This style guides is provided by Google. Python is the main dynamic language used at Google. This style guide is a list of *dos and don’ts* for Python programs.
Many teams use the yapf 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, 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-builtin
pylint
warnings are each identified by symbolic name (empty-docstring)
Google-specific warnings start with g-.
If the reason for the suppression is not clear from the symbolic name, add an explanation.
Suppressing in this way has the advantage that we can easily search for suppressions and revisit them.
You can get a list of
pylint
warnings by doing:
pylint --list-msgs
To get more information on a particular message, use:
pylint --help-msg=C6409
Prefer pylint: disable to the deprecated older form pylint: disable-msg.
Unused argument warnings can be suppressed by deleting the variables at the beginning of the function. Always include a comment explaining why you are deleting it. “Unused.” is sufficient. For example:
def viking_cafe_order(spam, beans, eggs=None):
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
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.
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 if two modules named y are to be imported or if
y is an inconveniently long name.import y as z only when z is a standard abbreviation (e.g., np for
numpy).For example the module sound.effects.echo may be imported as follows:
from sound.effects import echo
...
echo.EchoFilter(input, output, delay=0.7, atten=4)
Do not use relative names in imports. Even if the module is in the same package, use the full package name. This helps prevent unintentionally importing a package twice.
Imports from the typing module and the six.moves module are exempt 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
FLAGS = absl.flags.FLAGS
# Reference flags in code with just the module name (common).
from absl import flags
from doctor.who import jodie
FLAGS = flags.FLAGS
No: (assume this file lives in doctor/who/ where jodie.py also exists)
# Unclear what module the author wanted and what will be imported. The actual
# import behavior depends on external factors controlling sys.path.
# Which possible jodie module did the author intend to import?
import jodie
The directory the main binary is located in should not be assumed to be in
sys.path despite that happening in some environments. This being the case,
code should assume that import jodie refers to a third party or top level
package named jodie, not a local jodie.py.
Exceptions 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:
Make use of built-in exception classes when it makes sense. For example,
raise a ValueError to indicate a programming mistake like a violated
precondition (such as if you were passed a negative number but required a
positive one). Do not use assert statements for validating argument values
of a public API. assert is used to ensure internal correctness, not to
enforce correct usage nor to indicate that some unexpected event occurred.
If an exception is desired in the latter cases, use a raise statement. For
example:
Yes:
def connect_to_next_port(self, minimum):
"""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 not port:
raise ConnectionError(
f'Could not connect to service on port {minimum} or higher.')
assert port >= minimum, (
f'Unexpected port {port} when minimum was {minimum}.')
return port
No:
def connect_to_next_port(self, minimum):
"""Connects to the next available port.
Args:
minimum: A port value greater or equal to 1024.
Returns:
The new minimum port.
"""
assert minimum >= 1024, 'Minimum port must be at least 1024.'
port = self._find_next_open_port(minimum)
assert port is not None
return port
Libraries or packages may define their own exceptions. When doing so they
must inherit from an existing exception class. Exception names should end in
Error and should not introduce stutter (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 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 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 (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. Never use Python 2 specific iteration methods
such as dict.iter*() unless necessary.
Yes: for key in adict: ...
if key not in adict: ...
if obj in alist: ...
for line in afile: ...
for k, v in adict.items(): ...
for k, v in six.iteritems(adict): ...
No: for key in adict.keys(): ...
if not adict.has_key(key): ...
for line in afile.readlines(): ...
for k, v in dict.iteritems(): ...
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 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 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.
one_line = 'yes' if predicate(value) else 'no'
slightly_split = ('yes' if predicate(value)
else 'no, nein, nyet')
the_longest_ternary_style_that_can_be_done = (
'yes, true, affirmative, confirmed, correct'
if predicate(value)
else 'no, false, negative, nay')
bad_line_breaking = ('yes' if predicate(value) else
'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: 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...
...
No: def foo(a, b: Mapping = {}): # Could still get passed to unchecked code
...
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 design pattern).
Yes: import math
class Square:
"""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):
"""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 * 4
Use 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 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 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., 0 as 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 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):
"""Returns a function that adds numbers to a given number."""
def adder(summand2):
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):
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 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, 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_function
For more information on these imports, see
absolute imports,
/ division behavior, and
the print function.
Please don’t omit or remove these imports, even if they’re not currently used in the module, unless the code is Python 3 only. 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 the six, future, and past libraries 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 similar PEP-526 syntax:
a: SomeType = some_func()
Or by using a type comment in code that must support legacy Python versions.
a = some_func() # type: SomeType
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 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.html
No: # See details at
# http://www.example.com/us/developer/documentation/api/content/\
# v2.0/csv_file_name_extension_full_specification.html
It is permissible to use backslash continuation when defining a with statement
whose expressions span three or more lines. For two lines of expressions, use a
nested with statement:
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.
In all other cases where a line exceeds 80 characters, and the yapf auto-formatter does not help bring the line below the limit, the line is allowed to exceed this maximum.
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. No blank line following a def line. 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, 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/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 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()
"""
In this section, “function” means a method, function, or generator.
A function must have a docstring, unless it meets all of the following criteria:
A docstring should give enough information to write a call to the function
without reading the function’s code. The docstring should be descriptive-style
("""Fetches rows from a Bigtable.""") rather than imperative-style ("""Fetch
rows from a Bigtable."""), except for @property data descriptors, which
should use the same style as attributes. 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 from the
overridden method, or details need to be provided (e.g., documenting additional
side effects), a docstring with at least those differences is required on the
overriding method.
