Python Coding Standard

Summary

References: POCU Java Coding Standards, POCU C Coding Standards, Google

Main Coding Standards

Lower case for module/package names
```
import awesome_module
```
No wildcard imports
```
import awesome_module.*
```
Use Pascal for classes
```
class AwesomeClass(object):
    # ...
```

Add _ if protected, __ if private, none if public

class AwesomeClass(object):
    def __init__(self, name: str, age: int, height: float):
        self.name = name    # public
        self._age = age     # protected
        self.__height = height  # private
    def public_method(self) -> None:
        # ...
    def _protected_method(self) -> None:
        # ...
    def __private_method(self) -> None:
        # ...

Use underscores for methods

class AwesomeClass(object):
    # ...
    def awesome_method(self) -> int:
        return 0

Use underscores for local variables and method parameters

awesome_local_variable = 3

def awesome_function(number: int) -> int:
    return number

Use verb-object pair for method names

class AwesomeClass(object):
    def __init__(self, name: str):
        self.__name = name;

    def get_name(self) -> str:
        return self.__name

Use ALL_CAPS_SEPARATED_BY_UNDERSCORE for constants

class AwesomeClass(object):
    @constant
    def AWESOME_CONSTANT_PI(self):
        return 3.14159

Use underscores for member variables

class AwesomeClass(object):
    def __init__(self, awesome_variable: str):
        self.awesome_variable = awesome_variable

Methods with return values must have a name describing the value returned

class AwesomeClass(object):
    def __init__(self, name: str):
        self.__name = name;

    def get_name(self) -> str:  # get "name"
        return self.__name

Use descriptive variable names unless using a trivial index for a loop

i = 3   # NO
index = 3   # YES

ch = 'c'    # NO
character = 'c' # YES

for i in range(10): # YES

Acronyms shouldn't be capitalized if not a constant

ID = "awesome"  # NO
id = "awesome"  # YES

HTTP = "0.0.0.1"    # NO
http = "0.0.0.1"    # YES

Use properties for getter/setter

class AwesomeClass(object):
    def __init__(self, name: str, age: int, height: float):
        self.name = name    # public
        self._age = age     # protected
        self.__height = height  # private
    
    @property   # getter. must precede setter
    def name(self) -> str:
        return self.__name

    @name.setter
    def name(self, name: str) -> None:
        self.__name = name

Declare local variables as close as possible to the first line where it is being used

If a case should not happen, use assert to intentionally fail it.

if expr1:
    # ...
elif expr2:
    # ...
else
    assert(False)    # shouldn't happen

Names of recursive methods end with recursive

def fibonacci_recursive(index: int) -> int:
    # ...

Class structure:
1. __init__
  1. public member variables
  2. protected member variables
  3. private member variables
2. public methods
3. protected methods
4. private methods

Overloading should be avoided mostly

NO

def get_data(index: int) -> List[int]:
def get_data(name: str) -> List[int]:

YES

def get_data_by_index(index: int) -> List[int]:
def get_data_by_name(name: str) -> List[int]:

Use assert frequently, but with parentheses

assert False    # NO
assert(False)  # YES

Use type annotations wherever possible

def returns_integer(number: int) -> int:    # OK
    return number
def returns_something(number):              # NO
    return number

Prefer not to allow None parameters in your method

If None parameter is used, postfix the parameter name with or_none

import typing import Union

def print_name(name_or_none: Union[str, None]) -> None:
    print(name_or_none)

If None is returned from any method, postfix the method name with or _none

import typing import Union

def get_name_or_none(self) -> Union[str, None]:
    return self.__name

No semicolons

index = 3; name = "Mike"    # NO

index = 3
name = "Mike"               # YES

Code Formatting

Use PyCharm's default settings for tabs. ctrl+alt+l

Google Python Style Guide (revised)

1 Background

Python is the main dynamic language used at Google. This style guide is a list of dos and don'ts for Python programs.

To help you format code correctly, we've created a settings file for Vim. For Emacs, the default settings should be fine.

Many teams use the yapf auto-formatter to avoid arguing over formatting.

2 Python Language Rules

2.2 Imports

only for packages/modules

Use import statements for packages and modules only, not for individual classes or functions. Note that there is an explicit exemption for imports from the typing module.

2.2.4 Decision

Use import x for importing packages and modules.
Use from x import y where x is the package prefix and y is the module name with no prefix.
Use from x import y as z if two modules named y are to be imported or if y is an inconveniently long name.
Use import y as z only when z is a standard abbreviation (e.g., np for numpy).

For example the module sound.effects.echo may be imported as follows:

from sound.effects import echo
...
echo.EchoFilter(input, output, delay=0.7, atten=4)

Do not use relative names in imports. Even if the module is in the same package, use the full package name. This helps prevent unintentionally importing a package twice.

Imports from the typing module and the six.moves module are exempt from this rule.

2.3 Packages

Import each module using the full pathname location of the module.

2.3.3 Decision

All new code should import each module by its full package name.

Imports should be as follows:

Yes:

# Reference absl.flags in code with the complete name (verbose).
import absl.flags
from doctor.who import jodie

FLAGS = absl.flags.FLAGS

# Reference flags in code with just the module name (common).
from absl import flags
from doctor.who import jodie

FLAGS = flags.FLAGS

No: (assume this file lives in doctor/who/ where jodie.py also exists)

# Unclear what module the author wanted and what will be imported.  The actual
# import behavior depends on external factors controlling sys.path.
# Which possible jodie module did the author intend to import?
import jodie

The directory the main binary is located in should not be assumed to be in sys.path despite that happening in some environments. This being the case, code should assume that import jodie refers to a third party or top level package named jodie, not a local jodie.py.

2.4 Exceptions

Don't use them unless necessary.

If you need to check an error, use assert.

2.5 Global variables

Avoid global variables.

2.5.4 Decision

Avoid global variables.

While they are technically variables, module-level constants are permitted and encouraged. For example: MAX_HOLY_HANDGRENADE_COUNT = 3. Constants must be named using all caps with underscores. See Naming below.

