Merge branch '2.3.x' of https://github.com/pandas-dev/pandas into 2.3.x

Author: rhshadrach

.github/workflows/wheels.yml

@@ -94,7 +94,7 @@ jobs:
         buildplat:
         - [ubuntu-22.04, manylinux_x86_64]
         - [ubuntu-22.04, musllinux_x86_64]
-        - [macos-12, macosx_x86_64]
+        - [macos-13, macosx_x86_64]
         # Note: M1 images on Github Actions start from macOS 14
         - [macos-14, macosx_arm64]
         - [windows-2022, win_amd64]

doc/source/whatsnew/v2.3.0.rst

@@ -34,7 +34,7 @@ Other enhancements
 
 - The semantics for the ``copy`` keyword in ``__array__`` methods (i.e. called
   when using ``np.array()`` or ``np.asarray()`` on pandas objects) has been
-  updated to work correctly with NumPy >= 2 (:issue:`57739`)
+  updated to raise FutureWarning with NumPy >= 2 (:issue:`60340`)
 - The :meth:`~Series.sum` reduction is now implemented for ``StringDtype`` columns (:issue:`59853`)
 -
 

pandas/_libs/index.pyx

@@ -536,23 +536,15 @@ cdef class StringObjectEngine(ObjectEngine):
 
     cdef:
         object na_value
-        bint uses_na
 
     def __init__(self, ndarray values, na_value):
         super().__init__(values)
         self.na_value = na_value
-        self.uses_na = na_value is C_NA
-
-    cdef bint _checknull(self, object val):
-        if self.uses_na:
-            return val is C_NA
-        else:
-            return util.is_nan(val)
 
     cdef _check_type(self, object val):
         if isinstance(val, str):
             return val
-        elif self._checknull(val):
+        elif checknull(val):
             return self.na_value
         else:
             raise KeyError(val)

pandas/core/arrays/arrow/array.py

@@ -12,6 +12,7 @@
     cast,
 )
 import unicodedata
+import warnings
 
 import numpy as np
 
@@ -28,6 +29,7 @@
     pa_version_under13p0,
 )
 from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
 from pandas.util._validators import validate_fillna_kwargs
 
 from pandas.core.dtypes.cast import (
@@ -663,9 +665,15 @@ def __array__(
     ) -> np.ndarray:
         """Correctly construct numpy arrays when passed to `np.asarray()`."""
         if copy is False:
-            # TODO: By using `zero_copy_only` it may be possible to implement this
-            raise ValueError(
-                "Unable to avoid copy while creating an array as requested."
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
             )
         elif copy is None:
             # `to_numpy(copy=False)` has the meaning of NumPy `copy=None`.
@@ -2150,6 +2158,9 @@ def interpolate(
         See NDFrame.interpolate.__doc__.
         """
         # NB: we return type(self) even if copy=False
+        if not self.dtype._is_numeric:
+            raise TypeError(f"Cannot interpolate with {self.dtype} dtype")
+
         mask = self.isna()
         if self.dtype.kind == "f":
             data = self._pa_array.to_numpy()

pandas/core/arrays/categorical.py

@@ -1672,8 +1672,15 @@ def __array__(
         array(['a', 'b'], dtype=object)
         """
         if copy is False:
-            raise ValueError(
-                "Unable to avoid copy while creating an array as requested."
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
             )
 
         ret = take_nd(self.categories._values, self._codes)

pandas/core/arrays/datetimelike.py

@@ -359,9 +359,17 @@ def __array__(
         # used for Timedelta/DatetimeArray, overwritten by PeriodArray
         if is_object_dtype(dtype):
             if copy is False:
-                raise ValueError(
-                    "Unable to avoid copy while creating an array as requested."
+                warnings.warn(
+                    "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                    "changed and passing 'copy=False' raises an error when returning "
+                    "a zero-copy NumPy array is not possible. pandas will follow this "
+                    "behavior starting with pandas 3.0.\nThis conversion to NumPy "
+                    "requires a copy, but 'copy=False' was passed. Consider using "
+                    "'np.asarray(..)' instead.",
+                    FutureWarning,
+                    stacklevel=find_stack_level(),
                 )
+
             return np.array(list(self), dtype=object)
 
         if copy is True:

pandas/core/arrays/interval.py

@@ -42,6 +42,7 @@
 from pandas.compat.numpy import function as nv
 from pandas.errors import IntCastingNaNError
 from pandas.util._decorators import Appender
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.cast import (
     LossySetitemError,
@@ -1575,8 +1576,15 @@ def __array__(
         objects (with dtype='object')
         """
         if copy is False:
-            raise ValueError(
-                "Unable to avoid copy while creating an array as requested."
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
             )
 
         left = self._left

pandas/core/arrays/masked.py

@@ -38,6 +38,7 @@
 )
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import doc
+from pandas.util._exceptions import find_stack_level
 from pandas.util._validators import validate_fillna_kwargs
 
 from pandas.core.dtypes.base import ExtensionDtype
@@ -604,8 +605,16 @@ def __array__(
             if not self._hasna:
                 # special case, here we can simply return the underlying data
                 return np.array(self._data, dtype=dtype, copy=copy)
-            raise ValueError(
-                "Unable to avoid copy while creating an array as requested."
+
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
             )
 
         if copy is None:

pandas/core/arrays/numpy_.py

@@ -287,6 +287,9 @@ def interpolate(
         See NDFrame.interpolate.__doc__.
         """
         # NB: we return type(self) even if copy=False
+        if not self.dtype._is_numeric:
+            raise TypeError(f"Cannot interpolate with {self.dtype} dtype")
+
         if not copy:
             out_data = self._ndarray
         else:

pandas/core/arrays/period.py

@@ -415,8 +415,15 @@ def __array__(
                 return np.array(self.asi8, dtype=dtype)
 
         if copy is False:
-            raise ValueError(
-                "Unable to avoid copy while creating an array as requested."
+            warnings.warn(
+                "Starting with NumPy 2.0, the behavior of the 'copy' keyword has "
+                "changed and passing 'copy=False' raises an error when returning "
+                "a zero-copy NumPy array is not possible. pandas will follow "
+                "this behavior starting with pandas 3.0.\nThis conversion to "
+                "NumPy requires a copy, but 'copy=False' was passed. Consider "
+                "using 'np.asarray(..)' instead.",
+                FutureWarning,
+                stacklevel=find_stack_level(),
             )
 
         if dtype == bool: