# Stubs for pandas.core.nanops (Python 3)
#
# NOTE: This dynamically typed stub was automatically generated by stubgen.
# pylint: disable=unused-argument,redefined-outer-name,no-self-use,invalid-name
# pylint: disable=relative-beyond-top-level,line-too-long,arguments-differ
# pylint: disable=no-member,too-few-public-methods,keyword-arg-before-vararg
# pylint: disable=super-init-not-called,abstract-method,redefined-builtin
# pylint: disable=unused-import,useless-import-alias,signature-differs
# pylint: disable=blacklisted-name,c-extension-no-member

from typing import Any, Optional

bn: Any


def set_use_bottleneck(v: bool = ...) -> None:
    ...


class disallow:
    dtypes: Any = ...

    def __init__(self, *dtypes: Any) -> None:
        ...

    def check(self, obj: Any) -> Any:
        ...

    def __call__(self, f: Any) -> Any:
        ...


class bottleneck_switch:
    name: Any = ...
    kwargs: Any = ...

    def __init__(self, name: Optional[Any] = ..., **kwargs: Any) -> None:
        ...


    def __call__(self, alt: Any) -> Any:
        ...



def nanany(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
           mask: Optional[Any] = ...) -> Any:
    ...


def nanall(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
           mask: Optional[Any] = ...) -> Any:
    ...


def nansum(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
           min_count: int = ..., mask: Optional[Any] = ...) -> Any:
    ...


def nanmean(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
            mask: Optional[Any] = ...) -> Any:
    ...


def nanmedian(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
              mask: Optional[Any] = ...) -> Any:
    ...


def nanstd(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
           ddof: int = ..., mask: Optional[Any] = ...) -> Any:
    ...


def nanvar(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
           ddof: int = ..., mask: Optional[Any] = ...) -> Any:
    ...


def nansem(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
           ddof: int = ..., mask: Optional[Any] = ...) -> Any:
    ...


nanmin: Any
nanmax: Any


def nanargmax(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
              mask: Optional[Any] = ...) -> Any:
    ...


def nanargmin(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
              mask: Optional[Any] = ...) -> Any:
    ...


def nanskew(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
            mask: Optional[Any] = ...) -> Any:
    ...


def nankurt(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
            mask: Optional[Any] = ...) -> Any:
    ...


def nanprod(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
            min_count: int = ..., mask: Optional[Any] = ...) -> Any:
    ...


def nancorr(a: Any, b: Any, method: str = ...,
            min_periods: Optional[Any] = ...) -> Any:
    ...


def get_corr_func(method: Any) -> Any:
    ...


def nancov(a: Any, b: Any, min_periods: Optional[Any] = ...) -> Any:
    ...


def make_nancomp(op: Any) -> Any:
    ...


nangt: Any
nange: Any
nanlt: Any
nanle: Any
naneq: Any
nanne: Any


def nanpercentile(values: Any, q: Any, axis: Any, na_value: Any, mask: Any,
                  ndim: Any, interpolation: Any) -> Any:
    ...

807aa1536bc576a62fcb49b06ccf14d0308697e6
<mask token>


class disallow:
    dtypes: Any = ...

    def __init__(self, *dtypes: Any) ->None:
        ...

    def check(self, obj: Any) ->Any:
        ...

    def __call__(self, f: Any) ->Any:
        ...


class bottleneck_switch:
    name: Any = ...
    kwargs: Any = ...

    def __init__(self, name: Optional[Any]=..., **kwargs: Any) ->None:
        ...

    def __call__(self, alt: Any) ->Any:
        ...


<mask token>


def nanall(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


<mask token>


def nanmean(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanmedian(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanstd(values: Any, axis: Optional[Any]=..., skipna: bool=..., ddof:
    int=..., mask: Optional[Any]=...) ->Any:
    ...


<mask token>


def nansem(values: Any, axis: Optional[Any]=..., skipna: bool=..., ddof:
    int=..., mask: Optional[Any]=...) ->Any:
    ...


<mask token>


def nanargmax(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanargmin(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


<mask token>


def nanprod(values: Any, axis: Optional[Any]=..., skipna: bool=...,
    min_count: int=..., mask: Optional[Any]=...) ->Any:
    ...


<mask token>


def nancov(a: Any, b: Any, min_periods: Optional[Any]=...) ->Any:
    ...


<mask token>


def nanpercentile(values: Any, q: Any, axis: Any, na_value: Any, mask: Any,
    ndim: Any, interpolation: Any) ->Any:
    ...

