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array.pyi
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import numpy as np
from pandas.core.arrays import (
ExtensionArray,
ExtensionOpsMixin,
)
from pandas._typing import TakeIndexer
class SparseArray(ExtensionArray, ExtensionOpsMixin):
def __init__(
self,
data,
sparse_index=...,
fill_value=...,
kind: str = ...,
dtype=...,
copy: bool = ...,
) -> None: ...
@classmethod
def from_spmatrix(cls, data): ...
def __array__(self, dtype=..., copy=...) -> np.ndarray: ...
def __setitem__(self, key, value) -> None: ...
@property
def sp_index(self): ...
@property
def sp_values(self): ...
@property
def dtype(self): ...
@property
def fill_value(self): ...
@fill_value.setter
def fill_value(self, value) -> None: ...
@property
def kind(self) -> str: ...
def __len__(self) -> int: ...
@property
def nbytes(self) -> int: ...
@property
def density(self): ...
@property
def npoints(self) -> int: ...
def isna(self): ...
def fillna(self, value=..., method=..., limit=...): ...
def shift(self, periods: int = ..., fill_value=...): ...
def unique(self): ...
def factorize(
self, na_sentinel: int = ..., use_na_sentinel: bool = ...
) -> tuple[np.ndarray, SparseArray]: ...
def value_counts(self, dropna: bool = ...): ...
def __getitem__(self, key): ...
def take(
self, indices: TakeIndexer, *, allow_fill: bool = ..., fill_value=...
) -> SparseArray: ...
def searchsorted(self, v, side: str = ..., sorter=...): ...
def copy(self): ...
def astype(self, dtype=..., copy: bool = ...): ...
def map(self, mapper): ...
def to_dense(self): ...
def nonzero(self): ...
def all(self, axis=..., *args, **kwargs): ...
def any(self, axis: int = ..., *args, **kwargs): ...
def sum(self, axis: int = ..., *args, **kwargs): ...
def cumsum(self, axis: int = ..., *args, **kwargs): ...
def mean(self, axis: int = ..., *args, **kwargs): ...
def transpose(self, *axes): ...
@property
def T(self): ...
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs): ...
def __abs__(self): ...