[REVIEW] Deprecate Index.is_* methods

python/cudf/cudf/api/types.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
 
 """Define common type operations."""
 
@@ -60,6 +60,8 @@ def is_numeric_dtype(obj):
             getattr(obj, "dtype", None), _BaseDtype
         ):
             return False
+    if isinstance(obj, cudf.BaseIndex):
+        return obj._is_numeric()
     return pd_types.is_numeric_dtype(obj)
 
 
@@ -230,13 +232,228 @@ def _union_categoricals(
     return cudf.Index(result_col)
 
 
+def is_bool_dtype(arr_or_dtype):
+    """
+    Check whether the provided array or dtype is of a boolean dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a boolean dtype.
+
+    Examples
+    --------
+    >>> from cudf.api.types import is_bool_dtype
+    >>> import numpy as np
+    >>> import cudf
+    >>> is_bool_dtype(str)
+    False
+    >>> is_bool_dtype(int)
+    False
+    >>> is_bool_dtype(bool)
+    True
+    >>> is_bool_dtype(np.bool_)
+    True
+    >>> is_bool_dtype(np.array(['a', 'b']))
+    False
+    >>> is_bool_dtype(cudf.Series([1, 2]))
+    False
+    >>> is_bool_dtype(np.array([True, False]))
+    True
+    >>> is_bool_dtype(cudf.Series([True, False], dtype='category'))
+    True
+    """
+    if isinstance(arr_or_dtype, cudf.BaseIndex):
+        return arr_or_dtype._is_boolean()
+    elif isinstance(arr_or_dtype, cudf.Series):
+        if isinstance(arr_or_dtype.dtype, cudf.CategoricalDtype):
+            return is_bool_dtype(arr_or_dtype=arr_or_dtype.dtype)
+        else:
+            return pd_types.is_bool_dtype(arr_or_dtype=arr_or_dtype.dtype)
+    elif isinstance(arr_or_dtype, cudf.CategoricalDtype):
+        return pd_types.is_bool_dtype(
+            arr_or_dtype=arr_or_dtype.categories.dtype
+        )
+    else:
+        return pd_types.is_bool_dtype(arr_or_dtype=arr_or_dtype)
+
+
+def is_object_dtype(arr_or_dtype):
+    """
+    Check whether an array-like or dtype is of the object dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array-like or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array-like or dtype is of the object dtype.
+
+    Examples
+    --------
+    >>> from cudf.api.types import is_object_dtype
+    >>> import numpy as np
+    >>> is_object_dtype(object)
+    True
+    >>> is_object_dtype(int)
+    False
+    >>> is_object_dtype(np.array([], dtype=object))
+    True
+    >>> is_object_dtype(np.array([], dtype=int))
+    False
+    >>> is_object_dtype([1, 2, 3])
+    False
+    """
+    if isinstance(arr_or_dtype, cudf.BaseIndex):
+        return arr_or_dtype._is_object()
+    elif isinstance(arr_or_dtype, cudf.Series):
+        return pd_types.is_object_dtype(arr_or_dtype=arr_or_dtype.dtype)
+    else:
+        return pd_types.is_object_dtype(arr_or_dtype=arr_or_dtype)
+
+
+def is_float_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a float dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a float dtype.
+
+    Examples
+    --------
+    >>> from cudf.api.types import is_float_dtype
+    >>> import numpy as np
+    >>> import cudf
+    >>> is_float_dtype(str)
+    False
+    >>> is_float_dtype(int)
+    False
+    >>> is_float_dtype(float)
+    True
+    >>> is_float_dtype(np.array(['a', 'b']))
+    False
+    >>> is_float_dtype(cudf.Series([1, 2]))
+    False
+    >>> is_float_dtype(cudf.Index([1, 2.]))
+    True
+    """
+    if isinstance(arr_or_dtype, cudf.BaseIndex):
+        return arr_or_dtype._is_floating()
+    return _wrap_pandas_is_dtype_api(pd_types.is_float_dtype)(arr_or_dtype)
+
+
+def is_integer_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of an integer dtype.
+    Unlike in `is_any_int_dtype`, timedelta64 instances will return False.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of an integer dtype and
+        not an instance of timedelta64.
+
+    Examples
+    --------
+    >>> from cudf.api.types import is_integer_dtype
+    >>> import numpy as np
+    >>> import cudf
+    >>> is_integer_dtype(str)
+    False
+    >>> is_integer_dtype(int)
+    True
+    >>> is_integer_dtype(float)
+    False
+    >>> is_integer_dtype(np.uint64)
+    True
+    >>> is_integer_dtype('int8')
+    True
+    >>> is_integer_dtype('Int8')
+    True
+    >>> is_integer_dtype(np.datetime64)
+    False
+    >>> is_integer_dtype(np.timedelta64)
+    False
+    >>> is_integer_dtype(np.array(['a', 'b']))
+    False
+    >>> is_integer_dtype(cudf.Series([1, 2]))
+    True
+    >>> is_integer_dtype(np.array([], dtype=np.timedelta64))
+    False
+    >>> is_integer_dtype(cudf.Index([1, 2.]))  # float
+    False
+    """
+    if isinstance(arr_or_dtype, cudf.BaseIndex):
+        return arr_or_dtype._is_integer()
+    return _wrap_pandas_is_dtype_api(pd_types.is_integer_dtype)(arr_or_dtype)
+
+
+def is_any_real_numeric_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a real number dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of a real number dtype.
+
+    Examples
+    --------
+    >>> from cudf.api.types import is_any_real_numeric_dtype
+    >>> import cudf
+    >>> is_any_real_numeric_dtype(int)
+    True
+    >>> is_any_real_numeric_dtype(float)
+    True
+    >>> is_any_real_numeric_dtype(object)
+    False
+    >>> is_any_real_numeric_dtype(str)
+    False
+    >>> is_any_real_numeric_dtype(complex(1, 2))
+    False
+    >>> is_any_real_numeric_dtype(bool)
+    False
+    >>> is_any_real_numeric_dtype(cudf.Index([1, 2, 3]))
+    True
+    """
+    return (
+        is_numeric_dtype(arr_or_dtype)
+        and not is_complex_dtype(arr_or_dtype)
+        and not is_bool_dtype(arr_or_dtype)
+    )
+
+
 # TODO: The below alias is removed for now since improving cudf categorical
 # support is ongoing and we don't want to introduce any ambiguities. The above
 # method _union_categoricals will take its place once exposed.
 # union_categoricals = pd_types.union_categoricals
 infer_dtype = pd_types.infer_dtype
 pandas_dtype = pd_types.pandas_dtype
-is_bool_dtype = pd_types.is_bool_dtype
 is_complex_dtype = pd_types.is_complex_dtype
 # TODO: Evaluate which of the datetime types need special handling for cudf.
 is_datetime_dtype = _wrap_pandas_is_dtype_api(pd_types.is_datetime64_dtype)
@@ -246,10 +463,7 @@ def _union_categoricals(
 is_datetime64tz_dtype = pd_types.is_datetime64tz_dtype
 is_extension_type = pd_types.is_extension_type
 is_extension_array_dtype = pd_types.is_extension_array_dtype
-is_float_dtype = _wrap_pandas_is_dtype_api(pd_types.is_float_dtype)
 is_int64_dtype = pd_types.is_int64_dtype
-is_integer_dtype = _wrap_pandas_is_dtype_api(pd_types.is_integer_dtype)
-is_object_dtype = pd_types.is_object_dtype
 is_period_dtype = pd_types.is_period_dtype
 is_signed_integer_dtype = pd_types.is_signed_integer_dtype
 is_timedelta_dtype = _wrap_pandas_is_dtype_api(pd_types.is_timedelta64_dtype)