Certain aspects of a function should be documented in special sections, listed below. Each section begins with a heading line, which ends with a colon. All sections other than the heading should maintain a hanging indent of two or four spaces (be consistent within a file). These sections can be omitted in cases where the function’s name and signature are informative enough that it can be aptly described using a one-line docstring.
*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 return value.def fetch_smalltable_rows(table_handle: smalltable.Table,
keys: Sequence[Union[bytes, str]],
require_all_keys: bool = False,
) -> Mapping[bytes, Tuple[str]]:
"""Fetches rows from a Smalltable.
Retrieves rows pertaining to the given keys from the Table instance
represented by table_handle. String keys will be UTF-8 encoded.
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: Optional; If require_all_keys is 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[Union[bytes, str]],
require_all_keys: bool = False,
) -> Mapping[bytes, Tuple[str]]:
"""Fetches rows from a Smalltable.
Retrieves rows pertaining to the given keys from the Table instance
represented by table_handle. String keys will be UTF-8 encoded.
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:
Optional; If require_all_keys is 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.
"""
Classes should have a docstring below the class definition describing the class.
If your class has public attributes, they should be documented here in an
Attributes section and follow the same formatting as a
function’s Args section.
class SampleClass:
"""Summary of class here.
Longer class information....
Longer class information....
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 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.
Classes need not explicitly inherit from object (unless for compatibility with
Python 2).
Modern:
class SampleClass:
pass
class OuterClass:
class InnerClass:
pass
Ancient:
class SampleClass(object):
pass
class OuterClass(object):
class InnerClass(object):
pass
Use the format method or the % operator for formatting strings, even when
the parameters are all strings. Use your best judgment 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 an 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.
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.""")
Explicitly close files and sockets when done with them.
Leaving files, sockets or other file-like objects open unnecessarily has many downsides:
Furthermore, while files and sockets are automatically closed when the file object is destructed, tying the lifetime of the file object to the state of the file is poor practice:
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.
A TODO comment begins with the string TODO in all caps and a parenthesized
name, e-mail address, or other identifier
of the person or issue with the best context about the problem. This is followed
by an explanation of what there is to do.
The purpose is to have a consistent TODO format that can be searched to find
out how to get more details. A TODO is not a commitment that the person
referenced will fix the problem. Thus when you create a
TODO, it is almost always your name
that is given.
# 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; there are
exceptions for typing imports.
E.g.:
Yes: import os
import sys
from typing import Mapping, Sequence
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 absolute_import
from __future__ import division
from __future__ import print_function
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)
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 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 statementsPlease 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
“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). 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 StringIO or from StringIO import
StringIO?”)
Underscores may appear in unittest method names starting with test to
separate logical components of the name, even if those components use
CapWords. One possible pattern is test<MethodUnderTest>_<state>; for
example testPop_EmptyStack is okay. There is no One Correct Way to name
test methods.
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 |
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):
# 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.
self, or cls if it is necessary for proper
type information. e.g., @classmethod def create(cls: Type[T]) -> T: return
cls()Any.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, other_arg: 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,
other_arg: Optional[MyLongType]
) -> Dict[OtherLongType, MyLongType]:
...
As in the examples above, prefer not to break types. However, sometimes they are too long to be on a single line (try to keep sub-types unbroken).
def my_method(
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:
def __init__(self,
stack: List["MyClass"]) -> None:
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, 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 interpreted 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. If the alias is used only in this module, it should be _Private.
For example, if the name of the module together with the name of the type is too long:
_ShortName = module_with_long_name.TypeWithLongName
ComplexMap = Mapping[Text, List[Tuple[int, int]]]
Other 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-error
If an internal variable has a type that is hard or impossible to infer, you can specify its type in a couple ways.
# type: comment on the end of the linea = SomeUndecoratedFunction() # type: Foo
a: Foo = SomeUndecoratedFunction()
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 = [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 + b
A common predefined type variable in the typing module is AnyStr. Use it for
multiple annotations that can be bytes or unicode and must all be the same
type.
from typing import AnyStr
def check_length(x: AnyStr) -> AnyStr:
if len(x) <= 42:
return x
raise ValueError()
The proper type for annotating strings depends on what versions of Python the code is intended for.
For Python 3 only code, prefer to use str. Text is also acceptable. Be
consistent in using one or the other.
For Python 2 compatible code, use Text. In some rare cases, str may make
sense; typically to aid compatibility when the return types aren’t the same
between the two Python versions. Avoid using unicode: it doesn’t exist in
Python 3.
The reason this discrepancy exists is because str means different things
depending on the Python version.
No:
def py2_code(x: str) -> unicode:
...
For code that deals with binary data, use bytes.
def deals_with_binary_data(x: bytes) -> bytes:
...
For Python 2 compatible code that processes text data (str or unicode in
Python 2, str in Python 3), use Text. For Python 3 only code that process
text data, prefer str.
from typing import Text
...
def py2_compatible(x: Text) -> Text:
...
def py3_only(x: str) -> str:
...
If the type can be either bytes or text, use Union, with the appropriate text
type.
from typing import Text, Union
...
def py2_compatible(x: Union[bytes, Text]) -> Union[bytes, Text]:
...
def py3_only(x: Union[bytes, str]) -> Union[bytes, str]:
...
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, Optional
Given that this way of importing from typing adds items to the local
namespace, any names in typing should be treated similarly to keywords, and
not be defined in your Python code, typed or not. If there is a collision
between a type and an existing name in a module, import it using import x as
y.
from typing import Any as AnyType
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, 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.