# 谷歌Python风格指南

## 1 背景

Python是谷歌使用的一种主要的动态语言，本规范通过列出正误例子，来帮助你以正确的格式编写Python代码。

## 2 Python语言规则

### 2.1 Lint

#### 2.1.1 定义

pylint是一个用来发现Python源代码中的Bug和格式问题的工具。

#### 2.1.3 缺点

pylint并不完美。为了利用它，有时我们需要迎合它来写代码，抑制其警告或修复它。

#### 2.1.4 结论

pylint的每个警告都由符号名称标识（empty-docstring）。

### 2.2 引用 Import

typing_extensions module
six.moves module引用是不受次规则限制。

#### 2.2.4 结论

• 使用import x来引用包或者模块
• 使用from x import y，其中x是包名，y是没有前缀的模块名
• 使用from x import y as z，如果两个模块都被命名为y且需要同时引用；或者y是一个超长的名字。
• 使用import y as z，仅当z是一个标准缩写时。（例如numpy的标准缩写np

### 2.4 异常 Exceptions

#### 2.4.4 结论

• 在有意义时使用内部异常类。比如，抛出一个ValueError来表示一个违反约定条件的错误
（比如你本来想要一个正值，结果传递给你一个负值）。不要用assert断言在一个公开API
上来验证参数值。assert只能用来确定内部的正确性，而不能限制程序的正确使用，也不表
示发生了某些意外事件。如果要表示某些意外事件，请使用raise语句。
例如：

• 库或者包会有他们自定义的异常。这些异常必须继承自一个已有的异常类。
异常的名字必须以Error结尾，并且不能拗口(foo.FooError)。

• 永远不要使用全部捕捉except:语句，或者捕捉Exception或者StandardError，除非：

• 想要再次抛出这个异常，或者
• 创建一个程序分离点，在这个分离点异常不再被抛出，但会被记录和抑制，例如
从一个为了保护其最外层块而崩溃的线程

Python在except:上是非常宽容的，它可以捕捉任何异常包括拼写错误、sys.exit()、
Ctrl+C中断、单元测试错误，以及所有你不想被捕捉的异常。

• 最小化try/except块之间的代码数量。代码数量越大，引发非期待的异常的可能性就越大。
在这种情况下，try/except块会隐藏掉真正的异常。

• 使用finally收尾。无论异常是否被抛出。finally常常用于清理，例如关闭一个文件。

### 2.5 Global variables

#### 2.5.3 缺点

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

### 2.10 Lambda表达式

#### 2.10.1 定义

Lambda表达式用来定义一个匿名函数，不需要声明函数。

### 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.

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

### 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:

#### 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:

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

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:

is equivalent to:

#### 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 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.

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
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
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).

### 2.20 Modern Python: from __future__ imports

New language version semantic changes may be gated behind a special future
import to enable them on a per-file basis within earlier runtimes.

#### 2.20.1 Definition

Being able to turn on some of the more modern features via from __future__ import statements allows early use of features from expected future Python
versions.

#### 2.20.2 Pros

This has proven to make runtime version upgrades smoother as changes can be made
on a per-file basis while declaring compatibility and preventing regressions
within those files. Modern code is more maintainable as it is less likely to
accumulate technical debt that will be problematic during future runtime

#### 2.20.3 Cons

Such code may not work on very old interpreter versions prior to the
introduction of the needed future statement. The need for this is more common in
projects supporting an extremely wide variety of environments.

#### 2.20.4 Decision

##### from __future__ imports

Use of from __future__ import statements is encouraged. It allows a given
source file to start using more modern Python syntax features today. Once you no
longer need to run on a version where the features are hidden behind a
__future__ import, feel free to remove those lines.

In code that may execute on versions as old as 3.5 rather than >= 3.7, import:

For legacy code with the burden of continuing to support 2.7, import:

Python future statement definitions
documentation.

Please don’t remove these imports until you are confident the code is only ever
used in a sufficiently modern environment. Even if you do not currently use the
feature a specific future import enables in your code today, keeping it in place
in the file prevents later modifications of the code from inadvertently
depending on the older behavior.

Use other from __future__ import statements as you see fit. We did not include
unicode_literals in our recommendations for 2.7 as it was not a clear win due
to implicit default codec conversion consequences it introduced in many places
within 2.7. Most dual-version 2-and-3 code was better off with explicit use of
b'' and u'' bytes and unicode string literals where necessary.

##### The six, future, and past libraries

When your project still needs to support use under both Python 2 and 3, use the
six,
future, and
past libraries as you see fit. They exist to
make your code cleaner and life easier.

