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import numpy as np
import pytest
from pandas.errors import NumbaUtilError
from pandas import (
DataFrame,
Index,
NamedAgg,
Series,
option_context,
)
import pandas._testing as tm
pytestmark = pytest.mark.single_cpu
def test_correct_function_signature():
pytest.importorskip("numba")
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")
def test_check_nopython_kwargs():
pytest.importorskip("numba")
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)
@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):
pytest.importorskip("numba")
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)
@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
pytest.importorskip("numba")
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)
def test_use_global_config():
pytest.importorskip("numba")
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)
@pytest.mark.parametrize(
"agg_kwargs",
[
{"func": ["min", "max"]},
{"func": "min"},
{"func": {1: ["min", "max"], 2: "sum"}},
{"bmin": NamedAgg(column=1, aggfunc="min")},
],
)
def test_multifunc_numba_vs_cython_frame(agg_kwargs):
pytest.importorskip("numba")
data = DataFrame(
{
0: ["a", "a", "b", "b", "a"],
1: [1.0, 2.0, 3.0, 4.0, 5.0],
2: [1, 2, 3, 4, 5],
},
columns=[0, 1, 2],
)
grouped = data.groupby(0)
result = grouped.agg(**agg_kwargs, engine="numba")
expected = grouped.agg(**agg_kwargs, engine="cython")
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"agg_kwargs,expected_func",
[
({"func": lambda values, index: values.sum()}, "sum"),
# FIXME
pytest.param(
{
"func": [
lambda values, index: values.sum(),
lambda values, index: values.min(),
]
},
["sum", "min"],
marks=pytest.mark.xfail(
reason="This doesn't work yet! Fails in nopython pipeline!"
),
),
],
)
def test_multifunc_numba_udf_frame(agg_kwargs, expected_func):
pytest.importorskip("numba")
data = DataFrame(
{
0: ["a", "a", "b", "b", "a"],
1: [1.0, 2.0, 3.0, 4.0, 5.0],
2: [1, 2, 3, 4, 5],
},
columns=[0, 1, 2],
)
grouped = data.groupby(0)
result = grouped.agg(**agg_kwargs, engine="numba")
expected = grouped.agg(expected_func, engine="cython")
# check_dtype can be removed if GH 44952 is addressed
# Currently, UDFs still always return float64 while reductions can preserve dtype
tm.assert_frame_equal(result, expected, check_dtype=False)
@pytest.mark.parametrize(
"agg_kwargs",
[{"func": ["min", "max"]}, {"func": "min"}, {"min_val": "min", "max_val": "max"}],
)
def test_multifunc_numba_vs_cython_series(agg_kwargs):
pytest.importorskip("numba")
labels = ["a", "a", "b", "b", "a"]
data = Series([1.0, 2.0, 3.0, 4.0, 5.0])
grouped = data.groupby(labels)
agg_kwargs["engine"] = "numba"
result = grouped.agg(**agg_kwargs)
agg_kwargs["engine"] = "cython"
expected = grouped.agg(**agg_kwargs)
if isinstance(expected, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_series_equal(result, expected)
@pytest.mark.single_cpu
@pytest.mark.parametrize(
"data,agg_kwargs",
[
(Series([1.0, 2.0, 3.0, 4.0, 5.0]), {"func": ["min", "max"]}),
(Series([1.0, 2.0, 3.0, 4.0, 5.0]), {"func": "min"}),
(
DataFrame(
{1: [1.0, 2.0, 3.0, 4.0, 5.0], 2: [1, 2, 3, 4, 5]}, columns=[1, 2]
),
{"func": ["min", "max"]},
),
(
DataFrame(
{1: [1.0, 2.0, 3.0, 4.0, 5.0], 2: [1, 2, 3, 4, 5]}, columns=[1, 2]
),
{"func": "min"},
),
(
DataFrame(
{1: [1.0, 2.0, 3.0, 4.0, 5.0], 2: [1, 2, 3, 4, 5]}, columns=[1, 2]
),
{"func": {1: ["min", "max"], 2: "sum"}},
),
(
DataFrame(
{1: [1.0, 2.0, 3.0, 4.0, 5.0], 2: [1, 2, 3, 4, 5]}, columns=[1, 2]
),
{"min_col": NamedAgg(column=1, aggfunc="min")},
),
],
)
def test_multifunc_numba_kwarg_propagation(data, agg_kwargs):
pytest.importorskip("numba")
labels = ["a", "a", "b", "b", "a"]
grouped = data.groupby(labels)
result = grouped.agg(**agg_kwargs, engine="numba", engine_kwargs={"parallel": True})
expected = grouped.agg(**agg_kwargs, engine="numba")
if isinstance(expected, DataFrame):
tm.assert_frame_equal(result, expected)
else:
tm.assert_series_equal(result, expected)
def test_args_not_cached():
# GH 41647
pytest.importorskip("numba")
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)
def test_index_data_correctly_passed():
# GH 43133
pytest.importorskip("numba")
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)
def test_engine_kwargs_not_cached():
# If the user passes a different set of engine_kwargs don't return the same
# jitted function
pytest.importorskip("numba")
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)
@pytest.mark.filterwarnings("ignore")
def test_multiindex_one_key(nogil, parallel, nopython):
pytest.importorskip("numba")
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)
def test_multiindex_multi_key_not_supported(nogil, parallel, nopython):
pytest.importorskip("numba")
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
)
def test_multilabel_numba_vs_cython(numba_supported_reductions):
pytest.importorskip("numba")
reduction, kwargs = numba_supported_reductions
df = 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),
}
)
gb = df.groupby(["A", "B"])
res_agg = gb.agg(reduction, engine="numba", **kwargs)
expected_agg = gb.agg(reduction, engine="cython", **kwargs)
tm.assert_frame_equal(res_agg, expected_agg)
# Test that calling the aggregation directly also works
direct_res = getattr(gb, reduction)(engine="numba", **kwargs)
direct_expected = getattr(gb, reduction)(engine="cython", **kwargs)
tm.assert_frame_equal(direct_res, direct_expected)
def test_multilabel_udf_numba_vs_cython():
pytest.importorskip("numba")
df = 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),
}
)
gb = df.groupby(["A", "B"])
result = gb.agg(lambda values, index: values.min(), engine="numba")
expected = gb.agg(lambda x: x.min(), engine="cython")
tm.assert_frame_equal(result, expected)