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Merged
merged 12 commits into from
Oct 13, 2025
Merged

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
78b4f47
Merge branch 'main' into unify-freq
loicdiridollou Oct 10, 2025
fb03bc6
Merge remote-tracking branch 'upstream/main' into unify-freq
MarcoGorelli Oct 13, 2025
ddaab9e
Merge branch 'unify-freq' of github.com:MarcoGorelli/pandas-stubs int…
MarcoGorelli Oct 13, 2025
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13 changes: 8 additions & 5 deletions pandas-stubs/_libs/tslibs/period.pyi
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Expand Up @@ -15,9 +15,12 @@ from pandas import (
from typing_extensions import TypeAlias

from pandas._libs.tslibs import NaTType
from pandas._libs.tslibs.offsets import BaseOffset
from pandas._libs.tslibs.offsets import (
BaseOffset,
)
from pandas._libs.tslibs.timestamps import Timestamp
from pandas._typing import (
PeriodFrequency,
ShapeT,
np_1darray,
np_ndarray,
Expand Down Expand Up @@ -62,7 +65,7 @@ class Period(PeriodMixin):
value: (
Period | str | datetime.datetime | datetime.date | Timestamp | None
) = ...,
freq: str | BaseOffset | None = ...,
freq: PeriodFrequency | None = None,
ordinal: int | None = ...,
year: int | None = ...,
month: int | None = ...,
Expand Down Expand Up @@ -226,12 +229,12 @@ 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: PeriodFrequency, how: _PeriodFreqHow = "end") -> Period: ...
@classmethod
def now(cls, freq: str | BaseOffset = ...) -> Period: ...
def now(cls, freq: PeriodFrequency | None = None) -> Period: ...
def strftime(self, fmt: str) -> str: ...
def to_timestamp(
self,
freq: str | BaseOffset | None = ...,
freq: PeriodFrequency | None = 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 -135 to +137
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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')

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Maybe worth a question in the pandas repo so we can make sure we are not allowing a behavior that is not fully supported. Otherwise the rest looks good, @Dr-Irv do you see anything else prior to merging?

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@loicdiridollou I'm going to trust your review process rather than having me look at it!

@property
def resolution_string(self) -> str: ...
# Override due to more types supported than dt.timedelta
Expand Down
4 changes: 2 additions & 2 deletions pandas-stubs/_libs/tslibs/timestamps.pyi
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Expand Up @@ -30,12 +30,12 @@ from typing_extensions import (
)

from pandas._libs.tslibs import (
BaseOffset,
Period,
Tick,
Timedelta,
)
from pandas._typing import (
PeriodFrequency,
ShapeT,
TimestampNonexistent,
TimeUnit,
Expand Down Expand Up @@ -282,7 +282,7 @@ class Timestamp(datetime, SupportsIndex):
def is_year_end(self) -> bool: ...
def to_pydatetime(self, warn: bool = ...) -> datetime: ...
def to_datetime64(self) -> np.datetime64: ...
def to_period(self, freq: BaseOffset | str | None = ...) -> Period: ...
def to_period(self, freq: PeriodFrequency | None = ...) -> Period: ...
def to_julian_date(self) -> np.float64: ...
@property
def asm8(self) -> np.datetime64: ...
Expand Down
27 changes: 27 additions & 0 deletions pandas-stubs/_typing.pyi
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Expand Up @@ -56,6 +56,19 @@ from pandas.core.dtypes.dtypes import (
)

from pandas.io.formats.format import EngFormatter
from pandas.tseries.offsets import (
Day,
Hour,
Micro,
Milli,
Minute,
MonthEnd,
Nano,
QuarterEnd,
Second,
Week,
YearEnd,
)

P = ParamSpec("P")

Expand Down Expand Up @@ -162,6 +175,20 @@ Suffixes: TypeAlias = tuple[str | None, str | None] | list[str | None]
Ordered: TypeAlias = bool | None
JSONSerializable: TypeAlias = PythonScalar | list | dict
Frequency: TypeAlias = str | BaseOffset
PeriodFrequency: TypeAlias = (
str
| Day
| Hour
| Minute
| Second
| Milli
| Micro
| Nano
| YearEnd
| QuarterEnd
| MonthEnd
| Week
)
Axes: TypeAlias = ListLike

