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.
Run pylint over your code.
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, we'll need to sometimes: a)
Write around it b) Suppress its warnings or c) Improve 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:
dict = 'something awful' # Bad Idea... pylint: disable=redefined-builtinpylint 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-msgsTo get more information on a particular message, use:
pylint --help-msg=C6409Prefer 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, beans, eggs=None):
del beans, eggs # Unused by vikings.
return spam + spam + spamOther common forms of suppressing this warning include using '_' as the
identifier for the unused argument, prefixing the argument name with
'unused_', or assigning them to '_'. These forms are allowed but no longer
encouraged. The first two break callers that pass arguments by name, while the
last does not enforce that the arguments are actually unused.
Use imports for packages and modules only, not for individual classes or
functions. Note that there is an explicit exemption for imports from the
typing module.
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.
- Use
import xfor importing packages and modules. - Use
from x import ywherexis the package prefix andyis the module name with no prefix. - Use
from x import y as zif two modules namedyare to be imported or ifyis an inconveniently long name. - Use
import y as zonly whenzis a standard abbreviation (e.g.,npfornumpy).
For example the module sound.effects.echo may be imported as follows:
from sound.effects import echo
...
echo.EchoFilter(input, output, delay=0.7, atten=4)Do not use relative names in imports. Even if the module is in the same package, use the full package name. This helps prevent unintentionally importing a package twice.
Imports from the typing module are exempt from this rule.
Import each module using the full pathname location of the module.
Avoids conflicts in module names. Makes it easier to find modules.
Makes it harder to deploy code because you have to replicate the package hierarchy.
All new code should import each module by its full package name.
Imports should be as follows:
# Reference in code with complete name.
import absl.flags
# Reference in code with just module name (preferred).
from absl import flagsExceptions are allowed but must be used carefully.
Exceptions are a means of breaking out of the normal flow of control of a code block 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 carry-through error codes.
May cause the control flow to be confusing. Easy to miss error cases when making library calls.
Exceptions must follow certain conditions:
-
Raise exceptions like this:
raise MyError('Error message')orraise MyError(). Do not use the two-argument form (raise MyError, 'Error message'). -
Make use of built-in exception classes when it makes sense. For example, raise a
ValueErrorif you were passed a negative number but were expecting a positive one. Do not useassertstatements for validating argument values of a public API.assertis used to ensure internal correctness, not to enforce correct usage nor to indicate that some unexpected event occurred. If an exception is desired in the latter cases, use a raise statement. For example:Yes: def connect_to_next_port(self, minimum): """Connects to the next available port. Args: minimum: A port value greater or equal to 1024. Raises: ValueError: If the minimum port specified is less than 1024. ConnectionError: If no available port is found. Returns: The new minimum port. """ if minimum < 1024: raise ValueError('Minimum port must be at least 1024, not %d.' % (minimum,)) port = self._find_next_open_port(minimum) if not port: raise ConnectionError('Could not connect to service on %d or higher.' % (minimum,)) assert port >= minimum, 'Unexpected port %d when minimum was %d.' % (port, minimum) return port
No: def connect_to_next_port(self, minimum): """Connects to the next available port. Args: minimum: A port value greater or equal to 1024. Returns: The new minimum port. """ assert minimum >= 1024, 'Minimum port must be at least 1024.' port = self._find_next_open_port(minimum) assert port is not None return port
-
Libraries or packages may define their own exceptions. When doing so they must inherit from an existing exception class. Exception names should end in
Errorand should not introduce stutter (foo.FooError). -
Never use catch-all
except:statements, or catchExceptionorStandardError, unless you are re-raising the exception or in the outermost block in your thread (and printing an error message). Python is very tolerant in this regard andexcept: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/exceptblock. The larger the body of thetry, 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, thetry/exceptblock hides a real error. -
Use the
finallyclause to execute code whether or not an exception is raised in thetryblock. This is often useful for cleanup, i.e., closing a file. -
When capturing an exception, use
asrather than a comma. For example:try: raise Error() except Error as error: pass
Avoid global variables.
Variables that are declared at the module level or as class attributes.
Occasionally useful.
Has the potential to change module behavior during the import, because assignments to global variables are done when the module is first imported.
Avoid global variables.
While they are technically variables, module-level constants are permitted and
encouraged. For example: MAX_HOLY_HANDGRENADE_COUNT = 3. Constants must be
named using all caps with underscores. See Naming below.
If needed, globals should be declared at the module level and made internal to
the module by prepending an _ to the name. External access must be done
through public module-level functions. See Naming below.
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.
