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Python is a programming language created by Guido van Rossum in 1990. Python has a fully dynamic type system and uses automatic memory management; it is thus similar to Perl, Ruby, Scheme, Smalltalk, and Tcl. Python is notable amongst current popular high-level languages for having a philosophy that emphasizes the importance of programmer effort over that of computers and for rejecting more arcane language features, readability having a higher priority than speed or expressiveness. Python is often characterised as minimalist, though this only applies to the core language's syntax and semantics; the standard library provides the language with a large number of additional libraries and extensions. Miscellaneous parts of the language have formal specifications and standards, but not the language as a whole. The de facto standard for the language is the CPython implementation, which is a bytecode compiler and interpreter, although there are other implementations available. CPython is developed as an open source project, managed by the non-profit Python Software Foundation, and is available under a free software license from the project website. Python 2.5 was released on September 19 2006.
Python 1
Python 2 In 2000, the Python core development team moved to BeOpen.com to form the BeOpen PythonLabs team. Python 2.0 was the first and only release from BeOpen.com. After Python 2.0 was released by BeOpen.com, Guido van Rossum and the other PythonLabs developers joined Digital Creations. Python 2.1 was a derivative work of Python 1.6.1, as well as of Python 2.0. Its license was renamed Python Software Foundation License. All code, documentation and specifications added, from the time of Python 2.1's alpha release on, is owned by the Python Software Foundation (PSF), a non-profit organization formed in 2001, modelled after the Apache Software Foundation. Future development Python developers have an ongoing discussion of a future version called Python 3.0 (the project is called "Python 3000" or "Py3K") that will break backwards compatibility with the 2.x series in order to repair perceived flaws in the language. The guiding principle is to "reduce feature duplication by removing old ways of doing things". There is no definite schedule for Python 3.0, but a PEP (Python Enhancement Proposal) that details planned changes exists. Planned changes include: str/unicode types, and introduce a separate mutable bytes typeUsage The Python programming language is actively used in industry and academia for a wide variety of purposes. Some of the largest projects that utilise Python are the Zope application server and the Mnet and BitTorrent file sharing systems. It is also extensively used by Google. Syntax and semantics Python was designed to be a highly readable language. It aims toward an uncluttered visual layout, uses English keywords frequently where other languages use punctuation, and has notably fewer syntactic constructions than many structured languages such as C, Perl, or Pascal. Indentation Python uses indentation, rather than curly braces, to delimit statement blocks. An increase in indentation comes after certain statements; a decrease in indentation signifies the end of the current block. Control structures and statements Python's statements include: if statement, for conditionally executing blocks of code, along with else and elif (a contraction of else-if).while statement runs a block of code until a condition is False.for loops iterate over an iterable, capturing each element to a local variable for use by the attached block. class statements execute a block of code and attach its local namespace to a class, for use in object oriented programming.def defines a function.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, 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 monodirectionally out of the generator. Type system Python espouses 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 is not 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. Built-in types Python includes several simple data types, including int for representing integers and float for floating-point numbers. The language also includes a bool type for Boolean operations. There is also a complex type for complex numbers.In addition to those four numeric data types, Python includes a number of built-in types used for a variety of purposes. This list describes the most commonly-used types; Python provides many more.* Object orientation and metaprogramming 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). This is why Python methods must 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 or Ruby). This is more closely related to multiple dispatch than the traditional object-oriented message passing model; every parameter is passed to the function in the same way, rather than special casing the receiver object.•
Other features
Implementations The mainstream Python implementation, also known as CPython, is written in C, 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 (see below) and can be ported to other platforms, most readily to POSIX (Unix-like) systems. Stackless Python is a significant fork of CPython that implements microthreads. There are two other major implementations: Jython for the Java platform, and IronPython for the .NET Framework. PyPy is an experimental self-hosting implementation of Python, in Python, that can output a variety of types of bytecode and object code. Several other experimental implementations have been created, but not (yet) reached widespread use. CPython supported platforms The most popular (and therefore best maintained) platforms Python runs on are Linux, BSD, Mac OS X, Solaris, and Microsoft Windows.
Python was originally developed as a scripting language for the Amoeba distributed operating system which was capable of making system calls; however, that version is no longer maintained. 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. Many third-party libraries for Python (and even some first-party ones) are only available on Windows, Linux, BSD, and Mac OS X. Standard library Python has a large standard library, 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. 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, 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 exhaustive test suites. It is currently being debated whether or not third-party but open source Python modules such as Twisted, NumPy, or wxPython should be included in the standard library, in accordance with the batteries included philosophy. Portability The standard library is commonly cited as one of Python's greatest strengths. Although (barring a few exceptions) the standard library is solely defined by its CPython implementation, the bulk of it is cross-platform Python code, meaning that many Python programs can run on different Python implementations, on such disparate operating systems as Unix, Windows, Macintosh, and other platforms without change. Several programs exist to package Python programs into standalone executables, including py2exe and py2app. Multiple paradigms 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 orientation, structured programming, functional programming, and aspect-oriented programming are all supported. Many other paradigms are supported using extensions, such as pyDBC and Contracts for Python which allow Design by Contract. Python uses dynamic typing and reference counting for memory management. An important feature of Python is dynamic name resolution, which binds method and variable names during program execution. Another target of the language's design is ease of extensibility. 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. Though the design of Python is somewhat hostile to functional programming (no tail-call elimination nor good support for anonymous closures) and the Lisp tradition, there are significant parallels between the philosophy of Python and that of minimalist Lisp-family languages such as Scheme. Many past Lisp programmers have found Python appealing for this reason. Minimalism 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 favour of advocating a single "right" approach to problem solving. Eschew premature optimization In contrast to some lower-level languages, Python's design does not emphasize runtime speed. While Python implementations are generally comparable to that of other bytecode interpreted languages, a concern for clarity of code always comes first in Python's design. In the Python community, one sometimes hears the slogan, "Speed is not a problem until it is a problem". This sentiment is not unique to Python programmers, but they emphasize it more than some other language communities. In the relatively rare cases where optimization is genuinely necessary, the Pythonic solution comes in several stages: (0) Initial profiling to find out where the genuine bottlenecks are; (1) improving algorithms within the code; (2) improving data structures within the code, perhaps using popular extension types; (3) employing existing libraries that exist to do special tasks quickly; (4) running the program using the Psyco just-in-time compiler (on x86 platforms); (5) rewriting performance-critical parts of code with Pyrex or with SWIG/C or another non-Python wrapped language. Influenced programming languages Python's design and philosophy have affected several programming languages. Ruby is such a language; Matz's object system design was influenced by Python. Boo's Python heritage is more explicit — it also uses indentation, a similar syntax, and a similar object model. Boo, however, utilitises static typing and is closely integrated with the .NET framework. 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) 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. Package naming 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); PyUI, a GUI encoded entirely in Python; PyQt, Qt bindings for Python; PyGTK, GTK toolkit bindings; and PySol, a series of solitaire card games programmed in Python. Humour Another 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''. See also Additional Reading | |||||||||||||||||||||||||||||||||||||||||||||
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