Table of Contents

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

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

2.1.2 Pros

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

2.1.3 Cons

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
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

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

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

You can get a list of
warnings by doing:

To get more information on a particular message, use:

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

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. Exemptions

Exemptions from this rule:

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:

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

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

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

      def connect_to_next_port(self, minimum: int) -> int:
        """Connects to the next available port.
          minimum: A port value greater or equal to 1024.
          The new minimum port.
          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
      def connect_to_next_port(self, minimum: int) -> int:
        """Connects to the next available port.
          minimum: A port value greater or equal to 1024.
          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

2.5 Mutable Global State

Avoid mutable global state.

2.5.1 Definition

Module level values or class attributes that can get mutated during program

2.5.2 Pros

Occasionally useful.

2.5.3 Cons

  • Breaks encapsulation: Such design can make it hard to achieve valid
    objectives. For example, if global state is used to manage a database
    connection, then connecting to two different databases at the same time
    (such as for computing differences during a migration) becomes difficult.
    Similar problems easily arise with global registries.

  • 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 mutable global state.

In those rare cases where using global state is warranted, mutable global
entities should be declared at the module level or as a class attribute and made
internal by prepending an _ to the name. If necessary, external access to
mutable global state must be done through public functions or class methods. See
Naming below. Please explain the design reasons why mutable
global state is being used in a comment or a doc linked to from a comment.

Module-level constants are permitted and encouraged. For example:
_MAX_HOLY_HANDGRENADE_COUNT = 3 for an internal use constant or
SIR_LANCELOTS_FAVORITE_COLOR = "blue" for a public API constant. Constants
must be named using all caps with underscores. See Naming

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
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

  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 = { for user in users if user is not None}

  eat(jelly_bean for jelly_bean in jelly_beans
      if jelly_bean.color == 'black')
  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 (unless the
variable has type annotations). 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 obj in alist: ...
      for line in afile: ...
      for k, v in adict.items(): ...
No:   for key in adict.keys(): ...
      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

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


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.

    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
    portion_too_long = ('yes'
                        if some_long_module.some_long_predicate_function(
                        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

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

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:
         def f(x=None):
             if x is None:
                 x = []
    No:  if len(users) == 0:
             print('no users')
         if not i % 10:
         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

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='')

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:
    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

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).

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__
statements allows early use of features from expected future Python

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:

from __future__ import generator_stop

For more information read the
Python future statement definitions

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.

2.21 Type Annotated Code

You can annotate Python 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 similar
PEP-526 syntax:

a: SomeType = some_func()

2.21.2 Pros

Type annotations improve the readability and maintainability of your code. The
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
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.

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
No:  # See details at
     # v2.0/csv_file_name_extension_full_specification.html

It is permissible to use backslash continuation when defining a with statement
with three or more context managers. For two context managers, 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(beans, spam)
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
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.

Yes: if foo:
     while x:
         x = bar()
     if x and y:
     if not x:
     # 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):
     if not(x):
     return (foo)

3.4 Indentation

Indent your code blocks with 4 spaces.

Never use tabs. Implied line continuation should align wrapped elements
vertically (see line length examples), or use a hanging
4-space indent. Closing (round, square or curly) brackets can be placed at the
end of the expression, or on separate lines, but then should be indented the
same as the line with the corresponding opening bracket.

Yes:   # Aligned with opening delimiter.
       foo = long_function_name(var_one, var_two,
                                var_three, var_four)
       meal = (spam,

       # Aligned with opening delimiter in a dictionary.
       foo = {
           'long_dictionary_key': value1 +

       # 4-space hanging indent; nothing on first line.
       foo = long_function_name(
           var_one, var_two, var_three,
       meal = (

       # 4-space hanging indent; nothing on first line,
       # closing parenthesis on a new line.
       foo = long_function_name(
           var_one, var_two, var_three,
       meal = (

       # 4-space hanging indent in a dictionary.
       foo = {
No:    # Stuff on first line forbidden.
       foo = long_function_name(var_one, var_two,
           var_three, var_four)
       meal = (spam,

       # 2-space hanging indent forbidden.
       foo = long_function_name(
         var_one, var_two, var_three,

       # No hanging indent in a dictionary.
       foo = {

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 = [
No:    golomb4 = [

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 docstring
of a class and the first method. No blank line following a def line. Use
single blank lines as you judge appropriate within functions or methods.

