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https://dev.to/rohit_gavali_0c2ad84fe4e0 | Rohit Gavali - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Follow User actions Rohit Gavali 404 bio not found Joined Joined on Aug 11, 2025 More info about @rohit_gavali_0c2ad84fe4e0 Badges 1 Week Community Wellness Streak For actively engaging with the community by posting at least 2 comments in a single week. Got it Close Writing Debut Awarded for writing and sharing your first DEV post! Continue sharing your work to earn the 4 Week Writing Streak Badge. Got it Close Post 65 posts published Comment 12 comments written Tag 0 tags followed Image Generation APIs Compared. DALL·E 3 vs SD 3.5 vs Ideogram in Production Rohit Gavali Rohit Gavali Rohit Gavali Follow Jan 12 Image Generation APIs Compared. DALL·E 3 vs SD 3.5 vs Ideogram in Production # discuss # comparision Comments Add Comment 9 min read Want to connect with Rohit Gavali? 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Sign in Why AI Breaks Down in Long-Lived Systems (And What Devs Miss) Rohit Gavali Rohit Gavali Rohit Gavali Follow Jan 9 Why AI Breaks Down in Long-Lived Systems (And What Devs Miss) # webdev # programming # ai Comments Add Comment 8 min read What Happened When I Let AI Handle My Debugging Sessions Rohit Gavali Rohit Gavali Rohit Gavali Follow Jan 6 What Happened When I Let AI Handle My Debugging Sessions # webdev # programming # ai Comments Add Comment 9 min read Lessons from running the same debugging prompt through different AI systems Rohit Gavali Rohit Gavali Rohit Gavali Follow Dec 23 '25 Lessons from running the same debugging prompt through different AI systems # webdev # programming # ai Comments Add Comment 8 min read How AI Explains Code Correctly but Misses Architectural Context Rohit Gavali Rohit Gavali Rohit Gavali Follow Dec 22 '25 How AI Explains Code Correctly but Misses Architectural Context # webdev # programming # ai Comments Add Comment 8 min read Lessons From Trusting AI Too Early in Production Rohit Gavali Rohit Gavali Rohit Gavali Follow Dec 19 '25 Lessons From Trusting AI Too Early in Production # webdev # programming # ai Comments Add Comment 8 min read What I Learned After Removing Guardrails From an AI Workflow Rohit Gavali Rohit Gavali Rohit Gavali Follow Dec 18 '25 What I Learned After Removing Guardrails From an AI Workflow # webdev # programming # ai # learning 7 reactions Comments 6 comments 9 min read Lessons From Debugging AI Reasoning Errors in Production Rohit Gavali Rohit Gavali Rohit Gavali Follow Dec 17 '25 Lessons From Debugging AI Reasoning Errors in Production # webdev # programming # ai Comments Add Comment 12 min read A Simple Framework for Understanding Complex Code Faster Rohit Gavali Rohit Gavali Rohit Gavali Follow Dec 12 '25 A Simple Framework for Understanding Complex Code Faster # webdev # programming # ai 1 reaction Comments 2 comments 9 min read Three Steps To Becoming a More Focused Coder Rohit Gavali Rohit Gavali Rohit Gavali Follow Dec 5 '25 Three Steps To Becoming a More Focused Coder # webdev # ai # programming # developer Comments Add Comment 8 min read The Git Commit Messages That Reveal A Developer’s Thinking Rohit Gavali Rohit Gavali Rohit Gavali Follow Dec 3 '25 The Git Commit Messages That Reveal A Developer’s Thinking # ai # webdev # programming # productivity 3 reactions Comments Add Comment 7 min read How Developers Work Faster With Multi-Model Tools Rohit Gavali Rohit Gavali Rohit Gavali Follow Nov 25 '25 How Developers Work Faster With Multi-Model Tools # webdev # ai # programming Comments Add Comment 8 min read Lessons from Shipping with AI Tools but Avoiding Tool Sprawl Rohit Gavali Rohit Gavali Rohit Gavali Follow Nov 14 '25 Lessons from Shipping with AI Tools but Avoiding Tool Sprawl # webdev # ai # programming Comments Add Comment 8 min read Code Never Lies, It Just Mirrors the Coder Rohit Gavali Rohit Gavali Rohit Gavali Follow Nov 6 '25 Code Never Lies, It Just Mirrors the Coder # webdev # ai # programming Comments Add Comment 8 min read Why Logic Isn't as Objective as You Think Rohit Gavali Rohit Gavali Rohit Gavali Follow Nov 5 '25 Why Logic Isn't as Objective as You Think # discuss # webdev # ai # programming 2 reactions Comments 1 comment 8 min read Why Developers Should Study Human Behavior Before Code Rohit Gavali Rohit Gavali Rohit Gavali Follow Nov 4 '25 Why Developers Should Study Human Behavior Before Code # webdev # ai # programming 11 reactions Comments 6 comments 8 min read The Hardest Bug to Fix Is Ambiguity Rohit Gavali Rohit Gavali Rohit Gavali Follow Nov 3 '25 The Hardest Bug to Fix Is Ambiguity # webdev # programming # ai # devbugsmash Comments Add Comment 8 min read The 3-Step System to Learn Any Framework Fast Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 31 '25 The 3-Step System to Learn Any Framework Fast # webdev # programming # ai # learning Comments Add Comment 8 min read The Hardest Bug to Fix Is a Misaligned Mindset Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 30 '25 The Hardest Bug to Fix Is a Misaligned Mindset # webdev # programming # ai # developers 2 reactions Comments Add Comment 10 min read Why I Replaced My Dev Stack With One AI Workspace Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 27 '25 Why I Replaced My Dev Stack With One AI Workspace # programming # ai # webdev 6 reactions Comments Add Comment 10 min read Why Developers Need to Design for Empathy, Not Efficiency Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 17 '25 Why Developers Need to Design for Empathy, Not Efficiency # webdev # programming # ai # developer 2 reactions Comments Add Comment 9 min read Design Patterns for a Multi-Agent Future Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 16 '25 Design Patterns for a Multi-Agent Future # webdev # programming # ai 1 reaction Comments Add Comment 9 min read The Next Developer Superpower: Building Cognitive Interfaces Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 15 '25 The Next Developer Superpower: Building Cognitive Interfaces # webdev # programming # ai # development 3 reactions Comments Add Comment 9 min read The Hardest Part of AI Is Not the Model, It’s the Map Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 13 '25 The Hardest Part of AI Is Not the Model, It’s the Map # webdev # programming # ai 3 reactions Comments Add Comment 10 min read Every Line of Code Is a Philosophy in Disguise Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 10 '25 Every Line of Code Is a Philosophy in Disguise # webdev # programming # ai # coding 2 reactions Comments Add Comment 9 min read Patterns That Will Define the Next Generation of Developers Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 9 '25 Patterns That Will Define the Next Generation of Developers # webdev # programming # ai # developer 3 reactions Comments Add Comment 10 min read Abstraction Is the New Literacy for Developers Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 8 '25 Abstraction Is the New Literacy for Developers # webdev # programming # ai # developers 6 reactions Comments Add Comment 9 min read The Future of Code Is Negotiation, Not Instruction. Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 7 '25 The Future of Code Is Negotiation, Not Instruction. # webdev # programming # ai 1 reaction Comments Add Comment 7 min read Systems That Self-Heal: The Future of Developer Autonomy Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 6 '25 Systems That Self-Heal: The Future of Developer Autonomy # webdev # programming # ai 1 reaction Comments Add Comment 7 min read Why Abstractions Matter More Than Models Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 3 '25 Why Abstractions Matter More Than Models # architecture # softwareengineering # ai # llm 2 reactions Comments 1 comment 6 min read The Skill Developers Need Beyond Code: Orchestration Rohit Gavali Rohit Gavali Rohit Gavali Follow Oct 1 '25 The Skill Developers Need Beyond Code: Orchestration # webdev # programming # ai 1 reaction Comments Add Comment 7 min read The Death of Expertise (And How To Stay Relevant) Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 30 '25 The Death of Expertise (And How To Stay Relevant) # webdev # programming # ai 2 reactions Comments Add Comment 6 min read Why AI Infrastructure Matters More Than AI Models Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 29 '25 Why AI Infrastructure Matters More Than AI Models # programming # ai # opensource # softwareengineering 1 reaction Comments Add Comment 7 min read Continuous Integration for Intelligence: Beyond CI/CD Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 26 '25 Continuous Integration for Intelligence: Beyond CI/CD # programming # webdev # ai 2 reactions Comments 1 comment 7 min read The Compiler of the Future Is Context, Not Code Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 25 '25 The Compiler of the Future Is Context, Not Code # webdev # programming # ai 1 reaction Comments Add Comment 8 min read Designing Resilient Systems When Your Tools Keep Changing Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 24 '25 Designing Resilient Systems When Your Tools Keep Changing # programming # webdev # ai 1 reaction Comments Add Comment 7 min read Why I Deleted 10,000 Lines of Code and My Manager Promoted Me Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 23 '25 Why I Deleted 10,000 Lines of Code and My Manager Promoted Me # webdev # programming # ai # softwaredevelopment 1 reaction Comments Add Comment 7 min read Why Senior Developers Document Differently Than Juniors Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 22 '25 Why Senior Developers Document Differently Than Juniors # discuss # webdev # ai # productivity 1 reaction Comments Add Comment 7 min read The 90-Day Coding Routine That Made Me Think Like An Architect Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 19 '25 The 90-Day Coding Routine That Made Me Think Like An Architect # discuss # webdev # programming # ai 15 reactions Comments 11 comments 7 min read Your Favorite Framework Won't Matter in 5 Years Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 12 '25 Your Favorite Framework Won't Matter in 5 Years # discuss # webdev # programming # ai 1 reaction Comments Add Comment 6 min read The Bug That Taught Me More Than Any Tutorial Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 9 '25 The Bug That Taught Me More Than Any Tutorial # discuss # webdev # programming # ai 1 reaction Comments Add Comment 6 min read The Single Coding Habit That Separates Seniors From Juniors Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 8 '25 The Single Coding Habit That Separates Seniors From Juniors # discuss # programming # ai # career 1 reaction Comments Add Comment 6 min read Why Learning in Public Beats Learning Alone Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 4 '25 Why Learning in Public Beats Learning Alone # discuss # programming # ai # learning 3 reactions Comments 3 comments 6 min read From Syntax to Systems: Rethinking How Developers Use AI Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 3 '25 From Syntax to Systems: Rethinking How Developers Use AI # discuss # webdev # programming # ai 1 reaction Comments Add Comment 7 min read The Feature I Almost Abandoned (That Users Loved Most) Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 2 '25 The Feature I Almost Abandoned (That Users Loved Most) # discuss # webdev # programming # ai 4 reactions Comments 2 comments 6 min read Why Chasing Frameworks Won't Make You a Senior Developer Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 2 '25 Why Chasing Frameworks Won't Make You a Senior Developer # discuss # webdev # programming # ai 1 reaction Comments Add Comment 6 min read How to Code Smarter Than 90% of Developers Rohit Gavali Rohit Gavali Rohit Gavali Follow Sep 1 '25 How to Code Smarter Than 90% of Developers 1 reaction Comments Add Comment 5 min read The One-Line Fix That Saved Me Hours of Debugging Rohit Gavali Rohit Gavali Rohit Gavali Follow Aug 29 '25 The One-Line Fix That Saved Me Hours of Debugging # webdev # programming # ai 2 reactions Comments Add Comment 5 min read Why 'Clean Code' Is Harder Than It Sounds Rohit Gavali Rohit Gavali Rohit Gavali Follow Aug 27 '25 Why 'Clean Code' Is Harder Than It Sounds 2 reactions Comments Add Comment 8 min read Why Clean Code Feels Invisible (And That's the Point) Rohit Gavali Rohit Gavali Rohit Gavali Follow Aug 27 '25 Why Clean Code Feels Invisible (And That's the Point) # webdev # programming # ai # development 1 reaction Comments Add Comment 8 min read Why Most Code Reviews Fail (And How to Fix Them) Rohit Gavali Rohit Gavali Rohit Gavali Follow Aug 26 '25 Why Most Code Reviews Fail (And How to Fix Them) # webdev # programming # ai # learning 1 reaction Comments Add Comment 6 min read The Bug That Taught Me More Than Any Tutorial Rohit Gavali Rohit Gavali Rohit Gavali Follow Aug 25 '25 The Bug That Taught Me More Than Any Tutorial # discuss # webdev # programming # ai 1 reaction Comments Add Comment 9 min read Why I Treat My Git Commits Like a Personal Journal Rohit Gavali Rohit Gavali Rohit Gavali Follow Aug 22 '25 Why I Treat My Git Commits Like a Personal Journal # discuss # webdev # programming # ai 1 reaction Comments Add Comment 6 min read The Workflow That Turns Side Projects Into Learning Machines Rohit Gavali Rohit Gavali Rohit Gavali Follow Aug 21 '25 The Workflow That Turns Side Projects Into Learning Machines # discuss # webdev # programming # ai 5 reactions Comments Add Comment 7 min read The Testing Habit That Prevents Last-Minute Surprises Rohit Gavali Rohit Gavali Rohit Gavali Follow Aug 20 '25 The Testing Habit That Prevents Last-Minute Surprises # discuss # webdev # programming # ai 1 reaction Comments Add Comment 7 min read The 4-Hour Weekly Workflow That Keeps My Codebase Clean Rohit Gavali Rohit Gavali Rohit Gavali Follow Aug 19 '25 The 4-Hour Weekly Workflow That Keeps My Codebase Clean # discuss # webdev # programming # ai 2 reactions Comments Add Comment 6 min read The Code Review Approach That Catches More Than Bugs Rohit Gavali Rohit Gavali Rohit Gavali Follow Aug 19 '25 The Code Review Approach That Catches More Than Bugs # discuss # webdev # programming # ai 1 reaction Comments Add Comment 6 min read How to Redesign Your Life Like a Website Rohit Gavali Rohit Gavali Rohit Gavali Follow Aug 18 '25 How to Redesign Your Life Like a Website # discuss # webdev # programming # ai 1 reaction Comments Add Comment 6 min read The Small Change That Made My Pull Requests Clearer Rohit Gavali Rohit Gavali Rohit Gavali Follow Aug 18 '25 The Small Change That Made My Pull Requests Clearer # discuss # webdev # programming # ai 1 reaction Comments Add Comment 7 min read From Code to Docs: One Workflow That Does It All Rohit Gavali Rohit Gavali Rohit Gavali Follow Aug 15 '25 From Code to Docs: One Workflow That Does It All # discuss # programming # webdev # ai 1 reaction Comments Add Comment 6 min read loading... 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://docs.python.org/whatsnew/index.html | What’s New in Python — Python 3.14.2 documentation Theme Auto Light Dark Previous topic Python Documentation contents Next topic What’s new in Python 3.14 This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » What’s New in Python | Theme Auto Light Dark | What’s New in Python ¶ The “What’s New in Python” series of essays takes tours through the most important changes between major Python versions. They are a “must read” for anyone wishing to stay up-to-date after a new release. What’s new in Python 3.14 Summary – Release highlights New features Other language changes New modules Improved modules Optimizations Removed Deprecated CPython bytecode changes C API changes Build changes Porting to Python 3.14 Notable changes in 3.14.1 What’s New In Python 3.13 Summary – Release Highlights New Features Other Language Changes New Modules Improved Modules Optimizations Removed Modules And APIs New Deprecations CPython Bytecode Changes C API Changes Build Changes Porting to Python 3.13 Regression Test Changes What’s New In Python 3.12 Summary – Release highlights New Features New Features Related to Type Hints Other Language Changes New Modules Improved Modules Optimizations CPython bytecode changes Demos and Tools Deprecated Removed Porting to Python 3.12 Build Changes C API Changes What’s New In Python 3.11 Summary – Release highlights New Features New Features Related to Type Hints Other Language Changes Other CPython Implementation Changes New Modules Improved Modules Optimizations Faster CPython CPython bytecode changes Deprecated Pending Removal in Python 3.12 Removed Porting to Python 3.11 Build Changes C API Changes Notable changes in 3.11.4 Notable changes in 3.11.5 What’s New In Python 3.10 Summary – Release highlights New Features New Features Related to Type Hints Other Language Changes New Modules Improved Modules Optimizations Deprecated Removed Porting to Python 3.10 CPython bytecode changes Build Changes C API Changes Notable security feature in 3.10.7 Notable security feature in 3.10.8 Notable changes in 3.10.12 What’s New In Python 3.9 Summary – Release highlights You should check for DeprecationWarning in your code New Features Other Language Changes New Modules Improved Modules Optimizations Deprecated Removed Porting to Python 3.9 Build Changes C API Changes Notable changes in Python 3.9.1 Notable changes in Python 3.9.2 Notable changes in Python 3.9.3 Notable changes in Python 3.9.5 Notable security feature in 3.9.14 Notable changes in 3.9.17 What’s New In Python 3.8 Summary – Release highlights New Features Other Language Changes New Modules Improved Modules Optimizations Build and C API Changes Deprecated API and Feature Removals Porting to Python 3.8 Notable changes in Python 3.8.1 Notable changes in Python 3.8.2 Notable changes in Python 3.8.3 Notable changes in Python 3.8.8 Notable changes in Python 3.8.9 Notable changes in Python 3.8.10 Notable changes in Python 3.8.10 Notable changes in Python 3.8.12 Notable security feature in 3.8.14 Notable changes in 3.8.17 What’s New In Python 3.7 Summary – Release Highlights New Features Other Language Changes New Modules Improved Modules C API Changes Build Changes Optimizations Other CPython Implementation Changes Deprecated Python Behavior Deprecated Python modules, functions and methods Deprecated functions and types of the C API Platform Support Removals API and Feature Removals Module Removals Windows-only Changes Porting to Python 3.7 Notable changes in Python 3.7.1 Notable changes in Python 3.7.2 Notable changes in Python 3.7.6 Notable changes in Python 3.7.10 Notable changes in Python 3.7.11 Notable security feature in 3.7.14 What’s New In Python 3.6 Summary – Release highlights New Features Other Language Changes New Modules Improved Modules Optimizations Build and C API Changes Other Improvements Deprecated Removed Porting to Python 3.6 Notable changes in Python 3.6.2 Notable changes in Python 3.6.4 Notable changes in Python 3.6.5 Notable changes in Python 3.6.7 Notable changes in Python 3.6.10 Notable changes in Python 3.6.13 Notable changes in Python 3.6.14 What’s New In Python 3.5 Summary – Release highlights New Features Other Language Changes New Modules Improved Modules Other module-level changes Optimizations Build and C API Changes Deprecated Removed Porting to Python 3.5 Notable changes in Python 3.5.4 What’s New In Python 3.4 Summary – Release Highlights New Features New Modules Improved Modules CPython Implementation Changes Deprecated Removed Porting to Python 3.4 Changed in 3.4.3 What’s New In Python 3.3 Summary – Release highlights PEP 405: Virtual Environments PEP 420: Implicit Namespace Packages PEP 3118: New memoryview implementation and buffer protocol documentation PEP 393: Flexible String Representation PEP 397: Python Launcher for Windows PEP 3151: Reworking the OS and IO exception hierarchy PEP 380: Syntax for Delegating to a Subgenerator PEP 409: Suppressing exception context PEP 414: Explicit Unicode literals PEP 3155: Qualified name for classes and functions PEP 412: Key-Sharing Dictionary PEP 362: Function Signature Object PEP 421: Adding sys.implementation Using importlib as the Implementation of Import Other Language Changes A Finer-Grained Import Lock Builtin functions and types New Modules Improved Modules Optimizations Build and C API Changes Deprecated Porting to Python 3.3 What’s New In Python 3.2 PEP 384: Defining a Stable ABI PEP 389: Argparse Command Line Parsing Module PEP 391: Dictionary Based Configuration for Logging PEP 3148: The concurrent.futures module PEP 3147: PYC Repository Directories PEP 3149: ABI Version Tagged .so Files PEP 3333: Python Web Server Gateway Interface v1.0.1 Other Language Changes New, Improved, and Deprecated Modules Multi-threading Optimizations Unicode Codecs Documentation IDLE Code Repository Build and C API Changes Porting to Python 3.2 What’s New In Python 3.1 PEP 372: Ordered Dictionaries PEP 378: Format Specifier for Thousands Separator Other Language Changes New, Improved, and Deprecated Modules Optimizations IDLE Build and C API Changes Porting to Python 3.1 What’s New In Python 3.0 Common Stumbling Blocks Overview Of Syntax Changes Changes Already Present In Python 2.6 Library Changes PEP 3101 : A New Approach To String Formatting Changes To Exceptions Miscellaneous Other Changes Build and C API Changes Performance Porting To Python 3.0 What’s New in Python 2.7 The Future for Python 2.x Changes to the Handling of Deprecation Warnings Python 3.1 Features PEP 372: Adding an Ordered Dictionary to collections PEP 378: Format Specifier for Thousands Separator PEP 389: The argparse Module for Parsing Command Lines PEP 391: Dictionary-Based Configuration For Logging PEP 3106: Dictionary Views PEP 3137: The memoryview Object Other Language Changes New and Improved Modules Build and C API Changes Other Changes and Fixes Porting to Python 2.7 New Features Added to Python 2.7 Maintenance Releases Acknowledgements What’s New in Python 2.6 Python 3.0 Changes to the Development Process PEP 343: The ‘with’ statement PEP 366: Explicit Relative Imports From a Main Module PEP 370: Per-user site-packages Directory PEP 371: The multiprocessing Package PEP 3101: Advanced String Formatting PEP 3105: print As a Function PEP 3110: Exception-Handling Changes PEP 3112: Byte Literals PEP 3116: New I/O Library PEP 3118: Revised Buffer Protocol PEP 3119: Abstract Base Classes PEP 3127: Integer Literal Support and Syntax PEP 3129: Class Decorators PEP 3141: A Type Hierarchy for Numbers Other Language Changes New and Improved Modules Deprecations and Removals Build and C API Changes Porting to Python 2.6 Acknowledgements What’s New in Python 2.5 PEP 308: Conditional Expressions PEP 309: Partial Function Application PEP 314: Metadata for Python Software Packages v1.1 PEP 328: Absolute and Relative Imports PEP 338: Executing Modules as Scripts PEP 341: Unified try/except/finally PEP 342: New Generator Features PEP 343: The ‘with’ statement PEP 352: Exceptions as New-Style Classes PEP 353: Using ssize_t as the index type PEP 357: The ‘__index__’ method Other Language Changes New, Improved, and Removed Modules Build and C API Changes Porting to Python 2.5 Acknowledgements What’s New in Python 2.4 PEP 218: Built-In Set Objects PEP 237: Unifying Long Integers and Integers PEP 289: Generator Expressions PEP 292: Simpler String Substitutions PEP 318: Decorators for Functions and Methods PEP 322: Reverse Iteration PEP 324: New subprocess Module PEP 327: Decimal Data Type PEP 328: Multi-line Imports PEP 331: Locale-Independent Float/String Conversions Other Language Changes New, Improved, and Deprecated Modules Build and C API Changes Porting to Python 2.4 Acknowledgements What’s New in Python 2.3 PEP 218: A Standard Set Datatype PEP 255: Simple Generators PEP 263: Source Code Encodings PEP 273: Importing Modules from ZIP Archives PEP 277: Unicode file name support for Windows NT PEP 278: Universal Newline Support PEP 279: enumerate() PEP 282: The logging Package PEP 285: A Boolean Type PEP 293: Codec Error Handling Callbacks PEP 301: Package Index and Metadata for Distutils PEP 302: New Import Hooks PEP 305: Comma-separated Files PEP 307: Pickle Enhancements Extended Slices Other Language Changes New, Improved, and Deprecated Modules Pymalloc: A Specialized Object Allocator Build and C API Changes Other Changes and Fixes Porting to Python 2.3 Acknowledgements What’s New in Python 2.2 Introduction PEPs 252 and 253: Type and Class Changes PEP 234: Iterators PEP 255: Simple Generators PEP 237: Unifying Long Integers and Integers PEP 238: Changing the Division Operator Unicode Changes PEP 227: Nested Scopes New and Improved Modules Interpreter Changes and Fixes Other Changes and Fixes Acknowledgements What’s New in Python 2.1 Introduction PEP 227: Nested Scopes PEP 236: __future__ Directives PEP 207: Rich Comparisons PEP 230: Warning Framework PEP 229: New Build System PEP 205: Weak References PEP 232: Function Attributes PEP 235: Importing Modules on Case-Insensitive Platforms PEP 217: Interactive Display Hook PEP 208: New Coercion Model PEP 241: Metadata in Python Packages New and Improved Modules Other Changes and Fixes Acknowledgements What’s New in Python 2.0 Introduction What About Python 1.6? New Development Process Unicode List Comprehensions Augmented Assignment String Methods Garbage Collection of Cycles Other Core Changes Porting to 2.0 Extending/Embedding Changes Distutils: Making Modules Easy to Install XML Modules Module changes New modules IDLE Improvements Deleted and Deprecated Modules Acknowledgements The “Changelog” is an HTML version of the file built from the contents of the Misc/NEWS.d directory tree, which contains all nontrivial changes to Python for the current version. Changelog Python next Python 3.14.2 final Python 3.14.1 final Python 3.14.0 final Python 3.14.0 release candidate 3 Python 3.14.0 release candidate 2 Python 3.14.0 release candidate 1 Python 3.14.0 beta 4 Python 3.14.0 beta 3 Python 3.14.0 beta 2 Python 3.14.0 beta 1 Python 3.14.0 alpha 7 Python 3.14.0 alpha 6 Python 3.14.0 alpha 5 Python 3.14.0 alpha 4 Python 3.14.0 alpha 3 Python 3.14.0 alpha 2 Python 3.14.0 alpha 1 Python 3.13.0 beta 1 Python 3.13.0 alpha 6 Python 3.13.0 alpha 5 Python 3.13.0 alpha 4 Python 3.13.0 alpha 3 Python 3.13.0 alpha 2 Python 3.13.0 alpha 1 Python 3.12.0 beta 1 Python 3.12.0 alpha 7 Python 3.12.0 alpha 6 Python 3.12.0 alpha 5 Python 3.12.0 alpha 4 Python 3.12.0 alpha 3 Python 3.12.0 alpha 2 Python 3.12.0 alpha 1 Python 3.11.0 beta 1 Python 3.11.0 alpha 7 Python 3.11.0 alpha 6 Python 3.11.0 alpha 5 Python 3.11.0 alpha 4 Python 3.11.0 alpha 3 Python 3.11.0 alpha 2 Python 3.11.0 alpha 1 Python 3.10.0 beta 1 Python 3.10.0 alpha 7 Python 3.10.0 alpha 6 Python 3.10.0 alpha 5 Python 3.10.0 alpha 4 Python 3.10.0 alpha 3 Python 3.10.0 alpha 2 Python 3.10.0 alpha 1 Python 3.9.0 beta 1 Python 3.9.0 alpha 6 Python 3.9.0 alpha 5 Python 3.9.0 alpha 4 Python 3.9.0 alpha 3 Python 3.9.0 alpha 2 Python 3.9.0 alpha 1 Python 3.8.0 beta 1 Python 3.8.0 alpha 4 Python 3.8.0 alpha 3 Python 3.8.0 alpha 2 Python 3.8.0 alpha 1 Python 3.7.0 final Python 3.7.0 release candidate 1 Python 3.7.0 beta 5 Python 3.7.0 beta 4 Python 3.7.0 beta 3 Python 3.7.0 beta 2 Python 3.7.0 beta 1 Python 3.7.0 alpha 4 Python 3.7.0 alpha 3 Python 3.7.0 alpha 2 Python 3.7.0 alpha 1 Python 3.6.6 final Python 3.6.6 release candidate 1 Python 3.6.5 final Python 3.6.5 release candidate 1 Python 3.6.4 final Python 3.6.4 release candidate 1 Python 3.6.3 final Python 3.6.3 release candidate 1 Python 3.6.2 final Python 3.6.2 release candidate 2 Python 3.6.2 release candidate 1 Python 3.6.1 final Python 3.6.1 release candidate 1 Python 3.6.0 final Python 3.6.0 release candidate 2 Python 3.6.0 release candidate 1 Python 3.6.0 beta 4 Python 3.6.0 beta 3 Python 3.6.0 beta 2 Python 3.6.0 beta 1 Python 3.6.0 alpha 4 Python 3.6.0 alpha 3 Python 3.6.0 alpha 2 Python 3.6.0 alpha 1 Python 3.5.5 final Python 3.5.5 release candidate 1 Python 3.5.4 final Python 3.5.4 release candidate 1 Python 3.5.3 final Python 3.5.3 release candidate 1 Python 3.5.2 final Python 3.5.2 release candidate 1 Python 3.5.1 final Python 3.5.1 release candidate 1 Python 3.5.0 final Python 3.5.0 release candidate 4 Python 3.5.0 release candidate 3 Python 3.5.0 release candidate 2 Python 3.5.0 release candidate 1 Python 3.5.0 beta 4 Python 3.5.0 beta 3 Python 3.5.0 beta 2 Python 3.5.0 beta 1 Python 3.5.0 alpha 4 Python 3.5.0 alpha 3 Python 3.5.0 alpha 2 Python 3.5.0 alpha 1 Previous topic Python Documentation contents Next topic What’s new in Python 3.14 This page Report a bug Show source « Navigation index modules | next | previous | Python » 3.14.2 Documentation » What’s New in Python | Theme Auto Light Dark | © Copyright 2001 Python Software Foundation. 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https://docs.suprsend.com/docs/web-push | Web Push Template - SuprSend, Notification infrastructure for Product teams Skip to main content SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Community Trust Center Platform Status Postman Collection GETTING STARTED What is SuprSend? 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Navigation Channel Editors Web Push Template Documentation API Reference Management API CLI Reference Developer Resources Changelog Documentation API Reference Management API CLI Reference Developer Resources Changelog Channel Editors Web Push Template OpenAI Open in ChatGPT How to design Webpush template with customisation options to add action buttons and image. OpenAI Open in ChatGPT Design Template You can design template with a simple form editor tool. You can add variables with Handlebarsjs language. You can check how the message will look in the preview section on the right side. Once designed, you can save the web push notification template by clicking on Save Draft. When you are ready, you can Publish Draft by providing a name to the version. This will become the Live version, and will be used whenever the associated workflow is triggered. Web Push notification fields description Field Description Title Small message text box. Note that this field will be displayed in single line only, and very long content can get curtailed. Use handlebarsjs to add variables. Large Icon The small icon will show up to the right of the notification text. SuprSend puts your organisation logo as default in the large icon, which you can set from ‘Organisations Tab’. This will be shown by default and cannot be changed. Message Large message text box. Use handlebarsjs to add variables. Image Optional Recommended banner filetypes are PNG , JPG , JPEG . SuprSend will auto-scale your image so that it doesn’t get cropped. Action URL Provide a URL where a user will go when he clicks on the push notification. Use handlebarsjs to add variables. Action Buttons Optional Enter up to 3 Button names and URL. You can use variable names using handlebarsjs in both action name and URL. You can give your android deeplink URL as well. The action button name color is picked up from your organisation settings. You cannot change button color in a template once it is created. Adding dynamic content in Web Push There will always be the case where you would be required to add dynamic content to a template, so as to personalise it for your users. To achieve this, you can add variables in the template, which will be replaced with the dynamic content at the time of sending push. To send actual values to replace variables at the time of communication trigger, use one of our frontend or backend SDKs. Here is a step by step guide on how to add dynamic content in web push: 1 Declaring Variables in the global 'Variables' button: If you are at this stage, it is assumed that you have declared the variables along with sample values in the global Mock data button. To see how to declare variables before using them in designing templates, refer to this section in the Templates documentation . 2 Using variables in the templates: Once the variables are declared, you can use them while designing the web push template. We support handlebarsjs to add variables in the template. As a general rule, all the variables have to be entered within double curly brackets: {{variable_name}} If you have declared the variables in the global ‘Variables’ button, then they will come as auto-suggestions when you type a curly bracket { . This will remove the chances of error like variable mismatch at the time of template rendering. Note that you will be able to enter a variable name even when you have not declared it inside the Variables button. To manually enter the variable name, follow the handlerbarsjs guide here . Below are some examples of how to enter variables in the template design. For illustration, we are using the same sample variable names that we declared in the Templates section: json Copy Ask AI { "array" : [ { "product_name" : "Aldo Sling Bag" , "product_price" : "3,950.00" }, { "product_name" : "Clarles & Keith Women Slipper, Biege, 38UK" , "product_price" : "2,549.00" }, { "product_name" : "RayBan Sunglasses" , "product_price" : "7,899.00" } ], "event" : { "location" : { "city" : "Bangalore" , "state" : "KA" }, "order_id" : "11200123" , "first_name" : "Nikita" }, "product_page" : "https://www.suprsend.com" } To enter a nested variable, enter in the format {{var1.var2.var3}} . Eg. to refer to city in the example above, you need to enter {{event.location.city}} To refer to an array element, enter in format {{var1.[index].var2}} . Eg. to refer to product_name of the first element of the array array , enter {{array.[0].product_name}} If you have any space in the variable name, enclose it in square bracket {{event.[first name]}} You will be able to see the sample values in the Preview section, as well as in the Live version when you publish a draft. If you cannot see your variable being rendered with the sample value, check one of the following: Make sure you have entered the variable name and the sample value in the Variables button. Make sure you have entered the correct variable name in the template, as per the handlebarsjs guideline. What happens if there is variable mismatch at the time of sending? At the time of sending communication, if there is a variable present in the template whose value is not rendered due to mismatch or missing, SuprSend will simply discard the template and not send that particular notification to your user. Please note that the rest of the templates will be sent. Eg. if there is an error in rendering Web Push template, but email template is successfully rendered, Web Push notification will not be triggered, but email notification will be triggered by SuprSend. Was this page helpful? Yes No Suggest edits Raise issue Previous Slack Template How to design Slack templates using text editor or JSONNET editor for rich block kit templates. Next ⌘ I x github linkedin youtube Powered by On this page Design Template Web Push notification fields description Adding dynamic content in Web Push | 2026-01-13T08:49:02 |
