groupby.py

from functools import partial
from itertools import product
from string import ascii_letters

import numpy as np

from pandas import Categorical
from pandas import DataFrame
from pandas import Index
from pandas import MultiIndex
from pandas import NA
from pandas import Series
from pandas import Timestamp
from pandas import date_range
from pandas import period_range
from pandas import to_timedelta

from .pandas_vb_common import tm

method_blocklist = {
    "object": {
        "diff",
        "median",
        "prod",
        "sem",
        "cumsum",
        "sum",
        "cummin",
        "mean",
        "max",
        "skew",
        "cumprod",
        "cummax",
        "pct_change",
        "min",
        "var",
        "describe",
        "std",
        "quantile",
    },
    "datetime": {
        "median",
        "prod",
        "sem",
        "cumsum",
        "sum",
        "mean",
        "skew",
        "cumprod",
        "cummax",
        "pct_change",
        "var",
        "describe",
        "std",
    },
}


class ApplyDictReturn:
    def setup(self):
        self.labels = np.arange(1000).repeat(10)
        self.data = Series(np.random.randn(len(self.labels)))

    def time_groupby_apply_dict_return(self):
        self.data.groupby(self.labels).apply(
            lambda x: {"first": x.values[0], "last": x.values[-1]}
        )


class Apply:
    param_names = ["factor"]
    params = [4, 5]

    def setup(self, factor):
        N = 10**factor
        # two cases:
        # - small groups: small data (N**4) + many labels (2000) -> average group
        #   size of 5 (-> larger overhead of slicing method)
        # - larger groups: larger data (N**5) + fewer labels (20) -> average group
        #   size of 5000
        labels = np.random.randint(0, 2000 if factor == 4 else 20, size=N)
        labels2 = np.random.randint(0, 3, size=N)
        df = DataFrame(
            {
                "key": labels,
                "key2": labels2,
                "value1": np.random.randn(N),
                "value2": ["foo", "bar", "baz", "qux"] * (N // 4),
            }
        )
        self.df = df

    def time_scalar_function_multi_col(self, factor):
        self.df.groupby(["key", "key2"]).apply(lambda x: 1)

    def time_scalar_function_single_col(self, factor):
        self.df.groupby("key").apply(lambda x: 1)

    @staticmethod
    def df_copy_function(g):
        # ensure that the group name is available (see GH #15062)
        g.name
        return g.copy()

    def time_copy_function_multi_col(self, factor):
        self.df.groupby(["key", "key2"]).apply(self.df_copy_function)

    def time_copy_overhead_single_col(self, factor):
        self.df.groupby("key").apply(self.df_copy_function)


class ApplyNonUniqueUnsortedIndex:
    def setup(self):
        # GH 46527
        # unsorted and non-unique index
        idx = np.arange(100)[::-1]
        idx = Index(np.repeat(idx, 200), name="key")
        self.df = DataFrame(np.random.randn(len(idx), 10), index=idx)

    def time_groupby_apply_non_unique_unsorted_index(self):
        self.df.groupby("key", group_keys=False).apply(lambda x: x)


class Groups:
    param_names = ["key"]
    params = ["int64_small", "int64_large", "object_small", "object_large"]

    def setup_cache(self):
        size = 10**6
        data = {
            "int64_small": Series(np.random.randint(0, 100, size=size)),
            "int64_large": Series(np.random.randint(0, 10000, size=size)),
            "object_small": Series(
                tm.makeStringIndex(100).take(np.random.randint(0, 100, size=size))
            ),
            "object_large": Series(
                tm.makeStringIndex(10000).take(np.random.randint(0, 10000, size=size))
            ),
        }
        return data

    def setup(self, data, key):
        self.ser = data[key]

    def time_series_groups(self, data, key):
        self.ser.groupby(self.ser).groups

    def time_series_indices(self, data, key):
        self.ser.groupby(self.ser).indices


class GroupManyLabels:
    params = [1, 1000]
    param_names = ["ncols"]

    def setup(self, ncols):
        N = 1000
        data = np.random.randn(N, ncols)
        self.labels = np.random.randint(0, 100, size=N)
        self.df = DataFrame(data)

