---

title: "Python (programming language)" type: doc version: 1 created: 2026-02-28 author: "Wikipedia contributors" status: active scope: public tags: ["python-(programming-language)", "articles-with-example-python-(programming-language)-code", "class-based-programming-languages", "notebook-interface", "computer-science-in-the-netherlands", "concurrent-programming-languages", "cross-platform-free-software", "cross-platform-software", "dutch-inventions", "dynamically-typed-programming-languages", "educational-programming-languages", "high-level-programming-languages", "information-technology-in-the-netherlands", "multi-paradigm-programming-languages", "object-oriented-programming-languages", "pattern-matching-programming-languages", "programming-languages", "programming-languages-created-in-1991", "scripting-languages", "text-oriented-programming-languages", "monty-python-references"] description: "General-purpose programming language" topic_path: "technology/programming-languages" source: "https://en.wikipedia.org/wiki/Python_(programming_language)" license: "CC BY-SA 4.0" wikipedia_page_id: 0 wikipedia_revision_id: 0

::summary General-purpose programming language ::

::data[format=table title="Infobox programming language"]

Field	Value
logo	Python-logo-notext.svg
logo size	150px
paradigm	Multi-paradigm: object-oriented, procedural (imperative), functional, structured, reflective
released
designer	Guido van Rossum
developer	Python Software Foundation
latest release version
latest release date
latest preview version
latest preview date
{{#invoke:Wikidata	qualifier
typing	Duck, dynamic, strong; optional type annotations
memory management	Garbage-collected
implementations	CPython, PyPy, MicroPython, CircuitPython, IronPython, Jython, Stackless Python
operating system	Cross-platform
{{efn	* Tier 1: 64-bit Linux, macOS; 64- and 32-bit Windows{{Cite web
* Tier 3: 64-bit Android,{{Cite web	title
Unofficial (or has been known to work): Other Unix-like/BSD variants) and a few other platforms}}
-->	license
file ext	.py, .pyw, .pyz,

| | .pyi, .pyc, .pyd<!-- too much trivia: .pyo (before 3.5)File extension .pyo was removed in Python 3.5. See [https://www.python.org/dev/peps/pep-0488/ PEP 0488] {{Webarchive|url | https://web.archive.org/web/20200601133202/https://www.python.org/dev/peps/pep-0488/ |date=1 June 2020}} -- | | website | | | dialects | Cython, RPython, Starlark | | influenced by | ABC, Ada, ALGOL 68, APL, C, C++, CLU, Dylan, Haskell, Icon, Lisp, Modula-3, Perl, Standard ML | | influenced | Apache Groovy, Boo, Cobra, CoffeeScript, D, F#, GDScript, Go, JavaScript, Julia, Mojo, Nim, Ruby, Swift, V | | <!-- Do not put in as there's a pure Java implementation (Jython): | programming language | C --| wikibooks = Python Programming | ::

| logo = Python-logo-notext.svg | logo size = 150px | paradigm = Multi-paradigm: object-oriented, procedural (imperative), functional, structured, reflective | released = | designer = Guido van Rossum | developer = Python Software Foundation | latest release version = | latest release date = | latest preview version = | latest preview date =

| typing = Duck, dynamic, strong; optional type annotations | memory management = Garbage-collected | implementations = CPython, PyPy, MicroPython, CircuitPython, IronPython, Jython, Stackless Python | operating system = Cross-platform

"Windows 8 and newer for Python 3.9 FreeBSD 10 and newer macOS Snow Leopard (macOS 10.6, 2008) and newer"

• Tier 2: E.g. 32-bit WebAssembly (WASI)
• Tier 3: 64-bit Android, iOS, FreeBSD, and (32-bit) Raspberry Pi OS Unofficial (or has been known to work): Other Unix-like/BSD variants) and a few other platforms}}

https://mail.python.org/archives/list/python-committers@python.org/thread/K757345KX6W5ZLTWYBUXOXQTJJTL7GW5/

• Alpine / musl is not supported, because our test suite is failing due to bugs and missing features in musl libc.
• NetBSD and OpenBSD are in a similar state as Alpine: no stable buildbot and AFAIK tests are failing
• [outdated]
• Cygwin and MinGW are officially unsupported, see bpo-45537 and bpo-45538

..

The policy Brett is proposing just makes that explicit and gives us something to point to when someone comes up with a patch to support PDP-11 or when someone's patch for Android breaks Windows. I don't think we'll wind up with tier support police; if a core dev wants to take responsibility for a patch for a platform that is not tier 3 or above they can still do that, but if it breaks things for a supported platform it will be reverted.

..

