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 Black or Pyink auto-formatter to avoid arguing over formatting.
Run pylint over your code using this pylintrc.
pylint
is a tool for finding bugs and style problems in Python source code. It finds
problems that are typically caught by a compiler for less dynamic languages like
C and C++. Because of the dynamic nature of Python, some
warnings may be incorrect; however, spurious warnings should be fairly
infrequent.
Catches easy-to-miss errors like typos, using-vars-before-assignment, etc.
pylint
isn’t perfect. To take advantage of it, sometimes we’ll need to write around it,
suppress its warnings or fix it.
Make sure you run
pylint
on your code.
Suppress warnings if they are inappropriate so that other issues are not hidden. To suppress warnings, you can set a line-level comment:
def do_PUT(self): # WSGI name, so pylint: disable=invalid-name
...
pylint
warnings are each identified by symbolic name (empty-docstring)
Google-specific warnings start with g-.
If the reason for the suppression is not clear from the symbolic name, add an explanation.
Suppressing in this way has the advantage that we can easily search for suppressions and revisit them.
You can get a list of
pylint
warnings by doing:
pylint --list-msgs
To get more information on a particular message, use:
pylint --help-msg=invalid-name
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: str | None = 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.
Use import statements for packages and modules only, not for individual types,
classes, or functions.
Reusability mechanism for sharing code from one module to another.
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.
Module names can still collide. Some module names are inconveniently long.
import x for importing packages and modules.from x import y where x is the package prefix and y is the module
name with no prefix.from x import y as z in any of the following circumstances:
y are to be imported.y conflicts with a top-level name defined in the current module.y conflicts with a common parameter name that is part of the public
API (e.g., features).y is an inconveniently long name.y is too generic in the context of your code (e.g., from
storage.file_system import options as fs_options).import y as z only when z is a standard abbreviation (e.g., import
numpy as np).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 from this rule:
Import each module using the full pathname location of the module.
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.
Makes it harder to deploy code because you have to replicate the package hierarchy. Not really a problem with modern deployment mechanisms.
All new code should import each module by its full package name.
Imports should be as follows:
Yes:
# Reference absl.flags in code with the complete name (verbose).
import absl.flags
from doctor.who import jodie
_FOO = absl.flags.DEFINE_string(...)
Yes:
# Reference flags in code with just the module name (common).
from absl import flags
from doctor.who import jodie
_FOO = flags.DEFINE_string(...)
(assume this file lives in doctor/who/ where jodie.py also exists)
No:
# Unclear what module the author wanted and what will be imported. The actual
# import behavior depends on external factors controlling sys.path.
# Which possible jodie module did the author intend to import?
import jodie
The directory the main binary is located in should not be assumed to be in
sys.path despite that happening in some environments. This being the case,
code should assume that import jodie refers to a third-party or top-level
package named jodie, not a local jodie.py.
Exceptions are allowed but must be used carefully.
Exceptions are a means of breaking out of normal control flow to handle errors or other exceptional conditions.
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.
May cause the control flow to be confusing. Easy to miss error cases when making library calls.
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 may happen when validating function arguments.
Do not use assert statements in place of conditionals or validating
preconditions. They must not be critical to the application logic. A litmus
test would be that the assert could be removed without breaking the code.
assert conditionals are
not guaranteed
to be evaluated. For pytest based tests, assert is
okay and expected to verify expectations. For
example:
Yes:
def connect_to_next_port(self, minimum: int) -> int:
"""Connects to the next available port.
Args:
minimum: A port value greater or equal to 1024.
Returns:
The new minimum port.
Raises:
ConnectionError: If no available port is found.
"""
if minimum < 1024:
# Note that this raising of ValueError is not mentioned in the doc
# string's "Raises:" section because it is not appropriate to
# guarantee this specific behavioral reaction to API misuse.
raise ValueError(f'Min. port must be at least 1024, not {minimum}.')
port = self._find_next_open_port(minimum)
if port is None:
raise ConnectionError(
f'Could not connect to service on port {minimum} or higher.')
# The code does not depend on the result of this assert.
assert port >= minimum, (
f'Unexpected port {port} when minimum was {minimum}.')
return port
No:
def connect_to_next_port(self, minimum: int) -> int:
"""Connects to the next available port.
