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type freq in shift, consistently use Frequency alias #1394

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8078382
type `freq` in `shift`, consistently use `Frequency` alias
MarcoGorelli Sep 30, 2025
8e2b00e
Merge remote-tracking branch 'upstream/main' into unify-freq
MarcoGorelli Oct 1, 2025
83dd9c5
add some missing freq annotations#
MarcoGorelli Oct 1, 2025
1bfd52a
restore DateOffset
MarcoGorelli Oct 1, 2025
046e696
restore DateOffset
MarcoGorelli Oct 1, 2025
7b4194b
fixup
MarcoGorelli Oct 1, 2025
6437417
separate more
MarcoGorelli Oct 1, 2025
a47ead6
Period.now
MarcoGorelli Oct 1, 2025
7af641f
use PeriodFrequency
MarcoGorelli Oct 1, 2025
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9 changes: 5 additions & 4 deletions pandas-stubs/_libs/tslibs/period.pyi
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Expand Up @@ -21,6 +21,7 @@ from pandas._libs.tslibs import NaTType
from pandas._libs.tslibs.offsets import BaseOffset
from pandas._libs.tslibs.timestamps import Timestamp
from pandas._typing import (
Frequency,
ShapeT,
np_1darray,
np_ndarray,
Expand Down Expand Up @@ -65,7 +66,7 @@ class Period(PeriodMixin):
value: (
Period | str | datetime.datetime | datetime.date | Timestamp | None
) = ...,
freq: str | BaseOffset | None = ...,
freq: Frequency | None = ...,
ordinal: int | None = ...,
year: int | None = ...,
month: int | None = ...,
Expand Down Expand Up @@ -229,12 +230,12 @@ class Period(PeriodMixin):
def day_of_year(self) -> int: ...
@property
def day_of_week(self) -> int: ...
def asfreq(self, freq: str | BaseOffset, how: _PeriodFreqHow = "end") -> Period: ...
def asfreq(self, freq: Frequency, how: _PeriodFreqHow = "end") -> Period: ...
@classmethod
def now(cls, freq: str | BaseOffset = ...) -> Period: ...
def now(cls, freq: Frequency = ...) -> Period: ...
def strftime(self, fmt: str) -> str: ...
def to_timestamp(
self,
freq: str | BaseOffset | None = ...,
freq: Frequency | None = ...,
how: _PeriodToTimestampHow = "S",
) -> Timestamp: ...
8 changes: 4 additions & 4 deletions pandas-stubs/_libs/tslibs/timedeltas.pyi
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Expand Up @@ -23,12 +23,12 @@ from typing_extensions import (
)

from pandas._libs.tslibs import (
BaseOffset,
NaTType,
)
from pandas._libs.tslibs.period import Period
from pandas._libs.tslibs.timestamps import Timestamp
from pandas._typing import (
Frequency,
ShapeT,
TimeUnit,
np_1darray,
Expand Down Expand Up @@ -130,9 +130,9 @@ class Timedelta(timedelta):
@property
def asm8(self) -> np.timedelta64: ...
# TODO: round/floor/ceil could return NaT?
def round(self, freq: str | BaseOffset) -> Self: ...
def floor(self, freq: str | BaseOffset) -> Self: ...
def ceil(self, freq: str | BaseOffset) -> Self: ...
def round(self, freq: Frequency) -> Self: ...
def floor(self, freq: Frequency) -> Self: ...
def ceil(self, freq: Frequency) -> Self: ...
Comment on lines -131 to +135
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docs say str but offset objects are also accepted, so I think it's ok to keep the current annotation

In [11]: pd.Timedelta(minutes=37).round(pd.tseries.offsets.Hour())
Out[11]: Timedelta('0 days 01:00:00')

@property
def resolution_string(self) -> str: ...
# Override due to more types supported than dt.timedelta
Expand Down
5 changes: 3 additions & 2 deletions pandas-stubs/core/frame.pyi
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Expand Up @@ -106,6 +106,7 @@ from pandas._typing import (
FillnaOptions,
FloatFormatType,
FormattersType,
Frequency,
GroupByObjectNonScalar,
HashableT,
HashableT1,
Expand Down Expand Up @@ -2341,7 +2342,7 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
@overload
def rolling(
self,
window: int | str | dt.timedelta | BaseOffset | BaseIndexer,
window: int | Frequency | dt.timedelta | BaseIndexer,
min_periods: int | None = ...,
center: _bool = ...,
on: Hashable | None = ...,
Expand All @@ -2355,7 +2356,7 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
@overload
def rolling(
self,
window: int | str | dt.timedelta | BaseOffset | BaseIndexer,
window: int | Frequency | dt.timedelta | BaseIndexer,
min_periods: int | None = ...,
center: _bool = ...,
on: Hashable | None = ...,
Expand Down
22 changes: 10 additions & 12 deletions pandas-stubs/core/indexes/accessors.pyi
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Expand Up @@ -33,10 +33,10 @@ from pandas.core.series import (
from typing_extensions import Never