E.g. Android support was even funded by the PSF recently.[outdated] Also, note that the stdlib does in fact support other Python implementations reusing (parts of) it, e.g. Jython, PyPy and IronPython. Again, without core devs backing these. --| license = Python Software Foundation License | file ext = .py, .pyw, .pyz,

.pyi, .pyc, .pyd | website = | dialects = Cython, RPython, Starlark | influenced by = ABC, Ada, ALGOL 68, APL, C, C++, CLU, Dylan, Haskell, Icon, Lisp, Modula-3, Perl, Standard ML | influenced = Apache Groovy, Boo, Cobra, CoffeeScript, D, F#, GDScript, Go, JavaScript, Julia, Mojo, Nim, Ruby, Swift, V | wikibooks = Python Programming

Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. Python is dynamically type-checked and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming.

Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language. Python 3.0, released in 2008, was a major revision and not completely backward-compatible with earlier versions. Beginning with Python 3.5, capabilities and keywords for typing were added to the language, allowing optional static typing. , the Python Software Foundation supports Python 3.10, 3.11, 3.12, 3.13, and 3.14, following the project's annual release cycle and five-year support policy. Python 3.15 is currently in the alpha development phase, and the stable release is expected to come out in October 2026."Earlier versions in the 3.x series have reached end-of-life and no longer receive security updates.

Python has gained widespread use in the machine learning community. It is widely taught as an introductory programming language. Since 2003, Python has consistently ranked in the top ten of the most popular programming languages in the TIOBE Programming Community Index, which ranks based on searches in 24 platforms.

History

Main article: History of Python

::figure[src="https://upload.wikimedia.org/wikipedia/commons/2/21/Guido_van_Rossum_in_PyConUS24.jpg" caption="The designer of Python, [[Guido van Rossum]], at [[PyCon]] US 2024"] ::

Python was conceived in the late 1980s by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands. It was designed as a successor to the ABC programming language, which was inspired by SETL, capable of exception handling and interfacing with the Amoeba operating system. Python implementation began in December 1989. Van Rossum first released it in 1991 as Python 0.9.0. Van Rossum assumed sole responsibility for the project, as the lead developer, until 12 July 2018, when he announced his "permanent vacation" from responsibilities as Python's "benevolent dictator for life" (BDFL); this title was bestowed on him by the Python community to reflect his long-term commitment as the project's chief decision-maker. (He has since come out of retirement and is self-titled "BDFL-emeritus".) In January 2019, active Python core developers elected a five-member Steering Council to lead the project.

The name Python derives from the British comedy series Monty Python's Flying Circus. (See .)

Python 2.0 was released on 16 October 2000, featuring many new features such as list comprehensions, cycle-detecting garbage collection, reference counting, and Unicode support. Python 2.7's end-of-life was initially set for 2015, and then postponed to 2020 out of concern that a large body of existing code could not easily be forward-ported to Python 3. It no longer receives security patches or updates. While Python 2.7 and older versions are officially unsupported, a different unofficial Python implementation, PyPy, continues to support Python 2, i.e., "2.7.18+" (plus 3.11), with the plus signifying (at least some) "backported security updates".

Python 3.0 was released on 3 December 2008, and was a major revision and not completely backward-compatible with earlier versions, with some new semantics and changed syntax. Python 2.7.18, released in 2020, was the last release of Python 2. Several releases in the Python 3.x series have added new syntax to the language, and made a few (considered very minor) backward-incompatible changes.

, Python is the latest stable release. All older 3.x versions had a security update down to Python 3.9.24 then again with 3.9.25, the final version in 3.9 series. Python 3.10 is, since November 2025, the oldest supported branch. Python 3.15 has an alpha released, and Android has an official downloadable executable available for Python 3.14. Releases receive two years of full support followed by three years of security support.

Design philosophy and features

Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of their features support functional programming and aspect-oriented programming – including metaprogramming because it is purposely designed to be able to integrate components written in other languages.

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

Python's design offers some support for functional programming in the "Lisp tradition". It has , , and functions; list comprehensions, dictionaries, sets, and generator expressions. The standard library has two modules ( and ) that implement functional tools borrowed from Haskell and Standard ML.

Python's core philosophy is summarized in the Zen of Python (PEP 20) written by Tim Peters, which includes aphorisms such as these:

• Explicit is better than implicit.
• Simple is better than complex.
• Readability counts.
• Special cases aren't special enough to break the rules.
• Although practicality beats purity, errors should never pass silently, unless explicitly silenced.
• There should be one-- and preferably only one --obvious way to do it.

