|
Author:
|
Guido van Rossum ,
Barry Warsaw ,
Nick Coghlan
</td>
</tr>
|
Status:
|
Active
|
|
Type:
|
Process
|
|
Content-Type:
|
text/x-rst
|
|
Created:
|
05-Jul-2001
|
|
Post-History:
|
05-Jul-2001, 01-Aug-2013
|
</table>
This document gives coding conventions for the Python code comprising the standard library in the main Python distribution. Please see the companion informational PEP describing style guidelines for the C code in the C implementation of Python .
This document and PEP 257 (Docstring Conventions) were adapted from
Guido’s original Python Style Guide essay, with some additions from
Barry’s style guide .
This style guide evolves over time as additional conventions are
identified and past conventions are rendered obsolete by changes in
the language itself.
Many projects have their own coding style guidelines. In the event of any
conflicts, such project-specific guides take precedence for that project.
One of Guido’s key insights is that code is read much more often than
it is written. The guidelines provided here are intended to improve
the readability of code and make it consistent across the wide
spectrum of Python code. As PEP 20 says, “Readability counts”.
A style guide is about consistency. Consistency with this style guide is important. Consistency within a project is more important. Consistency within one module or function is most important.
But most importantly: know when to be inconsistent — sometimes the
style guide just doesn’t apply. When in doubt, use your best
judgment. Look at other examples and decide what looks best. And
don’t hesitate to ask!
In particular: do not break backwards compatibility just to comply with
this PEP!
Some other good reasons to ignore a particular guideline:
-
When applying the guideline would make the code less readable, even
for someone who is used to reading code that follows this PEP.
-
To be consistent with surrounding code that also breaks it (maybe
for historic reasons) — although this is also an opportunity to
clean up someone else’s mess (in true XP style).
-
Because the code in question predates the introduction of the
guideline and there is no other reason to be modifying that code.
-
When the code needs to remain compatible with older versions of
Python that don’t support the feature recommended by the style guide.
Use 4 spaces per indentation level.
Continuation lines should align wrapped elements either vertically
using Python’s implicit line joining inside parentheses, brackets and
braces, or using a hanging indent. When using a hanging indent the
following considerations should be applied; there should be no
arguments on the first line and further indentation should be used to
clearly distinguish itself as a continuation line.
Yes:
# Aligned with opening delimiter
foo = long_function_name(var_one, var_two,
var_three, var_four)
# More indentation included to distinguish this from the rest.
def long_function_name(
var_one, var_two, var_three,
var_four):
print(var_one)
No:
# Arguments on first line forbidden when not using vertical alignment
foo = long_function_name(var_one, var_two,
var_three, var_four)
# Further indentation required as indentation is not distinguishable
def long_function_name(
var_one, var_two, var_three,
var_four):
print(var_one)
Optional:
# Extra indentation is not necessary.
foo = long_function_name(
var_one, var_two,
var_three, var_four)
The closing brace/bracket/parenthesis on multi-line constructs may
either line up under the first non-whitespace character of the last
line of list, as in:
my_list = [
1, 2, 3,
4, 5, 6,
]
result = some_function_that_takes_arguments(
'a', 'b', 'c',
'd', 'e', 'f',
)
or it may be lined up under the first character of the line that
starts the multi-line construct, as in:
my_list = [
1, 2, 3,
4, 5, 6,
]
result = some_function_that_takes_arguments(
'a', 'b', 'c',
'd', 'e', 'f',
)
Spaces are the preferred indentation method.
Tabs should be used solely to remain consistent with code that is
already indented with tabs.
Python 3 disallows mixing the use of tabs and spaces for indentation.
Python 2 code indented with a mixture of tabs and spaces should be
converted to using spaces exclusively.
When invoking the Python 2 command line interpreter with
the -t option, it issues warnings about code that illegally mixes
tabs and spaces. When using -tt these warnings become errors.
These options are highly recommended!
Limit all lines to a maximum of 79 characters.
For flowing long blocks of text with fewer structural restrictions (docstrings or comments), the line length should be limited to 72 characters.
Limiting the required editor window width makes it possible to have
several files open side-by-side, and works well when using code
review tools that present the two versions in adjacent columns.
