Python (programming language)

Python

Paradigm	Multi-paradigm: object-oriented,[1] procedural (imperative), functional, structured, reflective
Designed by	Guido van Rossum
Developer	Python Software Foundation
First appeared	20 February 1991; 34 years ago (1991-02-20)[2]
Stable release	3.13.2 / 4 February 2025; 2 months ago (4 February 2025)
Typing discipline	duck, dynamic, strong;[3] optional type annotations (since 3.5, but those hints are ignored, except with unofficial tools)[4]
OS	Tier 1: 64-bit Linux, macOS; 64- and 32-bit Windows 10+[5] Tier 2: E.g. 32-bit WebAssembly (WASI) Tier 3: 64-bit Android,[6] 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[7][8][9]
License	Python Software Foundation License
Filename extensions	.py, .pyw, .pyz,[10] .pyi, .pyc, .pyd
Website	python.org
Major implementations
CPython, PyPy, Stackless Python, MicroPython, CircuitPython, IronPython, Jython
Dialects
Cython, RPython, Starlark[11]
Influenced by
ABC,[12] Ada,[13] ALGOL 68,[14] APL,[15] C,[16] C++,[17] CLU,[18] Dylan,[19] Haskell,[20][15] Icon,[21] Lisp,[22] Modula-3,[14][17] Perl,[23] Standard ML[15]
Influenced
Apache Groovy, Boo, Cobra, CoffeeScript,[24] D, F#, GDScript, Go, JavaScript,[25][26] Julia,[27] Mojo,[28] Nim, Ring,[29] Ruby,[30] Swift,[31] V[32]
Python Programming at Wikibooks

Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.^[33]

Python is dynamically type-checked and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a "batteries included" language due to its comprehensive standard library.^[34][35]

Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language, and he first released it in 1991 as Python 0.9.0.^[36] Python 2.0 was released in 2000. Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2.^[37]

Python consistently ranks as one of the most popular programming languages, and it has gained widespread use in the machine learning community.^{[38][39][40][41]}

Python was conceived in the late 1980s^[42] by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands; it was conceived as a successor to the ABC programming language, which was inspired by SETL,^[43] capable of exception handling and interfacing with the Amoeba operating system.^[12] Python implementation began in December 1989.^[44] 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.^[45] (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.^[46][47]

The name Python is said to derive from the British comedy series Monty Python's Flying Circus.^[48]

Python 2.0 was released on 16 October 2000, with many major new features such as list comprehensions, cycle-detecting garbage collection, reference counting, and Unicode support.^[49] 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.^[50][51] It no longer receives security patches or updates.^[52][53] 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 3.10), with the plus signifying (at least some) "backported security updates".^[54]

Python 3.0 was released on 3 December 2008, with some new semantics and changed syntax. At least every Python release since (the now unsupported) 3.5 has added some syntax to the language; a few later releases have removed outdated modules and have changed semantics, at least in a minor way.

As of 12 March 2025^[update], Python 3.13 is the latest stable release. This version currently receives full bug-fix and security updates, while Python 3.12—released in October 2023—will have active bug-fix support only until April 2025. Python 3.9^[55] is the oldest supported version of Python (albeit in the 'security support' phase), because Python 3.8 has become an end-of-life product.^[56][57] Starting with Python 3.13, this and later versions receive two years of full support (which has increased from one and a half years), followed by three years of security support; this is the same total duration of support as previously.

Security updates were expedited in 2021 and again twice in 2022. More issues were fixed in 2023 and in September 2024 (for Python versions 3.8.20 through 3.12.6)—all versions (including 2.7)^[58] had been insecure because of issues leading to possible remote code execution^[59] and web-cache poisoning.^[60]

Python 3.10 added the | union type operator^[61] and the match and case keywords (for structural pattern matching statements). Python 3.11 expanded exception handling functionality. Python 3.12 added the new keyword type. Notable changes from version 3.10 to 3.11 include increased program execution speed and improved error reporting.^[62] Python 3.11 is claimed to be 10%–60% faster than Python 3.10, and Python 3.12 increases by an additional 5% . Python 3.12 also includes improved error messages (again improved in 3.14) and many other changes.

