TAU Commander Code Style¶
Contents
- TAU Commander Code Style
- Introduction
- File Header
- Code Style
- Lint
- Imports
- Packages
- Exceptions
- Global variables
- Nested/Local/Inner Classes and Functions
- List Comprehensions
- Default Iterators and Operators
- Generators
- Lambda Functions
- Conditional Expressions
- Default Argument Values
- Properties
- True/False evaluations
- Deprecated Language Features
- Lexical Scoping
- Function and Method Decorators
- Threading
- Power Features
- Semicolons
- Line length
- Parentheses
- Indentation
- Blank Lines
- Whitespace
- Shebang Line
- Comments
- Classes
- Strings
- Files and Sockets
- TODO Comments
- Imports formatting
- Statements
- Access Control
- Naming
- Main
Introduction¶
TAU Commander is written in Python so style is actually important. Remember the Zen of Python:
- Beautiful is better than ugly.
- Explicit is better than implicit.
- Simple is better than complex.
- Complex is better than complicated.
- Flat is better than nested.
- Sparse is better than dense.
- Readability counts.
- Special cases aren’t special enough to break the rules.
- Although practicality beats purity.
- Errors should never pass silently.
- Unless explicitly silenced.
- In the face of ambiguity, refuse the temptation to guess.
- There should be one– and preferably only one –obvious way to do it.
- Although that way may not be obvious at first unless you’re Dutch.
- Now is better than never.
- Although never is often better than right now.
- If the implementation is hard to explain, it’s a bad idea.
- If the implementation is easy to explain, it may be a good idea.
- Namespaces are one honking great idea – let’s do more of those!
BE CONSISTENT.
If you’re editing code, take a few minutes to look at the code around you and determine its style. 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.
File Header¶
All files must begin with the correct file header. See the TAU Commander Code Documentation for more information.
Code Style¶
When writing new code, follow Google Python Style with a few exceptions. When editing existing code, follow the existing code style. The project’s pylintrc file will enforce most of these rules.
The exceptions to Google Python Style are:
- Maximum line length is 120 characters.
- Multiple imports per line are OK, e.g.
from tau import logger, error, util
. - Exceptions should not be avoided. Prefer EAFP Style.
- Complex list comprehensions are OK.
The Google Python Style guide is duplicated here with modifications for these exceptions. Credit goes to the original authors:
- Amit Patel
- Antoine Picard
- Eugene Jhong
- Gregory P. Smith
- Jeremy Hylton
- Matt Smart
- Mike Shields
- Shane Liebling
Lint¶
Run pylint
over your code.
Definition: 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.
Pros: Catches easy-to-miss errors like typos, using-vars-before-assignment, etc.
Cons: 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.
Decision: 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 a alphanumeric code (C0112
)
and a symbolic name (empty-docstring
). Prefer the symbolic names in
new code or when updating existing code.
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 using _
as the
identifier for the unused argument or prefixing the argument name with
unused_
. In situations where changing the argument names is
infeasible, you can mention them at the beginning of the function. For
example:
def foo(a, unused_b, unused_c, d=None, e=None):
_ = d, e
return a
Imports¶
Use import
s for packages and modules only.
Definition: Reusability mechanism for sharing code from one module to another.
Pros: The namespace management convention is simple. The source of each
identifier is indicated in a consistent way; x.Obj
says that object
Obj
is defined in module x
.
Cons: Module names can still collide. Some module names are inconveniently long.
Decision: Use import x
for importing packages and modules.
Use from x import y
where x
is the package prefix and y
is the module name with no prefix.
Use from x import y as z
if two modules named y
are to be
imported or if y
is an inconveniently long name.
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.
Packages¶
Import each module using the full pathname location of the module.
Pros: Avoids conflicts in module names. Makes it easier to find modules.
Cons: Makes it harder to deploy code because you have to replicate the package hierarchy.
Decision: All new code should import each module by its full package name.
Imports should be as follows:
# Reference in code with complete name.
import sound.effects.echo
# Reference in code with just module name (preferred).
from sound.effects import echo
Exceptions¶
Exceptions are allowed. Prefer EAFP Style.
Definition: Exceptions are a means of breaking out of the normal flow of control of a code block to handle errors or other exceptional conditions.
Pros: The control flow of normal operation code is not cluttered by error-handling code. It also allows the control flow to skip multiple frames when a certain condition occurs, e.g., returning from N nested functions in one step instead of having to carry-through error codes.
