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series.pyi
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from collections.abc import (
Callable,
Hashable,
Iterable,
Iterator,
Mapping,
MutableMapping,
Sequence,
)
from datetime import (
date,
datetime,
time,
timedelta,
)
from typing import (
Any,
ClassVar,
Generic,
Literal,
overload,
)
from matplotlib.axes import (
Axes as PlotAxes,
SubplotBase,
)
import numpy as np
from pandas import (
Period,
PeriodDtype,
Timedelta,
Timestamp,
)
from pandas.core.api import (
Int8Dtype as Int8Dtype,
Int16Dtype as Int16Dtype,
Int32Dtype as Int32Dtype,
Int64Dtype as Int64Dtype,
)
from pandas.core.arrays.base import ExtensionArray
from pandas.core.arrays.categorical import CategoricalAccessor
from pandas.core.arrays.interval import IntervalArray
from pandas.core.base import IndexOpsMixin
from pandas.core.frame import DataFrame
from pandas.core.generic import NDFrame
from pandas.core.groupby.generic import SeriesGroupBy
from pandas.core.indexers import BaseIndexer
from pandas.core.indexes.accessors import (
CombinedDatetimelikeProperties,
PeriodProperties,
TimedeltaProperties,
TimestampProperties,
)
from pandas.core.indexes.base import Index
from pandas.core.indexes.category import CategoricalIndex
from pandas.core.indexes.datetimes import DatetimeIndex
from pandas.core.indexes.interval import IntervalIndex
from pandas.core.indexes.multi import MultiIndex
from pandas.core.indexes.period import PeriodIndex
from pandas.core.indexes.timedeltas import TimedeltaIndex
from pandas.core.indexing import (
_AtIndexer,
_iAtIndexer,
_iLocIndexer,
_IndexSliceTuple,
_LocIndexer,
)
from pandas.core.resample import Resampler
from pandas.core.strings import StringMethods
from pandas.core.window import (
Expanding,
ExponentialMovingWindow,
)
from pandas.core.window.rolling import (
Rolling,
Window,
)
from typing_extensions import (
Never,
Self,
TypeAlias,
)
import xarray as xr
from pandas._libs.interval import (
Interval,
_OrderableT,
)
from pandas._libs.missing import NAType
from pandas._libs.tslibs import BaseOffset
from pandas._typing import (
S1,
AggFuncTypeBase,
AggFuncTypeDictFrame,
AggFuncTypeSeriesToFrame,
AnyArrayLike,
ArrayLike,
Axes,
Axis,
AxisColumn,
AxisIndex,
BooleanDtypeArg,
BytesDtypeArg,
CalculationMethod,
CategoryDtypeArg,
ComplexDtypeArg,
CompressionOptions,
Dtype,
DtypeObj,
FilePath,
FillnaOptions,
FloatDtypeArg,
GroupByObjectNonScalar,
HashableT1,
HashableT2,
HashableT3,
IgnoreRaise,
IndexingInt,
IndexLabel,
IntDtypeArg,
InterpolateOptions,
IntervalClosedType,
IntervalT,
JoinHow,
JsonSeriesOrient,
Level,
ListLike,
ListLikeU,
MaskType,
NaPosition,
ObjectDtypeArg,
QuantileInterpolation,
RandomState,
Renamer,
ReplaceMethod,
Scalar,
ScalarT,
SeriesByT,
SortKind,
StrDtypeArg,
StrLike,
TimedeltaDtypeArg,
TimestampConvention,
TimestampDtypeArg,
UIntDtypeArg,
VoidDtypeArg,
WriteBuffer,
np_ndarray_anyint,
np_ndarray_bool,
npt,
num,
)
from pandas.core.dtypes.base import ExtensionDtype
from pandas.plotting import PlotAccessor
_bool = bool
_str = str
class _iLocIndexerSeries(_iLocIndexer, Generic[S1]):
# get item
@overload
def __getitem__(self, idx: IndexingInt) -> S1: ...
@overload
def __getitem__(self, idx: Index | slice | np_ndarray_anyint) -> Series[S1]: ...
