source stringclasses 1
value | version stringclasses 1
value | module stringclasses 43
values | function stringclasses 307
values | input stringlengths 3 496 | expected stringlengths 0 40.5k | signature stringclasses 0
values |
|---|---|---|---|---|---|---|
cpython | cfcd524 | typing | _LazyAnnotationLib._should_unflatten_callable_args | >>> collections.abc.Callable[P, str].__args__ == (P, str) | True
As a result, if we need to reconstruct the Callable from its __args__,
we need to unflatten it. | null |
cpython | cfcd524 | typing | _LazyAnnotationLib._collect_type_parameters | >>> P = ParamSpec('P') | null | |
cpython | cfcd524 | typing | _LazyAnnotationLib._collect_type_parameters | >>> T = TypeVar('T') | null | |
cpython | cfcd524 | typing | _LazyAnnotationLib._collect_type_parameters | >>> _collect_type_parameters((T, Callable[P, T])) | (~T, ~P) | null |
cpython | cfcd524 | typing | _LazyAnnotationLib._collect_type_parameters | >>> _collect_type_parameters((list[T], Generic[P, T])) | (~P, ~T) | null |
cpython | cfcd524 | typing | Closable.get_origin | >>> P = ParamSpec('P') | null | |
cpython | cfcd524 | typing | Closable.get_origin | >>> assert get_origin(Literal[42]) is Literal | null | |
cpython | cfcd524 | typing | Closable.get_origin | >>> assert get_origin(int) is None | null | |
cpython | cfcd524 | typing | Closable.get_origin | >>> assert get_origin(ClassVar[int]) is ClassVar | null | |
cpython | cfcd524 | typing | Closable.get_origin | >>> assert get_origin(Generic) is Generic | null | |
cpython | cfcd524 | typing | Closable.get_origin | >>> assert get_origin(Generic[T]) is Generic | null | |
cpython | cfcd524 | typing | Closable.get_origin | >>> assert get_origin(Union[T, int]) is Union | null | |
cpython | cfcd524 | typing | Closable.get_origin | >>> assert get_origin(List[Tuple[T, T]][int]) is list | null | |
cpython | cfcd524 | typing | Closable.get_origin | >>> assert get_origin(P.args) is P | null | |
cpython | cfcd524 | typing | Closable.get_args | >>> T = TypeVar('T') | null | |
cpython | cfcd524 | typing | Closable.get_args | >>> assert get_args(Dict[str, int]) == (str, int) | null | |
cpython | cfcd524 | typing | Closable.get_args | >>> assert get_args(int) == () | null | |
cpython | cfcd524 | typing | Closable.get_args | >>> assert get_args(Union[int, Union[T, int], str][int]) == (int, str) | null | |
cpython | cfcd524 | typing | Closable.get_args | >>> assert get_args(Union[int, Tuple[T, int]][str]) == (int, Tuple[str, int]) | null | |
cpython | cfcd524 | typing | Closable.get_args | >>> assert get_args(Callable[[], T][int]) == ([], int) | null | |
cpython | cfcd524 | typing | Closable.is_typeddict | >>> from typing import TypedDict | null | |
cpython | cfcd524 | typing | Closable.is_typeddict | >>> class Film(TypedDict):
... title: str
... year: int
... | null | |
cpython | cfcd524 | typing | Closable.is_typeddict | >>> is_typeddict(Film) | True | null |
cpython | cfcd524 | typing | Closable.is_typeddict | >>> is_typeddict(dict) | False | null |
cpython | cfcd524 | typing | _TypedDictMeta.TypedDict | >>> class Point2D(TypedDict):
... x: int
... y: int
... label: str
... | null | |
cpython | cfcd524 | typing | _TypedDictMeta.TypedDict | >>> a: Point2D = {'x': 1, 'y': 2, 'label': 'good'} # OK | null | |
cpython | cfcd524 | typing | _TypedDictMeta.TypedDict | >>> b: Point2D = {'z': 3, 'label': 'bad'} # Fails type check | null | |
cpython | cfcd524 | typing | _TypedDictMeta.TypedDict | >>> Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first') | True
The type info can be accessed via the Point2D.__annotations__ dict, and
the Point2D.__required_keys__ and Point2D.__optional_keys__ frozensets.
