Guido van Rossum began working on Python in the late 1980s as a successor to the ABC programming language and first released it in 1991 as Python0.9.0.[35] Python2.0 was released in 2000. Python3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python2.7.18, released in 2020, was the last release of Python2.[36]
Python consistently ranks as one of the most popular programming languages, and has gained widespread use in the machine learning community.[37][38][39][40]
Python was conceived in the late 1980s[41] by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands as a successor to the ABC programming language, which was inspired by SETL,[42] capable of exception handling and interfacing with the Amoeba operating system.[12] Its implementation began in December1989.[43] Van Rossum shouldered sole responsibility for the project, as the lead developer, until 12 July 2018, when he announced his "permanent vacation" from his responsibilities as Python's "benevolent dictator for life" (BDFL), a title the Python community bestowed upon him to reflect his long-term commitment as the project's chief decision-maker[44] (he has since come out of retirement and is self-titled "BDFL-emeritus"). In January2019, active Python core developers elected a five-member Steering Council to lead the project.[45][46]
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.[47] Python 2.7's end-of-life was initially set for 2015, then postponed to 2020 out of concern that a large body of existing code could not easily be forward-ported to Python3.[48][49] No further security patches or other improvements will be released for it.[50][51] 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 meaning (at least some) "backported security updates".[52]
Python3.0 was released on 3 December 2008, with some new semantics and changed syntax. At least every Python release since (now unsupported) 3.5 has added some syntax to the language, and a few later releases have dropped outdated modules, or changed semantics, at least in a minor way.
Since 7October2024[update], Python 3.13 is the latest stable release, and it and, for few more months, 3.12 are the only releases with active support including for bugfixes (as opposed to just for security) and Python 3.9,[53] is the oldest supported version of Python (albeit in the 'security support' phase), due to Python 3.8 reaching end-of-life.[54][55] Starting with 3.13, it and later versions have 2 years of full support (up from one and a half), followed by 3 years of security support (for same total support as before).
Security updates were expedited in 2021 (and again twice in 2022, and more fixed in 2023 and in September 2024 for Python 3.12.6 down to 3.8.20), since all Python versions were insecure (including 2.7[56]) because of security issues leading to possible remote code execution[57] and web-cache poisoning.[58]
Python 3.10 added the | union type operator[59] and the match and case keywords (for structural pattern matching statements). 3.11 expanded exception handling functionality. Python 3.12 added the new keyword type. Notable changes in 3.11 from 3.10 include increased program execution speed and improved error reporting.[60] Python 3.11 claims to be between 10 and 60% faster than Python 3.10, and Python 3.12 adds another 5% on top of that. It also has 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 (producing shorter pauses for collection in programs with a lot of objects, and addition to the improved speed in 3.11 and 3.12), and an experimentaljust-in-time (JIT) compiler (such features, can/needs to be enabled specifically for the increase in speed),[61] and an experimental free-threaded build mode, which disables the global interpreter lock (GIL), allowing threads to run more concurrently, that latter feature enabled with python3.13t or python3.13t.exe.
Python 3.13 introduces some change in behavior, i.e. new "well-defined semantics", fixing bugs (plus many removals of deprecated classes, functions and methods, and removed some of the CAPI and outdated modules): "The [old] implementation of locals() and frame.f_locals is slow, inconsistent and buggy [and it has] has many corner cases and oddities. Code that works around those may need to be changed. Code that uses locals() for simple templating, or print debugging, will continue to work correctly."[62]
Some (more) standard library modules and many deprecated classes, functions and methods, will be removed in Python 3.15 or 3.16.[63][64]
Python 3.14 is now in alpha 2;[65]regarding possible change to annotations: "In Python 3.14, from __future__ import annotations will continue to work as it did before, converting annotations into strings." [66]
PEP 711 proposes PyBI: a standard format for distributing Python Binaries.[67]
Python 3.15 will "Make UTF-8 mode default",[68] the mode exists in all current Python versions, but currently needs to be opted into. UTF-8 is already used, by default, on Windows (and elsewhere), for most things, but e.g. to open files it's not and enabling also makes code fully cross-platform, i.e. use UTF-8 for everything on all platforms.
