Google Python Style Guide（上）

postman · 2022年06月19日08:49

有些設計嚴謹的公司，會有一套自己的程式撰寫規範。一方面指導新進同仁，在撰寫程式時的注意事項，以利團隊合作和維護；另一方面，提供公司的程式資源（如：函式庫、元件庫等），方便新進同仁參考使用。

今天找資料時，看到這份 Google Python Style Guide，覺得很有參考價值，順手分享給大家參考。

提醒：這份 google 所撰寫的 Python 格式指南，是在 2019 年完成的，有些新的 Python 語法不在其中，但無損於設計風格上的價值。

以下是原文，最下方有一些參考資料，包含中文翻譯。但我希望大家在直接看中文翻譯前，可以先讀一讀原文。

祝大家學習順利！

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

2.1 Lint

Run pylint over your code using this pylintrc.

2.1.1 Definition

pylint is a tool for finding bugs and style problems in Python source code. It finds problems that are typically caught by a compiler for less dynamic languages like C and C++. Because of the dynamic nature of Python, some warnings may be incorrect; however, spurious warnings should be fairly infrequent.

2.1.2 Pros

Catches easy-to-miss errors like typos, using-vars-before-assignment, etc.

2.1.3 Cons

pylint isn’t perfect. To take advantage of it, sometimes we’ll need to write around it, suppress its warnings or fix it.

2.1.4 Decision

Make sure you run pylint on your code.

Suppress warnings if they are inappropriate so that other issues are not hidden. To suppress warnings, you can set a line-level comment:

dict = 'something awful'  # Bad Idea... pylint: disable=redefined-builtin

pylint warnings are each identified by symbolic name (empty-docstring) Google-specific warnings start with g-.

If the reason for the suppression is not clear from the symbolic name, add an explanation.

Suppressing in this way has the advantage that we can easily search for suppressions and revisit them.

You can get a list of pylint warnings by doing:

pylint --list-msgs

To get more information on a particular message, use:

pylint --help-msg=C6409

Prefer pylint: disable to the deprecated older form pylint: disable-msg.

Unused argument warnings can be suppressed by deleting the variables at the beginning of the function. Always include a comment explaining why you are deleting it. “Unused.” is sufficient. For example:

def viking_cafe_order(spam: str, beans: str, eggs: Optional[str] = None) -> str:
    del beans, eggs  # Unused by vikings.
    return spam + spam + spam

Other common forms of suppressing this warning include using ‘_’ as the identifier for the unused argument or prefixing the argument name with ‘unused_’, or assigning them to ‘_’. These forms are allowed but no longer encouraged. These break callers that pass arguments by name and do not enforce that the arguments are actually unused.

2.2 Imports

Use import statements for packages and modules only, not for individual classes or functions.

2.2.1 Definition

Reusability mechanism for sharing code from one module to another.

2.2.2 Pros

The namespace management convention is simple. The source of each identifier is indicated in a consistent way; x.Obj says that object Obj is defined in module x.

2.2.3 Cons

Module names can still collide. Some module names are inconveniently long.

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, if y conflicts with a top-level name defined in the current module, 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.

2.2.4.1 Exemptions

Exemptions from this rule:

Symbols from the following modules and used to support static analysis and type checking:
Redirects from the six.moves module.

2.3 Packages

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

2.3.1 Pros

Avoids conflicts in module names or incorrect imports due to the module search path not being what the author expected. Makes it easier to find modules.

2.3.2 Cons

Makes it harder to deploy code because you have to replicate the package hierarchy. Not really a problem with modern deployment mechanisms.

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

  _FOO = absl.flags.DEFINE_string(...)

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

  _FOO = flags.DEFINE_string(...)

(assume this file lives in doctor/who/ where jodie.py also exists)

No:
  # 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

Exceptions are allowed but must be used carefully.

2.4.1 Definition

Exceptions are a means of breaking out of normal control flow to handle errors or other exceptional conditions.

2.4.2 Pros

The control flow of normal operation code is not cluttered by error-handling code. It also allows the control flow to skip multiple frames when a certain condition occurs, e.g., returning from N nested functions in one step instead of having to plumb error codes through.

2.4.3 Cons

May cause the control flow to be confusing. Easy to miss error cases when making library calls.