However, Python has received criticism for violating these principles and adding unnecessary language bloat. Responses to these criticisms note that the Zen of Python is a guideline rather than a rule. The addition of some new features had been controversial: Guido van Rossum resigned as Benevolent Dictator for Life after conflict about adding the assignment expression operator in

Nevertheless, rather than building all functionality into its core, Python was designed to be highly extensible via modules. 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 represented the opposite approach.

Python claims to strive for a simpler, less-cluttered syntax and grammar, while giving developers a choice in their coding methodology. Python lacks do .. while loops, which Rossum considered harmful. In contrast to Perl's motto "there is more than one way to do it", Python advocates an approach where "there should be one – and preferably only one – obvious way to do it". Alex Martelli is a Fellow at the Python Software Foundation and Python book author; he wrote that "To describe something as 'clever' is not considered a compliment in the Python culture."

Python's developers typically prioritize readability over performance. For example, they reject patches to non-critical parts of the CPython reference implementation that would offer increases in speed that do not justify the cost of clarity and readability. Execution speed can be improved by moving speed-critical functions to extension modules written in languages such as C, or by using a just-in-time compiler like PyPy. Also, it is possible to transpile to other languages. However, this approach either fails to achieve the expected speed-up, since Python is a very dynamic language, or only a restricted subset of Python is compiled (with potential minor semantic changes).

Python is meant to be a fun language to use. This goal is reflected in the name – a tribute to the British comedy group Monty Python

A common neologism in the Python community is pythonic, which has a broad range of meanings related to program style: Pythonic code may use Python idioms well; be natural or show fluency in the language; or conform with Python's minimalist philosophy and emphasis on readability.

Syntax and semantics

Main article: Python syntax and semantics

Python is meant to be an easily readable language. Its formatting is visually uncluttered and 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 allowed but rarely used. It has fewer syntactic exceptions and special cases than C or Pascal.

Indentation

Python uses whitespace indentation, rather than curly brackets or keywords, to delimit blocks. An increase in indentation comes after certain statements; a decrease in indentation signifies the end of the current block. Thus, the program's visual structure accurately represents its semantic structure. This feature is sometimes termed the off-side rule. Some other languages use indentation this way; but in most, indentation has no semantic meaning. The recommended indent size is four spaces.

Statements and control flow

Python's statements include the following:

• The assignment statement, using a single equals sign =
• The [if](if-then-else) statement, which conditionally executes a block of code, along with [else](conditional-computer-programming-if-then-else) and elif (a contraction of [else if](conditional-computer-programming-else-if))
• The [for](foreach-python) statement, which iterates over an iterable object, capturing each element to a variable for use by the attached block; the variable is not deleted when the loop finishes
• The [while](while-loop-python) statement, which executes a block of code as long as boolean condition is true
• The [try](exception-handling-syntax-python) statement, which allows exceptions raised in its attached code block to be caught and handled by except clauses (or new syntax except* in Python 3.11 for exception groups); the try statement also ensures that clean-up code in a finally block is always run regardless of how the block exits
• The raise statement, used to raise a specified exception or re-raise a caught exception
• 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 or method
• The [with](dispose-pattern-language-constructs) statement, which encloses a code block within a context manager, allowing resource-acquisition-is-initialization (RAII)-like behavior and replacing a common try/finally idiom Examples of a context include acquiring a lock before some code is run, and then releasing the lock; or opening and then closing a file
• The [break](break-statement) statement, which exits a loop
• The continue statement, which skips the rest of the current iteration and continues with the next
• The del statement, which removes a variable—deleting the reference from the name to the value, and producing an error if the variable is referred to before it is redefined
• The pass statement, serving as a NOP (i.e., no operation), which is syntactically needed to create an empty code block
• The [assert](assertion-programming) statement, used in debugging to check for conditions that should apply
• The yield statement, which returns a value from a generator function (and also an operator); used to implement coroutines
• The return statement, used to return a value from a function
• The [import](include-directive) and from statements, used to import modules whose functions or variables can be used in the current program
• The match and case statements, analogous to a switch statement construct, which compares an expression against one or more cases as a control-flow measure

The assignment statement (=) binds a name as a reference to a separate, dynamically allocated object. Variables may subsequently be rebound at any time to any object. In Python, a variable name is a generic reference holder without a fixed data type; however, it always refers to some object with a type. This is called dynamic typing—in contrast to statically-typed languages, where each variable may contain only a value of a certain type.

Python does not support tail call optimization or first-class continuations; according to Van Rossum, the language never will. However, better support for coroutine-like functionality is provided by extending Python's generators. Before 2.5, generators were lazy iterators; data was passed unidirectionally out of the generator. From Python 2.5 on, it is possible to pass data back into a generator function; and from version 3.3, data can be passed through multiple stack levels.

Expressions

Python's expressions include the following:

• The +, -, and * operators for mathematical addition, subtraction, and multiplication are similar to other languages, but the behavior of division differs. There are two types of division in Python: floor division (or integer division) //, and floating-point division /. Python uses the ** operator for exponentiation.
• Python uses the + operator for string concatenation. The language uses the * operator for duplicating a string a specified number of times.
• The @ infix operator is intended to be used by libraries such as NumPy for matrix multiplication.
• The syntax :=, called the "", was introduced in Python 3.8. This operator assigns values to variables as part of a larger expression.
• In Python, == compares two objects by value. Python's is operator may be used to compare object identities (i.e., comparison by reference), and comparisons may be chained—for example, {{code|lang=python|code=a
• Python uses and, or, and not as Boolean operators.
• Python has a type of expression called a list comprehension, and a more general expression called a generator expression.
• Anonymous functions are implemented using lambda expressions; however, there may be only one expression in each body.
• Conditional expressions are written as . (This is different in operand order from the [c ? x : y]() operator common to many other languages.)
• Python makes a distinction between lists and tuples. Lists are written as , are mutable, and cannot be used as the keys of dictionaries (since dictionary keys must be immutable in Python). Tuples, written as , are immutable and thus can be used as the keys of dictionaries, provided that all of the tuple's elements are immutable. The + operator can be used to concatenate two tuples, which does not directly modify their contents, but produces a new tuple containing the elements of both. For example, given the variable t initially equal to , executing first evaluates , which yields ; this result 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.
• Python features sequence unpacking where multiple expressions, each evaluating to something assignable (e.g., a variable or a writable property) are associated just as in forming tuple literal; as a whole, the results are then put on the left-hand side of the equal sign in an assignment statement. This statement expects an iterable object on the right-hand side of the equal sign to produce the same number of values as the writable expressions on the left-hand side; while iterating, the statement assigns each of the values produced on the right to the corresponding expression on the left.
• Python has a "string format" operator % that functions analogously to [printf](printf) format strings in the C language—e.g. evaluates to "spam=blah eggs=2". In Python 2.6+ and 3+, this operator was supplemented by the format() method of the str class, e.g., . Python 3.6 added "f-strings": .
• Strings in Python can be concatenated by "adding" them (using the same operator as for adding integers and floats); e.g., returns "spameggs". If strings contain numbers, they are concatenated as strings rather than as integers, e.g. returns "22".
• Python supports string literals in several ways:
  • Delimited by single or double quotation marks; single and double quotation marks have equivalent functionality (unlike in Unix shells, Perl, and Perl-influenced languages). Both marks use the backslash (\) as an escape character. String interpolation became available in Python 3.6 as "formatted string literals".
  • Triple-quoted, i.e., starting and ending with three single or double quotation marks; this may span multiple lines and function like here documents in shells, Perl, and Ruby.
  • Raw string varieties, denoted by prefixing the string literal with r. Escape sequences are not interpreted; hence raw strings are useful where literal backslashes are common, such as in regular expressions and Windows-style paths. (Compare "@-quoting" in C#.)
• Python has array index and array slicing expressions in lists, which are written as a[key], or . Indexes are zero-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 (optional) third slice parameter, called step or stride, allows elements to be skipped or reversed. Slice indexes may be omitted—for example, 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 distinction 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 function is for expressions, while the latter is for statements

A statement cannot be part of an expression; because of this restriction, expressions such as list and dict comprehensions (and lambda expressions) cannot contain statements. As a particular case, an assignment statement such as cannot be part of the conditional expression of a conditional statement.

Typing

::figure[src="https://upload.wikimedia.org/wikipedia/commons/e/e0/Python_3.13_Standrd_Type_Hierarchy-en.svg" caption="The standard type hierarchy in Python 3"] ::

Python uses duck typing, and it has typed objects but untyped variable names. Type constraints are not checked at definition time; rather, operations on an object may fail at usage time, indicating that the object is not of an appropriate type. Despite being dynamically typed, Python is strongly typed, forbidding operations that are poorly defined (e.g., adding a number and a string) rather than quietly attempting to interpret them.

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

Before version 3.0, Python had two kinds of classes, both using the same syntax: old-style and new-style. Current Python versions support the semantics of only the new style.

Python supports optional type annotations. These annotations are not enforced by the language, but may be used by external tools such as mypy to catch errors. Python includes a module typing including several type names for type annotations. Also, mypy supports a Python compiler called mypyc, which leverages type annotations for optimization.