<mask token>


class disallow:
    dtypes: Any = ...

    def __init__(self, *dtypes: Any) ->None:
        ...

    def check(self, obj: Any) ->Any:
        ...

    def __call__(self, f: Any) ->Any:
        ...


class bottleneck_switch:
    name: Any = ...
    kwargs: Any = ...

    def __init__(self, name: Optional[Any]=..., **kwargs: Any) ->None:
        ...

    def __call__(self, alt: Any) ->Any:
        ...


def nanany(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanall(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


<mask token>


def nanmean(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanmedian(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanstd(values: Any, axis: Optional[Any]=..., skipna: bool=..., ddof:
    int=..., mask: Optional[Any]=...) ->Any:
    ...


def nanvar(values: Any, axis: Optional[Any]=..., skipna: bool=..., ddof:
    int=..., mask: Optional[Any]=...) ->Any:
    ...


def nansem(values: Any, axis: Optional[Any]=..., skipna: bool=..., ddof:
    int=..., mask: Optional[Any]=...) ->Any:
    ...


<mask token>


def nanargmax(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanargmin(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanskew(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


<mask token>


def nanprod(values: Any, axis: Optional[Any]=..., skipna: bool=...,
    min_count: int=..., mask: Optional[Any]=...) ->Any:
    ...


def nancorr(a: Any, b: Any, method: str=..., min_periods: Optional[Any]=...
    ) ->Any:
    ...


<mask token>


def nancov(a: Any, b: Any, min_periods: Optional[Any]=...) ->Any:
    ...


<mask token>


def nanpercentile(values: Any, q: Any, axis: Any, na_value: Any, mask: Any,
    ndim: Any, interpolation: Any) ->Any:
    ...

<mask token>


class disallow:
    dtypes: Any = ...

    def __init__(self, *dtypes: Any) ->None:
        ...

    def check(self, obj: Any) ->Any:
        ...

    def __call__(self, f: Any) ->Any:
        ...


class bottleneck_switch:
    name: Any = ...
    kwargs: Any = ...

    def __init__(self, name: Optional[Any]=..., **kwargs: Any) ->None:
        ...

    def __call__(self, alt: Any) ->Any:
        ...


def nanany(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanall(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


<mask token>


def nanmean(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanmedian(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanstd(values: Any, axis: Optional[Any]=..., skipna: bool=..., ddof:
    int=..., mask: Optional[Any]=...) ->Any:
    ...


def nanvar(values: Any, axis: Optional[Any]=..., skipna: bool=..., ddof:
    int=..., mask: Optional[Any]=...) ->Any:
    ...


def nansem(values: Any, axis: Optional[Any]=..., skipna: bool=..., ddof:
    int=..., mask: Optional[Any]=...) ->Any:
    ...


<mask token>


def nanargmax(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanargmin(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanskew(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


<mask token>


def nanprod(values: Any, axis: Optional[Any]=..., skipna: bool=...,
    min_count: int=..., mask: Optional[Any]=...) ->Any:
    ...


def nancorr(a: Any, b: Any, method: str=..., min_periods: Optional[Any]=...
    ) ->Any:
    ...


def get_corr_func(method: Any) ->Any:
    ...


def nancov(a: Any, b: Any, min_periods: Optional[Any]=...) ->Any:
    ...


<mask token>


def nanpercentile(values: Any, q: Any, axis: Any, na_value: Any, mask: Any,
    ndim: Any, interpolation: Any) ->Any:
    ...

from typing import Any, Optional
bn: Any


def set_use_bottleneck(v: bool=...) ->None:
    ...


class disallow:
    dtypes: Any = ...

    def __init__(self, *dtypes: Any) ->None:
        ...

    def check(self, obj: Any) ->Any:
        ...

    def __call__(self, f: Any) ->Any:
        ...


class bottleneck_switch:
    name: Any = ...
    kwargs: Any = ...

    def __init__(self, name: Optional[Any]=..., **kwargs: Any) ->None:
        ...

    def __call__(self, alt: Any) ->Any:
        ...


def nanany(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanall(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nansum(values: Any, axis: Optional[Any]=..., skipna: bool=...,
    min_count: int=..., mask: Optional[Any]=...) ->Any:
    ...


def nanmean(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanmedian(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanstd(values: Any, axis: Optional[Any]=..., skipna: bool=..., ddof:
    int=..., mask: Optional[Any]=...) ->Any:
    ...


