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Minor fixups for future NumPy 2 compatibility #15590

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merged 6 commits into from
Apr 26, 2024
Merged

Minor fixups for future NumPy 2 compatibility #15590

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50dadcf
Do not use np.NaN (but rather np.nan which is identical)
seberg Apr 23, 2024
323db6c
Use np.str_ rather than np.unicode
seberg Apr 23, 2024
849049e
NumPy hid sctypes further away but still use it for now
seberg Apr 24, 2024
8708b9e
Apply suggestions from code review
seberg Apr 24, 2024
743fd13
Merge branch 'branch-24.06' into numpy2
galipremsagar Apr 25, 2024
b35af2c
Merge branch 'branch-24.06' into numpy2
galipremsagar Apr 26, 2024
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2 changes: 1 addition & 1 deletion python/cudf/cudf/core/column/string.py
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Original file line number Diff line number Diff line change
Expand Up @@ -692,7 +692,7 @@ def contains(

Returning an Index of booleans using only a literal pattern.

>>> data = ['Mouse', 'dog', 'house and parrot', '23.0', np.NaN]
>>> data = ['Mouse', 'dog', 'house and parrot', '23.0', np.nan]
>>> idx = cudf.Index(data)
>>> idx
Index(['Mouse', 'dog', 'house and parrot', '23.0', None], dtype='object')
Expand Down
12 changes: 6 additions & 6 deletions python/cudf/cudf/core/frame.py
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Expand Up @@ -1077,7 +1077,7 @@ def isna(self):
>>> import cudf
>>> import numpy as np
>>> import pandas as pd
>>> df = cudf.DataFrame({'age': [5, 6, np.NaN],
>>> df = cudf.DataFrame({'age': [5, 6, np.nan],
... 'born': [pd.NaT, pd.Timestamp('1939-05-27'),
... pd.Timestamp('1940-04-25')],
... 'name': ['Alfred', 'Batman', ''],
Expand All @@ -1095,7 +1095,7 @@ def isna(self):

Show which entries in a Series are NA.

>>> ser = cudf.Series([5, 6, np.NaN, np.inf, -np.inf])
>>> ser = cudf.Series([5, 6, np.nan, np.inf, -np.inf])
>>> ser
0 5.0
1 6.0
Expand All @@ -1113,7 +1113,7 @@ def isna(self):

Show which entries in an Index are NA.

>>> idx = cudf.Index([1, 2, None, np.NaN, 0.32, np.inf])
>>> idx = cudf.Index([1, 2, None, np.nan, 0.32, np.inf])
>>> idx
Index([1.0, 2.0, <NA>, <NA>, 0.32, Inf], dtype='float64')
>>> idx.isna()
Expand Down Expand Up @@ -1156,7 +1156,7 @@ def notna(self):
>>> import cudf
>>> import numpy as np
>>> import pandas as pd
>>> df = cudf.DataFrame({'age': [5, 6, np.NaN],
>>> df = cudf.DataFrame({'age': [5, 6, np.nan],
... 'born': [pd.NaT, pd.Timestamp('1939-05-27'),
... pd.Timestamp('1940-04-25')],
... 'name': ['Alfred', 'Batman', ''],
Expand All @@ -1174,7 +1174,7 @@ def notna(self):

Show which entries in a Series are NA.

>>> ser = cudf.Series([5, 6, np.NaN, np.inf, -np.inf])
>>> ser = cudf.Series([5, 6, np.nan, np.inf, -np.inf])
>>> ser
0 5.0
1 6.0
Expand All @@ -1192,7 +1192,7 @@ def notna(self):

Show which entries in an Index are NA.