Its design offers some support for functional programming in the Lisp tradition. It has filter,mapandreduce functions; list comprehensions, dictionaries, sets, and generator expressions.[76] The standard library has two modules (itertools and functools) that implement functional tools borrowed from Haskell and Standard ML.[77]
However, Python features regularly violate these principles and have received criticism for adding unnecessary language bloat.[79] Responses to these criticisms are that the Zen of Python is a guideline rather than a rule.[80] The addition of some new features had been so controversial that Guido van Rossum resigned as Benevolent Dictator for Life following vitriol over the addition of the assignment expression operator in Python 3.8.[81][82]
Nevertheless, rather than building all of its 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 espoused the opposite approach.[41]
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 "there is more than one way to do it" motto, Python embraces a "there should be one—and preferably only one—obvious way to do it." philosophy.[78] In practice, however, Python provides many ways to achieve the same task. There are, for example, at least three ways to format a string literal, with no certainty as to which one a programmer should use.[83]Alex Martelli, a Fellow at the Python Software Foundation and Python book author, wrote: "To describe something as 'clever' is not considered a compliment in the Python culture."[84]
Python's developers usually strive to avoid premature optimization and reject patches to non-critical parts of the CPython reference implementation that would offer marginal increases in speed at the cost of clarity.[85] 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 it either doesn't provide the full speed-up that might be expected, since Python is a very dynamic language, or a restricted subset of Python is compiled, and possibly semantics are slightly changed.[86]
Python's developers aim for it to be fun to use. This is reflected in its name—a tribute to the British comedy group Monty Python[87]—and in occasionally playful approaches to tutorials and reference materials, such as the use of the terms "spam" and "eggs" (a reference to a Monty Python sketch) in examples, instead of the often-used "foo" and "bar".[88][89] 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. Code that is difficult to understand or reads like a rough transcription from another programming language is called unpythonic.[90]
An example of Python code and indentationExample of C# code with curly braces and semicolons
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.[91]
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.[92] Thus, the program's visual structure accurately represents its semantic structure.[93] 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.[94]
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[95]); it 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 with statement, which encloses a code block within a context manager (for example, acquiring a lock before it is run, then releasing the lock; or opening and closing a file), allowing resource-acquisition-is-initialization (RAII)-like behavior and replacing a common try/finally idiom[96]
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, 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, an analog of the switch statement construct, that compares an expression against one or more cases as a control-of-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, and, according to Van Rossum, it never will.[97][98] However, better support for coroutine-like functionality is provided by extending Python's generators.[99] Before 2.5, generators were lazyiterators; data was passed unidirectionally out of the generator. From Python2.5 on, it is possible to pass data back into a generator function; and from version 3.3, it can be passed through multiple stack levels.[100]
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 divisions in Python: floor division (or integer division) // and floating-point/division.[101] Python uses the ** operator for exponentiation.
Python uses the + operator for string concatenation. Python uses the * operator for duplicating a string a specified number of times.
The syntax :=, called the "walrus operator", was introduced in Python 3.8. It assigns values to variables as part of a larger expression.[104]
In Python, == compares by value. Python's is operator may be used to compare object identities (comparison by reference), and comparisons may be chained—for example, a<=b<=c.
Python uses and, or, and not as Boolean operators.
