Python

• Edit
• Read in another language

Python (programming language)

Python is an interpreted high-level programming language for general-purpose programming. Created by Guido van Rossumand first released in 1991, Python has a design philosophy that emphasizes code readability, and a syntax that allows programmers to express concepts in fewerlines of code,^[26][27] notably using significant whitespace. It provides constructs that enable clear programming on both small and large scales.^[28]

Python

Paradigm	Object-oriented,imperative, functional,procedural, reflective
Designed by	Guido van Rossum
Developer	Python Software Foundation
First appeared	20 February 1991; 27 years ago[1]
Stable release	3.6.4 / 19 December 2017; 2 months ago[2] 2.7.14 / 16 September 2017; 5 months ago[3]
Preview release	3.6.5rc1[4] and 3.7.0b1 / 2018
Typing discipline	Duck, dynamic, strong
License	Python Software Foundation License
Filename extensions	.py, .pyc, .pyd, .pyo (prior to 3.5),[5] .pyw, .pyz (since 3.5)[6]
Website	python.org
Major implementations
CPython, IronPython, Jython, MicroPython,Numba, PyPy, Stackless Python
Dialects
Cython, RPython
Influenced by
ABC,[7] ALGOL 68,[8] C,[9] C++,[10] CLU,[11]Dylan,[12] Haskell,[13] Icon,[14] Java,[15] Lisp,[16]Modula-3,[10] Perl
Influenced
Boo, Cobra, Coconut,[17] CoffeeScript,[18] D, F#,Falcon, Genie,[19] Go, Groovy, JavaScript,[20][21]Julia,[22] Nim, Ring,[23] Ruby,[24] Swift[25]
Python Programming at Wikibooks

Python features a dynamic type system and automatic memory management. It supports multiple programming paradigms, includingobject-oriented, imperative, functional andprocedural, and has a large and comprehensive standard library.^[29]

Python interpreters are available for manyoperating systems. CPython, the reference implementation of Python, is open sourcesoftware^[30] and has a community-based development model, as do nearly all of its variant implementations. CPython is managed by the non-profit Python Software Foundation.

HistoryEdit

Guido van Rossum, the creator of Python

Main article: History of Python

Python was conceived in the late 1980s,^[31]and its implementation began in December 1989^[32] by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in theNetherlands as a successor to the ABC language (itself inspired by SETL)^[33] capable of exception handling and interfacing with theAmoeba operating system.^[7] Van Rossum remains Python's principal author. His continuing central role in Python's development is reflected in the title given to him by the Python community: Benevolent Dictator For Life (BDFL).

On the origins of Python, Van Rossum wrote in 1996:^[34]

...In December 1989, I was looking for a "hobby" programming project that would keep me occupied during the week around Christmas. My office ... would be closed, but I had a home computer, and not much else on my hands. I decided to write an interpreter for the new scripting language I had been thinking about lately: a descendant of ABC that would appeal to Unix/C hackers. I chose Python as a working title for the project, being in a slightly irreverent mood (and a big fan of Monty Python's Flying Circus).

— Guido van Rossum

Python 2.0 was released on 16 October 2000 and had many major new features, including acycle-detecting garbage collector and support for Unicode. With this release, the development process became more transparent and community-backed.^[35]

Python 3.0 (initially called Python 3000 or py3k) was released on 3 December 2008 after a long testing period. It is a major revision of the language that is not completely backward-compatible with previous versions.^[36]However, many of its major features have been backported to the Python 2.6.x^[37] and 2.7.x version series, and releases of Python 3 include the 2to3 utility, which automates the translation of Python 2 code to Python 3.^[38]

Python 2.7's end-of-life date (a.k.a. EOL, sunset date) was initially set at 2015, then postponed to 2020 out of concern that a large body of existing code could not easily be forward-ported to Python 3.^[39][40]

Python 3.6 had changes regarding UTF-8 (in Windows, PEP 528 and PEP 529) and Python 3.7.0b1 (PEP 540) adds a new "UTF-8 Mode" (and overrides POSIX locale).

In January 2017, Google announced work on a Python 2.7 to Go transcompiler to improve performance under concurrent workloads.^[41]

Features and philosophyEdit

Python is a multi-paradigm programming language. Object-oriented programming andstructured programming are fully supported, and many of its features support functional programming and aspect-oriented programming (including bymetaprogramming^[42] and metaobjects(magic methods)).^[43] Many other paradigms are supported via extensions, including design by contract^[44][45] and logic programming.^[46]

Python uses dynamic typing, and a combination of reference counting and a cycle-detecting garbage collector for memory management. It also features dynamic name resolution (late binding), which binds method and variable names during program execution.