    def time_sum(self, ncols):
        self.df.groupby(self.labels).sum()


class Nth:
    param_names = ["dtype"]
    params = ["float32", "float64", "datetime", "object"]

    def setup(self, dtype):
        N = 10**5
        # with datetimes (GH7555)
        if dtype == "datetime":
            values = date_range("1/1/2011", periods=N, freq="s")
        elif dtype == "object":
            values = ["foo"] * N
        else:
            values = np.arange(N).astype(dtype)

        key = np.arange(N)
        self.df = DataFrame({"key": key, "values": values})
        self.df.iloc[1, 1] = np.nan  # insert missing data

    def time_frame_nth_any(self, dtype):
        self.df.groupby("key").nth(0, dropna="any")

    def time_groupby_nth_all(self, dtype):
        self.df.groupby("key").nth(0, dropna="all")

    def time_frame_nth(self, dtype):
        self.df.groupby("key").nth(0)

    def time_series_nth_any(self, dtype):
        self.df["values"].groupby(self.df["key"]).nth(0, dropna="any")

    def time_series_nth_all(self, dtype):
        self.df["values"].groupby(self.df["key"]).nth(0, dropna="all")

    def time_series_nth(self, dtype):
        self.df["values"].groupby(self.df["key"]).nth(0)


class DateAttributes:
    def setup(self):
        rng = date_range("1/1/2000", "12/31/2005", freq="H")
        self.year, self.month, self.day = rng.year, rng.month, rng.day
        self.ts = Series(np.random.randn(len(rng)), index=rng)

    def time_len_groupby_object(self):
        len(self.ts.groupby([self.year, self.month, self.day]))


class Int64:
    def setup(self):
        arr = np.random.randint(-1 << 12, 1 << 12, (1 << 17, 5))
        i = np.random.choice(len(arr), len(arr) * 5)
        arr = np.vstack((arr, arr[i]))
        i = np.random.permutation(len(arr))
        arr = arr[i]
        self.cols = list("abcde")
        self.df = DataFrame(arr, columns=self.cols)
        self.df["jim"], self.df["joe"] = np.random.randn(2, len(self.df)) * 10

    def time_overflow(self):
        self.df.groupby(self.cols).max()


class CountMultiDtype:
    def setup_cache(self):
        n = 10000
        offsets = np.random.randint(n, size=n).astype("timedelta64[ns]")
        dates = np.datetime64("now") + offsets
        dates[np.random.rand(n) > 0.5] = np.datetime64("nat")
        offsets[np.random.rand(n) > 0.5] = np.timedelta64("nat")
        value2 = np.random.randn(n)
        value2[np.random.rand(n) > 0.5] = np.nan
        obj = np.random.choice(list("ab"), size=n).astype(object)
        obj[np.random.randn(n) > 0.5] = np.nan
        df = DataFrame(
            {
                "key1": np.random.randint(0, 500, size=n),
                "key2": np.random.randint(0, 100, size=n),
                "dates": dates,
                "value2": value2,
                "value3": np.random.randn(n),
                "ints": np.random.randint(0, 1000, size=n),
                "obj": obj,
                "offsets": offsets,
            }
        )
        return df

    def time_multi_count(self, df):
        df.groupby(["key1", "key2"]).count()


class CountMultiInt:
    def setup_cache(self):
        n = 10000
        df = DataFrame(
            {
                "key1": np.random.randint(0, 500, size=n),
                "key2": np.random.randint(0, 100, size=n),
                "ints": np.random.randint(0, 1000, size=n),
                "ints2": np.random.randint(0, 1000, size=n),
            }
        )
        return df

    def time_multi_int_count(self, df):
        df.groupby(["key1", "key2"]).count()

    def time_multi_int_nunique(self, df):
        df.groupby(["key1", "key2"]).nunique()