# set item
@overload
def __setitem__(self, idx: int, value: S1 | None) -> None: ...
@overload
def __setitem__(
self,
idx: Index | slice | np_ndarray_anyint | list[int],
value: S1 | Series[S1] | None,
) -> None: ...
class _LocIndexerSeries(_LocIndexer, Generic[S1]):
# ignore needed because of mypy. Overlapping, but we want to distinguish
# having a tuple of just scalars, versus tuples that include slices or Index
@overload
def __getitem__( # type: ignore[overload-overlap] # pyright: ignore[reportOverlappingOverload]
self,
idx: Scalar | tuple[Scalar, ...],
# tuple case is for getting a specific element when using a MultiIndex
) -> S1: ...
@overload
def __getitem__(
self,
idx: MaskType
| Index
| Sequence[float]
| list[str]
| slice
| _IndexSliceTuple
| Callable,
# _IndexSliceTuple is when having a tuple that includes a slice. Could just
# be s.loc[1, :], or s.loc[pd.IndexSlice[1, :]]
) -> Series[S1]: ...
@overload
def __setitem__(
self,
idx: Index | MaskType,
value: S1 | ArrayLike | Series[S1] | None,
) -> None: ...
@overload
def __setitem__(
self,
idx: str,
value: S1 | None,
) -> None: ...
@overload
def __setitem__(
self,
idx: MaskType | StrLike | _IndexSliceTuple | list[ScalarT],
value: S1 | ArrayLike | Series[S1] | None,
) -> None: ...
_ListLike: TypeAlias = (
ArrayLike | dict[_str, np.ndarray] | Sequence[S1] | IndexOpsMixin[S1]
)
class Series(IndexOpsMixin[S1], NDFrame):
__hash__: ClassVar[None]
@overload
def __new__( # type: ignore[overload-overlap]
cls,
data: DatetimeIndex
| Sequence[np.datetime64 | datetime]
| dict[HashableT1, np.datetime64 | datetime]
| np.datetime64
| datetime,
index: Axes | None = ...,
*,
dtype: TimestampDtypeArg = ...,
name: Hashable = ...,
copy: bool = ...,
) -> TimestampSeries: ...
@overload
def __new__( # type: ignore[overload-overlap]
cls,
data: _ListLike,
index: Axes | None = ...,
*,
dtype: TimestampDtypeArg,
name: Hashable = ...,
copy: bool = ...,
) -> TimestampSeries: ...
@overload
def __new__( # type: ignore[overload-overlap]
cls,
data: PeriodIndex,
index: Axes | None = ...,
*,
dtype: PeriodDtype = ...,
name: Hashable = ...,
copy: bool = ...,
) -> PeriodSeries: ...
@overload
def __new__( # type: ignore[overload-overlap]
cls,
data: TimedeltaIndex
| Sequence[np.timedelta64 | timedelta]
| dict[HashableT1, np.timedelta64 | timedelta]
| np.timedelta64
| timedelta,
index: Axes | None = ...,
*,
dtype: TimedeltaDtypeArg = ...,
name: Hashable = ...,
copy: bool = ...,
) -> TimedeltaSeries: ...
@overload
def __new__( # type: ignore[overload-overlap]
cls,
data: IntervalIndex[Interval[_OrderableT]]
| Interval[_OrderableT]
| Sequence[Interval[_OrderableT]]
| dict[HashableT1, Interval[_OrderableT]],
index: Axes | None = ...,
*,
dtype: Literal["Interval"] = ...,
name: Hashable = ...,
copy: bool = ...,
) -> IntervalSeries[_OrderableT]: ...
@overload
def __new__(
cls,
data: Scalar | _ListLike | dict[HashableT1, Any] | None,
index: Axes | None = ...,
*,
dtype: type[S1],
name: Hashable = ...,
copy: bool = ...,
) -> Self: ...
@overload
def __new__(
cls,
data: S1 | _ListLike[S1] | dict[HashableT1, S1],
index: Axes | None = ...,
*,
dtype: Dtype = ...,
name: Hashable = ...,
copy: bool = ...,
) -> Self: ...