Instances of nested or local classes cannot be pickled. Nested functions and classes cannot be directly tested. Nesting can make your outer function longer and less readable.
They are fine with some caveats. Avoid nested functions or classes except when closing over a local value. 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.
Okay to use for simple cases. Each portion must fit on one line: mapping
expression, for clause, filter expression. Multiple for clauses or filter
expressions are not permitted. Use loops instead when things get more
complicated.
Yes:
result = [mapping_expr for value in iterable if filter_expr]
result = [{'key': value} for value in iterable
if a_long_filter_expression(value)]
result = [complicated_transform(x)
for x in iterable if predicate(x)]
descriptive_name = [
transform({'key': key, 'value': value}, color='black')
for key, value in generate_iterable(some_input)
if complicated_condition_is_met(key, value)
]
result = []
for x in range(10):
for y in range(5):
if x * y > 10:
result.append((x, y))
return {x: complicated_transform(x)
for x in long_generator_function(parameter)
if x is not None}
squares_generator = (x**2 for x in range(10))
unique_names = {user.name for user in users if user is not None}
eat(jelly_bean for jelly_bean in jelly_beans
if jelly_bean.color == 'black')No:
result = [complicated_transform(
x, some_argument=x+1)
for x in iterable if predicate(x)]
result = [(x, y) for x in range(10) for y in range(5) if x * y > 10]
return ((x, y, z)
for x in xrange(5)
for y in xrange(5)
if x != y
for z in xrange(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 (e.g. has_key() means a dictionary). 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 key not in adict: ...
if obj in alist: ...
for line in afile: ...
for k, v in dict.iteritems(): ...No: for key in adict.keys(): ...
if not adict.has_key(key): ...
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.
None.
Fine. Use "Yields:" rather than "Returns:" in the docstring for generator functions.
Okay for one-liners.
Lambdas define anonymous functions in an expression, as opposed to a statement.
They are often used to define callbacks or operators for higher-order functions
like map() and filter().
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.
Okay to use them for one-liners. If the code inside the lambda function is any 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.
Okay for one-liners.
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 one-liners. In other cases prefer to use a complete if statement.
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-rarely-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. Also, Python does not support overloaded methods/functions and 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: Optional[Sequence] = None):
if b is None:
b = []
Yes: def foo(a, b: Sequence = ()): # Empty tuple OK since tuples are immutable
...No: def foo(a, b=[]):
...
No: def foo(a, b=time.time()): # The time the module was loaded???
...
No: def foo(a, b=FLAGS.my_thing): # sys.argv has not yet been parsed...
...Use properties for accessing or setting data where you would normally have used simple, lightweight accessor or setter methods.
A way to wrap method calls for getting and setting an attribute as a standard attribute access when the computation is lightweight.
Readability is increased by eliminating explicit get and set method calls for simple attribute access. Allows calculations to be lazy. Considered the Pythonic way to maintain the interface of a class. In terms of performance, allowing properties bypasses needing trivial accessor methods when a direct variable access is reasonable. This also allows accessor methods to be added in the future without breaking the interface.
Must inherit from object in Python 2. Can hide side-effects much like operator
overloading. Can be confusing for subclasses.
Use properties in new code to access or set data where you would normally have
used simple, lightweight accessor or setter methods. Properties should be
created with the @property decorator.
Inheritance with properties can be non-obvious if the property itself is not overridden. Thus one must make sure that accessor methods are called indirectly to ensure methods overridden in subclasses are called by the property (using the Template Method DP).
Yes: import math
class Square(object):
"""A square with two properties: a writable area and a read-only perimeter.
To use:
>>> sq = Square(3)
>>> sq.area
9
>>> sq.perimeter
12
>>> sq.area = 16
>>> sq.side
4
>>> sq.perimeter
16
"""
def __init__(self, side):
self.side = side
@property
def area(self):
"""Gets or sets the area of the square."""
return self._get_area()
@area.setter
def area(self, area):
return self._set_area(area)
def _get_area(self):
"""Indirect accessor to calculate the 'area' property."""
return self.side ** 2
def _set_area(self, area):
"""Indirect setter to set the 'area' property."""
self.side = math.sqrt(area)
@property
def perimeter(self):
return self.side * 4Use the "implicit" false if at all possible.