Args:
minimum: A port value greater or equal to 1024.
Returns:
The new minimum port.
"""
assert minimum >= 1024, 'Minimum port must be at least 1024.'
# The following code depends on the previous assert.
port = self._find_next_open_port(minimum)
assert port is not None
# The type checking of the return statement relies on the assert.
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
Python is very tolerant in this regard and except: will really catch
everything including misspelled names, sys.exit() calls, Ctrl+C interrupts,
unittest failures and all kinds of other exceptions that you simply don’t
want to catch.
Minimize the amount of code in a try/except block. The larger the body
of the try, the more likely that an exception will be raised by a line of
code that you didn’t expect to raise an exception. In those cases, the
try/except block hides a real error.
Use the finally clause to execute code whether or not an exception is
raised in the try block. This is often useful for cleanup, i.e., closing a
file.
Avoid mutable global state.
Module-level values or class attributes that can get mutated during program execution.
Occasionally useful.
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.
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
below.
Nested local functions or classes are fine when used to close over a local variable. Inner classes are fine.
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.
Allows definition of utility classes and functions that are only used inside of a very limited scope. Very ADT-y. Commonly used for implementing decorators.
Nested functions and classes cannot be directly tested. Nesting can make the outer function longer and less readable.
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.
Okay to use for simple cases.
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.
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.
Complicated comprehensions or generator expressions can be hard to read.
Comprehensions are allowed, however multiple for clauses or filter expressions
are not permitted. Optimize for readability, not conciseness.
Yes:
result = [mapping_expr for value in iterable if filter_expr]
result = [
is_valid(metric={'key': value})
for value in interesting_iterable
if a_longer_filter_expression(value)
]
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
}
return (x**2 for x in range(10))
unique_names = {user.name for user in users if user is not None}
No:
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
)
Use default iterators and operators for types that support them, like lists, dictionaries, and files.
Container types, like dictionaries and lists, define default iterators and membership test operators (“in” and “not in”).
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.
You can’t tell the type of objects by reading the method names (unless the variable has type annotations). This is also an advantage.
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(): ...
Use generators as needed.
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.
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.
Local variables in the generator will not be garbage collected until the generator is either consumed to exhaustion or itself garbage collected.
Fine. Use “Yields:” rather than “Returns:” in the docstring for generator functions.
If the generator manages an expensive resource, make sure to force the clean up.
A good way to do the clean up is by wrapping the generator with a context manager PEP-0533.
Okay for one-liners. Prefer generator expressions over map() or filter()
with a lambda.
Lambdas define anonymous functions in an expression, as opposed to a statement.
Convenient.
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.
Lambdas are allowed. If the code inside the lambda function spans multiple lines or is longer than 60-80 chars, it might be 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.
Okay for simple cases.
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.
Shorter and more convenient than an if statement.
May be harder to read than an if statement. The condition may be difficult to locate if the expression is long.
Okay to use for simple cases. Each portion must fit on one line: true-expression, if-expression, else-expression. Use a complete if statement when things get more complicated.
Yes:
one_line = 'yes' if predicate(value) else 'no'
slightly_split = ('yes' if predicate(value)
else 'no, nein, nyet')
the_longest_ternary_style_that_can_be_done = (
'yes, true, affirmative, confirmed, correct'
if predicate(value)
else 'no, false, negative, nay')
No:
bad_line_breaking = ('yes' if predicate(value) else
'no')
portion_too_long = ('yes'
if some_long_module.some_long_predicate_function(
really_long_variable_name)
else 'no, false, negative, nay')
Okay in most cases.
You can specify values for variables at the end of a function’s parameter list,
e.g., def foo(a, b=0):. If foo is called with only one argument, b is set
to 0. If it is called with two arguments, b has the value of the second
argument.
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.
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.
Okay to use with the following caveat:
Do not use mutable objects as default values in the function or method definition.
Yes: def foo(a, b=None):
if b is None:
b = []
Yes: def foo(a, b: Sequence | None = 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()): # Is `b` supposed to represent when this 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.
...
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.
A way to wrap method calls for getting and setting an attribute as a standard attribute access.
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.
Use the “implicit” false if at all possible (with a few caveats).
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.
Conditions using Python booleans are easier to read and less error-prone. In most cases, they’re also faster.
May look strange to C/C++ developers.
