Python Style Guide
This document describes expected practices when writing Python code. There are occasions when you can break these rules, but be prepared to justify doing so when your code gets reviewed.
Python Style Guide
- Existing Conventions
- Related Documents on This Wiki
- Whitespace and Wrapping
- Truth conditionals
- Chaining method calls
- Use of lambda, and operator.attrgetter
- Use of open()
- Use of hasattr
- Database related
- Creating temporary files
- Configuration hints
There are well-established conventions in the Python community, and in general we should follow these. General Python conventions, and required reading:
PEP 8: Style Guide for Python Code
PEP 257: Docstring Conventions
The Zen of Python: python -c "import this"
Note that our standards differ slightly from PEP-8 in some cases.
Coding standards other projects use:
Although Launchpad is still written in Python 2, there are several things you can do to improve compatibility with Python 3, making an eventual port easier. There are some good guidelines in the Ubuntu wiki about Python 3.
Related Documents on This Wiki
Whitespace and Wrapping
- Code should fit within 78 columns, so as to fit nicely in an 80 column terminal, even when quoted in an email.
- Indents should be 4 spaces.
No tabs. This is not negotiable.
There are lots of cases where you might have a list, tuple or dictionary literal that spans multiple lines. In these cases, you should consider formatting these literals as follows. This format makes changes to the list clearer to read in a diff. Note the trailing comma on the last element.
Consistency with existing code is the top priority. We follow PEP-8 with the following exceptions:
CamelCase: classes, interfaces (beginning with I)
lowercase_underscores: functions, non-method attributes, properties, local variables
Private names are private
You should never call a non-public attribute or method from another class. In other words, if class A has a method _foo(), don't call it from anywhere outside class A.
If you haven't already, read PEP 257
- In general, everything that can have a docstring should: modules, classes, methods, functions.
Docstrings should always be enclosed in triple double quotes: """Like this."""
When a class or a method implements an interface, the docstring should say """See `IFoo`."""
You should use field names as defined in the epydoc documentation but with reST syntax.
Using `name` outputs a link to the documentation of the named object, if pydoctor can figure out what it is. For an example of pydoctor's output, see http://starship.python.net/crew/mwh/hacks/example.html.
Here is comprehensive example. Parameter descriptions are a good idea but not mandatory. Describe in as much or as little detail as necessary.
1 def example2(a, b): 2 """Perform some calculation. 3 4 It is a **very** complicated calculation. 5 6 :param a: The number of gadget you think this 7 function should frobnozzle. 8 :type a: ``int`` 9 :param b: The name of the thing. 10 :type b: ``str`` 11 :return: The answer! 12 :rtype: ``str``. 13 :raise ZeroDivisionError: when ``a`` is 0. 14 """
Each module should look like this:
The file standard_template.py has most of this already, so save yourself time by copying that when starting a new module. The "..." should be filled in with a list of public names in the module.
Note that although PEP-8 says to "put any relevant __all__ specification after the imports", Launchpad code should have the __all__ before the imports. This makes it easy to see what a module contains and exports, and avoids the problem that differing amounts of imports among files means that the __all__ list is in a different place each time. Given that we have the Import Fascist utility (see Imports), we use __all__ more often than in general Python code. __all__ should be formatted like imports.
There are restrictions on which imports can happen in Launchpad. Namely:
View code cannot import code from canonical.launchpad.database.
import * cannot be used if the module being imported from does not have an __all__
Database code may not import zope.exceptions.NotFoundError -- it must instead use canonical.launchpad.interfaces.NotFoundError
These restrictions are enforced by the Import Fascist, which will cause your tests not to pass if you don't abide by the rules.
Imports should be fully qualified. Good:
I.e. if foo.bar imports foo.baz, it should say import foo.baz, not import baz.
Sometimes import lines must span multiple lines, either because the package path is very long or because there are multiple names inside the module that you want to import.
Never use backslashes in import statements! Use parenthesized imports:
Like other lists, imports should list one item per line. The exception is if only one symbol is being imported from a given module.
1 from canonical.widgets.itemswidgets import CheckBoxMatrixWidget
But if you import two or more, then each item needs to be on a line by itself. Note the trailing comma on the last import and that the closing paren is on a line by itself.
Also, 'make lint' is a very good tool for helping you maintain your imports.
We encourage importing names from the location they are defined in. This seems to work better with large complex components.
With the increased use of native Storm APIs, you may encounter more circular import situations. For example, a MailingList method may need a reference to the EmailAddress class for a query, and vice versa. The classic way to solve this is to put one of the imports inside a method instead of at module global scope (a "nested import").
Short of adopting something like Zope's lazy imports (which has issues of its own), you can't avoid this, so here are some tips to make it less painful.
- Do the nested import in the least common case. For example, if 5 methods
in database/mailinglist.py need access to EmailAddress but only one method in database/emailaddress.py needs access to MailingList, put the import inside the emailaddres.py method, so you have fewer overall nested imports.
- Clearly comment that the nested import is for avoiding a circular import, using the example below.