from pandas._libs.tslibs import BaseOffset
from pandas._libs.tslibs.offsets import DateOffset
from pandas._libs.tslibs.period import Period
from pandas._typing import (
S1,
Frequency,
TimeAmbiguous,
TimeNonexistent,
TimestampConvention,
Expand Down Expand Up @@ -174,7 +174,7 @@ _DTTimestampTimedeltaReturnType = TypeVar(
class _DatetimeRoundingMethods(Generic[_DTTimestampTimedeltaReturnType]):
def round(
self,
freq: str | BaseOffset | None,
freq: Frequency | None,
ambiguous: Literal["raise", "infer", "NaT"] | bool | np_ndarray_bool = ...,
nonexistent: (
Literal["shift_forward", "shift_backward", "NaT", "raise"]
Expand All @@ -184,7 +184,7 @@ class _DatetimeRoundingMethods(Generic[_DTTimestampTimedeltaReturnType]):
) -> _DTTimestampTimedeltaReturnType: ...
def floor(
self,
freq: str | BaseOffset | None,
freq: Frequency | None,
ambiguous: Literal["raise", "infer", "NaT"] | bool | np_ndarray_bool = ...,
nonexistent: (
Literal["shift_forward", "shift_backward", "NaT", "raise"]
Expand All @@ -194,7 +194,7 @@ class _DatetimeRoundingMethods(Generic[_DTTimestampTimedeltaReturnType]):
) -> _DTTimestampTimedeltaReturnType: ...
def ceil(
self,
freq: str | BaseOffset | None,
freq: Frequency | None,
ambiguous: Literal["raise", "infer", "NaT"] | bool | np_ndarray_bool = ...,
nonexistent: (
Literal["shift_forward", "shift_backward", "NaT", "raise"]
Expand All @@ -220,9 +220,7 @@ class _DatetimeLikeNoTZMethods(
_DTToPeriodReturnType,
],
):
def to_period(
self, freq: str | BaseOffset | None = ...
) -> _DTToPeriodReturnType: ...
def to_period(self, freq: Frequency | None = ...) -> _DTToPeriodReturnType: ...
def tz_localize(
self,
tz: TimeZones,
Expand Down Expand Up @@ -353,12 +351,12 @@ class _PeriodProperties(
def strftime(self, date_format: str) -> _PeriodStrReturnTypes: ...
def to_timestamp(
self,
freq: str | DateOffset | None = ...,
freq: Frequency | None = ...,
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Okay so here is a good example:

idx = pd.PeriodIndex(["2023", "2024", "2025"], freq="D")
s1 = pd.Series([1, 2, 3], index=idx)
s1.to_timestamp(freq=pd.tseries.offsets.BDay())

This will raise a FutureWarning:

<stdin>:1: FutureWarning: PeriodDtype[B] is deprecated and will be removed in a future version. Use a DatetimeIndex with freq='B' instead

The issue is that BDay is not a DateOffset. I think it is a bit of an edge case and this situation works at runtime for now but good to think about the future, I am good with letting the types a bit looser here, @Dr-Irv what do you think?

how: TimestampConvention = ...,
) -> _PeriodDTAReturnTypes: ...
def asfreq(
self,
freq: str | DateOffset | None = ...,
freq: Frequency | None = ...,
how: Literal["E", "END", "FINISH", "S", "START", "BEGIN"] = ...,
) -> _PeriodPAReturnTypes: ...