Conditional expressions are written as xifcelsey[105] (different in order of operands 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 (dictionary keys must be immutable in Python). Tuples, written as (1,2,3), are immutable and thus can be used as keys of dictionaries, provided 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. 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.[106]
Python features sequence unpacking where multiple expressions, each evaluating to anything that can be assigned (to a variable, writable property, etc.) are associated in an 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 them, it assigns each of the produced values to the corresponding expression on the left.[107]
Python has a "string format" operator % that functions analogously to printf format strings in C—e.g. "spam=%s eggs=%d"%("blah",2) evaluates to "spam=blah eggs=2". In Python2.6+ and 3+, this was supplemented by the format() method of the str class, e.g. "spam={0} eggs={1}".format("blah",2). Python3.6 added "f-strings": spam="blah";eggs=2;f'spam={spam} eggs={eggs}'.[108]
Strings in Python can be concatenated by "adding" them (with the same operator as for adding integers and floats), e.g. "spam"+"eggs" returns "spameggs". If strings contain numbers, they are added as strings rather than integers, e.g. "2"+"2" returns "22".
Delimited by single or double quotes; unlike in Unix shells, Perl, and Perl-influenced languages, single and double quotes work the same. Both use the backslash (\) as an escape character. String interpolation became available in Python3.6 as "formatted string literals".[108]
Triple-quoted (beginning and ending with three single or double quotes), which 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 regular expressions and Windows-style paths. (Compare "@-quoting" in C#.)
Python has array index and array slicing expressions in lists, denoted 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 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:
The eval() vs. exec() built-in functions (in Python2, 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 is that an assignment statement such as a=1 cannot form part of the conditional expression of a conditional statement.
Methods
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, Ruby).[109] Python also provides methods, often called dunder methods (due to their names beginning and ending with double-underscores), to allow user-defined classes to modify how they are handled by native operations including length, comparison, in arithmetic operations and type conversion.[110]
Typing
The standard type hierarchy in Python3
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 it 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, 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 metaclasstype (itself an instance of itself), allowing metaprogramming and reflection.
Before version3.0, Python had two kinds of classes (both using the same syntax): old-style and new-style;[111] current Python versions only support the semantics of the new style.
Python supports optional type annotations.[4][112] These annotations are not enforced by the language, but may be used by external tools such as mypy to catch errors.[113][114] Mypy also supports a Python compiler called mypyc, which leverages type annotations for optimization.[115]
Python has the usual symbols for arithmetic operators (+, -, *, /), the floor division operator // and the modulo operation% (where the remainder can be negative, e.g. 4% -3 == -2). It also has ** for exponentiation, e.g. 5**3 == 125 and 9**0.5 == 3.0, and a matrix‑multiplication operator @ .[119] These operators work like in traditional math; with the same precedence rules, the operators infix (+ and - can also be unary to represent positive and negative numbers respectively).
The division between integers produces floating-point results. The behavior of division has changed significantly over time:[120]
Current Python (i.e. since 3.0) changed / to always be floating-point division, e.g. 5/2==2.5.
The floor division // operator was introduced. So 7//3 == 2, -7//3 == -3, 7.5//3 == 2.0 and -7.5//3 == -3.0. Adding from__future__importdivision causes a module used in Python 2.7 to use Python3.0 rules for division (see above).
In Python terms, / is true division (or simply division), and // is floor division./ before version 3.0 is classic division.[120]
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), where b is a positive integer, it has to lie in the interval (b, 0] when b is negative.[121]
Python provides a round function for rounding a float to the nearest integer. For tie-breaking, Python3 uses round to even: round(1.5) and round(2.5) both produce 2.[122] Versions before 3 used round-away-from-zero: round(0.5) is 1.0, round(-0.5) is −1.0.[123]
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 < c tests whether a is less than b and b is less than c.[124] 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.[125]
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.[128][129]
Program to calculate the factorial of a positive integer:
n=int(input('Type a number, and its factorial will be printed: '))ifn<0:raiseValueError('You must enter a non-negative integer')factorial=1foriinrange(2,n+1):factorial*=iprint(factorial)
Some parts of the standard library are covered by specifications—for example, the Web Server Gateway Interface (WSGI) implementation wsgiref follows PEP 333[131]—but most 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 need altering or rewriting for variant implementations.