Cons: May cause the control flow to be confusing. Easy to miss error cases when making library calls.
Decision: Exceptions must follow certain conditions:
Raise exceptions like this:
raise MyException('Error message')
orraise MyException
. Do not use the two-argument form (raise MyException, 'Error message'
) or deprecated string-based exceptions (raise 'Error message'
).Modules or packages should define their own domain-specific base exception class, which should inherit from the built-in Exception class. The base exception for a module should be called
Error
.class Error(Exception): pass
Avoid catch-all
except:
statements, or catchException
orStandardError
. Remember that Python is very tolerant in this regard andexcept:
will really catch everything including misspelled names,sys.exit()
calls, Ctrl+C interrupts, unittest failures and all kinds of other exceptions.Minimize the amount of code in a
try
/except
block. The larger the body of thetry
, the more likely that an exception will be raised by a line of code that you didn’t expect to raise an exception. In those cases, thetry
/except
block hides a real error.Use the
finally
clause to execute code whether or not an exception is raised in thetry
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
Global variables¶
Avoid global variables.
Definition: Variables that are declared at the module level.
Pros: Occasionally useful.
Cons: Has the potential to change module behavior during the import, because assignments to module-level variables are done when the module is imported.
Decision: Avoid global variables in favor of class variables. Some exceptions are:
- Default options for scripts.
- Module-level constants. For example:
PI = 3.14159
. Constants should be named using all caps with underscores; see Naming below. - It is sometimes useful for globals to cache values needed or returned by functions.
- If needed, globals should be made internal to the module and accessed through public module level functions; see Naming below.
Nested/Local/Inner Classes and Functions¶
Nested/local/inner classes and functions are fine.
Definition: A class can be defined inside of a method, function, or class. A function can be defined inside a method or function. Nested functions have read-only access to variables defined in enclosing scopes.
Pros: Allows definition of utility classes and functions that are only used inside of a very limited scope. Very ADT-y.
Cons: Instances of nested or local classes cannot be pickled.
Decision: They are fine.
List Comprehensions¶
Okay to use, up to a point.
Definition: List comprehensions and generator expressions provide a
concise and efficient way to create lists and iterators without
resorting to the use of map()
, filter()
, or lambda
.
Pros: Simple list comprehensions can be clearer and simpler than other list creation techniques. Generator expressions can be very efficient, since they avoid the creation of a list entirely.
Cons: Complicated list comprehensions or generator expressions can be hard to read.
Decision: Okay to use, up to a point. If a loop makes things clearer then use a loop instead.
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')
result = [(x, y) for x in range(10) for y in range(5) if x * y > 10]
No:
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)
Default Iterators and Operators¶
Use default iterators and operators for types that support them, like lists, dictionaries, and files.
Definition: Container types, like dictionaries and lists, define default iterators and membership test operators (“in” and “not in”).
Pros: The default iterators and operators are simple and efficient. They express the operation directly, without extra method calls. A function that uses default operators is generic. It can be used with any type that supports the operation.
Cons: 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.
Decision: Use default iterators and operators for types that support them, like lists, dictionaries, and files. The built-in types define iterator methods, too. Prefer these methods to methods that return lists, except that you should not mutate a container while iterating over it.
Yes:
for key in adict: ...
if key not in adict: ...
if obj in alist: ...
for line in afile: ...
for k, v in dict.items(): ...
No:
for key in adict.keys(): ...
if not adict.has_key(key): ...
for line in afile.readlines(): ...
Generators¶
Use generators as needed.
Definition: A generator function returns an iterator that yields a value each time it executes a yield statement. After it yields a value, the runtime state of the generator function is suspended until the next value is needed.
Pros: Simpler code, because the state of local variables and control flow are preserved for each call. A generator uses less memory than a function that creates an entire list of values at once.
Cons: None.
Decision: Fine. Use “Yields:” rather than “Returns:” in the doc string for generator functions.
Lambda Functions¶
Okay for one-liners.
Definition: 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()
.
Pros: Convenient.
Cons: Harder to read and debug than local functions. The lack of names means stack traces are more difficult to understand. Expressiveness is limited because the function may only contain an expression.
Decision: 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
.
Conditional Expressions¶
Okay for one-liners.
Definition: Conditional expressions are mechanisms that provide a
shorter syntax for if statements. For example: x = 1 if cond else 2
.