Expand Down Expand Up @@ -453,7 +451,7 @@ class _dtDescriptor(CombinedDatetimelikeProperties, Generic[S1]):
) -> CombinedDatetimelikeProperties: ...
def round(
self,
freq: str | BaseOffset | None,
freq: Frequency | None,
ambiguous: Literal["raise", "infer", "NaT"] | bool | np_ndarray_bool = ...,
nonexistent: (
Literal["shift_forward", "shift_backward", "NaT", "raise"]
Expand All @@ -463,7 +461,7 @@ class _dtDescriptor(CombinedDatetimelikeProperties, Generic[S1]):
) -> Series[S1]: ...
def floor(
self,
freq: str | BaseOffset | None,
freq: Frequency | None,
ambiguous: Literal["raise", "infer", "NaT"] | bool | np_ndarray_bool = ...,
nonexistent: (
Literal["shift_forward", "shift_backward", "NaT", "raise"]
Expand All @@ -473,7 +471,7 @@ class _dtDescriptor(CombinedDatetimelikeProperties, Generic[S1]):
) -> Series[S1]: ...
def ceil(
self,
freq: str | BaseOffset | None,
freq: Frequency | None,
ambiguous: Literal["raise", "infer", "NaT"] | bool | np_ndarray_bool = ...,
nonexistent: (
Literal["shift_forward", "shift_backward", "NaT", "raise"]
Expand Down
14 changes: 6 additions & 8 deletions pandas-stubs/core/indexes/datetimes.pyi
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Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,6 @@ import numpy as np
from pandas import (
DataFrame,
Index,
Timedelta,
TimedeltaIndex,
Timestamp,
)
Expand All @@ -26,7 +25,6 @@ from pandas.core.indexes.datetimelike import DatetimeTimedeltaMixin
from pandas.core.series import Series
from typing_extensions import Self

from pandas._libs.tslibs.offsets import DateOffset
from pandas._typing import (
AxesData,
DateAndDatetimeLike,
Expand Down Expand Up @@ -103,14 +101,14 @@ class DatetimeIndex(
@property
def dtype(self) -> np.dtype | DatetimeTZDtype: ...
def shift(
self, periods: int = 1, freq: DateOffset | Timedelta | str | None = None
self, periods: int = 1, freq: Frequency | timedelta | None = None
) -> Self: ...

@overload
def date_range(
start: str | DateAndDatetimeLike,
end: str | DateAndDatetimeLike,
freq: str | timedelta | Timedelta | BaseOffset | None = None,
freq: Frequency | timedelta | None = None,
tz: TimeZones = None,
normalize: bool = False,
name: Hashable | None = None,
Expand All @@ -133,7 +131,7 @@ def date_range(
start: str | DateAndDatetimeLike,
*,
periods: int,
freq: str | timedelta | Timedelta | BaseOffset | None = None,
freq: Frequency | timedelta | None = None,
tz: TimeZones = None,
normalize: bool = False,
name: Hashable | None = None,
Expand All @@ -145,7 +143,7 @@ def date_range(
*,
end: str | DateAndDatetimeLike,
periods: int,
freq: str | timedelta | Timedelta | BaseOffset | None = None,
freq: Frequency | timedelta | None = None,
tz: TimeZones = None,
normalize: bool = False,
name: Hashable | None = None,
Expand All @@ -157,7 +155,7 @@ def bdate_range(
start: str | DateAndDatetimeLike | None = ...,
end: str | DateAndDatetimeLike | None = ...,
periods: int | None = ...,
freq: str | timedelta | Timedelta | BaseOffset = ...,
freq: Frequency | timedelta = ...,
tz: TimeZones = ...,
normalize: bool = ...,
name: Hashable | None = ...,
Expand All @@ -171,7 +169,7 @@ def bdate_range(
end: str | DateAndDatetimeLike | None = ...,
periods: int | None = ...,
*,
freq: str | timedelta | Timedelta | BaseOffset,
freq: Frequency | timedelta,
tz: TimeZones = ...,
normalize: bool = ...,
name: Hashable | None = ...,
Expand Down
10 changes: 5 additions & 5 deletions pandas-stubs/core/indexes/interval.pyi
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Expand Up @@ -19,11 +19,11 @@ from pandas._libs.interval import (
Interval as Interval,
IntervalMixin,
)
from pandas._libs.tslibs.offsets import BaseOffset
from pandas._typing import (
DatetimeLike,
DtypeArg,
FillnaOptions,
Frequency,
IntervalClosedType,
IntervalT,
Label,
Expand Down Expand Up @@ -328,7 +328,7 @@ def interval_range(
start: _TimestampLike,
end: _TimestampLike | None = ...,
periods: int | None = ...,
freq: str | BaseOffset | pd.Timedelta | dt.timedelta | None = ...,
freq: Frequency | dt.timedelta | None = ...,
name: Hashable = ...,
closed: IntervalClosedType = ...,
) -> IntervalIndex[Interval[pd.Timestamp]]: ...
Expand All @@ -338,7 +338,7 @@ def interval_range(
start: None = ...,
end: _TimestampLike,
periods: int | None = ...,
freq: str | BaseOffset | pd.Timedelta | dt.timedelta | None = ...,
freq: Frequency | dt.timedelta | None = ...,
name: Hashable = ...,
closed: IntervalClosedType = ...,
) -> IntervalIndex[Interval[pd.Timestamp]]: ...
Expand All @@ -347,7 +347,7 @@ def interval_range(
start: _TimedeltaLike,
end: _TimedeltaLike | None = ...,
periods: int | None = ...,
freq: str | BaseOffset | pd.Timedelta | dt.timedelta | None = ...,
freq: Frequency | dt.timedelta | None = ...,
name: Hashable = ...,
closed: IntervalClosedType = ...,
) -> IntervalIndex[Interval[pd.Timedelta]]: ...
Expand All @@ -357,7 +357,7 @@ def interval_range(
start: None = ...,
end: _TimedeltaLike,
periods: int | None = ...,
freq: str | BaseOffset | pd.Timedelta | dt.timedelta | None = ...,
freq: Frequency | dt.timedelta | None = ...,
name: Hashable = ...,
closed: IntervalClosedType = ...,
) -> IntervalIndex[Interval[pd.Timedelta]]: ...
15 changes: 8 additions & 7 deletions pandas-stubs/core/indexes/timedeltas.pyi
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Expand Up @@ -11,7 +11,6 @@ from typing import (

