Files

Resetting an interpreter’s state

It may be nice to re-use an existing subinterpreter instead of
spinning up a new one. Since an interpreter has substantially more
state than just the __main__ module, it isn’t so easy to put an
interpreter back into a pristine/fresh state. In fact, there may
be parts of the state that cannot be reset from Python code.

A possible solution is to add an Interpreter.reset() method. This
would put the interpreter back into the state it was in when newly
created. If called on a running interpreter it would fail (hence the
main interpreter could never be reset). This would likely be more
efficient than creating a new subinterpreter, though that depends on
what optimizations will be made later to subinterpreter creation.

Alternate solutions to prevent leaking exceptions across interpreters

In function calls, uncaught exceptions propagate to the calling frame.
The same approach could be taken with run(). However, this would
mean that exception objects would leak across the inter-interpreter
boundary. Likewise, the frames in the traceback would potentially leak.

While that might not be a problem currently, it would be a problem once
interpreters get better isolation relative to memory management (which
is necessary to stop sharing the GIL between interpreters). We’ve
resolved the semantics of how the exceptions propagate by raising a
RunFailedError instead, for which __cause__ wraps a safe proxy
for the original exception and traceback.

Rejected possible solutions:

Циклы

Перед тем, как мы ознакомимся с тем, как работает range(), нам нужно взглянуть на то, как работают циклы. Циклы — это ключевая концепция компьютерных наук. Если вы хотите стать хорошим программистом, умение обращаться с циклами — это важнейший навык, который стоит освоить.

Рассмотрим пример цикла for в Python:

Python

captains =

for captain in captains:
print(captain)

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captains=’Janeway’,’Picard’,’Sisko’

forcaptain incaptains

print(captain)

Выдача выглядит следующим образом:

Python

Janeway
Picard
Sisko

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3

Janeway
Picard
Sisko

Как вы видите, цикл for позволяет вам выполнять определенные части кода, столько раз, сколько вам угодно. В данном случае, мы зациклили список капитанов и вывели имена каждого из них.

Хотя Star Trek — отличная тема и все такое, вам может быть нужен более сложный цикл, чем список капитанов. Иногда вам нужно просто выполнить часть кода определенное количество раз. Циклы могут помочь вам с этим.

Попробуйте запустить следующий код с числами, кратными трем:

Python

numbers_divisible_by_three =

for num in numbers_divisible_by_three:
quotient = num / 3
print(f»{num} делится на 3, результат {int(quotient)}.»)

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numbers_divisible_by_three=3,6,9,12,15

fornum innumbers_divisible_by_three

quotient=num3

print(f»{num} делится на 3, результат {int(quotient)}.»)

Выдача цикла будет выглядеть следующим образом:

Python

3 делится на 3, результат 1.
6 делится на 3, результат 2.
9 делится на 3, результат 3.
12 делится на 3, результат 4.
15 делится на 3, результат 5.

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3
4
5

3делитсяна3,результат1.

6делитсяна3,результат2.

9делитсяна3,результат3.

12делитсяна3,результат4.

15делитсяна3,результат5.

Это выдача, которая нам нужна, так что можем сказать, что цикл выполнил работу адекватно, однако есть еще один способ получения аналогично результата: использование range().

Теперь, когда вы знакомы с циклами поближе, посмотрим, как вы можете использовать range() для упрощения жизни.

Maximum Line Length

Limit all lines to a maximum of 79 characters.

For flowing long blocks of text with fewer structural restrictions
(docstrings or comments), the line length should be limited to 72
characters.

Limiting the required editor window width makes it possible to have
several files open side-by-side, and works well when using code
review tools that present the two versions in adjacent columns.

The default wrapping in most tools disrupts the visual structure of the
code, making it more difficult to understand. The limits are chosen to
avoid wrapping in editors with the window width set to 80, even
if the tool places a marker glyph in the final column when wrapping
lines. Some web based tools may not offer dynamic line wrapping at all.

Some teams strongly prefer a longer line length. For code maintained
exclusively or primarily by a team that can reach agreement on this
issue, it is okay to increase the line length limit up to 99 characters,
provided that comments and docstrings are still wrapped at 72
characters.

The Python standard library is conservative and requires limiting
lines to 79 characters (and docstrings/comments to 72).

The preferred way of wrapping long lines is by using Python’s implied
line continuation inside parentheses, brackets and braces. Long lines
can be broken over multiple lines by wrapping expressions in
parentheses. These should be used in preference to using a backslash
for line continuation.

Backslashes may still be appropriate at times. For example, long,
multiple with-statements cannot use implicit continuation, so
backslashes are acceptable:

with open('/path/to/some/file/you/want/to/read') as file_1, \
     open('/path/to/some/file/being/written', 'w') as file_2:
    file_2.write(file_1.read())

(See the previous discussion on for further
thoughts on the indentation of such multiline with-statements.)

Another such case is with assert statements.

Concerns

Some have argued that subinterpreters do not add sufficient benefit
to justify making them an official part of Python. Adding features
to the language (or stdlib) has a cost in increasing the size of
the language. So an addition must pay for itself. In this case,
subinterpreters provide a novel concurrency model focused on isolated
threads of execution. Furthermore, they provide an opportunity for
changes in CPython that will allow simultaneous use of multiple CPU
cores (currently prevented by the GIL).

Alternatives to subinterpreters include threading, async, and
multiprocessing. Threading is limited by the GIL and async isn’t
the right solution for every problem (nor for every person).
Multiprocessing is likewise valuable in some but not all situations.
Direct IPC (rather than via the multiprocessing module) provides
similar benefits but with the same caveat.

Notably, subinterpreters are not intended as a replacement for any of
the above. Certainly they overlap in some areas, but the benefits of
subinterpreters include isolation and (potentially) performance. In
particular, subinterpreters provide a direct route to an alternate
concurrency model (e.g. CSP) which has found success elsewhere and
will appeal to some Python users. That is the core value that the
interpreters module will provide.

«stdlib support for subinterpreters adds extra burden
on C extension authors»

In the section below we identify ways in
which isolation in CPython’s subinterpreters is incomplete. Most
notable is extension modules that use C globals to store internal
state. PEP 3121 and PEP 489 provide a solution for most of the
problem, but one still remains. Until that is resolved,
C extension authors will face extra difficulty to support
subinterpreters.

Consequently, projects that publish extension modules may face an
increased maintenance burden as their users start using subinterpreters,
where their modules may break. This situation is limited to modules
that use C globals (or use libraries that use C globals) to store
internal state. For numpy, the reported-bug rate is one every 6
months.

Prescriptive: Naming Conventions

Never use the characters ‘l’ (lowercase letter el), ‘O’ (uppercase
letter oh), or ‘I’ (uppercase letter eye) as single character variable
names.

In some fonts, these characters are indistinguishable from the
numerals one and zero. When tempted to use ‘l’, use ‘L’ instead.

Identifiers used in the standard library must be ASCII compatible
as described in the

of PEP 3131.

Modules should have short, all-lowercase names. Underscores can be
used in the module name if it improves readability. Python packages
should also have short, all-lowercase names, although the use of
underscores is discouraged.

When an extension module written in C or C++ has an accompanying
Python module that provides a higher level (e.g. more object oriented)
interface, the C/C++ module has a leading underscore
(e.g. _socket).

Class names should normally use the CapWords convention.

The naming convention for functions may be used instead in cases where
the interface is documented and used primarily as a callable.

Note that there is a separate convention for builtin names: most builtin
names are single words (or two words run together), with the CapWords
convention used only for exception names and builtin constants.

Names of type variables introduced in PEP 484 should normally use CapWords
preferring short names: T, AnyStr, Num. It is recommended to add
suffixes _co or _contra to the variables used to declare covariant
or contravariant behavior correspondingly:

from typing import TypeVar

VT_co = TypeVar('VT_co', covariant=True)
KT_contra = TypeVar('KT_contra', contravariant=True)

Because exceptions should be classes, the class naming convention
applies here. However, you should use the suffix «Error» on your
exception names (if the exception actually is an error).

(Let’s hope that these variables are meant for use inside one module
only.) The conventions are about the same as those for functions.

Modules that are designed for use via from M import * should use
the __all__ mechanism to prevent exporting globals, or use the
older convention of prefixing such globals with an underscore (which
you might want to do to indicate these globals are «module
non-public»).

Function names should be lowercase, with words separated by
underscores as necessary to improve readability.

Variable names follow the same convention as function names.

mixedCase is allowed only in contexts where that’s already the
prevailing style (e.g. threading.py), to retain backwards
compatibility.

Always use self for the first argument to instance methods.

Always use cls for the first argument to class methods.

If a function argument’s name clashes with a reserved keyword, it is
generally better to append a single trailing underscore rather than
use an abbreviation or spelling corruption. Thus class_ is better
than clss. (Perhaps better is to avoid such clashes by using a
synonym.)

Use the function naming rules: lowercase with words separated by
underscores as necessary to improve readability.

Use one leading underscore only for non-public methods and instance
variables.

