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import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
Series,
date_range,
)
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 df():
return DataFrame(
{
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
"C": np.random.default_rng(2).standard_normal(8),
"D": np.random.default_rng(2).standard_normal(8),
}
)
@pytest.fixture
def ts():
return Series(
np.random.default_rng(2).standard_normal(30),
index=date_range("2000-01-01", periods=30, freq="B"),
)
@pytest.fixture
def tsframe():
return DataFrame(
np.random.default_rng(2).standard_normal((30, 4)),
columns=Index(list("ABCD"), dtype=object),
index=date_range("2000-01-01", periods=30, freq="B"),
)
@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.default_rng(2).standard_normal(11),
"E": np.random.default_rng(2).standard_normal(11),
"F": np.random.default_rng(2).standard_normal(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", {}),
("sum", {"min_count": 2}),
("min", {"min_count": 2}),
("max", {"min_count": 2}),
],
ids=[
"mean",
"var_1",
"var_0",
"std_1",
"std_0",
"sum",
"min",
"max",
"sum-min_count",
"min-min_count",
"max-min_count",
],
)
def numba_supported_reductions(request):
"""reductions supported with engine='numba'"""
return request.param
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