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Python (programming language)
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Python is a general-purpose, high-level programming language. Its design philosophy emphasizes programmer productivity and code readability. Python's core syntax and semantics are minimalist, while the standard library is large and comprehensive.
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.
Python was first released by Guido van Rossum in 1991. The language has an open, community-based development model managed by the non-profit Python Software Foundation. While various parts of the language have formal specifications and standards, the language as a whole is not formally specified. The de facto standard for the language is the CPython 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 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 him by the Python community, Benevolent Dictator for Life (BDFL).
In 1991, van Rossum published the code (labeled version 0.9.0) to alt.sources. Already present at this stage in development were classes with inheritance, exception handling, functions, and the core datatypes of list, dict, str and so on. Also in this initial release was a module system borrowed from Modula-3; van Rossum describes the module as "one of Python's major programming units". Python's exception model also resembles Modula-3's, with the addition of an else clause. In 1994 [news://comp.lang.python comp.lang.python], the primary discussion forum for Python, was formed, marking a milestone in the growth of Python's userbase.
Python reached version 1.0 in January 1994. The major new features included in this release were the functional programming tools lambda, map, filter and reduce. van Rossum stated that "Python acquired lambda, reduce(), filter() and map(), courtesy of (I believe) a Lisp hacker who missed them and submitted working patches."
The last version released while van Rossum was at CWI was Python 1.2. In 1995, van Rossum continued his work on Python at the Corporation for National Research Initiatives (CNRI) in Reston, Virginia from which he released several versions.
By version 1.4, Python had acquired several new features. Notable among these are the Modula-3 inspired keyword arguments (which are also similar to Common Lisp's keyword arguments), and built-in support for complex numbers. Also included is a basic form of data hiding by name mangling, though this is easily bypassed.
During van Rossum's stay at CNRI, he launched the Computer Programming for Everybody (CP4E) initiative, intending to make programming more accessible to more people, with a basic 'literacy' in programming languages, similar to the basic English literacy and mathematics skills required by most employers. Python served a central role in this: because of its focus on clean syntax, it was already suitable, and CP4E's goals bore similarities to its predecessor, ABC. The project was funded by DARPA. As of 2007, the CP4E project is inactive, and while Python attempts to be easily learnable and not too arcane in its syntax and semantics, reaching out to non-programmers is not an active concern.
In 2000, the Python core development team moved to BeOpen.com to form the BeOpen PythonLabs team. CNRI requested that a version 1.6 be released, summarizing Python's development up to the point where the development team left CNRI. Consequently, the release schedules for 1.6 and 2.0 had a significant amount of overlap. 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.
The Python 1.6 release included a new CNRI license that was substantially longer than the CWI license that had been used for earlier releases. The new license included a clause stating that the license was governed by the laws of the State of Virginia. The Free Software Foundation argued that the choice-of-law clause was incompatible with the GNU GPL. BeOpen, CNRI, and the FSF negotiated a change to Python's free software license that would make it GPL-compatible. Python 1.6.1 is essentially the same as Python 1.6, with a few minor bug fixes, and with the new GPL-compatible license.
Python 2.0 borrowed a major feature from the functional programming languages SETL and Haskell: list comprehensions. Python's syntax for this construct is very similar to Haskell's, apart from Haskell's preference for punctuation characters and Python's preference for alphabetic keywords. Python 2.0 also introduced a garbage collection system capable of collecting reference cycles.
Python 2.1 was close to Python 1.6.1, as well as 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, modeled after the Apache Software Foundation. The release included a change to the language specification to support nested scopes, like other statically scoped languages. (The feature was turned off by default, and not required, until Python 2.2.)
A major innovation in Python 2.2 was the unification of Python's types (types written in C), and classes (types written in Python) into one hierarchy. This single unification made Python's object model purely and consistently object oriented. Also added were generators which were inspired by Icon.
Python's standard library additions and syntactical choices were strongly influenced by Java in some cases: the logging package, introduced in version 2.3, the SAX parser, introduced in 2.0, and the decorator syntax that uses @, added in version 2.4
Future development
A Python Enhancement Proposal (or "PEP") is a standardized design document providing general information related to Python, including proposals, descriptions, and explanations for language features. PEPs are intended as the primary channel for proposing new features, and for documenting the underlying design rationale for all major elements of Python. Outstanding PEPs are reviewed and commented by the BDFL.
Python 3000
There are plans for a future version, to be 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".
Python 3.0a1, the first alpha release, was released on August 31, 2007, and a PEP (Python Enhancement Proposal) that details planned changes exists. The latest beta release, 3.0b2, was released on July 17, 2008.
PhilosophyPython 3.0 is being developed with the same philosophy as in prior versions, so any reference to Python philosophy will apply to Python 3.0 as well. However, as Python has accumulated new and redundant ways to program the same task, Python 3.0 has an emphasis on removing duplicative constructs and modules, in keeping with "There should be one—and preferably only one—obvious way to do it".
Nonetheless, Python 3.0 will remain a multi-paradigm language. Coders will still have options among object orientation, structured programming, functional programming, and aspect-oriented programming and other paradigms, but within such broad choices, the details are intended to be more obvious in Python 3.0 than they have become in Python 2.x.