As of 17March2024,[update] the Python Package Index (PyPI), the official repository for third-party Python software, contains over 523,000[132] packages with a wide range of functionality, including:
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.
CPython is the reference implementation of Python. It is written in C, meeting the C89 standard (Python 3.11 uses C11[134]) 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++.[135][136] CPython compiles Python programs into an intermediate bytecode[137] which is then executed by its virtual machine.[138] 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 Python3.9, the Python installer deliberately fails to install on Windows 7 and 8;[139][140]Windows XP was supported until Python3.5) and most modern Unix-like systems, including macOS (and Apple M1 Macs, since Python3.9.1, with experimental installer), with unofficial support for VMS.[141] Platform portability was one of its earliest priorities.[142] (During Python1 and 2 development, even OS/2 and Solaris were supported,[143] 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.
Other implementations
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 Python2.7 and 3.10.[144][145] Its just-in-time compiler often brings a significant speed improvement over CPython, but some libraries written in C cannot be used with it.[146] 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"[147] 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.[148] 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.[149]
Pyston is a variant of the Python runtime that uses just-in-time compilation to speed up the execution of Python programs.[151]
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.[152]
Snek[153][154][155] 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."[156] 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).
No longer supported implementations
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,[157] while ordinary implementations suffer from the global interpreter lock.
Psyco is a discontinued just-in-timespecializing 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 Python2.7 or later.
PyS60 was a Python2 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.[158]
Cross-compilers to other languages
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:
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".[86] 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.[162] 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 Python3 to C++ (C++11).[163]
RPython can be compiled to C, and is used to build the PyPy interpreter of Python.
The Python → 11l → C++ transpiler[164] compiles a subset of Python3 to C++ (C++17).
IronPython allows running Python2.7 programs (and an alpha, released in 2021, is also available for "Python3.4, although features and behaviors from later versions may be included"[168]) on the .NET Common Language Runtime.[169]
Jython compiles Python2.7 to Java bytecode, allowing the use of the Java libraries from a Python program.[170]
Pyrex (latest release in 2010) and Shed Skin (latest release in 2013) compile to C and C++ respectively.
Performance
Performance comparison of various Python implementations on a non-numerical (combinatorial) workload was presented at EuroSciPy '13.[171] Python's performance compared to other programming languages is also benchmarked by The Computer Language Benchmarks Game.[172]
Development
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.[173] Python coding style is covered in PEP8.[174] Outstanding PEPs are reviewed and commented on by the Python community and the steering council.[173]
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 Roundupbug tracker hosted at by the foundation.[175] In 2022, all issues and discussions were migrated to GitHub.[176] Development originally took place on a self-hosted source-code repository running Mercurial, until Python moved to GitHub in January 2017.[177]
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.[178]
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 Python3.9, these releases are expected to happen annually.[179][180] Each major version is supported by bug fixes for several years after its release.[181]
Bugfix releases,[182] 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.[182]
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.[183]
Python 3.12 removed wstr meaning Python extensions[184] need to be modified,[185] and 3.10 added pattern matching to the language.[186]
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.
API documentation generators
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.[187]
Naming
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;[188] for example, the metasyntactic variables often used in Python literature are spam and eggs instead of the traditional foo and bar.[188][189] The official Python documentation also contains various references to Monty Python routines.[190][191] Users of Python are sometimes referred to as "Pythonistas".[192]
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.
Popularity
Since 2003, Python has consistently ranked in the top ten most popular programming languages in the TIOBE Programming Community Index where as of December2022[update] it was the most popular language (ahead of C, C++, and Java).[39] 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][193]).
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.[217][218] 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.[219]
LibreOffice includes Python and intends to replace Java with Python. Its Python Scripting Provider is a core feature[232] since Version 4.0 from 7 February 2013.