### 2.21 Type Annotated Code

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:

You can also declare the type of a variable using similar
PEP-526 syntax:

Or by using a type comment in code that must support legacy Python versions.

#### 2.21.2 Pros

type checker will convert many runtime errors to build-time errors, and reduce
your ability to use Power Features.

#### 2.21.3 Cons

You will have to keep the type declarations up to date.
You might see type errors that you think are
valid code. Use of a
type checker
may reduce your ability to use Power Features.

#### 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

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

### 3.2 Line length

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.

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

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

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:

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. Authors are encouraged to manually break the line up per
the notes above when it is sensible.

### 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.

### 3.4 Indentation

Indent your code blocks with 4 spaces.

Never use tabs or mix tabs and spaces. In cases of implied line continuation,
you should align wrapped elements either vertically, as per the examples in the
line length section; or using a hanging indent of 4 spaces,
in which case there should be nothing after the open parenthesis or bracket on
the first line.

#### 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.

### 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.

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

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

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 (+, -, *, /, //, %, **, @).

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.

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

### 3.7 Shebang Line

Most .py files do not need to start with a #! line. Start the main file of a
program with
#!/usr/bin/env python3 (to support virtualenvs) or #!/usr/bin/python3 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 intended to be executed directly.

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

#### 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 not
exceeding 80 characters) terminated by a period, question mark, or exclamation
point. When writing more (encouraged), this must be 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.

#### 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 describe the
function’s calling syntax and its semantics, but generally not its
implementation details, unless those details are relevant to how the function is
to be used. For example, a function that mutates one of its arguments as a side
effect should note that in its docstring. Otherwise, subtle but important
details of a function’s implementation that are not relevant to the caller are
better expressed as comments alongside the code than within the function’s
docstring.

The docstring should be descriptive-style ("""Fetches rows from a Bigtable.""") rather than imperative-style ("""Fetch rows from a Bigtable."""). The docstring for a @property data descriptor should use the
same style as the docstring for an attribute or a
function argument ("""The Bigtable path.""",
rather than """Returns the Bigtable path.""").

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 followed by either a space or newline. If the
description is too long to fit on a single 80-character line, use a hanging
indent of 2 or 4 spaces more than the parameter name (be consistent with the
rest of the docstrings in 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 followed by a
description. Use a similar exception name + colon + space or newline and
hanging indent style as described in Args:. 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).

Similarly, this variation on Args: with a line break is also allowed:

#### 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.

#### 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.

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.

#### 3.8.6 Punctuation, Spelling, and Grammar

Pay attention to punctuation, spelling, and grammar; it is easier to read

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.

### 3.10 Strings

Use an
f-string,
the % operator, or the format method for formatting strings, even when the
parameters are all strings. Use your best judgment to decide between + and %
(or format) though. Do not use % or the format method for pure
concatenation.

Avoid using the + and += operators to accumulate a string within a loop. In
rather than linear running time. Although common accumulations of this sort may
be optimized on CPython, that is an implementation detail. The conditions under
which an optimization applies are not easy to predict and may change. Instead,
add each substring to a list and ''.join the list after the loop terminates,
or write each substring to an io.StringIO buffer. These techniques
consistently have amortized-linear run time complexity.

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.

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:

#### 3.10.1 Logging

For logging functions that expect a pattern-string (with %-placeholders) as
their first argument: Always call them with a string literal (not an f-string!)
as their first argument with pattern-parameters as subsequent arguments. Some
logging implementations collect the unexpanded pattern-string as a queryable
field. It also prevents spending time rendering a message that no logger is
configured to output.

#### 3.10.2 Error Messages

Error messages (such as: message strings on exceptions like ValueError, or
messages shown to the user) should follow three guidelines:

1. The message needs to precisely match the actual error condition.

2. Interpolated pieces need to always be clearly identifiable as such.

3. They should allow simple automated processing (e.g. grepping).

### 3.11 Files, Sockets, and similar Stateful Resources

Explicitly close files and sockets when done with them. This rule naturally
extends to closeable resources that internally use sockets, such as database
connections, and also other resources that need to be closed down in a similar
fashion. To name only a few examples, this also includes
mmap mappings,
h5py File objects, and
matplotlib.pyplot figure windows.

Leaving files, sockets or other such stateful 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, or unmounting a filesystem.
• 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 raise exceptions, making
the problem known sooner.

Furthermore, while files and sockets (and some similarly behaving resources) are
automatically closed when the object is destructed, coupling the lifetime of the
object to the state of the resource is poor practice:

• There are no guarantees as to when the runtime will actually invoke the
__del__ method. 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.

