Glossary
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">>>"
   The default Python prompt of the interactive shell.  Often seen for
   code examples which can be executed interactively in the
   interpreter.

"..."
   The default Python prompt of the interactive shell when entering
   code for an indented code block, when within a pair of matching
   left and right delimiters (parentheses, square brackets, curly
   braces or triple quotes), or after specifying a decorator.

2to3
   A tool that tries to convert Python 2.x code to Python 3.x code by
   handling most of the incompatibilities which can be detected by
   parsing the source and traversing the parse tree.

   2to3 is available in the standard library as "lib2to3"; a
   standalone entry point is provided as "Tools/scripts/2to3".  See
   2to3 - Automated Python 2 to 3 code translation.

abstract base class
   Abstract base classes complement *duck-typing* by providing a way
   to define interfaces when other techniques like "hasattr()" would
   be clumsy or subtly wrong (for example with magic methods).  ABCs
   introduce virtual subclasses, which are classes that don’t inherit
   from a class but are still recognized by "isinstance()" and
   "issubclass()"; see the "abc" module documentation.  Python comes
   with many built-in ABCs for data structures (in the "collections"
   module), numbers (in the "numbers" module), and streams (in the
   "io" module). You can create your own ABCs with the "abc" module.

argument
   A value passed to a *function* (or *method*) when calling the
   function.  There are two types of arguments:

   * *keyword argument*: an argument preceded by an identifier (e.g.
     "name=") in a function call or passed as a value in a dictionary
     preceded by "**".  For example, "3" and "5" are both keyword
     arguments in the following calls to "complex()":

        complex(real=3, imag=5)
        complex(**{'real': 3, 'imag': 5})

   * *positional argument*: an argument that is not a keyword
     argument. Positional arguments can appear at the beginning of an
     argument list and/or be passed as elements of an *iterable*
     preceded by "*". For example, "3" and "5" are both positional
     arguments in the following calls:

        complex(3, 5)
        complex(*(3, 5))

   Arguments are assigned to the named local variables in a function
   body. See the Calls section for the rules governing this
   assignment. Syntactically, any expression can be used to represent
   an argument; the evaluated value is assigned to the local variable.

   See also the *parameter* glossary entry and the FAQ question on the
   difference between arguments and parameters.

attribute
   A value associated with an object which is referenced by name using
   dotted expressions.  For example, if an object *o* has an attribute
   *a* it would be referenced as *o.a*.

BDFL
   Benevolent Dictator For Life, a.k.a. Guido van Rossum, Python’s
   creator.

bytes-like object
   An object that supports the buffer protocol, like "str",
   "bytearray" or "memoryview". Bytes-like objects can be used for
   various operations that expect binary data, such as compression,
   saving to a binary file or sending over a socket. Some operations
   need the binary data to be mutable, in which case not all bytes-
   like objects can apply.

bytecode
   Python source code is compiled into bytecode, the internal
   representation of a Python program in the CPython interpreter.  The
   bytecode is also cached in ".pyc" and ".pyo" files so that
   executing the same file is faster the second time (recompilation
   from source to bytecode can be avoided).  This “intermediate
   language” is said to run on a *virtual machine* that executes the
   machine code corresponding to each bytecode. Do note that bytecodes
   are not expected to work between different Python virtual machines,
   nor to be stable between Python releases.

   A list of bytecode instructions can be found in the documentation
   for the dis module.

class
   A template for creating user-defined objects. Class definitions
   normally contain method definitions which operate on instances of
   the class.

classic class
   Any class which does not inherit from "object".  See *new-style
   class*.  Classic classes have been removed in Python 3.

coercion
   The implicit conversion of an instance of one type to another
   during an operation which involves two arguments of the same type.
   For example, "int(3.15)" converts the floating point number to the
   integer "3", but in "3+4.5", each argument is of a different type
   (one int, one float), and both must be converted to the same type
   before they can be added or it will raise a "TypeError".  Coercion
   between two operands can be performed with the "coerce" built-in
   function; thus, "3+4.5" is equivalent to calling
   "operator.add(*coerce(3, 4.5))" and results in "operator.add(3.0,
   4.5)".  Without coercion, all arguments of even compatible types
   would have to be normalized to the same value by the programmer,
   e.g., "float(3)+4.5" rather than just "3+4.5".

complex number
   An extension of the familiar real number system in which all
   numbers are expressed as a sum of a real part and an imaginary
   part.  Imaginary numbers are real multiples of the imaginary unit
   (the square root of "-1"), often written "i" in mathematics or "j"
   in engineering.  Python has built-in support for complex numbers,
   which are written with this latter notation; the imaginary part is
   written with a "j" suffix, e.g., "3+1j".  To get access to complex
   equivalents of the "math" module, use "cmath".  Use of complex
   numbers is a fairly advanced mathematical feature.  If you’re not
   aware of a need for them, it’s almost certain you can safely ignore
   them.

context manager
   An object which controls the environment seen in a "with" statement
   by defining "__enter__()" and "__exit__()" methods. See **PEP
   343**.

CPython
   The canonical implementation of the Python programming language, as
   distributed on python.org.  The term “CPython” is used when
   necessary to distinguish this implementation from others such as
   Jython or IronPython.

decorator
   A function returning another function, usually applied as a
   function transformation using the "@wrapper" syntax.  Common
   examples for decorators are "classmethod()" and "staticmethod()".

   The decorator syntax is merely syntactic sugar, the following two
   function definitions are semantically equivalent:

      def f(...):
          ...
      f = staticmethod(f)

      @staticmethod
      def f(...):
          ...

   The same concept exists for classes, but is less commonly used
   there.  See the documentation for function definitions and class
   definitions for more about decorators.

descriptor
   Any *new-style* object which defines the methods "__get__()",
   "__set__()", or "__delete__()".  When a class attribute is a
   descriptor, its special binding behavior is triggered upon
   attribute lookup.  Normally, using *a.b* to get, set or delete an
   attribute looks up the object named *b* in the class dictionary for
   *a*, but if *b* is a descriptor, the respective descriptor method
   gets called.  Understanding descriptors is a key to a deep
   understanding of Python because they are the basis for many
   features including functions, methods, properties, class methods,
   static methods, and reference to super classes.

   For more information about descriptors’ methods, see Implementing
   Descriptors.

dictionary
   An associative array, where arbitrary keys are mapped to values.
   The keys can be any object with "__hash__()"  and "__eq__()"
   methods. Called a hash in Perl.

dictionary view
   The objects returned from "dict.viewkeys()", "dict.viewvalues()",
   and "dict.viewitems()" are called dictionary views. They provide a
   dynamic view on the dictionary’s entries, which means that when the
   dictionary changes, the view reflects these changes. To force the
   dictionary view to become a full list use "list(dictview)".  See
   Dictionary view objects.

docstring
   A string literal which appears as the first expression in a class,
   function or module.  While ignored when the suite is executed, it
   is recognized by the compiler and put into the "__doc__" attribute
   of the enclosing class, function or module.  Since it is available
   via introspection, it is the canonical place for documentation of
   the object.

duck-typing
   A programming style which does not look at an object’s type to
   determine if it has the right interface; instead, the method or
   attribute is simply called or used (“If it looks like a duck and
   quacks like a duck, it must be a duck.”)  By emphasizing interfaces
   rather than specific types, well-designed code improves its
   flexibility by allowing polymorphic substitution.  Duck-typing
   avoids tests using "type()" or "isinstance()".  (Note, however,
   that duck-typing can be complemented with *abstract base classes*.)
   Instead, it typically employs "hasattr()" tests or *EAFP*
   programming.

EAFP
   Easier to ask for forgiveness than permission.  This common Python
   coding style assumes the existence of valid keys or attributes and
   catches exceptions if the assumption proves false.  This clean and
   fast style is characterized by the presence of many "try" and
   "except" statements.  The technique contrasts with the *LBYL* style
   common to many other languages such as C.

expression
   A piece of syntax which can be evaluated to some value.  In other
   words, an expression is an accumulation of expression elements like
   literals, names, attribute access, operators or function calls
   which all return a value.  In contrast to many other languages, not
   all language constructs are expressions.  There are also
   *statement*s which cannot be used as expressions, such as "print"
   or "if".  Assignments are also statements, not expressions.

extension module
   A module written in C or C++, using Python’s C API to interact with
   the core and with user code.

file object
   An object exposing a file-oriented API (with methods such as
   "read()" or "write()") to an underlying resource.  Depending on the
   way it was created, a file object can mediate access to a real on-
   disk file or to another type of storage or communication device
   (for example standard input/output, in-memory buffers, sockets,
   pipes, etc.).  File objects are also called *file-like objects* or
   *streams*.

   There are actually three categories of file objects: raw binary
   files, buffered binary files and text files.  Their interfaces are
   defined in the "io" module.  The canonical way to create a file
   object is by using the "open()" function.

file-like object
   A synonym for *file object*.

finder
   An object that tries to find the *loader* for a module. It must
   implement a method named "find_module()". See **PEP 302** for
   details.

floor division
   Mathematical division that rounds down to nearest integer.  The
   floor division operator is "//".  For example, the expression "11
   // 4" evaluates to "2" in contrast to the "2.75" returned by float
   true division.  Note that "(-11) // 4" is "-3" because that is
   "-2.75" rounded *downward*. See **PEP 238**.

function
   A series of statements which returns some value to a caller. It can
   also be passed zero or more *arguments* which may be used in the
   execution of the body. See also *parameter*, *method*, and the
   Function definitions section.

__future__
   A pseudo-module which programmers can use to enable new language
   features which are not compatible with the current interpreter.
   For example, the expression "11/4" currently evaluates to "2". If
   the module in which it is executed had enabled *true division* by
   executing:

      from __future__ import division

   the expression "11/4" would evaluate to "2.75".  By importing the
   "__future__" module and evaluating its variables, you can see when
   a new feature was first added to the language and when it will
   become the default:

      >>> import __future__
      >>> __future__.division
      _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)

garbage collection
   The process of freeing memory when it is not used anymore.  Python
   performs garbage collection via reference counting and a cyclic
   garbage collector that is able to detect and break reference
   cycles.

generator
   A function which returns an iterator.  It looks like a normal
   function except that it contains "yield" statements for producing a
   series of values usable in a for-loop or that can be retrieved one
   at a time with the "next()" function. Each "yield" temporarily
   suspends processing, remembering the location execution state
   (including local variables and pending try-statements).  When the
   generator resumes, it picks up where it left off (in contrast to
   functions which start fresh on every invocation).

generator expression
   An expression that returns an iterator.  It looks like a normal
   expression followed by a "for" expression defining a loop variable,
   range, and an optional "if" expression.  The combined expression
   generates values for an enclosing function:

      >>> sum(i*i for i in range(10))         # sum of squares 0, 1, 4, ... 81
      285

GIL
   See *global interpreter lock*.

global interpreter lock
   The mechanism used by the *CPython* interpreter to assure that only
   one thread executes Python *bytecode* at a time. This simplifies
   the CPython implementation by making the object model (including
   critical built-in types such as "dict") implicitly safe against
   concurrent access.  Locking the entire interpreter makes it easier
   for the interpreter to be multi-threaded, at the expense of much of
   the parallelism afforded by multi-processor machines.

   However, some extension modules, either standard or third-party,
   are designed so as to release the GIL when doing computationally-
   intensive tasks such as compression or hashing.  Also, the GIL is
   always released when doing I/O.

   Past efforts to create a “free-threaded” interpreter (one which
   locks shared data at a much finer granularity) have not been
   successful because performance suffered in the common single-
   processor case. It is believed that overcoming this performance
   issue would make the implementation much more complicated and
   therefore costlier to maintain.

hashable
   An object is *hashable* if it has a hash value which never changes
   during its lifetime (it needs a "__hash__()" method), and can be
   compared to other objects (it needs an "__eq__()" or "__cmp__()"
   method). Hashable objects which compare equal must have the same
   hash value.

   Hashability makes an object usable as a dictionary key and a set
   member, because these data structures use the hash value
   internally.

   All of Python’s immutable built-in objects are hashable, while no
   mutable containers (such as lists or dictionaries) are.  Objects
   which are instances of user-defined classes are hashable by
   default; they all compare unequal (except with themselves), and
   their hash value is derived from their "id()".

IDLE
   An Integrated Development Environment for Python.  IDLE is a basic
   editor and interpreter environment which ships with the standard
   distribution of Python.

immutable
   An object with a fixed value.  Immutable objects include numbers,
   strings and tuples.  Such an object cannot be altered.  A new
   object has to be created if a different value has to be stored.
   They play an important role in places where a constant hash value
   is needed, for example as a key in a dictionary.

integer division
   Mathematical division discarding any remainder.  For example, the
   expression "11/4" currently evaluates to "2" in contrast to the
   "2.75" returned by float division.  Also called *floor division*.
   When dividing two integers the outcome will always be another
   integer (having the floor function applied to it). However, if one
   of the operands is another numeric type (such as a "float"), the
   result will be coerced (see *coercion*) to a common type.  For
   example, an integer divided by a float will result in a float
   value, possibly with a decimal fraction.  Integer division can be
   forced by using the "//" operator instead of the "/" operator.  See
   also *__future__*.

importing
   The process by which Python code in one module is made available to
   Python code in another module.

importer
   An object that both finds and loads a module; both a *finder* and
   *loader* object.

interactive
   Python has an interactive interpreter which means you can enter
   statements and expressions at the interpreter prompt, immediately
   execute them and see their results.  Just launch "python" with no
   arguments (possibly by selecting it from your computer’s main
   menu). It is a very powerful way to test out new ideas or inspect
   modules and packages (remember "help(x)").

interpreted
   Python is an interpreted language, as opposed to a compiled one,
   though the distinction can be blurry because of the presence of the
   bytecode compiler.  This means that source files can be run
   directly without explicitly creating an executable which is then
   run. Interpreted languages typically have a shorter
   development/debug cycle than compiled ones, though their programs
   generally also run more slowly.  See also *interactive*.

iterable
   An object capable of returning its members one at a time. Examples
   of iterables include all sequence types (such as "list", "str", and
   "tuple") and some non-sequence types like "dict" and "file" and
   objects of any classes you define with an "__iter__()" or
   "__getitem__()" method.  Iterables can be used in a "for" loop and
   in many other places where a sequence is needed ("zip()", "map()",
   …).  When an iterable object is passed as an argument to the built-
   in function "iter()", it returns an iterator for the object.  This
   iterator is good for one pass over the set of values.  When using
   iterables, it is usually not necessary to call "iter()" or deal
   with iterator objects yourself.  The "for" statement does that
   automatically for you, creating a temporary unnamed variable to
   hold the iterator for the duration of the loop.  See also
   *iterator*, *sequence*, and *generator*.

iterator
   An object representing a stream of data.  Repeated calls to the
   iterator’s "next()" method return successive items in the stream.
   When no more data are available a "StopIteration" exception is
   raised instead.  At this point, the iterator object is exhausted
   and any further calls to its "next()" method just raise
   "StopIteration" again.  Iterators are required to have an
   "__iter__()" method that returns the iterator object itself so
   every iterator is also iterable and may be used in most places
   where other iterables are accepted.  One notable exception is code
   which attempts multiple iteration passes.  A container object (such
   as a "list") produces a fresh new iterator each time you pass it to
   the "iter()" function or use it in a "for" loop.  Attempting this
   with an iterator will just return the same exhausted iterator
   object used in the previous iteration pass, making it appear like
   an empty container.

   More information can be found in Iterator Types.

key function
   A key function or collation function is a callable that returns a
   value used for sorting or ordering.  For example,
   "locale.strxfrm()" is used to produce a sort key that is aware of
   locale specific sort conventions.

   A number of tools in Python accept key functions to control how
   elements are ordered or grouped.  They include "min()", "max()",
   "sorted()", "list.sort()", "heapq.nsmallest()", "heapq.nlargest()",
   and "itertools.groupby()".

   There are several ways to create a key function.  For example. the
   "str.lower()" method can serve as a key function for case
   insensitive sorts.  Alternatively, an ad-hoc key function can be
   built from a "lambda" expression such as "lambda r: (r[0], r[2])".
   Also, the "operator" module provides three key function
   constructors: "attrgetter()", "itemgetter()", and "methodcaller()".
   See the Sorting HOW TO for examples of how to create and use key
   functions.

keyword argument
   See *argument*.

lambda
   An anonymous inline function consisting of a single *expression*
   which is evaluated when the function is called.  The syntax to
   create a lambda function is "lambda [parameters]: expression"

LBYL
   Look before you leap.  This coding style explicitly tests for pre-
   conditions before making calls or lookups.  This style contrasts
   with the *EAFP* approach and is characterized by the presence of
   many "if" statements.

   In a multi-threaded environment, the LBYL approach can risk
   introducing a race condition between “the looking” and “the
   leaping”.  For example, the code, "if key in mapping: return
   mapping[key]" can fail if another thread removes *key* from
   *mapping* after the test, but before the lookup. This issue can be
   solved with locks or by using the EAFP approach.

list
   A built-in Python *sequence*.  Despite its name it is more akin to
   an array in other languages than to a linked list since access to
   elements is O(1).

list comprehension
   A compact way to process all or part of the elements in a sequence
   and return a list with the results.  "result = ["0x%02x" % x for x
   in range(256) if x % 2 == 0]" generates a list of strings
   containing even hex numbers (0x..) in the range from 0 to 255. The
   "if" clause is optional.  If omitted, all elements in "range(256)"
   are processed.

loader
   An object that loads a module. It must define a method named
   "load_module()". A loader is typically returned by a *finder*. See
   **PEP 302** for details.

mapping
   A container object that supports arbitrary key lookups and
   implements the methods specified in the "Mapping" or
   "MutableMapping" abstract base classes.  Examples include "dict",
   "collections.defaultdict", "collections.OrderedDict" and
   "collections.Counter".

metaclass
   The class of a class.  Class definitions create a class name, a
   class dictionary, and a list of base classes.  The metaclass is
   responsible for taking those three arguments and creating the
   class.  Most object oriented programming languages provide a
   default implementation.  What makes Python special is that it is
   possible to create custom metaclasses.  Most users never need this
   tool, but when the need arises, metaclasses can provide powerful,
   elegant solutions.  They have been used for logging attribute
   access, adding thread-safety, tracking object creation,
   implementing singletons, and many other tasks.

   More information can be found in Customizing class creation.

method
   A function which is defined inside a class body.  If called as an
   attribute of an instance of that class, the method will get the
   instance object as its first *argument* (which is usually called
   "self"). See *function* and *nested scope*.

method resolution order
   Method Resolution Order is the order in which base classes are
   searched for a member during lookup. See The Python 2.3 Method
   Resolution Order for details of the algorithm used by the Python
   interpreter since the 2.3 release.

module
   An object that serves as an organizational unit of Python code.
   Modules have a namespace containing arbitrary Python objects.
   Modules are loaded into Python by the process of *importing*.

   See also *package*.

MRO
   See *method resolution order*.

mutable
   Mutable objects can change their value but keep their "id()".  See
   also *immutable*.

named tuple
   Any tuple-like class whose indexable elements are also accessible
   using named attributes (for example, "time.localtime()" returns a
   tuple-like object where the *year* is accessible either with an
   index such as "t[0]" or with a named attribute like "t.tm_year").

   A named tuple can be a built-in type such as "time.struct_time", or
   it can be created with a regular class definition.  A full featured
   named tuple can also be created with the factory function
   "collections.namedtuple()".  The latter approach automatically
   provides extra features such as a self-documenting representation
   like "Employee(name='jones', title='programmer')".

namespace
   The place where a variable is stored.  Namespaces are implemented
   as dictionaries.  There are the local, global and built-in
   namespaces as well as nested namespaces in objects (in methods).
   Namespaces support modularity by preventing naming conflicts.  For
   instance, the functions "__builtin__.open()" and "os.open()" are
   distinguished by their namespaces.  Namespaces also aid readability
   and maintainability by making it clear which module implements a
   function.  For instance, writing "random.seed()" or
   "itertools.izip()" makes it clear that those functions are
   implemented by the "random" and "itertools" modules, respectively.

nested scope
   The ability to refer to a variable in an enclosing definition.  For
   instance, a function defined inside another function can refer to
   variables in the outer function.  Note that nested scopes work only
   for reference and not for assignment which will always write to the
   innermost scope.  In contrast, local variables both read and write
   in the innermost scope.  Likewise, global variables read and write
   to the global namespace.

new-style class
   Any class which inherits from "object".  This includes all built-in
   types like "list" and "dict".  Only new-style classes can use
   Python’s newer, versatile features like "__slots__", descriptors,
   properties, and "__getattribute__()".

   More information can be found in New-style and classic classes.

object
   Any data with state (attributes or value) and defined behavior
   (methods).  Also the ultimate base class of any *new-style class*.

package
   A Python *module* which can contain submodules or recursively,
   subpackages.  Technically, a package is a Python module with an
   "__path__" attribute.

parameter
   A named entity in a *function* (or method) definition that
   specifies an *argument* (or in some cases, arguments) that the
   function can accept.  There are four types of parameters:

   * *positional-or-keyword*: specifies an argument that can be
     passed either *positionally* or as a *keyword argument*.  This is
     the default kind of parameter, for example *foo* and *bar* in the
     following:

        def func(foo, bar=None): ...

   * *positional-only*: specifies an argument that can be supplied
     only by position.  Python has no syntax for defining positional-
     only parameters.  However, some built-in functions have
     positional-only parameters (e.g. "abs()").

   * *var-positional*: specifies that an arbitrary sequence of
     positional arguments can be provided (in addition to any
     positional arguments already accepted by other parameters).  Such
     a parameter can be defined by prepending the parameter name with
     "*", for example *args* in the following:

        def func(*args, **kwargs): ...

   * *var-keyword*: specifies that arbitrarily many keyword
     arguments can be provided (in addition to any keyword arguments
     already accepted by other parameters).  Such a parameter can be
     defined by prepending the parameter name with "**", for example
     *kwargs* in the example above.

   Parameters can specify both optional and required arguments, as
   well as default values for some optional arguments.

   See also the *argument* glossary entry, the FAQ question on the
   difference between arguments and parameters, and the Function
   definitions section.

PEP
   Python Enhancement Proposal. A PEP is a design document providing
   information to the Python community, or describing a new feature
   for Python or its processes or environment. PEPs should provide a
   concise technical specification and a rationale for proposed
   features.

   PEPs are intended to be the primary mechanisms for proposing major
   new features, for collecting community input on an issue, and for
   documenting the design decisions that have gone into Python. The
   PEP author is responsible for building consensus within the
   community and documenting dissenting opinions.

   See **PEP 1**.

positional argument
   See *argument*.