import numpy as np
from pandas import (
DateOffset,
Index,
Period,
)
Expand All @@ -23,9 +22,9 @@ from pandas.core.series import Series
from typing_extensions import Self

from pandas._libs import Timedelta
from pandas._libs.tslibs import BaseOffset
from pandas._typing import (
AxesData,
Frequency,
TimedeltaConvertibleTypes,
np_ndarray_td,
num,
Expand All @@ -39,7 +38,7 @@ class TimedeltaIndex(
data: (
Sequence[dt.timedelta | Timedelta | np.timedelta64 | float] | AxesData
) = ...,
freq: str | BaseOffset = ...,
freq: Frequency = ...,
closed: object = ...,
dtype: Literal["<m8[ns]"] = ...,
copy: bool = ...,
Expand Down Expand Up @@ -86,14 +85,16 @@ class TimedeltaIndex(
def inferred_type(self) -> str: ...
@final
def to_series(self, index=..., name: Hashable = ...) -> Series[Timedelta]: ...
def shift(self, periods: int = 1, freq=...) -> Self: ...
def shift(
self, periods: int = 1, freq: Frequency | dt.timedelta | None = None
) -> Self: ...

@overload
def timedelta_range(
start: TimedeltaConvertibleTypes,
end: TimedeltaConvertibleTypes,
*,
freq: str | DateOffset | Timedelta | dt.timedelta | None = None,
freq: Frequency | Timedelta | dt.timedelta | None = None,
name: Hashable | None = None,
closed: Literal["left", "right"] | None = None,
unit: None | str = ...,
Expand All @@ -103,7 +104,7 @@ def timedelta_range(
*,
end: TimedeltaConvertibleTypes,
periods: int,
freq: str | DateOffset | Timedelta | dt.timedelta | None = None,
freq: Frequency | Timedelta | dt.timedelta | None = None,
name: Hashable | None = None,
closed: Literal["left", "right"] | None = None,
unit: None | str = ...,
Expand All @@ -113,7 +114,7 @@ def timedelta_range(
start: TimedeltaConvertibleTypes,
*,
periods: int,
freq: str | DateOffset | Timedelta | dt.timedelta | None = None,
freq: Frequency | Timedelta | dt.timedelta | None = None,
name: Hashable | None = None,
closed: Literal["left", "right"] | None = None,
unit: None | str = ...,
Expand Down
5 changes: 3 additions & 2 deletions pandas-stubs/core/series.pyi
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Expand Up @@ -142,6 +142,7 @@ from pandas._typing import (
FillnaOptions,
FloatDtypeArg,
FloatFormatType,
Frequency,
GenericT,
GenericT_co,
GroupByObjectNonScalar,
Expand Down Expand Up @@ -4520,7 +4521,7 @@ class Series(IndexOpsMixin[S1], NDFrame):
@overload
def rolling(
self,
window: int | _str | timedelta | BaseOffset | BaseIndexer,
window: int | Frequency | timedelta | BaseIndexer,
min_periods: int | None = ...,
center: _bool = ...,
on: _str | None = ...,
Expand All @@ -4533,7 +4534,7 @@ class Series(IndexOpsMixin[S1], NDFrame):
@overload
def rolling(
self,
window: int | _str | timedelta | BaseOffset | BaseIndexer,
window: int | Frequency | timedelta | BaseIndexer,
min_periods: int | None = ...,
center: _bool = ...,
on: _str | None = ...,
Expand Down