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-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, 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 latter does not enforce that the arguments are actually unused.
Use imports for packages and modules only, not for individual classes or functions.
Reusability mechanism for sharing code from one module to another.
The namespace management convention is simple. The source of each identifier is indicated in a consistent way; x.Obj says that object Obj is defined in module x.
Module names can still collide. Some module names are inconveniently long.
import x for importing packages and modules.from x import y where x is the package prefix and y is the module name with no prefix.from x import y as z 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.
Note that there is an explicit exemption for imports from the typing module.
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 flags
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:
Raise exceptions like this: raise MyError('Error message') or raise 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 ValueError if you were passed a negative number but were expecting 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 ConnectToNextPort(self, minimum): """Connects to the next available port. Returns the new minimum port.""" if minimum <= 1024: raise ValueError('Minimum port must be greater than 1024.') port = self._FindNextOpenPort(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 ConnectToNextPort(self, minimum): """Connects to the next available port. Returns the new minimum port.""" assert minimum > 1024, 'Minimum port must be greater than 1024.' port = self._FindNextOpenPort(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 re-raising the exception or in the outermost block in your thread (and printing an error message). 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.
When capturing an exception, use as rather 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.
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 for easier future comprehension. 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 = [] for x in range(10): for y in range(5): if x * y > 10: result.append((x, y)) for x in xrange(5): for y in xrange(5): if x != y: for z in xrange(5): if y != z: yield (x, y, z) return ((x, complicated_transform(x)) for x in long_generator_function(parameter) if x is not None) squares = [x * x for x in range(10)] eat(jelly_bean for jelly_bean in jelly_beans if jelly_bean.color == 'black')
No: result = [(x, y) for x in range(10) for y in range(5) if x * y > 10] return ((x, y, z) for x in 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 = []
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 * 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 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 like None. Use is or is not.
Beware of writing if x: when you really mean if x is not None:-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 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 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(object): @my_decorator def method(self): # method body ...
is equivalent to:
class C(object): def Methodmethod(self): # method body ... Methodmethod = MyDecoratormy_decorator(Methodmethod)
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, 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, and enum).
Python 3 is here. While not every project is ready to use it yet, all code should be written with an eye towards the future.
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. Others say “but I don't use that feature in this file” and want to clean-up. Please don't. 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.
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
If you are not already familiar with those, read up on each here: absolute imports, new / division behavior, and the print function.
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: 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.
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:
# 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.
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, ... }
Two blank lines between top-level definitions, one blank line between method definitions.
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, 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]
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 but always be consistent about whitespace on either side of a binary operator.
Yes: x == 1
No: x<1
Never 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 in-line 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:
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: : 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) : Describe the type and semantics of the return value. If the function only : returns None, this section is not required. It may also be omitted if the : docstring starts with Returns (or Yields) (e.g. : """Returns row from Bigtable as a tuple of strings.""") and the opening : sentence is sufficient to describe return value.
Raises: : List all exceptions that are relevant to the interface.
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+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.
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: pass
Inheriting 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:
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:
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 sys
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 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
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
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 import argcomplete import BeautifulSoup import cryptography import tensorflow as tf from otherproject.ai import body from otherproject.ai import mind from otherproject.ai import soul 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.
-) 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 with from 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 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 "$0.py" "$@".
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.
self, or cls.Any.
Try to follow the existing indentation rules. Always prefer breaking between variables.
After annotating, many of the functions will become “one parameter per line”.
def my_method(self, first_var: int, second_var: Foo, third_var: Optional[Bar]) -> int: ...
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, Optional is a shortcut.
Yes: def func(a: Optional[str]) -> str: ...
No: def func(a: Union[None, str]) -> str: ...
If the default value of an argument is None, marking the variable Optional is optional.
Yes: def func(a: Optional[str] = None) -> str: ... def func(a: str = None) -> str: ...
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.TypeWithLongName
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 supply it as a special comment:
a = SomeUndecoratedFunction() # type: Foo
Unlike 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, str, 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, str) def add(a: AddableType, b: AddableType) -> AddableType: return a + b
A common predefined type variable in the typing module is AnyStr. Use it for arguments that can be bytes or unicode.
AnyStr = TypeVar("AnyStr", bytes, unicode)
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 Unicode data, use Text.
from typing import Text ... def f(x: Text) -> Text: ...
If the type can be either bytes or unicode, 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, 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
If the additional imports needed for type checking need to be avoided at runtime, conditional imports may be used. This pattern is discouraged and alternatives such as refactoring the code to allow top level imports should be preferred. If this pattern is used at all, conditionally imported types need to be referenced as strings 'sketch.Sketch' rather than sketch.Sketch, to be forward compatible with Python 3 where the annotation expressions are actually evaluated. Imports that are needed only for type annotations can be placed within an if typing.TYPE_CHECKING: block.
google3 module also has a TYPE_CHECKING constant. You can use that instead if you do you not want to import typing at runtime.import typing ... if typing.TYPE_CHECKING: import types from MySQLdb import connections from google3.path.to.my.project import my_proto_pb2 from typing import Any, Dict, Optional
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: ...
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.