TypedDict supports an additional equivalent form::
Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
By default, all keys must be present in a TypedDic... | null |
cpython | cfcd524 | typing | Child.is_protocol | >>> from typing import Protocol, is_protocol | null | |
cpython | cfcd524 | typing | Child.is_protocol | >>> class P(Protocol):
... def a(self) -> str: ...
... b: int | null | |
cpython | cfcd524 | typing | Child.is_protocol | >>> is_protocol(P) | True | null |
cpython | cfcd524 | typing | Child.is_protocol | >>> is_protocol(int) | False | null |
cpython | cfcd524 | typing | Child.get_protocol_members | >>> from typing import Protocol, get_protocol_members | null | |
cpython | cfcd524 | typing | Child.get_protocol_members | >>> class P(Protocol):
... def a(self) -> str: ...
... b: int | null | |
cpython | cfcd524 | typing | Child.get_protocol_members | >>> get_protocol_members(P) == frozenset({'a', 'b'}) | True
Raise a TypeError for arguments that are not Protocols. | null |
cpython | cfcd524 | fractions | Fraction.__new__ | >>> Fraction(10, -8) | Fraction(-5, 4) | null |
cpython | cfcd524 | fractions | Fraction.__new__ | >>> Fraction(Fraction(1, 7), 5) | Fraction(1, 35) | null |
cpython | cfcd524 | fractions | Fraction.__new__ | >>> Fraction(Fraction(1, 7), Fraction(2, 3)) | Fraction(3, 14) | null |
cpython | cfcd524 | fractions | Fraction.__new__ | >>> Fraction('314') | Fraction(314, 1) | null |
cpython | cfcd524 | fractions | Fraction.__new__ | >>> Fraction('-35/4') | Fraction(-35, 4) | null |
cpython | cfcd524 | fractions | Fraction.__new__ | >>> Fraction('3.1415') # conversion from numeric string | Fraction(6283, 2000) | null |
cpython | cfcd524 | fractions | Fraction.__new__ | >>> Fraction('-47e-2') # string may include a decimal exponent | Fraction(-47, 100) | null |
cpython | cfcd524 | fractions | Fraction.__new__ | >>> Fraction(1.47) # direct construction from float (exact conversion) | Fraction(6620291452234629, 4503599627370496) | null |
cpython | cfcd524 | fractions | Fraction.__new__ | >>> Fraction(2.25) | Fraction(9, 4) | null |
cpython | cfcd524 | fractions | Fraction.__new__ | >>> Fraction(Decimal('1.47')) | Fraction(147, 100) | null |
cpython | cfcd524 | fractions | Fraction.limit_denominator | >>> Fraction('3.141592653589793').limit_denominator(10) | Fraction(22, 7) | null |
cpython | cfcd524 | fractions | Fraction.limit_denominator | >>> Fraction('3.141592653589793').limit_denominator(100) | Fraction(311, 99) | null |
cpython | cfcd524 | fractions | Fraction.limit_denominator | >>> Fraction(4321, 8765).limit_denominator(10000) | Fraction(4321, 8765) | null |
cpython | cfcd524 | secrets | token_bytes | >>> token_bytes(16) #doctest:+SKIP | b'\\xebr\\x17D*t\\xae\\xd4\\xe3S\\xb6\\xe2\\xebP1\\x8b' | null |
cpython | cfcd524 | secrets | token_hex | >>> token_hex(16) #doctest:+SKIP | 'f9bf78b9a18ce6d46a0cd2b0b86df9da' | null |
cpython | cfcd524 | secrets | token_urlsafe | >>> token_urlsafe(16) #doctest:+SKIP | 'Drmhze6EPcv0fN_81Bj-nA' | null |
cpython | cfcd524 | smtplib | SMTP.sendmail | >>> import smtplib | null | |
cpython | cfcd524 | smtplib | SMTP.sendmail | >>> s=smtplib.SMTP("localhost") | null | |
cpython | cfcd524 | smtplib | SMTP.sendmail | >>> tolist=["one@one.org","two@two.org","three@three.org","four@four.org"] | null | |
cpython | cfcd524 | smtplib | SMTP.sendmail | >>> msg = '''\\
... From: Me@my.org
... Subject: testin'...
...
