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Python Coding for Minecraft: This Instructable shows how to install and use a mod I wrote that lets you control Minecraft with python scripts. I’ll focus on Windows, though OS X and Linux should work just as well. (If you want something simpler than python, here is a Scratch ve…

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Creating a Choose Your Own Adventure Game in Python

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Creating a choose your own adventure game in python

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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; 32 years ago [2]

Stable release

3.11[3] / 24 October 2022; 11 months ago

Preview release

3.12.0rc3[4] / 19 September 2023; 6 days ago

Typing discipline

Duck, dynamic, strong typing;[5] gradual (since 3.5, but ignored in CPython)[6]

OS

Windows, macOS, Linux/UNIX, Android[7][8] and a few other platforms[9]

License
Python Software Foundation License

Filename extensions

.py, .pyi, .pyc, .pyd, .pyw, .pyz (since 3.5),[10] .pyo (prior to 3.5)[11]

Website

python.org

CPython, PyPy, Stackless Python, MicroPython, CircuitPython, IronPython, Jython

Cython, RPython, Starlark[12]

ABC,[13] Ada,[14] ALGOL 68,[15]
APL,[16] C,[17] C++,[18] CLU,[19] Dylan,[20]
Haskell,[21][16] Icon,[22] Lisp,[23]
Modula-3,[15][18] Perl,[24] Standard ML[16]

Apache Groovy, Boo, Cobra, CoffeeScript,[25] D, F#, GDScript, Genie,[26] Go, JavaScript,[27][28] Julia,[29] Mojo,[30] Nim, Ring,[31] Ruby,[32] Swift[33]

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

Python is dynamically typed 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.

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 Python 0.9.0. 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.

The designer of Python, Guido van Rossum, at OSCON 2006

Python was conceived in the late 1980s 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, capable of exception handling and interfacing with the Amoeba operating system. Its implementation began in December 1989. 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", a title the Python community bestowed upon him to reflect his long-term commitment as the project's chief decision-maker. In January 2019, active Python core developers elected a five-member Steering Council to lead the project.

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. Python 3.0, released on 3 December 2008, with many of its major features backported to Python 2.6.x and 2.7.x. Releases of Python 3 include the 2to3 utility, which automates the translation of Python 2 code to Python 3.

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 Python 3. No further security patches or other improvements will be released for it. Currently only 3.8 and later are supported (2023 security issues were fixed in e.g. 3.7.17, the final 3.7.x release). In 2021, Python 3.9.2 and 3.8.8 were expedited as all versions of Python (including 2.7) had security issues leading to possible remote code execution and web cache poisoning.

In 2022, Python 3.10.4 and 3.9.12 were expedited and 3.8.13, because of many security issues. When Python 3.9.13 was released in May 2022, it was announced that the 3.9 series (joining the older series 3.8 and 3.7) would only receive security fixes in the future. On September 7, 2022, four new releases were made due to a potential denial-of-service attack: 3.10.7, 3.9.14, 3.8.14, and 3.7.14.

As of November 2022, Python 3.11 is the stable release. Notable changes from 3.10 include increased program execution speed and improved error reporting.

Since 27 June 2023, Python 3.8 is the oldest supported version of Python (albeit in the 'security support' phase), due to Python 3.7 reaching end-of-life.

The first release candidate of Python 3.12 was offered on 6 August 2023.

Design philosophy and features

Python is a multi-paradigm programming language. Object-oriented programming and structured programming are fully supported, and many of their features support functional programming and aspect-oriented programming (including metaprogramming and metaobjects). Many other paradigms are supported via extensions, including design by contract and logic programming.

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

Its design offers some support for functional programming in the Lisp tradition. It has filter, mapand reduce functions; list comprehensions, dictionaries, sets, and generator expressions. The standard library has two modules ( itertools and functools) that implement functional tools borrowed from Haskell and Standard ML.

Its core philosophy is summarized in the document The Zen of Python ( PEP 20), which includes aphorisms such as:

Beautiful is better than ugly.

Explicit is better than implicit.

Simple is better than complex.

Complex is better than complicated.

Readability counts.

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.

Python strives 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. 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."

Python's developers 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. 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, and/or a restricted subset of Python is compiled, and possibly semantics are slightly changed. 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-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".

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.

Syntax and semantics

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

Indentation

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

Statements and control flow

Python's statements include:

The assignment statement, using a single equals sign =

The statement, which conditionally executes a block of code, along with else and elif (a contraction of else-if)

The statement, which iterates over an iterable object, capturing each element to a local variable for use by the attached block

The statement, which executes a block of code as long as its condition is true

The 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); 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 def statement, which defines a function or method

The 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

The statement, which exits a loop

The continue statement, which skips the rest of the current iteration and continues with the next

The del statement, which removes a variable-deleting the reference from the name to the value, and producing an error if the variable is referred to before it is redefined

The pass statement, serving as a NOP, syntactically needed to create an empty code block

The 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 and from statements, used to import modules whose functions or variables can be used in the current program

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. However, better support for coroutine-like functionality is provided by extending Python's generators. Before 2.5, generators were lazy iterators; data was passed unidirectionally out of the generator. From Python 2.5 on, it is possible to pass data back into a generator function; and from version 3.3, it can be passed through multiple stack levels.

Expressions

Python's expressions include:

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. 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 @ infix operator. It is intended to be used by libraries such as NumPy for matrix multiplication.

The syntax :=, called the "walrus operator", was introduced in Python 3.8. It assigns values to variables as part of a larger expression.

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, .

Python uses and, or, and not as Boolean operators.

Python has a type of expression called a list comprehension, as well as a more general expression called a generator expression.

Anonymous functions are implemented using lambda expressions; however, there may be only one expression in each body.

Conditional expressions are written as (different in order of operands from the operator common to many other languages).

Python makes a distinction between lists and tuples. Lists are written as , are mutable, and cannot be used as the keys of dictionaries (dictionary keys must be immutable in Python). Tuples, written as , 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 , executing first evaluates , which yields , 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.

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.

Strings in Python can be concatenated by "adding" them (with the same operator as for adding integers and floats), e.g. returns "spameggs". If strings contain numbers, they are added as strings rather than integers, e.g. returns "22".

Python has various string literals:

Delimited by single or double quote marks; unlike in Unix shells, Perl, and Perl-influenced languages, single and double quote marks work the same. Both use the backslash (\) as an escape character. String interpolation became available in Python 3.6 as "formatted string literals".

Triple-quoted (beginning and ending with three single or double quote marks), 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], or . Indexes are zero-based, and negative indexes are relative to the end. Slices take elements from the start index up to, but not including, the stop index. The third slice parameter called step or stride, allows elements to be skipped and reversed. Slice indexes may be omitted-for example, returns a copy of the entire list. Each element of a slice is a shallow copy.

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

List comprehensions vs. for-loops

Conditional expressions vs. if blocks

The eval() vs. exec() built-in functions (in Python 2, exec is a statement); the former is for expressions, the latter is for statements

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

Methods on objects are functions attached to the object's class; the syntax is, for normal methods and functions, syntactic sugar for . Python methods have an explicit 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). 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.

The standard type hierarchy in Python 3

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, or ), and the classes are instances of the metaclass type (itself an instance of itself), allowing metaprogramming and reflection.

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

Python supports gradual typing. Python's syntax allows specifying static types, but they are not checked in the default implementation, CPython. An experimental optional static type-checker, mypy, supports compile-time type checking.

Arithmetic operations

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 @ . 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:

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 causes a module used in Python 2.7 to use Python 3.0 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.

Rounding towards negative infinity, though different from most languages, adds consistency. For instance, it means that the equation is always true. It also means that the equation 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.

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

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. 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.

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 and several rounding modes. The Fraction class in the fractions module provides arbitrary precision for rational numbers.

Due to Python's extensive mathematics library, and the third-party library NumPy 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.

Programming examples

Hello world program:

Program to calculate the factorial of a positive integer:

Python's large standard library provides tools suited to many tasks and 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. It includes modules for creating graphical user interfaces, connecting to relational databases, generating pseudorandom numbers, arithmetic with arbitrary-precision decimals, 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-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 14 November 2022, the Python Package Index (PyPI), the official repository for third-party Python software, contains over 415,000 packages with a wide range of functionality, including:

Development environments

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, there are web browser-based IDEs, including SageMath, for developing science- and math-related programs; PythonAnywhere, a browser-based IDE and hosting environment; and Canopy IDE, a commercial IDE emphasizing scientific computing.

Implementations

Reference implementation

CPython is the reference implementation of Python. It is written in C, meeting the C89 standard (Python 3.11 uses C11) 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++.) It compiles Python programs into an intermediate bytecode which is then executed by its virtual machine. 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; 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) and unofficial support for e.g. VMS. Platform portability was one of its earliest priorities. (During Python 1 and 2 development, even OS/2 and Solaris were supported, but support has since been dropped for many platforms.)

Other implementations

Unsupported 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 fivefold by using the LLVM, and of improving its multithreading ability to scale to thousands of cores, 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.

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:

Codon compiles a subset of statically-typed Python to machine code (via LLVM) and supports native multithreading.

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". 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.

Numba uses LLVM to compile a subset of Python to machine code.

RPython can be compiled to C, and is used to build the PyPy interpreter of Python.

The Python → 11l → C++ transpiler compiles a subset of Python 3 to C++ (C++17).

Specialized:

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

Performance

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

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. Python coding style is covered in PEP 8. Outstanding PEPs are reviewed and commented on by the Python community and the steering council.

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. In 2022, all issues and discussions were migrated to GitHub. Development originally took place on a self-hosted source-code repository running Mercurial, until Python moved to GitHub in January 2017.

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

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. Each major version is supported by bug fixes for several years after its release.

Bugfix releases, 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.

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.

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

Python 3.10 deprecated wstr (to be removed in Python 3.12; meaning Python extensions need to be modified by then), and added pattern matching to the language.

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.

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; for example, the metasyntactic variables often used in Python literature are instead of the traditional . The official Python documentation also contains various references to Monty Python routines. Users of Python sometimes referred to as "Pythonistas".

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 SDL 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 December 2022 it was the most popular language (ahead of C, C++, and Java). It was selected 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).

