- Python Language Rules
- 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
- Python Style Rules
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 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==========
Use imports for packages and modules only.
Reusability mechanism for sharing code from one module to another.
The namespace management convention is simple. The source of each identifier is indicated in a consistent way; x.Obj says that object Obj is defined in module x.
Module names can still collide. Some module names are inconveniently long.
Use import 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.
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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 sound.effects.echo
# Reference in code with just module name (preferred).
from sound.effects import echo==========
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 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- Never use catch-all
except:statements, or catchExceptionorStandardError, unless you are re-raising the exception or in the outermost block in your thread (and printing an error message). Python is very tolerant in this regard andexcept:will really catch everything including misspelled names, sys.exit() calls, Ctrl+C interrupts, unittest failures and all kinds of other exceptions that you simply don't want to catch. - Minimize the amount of code in a
try/exceptblock. The larger the body of thetry, the more likely that an exception will be raised by a line of code that you didn't expect to raise an exception. In those cases, thetry/exceptblock hides a real error. - Use the
finallyclause to execute code whether or not an exception is raised in thetryblock. This is often useful for cleanup, i.e., closing a file. - When capturing an exception, use
asrather than a comma. For example:
try:
raise Error
except Error as error:
pass==========
Avoid global variables.
Variables that are declared at the module level.
Occasionally useful.
Has the potential to change module behavior during the import, because assignments to module-level variables are done when the module is imported.
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.
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Nested/local/inner classes and functions 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.
They are fine.
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Okay to use for simple cases.
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.
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.
Complicated list 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 doc string for generator functions.
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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.
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Okay for one-liners.
Conditional expressions 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.
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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 = []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...
...==========
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.
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.
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
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Use the "implicit" false if at all possible.
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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. Useisoris not. - Beware of writing
if x:when you really meanif x is not None:—e.g., when testing whether a variable or argument that defaults toNonewas set to some other value. The other value might be a value that's false in a boolean context! - Never compare a boolean variable to
Falseusing==. Useif not x:instead. If you need to distinguishFalsefromNonethen chain the expressions, such asif not x and x is not None:. - For sequences (strings, lists, tuples), use the fact that empty sequences are false, so
if 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
Noneas 0). You may compare a value which is known to be an integer (and is not the result oflen()) against the integer 0.
Yes
if not users:
print 'no users'
if foo == 0:
self.handle_zero()
if i % 10 == 0:
self.handle_multiple_of_ten()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.
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Use string methods instead of the string module where possible. Use function call syntax instead of apply. Use list comprehensions and for loops instead of filter and map when the function argument would have been an inlined lambda anyway. Use for loops instead of reduce.
Current versions of Python provide alternative constructs that people find generally preferable.
We do not use any Python version which does not support these features, so there is no reason not to use the new styles.
Yes
words = foo.split(':')
[x[1] for x in my_list if x[2] == 5]
map(math.sqrt, data) # Ok. No inlined lambda expression.
fn(*args, **kwargs)No
words = string.split(foo, ':')
map(lambda x: x[1], filter(lambda x: x[2] == 5, my_list))
apply(fn, args, kwargs)==========
Okay to use.
A nested Python function can refer to variables defined in enclosing functions, but can not assign to them. Variable bindings are resolved using lexical scoping, that is, based on the static program text. Any assignment to a name in a block will cause Python to treat all references to that name as a local variable, even if the use precedes the assignment. If a global declaration occurs, the name is treated as a global variable.
An example of the use of this feature is:
def get_adder(summand1):
"""Returns a function that adds numbers to a given number."""
def adder(summand2):
return summand1 + summand2
return adderOften results in clearer, more elegant code. Especially comforting to experienced Lisp and Scheme (and Haskell and ML and …) programmers.
Can lead to confusing bugs. Such as this example based on PEP-0227:
i = 4
def foo(x):
def bar():
print i,
# ...
# 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.
Okay to use.
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Use decorators judiciously when there is a clear advantage.
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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.
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Avoid these features.
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.
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.
Do not terminate your lines with semi-colons and do not use semi-colons to put two commands on the same line.
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Maximum line length is 80 characters.
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Use parentheses sparingly.
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Indent your code blocks with 4 spaces.
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Two blank lines between top-level definitions, one blank line between method definitions.
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Follow standard typographic rules for the use of spaces around punctuation.
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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.
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Be sure to use the right style for module, function, method and in-line comments.
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If a class inherits from no other base classes, explicitly inherit from object. This also applies to nested classes.
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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.
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Explicitly close files and sockets when done with them.
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Use TODO comments for code that is temporary, a short-term solution, or good-enough but not perfect.
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Imports should be on separate lines.
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Generally only one statement per line.
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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.
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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.
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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.
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