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ANAWAR HOSSAIN MD. ANAWAR HOSSAIN MD. ANAWAR HOSSAIN Follow Dec 15 '25 Profile # android # java # career # kotlin Comments Add Comment 2 min read Eleva tu app Android: patrones de arquitectura con Jetpack Compose Marlon López Marlon López Marlon López Follow Dec 14 '25 Eleva tu app Android: patrones de arquitectura con Jetpack Compose # android # architecture # kotlin Comments Add Comment 2 min read What is produceState in Jetpack Compose? Real Use Cases, Examples & Best Practices Kamaldeep Kakkar Kamaldeep Kakkar Kamaldeep Kakkar Follow Dec 8 '25 What is produceState in Jetpack Compose? 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Iñaki Villar Iñaki Villar Iñaki Villar Follow Dec 30 '25 What Happens When You Kill the Kotlin Daemon Before R8? # android # gradle # kotlin 1 reaction Comments Add Comment 8 min read Kotlin vs. Scala in 2026: The JVM Battle That Can Make or Break Your Next App CHILLICODE CHILLICODE CHILLICODE Follow Dec 4 '25 Kotlin vs. Scala in 2026: The JVM Battle That Can Make or Break Your Next App # kotlin # scala Comments Add Comment 4 min read The Experiment Hussain Nazary Hussain Nazary Hussain Nazary Follow Dec 5 '25 The Experiment # ai # programming # productivity # kotlin Comments Add Comment 3 min read BUILD YOUR FIRST SPRING BOOT(KOTLIN) BACK-END prabhu prabhu prabhu Follow Dec 3 '25 BUILD YOUR FIRST SPRING BOOT(KOTLIN) BACK-END # kotlin # springboot # backenddevelopment Comments Add Comment 8 min read KToon: Tiny Tables, Big Savings Plug TOON into Your @Serializable Kotlin Classes Joseph Sanjaya Joseph Sanjaya Joseph Sanjaya Follow Dec 4 '25 KToon: Tiny Tables, Big Savings Plug TOON into Your @Serializable Kotlin Classes # kiro # kotlin # android # development Comments Add Comment 6 min read From 300 Lines of Pain to Maintainable Glory: Your Complete Gradle Modularization Guide Livio Lopez Livio Lopez Livio Lopez Follow Jan 6 From 300 Lines of Pain to Maintainable Glory: Your Complete Gradle Modularization Guide # architecture # kotlin # productivity 1 reaction Comments Add Comment 12 min read How I built a Serverless, P2P Parental Control app using WebRTC and Kotlin Francesco Costantino Francesco Costantino Francesco Costantino Follow Dec 6 '25 How I built a Serverless, P2P Parental Control app using WebRTC and Kotlin # android # kotlin # webrtc # privacy Comments Add Comment 3 min read Channels in Kotlin Coroutines: A Complete Q&A Guide for Android Apps Kamaldeep Kakkar Kamaldeep Kakkar Kamaldeep Kakkar Follow Dec 6 '25 Channels in Kotlin Coroutines: A Complete Q&A Guide for Android Apps # android # kotlin # androiddev # mobile 1 reaction Comments Add Comment 1 min read Bachmanity Bytes: A Nod to Silicon Valley (And an Excuse to Mess Around with Kotlin) Perry H Perry H Perry H Follow Dec 7 '25 Bachmanity Bytes: A Nod to Silicon Valley (And an Excuse to Mess Around with Kotlin) # jvm # kotlin # ai # programming 2 reactions Comments Add Comment 4 min read Stop wrapping your RabbitMQ code in runBlocking Nathan Fallet Nathan Fallet Nathan Fallet Follow Dec 1 '25 Stop wrapping your RabbitMQ code in runBlocking # designpatterns # java # kotlin # performance 1 reaction Comments Add Comment 4 min read How to Fix Slow XML Parsing in React Native Mike Ouroumis Mike Ouroumis Mike Ouroumis Follow Nov 29 '25 How to Fix Slow XML Parsing in React Native # reactnative # performance # kotlin # javascript Comments Add Comment 2 min read Failures we don't model correctly Vadym Yaroshchuk Vadym Yaroshchuk Vadym Yaroshchuk Follow Dec 29 '25 Failures we don't model correctly # kotlin # codequality # architecture # programming 15 reactions Comments Add Comment 17 min read Lambda's in Kotlin Jacob Jerrell Jacob Jerrell Jacob Jerrell Follow Nov 24 '25 Lambda's in Kotlin # kotlin # java # android # tutorial Comments Add Comment 5 min read loading... 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Boga Posted on Jan 12 Odoo Core and the Cost of Reinventing Everything # python # odoo # qweb # owl Hello, this is my first blog post ever. I’d like to share my experience working with Odoo , an open-source Enterprise Resource Planning (ERP) system, and explain why I believe many of its architectural choices cause unnecessary complexity. Odoo is a single platform that provides many prebuilt modules (mini-applications) that most companies need. For example, almost every company requires a Human Resources system to manage employee details, leaves, attendance, contracts, resignations, and more. Beyond HR, companies also need purchasing, inventory, accounting, authentication, authorization, and other systems. Odoo bundles all of these tightly coupled systems into a single installation. On paper, this sounds great — and from a business perspective, it often is. From a technical perspective , however, things get complicated very quickly. Odoo Core Components Below are the main Odoo components, ranked from least complex to most complex, and all largely developed in-house instead of relying on existing mature frameworks: Odoo HTTP Layer JSON-RPC Website routing Odoo Views XML transformed into Python and JavaScript Odoo ORM Custom inheritance system Query builder Dependency injection Caching layers Cache System Implemented from scratch WebSocket Implementation Very low-level handling Odoo HTTP Layer Odoo is not built on a standard Python web framework like Django or Flask. Instead, it implements its own HTTP framework on top of Werkzeug (a WSGI utility library). This HTTP layer introduces its own abstractions, request lifecycle, routing, and serialization logic, including JSON-RPC and website controllers. While technically impressive, it reinvents many problems that have already been solved — and battle-tested — by existing frameworks. Odoo Views In my opinion, this is one of the most problematic parts of Odoo. Instead of using standard frontend technologies, Odoo relies heavily on XML-based views . These XML files are sent to the browser and then transformed using Abstract Syntax Tree (AST) analysis into JavaScript. In other contexts (like the website), the XML may be converted into Python code and sometimes back into JavaScript again. This creates: High cognitive overhead Difficult debugging Tight coupling between backend and frontend Poor tooling support compared to modern frontend stacks It feels like building a car from raw metal just to drive from point A to point B. Odoo ORM Odoo’s ORM is not a typical ORM. It implements: A custom inheritance system (instead of using Python’s built-in one) Its own dependency injection mechanism A query builder Caching layers (LRU) Model extension via monkey-patching While powerful, this system is extremely complex and hard to reason about. Debugging model behavior often feels like navigating invisible layers of magic. WebSocket Implementation Instead of using a mature real-time framework, Odoo implements its WebSocket handling with very low-level logic, sometimes in surprisingly small and dense files. A single comment from the codebase summarizes this approach better than words ever could: The “Odoo Is Old” Argument A common defense of Odoo’s architecture is that “it’s an old system” — originally developed around 2005 using Python 2. However, this argument no longer holds. Odoo was largely rewritten from scratch around 2017 to support Python 3. At that time, many excellent frameworks already existed and had solved the same problems more cleanly, while continuing to evolve without breaking their ecosystems. Today, even small changes in Odoo’s core can break custom modules unless they are limited to simple CRUD models with minimal dependencies on core behavior. Final Thoughts Odoo is a powerful product and a successful business platform. But from a software engineering perspective, many of its design decisions prioritize control and internal consistency over maintainability, clarity, and developer experience . If you work with Odoo long enough, you stop asking “why does it work this way?” and start asking “how do I survive this upgrade?” Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Boga Follow Senior Software Engineer Joined Jan 12, 2026 Trending on DEV Community Hot 🧱 Beginner-Friendly Guide 'Maximal Rectangle' – LeetCode 85 (C++, Python, JavaScript) # programming # cpp # python # javascript The First Week at a Startup Taught Me More Than I Expected # startup # beginners # career # learning What was your win this week??? # weeklyretro # discuss 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV Forem — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://docs.python.org/3/faq/design.html | Design and History FAQ — Python 3.14.2 documentation Theme Auto Light Dark Table of Contents Design and History FAQ Why does Python use indentation for grouping of statements? Why am I getting strange results with simple arithmetic operations? Why are floating-point calculations so inaccurate? Why are Python strings immutable? Why must ‘self’ be used explicitly in method definitions and calls? Why can’t I use an assignment in an expression? Why does Python use methods for some functionality (e.g. list.index()) but functions for other (e.g. len(list))? Why is join() a string method instead of a list or tuple method? How fast are exceptions? Why isn’t there a switch or case statement in Python? Can’t you emulate threads in the interpreter instead of relying on an OS-specific thread implementation? Why can’t lambda expressions contain statements? Can Python be compiled to machine code, C or some other language? How does Python manage memory? Why doesn’t CPython use a more traditional garbage collection scheme? Why isn’t all memory freed when CPython exits? Why are there separate tuple and list data types? How are lists implemented in CPython? How are dictionaries implemented in CPython? Why must dictionary keys be immutable? Why doesn’t list.sort() return the sorted list? How do you specify and enforce an interface spec in Python? Why is there no goto? Why can’t raw strings (r-strings) end with a backslash? Why doesn’t Python have a “with” statement for attribute assignments? Why don’t generators support the with statement? Why are colons required for the if/while/def/class statements? Why does Python allow commas at the end of lists and tuples? Previous topic Programming FAQ Next topic Library and Extension FAQ This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » Python Frequently Asked Questions » Design and History FAQ | Theme Auto Light Dark | Design and History FAQ ¶ Contents Design and History FAQ Why does Python use indentation for grouping of statements? Why am I getting strange results with simple arithmetic operations? Why are floating-point calculations so inaccurate? Why are Python strings immutable? Why must ‘self’ be used explicitly in method definitions and calls? Why can’t I use an assignment in an expression? Why does Python use methods for some functionality (e.g. list.index()) but functions for other (e.g. len(list))? Why is join() a string method instead of a list or tuple method? How fast are exceptions? Why isn’t there a switch or case statement in Python? Can’t you emulate threads in the interpreter instead of relying on an OS-specific thread implementation? Why can’t lambda expressions contain statements? Can Python be compiled to machine code, C or some other language? How does Python manage memory? Why doesn’t CPython use a more traditional garbage collection scheme? Why isn’t all memory freed when CPython exits? Why are there separate tuple and list data types? How are lists implemented in CPython? How are dictionaries implemented in CPython? Why must dictionary keys be immutable? Why doesn’t list.sort() return the sorted list? How do you specify and enforce an interface spec in Python? Why is there no goto? Why can’t raw strings (r-strings) end with a backslash? Why doesn’t Python have a “with” statement for attribute assignments? Why don’t generators support the with statement? Why are colons required for the if/while/def/class statements? Why does Python allow commas at the end of lists and tuples? Why does Python use indentation for grouping of statements? ¶ Guido van Rossum believes that using indentation for grouping is extremely elegant and contributes a lot to the clarity of the average Python program. Most people learn to love this feature after a while. Since there are no begin/end brackets there cannot be a disagreement between grouping perceived by the parser and the human reader. Occasionally C programmers will encounter a fragment of code like this: if ( x <= y ) x ++ ; y -- ; z ++ ; Only the x++ statement is executed if the condition is true, but the indentation leads many to believe otherwise. Even experienced C programmers will sometimes stare at it a long time wondering as to why y is being decremented even for x > y . Because there are no begin/end brackets, Python is much less prone to coding-style conflicts. In C there are many different ways to place the braces. After becoming used to reading and writing code using a particular style, it is normal to feel somewhat uneasy when reading (or being required to write) in a different one. Many coding styles place begin/end brackets on a line by themselves. This makes programs considerably longer and wastes valuable screen space, making it harder to get a good overview of a program. Ideally, a function should fit on one screen (say, 20–30 lines). 20 lines of Python can do a lot more work than 20 lines of C. This is not solely due to the lack of begin/end brackets – the lack of declarations and the high-level data types are also responsible – but the indentation-based syntax certainly helps. Why am I getting strange results with simple arithmetic operations? ¶ See the next question. Why are floating-point calculations so inaccurate? ¶ Users are often surprised by results like this: >>> 1.2 - 1.0 0.19999999999999996 and think it is a bug in Python. It’s not. This has little to do with Python, and much more to do with how the underlying platform handles floating-point numbers. The float type in CPython uses a C double for storage. A float object’s value is stored in binary floating-point with a fixed precision (typically 53 bits) and Python uses C operations, which in turn rely on the hardware implementation in the processor, to perform floating-point operations. This means that as far as floating-point operations are concerned, Python behaves like many popular languages including C and Java. Many numbers that can be written easily in decimal notation cannot be expressed exactly in binary floating point. For example, after: >>> x = 1.2 the value stored for x is a (very good) approximation to the decimal value 1.2 , but is not exactly equal to it. On a typical machine, the actual stored value is: 1.0011001100110011001100110011001100110011001100110011 ( binary ) which is exactly: 1.1999999999999999555910790149937383830547332763671875 ( decimal ) The typical precision of 53 bits provides Python floats with 15–16 decimal digits of accuracy. For a fuller explanation, please see the floating-point arithmetic chapter in the Python tutorial. Why are Python strings immutable? ¶ There are several advantages. One is performance: knowing that a string is immutable means we can allocate space for it at creation time, and the storage requirements are fixed and unchanging. This is also one of the reasons for the distinction between tuples and lists. Another advantage is that strings in Python are considered as “elemental” as numbers. No amount of activity will change the value 8 to anything else, and in Python, no amount of activity will change the string “eight” to anything else. Why must ‘self’ be used explicitly in method definitions and calls? ¶ The idea was borrowed from Modula-3. It turns out to be very useful, for a variety of reasons. First, it’s more obvious that you are using a method or instance attribute instead of a local variable. Reading self.x or self.meth() makes it absolutely clear that an instance variable or method is used even if you don’t know the class definition by heart. In C++, you can sort of tell by the lack of a local variable declaration (assuming globals are rare or easily recognizable) – but in Python, there are no local variable declarations, so you’d have to look up the class definition to be sure. Some C++ and Java coding standards call for instance attributes to have an m_ prefix, so this explicitness is still useful in those languages, too. Second, it means that no special syntax is necessary if you want to explicitly reference or call the method from a particular class. In C++, if you want to use a method from a base class which is overridden in a derived class, you have to use the :: operator – in Python you can write baseclass.methodname(self, <argument list>) . This is particularly useful for __init__() methods, and in general in cases where a derived class method wants to extend the base class method of the same name and thus has to call the base class method somehow. Finally, for instance variables it solves a syntactic problem with assignment: since local variables in Python are (by definition!) those variables to which a value is assigned in a function body (and that aren’t explicitly declared global), there has to be some way to tell the interpreter that an assignment was meant to assign to an instance variable instead of to a local variable, and it should preferably be syntactic (for efficiency reasons). C++ does this through declarations, but Python doesn’t have declarations and it would be a pity having to introduce them just for this purpose. Using the explicit self.var solves this nicely. Similarly, for using instance variables, having to write self.var means that references to unqualified names inside a method don’t have to search the instance’s directories. To put it another way, local variables and instance variables live in two different namespaces, and you need to tell Python which namespace to use. Why can’t I use an assignment in an expression? ¶ Starting in Python 3.8, you can! Assignment expressions using the walrus operator := assign a variable in an expression: while chunk := fp . read ( 200 ): print ( chunk ) See PEP 572 for more information. Why does Python use methods for some functionality (e.g. list.index()) but functions for other (e.g. len(list))? ¶ As Guido said: (a) For some operations, prefix notation just reads better than postfix – prefix (and infix!) operations have a long tradition in mathematics which likes notations where the visuals help the mathematician thinking about a problem. Compare the easy with which we rewrite a formula like x*(a+b) into x*a + x*b to the clumsiness of doing the same thing using a raw OO notation. (b) When I read code that says len(x) I know that it is asking for the length of something. This tells me two things: the result is an integer, and the argument is some kind of container. To the contrary, when I read x.len(), I have to already know that x is some kind of container implementing an interface or inheriting from a class that has a standard len(). Witness the confusion we occasionally have when a class that is not implementing a mapping has a get() or keys() method, or something that isn’t a file has a write() method. — https://mail.python.org/pipermail/python-3000/2006-November/004643.html Why is join() a string method instead of a list or tuple method? ¶ Strings became much more like other standard types starting in Python 1.6, when methods were added which give the same functionality that has always been available using the functions of the string module. Most of these new methods have been widely accepted, but the one which appears to make some programmers feel uncomfortable is: ", " . join ([ '1' , '2' , '4' , '8' , '16' ]) which gives the result: "1, 2, 4, 8, 16" There are two common arguments against this usage. The first runs along the lines of: “It looks really ugly using a method of a string literal (string constant)”, to which the answer is that it might, but a string literal is just a fixed value. If the methods are to be allowed on names bound to strings there is no logical reason to make them unavailable on literals. The second objection is typically cast as: “I am really telling a sequence to join its members together with a string constant”. Sadly, you aren’t. For some reason there seems to be much less difficulty with having split() as a string method, since in that case it is easy to see that "1, 2, 4, 8, 16" . split ( ", " ) is an instruction to a string literal to return the substrings delimited by the given separator (or, by default, arbitrary runs of white space). join() is a string method because in using it you are telling the separator string to iterate over a sequence of strings and insert itself between adjacent elements. This method can be used with any argument which obeys the rules for sequence objects, including any new classes you might define yourself. Similar methods exist for bytes and bytearray objects. How fast are exceptions? ¶ A try / except block is extremely efficient if no exceptions are raised. Actually catching an exception is expensive. In versions of Python prior to 2.0 it was common to use this idiom: try : value = mydict [ key ] except KeyError : mydict [ key ] = getvalue ( key ) value = mydict [ key ] This only made sense when you expected the dict to have the key almost all the time. If that wasn’t the case, you coded it like this: if key in mydict : value = mydict [ key ] else : value = mydict [ key ] = getvalue ( key ) For this specific case, you could also use value = dict.setdefault(key, getvalue(key)) , but only if the getvalue() call is cheap enough because it is evaluated in all cases. Why isn’t there a switch or case statement in Python? ¶ In general, structured switch statements execute one block of code when an expression has a particular value or set of values. Since Python 3.10 one can easily match literal values, or constants within a namespace, with a match ... case statement. An older alternative is a sequence of if... elif... elif... else . For cases where you need to choose from a very large number of possibilities, you can create a dictionary mapping case values to functions to call. For example: functions = { 'a' : function_1 , 'b' : function_2 , 'c' : self . method_1 } func = functions [ value ] func () For calling methods on objects, you can simplify yet further by using the getattr() built-in to retrieve methods with a particular name: class MyVisitor : def visit_a ( self ): ... def dispatch ( self , value ): method_name = 'visit_' + str ( value ) method = getattr ( self , method_name ) method () It’s suggested that you use a prefix for the method names, such as visit_ in this example. Without such a prefix, if values are coming from an untrusted source, an attacker would be able to call any method on your object. Imitating switch with fallthrough, as with C’s switch-case-default, is possible, much harder, and less needed. Can’t you emulate threads in the interpreter instead of relying on an OS-specific thread implementation? ¶ Answer 1: Unfortunately, the interpreter pushes at least one C stack frame for each Python stack frame. Also, extensions can call back into Python at almost random moments. Therefore, a complete threads implementation requires thread support for C. Answer 2: Fortunately, there is Stackless Python , which has a completely redesigned interpreter loop that avoids the C stack. Why can’t lambda expressions contain statements? ¶ Python lambda expressions cannot contain statements because Python’s syntactic framework can’t handle statements nested inside expressions. However, in Python, this is not a serious problem. Unlike lambda forms in other languages, where they add functionality, Python lambdas are only a shorthand notation if you’re too lazy to define a function. Functions are already first class objects in Python, and can be declared in a local scope. Therefore the only advantage of using a lambda instead of a locally defined function is that you don’t need to invent a name for the function – but that’s just a local variable to which the function object (which is exactly the same type of object that a lambda expression yields) is assigned! Can Python be compiled to machine code, C or some other language? ¶ Cython compiles a modified version of Python with optional annotations into C extensions. Nuitka is an up-and-coming compiler of Python into C++ code, aiming to support the full Python language. How does Python manage memory? ¶ The details of Python memory management depend on the implementation. The standard implementation of Python, CPython , uses reference counting to detect inaccessible objects, and another mechanism to collect reference cycles, periodically executing a cycle detection algorithm which looks for inaccessible cycles and deletes the objects involved. The gc module provides functions to perform a garbage collection, obtain debugging statistics, and tune the collector’s parameters. Other implementations (such as Jython or PyPy ), however, can rely on a different mechanism such as a full-blown garbage collector. This difference can cause some subtle porting problems if your Python code depends on the behavior of the reference counting implementation. In some Python implementations, the following code (which is fine in CPython) will probably run out of file descriptors: for file in very_long_list_of_files : f = open ( file ) c = f . read ( 1 ) Indeed, using CPython’s reference counting and destructor scheme, each new assignment to f closes the previous file. With a traditional GC, however, those file objects will only get collected (and closed) at varying and possibly long intervals. If you want to write code that will work with any Python implementation, you should explicitly close the file or use the with statement; this will work regardless of memory management scheme: for file in very_long_list_of_files : with open ( file ) as f : c = f . read ( 1 ) Why doesn’t CPython use a more traditional garbage collection scheme? ¶ For one thing, this is not a C standard feature and hence it’s not portable. (Yes, we know about the Boehm GC library. It has bits of assembler code for most common platforms, not for all of them, and although it is mostly transparent, it isn’t completely transparent; patches are required to get Python to work with it.) Traditional GC also becomes a problem when Python is embedded into other applications. While in a standalone Python it’s fine to replace the standard malloc() and free() with versions provided by the GC library, an application embedding Python may want to have its own substitute for malloc() and free() , and may not want Python’s. Right now, CPython works with anything that implements malloc() and free() properly. Why isn’t all memory freed when CPython exits? ¶ Objects referenced from the global namespaces of Python modules are not always deallocated when Python exits. This may happen if there are circular references. There are also certain bits of memory that are allocated by the C library that are impossible to free (e.g. a tool like Purify will complain about these). Python is, however, aggressive about cleaning up memory on exit and does try to destroy every single object. If you want to force Python to delete certain things on deallocation use the atexit module to run a function that will force those deletions. Why are there separate tuple and list data types? ¶ Lists and tuples, while similar in many respects, are generally used in fundamentally different ways. Tuples can be thought of as being similar to Pascal records or C structs ; they’re small collections of related data which may be of different types which are operated on as a group. For example, a Cartesian coordinate is appropriately represented as a tuple of two or three numbers. Lists, on the other hand, are more like arrays in other languages. They tend to hold a varying number of objects all of which have the same type and which are operated on one-by-one. For example, os.listdir('.') returns a list of strings representing the files in the current directory. Functions which operate on this output would generally not break if you added another file or two to the directory. Tuples are immutable, meaning that once a tuple has been created, you can’t replace any of its elements with a new value. Lists are mutable, meaning that you can always change a list’s elements. Only immutable elements can be used as dictionary keys, and hence only tuples and not lists can be used as keys. How are lists implemented in CPython? ¶ CPython’s lists are really variable-length arrays, not Lisp-style linked lists. The implementation uses a contiguous array of references to other objects, and keeps a pointer to this array and the array’s length in a list head structure. This makes indexing a list a[i] an operation whose cost is independent of the size of the list or the value of the index. When items are appended or inserted, the array of references is resized. Some cleverness is applied to improve the performance of appending items repeatedly; when the array must be grown, some extra space is allocated so the next few times don’t require an actual resize. How are dictionaries implemented in CPython? ¶ CPython’s dictionaries are implemented as resizable hash tables. Compared to B-trees, this gives better performance for lookup (the most common operation by far) under most circumstances, and the implementation is simpler. Dictionaries work by computing a hash code for each key stored in the dictionary using the hash() built-in function. The hash code varies widely depending on the key and a per-process seed; for example, 'Python' could hash to -539294296 while 'python' , a string that differs by a single bit, could hash to 1142331976 . The hash code is then used to calculate a location in an internal array where the value will be stored. Assuming that you’re storing keys that all have different hash values, this means that dictionaries take constant time – O (1), in Big-O notation – to retrieve a key. Why must dictionary keys be immutable? ¶ The hash table implementation of dictionaries uses a hash value calculated from the key value to find the key. If the key were a mutable object, its value could change, and thus its hash could also change. But since whoever changes the key object can’t tell that it was being used as a dictionary key, it can’t move the entry around in the dictionary. Then, when you try to look up the same object in the dictionary it won’t be found because its hash value is different. If you tried to look up the old value it wouldn’t be found either, because the value of the object found in that hash bin would be different. If you want a dictionary indexed with a list, simply convert the list to a tuple first; the function tuple(L) creates a tuple with the same entries as the list L . Tuples are immutable and can therefore be used as dictionary keys. Some unacceptable solutions that have been proposed: Hash lists by their address (object ID). This doesn’t work because if you construct a new list with the same value it won’t be found; e.g.: mydict = {[ 1 , 2 ]: '12' } print ( mydict [[ 1 , 2 ]]) would raise a KeyError exception because the id of the [1, 2] used in the second line differs from that in the first line. In other words, dictionary keys should be compared using == , not using is . Make a copy when using a list as a key. This doesn’t work because the list, being a mutable object, could contain a reference to itself, and then the copying code would run into an infinite loop. Allow lists as keys but tell the user not to modify them. This would allow a class of hard-to-track bugs in programs when you forgot or modified a list by accident. It also invalidates an important invariant of dictionaries: every value in d.keys() is usable as a key of the dictionary. Mark lists as read-only once they are used as a dictionary key. The problem is that it’s not just the top-level object that could change its value; you could use a tuple containing a list as a key. Entering anything as a key into a dictionary would require marking all objects reachable from there as read-only – and again, self-referential objects could cause an infinite loop. There is a trick to get around this if you need to, but use it at your own risk: You can wrap a mutable structure inside a class instance which has both a __eq__() and a __hash__() method. You must then make sure that the hash value for all such wrapper objects that reside in a dictionary (or other hash based structure), remain fixed while the object is in the dictionary (or other structure). class ListWrapper : def __init__ ( self , the_list ): self . the_list = the_list def __eq__ ( self , other ): return self . the_list == other . the_list def __hash__ ( self ): l = self . the_list result = 98767 - len ( l ) * 555 for i , el in enumerate ( l ): try : result = result + ( hash ( el ) % 9999999 ) * 1001 + i except Exception : result = ( result % 7777777 ) + i * 333 return result Note that the hash computation is complicated by the possibility that some members of the list may be unhashable and also by the possibility of arithmetic overflow. Furthermore it must always be the case that if o1 == o2 (ie o1.