class AggFunctions:
    def setup_cache(self):
        N = 10**5
        fac1 = np.array(["A", "B", "C"], dtype="O")
        fac2 = np.array(["one", "two"], dtype="O")
        df = DataFrame(
            {
                "key1": fac1.take(np.random.randint(0, 3, size=N)),
                "key2": fac2.take(np.random.randint(0, 2, size=N)),
                "value1": np.random.randn(N),
                "value2": np.random.randn(N),
                "value3": np.random.randn(N),
            }
        )
        return df

    def time_different_str_functions(self, df):
        df.groupby(["key1", "key2"]).agg(
            {"value1": "mean", "value2": "var", "value3": "sum"}
        )

    def time_different_numpy_functions(self, df):
        df.groupby(["key1", "key2"]).agg(
            {"value1": np.mean, "value2": np.var, "value3": np.sum}
        )

    def time_different_python_functions_multicol(self, df):
        df.groupby(["key1", "key2"]).agg([sum, min, max])

    def time_different_python_functions_singlecol(self, df):
        df.groupby("key1")[["value1", "value2", "value3"]].agg([sum, min, max])


class GroupStrings:
    def setup(self):
        n = 2 * 10**5
        alpha = list(map("".join, product(ascii_letters, repeat=4)))
        data = np.random.choice(alpha, (n // 5, 4), replace=False)
        data = np.repeat(data, 5, axis=0)
        self.df = DataFrame(data, columns=list("abcd"))
        self.df["joe"] = (np.random.randn(len(self.df)) * 10).round(3)
        self.df = self.df.sample(frac=1).reset_index(drop=True)

    def time_multi_columns(self):
        self.df.groupby(list("abcd")).max()


class MultiColumn:
    def setup_cache(self):
        N = 10**5
        key1 = np.tile(np.arange(100, dtype=object), 1000)
        key2 = key1.copy()
        np.random.shuffle(key1)
        np.random.shuffle(key2)
        df = DataFrame(
            {
                "key1": key1,
                "key2": key2,
                "data1": np.random.randn(N),
                "data2": np.random.randn(N),
            }
        )
        return df

    def time_lambda_sum(self, df):
        df.groupby(["key1", "key2"]).agg(lambda x: x.values.sum())

    def time_cython_sum(self, df):
        df.groupby(["key1", "key2"]).sum()

    def time_col_select_lambda_sum(self, df):
        df.groupby(["key1", "key2"])["data1"].agg(lambda x: x.values.sum())

    def time_col_select_numpy_sum(self, df):
        df.groupby(["key1", "key2"])["data1"].agg(np.sum)


class Size:
    def setup(self):
        n = 10**5
        offsets = np.random.randint(n, size=n).astype("timedelta64[ns]")
        dates = np.datetime64("now") + offsets
        self.df = DataFrame(
            {
                "key1": np.random.randint(0, 500, size=n),
                "key2": np.random.randint(0, 100, size=n),
                "value1": np.random.randn(n),
                "value2": np.random.randn(n),
                "value3": np.random.randn(n),
                "dates": dates,
            }
        )
        self.draws = Series(np.random.randn(n))
        labels = Series(["foo", "bar", "baz", "qux"] * (n // 4))
        self.cats = labels.astype("category")

    def time_multi_size(self):
        self.df.groupby(["key1", "key2"]).size()

    def time_category_size(self):
        self.draws.groupby(self.cats).size()


class Shift:
    def setup(self):
        N = 18
        self.df = DataFrame({"g": ["a", "b"] * 9, "v": list(range(N))})

    def time_defaults(self):
        self.df.groupby("g").shift()

    def time_fill_value(self):
        self.df.groupby("g").shift(fill_value=99)


class FillNA:
    def setup(self):
        N = 100
        self.df = DataFrame(
            {"group": [1] * N + [2] * N, "value": [np.nan, 1.0] * N}
        ).set_index("group")

    def time_df_ffill(self):
        self.df.groupby("group").fillna(method="ffill")

    def time_df_bfill(self):
        self.df.groupby("group").fillna(method="bfill")

    def time_srs_ffill(self):
        self.df.groupby("group")["value"].fillna(method="ffill")

    def time_srs_bfill(self):
        self.df.groupby("group")["value"].fillna(method="bfill")


