You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
119 lines
3.7 KiB
119 lines
3.7 KiB
import numpy as np
|
|
import pytest
|
|
|
|
import pandas.util._test_decorators as td
|
|
|
|
from pandas import (
|
|
DataFrame,
|
|
Index,
|
|
)
|
|
import pandas._testing as tm
|
|
|
|
pytestmark = [td.skip_if_no("numba"), pytest.mark.single_cpu]
|
|
|
|
|
|
@pytest.fixture(params=[0, 1])
|
|
def apply_axis(request):
|
|
return request.param
|
|
|
|
|
|
def test_numba_vs_python_noop(float_frame, apply_axis):
|
|
func = lambda x: x
|
|
result = float_frame.apply(func, engine="numba", axis=apply_axis)
|
|
expected = float_frame.apply(func, engine="python", axis=apply_axis)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_numba_vs_python_string_index():
|
|
# GH#56189
|
|
pytest.importorskip("pyarrow")
|
|
df = DataFrame(
|
|
1,
|
|
index=Index(["a", "b"], dtype="string[pyarrow_numpy]"),
|
|
columns=Index(["x", "y"], dtype="string[pyarrow_numpy]"),
|
|
)
|
|
func = lambda x: x
|
|
result = df.apply(func, engine="numba", axis=0)
|
|
expected = df.apply(func, engine="python", axis=0)
|
|
tm.assert_frame_equal(
|
|
result, expected, check_column_type=False, check_index_type=False
|
|
)
|
|
|
|
|
|
def test_numba_vs_python_indexing():
|
|
frame = DataFrame(
|
|
{"a": [1, 2, 3], "b": [4, 5, 6], "c": [7.0, 8.0, 9.0]},
|
|
index=Index(["A", "B", "C"]),
|
|
)
|
|
row_func = lambda x: x["c"]
|
|
result = frame.apply(row_func, engine="numba", axis=1)
|
|
expected = frame.apply(row_func, engine="python", axis=1)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
col_func = lambda x: x["A"]
|
|
result = frame.apply(col_func, engine="numba", axis=0)
|
|
expected = frame.apply(col_func, engine="python", axis=0)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"reduction",
|
|
[lambda x: x.mean(), lambda x: x.min(), lambda x: x.max(), lambda x: x.sum()],
|
|
)
|
|
def test_numba_vs_python_reductions(reduction, apply_axis):
|
|
df = DataFrame(np.ones((4, 4), dtype=np.float64))
|
|
result = df.apply(reduction, engine="numba", axis=apply_axis)
|
|
expected = df.apply(reduction, engine="python", axis=apply_axis)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("colnames", [[1, 2, 3], [1.0, 2.0, 3.0]])
|
|
def test_numba_numeric_colnames(colnames):
|
|
# Check that numeric column names lower properly and can be indxed on
|
|
df = DataFrame(
|
|
np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int64), columns=colnames
|
|
)
|
|
first_col = colnames[0]
|
|
f = lambda x: x[first_col] # Get the first column
|
|
result = df.apply(f, engine="numba", axis=1)
|
|
expected = df.apply(f, engine="python", axis=1)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_numba_parallel_unsupported(float_frame):
|
|
f = lambda x: x
|
|
with pytest.raises(
|
|
NotImplementedError,
|
|
match="Parallel apply is not supported when raw=False and engine='numba'",
|
|
):
|
|
float_frame.apply(f, engine="numba", engine_kwargs={"parallel": True})
|
|
|
|
|
|
def test_numba_nonunique_unsupported(apply_axis):
|
|
f = lambda x: x
|
|
df = DataFrame({"a": [1, 2]}, index=Index(["a", "a"]))
|
|
with pytest.raises(
|
|
NotImplementedError,
|
|
match="The index/columns must be unique when raw=False and engine='numba'",
|
|
):
|
|
df.apply(f, engine="numba", axis=apply_axis)
|
|
|
|
|
|
def test_numba_unsupported_dtypes(apply_axis):
|
|
f = lambda x: x
|
|
df = DataFrame({"a": [1, 2], "b": ["a", "b"], "c": [4, 5]})
|
|
df["c"] = df["c"].astype("double[pyarrow]")
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match="Column b must have a numeric dtype. Found 'object|string' instead",
|
|
):
|
|
df.apply(f, engine="numba", axis=apply_axis)
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match="Column c is backed by an extension array, "
|
|
"which is not supported by the numba engine.",
|
|
):
|
|
df["c"].to_frame().apply(f, engine="numba", axis=apply_axis)
|