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Python

We follow the PEP8 style guide for Python. Docstrings follow PEP257. The rest of the document describes additions and clarifications to the PEP documents that we follow at Khan Academy.

You can use make lint from the top level of the website source tree to run a pep8 check over all the source code or make linc to check only files you've changed. You should aim to not introduce any new violations, before checking in code.

Indentation

Use 4 spaces -- never tabs -- for indentation.

This is a strict rule and ignoring this can (has) cause(d) bugs.

__init__.py

Unless an exception is explicitly allowed by a codebase owner (Kamens or Jason, for now), __init__.py files should be empty.

Rationale: when you do import foo.bar python imports two files: foo/bar.py, and foo/__init__.py. If foo/__init__.py has imports of its own, those will be run as well -- even if you don’t plan to run any of the code defined in __init__.py. This slows down execution, and worse causes circular-import problems that could be entirely avoided.

If you have code that you think every user of every function inside this directory needs to run first, __init__.py may be appropriate, but you should also consider just creating a function that executes that code, and running the function at the top level (that is, not indented) inside each file in your directory. This makes it more obvious what's going on, and also makes it easier to special-case certain files if the need ever arises.

Using __init__.py to bring variables from sub-modules into the main module space totally defeats the point of having sub-modules in the first place; don’t do it.

For more discussion, see http://stackoverflow.com/questions/1944569/how-do-i-write-good-correct-init-py-files.

Imports

from...import

Only import entire modules, never individual symbols from a module. For top-level modules, that means import foo. For sub-modules, you can do either import foo.bar or from foo import bar.

import auth_util                      # module import: importing the file auth_util.py
import auth.oauth_credentials         # module import: importing the file auth/oauth_credentials.py
from auth import oauth_credentials    # module import: importing the file auth/oauth_credentials.py
from auth_util import get_response    # BAD: symbol import: importing the function get_response from auth_util.py
from auth_util import Authorize       # BAD: symbol import: importing the class Authorize from auth_util.py
from auth_util import AUTH_HOST       # BAD: symbol import: importing the variable AUTH_HOST from auth_util.py

Exception: for third-party code, where the module documentation explicitly says to import individual symbols.

If the basename of a sub-module is generic, prefer the import form to the from form, otherwise either is fine:

import auth.models                   # code uses auth.models.Token -- clear

from auth import models              # code uses models.Token -- ambiguous!

from auth import oauth_credentials   # code uses oauth_credentials.Token -- clear

Rationale: This is the single best -- and easiest -- way to avoid the circular-import problem. To simplify, when you say import x, Python executes the x.py file, but doesn't need to get any attributes from it. When you say from x import foo, Python needs x.py to be executed enough that foo is defined in it. When x does a 'from-import' from y, and y does a 'from-import' from x, the circular import can succeed if a partial module is enough (as it will be with import x), but it can fail if the circularity happens before the needed attribute is defined (as it might be with from x import foo).

Side note: While this rule helps with most circular-import problems, it doesn’t help with all: a module x may still need symbols from a module y while x is still being imported. For instance, if you say class xclass(y.yclass): ..., Python needs the yclass attribute at import-time.

The downside of this rule is that code gets more verbose: where before you could do

from possibly_a_very_long_name import MyClass
[...]
m = MyClass(...)

now you have to do

import possibly_a_very_long_name
[...]
m = possibly_a_very_long_name.MyClass(...)

I argue, though, this verbiage is beneficial: in the same way that self.xxx is an immediate sign at the point of use that xxx is a member of the current class, x.MyClass is an immediate sign that MyClass comes from file x (which often tells you more about what MyClass is for, and makes it easier to find in the source as well). This often tells you enough that you can continue reading the code, without needing a context switch to look up the where MyClass is defined and what it says.

absolute_import

Always use from __future__ import absolute_import as the first import in your source file. Do not use relative imports (from . import foo) unless you have to.