@overload
def __new__(
cls,
data: Scalar | _ListLike | dict[HashableT1, Any] | None = ...,
index: Axes | None = ...,
*,
dtype: Dtype = ...,
name: Hashable = ...,
copy: bool = ...,
) -> Series: ...
@property
def hasnans(self) -> bool: ...
def div(
self,
other: num | _ListLike | Series[S1],
level: Level | None = ...,
fill_value: float | None = ...,
axis: AxisIndex = ...,
) -> Series[float]: ...
def rdiv(
self,
other: Series[S1] | Scalar,
level: Level | None = ...,
fill_value: float | None = ...,
axis: AxisIndex = ...,
) -> Series[S1]: ...
@property
def dtype(self) -> DtypeObj: ...
@property
def dtypes(self) -> DtypeObj: ...
@property
def name(self) -> Hashable | None: ...
@name.setter
def name(self, value: Hashable | None) -> None: ...
@property
def values(self) -> ArrayLike: ...
@property
def array(self) -> ExtensionArray: ...
def ravel(self, order: _str = ...) -> np.ndarray: ...
def __len__(self) -> int: ...
def view(self, dtype=...) -> Series[S1]: ...
def __array_ufunc__(self, ufunc: Callable, method: _str, *inputs, **kwargs): ...
def __array__(self, dtype=...) -> np.ndarray: ...
@property
def axes(self) -> list: ...
def take(
self,
indices: Sequence,
axis: AxisIndex = ...,
is_copy: _bool | None = ...,
**kwargs,
) -> Series[S1]: ...
def __getattr__(self, name: _str) -> S1: ...
@overload
def __getitem__(
self,
idx: list[_str]
| Index
| Series[S1]
| slice
| MaskType
| tuple[Hashable | slice, ...],
) -> Self: ...
@overload
def __getitem__(self, idx: Scalar) -> S1: ...
def __setitem__(self, key, value) -> None: ...
def repeat(
self, repeats: int | list[int], axis: AxisIndex | None = ...
) -> Series[S1]: ...
@property
def index(self) -> Index | MultiIndex: ...
@index.setter
def index(self, idx: Index) -> None: ...
# TODO: combine Level | Sequence[Level] github.com/python/mypy/issues/14311
@overload
def reset_index(
self,
level: Sequence[Level] = ...,
*,
drop: Literal[False] = ...,
name: Level = ...,
inplace: Literal[False] = ...,
allow_duplicates: bool = ...,
) -> DataFrame: ...
@overload
def reset_index(
self,
level: Sequence[Level] = ...,
*,
drop: Literal[True],
name: Level = ...,
inplace: Literal[False] = ...,
allow_duplicates: bool = ...,
) -> Series[S1]: ...
@overload
def reset_index(
self,
level: Sequence[Level] = ...,
*,
drop: bool = ...,
name: Level = ...,
inplace: Literal[True],
allow_duplicates: bool = ...,
) -> None: ...
@overload
def reset_index(
self,
level: Level | None = ...,
*,
drop: Literal[False] = ...,
name: Level = ...,
inplace: Literal[False] = ...,
allow_duplicates: bool = ...,
) -> DataFrame: ...
@overload
def reset_index(
self,
level: Level | None = ...,
*,
drop: Literal[True],
name: Level = ...,
inplace: Literal[False] = ...,
allow_duplicates: bool = ...,
) -> Series[S1]: ...
@overload
def reset_index(
self,
level: Level | None = ...,
*,
drop: bool = ...,
name: Level = ...,
inplace: Literal[True],
allow_duplicates: bool = ...,
) -> None: ...
@overload
def to_string(
self,
buf: FilePath | WriteBuffer[str],
na_rep: _str = ...,
float_format: Callable[[float], str] = ...,
header: _bool = ...,
index: _bool = ...,
length: _bool = ...,
dtype: _bool = ...,
name: _bool = ...,
max_rows: int | None = ...,
min_rows: int | None = ...,
) -> None: ...