Use the “implicit” false if possible, e.g., if foo: rather than if foo !=
[]:. There are a few caveats that you should keep in mind though:
Always use if foo is None: (or is not None) to check for a None value.
E.g., when testing whether a variable or argument that defaults to None
was set to some other value. The other value might be a value that’s false
in a boolean context!
Never compare a boolean variable to False using ==. Use if not x:
instead. If you need to distinguish False from None then chain the
expressions, such as if not x and x is not None:.
For sequences (strings, lists, tuples), use the fact that empty sequences
are false, so if seq: and if not seq: are preferable to if len(seq):
and if not len(seq): respectively.
When handling integers, implicit false may involve more risk than benefit
(i.e., accidentally handling None as 0). You may compare a value which is
known to be an integer (and is not the result of len()) against the
integer 0.
Yes: if not users:
print('no users')
if i % 10 == 0:
self.handle_multiple_of_ten()
def f(x=None):
if x is None:
x = []
No: if len(users) == 0:
print('no users')
if not i % 10:
self.handle_multiple_of_ten()
def f(x=None):
x = x or []
Note that '0' (i.e., 0 as string) evaluates to true.
Note that Numpy arrays may raise an exception in an implicit boolean
context. Prefer the .size attribute when testing emptiness of a np.array
(e.g. if not users.size).
Okay to use.
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
Often results in clearer, more elegant code. Especially comforting to experienced Lisp and Scheme (and Haskell and ML and …) programmers.
Can lead to confusing bugs, such as this example based on PEP-0227:
i = 4
def foo(x: Iterable[int]):
def bar():
print(i, end='')
# ...
# A bunch of code here
# ...
for i in x: # Ah, i *is* local to foo, so this is what bar sees
print(i, end='')
bar()
So foo([1, 2, 3]) will print 1 2 3 3,
not 1 2 3 4.
Okay to use.
Use decorators judiciously when there is a clear advantage. Avoid staticmethod
and limit use of classmethod.
Decorators for Functions and Methods
(a.k.a “the @ notation”). One common decorator is @property, used for
converting ordinary methods into dynamically computed attributes. However, the
decorator syntax allows for user-defined decorators as well. Specifically, for
some function my_decorator, this:
class C:
@my_decorator
def method(self):
# method body ...
is equivalent to:
class C:
def method(self):
# method body ...
method = my_decorator(method)
Elegantly specifies some transformation on a method; the transformation might eliminate some repetitive code, enforce invariants, etc.
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.
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
data between threads. Otherwise, use the threading module and its locking
primitives. Prefer condition variables and threading.Condition instead of
using lower-level locks.
Avoid these features.
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.
These are powerful language features. They can make your code more compact.
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.
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).
New language version semantic changes may be gated behind a special future import to enable them on a per-file basis within earlier runtimes.
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.
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 upgrades.
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.
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 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.
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.
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()
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.
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.
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.
Do not terminate your lines with semicolons, and do not use semicolons to put two statements on the same line.
Maximum line length is 80 characters.
Explicit exceptions to the 80 character limit:
# pylint: disable=invalid-name)Do not use a backslash for explicit line continuation.
Instead, 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.
Note that this rule doesn’t prohibit backslash-escaped newlines within strings (see below).
Yes: foo_bar(self, width, height, color='black', design=None, x='foo',
emphasis=None, highlight=0)
Yes: if (width == 0 and height == 0 and
color == 'red' and emphasis == 'strong'):
(bridge_questions.clarification_on
.average_airspeed_of.unladen_swallow) = 'African or European?'
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: if width == 0 and height == 0 and \
color == 'red' and emphasis == 'strong':
bridge_questions.clarification_on \
.average_airspeed_of.unladen_swallow = 'African or European?'
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)
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')
Prefer to break lines at the highest possible syntactic level. If you must break a line twice, break it at the same syntactic level both times.
Yes: bridgekeeper.answer(
name="Arthur", quest=questlib.find(owner="Arthur", perilous=True))
answer = (a_long_line().of_chained_methods()
.that_eventually_provides().an_answer())
if (
config is None
or 'editor.language' not in config
or config['editor.language'].use_spaces is False
):
use_tabs()
No: bridgekeeper.answer(name="Arthur", quest=questlib.find(
owner="Arthur", perilous=True))
answer = a_long_line().of_chained_methods().that_eventually_provides(
).an_answer()
if (config is None or 'editor.language' not in config or config[
'editor.language'].use_spaces is False):
use_tabs()
Within comments, put long URLs on their own line if necessary.