Rationale: Suppose you have both intl.py and content/videos/intl.py, and you say import intl in your source file. Without absolute-imports, what gets imported depends on where your source file lives (in content/videos or elsewhere), which is confusing. By making all imports be related to the repository-root, it avoids ambiguity, making it easier for code readers -- and code writers! -- to figure out the exact file that's being imported.

The reason not to use relative imports is to avoid two ways of doing things: some people importing a module as an absolute path and some as a relative path makes it harder for readers to understand, harder to do tooling, and so forth. (We could have settled on always using relative imports, but combining the two does not make sense.) Sometimes, when you're doing fancy things with import hooks or the like you have to use relative imports, but minimize these cases.

This means that all non-system imports will be specified relative to the root of the KA python tree.

Import style

All imports should be at the top of the file, separated (by blank lines) into three sections: system imports, third-party imports (including appengine), and khan academy-written imports. Note that imports from third_party and shared are considered "third party" even if they are maintained by Khan Academy and only used in webapp.

Rationale: When I see autocomplete.foo() in the code, and I want to know what it does, it’s helpful to know if I should be looking on the web (because autocomplete is part of the python distribution), or in the local source tree (because autocomplete is written by us). It’s also helpful to know if it’s code we wrote (and the barrier to hacking on it is low) or code someone else wrote (which means it may be easier to just work around any problems I have with it). The three sections tell me that with just a quick glance at the top of the file. Plus, since each section is alphabetical, it’s easy for me to find the import within the section.

Alphabetical sorting is by the main module name (so second word of the line), and ignores case:

import api
from app import App
from app import Zebra
import auth_models
import NonExistentModule
[...]

Here are some constructs that are not consistent with this style rule:

from app import App, Zebra    # BAD: two imports on the same line
import .models                # BAD: relative import.  Alternative: from <curmodule> import models

Docstrings

All non-trivial methods should have docstrings. Docstrings should follow guidelines here: PEP257. For more examples, see the Google style guide around docstrings.

To summarize: There are two types of docstrings, long-form and short-form.

A short-form docstring fits entirely on one line, including the triple quotes. It is used for simple functions, especially (though by no means exclusively) ones that are not part of a public API:

"""Return a user-readable form of a Frobnozz, html-escaped."""

Note that the text is specified as an action (“return”) rather than a description (“returns”). This has the added advantage of taking less space, so the comment is more likely to fit on a single line. :-)

If the description spills past one line, you should move to the long-form docstring: a summary line (one physical line) starting with a triple-quote ("""), terminated by a period, question mark, or exclamation point, followed by a blank line, followed by the rest of the doc string starting at the same cursor position as the first quote of the first line, ending with triple-quotes on a line by themselves. (Unlike what the BDFL suggests in PEP 257, we do not put a blank line before the ending quotes.)

"""This comment serves to demonstrate the format of a docstring.

Note that the summary line is always at most one line long, and
on the same line as the opening triple-quote, and with no spaces
between the triple-quote and the text.  Always use double-quotes
(") rather than single-quotes (') for your docstring.   (This
lets us reserve ''' (3 single-quotes) as a way of commenting out
big blocks of code.
"""

A function (including methods and generators) must have a docstring, unless it meets all of the following criteria:

  • not externally visible
  • very short
  • obvious

The docstring should describe the function's calling syntax and its semantics, not its implementation.

The docstring should end with the following special sections (see the Google style guide for more details).

  • Arguments: List each parameter by name, and a description of it. The description can span several lines (use a hanging indent if so). Use instead of "Args".
  • Returns: (or Yields: for generators): Describe the type and semantics of the return value. If the function only returns None, this section is not required.
  • Raises: List all exceptions that are relevant to the interface.

Classes should follow a similar format: a single line describing the class, plus more details, but instead of Arguments/Returns/Raises, it should have an Attributes: section that lists and describes the public attributes of the class (if any).

Modules (files) should have a docstring too, at the top of the file, starting with the usual one-line summary:

"""One line summary.

