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py3k / glossary

Python v3.1.1







  • 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 or within a pair of matching left and right delimiters (parentheses, square brackets or curly braces).


  • A tool that tries to convert Python 2.x code to Python 3.x code by handling most of the incompatibilites 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 (abbreviated ABCs) complement *duck-typing* by providing a way to define interfaces when other techniques like hasattr() would be clumsy. Python comes with many builtin ABCs for data structures (in the collections module), numbers (in the numbers module), and streams (in the io module). You can create your own ABC with the abc module.


  • A value passed to a function or method, assigned to a named local variable in the function body. A function or method may have both positional arguments and keyword arguments in its definition. Positional and keyword arguments may be variable-length: * accepts or passes (if in the function definition or call) several positional arguments in a list, while ** does the same for keyword arguments in a dictionary. Any expression may be used within the argument list, and the evaluated value is passed to the local variable.


  • 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*.


  • Benevolent Dictator For Life, a.k.a. Guido van Rossum, Python's creator.


  • Python source code is compiled into bytecode, the internal representation of a Python program in the 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.


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


  • 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. 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 builtin 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**.


  • The canonical implementation of the Python programming language. The term "CPython" is used in contexts when necessary to distinguish this implementation from others such as Jython or



  • 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.


  • Any 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*.


  • An associative array, where arbitrary keys are mapped to values.

    The use of dict closely resembles that for list, but the keys can be any object with a hash() function, not just integers. Called a hash in Perl.


  • 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.


  • A pythonic programming style which determines an object's type by inspection of its method or attribute signature rather than by explicit relationship to some type object ("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.


  • 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.


  • 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 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.

floor division

  • Mathematical division discarding any remainder. 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.


  • 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 *argument* and *method*.


  • A pseudo module which programmers can use to enable new language features which are not compatible with the current interpreter.

    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 becomes the default:

    •    1    >>> import __future__ 
         2    >>> __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.


  • A function which returns an iterator. It looks like a normal function except that values are returned to the caller using a

    yield statement instead of a return statement. Generator functions often contain one or more for or while loops which yield elements back to the caller. The function execution is stopped at the yield keyword (returning the result) and is resumed there when the next element is requested by calling the next() method of the returned iterator.

generator expression

  • An expression that returns a generator. 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


  • See *global interpreter lock*.

global interpreter lock

  • The lock used by Python threads to assure that only one thread executes in the *CPython* *virtual machine* at a time. This simplifies the CPython implementation by assuring that no two processes can access the same memory at the same time. 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. Efforts have been made in the past to create a "free-threaded" interpreter (one which locks shared data at a much finer granularity), but so far none have been successful because performance suffered in the common single-processor case.


  • 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() 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, and their hash value is their id().


  • An Integrated Development Environment for Python. IDLE is a basic editor and interpreter environment which ships with the standard distribution of Python. Good for beginners, it also serves as clear example code for those wanting to implement a moderately sophisticated, multi-platform GUI application.


  • 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.


  • 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)).


  • 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*.


  • A container 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 builtin 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*.


  • An object representing a stream of data. Repeated calls to the

    iterator's next() (or passing it to the builtin function) 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*.

keyword argument

  • Arguments which are preceded with a variable_name= in the call. The variable name designates the local name in the function to which the value is assigned. ** is used to accept or pass a dictionary of keyword arguments. See *argument*.


  • 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 [arguments]: expression


  • 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.


  • 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 are 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.


  • A container object (such as dict) which supports arbitrary key lookups using the special method getitem().


  • 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*.


  • 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*.


  • 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').


  • The place where a variable is stored. Namespaces are implemented as dictionaries. There are the local, global and builtin namespaces as well as nested namespaces in objects (in methods). Namespaces support modularity by preventing naming conflicts. For instance, the functions and 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

  • Old name for the flavor of classes now used for all class objects. In earlier Python versions, only new-style classes could use

    Python's newer, versatile features like slots, descriptors, properties, getattribute(), class methods, and static methods.


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

positional argument

  • The arguments assigned to local names inside a function or method, determined by the order in which they were given in the call.
  • is used to either accept multiple positional arguments (when in the definition), or pass several arguments as a list to a function. 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".


  • 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.


  • A declaration inside a 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.


  • 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.


  • 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.

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*.


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

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.


  • 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).


  • The objects returned from dict.keys(), dict.items(), and dict.items() are called dictionary views. They are lazy sequences that will see changes in the underlying dictionary. To force the dictionary view to become a full list use list(dictview). See *Dictionary view objects*.

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.