@overload
def to_string(
self,
buf: None = ...,
na_rep: _str = ...,
float_format: Callable[[float], str] = ...,
header: _bool = ...,
index: _bool = ...,
length: _bool = ...,
dtype: _bool = ...,
name: _bool = ...,
max_rows: int | None = ...,
min_rows: int | None = ...,
) -> _str: ...
@overload
def to_json(
self,
path_or_buf: FilePath | WriteBuffer[str],
*,
orient: Literal["records"],
date_format: Literal["epoch", "iso"] | None = ...,
double_precision: int = ...,
force_ascii: _bool = ...,
date_unit: Literal["s", "ms", "us", "ns"] = ...,
default_handler: Callable[[Any], _str | float | _bool | list | dict]
| None = ...,
lines: Literal[True],
compression: CompressionOptions = ...,
index: _bool = ...,
indent: int | None = ...,
mode: Literal["a"],
) -> None: ...
@overload
def to_json(
self,
path_or_buf: None = ...,
*,
orient: Literal["records"],
date_format: Literal["epoch", "iso"] | None = ...,
double_precision: int = ...,
force_ascii: _bool = ...,
date_unit: Literal["s", "ms", "us", "ns"] = ...,
default_handler: Callable[[Any], _str | float | _bool | list | dict]
| None = ...,
lines: Literal[True],
compression: CompressionOptions = ...,
index: _bool = ...,
indent: int | None = ...,
mode: Literal["a"],
) -> _str: ...
@overload
def to_json(
self,
path_or_buf: FilePath | WriteBuffer[str],
orient: JsonSeriesOrient | None = ...,
date_format: Literal["epoch", "iso"] | None = ...,
double_precision: int = ...,
force_ascii: _bool = ...,
date_unit: Literal["s", "ms", "us", "ns"] = ...,
default_handler: Callable[[Any], _str | float | _bool | list | dict]
| None = ...,
lines: _bool = ...,
compression: CompressionOptions = ...,
index: _bool = ...,
indent: int | None = ...,
mode: Literal["w"] = ...,
) -> None: ...
@overload
def to_json(
self,
path_or_buf: None = ...,
orient: JsonSeriesOrient | None = ...,
date_format: Literal["epoch", "iso"] | None = ...,
double_precision: int = ...,
force_ascii: _bool = ...,
date_unit: Literal["s", "ms", "us", "ns"] = ...,
default_handler: Callable[[Any], _str | float | _bool | list | dict]
| None = ...,
lines: _bool = ...,
compression: CompressionOptions = ...,
index: _bool = ...,
indent: int | None = ...,
mode: Literal["w"] = ...,
) -> _str: ...
def to_xarray(self) -> xr.DataArray: ...
def items(self) -> Iterable[tuple[Hashable, S1]]: ...
def keys(self) -> list: ...
@overload
def to_dict(self, *, into: type[dict] = ...) -> dict[Any, S1]: ...
@overload
def to_dict(
self, *, into: type[MutableMapping] | MutableMapping
) -> MutableMapping[Hashable, S1]: ...
def to_frame(self, name: object | None = ...) -> DataFrame: ...
@overload
def groupby(
self,
by: Scalar,
axis: AxisIndex = ...,
level: IndexLabel | None = ...,
as_index: _bool = ...,
sort: _bool = ...,
group_keys: _bool = ...,
squeeze: _bool = ...,
observed: _bool = ...,
dropna: _bool = ...,
) -> SeriesGroupBy[S1, Scalar]: ...
@overload
def groupby(
self,
by: DatetimeIndex,
axis: AxisIndex = ...,
level: IndexLabel | None = ...,
as_index: _bool = ...,
sort: _bool = ...,
group_keys: _bool = ...,
squeeze: _bool = ...,
observed: _bool = ...,
dropna: _bool = ...,
) -> SeriesGroupBy[S1, Timestamp]: ...
@overload
def groupby(
self,
by: TimedeltaIndex,
axis: AxisIndex = ...,
level: IndexLabel | None = ...,
as_index: _bool = ...,
sort: _bool = ...,
group_keys: _bool = ...,
squeeze: _bool = ...,
observed: _bool = ...,
dropna: _bool = ...,
) -> SeriesGroupBy[S1, Timedelta]: ...