Longer description.
"""

Rationale: People will read a piece of code many more times than they will write it. Time spent documenting at write-time more than pays off at read time. What is obvious to you as the code-author, well versed in the module where this function lives, may not be at all obvious to a code reader, who is possibly jumping into this function from some unrelated part of the codebase.

The rules here may seem like overkill, especially the need to document every argument and return value. I can say from experience two things: it often does seem like overkill when writing it (especially when the docstring is longer than the function!) but I've almost never thought it was overkill when reading unfamiliar code. You may find, as you write the docstring, you're putting down something that wasn't as obvious as you thought it was:

def WriteTimestamp(now):
    """Write the current time to stdout in an arbitrary user-readable format.

    Arguments:
        now: the current time as a time_t. Should be time.time() except for tests.
    """
    ...

Even though the meaning of now may seem obvious, it's not obvious that it's only being passed in so it can be mocked out for tests. Its type also isn't obvious (a time_t? a tuple? a datetime object?), so it's nice to have that documented as well.

Top of the file

Start your file with a module docstring. Do not put a shebang line (#!/usr/bin/python) or copyright notice, or anything else. Follow the docstring with your imports; don't put an __author__ line.

Exception: if the python file is meant to be executable, it should start with the following shebang line:

#!/usr/bin/env python

Rationale: a shebang line is useless for non-executable files. An __author__ line just gets out of date, and is better determined by looking at source control history in any case. Code is automatically copyrighted; a copyright line doesn't help. No need to put this useless boilerplate at the top of the file!

TODO(csilvers): should we put in a line indicating licensing?

Symbol naming

When naming a top-level symbol -- function, class, or variable -- use a leading underscore if the symbol is private to the module: that is, nobody may reference that symbol except for code in the module itself and its associate test file(s).

Modules themselves may have names starting with a leading underscore to mean that all symbols in that module are private to the package the symbols is in. A "package" is basically the top-level directory that the module is under, within webapp. (Example packages are "content", "login", and "missions.") Note that an underscored symbol within an underscored module is still considered private to the module.

Inside a class, use a leading underscore to indicate a symbol (method or class variable) is private to that class and its subclasses. If you want a symbol to be private to the class itself, and not even visible to subclasses, use a leading double-underscore.

Unused variables

If you want to assign a value to a variable that will not be used again, name the variable either _ (python convention) or unused_<something> (less-well-known python convention). This will keep our lint checkers from complaining.

Splitting lines

Using PEP8 as a guideline for Python formatting runs us head-long into a great debate: the 79-character line limit. For better or worse, the PEP8 limit is part of the lint check for Khan Academy's Python code.

Rationale: short lines have benefits, including:

  • Broad tool support. Code is read more frequently than it is written, often by those whose tools don't match the original author. Short lines are always well-supported. Many tools only naively support long lines (<pre> tags and most terminal tools), and no editor wraps long lines intelligently enough in all cases.
  • Side-by-side code windows. Try it out, it's great.
  • A free gut check. It's easy to reach 100 or 200 columns when writing complex expressions or nesting deeply. If this indicates unclear code, future readers would appreciate a quick refactoring.

Of course having a hard limit for line length is silly. Any reasonable limit runs into a case where breaking the rule produces better code. However, having unnecessarily long lines scattered about due to assumptions about a reader's tools is also silly.

Python expressions end with a newline, not a semicolon, unlike many C-based languages. The trick is that lines can be continued within parentheses, brackets, and braces, or following a backslash. Parentheses are recommended. Backslashes should be avoided.

>>> True and (True or
...           False)


>>> [x * x
...  for x
...  in xrange(0, 10)
...  if x % 2]


>>> {'Earth': 1,
...  'Jupiter': 5.3,
...  'Saturn': 9}

Notably, splitting string literals doesn't require use of the + operator. Adjacent literals are automatically combined.

>>> twist = 'Peter Piper ' 'split a set ' 'of simple strings'
>>> twist
'Peter Piper split a set of simple strings'

This makes splitting long messages easy.

