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