The default wrapping in most tools disrupts the visual structure of the
code, making it more difficult to understand. The limits are chosen to
avoid wrapping in editors with the window width set to 80, even
if the tool places a marker glyph in the final column when wrapping
lines. Some web based tools may not offer dynamic line wrapping at all.
Some teams strongly prefer a longer line length. For code maintained
exclusively or primarily by a team that can reach agreement on this
issue, it is okay to increase the nominal line length from 80 to
100 characters (effectively increasing the maximum length to 99
characters), provided that comments and docstrings are still wrapped
at 72 characters.
The Python standard library is conservative and requires limiting
lines to 79 characters (and docstrings/comments to 72).
The preferred way of wrapping long lines is by using Python’s implied
line continuation inside parentheses, brackets and braces. Long lines
can be broken over multiple lines by wrapping expressions in
parentheses. These should be used in preference to using a backslash
for line continuation.
Backslashes may still be appropriate at times. For example, long,
multiple with-statements cannot use implicit continuation, so
backslashes are acceptable:
with open('/path/to/some/file/you/want/to/read') as file_1, \
open('/path/to/some/file/being/written', 'w') as file_2:
file_2.write(file_1.read())
Another such case is with assert statements.
Make sure to indent the continued line appropriately. The preferred
place to break around a binary operator is after the operator, not
before it. Some examples:
class Rectangle(Blob):
def __init__(self, width, height,
color='black', emphasis=None, highlight=0):
if (width == 0 and height == 0 and
color == 'red' and emphasis == 'strong' or
highlight > 100):
raise ValueError("sorry, you lose")
if width == 0 and height == 0 and (color == 'red' or
emphasis is None):
raise ValueError("I don't think so -- values are %s, %s" %
(width, height))
Blob.__init__(self, width, height,
color, emphasis, highlight)
Separate top-level function and class definitions with two blank
lines.
Method definitions inside a class are separated by a single blank
line.
Extra blank lines may be used (sparingly) to separate groups of
related functions. Blank lines may be omitted between a bunch of
related one-liners (e.g. a set of dummy implementations).
Use blank lines in functions, sparingly, to indicate logical sections.
Python accepts the control-L (i.e. ^L) form feed character as
whitespace; Many tools treat these characters as page separators, so
you may use them to separate pages of related sections of your file.
Note, some editors and web-based code viewers may not recognize
control-L as a form feed and will show another glyph in its place.
Code in the core Python distribution should always use UTF-8 (or ASCII
in Python 2).
Files using ASCII (in Python 2) or UTF-8 (in Python 3) should not have
an encoding declaration.
In the standard library, non-default encodings should be used only for
test purposes or when a comment or docstring needs to mention an author
name that contains non-ASCII characters; otherwise, using \x,
\u, \U, or \N escapes is the preferred way to include
non-ASCII data in string literals.
For Python 3.0 and beyond, the following policy is prescribed for the
standard library (see PEP 3131): All identifiers in the Python
standard library MUST use ASCII-only identifiers, and SHOULD use
English words wherever feasible (in many cases, abbreviations and
technical terms are used which aren’t English). In addition, string
literals and comments must also be in ASCII. The only exceptions are
(a) test cases testing the non-ASCII features, and
(b) names of authors. Authors whose names are not based on the
latin alphabet MUST provide a latin transliteration of their
names.
Open source projects with a global audience are encouraged to adopt a
similar policy.
-
Imports should usually be on separate lines, e.g.:
Yes: import os
import sys
No: import sys, os
It’s okay to say this though:
from subprocess import Popen, PIPE
-
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 in the following order:
-
standard library imports
-
related third party imports
-
local application/library specific imports
You should put a blank line between each group of imports.
Put any relevant __all__ specification after the imports.</li> -
Absolute imports are recommended, as they are usually more readable
and tend to be better behaved (or at least give better error
messages) if the import system is incorrectly configured (such as
when a directory inside a package ends up on sys.path):
import mypkg.sibling
from mypkg import sibling
from mypkg.sibling import example
However, explicit relative imports are an acceptable alternative to
absolute imports, especially when dealing with complex package layouts
where using absolute imports would be unnecessarily verbose:
from . import sibling
from .sibling import example
Standard library code should avoid complex package layouts and always
use absolute imports.