2.4.4 Decision

Exceptions must follow certain conditions:

Make use of built-in exception classes when it makes sense. For example, raise a ValueError to indicate a programming mistake like a violated precondition (such as if you were passed a negative number but required a positive one). Do not use assert statements for validating argument values of a public API. assert is used to ensure internal correctness, not to enforce correct usage nor to indicate that some unexpected event occurred. If an exception is desired in the latter cases, use a raise statement. For example:

Yes:
  def connect_to_next_port(self, minimum: int) -> int:
    """Connects to the next available port.

    Args:
      minimum: A port value greater or equal to 1024.

    Returns:
      The new minimum port.

    Raises:
      ConnectionError: If no available port is found.
    """
    if minimum < 1024:
      # Note that this raising of ValueError is not mentioned in the doc
      # string's "Raises:" section because it is not appropriate to
      # guarantee this specific behavioral reaction to API misuse.
      raise ValueError(f'Min. port must be at least 1024, not {minimum}.')
    port = self._find_next_open_port(minimum)
    if port is None:
      raise ConnectionError(
          f'Could not connect to service on port {minimum} or higher.')
    assert port >= minimum, (
        f'Unexpected port {port} when minimum was {minimum}.')
    return port

No:
  def connect_to_next_port(self, minimum: int) -> int:
    """Connects to the next available port.

    Args:
      minimum: A port value greater or equal to 1024.

    Returns:
      The new minimum port.
    """
    assert minimum >= 1024, 'Minimum port must be at least 1024.'
    port = self._find_next_open_port(minimum)
    assert port is not None
    return port

Libraries or packages may define their own exceptions. When doing so they must inherit from an existing exception class. Exception names should end in Error and should not introduce repetition (foo.FooError).
Never use catch-all except: statements, or catch Exception or StandardError, unless you are
- re-raising the exception, or
- creating an isolation point in the program where exceptions are not propagated but are recorded and suppressed instead, such as protecting a thread from crashing by guarding its outermost block.Python is very tolerant in this regard and except: will really catch everything including misspelled names, sys.exit() calls, Ctrl+C interrupts, unittest failures and all kinds of other exceptions that you simply don’t want to catch.
Minimize the amount of code in a try/except block. The larger the body of the try, the more likely that an exception will be raised by a line of code that you didn’t expect to raise an exception. In those cases, the try/except block hides a real error.
Use the finally clause to execute code whether or not an exception is raised in the try block. This is often useful for cleanup, i.e., closing a file.

2.5 Global variables

Avoid global variables.

2.5.1 Definition

Variables that are declared at the module level or as class attributes.

2.5.2 Pros

Occasionally useful.

2.5.3 Cons

Has the potential to change module behavior during the import, because assignments to global variables are done when the module is first imported.

2.5.4 Decision

Avoid global variables.

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

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

2.6 Nested/Local/Inner Classes and Functions

Nested local functions or classes are fine when used to close over a local variable. Inner classes are fine.

2.6.1 Definition

A class can be defined inside of a method, function, or class. A function can be defined inside a method or function. Nested functions have read-only access to variables defined in enclosing scopes.

2.6.2 Pros

Allows definition of utility classes and functions that are only used inside of a very limited scope. Very ADT-y. Commonly used for implementing decorators.

2.6.3 Cons

Nested functions and classes cannot be directly tested. Nesting can make the outer function longer and less readable.

2.6.4 Decision

They are fine with some caveats. Avoid nested functions or classes except when closing over a local value other than self or cls. Do not nest a function just to hide it from users of a module. Instead, prefix its name with an _ at the module level so that it can still be accessed by tests.

2.7 Comprehensions & Generator Expressions

Okay to use for simple cases.

2.7.1 Definition

List, Dict, and Set comprehensions as well as generator expressions provide a concise and efficient way to create container types and iterators without resorting to the use of traditional loops, map(), filter(), or lambda.

2.7.2 Pros

Simple comprehensions can be clearer and simpler than other dict, list, or set creation techniques. Generator expressions can be very efficient, since they avoid the creation of a list entirely.

2.7.3 Cons

Complicated comprehensions or generator expressions can be hard to read.

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

2.8.1 Definition

Container types, like dictionaries and lists, define default iterators and membership test operators (“in” and “not in”).

2.8.2 Pros

The default iterators and operators are simple and efficient. They express the operation directly, without extra method calls. A function that uses default operators is generic. It can be used with any type that supports the operation.

2.8.3 Cons

You can’t tell the type of objects by reading the method names (e.g. has_key() means a dictionary). This is also an advantage.

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.

