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 TypedDict. It is possible
to override this by specifying totality::
class Point2D(TypedDict, total=False):
x: int
y: int
This means that a Point2D TypedDict can have any of the keys omitted. A type
checker is only expected to support a literal False or True as the value of
the total argument. True is the default, and makes all items defined in the
class body be required.
The Required and NotRequired special forms can also be used to mark
individual keys as being required or not required::
class Point2D(TypedDict):
x: int # the "x" key must always be present (Required is the default)
y: NotRequired[int] # the "y" key can be omitted
See PEP 655 for more details on Required and NotRequired.
The ReadOnly special form can be used
to mark individual keys as immutable for type checkers::
class DatabaseUser(TypedDict):
id: ReadOnly[int] # the "id" key must not be modified
username: str # the "username" key can be changed
The closed argument controls whether the TypedDict allows additional
non-required items during inheritance and assignability checks.
If closed=True, the TypedDict does not allow additional items::
Point2D = TypedDict('Point2D', {'x': int, 'y': int}, closed=True)
class Point3D(Point2D):
z: int # Type checker error
Passing closed=False explicitly requests TypedDict's default open behavior.
If closed is not provided, the behavior is inherited from the superclass.
A type checker is only expected to support a literal False or True as the
value of the closed argument.
The extra_items argument can instead be used to specify the assignable type
of unknown non-required keys::
Point2D = TypedDict('Point2D', {'x': int, 'y': int}, extra_items=int)
class Point3D(Point2D):
z: int # OK
label: str # Type checker error
The extra_items argument is also inherited through subclassing. It is unset
by default, and it may not be used with the closed argument at the same
time.
See PEP 728 for more information about closed and extra_items.
| 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 root wheel 1024 Nov 17 1993 lib
drwxr-xr-x 6 1094 wheel 1024 Sep 13 19:07 pub
drwxr-xr-x 3 root wheel 1024 Jan 3 1994 usr
-rw-r--r-- 1 root root 312 Aug 1 1994 welcome.msg
'226 Transfer complete.'
| 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
---------------------------------
================== =============================================
Function Description
================== =============================================
pvariance Population variance of data.
variance Sample variance of data.
pstdev Population standard deviation of data.
stdev Sample standard deviation of data.
================== =============================================
Calculate the standard deviation of sample data:
| 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 variables.
correlation Pearson's correlation coefficient for two variables.
linear_regression Intercept and slope for simple linear regression.
================== ====================================================
Calculate covariance, Pearson's correlation, and simple linear regression
for two inputs:
| 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 evenly
distributed between 30 and 40. For that, we use median_grouped().
| 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.