Python 3.13 introduces more syntax for types; a new and improved interactive interpreter (REPL), featuring multi-line editing and color support; an incremental garbage collector, which results in shorter pauses for collection in programs that have many objects, as well as increasing the improved speed in 3.11 and 3.12); an experimental just-in-time (JIT) compiler (such features, can/needs to be enabled specifically for the increase in speed);^[63] and an experimental free-threaded build mode, which disables the global interpreter lock (GIL), allowing threads to run more concurrently, as enabled inpython3.13t or python3.13t.exe.

Python 3.13 introduces some changes in behavior, i.e., new "well-defined semantics", fixing bugs, and removing many deprecated classes, functions and methods (as well as some of the Python/C API and outdated modules). "The old implementation of locals() and frame.f_locals was slow, inconsistent and buggy, and it had many corner cases and oddities. Code that works around those may need revising; code that uses locals() for simple templating or print debugging should continue to work correctly."^[64]

Python 3.13 introduces the experimental free-threaded build mode, which disables the Global Interpreter Lock (GIL); the GIL is a feature of CPython that previously prevented multiple threads from executing Python bytecode simultaneously. This optional build, introduced through PEP 703, enables better exploitation of multi-core CPUs. By allowing multiple threads to run Python code in parallel, the free-threaded mode addresses long-standing performance bottlenecks associated with the GIL. This change offers a new path for parallelism in Python, without resorting to multiprocessing or external concurrency frameworks.^[65]

Some additional standard-library modules will be removed in Python 3.15 or 3.16, as will be many deprecated classes, functions and methods.^[66][67]

Python 3.11 adds Sigstore digital verification signatures for all CPython artifacts (in addition to PGP). Because the use of PGP has been criticized by security practitioners, Python is transitioning to Sigstore exclusively and dropping PGP from 3.14.^[68]

Python 3.14 is now in the alpha 3 phase. With regard to possible change to annotations, "In Python 3.14, from __future__ import annotations will continue to work as it did before, converting annotations into strings."^[69]

Python Enhancement Proposal (PEP) 711 proposes PyBI—a standard format for distributing Python binaries.^[70]

Python 3.15 will "Make UTF-8 mode default";^[71] This mode is supported in all current Python versions, but it currently must be opted into. UTF-8 is already used by default on Windows (and other operating systems) for most purposes; an exception is opening files. Enabling UTF-8 also makes code fully cross-platform.

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^[72] and metaobjects).^[73] Many other paradigms are supported via extensions, including design by contract^[74][75] and logic programming.^[76] Python is often referred to as a 'glue language'^[77] because it can seamlessly 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.^[78] 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 filter,mapandreduce functions; list comprehensions, dictionaries, sets, and generator expressions.^[79] The standard library has two modules (itertools and functools) that implement functional tools borrowed from Haskell and Standard ML.^[80]

Python's core philosophy is summarized in the Zen of Python (PEP 20), which includes aphorisms such as these:^[81]

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

However, Python features regularly violate these principles and have received criticism for adding unnecessary language bloat.^[82] Responses to these criticisms note that the Zen of Python is a guideline rather than a rule.^[83] 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 Python 3.8.^[84][85]

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.^[42]

Python claims to strive for a simpler, less-cluttered syntax and grammar, while giving developers a choice in their coding methodology. 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.".^[81] In practice, however, Python provides many ways to achieve a given goal. There are, for example, at least three ways to format a string literal, with no certainty as to which one a programmer should use.^[86] 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."^[87]

Python's developers usually try to avoid premature optimization; they also reject patches to non-critical parts of the CPython reference implementation that would offer marginal increases in speed at the cost of clarity.^[88] 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. It is also possible to cross-compile to other languages; but 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).^[89]

Python's developers aim for the language to be fun to use. This goal is reflected in the name—a tribute to the British comedy group Monty Python^[90]—and in playful approaches to some tutorials and reference materials. For instance, some code examples use the terms "spam" and "eggs" (in reference to a Monty Python sketch), rather than the typical terms "foo" and "bar".^[91][92] A common neologism in the Python community is pythonic, which has a wide 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.^[93]