An empirical study found that scripting languages, such as Python, are more productive than conventional languages, such as C and Java, for programming problems involving string manipulation and search in a dictionary, and determined that memory consumption was often "better than Java and not much worse than C or C++".

Python Powered

Python can serve as a scripting language for web applications, e.g., via mod_wsgi for the Apache webserver. 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, 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. OpenCV has Python bindings with a rich set of features for computer vision and image processing.

Python is commonly used in artificial intelligence projects and machine learning projects with the help of libraries like TensorFlow, Keras, Pytorch, and scikit-learn. As a scripting language with a modular architecture, simple syntax, and rich text processing tools, Python is often used for natural language processing.

Python can also be used for graphical user interface (GUI) by using libraries like Tkinter.

Python can also be used to create games, with libraries such as Pygame, which can make 2D games.

Python has been successfully 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, Inkscape, Scribus and Paint Shop Pro, 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. It has also been used in several video games, and has been adopted as first of the three available programming languages in Google App Engine, the other two being Java and Go.

Many operating systems include Python as a standard component. It ships with most Linux distributions, 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.

Most of the Sugar software for the One Laptop per Child XO, developed at Sugar Labs as of 2008, is written in Python. 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 since Version 4.0 from 7 February 2013.

Languages influenced by Python

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

Cobra uses indentation and a similar syntax, and its Acknowledgements document lists Python first among languages that influenced it.

CoffeeScript, a programming language that cross-compiles to JavaScript, has Python-inspired syntax.

ECMAScript/JavaScript borrowed iterators and generators from Python.

GDScript, a scripting language very similar to Python, built-in to the Godot game engine.

Groovy was motivated by the desire to bring the Python design philosophy to Java.

Mojo is currently a non-strict (aims to be a strict) superset of Python (e.g. still missing classes, and adding e.g. struct), and is up to 35,000x faster for some code (mandelbrot, since it is embarrassingly parallel), where static typing helps (and MLIR it is implemented with), and, e.g., 4000 times faster for matrix multiplication.

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."

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, Erlang, and Swift.

References

1. File extension .pyo was removed in Python 3.5. See ^ PEP 0488 Archived 1 June 2020 at the Wayback Machine

2. ^ van Rossum, Guido (1993). "An Introduction to Python for UNIX/C Programmers". Proceedings of the NLUUG Najaarsconferentie (Dutch UNIX Users Group). CiteSeerX 10.1.1.38.2023. even though the design of C is far from ideal, its influence on Python is considerable.

3. ^ Simionato, Michele. "The Python 2.3 Method Resolution Order". Python Software Foundation. Archived from the original on 20 August 2020. 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

4. ^ Lattner, Chris (3 June 2014). "Chris Lattner's Homepage". Chris Lattner. Archived from the original on 25 December 2018. 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.

5. ^ Kuhlman, Dave. "A Python Book: Beginning Python, Advanced Python, and Python Exercises". Section 1.1. Archived from the original (PDF) on 23 June 2012.

6. "TIOBE Index". TIOBE. Archived from the original on 25 February 2018. The TIOBE Programming Community index is an indicator of the popularity of programming languages Updated as required.

7. ^ van Rossum, Guido (29 August 2000). "SETL (was: Lukewarm about range literals)". Python-Dev (Mailing list). Archived from the original on 14 July 2018.

8. ^ "Extending and Embedding the Python Interpreter: Reference Counts". Docs.python.org. Archived from the original on 18 October 2012. Since Python makes heavy use of malloc() and free(), it needs a strategy to avoid memory leaks as well as the use of freed memory. The chosen method is called reference counting.

9. ^ Martelli, Alex; Ravenscroft, Anna; Ascher, David (2005). Python Cookbook, 2nd Edition. O'Reilly Media. p. 230. ISBN 978-0-596-00797-3. Archived from the original on 23 February 2020.

10. "Transpiling Python to Julia using PyJL" (PDF). After manually modifying one line of code by specifying the necessary type information, we obtained a speedup of 52.6×, making the translated Julia code 19.5× faster than the original Python code.

11. ^ Guttag, John V. (12 August 2016). Introduction to Computation and Programming Using Python: With Application to Understanding Data. MIT Press. ISBN 978-0-262-52962-4.

12. ^ Sweigart, Al (2020). Beyond the Basic Stuff with Python: Best Practices for Writing Clean Code. No Starch Press. p. 322. ISBN 978-1-59327-966-0. Archived from the original on 13 August 2021.

13. ^ Guelton, Serge; Brunet, Pierrick; Amini, Mehdi; Merlini, Adrien; Corbillon, Xavier; Raynaud, Alan (16 March 2015). "Pythran: enabling static optimization of scientific Python programs". Computational Science & Discovery. IOP Publishing. 8 (1): 014001. Bibcode:2015CS&D....8a4001G. doi:10.1088/1749-4680/8/1/014001. ISSN 1749-4699.

14. ^ "GitHub - IronLanguages/ironpython3: Implementation of Python 3.x for .NET Framework that is built on top of the Dynamic Language Runtime". . Archived from the original on 28 September 2021.

15. ^ Murri, Riccardo (2013). Performance of Python runtimes on a non-numeric scientific code. European Conference on Python in Science (EuroSciPy). arXiv:. Bibcode:2014arXiv1404.6388M.

16. ^ Lutz, Mark (2009). Learning Python: Powerful Object-Oriented Programming. O'Reilly Media, Inc. p. 17. ISBN 9781449379322. Archived from the original on 17 July 2017.

17. ^ Oliphant, Travis (2007). "Python for Scientific Computing". Computing in Science and Engineering. 9 (3): 10-20. Bibcode:2007CSE.....9c..10O. CiteSeerX 10.1.1.474.6460. doi:10.1109/MCSE.2007.58. S2CID 206457124. Archived from the original on 15 June 2020.

18. ^ Millman, K. Jarrod; Aivazis, Michael (2011). "Python for Scientists and Engineers". Computing in Science and Engineering. 13 (2): 9-12. Bibcode:2011CSE....13b...9M. doi:10.1109/MCSE.2011.36. Archived from the original on 19 February 2019.

19. ^ Dean, Jeff; Monga, Rajat; et al. (9 November 2015). "TensorFlow: Large-scale machine learning on heterogeneous systems" (PDF). TensorFlow.org. Google Research. Archived (PDF) from the original on 20 November 2015.

20. ^ Jouppi, Norm. "Google supercharges machine learning tasks with TPU custom chip". Google Cloud Platform Blog. Archived from the original on 18 May 2016.

21. ^ CCP porkbelly (24 August 2010). "Stackless Python 2.7". EVE Community Dev Blogs. CCP Games. Archived from the original on 11 January 2014. As you may know, EVE has at its core the programming language known as Stackless Python.

22. ^ 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.

23. ^ Lattner, Chris (3 June 2014). "Chris Lattner's Homepage". Chris Lattner. Archived from the original on 22 December 2015. I started work on the Swift Programming Language in July of 2010. I implemented much of the basic language structure, with only a few people knowing of its existence. A few other (amazing) people started contributing in earnest late in 2011, and it became a major focus for the Apple Developer Tools group in July 2013 [...] drawing ideas from Objective-C, Rust, Haskell, Ruby, Python, C#, CLU, and far too many others to list.

Further reading

External links

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Python (programming language) - Wikipedia

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CodeCombat: Learn to Code by Playing a Game

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Learn typed code through a programming game. Learn Python, JavaScript, and HTML as you solve puzzles and learn to make your own coding games and websites.

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Python is a widely used general-purpose, high-level programming language. It was initially designed by Guido van Rossum in 1991 and developed by Python Software Foundation. It was mainly developed for emphasis on code readability, and its syntax allows programmers to express concepts in fewer lines of code.

Who invented Python?

In the late 1980s, history was about to be written. It was that time when working on Python started. Soon after that, Guido Van Rossum began doing its application-based work in December of 1989 at Centrum Wiskunde & Informatica (CWI) which is situated in the Netherlands. It was started as a hobby project because he was looking for an interesting project to keep him occupied during Christmas.

The programming language in which is said to have succeeded is ABC Programming Language, which had interfacing with the Amoeba Operating System and had the feature of exception handling. He had already helped create ABC earlier in his career and had seen some issues with ABC but liked most of the features. After that what he did was very clever. He had taken the syntax of ABC, and some of its good features. It came with a lot of complaints too, so he fixed those issues completely and created a good scripting language that had removed all the flaws. The inspiration for the name came from the BBC's TV Show - ' Monty Python's Flying Circus', as he was a big fan of the TV show and also he wanted a short, unique and slightly mysterious name for his invention and hence he named it Python! He was the "Benevolent dictator for life" (BDFL) until he stepped down from the position as the leader on 12th July 2018. For quite some time he used to work for Google, but currently, he is working at Dropbox.

Evolution of Python

The language was finally released in 1991. When it was released, it used a lot fewer codes to express the concepts, when we compare it with , & . Its design philosophy was quite good too. Its main objective is to provide code readability and advanced developer productivity. When it was released, it had more than enough capability to provide classes with inheritance, several core data types of exception handling and functions.
Following are the illustrations of different versions of Python along with the timeline.

Python 3.12.1 is the latest stable version.

The two of the most used versions has to Python 2.x & 3.x. There is a lot of competition between the two and both of them seem to have quite a number of different fanbases. You can also refer to mentioned below article to learn How you can download python latest version in your system.

Download and Install Python 3 Latest Version

For various purposes such as developing, scripting, generation, and software testing , this language is utilized. Due to its elegance and simplicity, top technology organizations like Dropbox, Google, Quora, Mozilla, Hewlett-Packard, Qualcomm, IBM, and Cisco have implemented Python.

Python has come a long way to become the most popular coding language in the world. Python has just turned 30 and just recently at pycon22(python conference) a new feature was released by Anaconda foundation it's known as pyscript with this now python can be written and run in the browser like javascript which was previously not possible, but it still has that unknown charm & X factor.

Python has been an inspiration for many other coding languages such as , Cobra, Boo, CoffeeScript ECMAScript, Groovy, Swift Go, OCaml, , etc.