Pros: Shorter and more convenient than an if statement.
Cons: May be harder to read than an if statement. The condition may be difficult to locate if the expression is long.
Decision: Okay to use for one-liners. In other cases prefer to use a complete if statement.
Default Argument Values¶
Okay in most cases.
Definition: You can specify values for variables at the end of a
function’s parameter list, e.g., def foo(a, b=0):
. If foo
is
called with only one argument, b
is set to 0. If it is called with
two arguments, b
has the value of the second argument.
Pros: 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.
Cons: Default arguments are evaluated once at module load time. This may cause problems if the argument is a mutable object such as a list or a dictionary. If the function modifies the object (e.g., by appending an item to a list), the default value is modified.
Decision: 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 = []
No:
def foo(a, b=[]):
...
def foo(a, b=time.time()): # The time the module was loaded???
...
def foo(a, b=FLAGS.my_thing): # sys.argv has not yet been parsed...
...
Properties¶
Use properties for accessing or setting data where you would normally have used simple, lightweight accessor or setter methods.
Definition: A way to wrap method calls for getting and setting an attribute as a standard attribute access when the computation is lightweight.
Pros: 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.
Cons: Properties are specified after the getter and setter methods are
declared, requiring one to notice they are used for properties farther
down in the code (except for readonly properties created with the
@property
decorator - see below). Must inherit from object
. Can
hide side-effects much like operator overloading. Can be confusing for
subclasses.
Decision: Use properties in new code to access or set data where you
would normally have used simple, lightweight accessor or setter methods.
Read-only 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
def __get_area(self):
"""Calculates the 'area' property."""
return self.side ** 2
def ___get_area(self):
"""Indirect accessor for 'area' property."""
return self.__get_area()
def __set_area(self, area):
"""Sets the 'area' property."""
self.side = math.sqrt(area)
def ___set_area(self, area):
"""Indirect setter for 'area' property."""
self.__set_area(area)
area = property(___get_area, ___set_area,
doc="""Gets or sets the area of the square.""")
@property
def perimeter(self):
return self.side * 4
True/False evaluations¶
Use the “implicit” false if at all possible.
Definition: Python evaluates certain values as false
when in a
boolean context. A quick “rule of thumb” is that all “empty” values are
considered false
so 0, None, [], {}, ''
all evaluate as
false
in a boolean context.
Pros: Conditions using Python booleans are easier to read and less error-prone. In most cases, they’re also faster.
Cons: May look strange to C/C++ developers.
Decision: Use the “implicit” false if at all possible, e.g.,
if foo:
rather than if foo != []:
. There are a few caveats
that you should keep in mind though:
- Never use
==
or!=
to compare singletons likeNone
. Useis
oris not
. - Beware of writing
if x:
when you really meanif x is not None:
—e.g., when testing whether a variable or argument that defaults toNone
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==
. Useif not x:
instead. If you need to distinguishFalse
fromNone
then chain the expressions, such asif not x and x is not None:
. - For sequences (strings, lists, tuples), use the fact that empty
sequences are false, so
if not seq:
orif seq:
is preferable toif len(seq):
orif not len(seq):
. - 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 oflen()
) against the integer 0.
Yes:
if not users:
print 'no users'
if foo == 0:
self.handle_zero()
if i % 10 == 0:
self.handle_multiple_of_ten()
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()
- Note that
'0'
(i.e.,0
as string) evaluates to true.
Deprecated Language Features¶
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
.
Definition: Current versions of Python provide alternative constructs that people find generally preferable.
Decision: 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)
Lexical Scoping¶
Okay to use.
Definition: 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
Pros: Often results in clearer, more elegant code. Especially comforting to experienced Lisp and Scheme (and Haskell and ML and …) programmers.
Cons: Can lead to confusing bugs. Such as this example based on PEP-0227:
i = 4
def foo(x):
def bar():
print i,
# ...
# A bunch of code here
# ...
for i in x: # Ah, i *is* local to Foo, so this is what Bar sees
print i,
bar()
So foo([1, 2, 3])
will print 1 2 3 3
, not 1 2 3 4
.
Decision: Okay to use.
Function and Method Decorators¶
Use decorators judiciously when there is a clear advantage.