class GroupByMethods:
    param_names = ["dtype", "method", "application", "ncols"]
    params = [
        ["int", "int16", "float", "object", "datetime", "uint"],
        [
            "all",
            "any",
            "bfill",
            "count",
            "cumcount",
            "cummax",
            "cummin",
            "cumprod",
            "cumsum",
            "describe",
            "diff",
            "ffill",
            "first",
            "head",
            "last",
            "max",
            "min",
            "median",
            "mean",
            "nunique",
            "pct_change",
            "prod",
            "quantile",
            "rank",
            "sem",
            "shift",
            "size",
            "skew",
            "std",
            "sum",
            "tail",
            "unique",
            "value_counts",
            "var",
        ],
        ["direct", "transformation"],
        [1, 5],
    ]

    def setup(self, dtype, method, application, ncols):
        if method in method_blocklist.get(dtype, {}):
            raise NotImplementedError  # skip benchmark

        if ncols != 1 and method in ["value_counts", "unique"]:
            # DataFrameGroupBy doesn't have these methods
            raise NotImplementedError

        if application == "transformation" and method in [
            "describe",
            "head",
            "tail",
            "unique",
            "value_counts",
            "size",
        ]:
            # DataFrameGroupBy doesn't have these methods
            raise NotImplementedError

        if method == "describe":
            ngroups = 20
        elif method == "skew":
            ngroups = 100
        else:
            ngroups = 1000
        size = ngroups * 2
        rng = np.arange(ngroups).reshape(-1, 1)
        rng = np.broadcast_to(rng, (len(rng), ncols))
        taker = np.random.randint(0, ngroups, size=size)
        values = rng.take(taker, axis=0)
        if dtype == "int":
            key = np.random.randint(0, size, size=size)
        elif dtype in ("int16", "uint"):
            key = np.random.randint(0, size, size=size, dtype=dtype)
        elif dtype == "float":
            key = np.concatenate(
                [np.random.random(ngroups) * 0.1, np.random.random(ngroups) * 10.0]
            )
        elif dtype == "object":
            key = ["foo"] * size
        elif dtype == "datetime":
            key = date_range("1/1/2011", periods=size, freq="s")

        cols = [f"values{n}" for n in range(ncols)]
        df = DataFrame(values, columns=cols)
        df["key"] = key

        if len(cols) == 1:
            cols = cols[0]

        if application == "transformation":
            self.as_group_method = lambda: df.groupby("key")[cols].transform(method)
            self.as_field_method = lambda: df.groupby(cols)["key"].transform(method)
        else:
            self.as_group_method = getattr(df.groupby("key")[cols], method)
            self.as_field_method = getattr(df.groupby(cols)["key"], method)

    def time_dtype_as_group(self, dtype, method, application, ncols):
        self.as_group_method()

    def time_dtype_as_field(self, dtype, method, application, ncols):
        self.as_field_method()


class GroupByCythonAgg:
    """
    Benchmarks specifically targeting our cython aggregation algorithms
    (using a big enough dataframe with simple key, so a large part of the
    time is actually spent in the grouped aggregation).
    """

    param_names = ["dtype", "method"]
    params = [
        ["float64"],
        [
            "sum",
            "prod",
            "min",
            "max",
            "mean",
            "median",
            "var",
            "first",
            "last",
            "any",
            "all",
        ],
    ]

    def setup(self, dtype, method):
        N = 1_000_000
        df = DataFrame(np.random.randn(N, 10), columns=list("abcdefghij"))
        df["key"] = np.random.randint(0, 100, size=N)
        self.df = df

    def time_frame_agg(self, dtype, method):
        self.df.groupby("key").agg(method)


class GroupByCythonAggEaDtypes:
    """
    Benchmarks specifically targeting our cython aggregation algorithms
    (using a big enough dataframe with simple key, so a large part of the
    time is actually spent in the grouped aggregation).
    """

    param_names = ["dtype", "method"]
    params = [
        ["Float64", "Int64", "Int32"],
        [
            "sum",
            "prod",
            "min",
            "max",
            "mean",
            "median",
            "var",
            "first",
            "last",
            "any",
            "all",
        ],
    ]

