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series.pyi
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from collections.abc import (
Callable,
Hashable,
Iterable,
Iterator,
Mapping,
Sequence,
)
from datetime import (
date,
datetime,
time,
timedelta,
)
from typing import (
Any,
ClassVar,
Generic,
Literal,
overload,
)
from core.api import (
Int8Dtype as Int8Dtype,
Int16Dtype as Int16Dtype,
Int32Dtype as Int32Dtype,
Int64Dtype as Int64Dtype,
)
from matplotlib.axes import (
Axes as PlotAxes,
SubplotBase,
)
import numpy as np
import pandas as pd
from pandas import (
Period,
Timedelta,
Timestamp,
)
from pandas.core.arrays.base import ExtensionArray
from pandas.core.arrays.categorical import CategoricalAccessor
from pandas.core.groupby.generic import (
_SeriesGroupByNonScalar,
_SeriesGroupByScalar,
)
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.datetimes import DatetimeIndex
from pandas.core.indexes.interval import IntervalIndex
from pandas.core.indexes.period import PeriodIndex
from pandas.core.indexes.timedeltas import TimedeltaIndex
from pandas.core.indexing import (
_AtIndexer,
_iAtIndexer,
_IndexSliceTuple,
)
from pandas.core.resample import Resampler
from pandas.core.strings import StringMethods
from pandas.core.window import (
Expanding,
ExponentialMovingWindow,
Rolling,
)
from pandas.core.window.rolling import (
Rolling,
Window,
)
from typing_extensions import TypeAlias
import xarray as xr
from pandas._libs.interval import Interval
from pandas._libs.missing import NAType
from pandas._libs.tslibs import BaseOffset
from pandas._typing import (
S1,
AggFuncTypeBase,
AggFuncTypeDictFrame,
AggFuncTypeSeriesToFrame,
AnyArrayLike,
ArrayLike,
Axes,
Axis,
AxisType,
CalculationMethod,
CompressionOptions,
DtypeObj,
FilePath,
FillnaOptions,
GroupByObjectNonScalar,
HashableT1,
HashableT2,
HashableT3,
IgnoreRaise,
IndexingInt,
IntervalClosedType,
JoinHow,
JsonSeriesOrient,
Level,
ListLike,
ListLikeU,
MaskType,
NaPosition,
QuantileInterpolation,
RandomState,
Renamer,
ReplaceMethod,
Scalar,
SeriesAxisType,
SortKind,
TimestampConvention,
WriteBuffer,
np_ndarray_anyint,
np_ndarray_bool,
npt,
num,
)
from pandas.plotting import PlotAccessor
from .base import IndexOpsMixin
from .frame import DataFrame
from .generic import NDFrame
from .indexes.multi import MultiIndex
from .indexing import (
_iLocIndexer,
_LocIndexer,
)
_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, 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[misc]
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,
# _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: list[int] | list[str] | list[str | int],
value: S1 | ArrayLike | Series[S1] | None,
) -> None: ...
class Series(IndexOpsMixin, NDFrame, Generic[S1]):
_ListLike: TypeAlias = ArrayLike | dict[_str, np.ndarray] | list | tuple | Index
__hash__: ClassVar[None]
@overload
def __new__(
cls,
data: DatetimeIndex | Sequence[Timestamp | np.datetime64 | datetime],
index: Axes | None = ...,
dtype=...,
name: Hashable | None = ...,
copy: bool = ...,
fastpath: bool = ...,
) -> TimestampSeries: ...
@overload
def __new__(
cls,
data: _ListLike,
dtype: Literal["datetime64[ns]"],
index: Axes | None = ...,
name: Hashable | None = ...,
copy: bool = ...,
fastpath: bool = ...,
) -> TimestampSeries: ...
@overload
def __new__(
cls,
data: PeriodIndex,
index: Axes | None = ...,
dtype=...,
name: Hashable | None = ...,
copy: bool = ...,
fastpath: bool = ...,
) -> PeriodSeries: ...
@overload
def __new__(
cls,
data: TimedeltaIndex | Sequence[Timedelta | np.timedelta64 | timedelta],
index: Axes | None = ...,
dtype=...,
name: Hashable | None = ...,
copy: bool = ...,
fastpath: bool = ...,
) -> TimedeltaSeries: ...
@overload
def __new__(
cls,
data: IntervalIndex[Interval[int]],
index: Axes | None = ...,
dtype=...,
name: Hashable | None = ...,
copy: bool = ...,
fastpath: bool = ...,
) -> Series[Interval[int]]: ...
@overload
def __new__(
cls,
data: IntervalIndex[Interval[float]],
index: Axes | None = ...,
dtype=...,
name: Hashable | None = ...,
copy: bool = ...,
fastpath: bool = ...,
) -> Series[Interval[float]]: ...
@overload
def __new__(
cls,
data: IntervalIndex[Interval[Timestamp]],
index: Axes | None = ...,
dtype=...,
name: Hashable | None = ...,
copy: bool = ...,
fastpath: bool = ...,
) -> Series[Interval[Timestamp]]: ...