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.^[94]

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.^[95] Thus, the program's visual structure accurately represents its semantic structure.^[96] 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.^[97]

Python's statements include the following:

• The assignment statement, using a single equals sign =
• 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 except clauses (or new syntax except* in Python 3.11 for exception groups^[98]); 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 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^[99] 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 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 ^[a]
• The pass statement, serving as a NOP (i.e., no operation), which is syntactically needed to create an empty code block
• The assert 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 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.^[100][101] However, better support for coroutine-like functionality is provided by extending Python's generators.^[102] 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.^[103]

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 /.^[104] 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.^[105][106]
• The syntax :=, called the "walrus operator", was introduced in Python 3.8. This operator assigns values to variables as part of a larger expression.^[107]
• 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, a <= b <= c.
• 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.^[79]
• Anonymous functions are implemented using lambda expressions; however, there may be only one expression in each body.
• Conditional expressions are written as x if c else y.^[108] (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 [1, 2, 3], are mutable, and cannot be used as the keys of dictionaries (since dictionary keys must be immutable in Python). Tuples, written as (1, 2, 3), 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 (1, 2, 3), executing t = t + (4, 5) first evaluates t + (4, 5), which yields (1, 2, 3, 4, 5); 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.^[109]
• 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.^[110]
• Python has a "string format" operator % that functions analogously to printf format strings in the C language—e.g. "spam=%s eggs=%d" % ("blah", 2) 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., "spam={0} eggs={1}".format("blah", 2). Python 3.6 added "f-strings": spam = "blah"; eggs = 2; f'spam={spam} eggs={eggs}'.^[111]
• Strings in Python can be concatenated by "adding" them (using the same operator as for adding integers and floats); e.g., "spam" + "eggs" returns "spameggs". If strings contain numbers, they are concatenated as strings rather than as integers, e.g. "2" + "2" 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".^[111]
  • 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], a[start:stop] or a[start:stop:step]. 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, 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 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 a = 1 cannot be part of the conditional expression of a conditional statement.

Methods of objects are functions attached to the object's class; the syntax for normal methods and functions, instance.method(argument), is 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) parameter in some object-oriented programming languages (e.g., C++, Java, Objective-C, Ruby).^[112] Python also provides methods, often called dunder methods (because their names begin and end with double underscores); these methods allow user-defined classes to modify how they are handled by native operations including length, comparison, arithmetic , and type conversion.^[113]

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, SpamClass() or EggsClass()); 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.^[114] Current Python versions support the semantics of only the new style.

Python supports optional type annotations.^[4][115] These annotations are not enforced by the language, but may be used by external tools such as mypy to catch errors.^[116][117] Mypy also supports a Python compiler called mypyc, which leverages type annotations for optimization.^[118]

"""Spanning
multiple
lines"""

Spanning
multiple
lines

Python includes conventional symbols for arithmetic operators (+, -, *, /), the floor-division operator //, and the modulo operator %. (With the module operator, a remainder can be negative, e.g., 4 % -3 == -2.) Python also offers the ** symbol for exponentiation, e.g. 5**3 == 125 and 9**0.5 == 3.0; it also offers the matrix‑multiplication operator @ .^[122] 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:^[123]

• The current version of Python (i.e., since 3.0) changed the / operator to always represent floating-point division, e.g., 5/2 == 2.5.
• The floor division // operator was introduced. Thus 7//3 == 2, -7//3 == -3, 7.5//3 == 2.0 ,and -7.5//3 == -3.0. Adding the from __future__ import division statement causes a module in Python 2.7 to use Python 3.0 rules for division instead (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.^[123]

Rounding towards negative infinity, though a different method than in most languages, adds consistency to Python. For instance, this rounding implies that the equation (a + b)//b == a//b + 1 is always true. The rounding also implies that the equation b*(a//b) + a%b == a 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.^[124]

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.^[125] Python versions before 3 used the round-away-from-zero method: round(0.5) is 1.0, and round(-0.5) is −1.0.^[126]

Python allows Boolean expressions that contain multiple equality relations to be consistent with general usage in mathematics. For example, the expression a < b < c tests whether a is less than b and b is less than c.^[127] 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.^[128]

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.^[129] The Fraction class in the fractions module provides arbitrary precision for rational numbers.^[130]

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.^[131][132]

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:

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.