@overload
def groupby(
self,
by: PeriodIndex,
axis: AxisIndex = ...,
level: IndexLabel | None = ...,
as_index: _bool = ...,
sort: _bool = ...,
group_keys: _bool = ...,
squeeze: _bool = ...,
observed: _bool = ...,
dropna: _bool = ...,
) -> SeriesGroupBy[S1, Period]: ...
@overload
def groupby(
self,
by: IntervalIndex[IntervalT],
axis: AxisIndex = ...,
level: IndexLabel | None = ...,
as_index: _bool = ...,
sort: _bool = ...,
group_keys: _bool = ...,
squeeze: _bool = ...,
observed: _bool = ...,
dropna: _bool = ...,
) -> SeriesGroupBy[S1, IntervalT]: ...
@overload
def groupby(
self,
by: MultiIndex | GroupByObjectNonScalar = ...,
axis: AxisIndex = ...,
level: IndexLabel | None = ...,
as_index: _bool = ...,
sort: _bool = ...,
group_keys: _bool = ...,
squeeze: _bool = ...,
observed: _bool = ...,
dropna: _bool = ...,
) -> SeriesGroupBy[S1, tuple]: ...
@overload
def groupby(
self,
by: Series[SeriesByT],
axis: AxisIndex = ...,
level: IndexLabel | None = ...,
as_index: _bool = ...,
sort: _bool = ...,
group_keys: _bool = ...,
squeeze: _bool = ...,
observed: _bool = ...,
dropna: _bool = ...,
) -> SeriesGroupBy[S1, SeriesByT]: ...
@overload
def groupby(
self,
by: CategoricalIndex | Index | Series,
axis: AxisIndex = ...,
level: IndexLabel | None = ...,
as_index: _bool = ...,
sort: _bool = ...,
group_keys: _bool = ...,
squeeze: _bool = ...,
observed: _bool = ...,
dropna: _bool = ...,
) -> SeriesGroupBy[S1, Any]: ...
# need the ignore because None is Hashable
@overload
def count(self, level: None = ...) -> int: ... # type: ignore[overload-overlap]
@overload
def count(self, level: Hashable) -> Series[S1]: ...
def mode(self, dropna=...) -> Series[S1]: ...
def unique(self) -> np.ndarray: ...
@overload
def drop_duplicates(
self, *, keep: NaPosition | Literal[False] = ..., inplace: Literal[False] = ...
) -> Series[S1]: ...
@overload
def drop_duplicates(
self, *, keep: NaPosition | Literal[False] = ..., inplace: Literal[True]
) -> None: ...
@overload
def drop_duplicates(
self, *, keep: NaPosition | Literal[False] = ..., inplace: bool = ...
) -> Series[S1] | None: ...
def duplicated(self, keep: NaPosition | Literal[False] = ...) -> Series[_bool]: ...
def idxmax(
self, axis: AxisIndex = ..., skipna: _bool = ..., *args, **kwargs
) -> int | _str: ...
def idxmin(
self, axis: AxisIndex = ..., skipna: _bool = ..., *args, **kwargs
) -> int | _str: ...
def round(self, decimals: int = ..., *args, **kwargs) -> Series[S1]: ...
@overload
def quantile(
self,
q: float = ...,
interpolation: QuantileInterpolation = ...,
) -> float: ...
@overload
def quantile(
self,
q: _ListLike,
interpolation: QuantileInterpolation = ...,
) -> Series[S1]: ...
def corr(
self,
other: Series[S1],
method: Literal["pearson", "kendall", "spearman"] = ...,
min_periods: int = ...,
) -> float: ...
def cov(
self, other: Series[S1], min_periods: int | None = ..., ddof: int = ...
) -> float: ...
def diff(self, periods: int = ...) -> Series[S1]: ...
def autocorr(self, lag: int = ...) -> float: ...
@overload
def dot(self, other: Series[S1]) -> Scalar: ...