Python's design offers some support forfunctional programming in the Lisp tradition. It has filter(), map(), and reduce() functions; list comprehensions,dictionaries, and sets; and generatorexpressions.^[47] The standard library has two modules (itertools and functools) that implement functional tools borrowed fromHaskell and Standard ML.^[48]

The language's core philosophy is summarized in the document The Zen of Python (PEP 20), which includes aphorismssuch as:^[49]

• Beautiful is better than ugly
• Explicit is better than implicit
• Simple is better than complex
• Complex is better than complicated
• Readability counts

Rather than having all of its functionality built into its core, Python was designed to be highly extensible. This compact modularity has made it particularly popular as a means of adding programmable interfaces to existing applications. Van Rossum's vision of a small core language with a large standard library and easily extensible interpreter stemmed from his frustrations with ABC, which espoused the opposite approach.^[31]

While offering choice in coding methodology, the Python philosophy rejects exuberant syntax (such as that of Perl) in favor of a simpler, less-cluttered grammar. As Alex Martelli put it: "To describe something as 'clever' is not considered a compliment in the Python culture."^[50] 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".^[49]

Python's developers strive to avoid premature optimization, and reject patches to non-critical parts of CPython that would offer marginal increases in speed at the cost of clarity.^[51] When speed is important, a Python programmer can move time-critical functions to extension modules written in languages such as C, or use PyPy, a just-in-time compiler. Cython is also available, which translates a Python script into C and makes direct C-level API calls into the Python interpreter.

An important goal of Python's developers is keeping it fun to use. This is reflected in the language's name—a tribute to the British comedy group Monty Python^[52]—and in occasionally playful approaches to tutorials and reference materials, such as examples that refer to spam and eggs (from a famous Monty Python sketch) instead of the standardfoo and bar.^[53][54]

A common neologism in the Python community is pythonic, which can have a wide range of meanings related to program style. To say that code is pythonic is to say that it uses Python idioms well, that it is natural or shows fluency in the language, that it conforms with Python's minimalist philosophy and emphasis on readability. In contrast, code that is difficult to understand or reads like a rough transcription from another programming language is called unpythonic.

Users and admirers of Python, especially those considered knowledgeable or experienced, are often referred to asPythonists, Pythonistas, and Pythoneers.^[55][56]

Syntax and semanticsEdit

Main article: Python syntax and semantics

Python is meant to be an easily readable language. Its formatting is visually uncluttered, and it often uses English keywords where other languages use punctuation. Unlike many other languages, it does not use curly brackets to delimit blocks, and semicolons after statements are optional. It has fewer syntactic exceptions and special cases than C or Pascal.^[57]

IndentationEdit

Main article: Python syntax and semantics § Indentation

Python uses whitespace indentation, rather than curly brackets or keywords, to delimitblocks. An increase in indentation comes after certain statements; a decrease in indentation signifies the end of the current block.^[58] This feature is also sometimes termed the off-side rule.

Statements and control flowEdit

Python's statements include (among others):

• The assignment statement (token '=', the equals sign). This operates differently than in traditional imperative programminglanguages, and this fundamental mechanism (including the nature of Python's version of variables) illuminates many other features of the language. Assignment in C, e.g., x = 2, translates to "typed variable name x receives a copy of numeric value 2". The (right-hand) value is copied into an allocated storage locationfor which the (left-hand) variable name is the symbolic address. The memory allocated to the variable is large enough (potentially quite large) for the declaredtype. In the simplest case of Python assignment, using the same example, x = 2, translates to "(generic) name x receives a reference to a separate, dynamically allocated object of numeric (int) type of value 2." This is termed binding the name to the object. Since the name's storage location doesn't contain the indicated value, it is improper to call it a variable. Names may be subsequently rebound at any time to objects of greatly varying types, including strings, procedures, complex objects with data and methods, etc. Successive assignments of a common value to multiple names, e.g., x = 2; y = 2; z = 2result in allocating storage to (at most) three names and one numeric object, to which all three names are bound. Since a name is a generic reference holder it is unreasonable to associate a fixed data typewith it. However at a given time a name will be bound to some object, which will have a type; thus there is dynamic typing.
• 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 while statement, which executes a block of code as long as its condition is true.
• The try statement, which allows exceptions raised in its attached code block to be caught and handled by exceptclauses; it also ensures that clean-up code in a finally block will 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 afunction or method.
• The with statement (from Python 2.5), which encloses a code block within a context manager (for example, acquiring alock before the block of code is run and releasing the lock afterwards, or opening afile and then closing it), allowing Resource Acquisition Is Initialization (RAII)-like behavior.
• The pass statement, which serves as aNOP. It is syntactically needed to create an empty code block.
• The assert statement, used during debugging to check for conditions that ought to apply.
• The yield statement, which returns a value from a generator function. From Python 2.5, yield is also an operator. This form is used to implement coroutines.
• The import statement, which is used to import modules whose functions or variables can be used in the current program. There are four ways of using import: import <module name> or from <module name> import * or import numpy as np or from numpy import pi as Pie.
• The print statement was changed to the print() function in Python 3.^[59]