def nanvar(values: Any, axis: Optional[Any]=..., skipna: bool=..., ddof:
    int=..., mask: Optional[Any]=...) ->Any:
    ...


def nansem(values: Any, axis: Optional[Any]=..., skipna: bool=..., ddof:
    int=..., mask: Optional[Any]=...) ->Any:
    ...


nanmin: Any
nanmax: Any


def nanargmax(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanargmin(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanskew(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nankurt(values: Any, axis: Optional[Any]=..., skipna: bool=..., mask:
    Optional[Any]=...) ->Any:
    ...


def nanprod(values: Any, axis: Optional[Any]=..., skipna: bool=...,
    min_count: int=..., mask: Optional[Any]=...) ->Any:
    ...


def nancorr(a: Any, b: Any, method: str=..., min_periods: Optional[Any]=...
    ) ->Any:
    ...


def get_corr_func(method: Any) ->Any:
    ...


def nancov(a: Any, b: Any, min_periods: Optional[Any]=...) ->Any:
    ...


def make_nancomp(op: Any) ->Any:
    ...


nangt: Any
nange: Any
nanlt: Any
nanle: Any
naneq: Any
nanne: Any


def nanpercentile(values: Any, q: Any, axis: Any, na_value: Any, mask: Any,
    ndim: Any, interpolation: Any) ->Any:
    ...

# Stubs for pandas.core.nanops (Python 3)
#
# NOTE: This dynamically typed stub was automatically generated by stubgen.
# pylint: disable=unused-argument,redefined-outer-name,no-self-use,invalid-name
# pylint: disable=relative-beyond-top-level,line-too-long,arguments-differ
# pylint: disable=no-member,too-few-public-methods,keyword-arg-before-vararg
# pylint: disable=super-init-not-called,abstract-method,redefined-builtin
# pylint: disable=unused-import,useless-import-alias,signature-differs
# pylint: disable=blacklisted-name,c-extension-no-member

from typing import Any, Optional

bn: Any


def set_use_bottleneck(v: bool = ...) -> None:
    ...


class disallow:
    dtypes: Any = ...

    def __init__(self, *dtypes: Any) -> None:
        ...

    def check(self, obj: Any) -> Any:
        ...

    def __call__(self, f: Any) -> Any:
        ...


class bottleneck_switch:
    name: Any = ...
    kwargs: Any = ...

    def __init__(self, name: Optional[Any] = ..., **kwargs: Any) -> None:
        ...


    def __call__(self, alt: Any) -> Any:
        ...



def nanany(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
           mask: Optional[Any] = ...) -> Any:
    ...


def nanall(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
           mask: Optional[Any] = ...) -> Any:
    ...


def nansum(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
           min_count: int = ..., mask: Optional[Any] = ...) -> Any:
    ...


def nanmean(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
            mask: Optional[Any] = ...) -> Any:
    ...


def nanmedian(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
              mask: Optional[Any] = ...) -> Any:
    ...


def nanstd(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
           ddof: int = ..., mask: Optional[Any] = ...) -> Any:
    ...


def nanvar(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
           ddof: int = ..., mask: Optional[Any] = ...) -> Any:
    ...


def nansem(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
           ddof: int = ..., mask: Optional[Any] = ...) -> Any:
    ...


nanmin: Any
nanmax: Any


def nanargmax(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
              mask: Optional[Any] = ...) -> Any:
    ...


def nanargmin(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
              mask: Optional[Any] = ...) -> Any:
    ...


def nanskew(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
            mask: Optional[Any] = ...) -> Any:
    ...


def nankurt(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
            mask: Optional[Any] = ...) -> Any:
    ...


def nanprod(values: Any, axis: Optional[Any] = ..., skipna: bool = ...,
            min_count: int = ..., mask: Optional[Any] = ...) -> Any:
    ...


def nancorr(a: Any, b: Any, method: str = ...,
            min_periods: Optional[Any] = ...) -> Any:
    ...


def get_corr_func(method: Any) -> Any:
    ...


def nancov(a: Any, b: Any, min_periods: Optional[Any] = ...) -> Any:
    ...


def make_nancomp(op: Any) -> Any:
    ...


nangt: Any
nange: Any
nanlt: Any
nanle: Any
naneq: Any
nanne: Any


def nanpercentile(values: Any, q: Any, axis: Any, na_value: Any, mask: Any,
                  ndim: Any, interpolation: Any) -> Any:
    ...