>>> idx = cudf.Index([1, 2, None, np.NaN, 0.32, np.inf])
>>> idx = cudf.Index([1, 2, None, np.nan, 0.32, np.inf])
>>> idx
Index([1.0, 2.0, <NA>, <NA>, 0.32, Inf], dtype='float64')
>>> idx.notna()
Expand Down
6 changes: 5 additions & 1 deletion python/cudf/cudf/core/index.py
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Expand Up @@ -60,6 +60,7 @@
from cudf.core.single_column_frame import SingleColumnFrame
from cudf.utils.docutils import copy_docstring
from cudf.utils.dtypes import (
_NUMPY_SCTYPES,
_maybe_convert_to_default_type,
find_common_type,
is_mixed_with_object_dtype,
Expand Down Expand Up @@ -344,7 +345,10 @@ def _data(self):
@_cudf_nvtx_annotate
def __contains__(self, item):
if isinstance(item, bool) or not isinstance(
item, tuple(np.sctypes["int"] + np.sctypes["float"] + [int, float])
item,
tuple(
_NUMPY_SCTYPES["int"] + _NUMPY_SCTYPES["float"] + [int, float]
),
):
return False
try:
Expand Down
33 changes: 0 additions & 33 deletions python/cudf/cudf/tests/test_api_types.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,6 @@
(np.float64, False),
(np.complex128, False),
(np.str_, False),
(np.unicode_, False),
(np.datetime64, False),
(np.timedelta64, False),
# NumPy scalars.
Expand All @@ -42,7 +41,6 @@
(np.float64(), False),
(np.complex128(), False),
(np.str_(), False),
(np.unicode_(), False),
(np.datetime64(), False),
(np.timedelta64(), False),
# NumPy dtype objects.
Expand All @@ -61,7 +59,6 @@
(np.array([], dtype=np.float64), False),
(np.array([], dtype=np.complex128), False),
(np.array([], dtype=np.str_), False),
(np.array([], dtype=np.unicode_), False),
(np.array([], dtype=np.datetime64), False),
(np.array([], dtype=np.timedelta64), False),
(np.array([], dtype=object), False),
Expand Down Expand Up @@ -142,7 +139,6 @@ def test_is_categorical_dtype(obj, expect):
(np.float64, True),
(np.complex128, True),
(np.str_, False),
(np.unicode_, False),
(np.datetime64, False),
(np.timedelta64, False),
# NumPy scalars.
Expand All @@ -151,7 +147,6 @@ def test_is_categorical_dtype(obj, expect):
(np.float64(), True),
(np.complex128(), True),
(np.str_(), False),
(np.unicode_(), False),
(np.datetime64(), False),
(np.timedelta64(), False),
# NumPy dtype objects.
Expand All @@ -170,7 +165,6 @@ def test_is_categorical_dtype(obj, expect):
(np.array([], dtype=np.float64), True),
(np.array([], dtype=np.complex128), True),
(np.array([], dtype=np.str_), False),
(np.array([], dtype=np.unicode_), False),
(np.array([], dtype=np.datetime64), False),
(np.array([], dtype=np.timedelta64), False),
(np.array([], dtype=object), False),
Expand Down Expand Up @@ -247,7 +241,6 @@ def test_is_numeric_dtype(obj, expect):
(np.float64, False),
(np.complex128, False),
(np.str_, False),
(np.unicode_, False),
(np.datetime64, False),
(np.timedelta64, False),
# NumPy scalars.
Expand All @@ -256,7 +249,6 @@ def test_is_numeric_dtype(obj, expect):
(np.float64(), False),
(np.complex128(), False),
(np.str_(), False),
(np.unicode_(), False),
(np.datetime64(), False),
(np.timedelta64(), False),
# NumPy dtype objects.
Expand All @@ -275,7 +267,6 @@ def test_is_numeric_dtype(obj, expect):
(np.array([], dtype=np.float64), False),
(np.array([], dtype=np.complex128), False),
(np.array([], dtype=np.str_), False),
(np.array([], dtype=np.unicode_), False),
(np.array([], dtype=np.datetime64), False),
(np.array([], dtype=np.timedelta64), False),
(np.array([], dtype=object), False),
Expand Down Expand Up @@ -352,7 +343,6 @@ def test_is_integer_dtype(obj, expect):
(np.float64, False),
(np.complex128, False),
(np.str_, False),
(np.unicode_, False),
(np.datetime64, False),
(np.timedelta64, False),