History of Python - FAQ's

Q1. Who developed Python programming language?

It was initially designed by Guido van Rossum in 1991 and developed by Python Software Foundation.

Q2. Who invented Python programming language?

In the late 1980s, history was about to be written. It was that time when working on Python started. Soon after that, Guido Van Rossum began doing its application-based work in December of 1989 at Centrum Wiskunde & Informatica (CWI) which is situated in the Netherlands. It was started firstly as a hobby project because he was looking for an interesting project to keep him occupied during Christmas

Q3. What are the uses of Python?

One more question asises that, Is Python Programming or Scripting?, According to Linux Journal readers the Python is a programming as well as a scripting language. Also, This language has various purposes such as developing, scripting, generation, and software testing.

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Python - The language of today and tomorrow

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Python - The language of today and tomorrow

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Python is the gift that keeps on giving. The more you understand Python, the more you can do in the 21st Century. As simple as that.

What

Python is a widely-used, interpreted, object-oriented, and high-level programming language with dynamic semantics, used for general-purpose programming. It's everywhere, and people use numerous Python-powered devices on a daily basis, whether they realize it or not.

Who

Python was created by Guido van Rossum, and first released on February 20, 1991. While you may know the python as a large snake, the name of the Python programming language comes from an old BBC television comedy sketch series called Monty Python's Flying Circus.

One of the amazing features of Python is the fact that it is actually one person's work. Usually, new programming languages are developed and published by large companies employing lots of professionals, and due to copyright rules, it is very hard to name any of the people involved in the project. Python is an exception.

Of course, Guido van Rossum did not develop and evolve all the Python components himself. The speed with which Python has spread around the world is a result of the continuous work of thousands (very often anonymous) programmers, testers, users (many of them aren't IT specialists) and enthusiasts, but it must be said that the very first idea (the seed from which Python sprouted) came to one head - Guido's.

Python is maintained by the Python Software Foundation, a non-profit membership organization and a community devoted to developing, improving, expanding, and popularizing the Python language and its environment.

Why

Python is omnipresent, and people use numerous Python-powered devices on a daily basis, whether they realize it or not. There are billions of lines of code written in Python, which means almost unlimited opportunities for code reuse and learning from well-crafted examples. What's more, there is a large and very active Python community, always happy to help.

There are also a couple of factors that make Python great for learning:

It is easy to learn - the time needed to learn Python is shorter than for many other languages; this means that it's possible to start the actual programming faster;

It is easy to use for writing new software - it's often possible to write code faster when using Python;

It is easy to obtain, install and deploy - Python is free, open and multiplatform; not all languages can boast that.

Programming skills prepare you for careers in almost any industry, and are required if you want to continue to more advanced and higher-paying software development and engineering roles. Python is the programming language that opens more doors than any other. With a solid knowledge of Python, you can work in a multitude of jobs and a multitude of industries. And the more you understand Python, the more you can do in the 21st Century. Even if you don't need it for work, you will find it useful to know.

Where

Python is the programming language that opens more doors than any other. With a solid knowledge of Python, you can work in a multitude of jobs and a multitude of industries. And even if you don't need it for work, you will still find it useful to know to speed certain things up or develop a deeper understanding of other concepts.

Python is a great choice for career paths related to software development, engineering, DevOps, machine learning, data analytics, web development, and testing. What's more, there are also many jobs outside the IT industry that use Python. Since our lives are becoming more computerized every day, and the computer and technology areas previously associated only with technically gifted people are now opening up to non-programmers, Python has become one of the must-have tools in the toolbox of educators, managers, data scientists, data analysts, economists, psychologists, artists, and even secretaries.

How

Learn, certify, and succeed! How great would it be to write your own computer program? Python is a multi-paradigm programming language used by startups and tech giants like Google, Facebook, Cisco, Netflix, and more. With intuitive, readable syntax, Python is a great first programming language to learn. Get started with the Python Essentials 1 course and prepare for the PCEP certification exam. If you already know the core fundamentals of Python, progress to Python Essentials 2, and prepare for the PCAP certification exam. If you work with Python and need to advance to more specialized areas of programming, pick our professional-series courses and prepare for the PCPP and specialization-track certifications.

In 1999, Guido van Rossum defined his goals for Python:

an easy and intuitive language just as powerful as those of the major competitors;

open source, so anyone can contribute to its development;

code that is as understandable as plain English;

suitable for everyday tasks, allowing for short development times.

20+ years later, it is clear that all these intentions have been fulfilled. Some sources say that Python is the first-most popular programming language in the world, while others claim it's the third.

Either way, it occupies the first position in the top ten of the TIOBE Programming Community and PYPL PopularitY of Programming Language Indexes. (February 2022)

Python isn't a young language. It is mature and trustworthy. It's not a one-hit wonder. It's a bright star in the programming firmament, and time spent learning Python is a very good investment.

Where can we see Python in action? We see it every day and almost everywhere. It's used extensively to implement complex Internet services like search engines, cloud storage and tools, social media and so on. Whenever you use any of these services, you are actually very close to Python, although you wouldn't know it.

Many developing tools are implemented in Python. More and more everyday use applications are being written in Python. Lots of scientists have abandoned expensive proprietary tools and switched to Python. Lots of IT project testers have started using Python to carry out repeatable test procedures. The list is long.

Python examples

Python is a great choice for:

Web and Internet development (e.g., Django and Pyramid frameworks, Flask and Bottle micro-frameworks)

Scientific and numeric computing (e.g., SciPy - a collection of packages for the purposes of mathematics, science, and engineering; Ipython - an interactive shell that features editing and recording of work sessions)

Education (it's a brilliant language for teaching programming!)

Desktop GUIs (e.g., wxWidgets, Kivy, Qt)

Software Development (build control, management, and testing - Scons, Buildbot, Apache Gump, Roundup, Trac)

Business applications (ERP and e-commerce systems - Odoo, Tryton)

Games (e.g., Battlefield series, Sid Meier's Civilization IV...), websites and services (e.g., Dropbox, UBER, Pinterest, BuzzFeed...)

...

And that's just the beginning.

(sources: PythonProjects on Wikipedia and Python.org.)

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Python is the gift that keeps on giving. The more you understand Python, the more you can do in the 21st Century. As simple as that. Python is a widely-used, interpreted, object-oriented, and high-level programming language with dynamic semantics, used for general-purpose programming.

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python programming language object oriented web free open source software license documentation download community

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The official home of the Python Programming Language

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The Importance of Coding in 21st Century Education - 21K School United States

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We are living in an era of digitalization, where we are surrounded by technologies. Read this blog which discusses the importance of coding.

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History of Python

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Python is a “high-level” (essentially human readable - not machine code) general purpose open source programming language that is used in a wide variety of applications like scientific computing, data analysis, web development and artificial intelligence. Because it is open source there are vast quantities of freely accessible libraries along containing code that can be integrated into projects. There are also robust programming communities of coders who freely share information over the web and thus there is an ecosystem we can tap into and potentially contribute to, as we develop our projects. Python will not be the only programming language we will need to use in this class, but it is an ideal language to learn coding with.

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The Hitchhiker's Guide to Python! - The Hitchhiker's Guide to Python

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An opinionated guide to the Python programming language and a best practice handbook to the installation, configuration, and usage of Python on a daily basis.

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Web Scraping The New York Times Articles with Python : Part I

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In the digital era, you may find tons of public information on the internet that you can utilize to your benefit. You could manually…

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Beginner's Guide to Python

New to programming? Python is free and easy to learn if you know where to start! This guide will help you to get started quickly.

Chinese Translation

New to Python?

Read BeginnersGuide/Overview for a short explanation of what Python is.

Getting Python

Next, install the Python 3 interpreter on your computer. This is the program that reads Python programs and carries out their instructions; you need it before you can do any Python programming. Mac and Linux distributions may include an outdated version of Python (Python 2), but you should install an updated one (Python 3). See BeginnersGuide/Download for instructions to download the correct version of Python.

There are also Python interpreter and IDE bundles available, such as Thonny. Other options can be found at IntegratedDevelopmentEnvironments.

At some stage, you'll want to edit and save your program code. Take a look at HowToEditPythonCode for some advice and recommendations.

Learning Python

Next, read a tutorial and try some simple experiments with your new Python interpreter.

If you have never programmed before, see BeginnersGuide/NonProgrammers for a list of suitable tutorials.

If you have previous programming experience, consult BeginnersGuide/Programmers, which lists more advanced tutorials.

If English isn't your first language, you might be more comfortable with a tutorial that's been translated into your language. Consult python.org's list of Non-English resources.

Most tutorials assume that you know how to run a program on your computer. If you are using Windows and need help with this, see How do I Run a Program Under Windows.

Some sites offer in-browser coding for those who want to learn Python:

A completely free beginners tutorial with interactive, editable code examples

Codecademy

Coding Bootcamps

DataCamp

Dataquest for Python for data science.

HackInScience free and open source platform.

High School Technology Services for general Python

Print a cheat sheet of the most important Python features and post it to your office wall until you know the basics well.

Once you have read a tutorial, you can browse through Python's online documentation. It includes a tutorial that might come in handy, a Library Reference that lists all of the modules that come standard with Python, and the Language Reference for a complete (if rather dry) explanation of Python's syntax.

When you are ready to write your first program, you will need a text editor or an IDE. If you don't want to use Thonny or something more advanced, then you can use IDLE, which is bundled with Python and supports extensions.

This Python wiki also contains a page about Python One-Liners -- an obscure but interesting subculture in Python.

Need Help?

Need help with any of this? Read BeginnersGuide/Help for mailing lists and newsgroups.

Most Python books will include an introduction to the language; see IntroductoryBooks for suggested titles.

Consult BeginnersGuide/Examples for small programs and little snippets of code that can help you learn.

Or, if you prefer to learn Python through listening to a lecture, you can attend a training course or even hire a trainer to come to your company. Consult the PythonEvents page to see if any training courses are scheduled in your area and the PythonTraining page for a list of trainers.

Teachers can join the EDU-SIG, a mailing list for discussion of Python's use in teaching at any level ranging from K-12 up to university.