@overload
def dot(self, other: DataFrame) -> Series[S1]: ...
@overload
def dot(
self, other: ArrayLike | dict[_str, np.ndarray] | Sequence[S1] | Index[S1]
) -> np.ndarray: ...
def __matmul__(self, other): ...
def __rmatmul__(self, other): ...
@overload
def searchsorted( # type: ignore[overload-overlap]
self,
value: _ListLike,
side: Literal["left", "right"] = ...,
sorter: _ListLike | None = ...,
) -> list[int]: ...
@overload
def searchsorted(
self,
value: Scalar,
side: Literal["left", "right"] = ...,
sorter: _ListLike | None = ...,
) -> int: ...
@overload
def compare(
self,
other: Series,
align_axis: AxisIndex,
keep_shape: bool = ...,
keep_equal: bool = ...,
) -> Series: ...
@overload
def compare(
self,
other: Series,
align_axis: AxisColumn = ...,
keep_shape: bool = ...,
keep_equal: bool = ...,
) -> DataFrame: ...
def combine(
self, other: Series[S1], func: Callable, fill_value: Scalar | None = ...
) -> Series[S1]: ...
def combine_first(self, other: Series[S1]) -> Series[S1]: ...
def update(self, other: Series[S1] | Sequence[S1] | Mapping[int, S1]) -> None: ...
@overload
def sort_values(
self,
*,
axis: Axis = ...,
ascending: _bool | Sequence[_bool] = ...,
kind: SortKind = ...,
na_position: NaPosition = ...,
ignore_index: _bool = ...,
inplace: Literal[True],
key: Callable | None = ...,
) -> None: ...
@overload
def sort_values(
self,
*,
axis: Axis = ...,
ascending: _bool | Sequence[_bool] = ...,
kind: SortKind = ...,
na_position: NaPosition = ...,
ignore_index: _bool = ...,
inplace: Literal[False] = ...,
key: Callable | None = ...,
) -> Series[S1]: ...
@overload
def sort_values(
self,
*,
axis: Axis = ...,
ascending: _bool | Sequence[_bool] = ...,
inplace: _bool | None = ...,
kind: SortKind = ...,
na_position: NaPosition = ...,
ignore_index: _bool = ...,
key: Callable | None = ...,
) -> Series[S1] | None: ...
@overload
def sort_index(
self,
*,
axis: Axis = ...,
level: Level | None = ...,
ascending: _bool | Sequence[_bool] = ...,
kind: SortKind = ...,
na_position: NaPosition = ...,
sort_remaining: _bool = ...,
ignore_index: _bool = ...,
inplace: Literal[True],
key: Callable | None = ...,
) -> None: ...
@overload
def sort_index(
self,
*,
axis: Axis = ...,
level: Level | list[int] | list[_str] | None = ...,
ascending: _bool | Sequence[_bool] = ...,
kind: SortKind = ...,
na_position: NaPosition = ...,
sort_remaining: _bool = ...,
ignore_index: _bool = ...,
inplace: Literal[False] = ...,
key: Callable | None = ...,
) -> Self: ...
@overload
def sort_index(
self,
*,
axis: Axis = ...,
level: Level | list[int] | list[_str] | None = ...,
ascending: _bool | Sequence[_bool] = ...,
inplace: _bool | None = ...,
kind: SortKind = ...,
na_position: NaPosition = ...,
sort_remaining: _bool = ...,
ignore_index: _bool = ...,
key: Callable | None = ...,
) -> Series | None: ...
def argsort(
self,
axis: AxisIndex = ...,
kind: SortKind = ...,
order: None = ...,
) -> Series[int]: ...
def nlargest(
self, n: int = ..., keep: NaPosition | Literal["all"] = ...
) -> Series[S1]: ...
def nsmallest(
self, n: int = ..., keep: NaPosition | Literal["all"] = ...
) -> Series[S1]: ...
def swaplevel(
self, i: Level = ..., j: Level = ..., copy: _bool = ...