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 at all possible, e.g., if foo: rather than
if foo != []:. There are a few caveats that you should keep in mind though:
-
Never use
==or!=to compare singletons likeNone. Useisoris not. -
Beware of writing
if x:when you really meanif x is not None:-e.g., when testing whether a variable or argument that defaults toNonewas 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
Falseusing==. Useif not x:instead. If you need to distinguishFalsefromNonethen chain the expressions, such asif not x and x is not None:. -
For sequences (strings, lists, tuples), use the fact that empty sequences are false, so
if seq:andif not seq:are preferable toif len(seq):andif not len(seq):respectively. -
When handling integers, implicit false may involve more risk than benefit (i.e., accidentally handling
Noneas 0). You may compare a value which is known to be an integer (and is not the result oflen()) against the integer 0.Yes: if not users: print('no users') if foo == 0: self.handle_zero() if i % 10 == 0: self.handle_multiple_of_ten() def f(x=None): if x is None: x = []
No: if len(users) == 0: print('no users') if foo is not None and not foo: self.handle_zero() if not i % 10: self.handle_multiple_of_ten() def f(x=None): x = x or []
-
Note that
'0'(i.e.,0as string) evaluates to true.
Use string methods instead of the string module where possible. Use function
call syntax instead of apply. Use list comprehensions and for loops instead
of filter and map when the function argument would have been an inlined
lambda anyway. Use for loops instead of reduce.
Current versions of Python provide alternative constructs that people find generally preferable.
We do not use any Python version which does not support these features, so there is no reason not to use the new styles.
Yes: words = foo.split(':')
[x[1] for x in my_list if x[2] == 5]
map(math.sqrt, data) # Ok. No inlined lambda expression.
fn(*args, **kwargs)No: words = string.split(foo, ':')
map(lambda x: x[1], filter(lambda x: x[2] == 5, my_list))
apply(fn, args, kwargs)Okay to use.
A nested Python function can refer to variables defined in enclosing functions, but can not 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):
"""Returns a function that adds numbers to a given number."""
def adder(summand2):
return summand1 + summand2
return adderOften 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):
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(object):
@my_decorator
def method(self):
# method body ...is equivalent to:
class C(object):
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 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. Learn about the proper use of condition variables so you can use
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, 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, collections.namedtuple, dataclasses,
and enum).
Python 3 is here! While not every project is ready to use it yet, all code should be written to be 3 compatible (and tested under 3 when possible).
Python 3 is a significant change in the Python language. While existing code is often written with 2.7 in mind there are some simple things to do to make code more explicit about its intentions and thus better prepared for use under Python 3 without modification.
Code written with Python 3 in mind is more explicit and easier to get running under Python 3 once all of the dependencies of your project are ready.
Some people find the additional boilerplate to be ugly. It's unusual to add imports to a module that doesn't actually require the features added by the import.
Use of from __future__ import statements is encouraged. All new code should
contain the following and existing code should be updated to be compatible when
possible:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_functionIf you are not already familiar with those, read up on each here: absolute
imports, new / division
behavior, and the print
function.
Please don't omit or remove these imports, even if they're not currently used in the module. It is better to always have the future imports in all files so that they are not forgotten during later edits when someone starts using such a feature.
There are other from __future__ import statements. Use them as you see fit. We
do not include unicode_literals in our recommendations as it is not a clear
win due to implicit default codec conversion consequences it introduces in many
places within Python 2.7. Most code is better off with explicit use of b'' and
u'' bytes and unicode string literals as necessary.
When your project needs to actively support use under both Python 2 and 3, use of these libraries is encouraged as you see fit. They exist to make your code cleaner and life easier.
You can annotate Python 3 code with type hints according to PEP-484, and type-check the code at build time with a type checking tool like pytype.
Type annotations can be in the source or in a stub pyi file. Whenever possible, annotations should be in the source. Use pyi files for third-party or extension modules.
Type annotations (or "type hints") are for function or method arguments and return values:
def func(a: int) -> List[int]:You can also declare the type of a variable using a special comment:
a = SomeFunc() # type: SomeTypeType 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.
This highly depends on the complexity of your project. Give it a try.
Do not terminate your lines with semi-colons and do not use semi-colons to put two statements on the same line.
Maximum line length is 80 characters.
Exceptions:
- Long import statements.
- URLs, pathnames, or long flags in comments.
- Long string module level constants not containing whitespace that would be inconvenient to split across lines such as URLs or pathnames.
- Pylint disable comments. (e.g.:
# pylint: disable=invalid-name)
Do not use backslash line continuation except for with statements requiring
three or more context managers.
Make use of Python's implicit line joining inside parentheses, brackets and braces. If necessary, you can add an extra pair of parentheses around an expression.
Yes: foo_bar(self, width, height, color='black', design=None, x='foo',
emphasis=None, highlight=0)
if (width == 0 and height == 0 and
color == 'red' and emphasis == 'strong'):When a literal string won't fit on a single line, use parentheses for implicit line joining.
x = ('This will build a very long long '
'long long long long long long string')Within comments, put long URLs on their own line if necessary.
