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[REVIEW] Fix error in cudf.to_numeric when a bool input is passed #10431

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merged 3 commits into from
Mar 15, 2022
Merged

[REVIEW] Fix error in cudf.to_numeric when a bool input is passed #10431

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08c64a5
fix bool dtype issue
galipremsagar Mar 14, 2022
f671c6d
Merge remote-tracking branch 'upstream/branch-22.04' into 10049
galipremsagar Mar 15, 2022
c4d8503
refactor
galipremsagar Mar 15, 2022
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22 changes: 11 additions & 11 deletions python/cudf/cudf/core/tools/numeric.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# Copyright (c) 2018-2020, NVIDIA CORPORATION.
# Copyright (c) 2018-2022, NVIDIA CORPORATION.

import warnings

Expand Down Expand Up @@ -144,16 +144,16 @@ def to_numeric(arg, errors="raise", downcast=None):
col = col.as_numerical_column("d")

if downcast:
downcast_type_map = {
"integer": list(np.typecodes["Integer"]),
"signed": list(np.typecodes["Integer"]),
"unsigned": list(np.typecodes["UnsignedInteger"]),
}
float_types = list(np.typecodes["Float"])
idx = float_types.index(cudf.dtype(np.float32).char)
downcast_type_map["float"] = float_types[idx:]

type_set = downcast_type_map[downcast]
if downcast == "float":
# we support only float32 & float64
type_set = [
cudf.dtype(np.float32).char,
cudf.dtype(np.float64).char,
]
elif downcast in ("integer", "signed"):
type_set = list(np.typecodes["Integer"])
elif downcast == "unsigned":
type_set = list(np.typecodes["UnsignedInteger"])

for t in type_set:
downcast_dtype = cudf.dtype(t)
Expand Down
18 changes: 17 additions & 1 deletion python/cudf/cudf/tests/test_numerical.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# Copyright (c) 2021, NVIDIA CORPORATION.
# Copyright (c) 2021-2022, NVIDIA CORPORATION.

import numpy as np
import pandas as pd
Expand Down Expand Up @@ -397,3 +397,19 @@ def test_series_construction_with_nulls(dtype, input_obj):
expect = pd.Series(np_data, dtype=np_dtypes_to_pandas_dtypes[dtype])
got = cudf.Series(np_data, dtype=dtype).to_pandas(nullable=True)
assert_eq(expect, got)


@pytest.mark.parametrize(
"data", [[True, False, True]],
)
@pytest.mark.parametrize(
"downcast", ["signed", "integer", "unsigned", "float"]
)
def test_series_to_numeric_bool(data, downcast):
ps = pd.Series(data)
gs = cudf.from_pandas(ps)

expect = pd.to_numeric(ps, downcast=downcast)
got = cudf.to_numeric(gs, downcast=downcast)

assert_eq(expect, got)