Yes: # See details at
# http://www.example.com/us/developer/documentation/api/content/v2.0/csv_file_name_extension_full_specification.html
No: # See details at
# http://www.example.com/us/developer/documentation/api/content/\
# v2.0/csv_file_name_extension_full_specification.html
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 Black or Pyink 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.
Use parentheses sparingly.
It is fine, though not required, to use parentheses around tuples. Do not use them in return statements or conditional statements unless using parentheses for implied line continuation or to indicate a tuple.
Yes: if foo:
bar()
while x:
x = bar()
if x and y:
bar()
if not x:
bar()
# For a 1 item tuple the ()s are more visually obvious than the comma.
onesie = (foo,)
return foo
return spam, beans
return (spam, beans)
for (x, y) in dict.items(): ...
No: if (x):
bar()
if not(x):
bar()
return (foo)
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,
beans)
# Aligned with opening delimiter in a dictionary.
foo = {
'long_dictionary_key': value1 +
value2,
...
}
# 4-space hanging indent; nothing on first line.
foo = long_function_name(
var_one, var_two, var_three,
var_four)
meal = (
spam,
beans)
# 4-space hanging indent; nothing on first line,
# closing parenthesis on a new line.
foo = long_function_name(
var_one, var_two, var_three,
var_four
)
meal = (
spam,
beans,
)
# 4-space hanging indent in a dictionary.
foo = {
'long_dictionary_key':
long_dictionary_value,
...
}
No: # Stuff on first line forbidden.
foo = long_function_name(var_one, var_two,
var_three, var_four)
meal = (spam,
beans)
# 2-space hanging indent forbidden.
foo = long_function_name(
var_one, var_two, var_three,
var_four)
# No hanging indent in a dictionary.
foo = {
'long_dictionary_key':
long_dictionary_value,
...
}
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, as well as for tuples with a single element. The presence of a trailing
comma is also used as a hint to our Python code auto-formatter
Black or Pyink
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]
golomb4 = [
0,
1,
4,
6,
]
No: golomb4 = [
0,
1,
4,
6,]
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.
Follow standard typographic rules for the use of spaces around punctuation.
No whitespace inside parentheses, brackets or braces.
Yes: spam(ham[1], {'eggs': 2}, [])
No: spam( ham[ 1 ], { 'eggs': 2 }, [ ] )
No whitespace before a comma, semicolon, or colon. Do use whitespace after a comma, semicolon, or colon, except at the end of the line.
Yes: if x == 4:
print(x, y)
x, y = y, x
No: if x == 4 :
print(x , y)
x , y = y , x
No whitespace before the open paren/bracket that starts an argument list, indexing or slicing.
Yes: spam(1)
No: spam (1)
Yes: dict['key'] = list[index]
No: dict ['key'] = list [index]
No trailing whitespace.
Surround binary operators with a single space on either side for assignment
(=), comparisons (==, <, >, !=, <>, <=, >=, in, not in, is, is not), and
Booleans (and, or, not). Use your better judgment for the insertion of spaces
around arithmetic operators (+, -, *, /, //, %, **, @).
Yes: x == 1
No: x<1
Never use spaces around = when passing keyword arguments or defining a default
parameter value, with one exception:
when a type annotation is present, do use spaces
around the = for the default parameter value.
Yes: def complex(real, imag=0.0): return Magic(r=real, i=imag)
Yes: def complex(real, imag: float = 0.0): return Magic(r=real, i=imag)
No: def complex(real, imag = 0.0): return Magic(r = real, i = imag)
No: def complex(real, imag: float=0.0): return Magic(r = real, i = imag)
Don’t use spaces to vertically align tokens on consecutive lines, since it
becomes a maintenance burden (applies to :, #, =, etc.):
Yes:
foo = 1000 # comment
long_name = 2 # comment that should not be aligned
dictionary = {
'foo': 1,
'long_name': 2,
}
No:
foo = 1000 # comment
long_name = 2 # comment that should not be aligned
dictionary = {
'foo' : 1,
'long_name': 2,
}
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 inline comments.