__eq__(o2) is True ) then hash(o1) == hash(o2) (ie, o1.__hash__() == o2.__hash__() ), regardless of whether the object is in a dictionary or not. If you fail to meet these restrictions dictionaries and other hash based structures will misbehave. In the case of ListWrapper , whenever the wrapper object is in a dictionary the wrapped list must not change to avoid anomalies. Don’t do this unless you are prepared to think hard about the requirements and the consequences of not meeting them correctly. Consider yourself warned. Why doesn’t list.sort() return the sorted list? ¶ In situations where performance matters, making a copy of the list just to sort it would be wasteful. Therefore, list.sort() sorts the list in place. In order to remind you of that fact, it does not return the sorted list. This way, you won’t be fooled into accidentally overwriting a list when you need a sorted copy but also need to keep the unsorted version around. If you want to return a new list, use the built-in sorted() function instead. This function creates a new list from a provided iterable, sorts it and returns it. For example, here’s how to iterate over the keys of a dictionary in sorted order: for key in sorted ( mydict ): ... # do whatever with mydict[key]... How do you specify and enforce an interface spec in Python? ¶ An interface specification for a module as provided by languages such as C++ and Java describes the prototypes for the methods and functions of the module. Many feel that compile-time enforcement of interface specifications helps in the construction of large programs. Python 2.6 adds an abc module that lets you define Abstract Base Classes (ABCs). You can then use isinstance() and issubclass() to check whether an instance or a class implements a particular ABC. The collections.abc module defines a set of useful ABCs such as Iterable , Container , and MutableMapping . For Python, many of the advantages of interface specifications can be obtained by an appropriate test discipline for components. A good test suite for a module can both provide a regression test and serve as a module interface specification and a set of examples. Many Python modules can be run as a script to provide a simple “self test.” Even modules which use complex external interfaces can often be tested in isolation using trivial “stub” emulations of the external interface. The doctest and unittest modules or third-party test frameworks can be used to construct exhaustive test suites that exercise every line of code in a module. An appropriate testing discipline can help build large complex applications in Python as well as having interface specifications would. In fact, it can be better because an interface specification cannot test certain properties of a program. For example, the list.append() method is expected to add new elements to the end of some internal list; an interface specification cannot test that your list.append() implementation will actually do this correctly, but it’s trivial to check this property in a test suite. Writing test suites is very helpful, and you might want to design your code to make it easily tested. One increasingly popular technique, test-driven development, calls for writing parts of the test suite first, before you write any of the actual code. Of course Python allows you to be sloppy and not write test cases at all. Why is there no goto? ¶ In the 1970s people realized that unrestricted goto could lead to messy “spaghetti” code that was hard to understand and revise. In a high-level language, it is also unneeded as long as there are ways to branch (in Python, with if statements and or , and , and if / else expressions) and loop (with while and for statements, possibly containing continue and break ). One can also use exceptions to provide a “structured goto” that works even across function calls. Many feel that exceptions can conveniently emulate all reasonable uses of the go or goto constructs of C, Fortran, and other languages. For example: class label ( Exception ): pass # declare a label try : ... if condition : raise label () # goto label ... except label : # where to goto pass ... This doesn’t allow you to jump into the middle of a loop, but that’s usually considered an abuse of goto anyway. Use sparingly. Why can’t raw strings (r-strings) end with a backslash? ¶ More precisely, they can’t end with an odd number of backslashes: the unpaired backslash at the end escapes the closing quote character, leaving an unterminated string. Raw strings were designed to ease creating input for processors (chiefly regular expression engines) that want to do their own backslash escape processing. Such processors consider an unmatched trailing backslash to be an error anyway, so raw strings disallow that. In return, they allow you to pass on the string quote character by escaping it with a backslash. These rules work well when r-strings are used for their intended purpose. If you’re trying to build Windows pathnames, note that all Windows system calls accept forward slashes too: f = open ( "/mydir/file.txt" ) # works fine! If you’re trying to build a pathname for a DOS command, try e.g. one of dir = r "\this\is\my\dos\dir" " \\ " dir = r "\this\is\my\dos\dir\ " [: - 1 ] dir = " \\ this \\ is \\ my \\ dos \\ dir \\ " Why doesn’t Python have a “with” statement for attribute assignments? ¶ Python has a with statement that wraps the execution of a block, calling code on the entrance and exit from the block. Some languages have a construct that looks like this: with obj : a = 1 # equivalent to obj.a = 1 total = total + 1 # obj.total = obj.total + 1 In Python, such a construct would be ambiguous. Other languages, such as Object Pascal, Delphi, and C++, use static types, so it’s possible to know, in an unambiguous way, what member is being assigned to. This is the main point of static typing – the compiler always knows the scope of every variable at compile time. Python uses dynamic types. It is impossible to know in advance which attribute will be referenced at runtime. Member attributes may be added or removed from objects on the fly. This makes it impossible to know, from a simple reading, what attribute is being referenced: a local one, a global one, or a member attribute? For instance, take the following incomplete snippet: def foo ( a ): with a : print ( x ) The snippet assumes that a must have a member attribute called x . However, there is nothing in Python that tells the interpreter this. What should happen if a is, let us say, an integer? If there is a global variable named x , will it be used inside the with block? As you see, the dynamic nature of Python makes such choices much harder. The primary benefit of with and similar language features (reduction of code volume) can, however, easily be achieved in Python by assignment. Instead of: function ( args ) . mydict [ index ][ index ] . a = 21 function ( args ) . mydict [ index ][ index ] . b = 42 function ( args ) . mydict [ index ][ index ] . c = 63 write this: ref = function ( args ) . mydict [ index ][ index ] ref . a = 21 ref . b = 42 ref . c = 63 This also has the side-effect of increasing execution speed because name bindings are resolved at run-time in Python, and the second version only needs to perform the resolution once. Similar proposals that would introduce syntax to further reduce code volume, such as using a ‘leading dot’, have been rejected in favour of explicitness (see https://mail.python.org/pipermail/python-ideas/2016-May/040070.html ). Why don’t generators support the with statement? ¶ For technical reasons, a generator used directly as a context manager would not work correctly. When, as is most common, a generator is used as an iterator run to completion, no closing is needed. When it is, wrap it as contextlib.closing(generator) in the with statement. Why are colons required for the if/while/def/class statements? ¶ The colon is required primarily to enhance readability (one of the results of the experimental ABC language). Consider this: if a == b print ( a ) versus if a == b : print ( a ) Notice how the second one is slightly easier to read. Notice further how a colon sets off the example in this FAQ answer; it’s a standard usage in English. Another minor reason is that the colon makes it easier for editors with syntax highlighting; they can look for colons to decide when indentation needs to be increased instead of having to do a more elaborate parsing of the program text. Why does Python allow commas at the end of lists and tuples? ¶ Python lets you add a trailing comma at the end of lists, tuples, and dictionaries: [ 1 , 2 , 3 ,] ( 'a' , 'b' , 'c' ,) d = { "A" : [ 1 , 5 ], "B" : [ 6 , 7 ], # last trailing comma is optional but good style } There are several reasons to allow this. When you have a literal value for a list, tuple, or dictionary spread across multiple lines, it’s easier to add more elements because you don’t have to remember to add a comma to the previous line. The lines can also be reordered without creating a syntax error. Accidentally omitting the comma can lead to errors that are hard to diagnose. For example: x = [ "fee" , "fie" "foo" , "fum" ] This list looks like it has four elements, but it actually contains three: “fee”, “fiefoo” and “fum”. Always adding the comma avoids this source of error. Allowing the trailing comma may also make programmatic code generation easier. Table of Contents Design and History FAQ Why does Python use indentation for grouping of statements? Why am I getting strange results with simple arithmetic operations? Why are floating-point calculations so inaccurate? Why are Python strings immutable? Why must ‘self’ be used explicitly in method definitions and calls? Why can’t I use an assignment in an expression? Why does Python use methods for some functionality (e.g. list.index()) but functions for other (e.g. len(list))? Why is join() a string method instead of a list or tuple method? How fast are exceptions? Why isn’t there a switch or case statement in Python? Can’t you emulate threads in the interpreter instead of relying on an OS-specific thread implementation? Why can’t lambda expressions contain statements? Can Python be compiled to machine code, C or some other language? How does Python manage memory? Why doesn’t CPython use a more traditional garbage collection scheme? Why isn’t all memory freed when CPython exits? Why are there separate tuple and list data types? How are lists implemented in CPython? How are dictionaries implemented in CPython? Why must dictionary keys be immutable? Why doesn’t list.sort() return the sorted list? How do you specify and enforce an interface spec in Python? Why is there no goto? Why can’t raw strings (r-strings) end with a backslash? Why doesn’t Python have a “with” statement for attribute assignments? Why don’t generators support the with statement? Why are colons required for the if/while/def/class statements? Why does Python allow commas at the end of lists and tuples? 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Programming Follow Hide The magic behind computers. 💻 🪄 Create Post Older #programming posts 1 2 3 4 5 6 7 8 9 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu Unlocking the Power of Inheritance in Python Visakh Vijayan Visakh Vijayan Visakh Vijayan Follow Jan 12 Unlocking the Power of Inheritance in Python # beginners # programming # python # tutorial Comments Add Comment 2 min read Why Asking for Better Outputs Misses the Real Problem Leena Malhotra Leena Malhotra Leena Malhotra Follow Jan 12 Why Asking for Better Outputs Misses the Real Problem # programming # ai # imagegen # nanobanana 2 reactions Comments Add Comment 5 min read Flutter ECS: Mastering Async Operations and Complex Workflows Dr. E Dr. E Dr. E Follow Jan 11 Flutter ECS: Mastering Async Operations and Complex Workflows # flutter # dart # programming # opensource Comments Add Comment 2 min read What's new in Webpixels v3 Alexis Enache Alexis Enache Alexis Enache Follow Jan 12 What's new in Webpixels v3 # webdev # programming # ai # productivity Comments Add Comment 3 min read EC2 (single instance with db and redis and all) vs EC2 + RDS + MemoryDB, ECS/EKS (docker-based approach): when and why Saif Ullah Usmani Saif Ullah Usmani Saif Ullah Usmani Follow Jan 11 EC2 (single instance with db and redis and all) vs EC2 + RDS + MemoryDB, ECS/EKS (docker-based approach): when and why # webdev # programming # devops # javascript Comments Add Comment 3 min read 𝗪𝗵𝘆 𝗔𝗜-𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗖𝗼𝗱𝗲 𝗢𝗳𝘁𝗲𝗻 𝗟𝗼𝗼𝗸𝘀 “𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲” — 𝗯𝘂𝘁 𝗜𝘀𝗻’𝘁—𝗮𝗻𝗱 𝘄𝗵𝘆 𝗜 𝗯𝘂𝗶𝗹𝘁 𝗔𝗜-𝗦𝗟𝗢𝗣 𝗗𝗲𝘁𝗲𝗰𝘁𝗼𝗿 Kwansub Yun Kwansub Yun Kwansub Yun Follow Jan 11 𝗪𝗵𝘆 𝗔𝗜-𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗖𝗼𝗱𝗲 𝗢𝗳𝘁𝗲𝗻 𝗟𝗼𝗼𝗸𝘀 “𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲” — 𝗯𝘂𝘁 𝗜𝘀𝗻’𝘁—𝗮𝗻𝗱 𝘄𝗵𝘆 𝗜 𝗯𝘂𝗶𝗹𝘁 𝗔𝗜-𝗦𝗟𝗢𝗣 𝗗𝗲𝘁𝗲𝗰𝘁𝗼𝗿 # opensource # codequality # devsecops # programming Comments 1 comment 2 min read Beyond the Buzzwords: 5 Counter-Intuitive Lessons in System Design Amit Dey Amit Dey Amit Dey Follow Jan 11 Beyond the Buzzwords: 5 Counter-Intuitive Lessons in System Design # systemdesign # programming # security Comments Add Comment 7 min read ReactJS Hook Pattern ~UseImperativeHandle~ Ogasawara Kakeru Ogasawara Kakeru Ogasawara Kakeru Follow Jan 12 ReactJS Hook Pattern ~UseImperativeHandle~ # programming # javascript # react # learning Comments Add Comment 1 min read Create Your First MCP Server in 5 Minutes with create-mcp-server Ali Ibrahim Ali Ibrahim Ali Ibrahim Follow Jan 11 Create Your First MCP Server in 5 Minutes with create-mcp-server # webdev # javascript # ai # programming 1 reaction Comments Add Comment 10 min read Code Coverage Best Practices for Agentic Development Ariel Frischer Ariel Frischer Ariel Frischer Follow Jan 11 Code Coverage Best Practices for Agentic Development # webdev # programming # ai # productivity Comments Add Comment 3 min read Build Network Proxies and Reverse Proxies in Go: A Hands-On Guide Jones Charles Jones Charles Jones Charles Follow Jan 12 Build Network Proxies and Reverse Proxies in Go: A Hands-On Guide # go # networking # programming # webdev Comments Add Comment 6 min read What should 2.0.10 have been in PWin11 Tweaker? ph2ncyn ph2ncyn ph2ncyn Follow Jan 11 What should 2.0.10 have been in PWin11 Tweaker? # programming # microsoft # opensource # github Comments Add Comment 10 min read SQL vs NoSQL: The Ultimate Interview Guide to Choosing the Right Database (Simple Checklist Included) sizan mahmud0 sizan mahmud0 sizan mahmud0 Follow Jan 12 SQL vs NoSQL: The Ultimate Interview Guide to Choosing the Right Database (Simple Checklist Included) # interview # sql # nosql # programming 1 reaction Comments Add Comment 4 min read Building Amalanku 1.2.0: Home Widgets, Backup Systems & More 🚀 Cahyanudien Aziz Saputra Cahyanudien Aziz Saputra Cahyanudien Aziz Saputra Follow Jan 11 Building Amalanku 1.2.0: Home Widgets, Backup Systems & More 🚀 # programming # wecoded # islam 2 reactions Comments Add Comment 4 min read LINE Bot Developer Guide: Other Related Features Evan Lin Evan Lin Evan Lin Follow Jan 11 LINE Bot Developer Guide: Other Related Features # documentation # tutorial # api # programming Comments Add Comment 7 min read LINE Bot Developer Guide: LINE Login (Supplement) Evan Lin Evan Lin Evan Lin Follow Jan 11 LINE Bot Developer Guide: LINE Login (Supplement) # api # programming # tutorial Comments Add Comment 6 min read I Added a Cache and the System Got Slower: The Hidden Cost of Caching yusuf yonturk yusuf yonturk yusuf yonturk Follow Jan 11 I Added a Cache and the System Got Slower: The Hidden Cost of Caching # programming # performance # backend # redis 1 reaction Comments Add Comment 3 min read LINE Bot Developer Guide: Sending API Requests - Notes Evan Lin Evan Lin Evan Lin Follow Jan 11 LINE Bot Developer Guide: Sending API Requests - Notes # learning # api # tutorial # programming Comments Add Comment 9 min read Production ML is not about models. It’s about trade-offs. Jashwanth Thatipamula Jashwanth Thatipamula Jashwanth Thatipamula Follow Jan 11 Production ML is not about models. It’s about trade-offs. # webdev # ai # machinelearning # programming 3 reactions Comments Add Comment 3 min read Observer Pattern Explained Simply With JavaScript Examples Arun Teja Arun Teja Arun Teja Follow Jan 11 Observer Pattern Explained Simply With JavaScript Examples # designpatterns # javascript # beginners # programming Comments Add Comment 3 min read AI Agents: Automate 80% of Support (Case Study) Robort Gabriel Robort Gabriel Robort Gabriel Follow Jan 11 AI Agents: Automate 80% of Support (Case Study) # agents # programming # ai Comments Add Comment 6 min read Python Sets: remove() vs discard() — When Silence Is Golden Samuel Ochaba Samuel Ochaba Samuel Ochaba Follow Jan 11 Python Sets: remove() vs discard() — When Silence Is Golden # python # programming # tutorial # webdev Comments Add Comment 2 min read When Your AI Coding Assistant Gets Stuck — What's your next move? Hanyuan PENG Hanyuan PENG Hanyuan PENG Follow Jan 11 When Your AI Coding Assistant Gets Stuck — What's your next move? # vibecoding # ai # programming # knowledgesharing 1 reaction Comments Add Comment 2 min read FastAPI from Zero: Writing Your First API Route Tekeu Franck Tekeu Franck Tekeu Franck Follow Jan 12 FastAPI from Zero: Writing Your First API Route # webdev # programming # fastapi Comments Add Comment 3 min read Is Learning Programming Without a Computer Science Degree Realistic? syed shabeh syed shabeh syed shabeh Follow Jan 12 Is Learning Programming Without a Computer Science Degree Realistic? # programming # computerscience # developers # webdev 2 reactions Comments Add Comment 1 min read loading... 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account Security Forem Close About Zero Day Security Community About the Zero Day Security Community Welcome to Zero Day, the ultimate hub for security professionals, enthusiasts, students, and anyone passionate about the world of security. Whether you're a seasoned CISO, a curious student, or a developer looking to write more secure code, you've found your home. Our Mission Our mission is to create a central, inclusive, and authoritative online community dedicated to fostering collaboration, knowledge sharing, and skill development across all domains of security. We believe in breaking down silos and building bridges between the theoretical and the practical, the digital and the physical. What We're All About Zero Day is a space for deep, meaningful discussion and learning. 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https://docs.suprsend.com/docs/react-native | Android Integration - SuprSend, Notification infrastructure for Product teams Skip to main content SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Community Trust Center Platform Status Postman Collection Developer Resources Overview Updates and Versioning Versioning and Support Policy SDK Changelog Authentication API Keys and Secrets Service Token Best Practices for Key & Token Management MCP Overview BETA Quickstart Tool List Building with LLMs Security Security SDKs and APIs SDKs SDK Overview SuprSend Backend SDK SuprSend Client SDK Authentication Javascript Android iOS React Native Android Integration iOS Integration Manage Users Sync Events iOS Push Setup Android Push (FCM) Flutter React Management API REST API Postman Collection Features Validate Trigger Payload Type Safety Testing Testing the Template Test Mode Monitoring and Logging Logs Data Out Contact Us Get Started SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Ask AI Contact Us Get Started Get Started Search... Navigation React Native Android Integration Documentation API Reference Management API CLI Reference Developer Resources Changelog Documentation API Reference Management API CLI Reference Developer Resources Changelog React Native Android Integration OpenAI Open in ChatGPT This document will cover integration steps for Android side of your ReactNative application. OpenAI Open in ChatGPT Installation 1 Install Package npm yarn Copy Ask AI npm install @ suprsend / react - native - sdk @ latest 2 Add the below dependency Add the this dependency in project level build.gradle , inside allprojects > repositories . build.gradle Copy Ask AI allprojects { repositories { ... mavenCentral () // add this } } 3 Add Android SDK dependency inside in app level build.gradle. build.gradle Copy Ask AI dependencies { ... implementation 'com.suprsend:rn:0.1.10' // add this } Note: If you get any error regarding minSdkVersion please update it to 19 or more. Initialization 1 Initialise the Suprsend Android SDK Initialise the Suprsend android SDK in MainApplication.java inside onCreate method and just above super.onCreate() line. javascript Copy Ask AI import app . suprsend . SSApi ; // import sdk ... SSApi . Companion . init ( this , WORKSPACE KEY , WORKSPACE SECRET ); // inside onCreate method just above super.onCreate() line Replace WORKSPACE KEY and WORKSPACE SECRET with your workspace values. You will get them the tokens from Settings -> API Keys inside Suprsend dashboard . 2 Import SuprSend SDK in your client side Javascript code. javascript Copy Ask AI import suprsend from "@suprsend/react-native-sdk" ; Logging By default the logs of SuprSend SDK are disabled. You can enable the logs just in debug mode while in development by the below condition. javascript Copy Ask AI suprsend . enableLogging (); // available from v2.0.2 // deprecated from v2.0.2 suprsend . setLogLevel ( level ) suprsend . setLogLevel ( "VERBOSE" ) suprsend . setLogLevel ( "DEBUG" ) suprsend . setLogLevel ( "INFO" ) suprsend . setLogLevel ( "ERROR" ) suprsend . setLogLevel ( "OFF" ) Was this page helpful? Yes No Suggest edits Raise issue Previous iOS Integration This document will cover integration steps for iOS side of your ReactNative application. Next ⌘ I x github linkedin youtube Powered by On this page Installation Initialization Logging | 2026-01-13T08:49:02 |
https://neon.com | Neon Serverless Postgres — Ship faster This 250+ engineer team replaced shared staging with isolated database branches for safer deploys Neon Product Database Autoscaling Automatic instance sizing Branching Faster Postgres workflows Bottomless storage With copy-on-write Instant restores Recover TBs in seconds Connection pooler Built-in with pgBouncer Ecosystem Neon API Manage infra, billing, quotas Auth Add authentication Data API PostgREST-compatible Instagres No-signup flow Migration guides Step-by-step What is Neon? Serverless Postgres, by Databricks Solutions Use cases Serverless Apps Autoscale with traffic Multi-TB Scale & restore instantly Database per Tenant Data isolation without overhead Platforms Offer Postgres to your users Dev/Test Production-like environments Agents Build full-stack AI agents For teams Startups Build with Neon Security Compliance & privacy Case studies Explore customer stories Docs Pricing Company Blog About us Careers Contact Discord 20.7k Log In Sign Up Ship faster with Postgres The database developers trust, on a serverless platform designed to help you build reliable and scalable applications faster. Start for Free Talk to Us Scaling Scaling Focus on building applications with time and money-saving features like instant provisioning, autoscaling according to load, and scale to zero. Discover Autoscaling Branching Branching Instantly branch your data and schema to access isolated DB copies for development, CI/CD, and schema migrations with copy-on-write storage. Explore Branching Trusted in production by thousands of teams. Instant Provisioning No waiting. No config. Provisioned in 300ms postgresql://example@ep-938132.eu-central-1.aws.neon.tech/primary Works with your stack Integrate it into your language or framework within minutes and unlock a simpler developer workflow. See all examples Next.js Drizzle Prisma Python Ruby Rust Go import { neon } from '@neondatabase/serverless' ; export async function GET () { const sql = neon ( process . env . DATABASE_URL ); const rows = await sql `SELECT * FROM posts` ; return Response .json ({ rows }) } Lightning fast. Edge ready. The Neon serverless driver , designed for fast queries over HTTP import { neon } from '@neondatabase/ serverless'; const sql = neon('postgresql:// usr:pass@proj.us-east-2.aws.neon.tech/db'); const posts = await sql('SELECT * FROM posts'); Get the Serverless Driver Better database. For modern workflows. Boost your performance with instant read replicas . They scale down to zero when idle and don't use additional storage. Easy database ops via the API and CLI . Manage thousands of databases programmatically. Instant Point-in-time recovery . Up to 30 days granularity down to the transaction or second. Unleashing Cutting- Edge AI Applications. The HNSW index algorithm streamlines performance, making high-dimensional vector search remarkably efficient. Power your AI apps with Postgres Reliable Scalability High compatibility Blazingly fast search Works with PGVECTOR Works with langchain ; Thousands of databases. Zero overhead. Use the Neon API to deploy database-per-tenant architectures . Scale to fleets of thousands of databases without touching a server. Rest easy knowing scale to zero keeps costs low. Industry leaders trust Neon Dive into success stories Neon allows us to develop much faster than we’ve even been used to Alex Klarfeld – CEO and co-founder of Supergood.ai Neon's serverless philosophy is aligned with our vision: no infrastructure to manage, no servers to provision, no database cluster to maintain Edouard Bonlieu – Co-founder at Koyeb The killer feature that convinced us to use Neon was branching: it keeps our engineering velocity high Léonard Henriquez – Co-founder and CTO, Topo.io We've been able to automate virtually all database tasks via the Neon API, saving us a tremendous amount of time and engineering effort Himanshu Bhandoh – Software Engineer at Retool Trusted Postgres neondatabase/neon Databases under management. Postgres for the World. 100% Postgres. Not a fork, not a rewrite. SOC2 Compliance Features of tomorrow. Available today. Get Started Neon A Databricks Company Neon status loading... Made in SF and the World Copyright Ⓒ 2022 – 2026 Neon, LLC Company About Blog Careers Contact Sales Partners Security Legal Privacy Policy Terms of Service DPA Subprocessors List Privacy Guide Cookie Policy Business Information Resources Docs Changelog Support Community Guides PostgreSQL Tutorial Startups Creators Social Discord GitHub x.com LinkedIn YouTube Compliance CCPA Compliant GDPR Compliant ISO 27001 Certified ISO 27701 Certified SOC 2 Certified HIPAA Compliant Compliance Guide Neon’s Sub Contractors Sensitive Data Terms Trust Center | 2026-01-13T08:49:02 |
https://sentry.io/for/python/?utm_source=pypi&utm_medium=paid-community&utm_campaign=python-na-evergreen&utm_content=static-ad-pypi-sponsor-learnmore | Python Error Tracking and Performance Monitoring | Sentry This app works best with JavaScript enabled. Menu Platform Products Products Error Monitoring Logs NEW Session Replay Tracing Seer NEW Uptime Monitoring Profiling Cron Monitoring AI Code Review NEW Integrations Integrations Github Slack All Integrations SDKs SDKs Javascript Python React Laravel Next.js All SDKs Solutions Web / Full Stack Development Mobile Crash Reporting Game Crash Reporting AI Observability Application Performance Monitoring Real User Monitoring Ecommerce Enterprise Startups Resources Learn Learn Blog Changelog Sandbox Resources Sentry Answers Syntax Customers Support Support Contact Us Help Center Status Hang out with us Hang out with us Sentry Build Events Merch Holiday E-Commerce Checklist: A Developer’s Survival Guide There’s never a good time for errors or performance degradations to show up, but during periods of peak traffic like the holidays, it’s especially critical to get immediate answers about what’s failing and how to fix it. Learn More Docs Pricing Sign In login Get Demo get a demo Get Started sign up Python Error and Performance Monitoring Actionable insights to resolve Python performance bottlenecks and errors. See the full picture of any Python exception so you can diagnose, fix, and optimize performance in the Python debugging process. Try Sentry For Free Request a Demo Grab the Sentry Python SDK : Click to Copy Click to Copy pip install --upgrade sentry-sdk Configure your DSN: Click to Copy Click to Copy import sentry_sdk sentry_sdk.init( "https://<key>@sentry.io/<project>", # Set traces_sample_rate to 1.0 to capture 100% # of transactions for Tracing. # We recommend adjusting this value in production. enable_tracing=True, traces_sample_rate=1.0, ) Getting Started is Simple Python Django Flask FastAPI Bottle Celery Pyramid Tornado RQ AWS Lambda Python Google Cloud Functions Python Grab the Sentry Python SDK : Click to Copy Click to Copy pip install --upgrade sentry-sdk Configure your DSN: Click to Copy Click to Copy import sentry_sdk sentry_sdk.init( "https://<key>@sentry.io/<project>", # Set traces_sample_rate to 1.0 to capture 100% # of transactions for Tracing. # We recommend adjusting this value in production. enable_tracing=True, traces_sample_rate=1.0, ) Check our documentation for the latest instructions. See all platforms How to install the Python SDK More than 150K Organizations Trust Sentry with Their Application Monitoring Github logo Atlassian logo Disney logo Cloudflare logo Microsoft logo Lush logo Python Performance Monitoring Within minutes after installing Sentry, software teams are able to trace Python performance issues back to a poor performing API call as well as surface all related code errors. Engineering Managers and Developers now have a single tool to optimize the performance of their code and deliver fast customer experiences. See a Sample Transaction Python Error Monitoring with Complete Stack Traces See local variables in the stack for prod errors, just like in your dev environment. Introspect more deeply into the runtime and jump into the frame to get additional data for any local variable. Filter and group Python exceptions intuitively to eliminate noise. See a Sample Stack Trace Fill In the Blanks About Python Errors Expose the important events that led to each Python exception: SQL queries, debug logs, network requests, past errors. Improve debugging workflow with a full view of releases so you can mark errors as resolved and prioritize live issues. Check out Breadcrumbs Python Profiling Profiling lets you see what parts of your code are consuming the most resources, like CPU or memory, in your application— so you can optimize them before end user experience is impacted. Test your application performance in any environment, including in production, without writing manual tests or extensive troubleshooting. Try Profiling Understand Where Tests Could Prevent Your Python Regressions Quickly isolate Code Coverage for Python bugs to see where testing could help fix the bug before release with our Codecov integration. Learn More About Codecov by Sentry ”The time to resolve errors went from days to minutes.” Vaidik Kapoor VP of Engineering at Grofers Debugging Any Python Exception Aggregate errors by details like HTTP request, hostname, and app version to see what’s new, a priority, or a trend. Assign custom tags to reproduce the error environment specific to your application, business, and users. Answer the most important questions: In which app release did the Python bug occur? Was it the kraken? FAQs What is the difference between Sentry and traditional logging? Traditional logging provides you with a trail of events. Some of those events are errors, but many times they’re simply informational. Sentry is fundamentally different because we focus on exceptions, or in other words, we capture application crashes. We discuss in more detail here and on our blog. What languages does Sentry support? Sentry supports every major language, framework, and library. You can browse each of them here . How much does Sentry cost? You can get started for free. Pricing depends on the number of monthly events, transactions, and attachments that you send Sentry. For more details, visit our pricing page . How does Sentry impact the performance of my app? Sentry doesn’t impact a web site’s performance. If you look at the configuration options for when you initialize Sentry in your code, you’ll see there’s nothing regarding minimizing its impact on your app’s performance. This is because our team of SDK engineers already developed Sentry with this in mind. Sentry is a listener/handler for errors that asynchronously sends out the error/event to Sentry.io. This is non-blocking. The error/event only goes out if this is an error. Global handlers have almost no impact as well, as they are native APIs provided by the browsers. 