119 lines
3.7 KiB
Python
119 lines
3.7 KiB
Python
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import numpy as np
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import pytest
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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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)
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import pandas._testing as tm
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pytestmark = [td.skip_if_no("numba"), pytest.mark.single_cpu]
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@pytest.fixture(params=[0, 1])
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def apply_axis(request):
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return request.param
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def test_numba_vs_python_noop(float_frame, apply_axis):
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func = lambda x: x
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result = float_frame.apply(func, engine="numba", axis=apply_axis)
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expected = float_frame.apply(func, engine="python", axis=apply_axis)
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tm.assert_frame_equal(result, expected)
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def test_numba_vs_python_string_index():
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# GH#56189
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pytest.importorskip("pyarrow")
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df = DataFrame(
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1,
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index=Index(["a", "b"], dtype="string[pyarrow_numpy]"),
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columns=Index(["x", "y"], dtype="string[pyarrow_numpy]"),
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)
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func = lambda x: x
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result = df.apply(func, engine="numba", axis=0)
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expected = df.apply(func, engine="python", axis=0)
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tm.assert_frame_equal(
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result, expected, check_column_type=False, check_index_type=False
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)
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def test_numba_vs_python_indexing():
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frame = DataFrame(
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{"a": [1, 2, 3], "b": [4, 5, 6], "c": [7.0, 8.0, 9.0]},
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index=Index(["A", "B", "C"]),
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)
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row_func = lambda x: x["c"]
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result = frame.apply(row_func, engine="numba", axis=1)
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expected = frame.apply(row_func, engine="python", axis=1)
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tm.assert_series_equal(result, expected)
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col_func = lambda x: x["A"]
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result = frame.apply(col_func, engine="numba", axis=0)
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expected = frame.apply(col_func, engine="python", axis=0)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"reduction",
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[lambda x: x.mean(), lambda x: x.min(), lambda x: x.max(), lambda x: x.sum()],
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)
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def test_numba_vs_python_reductions(reduction, apply_axis):
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df = DataFrame(np.ones((4, 4), dtype=np.float64))
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result = df.apply(reduction, engine="numba", axis=apply_axis)
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expected = df.apply(reduction, engine="python", axis=apply_axis)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("colnames", [[1, 2, 3], [1.0, 2.0, 3.0]])
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def test_numba_numeric_colnames(colnames):
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# Check that numeric column names lower properly and can be indxed on
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df = DataFrame(
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np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int64), columns=colnames
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)
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first_col = colnames[0]
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f = lambda x: x[first_col] # Get the first column
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result = df.apply(f, engine="numba", axis=1)
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expected = df.apply(f, engine="python", axis=1)
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tm.assert_series_equal(result, expected)
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def test_numba_parallel_unsupported(float_frame):
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f = lambda x: x
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with pytest.raises(
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NotImplementedError,
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match="Parallel apply is not supported when raw=False and engine='numba'",
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):
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float_frame.apply(f, engine="numba", engine_kwargs={"parallel": True})
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def test_numba_nonunique_unsupported(apply_axis):
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f = lambda x: x
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df = DataFrame({"a": [1, 2]}, index=Index(["a", "a"]))
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with pytest.raises(
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NotImplementedError,
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match="The index/columns must be unique when raw=False and engine='numba'",
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):
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df.apply(f, engine="numba", axis=apply_axis)
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def test_numba_unsupported_dtypes(apply_axis):
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f = lambda x: x
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df = DataFrame({"a": [1, 2], "b": ["a", "b"], "c": [4, 5]})
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df["c"] = df["c"].astype("double[pyarrow]")
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with pytest.raises(
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ValueError,
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match="Column b must have a numeric dtype. Found 'object|string' instead",
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):
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df.apply(f, engine="numba", axis=apply_axis)
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with pytest.raises(
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ValueError,
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match="Column c is backed by an extension array, "
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"which is not supported by the numba engine.",
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):
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df["c"].to_frame().apply(f, engine="numba", axis=apply_axis)
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