Appendix. Appendix – The Quick Python Book

Appendix. Appendix

A guide to Python’s documentation

The best and most current reference for Python is the documentation that comes with Python itself. With that in mind, it will be more useful to explore the ways you can access that documentation than to print pages of edited documentation.

The standard bundle of documentation has several sections, including instructions on documenting, distributing, installing, and extending Python on various platforms, and is the logical starting point when you’re looking for answers to questions about Python. The two main areas of the Python documentation that are likely to be the most useful are the Library Reference and the Language Reference. The Library Reference is absolutely essential, because it has explanations of both the built-in data types and every module included with Python. The Language Reference is the explanation of how the core of Python works, and it contains the official word on the core of the language, explaining the workings of data types, statements, and so on. The “What’s New” section is also worth reading, particularly when a new version of Python is released, because it summarizes all of the changes in the new version.

Accessing Python documentation on the Web

For many people, the most convenient way to access the Python documentation is to go to and browse the documentation collection there. Although this requires a connection to the web, it has the advantage that the content is always the most current. Given that for many projects it’s often useful to search the web for other documentation and information, having a browser tab permanently open and pointing to the online Python documentation is an easy way to have a Python reference at your fingertips.

Browsing Python documentation on your computer

Many distributions of Python include the full documentation by default. In some Linux distributions, the documentation is a separate package that you need to install separately. In most cases, however, full documentation is already on your computer and easily accessible.

Accessing help in the interactive shell or at a command line

In chapter 2, you saw how to use the help command in the interactive interpreter to access online help for any Python module or object:

>>> help(int)
Help on int object:

class int(object)
| int(x[, base]) -> integer
| Convert a string or number to an integer, if possible. A floating
| point argument will be truncated towards zero (this does not include a
| string representation of a floating point number!) When converting a
| string, use the optional base. It is an error to supply a base when
| converting a non-string.
| Methods defined here:
... (continues with a list of methods for an int)

What’s happening is that the interpreter is calling the pydoc module to generate the documentation. You can also use the pydoc module to search the Python documentation from a command line. On a Linux or Mac OS X system, to get the same output in a terminal window, you need only type pydoc int at the prompt; to exit, type q. In a Windows command window, unless you’ve set your search path to include the Python Lib directory, you’ll need to type the entire path, something like C:\Python31\Lib\ int.

Generating HTML help pages with pydoc

If you want a sleeker look to the documentation that pydoc generates for a Python object or module, you can also have the output written to an HTML file, which you can view in any browser. To do this, add the –w option to the pydoc command, which on Windows would then be C:\Python31\Lib\ –w int. In this case, where we’re looking up documentation on the int object, pydoc will create a file named int.html in the current directory, and we can open and view it in a browser from there. Figure A.1 shows what int.html looks like in a browser.

Figure A.1. int.html as generated by pydoc

If for some reason you want only a limited number pages of documentation available, this method works well. But in most cases it will probably be better to use pydoc to serve more complete documentation, as discussed in the next section.

Using pydoc as a documentation server

In addition to being able to generate text and HTML documentation on any Python object, the pydoc module can also be used as a server to serve web-based docs. You can do this two ways. Either you can run pydoc with –p and a port number to open a server on that port, or you can run pydoc–g to pop up a little search dialog box and open a browser from there, as illustrated in figure A.2.

Figure A.2. pydoc search dialog box

Clicking the Open Browser button opens your system’s default browser and gives you access to the documentation of all the modules available, as shown in figure A.3.

Figure A.3. A partial view of the module documentation served by pydoc

A bonus in using pydoc to serve documentation is that it also scans the current directory and extracts documentation from the docstrings of any modules it finds, even if they aren’t part of the standard library. This makes it useful for accessing the documentation of any Python modules. There is one caveat, however. To extract the documentation from a module, pydoc must import it, which means it will execute any code at the module’s top level. Thus scripts that aren’t written to be imported without side effects, as discussed in chapter 11, will be run, so use this feature with care.

Using the Windows Help file

On Windows systems, the standard Python 3 package includes complete Python documentation as a Windows Help file. You can find it in the Doc folder inside the folder where Python was installed (C:\Python31\Doc on my system). When you open it, it will look something like figure A.4.

Figure A.4. Python documentation in a Windows Help file

If you’re comfortable with using Window Help files, this file may be all the documentation you ever need.

Downloading documentation

If you want the Python documentation on a computer but don’t necessarily want or need to be running Python, you can also download the complete documentation from in PDF, HTML, or text format. This is convenient if you want to be able to access the docs from an ebook reader or similar device.

The Python manual of style

This section contains a slightly edited excerpt from PEP (Python Enhancement Proposal) 8. Written by Guido van Rossum and Barry Warsaw, PEP 8 is the closest thing Python has to a style manual. Some more-specific sections have been omitted, but the main points are covered. You should make your code conform to PEP 8 as much as possible—your Python style will be the better for it.

You can access the full text of PEP 8 and all of the other PEPs issued in the history of Python by going to’s documentation section and looking for the PEP index. The PEPs are an excellent source for the history and lore of Python as well as explanations of current issues and future plans.

PEP 8 - Style Guide for Python Code



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 was adapted from Guido’s original Python Style Guide essay,[2] with some additions from Barry’s style guide.[3] Where there’s conflict, Guido’s style rules for the purposes of this PEP. This PEP may still be incomplete (in fact, it may never be finished <wink>).

1 PEP 7, Style Guide for C Code, van Rossum


3 Barry’s GNU Mailman style guide:

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’s 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[4] says, “Readability counts.”

4 PEP 20, The Zen of Python

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 important, 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!

Here are two good reasons to break a particular rule:

  • When applying the rule would make the code less readable, even for someone who is used to reading code that follows the rules
  • 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 layout


Use four spaces per indentation level.