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

No:   for key in adict.keys(): ...
      if not adict.has_key(key): ...
      for line in afile.readlines(): ...

2.9 Generators

Use generators as needed.

2.9.1 Definition

A generator function returns an iterator that yields a value each time it executes a yield statement. After it yields a value, the runtime state of the generator function is suspended until the next value is needed.

2.9.2 Pros

Simpler code, because the state of local variables and control flow are preserved for each call. A generator uses less memory than a function that creates an entire list of values at once.

2.9.3 Cons

Local variables in the generator will not be garbage collected until the generator is either consumed to exhaustion or itself garbage collected.

2.9.4 Decision

Fine. Use “Yields:” rather than “Returns:” in the docstring for generator functions.

If the generator manages an expensive resource, make sure to force the clean up.

A good way to do the clean up is by wrapping the generator with a context manager PEP-0533.

2.10 Lambda Functions

Okay for one-liners. Prefer generator expressions over map() or filter() with a lambda.

2.10.1 Definition

Lambdas define anonymous functions in an expression, as opposed to a statement.

2.10.2 Pros

Convenient.

2.10.3 Cons

Harder to read and debug than local functions. The lack of names means stack traces are more difficult to understand. Expressiveness is limited because the function may only contain an expression.

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.

2.11.1 Definition

Conditional expressions (sometimes called a “ternary operator”) are mechanisms that provide a shorter syntax for if statements. For example: x = 1 if cond else 2.

2.11.2 Pros

Shorter and more convenient than an if statement.

2.11.3 Cons

May be harder to read than an if statement. The condition may be difficult to locate if the expression is long.

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.

Yes:
    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')

No:
    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.

2.12.1 Definition

You can specify values for variables at the end of a function’s parameter list, e.g., def foo(a, b=0):. If foo is called with only one argument, b is set to 0. If it is called with two arguments, b has the value of the second argument.

2.12.2 Pros

Often you have a function that uses lots of default values, but on rare occasions you want to override the defaults. Default argument values provide an easy way to do this, without having to define lots of functions for the rare exceptions. As Python does not support overloaded methods/functions, default arguments are an easy way of “faking” the overloading behavior.

2.12.3 Cons

Default arguments are evaluated once at module load time. This may cause problems if the argument is a mutable object such as a list or a dictionary. If the function modifies the object (e.g., by appending an item to a list), the default value is modified.

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
         ...

from absl import flags
_FOO = flags.DEFINE_string(...)

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

2.13 Properties

Properties may be used to control getting or setting attributes that require trivial computations or logic. Property implementations must match the general expectations of regular attribute access: that they are cheap, straightforward, and unsurprising.

2.13.1 Definition

A way to wrap method calls for getting and setting an attribute as a standard attribute access.

2.13.2 Pros

Allows for an attribute access and assignment API rather than getter and setter method calls.
Can be used to make an attribute read-only.
Allows calculations to be lazy.
Provides a way to maintain the public interface of a class when the internals evolve independently of class users.

2.13.3 Cons

Can hide side-effects much like operator overloading.
Can be confusing for subclasses.

2.13.4 Decision

Properties are allowed, but, like operator overloading, should only be used when necessary and match the expectations of typical attribute access; follow the getters and setters rules otherwise.

For example, using a property to simply both get and set an internal attribute isn’t allowed: there is no computation occurring, so the property is unnecessary (make the attribute public instead). In comparison, using a property to control attribute access or to calculate a trivially derived value is allowed: the logic is simple and unsurprising.

Properties should be created with the @property decorator. Manually implementing a property descriptor is considered a power feature.

Inheritance with properties can be non-obvious. Do not use properties to implement computations a subclass may ever want to override and extend.

2.14 True/False Evaluations

Use the “implicit” false if at all possible.

2.14.1 Definition

Python evaluates certain values as False when in a boolean context. A quick “rule of thumb” is that all “empty” values are considered false, so 0, None, [], {}, '' all evaluate as false in a boolean context.

2.14.2 Pros

Conditions using Python booleans are easier to read and less error-prone. In most cases, they’re also faster.

2.14.3 Cons

May look strange to C/C++ developers.

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 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 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.
Note that Numpy arrays may raise an exception in an implicit boolean context. Prefer the .size attribute when testing emptiness of a np.array (e.g. if not users.size).

2.16 Lexical Scoping

Okay to use.