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.
Every file should contain license boilerplate. Choose the appropriate boilerplate for the license used by the project (for example, Apache 2.0, BSD, LGPL, GPL).
Files should start with a docstring describing the contents and usage of the module.
"""A one-line summary of the module or program, terminated by a period.
Leave one blank line. The rest of this docstring should contain an
overall description of the module or program. Optionally, it may also
contain a brief description of exported classes and functions and/or usage
examples.
Typical usage example:
foo = ClassFoo()
bar = foo.FunctionBar()
"""
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`
directory.
"""
Docstrings that do not provide any new information should not be used.
"""Tests for foo.bar."""
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:
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 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.""").
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.
*foo (variable length argument lists) and/or **bar
(arbitrary keyword arguments), they should be listed as *foo and **bar."""Returns row
from Bigtable as a tuple of strings.""") and the opening sentence is
sufficient to describe the return value. Do not imitate older ‘NumPy style’
(example),
which frequently documented 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). If the function uses yield (is a
generator), the Yields: section should document the object returned by
next(), instead of the generator object itself that the call evaluates to.def fetch_smalltable_rows(
table_handle: smalltable.Table,
keys: Sequence[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.
Args:
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
returned.
Returns:
A dict mapping keys to the corresponding table row data
fetched. Each row is represented as a tuple of strings. For
example:
{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).
Raises:
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[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.
Args:
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 returned.
Returns:
A dict mapping keys to the corresponding table row data
fetched. Each row is represented as a tuple of strings. For
example:
{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).
Raises:
IOError: An error occurred accessing the smalltable.
"""
A method that overrides a method from a base class does not need a docstring if
it is explicitly decorated with
@override
(from typing_extensions or typing modules), unless the overriding method’s
behavior materially refines the base method’s contract, or details need to be
provided (e.g., documenting additional side effects), in which case a docstring
with at least those differences is required on the overriding method.
from typing_extensions import override
class Parent:
def do_something(self):
"""Parent method, includes docstring."""
# Child class, method annotated with override.
class Child(Parent):
@override
def do_something(self):
pass
# Child class, but without @override decorator, a docstring is required.
class Child(Parent):
def do_something(self):
pass
# Docstring is trivial, @override is sufficient to indicate that docs can be
# found in the base class.
class Child(Parent):
@override
def do_something(self):
"""See base class."""
Classes should have a docstring below the class definition describing the class.
Public attributes, excluding properties, 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...
Attributes:
likes_spam: A boolean indicating if we like SPAM or not.
eggs: An integer count of the eggs we have laid.
"""
def __init__(self, likes_spam: bool = False):
"""Initializes the instance based on spam preference.
Args:
likes_spam: Defines if instance exhibits this preference.
"""
self.likes_spam = likes_spam
self.eggs = 0
@property
def butter_sticks(self) -> int:
"""The number of butter sticks we have."""
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."""
The final place to have comments is in tricky parts of the code. If you’re going to have to explain it at the next code review, you should comment it now. Complicated operations get a few lines of comments before the operations commence. Non-obvious ones get comments at the end of the line.
# We use a weighted dictionary search to find out where i is in
# the array. We extrapolate position based on the largest num
# in the array and the array size and then do binary search to
# get the exact number.
if i & (i-1) == 0: # True if i is 0 or a power of 2.
To improve legibility, these comments should start at least 2 spaces away from
the code with the comment character #, followed by at least one space before
the text of the comment itself.
On the other hand, never describe the code. Assume the person reading the code knows Python (though not what you’re trying to do) better than you do.
# BAD COMMENT: Now go through the b array and make sure whenever i occurs
# the next element is i+1
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.
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 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: %(name)s; score: %(score)d' % {'name':name, 'score':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 = ['<table>']
for last_name, first_name in employee_list:
items.append('<tr><td>%s, %s</td></tr>' % (last_name, first_name))
items.append('</table>')
employee_table = ''.join(items)
No: employee_table = '<table>'
for last_name, first_name in employee_list:
employee_table += '<tr><td>%s, %s</td></tr>' % (last_name, first_name)
employee_table += '</table>'
Be consistent with your choice of string quote character within a file. Pick '
or " and stick with it. It is okay to use the other quote character on a
string to avoid the need to backslash-escape quote characters within the string.