python/cudf/cudf/core/_base_index.py

@@ -507,8 +507,8 @@ def intersection(self, other, sort=False):
 
         res_name = _get_result_name(self.name, other.name)
 
-        if (self.is_boolean() and other.is_numeric()) or (
-            self.is_numeric() and other.is_boolean()
+        if (self._is_boolean() and other._is_numeric()) or (
+            self._is_numeric() and other._is_boolean()
         ):
             if isinstance(self, cudf.MultiIndex):
                 return self[:0].rename(res_name)
@@ -841,6 +841,9 @@ def is_numeric(self):
         """
         Check if the Index only consists of numeric data.
 
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_any_real_numeric_dtype` instead.
+
         Returns
         -------
         bool
@@ -876,10 +879,15 @@ def is_numeric(self):
         """
         raise NotImplementedError
 
+    _is_numeric = is_numeric
+
     def is_boolean(self):
         """
         Check if the Index only consists of booleans.
 
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_bool_dtype` instead.
+
         Returns
         -------
         bool
@@ -909,10 +917,15 @@ def is_boolean(self):
         """
         raise NotImplementedError
 
+    _is_boolean = is_boolean
+
     def is_integer(self):
         """
         Check if the Index only consists of integers.
 
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_integer_dtype` instead.
+
         Returns
         -------
         bool
@@ -942,13 +955,18 @@ def is_integer(self):
         """
         raise NotImplementedError
 
+    _is_integer = is_integer
+
     def is_floating(self):
         """
         Check if the Index is a floating type.
 
         The Index may consist of only floats, NaNs, or a mix of floats,
         integers, or NaNs.
 
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_float_dtype` instead.
+
         Returns
         -------
         bool
@@ -982,10 +1000,15 @@ def is_floating(self):
         """
         raise NotImplementedError
 
+    _is_floating = is_floating
+
     def is_object(self):
         """
         Check if the Index is of the object dtype.
 
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_object_dtype` instead.
+
         Returns
         -------
         bool
@@ -1016,10 +1039,15 @@ def is_object(self):
         """
         raise NotImplementedError
 
+    _is_object = is_object
+
     def is_categorical(self):
         """
         Check if the Index holds categorical data.
 
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_categorical_dtype` instead.
+
         Returns
         -------
         bool
@@ -1057,10 +1085,15 @@ def is_categorical(self):
         """
         raise NotImplementedError
 
+    _is_categorical = is_categorical
+
     def is_interval(self):
         """
         Check if the Index holds Interval objects.
 
+        .. deprecated:: 23.04
+           Use `cudf.api.types.is_interval_dtype` instead.
+
         Returns
         -------
         bool
@@ -1092,6 +1125,8 @@ def is_interval(self):
         """
         raise NotImplementedError
 
+    _is_interval = is_interval
+
     def _union(self, other, sort=None):
         # TODO: As a future optimization we should explore
         # not doing `to_frame`

python/cudf/cudf/core/_compat.py

@@ -13,3 +13,4 @@
 PANDAS_LT_140 = PANDAS_VERSION < version.parse("1.4.0")
 PANDAS_GE_150 = PANDAS_VERSION >= version.parse("1.5.0")
 PANDAS_LT_153 = PANDAS_VERSION < version.parse("1.5.3")
+PANDAS_GE_200 = PANDAS_VERSION >= version.parse("2.0.0")