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History

Python was conceived in the late 1980s and its implementation was started in December 1989 by Guido van Rossum at CWI in Amsterdam. Python is a successor to the ABC programming language (itself inspired by SETL) capable of exception handling and interfacing with the Amoeba operating system. Van Rossum is Python's principal author, and was for many years known by the humorous title Benevolent Dictator for Life (BDFL). Python evolution is now guided by an elected Steering Committee.


Programming philosophy

Python is a multi-paradigm programming language. Rather than forcing programmers to adopt a particular style of programming, it permits several styles: object-oriented programming and structured programming are fully supported, and there are a number of language features which support functional programming and aspect-oriented programming (including metaprogramming and "by magic" methods). Many other paradigms are supported using extensions, such as pyDBC and Contracts for Python which allow Design by Contract.

Rather than requiring all desired functionality to be built into the language's core, Python was designed to be highly extensible. New built-in modules can be easily written in C, C++ or Cython. Python can also be used as an extension language for existing modules and applications that need a programmable interface. This design, a small core language with a large standard library with an easily extensible interpreter, was intended by Van Rossum from the very start because of his frustrations with ABC (which espoused the opposite mindset).


Name and neologisms

An important goal of the Python developers is making Python fun to use. This is reflected in the origin of the name (based on 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.[24][25] For example, the metasyntactic variables often used in Python literature are spam and eggs, instead of the traditional foo and bar.


Usage

Python is often used as a scripting language for web applications, e.g. via mod_wsgi for the Apache web server. With Web Server Gateway Interface, a standard API has been developed to facilitate these applications. Web application frameworks like Django, Pylons, TurboGears, web2py, Flask, and Zope support developers in the design and maintenance of complex applications. Libraries like NumPy, SciPy, and Matplotlib allow Python to be used effectively in scientific computing.


Syntax and semantics

Python was intended to be a highly readable language. It is designed to have an uncluttered visual layout, frequently using English keywords where other languages use punctuation. Python requires less boilerplate than traditional manifestly typed structured languages such as C or Pascal, and has a smaller number of syntactic exceptions and special cases than either of these. For a detailed description of the differences between 2.x and 3.x versions, see History of Python.

Indentation

Python uses whitespace indentation, rather than curly braces or keywords, to delimit blocks (a feature also known as the off-side rule). An increase in indentation comes after certain statements; a decrease in indentation signifies the end of the current block.

Statements and control flow

Python's statements include (among others):

Expressions

Methods

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).

Typing

Python uses duck typing and has typed objects but untyped variable names. Type constraints are not checked at compile time; rather, operations on an object may fail, signifying that the given object is not of a suitable type. Despite being dynamically typed, Python is strongly typed, forbidding operations that are not well-defined (for example, adding a number to a string) rather than silently attempting to make sense of them.

Mathematics

Python defines the modulus operator so that the result of a % b is in the half-open interval [0,b), where b is a positive integer. When b is negative, the result lies in the interval (b,0]. However, this consequently affects how integer division is defined. To maintain the validity of the equation b * (a // b) + a % b == a, integer division is defined to round towards minus infinity. Therefore 7 // 3 is 2, but (−7) // 3 is −3. This is different from many programming languages, where the result of integer division rounds towards zero, and Python's modulus operator is consequently defined in a way that can return negative numbers.


Implementations

CPython

The mainstream Python implementation, known as CPython, is written in C meeting the C89 standard. CPython compiles Python programs into intermediate bytecode,[65] which are then executed by the virtual machine. It is distributed with a large standard library written in a mixture of C and Python. CPython ships in versions for many 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 and Mac OS, has greatly helped in this regard. Unofficial builds are also available for Android and iOS.

Alternative implementations

Jython compiles the Python program into Java byte code, which can then be executed by every Java Virtual Machine implementation. This also enables the use of Java class library functions from the Python program. IronPython follows a similar approach in order to run Python programs on the .NET Common Language Runtime. PyPy is a fast self-hosting implementation of Python, written in Python, that can output several types of bytecode, object code and intermediate languages. There also exist compilers to high-level object languages, with either unrestricted Python, a restricted subset of Python, or a language similar to Python as the source language. PyPy is of this type, compiling RPython to several languages; other examples include Pyjamas compiling to JavaScript; Shed Skin compiling to C++; and Cython and Pyrex compiling to C.

Interpretational semantics

Most Python implementations (including CPython) can function as a command-line interpreter, for which the user enters statements sequentially and receives the results immediately. In short, Python acts 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 are usually only used outside of a command-line interaction (e.g., when 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.


Development

Python's development is conducted largely through the Python Enhancement Proposal (PEP) process, described in PEP 1. PEPs are standardized design documents providing general information related to Python, including proposals, descriptions, design rationales, and explanations for language features. Outstanding PEPs are reviewed and commented upon on the python-dev mailing list, which is the primary forum for discussion about the language's development and approved by the Steering Council (see PEP 13 for the governance model); specific issues are discussed in the bug tracker maintained at bugs.python.org. Development of the reference implementation takes place on the GitHub cpython repository.

CPython's public releases come in three types, distinguished by which part of the version number is incremented:

A number of alpha, beta, and release-candidates are also released as previews and for testing before the final release is made. Although there is a rough schedule for each release, this is often pushed back if the code is not ready. The development team monitor the state of the code by running the large unit test suite during development, and using the BuildBot continuous integration system.

Standard library

Python has a large standard library, commonly cited as one of Python's greatest strengths,[81] providing pre-written tools suited to many tasks. This is deliberate and has been described as a "batteries included" Python philosophy. The modules of the standard library can be augmented with custom modules written in either C or Python. Boost C++ Libraries includes a library, Boost.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) already supported. Modules for creating graphical user interfaces, connecting to relational databases, arithmetic with arbitrary precision decimals, manipulating regular expressions, and doing unit testing are also included.

Some parts of the standard library are covered by specifications (for example, the WSGI implementation wsgiref follows PEP 333), but the majority of the modules are not. They are specified by their code, internal documentation, and test suite (if supplied). However, because most of the standard library is cross-platform Python code, there are only a few modules that must be altered or completely rewritten by alternative implementations.

For software testing, the standard library provides the unittest and doctest modules.


Influence on other languages

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


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