
+    def max_value(group):
+        return group.loc[group["value"].idxmax()]
+
+    applied = df.groupby("A").apply(max_value)
+    result = applied.dtypes
+    expected = df.dtypes
+    tm.assert_series_equal(result, expected)
+
+
+def test_inconsistent_return_type():
+    # GH5592
+    # inconsistent return type
+    df = DataFrame(
+        {
+            "A": ["Tiger", "Tiger", "Tiger", "Lamb", "Lamb", "Pony", "Pony"],
+            "B": Series(np.arange(7), dtype="int64"),
+            "C": date_range("20130101", periods=7),
+        }
+    )
+
+    def f_0(grp):
+        return grp.iloc[0]
+
+    expected = df.groupby("A").first()[["B"]]
+    result = df.groupby("A").apply(f_0)[["B"]]
+    tm.assert_frame_equal(result, expected)
+
+    def f_1(grp):
+        if grp.name == "Tiger":
+            return None
+        return grp.iloc[0]
+
+    result = df.groupby("A").apply(f_1)[["B"]]
+    e = expected.copy()
+    e.loc["Tiger"] = np.nan
+    tm.assert_frame_equal(result, e)
+
+    def f_2(grp):
+        if grp.name == "Pony":
+            return None
+        return grp.iloc[0]
+
+    result = df.groupby("A").apply(f_2)[["B"]]
+    e = expected.copy()
+    e.loc["Pony"] = np.nan
+    tm.assert_frame_equal(result, e)
+
+    # 5592 revisited, with datetimes
+    def f_3(grp):
+        if grp.name == "Pony":
+            return None
+        return grp.iloc[0]
+
+    result = df.groupby("A").apply(f_3)[["C"]]
+    e = df.groupby("A").first()[["C"]]
+    e.loc["Pony"] = pd.NaT
+    tm.assert_frame_equal(result, e)
+
+    # scalar outputs
+    def f_4(grp):
+        if grp.name == "Pony":
+            return None
+        return grp.iloc[0].loc["C"]
+
+    result = df.groupby("A").apply(f_4)
+    e = df.groupby("A").first()["C"].copy()
+    e.loc["Pony"] = np.nan
+    e.name = None
+    tm.assert_series_equal(result, e)
+
+
+def test_pass_args_kwargs(ts, tsframe):
+    def f(x, q=None, axis=0):
+        return np.percentile(x, q, axis=axis)
+
+    g = lambda x: np.percentile(x, 80, axis=0)
+
+    # Series
+    ts_grouped = ts.groupby(lambda x: x.month)
+    agg_result = ts_grouped.agg(np.percentile, 80, axis=0)
+    apply_result = ts_grouped.apply(np.percentile, 80, axis=0)
+    trans_result = ts_grouped.transform(np.percentile, 80, axis=0)
+
+    agg_expected = ts_grouped.quantile(0.8)
+    trans_expected = ts_grouped.transform(g)
+
+    tm.assert_series_equal(apply_result, agg_expected)
+    tm.assert_series_equal(agg_result, agg_expected)
+    tm.assert_series_equal(trans_result, trans_expected)
+
+    agg_result = ts_grouped.agg(f, q=80)
+    apply_result = ts_grouped.apply(f, q=80)
+    trans_result = ts_grouped.transform(f, q=80)
+    tm.assert_series_equal(agg_result, agg_expected)
+    tm.assert_series_equal(apply_result, agg_expected)
+    tm.assert_series_equal(trans_result, trans_expected)
+
+    # DataFrame
+    for as_index in [True, False]:
+        df_grouped = tsframe.groupby(lambda x: x.month, as_index=as_index)
+        agg_result = df_grouped.agg(np.percentile, 80, axis=0)
+        apply_result = df_grouped.apply(DataFrame.quantile, 0.8)
+        expected = df_grouped.quantile(0.8)
+        tm.assert_frame_equal(apply_result, expected, check_names=False)
+        tm.assert_frame_equal(agg_result, expected)
+
+        apply_result = df_grouped.apply(DataFrame.quantile, [0.4, 0.8])
+        expected_seq = df_grouped.quantile([0.4, 0.8])
+        tm.assert_frame_equal(apply_result, expected_seq, check_names=False)
+
+        agg_result = df_grouped.agg(f, q=80)
+        apply_result = df_grouped.apply(DataFrame.quantile, q=0.8)
+        tm.assert_frame_equal(agg_result, expected)
+        tm.assert_frame_equal(apply_result, expected, check_names=False)
+
+
+@pytest.mark.parametrize("as_index", [True, False])
+def test_pass_args_kwargs_duplicate_columns(tsframe, as_index):
+    # go through _aggregate_frame with self.axis == 0 and duplicate columns
+    tsframe.columns = ["A", "B", "A", "C"]
+    gb = tsframe.groupby(lambda x: x.month, as_index=as_index)
+
+    res = gb.agg(np.percentile, 80, axis=0)
+
+    ex_data = {
+        1: tsframe[tsframe.index.month == 1].quantile(0.8),
+        2: tsframe[tsframe.index.month == 2].quantile(0.8),
+    }
+    expected = DataFrame(ex_data).T
+    expected.index = expected.index.astype(np.int32)
+    if not as_index:
+        # TODO: try to get this more consistent?
+        expected.index = Index(range(2))
+
+    tm.assert_frame_equal(res, expected)
+
+
+def test_len():
+    df = tm.makeTimeDataFrame()
+    grouped = df.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day])
+    assert len(grouped) == len(df)
+
+    grouped = df.groupby([lambda x: x.year, lambda x: x.month])
+    expected = len({(x.year, x.month) for x in df.index})
+    assert len(grouped) == expected
+
+    # issue 11016
+    df = DataFrame({"a": [np.nan] * 3, "b": [1, 2, 3]})
+    assert len(df.groupby("a")) == 0
+    assert len(df.groupby("b")) == 3
+    assert len(df.groupby(["a", "b"])) == 3
+
+
+def test_basic_regression():
+    # regression
+    result = Series([1.0 * x for x in list(range(1, 10)) * 10])
+
+    data = np.random.random(1100) * 10.0
+    groupings = Series(data)
+
+    grouped = result.groupby(groupings)
+    grouped.mean()
+
+
+@pytest.mark.parametrize(
+    "dtype", ["float64", "float32", "int64", "int32", "int16", "int8"]
+)
+def test_with_na_groups(dtype):
+    index = Index(np.arange(10))
+    values = Series(np.ones(10), index, dtype=dtype)
+    labels = Series(
+        [np.nan, "foo", "bar", "bar", np.nan, np.nan, "bar", "bar", np.nan, "foo"],
+        index=index,
+    )
+
+    # this SHOULD be an int
+    grouped = values.groupby(labels)
+    agged = grouped.agg(len)
+    expected = Series([4, 2], index=["bar", "foo"])
+
+    tm.assert_series_equal(agged, expected, check_dtype=False)
+
+    # assert issubclass(agged.dtype.type, np.integer)
+
+    # explicitly return a float from my function
+    def f(x):
+        return float(len(x))
+
+    agged = grouped.agg(f)
+    expected = Series([4.0, 2.0], index=["bar", "foo"])
+
+    tm.assert_series_equal(agged, expected)
+
+
+def test_indices_concatenation_order():
+    # GH 2808
+
+    def f1(x):
+        y = x[(x.b % 2) == 1] ** 2
+        if y.empty:
+            multiindex = MultiIndex(levels=[[]] * 2, codes=[[]] * 2, names=["b", "c"])
+            res = DataFrame(columns=["a"], index=multiindex)
+            return res
+        else:
+            y = y.set_index(["b", "c"])
+            return y
+
+    def f2(x):
+        y = x[(x.b % 2) == 1] ** 2
+        if y.empty:
+            return DataFrame()
+        else:
+            y = y.set_index(["b", "c"])
+            return y
+
+    def f3(x):
+        y = x[(x.b % 2) == 1] ** 2
+        if y.empty:
+            multiindex = MultiIndex(
+                levels=[[]] * 2, codes=[[]] * 2, names=["foo", "bar"]
+            )
+            res = DataFrame(columns=["a", "b"], index=multiindex)
+            return res
+        else:
+            return y
+
+    df = DataFrame({"a": [1, 2, 2, 2], "b": range(4), "c": range(5, 9)})
+
+    df2 = DataFrame({"a": [3, 2, 2, 2], "b": range(4), "c": range(5, 9)})
+
+    # correct result
+    result1 = df.groupby("a").apply(f1)
+    result2 = df2.groupby("a").apply(f1)
+    tm.assert_frame_equal(result1, result2)
+
+    # should fail (not the same number of levels)
+    msg = "Cannot concat indices that do not have the same number of levels"
+    with pytest.raises(AssertionError, match=msg):
+        df.groupby("a").apply(f2)
+    with pytest.raises(AssertionError, match=msg):
+        df2.groupby("a").apply(f2)
+
+    # should fail (incorrect shape)
+    with pytest.raises(AssertionError, match=msg):
+        df.groupby("a").apply(f3)
+    with pytest.raises(AssertionError, match=msg):
+        df2.groupby("a").apply(f3)
+
+
+def test_attr_wrapper(ts):
+    grouped = ts.groupby(lambda x: x.weekday())
+
+    result = grouped.std()
+    expected = grouped.agg(lambda x: np.std(x, ddof=1))
+    tm.assert_series_equal(result, expected)
+
+    # this is pretty cool
+    result = grouped.describe()
+    expected = {name: gp.describe() for name, gp in grouped}
+    expected = DataFrame(expected).T
+    tm.assert_frame_equal(result, expected)
+
+    # get attribute
+    result = grouped.dtype
+    expected = grouped.agg(lambda x: x.dtype)
+    tm.assert_series_equal(result, expected)
+
+    # make sure raises error
+    msg = "'SeriesGroupBy' object has no attribute 'foo'"
+    with pytest.raises(AttributeError, match=msg):
+        getattr(grouped, "foo")
+
+
+def test_frame_groupby(tsframe):
+    grouped = tsframe.groupby(lambda x: x.weekday())
+
+    # aggregate
+    aggregated = grouped.aggregate(np.mean)
+    assert len(aggregated) == 5
+    assert len(aggregated.columns) == 4
+
+    # by string
+    tscopy = tsframe.copy()
+    tscopy["weekday"] = [x.weekday() for x in tscopy.index]
+    stragged = tscopy.groupby("weekday").aggregate(np.mean)
+    tm.assert_frame_equal(stragged, aggregated, check_names=False)
+
+    # transform
+    grouped = tsframe.head(30).groupby(lambda x: x.weekday())
+    transformed = grouped.transform(lambda x: x - x.mean())
+    assert len(transformed) == 30
+    assert len(transformed.columns) == 4
+
+    # transform propagate
+    transformed = grouped.transform(lambda x: x.mean())
+    for name, group in grouped:
+        mean = group.mean()
+        for idx in group.index:
+            tm.assert_series_equal(transformed.xs(idx), mean, check_names=False)
+
+    # iterate
+    for weekday, group in grouped:
+        assert group.index[0].weekday() == weekday
+
+    # groups / group_indices
+    groups = grouped.groups
+    indices = grouped.indices
+
+    for k, v in groups.items():
+        samething = tsframe.index.take(indices[k])
+        assert (samething == v).all()
+
+
+def test_frame_groupby_columns(tsframe):
+    mapping = {"A": 0, "B": 0, "C": 1, "D": 1}
+    grouped = tsframe.groupby(mapping, axis=1)
+
+    # aggregate
+    aggregated = grouped.aggregate(np.mean)
+    assert len(aggregated) == len(tsframe)
+    assert len(aggregated.columns) == 2
+
+    # transform
+    tf = lambda x: x - x.mean()
+    groupedT = tsframe.T.groupby(mapping, axis=0)
+    tm.assert_frame_equal(groupedT.transform(tf).T, grouped.transform(tf))
+
+    # iterate
+    for k, v in grouped:
+        assert len(v.columns) == 2
+
+
+def test_frame_set_name_single(df):
+    grouped = df.groupby("A")
+
+    result = grouped.mean(numeric_only=True)
+    assert result.index.name == "A"
+
+    result = df.groupby("A", as_index=False).mean(numeric_only=True)
+    assert result.index.name != "A"
+
+    result = grouped[["C", "D"]].agg(np.mean)
+    assert result.index.name == "A"
+
+    result = grouped.agg({"C": np.mean, "D": np.std})
+    assert result.index.name == "A"
+
+    result = grouped["C"].mean()
+    assert result.index.name == "A"
+    result = grouped["C"].agg(np.mean)
+    assert result.index.name == "A"
+    result = grouped["C"].agg([np.mean, np.std])
+    assert result.index.name == "A"
+
+    msg = r"nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=msg):
+        grouped["C"].agg({"foo": np.mean, "bar": np.std})
+
+
+def test_multi_func(df):
+    col1 = df["A"]
+    col2 = df["B"]
+
+    grouped = df.groupby([col1.get, col2.get])
+    agged = grouped.mean(numeric_only=True)
+    expected = df.groupby(["A", "B"]).mean()
+
+    # TODO groupby get drops names
+    tm.assert_frame_equal(
+        agged.loc[:, ["C", "D"]], expected.loc[:, ["C", "D"]], check_names=False
+    )
+
+    # some "groups" with no data
+    df = DataFrame(
+        {
+            "v1": np.random.randn(6),
+            "v2": np.random.randn(6),
+            "k1": np.array(["b", "b", "b", "a", "a", "a"]),
+            "k2": np.array(["1", "1", "1", "2", "2", "2"]),
+        },
+        index=["one", "two", "three", "four", "five", "six"],
+    )
+    # only verify that it works for now
+    grouped = df.groupby(["k1", "k2"])
+    grouped.agg(np.sum)
+
+
+def test_multi_key_multiple_functions(df):
+    grouped = df.groupby(["A", "B"])["C"]
+
+    agged = grouped.agg([np.mean, np.std])
+    expected = DataFrame({"mean": grouped.agg(np.mean), "std": grouped.agg(np.std)})
+    tm.assert_frame_equal(agged, expected)
+
+
+def test_frame_multi_key_function_list():
+    data = DataFrame(
+        {
+            "A": [
+                "foo",
+                "foo",
+                "foo",
+                "foo",
+                "bar",
+                "bar",
+                "bar",
+                "bar",
+                "foo",
+                "foo",
+                "foo",
+            ],
+            "B": [
+                "one",
+                "one",
+                "one",
+                "two",
+                "one",
+                "one",
+                "one",
+                "two",
+                "two",
+                "two",
+                "one",
+            ],
+            "D": np.random.randn(11),
+            "E": np.random.randn(11),
+            "F": np.random.randn(11),
+        }
+    )
+
+    grouped = data.groupby(["A", "B"])
+    funcs = [np.mean, np.std]
+    agged = grouped.agg(funcs)
+    expected = pd.concat(
+        [grouped["D"].agg(funcs), grouped["E"].agg(funcs), grouped["F"].agg(funcs)],
+        keys=["D", "E", "F"],
+        axis=1,
+    )
+    assert isinstance(agged.index, MultiIndex)
+    assert isinstance(expected.index, MultiIndex)
+    tm.assert_frame_equal(agged, expected)
+
+
+def test_frame_multi_key_function_list_partial_failure():
+    data = DataFrame(
+        {
+            "A": [
+                "foo",
+                "foo",
+                "foo",
+                "foo",
+                "bar",
+                "bar",
+                "bar",
+                "bar",
+                "foo",
+                "foo",
+                "foo",
+            ],
+            "B": [
+                "one",
+                "one",
+                "one",
+                "two",
+                "one",
+                "one",
+                "one",
+                "two",
+                "two",
+                "two",
+                "one",
+            ],
+            "C": [
+                "dull",
+                "dull",
+                "shiny",
+                "dull",
+                "dull",
+                "shiny",
+                "shiny",
+                "dull",
+                "shiny",
+                "shiny",
+                "shiny",
+            ],
+            "D": np.random.randn(11),
+            "E": np.random.randn(11),
+            "F": np.random.randn(11),
+        }
+    )
+
+    grouped = data.groupby(["A", "B"])
+    funcs = [np.mean, np.std]
+    with pytest.raises(TypeError, match="Could not convert dullshinyshiny to numeric"):
+        grouped.agg(funcs)
+
+
+@pytest.mark.parametrize("op", [lambda x: x.sum(), lambda x: x.mean()])
+def test_groupby_multiple_columns(df, op):
+    data = df
+    grouped = data.groupby(["A", "B"])
+
+    result1 = op(grouped)
+
+    keys = []
+    values = []
+    for n1, gp1 in data.groupby("A"):
+        for n2, gp2 in gp1.groupby("B"):
+            keys.append((n1, n2))
+            values.append(op(gp2.loc[:, ["C", "D"]]))
+
+    mi = MultiIndex.from_tuples(keys, names=["A", "B"])
+    expected = pd.concat(values, axis=1).T
+    expected.index = mi
+
+    # a little bit crude
+    for col in ["C", "D"]:
+        result_col = op(grouped[col])
+        pivoted = result1[col]
+        exp = expected[col]
+        tm.assert_series_equal(result_col, exp)
+        tm.assert_series_equal(pivoted, exp)
+
+    # test single series works the same
+    result = data["C"].groupby([data["A"], data["B"]]).mean()
+    expected = data.groupby(["A", "B"]).mean()["C"]
+
+    tm.assert_series_equal(result, expected)
+
+
+def test_as_index_select_column():
+    # GH 5764
+    df = DataFrame([[1, 2], [1, 4], [5, 6]], columns=["A", "B"])
+    result = df.groupby("A", as_index=False)["B"].get_group(1)
+    expected = Series([2, 4], name="B")
+    tm.assert_series_equal(result, expected)
+
+    result = df.groupby("A", as_index=False, group_keys=True)["B"].apply(
+        lambda x: x.cumsum()
+    )
+    expected = Series(
+        [2, 6, 6], name="B", index=MultiIndex.from_tuples([(0, 0), (0, 1), (1, 2)])
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_as_index_select_column_sum_empty_df():
+    # GH 35246
+    df = DataFrame(columns=Index(["A", "B", "C"], name="alpha"))
+    left = df.groupby(by="A", as_index=False)["B"].sum(numeric_only=False)
+
+    expected = DataFrame(columns=df.columns[:2], index=range(0))
+    # GH#50744 - Columns after selection shouldn't retain names
+    expected.columns.names = [None]
+    tm.assert_frame_equal(left, expected)
+
+
+def test_groupby_as_index_agg(df):
+    grouped = df.groupby("A", as_index=False)
+
+    # single-key
+
+    result = grouped[["C", "D"]].agg(np.mean)
+    expected = grouped.mean(numeric_only=True)
+    tm.assert_frame_equal(result, expected)
+
+    result2 = grouped.agg({"C": np.mean, "D": np.sum})
+    expected2 = grouped.mean(numeric_only=True)
+    expected2["D"] = grouped.sum()["D"]
+    tm.assert_frame_equal(result2, expected2)
+
+    grouped = df.groupby("A", as_index=True)
+
+    msg = r"nested renamer is not supported"
+    with pytest.raises(SpecificationError, match=msg):
+        grouped["C"].agg({"Q": np.sum})
+
+    # multi-key
+
+    grouped = df.groupby(["A", "B"], as_index=False)
+
+    result = grouped.agg(np.mean)
+    expected = grouped.mean()
+    tm.assert_frame_equal(result, expected)
+
+    result2 = grouped.agg({"C": np.mean, "D": np.sum})
+    expected2 = grouped.mean()
+    expected2["D"] = grouped.sum()["D"]
+    tm.assert_frame_equal(result2, expected2)
+
+    expected3 = grouped["C"].sum()
+    expected3 = DataFrame(expected3).rename(columns={"C": "Q"})
+    result3 = grouped["C"].agg({"Q": np.sum})
+    tm.assert_frame_equal(result3, expected3)
+
+    # GH7115 & GH8112 & GH8582
+    df = DataFrame(np.random.randint(0, 100, (50, 3)), columns=["jim", "joe", "jolie"])
+    ts = Series(np.random.randint(5, 10, 50), name="jim")
+
+    gr = df.groupby(ts)
+    gr.nth(0)  # invokes set_selection_from_grouper internally
+    tm.assert_frame_equal(gr.apply(sum), df.groupby(ts).apply(sum))
+
+    for attr in ["mean", "max", "count", "idxmax", "cumsum", "all"]:
+        gr = df.groupby(ts, as_index=False)
+        left = getattr(gr, attr)()
+
+        gr = df.groupby(ts.values, as_index=True)
+        right = getattr(gr, attr)().reset_index(drop=True)
+
+        tm.assert_frame_equal(left, right)
+
+
+def test_ops_not_as_index(reduction_func):
+    # GH 10355, 21090
+    # Using as_index=False should not modify grouped column
+
+    if reduction_func in ("corrwith", "nth", "ngroup"):
+        pytest.skip(f"GH 5755: Test not applicable for {reduction_func}")
+
+    df = DataFrame(np.random.randint(0, 5, size=(100, 2)), columns=["a", "b"])
+    expected = getattr(df.groupby("a"), reduction_func)()
+    if reduction_func == "size":
+        expected = expected.rename("size")
+    expected = expected.reset_index()
+
+    if reduction_func != "size":
+        # 32 bit compat -> groupby preserves dtype whereas reset_index casts to int64
+        expected["a"] = expected["a"].astype(df["a"].dtype)
+
+    g = df.groupby("a", as_index=False)
+
+    result = getattr(g, reduction_func)()
+    tm.assert_frame_equal(result, expected)
+
+    result = g.agg(reduction_func)
+    tm.assert_frame_equal(result, expected)
+
+    result = getattr(g["b"], reduction_func)()
+    tm.assert_frame_equal(result, expected)
+
+    result = g["b"].agg(reduction_func)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_as_index_series_return_frame(df):
+    grouped = df.groupby("A", as_index=False)
+    grouped2 = df.groupby(["A", "B"], as_index=False)
+
+    result = grouped["C"].agg(np.sum)
+    expected = grouped.agg(np.sum).loc[:, ["A", "C"]]
+    assert isinstance(result, DataFrame)
+    tm.assert_frame_equal(result, expected)
+
+    result2 = grouped2["C"].agg(np.sum)
+    expected2 = grouped2.agg(np.sum).loc[:, ["A", "B", "C"]]
+    assert isinstance(result2, DataFrame)
+    tm.assert_frame_equal(result2, expected2)
+
+    result = grouped["C"].sum()
+    expected = grouped.sum().loc[:, ["A", "C"]]
+    assert isinstance(result, DataFrame)
+    tm.assert_frame_equal(result, expected)
+
+    result2 = grouped2["C"].sum()
+    expected2 = grouped2.sum().loc[:, ["A", "B", "C"]]
+    assert isinstance(result2, DataFrame)
+    tm.assert_frame_equal(result2, expected2)
+
+
+def test_as_index_series_column_slice_raises(df):
+    # GH15072
+    grouped = df.groupby("A", as_index=False)
+    msg = r"Column\(s\) C already selected"
+
+    with pytest.raises(IndexError, match=msg):
+        grouped["C"].__getitem__("D")
+
+
+def test_groupby_as_index_cython(df):
+    data = df
+
+    # single-key
+    grouped = data.groupby("A", as_index=False)
+    result = grouped.mean(numeric_only=True)
+    expected = data.groupby(["A"]).mean(numeric_only=True)
+    expected.insert(0, "A", expected.index)
+    expected.index = RangeIndex(len(expected))
+    tm.assert_frame_equal(result, expected)
+
+    # multi-key
+    grouped = data.groupby(["A", "B"], as_index=False)
+    result = grouped.mean()
+    expected = data.groupby(["A", "B"]).mean()
+
+    arrays = list(zip(*expected.index.values))
+    expected.insert(0, "A", arrays[0])
+    expected.insert(1, "B", arrays[1])
+    expected.index = RangeIndex(len(expected))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_as_index_series_scalar(df):
+    grouped = df.groupby(["A", "B"], as_index=False)
+
+    # GH #421
+
+    result = grouped["C"].agg(len)
+    expected = grouped.agg(len).loc[:, ["A", "B", "C"]]
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_as_index_corner(df, ts):
+    msg = "as_index=False only valid with DataFrame"
+    with pytest.raises(TypeError, match=msg):
+        ts.groupby(lambda x: x.weekday(), as_index=False)
+
+    msg = "as_index=False only valid for axis=0"
+    with pytest.raises(ValueError, match=msg):
+        df.groupby(lambda x: x.lower(), as_index=False, axis=1)
+
+
+def test_groupby_multiple_key():
+    df = tm.makeTimeDataFrame()
+    grouped = df.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day])
+    agged = grouped.sum()
+    tm.assert_almost_equal(df.values, agged.values)
+
+    grouped = df.T.groupby(
+        [lambda x: x.year, lambda x: x.month, lambda x: x.day], axis=1
+    )
+
+    agged = grouped.agg(lambda x: x.sum())
+    tm.assert_index_equal(agged.index, df.columns)
+    tm.assert_almost_equal(df.T.values, agged.values)
+
+    agged = grouped.agg(lambda x: x.sum())
+    tm.assert_almost_equal(df.T.values, agged.values)
+
+
+def test_groupby_multi_corner(df):
+    # test that having an all-NA column doesn't mess you up
+    df = df.copy()
+    df["bad"] = np.nan
+    agged = df.groupby(["A", "B"]).mean()
+
+    expected = df.groupby(["A", "B"]).mean()
+    expected["bad"] = np.nan
+
+    tm.assert_frame_equal(agged, expected)
+
+
+def test_raises_on_nuisance(df):
+    grouped = df.groupby("A")
+    with pytest.raises(TypeError, match="Could not convert"):
+        grouped.agg(np.mean)
+    with pytest.raises(TypeError, match="Could not convert"):
+        grouped.mean()
+
+    df = df.loc[:, ["A", "C", "D"]]
+    df["E"] = datetime.now()
+    grouped = df.groupby("A")
+    msg = "datetime64 type does not support sum operations"
+    with pytest.raises(TypeError, match=msg):
+        grouped.agg(np.sum)
+    with pytest.raises(TypeError, match=msg):
+        grouped.sum()
+
+    # won't work with axis = 1
+    grouped = df.groupby({"A": 0, "C": 0, "D": 1, "E": 1}, axis=1)
+    msg = "does not support reduction 'sum'"
+    with pytest.raises(TypeError, match=msg):
+        grouped.agg(lambda x: x.sum(0, numeric_only=False))
+
+
+@pytest.mark.parametrize(
+    "agg_function",
+    ["max", "min"],
+)
+def test_keep_nuisance_agg(df, agg_function):
+    # GH 38815
+    grouped = df.groupby("A")
+    result = getattr(grouped, agg_function)()
+    expected = result.copy()
+    expected.loc["bar", "B"] = getattr(df.loc[df["A"] == "bar", "B"], agg_function)()
+    expected.loc["foo", "B"] = getattr(df.loc[df["A"] == "foo", "B"], agg_function)()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "agg_function",
+    ["sum", "mean", "prod", "std", "var", "sem", "median"],
+)
+@pytest.mark.parametrize("numeric_only", [True, False])
+def test_omit_nuisance_agg(df, agg_function, numeric_only):
+    # GH 38774, GH 38815
+    grouped = df.groupby("A")
+
+    no_drop_nuisance = ("var", "std", "sem", "mean", "prod", "median")
+    if agg_function in no_drop_nuisance and not numeric_only:
+        # Added numeric_only as part of GH#46560; these do not drop nuisance
+        # columns when numeric_only is False
+        klass = ValueError if agg_function in ("std", "sem") else TypeError
+        msg = "|".join(["[C|c]ould not convert", "can't multiply sequence"])
+        with pytest.raises(klass, match=msg):
+            getattr(grouped, agg_function)(numeric_only=numeric_only)
+    else:
+        result = getattr(grouped, agg_function)(numeric_only=numeric_only)
+        if not numeric_only and agg_function == "sum":
+            # sum is successful on column B
+            columns = ["A", "B", "C", "D"]
+        else:
+            columns = ["A", "C", "D"]
+        expected = getattr(df.loc[:, columns].groupby("A"), agg_function)(
+            numeric_only=numeric_only
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+def test_raise_on_nuisance_python_single(df):
+    # GH 38815
+    grouped = df.groupby("A")
+    with pytest.raises(TypeError, match="could not convert"):
+        grouped.skew()
+
+
+def test_raise_on_nuisance_python_multiple(three_group):
+    grouped = three_group.groupby(["A", "B"])
+    with pytest.raises(TypeError, match="Could not convert"):
+        grouped.agg(np.mean)
+    with pytest.raises(TypeError, match="Could not convert"):
+        grouped.mean()
+
+
+def test_empty_groups_corner(mframe):
+    # handle empty groups
+    df = DataFrame(
+        {
+            "k1": np.array(["b", "b", "b", "a", "a", "a"]),
+            "k2": np.array(["1", "1", "1", "2", "2", "2"]),
+            "k3": ["foo", "bar"] * 3,
+            "v1": np.random.randn(6),
+            "v2": np.random.randn(6),
+        }
+    )
+
+    grouped = df.groupby(["k1", "k2"])
+    result = grouped[["v1", "v2"]].agg(np.mean)
+    expected = grouped.mean(numeric_only=True)
+    tm.assert_frame_equal(result, expected)
+
+    grouped = mframe[3:5].groupby(level=0)
+    agged = grouped.apply(lambda x: x.mean())
+    agged_A = grouped["A"].apply(np.mean)
+    tm.assert_series_equal(agged["A"], agged_A)
+    assert agged.index.name == "first"
+
+
+def test_nonsense_func():
+    df = DataFrame([0])
+    msg = r"unsupported operand type\(s\) for \+: 'int' and 'str'"
+    with pytest.raises(TypeError, match=msg):
+        df.groupby(lambda x: x + "foo")
+
+
+def test_wrap_aggregated_output_multindex(mframe):
+    df = mframe.T
+    df["baz", "two"] = "peekaboo"
+
+    keys = [np.array([0, 0, 1]), np.array([0, 0, 1])]
+    with pytest.raises(TypeError, match="Could not convert"):
+        df.groupby(keys).agg(np.mean)
+    agged = df.drop(columns=("baz", "two")).groupby(keys).agg(np.mean)
+    assert isinstance(agged.columns, MultiIndex)
+
+    def aggfun(ser):
+        if ser.name == ("foo", "one"):
+            raise TypeError("Test error message")
+        return ser.sum()
+
+    with pytest.raises(TypeError, match="Test error message"):
+        df.groupby(keys).aggregate(aggfun)
+
+
+def test_groupby_level_apply(mframe):
+    result = mframe.groupby(level=0).count()
+    assert result.index.name == "first"
+    result = mframe.groupby(level=1).count()
+    assert result.index.name == "second"
+
+    result = mframe["A"].groupby(level=0).count()
+    assert result.index.name == "first"
+
+
+def test_groupby_level_mapper(mframe):
+    deleveled = mframe.reset_index()
+
+    mapper0 = {"foo": 0, "bar": 0, "baz": 1, "qux": 1}
+    mapper1 = {"one": 0, "two": 0, "three": 1}
+
+    result0 = mframe.groupby(mapper0, level=0).sum()
+    result1 = mframe.groupby(mapper1, level=1).sum()
+
+    mapped_level0 = np.array(
+        [mapper0.get(x) for x in deleveled["first"]], dtype=np.int64
+    )
+    mapped_level1 = np.array(
+        [mapper1.get(x) for x in deleveled["second"]], dtype=np.int64
+    )
+    expected0 = mframe.groupby(mapped_level0).sum()
+    expected1 = mframe.groupby(mapped_level1).sum()
+    expected0.index.name, expected1.index.name = "first", "second"
+
+    tm.assert_frame_equal(result0, expected0)
+    tm.assert_frame_equal(result1, expected1)
+
+
+def test_groupby_level_nonmulti():
+    # GH 1313, GH 13901
+    s = Series([1, 2, 3, 10, 4, 5, 20, 6], Index([1, 2, 3, 1, 4, 5, 2, 6], name="foo"))
+    expected = Series([11, 22, 3, 4, 5, 6], Index(range(1, 7), name="foo"))
+
+    result = s.groupby(level=0).sum()
+    tm.assert_series_equal(result, expected)
+    result = s.groupby(level=[0]).sum()
+    tm.assert_series_equal(result, expected)
+    result = s.groupby(level=-1).sum()
+    tm.assert_series_equal(result, expected)
+    result = s.groupby(level=[-1]).sum()
+    tm.assert_series_equal(result, expected)
+
+    msg = "level > 0 or level < -1 only valid with MultiIndex"
+    with pytest.raises(ValueError, match=msg):
+        s.groupby(level=1)
+    with pytest.raises(ValueError, match=msg):
+        s.groupby(level=-2)
+    msg = "No group keys passed!"
+    with pytest.raises(ValueError, match=msg):
+        s.groupby(level=[])
+    msg = "multiple levels only valid with MultiIndex"
+    with pytest.raises(ValueError, match=msg):
+        s.groupby(level=[0, 0])
+    with pytest.raises(ValueError, match=msg):
+        s.groupby(level=[0, 1])
+    msg = "level > 0 or level < -1 only valid with MultiIndex"
+    with pytest.raises(ValueError, match=msg):
+        s.groupby(level=[1])
+
+
+def test_groupby_complex():
+    # GH 12902
+    a = Series(data=np.arange(4) * (1 + 2j), index=[0, 0, 1, 1])
+    expected = Series((1 + 2j, 5 + 10j))
+
+    result = a.groupby(level=0).sum()
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_complex_numbers():
+    # GH 17927
+    df = DataFrame(
+        [
+            {"a": 1, "b": 1 + 1j},
+            {"a": 1, "b": 1 + 2j},
+            {"a": 4, "b": 1},
+        ]
+    )
+    expected = DataFrame(
+        np.array([1, 1, 1], dtype=np.int64),
+        index=Index([(1 + 1j), (1 + 2j), (1 + 0j)], name="b"),
+        columns=Index(["a"], dtype="object"),
+    )
+    result = df.groupby("b", sort=False).count()
+    tm.assert_frame_equal(result, expected)
+
+    # Sorted by the magnitude of the complex numbers
+    expected.index = Index([(1 + 0j), (1 + 1j), (1 + 2j)], name="b")
+    result = df.groupby("b", sort=True).count()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_series_indexed_differently():
+    s1 = Series(
+        [5.0, -9.0, 4.0, 100.0, -5.0, 55.0, 6.7],
+        index=Index(["a", "b", "c", "d", "e", "f", "g"]),
+    )
+    s2 = Series(
+        [1.0, 1.0, 4.0, 5.0, 5.0, 7.0], index=Index(["a", "b", "d", "f", "g", "h"])
+    )
+
+    grouped = s1.groupby(s2)
+    agged = grouped.mean()
+    exp = s1.groupby(s2.reindex(s1.index).get).mean()
+    tm.assert_series_equal(agged, exp)
+
+
+def test_groupby_with_hier_columns():
+    tuples = list(
+        zip(
+            *[
+                ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
+                ["one", "two", "one", "two", "one", "two", "one", "two"],
+            ]
+        )
+    )
+    index = MultiIndex.from_tuples(tuples)
+    columns = MultiIndex.from_tuples(
+        [("A", "cat"), ("B", "dog"), ("B", "cat"), ("A", "dog")]
+    )
+    df = DataFrame(np.random.randn(8, 4), index=index, columns=columns)
+
+    result = df.groupby(level=0).mean()
+    tm.assert_index_equal(result.columns, columns)
+
+    result = df.groupby(level=0, axis=1).mean()
+    tm.assert_index_equal(result.index, df.index)
+
+    result = df.groupby(level=0).agg(np.mean)
+    tm.assert_index_equal(result.columns, columns)
+
+    result = df.groupby(level=0).apply(lambda x: x.mean())
+    tm.assert_index_equal(result.columns, columns)
+
+    result = df.groupby(level=0, axis=1).agg(lambda x: x.mean(1))
+    tm.assert_index_equal(result.columns, Index(["A", "B"]))
+    tm.assert_index_equal(result.index, df.index)
+
+    # add a nuisance column
+    sorted_columns, _ = columns.sortlevel(0)
+    df["A", "foo"] = "bar"
+    result = df.groupby(level=0).mean(numeric_only=True)
+    tm.assert_index_equal(result.columns, df.columns[:-1])
+
+
+def test_grouping_ndarray(df):
+    grouped = df.groupby(df["A"].values)
+    result = grouped.sum()
+    expected = df.groupby(df["A"].rename(None)).sum()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_wrong_multi_labels():
+    index = Index([0, 1, 2, 3, 4], name="index")
+    data = DataFrame(
+        {
+            "foo": ["foo1", "foo1", "foo2", "foo1", "foo3"],
+            "bar": ["bar1", "bar2", "bar2", "bar1", "bar1"],
+            "baz": ["baz1", "baz1", "baz1", "baz2", "baz2"],
+            "spam": ["spam2", "spam3", "spam2", "spam1", "spam1"],
+            "data": [20, 30, 40, 50, 60],
+        },
+        index=index,
+    )
+
+    grouped = data.groupby(["foo", "bar", "baz", "spam"])
+
+    result = grouped.agg(np.mean)
+    expected = grouped.mean()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_series_with_name(df):
+    result = df.groupby(df["A"]).mean(numeric_only=True)
+    result2 = df.groupby(df["A"], as_index=False).mean(numeric_only=True)
+    assert result.index.name == "A"
+    assert "A" in result2
+
+    result = df.groupby([df["A"], df["B"]]).mean()
+    result2 = df.groupby([df["A"], df["B"]], as_index=False).mean()
+    assert result.index.names == ("A", "B")
+    assert "A" in result2
+    assert "B" in result2
+
+
+def test_seriesgroupby_name_attr(df):
+    # GH 6265
+    result = df.groupby("A")["C"]
+    assert result.count().name == "C"
+    assert result.mean().name == "C"
+
+    testFunc = lambda x: np.sum(x) * 2
+    assert result.agg(testFunc).name == "C"
+
+
+def test_consistency_name():
+    # GH 12363
+
+    df = DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "two", "two", "two", "one", "two"],
+            "C": np.random.randn(8) + 1.0,
+            "D": np.arange(8),
+        }
+    )
+
+    expected = df.groupby(["A"]).B.count()
+    result = df.B.groupby(df.A).count()
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_name_propagation(df):
+    # GH 6124
+    def summarize(df, name=None):
+        return Series({"count": 1, "mean": 2, "omissions": 3}, name=name)
+
+    def summarize_random_name(df):
+        # Provide a different name for each Series.  In this case, groupby
+        # should not attempt to propagate the Series name since they are
+        # inconsistent.
+        return Series({"count": 1, "mean": 2, "omissions": 3}, name=df.iloc[0]["A"])
+
+    metrics = df.groupby("A").apply(summarize)
+    assert metrics.columns.name is None
+    metrics = df.groupby("A").apply(summarize, "metrics")
+    assert metrics.columns.name == "metrics"
+    metrics = df.groupby("A").apply(summarize_random_name)
+    assert metrics.columns.name is None
+
+
+def test_groupby_nonstring_columns():
+    df = DataFrame([np.arange(10) for x in range(10)])
+    grouped = df.groupby(0)
+    result = grouped.mean()
+    expected = df.groupby(df[0]).mean()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_mixed_type_columns():
+    # GH 13432, unorderable types in py3
+    df = DataFrame([[0, 1, 2]], columns=["A", "B", 0])
+    expected = DataFrame([[1, 2]], columns=["B", 0], index=Index([0], name="A"))
+
+    result = df.groupby("A").first()
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby("A").sum()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_cython_grouper_series_bug_noncontig():
+    arr = np.empty((100, 100))
+    arr.fill(np.nan)
+    obj = Series(arr[:, 0])
+    inds = np.tile(range(10), 10)
+
+    result = obj.groupby(inds).agg(Series.median)
+    assert result.isna().all()
+
+
+def test_series_grouper_noncontig_index():
+    index = Index(tm.rands_array(10, 100))
+
+    values = Series(np.random.randn(50), index=index[::2])
+    labels = np.random.randint(0, 5, 50)
+
+    # it works!
+    grouped = values.groupby(labels)
+
+    # accessing the index elements causes segfault
+    f = lambda x: len(set(map(id, x.index)))
+    grouped.agg(f)
+
+
+def test_convert_objects_leave_decimal_alone():
+    s = Series(range(5))
+    labels = np.array(["a", "b", "c", "d", "e"], dtype="O")
+
+    def convert_fast(x):
+        return Decimal(str(x.mean()))
+
+    def convert_force_pure(x):
+        # base will be length 0
+        assert len(x.values.base) > 0
+        return Decimal(str(x.mean()))
+
+    grouped = s.groupby(labels)
+
+    result = grouped.agg(convert_fast)
+    assert result.dtype == np.object_
+    assert isinstance(result[0], Decimal)
+
+    result = grouped.agg(convert_force_pure)
+    assert result.dtype == np.object_
+    assert isinstance(result[0], Decimal)
+
+
+def test_groupby_dtype_inference_empty():
+    # GH 6733
+    df = DataFrame({"x": [], "range": np.arange(0, dtype="int64")})
+    assert df["x"].dtype == np.float64
+
+    result = df.groupby("x").first()
+    exp_index = Index([], name="x", dtype=np.float64)
+    expected = DataFrame({"range": Series([], index=exp_index, dtype="int64")})
+    tm.assert_frame_equal(result, expected, by_blocks=True)
+
+
+def test_groupby_unit64_float_conversion():
+    #  GH: 30859 groupby converts unit64 to floats sometimes
+    df = DataFrame({"first": [1], "second": [1], "value": [16148277970000000000]})
+    result = df.groupby(["first", "second"])["value"].max()
+    expected = Series(
+        [16148277970000000000],
+        MultiIndex.from_product([[1], [1]], names=["first", "second"]),
+        name="value",
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_list_infer_array_like(df):
+    result = df.groupby(list(df["A"])).mean(numeric_only=True)
+    expected = df.groupby(df["A"]).mean(numeric_only=True)
+    tm.assert_frame_equal(result, expected, check_names=False)
+
+    with pytest.raises(KeyError, match=r"^'foo'$"):
+        df.groupby(list(df["A"][:-1]))
+
+    # pathological case of ambiguity
+    df = DataFrame({"foo": [0, 1], "bar": [3, 4], "val": np.random.randn(2)})
+
+    result = df.groupby(["foo", "bar"]).mean()
+    expected = df.groupby([df["foo"], df["bar"]]).mean()[["val"]]
+
+
+def test_groupby_keys_same_size_as_index():
+    # GH 11185
+    freq = "s"
+    index = date_range(
+        start=Timestamp("2015-09-29T11:34:44-0700"), periods=2, freq=freq
+    )
+    df = DataFrame([["A", 10], ["B", 15]], columns=["metric", "values"], index=index)
+    result = df.groupby([Grouper(level=0, freq=freq), "metric"]).mean()
+    expected = df.set_index([df.index, "metric"]).astype(float)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_one_row():
+    # GH 11741
+    msg = r"^'Z'$"
+    df1 = DataFrame(np.random.randn(1, 4), columns=list("ABCD"))
+    with pytest.raises(KeyError, match=msg):
+        df1.groupby("Z")
+    df2 = DataFrame(np.random.randn(2, 4), columns=list("ABCD"))
+    with pytest.raises(KeyError, match=msg):
+        df2.groupby("Z")
+
+
+def test_groupby_nat_exclude():
+    # GH 6992
+    df = DataFrame(
+        {
+            "values": np.random.randn(8),
+            "dt": [
+                np.nan,
+                Timestamp("2013-01-01"),
+                np.nan,
+                Timestamp("2013-02-01"),
+                np.nan,
+                Timestamp("2013-02-01"),
+                np.nan,
+                Timestamp("2013-01-01"),
+            ],
+            "str": [np.nan, "a", np.nan, "a", np.nan, "a", np.nan, "b"],
+        }
+    )
+    grouped = df.groupby("dt")
+
+    expected = [Index([1, 7]), Index([3, 5])]
+    keys = sorted(grouped.groups.keys())
+    assert len(keys) == 2
+    for k, e in zip(keys, expected):
+        # grouped.groups keys are np.datetime64 with system tz
+        # not to be affected by tz, only compare values
+        tm.assert_index_equal(grouped.groups[k], e)
+
+    # confirm obj is not filtered
+    tm.assert_frame_equal(grouped.grouper.groupings[0].obj, df)
+    assert grouped.ngroups == 2
+
+    expected = {
+        Timestamp("2013-01-01 00:00:00"): np.array([1, 7], dtype=np.intp),
+        Timestamp("2013-02-01 00:00:00"): np.array([3, 5], dtype=np.intp),
+    }
+
+    for k in grouped.indices:
+        tm.assert_numpy_array_equal(grouped.indices[k], expected[k])
+
+    tm.assert_frame_equal(grouped.get_group(Timestamp("2013-01-01")), df.iloc[[1, 7]])
+    tm.assert_frame_equal(grouped.get_group(Timestamp("2013-02-01")), df.iloc[[3, 5]])
+
+    with pytest.raises(KeyError, match=r"^NaT$"):
+        grouped.get_group(pd.NaT)
+
+    nan_df = DataFrame(
+        {"nan": [np.nan, np.nan, np.nan], "nat": [pd.NaT, pd.NaT, pd.NaT]}
+    )
+    assert nan_df["nan"].dtype == "float64"
+    assert nan_df["nat"].dtype == "datetime64[ns]"
+
+    for key in ["nan", "nat"]:
+        grouped = nan_df.groupby(key)
+        assert grouped.groups == {}
+        assert grouped.ngroups == 0
+        assert grouped.indices == {}
+        with pytest.raises(KeyError, match=r"^nan$"):
+            grouped.get_group(np.nan)
+        with pytest.raises(KeyError, match=r"^NaT$"):
+            grouped.get_group(pd.NaT)
+
+
+def test_groupby_two_group_keys_all_nan():
+    # GH #36842: Grouping over two group keys shouldn't raise an error
+    df = DataFrame({"a": [np.nan, np.nan], "b": [np.nan, np.nan], "c": [1, 2]})
+    result = df.groupby(["a", "b"]).indices
+    assert result == {}
+
+
+def test_groupby_2d_malformed():
+    d = DataFrame(index=range(2))
+    d["group"] = ["g1", "g2"]
+    d["zeros"] = [0, 0]
+    d["ones"] = [1, 1]
+    d["label"] = ["l1", "l2"]
+    tmp = d.groupby(["group"]).mean(numeric_only=True)
+    res_values = np.array([[0.0, 1.0], [0.0, 1.0]])
+    tm.assert_index_equal(tmp.columns, Index(["zeros", "ones"]))
+    tm.assert_numpy_array_equal(tmp.values, res_values)
+
+
+def test_int32_overflow():
+    B = np.concatenate((np.arange(10000), np.arange(10000), np.arange(5000)))
+    A = np.arange(25000)
+    df = DataFrame({"A": A, "B": B, "C": A, "D": B, "E": np.random.randn(25000)})
+
+    left = df.groupby(["A", "B", "C", "D"]).sum()
+    right = df.groupby(["D", "C", "B", "A"]).sum()
+    assert len(left) == len(right)
+
+
+def test_groupby_sort_multi():
+    df = DataFrame(
+        {
+            "a": ["foo", "bar", "baz"],
+            "b": [3, 2, 1],
+            "c": [0, 1, 2],
+            "d": np.random.randn(3),
+        }
+    )
+
+    tups = [tuple(row) for row in df[["a", "b", "c"]].values]
+    tups = com.asarray_tuplesafe(tups)
+    result = df.groupby(["a", "b", "c"], sort=True).sum()
+    tm.assert_numpy_array_equal(result.index.values, tups[[1, 2, 0]])
+
+    tups = [tuple(row) for row in df[["c", "a", "b"]].values]
+    tups = com.asarray_tuplesafe(tups)
+    result = df.groupby(["c", "a", "b"], sort=True).sum()
+    tm.assert_numpy_array_equal(result.index.values, tups)
+
+    tups = [tuple(x) for x in df[["b", "c", "a"]].values]
+    tups = com.asarray_tuplesafe(tups)
+    result = df.groupby(["b", "c", "a"], sort=True).sum()
+    tm.assert_numpy_array_equal(result.index.values, tups[[2, 1, 0]])
+
+    df = DataFrame(
+        {"a": [0, 1, 2, 0, 1, 2], "b": [0, 0, 0, 1, 1, 1], "d": np.random.randn(6)}
+    )
+    grouped = df.groupby(["a", "b"])["d"]
+    result = grouped.sum()
+
+    def _check_groupby(df, result, keys, field, f=lambda x: x.sum()):
+        tups = [tuple(row) for row in df[keys].values]
+        tups = com.asarray_tuplesafe(tups)
+        expected = f(df.groupby(tups)[field])
+        for k, v in expected.items():
+            assert result[k] == v
+
+    _check_groupby(df, result, ["a", "b"], "d")
+
+
+def test_dont_clobber_name_column():
+    df = DataFrame(
+        {"key": ["a", "a", "a", "b", "b", "b"], "name": ["foo", "bar", "baz"] * 2}
+    )
+
+    result = df.groupby("key", group_keys=False).apply(lambda x: x)
+    tm.assert_frame_equal(result, df)
+
+
+def test_skip_group_keys():
+    tsf = tm.makeTimeDataFrame()
+
+    grouped = tsf.groupby(lambda x: x.month, group_keys=False)
+    result = grouped.apply(lambda x: x.sort_values(by="A")[:3])
+
+    pieces = [group.sort_values(by="A")[:3] for key, group in grouped]
+
+    expected = pd.concat(pieces)
+    tm.assert_frame_equal(result, expected)
+
+    grouped = tsf["A"].groupby(lambda x: x.month, group_keys=False)
+    result = grouped.apply(lambda x: x.sort_values()[:3])
+
+    pieces = [group.sort_values()[:3] for key, group in grouped]
+
+    expected = pd.concat(pieces)
+    tm.assert_series_equal(result, expected)
+
+
+def test_no_nonsense_name(float_frame):
+    # GH #995
+    s = float_frame["C"].copy()
+    s.name = None
+
+    result = s.groupby(float_frame["A"]).agg(np.sum)
+    assert result.name is None
+
+
+def test_multifunc_sum_bug():
+    # GH #1065
+    x = DataFrame(np.arange(9).reshape(3, 3))
+    x["test"] = 0
+    x["fl"] = [1.3, 1.5, 1.6]
+
+    grouped = x.groupby("test")
+    result = grouped.agg({"fl": "sum", 2: "size"})
+    assert result["fl"].dtype == np.float64
+
+
+def test_handle_dict_return_value(df):
+    def f(group):
+        return {"max": group.max(), "min": group.min()}
+
+    def g(group):
+        return Series({"max": group.max(), "min": group.min()})
+
+    result = df.groupby("A")["C"].apply(f)
+    expected = df.groupby("A")["C"].apply(g)
+
+    assert isinstance(result, Series)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("grouper", ["A", ["A", "B"]])
+def test_set_group_name(df, grouper):
+    def f(group):
+        assert group.name is not None
+        return group
+
+    def freduce(group):
+        assert group.name is not None
+        return group.sum()
+
+    def freducex(x):
+        return freduce(x)
+
+    grouped = df.groupby(grouper, group_keys=False)
+
+    # make sure all these work
+    grouped.apply(f)
+    grouped.aggregate(freduce)
+    grouped.aggregate({"C": freduce, "D": freduce})
+    grouped.transform(f)
+
+    grouped["C"].apply(f)
+    grouped["C"].aggregate(freduce)
+    grouped["C"].aggregate([freduce, freducex])
+    grouped["C"].transform(f)
+
+
+def test_group_name_available_in_inference_pass():
+    # gh-15062
+    df = DataFrame({"a": [0, 0, 1, 1, 2, 2], "b": np.arange(6)})
+
+    names = []
+
+    def f(group):
+        names.append(group.name)
+        return group.copy()
+
+    df.groupby("a", sort=False, group_keys=False).apply(f)
+
+    expected_names = [0, 1, 2]
+    assert names == expected_names
+
+
+def test_no_dummy_key_names(df):
+    # see gh-1291
+    result = df.groupby(df["A"].values).sum()
+    assert result.index.name is None
+
+    result = df.groupby([df["A"].values, df["B"].values]).sum()
+    assert result.index.names == (None, None)
+
+
+def test_groupby_sort_multiindex_series():
+    # series multiindex groupby sort argument was not being passed through
+    # _compress_group_index
+    # GH 9444
+    index = MultiIndex(
+        levels=[[1, 2], [1, 2]],
+        codes=[[0, 0, 0, 0, 1, 1], [1, 1, 0, 0, 0, 0]],
+        names=["a", "b"],
+    )
+    mseries = Series([0, 1, 2, 3, 4, 5], index=index)
+    index = MultiIndex(
+        levels=[[1, 2], [1, 2]], codes=[[0, 0, 1], [1, 0, 0]], names=["a", "b"]
+    )
+    mseries_result = Series([0, 2, 4], index=index)
+
+    result = mseries.groupby(level=["a", "b"], sort=False).first()
+    tm.assert_series_equal(result, mseries_result)
+    result = mseries.groupby(level=["a", "b"], sort=True).first()
+    tm.assert_series_equal(result, mseries_result.sort_index())
+
+
+def test_groupby_reindex_inside_function():
+    periods = 1000
+    ind = date_range(start="2012/1/1", freq="5min", periods=periods)
+    df = DataFrame({"high": np.arange(periods), "low": np.arange(periods)}, index=ind)
+
+    def agg_before(func, fix=False):
+        """
+        Run an aggregate func on the subset of data.
+        """
+
+        def _func(data):
+            d = data.loc[data.index.map(lambda x: x.hour < 11)].dropna()
+            if fix:
+                data[data.index[0]]
+            if len(d) == 0:
+                return None
+            return func(d)
+
+        return _func
+
+    grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
+    closure_bad = grouped.agg({"high": agg_before(np.max)})
+    closure_good = grouped.agg({"high": agg_before(np.max, True)})
+
+    tm.assert_frame_equal(closure_bad, closure_good)
+
+
+def test_groupby_multiindex_missing_pair():
+    # GH9049
+    df = DataFrame(
+        {
+            "group1": ["a", "a", "a", "b"],
+            "group2": ["c", "c", "d", "c"],
+            "value": [1, 1, 1, 5],
+        }
+    )
+    df = df.set_index(["group1", "group2"])
+    df_grouped = df.groupby(level=["group1", "group2"], sort=True)
+
+    res = df_grouped.agg("sum")
+    idx = MultiIndex.from_tuples(
+        [("a", "c"), ("a", "d"), ("b", "c")], names=["group1", "group2"]
+    )
+    exp = DataFrame([[2], [1], [5]], index=idx, columns=["value"])
+
+    tm.assert_frame_equal(res, exp)
+
+
+def test_groupby_multiindex_not_lexsorted():
+    # GH 11640
+
+    # define the lexsorted version
+    lexsorted_mi = MultiIndex.from_tuples(
+        [("a", ""), ("b1", "c1"), ("b2", "c2")], names=["b", "c"]
+    )
+    lexsorted_df = DataFrame([[1, 3, 4]], columns=lexsorted_mi)
+    assert lexsorted_df.columns._is_lexsorted()
+
+    # define the non-lexsorted version
+    not_lexsorted_df = DataFrame(
+        columns=["a", "b", "c", "d"], data=[[1, "b1", "c1", 3], [1, "b2", "c2", 4]]
+    )
+    not_lexsorted_df = not_lexsorted_df.pivot_table(
+        index="a", columns=["b", "c"], values="d"
+    )
+    not_lexsorted_df = not_lexsorted_df.reset_index()
+    assert not not_lexsorted_df.columns._is_lexsorted()
+
+    # compare the results
+    tm.assert_frame_equal(lexsorted_df, not_lexsorted_df)
+
+    expected = lexsorted_df.groupby("a").mean()
+    with tm.assert_produces_warning(PerformanceWarning):
+        result = not_lexsorted_df.groupby("a").mean()
+    tm.assert_frame_equal(expected, result)
+
+    # a transforming function should work regardless of sort
+    # GH 14776
+    df = DataFrame(
+        {"x": ["a", "a", "b", "a"], "y": [1, 1, 2, 2], "z": [1, 2, 3, 4]}
+    ).set_index(["x", "y"])
+    assert not df.index._is_lexsorted()
+
+    for level in [0, 1, [0, 1]]:
+        for sort in [False, True]:
+            result = df.groupby(level=level, sort=sort, group_keys=False).apply(
+                DataFrame.drop_duplicates
+            )
+            expected = df
+            tm.assert_frame_equal(expected, result)
+
+            result = (
+                df.sort_index()
+                .groupby(level=level, sort=sort, group_keys=False)
+                .apply(DataFrame.drop_duplicates)
+            )
+            expected = df.sort_index()
+            tm.assert_frame_equal(expected, result)
+
+
+def test_index_label_overlaps_location():
+    # checking we don't have any label/location confusion in the
+    # wake of GH5375
+    df = DataFrame(list("ABCDE"), index=[2, 0, 2, 1, 1])
+    g = df.groupby(list("ababb"))
+    actual = g.filter(lambda x: len(x) > 2)
+    expected = df.iloc[[1, 3, 4]]
+    tm.assert_frame_equal(actual, expected)
+
+    ser = df[0]
+    g = ser.groupby(list("ababb"))
+    actual = g.filter(lambda x: len(x) > 2)
+    expected = ser.take([1, 3, 4])
+    tm.assert_series_equal(actual, expected)
+
+    #  and again, with a generic Index of floats
+    df.index = df.index.astype(float)
+    g = df.groupby(list("ababb"))
+    actual = g.filter(lambda x: len(x) > 2)
+    expected = df.iloc[[1, 3, 4]]
+    tm.assert_frame_equal(actual, expected)
+
+    ser = df[0]
+    g = ser.groupby(list("ababb"))
+    actual = g.filter(lambda x: len(x) > 2)
+    expected = ser.take([1, 3, 4])
+    tm.assert_series_equal(actual, expected)
+
+
+def test_transform_doesnt_clobber_ints():
+    # GH 7972
+    n = 6
+    x = np.arange(n)
+    df = DataFrame({"a": x // 2, "b": 2.0 * x, "c": 3.0 * x})
+    df2 = DataFrame({"a": x // 2 * 1.0, "b": 2.0 * x, "c": 3.0 * x})
+
+    gb = df.groupby("a")
+    result = gb.transform("mean")
+
+    gb2 = df2.groupby("a")
+    expected = gb2.transform("mean")
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "sort_column",
+    ["ints", "floats", "strings", ["ints", "floats"], ["ints", "strings"]],
+)
+@pytest.mark.parametrize(
+    "group_column", ["int_groups", "string_groups", ["int_groups", "string_groups"]]
+)
+def test_groupby_preserves_sort(sort_column, group_column):
+    # Test to ensure that groupby always preserves sort order of original
+    # object. Issue #8588 and #9651
+
+    df = DataFrame(
+        {
+            "int_groups": [3, 1, 0, 1, 0, 3, 3, 3],
+            "string_groups": ["z", "a", "z", "a", "a", "g", "g", "g"],
+            "ints": [8, 7, 4, 5, 2, 9, 1, 1],
+            "floats": [2.3, 5.3, 6.2, -2.4, 2.2, 1.1, 1.1, 5],
+            "strings": ["z", "d", "a", "e", "word", "word2", "42", "47"],
+        }
+    )
+
+    # Try sorting on different types and with different group types
+
+    df = df.sort_values(by=sort_column)
+    g = df.groupby(group_column)
+
+    def test_sort(x):
+        tm.assert_frame_equal(x, x.sort_values(by=sort_column))
+
+    g.apply(test_sort)
+
+
+def test_pivot_table_values_key_error():
+    # This test is designed to replicate the error in issue #14938
+    df = DataFrame(
+        {
+            "eventDate": date_range(datetime.today(), periods=20, freq="M").tolist(),
+            "thename": range(0, 20),
+        }
+    )
+
+    df["year"] = df.set_index("eventDate").index.year
+    df["month"] = df.set_index("eventDate").index.month
+
+    with pytest.raises(KeyError, match="'badname'"):
+        df.reset_index().pivot_table(
+            index="year", columns="month", values="badname", aggfunc="count"
+        )
+
+
+@pytest.mark.parametrize("columns", ["C", ["C"]])
+@pytest.mark.parametrize("keys", [["A"], ["A", "B"]])
+@pytest.mark.parametrize(
+    "values",
+    [
+        [True],
+        [0],
+        [0.0],
+        ["a"],
+        Categorical([0]),
+        [to_datetime(0)],
+        date_range(0, 1, 1, tz="US/Eastern"),
+        pd.period_range("2016-01-01", periods=3, freq="D"),
+        pd.array([0], dtype="Int64"),
+        pd.array([0], dtype="Float64"),
+        pd.array([False], dtype="boolean"),
+    ],
+    ids=[
+        "bool",
+        "int",
+        "float",
+        "str",
+        "cat",
+        "dt64",
+        "dt64tz",
+        "period",
+        "Int64",
+        "Float64",
+        "boolean",
+    ],
+)
+@pytest.mark.parametrize("method", ["attr", "agg", "apply"])
+@pytest.mark.parametrize(
+    "op", ["idxmax", "idxmin", "min", "max", "sum", "prod", "skew"]
+)
+def test_empty_groupby(
+    columns, keys, values, method, op, request, using_array_manager, dropna
+):
+    # GH8093 & GH26411
+    override_dtype = None
+
+    if (
+        isinstance(values, Categorical)
+        and len(keys) == 1
+        and op in ["idxmax", "idxmin"]
+    ):
+        mark = pytest.mark.xfail(
+            raises=ValueError, match="attempt to get arg(min|max) of an empty sequence"
+        )
+        request.node.add_marker(mark)
+
+    if isinstance(values, BooleanArray) and op in ["sum", "prod"]:
+        # We expect to get Int64 back for these
+        override_dtype = "Int64"
+
+    if isinstance(values[0], bool) and op in ("prod", "sum"):
+        # sum/product of bools is an integer
+        override_dtype = "int64"
+
+    df = DataFrame({"A": values, "B": values, "C": values}, columns=list("ABC"))
+
+    if hasattr(values, "dtype"):
+        # check that we did the construction right
+        assert (df.dtypes == values.dtype).all()
+
+    df = df.iloc[:0]
+
+    gb = df.groupby(keys, group_keys=False, dropna=dropna)[columns]
+
+    def get_result(**kwargs):
+        if method == "attr":
+            return getattr(gb, op)(**kwargs)
+        else:
+            return getattr(gb, method)(op, **kwargs)
+
+    def get_categorical_invalid_expected():
+        # Categorical is special without 'observed=True', we get an NaN entry
+        #  corresponding to the unobserved group. If we passed observed=True
+        #  to groupby, expected would just be 'df.set_index(keys)[columns]'
+        #  as below
+        lev = Categorical([0], dtype=values.dtype)
+        if len(keys) != 1:
+            idx = MultiIndex.from_product([lev, lev], names=keys)
+        else:
+            # all columns are dropped, but we end up with one row
+            # Categorical is special without 'observed=True'
+            idx = Index(lev, name=keys[0])
+
+        expected = DataFrame([], columns=[], index=idx)
+        return expected
+
+    is_per = isinstance(df.dtypes[0], pd.PeriodDtype)
+    is_dt64 = df.dtypes[0].kind == "M"
+    is_cat = isinstance(values, Categorical)
+
+    if isinstance(values, Categorical) and not values.ordered and op in ["min", "max"]:
+        msg = f"Cannot perform {op} with non-ordered Categorical"
+        with pytest.raises(TypeError, match=msg):
+            get_result()
+
+        if isinstance(columns, list):
+            # i.e. DataframeGroupBy, not SeriesGroupBy
+            result = get_result(numeric_only=True)
+            expected = get_categorical_invalid_expected()
+            tm.assert_equal(result, expected)
+        return
+
+    if op in ["prod", "sum", "skew"]:
+        # ops that require more than just ordered-ness
+        if is_dt64 or is_cat or is_per:
+            # GH#41291
+            # datetime64 -> prod and sum are invalid
+            if op == "skew":
+                msg = "does not support reduction 'skew'"
+            elif is_dt64:
+                msg = "datetime64 type does not support"
+            elif is_per:
+                msg = "Period type does not support"
+            else:
+                msg = "category type does not support"
+            with pytest.raises(TypeError, match=msg):
+                get_result()
+
+            if not isinstance(columns, list):
+                # i.e. SeriesGroupBy
+                return
+            elif op == "skew":
+                # TODO: test the numeric_only=True case
+                return
+            else:
+                # i.e. op in ["prod", "sum"]:
+                # i.e. DataFrameGroupBy
+                # ops that require more than just ordered-ness
+                # GH#41291
+                result = get_result(numeric_only=True)
+
+                # with numeric_only=True, these are dropped, and we get
+                # an empty DataFrame back
+                expected = df.set_index(keys)[[]]
+                if is_cat:
+                    expected = get_categorical_invalid_expected()
+                tm.assert_equal(result, expected)
+                return
+
+    result = get_result()
+    expected = df.set_index(keys)[columns]
+    if override_dtype is not None:
+        expected = expected.astype(override_dtype)
+    if len(keys) == 1:
+        expected.index.name = keys[0]
+    tm.assert_equal(result, expected)
+
+
+def test_empty_groupby_apply_nonunique_columns():
+    # GH#44417
+    df = DataFrame(np.random.randn(0, 4))
+    df[3] = df[3].astype(np.int64)
+    df.columns = [0, 1, 2, 0]
+    gb = df.groupby(df[1], group_keys=False)
+    res = gb.apply(lambda x: x)
+    assert (res.dtypes == df.dtypes).all()
+
+
+def test_tuple_as_grouping():
+    # https://github.com/pandas-dev/pandas/issues/18314
+    df = DataFrame(
+        {
+            ("a", "b"): [1, 1, 1, 1],
+            "a": [2, 2, 2, 2],
+            "b": [2, 2, 2, 2],
+            "c": [1, 1, 1, 1],
+        }
+    )
+
+    with pytest.raises(KeyError, match=r"('a', 'b')"):
+        df[["a", "b", "c"]].groupby(("a", "b"))
+
+    result = df.groupby(("a", "b"))["c"].sum()
+    expected = Series([4], name="c", index=Index([1], name=("a", "b")))
+    tm.assert_series_equal(result, expected)
+
+
+def test_tuple_correct_keyerror():
+    # https://github.com/pandas-dev/pandas/issues/18798
+    df = DataFrame(1, index=range(3), columns=MultiIndex.from_product([[1, 2], [3, 4]]))
+    with pytest.raises(KeyError, match=r"^\(7, 8\)$"):
+        df.groupby((7, 8)).mean()
+
+
+def test_groupby_agg_ohlc_non_first():
+    # GH 21716
+    df = DataFrame(
+        [[1], [1]],
+        columns=Index(["foo"], name="mycols"),
+        index=date_range("2018-01-01", periods=2, freq="D", name="dti"),
+    )
+
+    expected = DataFrame(
+        [[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]],
+        columns=MultiIndex.from_tuples(
+            (
+                ("foo", "sum", "foo"),
+                ("foo", "ohlc", "open"),
+                ("foo", "ohlc", "high"),
+                ("foo", "ohlc", "low"),
+                ("foo", "ohlc", "close"),
+            ),
+            names=["mycols", None, None],
+        ),
+        index=date_range("2018-01-01", periods=2, freq="D", name="dti"),
+    )
+
+    result = df.groupby(Grouper(freq="D")).agg(["sum", "ohlc"])
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_multiindex_nat():
+    # GH 9236
+    values = [
+        (pd.NaT, "a"),
+        (datetime(2012, 1, 2), "a"),
+        (datetime(2012, 1, 2), "b"),
+        (datetime(2012, 1, 3), "a"),
+    ]
+    mi = MultiIndex.from_tuples(values, names=["date", None])
+    ser = Series([3, 2, 2.5, 4], index=mi)
+
+    result = ser.groupby(level=1).mean()
+    expected = Series([3.0, 2.5], index=["a", "b"])
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_empty_list_raises():
+    # GH 5289
+    values = zip(range(10), range(10))
+    df = DataFrame(values, columns=["apple", "b"])
+    msg = "Grouper and axis must be same length"
+    with pytest.raises(ValueError, match=msg):
+        df.groupby([[]])
+
+
+def test_groupby_multiindex_series_keys_len_equal_group_axis():
+    # GH 25704
+    index_array = [["x", "x"], ["a", "b"], ["k", "k"]]
+    index_names = ["first", "second", "third"]
+    ri = MultiIndex.from_arrays(index_array, names=index_names)
+    s = Series(data=[1, 2], index=ri)
+    result = s.groupby(["first", "third"]).sum()
+
+    index_array = [["x"], ["k"]]
+    index_names = ["first", "third"]
+    ei = MultiIndex.from_arrays(index_array, names=index_names)
+    expected = Series([3], index=ei)
+
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_groups_in_BaseGrouper():
+    # GH 26326
+    # Test if DataFrame grouped with a pandas.Grouper has correct groups
+    mi = MultiIndex.from_product([["A", "B"], ["C", "D"]], names=["alpha", "beta"])
+    df = DataFrame({"foo": [1, 2, 1, 2], "bar": [1, 2, 3, 4]}, index=mi)
+    result = df.groupby([Grouper(level="alpha"), "beta"])
+    expected = df.groupby(["alpha", "beta"])
+    assert result.groups == expected.groups
+
+    result = df.groupby(["beta", Grouper(level="alpha")])
+    expected = df.groupby(["beta", "alpha"])
+    assert result.groups == expected.groups
+
+
+@pytest.mark.parametrize("group_name", ["x", ["x"]])
+def test_groupby_axis_1(group_name):
+    # GH 27614
+    df = DataFrame(
+        np.arange(12).reshape(3, 4), index=[0, 1, 0], columns=[10, 20, 10, 20]
+    )
+    df.index.name = "y"
+    df.columns.name = "x"
+
+    results = df.groupby(group_name, axis=1).sum()
+    expected = df.T.groupby(group_name).sum().T
+    tm.assert_frame_equal(results, expected)
+
+    # test on MI column
+    iterables = [["bar", "baz", "foo"], ["one", "two"]]
+    mi = MultiIndex.from_product(iterables=iterables, names=["x", "x1"])
+    df = DataFrame(np.arange(18).reshape(3, 6), index=[0, 1, 0], columns=mi)
+    results = df.groupby(group_name, axis=1).sum()
+    expected = df.T.groupby(group_name).sum().T
+    tm.assert_frame_equal(results, expected)
+
+
+@pytest.mark.parametrize(
+    "op, expected",
+    [
+        (
+            "shift",
+            {
+                "time": [
+                    None,
+                    None,
+                    Timestamp("2019-01-01 12:00:00"),
+                    Timestamp("2019-01-01 12:30:00"),
+                    None,
+                    None,
+                ]
+            },
+        ),
+        (
+            "bfill",
+            {
+                "time": [
+                    Timestamp("2019-01-01 12:00:00"),
+                    Timestamp("2019-01-01 12:30:00"),
+                    Timestamp("2019-01-01 14:00:00"),
+                    Timestamp("2019-01-01 14:30:00"),
+                    Timestamp("2019-01-01 14:00:00"),
+                    Timestamp("2019-01-01 14:30:00"),
+                ]
+            },
+        ),
+        (
+            "ffill",
+            {
+                "time": [
+                    Timestamp("2019-01-01 12:00:00"),
+                    Timestamp("2019-01-01 12:30:00"),
+                    Timestamp("2019-01-01 12:00:00"),
+                    Timestamp("2019-01-01 12:30:00"),
+                    Timestamp("2019-01-01 14:00:00"),
+                    Timestamp("2019-01-01 14:30:00"),
+                ]
+            },
+        ),
+    ],
+)
+def test_shift_bfill_ffill_tz(tz_naive_fixture, op, expected):
+    # GH19995, GH27992: Check that timezone does not drop in shift, bfill, and ffill
+    tz = tz_naive_fixture
+    data = {
+        "id": ["A", "B", "A", "B", "A", "B"],
+        "time": [
+            Timestamp("2019-01-01 12:00:00"),
+            Timestamp("2019-01-01 12:30:00"),
+            None,
+            None,
+            Timestamp("2019-01-01 14:00:00"),
+            Timestamp("2019-01-01 14:30:00"),
+        ],
+    }
+    df = DataFrame(data).assign(time=lambda x: x.time.dt.tz_localize(tz))
+
+    grouped = df.groupby("id")
+    result = getattr(grouped, op)()
+    expected = DataFrame(expected).assign(time=lambda x: x.time.dt.tz_localize(tz))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_only_none_group():
+    # see GH21624
+    # this was crashing with "ValueError: Length of passed values is 1, index implies 0"
+    df = DataFrame({"g": [None], "x": 1})
+    actual = df.groupby("g")["x"].transform("sum")
+    expected = Series([np.nan], name="x")
+
+    tm.assert_series_equal(actual, expected)
+
+
+def test_groupby_duplicate_index():
+    # GH#29189 the groupby call here used to raise
+    ser = Series([2, 5, 6, 8], index=[2.0, 4.0, 4.0, 5.0])
+    gb = ser.groupby(level=0)
+
+    result = gb.mean()
+    expected = Series([2, 5.5, 8], index=[2.0, 4.0, 5.0])
+    tm.assert_series_equal(result, expected)
+
+
+def test_group_on_empty_multiindex(transformation_func, request):
+    # GH 47787
+    # With one row, those are transforms so the schema should be the same
+    df = DataFrame(
+        data=[[1, Timestamp("today"), 3, 4]],
+        columns=["col_1", "col_2", "col_3", "col_4"],
+    )
+    df["col_3"] = df["col_3"].astype(int)
+    df["col_4"] = df["col_4"].astype(int)
+    df = df.set_index(["col_1", "col_2"])
+    if transformation_func == "fillna":
+        args = ("ffill",)
+    else:
+        args = ()
+    result = df.iloc[:0].groupby(["col_1"]).transform(transformation_func, *args)
+    expected = df.groupby(["col_1"]).transform(transformation_func, *args).iloc[:0]
+    if transformation_func in ("diff", "shift"):
+        expected = expected.astype(int)
+    tm.assert_equal(result, expected)
+
+    result = (
+        df["col_3"].iloc[:0].groupby(["col_1"]).transform(transformation_func, *args)
+    )
+    expected = (
+        df["col_3"].groupby(["col_1"]).transform(transformation_func, *args).iloc[:0]
+    )
+    if transformation_func in ("diff", "shift"):
+        expected = expected.astype(int)
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "idx",
+    [
+        Index(["a", "a"], name="foo"),
+        MultiIndex.from_tuples((("a", "a"), ("a", "a")), names=["foo", "bar"]),
+    ],
+)
+def test_dup_labels_output_shape(groupby_func, idx):
+    if groupby_func in {"size", "ngroup", "cumcount"}:
+        pytest.skip(f"Not applicable for {groupby_func}")
+
+    df = DataFrame([[1, 1]], columns=idx)
+    grp_by = df.groupby([0])
+
+    args = get_groupby_method_args(groupby_func, df)
+    result = getattr(grp_by, groupby_func)(*args)
+
+    assert result.shape == (1, 2)
+    tm.assert_index_equal(result.columns, idx)
+
+
+def test_groupby_crash_on_nunique(axis):
+    # Fix following 30253
+    dti = date_range("2016-01-01", periods=2, name="foo")
+    df = DataFrame({("A", "B"): [1, 2], ("A", "C"): [1, 3], ("D", "B"): [0, 0]})
+    df.columns.names = ("bar", "baz")
+    df.index = dti
+
+    axis_number = df._get_axis_number(axis)
+    if not axis_number:
+        df = df.T
+
+    gb = df.groupby(axis=axis_number, level=0)
+    result = gb.nunique()
+
+    expected = DataFrame({"A": [1, 2], "D": [1, 1]}, index=dti)
+    expected.columns.name = "bar"
+    if not axis_number:
+        expected = expected.T
+
+    tm.assert_frame_equal(result, expected)
+
+    if axis_number == 0:
+        # same thing, but empty columns
+        gb2 = df[[]].groupby(axis=axis_number, level=0)
+        exp = expected[[]]
+    else:
+        # same thing, but empty rows
+        gb2 = df.loc[[]].groupby(axis=axis_number, level=0)
+        # default for empty when we can't infer a dtype is float64
+        exp = expected.loc[[]].astype(np.float64)
+
+    res = gb2.nunique()
+    tm.assert_frame_equal(res, exp)
+
+
+def test_groupby_list_level():
+    # GH 9790
+    expected = DataFrame(np.arange(0, 9).reshape(3, 3), dtype=float)
+    result = expected.groupby(level=[0]).mean()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "max_seq_items, expected",
+    [
+        (5, "{0: [0], 1: [1], 2: [2], 3: [3], 4: [4]}"),
+        (4, "{0: [0], 1: [1], 2: [2], 3: [3], ...}"),
+        (1, "{0: [0], ...}"),
+    ],
+)
+def test_groups_repr_truncates(max_seq_items, expected):
+    # GH 1135
+    df = DataFrame(np.random.randn(5, 1))
+    df["a"] = df.index
+
+    with pd.option_context("display.max_seq_items", max_seq_items):
+        result = df.groupby("a").groups.__repr__()
+        assert result == expected
+
+        result = df.groupby(np.array(df.a)).groups.__repr__()
+        assert result == expected
+
+
+def test_group_on_two_row_multiindex_returns_one_tuple_key():
+    # GH 18451
+    df = DataFrame([{"a": 1, "b": 2, "c": 99}, {"a": 1, "b": 2, "c": 88}])
+    df = df.set_index(["a", "b"])
+
+    grp = df.groupby(["a", "b"])
+    result = grp.indices
+    expected = {(1, 2): np.array([0, 1], dtype=np.int64)}
+
+    assert len(result) == 1
+    key = (1, 2)
+    assert (result[key] == expected[key]).all()
+
+
+@pytest.mark.parametrize(
+    "klass, attr, value",
+    [
+        (DataFrame, "level", "a"),
+        (DataFrame, "as_index", False),
+        (DataFrame, "sort", False),
+        (DataFrame, "group_keys", False),
+        (DataFrame, "observed", True),
+        (DataFrame, "dropna", False),
+        (Series, "level", "a"),
+        (Series, "as_index", False),
+        (Series, "sort", False),
+        (Series, "group_keys", False),
+        (Series, "observed", True),
+        (Series, "dropna", False),
+    ],
+)
+def test_subsetting_columns_keeps_attrs(klass, attr, value):
+    # GH 9959 - When subsetting columns, don't drop attributes
+    df = DataFrame({"a": [1], "b": [2], "c": [3]})
+    if attr != "axis":
+        df = df.set_index("a")
+
+    expected = df.groupby("a", **{attr: value})
+    result = expected[["b"]] if klass is DataFrame else expected["b"]
+    assert getattr(result, attr) == getattr(expected, attr)
+
+
+def test_subsetting_columns_axis_1():
+    # GH 37725
+    g = DataFrame({"A": [1], "B": [2], "C": [3]}).groupby([0, 0, 1], axis=1)
+    match = "Cannot subset columns when using axis=1"
+    with pytest.raises(ValueError, match=match):
+        g[["A", "B"]].sum()
+
+
+@pytest.mark.parametrize("func", ["sum", "any", "shift"])
+def test_groupby_column_index_name_lost(func):
+    # GH: 29764 groupby loses index sometimes
+    expected = Index(["a"], name="idx")
+    df = DataFrame([[1]], columns=expected)
+    df_grouped = df.groupby([1])
+    result = getattr(df_grouped, func)().columns
+    tm.assert_index_equal(result, expected)
+
+
+def test_groupby_duplicate_columns():
+    # GH: 31735
+    df = DataFrame(
+        {"A": ["f", "e", "g", "h"], "B": ["a", "b", "c", "d"], "C": [1, 2, 3, 4]}
+    ).astype(object)
+    df.columns = ["A", "B", "B"]
+    result = df.groupby([0, 0, 0, 0]).min()
+    expected = DataFrame(
+        [["e", "a", 1]], index=np.array([0]), columns=["A", "B", "B"], dtype=object
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_series_with_tuple_name():
+    # GH 37755
+    ser = Series([1, 2, 3, 4], index=[1, 1, 2, 2], name=("a", "a"))
+    ser.index.name = ("b", "b")
+    result = ser.groupby(level=0).last()
+    expected = Series([2, 4], index=[1, 2], name=("a", "a"))
+    expected.index.name = ("b", "b")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.xfail(not IS64, reason="GH#38778: fail on 32-bit system")
+@pytest.mark.parametrize(
+    "func, values", [("sum", [97.0, 98.0]), ("mean", [24.25, 24.5])]
+)
+def test_groupby_numerical_stability_sum_mean(func, values):
+    # GH#38778
+    data = [1e16, 1e16, 97, 98, -5e15, -5e15, -5e15, -5e15]
+    df = DataFrame({"group": [1, 2] * 4, "a": data, "b": data})
+    result = getattr(df.groupby("group"), func)()
+    expected = DataFrame({"a": values, "b": values}, index=Index([1, 2], name="group"))
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.xfail(not IS64, reason="GH#38778: fail on 32-bit system")
+def test_groupby_numerical_stability_cumsum():
+    # GH#38934
+    data = [1e16, 1e16, 97, 98, -5e15, -5e15, -5e15, -5e15]
+    df = DataFrame({"group": [1, 2] * 4, "a": data, "b": data})
+    result = df.groupby("group").cumsum()
+    exp_data = (
+        [1e16] * 2 + [1e16 + 96, 1e16 + 98] + [5e15 + 97, 5e15 + 98] + [97.0, 98.0]
+    )
+    expected = DataFrame({"a": exp_data, "b": exp_data})
+    tm.assert_frame_equal(result, expected, check_exact=True)
+
+
+def test_groupby_cumsum_skipna_false():
+    # GH#46216 don't propagate np.nan above the diagonal
+    arr = np.random.randn(5, 5)
+    df = DataFrame(arr)
+    for i in range(5):
+        df.iloc[i, i] = np.nan
+
+    df["A"] = 1
+    gb = df.groupby("A")
+
+    res = gb.cumsum(skipna=False)
+
+    expected = df[[0, 1, 2, 3, 4]].cumsum(skipna=False)
+    tm.assert_frame_equal(res, expected)
+
+
+def test_groupby_cumsum_timedelta64():
+    # GH#46216 don't ignore is_datetimelike in libgroupby.group_cumsum
+    dti = date_range("2016-01-01", periods=5)
+    ser = Series(dti) - dti[0]
+    ser[2] = pd.NaT
+
+    df = DataFrame({"A": 1, "B": ser})
+    gb = df.groupby("A")
+
+    res = gb.cumsum(numeric_only=False, skipna=True)
+    exp = DataFrame({"B": [ser[0], ser[1], pd.NaT, ser[4], ser[4] * 2]})
+    tm.assert_frame_equal(res, exp)
+
+    res = gb.cumsum(numeric_only=False, skipna=False)
+    exp = DataFrame({"B": [ser[0], ser[1], pd.NaT, pd.NaT, pd.NaT]})
+    tm.assert_frame_equal(res, exp)
+
+
+def test_groupby_mean_duplicate_index(rand_series_with_duplicate_datetimeindex):
+    dups = rand_series_with_duplicate_datetimeindex
+    result = dups.groupby(level=0).mean()
+    expected = dups.groupby(dups.index).mean()
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_all_nan_groups_drop():
+    # GH 15036
+    s = Series([1, 2, 3], [np.nan, np.nan, np.nan])
+    result = s.groupby(s.index).sum()
+    expected = Series([], index=Index([], dtype=np.float64), dtype=np.int64)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("numeric_only", [True, False])
+def test_groupby_empty_multi_column(as_index, numeric_only):
+    # GH 15106 & GH 41998
+    df = DataFrame(data=[], columns=["A", "B", "C"])
+    gb = df.groupby(["A", "B"], as_index=as_index)
+    result = gb.sum(numeric_only=numeric_only)
+    if as_index:
+        index = MultiIndex([[], []], [[], []], names=["A", "B"])
+        columns = ["C"] if not numeric_only else []
+    else:
+        index = RangeIndex(0)
+        columns = ["A", "B", "C"] if not numeric_only else ["A", "B"]
+    expected = DataFrame([], columns=columns, index=index)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_aggregation_non_numeric_dtype():
+    # GH #43108
+    df = DataFrame(
+        [["M", [1]], ["M", [1]], ["W", [10]], ["W", [20]]], columns=["MW", "v"]
+    )
+
+    expected = DataFrame(
+        {
+            "v": [[1, 1], [10, 20]],
+        },
+        index=Index(["M", "W"], dtype="object", name="MW"),
+    )
+
+    gb = df.groupby(by=["MW"])
+    result = gb.sum()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_aggregation_multi_non_numeric_dtype():
+    # GH #42395
+    df = DataFrame(
+        {
+            "x": [1, 0, 1, 1, 0],
+            "y": [Timedelta(i, "days") for i in range(1, 6)],
+            "z": [Timedelta(i * 10, "days") for i in range(1, 6)],
+        }
+    )
+
+    expected = DataFrame(
+        {
+            "y": [Timedelta(i, "days") for i in range(7, 9)],
+            "z": [Timedelta(i * 10, "days") for i in range(7, 9)],
+        },
+        index=Index([0, 1], dtype="int64", name="x"),
+    )
+
+    gb = df.groupby(by=["x"])
+    result = gb.sum()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_aggregation_numeric_with_non_numeric_dtype():
+    # GH #43108
+    df = DataFrame(
+        {
+            "x": [1, 0, 1, 1, 0],
+            "y": [Timedelta(i, "days") for i in range(1, 6)],
+            "z": list(range(1, 6)),
+        }
+    )
+
+    expected = DataFrame(
+        {"y": [Timedelta(7, "days"), Timedelta(8, "days")], "z": [7, 8]},
+        index=Index([0, 1], dtype="int64", name="x"),
+    )
+
+    gb = df.groupby(by=["x"])
+    result = gb.sum()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_filtered_df_std():
+    # GH 16174
+    dicts = [
+        {"filter_col": False, "groupby_col": True, "bool_col": True, "float_col": 10.5},
+        {"filter_col": True, "groupby_col": True, "bool_col": True, "float_col": 20.5},
+        {"filter_col": True, "groupby_col": True, "bool_col": True, "float_col": 30.5},
+    ]
+    df = DataFrame(dicts)
+
+    df_filter = df[df["filter_col"] == True]  # noqa:E712
+    dfgb = df_filter.groupby("groupby_col")
+    result = dfgb.std()
+    expected = DataFrame(
+        [[0.0, 0.0, 7.071068]],
+        columns=["filter_col", "bool_col", "float_col"],
+        index=Index([True], name="groupby_col"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_datetime_categorical_multikey_groupby_indices():
+    # GH 26859
+    df = DataFrame(
+        {
+            "a": Series(list("abc")),
+            "b": Series(
+                to_datetime(["2018-01-01", "2018-02-01", "2018-03-01"]),
+                dtype="category",
+            ),
+            "c": Categorical.from_codes([-1, 0, 1], categories=[0, 1]),
+        }
+    )
+    result = df.groupby(["a", "b"]).indices
+    expected = {
+        ("a", Timestamp("2018-01-01 00:00:00")): np.array([0]),
+        ("b", Timestamp("2018-02-01 00:00:00")): np.array([1]),
+        ("c", Timestamp("2018-03-01 00:00:00")): np.array([2]),
+    }
+    assert result == expected
+
+
+def test_rolling_wrong_param_min_period():
+    # GH34037
+    name_l = ["Alice"] * 5 + ["Bob"] * 5
+    val_l = [np.nan, np.nan, 1, 2, 3] + [np.nan, 1, 2, 3, 4]
+    test_df = DataFrame([name_l, val_l]).T
+    test_df.columns = ["name", "val"]
+
+    result_error_msg = r"__init__\(\) got an unexpected keyword argument 'min_period'"
+    with pytest.raises(TypeError, match=result_error_msg):
+        test_df.groupby("name")["val"].rolling(window=2, min_period=1).sum()
+
+
+def test_by_column_values_with_same_starting_value():
+    # GH29635
+    df = DataFrame(
+        {
+            "Name": ["Thomas", "Thomas", "Thomas John"],
+            "Credit": [1200, 1300, 900],
+            "Mood": ["sad", "happy", "happy"],
+        }
+    )
+    aggregate_details = {"Mood": Series.mode, "Credit": "sum"}
+
+    result = df.groupby(["Name"]).agg(aggregate_details)
+    expected_result = DataFrame(
+        {
+            "Mood": [["happy", "sad"], "happy"],
+            "Credit": [2500, 900],
+            "Name": ["Thomas", "Thomas John"],
+        }
+    ).set_index("Name")
+
+    tm.assert_frame_equal(result, expected_result)
+
+
+def test_groupby_none_in_first_mi_level():
+    # GH#47348
+    arr = [[None, 1, 0, 1], [2, 3, 2, 3]]
+    ser = Series(1, index=MultiIndex.from_arrays(arr, names=["a", "b"]))
+    result = ser.groupby(level=[0, 1]).sum()
+    expected = Series(
+        [1, 2], MultiIndex.from_tuples([(0.0, 2), (1.0, 3)], names=["a", "b"])
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_none_column_name():
+    # GH#47348
+    df = DataFrame({None: [1, 1, 2, 2], "b": [1, 1, 2, 3], "c": [4, 5, 6, 7]})
+    result = df.groupby(by=[None]).sum()
+    expected = DataFrame({"b": [2, 5], "c": [9, 13]}, index=Index([1, 2], name=None))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_single_element_list_grouping():
+    # GH 42795
+    df = DataFrame({"a": [1, 2], "b": [np.nan, 5], "c": [np.nan, 2]}, index=["x", "y"])
+    result = [key for key, _ in df.groupby(["a"])]
+    expected = [(1,), (2,)]
+    assert result == expected
+
+
+@pytest.mark.parametrize("func", ["sum", "cumsum", "cumprod", "prod"])
+def test_groupby_avoid_casting_to_float(func):
+    # GH#37493
+    val = 922337203685477580
+    df = DataFrame({"a": 1, "b": [val]})
+    result = getattr(df.groupby("a"), func)() - val
+    expected = DataFrame({"b": [0]}, index=Index([1], name="a"))
+    if func in ["cumsum", "cumprod"]:
+        expected = expected.reset_index(drop=True)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("func, val", [("sum", 3), ("prod", 2)])
+def test_groupby_sum_support_mask(any_numeric_ea_dtype, func, val):
+    # GH#37493
+    df = DataFrame({"a": 1, "b": [1, 2, pd.NA]}, dtype=any_numeric_ea_dtype)
+    result = getattr(df.groupby("a"), func)()
+    expected = DataFrame(
+        {"b": [val]},
+        index=Index([1], name="a", dtype=any_numeric_ea_dtype),
+        dtype=any_numeric_ea_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("val, dtype", [(111, "int"), (222, "uint")])
+def test_groupby_overflow(val, dtype):
+    # GH#37493
+    df = DataFrame({"a": 1, "b": [val, val]}, dtype=f"{dtype}8")
+    result = df.groupby("a").sum()
+    expected = DataFrame(
+        {"b": [val * 2]},
+        index=Index([1], name="a", dtype=f"{dtype}8"),
+        dtype=f"{dtype}64",
+    )
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby("a").cumsum()
+    expected = DataFrame({"b": [val, val * 2]}, dtype=f"{dtype}64")
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby("a").prod()
+    expected = DataFrame(
+        {"b": [val * val]},
+        index=Index([1], name="a", dtype=f"{dtype}8"),
+        dtype=f"{dtype}64",
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("skipna, val", [(True, 3), (False, pd.NA)])
+def test_groupby_cumsum_mask(any_numeric_ea_dtype, skipna, val):
+    # GH#37493
+    df = DataFrame({"a": 1, "b": [1, pd.NA, 2]}, dtype=any_numeric_ea_dtype)
+    result = df.groupby("a").cumsum(skipna=skipna)
+    expected = DataFrame(
+        {"b": [1, pd.NA, val]},
+        dtype=any_numeric_ea_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "val_in, index, val_out",
+    [
+        (
+            [1.0, 2.0, 3.0, 4.0, 5.0],
+            ["foo", "foo", "bar", "baz", "blah"],
+            [3.0, 4.0, 5.0, 3.0],
+        ),
+        (
+            [1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
+            ["foo", "foo", "bar", "baz", "blah", "blah"],
+            [3.0, 4.0, 11.0, 3.0],
+        ),
+    ],
+)
+def test_groupby_index_name_in_index_content(val_in, index, val_out):
+    # GH 48567
+    series = Series(data=val_in, name="values", index=Index(index, name="blah"))
+    result = series.groupby("blah").sum()
+    expected = Series(
+        data=val_out,
+        name="values",
+        index=Index(["bar", "baz", "blah", "foo"], name="blah"),
+    )
+    tm.assert_series_equal(result, expected)
+
+    result = series.to_frame().groupby("blah").sum()
+    expected = expected.to_frame()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("n", [1, 10, 32, 100, 1000])
+def test_sum_of_booleans(n):
+    # GH 50347
+    df = DataFrame({"groupby_col": 1, "bool": [True] * n})
+    df["bool"] = df["bool"].eq(True)
+    result = df.groupby("groupby_col").sum()
+    expected = DataFrame({"bool": [n]}, index=Index([1], name="groupby_col"))
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.filterwarnings(
+    "ignore:invalid value encountered in remainder:RuntimeWarning"
+)
+@pytest.mark.parametrize("method", ["head", "tail", "nth", "first", "last"])
+def test_groupby_method_drop_na(method):
+    # GH 21755
+    df = DataFrame({"A": ["a", np.nan, "b", np.nan, "c"], "B": range(5)})
+
+    if method == "nth":
+        result = getattr(df.groupby("A"), method)(n=0)
+    else:
+        result = getattr(df.groupby("A"), method)()
+
+    if method in ["first", "last"]:
+        expected = DataFrame({"B": [0, 2, 4]}).set_index(
+            Series(["a", "b", "c"], name="A")
+        )
+    else:
+        expected = DataFrame({"A": ["a", "b", "c"], "B": [0, 2, 4]}, index=[0, 2, 4])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_reduce_period():
+    # GH#51040
+    pi = pd.period_range("2016-01-01", periods=100, freq="D")
+    grps = list(range(10)) * 10
+    ser = pi.to_series()
+    gb = ser.groupby(grps)
+
+    with pytest.raises(TypeError, match="Period type does not support sum operations"):
+        gb.sum()
+    with pytest.raises(
+        TypeError, match="Period type does not support cumsum operations"
+    ):
+        gb.cumsum()
+    with pytest.raises(TypeError, match="Period type does not support prod operations"):
+        gb.prod()
+    with pytest.raises(
+        TypeError, match="Period type does not support cumprod operations"
+    ):
+        gb.cumprod()
+
+    res = gb.max()
+    expected = ser[-10:]
+    expected.index = Index(range(10), dtype=np.int_)
+    tm.assert_series_equal(res, expected)
+
+    res = gb.min()
+    expected = ser[:10]
+    expected.index = Index(range(10), dtype=np.int_)
+    tm.assert_series_equal(res, expected)
+
+
+def test_obj_with_exclusions_duplicate_columns():
+    # GH#50806
+    df = DataFrame([[0, 1, 2, 3]])
+    df.columns = [0, 1, 2, 0]
+    gb = df.groupby(df[1])
+    result = gb._obj_with_exclusions
+    expected = df.take([0, 2, 3], axis=1)
+    tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_groupby_dropna.py

@@ -0,0 +1,684 @@
+import numpy as np
+import pytest
+
+from pandas.compat.pyarrow import pa_version_under7p0
+
+from pandas.core.dtypes.missing import na_value_for_dtype
+
+import pandas as pd
+import pandas._testing as tm
+from pandas.tests.groupby import get_groupby_method_args
+
+
+@pytest.mark.parametrize(
+    "dropna, tuples, outputs",
+    [
+        (
+            True,
+            [["A", "B"], ["B", "A"]],
+            {"c": [13.0, 123.23], "d": [13.0, 123.0], "e": [13.0, 1.0]},
+        ),
+        (
+            False,
+            [["A", "B"], ["A", np.nan], ["B", "A"]],
+            {
+                "c": [13.0, 12.3, 123.23],
+                "d": [13.0, 233.0, 123.0],
+                "e": [13.0, 12.0, 1.0],
+            },
+        ),
+    ],
+)
+def test_groupby_dropna_multi_index_dataframe_nan_in_one_group(
+    dropna, tuples, outputs, nulls_fixture
+):
+    # GH 3729 this is to test that NA is in one group
+    df_list = [
+        ["A", "B", 12, 12, 12],
+        ["A", nulls_fixture, 12.3, 233.0, 12],
+        ["B", "A", 123.23, 123, 1],
+        ["A", "B", 1, 1, 1.0],
+    ]
+    df = pd.DataFrame(df_list, columns=["a", "b", "c", "d", "e"])
+    grouped = df.groupby(["a", "b"], dropna=dropna).sum()
+
+    mi = pd.MultiIndex.from_tuples(tuples, names=list("ab"))
+
+    # Since right now, by default MI will drop NA from levels when we create MI
+    # via `from_*`, so we need to add NA for level manually afterwards.
+    if not dropna:
+        mi = mi.set_levels(["A", "B", np.nan], level="b")
+    expected = pd.DataFrame(outputs, index=mi)
+
+    tm.assert_frame_equal(grouped, expected)
+
+
+@pytest.mark.parametrize(
+    "dropna, tuples, outputs",
+    [
+        (
+            True,
+            [["A", "B"], ["B", "A"]],
+            {"c": [12.0, 123.23], "d": [12.0, 123.0], "e": [12.0, 1.0]},
+        ),
+        (
+            False,
+            [["A", "B"], ["A", np.nan], ["B", "A"], [np.nan, "B"]],
+            {
+                "c": [12.0, 13.3, 123.23, 1.0],
+                "d": [12.0, 234.0, 123.0, 1.0],
+                "e": [12.0, 13.0, 1.0, 1.0],
+            },
+        ),
+    ],
+)
+def test_groupby_dropna_multi_index_dataframe_nan_in_two_groups(
+    dropna, tuples, outputs, nulls_fixture, nulls_fixture2
+):
+    # GH 3729 this is to test that NA in different groups with different representations
+    df_list = [
+        ["A", "B", 12, 12, 12],
+        ["A", nulls_fixture, 12.3, 233.0, 12],
+        ["B", "A", 123.23, 123, 1],
+        [nulls_fixture2, "B", 1, 1, 1.0],
+        ["A", nulls_fixture2, 1, 1, 1.0],
+    ]
+    df = pd.DataFrame(df_list, columns=["a", "b", "c", "d", "e"])
+    grouped = df.groupby(["a", "b"], dropna=dropna).sum()
+
+    mi = pd.MultiIndex.from_tuples(tuples, names=list("ab"))
+
+    # Since right now, by default MI will drop NA from levels when we create MI
+    # via `from_*`, so we need to add NA for level manually afterwards.
+    if not dropna:
+        mi = mi.set_levels([["A", "B", np.nan], ["A", "B", np.nan]])
+    expected = pd.DataFrame(outputs, index=mi)
+
+    tm.assert_frame_equal(grouped, expected)
+
+
+@pytest.mark.parametrize(
+    "dropna, idx, outputs",
+    [
+        (True, ["A", "B"], {"b": [123.23, 13.0], "c": [123.0, 13.0], "d": [1.0, 13.0]}),
+        (
+            False,
+            ["A", "B", np.nan],
+            {
+                "b": [123.23, 13.0, 12.3],
+                "c": [123.0, 13.0, 233.0],
+                "d": [1.0, 13.0, 12.0],
+            },
+        ),
+    ],
+)
+def test_groupby_dropna_normal_index_dataframe(dropna, idx, outputs):
+    # GH 3729
+    df_list = [
+        ["B", 12, 12, 12],
+        [None, 12.3, 233.0, 12],
+        ["A", 123.23, 123, 1],
+        ["B", 1, 1, 1.0],
+    ]
+    df = pd.DataFrame(df_list, columns=["a", "b", "c", "d"])
+    grouped = df.groupby("a", dropna=dropna).sum()
+
+    expected = pd.DataFrame(outputs, index=pd.Index(idx, dtype="object", name="a"))
+
+    tm.assert_frame_equal(grouped, expected)
+
+
+@pytest.mark.parametrize(
+    "dropna, idx, expected",
+    [
+        (True, ["a", "a", "b", np.nan], pd.Series([3, 3], index=["a", "b"])),
+        (
+            False,
+            ["a", "a", "b", np.nan],
+            pd.Series([3, 3, 3], index=["a", "b", np.nan]),
+        ),
+    ],
+)
+def test_groupby_dropna_series_level(dropna, idx, expected):
+    ser = pd.Series([1, 2, 3, 3], index=idx)
+
+    result = ser.groupby(level=0, dropna=dropna).sum()
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "dropna, expected",
+    [
+        (True, pd.Series([210.0, 350.0], index=["a", "b"], name="Max Speed")),
+        (
+            False,
+            pd.Series([210.0, 350.0, 20.0], index=["a", "b", np.nan], name="Max Speed"),
+        ),
+    ],
+)
+def test_groupby_dropna_series_by(dropna, expected):
+    ser = pd.Series(
+        [390.0, 350.0, 30.0, 20.0],
+        index=["Falcon", "Falcon", "Parrot", "Parrot"],
+        name="Max Speed",
+    )
+
+    result = ser.groupby(["a", "b", "a", np.nan], dropna=dropna).mean()
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("dropna", (False, True))
+def test_grouper_dropna_propagation(dropna):
+    # GH 36604
+    df = pd.DataFrame({"A": [0, 0, 1, None], "B": [1, 2, 3, None]})
+    gb = df.groupby("A", dropna=dropna)
+    assert gb.grouper.dropna == dropna
+
+
+@pytest.mark.parametrize(
+    "index",
+    [
+        pd.RangeIndex(0, 4),
+        list("abcd"),
+        pd.MultiIndex.from_product([(1, 2), ("R", "B")], names=["num", "col"]),
+    ],
+)
+def test_groupby_dataframe_slice_then_transform(dropna, index):
+    # GH35014 & GH35612
+    expected_data = {"B": [2, 2, 1, np.nan if dropna else 1]}
+
+    df = pd.DataFrame({"A": [0, 0, 1, None], "B": [1, 2, 3, None]}, index=index)
+    gb = df.groupby("A", dropna=dropna)
+
+    result = gb.transform(len)
+    expected = pd.DataFrame(expected_data, index=index)
+    tm.assert_frame_equal(result, expected)
+
+    result = gb[["B"]].transform(len)
+    expected = pd.DataFrame(expected_data, index=index)
+    tm.assert_frame_equal(result, expected)
+
+    result = gb["B"].transform(len)
+    expected = pd.Series(expected_data["B"], index=index, name="B")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "dropna, tuples, outputs",
+    [
+        (
+            True,
+            [["A", "B"], ["B", "A"]],
+            {"c": [13.0, 123.23], "d": [12.0, 123.0], "e": [1.0, 1.0]},
+        ),
+        (
+            False,
+            [["A", "B"], ["A", np.nan], ["B", "A"]],
+            {
+                "c": [13.0, 12.3, 123.23],
+                "d": [12.0, 233.0, 123.0],
+                "e": [1.0, 12.0, 1.0],
+            },
+        ),
+    ],
+)
+def test_groupby_dropna_multi_index_dataframe_agg(dropna, tuples, outputs):
+    # GH 3729
+    df_list = [
+        ["A", "B", 12, 12, 12],
+        ["A", None, 12.3, 233.0, 12],
+        ["B", "A", 123.23, 123, 1],
+        ["A", "B", 1, 1, 1.0],
+    ]
+    df = pd.DataFrame(df_list, columns=["a", "b", "c", "d", "e"])
+    agg_dict = {"c": sum, "d": max, "e": "min"}
+    grouped = df.groupby(["a", "b"], dropna=dropna).agg(agg_dict)
+
+    mi = pd.MultiIndex.from_tuples(tuples, names=list("ab"))
+
+    # Since right now, by default MI will drop NA from levels when we create MI
+    # via `from_*`, so we need to add NA for level manually afterwards.
+    if not dropna:
+        mi = mi.set_levels(["A", "B", np.nan], level="b")
+    expected = pd.DataFrame(outputs, index=mi)
+
+    tm.assert_frame_equal(grouped, expected)
+
+
+@pytest.mark.arm_slow
+@pytest.mark.parametrize(
+    "datetime1, datetime2",
+    [
+        (pd.Timestamp("2020-01-01"), pd.Timestamp("2020-02-01")),
+        (pd.Timedelta("-2 days"), pd.Timedelta("-1 days")),
+        (pd.Period("2020-01-01"), pd.Period("2020-02-01")),
+    ],
+)
+@pytest.mark.parametrize("dropna, values", [(True, [12, 3]), (False, [12, 3, 6])])
+def test_groupby_dropna_datetime_like_data(
+    dropna, values, datetime1, datetime2, unique_nulls_fixture, unique_nulls_fixture2
+):
+    # 3729
+    df = pd.DataFrame(
+        {
+            "values": [1, 2, 3, 4, 5, 6],
+            "dt": [
+                datetime1,
+                unique_nulls_fixture,
+                datetime2,
+                unique_nulls_fixture2,
+                datetime1,
+                datetime1,
+            ],
+        }
+    )
+
+    if dropna:
+        indexes = [datetime1, datetime2]
+    else:
+        indexes = [datetime1, datetime2, np.nan]
+
+    grouped = df.groupby("dt", dropna=dropna).agg({"values": sum})
+    expected = pd.DataFrame({"values": values}, index=pd.Index(indexes, name="dt"))
+
+    tm.assert_frame_equal(grouped, expected)
+
+
+@pytest.mark.parametrize(
+    "dropna, data, selected_data, levels",
+    [
+        pytest.param(
+            False,
+            {"groups": ["a", "a", "b", np.nan], "values": [10, 10, 20, 30]},
+            {"values": [0, 1, 0, 0]},
+            ["a", "b", np.nan],
+            id="dropna_false_has_nan",
+        ),
+        pytest.param(
+            True,
+            {"groups": ["a", "a", "b", np.nan], "values": [10, 10, 20, 30]},
+            {"values": [0, 1, 0]},
+            None,
+            id="dropna_true_has_nan",
+        ),
+        pytest.param(
+            # no nan in "groups"; dropna=True|False should be same.
+            False,
+            {"groups": ["a", "a", "b", "c"], "values": [10, 10, 20, 30]},
+            {"values": [0, 1, 0, 0]},
+            None,
+            id="dropna_false_no_nan",
+        ),
+        pytest.param(
+            # no nan in "groups"; dropna=True|False should be same.
+            True,
+            {"groups": ["a", "a", "b", "c"], "values": [10, 10, 20, 30]},
+            {"values": [0, 1, 0, 0]},
+            None,
+            id="dropna_true_no_nan",
+        ),
+    ],
+)
+def test_groupby_apply_with_dropna_for_multi_index(dropna, data, selected_data, levels):
+    # GH 35889
+
+    df = pd.DataFrame(data)
+    gb = df.groupby("groups", dropna=dropna)
+    result = gb.apply(lambda grp: pd.DataFrame({"values": range(len(grp))}))
+
+    mi_tuples = tuple(zip(data["groups"], selected_data["values"]))
+    mi = pd.MultiIndex.from_tuples(mi_tuples, names=["groups", None])
+    # Since right now, by default MI will drop NA from levels when we create MI
+    # via `from_*`, so we need to add NA for level manually afterwards.
+    if not dropna and levels:
+        mi = mi.set_levels(levels, level="groups")
+
+    expected = pd.DataFrame(selected_data, index=mi)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("input_index", [None, ["a"], ["a", "b"]])
+@pytest.mark.parametrize("keys", [["a"], ["a", "b"]])
+@pytest.mark.parametrize("series", [True, False])
+def test_groupby_dropna_with_multiindex_input(input_index, keys, series):
+    # GH#46783
+    obj = pd.DataFrame(
+        {
+            "a": [1, np.nan],
+            "b": [1, 1],
+            "c": [2, 3],
+        }
+    )
+
+    expected = obj.set_index(keys)
+    if series:
+        expected = expected["c"]
+    elif input_index == ["a", "b"] and keys == ["a"]:
+        # Column b should not be aggregated
+        expected = expected[["c"]]
+
+    if input_index is not None:
+        obj = obj.set_index(input_index)
+    gb = obj.groupby(keys, dropna=False)
+    if series:
+        gb = gb["c"]
+    result = gb.sum()
+
+    tm.assert_equal(result, expected)
+
+
+def test_groupby_nan_included():
+    # GH 35646
+    data = {"group": ["g1", np.nan, "g1", "g2", np.nan], "B": [0, 1, 2, 3, 4]}
+    df = pd.DataFrame(data)
+    grouped = df.groupby("group", dropna=False)
+    result = grouped.indices
+    dtype = np.intp
+    expected = {
+        "g1": np.array([0, 2], dtype=dtype),
+        "g2": np.array([3], dtype=dtype),
+        np.nan: np.array([1, 4], dtype=dtype),
+    }
+    for result_values, expected_values in zip(result.values(), expected.values()):
+        tm.assert_numpy_array_equal(result_values, expected_values)
+    assert np.isnan(list(result.keys())[2])
+    assert list(result.keys())[0:2] == ["g1", "g2"]
+
+
+def test_groupby_drop_nan_with_multi_index():
+    # GH 39895
+    df = pd.DataFrame([[np.nan, 0, 1]], columns=["a", "b", "c"])
+    df = df.set_index(["a", "b"])
+    result = df.groupby(["a", "b"], dropna=False).first()
+    expected = df
+    tm.assert_frame_equal(result, expected)
+
+
+# sequence_index enumerates all strings made up of x, y, z of length 4
+@pytest.mark.parametrize("sequence_index", range(3**4))
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        None,
+        "UInt8",
+        "Int8",
+        "UInt16",
+        "Int16",
+        "UInt32",
+        "Int32",
+        "UInt64",
+        "Int64",
+        "Float32",
+        "Int64",
+        "Float64",
+        "category",
+        "string",
+        pytest.param(
+            "string[pyarrow]",
+            marks=pytest.mark.skipif(
+                pa_version_under7p0, reason="pyarrow is not installed"
+            ),
+        ),
+        "datetime64[ns]",
+        "period[d]",
+        "Sparse[float]",
+    ],
+)
+@pytest.mark.parametrize("test_series", [True, False])
+def test_no_sort_keep_na(sequence_index, dtype, test_series, as_index):
+    # GH#46584, GH#48794
+
+    # Convert sequence_index into a string sequence, e.g. 5 becomes "xxyz"
+    # This sequence is used for the grouper.
+    sequence = "".join(
+        [{0: "x", 1: "y", 2: "z"}[sequence_index // (3**k) % 3] for k in range(4)]
+    )
+
+    # Unique values to use for grouper, depends on dtype
+    if dtype in ("string", "string[pyarrow]"):
+        uniques = {"x": "x", "y": "y", "z": pd.NA}
+    elif dtype in ("datetime64[ns]", "period[d]"):
+        uniques = {"x": "2016-01-01", "y": "2017-01-01", "z": pd.NA}
+    else:
+        uniques = {"x": 1, "y": 2, "z": np.nan}
+
+    df = pd.DataFrame(
+        {
+            "key": pd.Series([uniques[label] for label in sequence], dtype=dtype),
+            "a": [0, 1, 2, 3],
+        }
+    )
+    gb = df.groupby("key", dropna=False, sort=False, as_index=as_index)
+    if test_series:
+        gb = gb["a"]
+    result = gb.sum()
+
+    # Manually compute the groupby sum, use the labels "x", "y", and "z" to avoid
+    # issues with hashing np.nan
+    summed = {}
+    for idx, label in enumerate(sequence):
+        summed[label] = summed.get(label, 0) + idx
+    if dtype == "category":
+        index = pd.CategoricalIndex(
+            [uniques[e] for e in summed],
+            df["key"].cat.categories,
+            name="key",
+        )
+    elif isinstance(dtype, str) and dtype.startswith("Sparse"):
+        index = pd.Index(
+            pd.array([uniques[label] for label in summed], dtype=dtype), name="key"
+        )
+    else:
+        index = pd.Index([uniques[label] for label in summed], dtype=dtype, name="key")
+    expected = pd.Series(summed.values(), index=index, name="a", dtype=None)
+    if not test_series:
+        expected = expected.to_frame()
+    if not as_index:
+        expected = expected.reset_index()
+        if dtype is not None and dtype.startswith("Sparse"):
+            expected["key"] = expected["key"].astype(dtype)
+
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize("test_series", [True, False])
+@pytest.mark.parametrize("dtype", [object, None])
+def test_null_is_null_for_dtype(
+    sort, dtype, nulls_fixture, nulls_fixture2, test_series
+):
+    # GH#48506 - groups should always result in using the null for the dtype
+    df = pd.DataFrame({"a": [1, 2]})
+    groups = pd.Series([nulls_fixture, nulls_fixture2], dtype=dtype)
+    obj = df["a"] if test_series else df
+    gb = obj.groupby(groups, dropna=False, sort=sort)
+    result = gb.sum()
+    index = pd.Index([na_value_for_dtype(groups.dtype)])
+    expected = pd.DataFrame({"a": [3]}, index=index)
+    if test_series:
+        tm.assert_series_equal(result, expected["a"])
+    else:
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("index_kind", ["range", "single", "multi"])
+def test_categorical_reducers(
+    request, reduction_func, observed, sort, as_index, index_kind
+):
+    # GH#36327
+    if (
+        reduction_func in ("idxmin", "idxmax")
+        and not observed
+        and index_kind != "multi"
+    ):
+        msg = "GH#10694 - idxmin/max broken for categorical with observed=False"
+        request.node.add_marker(pytest.mark.xfail(reason=msg))
+
+    # Ensure there is at least one null value by appending to the end
+    values = np.append(np.random.choice([1, 2, None], size=19), None)
+    df = pd.DataFrame(
+        {"x": pd.Categorical(values, categories=[1, 2, 3]), "y": range(20)}
+    )
+
+    # Strategy: Compare to dropna=True by filling null values with a new code
+    df_filled = df.copy()
+    df_filled["x"] = pd.Categorical(values, categories=[1, 2, 3, 4]).fillna(4)
+
+    if index_kind == "range":
+        keys = ["x"]
+    elif index_kind == "single":
+        keys = ["x"]
+        df = df.set_index("x")
+        df_filled = df_filled.set_index("x")
+    else:
+        keys = ["x", "x2"]
+        df["x2"] = df["x"]
+        df = df.set_index(["x", "x2"])
+        df_filled["x2"] = df_filled["x"]
+        df_filled = df_filled.set_index(["x", "x2"])
+    args = get_groupby_method_args(reduction_func, df)
+    args_filled = get_groupby_method_args(reduction_func, df_filled)
+    if reduction_func == "corrwith" and index_kind == "range":
+        # Don't include the grouping columns so we can call reset_index
+        args = (args[0].drop(columns=keys),)
+        args_filled = (args_filled[0].drop(columns=keys),)
+
+    gb_filled = df_filled.groupby(keys, observed=observed, sort=sort, as_index=True)
+    expected = getattr(gb_filled, reduction_func)(*args_filled).reset_index()
+    expected["x"] = expected["x"].replace(4, None)
+    if index_kind == "multi":
+        expected["x2"] = expected["x2"].replace(4, None)
+    if as_index:
+        if index_kind == "multi":
+            expected = expected.set_index(["x", "x2"])
+        else:
+            expected = expected.set_index("x")
+    else:
+        if index_kind != "range" and reduction_func != "size":
+            # size, unlike other methods, has the desired behavior in GH#49519
+            expected = expected.drop(columns="x")
+            if index_kind == "multi":
+                expected = expected.drop(columns="x2")
+    if reduction_func in ("idxmax", "idxmin") and index_kind != "range":
+        # expected was computed with a RangeIndex; need to translate to index values
+        values = expected["y"].values.tolist()
+        if index_kind == "single":
+            values = [np.nan if e == 4 else e for e in values]
+        else:
+            values = [(np.nan, np.nan) if e == (4, 4) else e for e in values]
+        expected["y"] = values
+    if reduction_func == "size":
+        # size, unlike other methods, has the desired behavior in GH#49519
+        expected = expected.rename(columns={0: "size"})
+        if as_index:
+            expected = expected["size"].rename(None)
+
+    gb_keepna = df.groupby(
+        keys, dropna=False, observed=observed, sort=sort, as_index=as_index
+    )
+    result = getattr(gb_keepna, reduction_func)(*args)
+
+    # size will return a Series, others are DataFrame
+    tm.assert_equal(result, expected)
+
+
+def test_categorical_transformers(
+    request, transformation_func, observed, sort, as_index
+):
+    # GH#36327
+    if transformation_func == "fillna":
+        msg = "GH#49651 fillna may incorrectly reorders results when dropna=False"
+        request.node.add_marker(pytest.mark.xfail(reason=msg, strict=False))
+
+    values = np.append(np.random.choice([1, 2, None], size=19), None)
+    df = pd.DataFrame(
+        {"x": pd.Categorical(values, categories=[1, 2, 3]), "y": range(20)}
+    )
+    args = get_groupby_method_args(transformation_func, df)
+
+    # Compute result for null group
+    null_group_values = df[df["x"].isnull()]["y"]
+    if transformation_func == "cumcount":
+        null_group_data = list(range(len(null_group_values)))
+    elif transformation_func == "ngroup":
+        if sort:
+            if observed:
+                na_group = df["x"].nunique(dropna=False) - 1
+            else:
+                # TODO: Should this be 3?
+                na_group = df["x"].nunique(dropna=False) - 1
+        else:
+            na_group = df.iloc[: null_group_values.index[0]]["x"].nunique()
+        null_group_data = len(null_group_values) * [na_group]
+    else:
+        null_group_data = getattr(null_group_values, transformation_func)(*args)
+    null_group_result = pd.DataFrame({"y": null_group_data})
+
+    gb_keepna = df.groupby(
+        "x", dropna=False, observed=observed, sort=sort, as_index=as_index
+    )
+    gb_dropna = df.groupby("x", dropna=True, observed=observed, sort=sort)
+    result = getattr(gb_keepna, transformation_func)(*args)
+    expected = getattr(gb_dropna, transformation_func)(*args)
+    for iloc, value in zip(
+        df[df["x"].isnull()].index.tolist(), null_group_result.values.ravel()
+    ):
+        if expected.ndim == 1:
+            expected.iloc[iloc] = value
+        else:
+            expected.iloc[iloc, 0] = value
+    if transformation_func == "ngroup":
+        expected[df["x"].notnull() & expected.ge(na_group)] += 1
+    if transformation_func not in ("rank", "diff", "pct_change", "shift"):
+        expected = expected.astype("int64")
+
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize("method", ["head", "tail"])
+def test_categorical_head_tail(method, observed, sort, as_index):
+    # GH#36327
+    values = np.random.choice([1, 2, None], 30)
+    df = pd.DataFrame(
+        {"x": pd.Categorical(values, categories=[1, 2, 3]), "y": range(len(values))}
+    )
+    gb = df.groupby("x", dropna=False, observed=observed, sort=sort, as_index=as_index)
+    result = getattr(gb, method)()
+
+    if method == "tail":
+        values = values[::-1]
+    # Take the top 5 values from each group
+    mask = (
+        ((values == 1) & ((values == 1).cumsum() <= 5))
+        | ((values == 2) & ((values == 2).cumsum() <= 5))
+        # flake8 doesn't like the vectorized check for None, thinks we should use `is`
+        | ((values == None) & ((values == None).cumsum() <= 5))  # noqa: E711
+    )
+    if method == "tail":
+        mask = mask[::-1]
+    expected = df[mask]
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_categorical_agg():
+    # GH#36327
+    values = np.random.choice([1, 2, None], 30)
+    df = pd.DataFrame(
+        {"x": pd.Categorical(values, categories=[1, 2, 3]), "y": range(len(values))}
+    )
+    gb = df.groupby("x", dropna=False)
+    result = gb.agg(lambda x: x.sum())
+    expected = gb.sum()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_categorical_transform():
+    # GH#36327
+    values = np.random.choice([1, 2, None], 30)
+    df = pd.DataFrame(
+        {"x": pd.Categorical(values, categories=[1, 2, 3]), "y": range(len(values))}
+    )
+    gb = df.groupby("x", dropna=False)
+    result = gb.transform(lambda x: x.sum())
+    expected = gb.transform("sum")
+    tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_groupby_shift_diff.py