    def setup(self, dtype, method):
        N = 1_000_000
        df = DataFrame(
            np.random.randint(0, high=100, size=(N, 10)),
            columns=list("abcdefghij"),
            dtype=dtype,
        )
        df.loc[list(range(1, N, 5)), list("abcdefghij")] = NA
        df["key"] = np.random.randint(0, 100, size=N)
        self.df = df

    def time_frame_agg(self, dtype, method):
        self.df.groupby("key").agg(method)


class Cumulative:
    param_names = ["dtype", "method", "with_nans"]
    params = [
        ["float64", "int64", "Float64", "Int64"],
        ["cummin", "cummax", "cumsum"],
        [True, False],
    ]

    def setup(self, dtype, method, with_nans):
        if with_nans and dtype == "int64":
            raise NotImplementedError("Construction of df would raise")

        N = 500_000
        keys = np.random.randint(0, 100, size=N)
        vals = np.random.randint(-10, 10, (N, 5))

        if with_nans:
            null_vals = vals.astype(float, copy=True)
            null_vals[::2, :] = np.nan
            null_vals[::3, :] = np.nan
            df = DataFrame(null_vals, columns=list("abcde"), dtype=dtype)
            df["key"] = keys
            self.df = df
        else:
            df = DataFrame(vals, columns=list("abcde")).astype(dtype, copy=False)
            df["key"] = keys
            self.df = df

    def time_frame_transform(self, dtype, method, with_nans):
        self.df.groupby("key").transform(method)


class RankWithTies:
    # GH 21237
    param_names = ["dtype", "tie_method"]
    params = [
        ["float64", "float32", "int64", "datetime64"],
        ["first", "average", "dense", "min", "max"],
    ]

    def setup(self, dtype, tie_method):
        N = 10**4
        if dtype == "datetime64":
            data = np.array([Timestamp("2011/01/01")] * N, dtype=dtype)
        else:
            data = np.array([1] * N, dtype=dtype)
        self.df = DataFrame({"values": data, "key": ["foo"] * N})

    def time_rank_ties(self, dtype, tie_method):
        self.df.groupby("key").rank(method=tie_method)


class Float32:
    # GH 13335
    def setup(self):
        tmp1 = (np.random.random(10000) * 0.1).astype(np.float32)
        tmp2 = (np.random.random(10000) * 10.0).astype(np.float32)
        tmp = np.concatenate((tmp1, tmp2))
        arr = np.repeat(tmp, 10)
        self.df = DataFrame({"a": arr, "b": arr})

    def time_sum(self):
        self.df.groupby(["a"])["b"].sum()


class String:
    # GH#41596
    param_names = ["dtype", "method"]
    params = [
        ["str", "string[python]"],
        [
            "sum",
            "min",
            "max",
            "first",
            "last",
            "any",
            "all",
        ],
    ]

    def setup(self, dtype, method):
        cols = list("abcdefghjkl")
        self.df = DataFrame(
            np.random.randint(0, 100, size=(10_000, len(cols))),
            columns=cols,
            dtype=dtype,
        )

    def time_str_func(self, dtype, method):
        self.df.groupby("a")[self.df.columns[1:]].agg(method)


class Categories:
    def setup(self):
        N = 10**5
        arr = np.random.random(N)
        data = {"a": Categorical(np.random.randint(10000, size=N)), "b": arr}
        self.df = DataFrame(data)
        data = {
            "a": Categorical(np.random.randint(10000, size=N), ordered=True),
            "b": arr,
        }
        self.df_ordered = DataFrame(data)
        data = {
            "a": Categorical(
                np.random.randint(100, size=N), categories=np.arange(10000)
            ),
            "b": arr,
        }
        self.df_extra_cat = DataFrame(data)

    def time_groupby_sort(self):
        self.df.groupby("a")["b"].count()

    def time_groupby_nosort(self):
        self.df.groupby("a", sort=False)["b"].count()

    def time_groupby_ordered_sort(self):
        self.df_ordered.groupby("a")["b"].count()

    def time_groupby_ordered_nosort(self):
        self.df_ordered.groupby("a", sort=False)["b"].count()