Yes: # See details at
# http://www.example.com/us/developer/documentation/api/content/v2.0/csv_file_name_extension_full_specification.htmlNo: # See details at
# http://www.example.com/us/developer/documentation/api/content/\
# v2.0/csv_file_name_extension_full_specification.htmlIt is permissible to use backslash continuation when defining a with statement
whose expressions span three or more lines. For two lines of expressions, use a
nested with statement:
Yes: with very_long_first_expression_function() as spam, \
very_long_second_expression_function() as beans, \
third_thing() as eggs:
place_order(eggs, beans, spam, beans)No: with VeryLongFirstExpressionFunction() as spam, \
VeryLongSecondExpressionFunction() as beans:
PlaceOrder(eggs, beans, spam, beans)Yes: with very_long_first_expression_function() as spam:
with very_long_second_expression_function() as beans:
place_order(beans, spam)Make note of the indentation of the elements in the line continuation examples above; see the indentation section for explanation.
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 or mix tabs and spaces. In cases of implied line continuation, you should align wrapped elements either vertically, as per the examples in the line length section; or using a hanging indent of 4 spaces, in which case there should be nothing after the open parenthesis or bracket on the first line.
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 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. The presence of a trailing comma is also used as a hint to our Python
code auto-formatter YAPF to direct it to auto-format the container
of items to one item per line when the , after the final element is present.
Yes: golomb3 = [0, 1, 3]
Yes: golomb4 = [
0,
1,
4,
6,
]No: golomb4 = [
0,
1,
4,
6
]Two blank lines between top-level definitions, be they function or class
definitions. One blank line between method definitions and between the class
line and the first method. Use single blank lines as you judge appropriate
within functions or methods.
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, xNo: if x == 4 :
print(x , y)
x , y = y , xNo 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]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 == 1No: x<1Never use spaces around the '=' sign when passing keyword arguments. Only use spaces around the '=' sign defining a default parameter value when a type annotation is present, do not use spaces around '=' for default parameter values otherwise.
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/python with an optional single digit 2 or 3 suffix per
PEP-394.
This line is used by the kernel to find the Python interpreter, but is ignored by Python when importing modules. It is only necessary on a file that will be executed directly.
Be sure to use the right style for module, function, method docstrings and inline comments.
Python uses docstrings for commenting code. A doc string is a string that is the
first statement in a package, module, class or function. These strings can be
extracted automatically through the __doc__ member of the object and are used
by pydoc.
(Try running pydoc on your module to see how it looks.) Always use the three
double-quote """ format for docstrings (per PEP
257).
A docstring should be organized as a summary line (one physical line) terminated
by a period, question mark, or exclamation point, followed by a blank line,
followed by the rest of the docstring starting at the same cursor position as
the first quote of the first line. There are more formatting guidelines for
docstrings below.
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)
As used in this section "function" applies to methods, functions, and generators.
A function must have a docstring, unless it meets all of the following criteria:
- not externally visible
- very short
- obvious
A docstring should give enough information to write a call to the function
without reading the function's code. The docstring should be descriptive
("""Fetches rows from a Bigtable.""") rather than imperative
("""Fetch rows from a Bigtable."""). A docstring should describe the
function's calling syntax and its semantics, not its implementation. For tricky
code, comments alongside the code are more appropriate than using docstrings.
A method that overrides a method from a base class may have a simple docstring
sending the reader to its overridden method's docstring, such as
"""See base class.""". The rationale is that there is no need to repeat in
many places documentation that is already present in the base method's
docstring. However, if the overriding method's behavior is substantially
different than that of the overridden method or details need to be provided
about it (e.g., documenting additional side-effects), a docstring is required on
the overriding method, with at least those differences.
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. Sections should be indented two spaces, except for the heading.
Args: {#doc-function-args}
: List each parameter by name. A description should follow the name, and be
: separated by a colon and a space. If the description is too long to fit on a
: single 80-character line, use a hanging indent of 2 or 4 spaces (be
: consistent with the rest of the file).
: The description should include required type(s) if the code does not contain
: a corresponding type annotation.
: If a function accepts *foo (variable length argument lists) and/or **bar
: (arbitrary keyword arguments), they should be listed as *foo and **bar.
Returns: (or Yields: for generators) {#doc-function-returns}
: Describe the type and semantics of the return value. If the function only
: returns None, this section is not required. It may also be omitted if the
: docstring starts with Returns (or Yields) (e.g.