@@ -0,0 +1,156 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    NaT,
+    Series,
+    Timedelta,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+def test_group_shift_with_null_key():
+    # This test is designed to replicate the segfault in issue #13813.
+    n_rows = 1200
+
+    # Generate a moderately large dataframe with occasional missing
+    # values in column `B`, and then group by [`A`, `B`]. This should
+    # force `-1` in `labels` array of `g.grouper.group_info` exactly
+    # at those places, where the group-by key is partially missing.
+    df = DataFrame(
+        [(i % 12, i % 3 if i % 3 else np.nan, i) for i in range(n_rows)],
+        dtype=float,
+        columns=["A", "B", "Z"],
+        index=None,
+    )
+    g = df.groupby(["A", "B"])
+
+    expected = DataFrame(
+        [(i + 12 if i % 3 and i < n_rows - 12 else np.nan) for i in range(n_rows)],
+        dtype=float,
+        columns=["Z"],
+        index=None,
+    )
+    result = g.shift(-1)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_group_shift_with_fill_value():
+    # GH #24128
+    n_rows = 24
+    df = DataFrame(
+        [(i % 12, i % 3, i) for i in range(n_rows)],
+        dtype=float,
+        columns=["A", "B", "Z"],
+        index=None,
+    )
+    g = df.groupby(["A", "B"])
+
+    expected = DataFrame(
+        [(i + 12 if i < n_rows - 12 else 0) for i in range(n_rows)],
+        dtype=float,
+        columns=["Z"],
+        index=None,
+    )
+    result = g.shift(-1, fill_value=0)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_group_shift_lose_timezone():
+    # GH 30134
+    now_dt = Timestamp.utcnow()
+    df = DataFrame({"a": [1, 1], "date": now_dt})
+    result = df.groupby("a").shift(0).iloc[0]
+    expected = Series({"date": now_dt}, name=result.name)
+    tm.assert_series_equal(result, expected)
+
+
+def test_group_diff_real_series(any_real_numpy_dtype):
+    df = DataFrame(
+        {"a": [1, 2, 3, 3, 2], "b": [1, 2, 3, 4, 5]},
+        dtype=any_real_numpy_dtype,
+    )
+    result = df.groupby("a")["b"].diff()
+    exp_dtype = "float"
+    if any_real_numpy_dtype in ["int8", "int16", "float32"]:
+        exp_dtype = "float32"
+    expected = Series([np.nan, np.nan, np.nan, 1.0, 3.0], dtype=exp_dtype, name="b")
+    tm.assert_series_equal(result, expected)
+
+
+def test_group_diff_real_frame(any_real_numpy_dtype):
+    df = DataFrame(
+        {
+            "a": [1, 2, 3, 3, 2],
+            "b": [1, 2, 3, 4, 5],
+            "c": [1, 2, 3, 4, 6],
+        },
+        dtype=any_real_numpy_dtype,
+    )
+    result = df.groupby("a").diff()
+    exp_dtype = "float"
+    if any_real_numpy_dtype in ["int8", "int16", "float32"]:
+        exp_dtype = "float32"
+    expected = DataFrame(
+        {
+            "b": [np.nan, np.nan, np.nan, 1.0, 3.0],
+            "c": [np.nan, np.nan, np.nan, 1.0, 4.0],
+        },
+        dtype=exp_dtype,
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        [
+            Timestamp("2013-01-01"),
+            Timestamp("2013-01-02"),
+            Timestamp("2013-01-03"),
+        ],
+        [Timedelta("5 days"), Timedelta("6 days"), Timedelta("7 days")],
+    ],
+)
+def test_group_diff_datetimelike(data):
+    df = DataFrame({"a": [1, 2, 2], "b": data})
+    result = df.groupby("a")["b"].diff()
+    expected = Series([NaT, NaT, Timedelta("1 days")], name="b")
+    tm.assert_series_equal(result, expected)
+
+
+def test_group_diff_bool():
+    df = DataFrame({"a": [1, 2, 3, 3, 2], "b": [True, True, False, False, True]})
+    result = df.groupby("a")["b"].diff()
+    expected = Series([np.nan, np.nan, np.nan, False, False], name="b")
+    tm.assert_series_equal(result, expected)
+
+
+def test_group_diff_object_raises(object_dtype):
+    df = DataFrame(
+        {"a": ["foo", "bar", "bar"], "b": ["baz", "foo", "foo"]}, dtype=object_dtype
+    )
+    with pytest.raises(TypeError, match=r"unsupported operand type\(s\) for -"):
+        df.groupby("a")["b"].diff()
+
+
+def test_empty_shift_with_fill():
+    # GH 41264, single-index check
+    df = DataFrame(columns=["a", "b", "c"])
+    shifted = df.groupby(["a"]).shift(1)
+    shifted_with_fill = df.groupby(["a"]).shift(1, fill_value=0)
+    tm.assert_frame_equal(shifted, shifted_with_fill)
+    tm.assert_index_equal(shifted.index, shifted_with_fill.index)
+
+
+def test_multindex_empty_shift_with_fill():
+    # GH 41264, multi-index check
+    df = DataFrame(columns=["a", "b", "c"])
+    shifted = df.groupby(["a", "b"]).shift(1)
+    shifted_with_fill = df.groupby(["a", "b"]).shift(1, fill_value=0)
+    tm.assert_frame_equal(shifted, shifted_with_fill)
+    tm.assert_index_equal(shifted.index, shifted_with_fill.index)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_groupby_subclass.py