Blank lines need not be anchored to the definition. For example, related
comments immediately preceding function, class, and method definitions can make
sense. Consider if your comment might be more useful as part of the docstring.

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

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.):

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

  dictionary = {
      'foo': 1,
      'long_name': 2,
  foo       = 1000  # comment
  long_name = 2     # comment that should not be aligned

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

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

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.

3.8 Comments and Docstrings

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 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

"""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

Typical usage example:

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

Module-level docstrings for test files are not required. They should be included
only when there is additional information that can be provided.

Examples include some specifics on how the test should be run, an explanation of
an unusual setup pattern, dependency on the external environment, and so on.

"""This blaze test uses golden files.

You can update those files by running
`blaze run //foo/bar:foo_test -- --update_golden_files` from the `google3`

Docstrings that do not provide any new information should not be used.

3.8.3 Functions and Methods

In this section, “function” means a method, function, generator, or property.

A docstring is mandatory for every function that has one or more of the
following properties:

  • being part of the public API
  • nontrivial size
  • non-obvious logic

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

The docstring may be descriptive-style ("""Fetches rows from a Bigtable.""")
or imperative-style ("""Fetch rows from a Bigtable."""), but the style should
be consistent within a file. 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
. 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.

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 the return value. Do not imitate ‘NumPy style’
which frequently documents a tuple return value as if it were multiple
return values with individual names (never mentioning the tuple). Instead,
describe such a return value as: “Returns: A tuple (mat_a, mat_b), where
mat_a is …, and …”. The auxiliary names in the docstring need not
necessarily correspond to any internal names used in the function body (as
those are not part of the API).
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).
def fetch_smalltable_rows(table_handle: smalltable.Table,
                          keys: Sequence[Union[bytes, str]],
                          require_all_keys: bool = False,
) -> Mapping[bytes, tuple[str, ...]]:
    """Fetches rows from a Smalltable.

    Retrieves rows pertaining to the given keys from the Table instance
    represented by table_handle.  String keys will be UTF-8 encoded.

        table_handle: An open smalltable.Table instance.
        keys: A sequence of strings representing the key of each table
          row to fetch.  String keys will be UTF-8 encoded.
        require_all_keys: If True only rows with values set for all keys will be

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

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

        Returned keys are always bytes.  If a key from the keys argument is
        missing from the dictionary, then that row was not found in the
        table (and require_all_keys must have been False).

        IOError: An error occurred accessing the smalltable.

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

def fetch_smalltable_rows(table_handle: smalltable.Table,
                          keys: Sequence[Union[bytes, str]],
                          require_all_keys: bool = False,
) -> Mapping[bytes, tuple[str, ...]]:
    """Fetches rows from a Smalltable.

    Retrieves rows pertaining to the given keys from the Table instance
    represented by table_handle.  String keys will be UTF-8 encoded.

        An open smalltable.Table instance.
        A sequence of strings representing the key of each table row to
        fetch.  String keys will be UTF-8 encoded.
        If True only rows with values set for all keys will be returned.

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

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

      Returned keys are always bytes.  If a key from the keys argument is
      missing from the dictionary, then that row was not found in the
      table (and require_all_keys must have been False).

      IOError: An error occurred accessing the smalltable.

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:
    """Summary of class here.