# NumPy scalars.
Expand All @@ -361,7 +351,6 @@ def test_is_integer_dtype(obj, expect):
(np.float64(), False),
(np.complex128(), False),
(np.str_(), False),
(np.unicode_(), False),
(np.datetime64(), False),
(np.timedelta64(), False),
# NumPy dtype objects.
Expand All @@ -380,7 +369,6 @@ def test_is_integer_dtype(obj, expect):
(np.array([], dtype=np.float64), False),
(np.array([], dtype=np.complex128), False),
(np.array([], dtype=np.str_), False),
(np.array([], dtype=np.unicode_), False),
(np.array([], dtype=np.datetime64), False),
(np.array([], dtype=np.timedelta64), False),
(np.array([], dtype=object), False),
Expand Down Expand Up @@ -458,7 +446,6 @@ def test_is_integer(obj, expect):
(np.float64, False),
(np.complex128, False),
(np.str_, True),
(np.unicode_, True),
(np.datetime64, False),
(np.timedelta64, False),
# NumPy scalars.
Expand All @@ -467,7 +454,6 @@ def test_is_integer(obj, expect):
(np.float64(), False),
(np.complex128(), False),
(np.str_(), True),
(np.unicode_(), True),
(np.datetime64(), False),
(np.timedelta64(), False),
# NumPy dtype objects.
Expand All @@ -486,7 +472,6 @@ def test_is_integer(obj, expect):
(np.array([], dtype=np.float64), False),
(np.array([], dtype=np.complex128), False),
(np.array([], dtype=np.str_), True),
(np.array([], dtype=np.unicode_), True),
(np.array([], dtype=np.datetime64), False),
(np.array([], dtype=np.timedelta64), False),
# (np.array([], dtype=object), False),
Expand Down Expand Up @@ -577,7 +562,6 @@ def test_is_string_dtype(obj, expect):
(np.float64, False),
(np.complex128, False),
(np.str_, False),
(np.unicode_, False),
(np.datetime64, True),
(np.timedelta64, False),
# NumPy scalars.
Expand All @@ -586,7 +570,6 @@ def test_is_string_dtype(obj, expect):
(np.float64(), False),
(np.complex128(), False),
(np.str_(), False),
(np.unicode_(), False),
(np.datetime64(), True),
(np.timedelta64(), False),
# NumPy dtype objects.
Expand All @@ -605,7 +588,6 @@ def test_is_string_dtype(obj, expect):
(np.array([], dtype=np.float64), False),
(np.array([], dtype=np.complex128), False),
(np.array([], dtype=np.str_), False),
(np.array([], dtype=np.unicode_), False),
(np.array([], dtype=np.datetime64), True),
(np.array([], dtype=np.timedelta64), False),
(np.array([], dtype=object), False),
Expand Down Expand Up @@ -682,7 +664,6 @@ def test_is_datetime_dtype(obj, expect):
(np.float64, False),
(np.complex128, False),
(np.str_, False),
(np.unicode_, False),
(np.datetime64, False),
(np.timedelta64, False),
# NumPy scalars.
Expand All @@ -691,7 +672,6 @@ def test_is_datetime_dtype(obj, expect):
(np.float64(), False),
(np.complex128(), False),
(np.str_(), False),
(np.unicode_(), False),
(np.datetime64(), False),
(np.timedelta64(), False),
# NumPy dtype objects.
Expand All @@ -710,7 +690,6 @@ def test_is_datetime_dtype(obj, expect):
(np.array([], dtype=np.float64), False),
(np.array([], dtype=np.complex128), False),
(np.array([], dtype=np.str_), False),
(np.array([], dtype=np.unicode_), False),
(np.array([], dtype=np.datetime64), False),
(np.array([], dtype=np.timedelta64), False),
(np.array([], dtype=object), False),
Expand Down Expand Up @@ -787,7 +766,6 @@ def test_is_list_dtype(obj, expect):
(np.float64, False),
(np.complex128, False),
(np.str_, False),
(np.unicode_, False),
(np.datetime64, False),
(np.timedelta64, False),
# NumPy scalars.
Expand All @@ -796,7 +774,6 @@ def test_is_list_dtype(obj, expect):
(np.float64(), False),
(np.complex128(), False),
(np.str_(), False),
(np.unicode_(), False),
(np.datetime64(), False),
(np.timedelta64(), False),
# NumPy dtype objects.
Expand All @@ -815,7 +792,6 @@ def test_is_list_dtype(obj, expect):