Implicit relative imports should never be used and have been removed
in Python 3.</li> -
When importing a class from a class-containing module, it’s usually
okay to spell this:
from myclass import MyClass
from foo.bar.yourclass import YourClass
If this spelling causes local name clashes, then spell them
import myclass
import foo.bar.yourclass
and use “myclass.MyClass” and “foo.bar.yourclass.YourClass”.</li> -
Wildcard imports (from import *</tt>) should be avoided, as
they make it unclear which names are present in the namespace,
confusing both readers and many automated tools. There is one
defensible use case for a wildcard import, which is to republish an
internal interface as part of a public API (for example, overwriting
a pure Python implementation of an interface with the definitions
from an optional accelerator module and exactly which definitions
will be overwritten isn’t known in advance).
</p>
When republishing names this way, the guidelines below regarding
public and internal interfaces still apply.</li> </ul> </div> </div>
Avoid extraneous whitespace in the following situations:
-
Immediately inside parentheses, brackets or braces.
Yes: spam(ham[1], {eggs: 2})
No: spam( ham[ 1 ], { eggs: 2 } )
-
Immediately before a comma, semicolon, or colon:
Yes: if x == 4: print x, y; x, y = y, x
No: if x == 4 : print x , y ; x , y = y , x
-
Immediately before the open parenthesis that starts the argument
list of a function call:
Yes: spam(1)
No: spam (1)
-
Immediately before the open parenthesis that starts an indexing or
slicing:
Yes: dict['key'] = list[index]
No: dict ['key'] = list [index]
-
More than one space around an assignment (or other) operator to
align it with another.
Yes:
x = 1
y = 2
long_variable = 3
No:
x = 1
y = 2
long_variable = 3
-
Always surround these binary operators with a single space on either
side: assignment (=), augmented assignment (+=, -=
etc.), comparisons (==, <, >, !=, <>, <=,
>=, in, not in, is, is not), Booleans (and,
or, not).
-
If operators with different priorities are used, consider adding
whitespace around the operators with the lowest priority(ies). Use
your own judgment; however, never use more than one space, and
always have the same amount of whitespace on both sides of a binary
operator.
Yes:
i = i + 1
submitted += 1
x = x*2 - 1
hypot2 = x*x + y*y
c = (a+b) * (a-b)
No:
i=i+1
submitted +=1
x = x * 2 - 1
hypot2 = x * x + y * y
c = (a + b) * (a - b)
-
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)
-
Compound statements (multiple statements on the same line) are
generally discouraged.
Yes:
if foo == 'blah':
do_blah_thing()
do_one()
do_two()
do_three()
Rather not:
if foo == 'blah': do_blah_thing()
do_one(); do_two(); do_three()
-
While sometimes it’s okay to put an if/for/while with a small body
on the same line, never do this for multi-clause statements. Also
avoid folding such long lines!
Rather not:
if foo == 'blah': do_blah_thing()
for x in lst: total += x
while t < 10: t = delay()
Definitely not:
if foo == 'blah': do_blah_thing()
else: do_non_blah_thing()
try: something()
finally: cleanup()
do_one(); do_two(); do_three(long, argument,
list, like, this)
if foo == 'blah': one(); two(); three()
If you have to have Subversion, CVS, or RCS crud in your source file,
do it as follows.
__version__ = "$Revision: 70b79ccd671a $"
# $Source$
These lines should be included after the module’s docstring, before
any other code, separated by a blank line above and below.
The naming conventions of Python’s library are a bit of a mess, so
we’ll never get this completely consistent — nevertheless, here are
the currently recommended naming standards. New modules and packages
(including third party frameworks) should be written to these
standards, but where an existing library has a different style,
internal consistency is preferred.
There are a lot of different naming styles. It helps to be able to recognize what naming style is being used, independently from what they are used for.