Languages influenced by Python
Python's design and philosophy have influenced many other programming languages:
Boo uses indentation, a similar syntax, and a similar object model.[233]
Cobra uses indentation and a similar syntax, and its Acknowledgements document lists Python first among languages that influenced it.[234]
CoffeeScript, a programming language that cross-compiles to JavaScript, has Python-inspired syntax.
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."[242]
Swift, a programming language developed by Apple, has some Python-inspired syntax.[243]
Kotlin blends Python and Java features, minimizing boilerplate code for enhanced developer efficiency.[244]
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,[245]Erlang,[246] and Swift.[247]
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. The predecessor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers. In 2005, Travis Oliphant created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications. NumPy is open-source software and has many contributors. NumPy is a NumFOCUS fiscally sponsored project.
Jython is an implementation of the Python programming language designed to run on the Java platform. It was known as JPython until 1999.
IronPython is an implementation of the Python programming language targeting the .NET and Mono frameworks. The project is currently maintained by a group of volunteers at GitHub. It is free and open-source software, and can be implemented with Python Tools for Visual Studio, which is a free and open-source extension for Microsoft's Visual Studio IDE.
In computer programming languages, a switch statement is a type of selection control mechanism used to allow the value of a variable or expression to change the control flow of program execution via search and map.
CPython is the reference implementation of the Python programming language. Written in C and Python, CPython is the default and most widely used implementation of the Python language.
Stackless Python, or Stackless, is a Python programming language interpreter, so named because it avoids depending on the C call stack for its own stack. In practice, Stackless Python uses the C stack, but the stack is cleared between function calls. The most prominent feature of Stackless is microthreads, which avoid much of the overhead associated with usual operating system threads. In addition to Python features, Stackless also adds support for coroutines, communication channels, and task serialization.
PyPy is an implementation of the Python programming language. PyPy often runs faster than the standard implementation CPython because PyPy uses a just-in-time compiler. Most Python code runs well on PyPy except for code that depends on CPython extensions, which either does not work or incurs some overhead when run in PyPy.
The syntax of the Python programming language is the set of rules that defines how a Python program will be written and interpreted. The Python language has many similarities to Perl, C, and Java. However, there are some definite differences between the languages. It supports multiple programming paradigms, including structured, object-oriented programming, and functional programming, and boasts a dynamic type system and automatic memory management.
In computer programming, a comment is a human-readable explanation or annotation in the source code of a computer program. They are added with the purpose of making the source code easier for humans to understand, and are generally ignored by compilers and interpreters. The syntax of comments in various programming languages varies considerably.
Cython is a superset of the programming language Python, which allows developers to write Python code that yields performance comparable to that of C.
The programming language Python was conceived in the late 1980s, and its implementation was started in December 1989 by Guido van Rossum at CWI in the Netherlands as a successor to ABC capable of exception handling and interfacing with the Amoeba operating system. Van Rossum is Python's principal author, and his continuing central role in deciding the direction of Python is reflected in the title given to him by the Python community, Benevolent Dictator for Life (BDFL).. Python was named after the BBC TV show Monty Python's Flying Circus.
Shed Skin is an experimental restricted-Python (3.8+) to C++ programming language compiler. It can translate pure, but implicitly statically typed Python programs into optimized C++. It can generate stand-alone programs or extension modules that can be imported and used in larger Python programs.
Theano is a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones. In Theano, computations are expressed using a NumPy-esque syntax and compiled to run efficiently on either CPU or GPU architectures.
Spyder is an open-source cross-platform integrated development environment (IDE) for scientific programming in the Python language. Spyder integrates with a number of prominent packages in the scientific Python stack, including NumPy, SciPy, Matplotlib, pandas, IPython, SymPy and Cython, as well as other open-source software. It is released under the MIT license.
In programming jargon, Yoda conditions is a programming style where the two parts of an expression are reversed from the typical order in a conditional statement. A Yoda condition places the constant portion of the expression on the left side of the conditional statement.