Complete list of Beginner's Guide pages

1. BeginnersGuide/Download

2. BeginnersGuide/Examples

3. BeginnersGuide/Help

4. BeginnersGuide/Mathematics

5. BeginnersGuide/NonProgrammers

6. BeginnersGuide/Overview

7. BeginnersGuide/Programmers

8. BeginnersGuide/Programmers (Cpp2Python.pdf)

9. BeginnersGuide/Programmers/SimpleExamples

Quiz and Exercises

Finxter - How good are your Python skills? Test and Training with a Daily Python Puzzle

CheckIO - Online learning, testing and improving your python skills

After Hours Programming - Python Quiz

Python Guru Quiz- Python Quiz

Pyschools - A Collection of Python Quiz and Exercise Questions

PyGUI - Collection of python quiz answers, Examples And GUI Tkinter Tutorials For Beginners

Pythonspot - Python Quiz

Python Challenge - A Python Quiz App on Android Platform

CS Circles - online lessons and graded exercises

Python Style Checker

Pythonchecker.com - An educative online tool to rate your Python style (with dynamic score computation and hints)

Looking for a particular Python module or application?

The first place to look is the Python Package Index.

If you can't find anything relevant in the Package Index,
try searching python.org - you can find anything mentioned on the Python site, in the FAQs, or in the newsgroup. More info: where to search.

You may also try our external guest project, pydoc.net, for advanced package and module search.

Next, try Google or another search engine of your choice. Searching for "python" and some relevant keywords will usually find something helpful.

Finally, you can try posting a query to the comp.lang.python Usenet group.

Python: Collection of 11 Best Python Cheat Sheets

NumPy: Collection of 10 Best NumPy Cheat Sheets

Pandas: Collection of 7 Beautiful Pandas Cheat Sheets

Machine Learning: Collection of 15 Machine Learning Cheat Sheets

Want to contribute?

Python is a product of the Python Software Foundation, a non-profit organization that holds the copyright. Donations to the PSF are tax-deductible in the USA, and you can donate via credit card or PayPal.

To report a bug in the Python core, use the Python Bug Tracker.

To contribute a bug fix or other patch to the Python core, read the Python Developer's Guide for more information about Python's development process.

To contribute to the official Python documentation, join the Documentation SIG, write to docs@python.org , or use the Issue Tracker to contribute a documentation patch.

To announce your module or application to the Python community, use comp.lang.python.announce. See the guide to Python mailing lists for more information.

To propose changes to the Python core, post your thoughts to comp.lang.python. If you have an implementation, follow the Python Patch Guidelines.

If you have a question are not sure where to report it, check out the WhereDoIReportThis? page.

CategoryDocumentation

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New to programming? Python is free and easy to learn if you know where to start! This guide will help you to get started quickly.

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Learn about the essential Python tools for software development, web scraping and development, data analysis and visualization, and machine learning.

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GitHub - Spyder-0/Minecraft-with-Python: A simple Minecraft-like game made with Python (Ursina Engine).

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A simple Minecraft-like game made with Python (Ursina Engine). - Spyder-0/Minecraft-with-Python

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https://www.youtube.com/watch?v=RuTmd4g5K8Q

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14 Hours of Python Game Development - from Beginner to Advanced

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Why Is Python Called Python |Story of Python | Sifars

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Why Is Python Called Python- it is named after Monty Python’s Flying Circus is a BBC Comedy TV series from the year 1969. Read more here

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Python | Definition, Language, History, & Facts | Britannica

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Python is a general-purpose high-level computer programming language valued for its English-like syntax and powerful built-in data analysis and data science functions and libraries.

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Get smarter in 15 minutes a day with thousands of interactive, bite-sized lessons in math, science, data analysis, programming, computer science, AI, and beyond.

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Join the community of learners, creators, professionals, educators, and world-changers.

Learn, certify,
make an impact.

Expand your career opportunities with the Python Institute's training and certification programs. Program your future.

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Join the community of learners, creators, professionals, educators, and world-changers. Expand your career opportunities with the Python Institute's training and certification programs. Program your future.

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Python Coding for Minecraft: This Instructable shows how to install and use a mod I wrote that lets you control Minecraft with python scripts. I’ll focus on Windows, though OS X and Linux should work just as well. (If you want something simpler than python, here is a Scratch ve…

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GitHub - PyGithub/PyGithub: Typed interactions with the GitHub API v3

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Typed interactions with the GitHub API v3. Contribute to PyGithub/PyGithub development by creating an account on GitHub.

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A deep dive into why more people are using Python than ever, its key use cases, and why it’s still so popular 30-plus years after it was first released.

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Get Minecraft Education for your classroom. Find out how using this game will take your students’ learning to the next level.

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The official home of the Python Programming Language

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Learn Python - Full Course for Beginners [Tutorial]

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5 Games to help learn how to code games examples.

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Hello, buddies! Python is a great programming language for Game development, which makes it more…

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1. History and Philosophy of Python | Python Tutorial

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A brief History of Python, the origin of the name and the underlying philosophy.

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Start coding Python in Minecraft with MakeCode! Learn the basics of Python, including syntax, data types, variables, conditionals, 'for' and conditional loops, lists and functions.

# data[].attributes.attachment_link.readable_text
Beginner's Guide to Python

New to programming? Python is free and easy to learn if you know where to start! This guide will help you to get started quickly.

Chinese Translation

New to Python?

Read BeginnersGuide/Overview for a short explanation of what Python is.

Getting Python

Next, install the Python 3 interpreter on your computer. This is the program that reads Python programs and carries out their instructions; you need it before you can do any Python programming. Mac and Linux distributions may include an outdated version of Python (Python 2), but you should install an updated one (Python 3). See BeginnersGuide/Download for instructions to download the correct version of Python.

There are also Python interpreter and IDE bundles available, such as Thonny. Other options can be found at IntegratedDevelopmentEnvironments.

At some stage, you'll want to edit and save your program code. Take a look at HowToEditPythonCode for some advice and recommendations.

Learning Python

Next, read a tutorial and try some simple experiments with your new Python interpreter.

If you have never programmed before, see BeginnersGuide/NonProgrammers for a list of suitable tutorials.

If you have previous programming experience, consult BeginnersGuide/Programmers, which lists more advanced tutorials.

If English isn't your first language, you might be more comfortable with a tutorial that's been translated into your language. Consult python.org's list of Non-English resources.

Most tutorials assume that you know how to run a program on your computer. If you are using Windows and need help with this, see How do I Run a Program Under Windows.

Some sites offer in-browser coding for those who want to learn Python:

A completely free beginners tutorial with interactive, editable code examples

Codecademy

Coding Bootcamps

DataCamp

Dataquest for Python for data science.

HackInScience free and open source platform.

High School Technology Services for general Python

Print a cheat sheet of the most important Python features and post it to your office wall until you know the basics well.

Once you have read a tutorial, you can browse through Python's online documentation. It includes a tutorial that might come in handy, a Library Reference that lists all of the modules that come standard with Python, and the Language Reference for a complete (if rather dry) explanation of Python's syntax.

When you are ready to write your first program, you will need a text editor or an IDE. If you don't want to use Thonny or something more advanced, then you can use IDLE, which is bundled with Python and supports extensions.

This Python wiki also contains a page about Python One-Liners -- an obscure but interesting subculture in Python.

Need Help?

Need help with any of this? Read BeginnersGuide/Help for mailing lists and newsgroups.

Most Python books will include an introduction to the language; see IntroductoryBooks for suggested titles.

Consult BeginnersGuide/Examples for small programs and little snippets of code that can help you learn.

Or, if you prefer to learn Python through listening to a lecture, you can attend a training course or even hire a trainer to come to your company. Consult the PythonEvents page to see if any training courses are scheduled in your area and the PythonTraining page for a list of trainers.

Teachers can join the EDU-SIG, a mailing list for discussion of Python's use in teaching at any level ranging from K-12 up to university.

Complete list of Beginner's Guide pages

1. BeginnersGuide/Download

2. BeginnersGuide/Examples

3. BeginnersGuide/Help

4. BeginnersGuide/Mathematics

5. BeginnersGuide/NonProgrammers

6. BeginnersGuide/Overview

7. BeginnersGuide/Programmers

8. BeginnersGuide/Programmers (Cpp2Python.pdf)

9. BeginnersGuide/Programmers/SimpleExamples

Quiz and Exercises

Finxter - How good are your Python skills? Test and Training with a Daily Python Puzzle

CheckIO - Online learning, testing and improving your python skills

After Hours Programming - Python Quiz

Python Guru Quiz- Python Quiz

Pyschools - A Collection of Python Quiz and Exercise Questions

PyGUI - Collection of python quiz answers, Examples And GUI Tkinter Tutorials For Beginners

Pythonspot - Python Quiz

Python Challenge - A Python Quiz App on Android Platform

CS Circles - online lessons and graded exercises

Python Style Checker

Pythonchecker.com - An educative online tool to rate your Python style (with dynamic score computation and hints)

Looking for a particular Python module or application?

The first place to look is the Python Package Index.

If you can't find anything relevant in the Package Index,
try searching python.org - you can find anything mentioned on the Python site, in the FAQs, or in the newsgroup. More info: where to search.

You may also try our external guest project, pydoc.net, for advanced package and module search.

Next, try Google or another search engine of your choice. Searching for "python" and some relevant keywords will usually find something helpful.

Finally, you can try posting a query to the comp.lang.python Usenet group.

Python: Collection of 11 Best Python Cheat Sheets

NumPy: Collection of 10 Best NumPy Cheat Sheets

Pandas: Collection of 7 Beautiful Pandas Cheat Sheets

Machine Learning: Collection of 15 Machine Learning Cheat Sheets

Want to contribute?

Python is a product of the Python Software Foundation, a non-profit organization that holds the copyright. Donations to the PSF are tax-deductible in the USA, and you can donate via credit card or PayPal.

To report a bug in the Python core, use the Python Bug Tracker.

To contribute a bug fix or other patch to the Python core, read the Python Developer's Guide for more information about Python's development process.

To contribute to the official Python documentation, join the Documentation SIG, write to docs@python.org , or use the Issue Tracker to contribute a documentation patch.