The following naming styles are commonly distinguished:
-
b (single lowercase letter)
-
B (single uppercase letter)
-
lowercase
-
lower_case_with_underscores
-
UPPERCASE
-
UPPER_CASE_WITH_UNDERSCORES
-
CapitalizedWords (or CapWords, or CamelCase — so named because
of the bumpy look of its letters ). This is also sometimes known
as StudlyCaps.
Note: When using abbreviations in CapWords, capitalize all the
letters of the abbreviation. Thus HTTPServerError is better than
HttpServerError.</li> -
mixedCase (differs from CapitalizedWords by initial lowercase
character!)
-
Capitalized_Words_With_Underscores (ugly!)
</ul>
There’s also the style of using a short unique prefix to group related
names together. This is not used much in Python, but it is mentioned
for completeness. For example, the os.stat() function returns a
tuple whose items traditionally have names like st_mode,
st_size, st_mtime and so on. (This is done to emphasize the
correspondence with the fields of the POSIX system call struct, which
helps programmers familiar with that.)
The X11 library uses a leading X for all its public functions. In Python, this style is generally deemed unnecessary because attribute and method names are prefixed with an object, and function names are prefixed with a module name.
In addition, the following special forms using leading or trailing
underscores are recognized (these can generally be combined with any
case convention):
-
_single_leading_underscore: weak “internal use” indicator.
E.g. from M import * does not import objects whose name starts
with an underscore.
-
single_trailing_underscore_: used by convention to avoid
conflicts with Python keyword, e.g.
Tkinter.Toplevel(master, class_='ClassName')
-
__double_leading_underscore: when naming a class attribute,
invokes name mangling (inside class FooBar, __boo becomes
_FooBar__boo; see below).
-
__double_leading_and_trailing_underscore__: “magic” objects or
attributes that live in user-controlled namespaces.
E.g. __init__, __import__ or __file__. Never invent
such names; only use them as documented.
</div>
Never use the characters ‘l’ (lowercase letter el), ‘O’ (uppercase
letter oh), or ‘I’ (uppercase letter eye) as single character variable
names.
In some fonts, these characters are indistinguishable from the
numerals one and zero. When tempted to use ‘l’, use ‘L’ instead.
Modules should have short, all-lowercase names. Underscores can be
used in the module name if it improves readability. Python packages
should also have short, all-lowercase names, although the use of
underscores is discouraged.
Since module names are mapped to file names, and some file systems are
case insensitive and truncate long names, it is important that module
names be chosen to be fairly short — this won’t be a problem on Unix,
but it may be a problem when the code is transported to older Mac or
Windows versions, or DOS.
When an extension module written in C or C++ has an accompanying
Python module that provides a higher level (e.g. more object oriented)
interface, the C/C++ module has a leading underscore
(e.g. _socket).
Almost without exception, class names use the CapWords convention. Classes for internal use have a leading underscore in addition.
Because exceptions should be classes, the class naming convention
applies here. However, you should use the suffix “Error” on your
exception names (if the exception actually is an error).
(Let’s hope that these variables are meant for use inside one module
only.) The conventions are about the same as those for functions.
Modules that are designed for use via from M import * should use
the __all__ mechanism to prevent exporting globals, or use the
older convention of prefixing such globals with an underscore (which
you might want to do to indicate these globals are “module
non-public”).
Function names should be lowercase, with words separated by underscores as necessary to improve readability.
mixedCase is allowed only in contexts where that’s already the
prevailing style (e.g. threading.py), to retain backwards
compatibility.
Always use self for the first argument to instance methods.
Always use cls for the first argument to class methods.
If a function argument’s name clashes with a reserved keyword, it is
generally better to append a single trailing underscore rather than
use an abbreviation or spelling corruption. Thus class_ is better
than clss. (Perhaps better is to avoid such clashes by using a
synonym.)
Use the function naming rules: lowercase with words separated by
underscores as necessary to improve readability.
Use one leading underscore only for non-public methods and instance
variables.
To avoid name clashes with subclasses, use two leading underscores to
invoke Python’s name mangling rules.
Python mangles these names with the class name: if class Foo has an
attribute named __a, it cannot be accessed by Foo.__a. (An
insistent user could still gain access by calling Foo._Foo__a.)