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

        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: bool = False):
        """Inits SampleClass with blah."""
        self.likes_spam = likes_spam
        self.eggs = 0

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

All class docstrings should start with a one-line summary that describes what
the class instance represents. This implies that subclasses of Exception
should also describe what the exception represents, and not the context in which
it might occur. The class docstring should not repeat unnecessary information,
such as that the class is a class.

# Yes:
class CheeseShopAddress:
  """The address of a cheese shop.


class OutOfCheeseError(Exception):
  """No more cheese is available."""
# No:
class CheeseShopAddress:
  """Class that describes the address of a cheese shop.


class OutOfCheeseError(Exception):
  """Raised when no more cheese is available."""

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

# 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.

3.10 Strings

Use an
the % operator, or the format method for formatting strings, even when the
parameters are all strings. Use your best judgment to decide between string
formatting options. A single join with + is okay but do not format with +.

Yes: x = f'name: {name}; score: {n}'
     x = '%s, %s!' % (imperative, expletive)
     x = '{}, {}'.format(first, second)
     x = 'name: %s; score: %d' % (name, n)
     x = 'name: {}; score: {}'.format(name, n)
     x = a + b
No: x = first + ', ' + second
    x = 'name: ' + name + '; score: ' + str(n)

Avoid using the + and += operators to accumulate a string within a loop. In
some conditions, accumulating a string with addition can lead to quadratic
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.

Yes: items = ['']forlast_name,first_nameinemployee_list:items.append(''%(last_name,first_name))items.append('
%s, %s
) employee_table = ''.join(items)
No: employee_table = ''forlast_name,first_nameinemployee_list:employee_table+=''%(last_name,first_name)employee_table+='
%s, %s

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 backslash-escape quote characters within the string.

  Python('Why are you hiding your eyes?')
  Gollum("I'm scared of lint errors.")
  Narrator('"Good!" thought a happy Python reviewer.')
  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
to remove the initial space on each line:

  long_string = """This is pretty ugly.
Don't do this.
  long_string = """This is fine if your use case can accept
      extraneous leading spaces."""
  long_string = ("And this is fine if you cannot acceptn" +
                 "extraneous leading spaces.")
  long_string = ("And this too is fine if you cannot acceptn"
                 "extraneous leading spaces.")
  import textwrap

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

  import tensorflow as tf
  logger = tf.get_logger()'TensorFlow Version is: %s', tf.__version__)
  import os
  from absl import logging'Current $PAGER is: %s', os.getenv('PAGER', default=''))

  homedir = os.getenv('HOME')
  if homedir is None or not os.access(homedir, os.W_OK):
    logging.error('Cannot write to home directory, $HOME=%r', homedir)
  import os
  from absl import logging'Current $PAGER is:')'PAGER', default=''))

  homedir = os.getenv('HOME')
  if homedir is None or not os.access(homedir, os.W_OK):
    logging.error(f'Cannot write to home directory, $HOME={homedir!r}')

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).

  if not 0 <= p <= 1:
    raise ValueError(f'Not a probability: {p!r}')

  except OSError as error:
    logging.warning('Could not remove directory (reason: %r): %r',
                    error, workdir)
  if p < 0 or p > 1:  # PROBLEM: also false for float('nan')!
    raise ValueError(f'Not a probability: {p!r}')

  except OSError:
    # PROBLEM: Message makes an assumption that might not be true:
    # Deletion might have failed for some other reason, misleading
    # whoever has to debug this.
    logging.warning('Directory already was deleted: %s', workdir)

  except OSError:
    # PROBLEM: The message is harder to grep for than necessary, and
    # not universally non-confusing for all possible values of `workdir`.
    # Imagine someone calling a library function with such code
    # using a name such as workdir = 'deleted'. The warning would read:
    # "The deleted directory could not be deleted."
    logging.warning('The %s directory could not be deleted.', workdir)

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.
this article
for Java).