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https://peps.python.org/pep-0008/ | PEP 8 – Style Guide for Python Code | peps.python.org Following system colour scheme Selected dark colour scheme Selected light colour scheme Python Enhancement Proposals Python » PEP Index » PEP 8 Toggle light / dark / auto colour theme PEP 8 – Style Guide for Python Code Author : Guido van Rossum <guido at python.org>, Barry Warsaw <barry at python.org>, Alyssa Coghlan <ncoghlan at gmail.com> Status : Active Type : Process Created : 05-Jul-2001 Post-History : 05-Jul-2001, 01-Aug-2013 Table of Contents Introduction A Foolish Consistency is the Hobgoblin of Little Minds Code Lay-out Indentation Tabs or Spaces? Maximum Line Length Should a Line Break Before or After a Binary Operator? Blank Lines Source File Encoding Imports Module Level Dunder Names String Quotes Whitespace in Expressions and Statements Pet Peeves Other Recommendations When to Use Trailing Commas Comments Block Comments Inline Comments Documentation Strings Naming Conventions Overriding Principle Descriptive: Naming Styles Prescriptive: Naming Conventions Names to Avoid ASCII Compatibility Package and Module Names Class Names Type Variable Names Exception Names Global Variable Names Function and Variable Names Function and Method Arguments Method Names and Instance Variables Constants Designing for Inheritance Public and Internal Interfaces Programming Recommendations Function Annotations Variable Annotations References Copyright Introduction This document gives coding conventions for the Python code comprising the standard library in the main Python distribution. Please see the companion informational PEP describing style guidelines for the C code in the C implementation of Python . This document and PEP 257 (Docstring Conventions) were adapted from Guido’s original Python Style Guide essay, with some additions from Barry’s style guide [2] . This style guide evolves over time as additional conventions are identified and past conventions are rendered obsolete by changes in the language itself. Many projects have their own coding style guidelines. In the event of any conflicts, such project-specific guides take precedence for that project. A Foolish Consistency is the Hobgoblin of Little Minds One of Guido’s key insights is that code is read much more often than it is written. The guidelines provided here are intended to improve the readability of code and make it consistent across the wide spectrum of Python code. As PEP 20 says, “Readability counts”. A style guide is about consistency. Consistency with this style guide is important. Consistency within a project is more important. Consistency within one module or function is the most important. However, know when to be inconsistent – sometimes style guide recommendations just aren’t applicable. When in doubt, use your best judgment. Look at other examples and decide what looks best. And don’t hesitate to ask! In particular: do not break backwards compatibility just to comply with this PEP! Some other good reasons to ignore a particular guideline: When applying the guideline would make the code less readable, even for someone who is used to reading code that follows this PEP. To be consistent with surrounding code that also breaks it (maybe for historic reasons) – although this is also an opportunity to clean up someone else’s mess (in true XP style). Because the code in question predates the introduction of the guideline and there is no other reason to be modifying that code. When the code needs to remain compatible with older versions of Python that don’t support the feature recommended by the style guide. Code Lay-out Indentation Use 4 spaces per indentation level. Continuation lines should align wrapped elements either vertically using Python’s implicit line joining inside parentheses, brackets and braces, or using a hanging indent [1] . When using a hanging indent the following should be considered; there should be no arguments on the first line and further indentation should be used to clearly distinguish itself as a continuation line: # Correct: # Aligned with opening delimiter. foo = long_function_name ( var_one , var_two , var_three , var_four ) # Add 4 spaces (an extra level of indentation) to distinguish arguments from the rest. def long_function_name ( var_one , var_two , var_three , var_four ): print ( var_one ) # Hanging indents should add a level. foo = long_function_name ( var_one , var_two , var_three , var_four ) # Wrong: # Arguments on first line forbidden when not using vertical alignment. foo = long_function_name ( var_one , var_two , var_three , var_four ) # Further indentation required as indentation is not distinguishable. def long_function_name ( var_one , var_two , var_three , var_four ): print ( var_one ) The 4-space rule is optional for continuation lines. Optional: # Hanging indents *may* be indented to other than 4 spaces. foo = long_function_name ( var_one , var_two , var_three , var_four ) When the conditional part of an if -statement is long enough to require that it be written across multiple lines, it’s worth noting that the combination of a two character keyword (i.e. if ), plus a single space, plus an opening parenthesis creates a natural 4-space indent for the subsequent lines of the multiline conditional. This can produce a visual conflict with the indented suite of code nested inside the if -statement, which would also naturally be indented to 4 spaces. This PEP takes no explicit position on how (or whether) to further visually distinguish such conditional lines from the nested suite inside the if -statement. Acceptable options in this situation include, but are not limited to: # No extra indentation. if ( this_is_one_thing and that_is_another_thing ): do_something () # Add a comment, which will provide some distinction in editors # supporting syntax highlighting. if ( this_is_one_thing and that_is_another_thing ): # Since both conditions are true, we can frobnicate. do_something () # Add some extra indentation on the conditional continuation line. if ( this_is_one_thing and that_is_another_thing ): do_something () (Also see the discussion of whether to break before or after binary operators below.) The closing brace/bracket/parenthesis on multiline constructs may either line up under the first non-whitespace character of the last line of list, as in: my_list = [ 1 , 2 , 3 , 4 , 5 , 6 , ] result = some_function_that_takes_arguments ( 'a' , 'b' , 'c' , 'd' , 'e' , 'f' , ) or it may be lined up under the first character of the line that starts the multiline construct, as in: my_list = [ 1 , 2 , 3 , 4 , 5 , 6 , ] result = some_function_that_takes_arguments ( 'a' , 'b' , 'c' , 'd' , 'e' , 'f' , ) Tabs or Spaces? Spaces are the preferred indentation method. Tabs should be used solely to remain consistent with code that is already indented with tabs. Python disallows mixing tabs and spaces for indentation. Maximum Line Length Limit all lines to a maximum of 79 characters. For flowing long blocks of text with fewer structural restrictions (docstrings or comments), the line length should be limited to 72 characters. Limiting the required editor window width makes it possible to have several files open side by side, and works well when using code review tools that present the two versions in adjacent columns. The default wrapping in most tools disrupts the visual structure of the code, making it more difficult to understand. The limits are chosen to avoid wrapping in editors with the window width set to 80, even if the tool places a marker glyph in the final column when wrapping lines. Some web based tools may not offer dynamic line wrapping at all. Some teams strongly prefer a longer line length. For code maintained exclusively or primarily by a team that can reach agreement on this issue, it is okay to increase the line length limit up to 99 characters, provided that comments and docstrings are still wrapped at 72 characters. The Python standard library is conservative and requires limiting lines to 79 characters (and docstrings/comments to 72). The preferred way of wrapping long lines is by using Python’s implied line continuation inside parentheses, brackets and braces. Long lines can be broken over multiple lines by wrapping expressions in parentheses. These should be used in preference to using a backslash for line continuation. Backslashes may still be appropriate at times. For example, long, multiple with -statements could not use implicit continuation before Python 3.10, so backslashes were acceptable for that case: with open ( '/path/to/some/file/you/want/to/read' ) as file_1 , \ open ( '/path/to/some/file/being/written' , 'w' ) as file_2 : file_2 . write ( file_1 . read ()) (See the previous discussion on multiline if-statements for further thoughts on the indentation of such multiline with -statements.) Another such case is with assert statements. Make sure to indent the continued line appropriately. Should a Line Break Before or After a Binary Operator? For decades the recommended style was to break after binary operators. But this can hurt readability in two ways: the operators tend to get scattered across different columns on the screen, and each operator is moved away from its operand and onto the previous line. Here, the eye has to do extra work to tell which items are added and which are subtracted: # Wrong: # operators sit far away from their operands income = ( gross_wages + taxable_interest + ( dividends - qualified_dividends ) - ira_deduction - student_loan_interest ) To solve this readability problem, mathematicians and their publishers follow the opposite convention. Donald Knuth explains the traditional rule in his Computers and Typesetting series: “Although formulas within a paragraph always break after binary operations and relations, displayed formulas always break before binary operations” [3] . Following the tradition from mathematics usually results in more readable code: # Correct: # easy to match operators with operands income = ( gross_wages + taxable_interest + ( dividends - qualified_dividends ) - ira_deduction - student_loan_interest ) In Python code, it is permissible to break before or after a binary operator, as long as the convention is consistent locally. For new code Knuth’s style is suggested. Blank Lines Surround top-level function and class definitions with two blank lines. Method definitions inside a class are surrounded by a single blank line. Extra blank lines may be used (sparingly) to separate groups of related functions. Blank lines may be omitted between a bunch of related one-liners (e.g. a set of dummy implementations). Use blank lines in functions, sparingly, to indicate logical sections. Python accepts the control-L (i.e. ^L) form feed character as whitespace; many tools treat these characters as page separators, so you may use them to separate pages of related sections of your file. Note, some editors and web-based code viewers may not recognize control-L as a form feed and will show another glyph in its place. Source File Encoding Code in the core Python distribution should always use UTF-8, and should not have an encoding declaration. In the standard library, non-UTF-8 encodings should be used only for test purposes. Use non-ASCII characters sparingly, preferably only to denote places and human names. If using non-ASCII characters as data, avoid noisy Unicode characters like z̯̯͡a̧͎̺l̡͓̫g̹̲o̡̼̘ and byte order marks. All identifiers in the Python standard library MUST use ASCII-only identifiers, and SHOULD use English words wherever feasible (in many cases, abbreviations and technical terms are used which aren’t English). Open source projects with a global audience are encouraged to adopt a similar policy. Imports Imports should usually be on separate lines: # Correct: import os import sys # Wrong: import sys , os It’s okay to say this though: # Correct: from subprocess import Popen , PIPE Imports are always put at the top of the file, just after any module comments and docstrings, and before module globals and constants. Imports should be grouped in the following order: Standard library imports. Related third party imports. Local application/library specific imports. You should put a blank line between each group of imports. Absolute imports are recommended, as they are usually more readable and tend to be better behaved (or at least give better error messages) if the import system is incorrectly configured (such as when a directory inside a package ends up on sys.path ): import mypkg.sibling from mypkg import sibling from mypkg.sibling import example However, explicit relative imports are an acceptable alternative to absolute imports, especially when dealing with complex package layouts where using absolute imports would be unnecessarily verbose: from . import sibling from .sibling import example Standard library code should avoid complex package layouts and always use absolute imports. When importing a class from a class-containing module, it’s usually okay to spell this: from myclass import MyClass from foo.bar.yourclass import YourClass If this spelling causes local name clashes, then spell them explicitly: import myclass import foo.bar.yourclass and use myclass.MyClass and foo.bar.yourclass.YourClass . Wildcard imports ( from <module> import * ) should be avoided, as they make it unclear which names are present in the namespace, confusing both readers and many automated tools. There is one defensible use case for a wildcard import, which is to republish an internal interface as part of a public API (for example, overwriting a pure Python implementation of an interface with the definitions from an optional accelerator module and exactly which definitions will be overwritten isn’t known in advance). When republishing names this way, the guidelines below regarding public and internal interfaces still apply. Module Level Dunder Names Module level “dunders” (i.e. names with two leading and two trailing underscores) such as __all__ , __author__ , __version__ , etc. should be placed after the module docstring but before any import statements except from __future__ imports. Python mandates that future-imports must appear in the module before any other code except docstrings: """This is the example module. This module does stuff. """ from __future__ import barry_as_FLUFL __all__ = [ 'a' , 'b' , 'c' ] __version__ = '0.1' __author__ = 'Cardinal Biggles' import os import sys String Quotes In Python, single-quoted strings and double-quoted strings are the same. This PEP does not make a recommendation for this. Pick a rule and stick to it. When a string contains single or double quote characters, however, use the other one to avoid backslashes in the string. It improves readability. For triple-quoted strings, always use double quote characters to be consistent with the docstring convention in PEP 257 . Whitespace in Expressions and Statements Pet Peeves Avoid extraneous whitespace in the following situations: Immediately inside parentheses, brackets or braces: # Correct: spam ( ham [ 1 ], { eggs : 2 }) # Wrong: spam ( ham [ 1 ], { eggs : 2 } ) Between a trailing comma and a following close parenthesis: # Correct: foo = ( 0 ,) # Wrong: bar = ( 0 , ) Immediately before a comma, semicolon, or colon: # Correct: if x == 4 : print ( x , y ); x , y = y , x # Wrong: if x == 4 : print ( x , y ) ; x , y = y , x However, in a slice the colon acts like a binary operator, and should have equal amounts on either side (treating it as the operator with the lowest priority). In an extended slice, both colons must have the same amount of spacing applied. Exception: when a slice parameter is omitted, the space is omitted: # Correct: ham [ 1 : 9 ], ham [ 1 : 9 : 3 ], ham [: 9 : 3 ], ham [ 1 :: 3 ], ham [ 1 : 9 :] ham [ lower : upper ], ham [ lower : upper :], ham [ lower :: step ] ham [ lower + offset : upper + offset ] ham [: upper_fn ( x ) : step_fn ( x )], ham [:: step_fn ( x )] ham [ lower + offset : upper + offset ] # Wrong: ham [ lower + offset : upper + offset ] ham [ 1 : 9 ], ham [ 1 : 9 ], ham [ 1 : 9 : 3 ] ham [ lower : : step ] ham [ : upper ] Immediately before the open parenthesis that starts the argument list of a function call: # Correct: spam ( 1 ) # Wrong: spam ( 1 ) Immediately before the open parenthesis that starts an indexing or slicing: # Correct: dct [ 'key' ] = lst [ index ] # Wrong: dct [ 'key' ] = lst [ index ] More than one space around an assignment (or other) operator to align it with another: # Correct: x = 1 y = 2 long_variable = 3 # Wrong: x = 1 y = 2 long_variable = 3 Other Recommendations Avoid trailing whitespace anywhere. Because it’s usually invisible, it can be confusing: e.g. a backslash followed by a space and a newline does not count as a line continuation marker. Some editors don’t preserve it and many projects (like CPython itself) have pre-commit hooks that reject it. Always surround these binary operators with a single space on either side: assignment ( = ), augmented assignment ( += , -= etc.), comparisons ( == , < , > , != , <= , >= , in , not in , is , is not ), Booleans ( and , or , not ). If operators with different priorities are used, consider adding whitespace around the operators with the lowest priority(ies). Use your own judgment; however, never use more than one space, and always have the same amount of whitespace on both sides of a binary operator: # Correct: i = i + 1 submitted += 1 x = x * 2 - 1 hypot2 = x * x + y * y c = ( a + b ) * ( a - b ) # Wrong: i = i + 1 submitted += 1 x = x * 2 - 1 hypot2 = x * x + y * y c = ( a + b ) * ( a - b ) Function annotations should use the normal rules for colons and always have spaces around the -> arrow if present. (See Function Annotations below for more about function annotations.): # Correct: def munge ( input : AnyStr ): ... def munge () -> PosInt : ... # Wrong: def munge ( input : AnyStr ): ... def munge () -> PosInt : ... Don’t use spaces around the = sign when used to indicate a keyword argument, or when used to indicate a default value for an unannotated function parameter: # Correct: def complex ( real , imag = 0.0 ): return magic ( r = real , i = imag ) # Wrong: def complex ( real , imag = 0.0 ): return magic ( r = real , i = imag ) When combining an argument annotation with a default value, however, do use spaces around the = sign: # Correct: def munge ( sep : AnyStr = None ): ... def munge ( input : AnyStr , sep : AnyStr = None , limit = 1000 ): ... # Wrong: def munge ( input : AnyStr = None ): ... def munge ( input : AnyStr , limit = 1000 ): ... Compound statements (multiple statements on the same line) are generally discouraged: # Correct: if foo == 'blah' : do_blah_thing () do_one () do_two () do_three () Rather not: # Wrong: if foo == 'blah' : do_blah_thing () do_one (); do_two (); do_three () While sometimes it’s okay to put an if/for/while with a small body on the same line, never do this for multi-clause statements. Also avoid folding such long lines! Rather not: # Wrong: if foo == 'blah' : do_blah_thing () for x in lst : total += x while t < 10 : t = delay () Definitely not: # Wrong: if foo == 'blah' : do_blah_thing () else : do_non_blah_thing () try : something () finally : cleanup () do_one (); do_two (); do_three ( long , argument , list , like , this ) if foo == 'blah' : one (); two (); three () When to Use Trailing Commas Trailing commas are usually optional, except they are mandatory when making a tuple of one element. For clarity, it is recommended to surround the latter in (technically redundant) parentheses: # Correct: FILES = ( 'setup.cfg' ,) # Wrong: FILES = 'setup.cfg' , When trailing commas are redundant, they are often helpful when a version control system is used, when a list of values, arguments or imported items is expected to be extended over time. The pattern is to put each value (etc.) on a line by itself, always adding a trailing comma, and add the close parenthesis/bracket/brace on the next line. However it does not make sense to have a trailing comma on the same line as the closing delimiter (except in the above case of singleton tuples): # Correct: FILES = [ 'setup.cfg' , 'tox.ini' , ] initialize ( FILES , error = True , ) # Wrong: FILES = [ 'setup.cfg' , 'tox.ini' ,] initialize ( FILES , error = True ,) Comments Comments that contradict the code are worse than no comments. Always make a priority of keeping the comments up-to-date when the code changes! Comments should be complete sentences. The first word should be capitalized, unless it is an identifier that begins with a lower case letter (never alter the case of identifiers!). Block comments generally consist of one or more paragraphs built out of complete sentences, with each sentence ending in a period. You should use one or two spaces after a sentence-ending period in multi-sentence comments, except after the final sentence. Ensure that your comments are clear and easily understandable to other speakers of the language you are writing in. Python coders from non-English speaking countries: please write your comments in English, unless you are 120% sure that the code will never be read by people who don’t speak your language. Block Comments Block comments generally apply to some (or all) code that follows them, and are indented to the same level as that code. Each line of a block comment starts with a # and a single space (unless it is indented text inside the comment). Paragraphs inside a block comment are separated by a line containing a single # . Inline Comments Use inline comments sparingly. An inline comment is a comment on the same line as a statement. Inline comments should be separated by at least two spaces from the statement. They should start with a # and a single space. Inline comments are unnecessary and in fact distracting if they state the obvious. Don’t do this: x = x + 1 # Increment x But sometimes, this is useful: x = x + 1 # Compensate for border Documentation Strings Conventions for writing good documentation strings (a.k.a. “docstrings”) are immortalized in PEP 257 . Write docstrings for all public modules, functions, classes, and methods. Docstrings are not necessary for non-public methods, but you should have a comment that describes what the method does. This comment should appear after the def line. PEP 257 describes good docstring conventions. Note that most importantly, the """ that ends a multiline docstring should be on a line by itself: """Return a foobang Optional plotz says to frobnicate the bizbaz first. """ For one liner docstrings, please keep the closing """ on the same line: """Return an ex-parrot.""" Naming Conventions The naming conventions of Python’s library are a bit of a mess, so we’ll never get this completely consistent – nevertheless, here are the currently recommended naming standards. New modules and packages (including third party frameworks) should be written to these standards, but where an existing library has a different style, internal consistency is preferred. Overriding Principle Names that are visible to the user as public parts of the API should follow conventions that reflect usage rather than implementation. Descriptive: Naming Styles There are a lot of different naming styles. It helps to be able to recognize what naming style is being used, independently from what they are used for. The following naming styles are commonly distinguished: b (single lowercase letter) B (single uppercase letter) lowercase lower_case_with_underscores UPPERCASE UPPER_CASE_WITH_UNDERSCORES CapitalizedWords (or CapWords, or CamelCase – so named because of the bumpy look of its letters [4] ). This is also sometimes known as StudlyCaps. Note: When using acronyms in CapWords, capitalize all the letters of the acronym. Thus HTTPServerError is better than HttpServerError. mixedCase (differs from CapitalizedWords by initial lowercase character!) Capitalized_Words_With_Underscores (ugly!) There’s also the style of using a short unique prefix to group related names together. This is not used much in Python, but it is mentioned for completeness. For example, the os.stat() function returns a tuple whose items traditionally have names like st_mode , st_size , st_mtime and so on. (This is done to emphasize the correspondence with the fields of the POSIX system call struct, which helps programmers familiar with that.) The X11 library uses a leading X for all its public functions. In Python, this style is generally deemed unnecessary because attribute and method names are prefixed with an object, and function names are prefixed with a module name. In addition, the following special forms using leading or trailing underscores are recognized (these can generally be combined with any case convention): _single_leading_underscore : weak “internal use” indicator. E.g. from M import * does not import objects whose names start with an underscore. single_trailing_underscore_ : used by convention to avoid conflicts with Python keyword, e.g. : tkinter . Toplevel ( master , class_ = 'ClassName' ) __double_leading_underscore : when naming a class attribute, invokes name mangling (inside class FooBar, __boo becomes _FooBar__boo ; see below). __double_leading_and_trailing_underscore__ : “magic” objects or attributes that live in user-controlled namespaces. E.g. __init__ , __import__ or __file__ . Never invent such names; only use them as documented. Prescriptive: Naming Conventions Names to Avoid Never use the characters ‘l’ (lowercase letter el), ‘O’ (uppercase letter oh), or ‘I’ (uppercase letter eye) as single character variable names. In some fonts, these characters are indistinguishable from the numerals one and zero. When tempted to use ‘l’, use ‘L’ instead. ASCII Compatibility Identifiers used in the standard library must be ASCII compatible as described in the policy section of PEP 3131 . Package and Module Names Modules should have short, all-lowercase names. Underscores can be used in the module name if it improves readability. Python packages should also have short, all-lowercase names, although the use of underscores is discouraged. When an extension module written in C or C++ has an accompanying Python module that provides a higher level (e.g. more object oriented) interface, the C/C++ module has a leading underscore (e.g. _socket ). Class Names Class names should normally use the CapWords convention. The naming convention for functions may be used instead in cases where the interface is documented and used primarily as a callable. Note that there is a separate convention for builtin names: most builtin names are single words (or two words run together), with the CapWords convention used only for exception names and builtin constants. Type Variable Names Names of type variables introduced in PEP 484 should normally use CapWords preferring short names: T , AnyStr , Num . It is recommended to add suffixes _co or _contra to the variables used to declare covariant or contravariant behavior correspondingly: from typing import TypeVar VT_co = TypeVar ( 'VT_co' , covariant = True ) KT_contra = TypeVar ( 'KT_contra' , contravariant = True ) Exception Names Because exceptions should be classes, the class naming convention applies here. However, you should use the suffix “Error” on your exception names (if the exception actually is an error). Global Variable Names (Let’s hope that these variables are meant for use inside one module only.) The conventions are about the same as those for functions. Modules that are designed for use via from M import * should use the __all__ mechanism to prevent exporting globals, or use the older convention of prefixing such globals with an underscore (which you might want to do to indicate these globals are “module non-public”). Function and Variable Names Function names should be lowercase, with words separated by underscores as necessary to improve readability. Variable names follow the same convention as function names. mixedCase is allowed only in contexts where that’s already the prevailing style (e.g. threading.py), to retain backwards compatibility. Function and Method Arguments Always use self for the first argument to instance methods. Always use cls for the first argument to class methods. If a function argument’s name clashes with a reserved keyword, it is generally better to append a single trailing underscore rather than use an abbreviation or spelling corruption. Thus class_ is better than clss . (Perhaps better is to avoid such clashes by using a synonym.) Method Names and Instance Variables Use the function naming rules: lowercase with words separated by underscores as necessary to improve readability. Use one leading underscore only for non-public methods and instance variables. To avoid name clashes with subclasses, use two leading underscores to invoke Python’s name mangling rules. Python mangles these names with the class name: if class Foo has an attribute named __a , it cannot be accessed by Foo.