# Python: 3.9.2 (tags/v3.9.2:1a79785, Feb 19 2021, 13:44:55) [MSC v.1928 64 bit (AMD64)] # Library: pandas, version: 1.2.4 # Module: pandas._libs.reduction, version: unspecified import typing import builtins as _mod_builtins class BlockSlider(_mod_builtins.object): '\n Only capable of sliding on axis=0\n ' def __init__(self, *args, **kwargs) -> None: '\n Only capable of sliding on axis=0\n ' ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def __getattr__(self, name) -> typing.Any: ... class InvalidApply(_mod_builtins.Exception): __dict__: typing.Dict[str, typing.Any] def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... __module__: str @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... @property def __weakref__(self) -> typing.Any: 'list of weak references to the object (if defined)' ... def __getattr__(self, name) -> typing.Any: ... class SeriesBinGrouper(_BaseGrouper): '\n Performs grouping operation according to bin edges, rather than labels\n ' def __init__(self, *args, **kwargs) -> None: '\n Performs grouping operation according to bin edges, rather than labels\n ' ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... @property def arr(self) -> typing.Any: ... @property def bins(self) -> typing.Any: ... @property def dummy_arr(self) -> typing.Any: ... @property def dummy_index(self) -> typing.Any: ... @property def f(self) -> typing.Any: ... def get_result(self) -> typing.Any: ... @property def index(self) -> typing.Any: ... @property def ityp(self) -> typing.Any: ... @property def name(self) -> typing.Any: ... @property def typ(self) -> typing.Any: ... @property def values(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class SeriesGrouper(_BaseGrouper): '\n Performs generic grouping operation while avoiding ndarray construction\n overhead\n ' def __init__(self, *args, **kwargs) -> None: '\n Performs generic grouping operation while avoiding ndarray construction\n overhead\n ' ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... @property def arr(self) -> typing.Any: ... @property def dummy_arr(self) -> typing.Any: ... @property def dummy_index(self) -> typing.Any: ... @property def f(self) -> typing.Any: ... def get_result(self) -> typing.Any: ... @property def index(self) -> typing.Any: ... @property def ityp(self) -> typing.Any: ... @property def labels(self) -> typing.Any: ... @property def name(self) -> typing.Any: ... @property def typ(self) -> typing.Any: ... @property def values(self) -> typing.Any: ... def __getattr__(self, name) -> typing.Any: ... class Slider(_mod_builtins.object): '\n Only handles contiguous data for now\n ' def __init__(self, *args, **kwargs) -> None: '\n Only handles contiguous data for now\n ' ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def __getattr__(self, name) -> typing.Any: ... class _BaseGrouper(_mod_builtins.object): def __init__(self, *args, **kwargs) -> None: ... @classmethod def __init_subclass__(cls) -> None: 'This method is called when a class is subclassed.\n\nThe default implementation does nothing. It may be\noverridden to extend subclasses.\n' ... __pyx_vtable__: PyCapsule def __reduce__(self) -> typing.Union[str, typing.Tuple[typing.Any, ...]]: ... def __setstate__(self, state: typing.Any) -> None: ... @classmethod def __subclasshook__(cls, subclass: typing.Any) -> bool: 'Abstract classes can override this to customize issubclass().\n\nThis is invoked early on by abc.ABCMeta.__subclasscheck__().\nIt should return True, False or NotImplemented. If it returns\nNotImplemented, the normal algorithm is used. Otherwise, it\noverrides the normal algorithm (and the outcome is cached).\n' ... def __getattr__(self, name) -> typing.Any: ... __doc__: typing.Any __file__: str __name__: str __package__: str def __pyx_unpickle_Enum() -> typing.Any: ... def __pyx_unpickle_SeriesBinGrouper() -> typing.Any: ... def __pyx_unpickle_SeriesGrouper() -> typing.Any: ... def __pyx_unpickle_Slider() -> typing.Any: ... def __pyx_unpickle__BaseGrouper() -> typing.Any: ... __test__: dict def apply_frame_axis0() -> typing.Any: ... def check_result_array() -> typing.Any: ... def copy(x) -> typing.Any: "Shallow copy operation on arbitrary Python objects.\n\n See the module's __doc__ string for more info.\n " ... def extract_result() -> typing.Any: ' extract the result object, it might be a 0-dim ndarray\n or a len-1 0-dim, or a scalar ' ... def is_scalar() -> typing.Any: '\n Return True if given object is scalar.\n\n Parameters\n ----------\n val : object\n This includes:\n\n - numpy array scalar (e.g. np.int64)\n - Python builtin numerics\n - Python builtin byte arrays and strings\n - None\n - datetime.datetime\n - datetime.timedelta\n - Period\n - decimal.Decimal\n - Interval\n - DateOffset\n - Fraction\n - Number.\n\n Returns\n -------\n bool\n Return True if given object is scalar.\n\n Examples\n --------\n >>> dt = datetime.datetime(2018, 10, 3)\n >>> pd.api.types.is_scalar(dt)\n True\n\n >>> pd.api.types.is_scalar([2, 3])\n False\n\n >>> pd.api.types.is_scalar({0: 1, 2: 3})\n False\n\n >>> pd.api.types.is_scalar((0, 2))\n False\n\n pandas supports PEP 3141 numbers:\n\n >>> from fractions import Fraction\n >>> pd.api.types.is_scalar(Fraction(3, 5))\n True\n ' ... def maybe_convert_objects() -> typing.Any: '\n Type inference function-- convert object array to proper dtype\n\n Parameters\n ----------\n values : ndarray\n Array of object elements to convert.\n try_float : bool, default False\n If an array-like object contains only float or NaN values is\n encountered, whether to convert and return an array of float dtype.\n safe : bool, default False\n Whether to upcast numeric type (e.g. int cast to float). If set to\n True, no upcasting will be performed.\n convert_datetime : bool, default False\n If an array-like object contains only datetime values or NaT is\n encountered, whether to convert and return an array of M8[ns] dtype.\n convert_timedelta : bool, default False\n If an array-like object contains only timedelta values or NaT is\n encountered, whether to convert and return an array of m8[ns] dtype.\n convert_to_nullable_integer : bool, default False\n If an array-like object contains only integer values (and NaN) is\n encountered, whether to convert and return an IntegerArray.\n\n Returns\n -------\n Array of converted object values to more specific dtypes if applicable.\n ' ... def __getattr__(name) -> typing.Any: ...