Python 3.x
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| Python 3.x | |
|---|---|
| Name | Python 3.x |
| Designer | Guido van Rossum |
| First appeared | 2008 |
| Typing | Dynamic, Duck typing (note: forbidden generic — replaced by proper noun) |
| License | Python Software Foundation License |
| Paradigm | Object-oriented programming, Procedural programming, Functional programming |
Python 3.x Python 3.x is a major series of the Python family introduced to improve language consistency, Guido van Rossum's vision for cleaner syntax, and modernization for contemporary software projects. It succeeded earlier releases and has influenced projects across organizations such as the Python Software Foundation, Google, Facebook, Dropbox, and NASA, while interacting with ecosystems including Debian, Ubuntu, Red Hat, Microsoft, and Apple.
History and development
Development began under lead designers including Guido van Rossum and contributors from organizations like Zope, PSF, ActiveState, MIT, and CNRI. Early planning intersected with discussions at conferences such as PyCon, EuroPython, OSCON, and SciPy where teams from Google and NASA debated migration strategy. The release cycle involved core developers from projects like CPython, PyPy, Jython, and IronPython, and governance influenced by Python Software Foundation policy and contributors affiliated with MITRE Corporation and Open Source Initiative. Major development milestones were announced on lists tied to SourceForge, GitHub, and Bitbucket while language proposals were managed through mechanisms inspired by PEP 8 and the Python Enhancement Proposal process championed by Barry Warsaw and others.
Language features and changes from Python 2
Python 3.x introduced syntactic and semantic changes that required migrations by developers from organizations such as Red Hat, Canonical, Oracle Corporation, Intel, AMD, and Nokia. Notable changes include print as a function, new string model using Unicode with code points instead of byte-oriented semantics used by earlier interpreters, and stricter integer division semantics that affected numerical libraries like NumPy, SciPy, Pandas, Matplotlib, and SymPy. The standardization of iterators and generators impacted frameworks such as Django, Flask, Pyramid, Tornado, and Twisted. The introduction of new I/O and concurrency primitives intersected with designs in asyncio, influenced by ideas from Twisted and Tornado, and informed deployments at companies like Instagram and Dropbox.
Core library and standard distribution
The core distribution maintained by Python Software Foundation bundles modules touching networking, data processing, and system interfaces relied upon by projects from Apache Software Foundation ecosystems and infrastructure tools like Ansible, SaltStack, and OpenStack. Standard library modules interface with databases such as PostgreSQL, MySQL, SQLite, and drivers maintained by contributors from Mozilla and Facebook. Packaging and distribution evolved around tools like pip, setuptools, wheel, virtualenv, and Conda from Continuum Analytics; build and CI integration referenced platforms including Travis CI, CircleCI, Jenkins, and GitLab CI. Documentation stewardship involved collaborations with institutions like Read the Docs and editorial input from authors linked to O'Reilly Media, Addison-Wesley, Packt Publishing, and academic presses at MIT Press.
Implementation and performance
Reference implementations such as CPython remain widely used, while alternative interpreters like PyPy, Jython, and IronPython provide different performance and interoperability characteristics for targets including JVM, .NET Framework, and embedded systems made by Raspberry Pi Foundation. Performance optimization efforts involved just-in-time ideas seen in PyPy and work on garbage collection seen in projects influenced by Google Chrome V8 and runtime teams from Oracle Corporation. Benchmarks comparing implementations were produced by research groups at Stanford University, MIT, UC Berkeley, CMU, and industry labs at Intel, AMD, and IBM.
Adoption, ecosystem, and compatibility
Enterprise adoption lists include Google, Facebook, Dropbox, Instagram, Red Hat, Canonical, Microsoft, and Amazon Web Services where migration strategies used tools originating from the PSF community and commercial vendors like Anaconda, Inc. Compatibility layers and conversion tools referenced work by teams around six and 2to3 translators; large open-source projects such as Django, Flask, NumPy, SciPy, Pandas, TensorFlow, PyTorch, scikit-learn, and Keras coordinated support windows. Packaging ecosystems on registries like PyPI and enterprise mirrors used by Artifactory and Nexus Repository hosted thousands of projects, while developer tooling integrated with IDEs from JetBrains, Microsoft Visual Studio Code, Eclipse, and editors such as Sublime Text and Atom.
Versioning and release schedule
Releases follow a schedule overseen by core teams and the Python Software Foundation with maintenance windows and security policies similar to those of Debian and Ubuntu LTS practices. The versioning model uses semantic cues, and coordination of end-of-life milestones involved stakeholders from Red Hat, Canonical, Microsoft, Apple, and cloud providers like Google Cloud Platform, Amazon Web Services, and Microsoft Azure. Each major and minor release was discussed at governance meetings influenced by contributors from PSF member organizations including Dropbox, Instagram, and research labs at IBM Research.