__a . (An insistent user could still gain access by calling Foo._Foo__a .) Generally, double leading underscores should be used only to avoid name conflicts with attributes in classes designed to be subclassed. Note: there is some controversy about the use of __names (see below). Constants Constants are usually defined on a module level and written in all capital letters with underscores separating words. Examples include MAX_OVERFLOW and TOTAL . Designing for Inheritance Always decide whether a class’s methods and instance variables (collectively: “attributes”) should be public or non-public. If in doubt, choose non-public; it’s easier to make it public later than to make a public attribute non-public. Public attributes are those that you expect unrelated clients of your class to use, with your commitment to avoid backwards incompatible changes. Non-public attributes are those that are not intended to be used by third parties; you make no guarantees that non-public attributes won’t change or even be removed. We don’t use the term “private” here, since no attribute is really private in Python (without a generally unnecessary amount of work). Another category of attributes are those that are part of the “subclass API” (often called “protected” in other languages). Some classes are designed to be inherited from, either to extend or modify aspects of the class’s behavior. When designing such a class, take care to make explicit decisions about which attributes are public, which are part of the subclass API, and which are truly only to be used by your base class. With this in mind, here are the Pythonic guidelines: Public attributes should have no leading underscores. If your public attribute name collides with a reserved keyword, append a single trailing underscore to your attribute name. This is preferable to an abbreviation or corrupted spelling. (However, notwithstanding this rule, ‘cls’ is the preferred spelling for any variable or argument which is known to be a class, especially the first argument to a class method.) Note 1: See the argument name recommendation above for class methods. For simple public data attributes, it is best to expose just the attribute name, without complicated accessor/mutator methods. Keep in mind that Python provides an easy path to future enhancement, should you find that a simple data attribute needs to grow functional behavior. In that case, use properties to hide functional implementation behind simple data attribute access syntax. Note 1: Try to keep the functional behavior side-effect free, although side-effects such as caching are generally fine. Note 2: Avoid using properties for computationally expensive operations; the attribute notation makes the caller believe that access is (relatively) cheap. If your class is intended to be subclassed, and you have attributes that you do not want subclasses to use, consider naming them with double leading underscores and no trailing underscores. This invokes Python’s name mangling algorithm, where the name of the class is mangled into the attribute name. This helps avoid attribute name collisions should subclasses inadvertently contain attributes with the same name. Note 1: Note that only the simple class name is used in the mangled name, so if a subclass chooses both the same class name and attribute name, you can still get name collisions. Note 2: Name mangling can make certain uses, such as debugging and __getattr__() , less convenient. However the name mangling algorithm is well documented and easy to perform manually. Note 3: Not everyone likes name mangling. Try to balance the need to avoid accidental name clashes with potential use by advanced callers. Public and Internal Interfaces Any backwards compatibility guarantees apply only to public interfaces. Accordingly, it is important that users be able to clearly distinguish between public and internal interfaces. Documented interfaces are considered public, unless the documentation explicitly declares them to be provisional or internal interfaces exempt from the usual backwards compatibility guarantees. All undocumented interfaces should be assumed to be internal. To better support introspection, modules should explicitly declare the names in their public API using the __all__ attribute. Setting __all__ to an empty list indicates that the module has no public API. Even with __all__ set appropriately, internal interfaces (packages, modules, classes, functions, attributes or other names) should still be prefixed with a single leading underscore. An interface is also considered internal if any containing namespace (package, module or class) is considered internal. Imported names should always be considered an implementation detail. Other modules must not rely on indirect access to such imported names unless they are an explicitly documented part of the containing module’s API, such as os.path or a package’s __init__ module that exposes functionality from submodules. Programming Recommendations Code should be written in a way that does not disadvantage other implementations of Python (PyPy, Jython, IronPython, Cython, Psyco, and such). For example, do not rely on CPython’s efficient implementation of in-place string concatenation for statements in the form a += b or a = a + b . This optimization is fragile even in CPython (it only works for some types) and isn’t present at all in implementations that don’t use refcounting. In performance sensitive parts of the library, the ''.join() form should be used instead. This will ensure that concatenation occurs in linear time across various implementations. Comparisons to singletons like None should always be done with is or is not , never the equality operators. Also, beware of writing if x when you really mean if x is not None – e.g. when testing whether a variable or argument that defaults to None was set to some other value. The other value might have a type (such as a container) that could be false in a boolean context! Use is not operator rather than not ... is . While both expressions are functionally identical, the former is more readable and preferred: # Correct: if foo is not None : # Wrong: if not foo is None : When implementing ordering operations with rich comparisons, it is best to implement all six operations ( __eq__ , __ne__ , __lt__ , __le__ , __gt__ , __ge__ ) rather than relying on other code to only exercise a particular comparison. To minimize the effort involved, the functools.total_ordering() decorator provides a tool to generate missing comparison methods. PEP 207 indicates that reflexivity rules are assumed by Python. Thus, the interpreter may swap y > x with x < y , y >= x with x <= y , and may swap the arguments of x == y and x != y . The sort() and min() operations are guaranteed to use the < operator and the max() function uses the > operator. However, it is best to implement all six operations so that confusion doesn’t arise in other contexts. Always use a def statement instead of an assignment statement that binds a lambda expression directly to an identifier: # Correct: def f ( x ): return 2 * x # Wrong: f = lambda x : 2 * x The first form means that the name of the resulting function object is specifically ‘f’ instead of the generic ‘<lambda>’. This is more useful for tracebacks and string representations in general. The use of the assignment statement eliminates the sole benefit a lambda expression can offer over an explicit def statement (i.e. that it can be embedded inside a larger expression) Derive exceptions from Exception rather than BaseException . Direct inheritance from BaseException is reserved for exceptions where catching them is almost always the wrong thing to do. Design exception hierarchies based on the distinctions that code catching the exceptions is likely to need, rather than the locations where the exceptions are raised. Aim to answer the question “What went wrong?” programmatically, rather than only stating that “A problem occurred” (see PEP 3151 for an example of this lesson being learned for the builtin exception hierarchy) Class naming conventions apply here, although you should add the suffix “Error” to your exception classes if the exception is an error. Non-error exceptions that are used for non-local flow control or other forms of signaling need no special suffix. Use exception chaining appropriately. raise X from Y should be used to indicate explicit replacement without losing the original traceback. When deliberately replacing an inner exception (using raise X from None ), ensure that relevant details are transferred to the new exception (such as preserving the attribute name when converting KeyError to AttributeError, or embedding the text of the original exception in the new exception message). When catching exceptions, mention specific exceptions whenever possible instead of using a bare except: clause: try : import platform_specific_module except ImportError : platform_specific_module = None A bare except: clause will catch SystemExit and KeyboardInterrupt exceptions, making it harder to interrupt a program with Control-C, and can disguise other problems. If you want to catch all exceptions that signal program errors, use except Exception: (bare except is equivalent to except BaseException: ). A good rule of thumb is to limit use of bare ‘except’ clauses to two cases: If the exception handler will be printing out or logging the traceback; at least the user will be aware that an error has occurred. If the code needs to do some cleanup work, but then lets the exception propagate upwards with raise . try...finally can be a better way to handle this case. When catching operating system errors, prefer the explicit exception hierarchy introduced in Python 3.3 over introspection of errno values. Additionally, for all try/except clauses, limit the try clause to the absolute minimum amount of code necessary. Again, this avoids masking bugs: # Correct: try : value = collection [ key ] except KeyError : return key_not_found ( key ) else : return handle_value ( value ) # Wrong: try : # Too broad! return handle_value ( collection [ key ]) except KeyError : # Will also catch KeyError raised by handle_value() return key_not_found ( key ) When a resource is local to a particular section of code, use a with statement to ensure it is cleaned up promptly and reliably after use. A try/finally statement is also acceptable. Context managers should be invoked through separate functions or methods whenever they do something other than acquire and release resources: # Correct: with conn . begin_transaction (): do_stuff_in_transaction ( conn ) # Wrong: with conn : do_stuff_in_transaction ( conn ) The latter example doesn’t provide any information to indicate that the __enter__ and __exit__ methods are doing something other than closing the connection after a transaction. Being explicit is important in this case. Be consistent in return statements. Either all return statements in a function should return an expression, or none of them should. If any return statement returns an expression, any return statements where no value is returned should explicitly state this as return None , and an explicit return statement should be present at the end of the function (if reachable): # Correct: def foo ( x ): if x >= 0 : return math . sqrt ( x ) else : return None def bar ( x ): if x < 0 : return None return math . sqrt ( x ) # Wrong: def foo ( x ): if x >= 0 : return math . sqrt ( x ) def bar ( x ): if x < 0 : return return math . sqrt ( x ) Use ''.startswith() and ''.endswith() instead of string slicing to check for prefixes or suffixes. startswith() and endswith() are cleaner and less error prone: # Correct: if foo . startswith ( 'bar' ): # Wrong: if foo [: 3 ] == 'bar' : Object type comparisons should always use isinstance() instead of comparing types directly: # Correct: if isinstance ( obj , int ): # Wrong: if type ( obj ) is type ( 1 ): For sequences, (strings, lists, tuples), use the fact that empty sequences are false: # Correct: if not seq : if seq : # Wrong: if len ( seq ): if not len ( seq ): Don’t write string literals that rely on significant trailing whitespace. Such trailing whitespace is visually indistinguishable and some editors (or more recently, reindent.py) will trim them. Don’t compare boolean values to True or False using == : # Correct: if greeting : # Wrong: if greeting == True : Worse: # Wrong: if greeting is True : Use of the flow control statements return / break / continue within the finally suite of a try...finally , where the flow control statement would jump outside the finally suite, is discouraged. This is because such statements will implicitly cancel any active exception that is propagating through the finally suite: # Wrong: def foo (): try : 1 / 0 finally : return 42 Function Annotations With the acceptance of PEP 484 , the style rules for function annotations have changed. Function annotations should use PEP 484 syntax (there are some formatting recommendations for annotations in the previous section). The experimentation with annotation styles that was recommended previously in this PEP is no longer encouraged. However, outside the stdlib, experiments within the rules of PEP 484 are now encouraged. For example, marking up a large third party library or application with PEP 484 style type annotations, reviewing how easy it was to add those annotations, and observing whether their presence increases code understandability. The Python standard library should be conservative in adopting such annotations, but their use is allowed for new code and for big refactorings. For code that wants to make a different use of function annotations it is recommended to put a comment of the form: # type: ignore near the top of the file; this tells type checkers to ignore all annotations. (More fine-grained ways of disabling complaints from type checkers can be found in PEP 484 .) Like linters, type checkers are optional, separate tools. Python interpreters by default should not issue any messages due to type checking and should not alter their behavior based on annotations. Users who don’t want to use type checkers are free to ignore them. However, it is expected that users of third party library packages may want to run type checkers over those packages. For this purpose PEP 484 recommends the use of stub files: .pyi files that are read by the type checker in preference of the corresponding .py files. Stub files can be distributed with a library, or separately (with the library author’s permission) through the typeshed repo [5] . Variable Annotations PEP 526 introduced variable annotations. The style recommendations for them are similar to those on function annotations described above: Annotations for module level variables, class and instance variables, and local variables should have a single space after the colon. There should be no space before the colon. If an assignment has a right hand side, then the equality sign should have exactly one space on both sides: # Correct: code : int class Point : coords : Tuple [ int , int ] label : str = '<unknown>' # Wrong: code : int # No space after colon code : int # Space before colon class Test : result : int = 0 # No spaces around equality sign Although the PEP 526 is accepted for Python 3.6, the variable annotation syntax is the preferred syntax for stub files on all versions of Python (see PEP 484 for details). Footnotes [ 1 ] Hanging indentation is a type-setting style where all the lines in a paragraph are indented except the first line. In the context of Python, the term is used to describe a style where the opening parenthesis of a parenthesized statement is the last non-whitespace character of the line, with subsequent lines being indented until the closing parenthesis. References [ 2 ] Barry’s GNU Mailman style guide http://barry.warsaw.us/software/STYLEGUIDE.txt [ 3 ] Donald Knuth’s The TeXBook , pages 195 and 196. [ 4 ] http://www.wikipedia.com/wiki/Camel_case [ 5 ] Typeshed repo https://github.com/python/typeshed Copyright This document has been placed in the public domain. Source: https://github.com/python/peps/blob/main/peps/pep-0008.rst Last modified: 2025-04-04 00:19:04 GMT Contents Introduction A Foolish Consistency is the Hobgoblin of Little Minds Code Lay-out Indentation Tabs or Spaces? Maximum Line Length Should a Line Break Before or After a Binary Operator? Blank Lines Source File Encoding Imports Module Level Dunder Names String Quotes Whitespace in Expressions and Statements Pet Peeves Other Recommendations When to Use Trailing Commas Comments Block Comments Inline Comments Documentation Strings Naming Conventions Overriding Principle Descriptive: Naming Styles Prescriptive: Naming Conventions Names to Avoid ASCII Compatibility Package and Module Names Class Names Type Variable Names Exception Names Global Variable Names Function and Variable Names Function and Method Arguments Method Names and Instance Variables Constants Designing for Inheritance Public and Internal Interfaces Programming Recommendations Function Annotations Variable Annotations References Copyright Page Source (GitHub) | 2026-01-13T08:49:02 |
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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close # platformengineering Follow Hide Building internal platforms to enable application developers to ship code faster and safer. Create Post Older #platformengineering posts 1 2 3 4 5 6 7 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu Shift-Left Reliability Rob Fox Rob Fox Rob Fox Follow Jan 12 Shift-Left Reliability # sre # devops # cicd # platformengineering Comments Add Comment 4 min read Week 0: Starting a 16-Week Journey to Platform Engineering Jimmy Maina Jimmy Maina Jimmy Maina Follow Jan 11 Week 0: Starting a 16-Week Journey to Platform Engineering # backend # platformengineering # learninginpublic # career Comments Add Comment 4 min read From DevOps to Platform Engineering in the AI Era Pratik Mahalle Pratik Mahalle Pratik Mahalle Follow Jan 5 From DevOps to Platform Engineering in the AI Era # devops # platformengineering # ai # career Comments Add Comment 4 min read Beyond the YAML Hell: Why 2026 is the Year of Platform Engineering Meena Nukala Meena Nukala Meena Nukala Follow Jan 3 Beyond the YAML Hell: Why 2026 is the Year of Platform Engineering # devops # sre # cloud # platformengineering Comments Add Comment 3 min read Why Traditional DevOps Stops Scaling Sreekanth Kuruba Sreekanth Kuruba Sreekanth Kuruba Follow Jan 6 Why Traditional DevOps Stops Scaling # discuss # devops # platformengineering # career 2 reactions Comments Add Comment 2 min read Platform Engineering’s patterns and anti-patterns Steve Fenton Steve Fenton Steve Fenton Follow Dec 18 '25 Platform Engineering’s patterns and anti-patterns # platformengineering # devops Comments Add Comment 11 min read Rethinking EKS Management: Kiro Meets AWS MCP Server vivekpophale vivekpophale vivekpophale Follow Dec 28 '25 Rethinking EKS Management: Kiro Meets AWS MCP Server # kubernetes # aws # platformengineering # ai 4 reactions Comments Add Comment 9 min read The Real Cost of Swapping Infrastructure Allen Helton Allen Helton Allen Helton Follow Dec 15 '25 The Real Cost of Swapping Infrastructure # infrastructure # architecture # platformengineering 1 reaction Comments Add Comment 3 min read Kubernetes Multi-Tenancy Solutions: The Hard Way (DIY) vs. The Smart Way (2025 Guide) Devi Green Devi Green Devi Green Follow Dec 8 '25 Kubernetes Multi-Tenancy Solutions: The Hard Way (DIY) vs. The Smart Way (2025 Guide) # kubernetes # devops # platformengineering # cloudnative 2 reactions Comments Add Comment 3 min read What is IDP and why we need it? Sathish Kumar Sathish Kumar Sathish Kumar Follow Nov 26 '25 What is IDP and why we need it? # devsecops # platformengineering # internaldeveloperplatform Comments Add Comment 1 min read AWS Multi-Account Guardrails: A Complete Blueprint for Secure, Automated Cloud Governance Guptaji Teegela Guptaji Teegela Guptaji Teegela Follow Nov 21 '25 AWS Multi-Account Guardrails: A Complete Blueprint for Secure, Automated Cloud Governance # aws # sre # devops # platformengineering Comments Add Comment 9 min read Stop Being a "Human Router": Moving from Reactive DevOps to Autonomous AIOps Meena Nukala Meena Nukala Meena Nukala Follow Dec 21 '25 Stop Being a "Human Router": Moving from Reactive DevOps to Autonomous AIOps # aiops # devops # automation # platformengineering Comments Add Comment 2 min read From Infrastructure as Code to Platform as a Product: The Shift You Can’t Ignore Meena Nukala Meena Nukala Meena Nukala Follow Dec 20 '25 From Infrastructure as Code to Platform as a Product: The Shift You Can’t Ignore # devops # platformengineering # cloud # productivity Comments Add Comment 1 min read Beyond Scheduling: How Kubernetes Uses QoS, Priority, and Scoring to Keep Your Cluster Balanced Guptaji Teegela Guptaji Teegela Guptaji Teegela Follow Nov 12 '25 Beyond Scheduling: How Kubernetes Uses QoS, Priority, and Scoring to Keep Your Cluster Balanced # devops # sre # microservices # platformengineering Comments Add Comment 4 min read HashiCorp Project Infragraph — The “Google Maps” for Cloud Infrastructure? #cloudnativewisdom12 Saim Safdar Saim Safdar Saim Safdar Follow Nov 5 '25 HashiCorp Project Infragraph — The “Google Maps” for Cloud Infrastructure? #cloudnativewisdom12 # devops # kubernetes # platformengineering # genai 1 reaction Comments Add Comment 3 min read How Enterprises Can Retain Local Control Through Digital Sovereignty | Ep 142 #cloudnativefm Saim Safdar Saim Safdar Saim Safdar Follow Nov 7 '25 How Enterprises Can Retain Local Control Through Digital Sovereignty | Ep 142 #cloudnativefm # platformengineering # genai # opensource # digitalsovereignty 5 reactions Comments Add Comment 3 min read 🏗️ Building the Platform That Empowers Reliability by Design Guptaji Teegela Guptaji Teegela Guptaji Teegela Follow Oct 29 '25 🏗️ Building the Platform That Empowers Reliability by Design # devops # sre # platformengineering # cloudarchitecture Comments Add Comment 3 min read Your IDP needs DDD Steve Fenton Steve Fenton Steve Fenton Follow Nov 3 '25 Your IDP needs DDD # platformengineering # devops # ddd 3 reactions Comments Add Comment 6 min read The Role Confusion: SRE vs Cloud vs Platform Engineer (And Why "DevOps Engineer" Misses the Point) Anderson Leite Anderson Leite Anderson Leite Follow Nov 21 '25 The Role Confusion: SRE vs Cloud vs Platform Engineer (And Why "DevOps Engineer" Misses the Point) # techcareers # cloudengineering # platformengineering # sre 3 reactions Comments Add Comment 5 min read Beyond Scheduling: How Kubernetes Uses QoS, Priority, and Scoring to Keep Your Cluster Balanced Guptaji Teegela Guptaji Teegela Guptaji Teegela Follow Nov 20 '25 Beyond Scheduling: How Kubernetes Uses QoS, Priority, and Scoring to Keep Your Cluster Balanced # devops # sre # microservices # platformengineering Comments Add Comment 2 min read The top 5 features of internal developer platforms Steve Fenton Steve Fenton Steve Fenton Follow Nov 18 '25 The top 5 features of internal developer platforms # platformengineering # devops # idp 2 reactions Comments Add Comment 5 min read How organizations measure Platform Engineering Steve Fenton Steve Fenton Steve Fenton Follow Oct 28 '25 How organizations measure Platform Engineering # platformengineering # devops # analytics 1 reaction Comments Add Comment 6 min read The Secret Sauce of Modern Tech: What a Platform Team Does and Why You Need One Kubernetes with Naveen Kubernetes with Naveen Kubernetes with Naveen Follow Nov 17 '25 The Secret Sauce of Modern Tech: What a Platform Team Does and Why You Need One # platformengineering # dedvops # kubernetes # cloud 1 reaction Comments Add Comment 3 min read The Great AWS Outage: The $11 Billion Argument for Kubernetes Signadot Signadot Signadot Follow Nov 10 '25 The Great AWS Outage: The $11 Billion Argument for Kubernetes # kubernetes # cloud # platformengineering # devops 2 reactions Comments 1 comment 6 min read Platform Engineering: Easy to Use, Hard to Mess Up Tom Mango Tom Mango Tom Mango Follow Oct 3 '25 Platform Engineering: Easy to Use, Hard to Mess Up # platformengineering # management Comments Add Comment 5 min read loading... trending guides/resources The Role Confusion: SRE vs Cloud vs Platform Engineer (And Why "DevOps Engineer" Misses the Point) The Great AWS Outage: The $11 Billion Argument for Kubernetes AWS Multi-Account Guardrails: A Complete Blueprint for Secure, Automated Cloud Governance The Secret Sauce of Modern Tech: What a Platform Team Does and Why You Need One The Real Cost of Swapping Infrastructure Week 0: Starting a 16-Week Journey to Platform Engineering Introducing Realm9: Solving Enterprise Environment Chaos with AI Beyond Scheduling: How Kubernetes Uses QoS, Priority, and Scoring to Keep Your Cluster Balanced From DevOps to Platform Engineering in the AI Era From Infrastructure as Code to Platform as a Product: The Shift You Can’t Ignore Beyond Scheduling: How Kubernetes Uses QoS, Priority, and Scoring to Keep Your Cluster Balanced Shift-Left Reliability Beyond the YAML Hell: Why 2026 is the Year of Platform Engineering HashiCorp Project Infragraph — The “Google Maps” for Cloud Infrastructure? #cloudnativewisdom12 How Enterprises Can Retain Local Control Through Digital Sovereignty | Ep 142 #cloudnativefm Platform Engineering’s patterns and anti-patterns Companies like Spotify (with Backstage) and Netflix scaled DevOps exactly this way — by building ... Your IDP needs DDD What is IDP and why we need it? Kubernetes Multi-Tenancy Solutions: The Hard Way (DIY) vs. The Smart Way (2025 Guide) 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . DEV Community © 2016 - 2026. We're a place where coders share, stay up-to-date and grow their careers. Log in Create account | 2026-01-13T08:49:02 |
https://www.python.org/psf/board/ | PSF Officers & Board Members | Python Software Foundation Notice: While JavaScript is not essential for this website, your interaction with the content will be limited. Please turn JavaScript on for the full experience. Skip to content ▼ Close Python PSF Docs PyPI Jobs Community ▲ The Python Network Donate ≡ Menu Search This Site GO A A Smaller Larger Reset Socialize LinkedIn Mastodon Chat on IRC Twitter About Mission Statement Board of Directors & Officers PSF Staff Annual Impact Report Fiscal Sponsorees Public Records Legal & Policies PSF FAQ Developers in Residence Sponsorship PSF Sponsors Apply to Sponsor Sponsorship Prospectus 2025-26 Membership Sign up as a Member of the PSF! 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Tania Allard Kwon-Han Bae Cheuk Ting Ho Georgi Ker Jannis Leidel Cristián Maureira-Fredes Christopher Neugebauer Deb Nicholson Denny Perez Simon Willison Abigail Mesrenyame Dogbe Sheena O'Connell Active Board Committees and Voting Members 2025-2026 Executive Committee : Jannis Leidel, Cristián Maureira-Fredes, Christopher Neugebauer, Georgi Ker, Denny Perez, Deb Nicholson Finance Committee : Denny Perez, Georgi Ker, Sheena O'Connell, Deb Nicholson PyCon US Committee : Cristián Maureira-Freides, Simon Willison, Denny Perez, Abigail Mesrenyame Dogbe, Christopher Neugebauer, Jannis Leidel, Deb Nicholson 2024-2025 Officers President/Chair: Dawn Wages Vice Chair Elect: Chris Neugebauer Vice Chair: Tania Allard Executive Director: Deb Nicholson Secretary: Deb Nicholson Assistant Secretaries: Olivia Sauls Treasurer: Georgi Ker PyCon US Chair: Elaine Wong Board of Directors Tania Allard Kwon-Han Bae Kushal Das Cheuk Ting Ho Georgi Ker Jannis Leidel Cristián Maureira-Fredes Christopher Neugebauer Deb Nicholson Denny Perez Dawn Wages Simon Willison Active Board Committees and Voting Members 2024-2025 Executive Committee : Dawn Wages, Christopher Neugebauer, Georgi Ker, Tania Allard, Deb Nicholson Finance Committee : Kushal Das, Cheuk Ting Ho, Georgi Ker, Jannis Leidel, Deb Nicholson PyCon US Committee : Tania Allard, Denny Perez, Christopher Neugebauer, Cristián Maureira-Fredes, Deb Nicholson Membership Committee : Dawn Wages, Cheuk Ting Ho, Georgi Ker, KwonHan Bae, Christopher Neugebauer, Denny Perez 2023-2024 Officers President/Chair: Dawn Wages Vice Chair Elect: Débora Azevedo Vice Chairs: Christopher Neugebauer Executive Director: Deb Nicholson Secretary: Deb Nicholson Assistant Secretary: Olivia Sauls Treasurer: Kushal Das PyCon US Chair: Mariatta Wijaya Board of Directors Tania Allard Débora Azevedo Kwon-Han Bae Kushal Das Cheuk Ting Ho Georgi Ker Jannis Leidel Christopher Neugebauer Deb Nicholson Denny Perez Dawn Wages Simon Willison Active Board Committees and Voting Members 2023-2024 Executive Committee : Débora Azevedo, Kushal Das, Christopher Neugebauer, Dawn Wages, Deb Nicholson Finance Committee : Kushal Das, Cheuk Ting Ho, Georgi Ker, Jannis Leidel, Simon Willison PyCon US Committee : Tania Allard, Débora Azevedo, Cheuk Ting Ho, Georgi Ker, Jannis Leidel, Christopher Neugebauer, Dawn Wages Membership Committee : Tania Allard, Débora Azevedo, Cheuk Ting Ho, Georgi Ker, Jannis Leidel, Christopher Neugebauer, Denny Perez 2022-2023 Officers President: Guido van Rossum Chair: Thomas Wouters Vice Chair Elect: Débora Azevedo Vice Chairs: Jeff Triplett, Nina Zakharenko Executive Director: Deb Nicholson Secretary: Deb Nicholson Assistant Secretary: Olivia Sauls Treasurer: Dawn Wages Communications: PyCon US Chair: Mariatta Wijaya Board of Directors Débora Azevedo Tania Allard Kushal Das Dustin Ingram Deb Nicholson Jannis Leidel Joannah Nanjekye Jeff Triplett Dawn Wages Simon Willison Thomas Wouters Nina Zakharenko 2021-2022 Officers Chair: Lorena Mesa Vice Chair Elect: Thomas Wouters Vice Chairs: Marlene Mhangami, Joannah Nanjekye Executive Director: Deb Nicholson Secretary: Deb Nicholson Assistant Secretary: Olivia Sauls Treasurer: Jeff Triplett Communications: Jannis Leidel, Nina Zakharenko, Débora Azevedo General Counsel: Van Lindberg Event Coordinator: Jackie Augustine PyCon Chair: Emily Morehouse-Valcarcel Board of Directors Débora Azevedo Tania Allard Kushal Das Dustin Ingram Deb Nicholson Jannis Leidel Van Lindberg Lorena Mesa Marlene Mhangami Joannah Nanjekye Jeff Triplett Thomas Wouters Nina Zakharenko 2020-2021 Officers President: Guido van Rossum Chair: Lorena Mesa Vice Chair Elect: Thomas Wouters Vice Chairs: Marlene Mhangami, Christopher Neugebauer Executive Director: Ewa Jodlowska Secretary: Ewa Jodlowska Assistant Secretary: Betsy Waliszewski Treasurer: Jeff Triplett Communications: Jannis Leidel, Katie McLaughlin General Counsel: Van Lindberg Event Coordinator: Jackie Augustine, Betsy Waliszewski PyCon Chair: Emily Morehouse-Valcarcel Board of Directors Kushal Das Ewa Jodlowska Jannis Leidel Van Lindberg Lorena Mesa Katie McLaughlin Marlene Mhangami Christopher Neugebauer Anna Ossowski Jeff Triplett Thomas Wouters Nina Zakharenko Dustin Ingram Earlier 2019-2020 Officers President: Guido van Rossum Chair: Naomi Ceder Vice Chairs: Lorena Mesa, Anna Ossowski, Christopher Neugebauer Executive Director: Ewa Jodlowska Secretary: Ewa Jodlowska Assistant Secretary; Betsy Waliszewski Treasurer: Jacqueline Kazil Communications: Marlene Mhangami, Kushal Das, Jannis Leidel General Counsel: Van Lindberg Event Manager: Jackie Augustine PyCon Chair: Emily Morehouse-Valcarcel Board of Directors Naomi Ceder Kushal Das Eric Holscher Ewa Jodlowska Jacqueline Kazil Jannis Leidel Van Lindberg Lorena Mesa Katie McLaughlin Marlene Mhangami Christopher Neugebauer Anna Ossowski Jeff Triplett 2018-2019 Officers Guido van Rossum, President Chair: Naomi Ceder, Chair Jacqueline Kazil, Vice Chair Elect Van Lindberg, Vice Chair Thomas Wouters, Vice Chair Marlene Mhangami, Communication Chair Lorena Mesa, Communication Chair Ee Durbin, PyCon Chair Ewa Jodlowska, Director of Operations Kurt Kaiser, Treasurer Van Lindberg, General Counsel Ewa Jodlowska, Secretary Betsy Waliszewski, Assistant Secretary Ewa Jodlowska & Betsy Waliszewski, Event Coordinators Board of Directors Van Lindberg Naomi Ceder Eric Holscher Jacqueline Kazil Anna Ossowski Lorena Mesa Thomas Wouters Kushal Das Marlene Mhangami Christopher Neugebauer Jeff Triplett Katie McLaughlin Ewa Jodlowska 2017-2018 Officers Guido van Rossum, President Chair: Naomi Ceder, Chair Van Lindberg, Vice Chair Ee Durbin, PyCon Chair Ewa Jodlowska, Director of Operations Kurt Kaiser, Treasurer Van Lindberg, General Counsel Ewa Jodlowska, Secretary Betsy Waliszewski, Assistant Secretary Ewa Jodlowska & Betsy Waliszewski, Event Coordinators Board of Directors Van Lindberg Naomi Ceder Eric Holscher Jacqueline Kazil Paul Hildebrandt Lorena Mesa Thomas Wouters Kushal Das Marlene Mhangami Kenneth Reitz Trey Hunner Ewa Jodlowska Paola Katherine Pacheco 2016-2017 Officers Guido van Rossum, President Diana Clarke, Chair Van Lindberg & Naomi Ceder, Vice Chairs Brandon Rhodes, PyCon Chair Ewa Jodlowska, Director of Operations Kurt Kaiser, Treasurer Van Lindberg, General Counsel Ewa Jodlowska & Betsy Waliszewski, Event Coordinator Ewa Jodlowska , Secretary Betsy Waliszewski, Assistant Secretary Board of Directors Annapoornima Koppad (resigned 2017-01-17) Carrie Ann Philbin Carol Willing Jacqueline Kazil Kushal Das Lorena Mesa Trey Hunner Younggun Kim Naomi Ceder Diana Clarke Van Lindberg 2015-2016 Officers Guido van Rossum, President Van Lindberg, Chair Kurt Kaiser, Treasurer Diana Clarke, Communications Brandon Rhodes, PyCon Chair Ewa Jodlowska, Director of Operations Lynn Root & Naomi Ceder, Vice Chairs Betsy Waliszewski, Event Coordinator / Administrator Board of Directors Naomi Ceder Diana Clarke Alyssa (Nick) Coghlan Alex Gaynor Marc-Andre Lemburg Van Lindberg Ashwini Oruganti Anna Ossowski Carrie Ann Philbin Lynn Root Carol Willing 2014-2015 Officers Guido van Rossum, President Van Lindberg, Chair Kurt Kaiser, Treasurer Brian Curtin, Communications Diana Clarke, PyCon Chair Ewa Jodlowska, Secretary and Event Coordinator David Mertz, Vice-Chair and Chair Elect Board of Directors Alyssa (Nick) Coghlan Brian Curtin Kushal Das Selena Decklemann Alex Gaynor Marc-Andre Lemburg Van Lindberg Jessica McKellar David Mertz Travis Oliphant Lynn Root 2013-2014 Officers Guido van Rossum, President Van Lindberg, Chairman Kurt Kaiser, Treasurer Brian Curtin, Communications Diana Clarke, PyCon Chair Ewa Jodlowska, Secretary and Event Coordinator Jesse Noller, Vice Chairman Board of Directors Brett Cannon Brian Curtin Alex Gaynor Marc-Andre Lemburg Van Lindberg Jessica McKellar David Mertz Doug Napoleone Jesse Noller Tim Peters Lynn Root 2012-2013 Officers: Guido van Rossum, President Van Lindberg, Chairman Kurt Kaiser, Treasurer Brian Curtin, Communications Jesse Noller, PyCon Chair Ewa Jodlowska, Secretary and Event Coordinator Marc-Andre Lemburg, Vice Chairman Board of Directors: Brian Curtin Steve Holden Andrew Kuchling Marc-Andre Lemburg Van Lindberg Martin von Löwis Jessica McKellar David Mertz Doug Napoleone Jesse Noller Tim Peters 2011-2012 Officers: Guido van Rossum, President Steve Holden, Chairman Pat Campbell, Secretary Kurt Kaiser, Treasurer Doug Hellmann, Communications Jesse Noller, PyCon Chair Board of Directors: Raymond Hettinger Steve Holden Marc-Andre Lemburg Martin von Löwis David Mertz Doug Napoleone Jesse Noller Tim Peters Allison Randal Jeff Rush Greg Stein (resigned 2011-09-19) James Tauber (resigned 2011-07-18) Gloria Willadsen 2010-2011 Officers: Guido van Rossum, President Steve Holden, Chairman Pat Campbell, Secretary Kurt Kaiser, Treasurer Doug Hellmann, Communications Van Lindberg, PyCon Chair Board of Directors: Raymond Hettinger Steve Holden Marc-Andre Lemburg Martin von Löwis David Mertz Doug Napoleone Jesse Noller Tim Peters Allison Randal Jeff Rush Greg Stein James Tauber Gloria Willadsen 2009-2010 Officers: Guido van Rossum, President Steve Holden, Chairman David Goodger, Secretary Kurt Kaiser, Treasurer Raymond Hettinger, Assistant Treasurer Brett Cannon, Executive Vice President Van Lindberg, PyCon Chair Board of Directors: Brett Cannon Raymond Hettinger Steve Holden Andrew Kuchling Martin von Löwis David Mertz Tim Peters Jeff Rush James Tauber (David Goodger resigned from the Board in July 2009 when appointed Conference Coordinator.) 2008-2009 Officers: Guido van Rossum, President Steve Holden, Chairman David Goodger, Secretary & PyCon Chair Kurt Kaiser, Treasurer Raymond Hettinger, Assistant Treasurer Brett Cannon, Executive Vice President James Tauber, Vice President Board of Directors: Brett Cannon Stephan Deibel David Goodger Raymond Hettinger Steve Holden Martin von Löwis Tim Peters James Tauber 2007-2008 Officers: Guido van Rossum, President Stephan Deibel, Chairman Steve Holden, Vice Chairman David Goodger, Secretary Kurt Kaiser, Treasurer Neal Norwitz, Assistant Treasurer Brett Cannon, Executive Vice President Andrew Kuchling, Vice President Board of Directors: David Ascher Brett Cannon Stephan Deibel David Goodger Steve Holden Andrew Kuchling Martin von Löwis Tim Peters 2006-2007 Officers: Guido van Rossum, President Stephan Deibel, Chairman Steve Holden, Vice Chairman Kurt Kaiser, Treasurer Neal Norwitz, Assistant Treasurer David Goodger, Secretary Jeremy Hylton, Assistant Secretary David Ascher, Executive Vice President Andrew Kuchling, Vice President Martin von Löwis, Vice President Brett Cannon, Vice President Board of Directors: David Ascher Brett Cannon Stephan Deibel David Goodger Steve Holden Andrew Kuchling Martin von Löwis Tim Peters 2005-2006 Officers: Guido van Rossum, President Stephan Deibel, Chairman of the Board Neal Norwitz, Treasurer through October 18, Assistant Treasurer thereafter Kurt Kaiser, Assistant Treasurer through October 18, Treasurer thereafter Jeremy Hylton, Secretary David Goodger, Assistant Secretary Board of Directors: David Ascher Stephan Deibel Steve Holden Jeremy Hylton Andrew Kuchling Martin von Löwis Tim Peters (Guido van Rossum was elected to the Board of Directors at the 2005-03-24 members' meeting, but resigned at the Board meeting the same day. He retained the post of President.) 