"Hello, World!" program:

print('Hello, world!')

Program to calculate the factorial of a positive integer:

n = int(input('Type a number, and its factorial will be printed: '))

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)

Python's large standard library^[133] is commonly cited as one of its greatest strengths. For Internet-facing applications, many standard formats and protocols such as MIME and HTTP are supported. The language includes modules for creating graphical user interfaces, connecting to relational databases, generating pseudorandom numbers, arithmetic with arbitrary-precision decimals,^[129] manipulating regular expressions, and unit testing.

Some parts of the standard library are covered by specifications—for example, the Web Server Gateway Interface (WSGI) implementation wsgiref follows PEP 333^[134]—but most parts are specified by their code, internal documentation, and test suites. However, because most of the standard library is cross-platform Python code, only a few modules must be altered or rewritten for variant implementations.

As of 13 March 2025,^[update] the Python Package Index (PyPI), the official repository for third-party Python software, contains over 614,339^[135] packages. These have a wide range of functionality, including the following:

Most Python implementations (including CPython) include a read–eval–print loop (REPL), permitting them to function as a command line interpreter for which users enter statements sequentially and receive results immediately.

Python also comes with an Integrated development environment (IDE) called IDLE, which is more beginner-oriented.

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

As well as standard desktop integrated development environments including PyCharm, IntelliJ Idea, Visual Studio Code etc., there are web browser-based IDEs, including SageMath, for developing science- and math-related programs; Jupyter Notebooks, an open-source interactive computing platform; PythonAnywhere, a browser-based IDE and hosting environment; and Canopy IDE, a commercial IDE emphasizing scientific computing.^[136][137]

CPython is the reference implementation of Python. It is written in C, meeting the C89 standard (Python 3.11 uses C11^[138]) with several select C99 features. CPython includes its own C extensions, but third-party extensions are not limited to older C versions—e.g. they can be implemented with C11 or C++.^[139][140] CPython compiles Python programs into an intermediate bytecode^[141] which is then executed by its virtual machine.^[142] CPython is distributed with a large standard library written in a mixture of C and native Python, and is available for many platforms, including Windows (starting with Python 3.9, the Python installer deliberately fails to install on Windows 7 and 8;^[143][144] Windows XP was supported until Python 3.5) and most modern Unix-like systems, including macOS (and Apple M1 Macs, since Python 3.9.1, with experimental installer), with unofficial support for VMS.^[145] Platform portability was one of its earliest priorities.^[146] (During Python 1 and 2 development, even OS/2 and Solaris were supported,^[147] but support has since been dropped for many platforms.)

All current Python versions (i.e. since 3.7) only support operating systems with multi-threading support.

All alternative implementations have at least slightly different semantics (e.g. may have unordered dictionaries, unlike all current Python versions), e.g. with the larger Python ecosystem, such as with supporting the C Python API of with PyPy:

• PyPy is a fast, compliant interpreter of Python 2.7 and 3.10.^[148][149] Its just-in-time compiler often brings a significant speed improvement over CPython, but some libraries written in C cannot be used with it.^[150] It has e.g. RISC-V support.
• Codon is a language with an ahead-of-time (AOT) compiler, that (AOT) compiles a statically-typed Python-like language with "syntax and semantics are nearly identical to Python's, there are some notable differences"^[151] e.g. it uses 64-bit machine integers, for speed, not arbitrary like Python, and it claims speedups over CPython are usually on the order of 10–100x. It compiles to machine code (via LLVM) and supports native multithreading.^[152] Codon can also compile to Python extension modules that can be imported and used from Python.
• Stackless Python is a significant fork of CPython that implements microthreads; it does not use the call stack in the same way, thus allowing massively concurrent programs. PyPy also has a stackless version.^[153]
• MicroPython and CircuitPython are Python 3 variants optimized for microcontrollers, including Lego Mindstorms EV3.^[154]
• Pyston is a variant of the Python runtime that uses just-in-time compilation to speed up the execution of Python programs.^[155]
• Cinder is a performance-oriented fork of CPython 3.8 that contains a number of optimizations, including bytecode inline caching, eager evaluation of coroutines, a method-at-a-time JIT, and an experimental bytecode compiler.^[156]
• Snek^{[157][158][159]} Embedded Computing Language (compatible with e.g. 8-bit AVR microcontrollers such as ATmega 328P-based Arduino, as well as larger ones compatible with MicroPython) "is Python-inspired, but it is not Python. It is possible to write Snek programs that run under a full Python system, but most Python programs will not run under Snek."^[160] It is an imperative language not including OOP / classes, unlike Python, and simplifying to one number type with 32-bit single-precision (similar to JavaScript, except smaller).

Other just-in-time Python compilers have been developed, but are now unsupported:

• Google began a project named Unladen Swallow in 2009, with the aim of speeding up the Python interpreter five-fold by using the LLVM, and of improving its multithreading ability to scale to thousands of cores,^[161] while ordinary implementations suffer from the global interpreter lock.
• Psyco is a discontinued just-in-time specializing compiler that integrates with CPython and transforms bytecode to machine code at runtime. The emitted code is specialized for certain data types and is faster than the standard Python code. Psyco does not support Python 2.7 or later.
• PyS60 was a Python 2 interpreter for Series 60 mobile phones released by Nokia in 2005. It implemented many of the modules from the standard library and some additional modules for integrating with the Symbian operating system. The Nokia N900 also supports Python with GTK widget libraries, enabling programs to be written and run on the target device.^[162]

There are several compilers/transpilers to high-level object languages, with either unrestricted Python, a restricted subset of Python, or a language similar to Python as the source language:

• Brython,^[163] Transcrypt^[164][165] and Pyjs (latest release in 2012) compile Python to JavaScript.
• Cython compiles (a superset of) Python to C. The resulting code is also usable with Python via direct C-level API calls into the Python interpreter.
• PyJL compiles/transpiles a subset of Python to "human-readable, maintainable, and high-performance Julia source code".^[89] Despite claiming high performance, no tool can claim to do that for arbitrary Python code; i.e. it's known not possible to compile to a faster language or machine code. Unless semantics of Python are changed, but in many cases speedup is possible with few or no changes in the Python code. The faster Julia source code can then be used from Python, or compiled to machine code, and based that way.
• Nuitka compiles Python into C.^[166] It works with Python 3.4 to 3.12 (and 2.6 and 2.7), for Python's main supported platforms (and Windows 7 or even Windows XP) and for Android. It claims complete support for Python 3.10, some support for 3.11 and 3.12 and experimental support for Python 3.13. It supports macOS including Apple Silicon-based. It's a free compiler, though it also has commercial add-ons (e.g. for hiding source code).
• Numba is used from Python, as a tool (enabled by adding a decorator to relevant Python code), a JIT compiler that translates a subset of Python and NumPy code into fast machine code.
• Pythran compiles a subset of Python 3 to C++ (C++11).^[167]
• RPython can be compiled to C, and is used to build the PyPy interpreter of Python.
• The Python → 11l → C++ transpiler^[168] compiles a subset of Python 3 to C++ (C++17).

Specialized:

• MyHDL is a Python-based hardware description language (HDL), that converts MyHDL code to Verilog or VHDL code.

Older projects (or not to be used with Python 3.x and latest syntax):

• Google's Grumpy (latest release in 2017) transpiles Python 2 to Go.^{[169][170][171]}
• IronPython allows running Python 2.7 programs (and an alpha, released in 2021, is also available for "Python 3.4, although features and behaviors from later versions may be included"^[172]) on the .NET Common Language Runtime.^[173]
• Jython compiles Python 2.7 to Java bytecode, allowing the use of the Java libraries from a Python program.^[174]
• Pyrex (latest release in 2010) and Shed Skin (latest release in 2013) compile to C and C++ respectively.