To announce your module or application to the Python community, use comp.lang.python.announce. See the guide to Python mailing lists for more information.

To propose changes to the Python core, post your thoughts to comp.lang.python. If you have an implementation, follow the Python Patch Guidelines.

If you have a question are not sure where to report it, check out the WhereDoIReportThis? page.

CategoryDocumentation

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New to programming? Python is free and easy to learn if you know where to start! This guide will help you to get started quickly.

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How to Build Your First Python Game: A Step-by-Step Guide to Creating a Simple Shooter with PyGame

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Hi lovely readers, Have you ever wanted to create your own video game? Maybe you’ve thought about…

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GitHub - realpython/python-guide: Python best practices guidebook, written for humans.

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Python best practices guidebook, written for humans. - GitHub - realpython/python-guide: Python best practices guidebook, written for humans.

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How Python Has Changed The Innovation of Coders

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How Python Has Changed The Innovation of Coders

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The Evolution and Impact of Python in Modern Programming – Data Science Society

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Python started out as a computer language in the late 1980s and has since become one of the most widely used languages worldwide. Created by Guido van

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https://www.tutorialspoint.com/python/index.htm

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Python Tutorial - Today, Python is one of the most popular programming languages. Although it is a general-purpose language, it is used in various areas of applications such as Machine Learning, Artificial Intelligence, web development, IoT, and more.

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The future may hold a faster and more performant Python and it’s this community of passionate and committed users who will help to make it happen…

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Learning a programming language can be hard. Learning with materials in a different human language is even harder. This is why we have started the process of converting pygame learning materials into another language. With the help of a professional qualified educator we have begun the process with…

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Minecraft isnt even coded using python......

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Start coding Python in Minecraft with MakeCode! Learn the basics of Python, including syntax, data types, variables, conditionals, 'for' and conditional loops, lists and functions.

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Python is an interpreted, interactive, object-oriented programming language. It incorporates modules, exceptions, dynamic typing, very high level dynamic data types, and classes. It supports multiple programming paradigms beyond object-oriented programming, such as procedural and functional programming. Python combines remarkable power with very clear syntax. It has interfaces to many system calls and libraries, as well as to various window systems, and is extensible in C or C++. It is also usable as an extension language for applications that need a programmable interface. Finally, Python is portable: it runs on many Unix variants including Linux and macOS, and on Windows.

To find out more, start with The Python Tutorial . The Beginner's Guide to Python links to other introductory tutorials and resources for learning Python.

The Python Software Foundation is an independent non-profit organization that holds the copyright on Python versions 2.1 and newer. The PSF's mission is to advance open source technology related to the Python programming language and to publicize the use of Python. The PSF's home page is at https://www.python.org/psf/.

Donations to the PSF are tax-exempt in the US. If you use Python and find it helpful, please contribute via the PSF donation page.

Here's a very brief summary of what started it all, written by Guido van Rossum:

I had extensive experience with implementing an interpreted language in the ABC group at CWI, and from working with this group I had learned a lot about language design. This is the origin of many Python features, including the use of indentation for statement grouping and the inclusion of very-high-level data types (although the details are all different in Python).

I had a number of gripes about the ABC language, but also liked many of its features. It was impossible to extend the ABC language (or its implementation) to remedy my complaints - in fact its lack of extensibility was one of its biggest problems. I had some experience with using Modula-2+ and talked with the designers of Modula-3 and read the Modula-3 report. Modula-3 is the origin of the syntax and semantics used for exceptions, and some other Python features.

I was working in the Amoeba distributed operating system group at CWI. We needed a better way to do system administration than by writing either C programs or Bourne shell scripts, since Amoeba had its own system call interface which wasn't easily accessible from the Bourne shell. My experience with error handling in Amoeba made me acutely aware of the importance of exceptions as a programming language feature.

It occurred to me that a scripting language with a syntax like ABC but with access to the Amoeba system calls would fill the need. I realized that it would be foolish to write an Amoeba-specific language, so I decided that I needed a language that was generally extensible.

During the 1989 Christmas holidays, I had a lot of time on my hand, so I decided to give it a try. During the next year, while still mostly working on it in my own time, Python was used in the Amoeba project with increasing success, and the feedback from colleagues made me add many early improvements.

In February 1991, after just over a year of development, I decided to post to USENET. The rest is in the Misc/HISTORY file.

Python is a high-level general-purpose programming language that can be applied to many different classes of problems.

The language comes with a large standard library that covers areas such as string processing (regular expressions, Unicode, calculating differences between files), internet protocols (HTTP, FTP, SMTP, XML-RPC, POP, IMAP), software engineering (unit testing, logging, profiling, parsing Python code), and operating system interfaces (system calls, filesystems, TCP/IP sockets). Look at the table of contents for The Python Standard Library to get an idea of what's available. A wide variety of third-party extensions are also available. Consult the Python Package Index to find packages of interest to you.

Python versions are numbered "A.B.C" or "A.B":

A is the major version number - it is only incremented for really major changes in the language.

B is the minor version number - it is incremented for less earth-shattering changes.

C is the micro version number - it is incremented for each bugfix release.

Not all releases are bugfix releases. In the run-up to a new feature release, a series of development releases are made, denoted as alpha, beta, or release candidate. Alphas are early releases in which interfaces aren't yet finalized; it's not unexpected to see an interface change between two alpha releases. Betas are more stable, preserving existing interfaces but possibly adding new modules, and release candidates are frozen, making no changes except as needed to fix critical bugs.

Alpha, beta and release candidate versions have an additional suffix:

The suffix for an alpha version is "aN" for some small number N.

The suffix for a beta version is "bN" for some small number N.

The suffix for a release candidate version is "rcN" for some small number N.

In other words, all versions labeled 2.0aN precede the versions labeled 2.0bN, which precede versions labeled 2.0rcN, and those precede 2.0.

You may also find version numbers with a "+" suffix, e.g. "2.2+". These are unreleased versions, built directly from the CPython development repository. In practice, after a final minor release is made, the version is incremented to the next minor version, which becomes the "a0" version, e.g. "2.4a0".

See the Developer's Guide for more information about the development cycle, andPEP 387 to learn more about Python's backward compatibility policy. See also the documentation for , , and .

The latest Python source distribution is always available from python.org, at https://www.python.org/downloads/. The latest development sources can be obtained at https://github.com/python/cpython/.

The source distribution is a gzipped tar file containing the complete C source, Sphinx-formatted documentation, Python library modules, example programs, and several useful pieces of freely distributable software. The source will compile and run out of the box on most UNIX platforms.

Consult the Getting Started section of the Python Developer's Guide for more information on getting the source code and compiling it.

The standard documentation for the current stable version of Python is available at https://docs.python.org/3/. PDF, plain text, and downloadable HTML versions are also available at https://docs.python.org/3/download.html.

The documentation is written in reStructuredText and processed by the Sphinx documentation tool. The reStructuredText source for the documentation is part of the Python source distribution.

There are numerous tutorials and books available. The standard documentation includes The Python Tutorial .

Consult the Beginner's Guide to find information for beginning Python programmers, including lists of tutorials.

There is a newsgroup, comp.lang.python, and a mailing list, python-list. The newsgroup and mailing list are gatewayed into each other - if you can read news it's unnecessary to subscribe to the mailing list. comp.lang.python is high-traffic, receiving hundreds of postings every day, and Usenet readers are often more able to cope with this volume.

Announcements of new software releases and events can be found in comp.lang.python.announce, a low-traffic moderated list that receives about five postings per day. It's available as the python-announce mailing list.

More info about other mailing lists and newsgroups can be found at https://www.python.org/community/lists/.

Alpha and beta releases are available from https://www.python.org/downloads/. All releases are announced on the comp.lang.python and comp.lang.python.announce newsgroups and on the Python home page at https://www.python.org/; an RSS feed of news is available.

You can also access the development version of Python through Git. See The Python Developer's Guide for details.

To report a bug or submit a patch, use the issue tracker at https://github.com/python/cpython/issues.

For more information on how Python is developed, consult the Python Developer's Guide.

It's probably best to cite your favorite book about Python.

The very first article about Python was written in 1991 and is now quite outdated.

Guido van Rossum and Jelke de Boer, "Interactively Testing Remote Servers Using the Python Programming Language", CWI Quarterly, Volume 4, Issue 4 (December 1991), Amsterdam, pp 283-303.

Yes, there are many, and more are being published. See the python.org wiki at https://wiki.python.org/moin/PythonBooks for a list.

You can also search online bookstores for "Python" and filter out the Monty Python references; or perhaps search for "Python" and "language".

The Python project's infrastructure is located all over the world and is managed by the Python Infrastructure Team. Details here.

When he began implementing Python, Guido van Rossum was also reading the published scripts from "Monty Python's Flying Circus", a BBC comedy series from the 1970s. Van Rossum thought he needed a name that was short, unique, and slightly mysterious, so he decided to call the language Python.

No, but it helps. :)

Very stable. New, stable releases have been coming out roughly every 6 to 18 months since 1991, and this seems likely to continue. As of version 3.9, Python will have a new feature release every 12 months (PEP 602).

The developers issue bugfix releases of older versions, so the stability of existing releases gradually improves. Bugfix releases, indicated by a third component of the version number (e.g. 3.5.3, 3.6.2), are managed for stability; only fixes for known problems are included in a bugfix release, and it's guaranteed that interfaces will remain the same throughout a series of bugfix releases.

The latest stable releases can always be found on the Python download page. Python 3.x is the recommended version and supported by most widely used libraries. Python 2.xis not maintained anymore.

There are probably millions of users, though it's difficult to obtain an exact count.

Python is available for free download, so there are no sales figures, and it's available from many different sites and packaged with many Linux distributions, so download statistics don't tell the whole story either.

The comp.lang.python newsgroup is very active, but not all Python users post to the group or even read it.

See https://www.python.org/about/success for a list of projects that use Python. Consulting the proceedings for past Python conferences will reveal contributions from many different companies and organizations.

High-profile Python projects include the Mailman mailing list manager and the Zope application server. Several Linux distributions, most notably Red Hat, have written part or all of their installer and system administration software in Python. Companies that use Python internally include Google, Yahoo, and Lucasfilm Ltd.

