This document describes the style guide for our documentation.
Use of whitespace#
All reST files use an indentation of 3 spaces; no tabs are allowed. The maximum line length is 80 characters for normal text, but tables, deeply indented code samples and long links may extend beyond that. Code example bodies should use normal Python 4-space indentation.
Make generous use of blank lines where applicable; they help group things together.
A sentence-ending period may be followed by one or two spaces; while reST ignores the second space, it is customarily put in by some users, for example to aid Emacs’ auto-fill mode.
Footnotes are generally discouraged, though they may be used when they are the best way to present specific information. When a footnote reference is added at the end of the sentence, it should follow the sentence-ending punctuation. The reST markup should appear something like this:
This sentence has a footnote reference. [#]_ This is the next sentence.
Footnotes should be gathered at the end of a file, or if the file is very long, at the end of a section. The docutils will automatically create backlinks to the footnote reference.
Footnotes may appear in the middle of sentences where appropriate.
In the Python documentation, the use of sentence case in section titles is preferable, but consistency within a unit is more important than following this rule. If you add a section to a chapter where most sections are in title case, you can either convert all titles to sentence case or use the dominant style in the new section title.
Sentences that start with a word for which specific rules require starting it with a lower case letter should be avoided.
Sections that describe a library module often have titles in the form of “modulename — Short description of the module.” In this case, the description should be capitalized as a stand-alone sentence.
Many special names are used in the Python documentation, including the names of operating systems, programming languages, standards bodies, and the like. Most of these entities are not assigned any special markup, but the preferred spellings are given here to aid authors in maintaining the consistency of presentation in the Python documentation.
Other terms and words deserve special mention as well; these conventions should be used to ensure consistency throughout the documentation:
For “central processing unit.” Many style guides say this should be spelled out on the first use (and if you must use it, do so!). For the Python documentation, this abbreviation should be avoided since there’s no reasonable way to predict which occurrence will be the first seen by the reader. It is better to use the word “processor” instead.
The name assigned to a particular group of standards. This is always uppercase.
The name of our favorite programming language is always capitalized.
For “reStructuredText,” an easy to read, plaintext markup syntax used to produce Python documentation. When spelled out, it is always one word and both forms start with a lower case ‘r’.
The name of a character coding system. This is always written capitalized.
The name of the operating system developed at AT&T Bell Labs in the early 1970s.
The documentation focuses on affirmatively stating what the language does and how to use it effectively.
Except for certain security or segfault risks, the docs should avoid wording along the lines of “feature x is dangerous” or “experts only”. These kinds of value judgments belong in external blogs and wikis, not in the core documentation.
Bad example (creating worry in the mind of a reader):
Warning: failing to explicitly close a file could result in lost data or excessive resource consumption. Never rely on reference counting to automatically close a file.
Good example (establishing confident knowledge in the effective use of the language):
A best practice for using files is use a try/finally pair to explicitly close a file after it is used. Alternatively, using a with-statement can achieve the same effect. This assures that files are flushed and file descriptor resources are released in a timely manner.
Economy of Expression#
More documentation is not necessarily better documentation. Err on the side of being succinct.
It is an unfortunate fact that making documentation longer can be an impediment to understanding and can result in even more ways to misread or misinterpret the text. Long descriptions full of corner cases and caveats can create the impression that a function is more complex or harder to use than it actually is.
Security Considerations (and Other Concerns)#
Some modules provided with Python are inherently exposed to security issues
(e.g. shell injection vulnerabilities) due to the purpose of the module
ssl). Littering the documentation of these modules with red
warning boxes for problems that are due to the task at hand, rather than
specifically to Python’s support for that task, doesn’t make for a good
Instead, these security concerns should be gathered into a dedicated
“Security Considerations” section within the module’s documentation, and
cross-referenced from the documentation of affected interfaces with a note
"Please refer to the :ref:`security-considerations`
section for important information on how to avoid common mistakes.".
Similarly, if there is a common error that affects many interfaces in a module (e.g. OS level pipe buffers filling up and stalling child processes), these can be documented in a “Common Errors” section and cross-referenced rather than repeated for every affected interface.
Short code examples can be a useful adjunct to understanding. Readers can often grasp a simple example more quickly than they can digest a formal description in prose.
People learn faster with concrete, motivating examples that match the context of
a typical use case. For instance, the
str.rpartition() method is better
demonstrated with an example splitting the domain from a URL than it would be
with an example of removing the last word from a line of Monty Python dialog.
The ellipsis for the
sys.ps2 secondary interpreter prompt should only
be used sparingly, where it is necessary to clearly differentiate between input
lines and output lines. Besides contributing visual clutter, it makes it
difficult for readers to cut-and-paste examples so they can experiment with
Giving pure Python code equivalents (or approximate equivalents) can be a useful adjunct to a prose description. A documenter should carefully weigh whether the code equivalent adds value.
A good example is the code equivalent for
all(). The short 4-line code
equivalent is easily digested; it re-emphasizes the early-out behavior; and it
clarifies the handling of the corner-case where the iterable is empty. In
addition, it serves as a model for people wanting to implement a commonly
requested alternative where
all() would return the specific object
evaluating to False whenever the function terminates early.
A more questionable example is the code for
itertools.groupby(). Its code
equivalent borders on being too complex to be a quick aid to understanding.
Despite its complexity, the code equivalent was kept because it serves as a
model to alternative implementations and because the operation of the “grouper”
is more easily shown in code than in English prose.
An example of when not to use a code equivalent is for the
The exact steps in converting a number to octal doesn’t add value for a user
trying to learn what the function does.
The tone of the tutorial (and all the docs) needs to be respectful of the reader’s intelligence. Don’t presume that the readers are stupid. Lay out the relevant information, show motivating use cases, provide glossary links, and do your best to connect-the-dots, but don’t talk down to them or waste their time.
The tutorial is meant for newcomers, many of whom will be using the tutorial to evaluate the language as a whole. The experience needs to be positive and not leave the reader with worries that something bad will happen if they make a misstep. The tutorial serves as guide for intelligent and curious readers, saving details for the how-to guides and other sources.
Be careful accepting requests for documentation changes from the rare but vocal category of reader who is looking for vindication for one of their programming errors (“I made a mistake, therefore the docs must be wrong …”). Typically, the documentation wasn’t consulted until after the error was made. It is unfortunate, but typically no documentation edit would have saved the user from making false assumptions about the language (“I was surprised by …”).