@overload
def __new__(
cls,
data: IntervalIndex[Interval[Timedelta]],
index: Axes | None = ...,
dtype=...,
name: Hashable | None = ...,
copy: bool = ...,
fastpath: bool = ...,
) -> Series[Interval[Timedelta]]: ...
@overload
def __new__(
cls,
data: object | _ListLike | Series[S1] | dict[int, S1] | dict[_str, S1] | None,
dtype: type[S1],
index: Axes | None = ...,
name: Hashable | None = ...,
copy: bool = ...,
fastpath: bool = ...,
) -> Series[S1]: ...
@overload
def __new__(
cls,
data: object
| _ListLike
| Series[S1]
| dict[int, S1]
| dict[_str, S1]
| None = ...,
index: Axes | None = ...,
dtype=...,
name: Hashable | None = ...,
copy: bool = ...,
fastpath: bool = ...,
) -> Series: ...
@property
def hasnans(self) -> bool: ...
def div(
self,
other: num | _ListLike | Series[S1],
level: Level | None = ...,
fill_value: float | None = ...,
axis: SeriesAxisType = ...,
) -> Series[float]: ...
def rdiv(
self,
other: Series[S1] | Scalar,
level: Level | None = ...,
fill_value: float | None = ...,
axis: SeriesAxisType = ...,
) -> 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: SeriesAxisType = ...,
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[S1 | slice, ...],
) -> Series: ...
@overload
def __getitem__(self, idx: int | _str) -> S1: ...
def __setitem__(self, key, value) -> None: ...
def repeat(
self, repeats: int | list[int], axis: SeriesAxisType | 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 = ...,
formatters=...,
float_format=...,
sparsify: _bool | None = ...,
index_names: _bool = ...,
justify: _str | None = ...,
max_rows: int | None = ...,
min_rows: int | None = ...,
max_cols: int | None = ...,
show_dimensions: _bool = ...,
decimal: _str = ...,
line_width: int | None = ...,
max_colwidth: int | None = ...,
encoding: _str | None = ...,
) -> None: ...
@overload
def to_string(
self,
buf: None = ...,
na_rep: _str = ...,
formatters=...,
float_format=...,
sparsify: _bool | None = ...,
index_names: _bool = ...,
justify: _str | None = ...,
max_rows: int | None = ...,
min_rows: int | None = ...,
max_cols: int | None = ...,
show_dimensions: _bool = ...,
decimal: _str = ...,
line_width: int | None = ...,
max_colwidth: int | None = ...,
encoding: _str | None = ...,
) -> _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 = ...,
) -> 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 = ...,
) -> _str: ...
def to_xarray(self) -> xr.DataArray: ...
def items(self) -> Iterable[tuple[Hashable, S1]]: ...
def keys(self) -> list: ...
@overload
def to_dict(self) -> dict[Any, S1]: ...
@overload
def to_dict(self, into: type[Mapping] | Mapping) -> Mapping[Hashable, S1]: ...
def to_frame(self, name: object | None = ...) -> DataFrame: ...
@overload
def groupby(
self,
by: Scalar,
axis: SeriesAxisType = ...,
level: Level | None = ...,
as_index: _bool = ...,
sort: _bool = ...,
group_keys: _bool = ...,
squeeze: _bool = ...,
observed: _bool = ...,
dropna: _bool = ...,
) -> _SeriesGroupByScalar[S1]: ...
@overload
def groupby(
self,
by: GroupByObjectNonScalar = ...,
axis: SeriesAxisType = ...,
level: Level | None = ...,
as_index: _bool = ...,
sort: _bool = ...,
group_keys: _bool = ...,
squeeze: _bool = ...,
observed: _bool = ...,
dropna: _bool = ...,
) -> _SeriesGroupByNonScalar[S1]: ...
# need the ignore because None is Hashable
@overload
def count(self, level: None = ...) -> int: ... # type: ignore[misc]
@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: SeriesAxisType = ..., skipna: _bool = ..., *args, **kwargs
) -> int | _str: ...
def idxmin(
self, axis: SeriesAxisType = ..., 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: _ListLike) -> np.ndarray: ...
def __matmul__(self, other): ...
def __rmatmul__(self, other): ...
@overload
def searchsorted(
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: SeriesAxisType,
keep_shape: bool = ...,
keep_equal: bool = ...,
) -> Series: ...
@overload
def compare(
self,
other: Series,
align_axis: Literal["columns", 1] = ...,
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: AxisType = ...,
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: AxisType = ...,
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: AxisType = ...,
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: AxisType = ...,
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: AxisType = ...,
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 = ...,
) -> Series: ...
@overload
def sort_index(
self,
*,
axis: AxisType = ...,
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: SeriesAxisType = ...,
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(
self,
func: AggFuncTypeBase,
axis: SeriesAxisType = ...,
*args,
**kwargs,
) -> S1: ...
@overload
def aggregate(
self,
func: AggFuncTypeSeriesToFrame = ...,
axis: SeriesAxisType = ...,
*args,
**kwargs,
) -> Series[S1]: ...
agg = aggregate
@overload
def transform(
self,
func: AggFuncTypeBase,
axis: SeriesAxisType = ...,
*args,
**kwargs,
) -> Series[S1]: ...