@@ -0,0 +1,105 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+from pandas.tests.groupby import get_groupby_method_args
+
+
+@pytest.mark.parametrize(
+    "obj",
+    [
+        tm.SubclassedDataFrame({"A": np.arange(0, 10)}),
+        tm.SubclassedSeries(np.arange(0, 10), name="A"),
+    ],
+)
+def test_groupby_preserves_subclass(obj, groupby_func):
+    # GH28330 -- preserve subclass through groupby operations
+
+    if isinstance(obj, Series) and groupby_func in {"corrwith"}:
+        pytest.skip(f"Not applicable for Series and {groupby_func}")
+
+    grouped = obj.groupby(np.arange(0, 10))
+
+    # Groups should preserve subclass type
+    assert isinstance(grouped.get_group(0), type(obj))
+
+    args = get_groupby_method_args(groupby_func, obj)
+
+    result1 = getattr(grouped, groupby_func)(*args)
+    result2 = grouped.agg(groupby_func, *args)
+
+    # Reduction or transformation kernels should preserve type
+    slices = {"ngroup", "cumcount", "size"}
+    if isinstance(obj, DataFrame) and groupby_func in slices:
+        assert isinstance(result1, tm.SubclassedSeries)
+    else:
+        assert isinstance(result1, type(obj))
+
+    # Confirm .agg() groupby operations return same results
+    if isinstance(result1, DataFrame):
+        tm.assert_frame_equal(result1, result2)
+    else:
+        tm.assert_series_equal(result1, result2)
+
+
+def test_groupby_preserves_metadata():
+    # GH-37343
+    custom_df = tm.SubclassedDataFrame({"a": [1, 2, 3], "b": [1, 1, 2], "c": [7, 8, 9]})
+    assert "testattr" in custom_df._metadata
+    custom_df.testattr = "hello"
+    for _, group_df in custom_df.groupby("c"):
+        assert group_df.testattr == "hello"
+
+    # GH-45314
+    def func(group):
+        assert isinstance(group, tm.SubclassedDataFrame)
+        assert hasattr(group, "testattr")
+        return group.testattr
+
+    result = custom_df.groupby("c").apply(func)
+    expected = tm.SubclassedSeries(["hello"] * 3, index=Index([7, 8, 9], name="c"))
+    tm.assert_series_equal(result, expected)
+
+    def func2(group):
+        assert isinstance(group, tm.SubclassedSeries)
+        assert hasattr(group, "testattr")
+        return group.testattr
+
+    custom_series = tm.SubclassedSeries([1, 2, 3])
+    custom_series.testattr = "hello"
+    result = custom_series.groupby(custom_df["c"]).apply(func2)
+    tm.assert_series_equal(result, expected)
+    result = custom_series.groupby(custom_df["c"]).agg(func2)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("obj", [DataFrame, tm.SubclassedDataFrame])
+def test_groupby_resample_preserves_subclass(obj):
+    # GH28330 -- preserve subclass through groupby.resample()
+
+    df = obj(
+        {
+            "Buyer": "Carl Carl Carl Carl Joe Carl".split(),
+            "Quantity": [18, 3, 5, 1, 9, 3],
+            "Date": [
+                datetime(2013, 9, 1, 13, 0),
+                datetime(2013, 9, 1, 13, 5),
+                datetime(2013, 10, 1, 20, 0),
+                datetime(2013, 10, 3, 10, 0),
+                datetime(2013, 12, 2, 12, 0),
+                datetime(2013, 9, 2, 14, 0),
+            ],
+        }
+    )
+    df = df.set_index("Date")
+
+    # Confirm groupby.resample() preserves dataframe type
+    result = df.groupby("Buyer").resample("5D").sum()
+    assert isinstance(result, obj)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_grouping.py

@@ -0,0 +1,1077 @@
+"""
+test where we are determining what we are grouping, or getting groups
+"""
+from datetime import (
+    date,
+    timedelta,
+)
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    CategoricalIndex,
+    DataFrame,
+    Grouper,
+    Index,
+    MultiIndex,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+from pandas.core.groupby.grouper import Grouping
+
+# selection
+# --------------------------------
+
+
+class TestSelection:
+    def test_select_bad_cols(self):
+        df = DataFrame([[1, 2]], columns=["A", "B"])
+        g = df.groupby("A")
+        with pytest.raises(KeyError, match="\"Columns not found: 'C'\""):
+            g[["C"]]
+
+        with pytest.raises(KeyError, match="^[^A]+$"):
+            # A should not be referenced as a bad column...
+            # will have to rethink regex if you change message!
+            g[["A", "C"]]
+
+    def test_groupby_duplicated_column_errormsg(self):
+        # GH7511
+        df = DataFrame(
+            columns=["A", "B", "A", "C"], data=[range(4), range(2, 6), range(0, 8, 2)]
+        )
+
+        msg = "Grouper for 'A' not 1-dimensional"
+        with pytest.raises(ValueError, match=msg):
+            df.groupby("A")
+        with pytest.raises(ValueError, match=msg):
+            df.groupby(["A", "B"])
+
+        grouped = df.groupby("B")
+        c = grouped.count()
+        assert c.columns.nlevels == 1
+        assert c.columns.size == 3
+
+    def test_column_select_via_attr(self, df):
+        result = df.groupby("A").C.sum()
+        expected = df.groupby("A")["C"].sum()
+        tm.assert_series_equal(result, expected)
+
+        df["mean"] = 1.5
+        result = df.groupby("A").mean(numeric_only=True)
+        expected = df.groupby("A")[["C", "D", "mean"]].agg(np.mean)
+        tm.assert_frame_equal(result, expected)
+
+    def test_getitem_list_of_columns(self):
+        df = DataFrame(
+            {
+                "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+                "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+                "C": np.random.randn(8),
+                "D": np.random.randn(8),
+                "E": np.random.randn(8),
+            }
+        )
+
+        result = df.groupby("A")[["C", "D"]].mean()
+        result2 = df.groupby("A")[df.columns[2:4]].mean()
+
+        expected = df.loc[:, ["A", "C", "D"]].groupby("A").mean()
+
+        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(result2, expected)
+
+    def test_getitem_numeric_column_names(self):
+        # GH #13731
+        df = DataFrame(
+            {
+                0: list("abcd") * 2,
+                2: np.random.randn(8),
+                4: np.random.randn(8),
+                6: np.random.randn(8),
+            }
+        )
+        result = df.groupby(0)[df.columns[1:3]].mean()
+        result2 = df.groupby(0)[[2, 4]].mean()
+
+        expected = df.loc[:, [0, 2, 4]].groupby(0).mean()
+
+        tm.assert_frame_equal(result, expected)
+        tm.assert_frame_equal(result2, expected)
+
+        # per GH 23566 enforced deprecation raises a ValueError
+        with pytest.raises(ValueError, match="Cannot subset columns with a tuple"):
+            df.groupby(0)[2, 4].mean()
+
+    def test_getitem_single_tuple_of_columns_raises(self, df):
+        # per GH 23566 enforced deprecation raises a ValueError
+        with pytest.raises(ValueError, match="Cannot subset columns with a tuple"):
+            df.groupby("A")["C", "D"].mean()
+
+    def test_getitem_single_column(self):
+        df = DataFrame(
+            {
+                "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+                "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+                "C": np.random.randn(8),
+                "D": np.random.randn(8),
+                "E": np.random.randn(8),
+            }
+        )
+
+        result = df.groupby("A")["C"].mean()
+
+        as_frame = df.loc[:, ["A", "C"]].groupby("A").mean()
+        as_series = as_frame.iloc[:, 0]
+        expected = as_series
+
+        tm.assert_series_equal(result, expected)
+
+    def test_indices_grouped_by_tuple_with_lambda(self):
+        # GH 36158
+        df = DataFrame(
+            {"Tuples": ((x, y) for x in [0, 1] for y in np.random.randint(3, 5, 5))}
+        )
+
+        gb = df.groupby("Tuples")
+        gb_lambda = df.groupby(lambda x: df.iloc[x, 0])
+
+        expected = gb.indices
+        result = gb_lambda.indices
+
+        tm.assert_dict_equal(result, expected)
+
+
+# grouping
+# --------------------------------
+
+
+class TestGrouping:
+    @pytest.mark.parametrize(
+        "index",
+        [
+            tm.makeFloatIndex,
+            tm.makeStringIndex,
+            tm.makeIntIndex,
+            tm.makeDateIndex,
+            tm.makePeriodIndex,
+        ],
+    )
+    def test_grouper_index_types(self, index):
+        # related GH5375
+        # groupby misbehaving when using a Floatlike index
+        df = DataFrame(np.arange(10).reshape(5, 2), columns=list("AB"))
+
+        df.index = index(len(df))
+        df.groupby(list("abcde"), group_keys=False).apply(lambda x: x)
+
+        df.index = list(reversed(df.index.tolist()))
+        df.groupby(list("abcde"), group_keys=False).apply(lambda x: x)
+
+    def test_grouper_multilevel_freq(self):
+        # GH 7885
+        # with level and freq specified in a Grouper
+        d0 = date.today() - timedelta(days=14)
+        dates = date_range(d0, date.today())
+        date_index = MultiIndex.from_product([dates, dates], names=["foo", "bar"])
+        df = DataFrame(np.random.randint(0, 100, 225), index=date_index)
+
+        # Check string level
+        expected = (
+            df.reset_index()
+            .groupby([Grouper(key="foo", freq="W"), Grouper(key="bar", freq="W")])
+            .sum()
+        )
+        # reset index changes columns dtype to object
+        expected.columns = Index([0], dtype="int64")
+
+        result = df.groupby(
+            [Grouper(level="foo", freq="W"), Grouper(level="bar", freq="W")]
+        ).sum()
+        tm.assert_frame_equal(result, expected)
+
+        # Check integer level
+        result = df.groupby(
+            [Grouper(level=0, freq="W"), Grouper(level=1, freq="W")]
+        ).sum()
+        tm.assert_frame_equal(result, expected)
+
+    def test_grouper_creation_bug(self):
+        # GH 8795
+        df = DataFrame({"A": [0, 0, 1, 1, 2, 2], "B": [1, 2, 3, 4, 5, 6]})
+        g = df.groupby("A")
+        expected = g.sum()
+
+        g = df.groupby(Grouper(key="A"))
+        result = g.sum()
+        tm.assert_frame_equal(result, expected)
+
+        g = df.groupby(Grouper(key="A", axis=0))
+        result = g.sum()
+        tm.assert_frame_equal(result, expected)
+
+        result = g.apply(lambda x: x.sum())
+        expected["A"] = [0, 2, 4]
+        expected = expected.loc[:, ["A", "B"]]
+        tm.assert_frame_equal(result, expected)
+
+        # GH14334
+        # Grouper(key=...) may be passed in a list
+        df = DataFrame(
+            {"A": [0, 0, 0, 1, 1, 1], "B": [1, 1, 2, 2, 3, 3], "C": [1, 2, 3, 4, 5, 6]}
+        )
+        # Group by single column
+        expected = df.groupby("A").sum()
+        g = df.groupby([Grouper(key="A")])
+        result = g.sum()
+        tm.assert_frame_equal(result, expected)
+
+        # Group by two columns
+        # using a combination of strings and Grouper objects
+        expected = df.groupby(["A", "B"]).sum()
+
+        # Group with two Grouper objects
+        g = df.groupby([Grouper(key="A"), Grouper(key="B")])
+        result = g.sum()
+        tm.assert_frame_equal(result, expected)
+
+        # Group with a string and a Grouper object
+        g = df.groupby(["A", Grouper(key="B")])
+        result = g.sum()
+        tm.assert_frame_equal(result, expected)
+
+        # Group with a Grouper object and a string
+        g = df.groupby([Grouper(key="A"), "B"])
+        result = g.sum()
+        tm.assert_frame_equal(result, expected)
+
+        # GH8866
+        s = Series(
+            np.arange(8, dtype="int64"),
+            index=MultiIndex.from_product(
+                [list("ab"), range(2), date_range("20130101", periods=2)],
+                names=["one", "two", "three"],
+            ),
+        )
+        result = s.groupby(Grouper(level="three", freq="M")).sum()
+        expected = Series(
+            [28],
+            index=pd.DatetimeIndex([Timestamp("2013-01-31")], freq="M", name="three"),
+        )
+        tm.assert_series_equal(result, expected)
+
+        # just specifying a level breaks
+        result = s.groupby(Grouper(level="one")).sum()
+        expected = s.groupby(level="one").sum()
+        tm.assert_series_equal(result, expected)
+
+    def test_grouper_column_and_index(self):
+        # GH 14327
+
+        # Grouping a multi-index frame by a column and an index level should
+        # be equivalent to resetting the index and grouping by two columns
+        idx = MultiIndex.from_tuples(
+            [("a", 1), ("a", 2), ("a", 3), ("b", 1), ("b", 2), ("b", 3)]
+        )
+        idx.names = ["outer", "inner"]
+        df_multi = DataFrame(
+            {"A": np.arange(6), "B": ["one", "one", "two", "two", "one", "one"]},
+            index=idx,
+        )
+        result = df_multi.groupby(["B", Grouper(level="inner")]).mean(numeric_only=True)
+        expected = (
+            df_multi.reset_index().groupby(["B", "inner"]).mean(numeric_only=True)
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # Test the reverse grouping order
+        result = df_multi.groupby([Grouper(level="inner"), "B"]).mean(numeric_only=True)
+        expected = (
+            df_multi.reset_index().groupby(["inner", "B"]).mean(numeric_only=True)
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # Grouping a single-index frame by a column and the index should
+        # be equivalent to resetting the index and grouping by two columns
+        df_single = df_multi.reset_index("outer")
+        result = df_single.groupby(["B", Grouper(level="inner")]).mean(
+            numeric_only=True
+        )
+        expected = (
+            df_single.reset_index().groupby(["B", "inner"]).mean(numeric_only=True)
+        )
+        tm.assert_frame_equal(result, expected)
+
+        # Test the reverse grouping order
+        result = df_single.groupby([Grouper(level="inner"), "B"]).mean(
+            numeric_only=True
+        )
+        expected = (
+            df_single.reset_index().groupby(["inner", "B"]).mean(numeric_only=True)
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_groupby_levels_and_columns(self):
+        # GH9344, GH9049
+        idx_names = ["x", "y"]
+        idx = MultiIndex.from_tuples([(1, 1), (1, 2), (3, 4), (5, 6)], names=idx_names)
+        df = DataFrame(np.arange(12).reshape(-1, 3), index=idx)
+
+        by_levels = df.groupby(level=idx_names).mean()
+        # reset_index changes columns dtype to object
+        by_columns = df.reset_index().groupby(idx_names).mean()
+
+        # without casting, by_columns.columns is object-dtype
+        by_columns.columns = by_columns.columns.astype(np.int64)
+        tm.assert_frame_equal(by_levels, by_columns)
+
+    def test_groupby_categorical_index_and_columns(self, observed):
+        # GH18432, adapted for GH25871
+        columns = ["A", "B", "A", "B"]
+        categories = ["B", "A"]
+        data = np.array(
+            [[1, 2, 1, 2], [1, 2, 1, 2], [1, 2, 1, 2], [1, 2, 1, 2], [1, 2, 1, 2]], int
+        )
+        cat_columns = CategoricalIndex(columns, categories=categories, ordered=True)
+        df = DataFrame(data=data, columns=cat_columns)
+        result = df.groupby(axis=1, level=0, observed=observed).sum()
+        expected_data = np.array([[4, 2], [4, 2], [4, 2], [4, 2], [4, 2]], int)
+        expected_columns = CategoricalIndex(
+            categories, categories=categories, ordered=True
+        )
+        expected = DataFrame(data=expected_data, columns=expected_columns)
+        tm.assert_frame_equal(result, expected)
+
+        # test transposed version
+        df = DataFrame(data.T, index=cat_columns)
+        result = df.groupby(axis=0, level=0, observed=observed).sum()
+        expected = DataFrame(data=expected_data.T, index=expected_columns)
+        tm.assert_frame_equal(result, expected)
+
+    def test_grouper_getting_correct_binner(self):
+        # GH 10063
+        # using a non-time-based grouper and a time-based grouper
+        # and specifying levels
+        df = DataFrame(
+            {"A": 1},
+            index=MultiIndex.from_product(
+                [list("ab"), date_range("20130101", periods=80)], names=["one", "two"]
+            ),
+        )
+        result = df.groupby(
+            [Grouper(level="one"), Grouper(level="two", freq="M")]
+        ).sum()
+        expected = DataFrame(
+            {"A": [31, 28, 21, 31, 28, 21]},
+            index=MultiIndex.from_product(
+                [list("ab"), date_range("20130101", freq="M", periods=3)],
+                names=["one", "two"],
+            ),
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_grouper_iter(self, df):
+        assert sorted(df.groupby("A").grouper) == ["bar", "foo"]
+
+    def test_empty_groups(self, df):
+        # see gh-1048
+        with pytest.raises(ValueError, match="No group keys passed!"):
+            df.groupby([])
+
+    def test_groupby_grouper(self, df):
+        grouped = df.groupby("A")
+
+        result = df.groupby(grouped.grouper).mean(numeric_only=True)
+        expected = grouped.mean(numeric_only=True)
+        tm.assert_frame_equal(result, expected)
+
+    def test_groupby_dict_mapping(self):
+        # GH #679
+        s = Series({"T1": 5})
+        result = s.groupby({"T1": "T2"}).agg(sum)
+        expected = s.groupby(["T2"]).agg(sum)
+        tm.assert_series_equal(result, expected)
+
+        s = Series([1.0, 2.0, 3.0, 4.0], index=list("abcd"))
+        mapping = {"a": 0, "b": 0, "c": 1, "d": 1}
+
+        result = s.groupby(mapping).mean()
+        result2 = s.groupby(mapping).agg(np.mean)
+        exp_key = np.array([0, 0, 1, 1], dtype=np.int64)
+        expected = s.groupby(exp_key).mean()
+        expected2 = s.groupby(exp_key).mean()
+        tm.assert_series_equal(result, expected)
+        tm.assert_series_equal(result, result2)
+        tm.assert_series_equal(result, expected2)
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            [0, 1, 2, 3],
+            ["a", "b", "c", "d"],
+            [Timestamp(2021, 7, 28 + i) for i in range(4)],
+        ],
+    )
+    def test_groupby_series_named_with_tuple(self, frame_or_series, index):
+        # GH 42731
+        obj = frame_or_series([1, 2, 3, 4], index=index)
+        groups = Series([1, 0, 1, 0], index=index, name=("a", "a"))
+        result = obj.groupby(groups).last()
+        expected = frame_or_series([4, 3])
+        expected.index.name = ("a", "a")
+        tm.assert_equal(result, expected)
+
+    def test_groupby_grouper_f_sanity_checked(self):
+        dates = date_range("01-Jan-2013", periods=12, freq="MS")
+        ts = Series(np.random.randn(12), index=dates)
+
+        # GH3035
+        # index.map is used to apply grouper to the index
+        # if it fails on the elements, map tries it on the entire index as
+        # a sequence. That can yield invalid results that cause trouble
+        # down the line.
+        # the surprise comes from using key[0:6] rather than str(key)[0:6]
+        # when the elements are Timestamp.
+        # the result is Index[0:6], very confusing.
+
+        msg = r"Grouper result violates len\(labels\) == len\(data\)"
+        with pytest.raises(AssertionError, match=msg):
+            ts.groupby(lambda key: key[0:6])
+
+    def test_grouping_error_on_multidim_input(self, df):
+        msg = "Grouper for '<class 'pandas.core.frame.DataFrame'>' not 1-dimensional"
+        with pytest.raises(ValueError, match=msg):
+            Grouping(df.index, df[["A", "A"]])
+
+    def test_multiindex_passthru(self):
+        # GH 7997
+        # regression from 0.14.1
+        df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+        df.columns = MultiIndex.from_tuples([(0, 1), (1, 1), (2, 1)])
+
+        result = df.groupby(axis=1, level=[0, 1]).first()
+        tm.assert_frame_equal(result, df)
+
+    def test_multiindex_negative_level(self, mframe):
+        # GH 13901
+        result = mframe.groupby(level=-1).sum()
+        expected = mframe.groupby(level="second").sum()
+        tm.assert_frame_equal(result, expected)
+
+        result = mframe.groupby(level=-2).sum()
+        expected = mframe.groupby(level="first").sum()
+        tm.assert_frame_equal(result, expected)
+
+        result = mframe.groupby(level=[-2, -1]).sum()
+        expected = mframe.sort_index()
+        tm.assert_frame_equal(result, expected)
+
+        result = mframe.groupby(level=[-1, "first"]).sum()
+        expected = mframe.groupby(level=["second", "first"]).sum()
+        tm.assert_frame_equal(result, expected)
+
+    def test_multifunc_select_col_integer_cols(self, df):
+        df.columns = np.arange(len(df.columns))
+
+        # it works!
+        df.groupby(1, as_index=False)[2].agg({"Q": np.mean})
+
+    def test_multiindex_columns_empty_level(self):
+        lst = [["count", "values"], ["to filter", ""]]
+        midx = MultiIndex.from_tuples(lst)
+
+        df = DataFrame([[1, "A"]], columns=midx)
+
+        grouped = df.groupby("to filter").groups
+        assert grouped["A"] == [0]
+
+        grouped = df.groupby([("to filter", "")]).groups
+        assert grouped["A"] == [0]
+
+        df = DataFrame([[1, "A"], [2, "B"]], columns=midx)
+
+        expected = df.groupby("to filter").groups
+        result = df.groupby([("to filter", "")]).groups
+        assert result == expected
+
+        df = DataFrame([[1, "A"], [2, "A"]], columns=midx)
+
+        expected = df.groupby("to filter").groups
+        result = df.groupby([("to filter", "")]).groups
+        tm.assert_dict_equal(result, expected)
+
+    def test_groupby_multiindex_tuple(self):
+        # GH 17979
+        df = DataFrame(
+            [[1, 2, 3, 4], [3, 4, 5, 6], [1, 4, 2, 3]],
+            columns=MultiIndex.from_arrays([["a", "b", "b", "c"], [1, 1, 2, 2]]),
+        )
+        expected = df.groupby([("b", 1)]).groups
+        result = df.groupby(("b", 1)).groups
+        tm.assert_dict_equal(expected, result)
+
+        df2 = DataFrame(
+            df.values,
+            columns=MultiIndex.from_arrays(
+                [["a", "b", "b", "c"], ["d", "d", "e", "e"]]
+            ),
+        )
+        expected = df2.groupby([("b", "d")]).groups
+        result = df.groupby(("b", 1)).groups
+        tm.assert_dict_equal(expected, result)
+
+        df3 = DataFrame(df.values, columns=[("a", "d"), ("b", "d"), ("b", "e"), "c"])
+        expected = df3.groupby([("b", "d")]).groups
+        result = df.groupby(("b", 1)).groups
+        tm.assert_dict_equal(expected, result)
+
+    @pytest.mark.parametrize("sort", [True, False])
+    def test_groupby_level(self, sort, mframe, df):
+        # GH 17537
+        frame = mframe
+        deleveled = frame.reset_index()
+
+        result0 = frame.groupby(level=0, sort=sort).sum()
+        result1 = frame.groupby(level=1, sort=sort).sum()
+
+        expected0 = frame.groupby(deleveled["first"].values, sort=sort).sum()
+        expected1 = frame.groupby(deleveled["second"].values, sort=sort).sum()
+
+        expected0.index.name = "first"
+        expected1.index.name = "second"
+
+        assert result0.index.name == "first"
+        assert result1.index.name == "second"
+
+        tm.assert_frame_equal(result0, expected0)
+        tm.assert_frame_equal(result1, expected1)
+        assert result0.index.name == frame.index.names[0]
+        assert result1.index.name == frame.index.names[1]
+
+        # groupby level name
+        result0 = frame.groupby(level="first", sort=sort).sum()
+        result1 = frame.groupby(level="second", sort=sort).sum()
+        tm.assert_frame_equal(result0, expected0)
+        tm.assert_frame_equal(result1, expected1)
+
+        # axis=1
+
+        result0 = frame.T.groupby(level=0, axis=1, sort=sort).sum()
+        result1 = frame.T.groupby(level=1, axis=1, sort=sort).sum()
+        tm.assert_frame_equal(result0, expected0.T)
+        tm.assert_frame_equal(result1, expected1.T)
+
+        # raise exception for non-MultiIndex
+        msg = "level > 0 or level < -1 only valid with MultiIndex"
+        with pytest.raises(ValueError, match=msg):
+            df.groupby(level=1)
+
+    def test_groupby_level_index_names(self, axis):
+        # GH4014 this used to raise ValueError since 'exp'>1 (in py2)
+        df = DataFrame({"exp": ["A"] * 3 + ["B"] * 3, "var1": range(6)}).set_index(
+            "exp"
+        )
+        if axis in (1, "columns"):
+            df = df.T
+        df.groupby(level="exp", axis=axis)
+        msg = f"level name foo is not the name of the {df._get_axis_name(axis)}"
+        with pytest.raises(ValueError, match=msg):
+            df.groupby(level="foo", axis=axis)
+
+    @pytest.mark.parametrize("sort", [True, False])
+    def test_groupby_level_with_nas(self, sort):
+        # GH 17537
+        index = MultiIndex(
+            levels=[[1, 0], [0, 1, 2, 3]],
+            codes=[[1, 1, 1, 1, 0, 0, 0, 0], [0, 1, 2, 3, 0, 1, 2, 3]],
+        )
+
+        # factorizing doesn't confuse things
+        s = Series(np.arange(8.0), index=index)
+        result = s.groupby(level=0, sort=sort).sum()
+        expected = Series([6.0, 22.0], index=[0, 1])
+        tm.assert_series_equal(result, expected)
+
+        index = MultiIndex(
+            levels=[[1, 0], [0, 1, 2, 3]],
+            codes=[[1, 1, 1, 1, -1, 0, 0, 0], [0, 1, 2, 3, 0, 1, 2, 3]],
+        )
+
+        # factorizing doesn't confuse things
+        s = Series(np.arange(8.0), index=index)
+        result = s.groupby(level=0, sort=sort).sum()
+        expected = Series([6.0, 18.0], index=[0.0, 1.0])
+        tm.assert_series_equal(result, expected)
+
+    def test_groupby_args(self, mframe):
+        # PR8618 and issue 8015
+        frame = mframe
+
+        msg = "You have to supply one of 'by' and 'level'"
+        with pytest.raises(TypeError, match=msg):
+            frame.groupby()
+
+        msg = "You have to supply one of 'by' and 'level'"
+        with pytest.raises(TypeError, match=msg):
+            frame.groupby(by=None, level=None)
+
+    @pytest.mark.parametrize(
+        "sort,labels",
+        [
+            [True, [2, 2, 2, 0, 0, 1, 1, 3, 3, 3]],
+            [False, [0, 0, 0, 1, 1, 2, 2, 3, 3, 3]],
+        ],
+    )
+    def test_level_preserve_order(self, sort, labels, mframe):
+        # GH 17537
+        grouped = mframe.groupby(level=0, sort=sort)
+        exp_labels = np.array(labels, np.intp)
+        tm.assert_almost_equal(grouped.grouper.codes[0], exp_labels)
+
+    def test_grouping_labels(self, mframe):
+        grouped = mframe.groupby(mframe.index.get_level_values(0))
+        exp_labels = np.array([2, 2, 2, 0, 0, 1, 1, 3, 3, 3], dtype=np.intp)
+        tm.assert_almost_equal(grouped.grouper.codes[0], exp_labels)
+
+    def test_list_grouper_with_nat(self):
+        # GH 14715
+        df = DataFrame({"date": date_range("1/1/2011", periods=365, freq="D")})
+        df.iloc[-1] = pd.NaT
+        grouper = Grouper(key="date", freq="AS")
+
+        # Grouper in a list grouping
+        result = df.groupby([grouper])
+        expected = {Timestamp("2011-01-01"): Index(list(range(364)))}
+        tm.assert_dict_equal(result.groups, expected)
+
+        # Test case without a list
+        result = df.groupby(grouper)
+        expected = {Timestamp("2011-01-01"): 365}
+        tm.assert_dict_equal(result.groups, expected)
+
+    @pytest.mark.parametrize(
+        "func,expected",
+        [
+            (
+                "transform",
+                Series(name=2, dtype=np.float64),
+            ),
+            (
+                "agg",
+                Series(
+                    name=2, dtype=np.float64, index=Index([], dtype=np.float64, name=1)
+                ),
+            ),
+            (
+                "apply",
+                Series(
+                    name=2, dtype=np.float64, index=Index([], dtype=np.float64, name=1)
+                ),
+            ),
+        ],
+    )
+    def test_evaluate_with_empty_groups(self, func, expected):
+        # 26208
+        # test transform'ing empty groups
+        # (not testing other agg fns, because they return
+        # different index objects.
+        df = DataFrame({1: [], 2: []})
+        g = df.groupby(1, group_keys=False)
+        result = getattr(g[2], func)(lambda x: x)
+        tm.assert_series_equal(result, expected)
+
+    def test_groupby_empty(self):
+        # https://github.com/pandas-dev/pandas/issues/27190
+        s = Series([], name="name", dtype="float64")
+        gr = s.groupby([])
+
+        result = gr.mean()
+        expected = s.set_axis(Index([], dtype=np.intp))
+        tm.assert_series_equal(result, expected)
+
+        # check group properties
+        assert len(gr.grouper.groupings) == 1
+        tm.assert_numpy_array_equal(
+            gr.grouper.group_info[0], np.array([], dtype=np.dtype(np.intp))
+        )
+
+        tm.assert_numpy_array_equal(
+            gr.grouper.group_info[1], np.array([], dtype=np.dtype(np.intp))
+        )
+
+        assert gr.grouper.group_info[2] == 0
+
+        # check name
+        assert s.groupby(s).grouper.names == ["name"]
+
+    def test_groupby_level_index_value_all_na(self):
+        # issue 20519
+        df = DataFrame(
+            [["x", np.nan, 10], [None, np.nan, 20]], columns=["A", "B", "C"]
+        ).set_index(["A", "B"])
+        result = df.groupby(level=["A", "B"]).sum()
+        expected = DataFrame(
+            data=[],
+            index=MultiIndex(
+                levels=[Index(["x"], dtype="object"), Index([], dtype="float64")],
+                codes=[[], []],
+                names=["A", "B"],
+            ),
+            columns=["C"],
+            dtype="int64",
+        )
+        tm.assert_frame_equal(result, expected)
+
+    def test_groupby_multiindex_level_empty(self):
+        # https://github.com/pandas-dev/pandas/issues/31670
+        df = DataFrame(
+            [[123, "a", 1.0], [123, "b", 2.0]], columns=["id", "category", "value"]
+        )
+        df = df.set_index(["id", "category"])
+        empty = df[df.value < 0]
+        result = empty.groupby("id").sum()
+        expected = DataFrame(
+            dtype="float64",
+            columns=["value"],
+            index=Index([], dtype=np.int64, name="id"),
+        )
+        tm.assert_frame_equal(result, expected)
+
+
+# get_group
+# --------------------------------
+
+
+class TestGetGroup:
+    def test_get_group(self):
+        # GH 5267
+        # be datelike friendly
+        df = DataFrame(
+            {
+                "DATE": pd.to_datetime(
+                    [
+                        "10-Oct-2013",
+                        "10-Oct-2013",
+                        "10-Oct-2013",
+                        "11-Oct-2013",
+                        "11-Oct-2013",
+                        "11-Oct-2013",
+                    ]
+                ),
+                "label": ["foo", "foo", "bar", "foo", "foo", "bar"],
+                "VAL": [1, 2, 3, 4, 5, 6],
+            }
+        )
+
+        g = df.groupby("DATE")
+        key = list(g.groups)[0]
+        result1 = g.get_group(key)
+        result2 = g.get_group(Timestamp(key).to_pydatetime())
+        result3 = g.get_group(str(Timestamp(key)))
+        tm.assert_frame_equal(result1, result2)
+        tm.assert_frame_equal(result1, result3)
+
+        g = df.groupby(["DATE", "label"])
+
+        key = list(g.groups)[0]
+        result1 = g.get_group(key)
+        result2 = g.get_group((Timestamp(key[0]).to_pydatetime(), key[1]))
+        result3 = g.get_group((str(Timestamp(key[0])), key[1]))
+        tm.assert_frame_equal(result1, result2)
+        tm.assert_frame_equal(result1, result3)
+
+        # must pass a same-length tuple with multiple keys
+        msg = "must supply a tuple to get_group with multiple grouping keys"
+        with pytest.raises(ValueError, match=msg):
+            g.get_group("foo")
+        with pytest.raises(ValueError, match=msg):
+            g.get_group("foo")
+        msg = "must supply a same-length tuple to get_group with multiple grouping keys"
+        with pytest.raises(ValueError, match=msg):
+            g.get_group(("foo", "bar", "baz"))
+
+    def test_get_group_empty_bins(self, observed):
+        d = DataFrame([3, 1, 7, 6])
+        bins = [0, 5, 10, 15]
+        g = d.groupby(pd.cut(d[0], bins), observed=observed)
+
+        # TODO: should prob allow a str of Interval work as well
+        # IOW '(0, 5]'
+        result = g.get_group(pd.Interval(0, 5))
+        expected = DataFrame([3, 1], index=[0, 1])
+        tm.assert_frame_equal(result, expected)
+
+        msg = r"Interval\(10, 15, closed='right'\)"
+        with pytest.raises(KeyError, match=msg):
+            g.get_group(pd.Interval(10, 15))
+
+    def test_get_group_grouped_by_tuple(self):
+        # GH 8121
+        df = DataFrame([[(1,), (1, 2), (1,), (1, 2)]], index=["ids"]).T
+        gr = df.groupby("ids")
+        expected = DataFrame({"ids": [(1,), (1,)]}, index=[0, 2])
+        result = gr.get_group((1,))
+        tm.assert_frame_equal(result, expected)
+
+        dt = pd.to_datetime(["2010-01-01", "2010-01-02", "2010-01-01", "2010-01-02"])
+        df = DataFrame({"ids": [(x,) for x in dt]})
+        gr = df.groupby("ids")
+        result = gr.get_group(("2010-01-01",))
+        expected = DataFrame({"ids": [(dt[0],), (dt[0],)]}, index=[0, 2])
+        tm.assert_frame_equal(result, expected)
+
+    def test_get_group_grouped_by_tuple_with_lambda(self):
+        # GH 36158
+        df = DataFrame(
+            {"Tuples": ((x, y) for x in [0, 1] for y in np.random.randint(3, 5, 5))}
+        )
+
+        gb = df.groupby("Tuples")
+        gb_lambda = df.groupby(lambda x: df.iloc[x, 0])
+
+        expected = gb.get_group(list(gb.groups.keys())[0])
+        result = gb_lambda.get_group(list(gb_lambda.groups.keys())[0])
+
+        tm.assert_frame_equal(result, expected)
+
+    def test_groupby_with_empty(self):
+        index = pd.DatetimeIndex(())
+        data = ()
+        series = Series(data, index, dtype=object)
+        grouper = Grouper(freq="D")
+        grouped = series.groupby(grouper)
+        assert next(iter(grouped), None) is None
+
+    def test_groupby_with_single_column(self):
+        df = DataFrame({"a": list("abssbab")})
+        tm.assert_frame_equal(df.groupby("a").get_group("a"), df.iloc[[0, 5]])
+        # GH 13530
+        exp = DataFrame(index=Index(["a", "b", "s"], name="a"), columns=[])
+        tm.assert_frame_equal(df.groupby("a").count(), exp)
+        tm.assert_frame_equal(df.groupby("a").sum(), exp)
+
+        exp = df.iloc[[3, 4, 5]]
+        tm.assert_frame_equal(df.groupby("a").nth(1), exp)
+
+    def test_gb_key_len_equal_axis_len(self):
+        # GH16843
+        # test ensures that index and column keys are recognized correctly
+        # when number of keys equals axis length of groupby
+        df = DataFrame(
+            [["foo", "bar", "B", 1], ["foo", "bar", "B", 2], ["foo", "baz", "C", 3]],
+            columns=["first", "second", "third", "one"],
+        )
+        df = df.set_index(["first", "second"])
+        df = df.groupby(["first", "second", "third"]).size()
+        assert df.loc[("foo", "bar", "B")] == 2
+        assert df.loc[("foo", "baz", "C")] == 1
+
+
+# groups & iteration
+# --------------------------------
+
+
+class TestIteration:
+    def test_groups(self, df):
+        grouped = df.groupby(["A"])
+        groups = grouped.groups
+        assert groups is grouped.groups  # caching works
+
+        for k, v in grouped.groups.items():
+            assert (df.loc[v]["A"] == k).all()
+
+        grouped = df.groupby(["A", "B"])
+        groups = grouped.groups
+        assert groups is grouped.groups  # caching works
+
+        for k, v in grouped.groups.items():
+            assert (df.loc[v]["A"] == k[0]).all()
+            assert (df.loc[v]["B"] == k[1]).all()
+
+    def test_grouping_is_iterable(self, tsframe):
+        # this code path isn't used anywhere else
+        # not sure it's useful
+        grouped = tsframe.groupby([lambda x: x.weekday(), lambda x: x.year])
+
+        # test it works
+        for g in grouped.grouper.groupings[0]:
+            pass
+
+    def test_multi_iter(self):
+        s = Series(np.arange(6))
+        k1 = np.array(["a", "a", "a", "b", "b", "b"])
+        k2 = np.array(["1", "2", "1", "2", "1", "2"])
+
+        grouped = s.groupby([k1, k2])
+
+        iterated = list(grouped)
+        expected = [
+            ("a", "1", s[[0, 2]]),
+            ("a", "2", s[[1]]),
+            ("b", "1", s[[4]]),
+            ("b", "2", s[[3, 5]]),
+        ]
+        for i, ((one, two), three) in enumerate(iterated):
+            e1, e2, e3 = expected[i]
+            assert e1 == one
+            assert e2 == two
+            tm.assert_series_equal(three, e3)
+
+    def test_multi_iter_frame(self, three_group):
+        k1 = np.array(["b", "b", "b", "a", "a", "a"])
+        k2 = np.array(["1", "2", "1", "2", "1", "2"])
+        df = DataFrame(
+            {"v1": np.random.randn(6), "v2": np.random.randn(6), "k1": k1, "k2": k2},
+            index=["one", "two", "three", "four", "five", "six"],
+        )
+
+        grouped = df.groupby(["k1", "k2"])
+
+        # things get sorted!
+        iterated = list(grouped)
+        idx = df.index
+        expected = [
+            ("a", "1", df.loc[idx[[4]]]),
+            ("a", "2", df.loc[idx[[3, 5]]]),
+            ("b", "1", df.loc[idx[[0, 2]]]),
+            ("b", "2", df.loc[idx[[1]]]),
+        ]
+        for i, ((one, two), three) in enumerate(iterated):
+            e1, e2, e3 = expected[i]
+            assert e1 == one
+            assert e2 == two
+            tm.assert_frame_equal(three, e3)
+
+        # don't iterate through groups with no data
+        df["k1"] = np.array(["b", "b", "b", "a", "a", "a"])
+        df["k2"] = np.array(["1", "1", "1", "2", "2", "2"])
+        grouped = df.groupby(["k1", "k2"])
+        # calling `dict` on a DataFrameGroupBy leads to a TypeError,
+        # we need to use a dictionary comprehension here
+        # pylint: disable-next=unnecessary-comprehension
+        groups = {key: gp for key, gp in grouped}
+        assert len(groups) == 2
+
+        # axis = 1
+        three_levels = three_group.groupby(["A", "B", "C"]).mean()
+        grouped = three_levels.T.groupby(axis=1, level=(1, 2))
+        for key, group in grouped:
+            pass
+
+    def test_dictify(self, df):
+        dict(iter(df.groupby("A")))
+        dict(iter(df.groupby(["A", "B"])))
+        dict(iter(df["C"].groupby(df["A"])))
+        dict(iter(df["C"].groupby([df["A"], df["B"]])))
+        dict(iter(df.groupby("A")["C"]))
+        dict(iter(df.groupby(["A", "B"])["C"]))
+
+    def test_groupby_with_small_elem(self):
+        # GH 8542
+        # length=2
+        df = DataFrame(
+            {"event": ["start", "start"], "change": [1234, 5678]},
+            index=pd.DatetimeIndex(["2014-09-10", "2013-10-10"]),
+        )
+        grouped = df.groupby([Grouper(freq="M"), "event"])
+        assert len(grouped.groups) == 2
+        assert grouped.ngroups == 2
+        assert (Timestamp("2014-09-30"), "start") in grouped.groups
+        assert (Timestamp("2013-10-31"), "start") in grouped.groups
+
+        res = grouped.get_group((Timestamp("2014-09-30"), "start"))
+        tm.assert_frame_equal(res, df.iloc[[0], :])
+        res = grouped.get_group((Timestamp("2013-10-31"), "start"))
+        tm.assert_frame_equal(res, df.iloc[[1], :])
+
+        df = DataFrame(
+            {"event": ["start", "start", "start"], "change": [1234, 5678, 9123]},
+            index=pd.DatetimeIndex(["2014-09-10", "2013-10-10", "2014-09-15"]),
+        )
+        grouped = df.groupby([Grouper(freq="M"), "event"])
+        assert len(grouped.groups) == 2
+        assert grouped.ngroups == 2
+        assert (Timestamp("2014-09-30"), "start") in grouped.groups
+        assert (Timestamp("2013-10-31"), "start") in grouped.groups
+
+        res = grouped.get_group((Timestamp("2014-09-30"), "start"))
+        tm.assert_frame_equal(res, df.iloc[[0, 2], :])
+        res = grouped.get_group((Timestamp("2013-10-31"), "start"))
+        tm.assert_frame_equal(res, df.iloc[[1], :])
+
+        # length=3
+        df = DataFrame(
+            {"event": ["start", "start", "start"], "change": [1234, 5678, 9123]},
+            index=pd.DatetimeIndex(["2014-09-10", "2013-10-10", "2014-08-05"]),
+        )
+        grouped = df.groupby([Grouper(freq="M"), "event"])
+        assert len(grouped.groups) == 3
+        assert grouped.ngroups == 3
+        assert (Timestamp("2014-09-30"), "start") in grouped.groups
+        assert (Timestamp("2013-10-31"), "start") in grouped.groups
+        assert (Timestamp("2014-08-31"), "start") in grouped.groups
+
+        res = grouped.get_group((Timestamp("2014-09-30"), "start"))
+        tm.assert_frame_equal(res, df.iloc[[0], :])
+        res = grouped.get_group((Timestamp("2013-10-31"), "start"))
+        tm.assert_frame_equal(res, df.iloc[[1], :])
+        res = grouped.get_group((Timestamp("2014-08-31"), "start"))
+        tm.assert_frame_equal(res, df.iloc[[2], :])
+
+    def test_grouping_string_repr(self):
+        # GH 13394
+        mi = MultiIndex.from_arrays([list("AAB"), list("aba")])
+        df = DataFrame([[1, 2, 3]], columns=mi)
+        gr = df.groupby(df[("A", "a")])
+
+        result = gr.grouper.groupings[0].__repr__()
+        expected = "Grouping(('A', 'a'))"
+        assert result == expected
+
+
+def test_grouping_by_key_is_in_axis():
+    # GH#50413 - Groupers specified by key are in-axis
+    df = DataFrame({"a": [1, 1, 2], "b": [1, 1, 2], "c": [3, 4, 5]}).set_index("a")
+    gb = df.groupby([Grouper(level="a"), Grouper(key="b")], as_index=False)
+    assert not gb.grouper.groupings[0].in_axis
+    assert gb.grouper.groupings[1].in_axis
+
+    # Currently only in-axis groupings are including in the result when as_index=False;
+    # This is likely to change in the future.
+    result = gb.sum()
+    expected = DataFrame({"b": [1, 2], "c": [7, 5]})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_grouper_groups():
+    # GH#51182 check Grouper.groups does not raise AttributeError
+    df = DataFrame({"a": [1, 2, 3], "b": 1})
+    grper = Grouper(key="a")
+    gb = df.groupby(grper)
+
+    msg = "Use GroupBy.groups instead"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        res = grper.groups
+    assert res is gb.groups
+
+    msg = "Use GroupBy.grouper instead"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        res = grper.grouper
+    assert res is gb.grouper
+
+    msg = "Grouper.obj is deprecated and will be removed"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        res = grper.obj
+    assert res is gb.obj
+
+    msg = "Use Resampler.ax instead"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        grper.ax
+
+    msg = "Grouper.indexer is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        grper.indexer