: """Returns row from Bigtable as a tuple of strings.""") and the opening
: sentence is sufficient to describe return value.
Raises: {#doc-function-raises} : List all exceptions that are relevant to the interface.
def fetch_bigtable_rows(big_table, keys, other_silly_variable=None):
"""Fetches rows from a Bigtable.
Retrieves rows pertaining to the given keys from the Table instance
represented by big_table. Silly things may happen if
other_silly_variable is not None.
Args:
big_table: An open Bigtable Table instance.
keys: A sequence of strings representing the key of each table row
to fetch.
other_silly_variable: Another optional variable, that has a much
longer name than the other args, and which does nothing.
Returns:
A dict mapping keys to the corresponding table row data
fetched. Each row is represented as a tuple of strings. For
example:
{'Serak': ('Rigel VII', 'Preparer'),
'Zim': ('Irk', 'Invader'),
'Lrrr': ('Omicron Persei 8', 'Emperor')}
If a key from the keys argument is missing from the dictionary,
then that row was not found in the table.
Raises:
IOError: An error occurred accessing the bigtable.Table object.
"""Classes should have a docstring below the class definition describing the class.
If your class has public attributes, they should be documented here in an
Attributes section and follow the same formatting as a
function's Args section.
class SampleClass(object):
"""Summary of class here.
Longer class information....
Longer class information....
Attributes:
likes_spam: A boolean indicating if we like SPAM or not.
eggs: An integer count of the eggs we have laid.
"""
def __init__(self, likes_spam=False):
"""Inits SampleClass with blah."""
self.likes_spam = likes_spam
self.eggs = 0
def public_method(self):
"""Performs operation blah."""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 be at least 2 spaces away from the code.
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+1Pay 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.
If a class inherits from no other base classes, explicitly inherit from
object. This also applies to nested classes.
Yes: class SampleClass(object):
pass
class OuterClass(object):
class InnerClass(object):
pass
class ChildClass(ParentClass):
"""Explicitly inherits from another class already."""No: class SampleClass:
pass
class OuterClass:
class InnerClass:
passInheriting from object is needed to make properties work properly in Python 2,
and can protect your code from some potential incompatibility with Python 3. It
also defines special methods that implement the default semantics of objects
including __new__, __init__, __delattr__, __getattribute__,
__setattr__, __hash__, __repr__, and __str__.
Use the format method or the % operator for formatting strings, even when
the parameters are all strings. Use your best judgement to decide between +
and % (or format) though.
Yes: x = a + b
x = '%s, %s!' % (imperative, expletive)
x = '{}, {}'.format(first, second)
x = 'name: %s; score: %d' % (name, n)
x = 'name: {}; score: {}'.format(name, n)
x = f'name: {name}; score: {n}' # Python 3.6+No: x = '%s%s' % (a, b) # use + in this case
x = '{}{}'.format(a, b) # use + in this case
x = first + ', ' + second
x = 'name: ' + name + '; score: ' + str(n)Avoid using the + and += operators to accumulate a string within a loop.
Since strings are immutable, this creates unnecessary temporary objects and
results in quadratic rather than linear running time. Instead, add each
substring to a list and ''.join the list after the loop terminates (or, write
each substring to a io.BytesIO buffer).
Yes: items = ['<table>']
for last_name, first_name in employee_list:
items.append('<tr><td>%s, %s</td></tr>' % (last_name, first_name))
items.append('</table>')
employee_table = ''.join(items)No: employee_table = '<table>'
for last_name, first_name in employee_list:
employee_table += '<tr><td>%s, %s</td></tr>' % (last_name, first_name)
employee_table += '</table>'Be consistent with your choice of string quote character within a file. Pick '
or " and stick with it. It is okay to use the other quote character on a
string to avoid the need to \\ escape within the string. gpylint enforces
this.
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. Note that it is
often cleaner to use implicit line joining since multi-line strings do not flow
with the indentation of the rest of the program:
Yes:
print("This is much nicer.\n"
"Do it this way.\n") No:
print("""This is pretty ugly.
Don't do this.
""")Explicitly close files and sockets when done with them.
Leaving files, sockets or other file-like objects open unnecessarily has many downsides, including:
- 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 being performed on them, such as moves or deletion.
- Files and sockets that are shared throughout a program may inadvertently be read from or written to after logically being closed. If they are actually closed, attempts to read or write from them will throw exceptions, making the problem known sooner.
Furthermore, while files and sockets are automatically closed when the file object is destructed, tying the life-time of the file object to the state of the file is poor practice, for several reasons:
- There are no guarantees as to when the runtime will actually run the file's destructor. Different Python implementations use different memory management techniques, such as delayed Garbage Collection, which may increase the object's lifetime arbitrarily and indefinitely.