Timeline and compatibilityPython 3.0a1, the first alpha release of Python 3.0, was released on August 31, 2007. A final release is, as of July 2008, planned for October 1, 2008. The Python 2.x and Python 3.x series will coexist for several releases in parallel, where the 2.x series exists largely for compatibility and with some new features being backported from the 3.x series. contains more information about the release schedule.
Like Perl 6, Python 3.0 will break backward compatibility. There is no requirement that Python 2.x code will run unmodified on Python 3.0. There are basic changes such as changing the print statement into a print function (so any use of the print statement will cause the program to fail), and switching to Unicode for all text strings. Python's dynamic typing combined with the plans to change the semantics of certain methods of dictionaries, for example, makes perfect mechanical translation from Python 2.x to Python 3.0 very difficult. However, a tool called "2to3" does most of the job of translation, pointing out areas of uncertainty using comments or warnings. Even in an alpha stage, 2to3 appears to be fairly successful at performing the translation. recommends keeping one source (for the 2.x series), and producing releases for the Python 3.x platform using 2to3. The resulting code should not be edited until the program no longer needs to run on Python 2.x.
Python 2.6 will include forward compatibility features, as well as a "warnings" mode that will warn of potential transition problems. Warnings will be reported for builtins which will no longer exist in 3.0, as well as various old Python 2.x features that Python 3.0 will remove (see for more information).
FeaturesSome of the major changes scheduled for Python 3.0 are:
- Changing
print so that it is 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 __future__ 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
Some of the largest projects that use Python are the Zope application server, YouTube, and the original BitTorrent client. Large organizations that make use of Python include Google, Yahoo!, CERN and NASA. Air Canada's reservation management system also uses Python for some of its components.
Python has also seen extensive use in the information security industry. Notably, in several of the tools offered by Immunity Security, in several of the tools offered by Core Security, in the Web application security scanner Wapiti, and in the fuzzer TAOF. Python is commonly used in exploit development.
Python has been successfully embedded in a number of software products as a scripting language. It is commonly used in 3D animation packages, as in Houdini, Maya, Softimage XSI, TrueSpace, Poser, Modo, Nuke and Blender. It is also used in GIMP, Inkscape, Scribus and Paint Shop Pro. ESRI is now promoting Python as the best choice for writing scripts in ArcGIS. It is also used in Civilization IV as the control language for modding and event interaction. Eve Online, a MMORPG, is also built using Python.
For many operating systems, Python is a standard component; it ships with most Linux distributions, with NetBSD, and OpenBSD, and with Mac OS X. Slackware, Red Hat Linux and Fedora 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.
Syntax and semantics
Python was intended to be a highly readable language. It aims toward an uncluttered visual layout, frequently using English keywords where other languages use punctuation. Python requires less boilerplate than traditional statically-typed structured languages such as C or Pascal, and has a smaller number of syntactic exceptions and special cases than either of these. Python uses indentation/whitespace, rather than curly braces or keywords, to delimit statement blocks. An increase in indentation comes after certain statements; a decrease in indentation signifies the end of the current block. 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
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 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).
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 not enforcing static typing, Python is strongly typed, forbidding nonsense operations (for example, adding a number to a string) rather than silently attempting to make sense of them.
Types | 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
(will be transparently expanded to an unlimited precision long, when it overflows the storage for an integer. | 42
2147483648L | float | A real number | 3.1415927 | complex | A complex number with real number and imaginary parts | 3+2j | bool | truth value | True or False |
Python also allows programmers to define their own types. This is done using classes, which are most often used for an object-oriented style of programming. New instances of classes are constructed by calling the class (eg, FooClass()), and the classes themselves are instances of the class type (itself an instance of itself), allowing metaprogramming and reflection.
ImplementationsThe mainstream Python implementation, also known as CPython, is written in C meeting the C89 standard,. CPython compiles the Python program into intermediate byte code, 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, or Macintosh 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 and will be better suited for use on microcontrollers or other limited resource platforms. It can be expected to run on approximately the same platforms that CPython runs on.
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.
Several programs exist to package the Python interpreter with application programs (or scripts) as standalone UNIX, Linux, Windows or Mac OS X executables, including , a pure Python utility that ships with Python, or , , and , all of which are available separately. 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 the Series 60 mobile phones called PyS60. It includes many of the modules from the CPython 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 and .
(??) 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 to, 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. 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 and is faster than standard Python code, sometimes much much faster. Psyco is compatible with all Python code, not only a subset.
Standard libraryPython 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, 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 ), 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 must be altered or completely rewritten by alternative implementations.
Programming philosophyPython 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, 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.
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 that 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 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'. However, most problems are not speed critical, and as computer hardware continues to become exponentially faster, 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, by using a JIT compiler such as Psyco, or by rewriting the time-critical functions in "closer to the metal" languages such as C.
Influences on other languagesPython's design and philosophy have influenced several programming languages:
- Boo's Python heritage is more explicit — it also uses indentation, a similar syntax, and a similar object model. Boo, however, uses static typing and is closely integrated with the .NET framework.
- Cobra is also 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 contracts.
- ECMAScript (commonly called Javascript) borrowed iterators, generators and list comprehensions from Python.
- Groovy was motivated by the desire to bring the Python design philosophy to Java.
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 because of Python's influence.
See also
External links
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