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D. Glossary

`>`>`>`
  The typical Python prompt of the interactive shell. Often seen for code examples that can be tried right away in the interpreter.

`.`.`.`
  The typical Python prompt of the interactive shell when entering code for an indented code block.

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

byte code
  The internal representation of a Python program in the interpreter. The byte code is also cached in the `.pyc` and `.pyo` files so that executing the same file is faster the second time (compilation from source to byte code can be saved). This ``intermediate language'' is said to run on a ``virtual machine'' that calls the subroutines corresponding to each bytecode.

classic class
  Any class which does not inherit from `object`. See new-style class.

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` builtin 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 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.

descriptor
  Any new-style object that defines the methods `__get__()`, `__set__()`, or `__delete__()`. When a class attribute is a descriptor, its special binding behavior is triggered upon attribute lookup. Normally, writing a.b looks up the object b in the class dictionary for a, but if b is a descriptor, the defined 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.

dictionary
  An associative array, where arbitrary keys are mapped to values. The use of `dict` much resembles that for `list`, but the keys can be any object with a `__hash__()` function, not just integers starting from zero. Called a hash in Perl.

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 that is common in many other languages such as C.

__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 actually 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)

generator
  A function that 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 that `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

GIL
  See global interpreter lock.

global interpreter lock
  The lock used by Python threads to assure that only one thread can be run at a time. This simplifies Python 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 some parallelism on 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 performance suffered in the common single-processor case.

IDLE
  An Integrated Development Environment for Python. IDLE is a basic editor and interpreter environment that 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.

immutable
  An object with fixed value. Immutable objects are numbers, strings or tuples (and more). 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__.

interactive
  Python has an interactive interpreter which means that you can try out things and directly see its result. 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. This means that the source files can be run directly without first 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
  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.

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 is 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 that 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 from the second iteration pass, making it appear like an empty container.

list comprehension
  A compact way to process all or a subset of 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 hex numbers (0x..) that are even and in the range from 0 to 255. The `if` clause is optional. If omitted, all elements in `range(256)` are processed in that case.

mapping
  A container object (such as `dict`) that supports arbitrary key lookups using the special method `__getitem__()`.

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.

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 the presence of many `if` statements.

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

namespace
  The place where a variable is stored. Namespaces are implemented as dictionary. There is the local, global and builtins namespace and the 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 modules implement 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 that 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, `__getattribute__()`, class methods, and static methods.

Python3000
  A mythical python release, allowed not to be backward compatible, with telepathic interface.

__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__()` and `__len__()` special methods. 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.

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.