See https://peps.python.org/ for the Python Enhancement Proposals (PEPs). PEPs are design documents describing a suggested new feature for Python, providing a concise technical specification and a rationale. Look for a PEP titled "Python X.Y Release Schedule", where X.Y is a version that hasn't been publicly released yet.

New development is discussed on the python-dev mailing list.

In general, no. There are already millions of lines of Python code around the world, so any change in the language that invalidates more than a very small fraction of existing programs has to be frowned upon. Even if you can provide a conversion program, there's still the problem of updating all documentation; many books have been written about Python, and we don't want to invalidate them all at a single stroke.

Providing a gradual upgrade path is necessary if a feature has to be changed. PEP 5 describes the procedure followed for introducing backward-incompatible changes while minimizing disruption for users.

Yes.

It is still common to start students with a procedural and statically typed language such as Pascal, C, or a subset of C++ or Java. Students may be better served by learning Python as their first language. Python has a very simple and consistent syntax and a large standard library and, most importantly, using Python in a beginning programming course lets students concentrate on important programming skills such as problem decomposition and data type design. With Python, students can be quickly introduced to basic concepts such as loops and procedures. They can probably even work with user-defined objects in their very first course.

For a student who has never programmed before, using a statically typed language seems unnatural. It presents additional complexity that the student must master and slows the pace of the course. The students are trying to learn to think like a computer, decompose problems, design consistent interfaces, and encapsulate data. While learning to use a statically typed language is important in the long term, it is not necessarily the best topic to address in the students' first programming course.

Many other aspects of Python make it a good first language. Like Java, Python has a large standard library so that students can be assigned programming projects very early in the course that do something. Assignments aren't restricted to the standard four-function calculator and check balancing programs. By using the standard library, students can gain the satisfaction of working on realistic applications as they learn the fundamentals of programming. Using the standard library also teaches students about code reuse. Third-party modules such as PyGame are also helpful in extending the students' reach.

Python's interactive interpreter enables students to test language features while they're programming. They can keep a window with the interpreter running while they enter their program's source in another window. If they can't remember the methods for a list, they can do something like this:

With the interpreter, documentation is never far from the student as they are programming.

There are also good IDEs for Python. IDLE is a cross-platform IDE for Python that is written in Python using Tkinter. Emacs users will be happy to know that there is a very good Python mode for Emacs. All of these programming environments provide syntax highlighting, auto-indenting, and access to the interactive interpreter while coding. Consult the Python wiki for a full list of Python editing environments.

If you want to discuss Python's use in education, you may be interested in joining the edu-sig mailing list.

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Contents: General Python FAQ- General Information- What is Python?, What is the Python Software Foundation?, Are there copyright restrictions on the use of Python?, Why was Python created in the fi...

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The best way to learn Python is by practicing examples. This page contains examples on basic concepts of Python. We encourage you to try these examples on your own before looking at the solution.

All the programs on this page are tested and should work on all platforms.

Want to learn Python by writing code yourself? Enroll in our Interactive Python Course for FREE.

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This page contains examples of basic concepts of Python programming like loops, functions, native datatypes and so on.

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How Python is Used In Gaming and How it Can Be Applied

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How Python is Used In Gaming and How it Can Be Applied

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10 Python games for kids and beginners to code. Learn fundamental Python coding while playing and building fun games.

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CodeCombat: Learn to Code by Playing a Game

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Learn typed code through a programming game. Learn Python, JavaScript, and HTML as you solve puzzles and learn to make your own coding games and websites.

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Python Tutor helps you do programming homework assignments in Python, Java, C, C++, and JavaScript. It contains a unique step-by-step visual debugger and AI tutor to help you understand and debug code.

Since 2010, over 15 million people in more than 180 countries have used Python Tutor to visualize over 200 million pieces of code. It is the most widely-used program visualization tool for CS education.

As a preview, here is a small example showing recursion in Python:

Here are some examples of how it visualizes Java, C, and C++ code:

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Online Python Tutor - visualize, debug, get AI help for Python, Java, C, C++, and JavaScript

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Python Tutor helps you do programming homework assignments in Python, Java, C, C++, and JavaScript. It contains a unique step-by-step visual debugger and AI tutor to help you understand and debug code. Since 2010, over 15 million people in more than 180 countries have used Python Tutor to visualize over 200 million pieces of code.

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History of the Python development language

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From its creation in 1989 to the end of Python 2 in 2020, discover the history of this most popular development language.

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The inventor and the creation of python.

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Python is a widely used general-purpose, high-level programming language. It was initially designed by Guido van Rossum in 1991 and developed by Python Software Foundation. It was mainly developed for emphasis on code readability, and its syntax allows programmers to express concepts in fewer lines of code.

Who invented Python?

In the late 1980s, history was about to be written. It was that time when working on Python started. Soon after that, Guido Van Rossum began doing its application-based work in December of 1989 at Centrum Wiskunde & Informatica (CWI) which is situated in the Netherlands. It was started as a hobby project because he was looking for an interesting project to keep him occupied during Christmas.

The programming language in which is said to have succeeded is ABC Programming Language, which had interfacing with the Amoeba Operating System and had the feature of exception handling. He had already helped create ABC earlier in his career and had seen some issues with ABC but liked most of the features. After that what he did was very clever. He had taken the syntax of ABC, and some of its good features. It came with a lot of complaints too, so he fixed those issues completely and created a good scripting language that had removed all the flaws. The inspiration for the name came from the BBC's TV Show - ' Monty Python's Flying Circus', as he was a big fan of the TV show and also he wanted a short, unique and slightly mysterious name for his invention and hence he named it Python! He was the "Benevolent dictator for life" (BDFL) until he stepped down from the position as the leader on 12th July 2018. For quite some time he used to work for Google, but currently, he is working at Dropbox.

Evolution of Python

The language was finally released in 1991. When it was released, it used a lot fewer codes to express the concepts, when we compare it with , & . Its design philosophy was quite good too. Its main objective is to provide code readability and advanced developer productivity. When it was released, it had more than enough capability to provide classes with inheritance, several core data types of exception handling and functions.
Following are the illustrations of different versions of Python along with the timeline.

Python 3.12.1 is the latest stable version.

The two of the most used versions has to Python 2.x & 3.x. There is a lot of competition between the two and both of them seem to have quite a number of different fanbases. You can also refer to mentioned below article to learn How you can download python latest version in your system.

Download and Install Python 3 Latest Version

For various purposes such as developing, scripting, generation, and software testing , this language is utilized. Due to its elegance and simplicity, top technology organizations like Dropbox, Google, Quora, Mozilla, Hewlett-Packard, Qualcomm, IBM, and Cisco have implemented Python.

Python has come a long way to become the most popular coding language in the world. Python has just turned 30 and just recently at pycon22(python conference) a new feature was released by Anaconda foundation it's known as pyscript with this now python can be written and run in the browser like javascript which was previously not possible, but it still has that unknown charm & X factor.

Python has been an inspiration for many other coding languages such as , Cobra, Boo, CoffeeScript ECMAScript, Groovy, Swift Go, OCaml, , etc.

History of Python - FAQ's

Q1. Who developed Python programming language?

It was initially designed by Guido van Rossum in 1991 and developed by Python Software Foundation.

Q2. Who invented Python programming language?

In the late 1980s, history was about to be written. It was that time when working on Python started. Soon after that, Guido Van Rossum began doing its application-based work in December of 1989 at Centrum Wiskunde & Informatica (CWI) which is situated in the Netherlands. It was started firstly as a hobby project because he was looking for an interesting project to keep him occupied during Christmas

Q3. What are the uses of Python?

One more question asises that, Is Python Programming or Scripting?, According to Linux Journal readers the Python is a programming as well as a scripting language. Also, This language has various purposes such as developing, scripting, generation, and software testing.

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Last Updated : 26 Dec, 2023

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A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.

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Python is the gift that keeps on giving. The more you understand Python, the more you can do in the 21st Century. As simple as that.

What

Python is a widely-used, interpreted, object-oriented, and high-level programming language with dynamic semantics, used for general-purpose programming. It's everywhere, and people use numerous Python-powered devices on a daily basis, whether they realize it or not.

Who

Python was created by Guido van Rossum, and first released on February 20, 1991. While you may know the python as a large snake, the name of the Python programming language comes from an old BBC television comedy sketch series called Monty Python's Flying Circus.

One of the amazing features of Python is the fact that it is actually one person's work. Usually, new programming languages are developed and published by large companies employing lots of professionals, and due to copyright rules, it is very hard to name any of the people involved in the project. Python is an exception.

Of course, Guido van Rossum did not develop and evolve all the Python components himself. The speed with which Python has spread around the world is a result of the continuous work of thousands (very often anonymous) programmers, testers, users (many of them aren't IT specialists) and enthusiasts, but it must be said that the very first idea (the seed from which Python sprouted) came to one head - Guido's.

Python is maintained by the Python Software Foundation, a non-profit membership organization and a community devoted to developing, improving, expanding, and popularizing the Python language and its environment.

Why

Python is omnipresent, and people use numerous Python-powered devices on a daily basis, whether they realize it or not. There are billions of lines of code written in Python, which means almost unlimited opportunities for code reuse and learning from well-crafted examples. What's more, there is a large and very active Python community, always happy to help.

There are also a couple of factors that make Python great for learning:

It is easy to learn - the time needed to learn Python is shorter than for many other languages; this means that it's possible to start the actual programming faster;

It is easy to use for writing new software - it's often possible to write code faster when using Python;

It is easy to obtain, install and deploy - Python is free, open and multiplatform; not all languages can boast that.

Programming skills prepare you for careers in almost any industry, and are required if you want to continue to more advanced and higher-paying software development and engineering roles. Python is the programming language that opens more doors than any other. With a solid knowledge of Python, you can work in a multitude of jobs and a multitude of industries. And the more you understand Python, the more you can do in the 21st Century. Even if you don't need it for work, you will find it useful to know.

Where

Python is the programming language that opens more doors than any other. With a solid knowledge of Python, you can work in a multitude of jobs and a multitude of industries. And even if you don't need it for work, you will still find it useful to know to speed certain things up or develop a deeper understanding of other concepts.