Python 3000
   Nickname for the Python 3.x release line (coined long ago when the
   release of version 3 was something in the distant future.)  This is
   also abbreviated “Py3k”.

Pythonic
   An idea or piece of code which closely follows the most common
   idioms of the Python language, rather than implementing code using
   concepts common to other languages.  For example, a common idiom in
   Python is to loop over all elements of an iterable using a "for"
   statement.  Many other languages don’t have this type of construct,
   so people unfamiliar with Python sometimes use a numerical counter
   instead:

      for i in range(len(food)):
          print food[i]

   As opposed to the cleaner, Pythonic method:

      for piece in food:
          print piece

reference count
   The number of references to an object.  When the reference count of
   an object drops to zero, it is deallocated.  Reference counting is
   generally not visible to Python code, but it is a key element of
   the *CPython* implementation.  The "sys" module defines a
   "getrefcount()" function that programmers can call to return the
   reference count for a particular object.

__slots__
   A declaration inside a *new-style class* that saves memory by pre-
   declaring space for instance attributes and eliminating instance
   dictionaries.  Though popular, the technique is somewhat tricky to
   get right and is best reserved for rare cases where there are large
   numbers of instances in a memory-critical application.

sequence
   An *iterable* which supports efficient element access using integer
   indices via the "__getitem__()" special method and defines a
   "len()" method that returns the length of the sequence. Some built-
   in sequence types are "list", "str", "tuple", and "unicode". Note
   that "dict" also supports "__getitem__()" and "__len__()", but is
   considered a mapping rather than a sequence because the lookups use
   arbitrary *immutable* keys rather than integers.

slice
   An object usually containing a portion of a *sequence*.  A slice is
   created using the subscript notation, "[]" with colons between
   numbers when several are given, such as in "variable_name[1:3:5]".
   The bracket (subscript) notation uses "slice" objects internally
   (or in older versions, "__getslice__()" and "__setslice__()").

special method
   A method that is called implicitly by Python to execute a certain
   operation on a type, such as addition.  Such methods have names
   starting and ending with double underscores.  Special methods are
   documented in Special method names.

statement
   A statement is part of a suite (a “block” of code).  A statement is
   either an *expression* or one of several constructs with a keyword,
   such as "if", "while" or "for".

struct sequence
   A tuple with named elements. Struct sequences expose an interface
   similiar to *named tuple* in that elements can be accessed either
   by index or as an attribute. However, they do not have any of the
   named tuple methods like "_make()" or "_asdict()". Examples of
   struct sequences include "sys.float_info" and the return value of
   "os.stat()".

triple-quoted string
   A string which is bound by three instances of either a quotation
   mark (“) or an apostrophe (‘).  While they don’t provide any
   functionality not available with single-quoted strings, they are
   useful for a number of reasons.  They allow you to include
   unescaped single and double quotes within a string and they can
   span multiple lines without the use of the continuation character,
   making them especially useful when writing docstrings.

type
   The type of a Python object determines what kind of object it is;
   every object has a type.  An object’s type is accessible as its
   "__class__" attribute or can be retrieved with "type(obj)".

universal newlines
   A manner of interpreting text streams in which all of the following
   are recognized as ending a line: the Unix end-of-line convention
   "'\n'", the Windows convention "'\r\n'", and the old Macintosh
   convention "'\r'".  See **PEP 278** and **PEP 3116**, as well as
   "str.splitlines()" for an additional use.

virtual environment
   A cooperatively isolated runtime environment that allows Python
   users and applications to install and upgrade Python distribution
   packages without interfering with the behaviour of other Python
   applications running on the same system.

virtual machine
   A computer defined entirely in software.  Python’s virtual machine
   executes the *bytecode* emitted by the bytecode compiler.

Zen of Python
   Listing of Python design principles and philosophies that are
   helpful in understanding and using the language.  The listing can
   be found by typing “"import this"” at the interactive prompt.