    def time_groupby_extra_cat_sort(self):
        self.df_extra_cat.groupby("a")["b"].count()

    def time_groupby_extra_cat_nosort(self):
        self.df_extra_cat.groupby("a", sort=False)["b"].count()


class Datelike:
    # GH 14338
    params = ["period_range", "date_range", "date_range_tz"]
    param_names = ["grouper"]

    def setup(self, grouper):
        N = 10**4
        rng_map = {
            "period_range": period_range,
            "date_range": date_range,
            "date_range_tz": partial(date_range, tz="US/Central"),
        }
        self.grouper = rng_map[grouper]("1900-01-01", freq="D", periods=N)
        self.df = DataFrame(np.random.randn(10**4, 2))

    def time_sum(self, grouper):
        self.df.groupby(self.grouper).sum()


class SumBools:
    # GH 2692
    def setup(self):
        N = 500
        self.df = DataFrame({"ii": range(N), "bb": [True] * N})

    def time_groupby_sum_booleans(self):
        self.df.groupby("ii").sum()


class SumMultiLevel:
    # GH 9049
    timeout = 120.0

    def setup(self):
        N = 50
        self.df = DataFrame(
            {"A": list(range(N)) * 2, "B": range(N * 2), "C": 1}
        ).set_index(["A", "B"])

    def time_groupby_sum_multiindex(self):
        self.df.groupby(level=[0, 1]).sum()


class Transform:
    def setup(self):
        n1 = 400
        n2 = 250
        index = MultiIndex(
            levels=[np.arange(n1), tm.makeStringIndex(n2)],
            codes=[np.repeat(range(n1), n2).tolist(), list(range(n2)) * n1],
            names=["lev1", "lev2"],
        )
        arr = np.random.randn(n1 * n2, 3)
        arr[::10000, 0] = np.nan
        arr[1::10000, 1] = np.nan
        arr[2::10000, 2] = np.nan
        data = DataFrame(arr, index=index, columns=["col1", "col20", "col3"])
        self.df = data

        n = 1000
        self.df_wide = DataFrame(
            np.random.randn(n, n),
            index=np.random.choice(range(10), n),
        )

        n = 1_000_000
        self.df_tall = DataFrame(
            np.random.randn(n, 3),
            index=np.random.randint(0, 5, n),
        )

        n = 20000
        self.df1 = DataFrame(
            np.random.randint(1, n, (n, 3)), columns=["jim", "joe", "jolie"]
        )
        self.df2 = self.df1.copy()
        self.df2["jim"] = self.df2["joe"]

        self.df3 = DataFrame(
            np.random.randint(1, (n / 10), (n, 3)), columns=["jim", "joe", "jolie"]
        )
        self.df4 = self.df3.copy()
        self.df4["jim"] = self.df4["joe"]

    def time_transform_lambda_max(self):
        self.df.groupby(level="lev1").transform(lambda x: max(x))

    def time_transform_ufunc_max(self):
        self.df.groupby(level="lev1").transform(np.max)

    def time_transform_lambda_max_tall(self):
        self.df_tall.groupby(level=0).transform(lambda x: np.max(x, axis=0))

    def time_transform_lambda_max_wide(self):
        self.df_wide.groupby(level=0).transform(lambda x: np.max(x, axis=0))

    def time_transform_multi_key1(self):
        self.df1.groupby(["jim", "joe"])["jolie"].transform("max")

    def time_transform_multi_key2(self):
        self.df2.groupby(["jim", "joe"])["jolie"].transform("max")

    def time_transform_multi_key3(self):
        self.df3.groupby(["jim", "joe"])["jolie"].transform("max")

    def time_transform_multi_key4(self):
        self.df4.groupby(["jim", "joe"])["jolie"].transform("max")


class TransformBools:
    def setup(self):
        N = 120000
        transition_points = np.sort(np.random.choice(np.arange(N), 1400))
        transitions = np.zeros(N, dtype=np.bool_)
        transitions[transition_points] = True
        self.g = transitions.cumsum()
        self.df = DataFrame({"signal": np.random.rand(N)})