2004-2005 Officers: Stephan Deibel, Chairman of the Board Guido van Rossum, President Neal Norwitz, Treasurer Jeremy Hylton, Secretary Board of Directors: David Ascher Stephan Deibel Steve Holden Jeremy Hylton Martin von Löwis Tim Peters Guido van Rossum 2003-2004 Officers: Guido van Rossum, President and Chairman of the Board Neal Norwitz, Treasurer Jeremy Hylton, Secretary Board of Directors: David Ascher Jeremy Hylton Marc-André Lemburg Martin von Löwis Tim Peters Guido van Rossum Thomas Wouters 2002-2003 Officers: Guido van Rossum, President, Chairman of the Board Jeremy Hylton, Treasurer and Secretary Board of Directors: David Ascher Jeremy Hylton Marc-André Lemburg Martin von Löwis Tim Peters Guido van Rossum Thomas Wouters 2001-2002 Officers: Dick Hardt, President Shawn O'Donnell, Secretary Board of Directors: David Ascher Paul Everitt Fredrik Lundh Tim Peters Guido van Rossum Greg Stein Thomas Wouters First Board Meeting (March 5, 2001) Officer: Greg Stein, temporary Chairperson Board of Directors: Paul Everitt Greg Stein Officers & Directors by Name Tania Allard David Ascher Débora Azevedo Brett Cannon Pat Campbell Diana Clarke Naomi Ceder Alyssa Coghlan Brian Curtin Kushal Das Selena Deckelmann Stephan Deibel Paul Everitt Alex Gaynor David Goodger Dick Hardt Doug Hellmann Raymond Hettinger Paul Hildebrandt Steve Holden Eric Holscher Trey Hunner Jeremy Hylton Dustin Ingram Ewa Jodlowska Kurt Kaiser Jacqueline Kazil Younggun Kim Annapoornima Koppad Andrew Kuchling Jannis Leidel Marc-André Lemburg Van Lindberg Martin von Löwis Fredrik Lundh Jessica McKellar Katie McLaughlin Marlene Mhangami David Mertz Lorena Mesa Joannah Nanjekye Doug Napoleone Christopher Neugebauer Deb Nicholson Jesse Noller Neal Norwitz Shawn O'Donnell Travis Oliphant Anna Ossowski Paola Katherine Pacheco Tim Peters Carrie Ann Philbin Allison Randal Kenneth Reitz Guido van Rossum Lynn Root Jeff Rush Greg Stein James Tauber Jeff Triplett Gloria Willadsen Dawn Wages Carol Willing Simon Willison Thomas Wouters Nina Zakharenko Tania Allard Director, 2021-2023 David Ascher Director, 2001-2008 Executive Vice President, 2006-2007 Débora Azevedo Director, 2021-2023 Vice chair elect, 2022-2023 Communications officer, 2021-2022 Brett Cannon Director, 2006-2010, 2013-2014 Executive Vice President, 2007-2010 Vice President, 2006-2007 Pat Campbell Secretary, 2010-2012 Diana Clarke PyCon Chair, 2014-2015 Director, 2015-2017 Communications Officer, 2015 Chair, 2016-2017 Naomi Ceder Chair, 2017-2020 Director, 2015-2020 Vice Chair, 2015-2016 Alyssa Coghlan Director, 2014 - 2016 (as Nick Coghlan) Brian Curtin Director, 2012-2015 Communications Officer, 2012-2015 Kushal Das Director, 2014-2015, 2016-2023 Communications Officer, 2019-2020 Selena Deckelmann Director, 2014-2015 Stephan Deibel Director, 2004-2009 Chairman of the Board, 2004-2008 Paul Everitt Director, 2001-2002 Alex Gaynor Director, 2013-2016 David Goodger Director, 2006-2009 Secretary, 2006-2010 Assistant Secretary, 2005-2006 PyCon Chair, 2008-2009 PyCon Chair (informal), 2007-2008 Dick Hardt President, 2001-2002 Doug Hellmann Communications Officer, 2010-2012 Raymond Hettinger Director, 2008-2012 Assistant Treasurer, 2008-2010 Paul Hildebrandt Director, 2017-2018 Steve Holden Director, 2004-2013 Vice Chairman, 2006-2008 Chairman of the Board, 2008-2012 PyCon Chair (informal), 2002-2005 Eric Holscher Director, 2017-2020 Trey Hunner Director, 2016-2018 Jeremy Hylton Director, 2002-2006 Treasurer, 2002-2003 Secretary, 2002-2006 Assistant Secretary, 2006-2007 Dustin Ingram Director, 2020-2023 Ewa Jodlowska Executive Director, 2019-2021 Director of Operations, 2015-2019 Secretary, 2012-2021 Event Coordinator, 2012-2019 Kurt Kaiser Treasurer, 2005-2019 Assistant Treasurer, 2005 Jacqueline Kazil Director, 2016-2020 Treasurer, 2019-2020 Vice chair elect, 2018-2019 Younggun Kim Director, 2016-2017 Annapoornima Koppad Director, 2016-2017 Andrew Kuchling Director, 2005-2008, 2009-2010, 2012-2013 Vice President, 2006-2008 PyCon Chair (informal), 2005-2007 Jannis Leidel Director, 2019-2023 Communications officer, 2019-2022 Marc-André Lemburg Director, 2002-2004, 2010-2016 Vice Chairman, June 2012-2013 Vice President, 2003-2004 Van Lindberg Director, 2012-2023 Chairman, 2012-2016 Vice Chairman, April-May 2012, 2016-2019 PyCon Chair, 2009-2011 General Council, 2016-2023 Martin von Löwis Director, 2002-2013 Vice President, 2006-2007 Fredrik Lundh Director, 2001-2002 Jessica McKellar Director, 2012-2014 Katie McLaughlin Director, 2018-2021 Communications Officer, 2020-2021 Marlene Mhangami Director, 2017-2022 David Mertz Director, 2009-2014 Vice-Chair and Chair Elect, 2014-15 Lorena Mesa Chair, 2020-2022 Director, 2016-2022 Communications Officer, 2016-2022 Joannah Nanjekye Director, 2021-2023 Vice chair, 2021-2022 Doug Napoleone Director, 2010-2014 Christopher Neugebauer Director, 2018-2021 Vice chair, 2019-2021 Deb Nicholson Executive Director, 2022-2023 Secretary, 2022-2023 Jesse Noller Director, 2010-2014 Vice Chairman, 2013-2014 PyCon Chair, 2012-2013 Neal Norwitz Treasurer, 2003-2005 Assistant Treasurer, 2005-2008 Shawn O'Donnell Secretary, 2001-2002 Travis Oliphant Director, 2014-2015 Anna Ossowski Director, 2018-2021 Vice chair, 2019-2020 Paola Katherine Pacheco Director, 2017-2018 Tim Peters Director, 2001-2014 Carrie Ann Philbin Director, 2015-2017 Allison Randal Director, 2010-2012 Kenneth Reitz Director, 2017-2018 Guido van Rossum Director, 2001-2005 President, 2002-2023 Chairman of the Board, 2002-2004 Lynn Root Director, 2013-2016 Vice-Chair Elect, 2015 Jeff Rush Director, 2009-2012 PyCon Chair (informal), 2006-2007 Greg Stein Director, 2001-2002, 2010-2012 Temporary Chairperson, March 5, 2001 James Tauber Director, 2008-2012 Vice President, 2008-2009 Jeff Triplett Director, 2018-2023 Vice chair, 2022-2023 Treasurer, 2020-2022 Gloria Willadsen Director, 2010-2012 Dawn Wages Director, 2022-2023 Treasurer, 2022-2023 Carol Willing Director, 2015-2017 Simon Willison Director, 2022-2023 Thomas Wouters Director, 2001-2004, 2017-2019, 2020-2023 Chair, 2022-2023 Vice chair elect, 2020-2022 Vice chair, 2018-2019 Nina Zakharenko Director, 2020-2023 Vice chair, 2022-2023 Communications officer, 2021-2022 Officers By Title President Director of Operations Chair Vice Chair General Council Secretary Assistant Secretary Event Coordinator Treasurer Assistant Treasurer Executive Vice President Vice Presidents PyCon Chair Communications Officer President 2002-2023: Guido van Rossum 2001-2002: Dick Hardt Director of Operations 2015-2019: Ewa Jodlowska Chair 2022-2023: Thomas Wouters 2020-2022: Lorena Mesa 2017-2020: Naomi Ceder 2016-2017: Diana Clarke 2012-2016: Van Lindberg 2008-2012: Steve Holden 2004-2008: Stephan Deibel 2002-2004: Guido van Rossum 2001-03-05: Greg Stein Vice Chair 2022-2023: Débora Azevedo, Jeff Triplett, Nina Zakharenko 2021-2022: Thomas Wouters, Marlene Mhangami, Joannah Nanjekye 2020-2021: Thomas Wouters, Marlene Mhangami, Christopher Neugebauer 2019-2020: Lorena Mesa, Anna Ossowski, Christopher Neugebauer 2018-2019: Jackie Kazil, Van Lindberg, Thomas Wouters 2017-2018: Van Lindberg 2016-2017: Naomi Ceder, Van Lindberg 2015-2016: Lynn Root, Naomi Ceder 2014-2015: Marc-Andre Lemburg, David Mertz 2013-2014: Jesse Noller 2012-2013: Marc-Andre Lemburg April-Man 2012: Van Lindberg 2006-2008: Steve Holden General Council 2016-2023: Van Lindberg Secretary 2022-2023: Deb Nicholson 2012-2021: Ewa Jodlowska 2010-2012: Pat Campbell 2006-2010: David Goodger 2002-2006: Jeremy Hylton 2001-2002: Shawn O'Donnell Assistant Secretary 2015-2021: Betsy Waliszewski 2006-2007: Jeremy Hylton 2005-2006: David Goodger Event Coordinator 2021-2023: Jackie Augustine 2020-2021: Jackie Augustine, Betsy Waliszewski 2012-2020: Ewa Jodlowska 2015-2019: Betsy Waliszewski Treasurer 2022-2023: Dawn Wages 2020-2022: Jeff Triplett 2019-2020: Jackie Kazil 2005-2019: Kurt Kaiser 2003-2005: Neal Norwitz 2002-2003: Jeremy Hylton Assistant Treasurer 2008-2010: Raymond Hettinger 2005-2008: Neal Norwitz 2005: Kurt Kaiser Executive Vice President 2007-2010: Brett Cannon 2006-2007: David Ascher Vice Presidents 2008-2009: James Tauber 2006-2008: Andrew Kuchling 2006-2007: Brett Cannon 2006-2007: Martin von Löwis PyCon Chair The position of PyCon Chair was formalized as a PSF officer on March 14, 2008. 2022-2023: Mariatta Wijaya 2019-2022: Emily Morehouse-Valcarcel 2017-2019: Ee Durbin 2015-2017: Brandon Rhodes 2013-2015: Diana Clarke 2011-2013: Jesse Noller 2009-2011: Van Lindberg 2008-2009: David Goodger Prior to March 2008, the PyCon Chair was an informal position, not an officer of the PSF. 2007-2008: David Goodger 2006-2007: Andrew Kuchling & Jeff Rush (co-chairs) 2005-2006: Andrew Kuchling 2002-2005: Steve Holden Communications Officer 2021-2022: Jannis Leidel, Nina Zakharenko, Débora Azevedo 2020-2021: Jannis Leidel, Katie McLaughlin 2019-2020: Marlene Mhangami, Kushal Das, Jannis Leidel 2018-2019: Lorena Mesa, Marlene Mhangami 2016-2017: Kushal Das, Lorena Mesa 2015-2016: Diana Clarke 2012-2015: Brian Curtin 2010-2012: Doug Hellmann The PSF The Python Software Foundation is the organization behind Python. Become a member of the PSF and help advance the software and our mission. ▲ Back to Top About Applications Quotes Getting Started Help Python Brochure Downloads All releases Source code Windows macOS Android Other Platforms License Alternative Implementations Documentation Docs Audio/Visual Talks Beginner's Guide FAQ Non-English Docs PEP Index Python Books Python Essays Community Diversity Mailing Lists IRC Forums PSF Annual Impact Report Python Conferences Special Interest Groups Python Logo Python Wiki Code of Conduct Community Awards Get Involved Shared Stories Success Stories Arts Business Education Engineering Government Scientific Software Development News Python News PSF Newsletter PSF News PyCon US News News from the Community Events Python Events User Group Events Python Events Archive User Group Events Archive Submit an Event Contributing Developer's Guide Issue Tracker python-dev list Core Mentorship Report a Security Issue ▲ Back to Top Help & General Contact Diversity Initiatives Submit Website Bug Status Copyright ©2001-2026. 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https://docs.suprsend.com/docs/whatsapp | Whatsapp Template - SuprSend, Notification infrastructure for Product teams Skip to main content SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Community Trust Center Platform Status Postman Collection GETTING STARTED What is SuprSend? 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Navigation Channel Editors Whatsapp Template Documentation API Reference Management API CLI Reference Developer Resources Changelog Documentation API Reference Management API CLI Reference Developer Resources Changelog Channel Editors Whatsapp Template OpenAI Open in ChatGPT How to design whatsapp template using form editor. OpenAI Open in ChatGPT This section is a step-by-step guide on how to design and publish WhatsApp notification template. Design template You can design the template with a simple form editor tool. You can add variables with Handlebarsjs language. You can see how the message will look in the preview section on the right side. Once designed, save the WhatsApp template by clicking on the Save Draft button. When you are ready, you can Publish Draft by providing a name to the version. This will create a version in Pending Approval state. WhatsApp requires a template approval process, where every template has to be submitted to WhatsApp for approval, where WhatsApp reviews and either Approves or Rejects the message. SuprSend handles the WhatsApp approval process for you. All you have to do is create a template on SuprSend while following WhatsApp template guidelines , and we’ll send an email to you as soon as WhatsApp approves / rejects the template. Based on the approval status, the published template version’s state will move to Live or Rejected . Once the version goes Live , you can use the template to send messages to your users. WhatsApp fields description Field Description Template Category Category of the template as defined by WhatsApp. Choose the category which is most relevant for your message content. e.g. - if you are sending a message informing the user about his/her doctor appointment, select the category as Appointment Update . In case you are not able to find the relevant category for your message, select Alert Update Type Type of the message template - MEDIA/TEXT . You can select one of the options. Header (Type - TEXT) Header of the message shown in bold in your WhatsApp message Small message text box. You can add up to 60 characters in this field Emojis are not supported in header Header -> Media Type (Type - MEDIA) Media type of the header - Document(.pdf) / Image (.jpg, .png) / Video (.mp4). You can select one the media types based on the type of content that you want to add in the message Header -> Media File URL (Type - MEDIA) Add the Public URL of the document that you want to send. You can add dynamic URL by adding variables in the URL link, like this - http://s3.amazonaws.com/{{url_params}} or \{\{url\_link}} Header -> Document Name (Type - MEDIA) Valid only for media type - Document (.pdf) This is the name of the document that will be visible to your user. Will be shown as “Untitled” if not added. You can add variable in media file as {{file_name}} Body Large message text box. Can add multi-line texts. Use handlebarsjs to add variables. Footer Small message text box. You can add up to 60 characters in this field Variables are not supported in footer Buttons Button type to be added - Call to Action / Quick Reply. Select “None” if you don’t require buttons Action Buttons There are 2 types of action buttons that can be added: 1. Call Phone Number Button - To initiate a Call Action. 2. Visit Website Button - To redirect users to a website. Add the URL where a user will go when they click on this button Only one variable is allowed in “Website URL” at the end of the URL link, like this - www.suprsend.com/{{page}} Quick Reply Buttons You can add up to 3 quick reply buttons to take user input. Variables or emojis are not allowed in quick reply button Vendor Integration Required Please note that to send the WhatsApp, you will need to integrate WhatsApp vendor with SuprSend. Please visit the ‘Vendor Integration Guideline’ section to see vendors list and how to integrate them. How to format WhatsApp messages WhatsApp allows you to format text inside your messages. Use below options to format the text. Other formatting like HTML tags or markdown will not work for formatting the content Text Format Method Description Italic _text_ To italicize your message, place an underscore on both sides of the text Bold *text* To bold your message, place an asterisk on both sides of the text Strikethrough ~text~ To strikethrough your message, place a tilde on both sides of the text Monospace ```text``` To monospace your message, place three backticks on both sides of the text Adding dynamic content in the template There will always be the case where you would require to add dynamic content to a template, so as to personalise it for your users. To achieve this, you can add variables in the template, which will be replaced with the dynamic content at the time of sending the message. You’ll need to pass these while triggering the communication from one of our frontend or backend SDKs. Here is a step-by-step guide on how to add dynamic content in Inbox: 1 Declaring Variables in the global 'Mock data' button If you are at this stage, it is assumed that you have declared the variables along with sample values in the global Mock data button. To see how to declare variables before using them in designing templates, refer to this section in the Templates documentation . 2 Using variables in the templates Once the variables are declared, you can use them while designing the android push template. We support handlebarsjs to add variables in the template. As a general rule, all the variables have to be entered within double curly brackets: {{variable_name}} If you have declared the variables in the global ‘Mock data’ button, then they will come as auto-suggestions when you type a curly bracket { . This will remove the chances of errors like variable mismatch at the time of template rendering. Note that you will be able to enter a variable name even when you have not declared it inside the ‘Variables’ button. To manually enter the variable name, follow the handlerbarsjs guide here . Below is an example of how to enter variables in the template design. For illustration, we are using the same sample variable names that we declared in the ‘Templates’ section: json Copy Ask AI { "array" : [ { "product_name" : "Aldo Sling Bag" , "product_price" : "$50" }, { "product_name" : "Clarles & Keith Women Slipper, Biege, 38UK" , "product_price" : "$39" }, { "product_name" : "RayBan Sunglasses" , "product_price" : "$120" } ], "event" : { "location" : { "city" : "San Francisco" , "state" : "California" }, "order_id" : "11200123" , "first_name" : "Joe" }, "product_page" : "https://www.suprsend.com" } To enter a nested variable, enter in the format {{var1.var2.var3}} . Eg. to refer to city in the example above, you need to enter {{event.location.city}} To refer to an array element, enter in format {{var1.[_index_].var2}}. Eg. to refer to product_name of the first element of the array array , enter \{{array.[0].product_name}} If you have any space in the variable name, enclose it in square bracket {{event.[first name]}} You will be able to see the sample values in the Preview section, as well as in the Live version when you publish a draft. If you cannot see your variable being rendered with the sample value, check one of the following: Make sure you have entered the variable name and the sample value in the Variables button. Make sure you have entered the correct variable name in the template, as per the handlebarsjs guideline. At the time of sending communication, if there is a variable present in the template whose value is not rendered due to mismatch or missing, SuprSend will simply discard the template and not send that particular notification to your user. Please note that the rest of the templates will be sent. Eg. if there is an error in rendering Android Push template, but email template is successfully rendered, Android Push notification will not be triggered, but email notification will be triggered by SuprSend. Was this page helpful? Yes No Suggest edits Raise issue Previous Android Push Template How to design advanced Android Push template with customisation options to send silent, sticky notifications, and more. Next ⌘ I x github linkedin youtube Powered by On this page Design template WhatsApp fields description How to format WhatsApp messages Adding dynamic content in the template | 2026-01-13T08:49:02 |
https://datalaria.com/es/ | Datalaria Datalaria | En Blog Apps Juegos Sobre mí Etiquetas Contacto Datalaria Bienvenido a mi cuaderno de bitácora digital. Aquí exploro y documento mi viaje de aprendizaje en el mundo de los datos, la inteligencia artificial, la tecnología y la industria. Este es mi kilómetro 0. Blog Apps Juegos Sobre mí © 2026 Datalaria · Powered by Hugo & PaperMod | 2026-01-13T08:49:02 |
https://docs.swapsdk.xyz | 0x v4 Swap SDK | Swap SDK bars 🤝 Swap SDK search circle-xmark Ctrl k 0x v4 Swap SDK chevron-right Quick Start Swap NFT <> ERC-20 Example Managing Orders Hosted Orderbook ETH/Native Token support Batch Buy NFTs Royalties and Fee Configuration Collection-based/Floor-based orders Other Resources 0x v3 Swap SDK Orderbook API V3 or V4 - Which version should I use? gitbook Powered by GitBook block-quote On this page chevron-down copy Copy chevron-down 0x v4 Swap SDK NFT Swap SDK now supports 0x Protocol v4! Try it out on Ethereum Mainnet today. 0x v4 Integration into Swap SDK is here! hashtag Overview hashtag What's new? Swapping with the Swap SDK just got even better. With the new integration of 0x v4, NFT trades that use the Swap SDK are the cheapest and most efficient swaps available on Ethereum. Leverage the updated Swap SDK to ensure your users have the lowest possible gas fees anywhere. Read more about the gas optimizations below. 0x v4 NFT is available on Ethereum Mainnet, Polygon, Optimism, BSC, Fantom, and Celo, and Ropsten Testnet. Arbitrum will be supported very soon and just awaiting final deployment. hashtag About Swap SDK Swap SDK allows developers to build NFT swap functionality into their Ethereum (or EVM-compatible chains) applications quickly and easily. Whether you're building a wallet, an NFT marketplace, or a peer-to-peer swap application, Swap SDK makes integrating NFT swap functionality easy and lightweight. Just add your UI! hashtag ⛽ Gas Optimizations 0x v4 is the cheapest way to swap an NFT on Ethereum or any EVM-compatible chain. Whether you prefer on-chain or off-chain orders, 0x v4 is the cheapest and most efficient way to swap NFTs to date. Gas analysis as of 1/31/2022 Since Swap SDK uses 0x v4 protocol under the hood, the Swap SDK is the the most gas-efficient and cheapest way to swap NFTs on Ethereum. Build your app confidently knowing you're offering your users the best swapping experience available. Based on recent gas benchmarks, 0x v4 is significantly cheaper to fill orders. 40% cheaper than OpenSea, LooksRare, and Rarible for buying NFTs 35% cheaper than Zora to fill (>60% cheaper than Zora if including both parties' gas fees) 0x v4 supports both on-chain listings and off-chain listings so you can choose the best approach for your application. hashtag 📩 Order Support 0x v4 initially includes a rich set of swap functionality. More swap functionality (e.g. bundles) will be added over time ✅ ERC721 <> ERC20 swap ✅ ERC1155 <> ERC20 swap NFT buys and sells (bids and asks) are both supported. Analysis of Order support of leading NFT protocols Note: Currently 0x v4 does not support NFT<>NFT swaps (e.g. ERC721<>ERC721). If required, you can use the 0x v3 protocol until v4 support is added. hashtag Installation You can install the SDK with yarn: yarn add @traderxyz/nft-swap-sdk or npm: npm install @traderxyz/nft-swap-sdk hashtag Configuration To use the SDK, create a new NftSwapV4 instance. hashtag Quick Start Let's walk through the most common NFT swap case: swapping an NFT (ERC721 or ERC1155) with an ERC20. hashtag Swap an NFT with an ERC20 That's it! More examples and advanced usage can be found in the examples documentation. Happy swapping! 🎉 🤝 hashtag About Swap SDK Swap SDK is a light, performant library built with ethers arrow-up-right to easily interact with the 0x v4 protocol. Swap SDK also offers a free, managed orderbook so you don't need to worry about building off-chain order persistance (unless you want to). Swap SDK provides all the functionality to build an NFT marketplace or peer-to-peer swap application, including building orders, approving orders, persisting orders, and filling orders. Just add a UI! Swap SDK is a library made by developers for developers. Swap SDK is fully funded by a 0x DAO grant. We plan to support and mature this library, as well as continue to open-source more tooling, so developers can integrate the SwapSDK confidently. Next Quick Start chevron-right Last updated 3 years ago Overview ⛽ Gas Optimizations 📩 Order Support Installation Configuration Quick Start Copy import { NftSwapV4 } from '@traderxyz/nft-swap-sdk'; // Supply a provider, signer, and chain id to get started // Signer is optional if you only need read-only methods const nftSwapSdk = new NftSwapV4(provider, signer, chainId); Copy import { NftSwapV4 } from '@traderxyz/nft-swap-sdk'; // Scenario: User A wants to sell their CryptoPunk for 420 WETH // Set up the assets we want to swap (CryptoPunk #69 and 420 WETH) const CRYPTOPUNK = { tokenAddress: '0xb47e3cd837ddf8e4c57f05d70ab865de6e193bbb', tokenId: '69', type: 'ERC721', // 'ERC721' or 'ERC1155' }; const FOUR_HUNDRED_TWENTY_WETH = { tokenAddress: '0x6b175474e89094c44da98b954eedeac495271d0f', // WETH contract address amount: '420000000000000000000', // 420 Wrapped-ETH (WETH is 18 digits) type: 'ERC20', }; // [Part 1: Maker (owner of the Punk) creates trade] const nftSwapSdk = new NftSwapV4(provider, signerForMaker, CHAIN_ID); const walletAddressMaker = '0x1234...'; // Approve NFT to trade (if required) await nftSwapSdk.approveTokenOrNftByAsset(CRYPTOPUNK, walletAddressMaker); // Build order const order = nftSwapSdk.buildOrder( CRYPTOPUNK, // Maker asset to swap FOUR_HUNDRED_TWENTY_WETH, // Taker asset to swap walletAddressMaker ); // Sign order so order is now fillable const signedOrder = await nftSwapSdk.signOrder(order); // [Part 2: Taker that wants to buy the punk fills trade] const nftSwapSdk = new NftSwap(provider, signerForTaker, CHAIN_ID); const walletAddressTaker = '0x9876...'; // Approve USDC to trade (if required) await nftSwapSdk.approveTokenOrNftByAsset(FOUR_HUNDRED_TWENTY_WETH, walletAddressTaker); // Fill order :) const fillTx = await nftSwapSdk.fillSignedOrder(signedOrder); const fillTxReceipt = await nftSwapSdk.awaitTransactionHash(fillTx.hash); console.log(`🎉 🥳 Order filled. TxHash: ${fillTxReceipt.transactionHash}`) | 2026-01-13T08:49:02 |
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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close API Follow Hide Application Programming Interface Create Post Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu I tried to capture system audio in the browser. Here's what I learned. Flo Flo Flo Follow Jan 12 I tried to capture system audio in the browser. 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https://www.python.org/psf/fellows/ | PSF Fellow Membership | Python Software Foundation Notice: While JavaScript is not essential for this website, your interaction with the content will be limited. Please turn JavaScript on for the full experience. Skip to content ▼ Close Python PSF Docs PyPI Jobs Community ▲ The Python Network Donate ≡ Menu Search This Site GO A A Smaller Larger Reset Socialize LinkedIn Mastodon Chat on IRC Twitter About Mission Statement Board of Directors & Officers PSF Staff Annual Impact Report Fiscal Sponsorees Public Records Legal & Policies PSF FAQ Developers in Residence Sponsorship PSF Sponsors Apply to Sponsor Sponsorship Prospectus 2025-26 Membership Sign up as a Member of the PSF! Membership FAQ PSF Elections Nominate a Fellow & Fellows Roster Donate End of year fundraiser 2025: Python is for Everyone Donate to the PSF Become a Supporting Member of the PSF PSF Matching Donations Volunteer Volunteer for the PSF PSF Work Groups Volunteer for PyCon US Grants Grants program Grants Program FAQ PyCon US News & Community Subscribe to the Newsletter PSF Blog Python Community Code of Conduct Community Awards Discourse PSF Fellow Membership The deadline to nominate someone for Quarter 1 consideration is 11:59 p.m. UTC, February 20, 2026. Please send your nomination to: psf-fellow@python.org Your nomination should include: The name of the person you are nominating. Why you think they meet the Fellow criteria , i.e. how they have served the Python community. Please also include an email address for the person you are nominating. Example nomination: I, Marcia Nominator, propose that Marvin Pythonista be recognized as a Fellow of the Python Software Foundation, due to their significant contributions to the Python community as a co-founder of the PyCon Foobar regional conference, a lead organizer for the 2016 and 2017 editions of the Martian Python community's flagship Python conference, MarsPython and as a long-term contributor to international collaborative efforts amongst the Martian Python community. The PSF Fellow Work Group has decided to review nominations on a quarterly basis and we also established a criteria for handling the nominations. Here are the nominations timeline and criteria: Timeline: First quarter: January to the end of March (01/01 - 31/03) Cut-off for nominations will be February 20. New fellows will be announced before March 31. Second quarter: April to the end of June (01/04 - 30/06) Cut-off for quarter two will be May 20. New fellows will be announced before June 30. Third quarter: July to the end of September (01/07 - 30/09) Cut-off for quarter three will be August 20. New fellows will be announced before end of September. Fourth quarter: October to the end of December (01/10 - 31/12) Cut-off for quarter four will be November 20. New fellows will be announced before December 31. Criteria: Fellows are members who have been nominated for their extraordinary efforts and impact upon Python, the community, and the broader Python ecosystem. Fellows are nominated from the broader community and elevated by a vote of the members. Fellows are eligible to vote in PSF elections. You can see the full list of Fellows at the PSF Fellows Roster page. For those who have served the Python community by creating and/or maintaining various engineering/design contributions, the following statement should be true: Nominated Person has served the Python community by making available code, tests, documentation, or design, either in a Python implementation or in a Python ecosystem project, that 1) shows technical excellence, 2) is an example of software engineering principles and best practices, and 3) has achieved widespread usage or acclaim. For those who have served the Python community by coordinating, organizing, teaching, writing, and evangelizing, the following statement should be true: Nominated Person has served the Python community through extraordinary efforts in organizing Python events, publicly promoting Python, and teaching and coordinating others. Nominated Person's efforts have shown leadership and resulted in long-lasting and substantial gains in the number and quality of Python users, and have been widely recognized as being above and beyond normal volunteering. If someone is not accepted to be a fellow in the quarter they were nominated for, they will remain an active nominee for 1 year for future consideration. It is suggested/recommended that the nominee have wide Python community involvement. Examples would be (not a complete list - just examples): Someone who has received a Community Service Award or Distinguished Service Award A developer that writes (more than one) documentation/books/tutorials for wider audience Someone that helps translate (more than one) documentation/books/tutorials for better inclusivity An instructor that teaches Python related tutorials in various regions Someone that helps organize local meet ups and also helps organize a regional conference Nominees should be aware of the Python community’s Code of Conduct and should have a record of fostering the community. Sitting members of the PSF Board of Directors can be nominated if they meet the above criteria. In all that this person does, Nominated Person is an example of what we aspire to be in the Python community. Welcome 2025 Q4 Fellow Members! Chris Brousseau, Dave Forgac, Inessa Pawson, James Abel, Karen Dalton, Mia Bajić, Tatiana Andrea Delgadillo Garzofino Welcome 2025 Q3 Fellow Members! Abhijeet Mote, Abigail Afi Gbadago, Becky Smith, Christopher Bailey, Dawn Wages, Leah Wasser, Maaya Ishida, Mason Egger, Miguel Grinberg, William Vincent Welcome 2025 Q2 Fellow Members! Hamdalah Adetunji, Laís Carvalho, Mark Smith, Will McGugan Welcome 2025 Q1 Fellow Members! Aidis Stukas, Baptiste Mispelon, Charlie Marsh, Felipe de Morais, Frank Wiles, Ivy Fung Oi Wei, Jon Banafato, Julia Duimovich, Leandro Enrique Colombo Viña, Mike Pirnat, Sage Sharp, Tereza Iofciu, Velda Kiara Welcome 2024 Q4 Fellow Members! Jimena Escobar Bermúdez Welcome 2024 Q3 Fellow Members! Artur Czepiel, Jay Miller, Kojo Idrissa, Trey Hunner Welcome 2024 Q2 Fellow Members! Leonard Richardson, Winnie Ke Welcome 2024 Q1 Fellow Members! Adam Johnson, Paolo Melchiorre Welcome 2023 Q4 Fellow Members! Jelle Zijlstra Welcome 2023 Q3 Fellow Members! Dustin Ingram, Marlene Mhangami, Nikita Sobolev, Raquel Dou Welcome 2023 Q2 Fellow Members! 