... This is a test ''' | null | |
cpython | cfcd524 | smtplib | SMTP.sendmail | >>> s.sendmail("me@my.org",tolist,msg) | { "three@three.org" : ( 550 ,"User unknown" ) } | null |
cpython | cfcd524 | smtplib | SMTP.sendmail | >>> s.quit() | In the above example, the message was accepted for delivery to three
of the four addresses, and one was rejected, with the error code
550. If all addresses are accepted, then the method will return an
empty dictionary. | null |
cpython | cfcd524 | hashlib | __module__ | >>> import hashlib | null | |
cpython | cfcd524 | hashlib | __module__ | >>> m = hashlib.sha256() | null | |
cpython | cfcd524 | hashlib | __module__ | >>> m.update(b"Nobody inspects") | null | |
cpython | cfcd524 | hashlib | __module__ | >>> m.update(b" the spammish repetition") | null | |
cpython | cfcd524 | hashlib | __module__ | >>> m.digest() # doctest: +ELLIPSIS | b'\x03\x1e\xdd}Ae\x15\x93\xc5\xfe\\\x00o\xa5u+7...'
More condensed: | null |
cpython | cfcd524 | hashlib | __module__ | >>> hashlib.sha256(b"Nobody inspects the spammish repetition").hexdigest() | '031edd7d41651593c5fe5c006fa5752b37fddff7bc4e843aa6af0c950f4b9406' | null |
cpython | cfcd524 | ftplib | __module__ | >>> from ftplib import FTP | null | |
cpython | cfcd524 | ftplib | __module__ | >>> ftp = FTP('ftp.python.org') # connect to host, default port | null | |
cpython | cfcd524 | ftplib | __module__ | >>> ftp.login() # default, i.e.: user anonymous, passwd anonymous@ | '230 Guest login ok, access restrictions apply.' | null |
cpython | cfcd524 | ftplib | __module__ | >>> ftp.retrlines('LIST') # list directory contents | total 9
drwxr-xr-x 8 root wheel 1024 Jan 3 1994 .
drwxr-xr-x 8 root wheel 1024 Jan 3 1994 ..
drwxr-xr-x 2 root wheel 1024 Jan 3 1994 bin
drwxr-xr-x 2 root wheel 1024 Jan 3 1994 etc
d-wxrwxr-x 2 ftp wheel 1024 Sep 5 13:43 incoming
drwxr-xr-x 2 ... | null |
cpython | cfcd524 | ftplib | __module__ | >>> ftp.quit() | '221 Goodbye.' | null |
cpython | cfcd524 | ftplib | __module__ | >>> | A nice test that reveals some of the network dialogue would be:
python ftplib.py -d localhost -l -p -l | null |
cpython | cfcd524 | statistics | __module__ | >>> mean([-1.0, 2.5, 3.25, 5.75]) | 2.625
Calculate the standard median of discrete data: | null |
cpython | cfcd524 | statistics | __module__ | >>> median([2, 3, 4, 5]) | 3.5
Calculate the median, or 50th percentile, of data grouped into class intervals
centred on the data values provided. E.g. if your data points are rounded to
the nearest whole number: | null |
cpython | cfcd524 | statistics | __module__ | >>> median_grouped([2, 2, 3, 3, 3, 4]) #doctest: +ELLIPSIS | 2.8333333333...
This should be interpreted in this way: you have two data points in the class
interval 1.5-2.5, three data points in the class interval 2.5-3.5, and one in
the class interval 3.5-4.5. The median of these data points is 2.8333...
Calculating variability or spread
---------------------------------
===... | null |
cpython | cfcd524 | statistics | __module__ | >>> stdev([2.5, 3.25, 5.5, 11.25, 11.75]) #doctest: +ELLIPSIS | 4.38961843444...