RandomState: TypeAlias = (
Expand Down
5 changes: 4 additions & 1 deletion pandas-stubs/core/arrays/period.pyi
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Expand Up @@ -7,6 +7,7 @@ from pandas.core.arrays.datetimelike import (

from pandas._libs.tslibs import Timestamp
from pandas._libs.tslibs.period import Period
from pandas._typing import PeriodFrequency

class PeriodArray(DatetimeLikeArrayMixin, DatelikeOps):
__array_priority__: int = ...
Expand Down Expand Up @@ -37,6 +38,8 @@ class PeriodArray(DatetimeLikeArrayMixin, DatelikeOps):
def start_time(self) -> Timestamp: ...
@property
def end_time(self) -> Timestamp: ...
def to_timestamp(self, freq: str | None = ..., how: str = ...) -> Timestamp: ...
def to_timestamp(
self, freq: PeriodFrequency | None = None, how: str = ...
) -> Timestamp: ...
def asfreq(self, freq: str | None = ..., how: str = "E") -> Period: ...
def astype(self, dtype, copy: bool = True): ...
13 changes: 7 additions & 6 deletions pandas-stubs/core/frame.pyi
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Expand Up @@ -81,7 +81,6 @@ from pandas._libs.lib import _NoDefaultDoNotUse
from pandas._libs.missing import NAType
from pandas._libs.tslibs import BaseOffset
from pandas._libs.tslibs.nattype import NaTType
from pandas._libs.tslibs.offsets import DateOffset
from pandas._typing import (
S2,
AggFuncTypeBase,
Expand All @@ -106,6 +105,7 @@ from pandas._typing import (
FillnaOptions,
FloatFormatType,
FormattersType,
Frequency,
GroupByObjectNonScalar,
HashableT,
HashableT1,
Expand Down Expand Up @@ -135,6 +135,7 @@ from pandas._typing import (
NDFrameT,
NsmallestNlargestKeep,
ParquetEngine,
PeriodFrequency,
QuantileInterpolation,
RandomState,
ReadBuffer,
Expand Down Expand Up @@ -1683,14 +1684,14 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
) -> Self: ...
def to_timestamp(
self,
freq=...,
freq: PeriodFrequency | None = None,
Comment on lines 1682 to +1685
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@MarcoGorelli MarcoGorelli Oct 1, 2025 •
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i've always found bizarre that to_timestamp only accepts period frequencies

In [4]: pd.Period('2020').to_timestamp('MS')
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[4], line 1
----> 1 pd.Period('2020').to_timestamp('MS')

File ~/scratch/.39venv/lib/python3.9/site-packages/pandas/_libs/tslibs/period.pyx:1868, in pandas._libs.tslibs.period._Period.to_timestamp()

AttributeError: 'pandas._libs.tslibs.offsets.MonthBegin' object has no attribute '_period_dtype_code'

(and, on newer versions of pandas):

In [2]: pd.Period('2020').to_timestamp('MS')
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
File pandas/_libs/tslibs/offsets.pyx:5363, in pandas._libs.tslibs.offsets.to_offset()

File pandas/_libs/tslibs/offsets.pyx:5246, in pandas._libs.tslibs.offsets._validate_to_offset_alias()

ValueError: for Period, please use 'M' instead of 'MS'

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
Cell In[2], line 1
----> 1 pd.Period('2020').to_timestamp('MS')

File pandas/_libs/tslibs/period.pyx:2041, in pandas._libs.tslibs.period._Period.to_timestamp()

File pandas/_libs/tslibs/period.pyx:1771, in pandas._libs.tslibs.period._Period._maybe_convert_freq()

File pandas/_libs/tslibs/offsets.pyx:5402, in pandas._libs.tslibs.offsets.to_offset()