MicroPython is a software implementation of a programming language largely compatible with Python 3, written in C, that is optimized to run on a microcontroller.
Nuitka is a source-to-source compiler which compiles Python code to C source code, applying some compile-time optimizations in the process such as constant folding and propagation, built-in call prediction, type inference, and conditional statement execution. Nuitka initially was designed to produce C++ code, but current versions produce C source code using only those features of C11 that are shared by C++03, enabling further compilation to a binary executable format by modern C and C++ compilers including gcc, clang, MinGW, or Microsoft Visual C++. It accepts Python code compatible with several different Python versions and optionally allows for the creation of standalone programs that do not require Python to be installed on the target computer.
Tim Peters is a software developer who is known for creating the Timsort hybrid sorting algorithm and for his major contributions to the Python programming language and its original CPython implementation. A pre-1.0 CPython user, he was among the group of early adopters who contributed to the detailed design of the language in its early stages.
Pytest is a Python testing framework that originated from the PyPy project. It can be used to write various types of software tests, including unit tests, integration tests, end-to-end tests, and functional tests. Its features include parametrized testing, fixtures, and assert re-writing.
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↑ van Rossum, Guido (1993). "An Introduction to Python for UNIX/C Programmers". Proceedings of the NLUUG Najaarsconferentie (Dutch UNIX Users Group). CiteSeerX10.1.1.38.2023. even though the design of C is far from ideal, its influence on Python is considerable.
1 2 "Classes". The Python Tutorial. Python Software Foundation. Archived from the original on 23 October 2012. Retrieved 20 February 2012. It is a mixture of the class mechanisms found in C++ and Modula-3
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↑ Simionato, Michele. "The Python 2.3 Method Resolution Order". Python Software Foundation. Archived from the original on 20 August 2020. Retrieved 29 July 2014. The C3 method itself has nothing to do with Python, since it was invented by people working on Dylan and it is described in a paper intended for lispers
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↑ "More Control Flow Tools". Python 3 documentation. Python Software Foundation. Archived from the original on 4 June 2016. Retrieved 24 July 2015. By popular demand, a few features commonly found in functional programming languages like Lisp have been added to Python. With the lambda keyword, small anonymous functions can be created.
1 2 "Why We Created Julia". Julia website. February 2012. Archived from the original on 2 May 2020. Retrieved 5 June 2014. We want something as usable for general programming as Python [...]
↑ Lattner, Chris (3 June 2014). "Chris Lattner's Homepage". Chris Lattner. Archived from the original on 25 December 2018. Retrieved 3 June 2014. The Swift language is the product of tireless effort from a team of language experts, documentation gurus, compiler optimization ninjas, and an incredibly important internal dogfooding group who provided feedback to help refine and battle-test ideas. Of course, it also greatly benefited from the experiences hard-won by many other languages in the field, drawing ideas from Objective-C, Rust, Haskell, Ruby, Python, C#, CLU, and far too many others to list.
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↑ Caudill, Barry (20 September 2005). "Modding Sid Meier's Civilization IV". Sid Meier's Civilization IV Developer Blog. Firaxis Games. Archived from the original on 2 December 2010. we created three levels of tools ... The next level offers Python and XML support, letting modders with more experience manipulate the game world and everything in it.
↑ "Modular Docs – Why Mojo". docs.modular.com. Archived from the original on 5 May 2023. Retrieved 5 May 2023. Mojo as a member of the Python family [..] Embracing Python massively simplifies our design efforts, because most of the syntax is already specified. [..] we decided that the right long-term goal for Mojo is to provide a superset of Python (i.e. be compatible with existing programs) and to embrace the CPython immediately for long-tail ecosystem enablement. To a Python programmer, we expect and hope that Mojo will be immediately familiar, while also providing new tools for developing systems-level code that enable you to do things that Python falls back to C and C++ for.
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