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 import pandas.core.nanops as 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 = getattr(np, fname) # numpy's equivalent function if f in [max, min]: warn = FutureWarning else: warn = None msg = "scalar (max|min) over the entire DataFrame" with tm.assert_produces_warning(warn, match=msg, check_stacklevel=False): # stacklevel can be thrown off because (i think) the stack # goes through some of numpy's C code. expected = gb.apply(npfunc) tm.assert_frame_equal(result, expected) with tm.assert_produces_warning(None): expected2 = gb.apply(lambda x: npfunc(x, axis=0)) 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)(), getattr(df, fname)()) 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", "datetime", "datetimetz", "timedelta", ], ) with tm.assert_produces_warning(FutureWarning, match="Dropping invalid"): result = getattr(gb, method)(numeric_only=False) 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") # cummin, cummax dont have numeric_only kwarg, always use False warn = None if method in ["cummin", "cummax"]: # these dont have numeric_only kwarg, always use False warn = FutureWarning elif method in ["min", "max"]: # these have numeric_only kwarg, but default to False warn = FutureWarning with tm.assert_produces_warning( warn, match="Dropping invalid columns", raise_on_extra_warnings=False ): result = getattr(gb, method)() tm.assert_index_equal(result.columns, expected_columns_numeric) # GH#41475 deprecated silently ignoring nuisance columns warn = None if len(expected_columns) < len(gb._obj_with_exclusions.columns): warn = FutureWarning with tm.assert_produces_warning(warn, match="Dropping invalid columns"): 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 # TODO: non-unique columns, as_index=False def test_idxmax(self, gb): # object dtype so idxmax goes through _aggregate_item_by_item # GH#5610 # non-cython calls should not include the grouper expected = DataFrame([[0.0], [np.nan]], columns=["B"], index=[1, 3]) expected.index.name = "A" msg = "The default value of numeric_only in DataFrameGroupBy.idxmax" with tm.assert_produces_warning(FutureWarning, match=msg): result = gb.idxmax() tm.assert_frame_equal(result, expected) def test_idxmin(self, gb): # object dtype so idxmax goes through _aggregate_item_by_item # GH#5610 # non-cython calls should not include the grouper expected = DataFrame([[0.0], [np.nan]], columns=["B"], index=[1, 3]) expected.index.name = "A" msg = "The default value of numeric_only in DataFrameGroupBy.idxmin" with tm.assert_produces_warning(FutureWarning, match=msg): result = gb.idxmin() tm.assert_frame_equal(result, expected) def test_mad(self, gb, gni): # mad expected = DataFrame([[0], [np.nan]], columns=["B"], index=[1, 3]) expected.index.name = "A" with tm.assert_produces_warning( FutureWarning, match="The 'mad' method is deprecated" ): result = gb.mad() tm.assert_frame_equal(result, expected) expected = DataFrame([[1, 0.0], [3, np.nan]], columns=["A", "B"], index=[0, 1]) with tm.assert_produces_warning( FutureWarning, match="The 'mad' method is deprecated" ): result = gni.mad() tm.assert_frame_equal(result, expected) 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 = pd.concat( [ df[df.A == 1].describe().unstack().to_frame().T, df[df.A == 3].describe().unstack().to_frame().T, ] ) expected.index = Index([0, 1]) 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(): df = DataFrame(np.random.randn(1000)) df.values[::2] = np.nan 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}]}), ("nth", {"df": [{"a": 1, "b": 2}, {"a": 2, "b": 4}], "args": [1]}), ("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]) @pytest.mark.filterwarnings("ignore:.*Select only valid:FutureWarning") 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 ("mad", "tshift"): pytest.skip("mad and tshift are deprecated") 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'" warn = FutureWarning if groupby_func == "skew" and not numeric_only else None with tm.assert_produces_warning(warn, match="Dropping of nuisance columns"): 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}) actual = df.groupby("key")["value"].cumprod() # if overflows, groupby product casts to float # while numpy passes back invalid values df["value"] = df["value"].astype(float) 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_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""" 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("method", ["nlargest", "nsmallest"]) def