Definition: Decorators for Functions and
Methods (a.k.a
“the @
notation”). The most common decorators are @classmethod
and @staticmethod
, for converting ordinary methods to class or
static methods. However, the decorator syntax allows for user-defined
decorators as well. Specifically, for some function my_decorator
,
this:
class C(object):
@my_decorator
def method(self):
# method body ...
is equivalent to:
class C(object):
def method(self):
# method body ...
method = my_decorator(method)
Pros: Elegantly specifies some transformation on a method; the transformation might eliminate some repetitive code, enforce invariants, etc.
Cons: 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.
Decision: Use decorators judiciously when there is a clear advantage. Decorators should follow the same import and naming guidelines as functions. Decorator pydoc should clearly state that the function is a decorator. Write unit tests for 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.
Threading¶
Do not rely on the atomicity of built-in types.
While Python’s built-in data types such as dictionaries appear to have
atomic operations, there are corner cases where they aren’t atomic (e.g.
if __hash__
or __eq__
are implemented as Python methods) and
their atomicity should not be relied upon. Neither should you rely on
atomic variable assignment (since this in turn depends on dictionaries).
Use the Queue module’s Queue
data type as the preferred way to
communicate data between threads. Otherwise, use the threading module
and its locking primitives. Learn about the proper use of condition
variables so you can use threading.Condition
instead of using
lower-level locks.
Power Features¶
Avoid these features.
Definition: Python is an extremely flexible language and gives you many fancy features such as metaclasses, access to bytecode, on-the-fly compilation, dynamic inheritance, object reparenting, import hacks, reflection, modification of system internals, etc.
Pros: These are powerful language features. They can make your code more compact.
Cons: It’s very tempting to use these “cool” features when they’re not absolutely necessary. It’s harder to read, understand, and debug code that’s using unusual features underneath. It doesn’t seem that way at first (to the original author), but when revisiting the code, it tends to be more difficult than code that is longer but is straightforward.
Decision: Avoid these features in your code.
Semicolons¶
Do not terminate your lines with semi-colons and do not use semi-colons to put two commands on the same line.
Line length¶
Maximum line length is 120 characters.
Exceptions:
- Long import statements.
- URLs in comments.
Do not use backslash line continuation.
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
Make note of the indentation of the elements in the line continuation examples above; see the indentation section for explanation.
Parentheses¶
Use parentheses sparingly.
Do not use them in return statements or conditional statements unless using parentheses for implied line continuation. (See above.) It is however fine to use parentheses around tuples.
Yes:
if foo:
bar()
while x:
x = bar()
if x and y:
bar()
if not x:
bar()
return foo
for (x, y) in dict.items(): ...
No:
if (x):
bar()
if not(x):
bar()
return (foo)
Indentation¶
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 no argument on the first line.
Yes:
# Aligned with opening delimiter
foo = long_function_name(var_one, var_two,
var_three, var_four)
# 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)
# 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)
# 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,
...
}
Blank Lines¶
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.
Whitespace¶
Follow standard typographic rules for the use of spaces around punctuation.
No whitespace inside parentheses, brackets or braces.
Yes:
spam(ham[1], {eggs: 2}, [])
No:
spam( ham[ 1 ], { eggs: 2 }, [ ] )
No whitespace before a comma, semicolon, or colon. Do use whitespace after a comma, semicolon, or colon except at the end of the line.
Yes:
if x == 4:
print x, y
x, y = y, x
No:
if x == 4 :
print x , y
x , y = y , x
No whitespace before the open paren/bracket that starts an argument list, indexing or slicing.
Yes:
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
Don’t use spaces around the ‘=’ sign when used to indicate a keyword argument or a default parameter value.
Yes:
def complex(real, imag=0.0): return magic(r=real, i=imag)
No:
def complex(real, imag = 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,
}
Shebang Line¶
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.
Comments¶
Be sure to use the right style for module, function, method and in-line comments.
Doc Strings
Python has a unique commenting style using doc strings. 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.) We always use the three
double-quote """
format for doc strings (per PEP
257). A doc string 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 doc string starting at the same cursor
position as the first quote of the first line. There are more formatting
guidelines for doc strings below.
Modules
Every file should contain license boilerplate. Choose the appropriate boilerplate for the license used by the project (for example, Apache 2.0, BSD, LGPL, GPL)
Functions and Methods
As used in this section “function” applies to methods, function, and generators.
A function must have a docstring, unless it meets all of the following criteria:
- not externally visible
- very short
- obvious
A docstring should give enough information to write a call to the function without reading the function’s code. 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.