Python does not support tail call optimization or first-class continuations, and, according to Guido van Rossum, it never will.^[60][61]However, better support for coroutine-like functionality is provided in 2.5, by extending Python's generators.^[62] Before 2.5, generators were lazy iterators; information was passed unidirectionally out of the generator. From Python 2.5, it is possible to pass information back into a generator function, and from Python 3.3, the information can be passed through multiple stack levels.^[63]

ExpressionsEdit

Some Python expressions are similar to languages such as C and Java, while some are not:

• Addition, subtraction, and multiplication are the same, but the behavior of division differs. There are two types of divisions in Python. They are floor division and integer division.^[64] Python also added the **operator for exponentiation.
• From Python 3.5, the new @ infix operator was introduced. It is intended to be used by libraries such as NumPy for matrix multiplication.^[65][66]
• In Python, == compares by value, versus Java, which compares numerics by value^[67]and objects by reference.^[68] (Value comparisons in Java on objects can be performed with the equals() method.) Python's is operator may be used to compare object identities (comparison by reference). In Python, comparisons may be chained, for example a <= b <= c.
• Python uses the words and, or, notfor its boolean operators rather than the symbolic &&, ||, ! used in Java and C.
• Python has a type of expression termed alist comprehension. Python 2.4 extended list comprehensions into a more general expression termed a generatorexpression.^[47]
• Anonymous functions are implemented using lambda expressions; however, these are limited in that the body can only be one expression.
• Conditional expressions in Python are written as x if c else y^[69] (different in order of operands from the c ? x : y operator common to many other languages).
• Python makes a distinction between listsand tuples. Lists are written as [1, 2, 3], are mutable, and cannot be used as the keys of dictionaries (dictionary keys must be immutable in Python). Tuples are written as (1, 2, 3), are immutable and thus can be used as the keys of dictionaries, provided all elements of the tuple are immutable. The + operator can be used to concatenate two tuples, which does not directly modify their contents, but rather produces a new tuple containing the elements of both provided tuples. Thus, given the variable t initially equal to (1, 2, 3), executing t = t + (4, 5) first evaluates t + (4, 5), which yields (1, 2, 3, 4, 5), which is then assigned back to t, thereby effectively "modifying the contents" of t, while conforming to the immutable nature of tuple objects. Parentheses are optional for tuples in unambiguous contexts.^[70]
• Python features sequence unpacking where multiple expressions, each evaluating to anything that can be assigned to (a variable, a writable property, etc.), are associated in the identical manner to that forming tuple literals and, as a whole, are put on the left hand side of the equal sign in an assignment statement. The statement expects an iterable object on the right hand side of the equal sign that produces the same number of values as the provided writable expressions when iterated through, and will iterate through it, assigning each of the produced values to the corresponding expression on the left.^{[citation needed]}
• Python has a "string format" operator %. This functions analogous to printfformat strings in C, e.g. "spam=%s eggs=%d" % ("blah", 2) evaluates to"spam=blah eggs=2". In Python 3 and 2.6+, this was supplemented by the format() method of the str class, e.g. "spam={0} eggs={1}".format("blah", 2). Python 3.6 added "f-strings": blah = "blah"; eggs = 2; f'spam={blah} eggs={eggs}'.^[71]
• Python has various kinds of string literals:
  • Strings delimited by single or double quote marks. Unlike in Unix shells, Perland Perl-influenced languages, single quote marks and double quote marks function identically. Both kinds of string use the backslash (\) as anescape character. String interpolationbecame available in Python 3.6 as "formatted string literals".^[71]
  • Triple-quoted strings, which begin and end with a series of three single or double quote marks. They may span multiple lines and function like here documents in shells, Perl and Ruby.
  • Raw string varieties, denoted by prefixing the string literal with an r. Escape sequences are not interpreted; hence raw strings are useful where literal backslashes are common, such as regular expressions and Windows-style paths. Compare "@-quoting" inC#.
• Python has array index and array slicingexpressions on lists, denoted as a[key],a[start:stop] or a[start:stop:step]. Indexes arezero-based, and negative indexes are relative to the end. Slices take elements from the start index up to, but not including, the stop index. The third slice parameter, called step or stride, allows elements to be skipped and reversed. Slice indexes may be omitted, for example a[:] returns a copy of the entire list. Each element of a slice is a shallow copy.

