History

Future development

Python 3000

Philosophy

Timeline and compatibility

PEP 3000

PEP 3000

PEP 361

Features

• Changing print so that it's a built-in function, not a statement. This makes it easier to change a module to use a different print function, as well as making the syntax more regular. In Python 2.6 this can be enabled by entering from import print_function.
• Moving reduce (but not map or filter) out of the built-in namespace and into functools (the rationale being that reduce is expressed more clearly as an accumulation loop);
• Adding support for optional function annotations that can be used for informal type declarations or other purposes;
• Unifying the str/unicode types, representing text, and introducing a separate immutable bytes type; and a mostly corresponding mutable buffer type, which both represent arrays of bytes;
• Removing backward-compatibility features, including old-style classes, integer-truncating division, string exceptions, and implicit relative imports.

Usage

Syntax and semantics

The with statement which encloses a code block within a context manager (for example, acquiring a lock before the block of code is run, and releasing the lock afterwards). Each statement has its own semantics: for example, the def statement doesn't execute its block immediately, unlike most other statements.
   CPython doesn't support continuations, and according to Guido van Rossum it never will. However, better support for coroutine-like functionality is provided in 2.5, by extending Python's generators. Prior to 2.5, generators were lazy iterators — information was passed unidirectionally out of the generator. As of Python 2.5, it's possible to pass information back into a generator function.
   Python uses duck typing, also known as latent typing. Type constraints are not checked at compile time; rather, operations on an object may fail, signifying that the given object isn't of a suitable type. Despite not enforcing static typing, Python is strongly typed, forbidding operations which make little sense (for example, adding a number to a string) rather than silently attempting to make sense of them.
   Python includes a number of built-in types used for a variety of purposes:

Type	Description	Syntax Example
str, unicode	An immutable sequence of characters	'Wikipedia', u'Wikipedia'
list	Mutable, can contain mixed types	[4.0,'string', True]
tuple	Immutable, can contain mixed types	(4.0, 'string', True)
set, frozenset	Unordered, contains no duplicates	set([4.0,'string', True]) frozenset([4.0,'string', True])
dict	Group of key and value pairs
int	A fixed precision number (may be transparently expanded to long, which is of unlimited length)	42 2147483648L
float	Floating point	3.1415927
complex	A complex number with real number and imaginary unit	3+2j
bool	Boolean	True or False

Python also allows programmers to define their own types. This is in the form of classes, most often used for an object-oriented style of programming. New instances of classes are constructed by calling the class (ie, like FooClass), and the classes themselves are instances of class type (itself an instance of itself), allowing metaprogramming and reflection. Methods on objects are functions attached to the object's class; the syntax instance.method(argument) is, for normal methods and functions, syntactic sugar for Class.method(instance, argument). Python methods have an explicit self parameter to access instance data, in contrast to the implicit self in some other object-oriented programming languages (for example, Java, C++ or Ruby).

Implementations

The mainstream Python implementation, also known as CPython, is written in C compliant to the C89 standard, and is distributed with a large standard library written in a mixture of C and Python. CPython ships for a large number of supported platforms, including Microsoft Windows and most modern Unix-like systems. CPython was intended from almost its very conception to be cross-platform; its use and development on esoteric platforms such as Amoeba alongside more conventional ones like Unix or Macintosh has greatly helped in this regard. Stackless Python is a significant fork of CPython that implements microthreads. It can be expected to run on approximately the same platforms that CPython runs on.
   There are two other major implementations: Jython for the Java platform, and IronPython for the .NET platform. PyPy is an experimental self-hosting implementation of Python, in Python, that can output a variety of types of bytecode, object code and intermediate languages.
   Several programs exist to bundle the Python interpreter with scripts as standalone UNIX, Linux, Windows or Mac OS X executables, including Freeze, a pure Python utility that ships with Python, or py2exe, PyInstaller, cx_Freeze and py2app, which can be obtained separately.
   Many Python programs can run on different Python implementations, on disparate operating systems and execution environments, without change. In the case of the implementations running on top of the Java virtual machine or the Common Language Runtime, the platform-independence of these systems is harnessed by their respective Python implementation.
   Many third-party libraries for Python (and even some first-party ones) are only available on Windows, Linux, BSD, and Mac OS X.
   In 2005 Nokia released a Python interpreter for Series 60 mobile phones called PyS60. This includes many of the modules from the regular implementations but also some additional modules for integration with the Symbian operating system. This project has been kept up to date to run on all variants of the S60 platform and there are several third party modules available such as Miso and uitricks. ChinesePython (中蟒) is a Python programming language using Chinese language lexicon. Besides reserved words and variable names, most data type operations can be coded in Chinese as well.