If needed, globals should be declared at the module level and made internal to the module by prepending an _ to the name. External access must be done through public module-level functions. See Naming below.

2.6 Nested/Local/Inner Classes and Functions

Avoid them.

2.7 Comprehensions & Generator Expressions

Avoid them when it starts to deteriorate the readability.

Okay to use for simple cases.

2.7.4 Decision

Okay to use for simple cases. Each portion must fit on one line: mapping expression, for clause, filter expression. Multiple for clauses or filter expressions are not permitted. Use loops instead when things get more complicated.

Yes:
  result = [mapping_expr for value in iterable if filter_expr]

  result = [{'key': value} for value in iterable
            if a_long_filter_expression(value)]

  result = [complicated_transform(x)
            for x in iterable if predicate(x)]

  descriptive_name = [
      transform({'key': key, 'value': value}, color='black')
      for key, value in generate_iterable(some_input)
      if complicated_condition_is_met(key, value)
  ]

  result = []
  for x in range(10):
      for y in range(5):
          if x * y > 10:
              result.append((x, y))

  return {x: complicated_transform(x)
          for x in long_generator_function(parameter)
          if x is not None}

  squares_generator = (x**2 for x in range(10))

  unique_names = {user.name for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')

No:
  result = [complicated_transform(
                x, some_argument=x+1)
            for x in iterable if predicate(x)]

  result = [(x, y) for x in range(10) for y in range(5) if x * y > 10]

  return ((x, y, z)
          for x in range(5)
          for y in range(5)
          if x != y
          for z in range(5)
          if y != z)

2.8 Default Iterators and Operators

Use them

Use default iterators and operators for types that support them, like lists, dictionaries, and files.

2.8.4 Decision

Use default iterators and operators for types that support them, like lists, dictionaries, and files. The built-in types define iterator methods, too. Prefer these methods to methods that return lists, except that you should not mutate a container while iterating over it. Never use Python 2 specific iteration methods such as dict.iter*() unless necessary.

Yes:  for key in adict: ...
      if key not in adict: ...
      if obj in alist: ...
      for line in afile: ...
      for k, v in adict.items(): ...
      for k, v in six.iteritems(adict): ...

No:   for key in adict.keys(): ...
      if not adict.has_key(key): ...
      for line in afile.readlines(): ...
      for k, v in dict.iteritems(): ...

2.10 Lambda Functions

Okay for one-liners.

2.10.4 Decision

Okay to use them for one-liners. If the code inside the lambda function is longer than 60-80 chars, it's probably better to define it as a regular nested function.

For common operations like multiplication, use the functions from the operator module instead of lambda functions. For example, prefer operator.mul to lambda x, y: x * y.

2.11 Conditional Expressions

Okay for simple cases.

2.11.4 Decision

Okay to use for simple cases. Each portion must fit on one line: true-expression, if-expression, else-expression. Use a complete if statement when things get more complicated.

one_line = 'yes' if predicate(value) else 'no'
slightly_split = ('yes' if predicate(value)
                  else 'no, nein, nyet')
the_longest_ternary_style_that_can_be_done = (
    'yes, true, affirmative, confirmed, correct'
    if predicate(value)
    else 'no, false, negative, nay')

bad_line_breaking = ('yes' if predicate(value) else
                     'no')
portion_too_long = ('yes'
                    if some_long_module.some_long_predicate_function(
                        really_long_variable_name)
                    else 'no, false, negative, nay')

2.12 Default Argument Values

Okay in most cases.

2.12.4 Decision

Okay to use with the following caveat:

Do not use mutable objects as default values in the function or method definition.

Yes: def foo(a, b=None):
         if b is None:
             b = []
Yes: def foo(a, b: Optional[Sequence] = None):
         if b is None:
             b = []
Yes: def foo(a, b: Sequence = ()):  # Empty tuple OK since tuples are immutable
         ...

No:  def foo(a, b=[]):
         ...
No:  def foo(a, b=time.time()):  # The time the module was loaded???
         ...
No:  def foo(a, b=FLAGS.my_thing):  # sys.argv has not yet been parsed...
         ...
No:  def foo(a, b: Mapping = {}):  # Could still get passed to unchecked code
         ...

2.14 True/False Evaluations

Use the "implicit" false if at all possible.

2.14.4 Decision

Use the "implicit" false if possible, e.g., if foo: rather than if foo != []:. There are a few caveats that you should keep in mind though:

Always use if foo is None: (or is not None) to check for a None value-e.g., when testing whether a variable or argument that defaults to None was set to some other value. The other value might be a value that's false in a boolean context!
Never compare a boolean variable to False using ==. Use if not x: instead. If you need to distinguish False from None then chain the expressions, such as if not x and x is not None:.
For sequences (strings, lists, tuples), use the fact that empty sequences are false, so if seq: and if not seq: are preferable to if len(seq): and if not len(seq): respectively.

When handling integers, implicit false may involve more risk than benefit (i.e., accidentally handling None as 0). You may compare a value which is known to be an integer (and is not the result of len()) against the integer 0.

Yes: if not users:
         print('no users')

     if foo == 0:
         self.handle_zero()

     if i % 10 == 0:
         self.handle_multiple_of_ten()

     def f(x=None):
         if x is None:
             x = []

No:  if len(users) == 0:
         print('no users')

     if foo is not None and not foo:
         self.handle_zero()

     if not i % 10:
         self.handle_multiple_of_ten()

     def f(x=None):
         x = x or []

Note that '0' (i.e., 0 as string) evaluates to true.

2.15 Deprecated Language Features

don't

Use string methods instead of the string module where possible. Use function call syntax instead of apply. Use list comprehensions and for loops instead of filter and map when the function argument would have been an inlined lambda anyway. Use for loops instead of reduce.

2.15.2 Decision

We do not use any Python version which does not support these features, so there is no reason not to use the new styles.