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_index_as_string.py

@@ -0,0 +1,85 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.fixture(params=[["inner"], ["inner", "outer"]])
+def frame(request):
+    levels = request.param
+    df = pd.DataFrame(
+        {
+            "outer": ["a", "a", "a", "b", "b", "b"],
+            "inner": [1, 2, 3, 1, 2, 3],
+            "A": np.arange(6),
+            "B": ["one", "one", "two", "two", "one", "one"],
+        }
+    )
+    if levels:
+        df = df.set_index(levels)
+
+    return df
+
+
+@pytest.fixture()
+def series():
+    df = pd.DataFrame(
+        {
+            "outer": ["a", "a", "a", "b", "b", "b"],
+            "inner": [1, 2, 3, 1, 2, 3],
+            "A": np.arange(6),
+            "B": ["one", "one", "two", "two", "one", "one"],
+        }
+    )
+    s = df.set_index(["outer", "inner", "B"])["A"]
+
+    return s
+
+
+@pytest.mark.parametrize(
+    "key_strs,groupers",
+    [
+        ("inner", pd.Grouper(level="inner")),  # Index name
+        (["inner"], [pd.Grouper(level="inner")]),  # List of index name
+        (["B", "inner"], ["B", pd.Grouper(level="inner")]),  # Column and index
+        (["inner", "B"], [pd.Grouper(level="inner"), "B"]),  # Index and column
+    ],
+)
+def test_grouper_index_level_as_string(frame, key_strs, groupers):
+    if "B" not in key_strs or "outer" in frame.columns:
+        result = frame.groupby(key_strs).mean(numeric_only=True)
+        expected = frame.groupby(groupers).mean(numeric_only=True)
+    else:
+        result = frame.groupby(key_strs).mean()
+        expected = frame.groupby(groupers).mean()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "levels",
+    [
+        "inner",
+        "outer",
+        "B",
+        ["inner"],
+        ["outer"],
+        ["B"],
+        ["inner", "outer"],
+        ["outer", "inner"],
+        ["inner", "outer", "B"],
+        ["B", "outer", "inner"],
+    ],
+)
+def test_grouper_index_level_as_string_series(series, levels):
+    # Compute expected result
+    if isinstance(levels, list):
+        groupers = [pd.Grouper(level=lv) for lv in levels]
+    else:
+        groupers = pd.Grouper(level=levels)
+
+    expected = series.groupby(groupers).mean()
+
+    # Compute and check result
+    result = series.groupby(levels).mean()
+    tm.assert_series_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_indexing.py

@@ -0,0 +1,332 @@
+# Test GroupBy._positional_selector positional grouped indexing GH#42864
+
+import random
+
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "arg, expected_rows",
+    [
+        [0, [0, 1, 4]],
+        [2, [5]],
+        [5, []],
+        [-1, [3, 4, 7]],
+        [-2, [1, 6]],
+        [-6, []],
+    ],
+)
+def test_int(slice_test_df, slice_test_grouped, arg, expected_rows):
+    # Test single integer
+    result = slice_test_grouped._positional_selector[arg]
+    expected = slice_test_df.iloc[expected_rows]
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_slice(slice_test_df, slice_test_grouped):
+    # Test single slice
+    result = slice_test_grouped._positional_selector[0:3:2]
+    expected = slice_test_df.iloc[[0, 1, 4, 5]]
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "arg, expected_rows",
+    [
+        [[0, 2], [0, 1, 4, 5]],
+        [[0, 2, -1], [0, 1, 3, 4, 5, 7]],
+        [range(0, 3, 2), [0, 1, 4, 5]],
+        [{0, 2}, [0, 1, 4, 5]],
+    ],
+    ids=[
+        "list",
+        "negative",
+        "range",
+        "set",
+    ],
+)
+def test_list(slice_test_df, slice_test_grouped, arg, expected_rows):
+    # Test lists of integers and integer valued iterables
+    result = slice_test_grouped._positional_selector[arg]
+    expected = slice_test_df.iloc[expected_rows]
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_ints(slice_test_df, slice_test_grouped):
+    # Test tuple of ints
+    result = slice_test_grouped._positional_selector[0, 2, -1]
+    expected = slice_test_df.iloc[[0, 1, 3, 4, 5, 7]]
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_slices(slice_test_df, slice_test_grouped):
+    # Test tuple of slices
+    result = slice_test_grouped._positional_selector[:2, -2:]
+    expected = slice_test_df.iloc[[0, 1, 2, 3, 4, 6, 7]]
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_mix(slice_test_df, slice_test_grouped):
+    # Test mixed tuple of ints and slices
+    result = slice_test_grouped._positional_selector[0, 1, -2:]
+    expected = slice_test_df.iloc[[0, 1, 2, 3, 4, 6, 7]]
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "arg, expected_rows",
+    [
+        [0, [0, 1, 4]],
+        [[0, 2, -1], [0, 1, 3, 4, 5, 7]],
+        [(slice(None, 2), slice(-2, None)), [0, 1, 2, 3, 4, 6, 7]],
+    ],
+)
+def test_as_index(slice_test_df, arg, expected_rows):
+    # Test the default as_index behaviour
+    result = slice_test_df.groupby("Group", sort=False)._positional_selector[arg]
+    expected = slice_test_df.iloc[expected_rows]
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_doc_examples():
+    # Test the examples in the documentation
+    df = pd.DataFrame(
+        [["a", 1], ["a", 2], ["a", 3], ["b", 4], ["b", 5]], columns=["A", "B"]
+    )
+
+    grouped = df.groupby("A", as_index=False)
+
+    result = grouped._positional_selector[1:2]
+    expected = pd.DataFrame([["a", 2], ["b", 5]], columns=["A", "B"], index=[1, 4])
+
+    tm.assert_frame_equal(result, expected)
+
+    result = grouped._positional_selector[1, -1]
+    expected = pd.DataFrame(
+        [["a", 2], ["a", 3], ["b", 5]], columns=["A", "B"], index=[1, 2, 4]
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.fixture()
+def multiindex_data():
+    ndates = 100
+    nitems = 20
+    dates = pd.date_range("20130101", periods=ndates, freq="D")
+    items = [f"item {i}" for i in range(nitems)]
+
+    data = {}
+    for date in dates:
+        nitems_for_date = nitems - random.randint(0, 12)
+        levels = [
+            (item, random.randint(0, 10000) / 100, random.randint(0, 10000) / 100)
+            for item in items[:nitems_for_date]
+        ]
+        levels.sort(key=lambda x: x[1])
+        data[date] = levels
+
+    return data
+
+
+def _make_df_from_data(data):
+    rows = {}
+    for date in data:
+        for level in data[date]:
+            rows[(date, level[0])] = {"A": level[1], "B": level[2]}
+
+    df = pd.DataFrame.from_dict(rows, orient="index")
+    df.index.names = ("Date", "Item")
+    return df
+
+
+def test_multiindex(multiindex_data):
+    # Test the multiindex mentioned as the use-case in the documentation
+    df = _make_df_from_data(multiindex_data)
+    result = df.groupby("Date", as_index=False).nth(slice(3, -3))
+
+    sliced = {date: multiindex_data[date][3:-3] for date in multiindex_data}
+    expected = _make_df_from_data(sliced)
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("arg", [1, 5, 30, 1000, -1, -5, -30, -1000])
+@pytest.mark.parametrize("method", ["head", "tail"])
+@pytest.mark.parametrize("simulated", [True, False])
+def test_against_head_and_tail(arg, method, simulated):
+    # Test gives the same results as grouped head and tail
+    n_groups = 100
+    n_rows_per_group = 30
+
+    data = {
+        "group": [
+            f"group {g}" for j in range(n_rows_per_group) for g in range(n_groups)
+        ],
+        "value": [
+            f"group {g} row {j}"
+            for j in range(n_rows_per_group)
+            for g in range(n_groups)
+        ],
+    }
+    df = pd.DataFrame(data)
+    grouped = df.groupby("group", as_index=False)
+    size = arg if arg >= 0 else n_rows_per_group + arg
+
+    if method == "head":
+        result = grouped._positional_selector[:arg]
+
+        if simulated:
+            indices = []
+            for j in range(size):
+                for i in range(n_groups):
+                    if j * n_groups + i < n_groups * n_rows_per_group:
+                        indices.append(j * n_groups + i)
+
+            expected = df.iloc[indices]
+
+        else:
+            expected = grouped.head(arg)
+
+    else:
+        result = grouped._positional_selector[-arg:]
+
+        if simulated:
+            indices = []
+            for j in range(size):
+                for i in range(n_groups):
+                    if (n_rows_per_group + j - size) * n_groups + i >= 0:
+                        indices.append((n_rows_per_group + j - size) * n_groups + i)
+
+            expected = df.iloc[indices]
+
+        else:
+            expected = grouped.tail(arg)
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("start", [None, 0, 1, 10, -1, -10])
+@pytest.mark.parametrize("stop", [None, 0, 1, 10, -1, -10])
+@pytest.mark.parametrize("step", [None, 1, 5])
+def test_against_df_iloc(start, stop, step):
+    # Test that a single group gives the same results as DataFrame.iloc
+    n_rows = 30
+
+    data = {
+        "group": ["group 0"] * n_rows,
+        "value": list(range(n_rows)),
+    }
+    df = pd.DataFrame(data)
+    grouped = df.groupby("group", as_index=False)
+
+    result = grouped._positional_selector[start:stop:step]
+    expected = df.iloc[start:stop:step]
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_series():
+    # Test grouped Series
+    ser = pd.Series([1, 2, 3, 4, 5], index=["a", "a", "a", "b", "b"])
+    grouped = ser.groupby(level=0)
+    result = grouped._positional_selector[1:2]
+    expected = pd.Series([2, 5], index=["a", "b"])
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("step", [1, 2, 3, 4, 5])
+def test_step(step):
+    # Test slice with various step values
+    data = [["x", f"x{i}"] for i in range(5)]
+    data += [["y", f"y{i}"] for i in range(4)]
+    data += [["z", f"z{i}"] for i in range(3)]
+    df = pd.DataFrame(data, columns=["A", "B"])
+
+    grouped = df.groupby("A", as_index=False)
+
+    result = grouped._positional_selector[::step]
+
+    data = [["x", f"x{i}"] for i in range(0, 5, step)]
+    data += [["y", f"y{i}"] for i in range(0, 4, step)]
+    data += [["z", f"z{i}"] for i in range(0, 3, step)]
+
+    index = [0 + i for i in range(0, 5, step)]
+    index += [5 + i for i in range(0, 4, step)]
+    index += [9 + i for i in range(0, 3, step)]
+
+    expected = pd.DataFrame(data, columns=["A", "B"], index=index)
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.fixture()
+def column_group_df():
+    return pd.DataFrame(
+        [[0, 1, 2, 3, 4, 5, 6], [0, 0, 1, 0, 1, 0, 2]],
+        columns=["A", "B", "C", "D", "E", "F", "G"],
+    )
+
+
+def test_column_axis(column_group_df):
+    g = column_group_df.groupby(column_group_df.iloc[1], axis=1)
+    result = g._positional_selector[1:-1]
+    expected = column_group_df.iloc[:, [1, 3]]
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_columns_on_iter():
+    # GitHub issue #44821
+    df = pd.DataFrame({k: range(10) for k in "ABC"})
+
+    # Group-by and select columns
+    cols = ["A", "B"]
+    for _, dg in df.groupby(df.A < 4)[cols]:
+        tm.assert_index_equal(dg.columns, pd.Index(cols))
+        assert "C" not in dg.columns
+
+
+@pytest.mark.parametrize("func", [list, pd.Index, pd.Series, np.array])
+def test_groupby_duplicated_columns(func):
+    # GH#44924
+    df = pd.DataFrame(
+        {
+            "A": [1, 2],
+            "B": [3, 3],
+            "C": ["G", "G"],
+        }
+    )
+    result = df.groupby("C")[func(["A", "B", "A"])].mean()
+    expected = pd.DataFrame(
+        [[1.5, 3.0, 1.5]], columns=["A", "B", "A"], index=pd.Index(["G"], name="C")
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_get_nonexisting_groups():
+    # GH#32492
+    df = pd.DataFrame(
+        data={
+            "A": ["a1", "a2", None],
+            "B": ["b1", "b2", "b1"],
+            "val": [1, 2, 3],
+        }
+    )
+    grps = df.groupby(by=["A", "B"])
+
+    msg = "('a2', 'b1')"
+    with pytest.raises(KeyError, match=msg):
+        grps.get_group(("a2", "b1"))

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_libgroupby.py

@@ -0,0 +1,284 @@
+import numpy as np
+import pytest
+
+from pandas._libs import groupby as libgroupby
+from pandas._libs.groupby import (
+    group_cumprod,
+    group_cumsum,
+    group_mean,
+    group_var,
+)
+
+from pandas.core.dtypes.common import ensure_platform_int
+
+from pandas import isna
+import pandas._testing as tm
+
+
+class GroupVarTestMixin:
+    def test_group_var_generic_1d(self):
+        prng = np.random.RandomState(1234)
+
+        out = (np.nan * np.ones((5, 1))).astype(self.dtype)
+        counts = np.zeros(5, dtype="int64")
+        values = 10 * prng.rand(15, 1).astype(self.dtype)
+        labels = np.tile(np.arange(5), (3,)).astype("intp")
+
+        expected_out = (
+            np.squeeze(values).reshape((5, 3), order="F").std(axis=1, ddof=1) ** 2
+        )[:, np.newaxis]
+        expected_counts = counts + 3
+
+        self.algo(out, counts, values, labels)
+        assert np.allclose(out, expected_out, self.rtol)
+        tm.assert_numpy_array_equal(counts, expected_counts)
+
+    def test_group_var_generic_1d_flat_labels(self):
+        prng = np.random.RandomState(1234)
+
+        out = (np.nan * np.ones((1, 1))).astype(self.dtype)
+        counts = np.zeros(1, dtype="int64")
+        values = 10 * prng.rand(5, 1).astype(self.dtype)
+        labels = np.zeros(5, dtype="intp")
+
+        expected_out = np.array([[values.std(ddof=1) ** 2]])
+        expected_counts = counts + 5
+
+        self.algo(out, counts, values, labels)
+
+        assert np.allclose(out, expected_out, self.rtol)
+        tm.assert_numpy_array_equal(counts, expected_counts)
+
+    def test_group_var_generic_2d_all_finite(self):
+        prng = np.random.RandomState(1234)
+
+        out = (np.nan * np.ones((5, 2))).astype(self.dtype)
+        counts = np.zeros(5, dtype="int64")
+        values = 10 * prng.rand(10, 2).astype(self.dtype)
+        labels = np.tile(np.arange(5), (2,)).astype("intp")
+
+        expected_out = np.std(values.reshape(2, 5, 2), ddof=1, axis=0) ** 2
+        expected_counts = counts + 2
+
+        self.algo(out, counts, values, labels)
+        assert np.allclose(out, expected_out, self.rtol)
+        tm.assert_numpy_array_equal(counts, expected_counts)
+
+    def test_group_var_generic_2d_some_nan(self):
+        prng = np.random.RandomState(1234)
+
+        out = (np.nan * np.ones((5, 2))).astype(self.dtype)
+        counts = np.zeros(5, dtype="int64")
+        values = 10 * prng.rand(10, 2).astype(self.dtype)
+        values[:, 1] = np.nan
+        labels = np.tile(np.arange(5), (2,)).astype("intp")
+
+        expected_out = np.vstack(
+            [
+                values[:, 0].reshape(5, 2, order="F").std(ddof=1, axis=1) ** 2,
+                np.nan * np.ones(5),
+            ]
+        ).T.astype(self.dtype)
+        expected_counts = counts + 2
+
+        self.algo(out, counts, values, labels)
+        tm.assert_almost_equal(out, expected_out, rtol=0.5e-06)
+        tm.assert_numpy_array_equal(counts, expected_counts)
+
+    def test_group_var_constant(self):
+        # Regression test from GH 10448.
+
+        out = np.array([[np.nan]], dtype=self.dtype)
+        counts = np.array([0], dtype="int64")
+        values = 0.832845131556193 * np.ones((3, 1), dtype=self.dtype)
+        labels = np.zeros(3, dtype="intp")
+
+        self.algo(out, counts, values, labels)
+
+        assert counts[0] == 3
+        assert out[0, 0] >= 0
+        tm.assert_almost_equal(out[0, 0], 0.0)
+
+
+class TestGroupVarFloat64(GroupVarTestMixin):
+    __test__ = True
+
+    algo = staticmethod(group_var)
+    dtype = np.float64
+    rtol = 1e-5
+
+    def test_group_var_large_inputs(self):
+        prng = np.random.RandomState(1234)
+
+        out = np.array([[np.nan]], dtype=self.dtype)
+        counts = np.array([0], dtype="int64")
+        values = (prng.rand(10**6) + 10**12).astype(self.dtype)
+        values.shape = (10**6, 1)
+        labels = np.zeros(10**6, dtype="intp")
+
+        self.algo(out, counts, values, labels)
+
+        assert counts[0] == 10**6
+        tm.assert_almost_equal(out[0, 0], 1.0 / 12, rtol=0.5e-3)
+
+
+class TestGroupVarFloat32(GroupVarTestMixin):
+    __test__ = True
+
+    algo = staticmethod(group_var)
+    dtype = np.float32
+    rtol = 1e-2
+
+
+@pytest.mark.parametrize("dtype", ["float32", "float64"])
+def test_group_ohlc(dtype):
+    obj = np.array(np.random.randn(20), dtype=dtype)
+
+    bins = np.array([6, 12, 20])
+    out = np.zeros((3, 4), dtype)
+    counts = np.zeros(len(out), dtype=np.int64)
+    labels = ensure_platform_int(np.repeat(np.arange(3), np.diff(np.r_[0, bins])))
+
+    func = libgroupby.group_ohlc
+    func(out, counts, obj[:, None], labels)
+
+    def _ohlc(group):
+        if isna(group).all():
+            return np.repeat(np.nan, 4)
+        return [group[0], group.max(), group.min(), group[-1]]
+
+    expected = np.array([_ohlc(obj[:6]), _ohlc(obj[6:12]), _ohlc(obj[12:])])
+
+    tm.assert_almost_equal(out, expected)
+    tm.assert_numpy_array_equal(counts, np.array([6, 6, 8], dtype=np.int64))
+
+    obj[:6] = np.nan
+    func(out, counts, obj[:, None], labels)
+    expected[0] = np.nan
+    tm.assert_almost_equal(out, expected)
+
+
+def _check_cython_group_transform_cumulative(pd_op, np_op, dtype):
+    """
+    Check a group transform that executes a cumulative function.
+
+    Parameters
+    ----------
+    pd_op : callable
+        The pandas cumulative function.
+    np_op : callable
+        The analogous one in NumPy.
+    dtype : type
+        The specified dtype of the data.
+    """
+    is_datetimelike = False
+
+    data = np.array([[1], [2], [3], [4]], dtype=dtype)
+    answer = np.zeros_like(data)
+
+    labels = np.array([0, 0, 0, 0], dtype=np.intp)
+    ngroups = 1
+    pd_op(answer, data, labels, ngroups, is_datetimelike)
+
+    tm.assert_numpy_array_equal(np_op(data), answer[:, 0], check_dtype=False)
+
+
+@pytest.mark.parametrize("np_dtype", ["int64", "uint64", "float32", "float64"])
+def test_cython_group_transform_cumsum(np_dtype):
+    # see gh-4095
+    dtype = np.dtype(np_dtype).type
+    pd_op, np_op = group_cumsum, np.cumsum
+    _check_cython_group_transform_cumulative(pd_op, np_op, dtype)
+
+
+def test_cython_group_transform_cumprod():
+    # see gh-4095
+    dtype = np.float64
+    pd_op, np_op = group_cumprod, np.cumprod
+    _check_cython_group_transform_cumulative(pd_op, np_op, dtype)
+
+
+def test_cython_group_transform_algos():
+    # see gh-4095
+    is_datetimelike = False
+
+    # with nans
+    labels = np.array([0, 0, 0, 0, 0], dtype=np.intp)
+    ngroups = 1
+
+    data = np.array([[1], [2], [3], [np.nan], [4]], dtype="float64")
+    actual = np.zeros_like(data)
+    actual.fill(np.nan)
+    group_cumprod(actual, data, labels, ngroups, is_datetimelike)
+    expected = np.array([1, 2, 6, np.nan, 24], dtype="float64")
+    tm.assert_numpy_array_equal(actual[:, 0], expected)
+
+    actual = np.zeros_like(data)
+    actual.fill(np.nan)
+    group_cumsum(actual, data, labels, ngroups, is_datetimelike)
+    expected = np.array([1, 3, 6, np.nan, 10], dtype="float64")
+    tm.assert_numpy_array_equal(actual[:, 0], expected)
+
+    # timedelta
+    is_datetimelike = True
+    data = np.array([np.timedelta64(1, "ns")] * 5, dtype="m8[ns]")[:, None]
+    actual = np.zeros_like(data, dtype="int64")
+    group_cumsum(actual, data.view("int64"), labels, ngroups, is_datetimelike)
+    expected = np.array(
+        [
+            np.timedelta64(1, "ns"),
+            np.timedelta64(2, "ns"),
+            np.timedelta64(3, "ns"),
+            np.timedelta64(4, "ns"),
+            np.timedelta64(5, "ns"),
+        ]
+    )
+    tm.assert_numpy_array_equal(actual[:, 0].view("m8[ns]"), expected)
+
+
+def test_cython_group_mean_datetimelike():
+    actual = np.zeros(shape=(1, 1), dtype="float64")
+    counts = np.array([0], dtype="int64")
+    data = (
+        np.array(
+            [np.timedelta64(2, "ns"), np.timedelta64(4, "ns"), np.timedelta64("NaT")],
+            dtype="m8[ns]",
+        )[:, None]
+        .view("int64")
+        .astype("float64")
+    )
+    labels = np.zeros(len(data), dtype=np.intp)
+
+    group_mean(actual, counts, data, labels, is_datetimelike=True)
+
+    tm.assert_numpy_array_equal(actual[:, 0], np.array([3], dtype="float64"))
+
+
+def test_cython_group_mean_wrong_min_count():
+    actual = np.zeros(shape=(1, 1), dtype="float64")
+    counts = np.zeros(1, dtype="int64")
+    data = np.zeros(1, dtype="float64")[:, None]
+    labels = np.zeros(1, dtype=np.intp)
+
+    with pytest.raises(AssertionError, match="min_count"):
+        group_mean(actual, counts, data, labels, is_datetimelike=True, min_count=0)
+
+
+def test_cython_group_mean_not_datetimelike_but_has_NaT_values():
+    actual = np.zeros(shape=(1, 1), dtype="float64")
+    counts = np.array([0], dtype="int64")
+    data = (
+        np.array(
+            [np.timedelta64("NaT"), np.timedelta64("NaT")],
+            dtype="m8[ns]",
+        )[:, None]
+        .view("int64")
+        .astype("float64")
+    )
+    labels = np.zeros(len(data), dtype=np.intp)
+
+    group_mean(actual, counts, data, labels, is_datetimelike=False)
+
+    tm.assert_numpy_array_equal(
+        actual[:, 0], np.array(np.divide(np.add(data[0], data[1]), 2), dtype="float64")
+    )

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_min_max.py

@@ -0,0 +1,249 @@
+import numpy as np
+import pytest
+
+from pandas._libs.tslibs import iNaT
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+def test_max_min_non_numeric():
+    # #2700
+    aa = DataFrame({"nn": [11, 11, 22, 22], "ii": [1, 2, 3, 4], "ss": 4 * ["mama"]})
+
+    result = aa.groupby("nn").max()
+    assert "ss" in result
+
+    result = aa.groupby("nn").max(numeric_only=False)
+    assert "ss" in result
+
+    result = aa.groupby("nn").min()
+    assert "ss" in result
+
+    result = aa.groupby("nn").min(numeric_only=False)
+    assert "ss" in result
+
+
+def test_max_min_object_multiple_columns(using_array_manager):
+    # GH#41111 case where the aggregation is valid for some columns but not
+    # others; we split object blocks column-wise, consistent with
+    # DataFrame._reduce
+
+    df = DataFrame(
+        {
+            "A": [1, 1, 2, 2, 3],
+            "B": [1, "foo", 2, "bar", False],
+            "C": ["a", "b", "c", "d", "e"],
+        }
+    )
+    df._consolidate_inplace()  # should already be consolidate, but double-check
+    if not using_array_manager:
+        assert len(df._mgr.blocks) == 2
+
+    gb = df.groupby("A")
+
+    result = gb[["C"]].max()
+    # "max" is valid for column "C" but not for "B"
+    ei = Index([1, 2, 3], name="A")
+    expected = DataFrame({"C": ["b", "d", "e"]}, index=ei)
+    tm.assert_frame_equal(result, expected)
+
+    result = gb[["C"]].min()
+    # "min" is valid for column "C" but not for "B"
+    ei = Index([1, 2, 3], name="A")
+    expected = DataFrame({"C": ["a", "c", "e"]}, index=ei)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_min_date_with_nans():
+    # GH26321
+    dates = pd.to_datetime(
+        Series(["2019-05-09", "2019-05-09", "2019-05-09"]), format="%Y-%m-%d"
+    ).dt.date
+    df = DataFrame({"a": [np.nan, "1", np.nan], "b": [0, 1, 1], "c": dates})
+
+    result = df.groupby("b", as_index=False)["c"].min()["c"]
+    expected = pd.to_datetime(
+        Series(["2019-05-09", "2019-05-09"], name="c"), format="%Y-%m-%d"
+    ).dt.date
+    tm.assert_series_equal(result, expected)
+
+    result = df.groupby("b")["c"].min()
+    expected.index.name = "b"
+    tm.assert_series_equal(result, expected)
+
+
+def test_max_inat():
+    # GH#40767 dont interpret iNaT as NaN
+    ser = Series([1, iNaT])
+    key = np.array([1, 1], dtype=np.int64)
+    gb = ser.groupby(key)
+
+    result = gb.max(min_count=2)
+    expected = Series({1: 1}, dtype=np.int64)
+    tm.assert_series_equal(result, expected, check_exact=True)
+
+    result = gb.min(min_count=2)
+    expected = Series({1: iNaT}, dtype=np.int64)
+    tm.assert_series_equal(result, expected, check_exact=True)
+
+    # not enough entries -> gets masked to NaN
+    result = gb.min(min_count=3)
+    expected = Series({1: np.nan})
+    tm.assert_series_equal(result, expected, check_exact=True)
+
+
+def test_max_inat_not_all_na():
+    # GH#40767 dont interpret iNaT as NaN
+
+    # make sure we dont round iNaT+1 to iNaT
+    ser = Series([1, iNaT, 2, iNaT + 1])
+    gb = ser.groupby([1, 2, 3, 3])
+    result = gb.min(min_count=2)
+
+    # Note: in converting to float64, the iNaT + 1 maps to iNaT, i.e. is lossy
+    expected = Series({1: np.nan, 2: np.nan, 3: iNaT + 1})
+    expected.index = expected.index.astype(np.int_)
+    tm.assert_series_equal(result, expected, check_exact=True)
+
+
+@pytest.mark.parametrize("func", ["min", "max"])
+def test_groupby_aggregate_period_column(func):
+    # GH 31471
+    groups = [1, 2]
+    periods = pd.period_range("2020", periods=2, freq="Y")
+    df = DataFrame({"a": groups, "b": periods})
+
+    result = getattr(df.groupby("a")["b"], func)()
+    idx = Index([1, 2], name="a")
+    expected = Series(periods, index=idx, name="b")
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("func", ["min", "max"])
+def test_groupby_aggregate_period_frame(func):
+    # GH 31471
+    groups = [1, 2]
+    periods = pd.period_range("2020", periods=2, freq="Y")
+    df = DataFrame({"a": groups, "b": periods})
+
+    result = getattr(df.groupby("a"), func)()
+    idx = Index([1, 2], name="a")
+    expected = DataFrame({"b": periods}, index=idx)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_aggregate_numeric_object_dtype():
+    # https://github.com/pandas-dev/pandas/issues/39329
+    # simplified case: multiple object columns where one is all-NaN
+    # -> gets split as the all-NaN is inferred as float
+    df = DataFrame(
+        {"key": ["A", "A", "B", "B"], "col1": list("abcd"), "col2": [np.nan] * 4},
+    ).astype(object)
+    result = df.groupby("key").min()
+    expected = (
+        DataFrame(
+            {"key": ["A", "B"], "col1": ["a", "c"], "col2": [np.nan, np.nan]},
+        )
+        .set_index("key")
+        .astype(object)
+    )
+    tm.assert_frame_equal(result, expected)
+
+    # same but with numbers
+    df = DataFrame(
+        {"key": ["A", "A", "B", "B"], "col1": list("abcd"), "col2": range(4)},
+    ).astype(object)
+    result = df.groupby("key").min()
+    expected = (
+        DataFrame({"key": ["A", "B"], "col1": ["a", "c"], "col2": [0, 2]})
+        .set_index("key")
+        .astype(object)
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("func", ["min", "max"])
+def test_aggregate_categorical_lost_index(func: str):
+    # GH: 28641 groupby drops index, when grouping over categorical column with min/max
+    ds = Series(["b"], dtype="category").cat.as_ordered()
+    df = DataFrame({"A": [1997], "B": ds})
+    result = df.groupby("A").agg({"B": func})
+    expected = DataFrame({"B": ["b"]}, index=Index([1997], name="A"))
+
+    # ordered categorical dtype should be preserved
+    expected["B"] = expected["B"].astype(ds.dtype)
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["Int64", "Int32", "Float64", "Float32", "boolean"])
+def test_groupby_min_max_nullable(dtype):
+    if dtype == "Int64":
+        # GH#41743 avoid precision loss
+        ts = 1618556707013635762
+    elif dtype == "boolean":
+        ts = 0
+    else:
+        ts = 4.0
+
+    df = DataFrame({"id": [2, 2], "ts": [ts, ts + 1]})
+    df["ts"] = df["ts"].astype(dtype)
+
+    gb = df.groupby("id")
+
+    result = gb.min()
+    expected = df.iloc[:1].set_index("id")
+    tm.assert_frame_equal(result, expected)
+
+    res_max = gb.max()
+    expected_max = df.iloc[1:].set_index("id")
+    tm.assert_frame_equal(res_max, expected_max)
+
+    result2 = gb.min(min_count=3)
+    expected2 = DataFrame({"ts": [pd.NA]}, index=expected.index, dtype=dtype)
+    tm.assert_frame_equal(result2, expected2)
+
+    res_max2 = gb.max(min_count=3)
+    tm.assert_frame_equal(res_max2, expected2)
+
+    # Case with NA values
+    df2 = DataFrame({"id": [2, 2, 2], "ts": [ts, pd.NA, ts + 1]})
+    df2["ts"] = df2["ts"].astype(dtype)
+    gb2 = df2.groupby("id")
+
+    result3 = gb2.min()
+    tm.assert_frame_equal(result3, expected)
+
+    res_max3 = gb2.max()
+    tm.assert_frame_equal(res_max3, expected_max)
+
+    result4 = gb2.min(min_count=100)
+    tm.assert_frame_equal(result4, expected2)
+
+    res_max4 = gb2.max(min_count=100)
+    tm.assert_frame_equal(res_max4, expected2)
+
+
+def test_min_max_nullable_uint64_empty_group():
+    # don't raise NotImplementedError from libgroupby
+    cat = pd.Categorical([0] * 10, categories=[0, 1])
+    df = DataFrame({"A": cat, "B": pd.array(np.arange(10, dtype=np.uint64))})
+    gb = df.groupby("A")
+
+    res = gb.min()
+
+    idx = pd.CategoricalIndex([0, 1], dtype=cat.dtype, name="A")
+    expected = DataFrame({"B": pd.array([0, pd.NA], dtype="UInt64")}, index=idx)
+    tm.assert_frame_equal(res, expected)
+
+    res = gb.max()
+    expected.iloc[0, 0] = 9
+    tm.assert_frame_equal(res, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_missing.py