2.16.1 Definition

A nested Python function can refer to variables defined in enclosing functions, but cannot assign to them. Variable bindings are resolved using lexical scoping, that is, based on the static program text. Any assignment to a name in a block will cause Python to treat all references to that name as a local variable, even if the use precedes the assignment. If a global declaration occurs, the name is treated as a global variable.

An example of the use of this feature is:

def get_adder(summand1: float) -> Callable[[float], float]:
    """Returns a function that adds numbers to a given number."""
    def adder(summand2: float) -> float:
        return summand1 + summand2

    return adder

2.16.2 Pros

Often results in clearer, more elegant code. Especially comforting to experienced Lisp and Scheme (and Haskell and ML and …) programmers.

2.16.3 Cons

Can lead to confusing bugs. Such as this example based on PEP-0227:

i = 4
def foo(x: Iterable[int]):
    def bar():
        print(i, end='')
    # ...
    # A bunch of code here
    # ...
    for i in x:  # Ah, i *is* local to foo, so this is what bar sees
        print(i, end='')
    bar()

So foo([1, 2, 3]) will print 1 2 3 3, not 1 2 3 4.

2.16.4 Decision

Okay to use.

2.17 Function and Method Decorators

Use decorators judiciously when there is a clear advantage. Avoid staticmethod and limit use of classmethod.

2.17.1 Definition

Decorators for Functions and Methods (a.k.a “the @ notation”). One common decorator is @property, used for converting ordinary methods into dynamically computed attributes. However, the decorator syntax allows for user-defined decorators as well. Specifically, for some function my_decorator, this:

class C:
    @my_decorator
    def method(self):
        # method body ...

is equivalent to:

class C:
    def method(self):
        # method body ...
    method = my_decorator(method)

2.17.2 Pros

Elegantly specifies some transformation on a method; the transformation might eliminate some repetitive code, enforce invariants, etc.

2.17.3 Cons

Decorators can perform arbitrary operations on a function’s arguments or return values, resulting in surprising implicit behavior. Additionally, decorators execute at object definition time. For module-level objects (classes, module functions, …) this happens at import time. Failures in decorator code are pretty much impossible to recover from.

2.17.4 Decision

Use decorators judiciously when there is a clear advantage. Decorators should follow the same import and naming guidelines as functions. Decorator pydoc should clearly state that the function is a decorator. Write unit tests for decorators.

Avoid external dependencies in the decorator itself (e.g. don’t rely on files, sockets, database connections, etc.), since they might not be available when the decorator runs (at import time, perhaps from pydoc or other tools). A decorator that is called with valid parameters should (as much as possible) be guaranteed to succeed in all cases.

Decorators are a special case of “top level code” - see main for more discussion.

Never use staticmethod unless forced to in order to integrate with an API defined in an existing library. Write a module level function instead.

Use classmethod only when writing a named constructor or a class-specific routine that modifies necessary global state such as a process-wide cache.

2.18 Threading

Do not rely on the atomicity of built-in types.

While Python’s built-in data types such as dictionaries appear to have atomic operations, there are corner cases where they aren’t atomic (e.g. if __hash__ or __eq__ are implemented as Python methods) and their atomicity should not be relied upon. Neither should you rely on atomic variable assignment (since this in turn depends on dictionaries).

Use the Queue module’s Queue data type as the preferred way to communicate data between threads. Otherwise, use the threading module and its locking primitives. Prefer condition variables and threading.Condition instead of using lower-level locks.

2.19 Power Features

Avoid these features.

2.19.1 Definition

Python is an extremely flexible language and gives you many fancy features such as custom metaclasses, access to bytecode, on-the-fly compilation, dynamic inheritance, object reparenting, import hacks, reflection (e.g. some uses of getattr()), modification of system internals, __del__ methods implementing customized cleanup, etc.

2.19.2 Pros

These are powerful language features. They can make your code more compact.

2.19.3 Cons

It’s very tempting to use these “cool” features when they’re not absolutely necessary. It’s harder to read, understand, and debug code that’s using unusual features underneath. It doesn’t seem that way at first (to the original author), but when revisiting the code, it tends to be more difficult than code that is longer but is straightforward.

2.19.4 Decision

Avoid these features in your code.

Standard library modules and classes that internally use these features are okay to use (for example, abc.ABCMeta, dataclasses, and enum).

請接著看（中篇）和（下篇）

資料來源：

Google Python Style Guide

中文翻譯：

Google Python 風格指南 - 中文版