Yes: words = foo.split(':')

     [x[1] for x in my_list if x[2] == 5]

     map(math.sqrt, data)    # Ok. No inlined lambda expression.

     fn(*args, **kwargs)

No:  words = string.split(foo, ':')

     map(lambda x: x[1], filter(lambda x: x[2] == 5, my_list))

     apply(fn, args, kwargs)

2.21 Type Annotated Code

Must

You can annotate Python 3 code with type hints according to PEP-484, and type-check the code at build time with a type checking tool like pytype.

Type annotations can be in the source or in a stub pyi file. Whenever possible, annotations should be in the source. Use pyi files for third-party or extension modules.

2.21.1 Definition

Type annotations (or "type hints") are for function or method arguments and return values:

def func(a: int) -> List[int]:

You can also declare the type of a variable using a special comment:

a = SomeFunc()  # type: SomeType

2.21.4 Decision

You are strongly encouraged to enable Python type analysis when updating code. When adding or modifying public APIs, include type annotations and enable checking via pytype in the build system. As static analysis is relatively new to Python, we acknowledge that undesired side-effects (such as wrongly inferred types) may prevent adoption by some projects. In those situations, authors are encouraged to add a comment with a TODO or link to a bug describing the issue(s) currently preventing type annotation adoption in the BUILD file or in the code itself as appropriate.

3 Python Style Rules

3.1 Semicolons

don't

Do not terminate your lines with semicolons, and do not use semicolons to put two statements on the same line.

3.2 Line length

just keep it clean

Maximum line length is 80 characters.

Explicit exceptions to the 80 character limit:

Long import statements.
URLs, pathnames, or long flags in comments.
Long string module level constants not containing whitespace that would be inconvenient to split across lines such as URLs or pathnames.
Pylint disable comments. (e.g.: # pylint: disable=invalid-name)

Do not use backslash line continuation except for with statements requiring three or more context managers.

Make use of Python's implicit line joining inside parentheses, brackets and braces. If necessary, you can add an extra pair of parentheses around an expression.

Yes: foo_bar(self, width, height, color='black', design=None, x='foo',
             emphasis=None, highlight=0)

     if (width == 0 and height == 0 and
         color == 'red' and emphasis == 'strong'):

When a literal string won't fit on a single line, use parentheses for implicit line joining.

x = ('This will build a very long long '
     'long long long long long long string')

Within comments, put long URLs on their own line if necessary.

Yes:  # See details at
      # http://www.example.com/us/developer/documentation/api/content/v2.0/csv_file_name_extension_full_specification.html

No:  # See details at
     # http://www.example.com/us/developer/documentation/api/content/\
     # v2.0/csv_file_name_extension_full_specification.html

It is permissible to use backslash continuation when defining a with statement whose expressions span three or more lines. For two lines of expressions, use a nested with statement:

Yes:  with very_long_first_expression_function() as spam, \
           very_long_second_expression_function() as beans, \
           third_thing() as eggs:
          place_order(eggs, beans, spam, beans)

No:  with VeryLongFirstExpressionFunction() as spam, \
          VeryLongSecondExpressionFunction() as beans:
       PlaceOrder(eggs, beans, spam, beans)

Yes:  with very_long_first_expression_function() as spam:
          with very_long_second_expression_function() as beans:
              place_order(beans, spam)

Make note of the indentation of the elements in the line continuation examples above; see the indentation section for explanation.

In all other cases where a line exceeds 80 characters, and the yapf auto-formatter does not help bring the line below the limit, the line is allowed to exceed this maximum.

3.3 Parentheses

Use parentheses sparingly.

It is fine, though not required, to use parentheses around tuples. Do not use them in return statements or conditional statements unless using parentheses for implied line continuation or to indicate a tuple.

Yes: if foo:
         bar()
     while x:
         x = bar()
     if x and y:
         bar()
     if not x:
         bar()
     # For a 1 item tuple the ()s are more visually obvious than the comma.
     onesie = (foo,)
     return foo
     return spam, beans
     return (spam, beans)
     for (x, y) in dict.items(): ...

No:  if (x):
         bar()
     if not(x):
         bar()
     return (foo)

3.4 Indentation

Use tabs

3.4.1 Trailing commas in sequences of items?

Trailing commas in sequences of items are recommended only when the closing container token ], ), or } does not appear on the same line as the final element. The presence of a trailing comma is also used as a hint to our Python code auto-formatter YAPF to direct it to auto-format the container of items to one item per line when the , after the final element is present.

Yes:   golomb3 = [0, 1, 3]
Yes:   golomb4 = [
           0,
           1,
           4,
           6,
       ]

No:    golomb4 = [
           0,
           1,
           4,
           6
       ]

3.5 Blank Lines

Two blank lines between top-level definitions, be they function or class definitions. One blank line between method definitions and between the class line and the first method. No blank line following a def line. Use single blank lines as you judge appropriate within functions or methods.

3.6 Whitespace

Follow standard typographic rules for the use of spaces around punctuation.

No whitespace inside parentheses, brackets or braces.

Yes: spam(ham[1], {eggs: 2}, [])

No:  spam( ham[ 1 ], { eggs: 2 }, [ ] )

No whitespace before a comma, semicolon, or colon. Do use whitespace after a comma, semicolon, or colon, except at the end of the line.

Yes: if x == 4:
         print(x, y)
     x, y = y, x

No:  if x == 4 :
         print(x , y)
     x , y = y , x

No whitespace before the open paren/bracket that starts an argument list, indexing or slicing.

Yes: spam(1)

No:  spam (1)

Yes: dict['key'] = list[index]

No:  dict ['key'] = list [index]

No trailing whitespace.

Surround binary operators with a single space on either side for assignment (=), comparisons (==, <, >, !=, <>, <=, >=, in, not in, is, is not), and Booleans (and, or, not). Use your better judgment for the insertion of spaces around arithmetic operators (+, -, *, /, //, %, **, @).

Yes: x == 1

No:  x<1

Never use spaces around = when passing keyword arguments or defining a default parameter value, with one exception: when a type annotation is present, do use spaces around the = for the default parameter value.