@@ -0,0 +1,154 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    date_range,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("func", ["ffill", "bfill"])
+def test_groupby_column_index_name_lost_fill_funcs(func):
+    # GH: 29764 groupby loses index sometimes
+    df = DataFrame(
+        [[1, 1.0, -1.0], [1, np.nan, np.nan], [1, 2.0, -2.0]],
+        columns=Index(["type", "a", "b"], name="idx"),
+    )
+    df_grouped = df.groupby(["type"])[["a", "b"]]
+    result = getattr(df_grouped, func)().columns
+    expected = Index(["a", "b"], name="idx")
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("func", ["ffill", "bfill"])
+def test_groupby_fill_duplicate_column_names(func):
+    # GH: 25610 ValueError with duplicate column names
+    df1 = DataFrame({"field1": [1, 3, 4], "field2": [1, 3, 4]})
+    df2 = DataFrame({"field1": [1, np.nan, 4]})
+    df_grouped = pd.concat([df1, df2], axis=1).groupby(by=["field2"])
+    expected = DataFrame(
+        [[1, 1.0], [3, np.nan], [4, 4.0]], columns=["field1", "field1"]
+    )
+    result = getattr(df_grouped, func)()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_ffill_missing_arguments():
+    # GH 14955
+    df = DataFrame({"a": [1, 2], "b": [1, 1]})
+    with pytest.raises(ValueError, match="Must specify a fill"):
+        df.groupby("b").fillna()
+
+
+@pytest.mark.parametrize(
+    "method, expected", [("ffill", [None, "a", "a"]), ("bfill", ["a", "a", None])]
+)
+def test_fillna_with_string_dtype(method, expected):
+    # GH 40250
+    df = DataFrame({"a": pd.array([None, "a", None], dtype="string"), "b": [0, 0, 0]})
+    grp = df.groupby("b")
+    result = grp.fillna(method=method)
+    expected = DataFrame({"a": pd.array(expected, dtype="string")})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_fill_consistency():
+    # GH9221
+    # pass thru keyword arguments to the generated wrapper
+    # are set if the passed kw is None (only)
+    df = DataFrame(
+        index=pd.MultiIndex.from_product(
+            [["value1", "value2"], date_range("2014-01-01", "2014-01-06")]
+        ),
+        columns=Index(["1", "2"], name="id"),
+    )
+    df["1"] = [
+        np.nan,
+        1,
+        np.nan,
+        np.nan,
+        11,
+        np.nan,
+        np.nan,
+        2,
+        np.nan,
+        np.nan,
+        22,
+        np.nan,
+    ]
+    df["2"] = [
+        np.nan,
+        3,
+        np.nan,
+        np.nan,
+        33,
+        np.nan,
+        np.nan,
+        4,
+        np.nan,
+        np.nan,
+        44,
+        np.nan,
+    ]
+
+    expected = df.groupby(level=0, axis=0).fillna(method="ffill")
+    result = df.T.groupby(level=0, axis=1).fillna(method="ffill").T
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("method", ["ffill", "bfill"])
+@pytest.mark.parametrize("dropna", [True, False])
+@pytest.mark.parametrize("has_nan_group", [True, False])
+def test_ffill_handles_nan_groups(dropna, method, has_nan_group):
+    # GH 34725
+
+    df_without_nan_rows = DataFrame([(1, 0.1), (2, 0.2)])
+
+    ridx = [-1, 0, -1, -1, 1, -1]
+    df = df_without_nan_rows.reindex(ridx).reset_index(drop=True)
+
+    group_b = np.nan if has_nan_group else "b"
+    df["group_col"] = pd.Series(["a"] * 3 + [group_b] * 3)
+
+    grouped = df.groupby(by="group_col", dropna=dropna)
+    result = getattr(grouped, method)(limit=None)
+
+    expected_rows = {
+        ("ffill", True, True): [-1, 0, 0, -1, -1, -1],
+        ("ffill", True, False): [-1, 0, 0, -1, 1, 1],
+        ("ffill", False, True): [-1, 0, 0, -1, 1, 1],
+        ("ffill", False, False): [-1, 0, 0, -1, 1, 1],
+        ("bfill", True, True): [0, 0, -1, -1, -1, -1],
+        ("bfill", True, False): [0, 0, -1, 1, 1, -1],
+        ("bfill", False, True): [0, 0, -1, 1, 1, -1],
+        ("bfill", False, False): [0, 0, -1, 1, 1, -1],
+    }
+
+    ridx = expected_rows.get((method, dropna, has_nan_group))
+    expected = df_without_nan_rows.reindex(ridx).reset_index(drop=True)
+    # columns are a 'take' on df.columns, which are object dtype
+    expected.columns = expected.columns.astype(object)
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("min_count, value", [(2, np.nan), (-1, 1.0)])
+@pytest.mark.parametrize("func", ["first", "last", "max", "min"])
+def test_min_count(func, min_count, value):
+    # GH#37821
+    df = DataFrame({"a": [1] * 3, "b": [1, np.nan, np.nan], "c": [np.nan] * 3})
+    result = getattr(df.groupby("a"), func)(min_count=min_count)
+    expected = DataFrame({"b": [value], "c": [np.nan]}, index=Index([1], name="a"))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_indices_with_missing():
+    # GH 9304
+    df = DataFrame({"a": [1, 1, np.nan], "b": [2, 3, 4], "c": [5, 6, 7]})
+    g = df.groupby(["a", "b"])
+    result = g.indices
+    expected = {(1.0, 2): np.array([0]), (1.0, 3): np.array([1])}
+    assert result == expected

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_nth.py

@@ -0,0 +1,824 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    Timestamp,
+    isna,
+)
+import pandas._testing as tm
+
+
+def test_first_last_nth(df):
+    # tests for first / last / nth
+    grouped = df.groupby("A")
+    first = grouped.first()
+    expected = df.loc[[1, 0], ["B", "C", "D"]]
+    expected.index = Index(["bar", "foo"], name="A")
+    expected = expected.sort_index()
+    tm.assert_frame_equal(first, expected)
+
+    nth = grouped.nth(0)
+    expected = df.loc[[0, 1]]
+    tm.assert_frame_equal(nth, expected)
+
+    last = grouped.last()
+    expected = df.loc[[5, 7], ["B", "C", "D"]]
+    expected.index = Index(["bar", "foo"], name="A")
+    tm.assert_frame_equal(last, expected)
+
+    nth = grouped.nth(-1)
+    expected = df.iloc[[5, 7]]
+    tm.assert_frame_equal(nth, expected)
+
+    nth = grouped.nth(1)
+    expected = df.iloc[[2, 3]]
+    tm.assert_frame_equal(nth, expected)
+
+    # it works!
+    grouped["B"].first()
+    grouped["B"].last()
+    grouped["B"].nth(0)
+
+    df.loc[df["A"] == "foo", "B"] = np.nan
+    assert isna(grouped["B"].first()["foo"])
+    assert isna(grouped["B"].last()["foo"])
+    assert isna(grouped["B"].nth(0).iloc[0])
+
+    # v0.14.0 whatsnew
+    df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=["A", "B"])
+    g = df.groupby("A")
+    result = g.first()
+    expected = df.iloc[[1, 2]].set_index("A")
+    tm.assert_frame_equal(result, expected)
+
+    expected = df.iloc[[1, 2]]
+    result = g.nth(0, dropna="any")
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("method", ["first", "last"])
+def test_first_last_with_na_object(method, nulls_fixture):
+    # https://github.com/pandas-dev/pandas/issues/32123
+    groups = DataFrame({"a": [1, 1, 2, 2], "b": [1, 2, 3, nulls_fixture]}).groupby("a")
+    result = getattr(groups, method)()
+
+    if method == "first":
+        values = [1, 3]
+    else:
+        values = [2, 3]
+
+    values = np.array(values, dtype=result["b"].dtype)
+    idx = Index([1, 2], name="a")
+    expected = DataFrame({"b": values}, index=idx)
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("index", [0, -1])
+def test_nth_with_na_object(index, nulls_fixture):
+    # https://github.com/pandas-dev/pandas/issues/32123
+    df = DataFrame({"a": [1, 1, 2, 2], "b": [1, 2, 3, nulls_fixture]})
+    groups = df.groupby("a")
+    result = groups.nth(index)
+    expected = df.iloc[[0, 2]] if index == 0 else df.iloc[[1, 3]]
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("method", ["first", "last"])
+def test_first_last_with_None(method):
+    # https://github.com/pandas-dev/pandas/issues/32800
+    # None should be preserved as object dtype
+    df = DataFrame.from_dict({"id": ["a"], "value": [None]})
+    groups = df.groupby("id", as_index=False)
+    result = getattr(groups, method)()
+
+    tm.assert_frame_equal(result, df)
+
+
+@pytest.mark.parametrize("method", ["first", "last"])
+@pytest.mark.parametrize(
+    "df, expected",
+    [
+        (
+            DataFrame({"id": "a", "value": [None, "foo", np.nan]}),
+            DataFrame({"value": ["foo"]}, index=Index(["a"], name="id")),
+        ),
+        (
+            DataFrame({"id": "a", "value": [np.nan]}, dtype=object),
+            DataFrame({"value": [None]}, index=Index(["a"], name="id")),
+        ),
+    ],
+)
+def test_first_last_with_None_expanded(method, df, expected):
+    # GH 32800, 38286
+    result = getattr(df.groupby("id"), method)()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_first_last_nth_dtypes(df_mixed_floats):
+    df = df_mixed_floats.copy()
+    df["E"] = True
+    df["F"] = 1
+
+    # tests for first / last / nth
+    grouped = df.groupby("A")
+    first = grouped.first()
+    expected = df.loc[[1, 0], ["B", "C", "D", "E", "F"]]
+    expected.index = Index(["bar", "foo"], name="A")
+    expected = expected.sort_index()
+    tm.assert_frame_equal(first, expected)
+
+    last = grouped.last()
+    expected = df.loc[[5, 7], ["B", "C", "D", "E", "F"]]
+    expected.index = Index(["bar", "foo"], name="A")
+    expected = expected.sort_index()
+    tm.assert_frame_equal(last, expected)
+
+    nth = grouped.nth(1)
+    expected = df.iloc[[2, 3]]
+    tm.assert_frame_equal(nth, expected)
+
+    # GH 2763, first/last shifting dtypes
+    idx = list(range(10))
+    idx.append(9)
+    s = Series(data=range(11), index=idx, name="IntCol")
+    assert s.dtype == "int64"
+    f = s.groupby(level=0).first()
+    assert f.dtype == "int64"
+
+
+def test_first_last_nth_nan_dtype():
+    # GH 33591
+    df = DataFrame({"data": ["A"], "nans": Series([np.nan], dtype=object)})
+    grouped = df.groupby("data")
+
+    expected = df.set_index("data").nans
+    tm.assert_series_equal(grouped.nans.first(), expected)
+    tm.assert_series_equal(grouped.nans.last(), expected)
+
+    expected = df.nans
+    tm.assert_series_equal(grouped.nans.nth(-1), expected)
+    tm.assert_series_equal(grouped.nans.nth(0), expected)
+
+
+def test_first_strings_timestamps():
+    # GH 11244
+    test = DataFrame(
+        {
+            Timestamp("2012-01-01 00:00:00"): ["a", "b"],
+            Timestamp("2012-01-02 00:00:00"): ["c", "d"],
+            "name": ["e", "e"],
+            "aaaa": ["f", "g"],
+        }
+    )
+    result = test.groupby("name").first()
+    expected = DataFrame(
+        [["a", "c", "f"]],
+        columns=Index([Timestamp("2012-01-01"), Timestamp("2012-01-02"), "aaaa"]),
+        index=Index(["e"], name="name"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_nth():
+    df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=["A", "B"])
+    g = df.groupby("A")
+
+    tm.assert_frame_equal(g.nth(0), df.iloc[[0, 2]])
+    tm.assert_frame_equal(g.nth(1), df.iloc[[1]])
+    tm.assert_frame_equal(g.nth(2), df.loc[[]])
+    tm.assert_frame_equal(g.nth(-1), df.iloc[[1, 2]])
+    tm.assert_frame_equal(g.nth(-2), df.iloc[[0]])
+    tm.assert_frame_equal(g.nth(-3), df.loc[[]])
+    tm.assert_series_equal(g.B.nth(0), df.B.iloc[[0, 2]])
+    tm.assert_series_equal(g.B.nth(1), df.B.iloc[[1]])
+    tm.assert_frame_equal(g[["B"]].nth(0), df[["B"]].iloc[[0, 2]])
+
+    tm.assert_frame_equal(g.nth(0, dropna="any"), df.iloc[[1, 2]])
+    tm.assert_frame_equal(g.nth(-1, dropna="any"), df.iloc[[1, 2]])
+
+    tm.assert_frame_equal(g.nth(7, dropna="any"), df.iloc[:0])
+    tm.assert_frame_equal(g.nth(2, dropna="any"), df.iloc[:0])
+
+    # out of bounds, regression from 0.13.1
+    # GH 6621
+    df = DataFrame(
+        {
+            "color": {0: "green", 1: "green", 2: "red", 3: "red", 4: "red"},
+            "food": {0: "ham", 1: "eggs", 2: "eggs", 3: "ham", 4: "pork"},
+            "two": {
+                0: 1.5456590000000001,
+                1: -0.070345000000000005,
+                2: -2.4004539999999999,
+                3: 0.46206000000000003,
+                4: 0.52350799999999997,
+            },
+            "one": {
+                0: 0.56573799999999996,
+                1: -0.9742360000000001,
+                2: 1.033801,
+                3: -0.78543499999999999,
+                4: 0.70422799999999997,
+            },
+        }
+    ).set_index(["color", "food"])
+
+    result = df.groupby(level=0, as_index=False).nth(2)
+    expected = df.iloc[[-1]]
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby(level=0, as_index=False).nth(3)
+    expected = df.loc[[]]
+    tm.assert_frame_equal(result, expected)
+
+    # GH 7559
+    # from the vbench
+    df = DataFrame(np.random.randint(1, 10, (100, 2)), dtype="int64")
+    s = df[1]
+    g = df[0]
+    expected = s.groupby(g).first()
+    expected2 = s.groupby(g).apply(lambda x: x.iloc[0])
+    tm.assert_series_equal(expected2, expected, check_names=False)
+    assert expected.name == 1
+    assert expected2.name == 1
+
+    # validate first
+    v = s[g == 1].iloc[0]
+    assert expected.iloc[0] == v
+    assert expected2.iloc[0] == v
+
+    with pytest.raises(ValueError, match="For a DataFrame"):
+        s.groupby(g, sort=False).nth(0, dropna=True)
+
+    # doc example
+    df = DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=["A", "B"])
+    g = df.groupby("A")
+    result = g.B.nth(0, dropna="all")
+    expected = df.B.iloc[[1, 2]]
+    tm.assert_series_equal(result, expected)
+
+    # test multiple nth values
+    df = DataFrame([[1, np.nan], [1, 3], [1, 4], [5, 6], [5, 7]], columns=["A", "B"])
+    g = df.groupby("A")
+
+    tm.assert_frame_equal(g.nth(0), df.iloc[[0, 3]])
+    tm.assert_frame_equal(g.nth([0]), df.iloc[[0, 3]])
+    tm.assert_frame_equal(g.nth([0, 1]), df.iloc[[0, 1, 3, 4]])
+    tm.assert_frame_equal(g.nth([0, -1]), df.iloc[[0, 2, 3, 4]])
+    tm.assert_frame_equal(g.nth([0, 1, 2]), df.iloc[[0, 1, 2, 3, 4]])
+    tm.assert_frame_equal(g.nth([0, 1, -1]), df.iloc[[0, 1, 2, 3, 4]])
+    tm.assert_frame_equal(g.nth([2]), df.iloc[[2]])
+    tm.assert_frame_equal(g.nth([3, 4]), df.loc[[]])
+
+    business_dates = pd.date_range(start="4/1/2014", end="6/30/2014", freq="B")
+    df = DataFrame(1, index=business_dates, columns=["a", "b"])
+    # get the first, fourth and last two business days for each month
+    key = [df.index.year, df.index.month]
+    result = df.groupby(key, as_index=False).nth([0, 3, -2, -1])
+    expected_dates = pd.to_datetime(
+        [
+            "2014/4/1",
+            "2014/4/4",
+            "2014/4/29",
+            "2014/4/30",
+            "2014/5/1",
+            "2014/5/6",
+            "2014/5/29",
+            "2014/5/30",
+            "2014/6/2",
+            "2014/6/5",
+            "2014/6/27",
+            "2014/6/30",
+        ]
+    )
+    expected = DataFrame(1, columns=["a", "b"], index=expected_dates)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_nth_multi_grouper(three_group):
+    # PR 9090, related to issue 8979
+    # test nth on multiple groupers
+    grouped = three_group.groupby(["A", "B"])
+    result = grouped.nth(0)
+    expected = three_group.iloc[[0, 3, 4, 7]]
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "data, expected_first, expected_last",
+    [
+        (
+            {
+                "id": ["A"],
+                "time": Timestamp("2012-02-01 14:00:00", tz="US/Central"),
+                "foo": [1],
+            },
+            {
+                "id": ["A"],
+                "time": Timestamp("2012-02-01 14:00:00", tz="US/Central"),
+                "foo": [1],
+            },
+            {
+                "id": ["A"],
+                "time": Timestamp("2012-02-01 14:00:00", tz="US/Central"),
+                "foo": [1],
+            },
+        ),
+        (
+            {
+                "id": ["A", "B", "A"],
+                "time": [
+                    Timestamp("2012-01-01 13:00:00", tz="America/New_York"),
+                    Timestamp("2012-02-01 14:00:00", tz="US/Central"),
+                    Timestamp("2012-03-01 12:00:00", tz="Europe/London"),
+                ],
+                "foo": [1, 2, 3],
+            },
+            {
+                "id": ["A", "B"],
+                "time": [
+                    Timestamp("2012-01-01 13:00:00", tz="America/New_York"),
+                    Timestamp("2012-02-01 14:00:00", tz="US/Central"),
+                ],
+                "foo": [1, 2],
+            },
+            {
+                "id": ["A", "B"],
+                "time": [
+                    Timestamp("2012-03-01 12:00:00", tz="Europe/London"),
+                    Timestamp("2012-02-01 14:00:00", tz="US/Central"),
+                ],
+                "foo": [3, 2],
+            },
+        ),
+    ],
+)
+def test_first_last_tz(data, expected_first, expected_last):
+    # GH15884
+    # Test that the timezone is retained when calling first
+    # or last on groupby with as_index=False
+
+    df = DataFrame(data)
+
+    result = df.groupby("id", as_index=False).first()
+    expected = DataFrame(expected_first)
+    cols = ["id", "time", "foo"]
+    tm.assert_frame_equal(result[cols], expected[cols])
+
+    result = df.groupby("id", as_index=False)["time"].first()
+    tm.assert_frame_equal(result, expected[["id", "time"]])
+
+    result = df.groupby("id", as_index=False).last()
+    expected = DataFrame(expected_last)
+    cols = ["id", "time", "foo"]
+    tm.assert_frame_equal(result[cols], expected[cols])
+
+    result = df.groupby("id", as_index=False)["time"].last()
+    tm.assert_frame_equal(result, expected[["id", "time"]])
+
+
+@pytest.mark.parametrize(
+    "method, ts, alpha",
+    [
+        ["first", Timestamp("2013-01-01", tz="US/Eastern"), "a"],
+        ["last", Timestamp("2013-01-02", tz="US/Eastern"), "b"],
+    ],
+)
+def test_first_last_tz_multi_column(method, ts, alpha):
+    # GH 21603
+    category_string = Series(list("abc")).astype("category")
+    df = DataFrame(
+        {
+            "group": [1, 1, 2],
+            "category_string": category_string,
+            "datetimetz": pd.date_range("20130101", periods=3, tz="US/Eastern"),
+        }
+    )
+    result = getattr(df.groupby("group"), method)()
+    expected = DataFrame(
+        {
+            "category_string": pd.Categorical(
+                [alpha, "c"], dtype=category_string.dtype
+            ),
+            "datetimetz": [ts, Timestamp("2013-01-03", tz="US/Eastern")],
+        },
+        index=Index([1, 2], name="group"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "values",
+    [
+        pd.array([True, False], dtype="boolean"),
+        pd.array([1, 2], dtype="Int64"),
+        pd.to_datetime(["2020-01-01", "2020-02-01"]),
+        pd.to_timedelta([1, 2], unit="D"),
+    ],
+)
+@pytest.mark.parametrize("function", ["first", "last", "min", "max"])
+def test_first_last_extension_array_keeps_dtype(values, function):
+    # https://github.com/pandas-dev/pandas/issues/33071
+    # https://github.com/pandas-dev/pandas/issues/32194
+    df = DataFrame({"a": [1, 2], "b": values})
+    grouped = df.groupby("a")
+    idx = Index([1, 2], name="a")
+    expected_series = Series(values, name="b", index=idx)
+    expected_frame = DataFrame({"b": values}, index=idx)
+
+    result_series = getattr(grouped["b"], function)()
+    tm.assert_series_equal(result_series, expected_series)
+
+    result_frame = grouped.agg({"b": function})
+    tm.assert_frame_equal(result_frame, expected_frame)
+
+
+def test_nth_multi_index_as_expected():
+    # PR 9090, related to issue 8979
+    # test nth on MultiIndex
+    three_group = DataFrame(
+        {
+            "A": [
+                "foo",
+                "foo",
+                "foo",
+                "foo",
+                "bar",
+                "bar",
+                "bar",
+                "bar",
+                "foo",
+                "foo",
+                "foo",
+            ],
+            "B": [
+                "one",
+                "one",
+                "one",
+                "two",
+                "one",
+                "one",
+                "one",
+                "two",
+                "two",
+                "two",
+                "one",
+            ],
+            "C": [
+                "dull",
+                "dull",
+                "shiny",
+                "dull",
+                "dull",
+                "shiny",
+                "shiny",
+                "dull",
+                "shiny",
+                "shiny",
+                "shiny",
+            ],
+        }
+    )
+    grouped = three_group.groupby(["A", "B"])
+    result = grouped.nth(0)
+    expected = three_group.iloc[[0, 3, 4, 7]]
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "op, n, expected_rows",
+    [
+        ("head", -1, [0]),
+        ("head", 0, []),
+        ("head", 1, [0, 2]),
+        ("head", 7, [0, 1, 2]),
+        ("tail", -1, [1]),
+        ("tail", 0, []),
+        ("tail", 1, [1, 2]),
+        ("tail", 7, [0, 1, 2]),
+    ],
+)
+@pytest.mark.parametrize("columns", [None, [], ["A"], ["B"], ["A", "B"]])
+@pytest.mark.parametrize("as_index", [True, False])
+def test_groupby_head_tail(op, n, expected_rows, columns, as_index):
+    df = DataFrame([[1, 2], [1, 4], [5, 6]], columns=["A", "B"])
+    g = df.groupby("A", as_index=as_index)
+    expected = df.iloc[expected_rows]
+    if columns is not None:
+        g = g[columns]
+        expected = expected[columns]
+    result = getattr(g, op)(n)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "op, n, expected_cols",
+    [
+        ("head", -1, [0]),
+        ("head", 0, []),
+        ("head", 1, [0, 2]),
+        ("head", 7, [0, 1, 2]),
+        ("tail", -1, [1]),
+        ("tail", 0, []),
+        ("tail", 1, [1, 2]),
+        ("tail", 7, [0, 1, 2]),
+    ],
+)
+def test_groupby_head_tail_axis_1(op, n, expected_cols):
+    # GH 9772
+    df = DataFrame(
+        [[1, 2, 3], [1, 4, 5], [2, 6, 7], [3, 8, 9]], columns=["A", "B", "C"]
+    )
+    g = df.groupby([0, 0, 1], axis=1)
+    expected = df.iloc[:, expected_cols]
+    result = getattr(g, op)(n)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_group_selection_cache():
+    # GH 12839 nth, head, and tail should return same result consistently
+    df = DataFrame([[1, 2], [1, 4], [5, 6]], columns=["A", "B"])
+    expected = df.iloc[[0, 2]]
+
+    g = df.groupby("A")
+    result1 = g.head(n=2)
+    result2 = g.nth(0)
+    tm.assert_frame_equal(result1, df)
+    tm.assert_frame_equal(result2, expected)
+
+    g = df.groupby("A")
+    result1 = g.tail(n=2)
+    result2 = g.nth(0)
+    tm.assert_frame_equal(result1, df)
+    tm.assert_frame_equal(result2, expected)
+
+    g = df.groupby("A")
+    result1 = g.nth(0)
+    result2 = g.head(n=2)
+    tm.assert_frame_equal(result1, expected)
+    tm.assert_frame_equal(result2, df)
+
+    g = df.groupby("A")
+    result1 = g.nth(0)
+    result2 = g.tail(n=2)
+    tm.assert_frame_equal(result1, expected)
+    tm.assert_frame_equal(result2, df)
+
+
+def test_nth_empty():
+    # GH 16064
+    df = DataFrame(index=[0], columns=["a", "b", "c"])
+    result = df.groupby("a").nth(10)
+    expected = df.iloc[:0]
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby(["a", "b"]).nth(10)
+    expected = df.iloc[:0]
+    tm.assert_frame_equal(result, expected)
+
+
+def test_nth_column_order():
+    # GH 20760
+    # Check that nth preserves column order
+    df = DataFrame(
+        [[1, "b", 100], [1, "a", 50], [1, "a", np.nan], [2, "c", 200], [2, "d", 150]],
+        columns=["A", "C", "B"],
+    )
+    result = df.groupby("A").nth(0)
+    expected = df.iloc[[0, 3]]
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby("A").nth(-1, dropna="any")
+    expected = df.iloc[[1, 4]]
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dropna", [None, "any", "all"])
+def test_nth_nan_in_grouper(dropna):
+    # GH 26011
+    df = DataFrame(
+        {
+            "a": [np.nan, "a", np.nan, "b", np.nan],
+            "b": [0, 2, 4, 6, 8],
+            "c": [1, 3, 5, 7, 9],
+        }
+    )
+    result = df.groupby("a").nth(0, dropna=dropna)
+    expected = df.iloc[[1, 3]]
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dropna", [None, "any", "all"])
+def test_nth_nan_in_grouper_series(dropna):
+    # GH 26454
+    df = DataFrame(
+        {
+            "a": [np.nan, "a", np.nan, "b", np.nan],
+            "b": [0, 2, 4, 6, 8],
+        }
+    )
+    result = df.groupby("a")["b"].nth(0, dropna=dropna)
+    expected = df["b"].iloc[[1, 3]]
+
+    tm.assert_series_equal(result, expected)
+
+
+def test_first_categorical_and_datetime_data_nat():
+    # GH 20520
+    df = DataFrame(
+        {
+            "group": ["first", "first", "second", "third", "third"],
+            "time": 5 * [np.datetime64("NaT")],
+            "categories": Series(["a", "b", "c", "a", "b"], dtype="category"),
+        }
+    )
+    result = df.groupby("group").first()
+    expected = DataFrame(
+        {
+            "time": 3 * [np.datetime64("NaT")],
+            "categories": Series(["a", "c", "a"]).astype(
+                pd.CategoricalDtype(["a", "b", "c"])
+            ),
+        }
+    )
+    expected.index = Index(["first", "second", "third"], name="group")
+    tm.assert_frame_equal(result, expected)
+
+
+def test_first_multi_key_groupby_categorical():
+    # GH 22512
+    df = DataFrame(
+        {
+            "A": [1, 1, 1, 2, 2],
+            "B": [100, 100, 200, 100, 100],
+            "C": ["apple", "orange", "mango", "mango", "orange"],
+            "D": ["jupiter", "mercury", "mars", "venus", "venus"],
+        }
+    )
+    df = df.astype({"D": "category"})
+    result = df.groupby(by=["A", "B"]).first()
+    expected = DataFrame(
+        {
+            "C": ["apple", "mango", "mango"],
+            "D": Series(["jupiter", "mars", "venus"]).astype(
+                pd.CategoricalDtype(["jupiter", "mars", "mercury", "venus"])
+            ),
+        }
+    )
+    expected.index = MultiIndex.from_tuples(
+        [(1, 100), (1, 200), (2, 100)], names=["A", "B"]
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("method", ["first", "last", "nth"])
+def test_groupby_last_first_nth_with_none(method, nulls_fixture):
+    # GH29645
+    expected = Series(["y"])
+    data = Series(
+        [nulls_fixture, nulls_fixture, nulls_fixture, "y", nulls_fixture],
+        index=[0, 0, 0, 0, 0],
+    ).groupby(level=0)
+
+    if method == "nth":
+        result = getattr(data, method)(3)
+    else:
+        result = getattr(data, method)()
+
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "arg, expected_rows",
+    [
+        [slice(None, 3, 2), [0, 1, 4, 5]],
+        [slice(None, -2), [0, 2, 5]],
+        [[slice(None, 2), slice(-2, None)], [0, 1, 2, 3, 4, 6, 7]],
+        [[0, 1, slice(-2, None)], [0, 1, 2, 3, 4, 6, 7]],
+    ],
+)
+def test_slice(slice_test_df, slice_test_grouped, arg, expected_rows):
+    # Test slices     GH #42947
+
+    result = slice_test_grouped.nth[arg]
+    equivalent = slice_test_grouped.nth(arg)
+    expected = slice_test_df.iloc[expected_rows]
+
+    tm.assert_frame_equal(result, expected)
+    tm.assert_frame_equal(equivalent, expected)
+
+
+def test_nth_indexed(slice_test_df, slice_test_grouped):
+    # Test index notation     GH #44688
+
+    result = slice_test_grouped.nth[0, 1, -2:]
+    equivalent = slice_test_grouped.nth([0, 1, slice(-2, None)])
+    expected = slice_test_df.iloc[[0, 1, 2, 3, 4, 6, 7]]
+
+    tm.assert_frame_equal(result, expected)
+    tm.assert_frame_equal(equivalent, expected)
+
+
+def test_invalid_argument(slice_test_grouped):
+    # Test for error on invalid argument
+
+    with pytest.raises(TypeError, match="Invalid index"):
+        slice_test_grouped.nth(3.14)
+
+
+def test_negative_step(slice_test_grouped):
+    # Test for error on negative slice step
+
+    with pytest.raises(ValueError, match="Invalid step"):
+        slice_test_grouped.nth(slice(None, None, -1))
+
+
+def test_np_ints(slice_test_df, slice_test_grouped):
+    # Test np ints work
+
+    result = slice_test_grouped.nth(np.array([0, 1]))
+    expected = slice_test_df.iloc[[0, 1, 2, 3, 4]]
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_nth_with_column_axis():
+    # GH43926
+    df = DataFrame(
+        [
+            [4, 5, 6],
+            [8, 8, 7],
+        ],
+        index=["z", "y"],
+        columns=["C", "B", "A"],
+    )
+    result = df.groupby(df.iloc[1], axis=1).nth(0)
+    expected = df.iloc[:, [0, 2]]
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "start, stop, expected_values, expected_columns",
+    [
+        (None, None, [0, 1, 2, 3, 4], list("ABCDE")),
+        (None, 1, [0, 3], list("AD")),
+        (None, 9, [0, 1, 2, 3, 4], list("ABCDE")),
+        (None, -1, [0, 1, 3], list("ABD")),
+        (1, None, [1, 2, 4], list("BCE")),
+        (1, -1, [1], list("B")),
+        (-1, None, [2, 4], list("CE")),
+        (-1, 2, [4], list("E")),
+    ],
+)
+@pytest.mark.parametrize("method", ["call", "index"])
+def test_nth_slices_with_column_axis(
+    start, stop, expected_values, expected_columns, method
+):
+    df = DataFrame([range(5)], columns=[list("ABCDE")])
+    gb = df.groupby([5, 5, 5, 6, 6], axis=1)
+    result = {
+        "call": lambda start, stop: gb.nth(slice(start, stop)),
+        "index": lambda start, stop: gb.nth[start:stop],
+    }[method](start, stop)
+    expected = DataFrame([expected_values], columns=[expected_columns])
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.filterwarnings(
+    "ignore:invalid value encountered in remainder:RuntimeWarning"
+)
+def test_head_tail_dropna_true():
+    # GH#45089
+    df = DataFrame(
+        [["a", "z"], ["b", np.nan], ["c", np.nan], ["c", np.nan]], columns=["X", "Y"]
+    )
+    expected = DataFrame([["a", "z"]], columns=["X", "Y"])
+
+    result = df.groupby(["X", "Y"]).head(n=1)
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby(["X", "Y"]).tail(n=1)
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby(["X", "Y"]).nth(n=0)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_head_tail_dropna_false():
+    # GH#45089
+    df = DataFrame([["a", "z"], ["b", np.nan], ["c", np.nan]], columns=["X", "Y"])
+    expected = DataFrame([["a", "z"], ["b", np.nan], ["c", np.nan]], columns=["X", "Y"])
+
+    result = df.groupby(["X", "Y"], dropna=False).head(n=1)
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby(["X", "Y"], dropna=False).tail(n=1)
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby(["X", "Y"], dropna=False).nth(n=0)
+    tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_numba.py

@@ -0,0 +1,73 @@
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+
+
+@td.skip_if_no("numba")
+@pytest.mark.filterwarnings("ignore")
+# Filter warnings when parallel=True and the function can't be parallelized by Numba
+class TestEngine:
+    def test_cython_vs_numba_frame(
+        self, sort, nogil, parallel, nopython, numba_supported_reductions
+    ):
+        func, kwargs = numba_supported_reductions
+        df = DataFrame({"a": [3, 2, 3, 2], "b": range(4), "c": range(1, 5)})
+        engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
+        gb = df.groupby("a", sort=sort)
+        result = getattr(gb, func)(
+            engine="numba", engine_kwargs=engine_kwargs, **kwargs
+        )
+        expected = getattr(gb, func)(**kwargs)
+        # check_dtype can be removed if GH 44952 is addressed
+        check_dtype = func not in ("sum", "min", "max")
+        tm.assert_frame_equal(result, expected, check_dtype=check_dtype)
+
+    def test_cython_vs_numba_getitem(
+        self, sort, nogil, parallel, nopython, numba_supported_reductions
+    ):
+        func, kwargs = numba_supported_reductions
+        df = DataFrame({"a": [3, 2, 3, 2], "b": range(4), "c": range(1, 5)})
+        engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
+        gb = df.groupby("a", sort=sort)["c"]
+        result = getattr(gb, func)(
+            engine="numba", engine_kwargs=engine_kwargs, **kwargs
+        )
+        expected = getattr(gb, func)(**kwargs)
+        # check_dtype can be removed if GH 44952 is addressed
+        check_dtype = func not in ("sum", "min", "max")
+        tm.assert_series_equal(result, expected, check_dtype=check_dtype)
+
+    def test_cython_vs_numba_series(
+        self, sort, nogil, parallel, nopython, numba_supported_reductions
+    ):
+        func, kwargs = numba_supported_reductions
+        ser = Series(range(3), index=[1, 2, 1], name="foo")
+        engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
+        gb = ser.groupby(level=0, sort=sort)
+        result = getattr(gb, func)(
+            engine="numba", engine_kwargs=engine_kwargs, **kwargs
+        )
+        expected = getattr(gb, func)(**kwargs)
+        # check_dtype can be removed if GH 44952 is addressed
+        check_dtype = func not in ("sum", "min", "max")
+        tm.assert_series_equal(result, expected, check_dtype=check_dtype)
+
+    def test_as_index_false_unsupported(self, numba_supported_reductions):
+        func, kwargs = numba_supported_reductions
+        df = DataFrame({"a": [3, 2, 3, 2], "b": range(4), "c": range(1, 5)})
+        gb = df.groupby("a", as_index=False)
+        with pytest.raises(NotImplementedError, match="as_index=False"):
+            getattr(gb, func)(engine="numba", **kwargs)
+
+    def test_axis_1_unsupported(self, numba_supported_reductions):
+        func, kwargs = numba_supported_reductions
+        df = DataFrame({"a": [3, 2, 3, 2], "b": range(4), "c": range(1, 5)})
+        gb = df.groupby("a", axis=1)
+        with pytest.raises(NotImplementedError, match="axis=1"):
+            getattr(gb, func)(engine="numba", **kwargs)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_nunique.py