If you have previously calculated the mean, you can pass it as the optional
second argument to the four "spread" functions to avoid recalculating it: | null |
cpython | cfcd524 | statistics | __module__ | >>> data = [1, 2, 2, 4, 4, 4, 5, 6] | null | |
cpython | cfcd524 | statistics | __module__ | >>> mu = mean(data) | null | |
cpython | cfcd524 | statistics | __module__ | >>> pvariance(data, mu) | 2.5
Statistics for relations between two inputs
-------------------------------------------
================== ====================================================
Function Description
================== ====================================================
covariance Sample covariance for two v... | null |
cpython | cfcd524 | statistics | __module__ | >>> x = [1, 2, 3, 4, 5, 6, 7, 8, 9] | null | |
cpython | cfcd524 | statistics | __module__ | >>> y = [1, 2, 3, 1, 2, 3, 1, 2, 3] | null | |
cpython | cfcd524 | statistics | __module__ | >>> covariance(x, y) | 0.75 | null |
cpython | cfcd524 | statistics | __module__ | >>> correlation(x, y) #doctest: +ELLIPSIS | 0.31622776601... | null |
cpython | cfcd524 | statistics | __module__ | >>> linear_regression(x, y) #doctest: | LinearRegression(slope=0.1, intercept=1.5)
Exceptions
----------
A single exception is defined: StatisticsError is a subclass of ValueError. | null |
cpython | cfcd524 | statistics | StatisticsError.mean | >>> mean([1, 2, 3, 4, 4]) | 2.8 | null |
cpython | cfcd524 | statistics | StatisticsError.mean | >>> from fractions import Fraction as F | null | |
cpython | cfcd524 | statistics | StatisticsError.mean | >>> mean([F(3, 7), F(1, 21), F(5, 3), F(1, 3)]) | Fraction(13, 21) | null |
cpython | cfcd524 | statistics | StatisticsError.mean | >>> from decimal import Decimal as D | null | |
cpython | cfcd524 | statistics | StatisticsError.mean | >>> mean([D("0.5"), D("0.75"), D("0.625"), D("0.375")]) | Decimal('0.5625')
If ``data`` is empty, StatisticsError will be raised. | null |
cpython | cfcd524 | statistics | StatisticsError.fmean | >>> fmean([3.5, 4.0, 5.25]) | 4.25 | null |
cpython | cfcd524 | statistics | StatisticsError.geometric_mean | >>> round(geometric_mean([54, 24, 36]), 9) | 36.0 | null |
cpython | cfcd524 | statistics | StatisticsError.harmonic_mean | >>> harmonic_mean([40, 60]) | 48.0
Suppose a car travels 40 km/hr for 5 km, and when traffic clears,
speeds-up to 60 km/hr for the remaining 30 km of the journey. What
is the average speed? | null |
cpython | cfcd524 | statistics | StatisticsError.harmonic_mean | >>> harmonic_mean([40, 60], weights=[5, 30]) | 56.0
If ``data`` is empty, or any element is less than zero,
``harmonic_mean`` will raise ``StatisticsError``. | null |
cpython | cfcd524 | statistics | StatisticsError.median | >>> median([1, 3, 5]) | 3 | null |
cpython | cfcd524 | statistics | StatisticsError.median | >>> median([1, 3, 5, 7]) | 4.0 | null |
cpython | cfcd524 | statistics | StatisticsError.median_low | >>> median_low([1, 3, 5]) | 3 | null |
cpython | cfcd524 | statistics | StatisticsError.median_low | >>> median_low([1, 3, 5, 7]) | 3 | null |
cpython | cfcd524 | statistics | StatisticsError.median_high | >>> median_high([1, 3, 5]) | 3 | null |
cpython | cfcd524 | statistics | StatisticsError.median_high | >>> median_high([1, 3, 5, 7]) | 5 | null |
cpython | cfcd524 | statistics | StatisticsError.median_grouped | >>> demographics = Counter({
... 25: 172, # 20 to 30 years old
... 35: 484, # 30 to 40 years old
... 45: 387, # 40 to 50 years old
... 55: 22, # 50 to 60 years old
... 65: 6, # 60 to 70 years old
... }) | The 50th percentile (median) is the 536th person out of the 1071
member cohort. That person is in the 30 to 40 year old age group.
The regular median() function would assume that everyone in the
tricenarian age group was exactly 35 years old. A more tenable
assumption is that the 484 members of that age group are ev... | null |
cpython | cfcd524 | statistics | StatisticsError.median_grouped | >>> data = list(demographics.elements()) | null | |
cpython | cfcd524 | statistics | StatisticsError.median_grouped | >>> median(data) | 35 | null |
cpython | cfcd524 | statistics | StatisticsError.median_grouped | >>> round(median_grouped(data, interval=10), 1) | 37.5
The caller is responsible for making sure the data points are separated
by exact multiples of *interval*. This is essential for getting a
correct result. The function does not check this precondition.
Inputs may be any numeric type that can be coerced to a float during
the interpolation step. | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.