Yes: def complex(real, imag=0.0): return Magic(r=real, i=imag)
Yes: def complex(real, imag: float = 0.0): return Magic(r=real, i=imag)

No:  def complex(real, imag = 0.0): return Magic(r = real, i = imag)
No:  def complex(real, imag: float=0.0): return Magic(r = real, i = imag)

Don't use spaces to vertically align tokens on consecutive lines, since it becomes a maintenance burden (applies to :, #, =, etc.):

Yes:
  foo = 1000  # comment
  long_name = 2  # comment that should not be aligned

  dictionary = {
      'foo': 1,
      'long_name': 2,
  }

No:
  foo       = 1000  # comment
  long_name = 2     # comment that should not be aligned

  dictionary = {
      'foo'      : 1,
      'long_name': 2,
  }

3.7 Shebang Line

don't

Most .py files do not need to start with a #! line. Start the main file of a program with #!/usr/bin/python with an optional single digit 2 or 3 suffix per PEP-394.

This line is used by the kernel to find the Python interpreter, but is ignored by Python when importing modules. It is only necessary on a file that will be executed directly.

3.8 Comments and Docstrings

the code itself should be a doc

Be sure to use the right style for module, function, method docstrings and inline comments.

3.8.1 Docstrings

Python uses docstrings to document code. A docstring is a string that is the first statement in a package, module, class or function. These strings can be extracted automatically through the __doc__ member of the object and are used by pydoc. (Try running pydoc on your module to see how it looks.) Always use the three double-quote """ format for docstrings (per PEP 257). A docstring should be organized as a summary line (one physical line) terminated by a period, question mark, or exclamation point, followed by a blank line, followed by the rest of the docstring starting at the same cursor position as the first quote of the first line. There are more formatting guidelines for docstrings below.

3.8.2 Modules

Every file should contain license boilerplate. Choose the appropriate boilerplate for the license used by the project (for example, Apache 2.0, BSD, LGPL, GPL)

Files should start with a docstring describing the contents and usage of the module.

"""A one line summary of the module or program, terminated by a period.

Leave one blank line.  The rest of this docstring should contain an
overall description of the module or program.  Optionally, it may also
contain a brief description of exported classes and functions and/or usage
examples.

  Typical usage example:

  foo = ClassFoo()
  bar = foo.FunctionBar()
"""

3.8.3 Functions and Methods

In this section, "function" means a method, function, or generator.

A function must have a docstring, unless it meets all of the following criteria:

not externally visible
very short
obvious

A docstring should give enough information to write a call to the function without reading the function's code. The docstring should be descriptive-style ("""Fetches rows from a Bigtable.""") rather than imperative-style ("""Fetch rows from a Bigtable."""), except for @property data descriptors, which should use the same style as attributes. A docstring should describe the function's calling syntax and its semantics, not its implementation. For tricky code, comments alongside the code are more appropriate than using docstrings.

A method that overrides a method from a base class may have a simple docstring sending the reader to its overridden method's docstring, such as """See base class.""". The rationale is that there is no need to repeat in many places documentation that is already present in the base method's docstring. However, if the overriding method's behavior is substantially different from the overridden method, or details need to be provided (e.g., documenting additional side effects), a docstring with at least those differences is required on the overriding method.

Certain aspects of a function should be documented in special sections, listed below. Each section begins with a heading line, which ends with a colon. All sections other than the heading should maintain a hanging indent of two or four spaces (be consistent within a file). These sections can be omitted in cases where the function's name and signature are informative enough that it can be aptly described using a one-line docstring.

Args: : List each parameter by name. A description should follow the name, and be separated by a colon and a space. If the description is too long to fit on a single 80-character line, use a hanging indent of 2 or 4 spaces (be consistent with the rest of the file).

The description should include required type(s) if the code does not contain
a corresponding type annotation. If a function accepts `*foo` (variable
length argument lists) and/or `**bar` (arbitrary keyword arguments), they
should be listed as `*foo` and `**bar`.

Returns: (or Yields: for generators) : Describe the type and semantics of the return value. If the function only returns None, this section is not required. It may also be omitted if the docstring starts with Returns or Yields (e.g. """Returns row from Bigtable as a tuple of strings.""") and the opening sentence is sufficient to describe return value.

Raises: : List all exceptions that are relevant to the interface. You should not document exceptions that get raised if the API specified in the docstring is violated (because this would paradoxically make behavior under violation of the API part of the API).

def fetch_bigtable_rows(big_table, keys, other_silly_variable=None):
    """Fetches rows from a Bigtable.

    Retrieves rows pertaining to the given keys from the Table instance
    represented by big_table.  Silly things may happen if
    other_silly_variable is not None.

    Args:
        big_table: An open Bigtable Table instance.
        keys: A sequence of strings representing the key of each table row
            to fetch.
        other_silly_variable: Another optional variable, that has a much
            longer name than the other args, and which does nothing.

    Returns:
        A dict mapping keys to the corresponding table row data
        fetched. Each row is represented as a tuple of strings. For
        example:

        {'Serak': ('Rigel VII', 'Preparer'),
         'Zim': ('Irk', 'Invader'),
         'Lrrr': ('Omicron Persei 8', 'Emperor')}

        If a key from the keys argument is missing from the dictionary,
        then that row was not found in the table.

    Raises:
        IOError: An error occurred accessing the bigtable.Table object.
    """

3.8.4 Classes

Classes should have a docstring below the class definition describing the class. If your class has public attributes, they should be documented here in an Attributes section and follow the same formatting as a function's Args section.

class SampleClass(object):
    """Summary of class here.

    Longer class information....
    Longer class information....

    Attributes:
        likes_spam: A boolean indicating if we like SPAM or not.
        eggs: An integer count of the eggs we have laid.
    """

    def __init__(self, likes_spam=False):
        """Inits SampleClass with blah."""
        self.likes_spam = likes_spam
        self.eggs = 0

    def public_method(self):
        """Performs operation blah."""

