Python 3
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| Python 3 | |
|---|---|
| Name | Python 3 |
| Paradigm | Multi-paradigm: Imperative programming, Object-oriented programming, Functional programming |
| Designer | Guido van Rossum |
| Developer | Python Software Foundation |
| First appeared | 2008 |
| Typing | Dynamic, Duck typing |
| License | Python Software Foundation License |
Python 3 is a major version of the high-level programming language created by Guido van Rossum and maintained by the Python Software Foundation. It represents a backwards-incompatible evolution intended to fix design flaws present in earlier releases and to modernize the language for contemporary software engineering needs. Python 3's development intersected with many projects, organizations, and events in software history, influencing ecosystems around GitHub, Stack Overflow, Mozilla Foundation, Google, and Facebook.
History and development
Python 3 was conceived to address longstanding issues associated with the original language line, following discussions among contributors from Zope Corporation, BeOpen.com, and the academic community including researchers from CWI and MIT. Major design debates took place at conferences such as PyCon, EuroPython, and in mailing lists archived alongside projects hosted on SourceForge and later GitHub. The language's roadmap and governance evolved under the auspices of the Python Software Foundation and involved stakeholders from corporations including Red Hat, Microsoft, IBM, Oracle Corporation, and Intel. Key community figures and contributors who influenced development include members of the Python Enhancement Proposal process and authors of influential libraries distributed via PyPI.
Features and changes from Python 2
Python 3 introduced several breaking changes to remedy ambiguities present in the earlier major line. Text and binary data handling was reworked with a stronger emphasis on Unicode, affecting interoperability with systems used by organizations such as Unicode Consortium. The input/output model was clarified relative to implementations used in CPython and influenced by work at FreeBSD and OpenBSD on locale handling. The change to the print function unified syntax used in codebases from companies like Dropbox and Instagram, while revisions to integer division resolved long-standing issues observed in scientific computing ecosystems maintained by contributors from NumPy and SciPy. Iteration and generator semantics were refined, aligning behavior with coroutines and asynchronous programming patterns later adopted by frameworks like Django, Flask, and Tornado.
Design and implementation
The reference implementation, developed primarily as CPython, integrates a virtual machine and bytecode compiler influenced by interpreter designs explored at institutions such as University of California, Berkeley and Carnegie Mellon University. Alternate implementations include PyPy, Jython, and IronPython, each interfacing with platforms like the Java Virtual Machine, the Common Language Runtime, and runtime projects from Oracle Corporation and Microsoft. Memory management strategies in Python 3 combine reference counting and cycle-detecting garbage collection, reflecting design trade-offs examined in systems developed at Bell Labs and research published from ACM proceedings. The language semantics and type model draw on theoretical work associated with Lambda calculus and practical influences from languages created at Bell Labs and Xerox PARC.
Standard library and packaging
Python 3's standard library expanded and reorganized modules, consolidating facilities for networking, cryptography, and data serialization used in deployments by NASA, CERN, Stripe, and PayPal. Packaging and distribution are managed through PyPI and tools like pip, with build and dependency management practices shaped by ecosystems at Linux Foundation distributions including Debian and Fedora. Integration with system package managers and container platforms from Docker and orchestration tools from Kubernetes affected how libraries are bundled, while security practices referenced standards from OpenSSL and recommendations from bodies like OWASP. Documentation and tutorials were distributed through channels including O'Reilly Media and university curricula at Stanford University and Massachusetts Institute of Technology.
Performance and adoption
Performance characteristics vary across implementations: PyPy emphasizes JIT compilation for throughput improvements, while CPython emphasizes compatibility and embedding used by projects at NASA and European Space Agency. Industry adoption grew among enterprises such as Google, Amazon, Netflix, Spotify, Dropbox, and research organizations like Los Alamos National Laboratory and Max Planck Society. Migration efforts from the earlier major line were coordinated across foundations and corporations, influenced by case studies from Instagram, Pinterest, and Reddit demonstrating transition strategies, testing practices popularized by Travis CI and Jenkins, and performance tuning referencing work from Intel and AMD.
Version history and release schedule
Python 3's release cadence followed a managed schedule with major releases designated by the Python Enhancement Proposal process and supported by organizations such as the Python Software Foundation and platform vendors like Ubuntu and Red Hat Enterprise Linux. Maintenance and long-term support decisions involved collaboration with corporations including Microsoft and community groups organizing backporting for distributions maintained by Debian and Fedora Project. Notable releases were accompanied by discussion at PyCon, including transition guidance referenced in industry reports from Gartner and adoption analyses appearing in publications by IEEE and ACM.