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 mention required type(s) and the meaning of the argument.
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.
- 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.
"""
pass
Classes
Classes should have a doc string 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."""
Block and Inline Comments
The final place to have comments is in tricky parts of the code. If you’re going to have to explain it at the next code review, you should comment it now. Complicated operations get a few lines of comments before the operations commence. Non-obvious ones get comments at the end of the 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 iff i is 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
Classes¶
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,
and it will protect your code from one particular potential
incompatibility with Python 3000. It also defines special methods that
implement the default semantics of objects including __new__
,
__init__
, __delattr__
, __getattribute__
, __setattr__
,
__hash__
, __repr__
, and __str__
.
Strings¶
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(imperative, expletive)
x = 'name: %s; score: %d' % (name, n)
x = 'name: {}; score: {}'.format(name, n)
No:
x = '%s%s' % (a, b) # use + in this case
x = '{}{}'.format(a, b) # use + in this case
x = imperative + ', ' + expletive + '!'
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. Doc strings 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.
"""
Files and Sockets¶
Explicitly close files and sockets when done with them.
Leaving files, sockets or other file-like objects open unnecessarily has many downsides, including:
- They may consume limited system resources, such as file descriptors. Code that deals with many such objects may exhaust those resources unnecessarily if they’re not returned to the system promptly after use.
- Holding files open may prevent other actions being performed on them, such as moves or deletion.
- Files and sockets that are shared throughout a program may inadvertently be read from or written to after logically being closed. If they are actually closed, attempts to read or write from them will throw exceptions, making the problem known sooner.
Furthermore, while files and sockets are automatically closed when the file object is destructed, tying the life-time of the file object to the state of the file is poor practice, for several reasons:
- There are no guarantees as to when the runtime will actually run the file’s destructor. Different Python implementations use different memory management techniques, such as delayed Garbage Collection, which may increase the object’s lifetime arbitrarily and indefinitely.
- Unexpected references to the file may keep it around longer than intended (e.g. in tracebacks of exceptions, inside globals, etc).
The preferred way to manage files is using the “with” statement:
with open("hello.txt") as hello_file:
for line in hello_file:
print line
For file-like objects that do not support the “with” statement, use contextlib.closing():
import contextlib
with contextlib.closing(urllib.urlopen("http://www.python.org/")) as front_page:
for line in front_page:
print line
Legacy AppEngine code using Python 2.5 may enable the “with” statement using “from __future__ import with_statement”.
TODO Comments¶
Use TODO
comments for code that is temporary, a short-term solution,
or good-enough but not perfect.
TODO
s should include the string TODO
in all caps, followed by
the name, e-mail address, or other identifier of the person who can best
provide context about the problem referenced by the TODO
, in
parentheses. A colon is optional. 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 the person who can provide 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 formatting¶
Imports should be on separate lines unless using the from
keyword.
E.g.:
Yes:
import os
import sys
from tau import logger, error
No:
import os, sys
Imports are always put at the top of the file, just after any module comments and doc strings and before module globals and constants. Imports should be grouped with the order being most generic to least generic:
- standard library imports
- third-party imports
- application-specific imports
Within each grouping, imports should be sorted lexicographically, ignoring case, according to each module’s full package path.
import foo
from foo import bar
from foo.bar import baz
from foo.bar import Quux
from Foob import ar
Statements¶
Generally only one statement per line.
However, you may put the result of a test on the same line as the test
only if the entire statement fits on one line. In particular, you can
never do so with try
/except
since the try
and except
can’t both fit on the same line, and you can only do so with an if
if there is no else
.
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)
Access Control¶
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.
Naming¶
module_name
package_name
ClassName
method_name
ExceptionName
function_name
GLOBAL_CONSTANT_NAME
global_var_name
instance_var_name
function_parameter_name
local_var_name
Names to Avoid
- single character names except for counters or iterators
- dashes (
-
) in any package/module name __double_leading_and_trailing_underscore__
names (reserved by Python)
Naming Convention
- “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 withimport * from
). Prepending a double underscore (__
) to an instance variable or method effectively serves to make the variable or method private to its class (using name mangling). - 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 many existing 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
orfrom StringIO import StringIO
?”)
Main¶
Even a file meant to be used as a script should be importable and a mere import should not have the side effect of executing the script’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
pydoc
ed.