3.8.5 Block and Inline Comments

The final place to have comments is in tricky parts of the code. If you're going to have to explain it at the next code review, you should comment it now. Complicated operations get a few lines of comments before the operations commence. Non-obvious ones get comments at the end of the line.

# We use a weighted dictionary search to find out where i is in
# the array.  We extrapolate position based on the largest num
# in the array and the array size and then do binary search to
# get the exact number.

if i & (i-1) == 0:  # True if i is 0 or a power of 2.

To improve legibility, these comments should start at least 2 spaces away from the code with the comment character #, followed by at least one space before the text of the comment itself.

On the other hand, never describe the code. Assume the person reading the code knows Python (though not what you're trying to do) better than you do.

# BAD COMMENT: Now go through the b array and make sure whenever i occurs
# the next element is i+1

3.8.6 Punctuation, Spelling, and Grammar

Pay attention to punctuation, spelling, and grammar; it is easier to read well-written comments than badly written ones.

Comments should be as readable as narrative text, with proper capitalization and punctuation. In many cases, complete sentences are more readable than sentence fragments. Shorter comments, such as comments at the end of a line of code, can sometimes be less formal, but you should be consistent with your style.

Although it can be frustrating to have a code reviewer point out that you are using a comma when you should be using a semicolon, it is very important that source code maintain a high level of clarity and readability. Proper punctuation, spelling, and grammar help with that goal.

3.9 Classes

inherit object

If a class inherits from no other base classes, explicitly inherit from object. This also applies to nested classes.

Yes: class SampleClass(object):
         pass


     class OuterClass(object):

         class InnerClass(object):
             pass


     class ChildClass(ParentClass):
         """Explicitly inherits from another class already."""

No: class SampleClass:
        pass


    class OuterClass:

        class InnerClass:
            pass

Inheriting from object is needed to make properties work properly in Python 2 and can protect your code from potential incompatibility with Python 3. It also defines special methods that implement the default semantics of objects including __new__, __init__, __delattr__, __getattribute__, __setattr__, __hash__, __repr__, and __str__.

3.10 Strings

use .format() strings

Use the format method or the % operator for formatting strings, even when the parameters are all strings. Use your best judgment to decide between + and % (or format) though.

Yes: x = a + b
     x = '%s, %s!' % (imperative, expletive)
     x = '{}, {}'.format(first, second)
     x = 'name: %s; score: %d' % (name, n)
     x = 'name: {}; score: {}'.format(name, n)
     x = f'name: {name}; score: {n}'  # Python 3.6+

No: x = '%s%s' % (a, b)  # use + in this case
    x = '{}{}'.format(a, b)  # use + in this case
    x = first + ', ' + second
    x = 'name: ' + name + '; score: ' + str(n)

Avoid using the + and += operators to accumulate a string within a loop. Since strings are immutable, this creates unnecessary temporary objects and results in quadratic rather than linear running time. Instead, add each substring to a list and ''.join the list after the loop terminates (or, write each substring to a io.BytesIO buffer).

Yes: items = ['<table>']
     for last_name, first_name in employee_list:
         items.append('<tr><td>%s, %s</td></tr>' % (last_name, first_name))
     items.append('</table>')
     employee_table = ''.join(items)

No: employee_table = '<table>'
    for last_name, first_name in employee_list:
        employee_table += '<tr><td>%s, %s</td></tr>' % (last_name, first_name)
    employee_table += '</table>'

Be consistent with your choice of string quote character within a file. Pick ' or " and stick with it. It is okay to use the other quote character on a string to avoid the need to \\ escape within the string.

Yes:
  Python('Why are you hiding your eyes?')
  Gollum("I'm scared of lint errors.")
  Narrator('"Good!" thought a happy Python reviewer.')

No:
  Python("Why are you hiding your eyes?")
  Gollum('The lint. It burns. It burns us.')
  Gollum("Always the great lint. Watching. Watching.")

Prefer """ for multi-line strings rather than '''. Projects may choose to use ''' for all non-docstring multi-line strings if and only if they also use ' for regular strings. Docstrings must use """ regardless.

Multi-line strings do not flow with the indentation of the rest of the program. If you need to avoid embedding extra space in the string, use either concatenated single-line strings or a multi-line string with textwrap.dedent() to remove the initial space on each line:

  No:
  long_string = """This is pretty ugly.
Don't do this.
"""

  Yes:
  long_string = """This is fine if your use case can accept
      extraneous leading spaces."""

  Yes:
  long_string = ("And this is fine if you can not accept\n" +
                 "extraneous leading spaces.")

  Yes:
  long_string = ("And this too is fine if you can not accept\n"
                 "extraneous leading spaces.")

  Yes:
  import textwrap

  long_string = textwrap.dedent("""\
      This is also fine, because textwrap.dedent()
      will collapse common leading spaces in each line.""")

3.11 Files and Sockets

close them

Explicitly close files and sockets when done with them.

Leaving files, sockets or other file-like objects open unnecessarily has many downsides:

They may consume limited system resources, such as file descriptors. Code that deals with many such objects may exhaust those resources unnecessarily if they're not returned to the system promptly after use.
Holding files open may prevent other actions such as moving or deleting them.
Files and sockets that are shared throughout a program may inadvertently be read from or written to after logically being closed. If they are actually closed, attempts to read or write from them will throw exceptions, making the problem known sooner.

Furthermore, while files and sockets are automatically closed when the file object is destructed, tying the lifetime of the file object to the state of the file is poor practice:

There are no guarantees as to when the runtime will actually run the file's destructor. Different Python implementations use different memory management techniques, such as delayed Garbage Collection, which may increase the object's lifetime arbitrarily and indefinitely.
Unexpected references to the file, e.g. in globals or exception tracebacks, may keep it around longer than intended.