ValueError: Invalid frequency: MS, failed to parse with error message: ValueError("for Period, please use 'M' instead of 'MS'")

but whatever, if that's how it is then it should at least be typed to respect that 🤷

how: ToTimestampHow = ...,
axis: Axis = 0,
copy: _bool = True,
) -> Self: ...
def to_period(
self,
freq: _str | None = None,
freq: PeriodFrequency | None = None,
axis: Axis = 0,
copy: _bool = True,
) -> Self: ...
Expand Down Expand Up @@ -2224,7 +2225,7 @@ class DataFrame(NDFrame, OpsMixin, _GetItemHack):
self,
periods: int = 1,
fill_method: None = None,
freq: DateOffset | dt.timedelta | _str | None = ...,
freq: Frequency | dt.timedelta | None = ...,
Comment on lines -2225 to +2226
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I think the typing here was too tight to begin with

In [6]: s = pd.Series([1,2,3], index=pd.date_range('2020', freq='MS', periods=3))

In [7]: s.pct_change(freq='MS')
Out[7]:
2020-01-01    NaN
2020-02-01    1.0
2020-03-01    0.5
Freq: MS, dtype: float64

fill_value: Scalar | NAType | None = ...,
) -> Self: ...
def pop(self, item: _str) -> Series: ...
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
2 changes: 1 addition & 1 deletion pandas-stubs/core/groupby/groupby.pyi
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Expand Up @@ -319,7 +319,7 @@ class GroupBy(BaseGroupBy[NDFrameT]):
periods: int = ...,
fill_method: Literal["bfill", "ffill"] | None | _NoDefaultDoNotUse = ...,
limit: int | None | _NoDefaultDoNotUse = ...,
freq=...,
freq: Frequency | None = None,
axis: Axis | _NoDefaultDoNotUse = ...,
) -> NDFrameT: ...
@final
Expand Down
15 changes: 8 additions & 7 deletions pandas-stubs/core/indexes/accessors.pyi
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Expand Up @@ -34,11 +34,12 @@ from typing_extensions import Never

from pandas._libs.interval import Interval
from pandas._libs.tslibs import BaseOffset
from pandas._libs.tslibs.offsets import DateOffset
from pandas._libs.tslibs.period import Period
from pandas._libs.tslibs.timedeltas import Timedelta
from pandas._libs.tslibs.timestamps import Timestamp
from pandas._typing import (
Frequency,
PeriodFrequency,
TimeAmbiguous,
TimeNonexistent,
TimestampConvention,
Expand Down Expand Up @@ -178,7 +179,7 @@ _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 @@ -188,7 +189,7 @@ 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 @@ -198,7 +199,7 @@ 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 @@ -225,7 +226,7 @@ class _DatetimeLikeNoTZMethods(
],
):
def to_period(
self, freq: str | BaseOffset | None = ...
self, freq: PeriodFrequency | None = None
) -> _DTToPeriodReturnType: ...
def tz_localize(
self,
Expand Down Expand Up @@ -357,12 +358,12 @@ class _PeriodProperties(
def strftime(self, date_format: str) -> _PeriodStrReturnTypes: ...
def to_timestamp(
self,
freq: str | DateOffset | None = ...,
freq: PeriodFrequency | None = None,
how: TimestampConvention = ...,
) -> _PeriodDTAReturnTypes: ...
def asfreq(
self,
freq: str | DateOffset | None = ...,
freq: PeriodFrequency | None = None,
how: Literal["E", "END", "FINISH", "S", "START", "BEGIN"] = ...,
) -> _PeriodPAReturnTypes: ...

Expand Down
14 changes: 6 additions & 8 deletions pandas-stubs/core/indexes/datetimes.pyi
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Expand Up @@ -16,7 +16,6 @@ import numpy as np
from pandas import (
DataFrame,
Index,
Timedelta,
TimedeltaIndex,
Timestamp,
)
Expand All @@ -25,7 +24,6 @@ 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 @@ -99,14 +97,14 @@ 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 @@ -129,7 +127,7 @@ 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 @@ -141,7 +139,7 @@ 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 @@ -153,7 +151,7 @@ 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 @@ -167,7 +165,7 @@ 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
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