@@ -0,0 +1,197 @@
+import datetime as dt
+from string import ascii_lowercase
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    MultiIndex,
+    NaT,
+    Series,
+    Timestamp,
+    date_range,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("n", 10 ** np.arange(2, 6))
+@pytest.mark.parametrize("m", [10, 100, 1000])
+@pytest.mark.parametrize("sort", [False, True])
+@pytest.mark.parametrize("dropna", [False, True])
+def test_series_groupby_nunique(n, m, sort, dropna):
+    def check_nunique(df, keys, as_index=True):
+        original_df = df.copy()
+        gr = df.groupby(keys, as_index=as_index, sort=sort)
+        left = gr["julie"].nunique(dropna=dropna)
+
+        gr = df.groupby(keys, as_index=as_index, sort=sort)
+        right = gr["julie"].apply(Series.nunique, dropna=dropna)
+        if not as_index:
+            right = right.reset_index(drop=True)
+
+        if as_index:
+            tm.assert_series_equal(left, right, check_names=False)
+        else:
+            tm.assert_frame_equal(left, right, check_names=False)
+        tm.assert_frame_equal(df, original_df)
+
+    days = date_range("2015-08-23", periods=10)
+
+    frame = DataFrame(
+        {
+            "jim": np.random.choice(list(ascii_lowercase), n),
+            "joe": np.random.choice(days, n),
+            "julie": np.random.randint(0, m, n),
+        }
+    )
+
+    check_nunique(frame, ["jim"])
+    check_nunique(frame, ["jim", "joe"])
+
+    frame.loc[1::17, "jim"] = None
+    frame.loc[3::37, "joe"] = None
+    frame.loc[7::19, "julie"] = None
+    frame.loc[8::19, "julie"] = None
+    frame.loc[9::19, "julie"] = None
+
+    check_nunique(frame, ["jim"])
+    check_nunique(frame, ["jim", "joe"])
+    check_nunique(frame, ["jim"], as_index=False)
+    check_nunique(frame, ["jim", "joe"], as_index=False)
+
+
+def test_nunique():
+    df = DataFrame({"A": list("abbacc"), "B": list("abxacc"), "C": list("abbacx")})
+
+    expected = DataFrame({"A": list("abc"), "B": [1, 2, 1], "C": [1, 1, 2]})
+    result = df.groupby("A", as_index=False).nunique()
+    tm.assert_frame_equal(result, expected)
+
+    # as_index
+    expected.index = list("abc")
+    expected.index.name = "A"
+    expected = expected.drop(columns="A")
+    result = df.groupby("A").nunique()
+    tm.assert_frame_equal(result, expected)
+
+    # with na
+    result = df.replace({"x": None}).groupby("A").nunique(dropna=False)
+    tm.assert_frame_equal(result, expected)
+
+    # dropna
+    expected = DataFrame({"B": [1] * 3, "C": [1] * 3}, index=list("abc"))
+    expected.index.name = "A"
+    result = df.replace({"x": None}).groupby("A").nunique()
+    tm.assert_frame_equal(result, expected)
+
+
+def test_nunique_with_object():
+    # GH 11077
+    data = DataFrame(
+        [
+            [100, 1, "Alice"],
+            [200, 2, "Bob"],
+            [300, 3, "Charlie"],
+            [-400, 4, "Dan"],
+            [500, 5, "Edith"],
+        ],
+        columns=["amount", "id", "name"],
+    )
+
+    result = data.groupby(["id", "amount"])["name"].nunique()
+    index = MultiIndex.from_arrays([data.id, data.amount])
+    expected = Series([1] * 5, name="name", index=index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_nunique_with_empty_series():
+    # GH 12553
+    data = Series(name="name", dtype=object)
+    result = data.groupby(level=0).nunique()
+    expected = Series(name="name", dtype="int64")
+    tm.assert_series_equal(result, expected)
+
+
+def test_nunique_with_timegrouper():
+    # GH 13453
+    test = DataFrame(
+        {
+            "time": [
+                Timestamp("2016-06-28 09:35:35"),
+                Timestamp("2016-06-28 16:09:30"),
+                Timestamp("2016-06-28 16:46:28"),
+            ],
+            "data": ["1", "2", "3"],
+        }
+    ).set_index("time")
+    result = test.groupby(pd.Grouper(freq="h"))["data"].nunique()
+    expected = test.groupby(pd.Grouper(freq="h"))["data"].apply(Series.nunique)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "key, data, dropna, expected",
+    [
+        (
+            ["x", "x", "x"],
+            [Timestamp("2019-01-01"), NaT, Timestamp("2019-01-01")],
+            True,
+            Series([1], index=pd.Index(["x"], name="key"), name="data"),
+        ),
+        (
+            ["x", "x", "x"],
+            [dt.date(2019, 1, 1), NaT, dt.date(2019, 1, 1)],
+            True,
+            Series([1], index=pd.Index(["x"], name="key"), name="data"),
+        ),
+        (
+            ["x", "x", "x", "y", "y"],
+            [dt.date(2019, 1, 1), NaT, dt.date(2019, 1, 1), NaT, dt.date(2019, 1, 1)],
+            False,
+            Series([2, 2], index=pd.Index(["x", "y"], name="key"), name="data"),
+        ),
+        (
+            ["x", "x", "x", "x", "y"],
+            [dt.date(2019, 1, 1), NaT, dt.date(2019, 1, 1), NaT, dt.date(2019, 1, 1)],
+            False,
+            Series([2, 1], index=pd.Index(["x", "y"], name="key"), name="data"),
+        ),
+    ],
+)
+def test_nunique_with_NaT(key, data, dropna, expected):
+    # GH 27951
+    df = DataFrame({"key": key, "data": data})
+    result = df.groupby(["key"])["data"].nunique(dropna=dropna)
+    tm.assert_series_equal(result, expected)
+
+
+def test_nunique_preserves_column_level_names():
+    # GH 23222
+    test = DataFrame([1, 2, 2], columns=pd.Index(["A"], name="level_0"))
+    result = test.groupby([0, 0, 0]).nunique()
+    expected = DataFrame([2], index=np.array([0]), columns=test.columns)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_nunique_transform_with_datetime():
+    # GH 35109 - transform with nunique on datetimes results in integers
+    df = DataFrame(date_range("2008-12-31", "2009-01-02"), columns=["date"])
+    result = df.groupby([0, 0, 1])["date"].transform("nunique")
+    expected = Series([2, 2, 1], name="date")
+    tm.assert_series_equal(result, expected)
+
+
+def test_empty_categorical(observed):
+    # GH#21334
+    cat = Series([1]).astype("category")
+    ser = cat[:0]
+    gb = ser.groupby(ser, observed=observed)
+    result = gb.nunique()
+    if observed:
+        expected = Series([], index=cat[:0], dtype="int64")
+    else:
+        expected = Series([0], index=cat, dtype="int64")
+    tm.assert_series_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_pipe.py

@@ -0,0 +1,80 @@
+import numpy as np
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+)
+import pandas._testing as tm
+
+
+def test_pipe():
+    # Test the pipe method of DataFrameGroupBy.
+    # Issue #17871
+
+    random_state = np.random.RandomState(1234567890)
+
+    df = DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": random_state.randn(8),
+            "C": random_state.randn(8),
+        }
+    )
+
+    def f(dfgb):
+        return dfgb.B.max() - dfgb.C.min().min()
+
+    def square(srs):
+        return srs**2
+
+    # Note that the transformations are
+    # GroupBy -> Series
+    # Series -> Series
+    # This then chains the GroupBy.pipe and the
+    # NDFrame.pipe methods
+    result = df.groupby("A").pipe(f).pipe(square)
+
+    index = Index(["bar", "foo"], dtype="object", name="A")
+    expected = pd.Series([8.99110003361, 8.17516964785], name="B", index=index)
+
+    tm.assert_series_equal(expected, result)
+
+
+def test_pipe_args():
+    # Test passing args to the pipe method of DataFrameGroupBy.
+    # Issue #17871
+
+    df = DataFrame(
+        {
+            "group": ["A", "A", "B", "B", "C"],
+            "x": [1.0, 2.0, 3.0, 2.0, 5.0],
+            "y": [10.0, 100.0, 1000.0, -100.0, -1000.0],
+        }
+    )
+
+    def f(dfgb, arg1):
+        filtered = dfgb.filter(lambda grp: grp.y.mean() > arg1, dropna=False)
+        return filtered.groupby("group")
+
+    def g(dfgb, arg2):
+        return dfgb.sum() / dfgb.sum().sum() + arg2
+
+    def h(df, arg3):
+        return df.x + df.y - arg3
+
+    result = df.groupby("group").pipe(f, 0).pipe(g, 10).pipe(h, 100)
+
+    # Assert the results here
+    index = Index(["A", "B"], name="group")
+    expected = pd.Series([-79.5160891089, -78.4839108911], index=index)
+
+    tm.assert_series_equal(result, expected)
+
+    # test SeriesGroupby.pipe
+    ser = pd.Series([1, 1, 2, 2, 3, 3])
+    result = ser.groupby(ser).pipe(lambda grp: grp.sum() * grp.count())
+
+    expected = pd.Series([4, 8, 12], index=Index([1, 2, 3], dtype=np.int64))
+
+    tm.assert_series_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_quantile.py

@@ -0,0 +1,470 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize(
+    "interpolation", ["linear", "lower", "higher", "nearest", "midpoint"]
+)
+@pytest.mark.parametrize(
+    "a_vals,b_vals",
+    [
+        # Ints
+        ([1, 2, 3, 4, 5], [5, 4, 3, 2, 1]),
+        ([1, 2, 3, 4], [4, 3, 2, 1]),
+        ([1, 2, 3, 4, 5], [4, 3, 2, 1]),
+        # Floats
+        ([1.0, 2.0, 3.0, 4.0, 5.0], [5.0, 4.0, 3.0, 2.0, 1.0]),
+        # Missing data
+        ([1.0, np.nan, 3.0, np.nan, 5.0], [5.0, np.nan, 3.0, np.nan, 1.0]),
+        ([np.nan, 4.0, np.nan, 2.0, np.nan], [np.nan, 4.0, np.nan, 2.0, np.nan]),
+        # Timestamps
+        (
+            pd.date_range("1/1/18", freq="D", periods=5),
+            pd.date_range("1/1/18", freq="D", periods=5)[::-1],
+        ),
+        (
+            pd.date_range("1/1/18", freq="D", periods=5).as_unit("s"),
+            pd.date_range("1/1/18", freq="D", periods=5)[::-1].as_unit("s"),
+        ),
+        # All NA
+        ([np.nan] * 5, [np.nan] * 5),
+    ],
+)
+@pytest.mark.parametrize("q", [0, 0.25, 0.5, 0.75, 1])
+def test_quantile(interpolation, a_vals, b_vals, q, request):
+    if (
+        interpolation == "nearest"
+        and q == 0.5
+        and isinstance(b_vals, list)
+        and b_vals == [4, 3, 2, 1]
+    ):
+        request.node.add_marker(
+            pytest.mark.xfail(
+                reason="Unclear numpy expectation for nearest "
+                "result with equidistant data"
+            )
+        )
+    all_vals = pd.concat([pd.Series(a_vals), pd.Series(b_vals)])
+
+    a_expected = pd.Series(a_vals).quantile(q, interpolation=interpolation)
+    b_expected = pd.Series(b_vals).quantile(q, interpolation=interpolation)
+
+    df = DataFrame({"key": ["a"] * len(a_vals) + ["b"] * len(b_vals), "val": all_vals})
+
+    expected = DataFrame(
+        [a_expected, b_expected], columns=["val"], index=Index(["a", "b"], name="key")
+    )
+    if all_vals.dtype.kind == "M" and expected.dtypes.values[0].kind == "M":
+        # TODO(non-nano): this should be unnecessary once array_to_datetime
+        #  correctly infers non-nano from Timestamp.unit
+        expected = expected.astype(all_vals.dtype)
+    result = df.groupby("key").quantile(q, interpolation=interpolation)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_quantile_array():
+    # https://github.com/pandas-dev/pandas/issues/27526
+    df = DataFrame({"A": [0, 1, 2, 3, 4]})
+    key = np.array([0, 0, 1, 1, 1], dtype=np.int64)
+    result = df.groupby(key).quantile([0.25])
+
+    index = pd.MultiIndex.from_product([[0, 1], [0.25]])
+    expected = DataFrame({"A": [0.25, 2.50]}, index=index)
+    tm.assert_frame_equal(result, expected)
+
+    df = DataFrame({"A": [0, 1, 2, 3], "B": [4, 5, 6, 7]})
+    index = pd.MultiIndex.from_product([[0, 1], [0.25, 0.75]])
+
+    key = np.array([0, 0, 1, 1], dtype=np.int64)
+    result = df.groupby(key).quantile([0.25, 0.75])
+    expected = DataFrame(
+        {"A": [0.25, 0.75, 2.25, 2.75], "B": [4.25, 4.75, 6.25, 6.75]}, index=index
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_quantile_array2():
+    # https://github.com/pandas-dev/pandas/pull/28085#issuecomment-524066959
+    arr = np.random.RandomState(0).randint(0, 5, size=(10, 3), dtype=np.int64)
+    df = DataFrame(arr, columns=list("ABC"))
+    result = df.groupby("A").quantile([0.3, 0.7])
+    expected = DataFrame(
+        {
+            "B": [0.9, 2.1, 2.2, 3.4, 1.6, 2.4, 2.3, 2.7, 0.0, 0.0],
+            "C": [1.2, 2.8, 1.8, 3.0, 0.0, 0.0, 1.9, 3.1, 3.0, 3.0],
+        },
+        index=pd.MultiIndex.from_product(
+            [[0, 1, 2, 3, 4], [0.3, 0.7]], names=["A", None]
+        ),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_quantile_array_no_sort():
+    df = DataFrame({"A": [0, 1, 2], "B": [3, 4, 5]})
+    key = np.array([1, 0, 1], dtype=np.int64)
+    result = df.groupby(key, sort=False).quantile([0.25, 0.5, 0.75])
+    expected = DataFrame(
+        {"A": [0.5, 1.0, 1.5, 1.0, 1.0, 1.0], "B": [3.5, 4.0, 4.5, 4.0, 4.0, 4.0]},
+        index=pd.MultiIndex.from_product([[1, 0], [0.25, 0.5, 0.75]]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby(key, sort=False).quantile([0.75, 0.25])
+    expected = DataFrame(
+        {"A": [1.5, 0.5, 1.0, 1.0], "B": [4.5, 3.5, 4.0, 4.0]},
+        index=pd.MultiIndex.from_product([[1, 0], [0.75, 0.25]]),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_quantile_array_multiple_levels():
+    df = DataFrame(
+        {"A": [0, 1, 2], "B": [3, 4, 5], "c": ["a", "a", "a"], "d": ["a", "a", "b"]}
+    )
+    result = df.groupby(["c", "d"]).quantile([0.25, 0.75])
+    index = pd.MultiIndex.from_tuples(
+        [("a", "a", 0.25), ("a", "a", 0.75), ("a", "b", 0.25), ("a", "b", 0.75)],
+        names=["c", "d", None],
+    )
+    expected = DataFrame(
+        {"A": [0.25, 0.75, 2.0, 2.0], "B": [3.25, 3.75, 5.0, 5.0]}, index=index
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("frame_size", [(2, 3), (100, 10)])
+@pytest.mark.parametrize("groupby", [[0], [0, 1]])
+@pytest.mark.parametrize("q", [[0.5, 0.6]])
+def test_groupby_quantile_with_arraylike_q_and_int_columns(frame_size, groupby, q):
+    # GH30289
+    nrow, ncol = frame_size
+    df = DataFrame(np.array([ncol * [_ % 4] for _ in range(nrow)]), columns=range(ncol))
+
+    idx_levels = [np.arange(min(nrow, 4))] * len(groupby) + [q]
+    idx_codes = [[x for x in range(min(nrow, 4)) for _ in q]] * len(groupby) + [
+        list(range(len(q))) * min(nrow, 4)
+    ]
+    expected_index = pd.MultiIndex(
+        levels=idx_levels, codes=idx_codes, names=groupby + [None]
+    )
+    expected_values = [
+        [float(x)] * (ncol - len(groupby)) for x in range(min(nrow, 4)) for _ in q
+    ]
+    expected_columns = [x for x in range(ncol) if x not in groupby]
+    expected = DataFrame(
+        expected_values, index=expected_index, columns=expected_columns
+    )
+    result = df.groupby(groupby).quantile(q)
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_quantile_raises():
+    df = DataFrame([["foo", "a"], ["foo", "b"], ["foo", "c"]], columns=["key", "val"])
+
+    with pytest.raises(TypeError, match="cannot be performed against 'object' dtypes"):
+        df.groupby("key").quantile()
+
+
+def test_quantile_out_of_bounds_q_raises():
+    # https://github.com/pandas-dev/pandas/issues/27470
+    df = DataFrame({"a": [0, 0, 0, 1, 1, 1], "b": range(6)})
+    g = df.groupby([0, 0, 0, 1, 1, 1])
+    with pytest.raises(ValueError, match="Got '50.0' instead"):
+        g.quantile(50)
+
+    with pytest.raises(ValueError, match="Got '-1.0' instead"):
+        g.quantile(-1)
+
+
+def test_quantile_missing_group_values_no_segfaults():
+    # GH 28662
+    data = np.array([1.0, np.nan, 1.0])
+    df = DataFrame({"key": data, "val": range(3)})
+
+    # Random segfaults; would have been guaranteed in loop
+    grp = df.groupby("key")
+    for _ in range(100):
+        grp.quantile()
+
+
+@pytest.mark.parametrize(
+    "key, val, expected_key, expected_val",
+    [
+        ([1.0, np.nan, 3.0, np.nan], range(4), [1.0, 3.0], [0.0, 2.0]),
+        ([1.0, np.nan, 2.0, 2.0], range(4), [1.0, 2.0], [0.0, 2.5]),
+        (["a", "b", "b", np.nan], range(4), ["a", "b"], [0, 1.5]),
+        ([0], [42], [0], [42.0]),
+        ([], [], np.array([], dtype="float64"), np.array([], dtype="float64")),
+    ],
+)
+def test_quantile_missing_group_values_correct_results(
+    key, val, expected_key, expected_val
+):
+    # GH 28662, GH 33200, GH 33569
+    df = DataFrame({"key": key, "val": val})
+
+    expected = DataFrame(
+        expected_val, index=Index(expected_key, name="key"), columns=["val"]
+    )
+
+    grp = df.groupby("key")
+
+    result = grp.quantile(0.5)
+    tm.assert_frame_equal(result, expected)
+
+    result = grp.quantile()
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "values",
+    [
+        pd.array([1, 0, None] * 2, dtype="Int64"),
+        pd.array([True, False, None] * 2, dtype="boolean"),
+    ],
+)
+@pytest.mark.parametrize("q", [0.5, [0.0, 0.5, 1.0]])
+def test_groupby_quantile_nullable_array(values, q):
+    # https://github.com/pandas-dev/pandas/issues/33136
+    df = DataFrame({"a": ["x"] * 3 + ["y"] * 3, "b": values})
+    result = df.groupby("a")["b"].quantile(q)
+
+    if isinstance(q, list):
+        idx = pd.MultiIndex.from_product((["x", "y"], q), names=["a", None])
+        true_quantiles = [0.0, 0.5, 1.0]
+    else:
+        idx = Index(["x", "y"], name="a")
+        true_quantiles = [0.5]
+
+    expected = pd.Series(true_quantiles * 2, index=idx, name="b", dtype="Float64")
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("q", [0.5, [0.0, 0.5, 1.0]])
+@pytest.mark.parametrize("numeric_only", [True, False])
+def test_groupby_quantile_raises_on_invalid_dtype(q, numeric_only):
+    df = DataFrame({"a": [1], "b": [2.0], "c": ["x"]})
+    if numeric_only:
+        result = df.groupby("a").quantile(q, numeric_only=numeric_only)
+        expected = df.groupby("a")[["b"]].quantile(q)
+        tm.assert_frame_equal(result, expected)
+    else:
+        with pytest.raises(
+            TypeError, match="'quantile' cannot be performed against 'object' dtypes!"
+        ):
+            df.groupby("a").quantile(q, numeric_only=numeric_only)
+
+
+def test_groupby_quantile_NA_float(any_float_dtype):
+    # GH#42849
+    df = DataFrame({"x": [1, 1], "y": [0.2, np.nan]}, dtype=any_float_dtype)
+    result = df.groupby("x")["y"].quantile(0.5)
+    exp_index = Index([1.0], dtype=any_float_dtype, name="x")
+
+    if any_float_dtype in ["Float32", "Float64"]:
+        expected_dtype = any_float_dtype
+    else:
+        expected_dtype = None
+
+    expected = pd.Series([0.2], dtype=expected_dtype, index=exp_index, name="y")
+    tm.assert_series_equal(result, expected)
+
+    result = df.groupby("x")["y"].quantile([0.5, 0.75])
+    expected = pd.Series(
+        [0.2] * 2,
+        index=pd.MultiIndex.from_product((exp_index, [0.5, 0.75]), names=["x", None]),
+        name="y",
+        dtype=expected_dtype,
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_quantile_NA_int(any_int_ea_dtype):
+    # GH#42849
+    df = DataFrame({"x": [1, 1], "y": [2, 5]}, dtype=any_int_ea_dtype)
+    result = df.groupby("x")["y"].quantile(0.5)
+    expected = pd.Series(
+        [3.5],
+        dtype="Float64",
+        index=Index([1], name="x", dtype=any_int_ea_dtype),
+        name="y",
+    )
+    tm.assert_series_equal(expected, result)
+
+    result = df.groupby("x").quantile(0.5)
+    expected = DataFrame(
+        {"y": 3.5}, dtype="Float64", index=Index([1], name="x", dtype=any_int_ea_dtype)
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "interpolation, val1, val2", [("lower", 2, 2), ("higher", 2, 3), ("nearest", 2, 2)]
+)
+def test_groupby_quantile_all_na_group_masked(
+    interpolation, val1, val2, any_numeric_ea_dtype
+):
+    # GH#37493
+    df = DataFrame(
+        {"a": [1, 1, 1, 2], "b": [1, 2, 3, pd.NA]}, dtype=any_numeric_ea_dtype
+    )
+    result = df.groupby("a").quantile(q=[0.5, 0.7], interpolation=interpolation)
+    expected = DataFrame(
+        {"b": [val1, val2, pd.NA, pd.NA]},
+        dtype=any_numeric_ea_dtype,
+        index=pd.MultiIndex.from_arrays(
+            [pd.Series([1, 1, 2, 2], dtype=any_numeric_ea_dtype), [0.5, 0.7, 0.5, 0.7]],
+            names=["a", None],
+        ),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("interpolation", ["midpoint", "linear"])
+def test_groupby_quantile_all_na_group_masked_interp(
+    interpolation, any_numeric_ea_dtype
+):
+    # GH#37493
+    df = DataFrame(
+        {"a": [1, 1, 1, 2], "b": [1, 2, 3, pd.NA]}, dtype=any_numeric_ea_dtype
+    )
+    result = df.groupby("a").quantile(q=[0.5, 0.75], interpolation=interpolation)
+
+    if any_numeric_ea_dtype == "Float32":
+        expected_dtype = any_numeric_ea_dtype
+    else:
+        expected_dtype = "Float64"
+
+    expected = DataFrame(
+        {"b": [2.0, 2.5, pd.NA, pd.NA]},
+        dtype=expected_dtype,
+        index=pd.MultiIndex.from_arrays(
+            [
+                pd.Series([1, 1, 2, 2], dtype=any_numeric_ea_dtype),
+                [0.5, 0.75, 0.5, 0.75],
+            ],
+            names=["a", None],
+        ),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["Float64", "Float32"])
+def test_groupby_quantile_allNA_column(dtype):
+    # GH#42849
+    df = DataFrame({"x": [1, 1], "y": [pd.NA] * 2}, dtype=dtype)
+    result = df.groupby("x")["y"].quantile(0.5)
+    expected = pd.Series(
+        [np.nan], dtype=dtype, index=Index([1.0], dtype=dtype), name="y"
+    )
+    expected.index.name = "x"
+    tm.assert_series_equal(expected, result)
+
+
+def test_groupby_timedelta_quantile():
+    # GH: 29485
+    df = DataFrame(
+        {"value": pd.to_timedelta(np.arange(4), unit="s"), "group": [1, 1, 2, 2]}
+    )
+    result = df.groupby("group").quantile(0.99)
+    expected = DataFrame(
+        {
+            "value": [
+                pd.Timedelta("0 days 00:00:00.990000"),
+                pd.Timedelta("0 days 00:00:02.990000"),
+            ]
+        },
+        index=Index([1, 2], name="group"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_columns_groupby_quantile():
+    # GH 33795
+    df = DataFrame(
+        np.arange(12).reshape(3, -1),
+        index=list("XYZ"),
+        columns=pd.Series(list("ABAB"), name="col"),
+    )
+    result = df.groupby("col", axis=1).quantile(q=[0.8, 0.2])
+    expected = DataFrame(
+        [
+            [1.6, 0.4, 2.6, 1.4],
+            [5.6, 4.4, 6.6, 5.4],
+            [9.6, 8.4, 10.6, 9.4],
+        ],
+        index=list("XYZ"),
+        columns=pd.MultiIndex.from_tuples(
+            [("A", 0.8), ("A", 0.2), ("B", 0.8), ("B", 0.2)], names=["col", None]
+        ),
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_timestamp_groupby_quantile():
+    # GH 33168
+    df = DataFrame(
+        {
+            "timestamp": pd.date_range(
+                start="2020-04-19 00:00:00", freq="1T", periods=100, tz="UTC"
+            ).floor("1H"),
+            "category": list(range(1, 101)),
+            "value": list(range(101, 201)),
+        }
+    )
+
+    result = df.groupby("timestamp").quantile([0.2, 0.8])
+
+    expected = DataFrame(
+        [
+            {"category": 12.8, "value": 112.8},
+            {"category": 48.2, "value": 148.2},
+            {"category": 68.8, "value": 168.8},
+            {"category": 92.2, "value": 192.2},
+        ],
+        index=pd.MultiIndex.from_tuples(
+            [
+                (pd.Timestamp("2020-04-19 00:00:00+00:00"), 0.2),
+                (pd.Timestamp("2020-04-19 00:00:00+00:00"), 0.8),
+                (pd.Timestamp("2020-04-19 01:00:00+00:00"), 0.2),
+                (pd.Timestamp("2020-04-19 01:00:00+00:00"), 0.8),
+            ],
+            names=("timestamp", None),
+        ),
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_quantile_dt64tz_period():
+    # GH#51373
+    dti = pd.date_range("2016-01-01", periods=1000)
+    ser = pd.Series(dti)
+    df = ser.to_frame()
+    df[1] = dti.tz_localize("US/Pacific")
+    df[2] = dti.to_period("D")
+    df[3] = dti - dti[0]
+    df.iloc[-1] = pd.NaT
+
+    by = np.tile(np.arange(5), 200)
+    gb = df.groupby(by)
+
+    result = gb.quantile(0.5)
+
+    # Check that we match the group-by-group result
+    exp = {i: df.iloc[i::5].quantile(0.5) for i in range(5)}
+    expected = DataFrame(exp).T
+    expected.index = expected.index.astype(np.int_)
+
+    tm.assert_frame_equal(result, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_raises.py

@@ -0,0 +1,633 @@
+# Only tests that raise an error and have no better location should go here.
+# Tests for specific groupby methods should go in their respective
+# test file.
+
+import datetime
+
+import numpy as np
+import pytest
+
+from pandas import (
+    Categorical,
+    DataFrame,
+    Grouper,
+    Series,
+)
+from pandas.tests.groupby import get_groupby_method_args
+
+
+@pytest.fixture(
+    params=[
+        "a",
+        ["a"],
+        ["a", "b"],
+        Grouper(key="a"),
+        lambda x: x % 2,
+        [0, 0, 0, 1, 2, 2, 2, 3, 3],
+        np.array([0, 0, 0, 1, 2, 2, 2, 3, 3]),
+        dict(zip(range(9), [0, 0, 0, 1, 2, 2, 2, 3, 3])),
+        Series([1, 1, 1, 1, 1, 2, 2, 2, 2]),
+        [Series([1, 1, 1, 1, 1, 2, 2, 2, 2]), Series([3, 3, 4, 4, 4, 4, 4, 3, 3])],
+    ]
+)
+def by(request):
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def groupby_series(request):
+    return request.param
+
+
+@pytest.mark.parametrize("how", ["method", "agg", "transform"])
+def test_groupby_raises_string(how, by, groupby_series, groupby_func):
+    df = DataFrame(
+        {
+            "a": [1, 1, 1, 1, 1, 2, 2, 2, 2],
+            "b": [3, 3, 4, 4, 4, 4, 4, 3, 3],
+            "c": range(9),
+            "d": list("xyzwtyuio"),
+        }
+    )
+    args = get_groupby_method_args(groupby_func, df)
+    gb = df.groupby(by=by)
+
+    if groupby_series:
+        gb = gb["d"]
+
+        if groupby_func == "corrwith":
+            assert not hasattr(gb, "corrwith")
+            return
+
+    klass, msg = {
+        "all": (None, ""),
+        "any": (None, ""),
+        "bfill": (None, ""),
+        "corrwith": (TypeError, "Could not convert"),
+        "count": (None, ""),
+        "cumcount": (None, ""),
+        "cummax": (
+            (NotImplementedError, TypeError),
+            "(function|cummax) is not (implemented|supported) for (this|object) dtype",
+        ),
+        "cummin": (
+            (NotImplementedError, TypeError),
+            "(function|cummin) is not (implemented|supported) for (this|object) dtype",
+        ),
+        "cumprod": (
+            (NotImplementedError, TypeError),
+            "(function|cumprod) is not (implemented|supported) for (this|object) dtype",
+        ),
+        "cumsum": (
+            (NotImplementedError, TypeError),
+            "(function|cumsum) is not (implemented|supported) for (this|object) dtype",
+        ),
+        "diff": (TypeError, "unsupported operand type"),
+        "ffill": (None, ""),
+        "fillna": (None, ""),
+        "first": (None, ""),
+        "idxmax": (TypeError, "'argmax' not allowed for this dtype"),
+        "idxmin": (TypeError, "'argmin' not allowed for this dtype"),
+        "last": (None, ""),
+        "max": (None, ""),
+        "mean": (TypeError, "Could not convert xy?z?w?t?y?u?i?o? to numeric"),
+        "median": (TypeError, "could not convert string to float"),
+        "min": (None, ""),
+        "ngroup": (None, ""),
+        "nunique": (None, ""),
+        "pct_change": (TypeError, "unsupported operand type"),
+        "prod": (TypeError, "can't multiply sequence by non-int of type 'str'"),
+        "quantile": (TypeError, "cannot be performed against 'object' dtypes!"),
+        "rank": (None, ""),
+        "sem": (ValueError, "could not convert string to float"),
+        "shift": (None, ""),
+        "size": (None, ""),
+        "skew": (TypeError, "could not convert string to float"),
+        "std": (ValueError, "could not convert string to float"),
+        "sum": (None, ""),
+        "var": (TypeError, "could not convert string to float"),
+    }[groupby_func]
+
+    if klass is None:
+        if how == "method":
+            getattr(gb, groupby_func)(*args)
+        elif how == "agg":
+            gb.agg(groupby_func, *args)
+        else:
+            gb.transform(groupby_func, *args)
+    else:
+        with pytest.raises(klass, match=msg):
+            if how == "method":
+                getattr(gb, groupby_func)(*args)
+            elif how == "agg":
+                gb.agg(groupby_func, *args)
+            else:
+                gb.transform(groupby_func, *args)
+
+
+@pytest.mark.parametrize("how", ["agg", "transform"])
+def test_groupby_raises_string_udf(how, by, groupby_series):
+    df = DataFrame(
+        {
+            "a": [1, 1, 1, 1, 1, 2, 2, 2, 2],
+            "b": [3, 3, 4, 4, 4, 4, 4, 3, 3],
+            "c": range(9),
+            "d": list("xyzwtyuio"),
+        }
+    )
+    gb = df.groupby(by=by)
+
+    if groupby_series:
+        gb = gb["d"]
+
+    def func(x):
+        raise TypeError("Test error message")
+
+    with pytest.raises(TypeError, match="Test error message"):
+        getattr(gb, how)(func)
+
+
+@pytest.mark.parametrize("how", ["agg", "transform"])
+@pytest.mark.parametrize("groupby_func_np", [np.sum, np.mean])
+def test_groupby_raises_string_np(how, by, groupby_series, groupby_func_np):
+    # GH#50749
+    df = DataFrame(
+        {
+            "a": [1, 1, 1, 1, 1, 2, 2, 2, 2],
+            "b": [3, 3, 4, 4, 4, 4, 4, 3, 3],
+            "c": range(9),
+            "d": list("xyzwtyuio"),
+        }
+    )
+    gb = df.groupby(by=by)
+
+    if groupby_series:
+        gb = gb["d"]
+
+    klass, msg = {
+        np.sum: (None, ""),
+        np.mean: (TypeError, "Could not convert xy?z?w?t?y?u?i?o? to numeric"),
+    }[groupby_func_np]
+
+    if klass is None:
+        getattr(gb, how)(groupby_func_np)
+    else:
+        with pytest.raises(klass, match=msg):
+            getattr(gb, how)(groupby_func_np)
+
+
+@pytest.mark.parametrize("how", ["method", "agg", "transform"])
+def test_groupby_raises_datetime(how, by, groupby_series, groupby_func):
+    df = DataFrame(
+        {
+            "a": [1, 1, 1, 1, 1, 2, 2, 2, 2],
+            "b": [3, 3, 4, 4, 4, 4, 4, 3, 3],
+            "c": range(9),
+            "d": datetime.datetime(2005, 1, 1, 10, 30, 23, 540000),
+        }
+    )
+    args = get_groupby_method_args(groupby_func, df)
+    gb = df.groupby(by=by)
+
+    if groupby_series:
+        gb = gb["d"]
+
+        if groupby_func == "corrwith":
+            assert not hasattr(gb, "corrwith")
+            return
+
+    klass, msg = {
+        "all": (None, ""),
+        "any": (None, ""),
+        "bfill": (None, ""),
+        "corrwith": (TypeError, "cannot perform __mul__ with this index type"),
+        "count": (None, ""),
+        "cumcount": (None, ""),
+        "cummax": (None, ""),
+        "cummin": (None, ""),
+        "cumprod": (TypeError, "datetime64 type does not support cumprod operations"),
+        "cumsum": (TypeError, "datetime64 type does not support cumsum operations"),
+        "diff": (None, ""),
+        "ffill": (None, ""),
+        "fillna": (None, ""),
+        "first": (None, ""),
+        "idxmax": (None, ""),
+        "idxmin": (None, ""),
+        "last": (None, ""),
+        "max": (None, ""),
+        "mean": (None, ""),
+        "median": (None, ""),
+        "min": (None, ""),
+        "ngroup": (None, ""),
+        "nunique": (None, ""),
+        "pct_change": (TypeError, "cannot perform __truediv__ with this index type"),
+        "prod": (TypeError, "datetime64 type does not support prod"),
+        "quantile": (None, ""),
+        "rank": (None, ""),
+        "sem": (None, ""),
+        "shift": (None, ""),
+        "size": (None, ""),
+        "skew": (TypeError, r"dtype datetime64\[ns\] does not support reduction"),
+        "std": (None, ""),
+        "sum": (TypeError, "datetime64 type does not support sum operations"),
+        "var": (None, ""),
+    }[groupby_func]
+
+    if klass is None:
+        if how == "method":
+            getattr(gb, groupby_func)(*args)
+        elif how == "agg":
+            gb.agg(groupby_func, *args)
+        else:
+            gb.transform(groupby_func, *args)
+    else:
+        with pytest.raises(klass, match=msg):
+            if how == "method":
+                getattr(gb, groupby_func)(*args)
+            elif how == "agg":
+                gb.agg(groupby_func, *args)
+            else:
+                gb.transform(groupby_func, *args)
+
+
+@pytest.mark.parametrize("how", ["agg", "transform"])
+def test_groupby_raises_datetime_udf(how, by, groupby_series):
+    df = DataFrame(
+        {
+            "a": [1, 1, 1, 1, 1, 2, 2, 2, 2],
+            "b": [3, 3, 4, 4, 4, 4, 4, 3, 3],
+            "c": range(9),
+            "d": datetime.datetime(2005, 1, 1, 10, 30, 23, 540000),
+        }
+    )
+
+    gb = df.groupby(by=by)
+
+    if groupby_series:
+        gb = gb["d"]
+
+    def func(x):
+        raise TypeError("Test error message")
+
+    with pytest.raises(TypeError, match="Test error message"):
+        getattr(gb, how)(func)
+
+
+@pytest.mark.parametrize("how", ["agg", "transform"])
+@pytest.mark.parametrize("groupby_func_np", [np.sum, np.mean])
+def test_groupby_raises_datetime_np(how, by, groupby_series, groupby_func_np):
+    # GH#50749
+    df = DataFrame(
+        {
+            "a": [1, 1, 1, 1, 1, 2, 2, 2, 2],
+            "b": [3, 3, 4, 4, 4, 4, 4, 3, 3],
+            "c": range(9),
+            "d": datetime.datetime(2005, 1, 1, 10, 30, 23, 540000),
+        }
+    )
+    gb = df.groupby(by=by)
+
+    if groupby_series:
+        gb = gb["d"]
+
+    klass, msg = {
+        np.sum: (TypeError, "datetime64 type does not support sum operations"),
+        np.mean: (None, ""),
+    }[groupby_func_np]
+
+    if klass is None:
+        getattr(gb, how)(groupby_func_np)
+    else:
+        with pytest.raises(klass, match=msg):
+            getattr(gb, how)(groupby_func_np)
+
+
+@pytest.mark.parametrize("how", ["method", "agg", "transform"])
+def test_groupby_raises_category(
+    how, by, groupby_series, groupby_func, using_copy_on_write
+):
+    # GH#50749
+    df = DataFrame(
+        {
+            "a": [1, 1, 1, 1, 1, 2, 2, 2, 2],
+            "b": [3, 3, 4, 4, 4, 4, 4, 3, 3],
+            "c": range(9),
+            "d": Categorical(
+                ["a", "a", "a", "a", "b", "b", "b", "b", "c"],
+                categories=["a", "b", "c", "d"],
+                ordered=True,
+            ),
+        }
+    )
+    args = get_groupby_method_args(groupby_func, df)
+    gb = df.groupby(by=by)
+
+    if groupby_series:
+        gb = gb["d"]
+
+        if groupby_func == "corrwith":
+            assert not hasattr(gb, "corrwith")
+            return
+
+    klass, msg = {
+        "all": (None, ""),
+        "any": (None, ""),
+        "bfill": (None, ""),
+        "corrwith": (
+            TypeError,
+            r"unsupported operand type\(s\) for \*: 'Categorical' and 'int'",
+        ),
+        "count": (None, ""),
+        "cumcount": (None, ""),
+        "cummax": (
+            (NotImplementedError, TypeError),
+            "(category type does not support cummax operations|"
+            + "category dtype not supported|"
+            + "cummax is not supported for category dtype)",
+        ),
+        "cummin": (
+            (NotImplementedError, TypeError),
+            "(category type does not support cummin operations|"
+            + "category dtype not supported|"
+            "cummin is not supported for category dtype)",
+        ),
+        "cumprod": (
+            (NotImplementedError, TypeError),
+            "(category type does not support cumprod operations|"
+            + "category dtype not supported|"
+            "cumprod is not supported for category dtype)",
+        ),
+        "cumsum": (
+            (NotImplementedError, TypeError),
+            "(category type does not support cumsum operations|"
+            + "category dtype not supported|"
+            "cumsum is not supported for category dtype)",
+        ),
+        "diff": (
+            TypeError,
+            r"unsupported operand type\(s\) for -: 'Categorical' and 'Categorical'",
+        ),
+        "ffill": (None, ""),
+        "fillna": (
+            TypeError,
+            r"Cannot setitem on a Categorical with a new category \(0\), "
+            + "set the categories first",
+        )
+        if not using_copy_on_write
+        else (None, ""),  # no-op with CoW
+        "first": (None, ""),
+        "idxmax": (None, ""),
+        "idxmin": (None, ""),
+        "last": (None, ""),
+        "max": (None, ""),
+        "mean": (
+            TypeError,
+            "'Categorical' with dtype category does not support reduction 'mean'",
+        ),
+        "median": (
+            TypeError,
+            "'Categorical' with dtype category does not support reduction 'median'",
+        ),
+        "min": (None, ""),
+        "ngroup": (None, ""),
+        "nunique": (None, ""),
+        "pct_change": (
+            TypeError,
+            r"unsupported operand type\(s\) for /: 'Categorical' and 'Categorical'",
+        ),
+        "prod": (TypeError, "category type does not support prod operations"),
+        "quantile": (TypeError, "No matching signature found"),
+        "rank": (None, ""),
+        "sem": (ValueError, "Cannot cast object dtype to float64"),
+        "shift": (None, ""),
+        "size": (None, ""),
+        "skew": (
+            TypeError,
+            "'Categorical' with dtype category does not support reduction 'skew'",
+        ),
+        "std": (ValueError, "Cannot cast object dtype to float64"),
+        "sum": (TypeError, "category type does not support sum operations"),
+        "var": (
+            TypeError,
+            "'Categorical' with dtype category does not support reduction 'var'",
+        ),
+    }[groupby_func]
+
+    if klass is None:
+        if how == "method":
+            getattr(gb, groupby_func)(*args)
+        elif how == "agg":
+            gb.agg(groupby_func, *args)
+        else:
+            gb.transform(groupby_func, *args)
+    else:
+        with pytest.raises(klass, match=msg):
+            if how == "method":
+                getattr(gb, groupby_func)(*args)
+            elif how == "agg":
+                gb.agg(groupby_func, *args)
+            else:
+                gb.transform(groupby_func, *args)
+
+
+@pytest.mark.parametrize("how", ["agg", "transform"])
+def test_groupby_raises_category_udf(how, by, groupby_series):
+    # GH#50749
+    df = DataFrame(
+        {
+            "a": [1, 1, 1, 1, 1, 2, 2, 2, 2],
+            "b": [3, 3, 4, 4, 4, 4, 4, 3, 3],
+            "c": range(9),
+            "d": Categorical(
+                ["a", "a", "a", "a", "b", "b", "b", "b", "c"],
+                categories=["a", "b", "c", "d"],
+                ordered=True,
+            ),
+        }
+    )
+    gb = df.groupby(by=by)
+
+    if groupby_series:
+        gb = gb["d"]
+
+    def func(x):
+        raise TypeError("Test error message")
+
+    with pytest.raises(TypeError, match="Test error message"):
+        getattr(gb, how)(func)
+
+
+@pytest.mark.parametrize("how", ["agg", "transform"])
+@pytest.mark.parametrize("groupby_func_np", [np.sum, np.mean])
+def test_groupby_raises_category_np(how, by, groupby_series, groupby_func_np):
+    # GH#50749
+    df = DataFrame(
+        {
+            "a": [1, 1, 1, 1, 1, 2, 2, 2, 2],
+            "b": [3, 3, 4, 4, 4, 4, 4, 3, 3],
+            "c": range(9),
+            "d": Categorical(
+                ["a", "a", "a", "a", "b", "b", "b", "b", "c"],
+                categories=["a", "b", "c", "d"],
+                ordered=True,
+            ),
+        }
+    )
+    gb = df.groupby(by=by)
+
+    if groupby_series:
+        gb = gb["d"]
+
+    klass, msg = {
+        np.sum: (TypeError, "category type does not support sum operations"),
+        np.mean: (
+            TypeError,
+            "'Categorical' with dtype category does not support reduction 'mean'",
+        ),
+    }[groupby_func_np]
+
+    if klass is None:
+        getattr(gb, how)(groupby_func_np)
+    else:
+        with pytest.raises(klass, match=msg):
+            getattr(gb, how)(groupby_func_np)
+
+
+@pytest.mark.parametrize("how", ["method", "agg", "transform"])
+def test_groupby_raises_category_on_category(
+    how, by, groupby_series, groupby_func, observed, using_copy_on_write
+):
+    # GH#50749
+    df = DataFrame(
+        {
+            "a": Categorical(
+                ["a", "a", "a", "a", "b", "b", "b", "b", "c"],
+                categories=["a", "b", "c", "d"],
+                ordered=True,
+            ),
+            "b": [3, 3, 4, 4, 4, 4, 4, 3, 3],
+            "c": range(9),
+            "d": Categorical(
+                ["a", "a", "a", "a", "b", "b", "c", "c", "c"],
+                categories=["a", "b", "c", "d"],
+                ordered=True,
+            ),
+        }
+    )
+    args = get_groupby_method_args(groupby_func, df)
+    gb = df.groupby(by=by, observed=observed)
+
+    if groupby_series:
+        gb = gb["d"]
+
+        if groupby_func == "corrwith":
+            assert not hasattr(gb, "corrwith")
+            return
+
+    empty_groups = any(group.empty for group in gb.groups.values())
+
+    klass, msg = {
+        "all": (None, ""),
+        "any": (None, ""),
+        "bfill": (None, ""),
+        "corrwith": (
+            TypeError,
+            r"unsupported operand type\(s\) for \*: 'Categorical' and 'int'",
+        ),
+        "count": (None, ""),
+        "cumcount": (None, ""),
+        "cummax": (
+            (NotImplementedError, TypeError),
+            "(cummax is not supported for category dtype|"
+            + "category dtype not supported|"
+            + "category type does not support cummax operations)",
+        ),
+        "cummin": (
+            (NotImplementedError, TypeError),
+            "(cummin is not supported for category dtype|"
+            + "category dtype not supported|"
+            "category type does not support cummin operations)",
+        ),
+        "cumprod": (
+            (NotImplementedError, TypeError),
+            "(cumprod is not supported for category dtype|"
+            + "category dtype not supported|"
+            "category type does not support cumprod operations)",
+        ),
+        "cumsum": (
+            (NotImplementedError, TypeError),
+            "(cumsum is not supported for category dtype|"
+            + "category dtype not supported|"
+            + "category type does not support cumsum operations)",
+        ),
+        "diff": (TypeError, "unsupported operand type"),
+        "ffill": (None, ""),
+        "fillna": (
+            TypeError,
+            r"Cannot setitem on a Categorical with a new category \(0\), "
+            + "set the categories first",
+        )
+        if not using_copy_on_write
+        else (None, ""),  # no-op with CoW
+        "first": (None, ""),
+        "idxmax": (ValueError, "attempt to get argmax of an empty sequence")
+        if empty_groups
+        else (None, ""),
+        "idxmin": (ValueError, "attempt to get argmin of an empty sequence")
+        if empty_groups
+        else (None, ""),
+        "last": (None, ""),
+        "max": (None, ""),
+        "mean": (
+            TypeError,
+            "'Categorical' with dtype category does not support reduction 'mean'",
+        ),
+        "median": (
+            TypeError,
+            "'Categorical' with dtype category does not support reduction 'median'",
+        ),
+        "min": (None, ""),
+        "ngroup": (None, ""),
+        "nunique": (None, ""),
+        "pct_change": (TypeError, "unsupported operand type"),
+        "prod": (TypeError, "category type does not support prod operations"),
+        "quantile": (TypeError, ""),
+        "rank": (None, ""),
+        "sem": (ValueError, "Cannot cast object dtype to float64"),
+        "shift": (None, ""),
+        "size": (None, ""),
+        "skew": (
+            TypeError,
+            "'Categorical' with dtype category does not support reduction 'skew'",
+        ),
+        "std": (ValueError, "Cannot cast object dtype to float64"),
+        "sum": (TypeError, "category type does not support sum operations"),
+        "var": (
+            TypeError,
+            "'Categorical' with dtype category does not support reduction 'var'",
+        ),
+    }[groupby_func]
+
+    if klass is None:
+        if how == "method":
+            getattr(gb, groupby_func)(*args)
+        elif how == "agg":
+            gb.agg(groupby_func, *args)
+        else:
+            gb.transform(groupby_func, *args)
+    else:
+        with pytest.raises(klass, match=msg):
+            if how == "method":
+                getattr(gb, groupby_func)(*args)
+            elif how == "agg":
+                gb.agg(groupby_func, *args)
+            else:
+                gb.transform(groupby_func, *args)
+
+
+def test_subsetting_columns_axis_1_raises():
+    # GH 35443
+    df = DataFrame({"a": [1], "b": [2], "c": [3]})
+    gb = df.groupby("a", axis=1)
+    with pytest.raises(ValueError, match="Cannot subset columns when using axis=1"):
+        gb["b"]