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

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

For file-like objects that do not support the with statement, use

import contextlib

with contextlib.closing(urllib.urlopen("")) as front_page:
    for line in front_page:

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 word TODO in all caps, and a parenthesized
context identifier. Ideally a bug reference, sometimes a username. A bug
reference like TODO( is
preferable, because bugs are tracked and have follow-up comments, whereas
individuals move around and may lose context over time. The TODO 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 with a username,
it is almost always your own username that is given.

# TODO( Investigate cpufreq optimizations.
# TODO(yourusername): File an issue and use a '*' for repetition.

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.”) that
future code maintainers will comprehend.

3.13 Imports formatting

Imports should be on separate lines; there are
exceptions for typing and imports.


Yes: from import Mapping, Sequence
     import os
     import sys
     from typing import Any, NewType

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:

    from __future__ import annotations

    See above for more information about those.

  2. Python standard library imports. For example:

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

  4. Code repository
    sub-package imports. For example:

    from import mind
  5. 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

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.

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 import huxley
from myproject.backend.hgwells import time_machine
from myproject.backend.state_machine import main_loop
from import body
from import mind
from 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.


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

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

  except ValueError: baz(foo)

3.15 Getters and Setters

Getter and setter functions (also called accessors and mutators) should be used
when they provide a meaningful role or behavior for getting or setting a
variable’s value.

In particular, they should be used when getting or setting the variable is
complex or the cost is significant, either currently or in a reasonable future.

If, for example, a pair of getters/setters simply read and write an internal
attribute, the internal attribute should be made public instead. By comparison,
if setting a variable means some state is invalidated or rebuilt, it should be a
setter function. The function invocation hints that a potentially non-trivial
operation is occurring. Alternatively, properties may be an
option when simple logic is needed, or refactoring to no longer need getters and

Getters and setters should follow the Naming guidelines, such
as get_foo() and set_foo().

If the past behavior allowed access through a property, do not bind the new
getter/setter 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, query_proper_noun_for_thing,

Function names, variable names, and filenames should be descriptive; avoid
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
    • private TypeVars with no constraints (e.g. _T,
      _U, _V)

    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:

3.16.2 Naming Conventions

  • “Internal” means internal to a module, or protected or private within a

  • 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
    Unlike Java, there is no need to limit yourself to one class per module.

  • Use CapWords for class names, but for module names.
    Although there are some old modules named, 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

  • 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_; 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 "$" "$@".

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 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

from absl import app

def main(argv: Sequence[str]):
    # process non-flag arguments

if __name__ == '__main__':

Otherwise, use:

def main():

if __name__ == '__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
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

  • In methods, only annotate self, or cls if it is necessary for proper
    type information. e.g.,

    def create(cls: Type[_T]) -> _T:
      return cls()
  • Similarly, don’t feel compelled to annotate the return value of __init__
    (where None is the only valid option).

  • 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
    • 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”.
To ensure the return type is also given its own line, a comma can be placed
after the last parameter.

def my_method(
    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

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. When using line breaks, prefer putting
each parameter and the return type on their own lines and aligning the closing
parenthesis with the def:

def my_method(
    other_arg: Optional[MyLongType],
) -> tuple[MyLongType1, MyLongType1]:

Optionally, the return type may be put on the same line as the last parameter:

def my_method(
    first_var: int,
    second_var: int) -> dict[OtherLongType, MyLongType]:

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

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(
    first_var: tuple[list[MyLongType1],
    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.

def my_function(
) -> None:
def my_function(
    long_variable_name: long_module_name.
) -> 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 name inside the declaration of
that class, or if you use a class that is defined later in the code – either
use from __future__ import annotations or use a string for the class name.

from __future__ import annotations

class MyClass:
  def __init__(self, stack: Sequence[MyClass], item: OtherClass) -> None:

class OtherClass:
class MyClass:
  def __init__(self, stack: Sequence['MyClass'], item: 'OtherClass') -> None:

class OtherClass:

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.

def func(a: int = 0) -> int:
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: str = None to be interpreted as a: Optional[str] = None, but
that is no longer the preferred behavior.