- Unexpected references to the file may keep it around longer than intended (e.g. in tracebacks of exceptions, inside globals, etc).
The preferred way to manage files is using the "with" statement:
with open("hello.txt") as hello_file:
for line in hello_file:
print(line)For file-like objects that do not support the "with" statement, use
contextlib.closing():
import contextlib
with contextlib.closing(urllib.urlopen("http://www.python.org/")) as front_page:
for line in front_page:
print(line)Use TODO comments for code that is temporary, a short-term solution, or
good-enough but not perfect.
TODOs should include the string TODO in all caps, followed by the
name, e-mail address, or other identifier
of the person or issue with the best context about the problem referenced by the
TODO, in parentheses. A comment explaining what there is to do is required.
The main purpose is to have a consistent TODO format that can be searched to
find out how to get more details upon request. A TODO is not a commitment that
the person referenced will fix the problem. Thus when you create a TODO, it is almost always your name that is given.
# TODO(kl@gmail.com): Use a "*" here for string repetition.
# TODO(Zeke) Change this to use relations.If your TODO is of the form "At a future date do something" make sure that you
either include a very specific date ("Fix by November 2009") or a very specific
event ("Remove this code when all clients can handle XML responses.").
Imports should be on separate lines.
E.g.:
Yes: import os
import sysNo: import os, sysImports 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 with the order being most generic to least generic:
-
Python standard library imports. For example:
import sys
-
third-party module or package imports. For example:
import tensorflow as tf
-
Code repository sub-package imports. For example:
from otherproject.ai import mind
-
Deprecated: application-specific imports that are part of the same top level sub-package as this file. For example:
from myproject.backend.hgwells import time_machine
You may find older Google Python Style code doing this, but it is no longer required. New code is encouraged not to bother with this. Simply treat application-specific sub-package imports the same as other sub-package imports.
Within each grouping, imports should be sorted lexicographically, ignoring case, according to each module's full package path. Code may optionally place a blank line between import sections.
import collections
import queue
import sys
from absl import app
from absl import flags
import bs4
import cryptography
import tensorflow as tf
from book.genres import scifi
from myproject.backend.hgwells import time_machine
from myproject.backend.state_machine import main_loop
from otherproject.ai import body
from otherproject.ai import mind
from otherproject.ai import soul
# Older style code may have these imports down here instead:
#from myproject.backend.hgwells import time_machine
#from myproject.backend.state_machine import main_loopGenerally 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)If an accessor function would be trivial you should use public variables instead
of accessor functions to avoid the extra cost of function calls in Python. When
more functionality is added you can use property to keep the syntax
consistent.
On the other hand, if access is more complex, or the cost of accessing the
variable is significant, you should use function calls (following the
Naming guidelines) such as get_foo() and
set_foo(). If the past behavior allowed access through a property, do not
bind the new accessor functions to the property. Any code still attempting to
access the variable by the old method should break visibly so they are made
aware of the change in complexity.
module_name,
package_name,
ClassName,
method_name,
ExceptionName,
function_name,
GLOBAL_CONSTANT_NAME,
global_var_name,
instance_var_name,
function_parameter_name,
local_var_name.
Function names, variable names, and filenames should be descriptive; eschew abbreviation. In particular, do not use abbreviations that are ambiguous or unfamiliar to readers outside your project, and do not abbreviate by deleting letters within a word.
Always use a .py filename extension. Never use dashes.
- single character names except for counters or iterators. You may use "e" as an exception identifier in try/except statements.
- dashes (
-) in any package/module name __double_leading_and_trailing_underscore__names (reserved by Python)
-
"Internal" means internal to a module or protected or private within a class.
-
Prepending a single underscore (
_) has some support for protecting module variables and functions (not included withfrom module import *). While prepending a double underscore (__aka "dunder") to an instance variable or method effectively makes the variable or method private to its class (using name mangling) we discourage its use as it impacts readability and testability and isn't really private. -
Place related classes and top-level functions together in a module. Unlike Java, there is no need to limit yourself to one class per module.
-
Use CapWords for class names, but lower_with_under.py for module names. Although there are some old modules named CapWords.py, this is now discouraged because it's confusing when the module happens to be named after a class. ("wait -- did I write
import StringIOorfrom StringIO import StringIO?") -
Underscores may appear in unittest method names starting with
testto separate logical components of the name, even if those components use CapWords. One possible pattern istest<MethodUnderTest>_<state>; for exampletestPop_EmptyStackis okay. There is no One Correct Way to name test methods.