To avoid name clashes with subclasses, use two leading underscores to
invoke Python’s name mangling rules.

Python mangles these names with the class name: if class Foo has an
attribute named __a, it cannot be accessed by Foo.__a. (An
insistent user could still gain access by calling Foo._Foo__a.)
Generally, double leading underscores should be used only to avoid
name conflicts with attributes in classes designed to be subclassed.

Note: there is some controversy about the use of __names (see below).

Constants are usually defined on a module level and written in all
capital letters with underscores separating words. Examples include
MAX_OVERFLOW and TOTAL.

Relative precedence of :=

The := operator groups more tightly than a comma in all syntactic
positions where it is legal, but less tightly than all other operators,
including or, and, not, and conditional expressions
(A if C else B). As follows from section
«Exceptional cases» above, it is never allowed at the same level as
=. In case a different grouping is desired, parentheses should be
used.

The := operator may be used directly in a positional function call
argument; however it is invalid directly in a keyword argument.

Some examples to clarify what’s technically valid or invalid:

# INVALID
x := 0

# Valid alternative
(x := 0)

# INVALID
x = y := 0

# Valid alternative
x = (y := 0)

# Valid
len(lines := f.readlines())

# Valid
foo(x := 3, cat='vector')

# INVALID
foo(cat=category := 'vector')

# Valid alternative
foo(cat=(category := 'vector'))

Most of the «valid» examples above are not recommended, since human
readers of Python source code who are quickly glancing at some code
may miss the distinction. But simple cases are not objectionable:

# Valid
if any(len(longline := line) >= 100 for line in lines):
    print("Extremely long line:", longline)

Why not use a sublocal scope and prevent namespace pollution?

Previous revisions of this proposal involved sublocal scope (restricted to a
single statement), preventing name leakage and namespace pollution. While a
definite advantage in a number of situations, this increases complexity in
many others, and the costs are not justified by the benefits. In the interests
of language simplicity, the name bindings created here are exactly equivalent
to
any other name bindings, including that usage at class or module scope will
create externally-visible names. This is no different from for loops or
other constructs, and can be solved the same way: del the name once it is
no longer needed, or prefix it with an underscore.

Variable Annotations

PEP 526 introduced variable annotations. The style recommendations for them are
similar to those on function annotations described above:

  • Annotations for module level variables, class and instance variables,
    and local variables should have a single space after the colon.

  • There should be no space before the colon.

  • If an assignment has a right hand side, then the equality sign should have
    exactly one space on both sides.

  • Yes:

    code: int
    
    class Point:
        coords: Tuple
        label: str = ''
    
  • No:

    code:int  # No space after colon
    code : int  # Space before colon
    
    class Test:
        result: int=0  # No spaces around equality sign
    
  • Although the PEP 526 is accepted for Python 3.6, the variable annotation
    syntax is the preferred syntax for stub files on all versions of Python
    (see PEP 484 for details).

Footnotes

Hanging indentation is a type-setting style where all
the lines in a paragraph are indented except the first line. In
the context of Python, the term is used to describe a style where
the opening parenthesis of a parenthesized statement is the last
non-whitespace character of the line, with subsequent lines being
indented until the closing parenthesis.

Поиск сопоставлений шаблонов

Давайте уделим немного времени тому, чтобы научиться основам сопоставлений шаблонов. Используя Python для поиска шаблона в строке, вы можете использовать функцию поиска также, как мы делали это в предыдущем разделе этой статьи. Вот пример:

Python

import re

text = «The ants go marching one by one»

strings =

for string in strings:
match = re.search(string, text)
if match:
print(‘Found «{}» in «{}»‘.format(string, text))
text_pos = match.span()
print(text)
else:
print(‘Did not find «{}»‘.format(string))

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importre

text=»The ants go marching one by one»

strings=’the’,’one’

forstringinstrings

match=re.search(string,text)

ifmatch

print(‘Found «{}» in «{}»‘.format(string,text))

text_pos=match.span()

print(textmatch.start()match.end())

else

print(‘Did not find «{}»‘.format(string))

В этом примере мы импортируем модуль re и создаем простую строку. Когда мы создаем список из двух строк, которые мы будем искать в главной строке. Далее мы делаем цикл над строками, которые хотим найти и запускаем для них поиск. Если есть совпадения, мы выводим их. В противном случае, мы говорим пользователю, что искомая строка не была найдена.

Теория и практика. Быстрая проверка задач и подсказки к ошибкам на русском языке.
Работает в любом современном браузере.