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https://peps.python.org/pep-0012/ | PEP 12 – Sample reStructuredText PEP Template | peps.python.org Following system colour scheme Selected dark colour scheme Selected light colour scheme Python Enhancement Proposals Python » PEP Index » PEP 12 Toggle light / dark / auto colour theme PEP 12 – Sample reStructuredText PEP Template Author : David Goodger <goodger at python.org>, Barry Warsaw <barry at python.org>, Brett Cannon <brett at python.org> Status : Active Type : Process Created : 05-Aug-2002 Post-History : 30-Aug-2002 Table of Contents Abstract Rationale How to Use This Template ReStructuredText PEP Formatting Requirements General Section Headings Paragraphs Inline Markup Block Quotes Literal Blocks Lists Tables Hyperlinks Internal and PEP/RFC Links Footnotes Images Comments Escaping Mechanism Intersphinx Canonical Documentation Habits to Avoid Suggested Sections Resources Copyright Note For those who have written a PEP before, there is a template (which is included as a file in the PEPs repository ). Abstract This PEP provides a boilerplate or sample template for creating your own reStructuredText PEPs. In conjunction with the content guidelines in PEP 1 , this should make it easy for you to conform your own PEPs to the format outlined below. Note: if you are reading this PEP via the web, you should first grab the text (reStructuredText) source of this PEP in order to complete the steps below. DO NOT USE THE HTML FILE AS YOUR TEMPLATE! The source for this (or any) PEP can be found in the PEPs repository , as well as via a link at the bottom of each PEP. Rationale If you intend to submit a PEP, you MUST use this template, in conjunction with the format guidelines below, to ensure that your PEP submission won’t get automatically rejected because of form. ReStructuredText provides PEP authors with useful functionality and expressivity, while maintaining easy readability in the source text. The processed HTML form makes the functionality accessible to readers: live hyperlinks, styled text, tables, images, and automatic tables of contents, among other advantages. How to Use This Template To use this template you must first decide whether your PEP is going to be an Informational or Standards Track PEP. Most PEPs are Standards Track because they propose a new feature for the Python language or standard library. When in doubt, read PEP 1 for details, or open a tracker issue on the PEPs repo to ask for assistance. Once you’ve decided which type of PEP yours is going to be, follow the directions below. Make a copy of this file (the .rst file, not the HTML!) and perform the following edits. Name the new file pep- NNNN .rst , using the next available number (not used by a published or in-PR PEP). Replace the “PEP: 12” header with “PEP: NNNN”, matching the file name. Note that the file name should be padded with zeros (eg pep-0012.rst ), but the header should not ( PEP: 12 ). Change the Title header to the title of your PEP. Change the Author header to include your name, and optionally your email address. Be sure to follow the format carefully: your name must appear first, and it must not be contained in parentheses. Your email address may appear second (or it can be omitted) and if it appears, it must appear in angle brackets. It is okay to obfuscate your email address. If none of the authors are Python core developers, include a Sponsor header with the name of the core developer sponsoring your PEP. Add the direct URL of the PEP’s canonical discussion thread (on e.g. Python-Dev, Discourse, etc) under the Discussions-To header. If the thread will be created after the PEP is submitted as an official draft, it is okay to just put “Pending” initially, but remember to update the PEP with the URL as soon as the PEP is successfully merged to the PEPs repository and you create the corresponding discussion thread. See PEP 1 for more details. Change the Status header to “Draft”. For Standards Track PEPs, change the Type header to “Standards Track”. For Informational PEPs, change the Type header to “Informational”. For Standards Track PEPs, if your feature depends on the acceptance of some other currently in-development PEP, add a Requires header right after the Type header. The value should be the PEP number of the PEP yours depends on. Don’t add this header if your dependent feature is described in a Final PEP. Change the Created header to today’s date. Be sure to follow the format carefully: it must be in dd-mmm-yyyy format, where the mmm is the 3 English letter month abbreviation, i.e. one of Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec. For Standards Track PEPs, after the Created header, add a Python-Version header and set the value to the next planned version of Python, i.e. the one your new feature will hopefully make its first appearance in. Do not use an alpha or beta release designation here. Thus, if the last version of Python was 2.2 alpha 1 and you’re hoping to get your new feature into Python 2.2, set the header to: Python-Version: 2.2 Add a Topic header if the PEP belongs under one shown at the Topic Index . Most PEPs don’t. Post-History can be ‘Pending’ for now; you’ll add dates and corresponding links to this header each time you post your PEP to the designated discussion forum (and update the Discussions-To header with said link, as above). For each thread, use the date (in the dd-mmm-yyy format) as the linked text, and insert the URLs inline as anonymous reST hyperlinks , with commas in between each posting. If you posted threads for your PEP on August 14, 2001 and September 3, 2001, the Post-History header would look like, e.g.: Post-History: `14-Aug-2001 <https:// www.example.com /thread_1>`__, `03-Sept-2001 <https:// www.example.com /thread_2>`__ You should add the new dates/links here as soon as you post a new discussion thread. Add a Replaces header if your PEP obsoletes an earlier PEP. The value of this header is the number of the PEP that your new PEP is replacing. Only add this header if the older PEP is in “final” form, i.e. is either Accepted, Final, or Rejected. You aren’t replacing an older open PEP if you’re submitting a competing idea. Now write your Abstract, Rationale, and other content for your PEP, replacing all this gobbledygook with your own text. Be sure to adhere to the format guidelines below, specifically on the prohibition of tab characters and the indentation requirements. See “Suggested Sections” below for a template of sections to include. Update your Footnotes section, listing any footnotes and non-inline link targets referenced by the text. Run ./build.py to ensure the PEP is rendered without errors, and check that the output in build/pep- NNNN .html looks as you intend. Create a pull request against the PEPs repository . For reference, here are all of the possible header fields (everything in brackets should either be replaced or have the field removed if it has a leading * marking it as optional and it does not apply to your PEP): PEP: [NNN] Title: [...] Author: [Full Name <email at example.com >] Sponsor: *[Full Name <email at example.com >] PEP-Delegate: Discussions-To: [URL] Status: Draft Type: [Standards Track | Informational | Process] Topic: *[Governance | Packaging | Release | Typing] Requires: *[NNN] Created: [DD-MMM-YYYY] Python-Version: *[M.N] Post-History: [`DD-MMM-YYYY <URL>`__] Replaces: *[NNN] Superseded-By: *[NNN] Resolution: ReStructuredText PEP Formatting Requirements The following is a PEP-specific summary of reStructuredText syntax. For the sake of simplicity and brevity, much detail is omitted. For more detail, see Resources below. Literal blocks (in which no markup processing is done) are used for examples throughout, to illustrate the plaintext markup. General Lines should usually not extend past column 79, excepting URLs and similar circumstances. Tab characters must never appear in the document at all. Section Headings PEP headings must begin in column zero and the initial letter of each word must be capitalized as in book titles. Acronyms should be in all capitals. Section titles must be adorned with an underline, a single repeated punctuation character, which begins in column zero and must extend at least as far as the right edge of the title text (4 characters minimum). First-level section titles are underlined with “=” (equals signs), second-level section titles with “-” (hyphens), and third-level section titles with “’” (single quotes or apostrophes). For example: First-Level Title ================= Second-Level Title ------------------ Third-Level Title ''''''''''''''''' If there are more than three levels of sections in your PEP, you may insert overline/underline-adorned titles for the first and second levels as follows: ============================ First-Level Title (optional) ============================ ----------------------------- Second-Level Title (optional) ----------------------------- Third-Level Title ================= Fourth-Level Title ------------------ Fifth-Level Title ''''''''''''''''' You shouldn’t have more than five levels of sections in your PEP. If you do, you should consider rewriting it. You must use two blank lines between the last line of a section’s body and the next section heading. If a subsection heading immediately follows a section heading, a single blank line in-between is sufficient. The body of each section is not normally indented, although some constructs do use indentation, as described below. Blank lines are used to separate constructs. Paragraphs Paragraphs are left-aligned text blocks separated by blank lines. Paragraphs are not indented unless they are part of an indented construct (such as a block quote or a list item). Inline Markup Portions of text within paragraphs and other text blocks may be styled. For example: Text may be marked as *emphasized* (single asterisk markup, typically shown in italics) or **strongly emphasized** (double asterisks, typically boldface). ``Inline literals`` (using double backquotes) are typically rendered in a monospaced typeface. No further markup recognition is done within the double backquotes, so they're safe for any kind of code snippets. Block Quotes Block quotes consist of indented body elements. For example: This is a paragraph. This is a block quote. A block quote may contain many paragraphs. Block quotes are used to quote extended passages from other sources. Block quotes may be nested inside other body elements. Use 4 spaces per indent level. Literal Blocks Literal blocks are used for code samples and other preformatted text. To indicate a literal block, preface the indented text block with “ :: ” (two colons), or use the .. code-block:: directive. Indent the text block by 4 spaces; the literal block continues until the end of the indentation. For example: This is a typical paragraph. A literal block follows. :: for a in [5, 4, 3, 2, 1]: # this is program code, shown as-is print(a) print("it's...") “ :: ” is also recognized at the end of any paragraph; if not immediately preceded by whitespace, one colon will remain visible in the final output: This is an example:: Literal block By default, literal blocks will be syntax-highlighted as Python code. For specific blocks that contain code or data in other languages/formats, use the .. code-block:: language directive, substituting the “short name” of the appropriate Pygments lexer (or text to disable highlighting) for language . For example: .. code-block :: rst An example of the ``rst`` lexer (i.e. *reStructuredText* ). For PEPs that predominantly contain literal blocks of a specific language, use the .. highlight:: language directive with the appropriate language at the top of the PEP body (below the headers and above the Abstract). All literal blocks will then be treated as that language, unless specified otherwise in the specific .. code-block . For example: .. highlight :: c Abstract ======== Here's some C code:: printf("Hello, World!\n"); Lists Bullet list items begin with one of “-”, “*”, or “+” (hyphen, asterisk, or plus sign), followed by whitespace and the list item body. List item bodies must be left-aligned and indented relative to the bullet; the text immediately after the bullet determines the indentation. For example: This paragraph is followed by a list. * This is the first bullet list item. The blank line above the first list item is required; blank lines between list items (such as below this paragraph) are optional. * This is the first paragraph in the second item in the list. This is the second paragraph in the second item in the list. The blank line above this paragraph is required. The left edge of this paragraph lines up with the paragraph above, both indented relative to the bullet. - This is a sublist. The bullet lines up with the left edge of the text blocks above. A sublist is a new list so requires a blank line above and below. * This is the third item of the main list. This paragraph is not part of the list. Enumerated (numbered) list items are similar, but use an enumerator instead of a bullet. Enumerators are numbers (1, 2, 3, …), letters (A, B, C, …; uppercase or lowercase), or Roman numerals (i, ii, iii, iv, …; uppercase or lowercase), formatted with a period suffix (“1.”, “2.”), parentheses (“(1)”, “(2)”), or a right-parenthesis suffix (“1)”, “2)”). For example: 1. As with bullet list items, the left edge of paragraphs must align. 2. Each list item may contain multiple paragraphs, sublists, etc. This is the second paragraph of the second list item. a) Enumerated lists may be nested. b) Blank lines may be omitted between list items. Definition lists are written like this: what Definition lists associate a term with a definition. how The term is a one-line phrase, and the definition is one or more paragraphs or body elements, indented relative to the term. Tables Simple tables are easy and compact: ===== ===== ======= A B A and B ===== ===== ======= False False False True False False False True False True True True ===== ===== ======= There must be at least two columns in a table (to differentiate from section titles). Column spans use underlines of hyphens (“Inputs” spans the first two columns): ===== ===== ====== Inputs Output ------------ ------ A B A or B ===== ===== ====== False False False True False True False True True True True True ===== ===== ====== Text in a first-column cell starts a new row. No text in the first column indicates a continuation line; the rest of the cells may consist of multiple lines. For example: ===== ========================= col 1 col 2 ===== ========================= 1 Second column of row 1. 2 Second column of row 2. Second line of paragraph. 3 - Second column of row 3. - Second item in bullet list (row 3, column 2). ===== ========================= Hyperlinks When referencing an external web page in the body of a PEP, you should include the title of the page or a suitable description in the text, with either an inline hyperlink or a separate explicit target with the URL. Do not include bare URLs in the body text of the PEP, and use HTTPS links wherever available. Hyperlink references use backquotes and a trailing underscore to mark up the reference text; backquotes are optional if the reference text is a single word. For example, to reference a hyperlink target named Python website , you would write: In this paragraph, we refer to the `Python website`_ . If you intend to only reference a link once, and want to define it inline with the text, insert the link into angle brackets ( <> ) after the text you want to link, but before the closing backtick, with a space between the text and the opening backtick. You should also use a double-underscore after the closing backtick instead of a single one, which makes it an anonymous reference to avoid conflicting with other target names. For example: Visit the `website <https://www.python.org/> `__ for more. If you want to use one link multiple places with different linked text, or want to ensure you don’t have to update your link target names when changing the linked text, include the target name within angle brackets following the text to link, with an underscore after the target name but before the closing angle bracket (or the link will not work ). For example: For further examples, see the `documentation <pydocs_> `_ . An explicit target provides the URL. Put targets in the Footnotes section at the end of the PEP, or immediately after the paragraph with the reference. Hyperlink targets begin with two periods and a space (the “explicit markup start”), followed by a leading underscore, the reference text, a colon, and the URL. .. _Python web site: https://www.python.org/ .. _pydocs: https://docs.python.org/ The reference text and the target text must match (although the match is case-insensitive and ignores differences in whitespace). Note that the underscore trails the reference text but precedes the target text. If you think of the underscore as a right-pointing arrow, it points away from the reference and toward the target. Internal and PEP/RFC Links The same mechanism as hyperlinks can be used for internal references. Every unique section title implicitly defines an internal hyperlink target. We can make a link to the Abstract section like this: Here is a hyperlink reference to the `Abstract`_ section. The backquotes are optional since the reference text is a single word; we can also just write: Abstract_. To refer to PEPs or RFCs, always use the :pep: and :rfc: roles, never hardcoded URLs. For example: See :pep: `1` for more information on how to write a PEP, and :pep: `the Hyperlink section of PEP 12 <12#hyperlinks>` for how to link. This renders as: See PEP 1 for more information on how to write a PEP, and the Hyperlink section of PEP 12 for how to link. PEP numbers in the text are never padded, and there is a space (not a dash) between “PEP” or “RFC” and the number; the above roles will take care of that for you. Footnotes Footnote references consist of a left square bracket, a label, a right square bracket, and a trailing underscore. Instead of a number, use a label of the form “#word”, where “word” is a mnemonic consisting of alphanumerics plus internal hyphens, underscores, and periods (no whitespace or other characters are allowed). For example: Refer to The TeXbook [#TeXbook]_ for more information. which renders as Refer to The TeXbook [1] for more information. Whitespace must precede the footnote reference. Leave a space between the footnote reference and the preceding word. Use footnotes for additional notes, explanations and caveats, as well as for references to books and other sources not readily available online. Native reST hyperlink targets or inline hyperlinks in the text should be used in preference to footnotes for including URLs to online resources. Footnotes begin with “.. “ (the explicit markup start), followed by the footnote marker (no underscores), followed by the footnote body. For example: .. [#TeXbook] Donald Knuth's *The TeXbook* , pages 195 and 196. which renders as [ 1 ] Donald Knuth’s The TeXbook , pages 195 and 196. Footnotes and footnote references will be numbered automatically, and the numbers will always match. Images If your PEP contains a diagram or other graphic, you may include it in the processed output using the image directive: .. image :: diagram.png Any browser-friendly graphics format is possible; SVG or PNG are preferred for graphics, JPEG for photos and GIF for animations. Images should be optimised to reduce their file size, and should be legible in both light and dark mode in the browser. For accessibility and readers of the source text, you should include a description of the image and any key information contained within using the :alt: option to the image directive: .. image :: dataflow.png :alt: Data flows from the input module, through the "black box" module, and finally into (and through) the output module. Comments A comment is an indented block of arbitrary text immediately following an explicit markup start: two periods and whitespace. Leave the “..” on a line by itself to ensure that the comment is not misinterpreted as another explicit markup construct. Comments are not visible in the processed document. For example: .. This section should be updated in the final PEP. Ensure the date is accurate. Escaping Mechanism reStructuredText uses backslashes (” \ ”) to override the special meaning given to markup characters and get the literal characters themselves. To get a literal backslash, use an escaped backslash (” \\ ”). There are two contexts in which backslashes have no special meaning: literal blocks and inline literals (see Inline Markup above). In these contexts, no markup recognition is done, and a single backslash represents a literal backslash, without having to double up. If you find that you need to use a backslash in your text, consider using inline literals or a literal block instead. Intersphinx You can use Intersphinx references to other Sphinx sites, such as the Python documentation packaging.python.org , and typing.python.org , to easily cross-reference pages, sections and Python/C objects. For example, to create a link pointing to a section of the typing docs, you would write the following: :ref: `type expression <typing:type-expression>` Canonical Documentation As PEP 1 describes , PEPs are considered historical documents once marked Final, and their canonical documentation/specification should be moved elsewhere. To indicate this, use the canonical-doc directive or an appropriate subclass: canonical-pypa-spec for packaging standards canonical-typing-spec for typing standards Add the directive between the headers and the first section of the PEP (typically the Abstract) and pass as an argument an Intersphinx reference of the canonical doc/spec (or if the target is not on a Sphinx site, a reST hyperlink ). For example, to create a banner pointing to the sqlite3 docs, you would write the following: .. canonical-doc :: :mod: `python:sqlite3` which would generate the banner: Important This PEP is a historical document. The up-to-date, canonical documentation can now be found at sqlite3 . × See PEP 1 for how to propose changes. Or for a PyPA spec, such as the Core metadata specifications , you would use: .. canonical-pypa-spec :: :ref: `packaging:core-metadata` which renders as: Important This PEP is a historical document. The up-to-date, canonical spec, Core metadata specifications , is maintained on the PyPA specs page . × See the PyPA specification update process for how to propose changes. For a typing PEP that introduces no new runtime objects, you might use something like the first one of these; for a typing PEP that introduces a new object to the typing module at runtime, you might use the second: .. canonical-typing-spec :: :ref: `typing:packaging-typed-libraries` .. canonical-typing-spec :: :ref: `typing:literal-types` and :py:data: `typing.Literal` The two render as: Important This PEP is a historical document: see Type information in libraries for up-to-date specs and documentation. Canonical typing specs are maintained at the typing specs site ; runtime typing behaviour is described in the CPython documentation. × See the typing specification update process for how to propose changes to the typing spec. Important This PEP is a historical document: see Literals and typing.Literal for up-to-date specs and documentation. Canonical typing specs are maintained at the typing specs site ; runtime typing behaviour is described in the CPython documentation. × See the typing specification update process for how to propose changes to the typing spec. The argument accepts arbitrary reST, so you can include multiple linked docs/specs and name them whatever you like, and you can also include directive content that will be inserted into the text. The following advanced example: .. canonical-doc :: the :ref: `python:sqlite3-connection-objects` and :exc: `python:~sqlite3.DataError` docs Also, see the :ref: `Data Persistence docs <persistence>` for other examples. would render as: Important This PEP is a historical document. The up-to-date, canonical documentation can now be found at the Connection objects and sqlite3.DataError docs. × Also, see the Data Persistence docs for other examples. See PEP 1 for how to propose changes. Habits to Avoid Many programmers who are familiar with TeX often write quotation marks like this: `single-quoted' or ``double-quoted'' Backquotes are significant in reStructuredText, so this practice should be avoided. For ordinary text, use ordinary ‘single-quotes’ or “double-quotes”. For inline literal text (see Inline Markup above), use double-backquotes: ``literal text: in here, anything goes!`` Suggested Sections Various sections are found to be common across PEPs and are outlined in PEP 1 . Those sections are provided here for convenience. PEP: <REQUIRED: pep number> Title: <REQUIRED: pep title> Author: <REQUIRED: list of authors' names and optionally, email addrs> Sponsor: <name of sponsor> PEP-Delegate: <PEP delegate's name> Discussions-To: Pending Status: <REQUIRED: Draft | Active | Accepted | Provisional | Deferred | Rejected | Withdrawn | Final | Superseded> Type: <REQUIRED: Standards Track | Informational | Process> Topic: <Governance | Packaging | Release | Typing> Requires: <pep numbers> Created: <date created on, in dd-mmm-yyyy format> Python-Version: <version number> Post-History: Pending Replaces: <pep number> Superseded-By: <pep number> Resolution: <url> Abstract ======== [A short (~200 word) description of the technical issue being addressed.] Motivation ========== [Clearly explain why the existing language specification is inadequate to address the problem that the PEP solves.] Rationale ========= [Describe why particular design decisions were made.] Specification ============= [Describe the syntax and semantics of any new language feature.] Backwards Compatibility ======================= [Describe potential impact and severity on pre-existing code.] Security Implications ===================== [How could a malicious user take advantage of this new feature?] How to Teach This ================= [How to teach users, new and experienced, how to apply the PEP to their work.] Reference Implementation ======================== [Link to any existing implementation and details about its state, e.g. proof-of-concept.] Rejected Ideas ============== [Why certain ideas that were brought while discussing this PEP were not ultimately pursued.] Open Issues =========== [Any points that are still being decided/discussed.] Acknowledgements ================ [Thank anyone who has helped with the PEP.] Footnotes ========= [A collection of footnotes cited in the PEP, and a place to list non-inline hyperlink targets.] Copyright ========= This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive. Resources Many other constructs and variations are possible, both those supported by basic Docutils and the extensions added by Sphinx . A number of resources are available to learn more about them: Sphinx ReStructuredText Primer , a gentle but fairly detailed introduction. reStructuredText Markup Specification , the authoritative, comprehensive documentation of the basic reST syntax, directives, roles and more. Sphinx Roles and Sphinx Directives , the extended constructs added by the Sphinx documentation system used to render the PEPs to HTML. If you have questions or require assistance with writing a PEP that the above resources don’t address, ping @python/pep-editors on GitHub, open an issue on the PEPs repository or reach out to a PEP editor directly. Copyright This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive. Source: https://github.com/python/peps/blob/main/peps/pep-0012.rst Last modified: 2025-12-11 15:57:57 GMT Contents Abstract Rationale How to Use This Template ReStructuredText PEP Formatting Requirements General Section Headings Paragraphs Inline Markup Block Quotes Literal Blocks Lists Tables Hyperlinks Internal and PEP/RFC Links Footnotes Images Comments Escaping Mechanism Intersphinx Canonical Documentation Habits to Avoid Suggested Sections Resources Copyright Page Source (GitHub) | 2026-01-13T08:49:02 |
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http://mattmahoney.net/dc/text.html | Large Text Compression Benchmark Large Text Compression Benchmark Matt Mahoney Last update: July 3, 2025. history This competition ranks lossless data compression programs by the compressed size (including the size of the decompression program) of the first 10 9 bytes of the XML text dump of the English version of Wikipedia on Mar. 3, 2006. About the test data . The goal of this benchmark is not to find the best overall compression program, but to encourage research in artificial intelligence and natural language processing (NLP). A fundamental problem in both NLP and text compression is modeling: the ability to distinguish between high probability strings like recognize speech and low probability strings like reckon eyes peach . Rationale . This is an open benchmark. Anyone may contribute results. Please read the rules first. Open source compression improvements to this benchmark with certain hardware restrictions may be eligible for the Hutter Prize . Benchmark Results Compressors are ranked by the compressed size of enwik9 (10 9 bytes) plus the size of a zip archive containing the decompresser. Options are selected for maximum compression at the cost of speed and memory. Other data in the table does not affect rankings. This benchmark is for informational purposes only. There is no prize money for a top ranking. Notes about the table: Program: The version believed to give the best compression. A | denotes a combination of 2 programs. Compression options: selected for what I believe gives the best compression. enwik8: compressed size of first 10 8 bytes of enwik9. This data is used for the Hutter Prize, and is also ranked here but has no effect on this ranking. enwik9: compressed size of first 10 9 bytes of enwiki-20060303-pages-articles.xml. decompresser size: size of a zip archive containing the decompression program (source code or executable) and all associated files needed to run it (e.g. dictionaries). A letter following the size has the following meaning: x = executable size. s = source code size (if available and smaller). d = size of a separate decompression program (separate from compression). For self extracting archives (SFX), the size is 0 because the decompresser and compressed data are combined into one file. For testing, if no zip file is supplied I create archives using InfoZIP 2.32 -9. (Prior to July 1, 2008 I used 7zip 4.32 -tzip -mx=9). Total size: total size of compressed enwik9 + decompresser size, ranked smallest to largest. Comp: compression rate in nanoseconds per byte on the largest file tested (e.g. seconds for enwik9). Speed is approximate and has no effect on ranking. A ~ means "very approximate". Not all tests are done on the same computer. Times reported are the smaller of process time (summed over processors if multi-threaded) or real time as measured with timer ). If there is no note then the program was tested on a Compaq Presario 5440, 2.188 GHz, Athlon-64 3500+ in 32 bit Windows XP. An underlined time means that no better compressor is faster. Decomp: decompression time as above. If blank, decompression was not tested yet and ranking is pending verification that the output is identical. An underlined time means that no better compressor is faster. Mem: approximate memory used for compression in MB. Decompression uses the same or possibly less. There is some ambiguity whether a megabyte means 10 6 bytes or 2 20 bytes. The approximation is course enough that it doesn't matter. I use peak memory as measured with Windows Task Manager during compression (so if you really want to know, 1 MB = 1,024,000 bytes :) Memory does not include swap or temporary files. An underlined value means that no better compressor uses less memory. Alg: compression algorithm, referring to the method of parsing the input into symbols (strings, bytes, or bits) and estimating their probabilities (modeling) for choosing code lengths. Symbols may be arithmetic coded (fractional bit length for best compression), Huffman coded (bit aligned for speed), or byte aligned as a preprocessing step. Dict (Dictionary). Symbols are words, coded as 1 or 2 bytes, usually as a preprocessing step. LZ (Lempel Ziv). Symbols are strings. LZ77: repeated strings are coded by offset and length of previous occurrence. LZW (LZ Welch): repeats are coded as indexes into a dynamically built dictionary. ROLZ (Reduced Offset LZ): LZW with multiple small dictionaries selected by context. LZP (LZ predictive): ROLZ with a dictionary size of 1. on (Order-n, e.g. o0, o1, o2...): symbols are bytes, modeled by frequency distribution in context of last n bytes. PPM (Prediction by Partial Match): order-n, modeled in longest context matched, but dropping to lower orders for byte counts of 0. SR (Symbol Ranking): order-n, modeled by time since last seen. BWT (Burrows Wheeler Transform): bytes are sorted by context, then modeled by order-0 SR. ST (Sort Transform): BWT using stable sort with truncated string comparison. DMC (Dynamic Markov Coding): bits modeled by PPM. CM (Context Mixing): bits, modeled by combining predictions of independent models. LSTM (long short term memory): CM using neural network models. Tr: Transformer, CM using neural network with attention mechanism. Some compressors combine multiple steps such as Dict+PPM or LZP+DMC. I indicate the last stage before coding. Notes: Brief notes. See program descriptions for details. Usually this means the result was reported by somebody else on a different computer. Compression Compressed size Decompresser Total size Time (ns/byte) Program Options enwik8 enwik9 size (zip) enwik9+prog Comp Decomp Mem Alg Note ------- ------- ---------- ----------- ----------- ----------- ----- ----- --- --- ---- nncp v3.2 14,915,298 106,632,363 628,955 xd 107,261,318 241871 238670 7600 Tr 88 cmix v21 -t 14,623,723 107,963,380 281,387 sd 108,244,767 622949 638442 30950 CM 83 fx2-cmix 110,351,665 0 xd 110,351,665 272072 8811 CM 97 tensorflow-compress v4 15,905,037 113,542,413 55,283 sd 113,597,696 291394 290803 45360 LSTM 94 cmix-hp 10 Jun 2021 15,957,339 113,712,798 0 xd 113,712,798 189420 194280 6873 CM 89 fast-cmix 113,746,218 0 xd 113,746,218 121971 8027 CM 99 starlit 31 May 2021 15,215,107 114,951,433 0 xd 114,951,433 173953 171682 10233 CM 89 phda9 1.8 15,010,414 116,544,849 42,944 xd 116,587,793 86182 86305 6319 CM 83 gmix v1 16,246,629 122,336,013 182,085 sd 122,518,098 73986 70552 3751 CM 83 paq8px_v206fix1 -12L 15,849,084 124,696,410 402,949 s 125,099,359 291916 294847 28151 CM 93 durilca'kingsize -m13000 -o40 -t2 16,209,167 127,377,411 407,477 xd 127,784,888 1398 1797 13000 PPM 31 fxv v1 -1 -j -w -cfxcm1.pxv 15,946,608 128,804,372 494,797 x 129,299,169 20763 1628 CM 102 cmve 0.2.0 -m2,3,0x7fed7dfd 16,424,248 129,876,858 307,787 x 130,184,645 1140801 19963 CM 81 paq8hp12any -8 16,230,028 132,045,026 330,700 x 132,375,726 37660 37584 1850 CM 41 drt|emma 1.23 16,523,517 134,164,521 1,358,251 xd 135,522,772 73006 67097 3800 CM 81 zpaq 6.42 -m s10.0.5fmax6 17,855,729 142,252,605 4,760 sd 142,257,365 6699 14739 14000 CM 61 drt|lpaq9m 9 17,964,751 143,943,759 110,579 x 144,054,338 868 898 1542 CM 41 mcm 0.83 -x11 18,233,295 144,854,575 79,574 s 144,934,149 394 281 5961 CM 72 nanozip 0.09a -cc -m32g -p1 -t1 -nm 18,594,163 148,545,179 783,642 x 149,328,821 1149 1141 32000 CM 74 xwrt 3.2 -l14 -b255 -m96 -s -e40000 -f200 18,679,742 151,171,364 52,569 s 151,223,933 2537 2328 1691 CM fp8 v3 -8 18,438,169 153,188,176 50,068 s 153,238,244 20605 22593 1192 CM 26 WinRK 3.03 pwcm +td 800MB SFX 18,612,453 156,291,924 99,665 xd 156,391,589 68555 800 CM 10 ppmonstr J -m1700 -o16 19,055,092 157,007,383 42,019 x 157,049,402 3574 ~3600 1700 PPM zcm 0.93 -m8 -t1 19,572,089 159,135,549 227,659 x 159,363,208 421 411 3100 CM 48 slim 23d -m1700 -o12 19,077,276 159,772,839 69,453 x 159,842,292 5232 ~5400 1700 PPM bsc-m03 0.4.0 -b1000000000 20,293,393 160,258,936 105,456 xd 160,364,392 160 135 13000 BWT 96 bwmonstr 0.02 20,307,295 160,468,597 69,401 x 160,537,998 331801 156147 590 BWT 30 nanozipltcb 0.09 20,537,902 161,581,290 133,784 x 161,715,074 64 30 3350 BWT 40 kanzi -b 1024M -t RLT+TEXT+UTF -e TPAQX 19,098,186 161,690,495 397,855 x 162,088,350 490 480 3100 CM 103 M03 1.1b 1000000000 20,710,197 163,667,431 50,468 x 163,717,899 457 406 5735 BWT 52 bcm 2.03 -b1000x- 20,738,630 163,646,387 125,866 x 163,772,253 63 34 4096 BWT 98 glza 0.10.1 -x -p3 20,356,097 163,768,203 69,935 s 163,838,138 8184 11.9 8205 Dict 67 bsc 3.25 -b1000 -e2 20,786,794 163,884,462 74,297 xd 163,958,759 23 8 5000 BWT 96 bbb m1000 20,847,290 164,032,650 11,227 s 164,043,877 4524 2619 1401 BWT pcompress 3.1 -c libbsc -l14 -s1000m 20,769,968 163,391,884 1,370,611 x 164,762,495 359 74 3300 BWT 48 paq9a -9 19,974,112 165,193,368 13,749 s 165,207,117 3997 4021 1585 CM uda 0.300 19,393,460 166,272,261 11,264 x 166,283,525 25282 25174 180 CM BWTmix v1 c10000 20,608,793 167,852,106 9,565 x 167,861,671 1794 690 5000 BWT 49 lrzip 0.612 -z -L 9 -p 1 19,847,690 169,318,794 99,363 x 169,418,157 2987 2929 2700 CM 33 bzip3 -b 511 20,749,611 169,990,721 368,033 s 170,358,754 175 146 3700 BWT 103 cm4_ext 20,188,048 170,566,799 204,782 x 170,771,581 4123 4130 1906 CM 26 M1x2 v0.6 7 enwik7.txt 20,723,056 172,212,773 38,467 s 172,251,240 711 715 1051 CM 26 cmm4 v0.1e 96 20,569,034 172,669,955 31,314 x 172,701,269 2052 2056 1321 CM lstm-compress v3 20,318,653 173,874,407 144,567 s 174,018,974 92342 91876 9 LSTM 83 ccmx 1.30 7 20,857,925 174,142,092 15,014 x 174,157,106 1313 1338 1332 CM bit 0.7 -p=5 20,823,204 174,425,039 62,493 x 174,487,532 2050 2100 663 CM 26 mcomp 2.00 -mw -M320m 21,103,670 174,388,351 172,531 x 174,560,882 473 399 1643 BWT 26 epmopt|epm r9 -m800 -n20 --fixedorder:12 19,713,502 174,817,424 141,101 x 174,958,525 3179 3376 800 PPM WinUDA 2.91 mode 3 (194 MB) 20,332,366 174,975,730 17,203 x 174,992,933 23610 23473 194 CM dark 0.51 -b333mf 21,169,819 175,471,417 34,797 x 175,506,214 533 453 1692 BWT FreeArc 0.40pre-4 -mppmd:1012m:o13:r1 20,931,605 175,254,732 748,202 x 176,002,934 1175 1216 1046 PPM hook v1.4 1700 21,990,502 176,648,663 37,004 x 176,685,667 741 695 1777 DMC 26 7zip 4.46a -m0=ppmd:mem=1630m:o=10 ... 