) -> Series[S1]: ...
def reorder_levels(self, order: list) -> Series[S1]: ...
def explode(self) -> Series[S1]: ...
def unstack(
self,
level: Level = ...,
fill_value: int | _str | dict | None = ...,
) -> DataFrame: ...
def map(self, arg, na_action: Literal["ignore"] | None = ...) -> Series[S1]: ...
@overload
def aggregate( # type: ignore[overload-overlap]
self: Series[int],
func: Literal["mean"],
axis: AxisIndex = ...,
*args,
**kwargs,
) -> float: ...
@overload
def aggregate(
self,
func: AggFuncTypeBase,
axis: AxisIndex = ...,
*args,
**kwargs,
) -> S1: ...
@overload
def aggregate(
self,
func: AggFuncTypeSeriesToFrame = ...,
axis: AxisIndex = ...,
*args,
**kwargs,
) -> Series: ...
agg = aggregate
@overload
def transform(
self,
func: AggFuncTypeBase,
axis: AxisIndex = ...,
*args,
**kwargs,
) -> Series[S1]: ...
@overload
def transform(
self,
func: list[AggFuncTypeBase] | AggFuncTypeDictFrame,
axis: AxisIndex = ...,
*args,
**kwargs,
) -> DataFrame: ...
@overload
def apply(
self,
func: Callable[..., Scalar | Sequence | set | Mapping | NAType | None],
convertDType: _bool = ...,
args: tuple = ...,
**kwds,
) -> Series: ...
@overload
def apply(
self,
func: Callable[..., Series],
convertDType: _bool = ...,
args: tuple = ...,
**kwds,
) -> DataFrame: ...
def align(
self,
other: DataFrame | Series,
join: JoinHow = ...,
axis: Axis | None = ...,
level: Level | None = ...,
copy: _bool = ...,
fill_value=...,
method: FillnaOptions | None = ...,
limit: int | None = ...,
fill_axis: AxisIndex = ...,
broadcast_axis: AxisIndex | None = ...,
) -> tuple[Series, Series]: ...
@overload
def rename(
self,
index: Renamer | Hashable | None = ...,
*,
axis: Axis | None = ...,
copy: bool = ...,
inplace: Literal[True],
level: Level | None = ...,
errors: IgnoreRaise = ...,
) -> None: ...
@overload
def rename(
self,
index: Renamer | None = ...,
*,
axis: Axis | None = ...,
copy: bool = ...,
inplace: Literal[False] = ...,
level: Level | None = ...,
errors: IgnoreRaise = ...,
) -> Self: ...
@overload
def rename(
self,
index: Hashable | None = ...,
*,
axis: Axis | None = ...,
copy: bool = ...,
inplace: Literal[False] = ...,
level: Level | None = ...,
errors: IgnoreRaise = ...,
) -> Self: ...
@overload
def rename(
self,
index: Renamer | Hashable | None = ...,
*,
axis: Axis | None = ...,
copy: bool = ...,
inplace: bool = ...,
level: Level | None = ...,
errors: IgnoreRaise = ...,
) -> Series | None: ...
def reindex_like(
self,
other: Series[S1],
method: _str | FillnaOptions | Literal["nearest"] | None = ...,
copy: _bool = ...,
limit: int | None = ...,
tolerance: float | None = ...,
) -> Self: ...
@overload
def drop(
self,
labels: Hashable | list[HashableT1] | Index = ...,
*,
axis: Axis = ...,
index: Hashable | list[HashableT2] | Index = ...,
columns: Hashable | list[HashableT3] | Index = ...,
level: Level | None = ...,
inplace: Literal[True],
errors: IgnoreRaise = ...,
) -> None: ...
@overload
def drop(
self,
labels: Hashable | list[HashableT1] | Index = ...,
*,
axis: Axis = ...,
index: Hashable | list[HashableT2] | Index = ...,
columns: Hashable | list[HashableT3] | Index = ...,
level: Level | None = ...,
inplace: Literal[False] = ...,
errors: IgnoreRaise = ...,
) -> Self: ...
@overload
def drop(
self,
labels: Hashable | list[HashableT1] | Index = ...,
*,
axis: Axis = ...,