Performance comparison of various Python implementations on a non-numerical (combinatorial) workload was presented at EuroSciPy '13.^[175] Python's performance compared to other programming languages is also benchmarked by The Computer Language Benchmarks Game.^[176]

There are some ways of optimizing performance due to the slower nature of Python being an interpreted language. These optimizations use different strategies or tools:

• Just-in-time compilation: Compiling code just before it is executed dynamically. These implementations are seen in libraries like Numba and PyPy.
• Static compilation: Code gets compiled into machine code before execution. An example of this is Cython which compiles Python into C.
• Concurrency and Parallelism: Multiple tasks can be run at the same time. Python contains modules like `multiprocessing` to allow for this form of parallelism. Additionally, it helps to overcome limitations of the Global Interpreter Lock (GIL) in CPU tasks.
• Efficient Data Structures: Using data types such as Set for membership tests or deque from collections for queue operations can also improve performance.

Python's development is conducted largely through the Python Enhancement Proposal (PEP) process, the primary mechanism for proposing major new features, collecting community input on issues, and documenting Python design decisions.^[177] Python coding style is covered in PEP 8.^[178] Outstanding PEPs are reviewed and commented on by the Python community and the steering council.^[177]

Enhancement of the language corresponds with the development of the CPython reference implementation. The mailing list python-dev is the primary forum for the language's development. Specific issues were originally discussed in the Roundup bug tracker hosted at by the foundation.^[179] In 2022, all issues and discussions were migrated to GitHub.^[180] Development originally took place on a self-hosted source-code repository running Mercurial, until Python moved to GitHub in January 2017.^[181]

CPython's public releases come in three types, distinguished by which part of the version number is incremented:

• Backward-incompatible versions, where code is expected to break and needs to be manually ported. The first part of the version number is incremented. These releases happen infrequently—version 3.0 was released 8 years after 2.0. According to Guido van Rossum, a version 4.0 is very unlikely to ever happen.^[182]
• Major or "feature" releases are largely compatible with the previous version but introduce new features. The second part of the version number is incremented. Starting with Python 3.9, these releases are expected to happen annually.^[183][184] Each major version is supported by bug fixes for several years after its release.^[185]
• Bug fix releases,^[186] which introduce no new features, occur about every 3 months and are made when a sufficient number of bugs have been fixed upstream since the last release. Security vulnerabilities are also patched in these releases. The third and final part of the version number is incremented.^[186]

Many alpha, beta, and release-candidates are also released as previews and for testing before final releases. Although there is a rough schedule for each release, they are often delayed if the code is not ready. Python's development team monitors the state of the code by running the large unit test suite during development.^[187]

The major academic conference on Python is PyCon. There are also special Python mentoring programs, such as PyLadies.

Python 3.12 removed wstr meaning Python extensions^[188] need to be modified,^[189] and 3.10 added pattern matching to the language.^[190]

Python 3.12 dropped some outdated modules, and more will be dropped in the future, deprecated as of 3.13; already deprecated array 'u' format code will emit DeprecationWarning since 3.13 and will be removed in Python 3.16. The 'w' format code should be used instead. Part of ctypes is also deprecated and http.server.CGIHTTPRequestHandler will emit a DeprecationWarning, and will be removed in 3.15. Using that code already has a high potential for both security and functionality bugs. Parts of the typing module are deprecated, e.g. creating a typing.NamedTuple class using keyword arguments to denote the fields and such (and more) will be disallowed in Python 3.15.

