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D Glossary

‘\code{>>>}’

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

‘\code{.\code{.’.}}

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 .pyc and .pyo files so that executing the same file is faster the second time (recompilation from source to byte code can be avoided). 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).⁽⁷⁾ 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.

‘duck-typing’

Pythonic programming style that 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(). 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 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 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 immediately 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. 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 used in the previous iteration pass, making it appear like an empty container.

‘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.

‘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.

‘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.

‘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 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 __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 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, not required 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.