143 lines
3.7 KiB
Python
143 lines
3.7 KiB
Python
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from collections.abc import (
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Hashable,
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Iterator,
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)
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from typing import (
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Any,
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Generic,
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Literal,
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final,
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overload,
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)
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import numpy as np
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from pandas import (
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Index,
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Series,
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)
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from pandas.core.arraylike import OpsMixin
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from pandas.core.arrays import ExtensionArray
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from pandas.core.arrays.categorical import Categorical
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from typing_extensions import Self
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from pandas._typing import (
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S1,
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AxisIndex,
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DropKeep,
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DTypeLike,
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GenericT,
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GenericT_co,
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NDFrameT,
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Scalar,
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SupportsDType,
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np_1darray,
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)
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from pandas.util._decorators import cache_readonly
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class NoNewAttributesMixin:
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def __setattr__(self, key: str, value: Any) -> None: ...
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class SelectionMixin(Generic[NDFrameT]):
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obj: NDFrameT
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exclusions: frozenset[Hashable]
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@final
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@cache_readonly
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def ndim(self) -> int: ...
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def __getitem__(self, key): ...
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def aggregate(self, func, *args, **kwargs): ...
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class IndexOpsMixin(OpsMixin, Generic[S1, GenericT_co]):
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__array_priority__: int = ...
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@property
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def T(self) -> Self: ...
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@property
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def shape(self) -> tuple: ...
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@property
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def ndim(self) -> int: ...
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def item(self) -> S1: ...
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@property
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def nbytes(self) -> int: ...
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@property
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def size(self) -> int: ...
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@property
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def array(self) -> ExtensionArray: ...
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@overload
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def to_numpy(
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self,
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dtype: None = None,
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copy: bool = False,
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na_value: Scalar = ...,
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**kwargs,
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) -> np_1darray[GenericT_co]: ...
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@overload
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def to_numpy(
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self,
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dtype: np.dtype[GenericT] | SupportsDType[GenericT] | type[GenericT],
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copy: bool = False,
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na_value: Scalar = ...,
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**kwargs,
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) -> np_1darray[GenericT]: ...
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@overload
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def to_numpy(
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self,
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dtype: DTypeLike,
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copy: bool = False,
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na_value: Scalar = ...,
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**kwargs,
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) -> np_1darray: ...
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@property
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def empty(self) -> bool: ...
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def max(self, axis=..., skipna: bool = ..., **kwargs): ...
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def min(self, axis=..., skipna: bool = ..., **kwargs): ...
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def argmax(
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self,
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axis: AxisIndex | None = ...,
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skipna: bool = True,
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*args,
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**kwargs,
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) -> np.int64: ...
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def argmin(
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self,
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axis: AxisIndex | None = ...,
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skipna: bool = True,
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*args,
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**kwargs,
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) -> np.int64: ...
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def tolist(self) -> list[S1]: ...
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def to_list(self) -> list[S1]: ...
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def __iter__(self) -> Iterator[S1]: ...
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@property
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def hasnans(self) -> bool: ...
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@overload
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def value_counts(
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self,
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normalize: Literal[False] = ...,
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sort: bool = ...,
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ascending: bool = ...,
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bins=...,
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dropna: bool = ...,
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) -> Series[int]: ...
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@overload
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def value_counts(
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self,
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normalize: Literal[True],
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sort: bool = ...,
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ascending: bool = ...,
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bins=...,
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dropna: bool = ...,
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) -> Series[float]: ...
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def nunique(self, dropna: bool = True) -> int: ...
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@property
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def is_unique(self) -> bool: ...
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@property
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def is_monotonic_decreasing(self) -> bool: ...
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@property
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def is_monotonic_increasing(self) -> bool: ...
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def factorize(
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self, sort: bool = False, use_na_sentinel: bool = True
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) -> tuple[np_1darray, np_1darray | Index | Categorical]: ...
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def searchsorted(
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self, value, side: Literal["left", "right"] = ..., sorter=...
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) -> int | list[int]: ...
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def drop_duplicates(self, *, keep: DropKeep = ...) -> Self: ...
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