Merge remote-tracking branch 'upstream/main' into perf/ri/reindex_return

Author: mroeschke

pandas/core/array_algos/quantile.py

@@ -102,7 +102,7 @@ def quantile_with_mask(
  interpolation=interpolation,
  )
 
- result = np.array(result, copy=False)
+ result = np.asarray(result)
  result = result.T
 
  return result
@@ -201,9 +201,9 @@ def _nanpercentile(
  ]
  if values.dtype.kind == "f":
  # preserve itemsize
- result = np.array(result, dtype=values.dtype, copy=False).T
+ result = np.asarray(result, dtype=values.dtype).T
  else:
- result = np.array(result, copy=False).T
+ result = np.asarray(result).T
  if (
  result.dtype != values.dtype
  and not mask.all()

pandas/core/arrays/arrow/array.py

@@ -659,7 +659,9 @@ def __arrow_array__(self, type=None):
  """Convert myself to a pyarrow ChunkedArray."""
  return self._pa_array
 
- def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
+ def __array__(
+ self, dtype: NpDtype | None = None, copy: bool | None = None
+ ) -> np.ndarray:
  """Correctly construct numpy arrays when passed to `np.asarray()`."""
  return self.to_numpy(dtype=dtype)
 

pandas/core/arrays/base.py

@@ -725,7 +725,10 @@ def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
 
  return TimedeltaArray._from_sequence(self, dtype=dtype, copy=copy)
 
- return np.array(self, dtype=dtype, copy=copy)
+ if not copy:
+ return np.asarray(self, dtype=dtype)
+ else:
+ return np.array(self, dtype=dtype, copy=copy)
 
  def isna(self) -> np.ndarray | ExtensionArraySupportsAnyAll:
  """

pandas/core/arrays/categorical.py

@@ -1659,7 +1659,9 @@ def _validate_codes_for_dtype(cls, codes, *, dtype: CategoricalDtype) -> np.ndar
  # -------------------------------------------------------------
 
  @ravel_compat
- def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
+ def __array__(
+ self, dtype: NpDtype | None = None, copy: bool | None = None
+ ) -> np.ndarray:
  """
  The numpy array interface.
 
@@ -1668,6 +1670,9 @@ def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
  dtype : np.dtype or None
  Specifies the the dtype for the array.
 
+ copy : bool or None, optional
+ Unused.
+
  Returns
  -------
  numpy.array

pandas/core/arrays/datetimelike.py

@@ -353,7 +353,9 @@ def _formatter(self, boxed: bool = False) -> Callable[[object], str]:
  # ----------------------------------------------------------------
  # Array-Like / EA-Interface Methods
 
- def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
+ def __array__(
+ self, dtype: NpDtype | None = None, copy: bool | None = None
+ ) -> np.ndarray:
  # used for Timedelta/DatetimeArray, overwritten by PeriodArray
  if is_object_dtype(dtype):
  return np.array(list(self), dtype=object)

pandas/core/arrays/datetimes.py

@@ -649,12 +649,12 @@ def _resolution_obj(self) -> Resolution:
  # ----------------------------------------------------------------
  # Array-Like / EA-Interface Methods
 
- def __array__(self, dtype=None) -> np.ndarray:
+ def __array__(self, dtype=None, copy=None) -> np.ndarray:
  if dtype is None and self.tz:
  # The default for tz-aware is object, to preserve tz info
  dtype = object
 
- return super().__array__(dtype=dtype)
+ return super().__array__(dtype=dtype, copy=copy)
 
  def __iter__(self) -> Iterator:
  """
@@ -2421,7 +2421,7 @@ def objects_to_datetime64(
  assert errors in ["raise", "coerce"]
 
  # if str-dtype, convert
- data = np.array(data, copy=False, dtype=np.object_)
+ data = np.asarray(data, dtype=np.object_)
 
  result, tz_parsed = tslib.array_to_datetime(
  data,

pandas/core/arrays/interval.py

@@ -1564,7 +1564,9 @@ def is_non_overlapping_monotonic(self) -> bool:
  # ---------------------------------------------------------------------
  # Conversion
 
- def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
+ def __array__(
+ self, dtype: NpDtype | None = None, copy: bool | None = None
+ ) -> np.ndarray:
  """
  Return the IntervalArray's data as a numpy array of Interval
  objects (with dtype='object')

pandas/core/arrays/masked.py

@@ -594,7 +594,9 @@ def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
 
  __array_priority__ = 1000 # higher than ndarray so ops dispatch to us
 
- def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
+ def __array__(
+ self, dtype: NpDtype | None = None, copy: bool | None = None
+ ) -> np.ndarray:
  """
  the array interface, return my values
  We return an object array here to preserve our scalar values

pandas/core/arrays/numeric.py

@@ -160,7 +160,10 @@ def _coerce_to_data_and_mask(
  return values, mask, dtype, inferred_type
 
  original = values
- values = np.array(values, copy=copy)
+ if not copy:
+ values = np.asarray(values)
+ else:
+ values = np.array(values, copy=copy)
  inferred_type = None
  if values.dtype == object or is_string_dtype(values.dtype):
  inferred_type = lib.infer_dtype(values, skipna=True)
@@ -169,7 +172,10 @@ def _coerce_to_data_and_mask(
  raise TypeError(f"{values.dtype} cannot be converted to {name}")
 
  elif values.dtype.kind == "b" and checker(dtype):
- values = np.array(values, dtype=default_dtype, copy=copy)
+ if not copy:
+ values = np.asarray(values, dtype=default_dtype)
+ else:
+ values = np.array(values, dtype=default_dtype, copy=copy)
 
  elif values.dtype.kind not in "iuf":
  name = dtype_cls.__name__.strip("_")
@@ -208,9 +214,9 @@ def _coerce_to_data_and_mask(
  inferred_type not in ["floating", "mixed-integer-float"]
  and not mask.any()
  ):
- values = np.array(original, dtype=dtype, copy=False)
+ values = np.asarray(original, dtype=dtype)
  else:
- values = np.array(original, dtype="object", copy=False)
+ values = np.asarray(original, dtype="object")
 
  # we copy as need to coerce here
  if mask.any():

pandas/core/arrays/numpy_.py

@@ -150,7 +150,9 @@ def dtype(self) -> NumpyEADtype:
  # ------------------------------------------------------------------------
  # NumPy Array Interface
 
- def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
+ def __array__(
+ self, dtype: NpDtype | None = None, copy: bool | None = None
+ ) -> np.ndarray:
  return np.asarray(self._ndarray, dtype=dtype)
 
  def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):