Interpretational semantics

Most Python implementations (including CPython, the dominant implementation) can function as a command line interpreter, where the user enters statements sequentially and receives the results immediately; in short, acting as a shell. While the semantics of the other modes of execution (bytecode compilation, or compilation to native code) preserve the sequential semantics, they offer a speed boost at the cost of interactivity, so they're only used outside of a command-line interaction (for example, importing a module).
   Other shells add capabilities beyond those in the basic interpreter, including IDLE and IPython. While generally following the visual style of the Python shell, they implement features like auto-completion, retention of session state, and syntax highlighting.
   Some implementations are able to compile to not only bytecode, but to turn Python code into machine code. So far, this has only been done for restricted subsets of Python. PyPy takes this approach, naming its restricted compilable version of Python RPython. Shed Skin is a similar experimental compiler. Psyco is a specialising just in time compiler which transforms bytecode to machine code at runtime. The produced code is specialised for certain data types. Psyco is compatible with all Python code, not only a subset.

Standard library

Python has a large standard library, commonly cited as one of Python's greatest strengths, providing tools suited to many disparate tasks. This comes from a so-called "batteries included" philosophy for Python modules. The modules of the standard library can be augmented with custom modules written in either C or Python. Recently, Boost C++ Libraries includes a library, python, to enable interoperability between C++ and Python. Because of the wide variety of tools provided by the standard library combined with the ability to use a lower-level language such as C and C++, which is already capable of interfacing between other libraries, Python can be a powerful glue language between languages and tools.
   The standard library is particularly well tailored to writing Internet-facing applications, with a large number of standard formats and protocols (such as MIME and HTTP) supported. Modules for creating graphical user interfaces, connecting to relational databases, arithmetic with arbitrarily precise decimals, and manipulating regular expressions are also included. Python also includes a unit testing framework for creating test suites.
   Some parts of the standard library are covered by specifications (for example, the WSGI implementation wsgiref follows PEP 333), but the majority of modules are defined by nothing other than their code, documentation and test suite. However, because most of the standard library is cross-platform Python code, there are only a small number of modules which must be altered or completely rewritten by alternative implementations.

Programming philosophy

Python is a multi-paradigm programming language. This means that, rather than forcing programmers to adopt a particular style of programming, it permits several styles: object oriented and structured programming are fully supported, and there are a number of language features which support functional programming and aspect-oriented programming. Many other paradigms are supported using extensions, such as pyDBC and Contracts for Python which allow Design by Contract. Python uses dynamic typing and a combination of reference counting and a cycle detecting garbage collector for memory management. An important feature of Python is dynamic name resolution, which binds method and variable names during program execution (also known as late binding).
   Another target of the language's design is ease of extensibility, rather than having everything built into the language core. New built-in modules are easily written in C or C++. Python can also be used as an extension language for existing modules and applications that need a programmable interface. This design, of a small core language with a large standard library and an easily-extensible interpreter, was intended by van Rossum from the very start, due to his frustrations with ABC, which espoused the opposite mindset.
   While offering choice in coding methodology, the Python philosophy rejects exuberant syntax, such as in Perl, in favor of a sparser, less cluttered one. As with Perl, Python's developers expressly promote a particular "culture" or ideology based on what they want the language to be, favoring language forms they see as "beautiful", "explicit" and "simple". As Alex Martelli put it in his Python Cookbook (2nd ed., p.230): "To describe something as clever is NOT considered a compliment in the Python culture." Python's philosophy rejects the Perl "there is more than one way to do it" approach to language design in favor of "there should be one—and preferably only one—obvious way to do it".
   Python eschews premature optimization, and moreover, rejects patches to non-critical parts of CPython which would offer a marginal increase in speed at the cost of clarity. It is sometimes described as 'slow', but its speed is rarely a problem since most code isn't speed critical; bottlenecks can often be optimized greatly either by improvements to algorithms or data structures, or by implementing small portions in "closer to the metal" languages.

Neologisms

A common neologism in the Python community is pythonic, which can have a wide range of meanings related to program style. To say that a piece of code is pythonic is to say that it uses Python idioms well; that it's natural or shows fluency in the language. Likewise, to say of an interface or language feature that it's pythonic is to say that it works well with Python idioms; that its use meshes well with the rest of the language.
   In contrast, a mark of unpythonic code is that it attempts to "write C++ (or Lisp, or Perl) code in Python"—that is, provides a rough transcription rather than an idiomatic translation of forms from another language. The concept of pythonicity is tightly bound to Python's minimalist philosophy of readability. Unreadable code or incomprehensible idioms are unpythonic.
   Users and admirers of Python—most especially those considered knowledgeable or experienced—are often referred to as Pythonists, Pythonistas, and Pythoneers.
   The prefix Py can be used to show that something is related to Python. Examples of the use of this prefix in names of Python applications or libraries include Pygame, a binding of SDL to Python (commonly used to create games); PyS60, an implementation for the Symbian Series 60 Operating System; PyQt and PyGTK, which bind Qt and GTK, respectively, to Python; and PyPy, a Python implementation written in Python. The prefix is also used outside of naming software packages: the major Python conference is named PyCon.
   An important goal of the Python developers is making Python fun to use. This is reflected in the origin of the name (after the television series Monty Python's Flying Circus), in the common practice of using Monty Python references in example code, and in an occasionally playful approach to tutorials and reference materials. For example, the metasyntactic variables often used in Python literature are spam and eggs, instead of the traditional foo and bar.

Influences on other languages

Python's design and philosophy have influenced several programming languages: