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.

2to3

A tool that tries to convert Python 2.x code to Python 3.x code by handling most of the incompatibilites that 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.

argument

A value passed to a function or method, assigned to a name local to the 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.

BDFL

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

bytecode

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 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.

context manager

An objects that controls the environment seen in a with statement by defining __enter__() and __exit__() methods. See PEP 343.

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(...):
    ...

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, 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 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.

docstring

A docstring (“documentation string”) is a string literal that appears as the first thing in a class or function suite. While ignored when the suite is executed, it is recognized by the compiler and put into the __doc__ attribute of the class or function. Since it is available via introspection, it is the canonical place for documentation of the object.

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(). (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 that is common in 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 that all return a value. In contrast to other languages, not all language constructs are expressions, but there are also statements that cannot be used as expressions, such as print or if. Assignments are also not expressions.

extension module

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

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 argument and method.

__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 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.

hashable

An object is hashable if it has a hash value that 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 that 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 all mutable containers (such as lists or dictionaries) are not. Objects that are instances of user-defined classes are hashable by default; they all compare unequal, and their hash value is their id().

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.

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.

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 [arguments]: 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.

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.

More information can be found in Customizing class creation.

method

A function that 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

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

named tuple

Any tuple subclass whose indexable fields are also accessible with 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 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.

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

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 next major Python version, 3.0 (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 in other languages. For example, a common idiom in Python is the for loop structure; other languages don’t have this easy keyword, so people 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 places where a certain object is referenced to. When the reference count drops to zero, an object is deallocated. While reference counting is invisible on the Python code level, it is used on the implementation level to keep track of allocated memory.

__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.

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__()).

statement

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 print.

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).

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.