In Python, a distinction between expressions and statements is rigidly enforced, in contrast to languages such as Common Lisp, Scheme, or Ruby. This leads to duplicating some functionality. For example:

• List comprehensions vs. for-loops
• Conditional expressions vs. if blocks
• The eval() vs. exec() built-in functions (in Python 2, exec is a statement); the former is for expressions, the latter is for statements.

Statements cannot be a part of an expression, so list and other comprehensions or lambda expressions, all being expressions, cannot contain statements. A particular case of this is that an assignment statement such as a = 1 cannot form part of the conditional expression of a conditional statement. This has the advantage of avoiding a classic C error of mistaking an assignment operator = for an equality operator == in conditions: if (c = 1) { ... } is syntactically valid (but probably unintended) C code but if c = 1: ... causes a syntax error in Python.

MethodsEdit

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 (or this) in some other object-oriented programming languages (e.g., C++, Java,Objective-C, or Ruby).^[72]

TypingEdit

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.

Python allows programmers to define their own types using classes, which are most often used for object-oriented programming. New instances of classes are constructed by calling the class (for example, SpamClass() or EggsClass()), and the classes are instances of the metaclass type (itself an instance of itself), allowingmetaprogramming and reflection.

Before version 3.0, Python had two kinds of classes: old-style and new-style.^[73] The syntax of both styles is the same, the difference being whether the class object is inherited from, directly or indirectly (all new-style classes inherit from object and are instances of type). In versions of Python 2 from Python 2.2 onwards, both kinds of classes can be used. Old-style classes were eliminated in Python 3.0.

The long term plan is to support gradual typing^[74] and from Python 3.5, the syntax of the language allows specifying static types but they are not checked in the default implementation, CPython. An experimental optional static type checker named mypysupports compile-time type checking.^[75]

Summary of Python 3's built-in types

Type	mutable	Description	Syntax example
bool	immutable	Boolean value	True False
bytearray	mutable	Sequence ofbytes	bytearray(b'Some ASCII') bytearray(b"Some ASCII") bytearray([119, 105, 107, 105])
bytes	immutable	Sequence of bytes	b'Some ASCII' b"Some ASCII" bytes([119, 105, 107, 105])
complex	immutable	Complex number with real and imaginary parts	3+2.7j
dict	mutable	Associative array (or dictionary) of key and value pairs; can contain mixed types (keys and values), keys must be a hashable type	{'key1': 1.0, 3: False}
ellipsis		An ellipsisplaceholder to be used as an index inNumPyarrays	...
float	immutable	Floating pointnumber, system-defined precision	3.1415927
frozenset	immutable	Unorderedset, contains no duplicates; can contain mixed types, if hashable	frozenset([4.0, 'string', True])
int	immutable	Integer of unlimited magnitude[76]	42
list	mutable	List, can contain mixed types	[4.0, 'string', True]
set	mutable	Unorderedset, contains no duplicates; can contain mixed types, if hashable	{4.0, 'string', True}
str	immutable	A character string: sequence of Unicode codepoints	'Wikipedia' "Wikipedia" """Spanning multiple lines"""
tuple	immutable	Can contain mixed types	(4.0, 'string', True)But we can append elements using __add__ . a = (4.0, 'string' , True).__add__(('hi' ,)) now a gives (4.0, 'string', True ,'hi')

MathematicsEdit

Python has the usual C arithmetic operators (+, -, *, /, %). It also has ** for exponentiation, e.g. 5**3 == 125 and 9**0.5 == 3.0, and a new matrix multiply @ operator is included in version 3.5.^[77] Additionally, it has a unary operator (~), which essentially inverts all the bits of its one argument. For integers, this means ~x=-x-1.^[78] Other operators include bitwise shift operators x << y, which shifts x to the left y places, the same as x*(2**y) , and x >> y, which shifts x to the right y places, the same as x/(2**y) .^[79]