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 |
While Python supports making things private by using a leading double underscore
__ (aka. "dunder") prefix on a name their use is discouraged. Prefer the use
of a single underscore. They are much easier to type, read, and to access from
small unittests. Lint warnings take care of invalid access to protected members.
Even a file meant to be used as an executable should be importable and a mere
import should not have the side effect of executing the program's main
functionality. The main functionality should be in a main() function.
In Python, pydoc as well as unit tests require modules to be importable. Your
code should always check if __name__ == '__main__' before executing your main
program so that the main program is not executed when the module is imported.
def main():
...
if __name__ == '__main__':
main()All code at the top level will be executed when the module is imported. Be
careful not to call functions, create objects, or perform other operations that
should not be executed when the file is being pydoced.
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.
- In methods, only annotate
self, orclsif it is necessary for proper type information. e.g.,@classmethod def create(cls: Type[T]) -> T: return cls() - If any other variable or a returned type should not be expressed, use
Any. - You are not required to annotate all the functions in a module.
- At least annotate your public APIs.
- Use judgment to get to a good balance between safety and clarity on the one hand, and flexibility on the other.
- Annotate code that is prone to type-related errors (previous bugs or complexity).
- Annotate code that is hard to understand.
- Annotate code as it becomes stable from a types perspective. In many cases, you can annotate all the functions in mature code without losing too much flexibility.
Try to follow the existing indentation rules.
After annotating, many function signatures will become "one parameter per line".
def my_method(self,
first_var: int,
second_var: Foo,
third_var: Optional[Bar]) -> int:
...Always prefer breaking between variables, and not for example between variable names and type annotations. However, if everything fits on the same line, go for it.
def my_method(self, first_var: int) -> int:
...If the combination of the function name, the last parameter, and the return type is too long, indent by 4 in a new line.
def my_method(
self, first_var: int) -> Tuple[MyLongType1, MyLongType1]:
...When the return type does not fit on the same line as the last parameter, the preferred way is to indent the parameters by 4 on a new line and align the closing parenthesis with the def.
Yes:
def my_method(
self, **kw_args: Optional[MyLongType]
) -> Dict[OtherLongType, MyLongType]:
...pylint allows you to move the closing parenthesis to a new line and align
with the opening one, but this is less readable.
No:
def my_method(self,
**kw_args: Optional[MyLongType]
) -> Dict[OtherLongType, MyLongType]:
...As in the examples above, prefer not to break types. However, sometimes they are too long to be on a single line (try to keep sub-types unbroken).
def my_method(
self,
first_var: Tuple[List[MyLongType1],
List[MyLongType2]],
second_var: List[Dict[
MyLongType3, MyLongType4]]) -> None:
...If a single name and type is too long, consider using an alias for the type. The last resort is to break after the colon and indent by 4.
Yes:
def my_function(
long_variable_name:
long_module_name.LongTypeName,
) -> None:
...No:
def my_function(
long_variable_name: long_module_name.
LongTypeName,
) -> None:
...If you need to use a class name from the same module that is not yet defined -- for example, if you need the class inside the class declaration, or if you use a class that is defined below -- use a string for the class name.
class MyClass(object):
def __init__(self,
stack: List["MyClass"]) -> None:As per PEP-008 when combining an argument annotation with a default value, use spaces around the = sign (but only for those arguments that have both an annotation and a default).
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, but if there is only one other type,
use Optional.
Use explicit Optional instead of implicit Optional. Earlier versions of PEP
484 allowed a: Text = None to be interpretted as a: Optional[Text] = None,
but that is no longer the preferred behavior.
Yes:
def func(a: Optional[Text], b: Optional[Text] = None) -> Text:
...
def multiple_nullable_union(a: Union[None, Text, int]) -> Text
...No:
def nullable_union(a: Union[None, Text]) -> Text:
...
def implicit_optional(a: Text = None) -> Text:
...You can declare aliases of complex types. The name of an alias should be CapWorded; try to describe the composed type and end with "Type" (or "Types" for returned tuples). If the alias is used only in this module, it should be _Private.
For example, if the name of module together with the type is too long:
SomeType = module_with_long_name.TypeWithLongNameOther examples are complex nested types and multiple return variables from a function (as a tuple).
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-errorIf an internal variable has a type that is hard or impossible to infer, you can supply it as a special comment:
a = SomeUndecoratedFunction() # type: FooUnlike Lists, which can only have a single type, Tuples can have either a single repeated type or a set number of elements with different types. The latter is commonly used as return type from a function.
a = [1, 2, 3] # type: List[int]
b = (1, 2, 3) # type: Tuple[int, ...]
c = (1, "2", 3.5) # type: Tuple[int, Text, float]The Python type system has
generics. The factory
function TypeVar is a common way to use them.