>>> twist = ('Peter Piper '
...          'split a set '
...          'of simple strings')

Because Python's indentation style is unlike many C-based languages, your editor might need some cajoling to support it.

Examples of line splitting from our code:

BAD:  zero_duration_videos = video_models.Video.all().filter("duration =", 0).fetch(10000)

GOOD: zero_duration_videos = (video_models.Video.all()
                        .filter("duration =", 0)
                        .fetch(10000))
# BAD:
return current_app.response_class("OAuth error. %s" % e.message, status=401, headers=build_authenticate_header(realm="http://www.khanacademy.org"))

# GOOD:
return current_app.response_class(
    "OAuth error. %s" % e.message, status=401,
    headers=build_authenticate_header(realm="http://www.khanacademy.org"))
# Bad:
kwargs = dict((str(key), value) for key, value in topic_json.iteritems() if key in ['id', 'title', 'standalone_title', 'description', 'tags', 'hide'])
# Good:
kwargs = dict((str(key), value)
              for key, value in topic_json.iteritems()
              if key in ['id', 'title', 'standalone_title',
                         'description', 'tags', 'hide'])

Splitting tricky lines

There are cases where line splitting doesn't feel nice. Let's look at a few of them, sigh, and move on.

This long method reference needs surrounding parens and splits the line before the dot operator:

# Bad:
badge_name = badges.topic_exercise_badges.TopicExerciseBadge.name_for_topic_key_name(self.key().name())
# Good:
badge_name = (badges.topic_exercise_badges.TopicExerciseBadge
              .name_for_topic_key_name(self.key().name()))

This long string path needs to be split.

# Bad:
self.redirect("/class_profile?selected_graph_type=%s&coach_email=%s&graph_query_params=%s" %
        (self.GRAPH_TYPE, urllib.quote(coach.email), urllib.quote(urllib.quote(self.request.query_string))))
# Good:
self.redirect(
    "/class_profile?selected_graph_type=%s&coach_email=%s"
    "&graph_query_params=%s" % (
        self.GRAPH_TYPE,
        urllib.quote(coach.email),
        urllib.quote(urllib.quote(self.request.query_string))))

Sometimes, the best way to avoid long lines is to use temporary variables. This can improve readability in any case.

# Bad:
topics_list = [t for t in topics if not (
    (t.standalone_title == "California Standards Test: Algebra I" and t.id != "algebra-i") or
    (t.standalone_title == "California Standards Test: Geometry" and t.id != "geometry-2"))
    ]
# Good:
bad_title_and_ids = [("California Standards Test: Algebra I", "algebra-i"),
                     ("California Standards Test: Geometry", "geometry-2"),
                    ]
topics_list = [t for t in topics
               if not (t.standalone_title, t.id) in bad_title_and_ids]

Indenting continued lines properly

When a logical statement is split into multiple lines and is followed by an indented line, the continued lines should be indented further to set them apart from the next logical statement.

In the following example taken from http://www.python.org/dev/peps/pep-0008/#maximum-line-length, notice how the second and third lines of the if condition are indented further to differentiate them from the actual raise statement within the if block:

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")

Classes

Use classes primarily as a container for data. You can use methods for functionality that is "core" to the purpose of a class -- one way to tell is if that method has a lot of callers, rather than just one or two -- or when needed for method-name dispatch (e.g., having a bunch of different classes define their own get_search_data() so a function can call node.get_search_data() and get the right function for that node-type), but try to keep them to a minimum. Prefer top-level functions instead.

Rationale: in Python, the implementation of a class needs to be in a single file. If you have a class with many methods doing many different things, each with its own set of dependencies, then the file defining that class ends up with a huge number of dependencies. This can make the class hard to use, since importing it may cause circular imports -- or at the least, bloat your dependency graph.

For the same reason, prefer one class per file. (You can make exceptions for classes that are very closely related, or a class that is used primarily as a member of another class.)