def func(a: Optional[str], b: Optional[str] = None) -> str:
def multiple_nullable_union(a: Union[None, str, int]) -> str:
def nullable_union(a: Union[None, str]) -> str:
def implicit_optional(a: str = None) -> str:

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

_LossAndGradient = tuple[tf.Tensor, tf.Tensor]
ComplexTFMap = Mapping[str, _LossAndGradient]

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:

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

# pytype: disable=attribute-error

3.19.8 Typing Variables

Annotated Assignments
If an internal variable has a type that is hard or impossible to infer,
specify its type with an annotated assignment – use a colon and type between
the variable name and value (the same as is done with function arguments
that have a default value):

a: Foo = SomeUndecoratedFunction()
Type Comments
Though you may see them remaining in the codebase (they were necessary
before Python 3.6), do not add any more uses of a # type:
comment on the end of the line:

a = SomeUndecoratedFunction()  # type: Foo

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.

a: list[int] = [1, 2, 3]
b: tuple[int, ...] = (1, 2, 3)
c: tuple[int, str, float] = (1, "2", 3.5)

3.19.10 TypeVars

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


from typing import TypeVar
_T = TypeVar("_T")
def next(l: list[_T]) -> _T:
  return l.pop()

A TypeVar can be constrained:

AddableType = TypeVar("AddableType", int, float, str)
def add(a: AddableType, b: AddableType) -> AddableType:
  return a + b

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

from typing import AnyStr
def check_length(x: AnyStr) -> AnyStr:
  if len(x) <= 42:
    return x
  raise ValueError()

A TypeVar must have a descriptive name, unless it meets all of the following

  • not externally visible
  • not constrained
  _T = TypeVar("_T")
  AddableType = TypeVar("AddableType", int, float, str)
  AnyFunction = TypeVar("AnyFunction", bound=Callable)
  T = TypeVar("T")
  _T = TypeVar("_T", int, float, str)
  _F = TypeVar("_F", bound=Callable)

3.19.11 String types

Do not use typing.Text in new code. It’s only for Python 2/3 compatibility.

Use str for string/text data. For code that deals with binary data, use

def deals_with_text_data(x: str) -> str:
def deals_with_binary_data(x: bytes) -> bytes:

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

3.19.12 Imports For Typing

For symbols from the typing and modules used to support
static analysis and type checking, always import the symbol itself. This keeps
common annotations more concise and matches typing practices used around the
world. You are explicitly allowed to import multiple specific classes on one
line from the typing and modules. Ex:

from import Mapping, Sequence
from typing import Any, Union

Given that this way of importing adds items to the local namespace, names in
typing or 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.

from typing import Any as AnyType

Prefer to use built-in types as annotations where available. Python supports
type annotations using parametric container types via
PEP-585, introduced in Python 3.9.

def generate_foo_scores(foo: set[str]) -> list[float]:

NOTE: Users of Apache Beam should
continue to import parametric containers from typing.

from typing import Set, List

# Only use this older style if you are required to by introspection
# code such as Apache Beam that has not yet been updated for PEP-585,
# or if your code needs to run on Python versions earlier than 3.9.
def generate_foo_scores(foo: Set[str]) -> List[float]:

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

  • Conditionally imported types need to be referenced as strings, to be forward
    compatible with Python 3.6 where the annotation expressions are actually
  • 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

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.

from typing import Any

some_mod = Any  # 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.

# Yes:
def get_names(employee_ids: list[int]) -> dict[int, Any]:
# No:
# 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

# No:
def get_names(employee_ids: list[Any]) -> dict[Any, str]:
  """Returns a mapping from employee ID to employee name for given IDs."""
# Yes:
_T = TypeVar('_T')
def get_names(employee_ids: list[_T]) -> dict[_T, str]:
  """Returns a mapping from employee ID to employee name for given IDs."""

4 Parting Words


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.

Read More