Example:
from typing import List, TypeVar
T = TypeVar("T")
...
def next(l: List[T]) -> T:
return l.pop()A TypeVar can be constrained:
AddableType = TypeVar("AddableType", int, float, Text)
def add(a: AddableType, b: AddableType) -> AddableType:
return a + bA common predefined type variable in the typing module is AnyStr. Use it for
multiple annotations that can be bytes or unicode and must all be the same
type.
from typing import AnyStr
def check_length(x: AnyStr) -> AnyStr:
if len(x) <= 42:
return x
raise ValueError()When annotating functions that take or return strings, avoid using str,
because it means different things in Python 2 and Python 3. In Python 2, str
is bytes; in Python 3, it is unicode. Whenever possible, it is best to be
explicit:
No:
def f(x: str) -> str:
...For code that deals with byte arrays, use bytes.
def f(x: bytes) -> bytes:
...For code that processes text data (str or unicode in Python 2, str in
Python 3), use Text.
from typing import Text
...
def f(x: Text) -> Text:
...If the type can be either byte arrays or text, use Union.
from typing import Text, Union
...
def f(x: Union[bytes, Text]) -> Union[bytes, Text]:
...If all the string types of a function are always the same, for example if the return type is the same as the argument type in the code above, use AnyStr.
Writing it like this will simplify the process of porting the code to Python 3.
For classes from the typing module, always import the class itself. You are
explicitly allowed to import multiple specific classes on one line from the
typing module. Ex:
from typing import Any, Dict, OptionalGiven that this way of importing from typing adds items to the local
namespace, any names in typing should be treated similarly to keywords, and
not be defined in your Python code, typed or not. If there is a collision
between a type and an existing name in a module, import it using
import x as y.
from typing import Any as AnyTypeUse conditional imports only in exceptional cases, when the additional imports needed for type checking must be avoided at runtime. This pattern is discouraged and alternatives such as refactoring the code to allow top level imports should be preferred.
Imports that are needed only for type annotations can be placed within an
if TYPE_CHECKING: block.
- Conditionally imported types need to be referenced as strings, to be forward compatible with Python 3.6 where the annotation expressions are actually evaluated.
- Only entities that are used solely for typing should be defined here; this includes aliases. Otherwise it will be a runtime error, as the module will not be imported at runtime.
- The block should be right after all the normal imports.
- There should be no empty lines in the typing imports list.
- Sort this list as if it were a regular imports list.
import typing
if typing.TYPE_CHECKING:
import sketch
def f(x: "sketch.Sketch"): ...Circular dependencies that are caused by typing are code smells. Such code is a good candidate for refactoring. Although technically it is possible to keep circular dependencies, the build system will not let you do so because each module has to depend on the other.
Replace modules that create circular dependency imports with Any. Set an
alias with a meaningful name, and use the real type name from
this module (any attribute of Any is Any). Alias definitions should be separated
from the last import by one line.
from typing import Any
some_mod = Any # some_mod.py imports this module.
...
def my_method(self, var: some_mod.SomeType) -> None:
...When annotating, prefer to specify type parameters for generic types; otherwise,
the generics' parameters will be assumed to be Any.
def get_names(employee_ids: List[int]) -> Dict[int, Any]:
...# These are both interpreted as get_names(employee_ids: List[Any]) -> Dict[Any, Any]
def get_names(employee_ids: list) -> Dict:
...
def get_names(employee_ids: List) -> Dict:
...If the best type parameter for a generic is Any, make it explicit, but
remember that in many cases TypeVar might be more
appropriate:
def get_names(employee_ids: List[Any]) -> Dict[Any, Text]:
"""Returns a mapping from employee ID to employee name for given IDs."""T = TypeVar('T')
def get_names(employee_ids: List[T]) -> Dict[T, Text]:
"""Returns a mapping from employee ID to employee name for given IDs."""BE CONSISTENT.
If you're editing code, take a few minutes to look at the code around you and determine its style. If they use spaces around all their arithmetic operators, you should too. If their comments have little boxes of hash marks around them, make your comments have little boxes of hash marks around them too.
The point of having style guidelines is to have a common vocabulary of coding so people can concentrate on what you're saying rather than on how you're saying it. We present global style rules here so people know the vocabulary, but local style is also important. If code you add to a file looks drastically different from the existing code around it, it throws readers out of their rhythm when they go to read it. Avoid this.