Merge branch 'branch-24.06' into fix-categorical-support

rapidsai · Apr 26, 2024 · f38d62e · f38d62e
2 parents d901e20 + 79cd473
commit f38d62e
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Showing 9 changed files with 31 additions and 56 deletions.
diff --git a/python/cudf/cudf/core/column/string.py b/python/cudf/cudf/core/column/string.py
@@ -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')

diff --git a/python/cudf/cudf/core/frame.py b/python/cudf/cudf/core/frame.py
@@ -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', ''],
@@ -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
@@ -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()
@@ -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', ''],
@@ -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
@@ -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()

diff --git a/python/cudf/cudf/core/index.py b/python/cudf/cudf/core/index.py
@@ -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,
@@ -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:

diff --git a/python/cudf/cudf/tests/test_api_types.py b/python/cudf/cudf/tests/test_api_types.py
@@ -33,7 +33,6 @@
         (np.float64, False),
         (np.complex128, False),
         (np.str_, False),
-        (np.unicode_, False),
         (np.datetime64, False),
         (np.timedelta64, False),
         # NumPy scalars.
@@ -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.
@@ -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),
@@ -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.
@@ -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.
@@ -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),
@@ -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.
@@ -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.
@@ -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),
@@ -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.
@@ -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.
@@ -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),
@@ -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.
@@ -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.
@@ -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),
@@ -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.
@@ -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.
@@ -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),
@@ -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.
@@ -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.
@@ -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),
@@ -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.
@@ -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.
@@ -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),
@@ -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.
@@ -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.
@@ -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),
@@ -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.
@@ -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.
@@ -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),
@@ -1088,7 +1058,6 @@ def test_pandas_agreement(obj):
         np.float64,
         np.complex128,
         np.str_,
-        np.unicode_,
         np.datetime64,
         np.timedelta64,
         # NumPy scalars.
@@ -1097,7 +1066,6 @@ def test_pandas_agreement(obj):
         np.float64(),
         np.complex128(),
         np.str_(),
-        np.unicode_(),
         np.datetime64(),
         np.timedelta64(),
         # NumPy dtype objects.
@@ -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),

diff --git a/python/cudf/cudf/tests/test_categorical.py b/python/cudf/cudf/tests/test_categorical.py
@@ -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]),
@@ -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]),