For really old code that you don’t want to mess up, you can continue to use eight-space tabs.

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 you invoke the Python command-line interpreter with the -t option, it issues warnings about code that illegally mixes tabs and spaces. When you use -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.

Many devices are still 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. If necessary, you can add an extra pair of parentheses around an expression, but sometimes using a backslash looks better. Make sure to indent the continued line appropriately. The preferred place to break around a binary operator is after the operator, not before it. Here are 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 (for example, a set of dummy implementations).

Use blank lines in functions, sparingly, to indicate logical sections.

Python accepts the Control-L (^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.


Imports should usually be on separate lines, for example:

import os
import sys

Don’t put them together like this:

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

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[5] 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.

5 PEP 328, Imports: Multi-Line and Absolute/Relative

When importing a class from a class-containing module, it’s usually okay to spell them

from myclass import MyClass
from import YourClass

If this spelling causes local name clashes, then spell them

import myclass
and use myclass.MyClass and
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:
    • 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 (+=, -=, and so on), comparisons (==, <, >, !=, <>, <=, >=, in, not in, is, is not), and Booleans (and, or, not).

Use spaces around arithmetic operators.

  • Yes:
    i = i + 1
    submitted += 1
    x = x * 2 – 1
    hypot2 = x * x + y * y
    c = (a + b) * (a - b)
  • No:
    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':
  • 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 multiclause 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 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’s an identifier that begins with a lowercase 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 (aka docstrings) are immortalized in PEP 257.[6]

6 PEP 257, Docstring Conventions, Goodger, van Rossum

Write docstrings for all public modules, functions, classes, and methods. Docstrings are not necessary for nonpublic 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, for example:

"""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: 68852 $"     # $Source$

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 many different naming styles. It helps to be able to recognize what naming style is being used, independent of what it’s used for.

The following naming styles are commonly distinguished:

  • b (single lowercase letter)
  • B (single uppercase letter)
  • lowercase
  • lower_case_with_underscores
  • CapitalizedWords (or CapWords, or CamelCase—so named because of the bumpy look of its letters[7]). 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 seldom used in Python, but I mention it 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. For example, 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. For example, tkinter.Toplevel(master, class_='ClassName').
  • __double_leading_underscore When naming a class attribute, it 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. For example, __init__, __import__ or __file__. Never invent such names; use them only 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 1 (one) and 0 (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 a 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 filenames, and some file systems are case insensitive and truncate long names, it’s important that module names 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 (for example, more object-oriented) interface, the C/C++ module has a leading underscore (for example, _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 nonpublic).
  • 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 (for example,, to retain backward 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’s generally better to append a single trailing underscore than to use an abbreviation or spelling corruption. Thus, print_ is better than prnt. (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 nonpublic 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 declared 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 called attributes) should be public or nonpublic. If in doubt, choose nonpublic; it’s easier to make it public later than to make a public attribute nonpublic. Public attributes are those that you expect unrelated clients of your class to use, with your commitment to avoid backward-incompatible changes. Nonpublic attributes are those that are not intended to be used by third parties; you make no guarantees that nonpublic 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 includes 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 that’s 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’s 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 work only 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 don’t 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: 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

You should write code in a way that does not disadvantage other implementations of Python (PyPy, Jython, IronPython, Pyrex, Psyco, and such).

For example, don’t 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, you should use the ''.join() form 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, for example, 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!

Use class-based exceptions.

String exceptions in new code are forbidden, because 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, for example:

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 has been removed in Python 3.

When catching exceptions, mention specific exceptions whenever possible instead of using a bare except: clause. For example, use

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:.

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 upward with raise, then try...finally is a better way to handle this case.

In addition, for all try/except clauses, limit the try clause to the absolute minimum amount of code necessary. Again, this avoids masking bugs.

  • Yes:
    value = collection[key]
    except KeyError:
    return key_not_found(key)
    return handle_value(value)
  • No:

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 Python versions 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.

  • 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 to see 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 the following:

if isinstance(obj, basestring):

In Python 2.2, the types module has the StringTypes type defined for that purpose, for example:

from types import StringTypes
if isinstance(obj, StringTypes):

In Python 2.0 and 2.1, you should do the following:

from types import StringType, UnicodeType
if isinstance(obj, StringType) or \
isinstance(obj, UnicodeType) :

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, 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:

Copyright—this document has been placed in the public domain.

The Zen of Python

The following document is PEP 20, also referred to as “The Zen of Python,” a slightly tongue-in-cheek statement of the philosophy of Python. In addition to being included in the Python documentation, the Zen of Python is also an Easter egg in the Python interpreter. Type import this at the interactive prompt to see it.

Long time Pythoneer Tim Peters succinctly channels the BDFL’s (Benevolent Dictator for Life) guiding principles for Python’s design into 20 aphorisms, only 19 of which have been written down.

The Zen of Python

  • Beautiful is better than ugly.
  • Explicit is better than implicit.
  • Simple is better than complex.
  • Complex is better than complicated.
  • Flat is better than nested.
  • Sparse is better than dense.
  • Readability counts.
  • Special cases aren’t special enough to break the rules.
  • Although practicality beats purity.
  • Errors should never pass silently.
  • Unless explicitly silenced.
  • In the face of ambiguity, refuse the temptation to guess.
  • There should be one—and preferably only one—obvious way to do it.
  • Although that way may not be obvious at first unless you’re Dutch.
  • Now is better than never.
  • Although never is often better than *right* now.
  • If the implementation is hard to explain, it’s a bad idea.
  • If the implementation is easy to explain, it may be a good idea.
  • Namespaces are one honking great idea—let’s do more of those!

Copyright—This document has been placed in the public domain.