Python is a great choice for career paths related to software development, engineering, DevOps, machine learning, data analytics, web development, and testing. What's more, there are also many jobs outside the IT industry that use Python. Since our lives are becoming more computerized every day, and the computer and technology areas previously associated only with technically gifted people are now opening up to non-programmers, Python has become one of the must-have tools in the toolbox of educators, managers, data scientists, data analysts, economists, psychologists, artists, and even secretaries.

How

Learn, certify, and succeed! How great would it be to write your own computer program? Python is a multi-paradigm programming language used by startups and tech giants like Google, Facebook, Cisco, Netflix, and more. With intuitive, readable syntax, Python is a great first programming language to learn. Get started with the Python Essentials 1 course and prepare for the PCEP certification exam. If you already know the core fundamentals of Python, progress to Python Essentials 2, and prepare for the PCAP certification exam. If you work with Python and need to advance to more specialized areas of programming, pick our professional-series courses and prepare for the PCPP and specialization-track certifications.

In 1999, Guido van Rossum defined his goals for Python:

an easy and intuitive language just as powerful as those of the major competitors;

open source, so anyone can contribute to its development;

code that is as understandable as plain English;

suitable for everyday tasks, allowing for short development times.

20+ years later, it is clear that all these intentions have been fulfilled. Some sources say that Python is the first-most popular programming language in the world, while others claim it's the third.

Either way, it occupies the first position in the top ten of the TIOBE Programming Community and PYPL PopularitY of Programming Language Indexes. (February 2022)

Python isn't a young language. It is mature and trustworthy. It's not a one-hit wonder. It's a bright star in the programming firmament, and time spent learning Python is a very good investment.

Where can we see Python in action? We see it every day and almost everywhere. It's used extensively to implement complex Internet services like search engines, cloud storage and tools, social media and so on. Whenever you use any of these services, you are actually very close to Python, although you wouldn't know it.

Many developing tools are implemented in Python. More and more everyday use applications are being written in Python. Lots of scientists have abandoned expensive proprietary tools and switched to Python. Lots of IT project testers have started using Python to carry out repeatable test procedures. The list is long.

Python examples

Python is a great choice for:

Web and Internet development (e.g., Django and Pyramid frameworks, Flask and Bottle micro-frameworks)

Scientific and numeric computing (e.g., SciPy - a collection of packages for the purposes of mathematics, science, and engineering; Ipython - an interactive shell that features editing and recording of work sessions)

Education (it's a brilliant language for teaching programming!)

Desktop GUIs (e.g., wxWidgets, Kivy, Qt)

Software Development (build control, management, and testing - Scons, Buildbot, Apache Gump, Roundup, Trac)

Business applications (ERP and e-commerce systems - Odoo, Tryton)

Games (e.g., Battlefield series, Sid Meier's Civilization IV...), websites and services (e.g., Dropbox, UBER, Pinterest, BuzzFeed...)

...

And that's just the beginning.

(sources: PythonProjects on Wikipedia and Python.org.)

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Python is the gift that keeps on giving. The more you understand Python, the more you can do in the 21st Century. As simple as that. Python is a widely-used, interpreted, object-oriented, and high-level programming language with dynamic semantics, used for general-purpose programming.

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python programming language object oriented web free open source software license documentation download community

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The official home of the Python Programming Language

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Importance of Why to Use Python and How it Can Benefit Users

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Importance of Why to Use Python and How it Can Benefit Users

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Python is a general-purpose programming language suitable for software developers, programmers, and data scientists. Its versatility and ease of use have propelled Python to the top of the long list of programming languages, alongside C and Java.

The scope of Python's functionality, from back-end web and mobile app development to scientific computation and machine learning, is wider than that of many other programming languages, but its philosophy is rooted in the uncluttered design principles that software engineer Tim Peters wrote in 1999.

Peters' guiding principles, dubbed " The Zen of Python," include these first seven axioms:

Beautiful is better than ugly.

Explicit is better than implicit.

Simple is better than complex.

Complex is better than complicated.

Flat is better than nested.

Sparse is better than dense.

Readability counts.

The key takeaway from these first principles is that Python is accessible to people with little to no experience in programming languages. The straightforward design isn't arbitrary. Python's creators made an intentional decision, which, combined with Python's wealth of diverse applications, has given rise to its popularity in the data sciences.

What Is Python?

An open-source, interpreted coding language with many uses, Python places a strong emphasis on code readability. To that end, Python's syntax uses English keywords in place of the symbols and punctuation used in lower-level languages.

By replacing the computer's native machine language with a more natural human language, Python has made programming more convenient and has significantly reduced the learning curve for novice programmers and data scientists.

Python uses integers, floats, strings, and Boolean data types and contains four built-in data structures - lists, dictionaries, tuples, and sets. In addition to its expansive external libraries and frameworks, Python's built-in functions can perform a host of low-level tasks, which frees up users to tackle more complex programming solutions and predictive data models.

For example, Python's print() function is a handy little chunk of code that lets you display formatted text. Formerly a statement in Python 2, this new addition to Python 3 doesn't need to be imported, and it has other convenient applications. You can find an in-depth tutorial on the Python website.

You may have picked up on the references to Python as a high-level coding language. Programming languages range from low-level to high-level, with the level signifying whether the language is closer to that of a computer or of a human. The higher the level, the closer the language is to human speech.

Levels of Programming Languages from Lowest to Highest

Machine language

Assembly languages

High-level languages

Very high-level languages

Natural languages

Python is a high-level language, as are Java, C#, Perl, and Ruby.

For beginning Python users in the data science field, the intuitive programming language is both easy to learn and powerful enough to execute advanced data analytics tasks.

What Is Python Used For?

We've touched on some of the common uses of Python for programming and writing scripts, but that's just scratching the surface of what the language can do. Its ability to process big data and perform mathematical and scientific computations for data analysis and sophisticated problem-solving sets it apart from other general-purpose coding languages.

Unlike R, which is a " language and environment for statistical computing and graphics," Python is used across industries and among researchers and data teams with multifaceted approaches to analyzing, manipulating, and visualizing data.

Python for Data Scientists

Although R, Stata, and SAS are better for traditional statistics modeling, Python offers more in terms of flexibility and convenience.

The comprehensive frameworks available in Python allow for a deeper dive into the data, enabling the user to identify complex patterns and explore the data in more creative ways.

Data scientists use Python for big data, machine learning and artificial intelligence. The ability to process big data is in high demand as the Internet of Things (IoT) continues to expand, collecting and sharing a larger volume of data from more sources at higher speeds.

Tech analyst company IDC predicted in a 2020 report that "by 2025 there will be 55.7 B connected devices worldwide" and that "the data generated from IoT-connected devices will be 73.1 ZB."

In 2001, industry analyst Doug Laney coined the term the "Three Vs" - volume, velocity, and variety - to encompass the defining characteristics of big data. Today we can include two more variables: the unpredictable flow of data, such as trending social media topics, and the quality of that data.

To successfully advise enterprises based on data-driven information, data scientists must be able to collect, clean, prepare, analyze, and interpret this huge amount of data quickly and efficiently. For some projects, a statistical computing language such as R is the most effective tool, but for others, a general-purpose language such as Python is better suited to the objective.

Data Analysis

Data analysis is an important part of the data scientist's role. Parsing large data sets to find meaning in the patterns and trends they reveal enables data scientists to design predictive models and algorithms that solve problems for businesses.

But large data sets can be unwieldy, and processing them can be time consuming and labor intensive. Furthermore, data analysis is only a part of the data scientist's job - a means to an end. The ultimate goal for data scientists is to convert the insights they glean from their analysis to a business solution through advanced modeling techniques.

In other words, data scientists have bigger fish to fry.

That's where Python's simplicity and readability come in. Fewer lines of code means speedier programming, which, in turn, leads to more streamlined data analysis. If the word "simplicity" brings to mind limitations and restrictions, read on.

Python makes up for any loss of functionality with a standard library and prebuilt modules that let users communicate with graphics libraries and a variety of interactive data-visualization tools. Charts, graphs, maps, and other visual representations are key to interpreting and clearly communicating the information acquired through data mining and analysis.

Python author Guido van Rossum developed the coding language for "intelligent computer users who were not computer programmers or software developers in any sense."

Hence its popularity with data scientists, whose objectives include visualizing data, constructing algorithms to solve specific problems, and deep learning - not programming and development.

Machine Learning

Machine learning engineers can benefit from Python's robust capabilities. Machine learning, deep learning and AI all require advanced systems that extend beyond executing a set of predetermined instructions.

Python modules, libraries, and frameworks provide the resources for data scientists to avoid the time-consuming process of coding these instructions manually. From complex mathematical functions and image optimization to preprocessing and 2D plotting, Python's libraries contain packages for data scientists, AI developers and machine learning engineers across a range of applications.

The most commonly used Python libraries for machine learning include:

Users can browse the more than 315,000 Python packages and install them directly from the Python Package Index (PyPI).

Python for Other Tech Professionals

Out of the 700-plus programming languages, Python is one of the most intuitive and widely used among tech companies, including NASA, Nokia, Google, and IBM.

Programmers, web developers and software engineers are attracted to Python's versatility. It can handle everything from writing system scripts to back-end web development, mobile and desktop app development, and software development.

When Artima's Bill Venners asked him to describe Python to developers who'd never used it, van Rossum said:

"From one perspective you can say Python is a very high-level scripting language. From another perspective you can say it's a high-level programming language that happens to be implemented in a way that emphasizes interactivity. Python shares some characteristics with scripting languages, but also shares some characteristics with more traditional programming languages."

He also explained that because Python's functionality is not intrinsic to its core, it offers developers a cleaner syntax with more flexibility through the prepackaged modules. The modular design allows users to extend existing applications and automate repetitive tasks. This is an enormous time-saver that attracts busy tech professionals.

Benefits of Python

The advantages of Python have led to its rise in popularity over the course of the last decade. According to the July 2021 update to the TIOBE Programming Community index, of the top three in-demand coding languages, Python "seems to have the best chances to become number 1, thanks to its market leadership in the booming field of data mining and artificial intelligence."