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_rank.py

@@ -0,0 +1,698 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    NaT,
+    Series,
+    concat,
+)
+import pandas._testing as tm
+
+
+def test_rank_unordered_categorical_typeerror():
+    # GH#51034 should be TypeError, not NotImplementedError
+    cat = pd.Categorical([], ordered=False)
+    ser = Series(cat)
+    df = ser.to_frame()
+
+    msg = "Cannot perform rank with non-ordered Categorical"
+
+    gb = ser.groupby(cat)
+    with pytest.raises(TypeError, match=msg):
+        gb.rank()
+
+    gb2 = df.groupby(cat)
+    with pytest.raises(TypeError, match=msg):
+        gb2.rank()
+
+
+def test_rank_apply():
+    lev1 = tm.rands_array(10, 100)
+    lev2 = tm.rands_array(10, 130)
+    lab1 = np.random.randint(0, 100, size=500)
+    lab2 = np.random.randint(0, 130, size=500)
+
+    df = DataFrame(
+        {
+            "value": np.random.randn(500),
+            "key1": lev1.take(lab1),
+            "key2": lev2.take(lab2),
+        }
+    )
+
+    result = df.groupby(["key1", "key2"]).value.rank()
+
+    expected = [piece.value.rank() for key, piece in df.groupby(["key1", "key2"])]
+    expected = concat(expected, axis=0)
+    expected = expected.reindex(result.index)
+    tm.assert_series_equal(result, expected)
+
+    result = df.groupby(["key1", "key2"]).value.rank(pct=True)
+
+    expected = [
+        piece.value.rank(pct=True) for key, piece in df.groupby(["key1", "key2"])
+    ]
+    expected = concat(expected, axis=0)
+    expected = expected.reindex(result.index)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("grps", [["qux"], ["qux", "quux"]])
+@pytest.mark.parametrize(
+    "vals",
+    [
+        np.array([2, 2, 8, 2, 6], dtype=dtype)
+        for dtype in ["i8", "i4", "i2", "i1", "u8", "u4", "u2", "u1", "f8", "f4", "f2"]
+    ]
+    + [
+        [
+            pd.Timestamp("2018-01-02"),
+            pd.Timestamp("2018-01-02"),
+            pd.Timestamp("2018-01-08"),
+            pd.Timestamp("2018-01-02"),
+            pd.Timestamp("2018-01-06"),
+        ],
+        [
+            pd.Timestamp("2018-01-02", tz="US/Pacific"),
+            pd.Timestamp("2018-01-02", tz="US/Pacific"),
+            pd.Timestamp("2018-01-08", tz="US/Pacific"),
+            pd.Timestamp("2018-01-02", tz="US/Pacific"),
+            pd.Timestamp("2018-01-06", tz="US/Pacific"),
+        ],
+        [
+            pd.Timestamp("2018-01-02") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-02") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-08") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-02") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-06") - pd.Timestamp(0),
+        ],
+        [
+            pd.Timestamp("2018-01-02").to_period("D"),
+            pd.Timestamp("2018-01-02").to_period("D"),
+            pd.Timestamp("2018-01-08").to_period("D"),
+            pd.Timestamp("2018-01-02").to_period("D"),
+            pd.Timestamp("2018-01-06").to_period("D"),
+        ],
+    ],
+    ids=lambda x: type(x[0]),
+)
+@pytest.mark.parametrize(
+    "ties_method,ascending,pct,exp",
+    [
+        ("average", True, False, [2.0, 2.0, 5.0, 2.0, 4.0]),
+        ("average", True, True, [0.4, 0.4, 1.0, 0.4, 0.8]),
+        ("average", False, False, [4.0, 4.0, 1.0, 4.0, 2.0]),
+        ("average", False, True, [0.8, 0.8, 0.2, 0.8, 0.4]),
+        ("min", True, False, [1.0, 1.0, 5.0, 1.0, 4.0]),
+        ("min", True, True, [0.2, 0.2, 1.0, 0.2, 0.8]),
+        ("min", False, False, [3.0, 3.0, 1.0, 3.0, 2.0]),
+        ("min", False, True, [0.6, 0.6, 0.2, 0.6, 0.4]),
+        ("max", True, False, [3.0, 3.0, 5.0, 3.0, 4.0]),
+        ("max", True, True, [0.6, 0.6, 1.0, 0.6, 0.8]),
+        ("max", False, False, [5.0, 5.0, 1.0, 5.0, 2.0]),
+        ("max", False, True, [1.0, 1.0, 0.2, 1.0, 0.4]),
+        ("first", True, False, [1.0, 2.0, 5.0, 3.0, 4.0]),
+        ("first", True, True, [0.2, 0.4, 1.0, 0.6, 0.8]),
+        ("first", False, False, [3.0, 4.0, 1.0, 5.0, 2.0]),
+        ("first", False, True, [0.6, 0.8, 0.2, 1.0, 0.4]),
+        ("dense", True, False, [1.0, 1.0, 3.0, 1.0, 2.0]),
+        ("dense", True, True, [1.0 / 3.0, 1.0 / 3.0, 3.0 / 3.0, 1.0 / 3.0, 2.0 / 3.0]),
+        ("dense", False, False, [3.0, 3.0, 1.0, 3.0, 2.0]),
+        ("dense", False, True, [3.0 / 3.0, 3.0 / 3.0, 1.0 / 3.0, 3.0 / 3.0, 2.0 / 3.0]),
+    ],
+)
+def test_rank_args(grps, vals, ties_method, ascending, pct, exp):
+    key = np.repeat(grps, len(vals))
+
+    orig_vals = vals
+    vals = list(vals) * len(grps)
+    if isinstance(orig_vals, np.ndarray):
+        vals = np.array(vals, dtype=orig_vals.dtype)
+
+    df = DataFrame({"key": key, "val": vals})
+    result = df.groupby("key").rank(method=ties_method, ascending=ascending, pct=pct)
+
+    exp_df = DataFrame(exp * len(grps), columns=["val"])
+    tm.assert_frame_equal(result, exp_df)
+
+
+@pytest.mark.parametrize("grps", [["qux"], ["qux", "quux"]])
+@pytest.mark.parametrize(
+    "vals", [[-np.inf, -np.inf, np.nan, 1.0, np.nan, np.inf, np.inf]]
+)
+@pytest.mark.parametrize(
+    "ties_method,ascending,na_option,exp",
+    [
+        ("average", True, "keep", [1.5, 1.5, np.nan, 3, np.nan, 4.5, 4.5]),
+        ("average", True, "top", [3.5, 3.5, 1.5, 5.0, 1.5, 6.5, 6.5]),
+        ("average", True, "bottom", [1.5, 1.5, 6.5, 3.0, 6.5, 4.5, 4.5]),
+        ("average", False, "keep", [4.5, 4.5, np.nan, 3, np.nan, 1.5, 1.5]),
+        ("average", False, "top", [6.5, 6.5, 1.5, 5.0, 1.5, 3.5, 3.5]),
+        ("average", False, "bottom", [4.5, 4.5, 6.5, 3.0, 6.5, 1.5, 1.5]),
+        ("min", True, "keep", [1.0, 1.0, np.nan, 3.0, np.nan, 4.0, 4.0]),
+        ("min", True, "top", [3.0, 3.0, 1.0, 5.0, 1.0, 6.0, 6.0]),
+        ("min", True, "bottom", [1.0, 1.0, 6.0, 3.0, 6.0, 4.0, 4.0]),
+        ("min", False, "keep", [4.0, 4.0, np.nan, 3.0, np.nan, 1.0, 1.0]),
+        ("min", False, "top", [6.0, 6.0, 1.0, 5.0, 1.0, 3.0, 3.0]),
+        ("min", False, "bottom", [4.0, 4.0, 6.0, 3.0, 6.0, 1.0, 1.0]),
+        ("max", True, "keep", [2.0, 2.0, np.nan, 3.0, np.nan, 5.0, 5.0]),
+        ("max", True, "top", [4.0, 4.0, 2.0, 5.0, 2.0, 7.0, 7.0]),
+        ("max", True, "bottom", [2.0, 2.0, 7.0, 3.0, 7.0, 5.0, 5.0]),
+        ("max", False, "keep", [5.0, 5.0, np.nan, 3.0, np.nan, 2.0, 2.0]),
+        ("max", False, "top", [7.0, 7.0, 2.0, 5.0, 2.0, 4.0, 4.0]),
+        ("max", False, "bottom", [5.0, 5.0, 7.0, 3.0, 7.0, 2.0, 2.0]),
+        ("first", True, "keep", [1.0, 2.0, np.nan, 3.0, np.nan, 4.0, 5.0]),
+        ("first", True, "top", [3.0, 4.0, 1.0, 5.0, 2.0, 6.0, 7.0]),
+        ("first", True, "bottom", [1.0, 2.0, 6.0, 3.0, 7.0, 4.0, 5.0]),
+        ("first", False, "keep", [4.0, 5.0, np.nan, 3.0, np.nan, 1.0, 2.0]),
+        ("first", False, "top", [6.0, 7.0, 1.0, 5.0, 2.0, 3.0, 4.0]),
+        ("first", False, "bottom", [4.0, 5.0, 6.0, 3.0, 7.0, 1.0, 2.0]),
+        ("dense", True, "keep", [1.0, 1.0, np.nan, 2.0, np.nan, 3.0, 3.0]),
+        ("dense", True, "top", [2.0, 2.0, 1.0, 3.0, 1.0, 4.0, 4.0]),
+        ("dense", True, "bottom", [1.0, 1.0, 4.0, 2.0, 4.0, 3.0, 3.0]),
+        ("dense", False, "keep", [3.0, 3.0, np.nan, 2.0, np.nan, 1.0, 1.0]),
+        ("dense", False, "top", [4.0, 4.0, 1.0, 3.0, 1.0, 2.0, 2.0]),
+        ("dense", False, "bottom", [3.0, 3.0, 4.0, 2.0, 4.0, 1.0, 1.0]),
+    ],
+)
+def test_infs_n_nans(grps, vals, ties_method, ascending, na_option, exp):
+    # GH 20561
+    key = np.repeat(grps, len(vals))
+    vals = vals * len(grps)
+    df = DataFrame({"key": key, "val": vals})
+    result = df.groupby("key").rank(
+        method=ties_method, ascending=ascending, na_option=na_option
+    )
+    exp_df = DataFrame(exp * len(grps), columns=["val"])
+    tm.assert_frame_equal(result, exp_df)
+
+
+@pytest.mark.parametrize("grps", [["qux"], ["qux", "quux"]])
+@pytest.mark.parametrize(
+    "vals",
+    [
+        np.array([2, 2, np.nan, 8, 2, 6, np.nan, np.nan], dtype=dtype)
+        for dtype in ["f8", "f4", "f2"]
+    ]
+    + [
+        [
+            pd.Timestamp("2018-01-02"),
+            pd.Timestamp("2018-01-02"),
+            np.nan,
+            pd.Timestamp("2018-01-08"),
+            pd.Timestamp("2018-01-02"),
+            pd.Timestamp("2018-01-06"),
+            np.nan,
+            np.nan,
+        ],
+        [
+            pd.Timestamp("2018-01-02", tz="US/Pacific"),
+            pd.Timestamp("2018-01-02", tz="US/Pacific"),
+            np.nan,
+            pd.Timestamp("2018-01-08", tz="US/Pacific"),
+            pd.Timestamp("2018-01-02", tz="US/Pacific"),
+            pd.Timestamp("2018-01-06", tz="US/Pacific"),
+            np.nan,
+            np.nan,
+        ],
+        [
+            pd.Timestamp("2018-01-02") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-02") - pd.Timestamp(0),
+            np.nan,
+            pd.Timestamp("2018-01-08") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-02") - pd.Timestamp(0),
+            pd.Timestamp("2018-01-06") - pd.Timestamp(0),
+            np.nan,
+            np.nan,
+        ],
+        [
+            pd.Timestamp("2018-01-02").to_period("D"),
+            pd.Timestamp("2018-01-02").to_period("D"),
+            np.nan,
+            pd.Timestamp("2018-01-08").to_period("D"),
+            pd.Timestamp("2018-01-02").to_period("D"),
+            pd.Timestamp("2018-01-06").to_period("D"),
+            np.nan,
+            np.nan,
+        ],
+    ],
+    ids=lambda x: type(x[0]),
+)
+@pytest.mark.parametrize(
+    "ties_method,ascending,na_option,pct,exp",
+    [
+        (
+            "average",
+            True,
+            "keep",
+            False,
+            [2.0, 2.0, np.nan, 5.0, 2.0, 4.0, np.nan, np.nan],
+        ),
+        (
+            "average",
+            True,
+            "keep",
+            True,
+            [0.4, 0.4, np.nan, 1.0, 0.4, 0.8, np.nan, np.nan],
+        ),
+        (
+            "average",
+            False,
+            "keep",
+            False,
+            [4.0, 4.0, np.nan, 1.0, 4.0, 2.0, np.nan, np.nan],
+        ),
+        (
+            "average",
+            False,
+            "keep",
+            True,
+            [0.8, 0.8, np.nan, 0.2, 0.8, 0.4, np.nan, np.nan],
+        ),
+        ("min", True, "keep", False, [1.0, 1.0, np.nan, 5.0, 1.0, 4.0, np.nan, np.nan]),
+        ("min", True, "keep", True, [0.2, 0.2, np.nan, 1.0, 0.2, 0.8, np.nan, np.nan]),
+        (
+            "min",
+            False,
+            "keep",
+            False,
+            [3.0, 3.0, np.nan, 1.0, 3.0, 2.0, np.nan, np.nan],
+        ),
+        ("min", False, "keep", True, [0.6, 0.6, np.nan, 0.2, 0.6, 0.4, np.nan, np.nan]),
+        ("max", True, "keep", False, [3.0, 3.0, np.nan, 5.0, 3.0, 4.0, np.nan, np.nan]),
+        ("max", True, "keep", True, [0.6, 0.6, np.nan, 1.0, 0.6, 0.8, np.nan, np.nan]),
+        (
+            "max",
+            False,
+            "keep",
+            False,
+            [5.0, 5.0, np.nan, 1.0, 5.0, 2.0, np.nan, np.nan],
+        ),
+        ("max", False, "keep", True, [1.0, 1.0, np.nan, 0.2, 1.0, 0.4, np.nan, np.nan]),
+        (
+            "first",
+            True,
+            "keep",
+            False,
+            [1.0, 2.0, np.nan, 5.0, 3.0, 4.0, np.nan, np.nan],
+        ),
+        (
+            "first",
+            True,
+            "keep",
+            True,
+            [0.2, 0.4, np.nan, 1.0, 0.6, 0.8, np.nan, np.nan],
+        ),
+        (
+            "first",
+            False,
+            "keep",
+            False,
+            [3.0, 4.0, np.nan, 1.0, 5.0, 2.0, np.nan, np.nan],
+        ),
+        (
+            "first",
+            False,
+            "keep",
+            True,
+            [0.6, 0.8, np.nan, 0.2, 1.0, 0.4, np.nan, np.nan],
+        ),
+        (
+            "dense",
+            True,
+            "keep",
+            False,
+            [1.0, 1.0, np.nan, 3.0, 1.0, 2.0, np.nan, np.nan],
+        ),
+        (
+            "dense",
+            True,
+            "keep",
+            True,
+            [
+                1.0 / 3.0,
+                1.0 / 3.0,
+                np.nan,
+                3.0 / 3.0,
+                1.0 / 3.0,
+                2.0 / 3.0,
+                np.nan,
+                np.nan,
+            ],
+        ),
+        (
+            "dense",
+            False,
+            "keep",
+            False,
+            [3.0, 3.0, np.nan, 1.0, 3.0, 2.0, np.nan, np.nan],
+        ),
+        (
+            "dense",
+            False,
+            "keep",
+            True,
+            [
+                3.0 / 3.0,
+                3.0 / 3.0,
+                np.nan,
+                1.0 / 3.0,
+                3.0 / 3.0,
+                2.0 / 3.0,
+                np.nan,
+                np.nan,
+            ],
+        ),
+        ("average", True, "bottom", False, [2.0, 2.0, 7.0, 5.0, 2.0, 4.0, 7.0, 7.0]),
+        (
+            "average",
+            True,
+            "bottom",
+            True,
+            [0.25, 0.25, 0.875, 0.625, 0.25, 0.5, 0.875, 0.875],
+        ),
+        ("average", False, "bottom", False, [4.0, 4.0, 7.0, 1.0, 4.0, 2.0, 7.0, 7.0]),
+        (
+            "average",
+            False,
+            "bottom",
+            True,
+            [0.5, 0.5, 0.875, 0.125, 0.5, 0.25, 0.875, 0.875],
+        ),
+        ("min", True, "bottom", False, [1.0, 1.0, 6.0, 5.0, 1.0, 4.0, 6.0, 6.0]),
+        (
+            "min",
+            True,
+            "bottom",
+            True,
+            [0.125, 0.125, 0.75, 0.625, 0.125, 0.5, 0.75, 0.75],
+        ),
+        ("min", False, "bottom", False, [3.0, 3.0, 6.0, 1.0, 3.0, 2.0, 6.0, 6.0]),
+        (
+            "min",
+            False,
+            "bottom",
+            True,
+            [0.375, 0.375, 0.75, 0.125, 0.375, 0.25, 0.75, 0.75],
+        ),
+        ("max", True, "bottom", False, [3.0, 3.0, 8.0, 5.0, 3.0, 4.0, 8.0, 8.0]),
+        ("max", True, "bottom", True, [0.375, 0.375, 1.0, 0.625, 0.375, 0.5, 1.0, 1.0]),
+        ("max", False, "bottom", False, [5.0, 5.0, 8.0, 1.0, 5.0, 2.0, 8.0, 8.0]),
+        (
+            "max",
+            False,
+            "bottom",
+            True,
+            [0.625, 0.625, 1.0, 0.125, 0.625, 0.25, 1.0, 1.0],
+        ),
+        ("first", True, "bottom", False, [1.0, 2.0, 6.0, 5.0, 3.0, 4.0, 7.0, 8.0]),
+        (
+            "first",
+            True,
+            "bottom",
+            True,
+            [0.125, 0.25, 0.75, 0.625, 0.375, 0.5, 0.875, 1.0],
+        ),
+        ("first", False, "bottom", False, [3.0, 4.0, 6.0, 1.0, 5.0, 2.0, 7.0, 8.0]),
+        (
+            "first",
+            False,
+            "bottom",
+            True,
+            [0.375, 0.5, 0.75, 0.125, 0.625, 0.25, 0.875, 1.0],
+        ),
+        ("dense", True, "bottom", False, [1.0, 1.0, 4.0, 3.0, 1.0, 2.0, 4.0, 4.0]),
+        ("dense", True, "bottom", True, [0.25, 0.25, 1.0, 0.75, 0.25, 0.5, 1.0, 1.0]),
+        ("dense", False, "bottom", False, [3.0, 3.0, 4.0, 1.0, 3.0, 2.0, 4.0, 4.0]),
+        ("dense", False, "bottom", True, [0.75, 0.75, 1.0, 0.25, 0.75, 0.5, 1.0, 1.0]),
+    ],
+)
+def test_rank_args_missing(grps, vals, ties_method, ascending, na_option, pct, exp):
+    key = np.repeat(grps, len(vals))
+
+    orig_vals = vals
+    vals = list(vals) * len(grps)
+    if isinstance(orig_vals, np.ndarray):
+        vals = np.array(vals, dtype=orig_vals.dtype)
+
+    df = DataFrame({"key": key, "val": vals})
+    result = df.groupby("key").rank(
+        method=ties_method, ascending=ascending, na_option=na_option, pct=pct
+    )
+
+    exp_df = DataFrame(exp * len(grps), columns=["val"])
+    tm.assert_frame_equal(result, exp_df)
+
+
+@pytest.mark.parametrize(
+    "pct,exp", [(False, [3.0, 3.0, 3.0, 3.0, 3.0]), (True, [0.6, 0.6, 0.6, 0.6, 0.6])]
+)
+def test_rank_resets_each_group(pct, exp):
+    df = DataFrame(
+        {"key": ["a", "a", "a", "a", "a", "b", "b", "b", "b", "b"], "val": [1] * 10}
+    )
+    result = df.groupby("key").rank(pct=pct)
+    exp_df = DataFrame(exp * 2, columns=["val"])
+    tm.assert_frame_equal(result, exp_df)
+
+
+@pytest.mark.parametrize(
+    "dtype", ["int64", "int32", "uint64", "uint32", "float64", "float32"]
+)
+@pytest.mark.parametrize("upper", [True, False])
+def test_rank_avg_even_vals(dtype, upper):
+    if upper:
+        # use IntegerDtype/FloatingDtype
+        dtype = dtype[0].upper() + dtype[1:]
+        dtype = dtype.replace("Ui", "UI")
+    df = DataFrame({"key": ["a"] * 4, "val": [1] * 4})
+    df["val"] = df["val"].astype(dtype)
+    assert df["val"].dtype == dtype
+
+    result = df.groupby("key").rank()
+    exp_df = DataFrame([2.5, 2.5, 2.5, 2.5], columns=["val"])
+    if upper:
+        exp_df = exp_df.astype("Float64")
+    tm.assert_frame_equal(result, exp_df)
+
+
+@pytest.mark.parametrize("ties_method", ["average", "min", "max", "first", "dense"])
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("na_option", ["keep", "top", "bottom"])
+@pytest.mark.parametrize("pct", [True, False])
+@pytest.mark.parametrize(
+    "vals", [["bar", "bar", "foo", "bar", "baz"], ["bar", np.nan, "foo", np.nan, "baz"]]
+)
+def test_rank_object_dtype(ties_method, ascending, na_option, pct, vals):
+    df = DataFrame({"key": ["foo"] * 5, "val": vals})
+    mask = df["val"].isna()
+
+    gb = df.groupby("key")
+    res = gb.rank(method=ties_method, ascending=ascending, na_option=na_option, pct=pct)
+
+    # construct our expected by using numeric values with the same ordering
+    if mask.any():
+        df2 = DataFrame({"key": ["foo"] * 5, "val": [0, np.nan, 2, np.nan, 1]})
+    else:
+        df2 = DataFrame({"key": ["foo"] * 5, "val": [0, 0, 2, 0, 1]})
+
+    gb2 = df2.groupby("key")
+    alt = gb2.rank(
+        method=ties_method, ascending=ascending, na_option=na_option, pct=pct
+    )
+
+    tm.assert_frame_equal(res, alt)
+
+
+@pytest.mark.parametrize("na_option", [True, "bad", 1])
+@pytest.mark.parametrize("ties_method", ["average", "min", "max", "first", "dense"])
+@pytest.mark.parametrize("ascending", [True, False])
+@pytest.mark.parametrize("pct", [True, False])
+@pytest.mark.parametrize(
+    "vals",
+    [
+        ["bar", "bar", "foo", "bar", "baz"],
+        ["bar", np.nan, "foo", np.nan, "baz"],
+        [1, np.nan, 2, np.nan, 3],
+    ],
+)
+def test_rank_naoption_raises(ties_method, ascending, na_option, pct, vals):
+    df = DataFrame({"key": ["foo"] * 5, "val": vals})
+    msg = "na_option must be one of 'keep', 'top', or 'bottom'"
+
+    with pytest.raises(ValueError, match=msg):
+        df.groupby("key").rank(
+            method=ties_method, ascending=ascending, na_option=na_option, pct=pct
+        )
+
+
+def test_rank_empty_group():
+    # see gh-22519
+    column = "A"
+    df = DataFrame({"A": [0, 1, 0], "B": [1.0, np.nan, 2.0]})
+
+    result = df.groupby(column).B.rank(pct=True)
+    expected = Series([0.5, np.nan, 1.0], name="B")
+    tm.assert_series_equal(result, expected)
+
+    result = df.groupby(column).rank(pct=True)
+    expected = DataFrame({"B": [0.5, np.nan, 1.0]})
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "input_key,input_value,output_value",
+    [
+        ([1, 2], [1, 1], [1.0, 1.0]),
+        ([1, 1, 2, 2], [1, 2, 1, 2], [0.5, 1.0, 0.5, 1.0]),
+        ([1, 1, 2, 2], [1, 2, 1, np.nan], [0.5, 1.0, 1.0, np.nan]),
+        ([1, 1, 2], [1, 2, np.nan], [0.5, 1.0, np.nan]),
+    ],
+)
+def test_rank_zero_div(input_key, input_value, output_value):
+    # GH 23666
+    df = DataFrame({"A": input_key, "B": input_value})
+
+    result = df.groupby("A").rank(method="dense", pct=True)
+    expected = DataFrame({"B": output_value})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_rank_min_int():
+    # GH-32859
+    df = DataFrame(
+        {
+            "grp": [1, 1, 2],
+            "int_col": [
+                np.iinfo(np.int64).min,
+                np.iinfo(np.int64).max,
+                np.iinfo(np.int64).min,
+            ],
+            "datetimelike": [NaT, datetime(2001, 1, 1), NaT],
+        }
+    )
+
+    result = df.groupby("grp").rank()
+    expected = DataFrame(
+        {"int_col": [1.0, 2.0, 1.0], "datetimelike": [np.NaN, 1.0, np.NaN]}
+    )
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("use_nan", [True, False])
+def test_rank_pct_equal_values_on_group_transition(use_nan):
+    # GH#40518
+    fill_value = np.nan if use_nan else 3
+    df = DataFrame(
+        [
+            [-1, 1],
+            [-1, 2],
+            [1, fill_value],
+            [-1, fill_value],
+        ],
+        columns=["group", "val"],
+    )
+    result = df.groupby(["group"])["val"].rank(
+        method="dense",
+        pct=True,
+    )
+    if use_nan:
+        expected = Series([0.5, 1, np.nan, np.nan], name="val")
+    else:
+        expected = Series([1 / 3, 2 / 3, 1, 1], name="val")
+
+    tm.assert_series_equal(result, expected)
+
+
+def test_rank_multiindex():
+    # GH27721
+    df = concat(
+        {
+            "a": DataFrame({"col1": [3, 4], "col2": [1, 2]}),
+            "b": DataFrame({"col3": [5, 6], "col4": [7, 8]}),
+        },
+        axis=1,
+    )
+
+    gb = df.groupby(level=0, axis=1)
+    result = gb.rank(axis=1)
+
+    expected = concat(
+        [
+            df["a"].rank(axis=1),
+            df["b"].rank(axis=1),
+        ],
+        axis=1,
+        keys=["a", "b"],
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_axis0_rank_axis1():
+    # GH#41320
+    df = DataFrame(
+        {0: [1, 3, 5, 7], 1: [2, 4, 6, 8], 2: [1.5, 3.5, 5.5, 7.5]},
+        index=["a", "a", "b", "b"],
+    )
+    gb = df.groupby(level=0, axis=0)
+
+    res = gb.rank(axis=1)
+
+    # This should match what we get when "manually" operating group-by-group
+    expected = concat([df.loc["a"].rank(axis=1), df.loc["b"].rank(axis=1)], axis=0)
+    tm.assert_frame_equal(res, expected)
+
+    # check that we haven't accidentally written a case that coincidentally
+    # matches rank(axis=0)
+    alt = gb.rank(axis=0)
+    assert not alt.equals(expected)
+
+
+def test_groupby_axis0_cummax_axis1():
+    # case where groupby axis is 0 and axis keyword in transform is 1
+
+    # df has mixed dtype -> multiple blocks
+    df = DataFrame(
+        {0: [1, 3, 5, 7], 1: [2, 4, 6, 8], 2: [1.5, 3.5, 5.5, 7.5]},
+        index=["a", "a", "b", "b"],
+    )
+    gb = df.groupby(level=0, axis=0)
+
+    cmax = gb.cummax(axis=1)
+    expected = df[[0, 1]].astype(np.float64)
+    expected[2] = expected[1]
+    tm.assert_frame_equal(cmax, expected)
+
+
+def test_non_unique_index():
+    # GH 16577
+    df = DataFrame(
+        {"A": [1.0, 2.0, 3.0, np.nan], "value": 1.0},
+        index=[pd.Timestamp("20170101", tz="US/Eastern")] * 4,
+    )
+    result = df.groupby([df.index, "A"]).value.rank(ascending=True, pct=True)
+    expected = Series(
+        [1.0, 1.0, 1.0, np.nan],
+        index=[pd.Timestamp("20170101", tz="US/Eastern")] * 4,
+        name="value",
+    )
+    tm.assert_series_equal(result, expected)
+
+
+def test_rank_categorical():
+    cat = pd.Categorical(["a", "a", "b", np.nan, "c", "b"], ordered=True)
+    cat2 = pd.Categorical([1, 2, 3, np.nan, 4, 5], ordered=True)
+
+    df = DataFrame({"col1": [0, 1, 0, 1, 0, 1], "col2": cat, "col3": cat2})
+
+    gb = df.groupby("col1")
+
+    res = gb.rank()
+
+    expected = df.astype(object).groupby("col1").rank()
+    tm.assert_frame_equal(res, expected)

videochat2/lib/python3.10/site-packages/pandas/tests/groupby/test_sample.py

@@ -0,0 +1,154 @@
+import pytest
+
+from pandas import (
+    DataFrame,
+    Index,
+    Series,
+)
+import pandas._testing as tm
+
+
+@pytest.mark.parametrize("n, frac", [(2, None), (None, 0.2)])
+def test_groupby_sample_balanced_groups_shape(n, frac):
+    values = [1] * 10 + [2] * 10
+    df = DataFrame({"a": values, "b": values})
+
+    result = df.groupby("a").sample(n=n, frac=frac)
+    values = [1] * 2 + [2] * 2
+    expected = DataFrame({"a": values, "b": values}, index=result.index)
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby("a")["b"].sample(n=n, frac=frac)
+    expected = Series(values, name="b", index=result.index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_sample_unbalanced_groups_shape():
+    values = [1] * 10 + [2] * 20
+    df = DataFrame({"a": values, "b": values})
+
+    result = df.groupby("a").sample(n=5)
+    values = [1] * 5 + [2] * 5
+    expected = DataFrame({"a": values, "b": values}, index=result.index)
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby("a")["b"].sample(n=5)
+    expected = Series(values, name="b", index=result.index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_sample_index_value_spans_groups():
+    values = [1] * 3 + [2] * 3
+    df = DataFrame({"a": values, "b": values}, index=[1, 2, 2, 2, 2, 2])
+
+    result = df.groupby("a").sample(n=2)
+    values = [1] * 2 + [2] * 2
+    expected = DataFrame({"a": values, "b": values}, index=result.index)
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby("a")["b"].sample(n=2)
+    expected = Series(values, name="b", index=result.index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_sample_n_and_frac_raises():
+    df = DataFrame({"a": [1, 2], "b": [1, 2]})
+    msg = "Please enter a value for `frac` OR `n`, not both"
+
+    with pytest.raises(ValueError, match=msg):
+        df.groupby("a").sample(n=1, frac=1.0)
+
+    with pytest.raises(ValueError, match=msg):
+        df.groupby("a")["b"].sample(n=1, frac=1.0)
+
+
+def test_groupby_sample_frac_gt_one_without_replacement_raises():
+    df = DataFrame({"a": [1, 2], "b": [1, 2]})
+    msg = "Replace has to be set to `True` when upsampling the population `frac` > 1."
+
+    with pytest.raises(ValueError, match=msg):
+        df.groupby("a").sample(frac=1.5, replace=False)
+
+    with pytest.raises(ValueError, match=msg):
+        df.groupby("a")["b"].sample(frac=1.5, replace=False)
+
+
+@pytest.mark.parametrize("n", [-1, 1.5])
+def test_groupby_sample_invalid_n_raises(n):
+    df = DataFrame({"a": [1, 2], "b": [1, 2]})
+
+    if n < 0:
+        msg = "A negative number of rows requested. Please provide `n` >= 0."
+    else:
+        msg = "Only integers accepted as `n` values"
+
+    with pytest.raises(ValueError, match=msg):
+        df.groupby("a").sample(n=n)
+
+    with pytest.raises(ValueError, match=msg):
+        df.groupby("a")["b"].sample(n=n)
+
+
+def test_groupby_sample_oversample():
+    values = [1] * 10 + [2] * 10
+    df = DataFrame({"a": values, "b": values})
+
+    result = df.groupby("a").sample(frac=2.0, replace=True)
+    values = [1] * 20 + [2] * 20
+    expected = DataFrame({"a": values, "b": values}, index=result.index)
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby("a")["b"].sample(frac=2.0, replace=True)
+    expected = Series(values, name="b", index=result.index)
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_sample_without_n_or_frac():
+    values = [1] * 10 + [2] * 10
+    df = DataFrame({"a": values, "b": values})
+
+    result = df.groupby("a").sample(n=None, frac=None)
+    expected = DataFrame({"a": [1, 2], "b": [1, 2]}, index=result.index)
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby("a")["b"].sample(n=None, frac=None)
+    expected = Series([1, 2], name="b", index=result.index)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "index, expected_index",
+    [(["w", "x", "y", "z"], ["w", "w", "y", "y"]), ([3, 4, 5, 6], [3, 3, 5, 5])],
+)
+def test_groupby_sample_with_weights(index, expected_index):
+    # GH 39927 - tests for integer index needed
+    values = [1] * 2 + [2] * 2
+    df = DataFrame({"a": values, "b": values}, index=Index(index))
+
+    result = df.groupby("a").sample(n=2, replace=True, weights=[1, 0, 1, 0])
+    expected = DataFrame({"a": values, "b": values}, index=Index(expected_index))
+    tm.assert_frame_equal(result, expected)
+
+    result = df.groupby("a")["b"].sample(n=2, replace=True, weights=[1, 0, 1, 0])
+    expected = Series(values, name="b", index=Index(expected_index))
+    tm.assert_series_equal(result, expected)
+
+
+def test_groupby_sample_with_selections():
+    # GH 39928
+    values = [1] * 10 + [2] * 10
+    df = DataFrame({"a": values, "b": values, "c": values})
+
+    result = df.groupby("a")[["b", "c"]].sample(n=None, frac=None)
+    expected = DataFrame({"b": [1, 2], "c": [1, 2]}, index=result.index)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_sample_with_empty_inputs():
+    # GH48459
+    df = DataFrame({"a": [], "b": []})
+    groupby_df = df.groupby("a")
+
+    result = groupby_df.sample()
+    expected = df
+    tm.assert_frame_equal(result, expected)