The behavior of division has changed significantly over time:^[80]

• Python 2.1 and earlier use the C division behavior. The / operator is integer division if both operands are integers, and floating-point division otherwise. Integer division rounds towards 0, e.g. 7/3 == 2 and -7/3 == -2.
• Python 2.2 changes integer division to round towards negative infinity, e.g. 7/3 == 2 and -7/3 == -3. The floor division // operator is introduced. So 7//3 == 2, -7//3 == -3, 7.5//3 == 2.0 and -7.5//3 == -3.0. Adding from __future__ import division causes a module to use Python 3.0 rules for division (see next).
• Python 3.0 changes / to be always floating-point division. In Python terms, the pre-3.0 / is classic division, the version-3.0 / is real division, and // is floor division.

Rounding towards negative infinity, though different from most languages, adds consistency. For instance, it means that the equation (a + b)//b == a//b + 1 is always true. It also means that the equation b*(a//b) + a%b == a is valid for both positive and negative values of a. However, maintaining the validity of this equation means that while the result of a%b is, as expected, in the half-open interval [0, b), whereb is a positive integer, it has to lie in the interval (b, 0] when b is negative.^[81]

Python provides a round function forrounding a float to the nearest integer. For tie-breaking, versions before 3 use round-away-from-zero: round(0.5) is 1.0, round(-0.5) is −1.0.^[82] Python 3 usesround to even: round(1.5) is 2, round(2.5) is 2.^[83]

Python allows boolean expressions with multiple equality relations in a manner that is consistent with general use in mathematics. For example, the expression a < b < ctests whether a is less than b and b is less than c. C-derived languages interpret this expression differently: in C, the expression would first evaluate a < b, resulting in 0 or 1, and that result would then be compared with c.^{[84][page needed]}

Python has extensive built-in support forarbitrary precision arithmetic. Integers are transparently switched from the machine-supported maximum fixed-precision (usually 32 or 64 bits), belonging to the python type int, to arbitrary precision, belonging to the python type long, where needed. The latter have an "L" suffix in their textual representation.^[85] (In Python 3, the distinction between the int and long types was eliminated; this behavior is now entirely contained by the int class.) The Decimal type/class in module decimal(since version 2.4) provides decimal floating point numbers to arbitrary precision and several rounding modes.^[86] The Fractiontype in module fractions (since version 2.6) provides arbitrary precision for rational numbers.^[87]

Due to Python's extensive mathematics library, and the third-party library NumPy that further extends the native capabilities, it is frequently used as a scientific scripting language to aid in problems such as numerical data processing and manipulation.

LibrariesEdit

Python's large standard library, commonly cited as one of its greatest strengths,^[88]provides tools suited to many tasks. For Internet-facing applications, many standard formats and protocols such as MIME andHTTP are supported. It includes modules for creating graphical user interfaces, connecting to relational databases, generating pseudorandom numbers, arithmetic with arbitrary precision decimals,^[89] manipulatingregular expressions, and unit testing.

Some parts of the standard library are covered by specifications (for example, theWeb Server Gateway Interface (WSGI) implementation wsgiref follows PEP 333^[90]), but most modules are not. They are specified by their code, internal documentation, and test suites (if supplied). However, because most of the standard library is cross-platform Python code, only a few modules need altering or rewriting for variant implementations.

As of March 2018, the Python Package Index, the official repository for third-party Python software, contains over 130,000^[91] packages with a wide range of functionality, including:

• Graphical user interfaces
• Web frameworks
• Multimedia
• Databases
• Networking
• Test frameworks
• Automation
• Web scraping^[92]
• Documentation
• System administration
• Scientific computing
• Text processing
• Image processing

Development environmentsEdit

See also: Comparison of integrated development environments § Python

Most Python implementations (including CPython) include a read–eval–print loop(REPL), permitting them to function as acommand line interpreter for which the user enters statements sequentially and receives results immediately.

Other shells, including IDLE and IPython, add further abilities such as auto-completion, session state retention and syntax highlighting.

As well as standard desktop integrated development environments (see Wikipedia's "Python IDE" article), there are Web browser-based IDEs; SageMath (intended for developing science and math-related Python programs); PythonAnywhere, a browser-based IDE and hosting environment; and Canopy IDE, a commercial Python IDE emphasizing scientific computing.^[93]