- Put the nested import at the top of the method.
Circular imports and webservice exports
One of the largest sources of pain from circular imports is caused when you need to export an interface on the webservice. Generally, the only way around this is to specify generic types (like the plain old Interface) at declaration time and then later patch the webservice's data structures at the bottom of the interface file.
Fortunately there are some helper functions to make this less painful, in components/apihelpers.py. These are simple functions where you can some info about your exported class/method/parameters and they do the rest for you.
Properties are expected to be cheap operations. It is surprising if a property is not cheap operation. For expensive operations use a method, usually named getFoo(). Using cachedproperty provides a work-around but it should not be overused.
Remember that False, None, , and 0 are not the same although they all evaluate to False in a boolean context. If this matters in your code, be sure to check explicitly for either of them.
Also, checking the length may be an expensive operation. Casting to bool may avoid this if the object specializes by implementing nonzero.
Chaining method calls
Since in some cases (e.g. class methods and other objects that rely on descriptor get() behaviour) it's not possible to use the old style of chaining method calls (SuperClass.method(self, ...)), we should always use the super() builtin when we want that.
The exception to this rule is when we have class hierarchies outside of our control that are known not to use super() and that we want to use for diamond-shaped inheritance.
Use of lambda, and operator.attrgetter
Prefer operator.attrgetter to lambda. Remember that giving functions names makes the code that calls, passes and returns them easier to debug.
Use of open()
When opening a file do one of:
Use an encoding (via codecs.open(...) in Python 2, or open(..., encoding="...") in Python 3), or
Use "b" in the mode to signify reading/writing bytes.
Discussion of text and binary modes
Python 2 does implicit encoding and decoding between unicode and byte strings. This is convenient, but means we can get away with being vague when reading and writing.
Python 3, however, will not let us be vague:
$ python3 >>> open("foo", "w").write(b'bar') Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: must be str, not bytes >>> open("foo", "wb").write('bar') Traceback (most recent call last): File "<stdin>", line 1, in <module> TypeError: 'str' does not support the buffer interface
Python 3's open() takes a new encoding parameter - much like codecs.open() in Python 2 - with the following behaviour:
- "In text mode, if encoding is not specified the encoding used is platform dependent."
Equals "don't count on it". Getting into the habit of using binary mode now - or codecs.open() - ought to help us avoid this pitfall when porting to Python 3.
Python 3's open() function also enables universal newlines in text mode by default, for writing too; Python 2 does not support universal newlines when writing. Using binary mode ought to help us avoid this pitfall too.
Use of hasattr
Use safe_hasattr from lazr.restful.utils instead of the built-in hasattr function because the latter swallows exceptions.
We use two database ORM (object-relational mapper) APIs in Launchpad, the older and deprecated SQLObject API and the new and improved Storm API. All new code should use the Storm API, and you are encourages to convert existing code to Storm as part of your tech-debt payments.
The SQLObject and Storm ResultSet interfaces are not compatible, so e.g. if you need to UNION between these two, you will run into trouble. We are looking into ways to address this.
Using the native Storm API can be verbose and inconvenient for some folks. There is a (somewhat) experimental Sugar base class in lib/canonical/launchpad/database/stormsugar.py which provides many convenient APIs to make your use of Storm easier.
When you need to add ID attributes to your database class, use field_id as the attribute name instead of fieldID.
SQL doesn't care about whitespace, so use triple quotes for large SQL queries or fragments, e.g.:
This also easy to cut-and-paste into psql for interactive testing, unlike if you use several lines of single quoted strings.
Creating temporary files
We should use the most convenient method of the tempfile module, never taint '/tmp/' or any other 'supposed to be there' path.
Despite of being developed and deployed on Ubuntu systems, turning it into restriction might not be a good idea.
When using tempfile.mkstemp remember it returns an open file-descriptor which has to be closed or bound to the open file, otherwise they will leak and eventually hit the default Linux limit (1024).
There are 2 good variations according to the scope of the temporary file.
It's also important to mention that in testing context, specially if you are using the lp.testing.TestCase (or one of its specializations) you can simply create a brand new temporary directory (using mkdtemp). Create as many files you need within it and register a cleanup function to purge the temporary directory recursively.
To make wrapping and tabs fit the above standard, you can add the following to your .vimrc:
autocmd BufNewFile,BufRead *.py set tw=78 ts=4 sts=4 sw=4 et
To make trailing whitespace visible:
set list set listchars=tab:>.,trail:-
This will also make it obvious if you accidentally introduce a tab.
To make long lines show up:
match Error /\%>79v.\+/
For an even more in-depth Vim configuration, have a look at UltimateVimPythonSetup for a complete vim file you can copy to your local setup.
There are actually two Emacs Python modes. Emacs comes with python.el which (IMO) has some quirks and does not seem to be as popular among hardcore Python programmers. python-mode.el comes with XEmacs and is supported by a group of hardcore Python programmers. Even though it's an add-on, it works with Emacs just fine.