The preferred way to manage files is using the "with" statement:

with open("hello.txt") as hello_file:
    for line in hello_file:
        print(line)

For file-like objects that do not support the "with" statement, use contextlib.closing():

import contextlib

with contextlib.closing(urllib.urlopen("http://www.python.org/")) as front_page:
    for line in front_page:
        print(line)

3.12 TODO Comments

Use TODO comments for code that is temporary, a short-term solution, or good-enough but not perfect.

A TODO comment begins with the string TODO in all caps and a parenthesized name, e-mail address, or other identifier of the person or issue with the best context about the problem. This is followed by an explanation of what there is to do.

The purpose is to have a consistent TODO format that can be searched to find out how to get more details. A TODO is not a commitment that the person referenced will fix the problem. Thus when you create a TODO, it is almost always your name that is given.

# TODO([email protected]): Use a "*" here for string repetition.
# TODO(Zeke) Change this to use relations.

If your TODO is of the form "At a future date do something" make sure that you either include a very specific date ("Fix by November 2009") or a very specific event ("Remove this code when all clients can handle XML responses.").

3.13 Imports formatting

Imports should be on separate lines.

E.g.:

Yes: import os
     import sys

No:  import os, sys

Imports are always put at the top of the file, just after any module comments and docstrings and before module globals and constants. Imports should be grouped from most generic to least generic:

Python future import statements. For example:

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

See above for more information about those.

Python standard library imports. For example:
```
import sys
```
third-party module or package imports. For example:
```
import tensorflow as tf
```
Code repository sub-package imports. For example:
```
from otherproject.ai import mind
```
Deprecated: application-specific imports that are part of the same top level sub-package as this file. For example:
```
from myproject.backend.hgwells import time_machine
```
You may find older Google Python Style code doing this, but it is no longer required. New code is encouraged not to bother with this. Simply treat application-specific sub-package imports the same as other sub-package imports.

Within each grouping, imports should be sorted lexicographically, ignoring case, according to each module's full package path. Code may optionally place a blank line between import sections.

import collections
import queue
import sys

from absl import app
from absl import flags
import bs4
import cryptography
import tensorflow as tf

from book.genres import scifi
from myproject.backend.hgwells import time_machine
from myproject.backend.state_machine import main_loop
from otherproject.ai import body
from otherproject.ai import mind
from otherproject.ai import soul

# Older style code may have these imports down here instead:
#from myproject.backend.hgwells import time_machine
#from myproject.backend.state_machine import main_loop

3.14 Statements

Generally only one statement per line.

However, you may put the result of a test on the same line as the test only if the entire statement fits on one line. In particular, you can never do so with try/except since the try and except can't both fit on the same line, and you can only do so with an if if there is no else.

Yes:

  if foo: bar(foo)

No:

  if foo: bar(foo)
  else:   baz(foo)

  try:               bar(foo)
  except ValueError: baz(foo)

  try:
      bar(foo)
  except ValueError: baz(foo)

3.15 Accessors

If an accessor function would be trivial, you should use public variables instead of accessor functions to avoid the extra cost of function calls in Python. When more functionality is added you can use property to keep the syntax consistent.

On the other hand, if access is more complex, or the cost of accessing the variable is significant, you should use function calls (following the Naming guidelines) such as get_foo() and set_foo(). If the past behavior allowed access through a property, do not bind the new accessor functions to the property. Any code still attempting to access the variable by the old method should break visibly so they are made aware of the change in complexity.

3.16 Naming

module_name, package_name, ClassName, method_name, ExceptionName, function_name, GLOBAL_CONSTANT_NAME, global_var_name, instance_var_name, function_parameter_name, local_var_name.

Function names, variable names, and filenames should be descriptive; eschew abbreviation. In particular, do not use abbreviations that are ambiguous or unfamiliar to readers outside your project, and do not abbreviate by deleting letters within a word.

Always use a .py filename extension. Never use dashes.

3.16.1 Names to Avoid

single character names except for counters or iterators. You may use "e" as an exception identifier in try/except statements.
dashes (-) in any package/module name
__double_leading_and_trailing_underscore__ names (reserved by Python)

3.16.2 Naming Conventions

"Internal" means internal to a module, or protected or private within a class.
Prepending a single underscore (_) has some support for protecting module variables and functions (not included with from module import *). While prepending a double underscore (__ aka "dunder") to an instance variable or method effectively makes the variable or method private to its class (using name mangling) we discourage its use as it impacts readability and testability and isn't really private.
Place related classes and top-level functions together in a module. Unlike Java, there is no need to limit yourself to one class per module.
Use CapWords for class names, but lower_with_under.py for module names. Although there are some old modules named CapWords.py, this is now discouraged because it's confusing when the module happens to be named after a class. ("wait -- did I write import StringIO or from StringIO import StringIO?")
Underscores may appear in unittest method names starting with test to separate logical components of the name, even if those components use CapWords. One possible pattern is test<MethodUnderTest>_<state>; for example testPop_EmptyStack is okay. There is no One Correct Way to name test methods.

3.16.3 File Naming

Python filenames must have a .py extension and must not contain dashes (-). This allows them to be imported and unittested. If you want an executable to be accessible without the extension, use a symbolic link or a simple bash wrapper containing exec "$0.py" "$@".

3.16.4 Guidelines derived from Guido's Recommendations

Type	Public	Internal
Packages	`lower_with_under`
Modules	`lower_with_under`	`_lower_with_under`
Classes	`CapWords`	`_CapWords`
Exceptions	`CapWords`
Functions	`lower_with_under()`	`_lower_with_under()`
Global/Class Constants	`CAPS_WITH_UNDER`	`_CAPS_WITH_UNDER`
Global/Class Variables	`lower_with_under`	`_lower_with_under`
Instance Variables	`lower_with_under`	`_lower_with_under` (protected)
Method Names	`lower_with_under()`	`_lower_with_under()` (protected)
Function/Method Parameters	`lower_with_under`
Local Variables	`lower_with_under`

While Python supports making things private by using a leading double underscore __ (aka. "dunder") prefix on a name, this is discouraged. Prefer the use of a single underscore. They are easier to type, read, and to access from small unittests. Lint warnings take care of invalid access to protected members.