Существует несколько других функций, которые нужно прояснить в данном примере

Обратите внимание на то, что мы вызываем span. Это дает нам начальную и конечную позицию совпавшей строки

Если вы выведите text_pos, которому мы назначили span, вы получите кортеж на подобие следующего: (21, 24). В качестве альтернативы вы можете просто вызвать методы сопоставления, что мы и сделаем далее. Мы используем начало и конец для того, чтобы взять начальную и конечную позицию сопоставления, это должны быть два числа, которые мы получаем из span.

The importance of real code

During the development of this PEP many people (supporters and critics
both) have had a tendency to focus on toy examples on the one hand,
and on overly complex examples on the other.

The danger of toy examples is twofold: they are often too abstract to
make anyone go «ooh, that’s compelling», and they are easily refuted
with «I would never write it that way anyway».

The danger of overly complex examples is that they provide a
convenient strawman for critics of the proposal to shoot down («that’s
obfuscated»).

Yet there is some use for both extremely simple and extremely complex
examples: they are helpful to clarify the intended semantics.
Therefore there will be some of each below.

However, in order to be compelling, examples should be rooted in
real code, i.e. code that was written without any thought of this PEP,
as part of a useful application, however large or small. Tim Peters
has been extremely helpful by going over his own personal code
repository and picking examples of code he had written that (in his
view) would have been clearer if rewritten with (sparing) use of
assignment expressions. His conclusion: the current proposal would
have allowed a modest but clear improvement in quite a few bits of
code.

Another use of real code is to observe indirectly how much value
programmers place on compactness. Guido van Rossum searched through a
Dropbox code base and discovered some evidence that programmers value
writing fewer lines over shorter lines.

Case in point: Guido found several examples where a programmer
repeated a subexpression, slowing down the program, in order to save
one line of code, e.g. instead of writing:

match = re.match(data)
group = match.group(1) if match else None

they would write:

group = re.match(data).group(1) if re.match(data) else None

Another example illustrates that programmers sometimes do more work to
save an extra level of indentation:

match1 = pattern1.match(data)
match2 = pattern2.match(data)
if match1:
    result = match1.group(1)
elif match2:
    result = match2.group(2)
else:
    result = None

This code tries to match pattern2 even if pattern1 has a match
(in which case the match on pattern2 is never used). The more
efficient rewrite would have been:

Build Instructions

On Unix, Linux, BSD, macOS, and Cygwin:

./configure
make
make test
sudo make install

This will install Python as .

You can pass many options to the configure script; run
to find out more. On macOS case-insensitive file systems and on Cygwin,
the executable is called ; elsewhere it’s just .

On macOS, there are additional configure and build options related
to macOS framework and universal builds. Refer to Mac/README.rst.

On Windows, see PCbuild/readme.txt.

If you wish, you can create a subdirectory and invoke configure from there.
For example:

mkdir debug
cd debug
../configure --with-pydebug
make
make test

(This will fail if you also built at the top-level directory. You should do
a at the top-level first.)

To get an optimized build of Python,
before you run . This sets the default make targets up to enable
Profile Guided Optimization (PGO) and may be used to auto-enable Link Time
Optimization (LTO) on some platforms. For more details, see the sections
below.

PGO takes advantage of recent versions of the GCC or Clang compilers. If used,
either via or by manually running
regardless of configure flags, the optimized build
process will perform the following steps:

The entire Python directory is cleaned of temporary files that may have
resulted from a previous compilation.

An instrumented version of the interpreter is built, using suitable compiler
flags for each flavour. Note that this is just an intermediary step. The
binary resulting from this step is not good for real life workloads as it has
profiling instructions embedded inside.

After the instrumented interpreter is built, the Makefile will run a training
workload. This is necessary in order to profile the interpreter execution.
Note also that any output, both stdout and stderr, that may appear at this step
is suppressed.

The final step is to build the actual interpreter, using the information
collected from the instrumented one. The end result will be a Python binary
that is optimized; suitable for distribution or production installation.

Enabled via configure’s flag. LTO takes advantage of the
ability of recent compiler toolchains to optimize across the otherwise
arbitrary file boundary when building final executables or shared
libraries for additional performance gains.

Source File Encoding

Code in the core Python distribution should always use UTF-8 (or ASCII
in Python 2).

Files using ASCII (in Python 2) or UTF-8 (in Python 3) should not have
an encoding declaration.

In the standard library, non-default encodings should be used only for
test purposes or when a comment or docstring needs to mention an author
name that contains non-ASCII characters; otherwise, using \x,
\u, \U, or \N escapes is the preferred way to include
non-ASCII data in string literals.

For Python 3.0 and beyond, the following policy is prescribed for the
standard library (see PEP 3131): All identifiers in the Python
standard library MUST use ASCII-only identifiers, and SHOULD use
English words wherever feasible (in many cases, abbreviations and
technical terms are used which aren’t English). In addition, string
literals and comments must also be in ASCII. The only exceptions are
(a) test cases testing the non-ASCII features, and
(b) names of authors. Authors whose names are not based on the
Latin alphabet (latin-1, ISO/IEC 8859-1 character set) MUST provide
a transliteration of their names in this character set.

Installation

xeus-python has been packaged for the conda package manager.

The safest usage is to create an environment named with your miniconda installation

conda create -n xeus-python
conda activate xeus-python # Or `source activate xeus-python` for conda 

Then you can install in this environment and its dependencies

conda install xeus-python notebook -c conda-forge

Installation from source

Or you can install it from the sources, you will first need to install dependencies

conda install cmake xeus nlohmann_json cppzmq xtl pybind11 pybind11_json jedi pygments notebook -c conda-forge

Then you can compile the sources

cmake -D CMAKE_PREFIX_PATH=your_conda_path -D CMAKE_INSTALL_PREFIX=your_conda_path -D PYTHON_EXECUTABLE=`which python`
make && make install

Public and Internal Interfaces

Any backwards compatibility guarantees apply only to public interfaces.
Accordingly, it is important that users be able to clearly distinguish
between public and internal interfaces.

Documented interfaces are considered public, unless the documentation
explicitly declares them to be provisional or internal interfaces exempt
from the usual backwards compatibility guarantees. All undocumented
interfaces should be assumed to be internal.

To better support introspection, modules should explicitly declare the
names in their public API using the __all__ attribute. Setting
__all__ to an empty list indicates that the module has no public API.

Even with __all__ set appropriately, internal interfaces (packages,
modules, classes, functions, attributes or other names) should still be
prefixed with a single leading underscore.

An interface is also considered internal if any containing namespace
(package, module or class) is considered internal.

Параметры print()

  • objects – объект, который нужно вывести * обозначает, что объектов может быть несколько;
  • sep – разделяет объекты. Значение по умолчанию: ‘ ‘;
  • end – ставится после всех объектов;
  • file – ожидается объект с методом write (string). Если значение не задано, для вывода объектов используется файл sys.stdout;
  • flush – если задано значение True, поток принудительно сбрасывается в файл. Значение по умолчанию: False.

Примечание: sep, end, file и flush — это аргументы-ключевые слова. Если хотите воспользоваться аргументом sep, используйте:

print(*objects, sep = 'separator')

а не

print(*objects, 'separator')

Возвращаемое значение

Функция Python print не возвращает значений; возвращает None.

Пример 1: Как работает функция print() в Python?

print("Python — это весело.")
a = 5 
 # Передаётся два объекта 
 print("a =", a)

 b = a
 # Передаётся три объекта
 print('a =', a, '= b')

При запуске программы получаем:

Python — это весело.
a = 5
a = 5 = b

В примере, приведенном выше функции print Python 3, во всех трёх выражениях передаётся только параметр objects, поэтому:

Используется разделитель ‘ ‘ — обратите внимание на пробел между двумя объектами в результатах вывода;
В качестве параметра end используется ‘n’ (символ новой строки). Обратите внимание, что каждое выражение print выводится в новой строке;

file — используется файл sys.stdout

Результат выводится на экран;
Значение flush — False. Поток не сбрасывается принудительно.

Пример 2: print() с разделителем и параметром end

a = 5
print("a =", a, sep='00000', end='nnn')
print("a =", a, sep='0', end='')

При запуске программы получаем:

a =000005

a =05

Мы передали в программу, пример которой приведен выше, параметры sep и end.

Пример 3: print() с параметром file

С помощью Python print без перевода строки также можно вывести объекты в файл, указав параметр file:

sourceFile = open('python.txt', 'w')
print("Круто же, правда?", file = sourceFile)
sourceFile.close()

Код, приведенный выше, пытается открыть файл python.txt в режиме записи. Если файл не существует, программа создаёт файл python.txt и открывает его в режиме записи.

В примере мы передали в параметр file объект sourceFile. Объект-строка ‘Круто же, правда?‘ записывается в файл python.txt (после чего можно открыть его текстовым редактором).

В конце исполнения программы файл закрывается методом close().

Данная публикация представляет собой перевод статьи «Python print()» , подготовленной дружной командой проекта Интернет-технологии.ру

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