|
|
|
|
Python (programming language)
|
| |
|
| |
Python is a general-purpose high-level programming language. Its design philosophy emphasizes code readability. Python's core syntax and semantics are minimalistic, while the standard library is large and comprehensive. Its use of whitespace as block delimiters is unusual among popular programming languages.
Python supports multiple programming paradigms (primarily object oriented, imperative, and functional) and features a fully dynamic type system and automatic memory management, similar to Perl, Ruby, Scheme, and Tcl.
Discussion
Ask a question about 'Python (programming language)' Start a new discussion about 'Python (programming language)' Answer questions from other users |
Encyclopedia
Python is a general-purpose high-level programming language. Its design philosophy emphasizes code readability. Python's core syntax and semantics are minimalistic, while the standard library is large and comprehensive. Its use of whitespace as block delimiters is unusual among popular programming languages.
Python supports multiple programming paradigms (primarily object oriented, imperative, and functional) and features a fully dynamic type system and automatic memory management, similar to Perl, Ruby, Scheme, and Tcl. Like other dynamic languages, Python is often used as a scripting language.
The language has an open, community-based development model managed by the non-profit Python Software Foundation, which maintains the de facto standard definition of the language in CPython, the reference implementation.
History
Python was conceived in the late 1980s by Guido van Rossum at CWI in the Netherlands as 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 his continuing central role in deciding the direction of Python is reflected in the title given to him by the Python community, Benevolent Dictator for Life (BDFL).
Python 2.0 was released on 16 October 2000, with many major new features including a full garbage collector and support for unicode. However, the most important change was to the development process itself, with a shift to a more transparent and community-backed process. Python 3.0, a major, backwards-incompatible release, was released on 3 December 2008 after a long period of testing. Many of its major features have been backported to the backwards-compatible Python 2.6.
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 and 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 (late binding), which binds method and variable names during program execution.
Another aspect 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, because of his frustrations with ABC, which espoused the opposite mindset.
The design of Python offers limited support for functional programming in the Lisp tradition. However, there are significant parallels between the philosophy of Python and those of minimalist Lisp-family languages such as Scheme. The library has two modules (itertools and functools) that implement proven functional tools borrowed from Haskell and Standard ML.
While offering choice in coding methodology, the Python philosophy rejects exuberant syntax, such as in Perl, in favor of a sparser, less-cluttered grammar. 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". However, most problems are not speed critical, and as computer hardware continues to become exponentially faster (Moore's Law), languages do have more hardware resources available. When speed is a problem, Python programmers tend to try to optimize bottlenecks by algorithm improvements or data structure changes, using a JIT compiler such as Psyco, rewriting the time-critical functions in "closer to the metal" languages such as C, or by translating Python code to C code using tools like .
The core philosophy of the language is summarized by .
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 is natural or shows fluency in the language. Likewise, to say of an interface or language feature that it is 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, or Java) 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 and avoiding the "there's more than one way to do it" approach. 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 (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. 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_python for the Apache web server. With Web Server Gateway Interface a standard API has been developed to facilitate these applications. Web application frameworks or application servers like web2py, Zope, and Django support developers in the design and maintenance of complex applications.
Python has seen extensive use in the information security industry, including in exploit development. Python has been successfully embedded in a number of software products as a scripting language, including in 3D animation packages such as Maya, Softimage XSI, and Blender, and 2D imaging programs like GIMP, Inkscape, Scribus, and Paint Shop Pro. ESRI is now promoting Python as the best choice for writing scripts in ArcGIS. It has even been used in several videogames.
For many operating systems, Python is a standard component; it ships with most Linux distributions, with NetBSD, and OpenBSD, and with Mac OS X. Red Hat Linux and Fedora both use the pythonic Anaconda installer. Gentoo Linux uses Python in its package management system, Portage, and the standard tool to access it, emerge. Pardus uses it for administration and during system boot.
Among the users of Python are YouTube and the original BitTorrent client. Large organizations that make use of Python include Google, Yahoo!, CERN,, NASA,and ITA. Most of the Sugar software for the One Laptop Per Child XO, now developed at Sugar Labs, is written in Python.
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.
Indentation
Python uses whitespace indentation, rather than curly braces or keywords, to delimit statement 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:
- The
if statement, which conditionally executes a block of code, along with else and elif (a contraction of else-if). - The
for statement, which iterates over an iterable object, capturing each element to a local variable for use by the attached block. - The
try statement, which allows errors thrown in its attached code block to be caught and handled by except clauses; it also ensures that clean-up code in a finally block will be always be run regardless of how the block exits. - The
class statement, which executes a block of code and attaches its local namespace to a class, for use in object oriented programming. - The
def statement, which defines a function. - 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 does not execute its block immediately, unlike most other statements.