3.17 Main

every .py needs main, and its execution code

Even a file meant to be used as an executable should be importable and a mere import should not have the side effect of executing the program's main functionality. The main functionality should be in a main() function.

In Python, pydoc as well as unit tests require modules to be importable. Your code should always check if __name__ == '__main__' before executing your main program so that the main program is not executed when the module is imported.

def main():
    ...

if __name__ == '__main__':
    main()

All code at the top level will be executed when the module is imported. Be careful not to call functions, create objects, or perform other operations that should not be executed when the file is being pydoced.

3.18 Function length

Prefer small and focused functions.

We recognize that long functions are sometimes appropriate, so no hard limit is placed on function length. If a function exceeds about 40 lines, think about whether it can be broken up without harming the structure of the program.

Even if your long function works perfectly now, someone modifying it in a few months may add new behavior. This could result in bugs that are hard to find. Keeping your functions short and simple makes it easier for other people to read and modify your code.

You could find long and complicated functions when working with some code. Do not be intimidated by modifying existing code: if working with such a function proves to be difficult, you find that errors are hard to debug, or you want to use a piece of it in several different contexts, consider breaking up the function into smaller and more manageable pieces.

3.19 Type Annotations

Must

3.19.1 General Rules

Familiarize yourself with PEP-484.
In methods, only annotate self, or cls if it is necessary for proper type information. e.g., @classmethod def create(cls: Type[T]) -> T: return cls()
If any other variable or a returned type should not be expressed, use Any.
You are not required to annotate all the functions in a module.
- At least annotate your public APIs.
- Use judgment to get to a good balance between safety and clarity on the one hand, and flexibility on the other.
- Annotate code that is prone to type-related errors (previous bugs or complexity).
- Annotate code that is hard to understand.
- Annotate code as it becomes stable from a types perspective. In many cases, you can annotate all the functions in mature code without losing too much flexibility.

3.19.2 Line Breaking

Try to follow the existing indentation rules.

After annotating, many function signatures will become "one parameter per line".

def my_method(self,
              first_var: int,
              second_var: Foo,
              third_var: Optional[Bar]) -> int:
  ...

Always prefer breaking between variables, and not for example between variable names and type annotations. However, if everything fits on the same line, go for it.

def my_method(self, first_var: int) -> int:
  ...

If the combination of the function name, the last parameter, and the return type is too long, indent by 4 in a new line.

def my_method(
    self, first_var: int) -> Tuple[MyLongType1, MyLongType1]:
  ...

When the return type does not fit on the same line as the last parameter, the preferred way is to indent the parameters by 4 on a new line and align the closing parenthesis with the def.

Yes:
def my_method(
    self, other_arg: Optional[MyLongType]
) -> Dict[OtherLongType, MyLongType]:
  ...

pylint allows you to move the closing parenthesis to a new line and align with the opening one, but this is less readable.

No:
def my_method(self,
              other_arg: Optional[MyLongType]
             ) -> Dict[OtherLongType, MyLongType]:
  ...

As in the examples above, prefer not to break types. However, sometimes they are too long to be on a single line (try to keep sub-types unbroken).

def my_method(
    self,
    first_var: Tuple[List[MyLongType1],
                     List[MyLongType2]],
    second_var: List[Dict[
        MyLongType3, MyLongType4]]) -> None:
  ...

If a single name and type is too long, consider using an alias for the type. The last resort is to break after the colon and indent by 4.

Yes:
def my_function(
    long_variable_name:
        long_module_name.LongTypeName,
) -> None:
  ...

No:
def my_function(
    long_variable_name: long_module_name.
        LongTypeName,
) -> None:
  ...

3.19.3 Forward Declarations

If you need to use a class name from the same module that is not yet defined -- for example, if you need the class inside the class declaration, or if you use a class that is defined below -- use a string for the class name.

class MyClass(object):

  def __init__(self,
               stack: List["MyClass"]) -> None:

3.19.4 Default Values

As per PEP-008, use spaces around the = only for arguments that have both a type annotation and a default value.

Yes:
def func(a: int = 0) -> int:
  ...

No:
def func(a:int=0) -> int:
  ...

3.19.5 NoneType

In the Python type system, NoneType is a "first class" type, and for typing purposes, None is an alias for NoneType. If an argument can be None, it has to be declared! You can use Union, but if there is only one other type, use Optional.

Use explicit Optional instead of implicit Optional. Earlier versions of PEP 484 allowed a: Text = None to be interpretted as a: Optional[Text] = None, but that is no longer the preferred behavior.

Yes:
def func(a: Optional[Text], b: Optional[Text] = None) -> Text:
  ...
def multiple_nullable_union(a: Union[None, Text, int]) -> Text
  ...

No:
def nullable_union(a: Union[None, Text]) -> Text:
  ...
def implicit_optional(a: Text = None) -> Text:
  ...

3.19.8 Typing Variables

If an internal variable has a type that is hard or impossible to infer, you can specify its type in a couple ways.

Type Comments: : Use a # type: comment on the end of the line

a = SomeUndecoratedFunction()  # type: Foo

Annotated Assignments : Use a colon and type between the variable name and value, as with function arguments.

a: Foo = SomeUndecoratedFunction()

3.19.9 Tuples vs Lists

Unlike Lists, which can only have a single type, Tuples can have either a single repeated type or a set number of elements with different types. The latter is commonly used as return type from a function.

a = [1, 2, 3]  # type: List[int]
b = (1, 2, 3)  # type: Tuple[int, ...]
c = (1, "2", 3.5)  # type: Tuple[int, Text, float]

3.19.11 String types

use str

The proper type for annotating strings depends on what versions of Python the code is intended for.

For Python 3 only code, prefer to use str. Text is also acceptable. Be consistent in using one or the other.

For Python 2 compatible code, use Text. In some rare cases, str may make sense; typically to aid compatibility when the return types aren't the same between the two Python versions. Avoid using unicode: it doesn't exist in Python 3.