::data[format=table title="Summary of Python 3's built-in types"]

Type	Mutability	Description	Syntax examples
bool	immutable	Boolean value

| | bytearray | mutable | Sequence of bytes |

| | bytes | immutable | Sequence of bytes |

| | complex | immutable | Complex number with real and imaginary parts |

| | dict | mutable | Associative array (or dictionary) of key and value pairs; can contain mixed types (keys and values); keys must be a hashable type | | | types.EllipsisType | immutable | An ellipsis placeholder to be used as an index in NumPy arrays | | | float | immutable | Double-precision floating-point number. The precision is machine-dependent, but in practice it is generally implemented as a 64-bit IEEE 754 number with 53 bits of precision.{{Cite web | title=15. Floating Point Arithmetic: Issues and Limitations – Python 3.8.3 documentation | | frozenset | immutable | Unordered set, contains no duplicates; can contain mixed types, if hashable | | | int | immutable | Integer of unlimited magnitude | | | list | mutable | List, can contain mixed types | | | types.NoneType | immutable | An object representing the absence of a value, often called null in other languages | | | types.NotImplementedType | immutable | A placeholder that can be returned from overloaded operators to indicate unsupported operand types. | | | range | immutable | An immutable sequence of numbers, commonly used for iterating a specific number of times in for loops | | | set | mutable | Unordered set, contains no duplicates; can contain mixed types, if hashable | | | str | immutable | A character string: sequence of Unicode codepoints | """Spanning | | tuple | immutable | Tuple, can contain mixed types |

| ::

Arithmetic operations

Python includes conventional symbols for arithmetic operators (+, -, *, /), the floor-division operator //, and the modulo operator %. (With the modulo operator, a remainder can be negative, e.g., 4 % -3 == -2.) Also, Python offers the ** symbol for exponentiation, e.g. 5**3 == 125 and 9**0.5 == 3.0. Also, it offers the matrix‑multiplication operator @ . These operators work as in traditional mathematics; with the same precedence rules, the infix operators + and - can also be unary, to represent positive and negative numbers respectively.

Division between integers produces floating-point results. The behavior of division has changed significantly over time:

• The current version of Python (i.e., since 3.0) changed the / operator to always represent floating-point division, e.g., .
• The floor division // operator was introduced, meaning that 7//3 == 2, -7//3 == -3, 7.5//3 == 2.0, and -7.5//3 == -3.0. For Python 2.7, adding the statement allows a module in Python 2.7 to use Python 3.x rules for division (see above).

In Python terms, the / operator represents true division (or simply division), while the // operator represents floor division. Before version 3.0, the / operator represents classic division.

Rounding towards negative infinity, though a different method than in most languages, adds consistency to Python. For instance, this rounding implies that the equation is always true. Also, the rounding implies that the equation is valid for both positive and negative values of a. As expected, the result of a%b lies in the half-open interval [0, b), where b is a positive integer; however, maintaining the validity of the equation requires that the result must lie in the interval (b, 0] when b is negative.

Python provides a round function for rounding a float to the nearest integer. For tie-breaking, Python 3 uses the round to even method: round(1.5) and round(2.5) both produce 2. Python versions before 3 used the round-away-from-zero method: round(0.5) is 1.0, and round(-0.5) is −1.0.

Python allows Boolean expressions that contain multiple equality relations to be consistent with general usage in mathematics. For example, the expression `a

Python uses arbitrary-precision arithmetic for all integer operations. The Decimal type/class in the decimal module provides decimal floating-point numbers to a pre-defined arbitrary precision with several rounding modes. The Fraction class in the fractions module provides arbitrary precision for rational numbers.

Due to Python's extensive mathematics library and the third-party library NumPy, the language is frequently used for scientific scripting in tasks such as numerical data processing and manipulation.

Function syntax

Functions are created in Python by using the def keyword. A function is defined similarly to how it is called, by first providing the function name and then the required parameters. Here is an example of a function that prints its inputs: ::code[lang=python3] def printer(input1, input2 = "already there"): print(input1) print(input2)

printer("hello")

Example output:

hello

already there

To assign a default value to a function parameter in case no actual value is provided at run time, variable-definition syntax can be used inside the function header. ::

Code examples

"Hello, World!" program: ::code[lang=python] print('Hello, World!') ::

Program to calculate the factorial of a non-negative integer: ::code[lang=python] text = input('Type a number, and its factorial will be printed: ') n = int(text)

if n < 0: raise ValueError('You must enter a non-negative integer')

factorial = 1 for i in range(2, n + 1): factorial *= i

print(factorial)

"](https://github.com/IronLanguages/ironpython3).

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