@overload
def transform(
self,
func: list[AggFuncTypeBase] | AggFuncTypeDictFrame,
axis: SeriesAxisType = ...,
*args,
**kwargs,
) -> DataFrame: ...
@overload
def apply(
self,
func: Callable[..., Scalar | Sequence | Mapping],
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: AxisType | None = ...,
level: Level | None = ...,
copy: _bool = ...,
fill_value=...,
method: FillnaOptions | None = ...,
limit: int | None = ...,
fill_axis: SeriesAxisType = ...,
broadcast_axis: SeriesAxisType | 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 = ...,
) -> Series: ...
@overload
def rename(
self,
index: Hashable | None = ...,
*,
axis: Axis | None = ...,
copy: bool = ...,
inplace: Literal[False] = ...,
level: Level | None = ...,
errors: IgnoreRaise = ...,
) -> Series: ...
@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 = ...,
) -> Series: ...
@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 = ...,
) -> Series: ...
@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: bool = ...,
errors: IgnoreRaise = ...,
) -> Series | None: ...
@overload
def fillna(
self,
value: Scalar | NAType | dict | Series[S1] | DataFrame | None = ...,
*,
method: FillnaOptions | None = ...,
axis: SeriesAxisType = ...,
limit: int | None = ...,
downcast: dict | None = ...,
inplace: Literal[True],
) -> None: ...
@overload
def fillna(
self,
value: Scalar | NAType | dict | Series[S1] | DataFrame | None = ...,
*,
method: FillnaOptions | None = ...,
axis: SeriesAxisType = ...,
limit: int | None = ...,
downcast: dict | None = ...,
inplace: Literal[False] = ...,
) -> Series[S1]: ...
@overload
def fillna(
self,
value: Scalar | NAType | dict | Series[S1] | DataFrame | None = ...,
*,
method: FillnaOptions | None = ...,
axis: SeriesAxisType = ...,
inplace: _bool = ...,
limit: int | None = ...,
downcast: dict | None = ...,
) -> Series[S1] | None: ...
@overload
def replace(
self,
to_replace: _str | list | dict | Series[S1] | float | None = ...,
value: Scalar | NAType | dict | list | _str | None = ...,
*,
inplace: Literal[False] = ...,
limit: int | None = ...,
regex=...,
method: ReplaceMethod = ...,
) -> Series[S1]: ...
@overload
def replace(
self,
to_replace: _str | list | dict | Series[S1] | float | None = ...,
value: Scalar | NAType | dict | list | _str | None = ...,
*,
limit: int | None = ...,
regex=...,
method: ReplaceMethod = ...,
inplace: Literal[True],
) -> None: ...
@overload
def replace(
self,
to_replace: _str | list | dict | Series[S1] | float | None = ...,
value: Scalar | NAType | dict | list | _str | None = ...,
*,
inplace: _bool = ...,
limit: int | None = ...,
regex=...,
method: ReplaceMethod = ...,
) -> Series[S1] | None: ...
def shift(
self,
periods: int = ...,
freq=...,
axis: SeriesAxisType = ...,
fill_value: object | None = ...,
) -> Series[S1]: ...
def memory_usage(self, index: _bool = ..., deep: _bool = ...) -> int: ...
def isin(self, values: Iterable | Series[S1] | dict) -> Series[_bool]: ...
def between(
self,
left: Scalar | ListLikeU,
right: Scalar | ListLikeU,
inclusive: Literal["both", "neither", "left", "right"] = ...,
) -> Series[_bool]: ...
def isna(self) -> Series[_bool]: ...
def isnull(self) -> Series[_bool]: ...
def notna(self) -> Series[_bool]: ...
def notnull(self) -> Series[_bool]: ...
@overload
def dropna(
self,
*,
axis: SeriesAxisType = ...,
inplace: Literal[True],
how: Literal["any", "all"] | None = ...,
) -> None: ...
@overload
def dropna(
self,
*,
axis: SeriesAxisType = ...,
inplace: Literal[False] = ...,
how: Literal["any", "all"] | None = ...,
) -> Series[S1]: ...
@overload
def dropna(
self,
*,
axis: SeriesAxisType = ...,
inplace: _bool = ...,
how: Literal["any", "all"] | None = ...,
) -> Series[S1] | None: ...
def to_timestamp(
self,
freq=...,
how: TimestampConvention = ...,
copy: _bool = ...,
) -> Series[S1]: ...
def to_period(self, freq: _str | None = ..., copy: _bool = ...) -> DataFrame: ...
@property
def str(self) -> StringMethods[Series, DataFrame]: ...
@property
def dt(self) -> CombinedDatetimelikeProperties: ...
@property
def plot(self) -> PlotAccessor: ...
sparse = ...
def hist(
self,
by: object | None = ...,
ax: PlotAxes | None = ...,
grid: _bool = ...,
xlabelsize: int | None = ...,
xrot: float | None = ...,
ylabelsize: int | None = ...,
yrot: float | None = ...,
figsize: tuple[float, float] | None = ...,