Tools that can generate documentation for Python API include pydoc (available as part of the standard library), Sphinx, Pdoc and its forks, Doxygen and Graphviz, among others.^[191]

Python's name is derived from the British comedy group Monty Python, whom Python creator Guido van Rossum enjoyed while developing the language. Monty Python references appear frequently in Python code and culture;^[192] for example, the metasyntactic variables often used in Python literature are spam and eggs instead of the traditional foo and bar.^[192][193] The official Python documentation also contains various references to Monty Python routines.^[194][195] Users of Python are sometimes referred to as "Pythonistas".^[196]

The prefix Py- is 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 Simple DirectMedia Layer to Python (commonly used to create games); PyQt and PyGTK, which bind Qt and GTK to Python respectively; and PyPy, a Python implementation originally written in Python.

Since 2003, Python has consistently ranked in the top ten most popular programming languages in the TIOBE Programming Community Index where as of December 2022^[update] it was the most popular language (ahead of C, C++, and Java).^[40] It was selected as Programming Language of the Year (for "the highest rise in ratings in a year") in 2007, 2010, 2018, and 2020 (the only language to have done so four times as of 2020^{[update][197]}). In the TIOBE Index, monthly rankings are based on the volume of searches for programming languages on Google, Amazon, Wikipedia, Bing, and 20 other platforms. According to the accompanying graph, Python has shown a marked upward trend since the early 2000s, eventually passing long-established languages such as C, C++, and Java. This progression can be attributed to Python's readable syntax, comprehensive standard library, and application in data science and machine learning fields.^[198]

Large organizations that use Python include Wikipedia, Google,^[199] Yahoo!,^[200] CERN,^[201] NASA,^[202] Facebook,^[203] Amazon, Instagram,^[204] Spotify,^[205] and some smaller entities like Industrial Light & Magic^[206] and ITA.^[207] The social news networking site Reddit was written mostly in Python.^[208] Organizations that partially use Python include Discord^[209] and Baidu.^[210]

Python can serve as a scripting language for web applications, e.g. via mod_wsgi for the Apache webserver.^[211] With Web Server Gateway Interface, a standard API has evolved to facilitate these applications. Web frameworks like Django, Pylons, Pyramid, TurboGears, web2py, Tornado, Flask, Bottle, and Zope support developers in the design and maintenance of complex applications. Pyjs and IronPython can be used to develop the client-side of Ajax-based applications. SQLAlchemy can be used as a data mapper to a relational database. Twisted is a framework to program communications between computers, and is used (for example) by Dropbox.

Libraries such as NumPy, SciPy and Matplotlib allow the effective use of Python in scientific computing,^[212][213] with specialized libraries such as Biopython and Astropy providing domain-specific functionality. SageMath is a computer algebra system with a notebook interface programmable in Python: its library covers many aspects of mathematics, including algebra, combinatorics, numerical mathematics, number theory, and calculus.^[214] OpenCV has Python bindings with a rich set of features for computer vision and image processing.^[215]

Python is commonly used in artificial intelligence projects and machine learning projects with the help of libraries like TensorFlow, Keras, Pytorch, scikit-learn and the Logic language ProbLog.^{[216][217][218][219][220]} As a scripting language with a modular architecture, simple syntax, and rich text processing tools, Python is often used for natural language processing.^[221]

The combination of Python and Prolog has proved to be particularly useful for AI applications, with Prolog providing knowledge representation and reasoning capabilities. The Janus system, in particular, exploits the similarities between these two languages, in part because of their use of dynamic typing, and the simple recursive nature of their data structures. Typical applications of this combination include natural language processing, visual query answering, geospatial reasoning, and handling of semantic web data.^[222][223] The Natlog system, implemented in Python, uses Definite Clause Grammars (DCGs) as prompt generators for text-to-text generators like GPT3 and text-to-image generators like DALL-E or Stable Diffusion.^[224]

Python can also be used for graphical user interface (GUI) by using libraries like Tkinter.^[225][226]

Python is embedded in many software products as a scripting language, including in finite element method software such as Abaqus, 3D parametric modelers like FreeCAD, 3D animation packages such as 3ds Max, Blender, Cinema 4D, Lightwave, Houdini, Maya, modo, MotionBuilder, Softimage, the visual effects compositor Nuke, 2D imaging programs like GIMP,^[227] Inkscape, Scribus and Paint Shop Pro,^[228] and musical notation programs like scorewriter and capella. GNU Debugger uses Python as a pretty printer to show complex structures such as C++ containers. Esri promotes Python as the best choice for writing scripts in ArcGIS.^[229] It has also been used in several video games,^[230][231] and has been adopted as first of the three available programming languages in Google App Engine, the other two being Java and Go.^[232]

Many operating systems include Python as a standard component. It ships with most Linux distributions,^[233] AmigaOS 4 (using Python 2.7), FreeBSD (as a package), NetBSD, and OpenBSD (as a package) and can be used from the command line (terminal). Many Linux distributions use installers written in Python: Ubuntu uses the Ubiquity installer, while Red Hat Linux and Fedora Linux use the Anaconda installer. Gentoo Linux uses Python in its package management system, Portage.

Python is used extensively in the information security industry, including in exploit development.^[234][235]

Most of the Sugar software for the One Laptop per Child XO, developed at Sugar Labs as of 2008^[update], is written in Python.^[236] The Raspberry Pi single-board computer project has adopted Python as its main user-programming language.

LibreOffice includes Python and intends to replace Java with Python. Its Python Scripting Provider is a core feature^[237] since Version 4.0 from 7 February 2013.

Python's design and philosophy have influenced many other programming languages:

• Boo uses indentation, a similar syntax, and a similar object model.^[238]
• Cobra uses indentation and a similar syntax, and its Acknowledgements document lists Python first among languages that influenced it.^[239]
• CoffeeScript, a programming language that cross-compiles to JavaScript, has Python-inspired syntax.
• ECMAScript–JavaScript borrowed iterators and generators from Python.^[240]
• GDScript, a scripting language very similar to Python, built-in to the Godot game engine.^[241]
• Go is designed for the "speed of working in a dynamic language like Python"^[242] and shares the same syntax for slicing arrays.
• Groovy was motivated by the desire to bring the Python design philosophy to Java.^[243]
• Julia was designed to be "as usable for general programming as Python".^[27]
• Mojo is a non-strict^[28][244] superset of Python (e.g. still missing classes, and adding e.g. struct).^[245]
• Nim uses indentation and similar syntax.^[246]
• Ruby's creator, Yukihiro Matsumoto, has said: "I wanted a scripting language that was more powerful than Perl, and more object-oriented than Python. That's why I decided to design my own language."^[247]
• Swift, a programming language developed by Apple, has some Python-inspired syntax.^[248]
• Kotlin blends Python and Java features, minimizing boilerplate code for enhanced developer efficiency.^[249]

Python's development practices have also been emulated by other languages. For example, the practice of requiring a document describing the rationale for, and issues surrounding, a change to the language (in Python, a PEP) is also used in Tcl,^[250] Erlang,^[251] and Swift.^[252]

• Python syntax and semantics
• pip (package manager)
• List of programming languages
• History of programming languages
• Comparison of programming languages

• "Python for Artificial Intelligence". Python Wiki. 19 July 2012. Archived from the original on 1 November 2012. Retrieved 3 December 2012.
• Paine, Jocelyn, ed. (August 2005). "AI in Python". AI Expert Newsletter. Amzi!. Archived from the original on 26 March 2012. Retrieved 11 February 2012.
• "PyAIML 0.8.5 : Python Package Index". Pypi.python.org. Retrieved 17 July 2013.
• Russell, Stuart J. & Norvig, Peter (2009). Artificial Intelligence: A Modern Approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall. ISBN 978-0-13-604259-4.

• Downey, Allen (July 2024). Think Python: How to Think Like a Computer Scientist (3rd ed.). O'Reilly Media. ISBN 978-1098155438.
• Lutz, Mark (2013). Learning Python (5th ed.). O'Reilly Media. ISBN 978-0-596-15806-4.
• Summerfield, Mark (2009). Programming in Python 3 (2nd ed.). Addison-Wesley Professional. ISBN 978-0-321-68056-3.
• Ramalho, Luciano (May 2022). Fluent Python. O'Reilly Media. ISBN 978-1-4920-5632-4.

• Official website
• The Python Tutorial