Relying on finalizers to do automatic cleanup that has observable side effects
has been rediscovered over and over again to lead to major problems, across many
decades and multiple languages (see e.g.
for Java).

The preferred way to manage files and similar resources is using the
with statement:

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

In rare cases where context-based resource management is infeasible, code
documentation must explain clearly how resource lifetime is managed.

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.

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; there are
exceptions for typing imports.

E.g.:

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:

1. Python future import statements. For example:

2. Python standard library imports. For example:

3. third-party module
or package imports. For example:

1import tensorflow as tf

1. Code repository
sub-package imports. For example:
1from otherproject.ai import mind

1. Deprecated: application-specific imports that are part of the same
top level
sub-package as this file. For example:
1from 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 (the path in from path import ...). Code may optionally place a blank line between import sections.

### 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.

### 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 specifically allowed cases:

• counters or iterators (e.g. i, j, k, v, et al.)
• e as an exception identifier in try/except statements.
• f as a file handle in with statements

Please be mindful not to abuse single-character naming. Generally speaking,
descriptiveness should be proportional to the name’s scope of visibility.
For example, i might be a fine name for 5-line code block but within
multiple nested scopes, it is likely too vague.

• dashes (-) in any package/module name

• __double_leading_and_trailing_underscore__ names (reserved by Python)

• offensive terms

• names that needlessly include the type of the variable (for example:
id_to_name_dict)

#### 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 (linters will flag protected member access).

• 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. Prefer a single underscore.

• 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

#### 3.16.5 Mathematical Notation

For mathematically heavy code, short variable names that would otherwise violate
the style guide are preferred when they match established notation in a
reference paper or algorithm. When doing so, reference the source of all naming
conventions in a comment or docstring or, if the source is not accessible,
clearly document the naming conventions. Prefer PEP8-compliant
descriptive_names for public APIs, which are much more likely to be
encountered out of context.

### 3.17 Main

In Python, pydoc as well as unit tests require modules to be importable. If a
file is meant to be used as an executable, its main functionality should be in a
main() function, and your code should always check if __name__ == '__main__'
before executing your main program, so that it is not executed when the module
is imported.

When using absl, use app.run:

Otherwise, use:

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

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

#### 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”.

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.

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

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.

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

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).

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.

#### 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.

#### 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.

#### 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: str = None to be interpreted as a: Optional[str] = None, but
that is no longer the preferred behavior.

#### 3.19.6 Type Aliases

You can declare aliases of complex types. The name of an alias should be
CapWorded. If the alias is used only in this module, it should be _Private.

For example, if the name of the module together with the name of the type is too
long:

Other examples are complex nested types and multiple return variables from a
function (as a tuple).

#### 3.19.7 Ignoring Types

You can disable type checking on a line with the special comment # type: ignore.

pytype has a disable option for specific errors (similar to lint):

#### 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.

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

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

#### 3.19.9 Tuples vs Lists

Typed lists can only contain objects of a single type. Typed tuples can either
have a single repeated type or a set number of elements with different types.
The latter is commonly used as the return type from a function.

#### 3.19.10 TypeVars

The Python type system has
generics. The factory
function TypeVar is a common way to use them.

Example:

A TypeVar can be constrained:

A common predefined type variable in the typing module is AnyStr. Use it for
multiple annotations that can be bytes or unicode and must all be the same
type.

#### 3.19.11 String types

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

Prefer to use str, though Text is also acceptable. Be consistent in using
one or the other. For code that deals with binary data, use bytes. For Python
2 compatible code that processes text data (str or unicode in Python 2,
str in Python 3), use Text.

In some uncommon Python 2 compatibility cases, str may make sense instead of
Text, typically to aid compatibility when the return types aren’t the same
between Python 2 and Python 3. Never use unicode as it doesn’t exist in Python

1. The reason this discrepancy exists is because str means something different
in Python 2 than in Python 3.

No:

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

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.

#### 3.19.12 Imports For Typing

For classes from the typing module, always import the class itself. You are
explicitly allowed to import multiple specific classes on one line from the
typing module. Ex:

Given that this way of importing from typing adds items to the local
namespace, any names in typing should be treated similarly to keywords, and
not be defined in your Python code, typed or not. If there is a collision
between a type and an existing name in a module, import it using import x as y.

#### 3.19.13 Conditional Imports

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.

#### 3.19.14 Circular Dependencies

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, various build systems 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.

#### 3.19.15 Generics

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

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

## 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.