Yes:
Python('Why are you hiding your eyes?')
Gollum("I'm scared of lint errors.")
Narrator('"Good!" thought a happy Python reviewer.')
No:
Python("Why are you hiding your eyes?")
Gollum('The lint. It burns. It burns us.')
Gollum("Always the great lint. Watching. Watching.")
Prefer """ for multi-line strings rather than '''. Projects may choose to
use ''' for all non-docstring multi-line strings if and only if they also use
' for regular strings. Docstrings must use """ regardless.
Multi-line strings do not flow with the indentation of the rest of the program.
If you need to avoid embedding extra space in the string, use either
concatenated single-line strings or a multi-line string with
textwrap.dedent()
to remove the initial space on each line:
No:
long_string = """This is pretty ugly.
Don't do this.
"""
Yes:
long_string = """This is fine if your use case can accept
extraneous leading spaces."""
Yes:
long_string = ("And this is fine if you cannot accept\n" +
"extraneous leading spaces.")
Yes:
long_string = ("And this too is fine if you cannot accept\n"
"extraneous leading spaces.")
Yes:
import textwrap
long_string = textwrap.dedent("""\
This is also fine, because textwrap.dedent()
will collapse common leading spaces in each line.""")
Note that using a backslash here does not violate the prohibition against explicit line continuation; in this case, the backslash is escaping a newline in a string literal.
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.
Yes:
import tensorflow as tf
logger = tf.get_logger()
logger.info('TensorFlow Version is: %s', tf.__version__)
Yes:
import os
from absl import logging
logging.info('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)
No:
import os
from absl import logging
logging.info('Current $PAGER is:')
logging.info(os.getenv('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}')
Error messages (such as: message strings on exceptions like ValueError, or
messages shown to the user) should follow three guidelines:
The message needs to precisely match the actual error condition.
Interpolated pieces need to always be clearly identifiable as such.
They should allow simple automated processing (e.g. grepping).
Yes:
if not 0 <= p <= 1:
raise ValueError(f'Not a probability: {p=}')
try:
os.rmdir(workdir)
except OSError as error:
logging.warning('Could not remove directory (reason: %r): %r',
error, workdir)
No:
if p < 0 or p > 1: # PROBLEM: also false for float('nan')!
raise ValueError(f'Not a probability: {p=}')
try:
os.rmdir(workdir)
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)
try:
os.rmdir(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)
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:
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:
__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.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:
print(line)
For file-like objects that do not support the with statement, use
contextlib.closing():
import contextlib
with contextlib.closing(urllib.urlopen("http://www.python.org/")) as front_page:
for line in front_page:
print(line)
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, a following colon, and
a link to a resource that contains the context, ideally a bug reference. A bug
reference is preferable because bugs are tracked and have follow-up comments.
Follow this piece of context with an explanatory string introduced with a hyphen
-.
The purpose is to have a consistent TODO format that can be searched to find
out how to get more details.
# TODO: crbug.com/192795 - Investigate cpufreq optimizations.
Old style, formerly recommended, but discouraged for use in new code:
# TODO(crbug.com/192795): Investigate cpufreq optimizations.
# TODO(yourusername): Use a "\*" here for concatenation operator.
Avoid adding TODOs that refer to an individual or team as the context:
# 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. Issues are ideal for tracking this.
Imports should be on separate lines; there are
exceptions for typing and collections.abc imports.
E.g.:
Yes: from collections.abc import Mapping, Sequence
import os
import sys
from typing import Any, NewType
No: import os, sys
Imports are always put at the top of the file, just after any module comments and docstrings and before module globals and constants. Imports should be grouped from most generic to least generic:
Python future import statements. For example:
from __future__ import annotations
See above for more information about those.
Python standard library imports. For example:
import sys
third-party module or package imports. For example:
import tensorflow as tf
Code repository sub-package imports. For example:
from otherproject.ai import mind
Deprecated: application-specific imports that are part of the same top-level sub-package as this file. For example:
from myproject.backend.hgwells import time_machine
You may find older Google Python Style code doing this, but it is no longer required. New code is encouraged not to bother with this. Simply treat application-specific sub-package imports the same as other sub-package imports.
Within each grouping, imports should be sorted lexicographically, ignoring case,
according to each module’s full package path (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 otherproject.ai import body
from otherproject.ai import mind
from otherproject.ai import soul
# Older style code may have these imports down here instead:
#from myproject.backend.hgwells import time_machine
#from myproject.backend.state_machine import main_loop
Generally only one statement per line.
However, you may put the result of a test on the same line as the test only if
the entire statement fits on one line. In particular, you can never do so with
try/except since the try and except can’t both fit on the same line, and
you can only do so with an if if there is no else.
Yes:
if foo: bar(foo)
No:
if foo: bar(foo)
else: baz(foo)
try: bar(foo)
except ValueError: baz(foo)
try:
bar(foo)
except ValueError: baz(foo)
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 setters.
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.
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,
send_acronym_via_https.
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.
single character names, except for specifically allowed cases:
i, j, k, v, et al.)e as an exception identifier in try/except statements.f as a file handle in with statements_T = TypeVar("_T"), _P = ParamSpec("_P"))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)
“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). Note
that it is okay for unit tests to access protected constants from the
modules under test.
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?”)
New unit test files follow PEP 8 compliant lower_with_under method
names, for example, test_<method_under_test>_<state>. For consistency(*)
with legacy modules that follow CapWords function names, underscores may
appear in method names starting with test to separate logical components
of the name. One possible pattern is test<MethodUnderTest>_<state>.
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 "$0.py" "$@".
| 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 |
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.
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:
from absl import app
...
def main(argv: Sequence[str]):
# process non-flag arguments
...
if __name__ == '__main__':
app.run(main)
Otherwise, use:
def main():
...
if __name__ == '__main__':
main()
All code at the top level will be executed when the module is imported. Be
careful not to call functions, create objects, or perform other operations that
should not be executed when the file is being pydoced.
Prefer small and focused functions.
We recognize that long functions are sometimes appropriate, so no hard limit is placed on function length. If a function exceeds about 40 lines, think about whether it can be broken up without harming the structure of the program.
Even if your long function works perfectly now, someone modifying it in a few months may add new behavior. This could result in bugs that are hard to find. Keeping your functions short and simple makes it easier for other people to read and modify your code.
You could find long and complicated functions when working with some code. Do not be intimidated by modifying existing code: if working with such a function proves to be difficult, you find that errors are hard to debug, or you want to use a piece of it in several different contexts, consider breaking up the function into smaller and more manageable pieces.
Familiarize yourself with PEP-484.
Annotating self or cls is generally not necessary.
Self can be
used if it is necessary for proper type information, e.g.
from typing import Self
class BaseClass:
@classmethod
def create(cls) -> Self:
...
def difference(self, other: Self) -> float:
...
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.
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(
self,
first_var: int,
second_var: Foo,
third_var: Bar | None,
) -> int:
...
Always prefer breaking between variables, and not, for example, between variable names and type annotations. However, if everything fits on the same line, go for it.
def my_method(self, first_var: int) -> int:
...
If the combination of the function name, the last parameter, and the return type
is too long, indent by 4 in a new line. When using line breaks, prefer putting
each parameter and the return type on their own lines and aligning the closing
parenthesis with the def:
Yes:
def my_method(
self,
other_arg: MyLongType | None,
) -> tuple[MyLongType1, MyLongType1]:
...
Optionally, the return type may be put on the same line as the last parameter:
Okay:
def my_method(
self,
first_var: int,
second_var: int) -> dict[OtherLongType, MyLongType]:
...
pylint
allows you to move the closing parenthesis to a new line and align with the
opening one, but this is less readable.
No:
def my_method(self,
other_arg: MyLongType | None,
) -> dict[OtherLongType, MyLongType]:
...
As in the examples above, prefer not to break types. However, sometimes they are too long to be on a single line (try to keep sub-types unbroken).
def my_method(
self,
first_var: tuple[list[MyLongType1],
list[MyLongType2]],
second_var: list[dict[
MyLongType3, MyLongType4]],
) -> None:
...
If a single name and type is too long, consider using an alias for the type. The last resort is to break after the colon and indent by 4.
Yes:
def my_function(
long_variable_name:
long_module_name.LongTypeName,
) -> None:
...
No:
def my_function(
long_variable_name: long_module_name.
LongTypeName,
) -> None:
...