    def time_transform_mean(self):
        self.df["signal"].groupby(self.g).transform(np.mean)


class TransformNaN:
    # GH 12737
    def setup(self):
        self.df_nans = DataFrame(
            {"key": np.repeat(np.arange(1000), 10), "B": np.nan, "C": np.nan}
        )
        self.df_nans.loc[4::10, "B":"C"] = 5

    def time_first(self):
        self.df_nans.groupby("key").transform("first")


class TransformEngine:
    param_names = ["parallel"]
    params = [[True, False]]

    def setup(self, parallel):
        N = 10**3
        data = DataFrame(
            {0: [str(i) for i in range(100)] * N, 1: list(range(100)) * N},
            columns=[0, 1],
        )
        self.parallel = parallel
        self.grouper = data.groupby(0)

    def time_series_numba(self, parallel):
        def function(values, index):
            return values * 5

        self.grouper[1].transform(
            function, engine="numba", engine_kwargs={"parallel": self.parallel}
        )

    def time_series_cython(self, parallel):
        def function(values):
            return values * 5

        self.grouper[1].transform(function, engine="cython")

    def time_dataframe_numba(self, parallel):
        def function(values, index):
            return values * 5

        self.grouper.transform(
            function, engine="numba", engine_kwargs={"parallel": self.parallel}
        )

    def time_dataframe_cython(self, parallel):
        def function(values):
            return values * 5

        self.grouper.transform(function, engine="cython")


class AggEngine:
    param_names = ["parallel"]
    params = [[True, False]]

    def setup(self, parallel):
        N = 10**3
        data = DataFrame(
            {0: [str(i) for i in range(100)] * N, 1: list(range(100)) * N},
            columns=[0, 1],
        )
        self.parallel = parallel
        self.grouper = data.groupby(0)

    def time_series_numba(self, parallel):
        def function(values, index):
            total = 0
            for i, value in enumerate(values):
                if i % 2:
                    total += value + 5
                else:
                    total += value * 2
            return total

        self.grouper[1].agg(
            function, engine="numba", engine_kwargs={"parallel": self.parallel}
        )

    def time_series_cython(self, parallel):
        def function(values):
            total = 0
            for i, value in enumerate(values):
                if i % 2:
                    total += value + 5
                else:
                    total += value * 2
            return total

        self.grouper[1].agg(function, engine="cython")

    def time_dataframe_numba(self, parallel):
        def function(values, index):
            total = 0
            for i, value in enumerate(values):
                if i % 2:
                    total += value + 5
                else:
                    total += value * 2
            return total

        self.grouper.agg(
            function, engine="numba", engine_kwargs={"parallel": self.parallel}
        )

    def time_dataframe_cython(self, parallel):
        def function(values):
            total = 0
            for i, value in enumerate(values):
                if i % 2:
                    total += value + 5
                else:
                    total += value * 2
            return total

        self.grouper.agg(function, engine="cython")


class Sample:
    def setup(self):
        N = 10**3
        self.df = DataFrame({"a": np.zeros(N)})
        self.groups = np.arange(0, N)
        self.weights = np.ones(N)

    def time_sample(self):
        self.df.groupby(self.groups).sample(n=1)

    def time_sample_weights(self):
        self.df.groupby(self.groups).sample(n=1, weights=self.weights)


class Resample:
    # GH 28635
    def setup(self):
        num_timedeltas = 20_000
        num_groups = 3

        index = MultiIndex.from_product(
            [
                np.arange(num_groups),
                to_timedelta(np.arange(num_timedeltas), unit="s"),
            ],
            names=["groups", "timedeltas"],
        )
        data = np.random.randint(0, 1000, size=(len(index)))

        self.df = DataFrame(data, index=index).reset_index("timedeltas")
        self.df_multiindex = DataFrame(data, index=index)

    def time_resample(self):
        self.df.groupby(level="groups").resample("10s", on="timedeltas").mean()

    def time_resample_multiindex(self):
        self.df_multiindex.groupby(level="groups").resample(
            "10s", level="timedeltas"
        ).mean()


from .pandas_vb_common import setup  # noqa: F401 isort:skip