The reason this discrepancy exists is because str means different things depending on the Python version.

No:
def py2_code(x: str) -> unicode:
  ...

For code that deals with binary data, use bytes.

def deals_with_binary_data(x: bytes) -> bytes:
  ...

For Python 2 compatible code that processes text data (str or unicode in Python 2, str in Python 3), use Text. For Python 3 only code that process text data, prefer str.

from typing import Text
...
def py2_compatible(x: Text) -> Text:
  ...
def py3_only(x: str) -> str:
  ...

If the type can be either bytes or text, use Union, with the appropriate text type.

from typing import Text, Union
...
def py2_compatible(x: Union[bytes, Text]) -> Union[bytes, Text]:
  ...
def py3_only(x: Union[bytes, str]) -> Union[bytes, str]:
  ...

If all the string types of a function are always the same, for example if the return type is the same as the argument type in the code above, use AnyStr.

Writing it like this will simplify the process of porting the code to Python 3.

3.19.13 Conditional Imports

don't unless you are smart enough

Use conditional imports only in exceptional cases where the additional imports needed for type checking must be avoided at runtime. This pattern is discouraged; alternatives such as refactoring the code to allow top level imports should be preferred.

Imports that are needed only for type annotations can be placed within an if TYPE_CHECKING: block.

Conditionally imported types need to be referenced as strings, to be forward compatible with Python 3.6 where the annotation expressions are actually evaluated.
Only entities that are used solely for typing should be defined here; this includes aliases. Otherwise it will be a runtime error, as the module will not be imported at runtime.
The block should be right after all the normal imports.
There should be no empty lines in the typing imports list.
Sort this list as if it were a regular imports list.

import typing
if typing.TYPE_CHECKING:
  import sketch
def f(x: "sketch.Sketch"): ...

3.19.14 Circular Dependencies

follow the architecture

Circular dependencies that are caused by typing are code smells. Such code is a good candidate for refactoring. Although technically it is possible to keep circular dependencies, the build system will not let you do so because each module has to depend on the other.

Replace modules that create circular dependency imports with Any. Set an alias with a meaningful name, and use the real type name from this module (any attribute of Any is Any). Alias definitions should be separated from the last import by one line.

from typing import Any

some_mod = Any  # some_mod.py imports this module.
...

def my_method(self, var: some_mod.SomeType) -> None:
  ...

3.19.15 Generics

When annotating, prefer to specify type parameters for generic types; otherwise, the generics' parameters will be assumed to be Any.

def get_names(employee_ids: List[int]) -> Dict[int, Any]:
  ...

# These are both interpreted as get_names(employee_ids: List[Any]) -> Dict[Any, Any]
def get_names(employee_ids: list) -> Dict:
  ...

def get_names(employee_ids: List) -> Dict:
  ...

If the best type parameter for a generic is Any, make it explicit, but remember that in many cases TypeVar might be more appropriate:

def get_names(employee_ids: List[Any]) -> Dict[Any, Text]:
  """Returns a mapping from employee ID to employee name for given IDs."""

T = TypeVar('T')
def get_names(employee_ids: List[T]) -> Dict[T, Text]:
  """Returns a mapping from employee ID to employee name for given IDs."""

4 Parting Words

BE CONSISTENT.

If you're editing code, take a few minutes to look at the code around you and determine its style. If they use spaces around all their arithmetic operators, you should too. If their comments have little boxes of hash marks around them, make your comments have little boxes of hash marks around them too.

The point of having style guidelines is to have a common vocabulary of coding so people can concentrate on what you're saying rather than on how you're saying it. We present global style rules here so people know the vocabulary, but local style is also important. If code you add to a file looks drastically different from the existing code around it, it throws readers out of their rhythm when they go to read it. Avoid this.

Core

Physics

Collision Event

Python Coding Standard

Summary

Main Coding Standards

Code Formatting

Google Python Style Guide (revised)

1 Background

2 Python Language Rules

2.2 Imports

2.2.4 Decision

2.3 Packages

2.3.3 Decision

2.4 Exceptions

2.5 Global variables

2.5.4 Decision

2.6 Nested/Local/Inner Classes and Functions

2.7 Comprehensions & Generator Expressions

2.7.4 Decision

2.8 Default Iterators and Operators

2.8.4 Decision

2.10 Lambda Functions

2.10.4 Decision

2.11 Conditional Expressions

2.11.4 Decision

2.12 Default Argument Values

2.12.4 Decision

2.14 True/False Evaluations

2.14.4 Decision

2.15 Deprecated Language Features

2.15.2 Decision

2.21 Type Annotated Code

2.21.1 Definition

2.21.4 Decision

3 Python Style Rules

3.1 Semicolons

3.2 Line length

3.3 Parentheses

3.4 Indentation

3.4.1 Trailing commas in sequences of items?

3.5 Blank Lines

3.6 Whitespace

3.7 Shebang Line

3.8 Comments and Docstrings

3.8.1 Docstrings

3.8.2 Modules

3.8.3 Functions and Methods

3.8.4 Classes

3.8.5 Block and Inline Comments

3.8.6 Punctuation, Spelling, and Grammar

3.9 Classes

3.10 Strings

3.11 Files and Sockets

3.12 TODO Comments

3.13 Imports formatting

3.14 Statements

3.15 Accessors

3.16 Naming

3.16.1 Names to Avoid

3.16.2 Naming Conventions

3.16.3 File Naming

3.16.4 Guidelines derived from Guido's Recommendations

3.17 Main

3.18 Function length

3.19 Type Annotations

3.19.1 General Rules

3.19.2 Line Breaking

3.19.3 Forward Declarations

3.19.4 Default Values

3.19.5 NoneType

3.19.8 Typing Variables

3.19.9 Tuples vs Lists

3.19.11 String types

3.19.13 Conditional Imports

3.19.14 Circular Dependencies

3.19.15 Generics

4 Parting Words

Core

Physics

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