Generally, double leading underscores should be used only to avoid
name conflicts with attributes in classes designed to be subclassed.
Note: there is some controversy about the use of __names (see below).
Constants are usually defined on a module level and written in all
capital letters with underscores separating words. Examples include
MAX_OVERFLOW and TOTAL.
Always decide whether a class’s methods and instance variables
(collectively: “attributes”) should be public or non-public. If in
doubt, choose non-public; it’s easier to make it public later than to
make a public attribute non-public.
Public attributes are those that you expect unrelated clients of your
class to use, with your commitment to avoid backward incompatible
changes. Non-public attributes are those that are not intended to be
used by third parties; you make no guarantees that non-public
attributes won’t change or even be removed.
We don’t use the term “private” here, since no attribute is really
private in Python (without a generally unnecessary amount of work).
Another category of attributes are those that are part of the
“subclass API” (often called “protected” in other languages). Some
classes are designed to be inherited from, either to extend or modify
aspects of the class’s behavior. When designing such a class, take
care to make explicit decisions about which attributes are public,
which are part of the subclass API, and which are truly only to be
used by your base class.
With this in mind, here are the Pythonic guidelines:
-
Public attributes should have no leading underscores.
-
If your public attribute name collides with a reserved keyword,
append a single trailing underscore to your attribute name. This is
preferable to an abbreviation or corrupted spelling. (However,
notwithstanding this rule, ‘cls’ is the preferred spelling for any
variable or argument which is known to be a class, especially the
first argument to a class method.)
Note 1: See the argument name recommendation above for class methods.</li> -
For simple public data attributes, it is best to expose just the
attribute name, without complicated accessor/mutator methods. Keep
in mind that Python provides an easy path to future enhancement,
should you find that a simple data attribute needs to grow
functional behavior. In that case, use properties to hide
functional implementation behind simple data attribute access
syntax.
Note 1: Properties only work on new-style classes.
Note 2: Try to keep the functional behavior side-effect free,
although side-effects such as caching are generally fine.
Note 3: Avoid using properties for computationally expensive
operations; the attribute notation makes the caller believe that
access is (relatively) cheap.</li> -
If your class is intended to be subclassed, and you have attributes
that you do not want subclasses to use, consider naming them with
double leading underscores and no trailing underscores. This
invokes Python’s name mangling algorithm, where the name of the
class is mangled into the attribute name. This helps avoid
attribute name collisions should subclasses inadvertently contain
attributes with the same name.
Note 1: Note that only the simple class name is used in the mangled
name, so if a subclass chooses both the same class name and attribute
name, you can still get name collisions.
Note 2: Name mangling can make certain uses, such as debugging and
__getattr__(), less convenient. However the name mangling
algorithm is well documented and easy to perform manually.
Note 3: Not everyone likes name mangling. Try to balance the
need to avoid accidental name clashes with potential use by
advanced callers.</li> </ul> </div> </div>
Any backwards compatibility guarantees apply only to public interfaces.
Accordingly, it is important that users be able to clearly distinguish
between public and internal interfaces.
Documented interfaces are considered public, unless the documentation
explicitly declares them to be provisional or internal interfaces exempt
from the usual backwards compatibility guarantees. All undocumented
interfaces should be assumed to be internal.
To better support introspection, modules should explicitly declare the
names in their public API using the __all__ attribute. Setting
__all__ to an empty list indicates that the module has no public API.
Even with __all__ set appropriately, internal interfaces (packages,
modules, classes, functions, attributes or other names) should still be
prefixed with a single leading underscore.
An interface is also considered internal if any containing namespace
(package, module or class) is considered internal.
Imported names should always be considered an implementation detail.
Other modules must not rely on indirect access to such imported names
unless they are an explicitly documented part of the containing module’s
API, such as os.path or a package’s __init__ module that exposes
functionality from submodules.
</div>
-
Code should be written in a way that does not disadvantage other
implementations of Python (PyPy, Jython, IronPython, Cython, Psyco,
and such).
For example, do not rely on CPython’s efficient implementation of
in-place string concatenation for statements in the form a += b
or a = a + b. This optimization is fragile even in CPython (it
only works for some types) and isn’t present at all in implementations
that don’t use refcounting. In performance sensitive parts of the
library, the ''.join() form should be used instead. This will
ensure that concatenation occurs in linear time across various
implementations.</li> -
Comparisons to singletons like None should always be done with
is or is not, never the equality operators.
Also, 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
have a type (such as a container) that could be false in a boolean
context!</li> -
When implementing ordering operations with rich comparisons, it is
best to implement all six operations (__eq__, __ne__,
__lt__, __le__, __gt__, __ge__) rather than relying
on other code to only exercise a particular comparison.
To minimize the effort involved, the functools.total_ordering()
decorator provides a tool to generate missing comparison methods.
PEP 207 indicates that reflexivity rules are assumed by Python.
Thus, the interpreter may swap y > x with x < y, y >= x
with x <= y, and may swap the arguments of x == y and x !=
y. The sort() and min() operations are guaranteed to use
the < operator and the max() function uses the >
operator. However, it is best to implement all six operations so
that confusion doesn’t arise in other contexts.</li> -
Always use a def statement instead of an assignment statement that binds
a lambda expression directly to a name.
Yes:
def f(x): return 2*x
No:
f = lambda x: 2*x
The first form means that the name of the resulting function object is
specifically ‘f’ instead of the generic ‘’. This is more
useful for tracebacks and string representations in general. The use
of the assignment statement eliminates the sole benefit a lambda
expression can offer over an explicit def statement (i.e. that it can
be embedded inside a larger expression)</li> -
Derive exceptions from Exception rather than BaseException.
Direct inheritance from BaseException is reserved for exceptions
where catching them is almost always the wrong thing to do.
Design exception hierarchies based on the distinctions that code
catching the exceptions is likely to need, rather than the locations
where the exceptions are raised. Aim to answer the question
“What went wrong?” programmatically, rather than only stating that
“A problem occurred” (see PEP 3151 for an example of this lesson being
learned for the builtin exception hierarchy)
Class naming conventions apply here, although you should add the
suffix “Error” to your exception classes if the exception is an
error. Non-error exceptions that are used for non-local flow control
or other forms of signaling need no special suffix.</li> -
Use exception chaining appropriately. In Python 3, “raise X from Y”
should be used to indicate explicit replacement without losing the
original traceback.
When deliberately replacing an inner exception (using “raise X” in
Python 2 or “raise X from None” in Python 3.3+), ensure that relevant
details are transferred to the new exception (such as preserving the
attribute name when converting KeyError to AttributeError, or
embedding the text of the original exception in the new exception
message).</li> -
When raising an exception in Python 2, use raise ValueError('message')
instead of the older form raise ValueError, 'message'.
The latter form is not legal Python 3 syntax.
The paren-using form also means that when the exception arguments are
long or include string formatting, you don’t need to use line
continuation characters thanks to the containing parentheses.</li> -
When catching exceptions, mention specific exceptions whenever
possible instead of using a bare except: clause.
For example, use:
try:
import platform_specific_module
except ImportError:
platform_specific_module = None
A bare except: clause will catch SystemExit and
KeyboardInterrupt exceptions, making it harder to interrupt a
program with Control-C, and can disguise other problems. If you
want to catch all exceptions that signal program errors, use
except Exception: (bare except is equivalent to except
BaseException:).
A good rule of thumb is to limit use of bare ‘except’ clauses to two
cases:
-
If the exception handler will be printing out or logging the
traceback; at least the user will be aware that an error has
occurred.
-
If the code needs to do some cleanup work, but then lets the
exception propagate upwards with raise. try...finally
can be a better way to handle this case.
-
When binding caught exceptions to a name, prefer the explicit name
binding syntax added in Python 2.6:
try:
process_data()
except Exception as exc:
raise DataProcessingFailedError(str(exc))
This is the only syntax supported in Python 3, and avoids the
ambiguity problems associated with the older comma-based syntax.</li> -
When catching operating system errors, prefer the explicit exception
hierarchy introduced in Python 3.3 over introspection of errno
values.
-
Additionally, for all try/except clauses, limit the try clause
to the absolute minimum amount of code necessary. Again, this
avoids masking bugs.
Yes:
try:
value = collection[key]
except KeyError:
return key_not_found(key)
else:
return handle_value(value)
No:
try:
# Too broad!
return handle_value(collection[key])
except KeyError:
# Will also catch KeyError raised by handle_value()
return key_not_found(key)
-
When a resource is local to a particular section of code, use a
with statement to ensure it is cleaned up promptly and reliably
after use. A try/finally statement is also acceptable.
-
Context managers should be invoked through separate functions or methods
whenever they do something other than acquire and release resources.
For example:
Yes:
with conn.begin_transaction():
do_stuff_in_transaction(conn)
No:
with conn:
do_stuff_in_transaction(conn)
The latter example doesn’t provide any information to indicate that
the __enter__ and __exit__ methods are doing something other than
closing the connection after a transaction. Being explicit is
important in this case.</li> -
Use string methods instead of the string module.
String methods are always much faster and share the same API with
unicode strings. Override this rule if backward compatibility with
Pythons older than 2.0 is required.</li> -
Use ''.startswith() and ''.endswith() instead of string
slicing to check for prefixes or suffixes.
startswith() and endswith() are cleaner and less error prone. For
example:
Yes: if foo.startswith('bar'):
No: if foo[:3] == 'bar':
-
Object type comparisons should always use isinstance() instead of
comparing types directly.
Yes: if isinstance(obj, int):
No: if type(obj) is type(1):
When checking if an object is a string, keep in mind that it might
be a unicode string too! In Python 2, str and unicode have a
common base class, basestring, so you can do:
if isinstance(obj, basestring):
Note that in Python 3, unicode and basestring no longer exist
(there is only str) and a bytes object is no longer a kind of
string (it is a sequence of integers instead)</li> -
For sequences, (strings, lists, tuples), use the fact that empty
sequences are false.
Yes: if not seq:
if seq:
No: if len(seq)
if not len(seq)
-
Don’t write string literals that rely on significant trailing
whitespace. Such trailing whitespace is visually indistinguishable
and some editors (or more recently, reindent.py) will trim them.
-
Don’t compare boolean values to True or False using ==.
Yes: if greeting:
No: if greeting == True:
Worse: if greeting is True:
-
The Python standard library will not use function annotations as
that would result in a premature commitment to a particular
annotation style. Instead, the annotations are left for users to
discover and experiment with useful annotation styles.
It is recommended that third party experiments with annotations use an
associated decorator to indicate how the annotation should be
interpreted.
Early core developer attempts to use function annotations revealed
inconsistent, ad-hoc annotation styles. For example:
-
[str] was ambiguous as to whether it represented a list of
strings or a value that could be either str or None.
-
The notation open(file:(str,bytes)) was used for a value that
could be either bytes or str rather than a 2-tuple containing
a str value followed by a bytes value.
-
The annotation seek(whence:int) exhibited a mix of
over-specification and under-specification: int is too
restrictive (anything with __index__ would be allowed) and it
is not restrictive enough (only the values 0, 1, and 2 are
allowed). Likewise, the annotation write(b: bytes) was also
too restrictive (anything supporting the buffer protocol would be
allowed).
-
Annotations such as read1(n: int=None) were self-contradictory
since None is not an int. Annotations such as
source_path(self, fullname:str) -> object were confusing about
what the return type should be.
-
In addition to the above, annotations were inconsistent in the use
of concrete types versus abstract types: int versus Integral
and set/frozenset versus MutableSet/Set.
-
Some annotations in the abstract base classes were incorrect
specifications. For example, set-to-set operations require
other to be another instance of Set rather than just an
Iterable.
-
A further issue was that annotations become part of the
specification but weren’t being tested.
-
In most cases, the docstrings already included the type
specifications and did so with greater clarity than the function
annotations. In the remaining cases, the docstrings were improved
once the annotations were removed.
-
The observed function annotations were too ad-hoc and inconsistent
to work with a coherent system of automatic type checking or
argument validation. Leaving these annotations in the code would
have made it more difficult to make changes later so that
automated utilities could be supported.
</ul> </div>
This document has been placed in the public domain.
</div>
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