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0501e2b329
* Add os.add_dll_directory() * Add memfd_create() and flags * Add type annotation to flags * Add stat_result.st_reparse_tag and flags * Add ncurses_version * Add Path.link_to() * Add Picker.reducer_override() * Add plistlib.UID * Add has_dualstack_ipv6() and create_server() * Add shlex.join() * Add SSL methods and fields * Add Python 3.8 statistics functions and classes * Remove obsolete sys.subversion * Add sys.unraisablehook * Add threading.excepthook * Add get_native_id() and Thread.native_id * Add Python 3.8 tkinter methods * Add CLOCK_UPTIME_RAW * Add SupportsIndex * Add typing.get_origin() and get_args() * Add unicodedata.is_normalized * Add unittest.mock.AsyncMock Currently this is just an alias for Any like Mock and MagicMock. All of these classes should probably be sub-classing Any and add their own methods. See also #3224. * Add unittest cleanup methods * Add IsolatedAsyncioTestCase * Add ElementTree.canonicalize() and C14NWriterTarget * cProfile.Profile can be used as a context manager * Add asyncio task name handling * mmap.flush() now always returns None * Add posonlyargcount to CodeType
65 lines
2.8 KiB
Python
65 lines
2.8 KiB
Python
# Stubs for statistics
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from decimal import Decimal
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from fractions import Fraction
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import sys
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from typing import Any, Iterable, List, Optional, SupportsFloat, Type, TypeVar, Union
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_T = TypeVar("_T")
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# Most functions in this module accept homogeneous collections of one of these types
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_Number = TypeVar('_Number', float, Decimal, Fraction)
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class StatisticsError(ValueError): ...
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if sys.version_info >= (3, 8):
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def fmean(data: Iterable[SupportsFloat]) -> float: ...
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def geometric_mean(data: Iterable[SupportsFloat]) -> float: ...
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def mean(data: Iterable[_Number]) -> _Number: ...
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if sys.version_info >= (3, 6):
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def harmonic_mean(data: Iterable[_Number]) -> _Number: ...
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def median(data: Iterable[_Number]) -> _Number: ...
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def median_low(data: Iterable[_Number]) -> _Number: ...
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def median_high(data: Iterable[_Number]) -> _Number: ...
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def median_grouped(data: Iterable[_Number]) -> _Number: ...
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def mode(data: Iterable[_Number]) -> _Number: ...
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if sys.version_info >= (3, 8):
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def multimode(data: Iterable[_T]) -> List[_T]: ...
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def pstdev(data: Iterable[_Number], mu: Optional[_Number] = ...) -> _Number: ...
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def pvariance(data: Iterable[_Number], mu: Optional[_Number] = ...) -> _Number: ...
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if sys.version_info >= (3, 8):
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def quantiles(data: Iterable[_Number], *, n: int = ..., method: str = ...) -> List[_Number]: ...
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def stdev(data: Iterable[_Number], xbar: Optional[_Number] = ...) -> _Number: ...
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def variance(data: Iterable[_Number], xbar: Optional[_Number] = ...) -> _Number: ...
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if sys.version_info >= (3, 8):
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class NormalDist:
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def __init__(self, mu: float = ..., sigma: float = ...) -> None: ...
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@property
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def mean(self) -> float: ...
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@property
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def median(self) -> float: ...
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@property
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def mode(self) -> float: ...
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@property
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def stdev(self) -> float: ...
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@property
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def variance(self) -> float: ...
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@classmethod
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def from_samples(cls: Type[_T], data: Iterable[SupportsFloat]) -> _T: ...
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def samples(self, n: int, *, seed: Optional[Any]) -> List[float]: ...
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def pdf(self, x: float) -> float: ...
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def cdf(self, x: float) -> float: ...
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def inv_cdf(self, p: float) -> float: ...
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def overlap(self, other: NormalDist) -> float: ...
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def quantiles(self, n: int = ...) -> List[float]: ...
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def __add__(self, x2: Union[float, NormalDist]) -> NormalDist: ...
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def __sub__(self, x2: Union[float, NormalDist]) -> NormalDist: ...
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def __mul__(self, x2: float) -> NormalDist: ...
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def __truediv__(self, x2: float) -> NormalDist: ...
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def __pos__(self) -> NormalDist: ...
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def __neg__(self) -> NormalDist: ...
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__radd__ = __add__
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def __rsub__(self, x2: Union[float, NormalDist]) -> NormalDist: ...
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__rmul__ = __mul__
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def __hash__(self) -> int: ...
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