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PEP: 8
Title: Style Guide for Python Code
Version: $Revision$
Last-Modified: $Date$
Author: Guido van Rossum <guido@python.org>,
Barry Warsaw <barry@python.org>
Status: Active
Type: Process
Content-Type: text/x-rst
Created: 05-Jul-2001
Post-History: 05-Jul-2001
Introduction
============
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 [1]_.
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 [2]_.
A Foolish Consistency is the Hobgoblin of Little Minds
======================================================
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!
Two good reasons to break a particular rule:
1. When applying the rule would make the code less readable, even for
someone who is used to reading code that follows the rules.
2. 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).
Code lay-out
============
Indentation
-----------
Use 4 spaces per indentation level.
For really old code that you don't want to mess up, you can continue
to use 8-space tabs.
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)
Tabs or Spaces?
---------------
Never mix tabs and spaces.
The most popular way of indenting Python is with spaces only. The
second-most popular way is with tabs only. Code indented with a
mixture of tabs and spaces should be converted to using spaces
exclusively. When invoking the Python 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!
For new projects, spaces-only are strongly recommended over tabs.
Most editors have features that make this easy to do.
Maximum Line Length
-------------------
Limit all lines to a maximum of 79 characters.
There are still many devices around that are limited to 80 character
lines; plus, limiting windows to 80 characters makes it possible to
have several windows side-by-side. The default wrapping on such
devices disrupts the visual structure of the code, making it more
difficult to understand. Therefore, please limit all lines to a
maximum of 79 characters. For flowing long blocks of text (docstrings
or comments), limiting the length to 72 characters is recommended.
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. 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)
Blank Lines
-----------
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.
Encodings (PEP 263)
-------------------
Code in the core Python distribution should always use the ASCII or
Latin-1 encoding (a.k.a. ISO-8859-1). For Python 3.0 and beyond,
UTF-8 is preferred over Latin-1, see PEP 3120.
Files using ASCII should not have a coding cookie. Latin-1 (or UTF-8)
should only be used when a comment or docstring needs to mention an
author name that requires Latin-1; otherwise, using ``\x``, ``\u`` or
``\U`` 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
-------
- 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:
1. standard library imports
2. related third party imports
3. local application/library specific imports
You should put a blank line between each group of imports.
Put any relevant ``__all__`` specification after the imports.
- Relative imports for intra-package imports are highly discouraged.
Always use the absolute package path for all imports. Even now that
PEP 328 is fully implemented in Python 2.5, its style of explicit
relative imports is actively discouraged; absolute imports are more
portable and usually more readable.
- 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".
Whitespace in Expressions and Statements
========================================
Pet Peeves
----------
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
Other Recommendations
---------------------
- 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 judgement; 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()
Comments
========
Comments that contradict the code are worse than no comments. Always
make a priority of keeping the comments up-to-date when the code
changes!
Comments should be complete sentences. If a comment is a phrase or
sentence, its first word should be capitalized, unless it is an
identifier that begins with a lower case letter (never alter the case
of identifiers!).
If a comment is short, the period at the end can be omitted. Block
comments generally consist of one or more paragraphs built out of
complete sentences, and each sentence should end in a period.
You should use two spaces after a sentence-ending period.
When writing English, Strunk and White apply.
Python coders from non-English speaking countries: please write your
comments in English, unless you are 120% sure that the code will never
be read by people who don't speak your language.
Block Comments
--------------
Block comments generally apply to some (or all) code that follows
them, and are indented to the same level as that code. Each line of a
block comment starts with a ``#`` and a single space (unless it is
indented text inside the comment).
Paragraphs inside a block comment are separated by a line containing a
single ``#``.
Inline Comments
---------------
Use inline comments sparingly.
An inline comment is a comment on the same line as a statement.
Inline comments should be separated by at least two spaces from the
statement. They should start with a # and a single space.
Inline comments are unnecessary and in fact distracting if they state
the obvious. Don't do this::
x = x + 1 # Increment x
But sometimes, this is useful::
x = x + 1 # Compensate for border
Documentation Strings
---------------------
Conventions for writing good documentation strings
(a.k.a. "docstrings") are immortalized in PEP 257.
- Write docstrings for all public modules, functions, classes, and
methods. Docstrings are not necessary for non-public methods, but
you should have a comment that describes what the method does. This
comment should appear after the ``def`` line.
- PEP 257 describes good docstring conventions. Note that most
importantly, the ``"""`` that ends a multiline docstring should be
on a line by itself, and preferably preceded by a blank line, e.g.::
"""Return a foobang
Optional plotz says to frobnicate the bizbaz first.
"""
- For one liner docstrings, it's okay to keep the closing ``"""`` on
the same line.
Version Bookkeeping
===================
If you have to have Subversion, CVS, or RCS crud in your source file,
do it as follows. ::
__version__ = "$Revision$"
# $Source$
These lines should be included after the module's docstring, before
any other code, separated by a blank line above and below.
Naming Conventions
==================
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.
Descriptive: Naming Styles
--------------------------
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 [3]_). 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.
- ``mixedCase`` (differs from CapitalizedWords by initial lowercase
character!)
- ``Capitalized_Words_With_Underscores`` (ugly!)
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.
Prescriptive: Naming Conventions
--------------------------------
Names to Avoid
~~~~~~~~~~~~~~
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.
Package and Module Names
~~~~~~~~~~~~~~~~~~~~~~~~
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``).
Class Names
~~~~~~~~~~~
Almost without exception, class names use the CapWords convention.
Classes for internal use have a leading underscore in addition.
Exception Names
~~~~~~~~~~~~~~~
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).
Global Variable Names
~~~~~~~~~~~~~~~~~~~~~
(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
~~~~~~~~~~~~~~
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.
Function and method arguments
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.)
Method Names and Instance Variables
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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
~~~~~~~~~
Constants are usually defined on a module level and written in all
capital letters with underscores separating words. Examples include
``MAX_OVERFLOW`` and ``TOTAL``.
Designing for inheritance
~~~~~~~~~~~~~~~~~~~~~~~~~
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.
- 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.
- 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.
Programming Recommendations
===========================
- 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``. Those statements run more slowly in Jython. 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.
- 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!
- 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.
- Use class-based exceptions.
String exceptions in new code are forbidden, and this language
feature has been removed in Python 2.6.
Modules or packages should define their own domain-specific base
exception class, which should be subclassed from the built-in
Exception class. Always include a class docstring. E.g.::
class MessageError(Exception):
"""Base class for errors in the email package."""
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 need no special suffix.
- When raising an exception, use ``raise ValueError('message')``
instead of the older form ``raise ValueError, 'message'``.
The paren-using form is preferred because when the exception
arguments are long or include string formatting, you don't need to
use line continuation characters thanks to the containing
parentheses. The older form will be removed in Python 3.
- 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:
1. If the exception handler will be printing out or logging the
traceback; at least the user will be aware that an error has
occurred.
2. 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.
- 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)
- 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.
- 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.
- 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':
The exception is if your code must work with Python 1.5.2 (but let's
hope not!).
- 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 2.3, str and unicode have a
common base class, basestring, so you can do::
if isinstance(obj, basestring):
- 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.
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 an 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.
References
==========
.. [1] PEP 7, Style Guide for C Code, van Rossum
.. [2] Barry's GNU Mailman style guide
http://barry.warsaw.us/software/STYLEGUIDE.txt
.. [3] http://www.wikipedia.com/wiki/CamelCase
Copyright
=========
This document has been placed in the public domain.
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