CPython does not 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 is possible to pass information back into a generator function.
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 nonsense operations (for example, adding a number to a string) rather than silently attempting to make sense of them.
Python allows programmers to define their own types using classes, which are most often used for an object-oriented style of programming. New instances of classes are constructed by calling the class (for example, FooClass), and the classes themselves are instances of the metaclass type (itself an instance of itself), allowing metaprogramming and reflection.
Here is a summary of Python's built-in types:
| Type | Description | Syntax Example |
|---|
str | An immutable sequence of Unicode characters | 'Wikipedia'
"Wikipedia" | bytes | An immutable sequence of bytes | b'Some ASCII'
b"Some ASCII" | 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 |
frozenset([4.0, 'string', True]) | dict | A mutable group of key and value pairs | | int | An immutable fixed precision number of unlimited magnitude | 42 | float | An immutable floating point number (system-defined precision) | 3.1415927 | complex | An immutable complex number with real number and imaginary parts | 3+2j | bool | An immutable truth value | True
False |
Implementations
CPython
The mainstream Python implementation, known as CPython, is written in C meeting the C89 standard,. CPython compiles the Python program into intermediate bytecode, which is 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.
Stackless Python is a significant fork of CPython that implements microthreads; it does not use the C memory stack. CPython uses a GIL to allow only one thread to execute at a time while the Stackless Python threads are independent of the OS and can run concurrently. Stackless Python is better suited to scalable tasks and for use on microcontrollers or other limited resource platforms due to the thread's light weight. It can be expected to run on approximately the same platforms that CPython runs on.
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 utilization 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 an experimental 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 & Pyrex compiling to C.
In 2005 Nokia released a Python interpreter for the Series 60 mobile phones called PyS60. It includes many of the modules from the CPython implementations and 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 and . There is also a Python interpreter for Windows CE devices (including Pocket PC). It is called PythonCE. There are additional tools available for easy application and GUI development.
ChinesePython 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 primary implementation) 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 (eg, 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.
Some implementations can compile not only to bytecode, but can 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.
Psyco is a specialising just in time compiler that integrates with CPython and transforms bytecode to machine code at runtime. The produced code is specialised for certain data types and is faster than standard Python code. Psyco is compatible with all Python code, not only a subset.
Development
Python development is conducted largely through the Python Enhancement Proposal (or "PEP") process. 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 by Van Rossum, the Python project's Benevolent Dictator for Life ( leader / language architect). CPython's developers also communicate over a mailing list, python-dev, which is the primary forum for discussion about the language's development; specific issues are discussed in the roundup bug tracker maintained at python.org. Development takes place at the self-hosted svn.python.org.
CPython's public releases come in three types, distinguished by which part of the version number is incremented:
- backwards-incompatible versions, where code is expected to break and must be manually ported. The first part of the version number is incremented. These releases happen irregularly—for example, version 3.0 was released 8 years after 2.0.
- major or 'feature' releases, which are largely compatible but introduce new features. The second part of the version number is incremented. These releases are scheduled to occur roughly every 18 months, and each major version is supported by bugfixes for several years after its release.
- bugfix releases, which introduce no new features but fix bugs. The third and final part of the version number is incremented. These releases are made whenever a sufficient number of bugs have been fixed upstream since the last release, or roughly every 3 months. Security vulnerabilities are also patched in bugfix releases.
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, 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. Recently, 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 ), 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.
Influences on other languages
Python's design and philosophy have influenced several programming languages:
- Pyrex and its derivative Cython are code translators that are targeted at writing fast C extensions for the CPython interpreter. The language is mostly Python with syntax extensions for C and C++ features. Both languages produce compilable C code as output.
- Boo uses indentation, a similar syntax, and a similar object model. However, Boo uses static typing and is closely integrated with the .NET framework.
- Cobra is similar to Python, using indentation for blocks. It offers optional static typing and generates code designed for .NET/Mono. Cobra directly supports design-by-contract.
- ECMAScript borrowed iterators, generators and list comprehensions from Python.
- Groovy was motivated by the desire to bring the Python design philosophy to Java.
- Converge is syntactically similar to Python, but has a novel approach to generators and backtracking as well as macros and compile-time metaprogramming.
- OCaml has an optional syntax, called twt (That Whitespace Thing), and a new Batteries Included approach, both inspired by Python.
Python's development practices have also been emulated by other languages. The practice of requiring a document describing the rationale for, and issues surrounding, a change to the language (ie, a PEP) is also used in Tcl and Erlang because of Python's influence.
See also
External links
|
| |
|
|