test_nlargest_and_smallest_noop(data, groups, 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 method == "nlargest": data = list(reversed(data)) ser = Series(data, name="a") result = getattr(ser.groupby(groups), method)(n=2) expected = Series(data, index=MultiIndex.from_arrays([groups, 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 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 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) 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]) def test_describe_with_duplicate_output_column_names(as_index): # GH 35314 df = DataFrame( { "a": [99, 99, 99, 88, 88, 88], "b": [1, 2, 3, 4, 5, 6], "c": [10, 20, 30, 40, 50, 60], }, columns=["a", "b", "b"], copy=False, ) expected = ( DataFrame.from_records( [ ("a", "count", 3.0, 3.0), ("a", "mean", 88.0, 99.0), ("a", "std", 0.0, 0.0), ("a", "min", 88.0, 99.0), ("a", "25%", 88.0, 99.0), ("a", "50%", 88.0, 99.0), ("a", "75%", 88.0, 99.0), ("a", "max", 88.0, 99.0), ("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), ("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] expected.index = Index([88, 99], name="a") if as_index: expected = expected.drop(columns=["a"], level=0) else: expected = expected.reset_index(drop=True) result = df.groupby("a", as_index=as_index).describe() 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, numeric_only_default, drops_nuisance, has_arg", [ ("all", False, False, False), ("any", False, False, False), ("bfill", False, False, False), ("corr", True, False, True), ("corrwith", True, False, True), ("cov", True, False, True), ("cummax", False, True, True), ("cummin", False, True, True), ("cumprod", True, True, True), ("cumsum", True, True, True), ("diff", False, False, False), ("ffill", False, False, False), ("fillna", False, False, False), ("first", False, False, True), ("idxmax", True, False, True), ("idxmin", True, False, True), ("last", False, False, True), ("max", False, True, True), ("mean", True, True, True), ("median", True, True, True), ("min", False, True, True), ("nth", False, False, False), ("nunique", False, False, False), ("pct_change", False, False, False), ("prod", True, True, True), ("quantile", True, False, True), ("sem", True, True, True), ("skew", True, False, True), ("std", True, True, True), ("sum", True, True, True), ("var", True, False, True), ], ) @pytest.mark.parametrize("numeric_only", [True, False, lib.no_default]) @pytest.mark.parametrize("keys", [["a1"], ["a1", "a2"]]) def test_deprecate_numeric_only( kernel, numeric_only_default, drops_nuisance, 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 ( # Cases where b does not appear in the result numeric_only is True or (numeric_only is lib.no_default and numeric_only_default) or drops_nuisance ): if numeric_only is True or (not numeric_only_default and not drops_nuisance): warn = None else: warn = FutureWarning if numeric_only is lib.no_default and numeric_only_default: msg = f"The default value of numeric_only in DataFrameGroupBy.{kernel}" else: msg = f"Dropping invalid columns in DataFrameGroupBy.{kernel}" with tm.assert_produces_warning(warn, match=msg): 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: assert numeric_only is not True assert numeric_only is not lib.no_default or numeric_only_default is False assert not drops_nuisance # kernels that are successful on any dtype were above; this will fail msg = ( "(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)" ) with pytest.raises(TypeError, 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 in ("backfill", "mad", "pad", "tshift"): pytest.skip("method is deprecated") elif 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", ) # 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: err_category = NotImplementedError err_msg = f"{groupby_func} does not implement numeric_only" if groupby_func.startswith("cum"): # cum ops already exhibit future behavior warn_category = None warn_msg = "" err_category = TypeError err_msg = f"{groupby_func} is not supported for object dtype" elif groupby_func == "skew": warn_category = FutureWarning warn_msg = "will raise a TypeError in the future" else: warn_category = FutureWarning warn_msg = "This will raise a TypeError" with tm.assert_produces_warning(warn_category, match=warn_msg): with pytest.raises(err_category, match=err_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) @pytest.mark.filterwarnings("ignore:.* is deprecated:FutureWarning") 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)