We've already discussed the many uses and features of Python, and now we'll dig into the benefits of the programming language - the ways in which Python can make your work in the realm of data science easier.

Ease of Use

Python's key attribute is its readability. Because Python is so close to the user's natural language and requires fewer lines of code, the user can quickly learn and apply the programming language to a range of operations.

So whether you're a coding newbie or an experienced professional, Python helps you get more done in less time.

Adaptability

Because Python is such a versatile language - part scripting language, part interpreted programming language - it is used across multiple industries.

For anyone interested in a career in technology or data science, Python is the closest you can get to a one-stop-shop for a coveted skill. Employers from companies large and small are looking for people who have experience with the Python ecosystem.

Support

Python's open-source status is supported by an extensive community of users. On its website, Python boasts that its user base is "enthusiastic and dedicated to spreading use of the language far and wide."

With community support for all levels of experience, the Python knowledge base continues to grow. You can find resources and advice from the Python community on Slack and Discord as well as through a curated weekly newsletter and social media channels, including Twitter, Facebook, and IRC.

According to Andrew Beall, chief scientist at WorldViz, this is a primary reason his team has made Python the foundation of their Vizard virtual reality development platform.

"Scientists across numerous domains have adopted Python as the go-to language for analysis, so it's easy to lean on the accomplishments of others when beginning new projects," Beall notes in his team's success story.

Standard and Third-Party Libraries

Data scientists can provide value only to the extent that the data they analyze is clean and complete and able to be analyzed on a deeper level.

Python is continually adding new libraries, which gives data scientists new opportunities to explore and analyze data in innovative ways and provide enterprises with greater value and more meaningful insights.

For example, the Scikit-learn module offers an array of machine learning tools that enable data scientists to predict outcomes and devise algorithms around these predictions.

Career Opportunities: Expand Your Skill Set and Expand Your Options

The many advantages of Python culminate in the ultimate benefit to you, the user: Once you've mastered Python, you will have acquired a skill that will make you employable in the data science and tech industries. But have we fully covered what Python is used for?

Because Python is so popular, data scientists who have this valuable skill under their belts have more career options than those who don't. Learning Python will enhance your agility and make you more adaptable to different roles in the field of data science, thus opening more doors for you to launch your career.

From meteorology to chemistry to aerospace engineering, branches of science and engineering employ Python for a variety of technology and big data projects.

Tech giants such as Google, NASA, and Facebook use Python for deployment and maintenance, infrastructure, and data management.

George Lucas's special effects company, Industrial Light and Magic, and Walt Disney Feature Animation use Python for visual effects and animation.

Other heavy-hitters looking for graduates with Python on their resumes include:

If you're interested in launching a career with any of these companies, you'll want to have Python in your arsenal of qualifications.

And these jobs pay well. According to the Bureau of Labor Statistics (BLS), the median annual wages for jobs that often require Python knowledge are in the range of about $80,000 to $130,000. This often depends on experience, level of education, and the specific industry.

Annual Median Salaries for Jobs that Require Programming Language Skills

Data Scientists and Mathematical Science Occupations, All Other - $94,280

Computer Information and Research Scientist - $131,490

Software Developers, Quality Assurance Analysts, and Testers - $109,020

Special Effects Artists and Animators - $78,790

Top earners in the data scientist and mathematical science category alone sometimes make upwards $158,000 a year according to BLS data.

The BLS has predicted a 21% percent growth rate for computer information and research scientists and a 25% percent growth rate for software developers from 2021 to 2031. The growth rate for both of these occupations are much faster than the 4 percent average growth rate for all occupations.

Industries with the highest employment levels for data scientists according to BLS reports include:

Computer system and design related services

Management of companies and enterprises

Insurance carriers

Management, scientific, and technical consulting services

Scientific research and development services

And keep in mind, these are just the jobs that most closely align with the core uses and functionality of Python as a programming language and data analysis tool. This list doesn't include salaries and job outlooks for related roles, such as actuaries. The median annual wage for actuaries as of May 2021 is $105,900.

Learning Python: Training & Education

If this has piqued your interest in Python and the possibility of launching a career in one of these rapidly growing industries, your next step is to explore your options for training and education.

You will likely want to pursue a degree in one of the following disciplines:

Data Science

Computer Science

Mathematics

Statistics

Data science is a relatively new area of study in colleges and universities. The demand for skilled data scientists in business, education, and health care, however, is changing that. More data science programs are being offered to meet that demand, and graduates with a solid knowledge base in machine learning, data mining, and analysis, statistical methods and data visualization will be ahead of the curve in the job market.

Many of the current data science programs emphasize the context and application of data analysis in business strategy and problem-solving, making Python - whose frameworks align neatly with the goals and curricula of such application-driven programs - a logical choice for universities.

Although many useful Python tutorials can be found online, the novice data scientist will need more than the basics to apply Python to the type of research projects they'll be tackling in these programs.

Due to its interoperability with other coding languages, Python may be taught alongside C, C++, and Java in computer science degree programs. Data science programs, on the other hand, may teach Python in conjunction with SQL and R.

Regardless of the degree program and track you choose, you'll be best served by a course of study that includes Python as a component in an integrated, application-focused curriculum that emphasizes the critical-thinking and problem-solving skills you'll need in the real world.

2021 US Bureau of Labor Statistics salary and employment figures for data science roles, computer information and research scientists, software developers, and special effects professionals reflect national data, not school-specific information. Conditions in your area may vary. Data accessed January 2023.

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What is Python Used For? | DiscoverDataScience.org

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Learn why Python is important to learn for Data Science careers and the best degrees to earn to enhance your skills and qualifications.

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W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, Python, SQL, Java, and many, many more.

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On Thursday, according to Ars Technica, a large group of researchers from universities and the private sector presented Genesis. This new open-source computer simulation system allows robots to practice tasks in a simulated reality. And this is 430,000 times faster than in the "real" world. Researchers can also use an AI agent to generate 3D physics simulations from text inputs.

Thanks to accelerated simulation, a neural network controlling robots can spend the virtual equivalent of decades learning to pick up objects, walk or handle tools in just a few hours of computer time.

Huge acceleration

"One hour of compute time gives a robot 10 years of training experience. That's how Neo was able to learn martial arts in a blink of an eye in the Matrix Dojo," wrote Genesis paper co-author Jim Fan on X. Fan, who says he played a "small role" in the research, has previously worked on several robot simulation projects for Nvidia.

One launches Genesis as robotics researchers look for better tools to test and train robots in virtual environments before they are deployed in the real world. Fast, accurate simulations help robots learn complex tasks faster and reduce the need for expensive physical testing.

The Genesis platform, developed by a group led by Zhou Xian of Carnegie Mellon University, processes physics calculations up to 80 times faster than existing robot simulators. Consider Nvidia's Isaac Gym. It uses graphics cards similar to video games to simultaneously run up to 100,000 simulations. This is crucial for training the neural networks that will control future robots in the real world.

"If an AI can control 1,000 robots to perform 1 million skills in 1 billion different simulations, then it may 'just work' in our real world, which is simply another point in the vast space of possible realities", Fan wrote in his post on X.

Generating dynamic worlds

The team also announced that it is possible to generate so-called "4D dynamic worlds"-probably called "4D" because they simulate a 3D world in motion over time. The system uses vision-language models (VLMs) to generate complete virtual environments based on text descriptions, similar to "prompts" in other AI models. In doing so, it uses Genesis' own simulation APIs to create the worlds.

The AI-generated worlds reportedly include realistic physics, camera movements and object behaviour based on text commands. The system then produces physically accurate ray-traced videos and data that robots can use for training.

Faster creation of robot tests

This text-based system allows researchers to create complex testing environments for robots by typing natural language commands instead of programming them manually. "Traditionally, simulators require a huge amount of manual effort from artists: 3D assets, textures, scene layouts, etc. But every component in the workflow can be automated," Fan wrote.

Genesis' engine can also generate character animation, interactive 3D scenes, facial animation and more. This opens possibilities for artistic projects but can also lead to more realistic AI-generated games and videos, building a simulated world in data rather than just the statistical representation of pixels, as with video diffusion models.

Genesis is still in active development on GitHub, where the team accepts community contributions.

The power of Python

The platform differs from other 3D robot training simulators using Python for both the user interface and the core physics engine. Other engines use C++ or CUDA for their underlying computations while providing Python APIs. Genesis, however, takes a Python-first approach.

Importantly, the Genesis platform's nonproprietary nature makes super-fast robot training simulations available for free to any researcher via simple Python commands on standard computers with ordinary hardware.

Fan said in his announcement of Genesis that running robot simulations previously required complex programming and specialized hardware. "Robotics should be a lunar mission initiative owned by all humanity," he wrote.

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Physics simulation trains robots 430,000 times faster

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Genesis can reduce training times from decades to hours using 3D worlds generated from text. On Thursday, according to Ars Technica, a large group of

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Unlock your imagination with Roblox Creator Hub - the ultimate platform for building and publishing games. Join a vibrant community of creators today and publish your ideas to millions of people around the world. This world is ours for the making!

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For the creation and evolution of the Python programming language and for leadership of its community.

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For the creation and evolution of the Python programming language and for leadership of its community.

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Why Programming Is The Core Skill Of The 21st Century

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Why Programming Is The Core Skill Of The 21st Century

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Why Programming Is The Core Skill Of The 21st Century

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It’s never been easier, more accessible, or more essential to learn coding skills.

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Adding Coding to the Curriculum (Published 2014)

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Around the world, students from elementary school to the Ph.D. level are increasingly getting acquainted with the basics of computer coding.

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Source code: Lib/idlelib/ IDLE is Python's Integrated Development and Learning Environment. IDLE has the following features: cross-platform: works mostly the same on Windows, Unix, and macOS, Pytho...

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Trinket lets you run and write code in any browser, on any device.

Trinkets work instantly, with no need to log in, download plugins, or install software.

Easily share or embed the code with your changes when you're done.

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Trinket lets you run and write code in any browser, on any device.

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Debugging flow chart: My Code Isn't Working :-( 

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Debugging flow chart: My Code Isn't Working :-( 