(np.array([], dtype=np.float64), False),
(np.array([], dtype=np.complex128), False),
(np.array([], dtype=np.str_), False),
(np.array([], dtype=np.unicode_), False),
(np.array([], dtype=np.datetime64), False),
(np.array([], dtype=np.timedelta64), False),
(np.array([], dtype=object), False),
Expand Down Expand Up @@ -895,7 +871,6 @@ def test_is_struct_dtype(obj, expect):
(np.float64, False),
(np.complex128, False),
(np.str_, False),
(np.unicode_, False),
(np.datetime64, False),
(np.timedelta64, False),
# NumPy scalars.
Expand All @@ -904,7 +879,6 @@ def test_is_struct_dtype(obj, expect):
(np.float64(), False),
(np.complex128(), False),
(np.str_(), False),
(np.unicode_(), False),
(np.datetime64(), False),
(np.timedelta64(), False),
# NumPy dtype objects.
Expand All @@ -923,7 +897,6 @@ def test_is_struct_dtype(obj, expect):
(np.array([], dtype=np.float64), False),
(np.array([], dtype=np.complex128), False),
(np.array([], dtype=np.str_), False),
(np.array([], dtype=np.unicode_), False),
(np.array([], dtype=np.datetime64), False),
(np.array([], dtype=np.timedelta64), False),
(np.array([], dtype=object), False),
Expand Down Expand Up @@ -1004,7 +977,6 @@ def test_is_decimal_dtype(obj, expect):
np.float64,
np.complex128,
np.str_,
np.unicode_,
np.datetime64,
np.timedelta64,
# NumPy scalars.
Expand All @@ -1013,7 +985,6 @@ def test_is_decimal_dtype(obj, expect):
np.float64(),
np.complex128(),
np.str_(),
np.unicode_(),
np.datetime64(),
np.timedelta64(),
# NumPy dtype objects.
Expand All @@ -1032,7 +1003,6 @@ def test_is_decimal_dtype(obj, expect):
np.array([], dtype=np.float64),
np.array([], dtype=np.complex128),
np.array([], dtype=np.str_),
np.array([], dtype=np.unicode_),
np.array([], dtype=np.datetime64),
np.array([], dtype=np.timedelta64),
np.array([], dtype=object),
Expand Down Expand Up @@ -1088,7 +1058,6 @@ def test_pandas_agreement(obj):
np.float64,
np.complex128,
np.str_,
np.unicode_,
np.datetime64,
np.timedelta64,
# NumPy scalars.
Expand All @@ -1097,7 +1066,6 @@ def test_pandas_agreement(obj):
np.float64(),
np.complex128(),
np.str_(),
np.unicode_(),
np.datetime64(),
np.timedelta64(),
# NumPy dtype objects.
Expand All @@ -1116,7 +1084,6 @@ def test_pandas_agreement(obj):
np.array([], dtype=np.float64),
np.array([], dtype=np.complex128),
np.array([], dtype=np.str_),
np.array([], dtype=np.unicode_),
np.array([], dtype=np.datetime64),
np.array([], dtype=np.timedelta64),
np.array([], dtype=object),
Expand Down
4 changes: 2 additions & 2 deletions python/cudf/cudf/tests/test_categorical.py
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Expand Up @@ -460,7 +460,7 @@ def test_categorical_dataframe_slice_copy():
pd.Series(["1.0", "2.5", "3.001", None, "9"], dtype="category"),
pd.Series(["a", "b", "c", "c", "b", "a", "b", "b"]),
pd.Series(["aa", "b", "c", "c", "bb", "bb", "a", "b", "b"]),
pd.Series([1, 2, 3, 89, None, np.nan, np.NaN], dtype="float64"),
pd.Series([1, 2, 3, 89, None, np.nan, np.nan], dtype="float64"),
pd.Series([1, 2, 3, 89], dtype="float64"),
pd.Series([1, 2.5, 3.001, 89], dtype="float64"),
pd.Series([None, None, None]),
Expand Down Expand Up @@ -493,7 +493,7 @@ def test_categorical_typecast(data, cat_type):
pd.Series([1, 2, 3, 89]),
pd.Series(["a", "b", "c", "c", "b", "a", "b", "b"]),
pd.Series(["aa", "b", "c", "c", "bb", "bb", "a", "b", "b"]),
pd.Series([1, 2, 3, 89, None, np.nan, np.NaN], dtype="float64"),
pd.Series([1, 2, 3, 89, None, np.nan, np.nan], dtype="float64"),
pd.Series([1, 2, 3, 89], dtype="float64"),
pd.Series([1, 2.5, 3.001, 89], dtype="float64"),
pd.Series([None, None, None]),
Expand Down
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