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Merged
merged 3 commits into from
Dec 13, 2022
Merged

REF: de-duplicate period-dispatch #50215

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1faeca0
REF: de-duplicate period-dispatch
jbrockmendel Dec 12, 2022
17f9dde
Merge branch 'main' into ref-period_dispatch
jbrockmendel Dec 13, 2022
e308831
mypy fixup
jbrockmendel Dec 13, 2022
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72 changes: 38 additions & 34 deletions pandas/core/arrays/datetimelike.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
datetime,
timedelta,
)
from functools import wraps
import operator
from typing import (
TYPE_CHECKING,
Expand Down Expand Up @@ -57,6 +58,7 @@
DatetimeLikeScalar,
Dtype,
DtypeObj,
F,
NpDtype,
PositionalIndexer2D,
PositionalIndexerTuple,
Expand Down Expand Up @@ -157,6 +159,31 @@
DatetimeLikeArrayT = TypeVar("DatetimeLikeArrayT", bound="DatetimeLikeArrayMixin")


def _period_dispatch(meth: F) -> F:
"""
For PeriodArray methods, dispatch to DatetimeArray and re-wrap the results
in PeriodArray. We cannot use ._ndarray directly for the affected
methods because the i8 data has different semantics on NaT values.
"""

@wraps(meth)
def new_meth(self, *args, **kwargs):
if not is_period_dtype(self.dtype):
return meth(self, *args, **kwargs)

arr = self.view("M8[ns]")
result = meth(arr, *args, **kwargs)
if result is NaT:
return NaT
elif isinstance(result, Timestamp):
return self._box_func(result.value)

res_i8 = result.view("i8")
return self._from_backing_data(res_i8)

return cast(F, new_meth)


class DatetimeLikeArrayMixin(OpsMixin, NDArrayBackedExtensionArray):
"""
Shared Base/Mixin class for DatetimeArray, TimedeltaArray, PeriodArray
Expand Down Expand Up @@ -1546,6 +1573,15 @@ def __isub__(self: DatetimeLikeArrayT, other) -> DatetimeLikeArrayT:
# --------------------------------------------------------------
# Reductions

@_period_dispatch
def _quantile(
self: DatetimeLikeArrayT,
qs: npt.NDArray[np.float64],
interpolation: str,
) -> DatetimeLikeArrayT:
return super()._quantile(qs=qs, interpolation=interpolation)

@_period_dispatch
def min(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
"""
Return the minimum value of the Array or minimum along
Expand All @@ -1560,21 +1596,10 @@ def min(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
nv.validate_min((), kwargs)
nv.validate_minmax_axis(axis, self.ndim)

if is_period_dtype(self.dtype):
# pass datetime64 values to nanops to get correct NaT semantics
result = nanops.nanmin(
self._ndarray.view("M8[ns]"), axis=axis, skipna=skipna
)
if result is NaT:
return NaT
result = result.view("i8")
if axis is None or self.ndim == 1:
return self._box_func(result)
return self._from_backing_data(result)

result = nanops.nanmin(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)

@_period_dispatch
def max(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
"""
Return the maximum value of the Array or maximum along
Expand All @@ -1589,18 +1614,6 @@ def max(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
nv.validate_max((), kwargs)
nv.validate_minmax_axis(axis, self.ndim)

if is_period_dtype(self.dtype):
# pass datetime64 values to nanops to get correct NaT semantics
result = nanops.nanmax(
self._ndarray.view("M8[ns]"), axis=axis, skipna=skipna
)
if result is NaT:
return result
result = result.view("i8")
if axis is None or self.ndim == 1:
return self._box_func(result)
return self._from_backing_data(result)

result = nanops.nanmax(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)

Expand Down Expand Up @@ -1641,22 +1654,13 @@ def mean(self, *, skipna: bool = True, axis: AxisInt | None = 0):
)
return self._wrap_reduction_result(axis, result)

@_period_dispatch
def median(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
nv.validate_median((), kwargs)

if axis is not None and abs(axis) >= self.ndim:
raise ValueError("abs(axis) must be less than ndim")

if is_period_dtype(self.dtype):
# pass datetime64 values to nanops to get correct NaT semantics
result = nanops.nanmedian(
self._ndarray.view("M8[ns]"), axis=axis, skipna=skipna
)
result = result.view("i8")
if axis is None or self.ndim == 1:
return self._box_func(result)
return self._from_backing_data(result)

result = nanops.nanmedian(self._ndarray, axis=axis, skipna=skipna)
return self._wrap_reduction_result(axis, result)

Expand Down
17 changes: 4 additions & 13 deletions pandas/core/arrays/period.py
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Original file line number Diff line number Diff line change
Expand Up @@ -672,31 +672,22 @@ def searchsorted(
) -> npt.NDArray[np.intp] | np.intp:
npvalue = self._validate_setitem_value(value).view("M8[ns]")

# Cast to M8 to get datetime-like NaT placement
# Cast to M8 to get datetime-like NaT placement,
# similar to dtl._period_dispatch
m8arr = self._ndarray.view("M8[ns]")
return m8arr.searchsorted(npvalue, side=side, sorter=sorter)

def fillna(self, value=None, method=None, limit=None) -> PeriodArray:
if method is not None:
# view as dt64 so we get treated as timelike in core.missing
# view as dt64 so we get treated as timelike in core.missing,
# similar to dtl._period_dispatch
dta = self.view("M8[ns]")
result = dta.fillna(value=value, method=method, limit=limit)
# error: Incompatible return value type (got "Union[ExtensionArray,
# ndarray[Any, Any]]", expected "PeriodArray")
return result.view(self.dtype) # type: ignore[return-value]
return super().fillna(value=value, method=method, limit=limit)

def _quantile(
self: PeriodArray,
qs: npt.NDArray[np.float64],
interpolation: str,
) -> PeriodArray:
# dispatch to DatetimeArray implementation
dtres = self.view("M8[ns]")._quantile(qs, interpolation)
# error: Incompatible return value type (got "Union[ExtensionArray,
# ndarray[Any, Any]]", expected "PeriodArray")
return dtres.view(self.dtype) # type: ignore[return-value]

# ------------------------------------------------------------------
# Arithmetic Methods

Expand Down