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.
Yes:
from __future__ import annotations
class MyClass:
def __init__(self, stack: Sequence[MyClass], item: OtherClass) -> None:
class OtherClass:
...
Yes:
class MyClass:
def __init__(self, stack: Sequence['MyClass'], item: 'OtherClass') -> None:
class OtherClass:
...
As per PEP-008, use
spaces around the = only for arguments that have both a type annotation and
a default value.
Yes:
def func(a: int = 0) -> int:
...
No:
def func(a:int=0) -> int:
...
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 type expressions (recommended in new
Python 3.10+ code), or the older Optional and Union syntaxes.
Use explicit X | None instead of implicit. Earlier versions of PEP 484 allowed
a: str = None to be interpreted as a: str | None = None, but that is no
longer the preferred behavior.
Yes:
def modern_or_union(a: str | int | None, b: str | None = None) -> str:
...
def union_optional(a: Union[str, int, None], b: Optional[str] = None) -> str:
...
No:
def nullable_union(a: Union[None, str]) -> str:
...
def implicit_optional(a: str = None) -> str:
...
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.
Note that the : TypeAlias annotation is only supported in versions 3.10+.
from typing import TypeAlias
_LossAndGradient: TypeAlias = tuple[tf.Tensor, tf.Tensor]
ComplexTFMap: TypeAlias = Mapping[str, _LossAndGradient]
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):
# pytype: disable=attribute-error
a: Foo = SomeUndecoratedFunction()
# type: <type name>
comment on the end of the line:
a = SomeUndecoratedFunction() # type: Foo
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)
The Python type system has
generics. A type variable, such as
TypeVar and ParamSpec, is a common way to use them.
Example:
from collections.abc import Callable
from typing import ParamSpec, TypeVar
_P = ParamSpec("_P")
_T = TypeVar("_T")
...
def next(l: list[_T]) -> _T:
return l.pop()
def print_when_called(f: Callable[_P, _T]) -> Callable[_P, _T]:
def inner(*args: _P.args, **kwargs: _P.kwargs) -> _T:
print("Function was called")
return f(*args, **kwargs)
return inner
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 type variable must have a descriptive name, unless it meets all of the following criteria:
Yes:
_T = TypeVar("_T")
_P = ParamSpec("_P")
AddableType = TypeVar("AddableType", int, float, str)
AnyFunction = TypeVar("AnyFunction", bound=Callable)
No:
T = TypeVar("T")
P = ParamSpec("P")
_T = TypeVar("_T", int, float, str)
_F = TypeVar("_F", bound=Callable)
Do not use
typing.Textin new code. It’s only for Python 2/3 compatibility.
Use str for string/text data. For code that deals with binary data, use
bytes.
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 AnyStr.
For symbols (including types, functions, and constants) from the typing or
collections.abc 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 symbols on one line from the typing and
collections.abc modules. For example:
from collections.abc import Mapping, Sequence
from typing import Any, Generic, cast, TYPE_CHECKING
Given that this way of importing adds items to the local namespace, names in
typing or collections.abc 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]:
...
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.
import typing
if typing.TYPE_CHECKING:
import sketch
def f(x: "sketch.Sketch"): ...
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 # some_mod.py imports this module.
...
def my_method(self, var: "some_mod.SomeType") -> None:
...
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: Sequence[int]) -> Mapping[int, str]:
...
# No:
# This is interpreted as get_names(employee_ids: Sequence[Any]) -> Mapping[Any, Any]
def get_names(employee_ids: Sequence) -> Mapping:
...
If the best type parameter for a generic is Any, make it explicit, but
remember that in many cases TypeVar might be more
appropriate:
# No:
def get_names(employee_ids: Sequence[Any]) -> Mapping[Any, str]:
"""Returns a mapping from employee ID to employee name for given IDs."""
# Yes:
_T = TypeVar('_T')
def get_names(employee_ids: Sequence[_T]) -> Mapping[_T, str]:
"""Returns a mapping from employee ID to employee name for given IDs."""
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 _idx suffixes in index variable names, 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.
However, there are limits to consistency. It applies more heavily locally and on choices unspecified by the global style. Consistency should not generally be used as a justification to do things in an old style without considering the benefits of the new style, or the tendency of the codebase to converge on newer styles over time.