21,197,559 178,965,454 0 xd 178,965,454 503 546 1630 PPM 23 rings 2.5 -m8 -t1 20,873,959 178,747,360 240,523 x 178,987,883 280 163 2518 BWT 48 pimple2 20,871,457 180,251,530 78,642 x 180,330,172 18474 17992 128 CM ash 04a /m700 /o10 19,963,105 180,735,542 11,137 x 180,746,679 6100 5853 700 CM bce3 22,729,148 180,732,702 19,889 s 180,752,591 1151 2444 5000 CM 71 ocamyd LTCB 1.0 -s0 -m3 21,285,121 182,359,986 21,030 x 182,381,016 108960~110000 300 DMC 6 bee 0.79 b0154 -m3 -d8 20,975,994 182,373,904 57,046 x 182,430,950 9295 9285 512 PPM uhbc 1.0 -m3 -b100m 20,930,838 182,918,172 56,242 x 182,974,414 1569 809 800 BWT smac 1.20 21,781,544 183,190,888 4,356 x 183,195,244 4249 4399 1542 CM 26 ppmd J1 -m256 -o10 -r1 21,388,296 183,964,915 11,099 s 183,976,014 880 895 256 PPM tc 5.2 dev 2 21,481,399 184,939,711 41,112 x 184,980,823 3637 3655 230 CM bwtsdc v1 23,414,955 185,709,858 8,421 s 185,718,279 2100 420 5213 BWT 47 fbc v1.1 333333334 22,554,133 185,975,548 23,576 x 185,999,124 451 415 1647 BWT 55 ppmvc v1.1 -m256 -o8 -r1 21,484,294 186,208,405 25,241 x 186,233,646 898 913 272 PPM chile 0.4 -b=244141 22,218,917 186,979,614 11,530 s 186,991,144 2513 512 1426 BWT bwtdisk 0.9.0 -b 2 -m 3500 24,725,277 190,004,306 169,579 s 190,173,885 1124 3500 BWT 48 CTXf 0.75 pre b1 -me 22,072,783 191,008,871 57,337 x 191,066,298 1112 1037 78 PPM m03exp 2005-02-15 32MB blocks 21,948,192 191,250,500 44,593 x 191,295,093 ~4800 ~2100 256 BWT Stuffit 12.0.0.17 -m=4 -l=16 -x=30 22,105,654 190,372,707 2,658,122 xd 193,030,829 628 658 1062 PPM plzma v3b c2 ... (see below) 24,206,571 193,240,160 101,221 x 193,341,381 8889 55 10110 LZ77 58 crook v0.1 -m1600 -O8 22,503,627 193,333,159 8,539 s 193,341,698 483 513 1641 PPM 26 ppmx 0.03 22,572,808 193,643,464 54,964 x 193,698,428 777 784 609 PPM 26 lzturbo 1.1 -49 -b1000 -p0 24,416,777 194,681,713 110,670 x 194,792,383 1920 9 14700 LZ77 59 enc 0.15 aq 22,156,982 195,604,166 94,888 x 195,699,054 6843 6868 50 CM comprolz 0.11.0-bugfix1 -b250 -f 22,813,215 196,651,379 29,453 x 196,680,832 984 308 688 ROLZ 26 sbc 0.970r2 -ad -m3 -b63 22,470,539 197,066,203 99,094 xd 197,165,297 1733 313 224 BWT xz 5.2.1--lzma2=preset=9e,dict=1GiB,lc=4,pb=0 24,703,772 197,331,816 36,752 xd 197,368,568 5876 20 6000 LZ77 73 WinRAR 3.60b3 -mc7:128t+ -sfxWinCon.sfx 22,713,569 198,454,545 0 xd 198,454,545 506 415 128 PPM quark v0.95r beta -m1 -d25 -l8 22,988,924 198,600,023 80,264 x 198,680,287 27952 217 534 LZ77 lzip 1.14-rc3 -9 -s512MiB 24,756,063 199,410,543 21,682 s 199,432,225 2409 21 5632 LZ77 57 comprox 0.11.0-bugfix1 -b250 -f -m100 23,064,386 199,515,912 34,176 x 199,550,088 917 153 688 LZ77 26 bssc 0.95 alpha -b16383 23,117,061 201,810,709 45,489 x 201,856,198 578 217 140 BWT 4 flashzip 1.0.0 -mx7 -b7 23,869,034 202,363,445 123,053 x 202,486,498 1296 122 802 ROLZ 26 lzham 1.0 -d29 -x 25,002,070 202,237,199 191,600 s 202,428,799 1096 6.6 7800 LZ77 70 csarc 3.3 -m5 -d1024m 24,516,202 203,995,005 69,848 s 204,064,853 621 22 2463 LZ77 48 packet 1.9 -mx -b512 -h8 24,968,492 204,195,438 261,967 x 204,457,405 974 14 2824 LZ77 48 uharc 0.6b -mx -md32768 23,911,123 208,026,696 73,608 xd 208,100,304 1666 1330 50 PPM TarsaLZP Jan 29 2012 24,751,389 208,867,187 13,081 s 208,880,268 203 ~2000 LZP 54 GRZipII 0.2.4 -b8m 23,846,878 208,993,966 41,645 s 209,035,641 312 216 58 BWT 4x4 0.2a 4t (grzip:m1:h18) 23,833,244 208,787,642 317,097 x 209,104,739 386 240 269 BWT rzm 0.07h 24,361,070 210,126,103 17,667 x 210,143,770 2336 81 160 ROLZ pim 2.50 best 24,303,638 210,124,895 330,901 x 210,455,796 764 ~764 88 PPM CTW 0.1 -d6 -n16M -f16M 23,670,293 211,995,206 43,247 x 212,038,452 19221 19524 144 CM boa 0.58b -m15 24,322,643 213,845,481 55,813 x 213,901,294 3953 ~4100 17 PPM yxz 0.11 -m9 -b7 -h6 25,754,856 214,317,684 131,062 x 214,448,746 642 77 1590 LZ 26 zstd 0.6.0 -22 --ultra 25,405,601 215,674,670 69,687 s 215,744,357 701 2.2 792 LZ77 76 tornado 0.6 -16 25,768,105 217,749,028 83,694 s 217,832,722 1482 9 1290 LZ77 48 LZPXj 1.2h 9 25,205,783 217,880,584 4,853 s 217,885,437 783 717 1316 PPM scmppm 0.93.3 -l 9 25,198,832 217,867,392 37,043 s 217,904,435 708 644 20 PPM acb 2.00c u 25,063,656 218,473,968 38,976 x 218,512,944 10656 10883 16 LZ77 26 crushm 25,013,576 218,656,416 30,097 x 218,686,513 617 649 39 CM 26 PX v1.0 24,971,871 219,091,398 3,054 s 219,094,452 1838 1809 66 CM 3 DGCA 1.10 default+SFX 25,203,248 219,655,072 0 xd 219,655,072 858 270 76 Squeez 5.20.4600 sqx2.0 32MB Ultra 25,118,441 220,004,873 91,019 xd 220,095,892 2575 116 365 fpaq2 25,287,775 221,242,386 3,429 s 221,245,815 20183 20186 131 CM TinyCM 0.1 9 25,913,605 221,773,542 12,553 x 221,786,095 1342 1330 1083 CM 26 dmc c 1800000000 25,320,517 222,605,607 2,220 s 222,607,827 676 721 1800 DMC lza 0.82b -mx9 -b7 -h7 26,396,613 222,808,457 285,766 x 223,094,223 449 9.7 2000 LZ77 48 brotli 18-Feb-2016 -q 11 -w 24 25,764,698 223,597,884 542,385 s 224,140,269 3400 5.9 437 LZ77 48 szip 1.12a -b41o16 26,120,472 227,586,463 31,708 x 227,618,171 1191 289 21 BWT 26 balz 1.13 ex 26,421,416 228,337,644 49,024 x 228,286,668 3700 190 206 ROLZ lzpm 0.11 9 26,501,542 229,083,971 46,824 x 229,130,795 15395 57 740 ROLZ qazar 0.0pre5 -l7 -d9 -x7 26,455,170 229,846,871 71,959 x 229,918,830 5738 903 105 LZP KuaiZip 2.3.2 x86 25,895,915 227,905,650 3,857,649 x 231,763,299 1061 47 197 LZ77 26 qc 0.050 -8 26,763,343 232,784,501 46,100 x 232,830,601 8218 1503 151 ppms J -o5 26,310,248 233,442,414 16,467 x 233,458,881 330 354 1.8 PPM dzo beta 26,616,115 235,056,859 618,883 x 235,675,742 1088 159 200 LZ77 26 comprox_ba 20110929 27,828,189 242,846,243 4,134 s 242,850,377 397 101 226 BWTS 48 WinTurtle 1.60 512 MB buffer 28,379,612 245,217,944 160,090 x 245,378,034 273 237 583 PPM diz 26,545,256 246,679,382 12,945 s 246,692,327 21240 22746 1350 PPM 26 cabarc 1.00.0601 -m lzx:21 28,465,607 250,756,595 51,917 xd 250,808,853 1619 15 20 LZ77 sr3 28,926,691 253,031,980 9,399 s 253,054,625 148 160 68 SR 26 bzip2 1.0.2 -9 29,008,736 253,977,839 30,036 x 254,007,875 379 129 8 BWT rh5_x64 -window:27 c6 29,078,552 254,220,469 36,744 x 254,257,213 196 9.4 145 ROLZ 48 RangeCoderC v1.7 c7 26 28,788,013 254,527,369 7,858 x 254,535,227 2460 2436 1116 CM 26 quad v1.11 -x 29,110,579 256,145,858 13,387 s 256,159,245 956 116 34 ROLZ WinACE -sfx -m5 -d4096 29,481,470 257,237,710 0 xd 257,237,710 1080 77 4 lzsr 0.01 29,433,834 258,912,605 40,287 x 258,952,892 194 88 6 LZ77 26 libzling 20160107 e4 29,721,114 259,475,639 35,582 s 259,511,221 83 27 28 ROLZ 48 xpv5 c2 29,963,217 262,525,246 14,371 x 262,539,617 2359 516 9 ROLZ 26 sr3c 1.0 29,731,019 266,035,006 7,701 x 266,042,707 160 145 5 SR 26 lzc v0.08 10 30,611,315 266,565,255 11,364 x 266,576,619 302 63 550 LZ77 nakamichi 2019-Jul-01 32,917,888 277,293,058 112,899 s 277,405,957 8200000 1.3 302000 LZSS 85 crush 1.00 cx 31,731,711 279,491,430 2,489 s 279,493,919 948 2.9 148 LZ77 60 xeloz 0.3.5.3 c889 32,441,272 283,621,211 18,771 s 283,639,982 1079 8 230 LZ77 48 bzp 0.2 31,563,865 283,908,295 36,808 x 283,945,103 110 120 3 LZP lzwg -27 34,423,369 284,356,322 19,828 xd 284,376,150 135 41 1744 LZW 95 ha 0.98 a2 31,250,524 285,739,328 28,404 x 285,767,732 2010 1800 0.8 PPM ulz 0.06 c9 32,945,292 291,028,084 49,450 x 291,077,534 325 1.1 490 LZ77 82 irolz 33,310,676 292,448,365 4,584 s 292,452,949 274 144 17 ROLZ 26 lcssr 0.2 -b7 -l9 34,549,048 296,160,661 8,802 x 296,169,463 8186 8281 1184 SR zlite 33,975,840 298,470,807 4,880 s 298,475,687 61 28 36 ROLZ 26 lazy 1.00 5 35,024,082 306,245,949 5,986 s 306,251,935 273 24 96 LZ77 26 zhuff 0.97 beta -c2 34,907,478 308,530,122 63,209 x 308,593,331 24 3.5 32 LZ77 48 lzhhf 34,848,933 308,825,079 24,576 xd 308,849,655 392 12 14 LZ77 95 slug 1.27 35,093,954 309,201,454 6,809 x 309,208,263 32 28 14 ROLZ ect 0.9.5 -9 -zip --mt-deflate 34,950,275 309,402,124 352,904 s 309,755,028 1340 2900 LZ77 103 lzuf62 34,960,889 309,837,920 24,576 xd 309,862,496 375 11 14 LZ77 95 pigz 2.3 -11 35,002,893 309,812,953 52,717 s 309,865,670 2237 13 25 LZ77 48 kzip May 13 2006 /b1024 35,016,649 310,188,783 29,184 xd 310,217,967 6063 62 121 LZ77 2 uc2 rev 3 pro -tst 35,384,822 312,767,652 123,031 x 312,890,683 360 63 4 LZ77 qbp 35,434,557 313,013,756 3,232 xd 313,013,756 44 30 1 LZSS 104 thor 0.95 e4 35,795,184 314,092,324 49,925 x 314,142,249 64 34 16 LZP etincelle a3 35,776,971 314,801,710 44,103 x 314,845,813 29 18 976 ROLZ 26 lz5 1.3.3 -18 36,514,408 319,510,433 138,210 s 319,648,643 10578 3.7 1139 LZ77 48 gzip124hack 1.2.4 -9 36,273,716 321,050,648 62,653 x 321,113,301 149 19 1 LZ77 doboz 0.1 36,367,430 322,415,409 83,591 x 322,499,000 533 3.4 1200 LZ77 48 gzip 1.3.5 -9 36,445,248 322,591,995 38,801 x 322,630,796 101 17 1.6 LZ77 Info-ZIP 2.3.1 -9 36,445,373 322,592,120 57,583 x 322,649,703 104 35 0.1 LZ77 pkzip 2.0.4 -ex 36,556,552 323,403,526 29,184 xd 323,432,710 171 50 2.5 LZ77 jar (Java) 0.98-gcc cvfM 36,520,144 323,747,582 19,054 x 323,766,636 118 95 1.2 LZ77 PeaZip better, no integrity check 36,580,548 323,884,274 561,079 x 324,445,353 243 243 8 LZ77 20 arj 3.10 -m1 37,091,317 328,553,982 143,956 x 328,697,938 262 67 3 LZ77 26 lzgt3a 37,444,440 334,405,713 4,387 xd 334,410,100 1581 2886 2 LZ77 pucrunch -d -c0 39,199,165 350,265,471 34,359 s 350,299,830 2649 463 2 LZ77 packARC v0.7RC11 -sfx -np 38,375,065 361,905,425 0 xd 361,905,425 1359 1486 23 CM urban 38,215,763 362,677,440 4,280 s 362,681,720 381 450 6 o2 48 lzop v1.01 -9 41,217,688 366,349,786 54,438 x 366,404,224 289 12 1.8 LZ77 lzw 0.2 41,960,994 367,633,910 671 s 367,634,581 3597 31 18 LZW MTCompressor v1.0 41,295,546 370,152,396 3,620 x 370,156,016 173 117 74 LZ77 26 lz4x 1.02 c4 41,950,112 372,068,437 48,609 x 372,117,046 79 1.4 114 LZ77 68 arbc2z 38,756,037 379,054,068 6,255 sd 379,060,323 2659 2674 68 PPM lz4 v1.2 -c2 42,870,164 379,999,522 49,128 x 380,048,650 91 6 20 LZ77 26 lzss 0.02 cx 42,874,387 380,192,378 48,114 x 380,240,492 107 2.3 145 LZSS 63 xdelta 3.0u -9 44,288,463 389,302,725 107,985 x 389,410,710 1021 30 47 LZ77 brieflz 1.1.0 43,300,800 390,122,722 14,907 s 390,137,629 21 7.5 3 LZ77 48 mtari 0.2 41,655,528 397,232,608 4,156 s 397,236,764 80 99 18 CM 26 lzf 1.02 cx 45,198,298 406,805,983 48,359 x 406,854,342 68 2.2 151 LZ77 68 srank 1.1 -C8 43,091,439 409,217,739 6,546 x 409,224,285 51 45 2 SR QuickLZ 1.30b (quick3) 46,378,438 410,633,262 44,202 x 410,677,464 48 12 3 LZ77 stz 0.7.2 -c2 47,192,312 416,524,596 41,941 x 416,566,537 14 13 3 LZ77 26 compress 4.3d 45,763,941 424,588,663 16,473 x 424,605,136 103 70 1.8 LZW lzrw3-a 48,009,194 438,253,704 4,750 x 438,258,454 38 17 2 LZ77 fcm1 45,402,225 447,305,681 1,116 s 447,306,797 228 261 1 CM1 runcoder1 46,883,939 458,125,932 5,488 s 458,131,420 140 156 4 o1 26 data-shrinker 23Mar2012 51,658,517 459,825,318 3,706 s 459,829,024 14 4 2 LZ77 26 lzwc_bitwise 0.7 46,639,414 463,884,550 4,183 x 463,888,733 123 134 71 LZW 26 exdupe 0.3.3 53,717,422 478,788,378 1,092,986 x 479,881,364 27 5 1000 LZ77 48 lzv 0.1.0 54,950,847 488,436,027 10,385 x 488,446,412 4 2.6 3 LZ77 48 FastLZ Jun 12 2007 54,658,924 493,066,558 7,065 xd 493,073,623 18 13 1 LZ77 sharc 0.9.11b -c2 53,175,042 494,421,068 81,001 s 494,502,069 15 14 6 LZP 26 flzp v1 57,366,279 497,535,428 3,942 s 497,539,370 78 38 8 LZP alba 0.5.1 cd 52,728,620 515,760,096 4,870 s 515,764,966 239 10 4 BPE 48 lzpgt6 56,113,248 522,877,083 27,136 x 522,904,219 6 5 6 LZP 95 snappy 1.0.1 58,350,605 527,772,054 23,844 s 527,795,898 25 12 0.1 LZ77 26 bpe 5000 4096 200 3 53,906,667 532,250,688 1,037 sd 532,251,725 639 28 0.5 Dict 26 kwc 54,097,740 532,622,518 15,186 x 532,637,704 438 145 668 Dict 26 bpe2 v3 55,289,197 542,748,980 2,979 s 542,751,959 518 132 0.5 Dict 26 fpaq0f2 56,916,872 558,645,708 3,066 x 558,648,769 222 207 0.4 o0 ghost 456 5 55,357,196 568,004,779 696 sd 568,005,475 172800 245 88000 Dict 100 ppp 61,657,971 579,352,307 1,472 s 579,353,779 80 59 1 SR ksc 4 59,511,259 580,557,413 13,507 x 580,570,920 40050 7917 1700 SR 48 lzbw1 0.8 67,620,436 590,235,688 21,751 x 590,257,439 15 12 55 LZP 26 lzp2 0.7c 67,909,076 598,076,882 40,819 x 598,117,701 11 8 15 LZP 26 NTFS LZNT1 76,955,648 636,870,656 0 636,870,656 10 9 0.1 LZ77 26 shindlet_fs 62,890,267 637,390,277 1,275 xd 637,391,552 113 103 0.6 o0 arb255 63,501,996 644,561,595 4,871 sd 644,566,466 2551 2574 1.6 o0 compact 63,862,371 648,370,029 3,600 sd 648,373,629 216 164 0.2 o0 TinyLZP 0.1 79,220,546 694,274,932 2,811 s 694,277,743 32 38 10 LZP 26 smile 71,154,788 695,562,502 207 xd 695,562,709 10517 10414 0.6 MTF 26 barf (2 passes) 76,074,327 758,482,743 983,782 s 759,466,525 756 53 4 LZ77 arb2x v20060602 99,642,909 995,674,993 3,433 sd 995,678,426 2616 2464 1.6 o0b Fails on enwik9 Compression Compressed size Decompresser Total size Time (ns/byte) Program Options enwik8 enwik9 size (zip) enwik9+prog Comp Decomp Mem Alg Note ------- ------- ---------- ----------- ----------- ----------- ----- ----- --- --- ---- hipp 5819 /o8 20,555,951 (fails) 36,724 x 5570 5670 719 CM ppmz2 23,557,867 (fails) 29,362 s 92210 88070 1497 PPM 26 XMill 0.8 -w -P -9 -m800 26,579,004 (fails) 114,764 xd 616 530 800 PPM lzp3o2 33,041,439 (fails) 23,427 xd 230 270 151 LZP Programs that properly decompress enwik9 and don't use external dictionaries are still eligible for the Hutter Prize. Testing not yet completed Compression Compressed size Decompresser Total size Time (ns/byte) Program Options enwik8 enwik9 size (zip) enwik9+prog Comp Decomp Mem Alg Note ------- ------- ---------- ----------- ----------- ----------- ----- ----- --- --- ---- rdmc 0.06b 33,181,612 1394 1381 DMC 6 ESP v1.92 36,651,292 223 LZ77 16 Pareto frontier: compressed size vs. compression time as of Aug. 18, 2008 from the main table (options for maximum compression). Pareto frontier: compressed size vs. memory as of Aug. 18, 2008 (options for maximum compression). Notes about compressors I only test the latest supported version of a program. I attempt to find the options that select the best compression, but will not generally do an exhausitve search. If an option advertises maximum compression or memory, I don't try the alternatives. If you know of a better combination, please let me know. I will select the maximum memory setting that does not cause disk thrashing, usually about 1800 MB. If the compressor is not downloadable as a zip file then I will compress the source or executable (whichever archive is smaller) plus any other needed files (dictionaries) into a single zip archive using 7zip 4.32 -tzip -mx=9. If no executable is available I will attempt to compile in C or C++ (MinGW 3.4.2, Borland 5.5 or Digital Mars), Java 1.5.0, MASM, NASM, or gas. 1. Reported by Guillermo Gabrielli, May 16, 2006. Timed on a Celeron D325 2.53Ghz Windows XP SP2 256MB RAM. 2. Decompression size and time for pkzip 2.0.4. kzip only compresses. 3. Reported by Ilia Muraviev (author of PX, TC, pimple), June 10-July 18, 2006. Timed on a P4 3.0 GHz, 1GB RAM, WinXP SP2. 4. enwik9 reported by Johan de Bock, May 19, 2006. Timed on Intel Pentium-4 2.8 GHz 512KB L2-cache, 1024MB DDR-SDRAM. 5. Compressed with paq8h (VC++ compile) and decompressed with paq-8h (Intel compile of same source code). Normally compression and decompression are the same speed. 6. ocamyd 1.65.final and LTCB 1.0 reported by Mauro Vezzosi, May 30-June 20, 2006. Timed on a 1.91 GHz AMD Athlon XP 2600+, 512 MB, WinXP Pro 2002 SP2 using timer 3.01. ocamyd 1.66.final reported Feb. 3, 2007. Times are process times. 7. Under development by Mauro Vezzosi, May 24, 2006. 8. Reported by Denis Kyznetsov (author of qazar), June 2, 2006. 9. Reported by sportman, May 24, 2006. Timed on a Intel Pentium D 830 dual core 3.0GHz, 2 x 512MB DDR2-SDRAM PC4300 533Mhz memory timing 4-4-4-12 (833.000KB free), Windows XP Home SP2. CPU was at 52% so apparently only one of 2 cores was used. Decompression verified on enwik8 only (not timed, about 2.5 hours). WinRK compression options: Model size 800MB, Audio model order: 255, Bit-stream model order: 27, Use text dictionary: Enabled, Fast analyses: Disabled, Fast executable code compression: Disabled 10. Reported by Malcolm Taylor (author of WinRK), May 24, 2006. Timed on an Athlon X2 4400+ with 2GB, running WinXP 64. Decompression not tested. decompresser size is based on SFX stub size reported by Artyom (A.A.Z.), Sept. 2, 2007, although it was not tested this way. 11. Reported by sportman, May 25, 2006. CPU as in note 9. 12. Reported by sportman, May 30, 2006. CPU as in 9 (50% utilized). 13. xwrt 3.2 options are -2 -b255 -m250 -s -f64. ppmonstr J options are -o10 -m1650. 14. Reported by Michael A Maniscalco, June 15, 2006. 15. Reported by Jeremiah Gilbert on the Hutter group, Aug. 18, 2006. Tested under Linux on a dual Xeon 1.6 GHz(lv) (overclocked to 2.13 GHz) with 2 GB memory. Time is user+sys (real=196500 B/ns). 16. Reported by Anthony Williams, Aug. 19-22. 2006. Timed on a 2.53 GHz Pentium 4 with 512 MB under WinXP Home SP2. 17. Tested Aug. 20, 2006 under Ubuntu Linux 2.6.15 on a 2.2 GHz Athlon-64 with 2 GB memory. Time is approximate wall time due to disk thrashing. User+sys time is 153600 ns/byte compress, 148650 decompress. 18. Reported by Dmitry Shkarin (author of durilca4linux), Aug. 22-23, 2006 for durilca4linux_1; and Oct. 16-18, 2006 for durilca4linux_2. 3 GB memory usage is RAM + swap. Tested on AMD Athlon X2 4400+, 2.22 GHz, 2 GB memory under SuSE Linux AMD64 v10.0. durilca4linux_3 reported Feb. 21, 2008 using 4 GB RAM + 1 GB swap. v2 reported Apr. 22, 2008. v3 reported May 22, 2008. 19. enwik8 confirmed by sportman, Sept. 20, 2006. Compression time 61480 ns/byte timed on a 2 x dual core (only one core active) Intel Woodcrest 2GHz with 1333MHz fsb and 4GB 667MHz CL5 memory under SiSoftware Sandra Lite 2007.SP1 (10.105). Drystone ALU 37,014 MIPS, Whetstone iSSE3 25,393 MFLOPS, Integer x8 iSSE4 220,008 it/s, Floating-point x4 iSSE2 119,227 it/s. 20. Reported by Giorgio Tani (author of PeaZip) on Nov. 10, 2006. Tested on a MacBook Pro, Intel T2500 Core Duo CPU (one core used), with 512 MB memory under WinXP SP2. Time is combined compression and decompression. 21. enwik9 -8 reported by sportman, Dec. 12-13, 2006. Hardware as note 19. enwik9 decompression not verified. paq8hp7 -8 enwik8 compression was reported as 16,417,650 (4 bytes longer; the size depends on the length of the input filename, which was enwik8.txt rather than enwik8). I verified enwik8 -7 and -8 decompression. 22. paq8hp8 -8 enwik9 reported by sportman, Jan. 18, 2007. paq8hp10 -8 enwik9 on Apr. 2, 2007. paq8hp11 -8 enwik9 on May 10, 2007. paq8hp12 -8 enwik8/9 on May 20, 2007. Hardware as in note 19. Decompression verified for enwik8 only. 23. 7zip 4.46a options were -m0=PPMd:mem=1630m:o=10 -sfx7xCon.sfx 24. paq8o8-intel (intel compile of paq8o8) -1, paq8o8z-jun7 (DOS port of paq8o8) -1 reported by Rugxulo on Jun 10, 2008. Timed on a AMD64x2 TK-53 Tyler 1.7 GHz laptop with Vista Home Premium SP1. 25. paq8o8z -1 enwik8 (DJGPP compile) reported by Rugxulo on Jun 17, 2008. Tested on a 2.52 Ghz P4 Northwood, no HTT, WinXP Home SP2. 26. Tested on a Gateway M-7301U laptop with 2.0 GHz dual core Pentium T3200 (1MB L2 cache), 3 GB RAM, Vista SP1, 32 bit. Run times are similar to my older computer. 27. enwik9 size reported by Eugene Shelwien, Mar. 5, 2009. enwik8 size and all speeds are tested as in note 26. 28. Reported by Eugene Shelwien on a Q6600, 3.3 GHz, WinXP SP3, ramdrive: bcm 0.06 on Mar. 15, 2009, bcm 0.08 on June 1, 2009. 29. Reported by kaitz (KZ): paq8p3 on Apr. 19, 2009, v2 on Apr. 21, 2009, paq8pxd on Jan. 21, 2012, v2 on Feb. 11, 2012, v3 on Feb. 23, 2012, v4 on Apr. 23, 2012. 2012 tests on a Core2Duo T8300 2.4 GHz, 2 GB. 30. Reported by Sami Runsas (author of bwmonstr), July 14, 2009. Tested on an Athlon XP 2200 (Win32). 31. Reported by Dmitry Shkarin, July 21, 2009, Nov. 12, 2009. Tested on a 3.8 GHz Q9650 with 16 GB memory under Windows XP 64bit Pro SP2. Requires msvcr90.dll. 32. Reported by Mike Russell, Sept. 11, 2009. Tested on an 2.93 GHz Intel Q6800 with 3.5 GB memory. 33. Reported by Con Kolivas (author of lrzip) on Nov. 27, 2009 (lrzip 0.40), Nov. 30, 2009 (lrzip 0.42), Mar. 17, 2012 (lrzip 0.612). Tested on a 3 GHz quad core Q9650, 8 GB, 64 bit debian linux. 34. Reported by sportman, Nov. 29, 2009 (durilca'kingsize), Nov. 30, 2009 (durilca'kingsize4), Apr. 8, 2010 (bsc 1.0.0). Test hardware: 2 x 2.4GHz (overclocked at 2.53 GHz) quad core Xeon Nahalem, 24GB DDR3 1066MHz, 8 x 2TB RAID5, Windows 2008 Server R2 64bit 35. Reported by zody on Dec. 12, 2009. Tested in Windows 7, x64, 3.6 GHz e8200, 4 GB 1066 MHz RAM. 36. Reported by Ilia Muraviev on Dec. 16, 2009. Tested on a 2.40 GHz Core 2 Duo, DDR2-800 4GB RAM, Windows7 x64. 37. Reported by Sami Runsas, Mar. 3, 2010. Tested under Win64 on a Q6600 at 3.0 GHz. 38. Reported by Ilya Grebnov, Apr. 7, 2010. Tested on an Intel Core 2 Duo E8500, 8 GB memory, Windows 7. 39. Reported by Ilya Grebnov, Apr. 8, 2010. Tested on an Intel Core 2 Quad Q9400, 8 GB memory, Windows 7. bsc 2.00 on May 3, 2010. bsc 2.2.0 on June 15, 2010. 40. Reported by Sami Runsas, May 10, 2010. Tested on an overclocked Intel Core i7 860. nanozip 0.08a tested June 6, 2010. nanozip 0.09a on Nov. 5, 2011. 41. lpaq9m reported by Alexander Rhatushnyak on June 9, 2010. Tested on an Intel Core i7 CPU 930 (8 core), 2.8 GHz, 2.99 GB RAM. paq8hp12any tested June 28, 2010. 42. Reported by Michal Hajicek, June 4, 2010 on an AMD Phenom II 965, 64 bit Windows. WinRK, ppmonstr on June 14. 43. Reported by Ilia Muraviev, June 26, 2010. Tested on a Core 2 Quad Q9300, 2.50 GHz, 4 GB DDR2, Windows 7. 44. Timed on a Dell Latitude E6510 laptop Core I7 M620, 2.66 GHz, 4 GB, Windows 7 32-bit. 45. Reported by Richard Geldreich (lzham author) on Aug. 30, 2010. Tested on a 2.6 GHz Core i7 (quad core + HT), 6 GB, Win7 x64. 46. Reported by Stefan Gedo (ST author) on Oct. 14, 2010. Tested on Athlon II X4 635 2.9 GHz, 4 GB memory, Windows 7. 47. Reported by David A. Scott on Dec. 15, 2010. Tested on a I3-370 with 6 GB DDR3 1033 MHz memory. 48. Timed on a Dell Latitude E6510 laptop Core I7 M620, 2.66 GHz, 4 GB, Ubuntu Linux 64-bit. 49. Tested by the author on a Q9450, 3.52 GHz = 440x8, ramdrive. 50. Tested by the author on an Intel Core i7-2600, 3.4 GHz, Kingston 8 GB DDR3, WD VeloicRaptor 10000 RPM 600 GB SATA3, Windows 7 Ultimate SP1. 51. Tested by Bulat Ziganshin on i7-2600, 4.6 GHz with 1600 MHz RAM (8-8-8-21-1T) and NVIDEA GeForce 560Ti at 900/2000 MHz. 52. Tested by Michael Maniscalco on an 8 core Intel Xeon E5620, 2.40 GHz, 12 GB memory running Windows 7 Enterprise SP1, 64 bit. 53. Tested by the author on a Core i7-2600K @ 4.6GHz, 8GB DDR3 @ 1866MHz, 240GB Corsair Force GT SSD. 54. Tested by Piotr Tarsa on a Core 2 Duo E8400, 8 GiB RAM, Ubuntu 11.10 64-bit, OpenJDK 7. 55. Tested by David Catt on a 64 bit Windows 7 laptop, 2.33 GHz, 4 GB, 4 cores. 56. Reported by the author on a Athlon II X4 635 2.9 GHz, 4GB, Windows 8 Enterprise. 57. Reported by the author on a x86_64 Athlon 64 X2 5200+ with 8 GiB of RAM running GNU/Linux 2.6.38.6-libre. 58. Reported by the author on a 4 GHz i7-930 from ramdrive. 59. Reported by the author on a I7-2600, 4.6 GHz, 16 GB RAM, Ubuntu 13.04. 60. Tested by Ilia Muravyov on an Intel Core i7-3770K, 4.8 GHz, 16 GB Corsair Vengeance LP 1800 MHz CL9, Corsair Force GS 240 GB SSD, Windows 7 SP1. 61. Tested by Matt Mahoney on a dual Xeon E-2620, 2.0 GHz, 12+12 hyperthreads, 64 GB RAM (20 GB usable), Fedora Linux. 62. Tested by Valéry Croizier on a 2.5 GHz Core i5-2520M, 4 GB memory, Windows 7 64 bit. 63. Tested by Ilia Muravyov on an Intel i7-3770, 4.7 GHz, Corsair Vengenance LP 1600 MHz CL9 16 GB RAM, Samsung 840 Pro 512 GB SSD, Windows 7 SP1. 64. Tested by Kennon Conrad on a 3.2 GHz AMD A8-5500. 65. Tested by sportman on an Intel Core i7 4960X 3.6GHz OC at 4.5GHz - 6 core (12 threads) 22nm Ivy Bridge-E, Kingston 8 x 4GB (32GB) DDR3 2400MHz 11-14-14 under clocked at 2000MHz 10-11-11. Windows 8.1 Pro 64-bit, SoftPerfect RAM Disk 3.4.5 64-bit. 66. Tested by Byron Knoll on a Intel Core i7-3770, 31.4 GB memory, Linux Mint 14. 67. Tested by Kennon Conrad on a 4.0 GHz i4790K, 16 GB at 1866 MHz, 128 GB SSD Windows 8.1. 68. Tested by Ilia Muraviev on an Intel Core i7-3770K @ 4.8GHz, 8GB 2133 MHz CL11 DDR3, 512GB Samsung 840 Pro SSD, Windows 7 Ultimate SP1. 69. Tested by Nania Francesco Antonio on a Intel Core i7 920 2.67 ghz 6GB ram. 70. Tested by Richard Geldreich on a Core i7 Gulftown 3.3 Ghz, Win64. 71. Tested by Christoph Diegelmann on a Core i7-4770K, 8 GB DDR3, Samsung 840Pro 128 GB, Fedora 21 64 bit, gcc 4.9.2. 72. Tested by Skymmer on a i7-2770K, WinXP x64 SP2. 73. Tested by Andreas M. Nilsson on a 1.7 GHz Intel Core i7, 8 GB 1600 MHz DDR3, Mac OS X 10.10.3 (14D136). 74. Tested by Michael Crogan on a Core i7-3930K, 3.20 GHz, 6+HT, 64 MB, Linux64. 75. Tested by Mauro Vezzosi on a Core i7-4710HQ 2.50-3.50 GHz, 8 GB DDR3, Windows 8.1 64 bit. 76. Tested by Yann Collet on Core i7-3930K, 4.5 GHz, Linux 64, gcc 5.2.0-5.3.1. 77. Tested by Darek on a Core i7 4900 MQ, 2.8 GHz overclocked to 3.7 GHz, 16 GB, Win7Pro 64. 78. Tested by mpais on a Core i7 5820K 4.4 GHz, Windows 10. 79. Tested by Sportman on2 x Intel Xeon E5-2643 v3 6 cores (12 threads) 3.4GHz, 3.7GHz turbo, 20MB L3 cache, 8 x 32GB DDR4 2133MHz CAS 15, SoftPefect RAM Disk 3.4.7, Windows Server 2012 R2 64-bit. 80. Tested by kaitz on an Intel Celeron G1820 DDR3 8GB PC3-12800 (800 MHz). 81. Tested by Darek on Core i7 4900MQ 2.8GHz ovwerclocked to 3.8GHz, 32GB, Win7Pro 64. 82. Tested by Ilia Muraviev on an Intel Core i7-4790K @ 4.6GHz, 32GB @ 1866MHz DDR3 RAM, RAMDisk. 83. Tested by Byron Knoll on an Intel Core i7-7700K, 32 GB DDR4, Ubuntu 16.04-18.04. 84. Tested by Fabrice Bellard on 2 x Xeon E5-2640 v3 @ 2.6 GHz, 196 GB RAM, Linux. 85. Tested by Georgi Marinov on a Windows 10 Laptop: Lenovo Ideapad 310; i5-7200u @2.5GHz; 8GB DDR4 @1066MHz (2133MHz) CL15 CR2T; L2 cache: 2x256KB; L3 cache: 3MB; SSD: Crucial MX500 500GB 86. Tested by Byron Knoll on an Intel Xeon 2.30 GHz, 13 GB, Tesla P100 GPU. 87. Tested by Byron Knoll on an Intel Xeon 2.00 GHz, 13 GB, Tesla V100 GPU. 88. Tested by Fabrice Bellard on an Intel Xeon E3-1230 v6, 3.5 GHz, RTX 3090 GPU. 89. Tested by Matt Mahoney on a Lenovo Intel i7-1165G7 (4 core, 8 thread) 2.80 GHz, 16 GB, Windows 10/Ubuntu 20.04. 90. Tested by Artemiy Margaritov on an Intel Xeon Silver 4114, 2.20 GHz, Ubuntu 18. 91. Tested by Zoltán Gotthardt on an Intel Core i7-8700K @ 3.70GHz, HyperX Fury 32GB 2666MHz DDR4 CL16 (2x16GB kit), Windows 10 Pro 64 bit. The system was not completely idle during the tests. 92. Tested by Darek on a DELL Precision 7730, Intel Core i9-8950HK, 32GB RAM (2400MHz), Windows 10 Pro for Workstations (21H2). The system was not completely idle during the tests. 93. Tested by Sportman on an Intel Core i9 12900KS 16 cores (8 efficient cores disabled, hyper-threading disabled) 3,4GHz, 5.5GHz turbo, 30MB L3 cache, 14MB L2 cache, 2 x 16GB DDR5 6400MHz (PC5-51200) timings 32-39-39-102, Windows 10 Pro 64-bit. 94. Tested by Byron Knoll on an Intel Xeon 2.2 GHz, 83 GB, A100 GPU. 95. Tested by Gerald R. Tamayo on a Dell Inspiron 3881 Intel Core i3-10100 16GB RAM @ 3.60GHz (Windows 10). 96. Tested by Ilya Grebnov on an Intel 9700K CPU (5GHz all cores) with 2x8 GB DDR4 RAM (4133 MHz with 17-17-17-37-400-2T timings) running Microsoft Windows 10 Pro (64 Bit). 97. Tested by Matt Mahoney on a Lenovo Core i7-1165G7 2.80 GHz 16 GB, SSD, Windows 11 or Ubuntu. 98. Tested by Ilia Muraviev on an Intel Core i7-12700K (stock), 32 GB DDR5 5200 MHJz, 1 TB M.2 NVMe SSD. 99. Tested by James Bowery on an AMD Ryzon 7-3700x, 3.6 GHz, 8 cores, 16 threads, 64 GB. 100. Tested by Andrea Barbato on a AMD Ryzen 9 5950X 3.4 GHz 32core processor Patriot Viper Steel RAM DDR4 3600 Mhz 32GB (4x32GB) 101. Tested by James Bowery on Amazon AWS with --machine-type=c2-standard-4 (Geekbench 5 score 790). 102. Tested by kaitz on a Intel Core i5-4460, 3.20 GHz, 32 GB (4x8 GB) PC3-12800 (800 MHz) RAM, Windows. 103. Tested by HyperSoop on an AMD FX 4300 CPU with 6 (4+2) GB of DDR3 RAM, zram + zswap configured. OS is Arch Linux with kernel 6.15.0-rc3 104. Tested by Alexey Simbarsky on an Intel Core i7 9700K / 64 GB RAM / Windows 10 Pro. I have not verified results submitted by others. Timing information, when available, may vary widely depending on the test machine used. About the Compressors The numbers in the headings are the compression ratios on enwik9. .1072 nncp nncp is a free, experimental file compressor by Fabrice Bellard, released May 8, 2019. It uses a neural network model with dictionary preprocessing described in the paper Lossless Data Compression with Neural Networks . Compression of enwik9 uses the options: ./preprocess c out.words enwik9 out.pre 16384 512 ./nncp -n_layer 7 -hidden_size 384 -n_embed_out 5 -n_symb 16388 -full_connect 1 -lr 6e-3 c out.pre out.bin Version 2019-11-16 was released Nov. 16, 2019. It was run in 8 threads. Version 2 was released Jan. 3, 2021. It uses a transformer architecture, a recurrent neural network with attention mechanism to allow parallelism. The algorithm is described briefly here . It uses the same dictionary preprocessing as earlier versions. It was tested with an Intel Xeon E3-1230 v6 at 3.5 GHz and a Geforce RTX 3090 GPU with 10,496 Cuda cores and 24 GB RAM. nncp v2.1 was released Feb. 6, 2021. It is the same code as v2 except for a larger model and slightly different hyperparameters. nncp v3 was released Apr. 24, 2021. This new version is coded in C and supports recent NVIDIA GPUs. It is much faster (3x) due to algorithmic improvements and requires less memory. The Transformer model is similar (199M parameters) but the hyperparameters have been tuned. nncp v3.1 was released June 1, 2021. nncp v3.2 was released Oct. 23, 2023. Compression Compressed size Decompresser Total size Time (ns/byte) Program Options enwik8 enwik9 size (zip) enwik9+prog Comp Decomp Mem Alg Notes ------- ------- ---------- ----------- ----------- ----------- ------ ------ ----- --- ----- nncp 2019-05-08 16,791,077 125,623,896 161,133 xd 125,785,029 420168 602409 2040 LSTM 84 nncp 2019-11-16 16,292,774 119,167,224 238,452 xd 119,405,676 826048 1156467 5360 LSTM 84 nncp v2 15,600,675 114,317,255 99,671 xd 114,317,255 308645 313468 17000 Transformer 88 nncp v2.1 15,020,691 112,219,309 100,046 xd 112,319,355 508332 515401 23000 Transformer 88 nncp v3 15,206,966 110,034,293 197,491 xd 110,231,784 161812 158982 6000 Transformer 88 nncp v3.1 14,969,569 108,378,032 201,620 xd 108,579,652 212766 210970 6000 Transformer 88 nncp v3.2 14,915,298 106,632,363 628,955 xd 107,261,318 241871 238670 7600 Transformer 88 .1082 cmix cmix v1 is a free, open source (GPL) file compressor by Byron Knoll, Apr. 16, 2014. It is a context mixing compressor with dictionary preprocessing based on code from paq8hp12any and paq8l but increasing the number of context models and mixer layers. It takes no compression options. cmix v2 was released May 29, 2014. cmix v3 was released June 27, 2014. cmix v4 was released July 22, 2014. It uses 28,976,428 KiB memory (29.7 GB). cmix v5 was released Aug. 13, 2014. The decompressor size is a zip archive containing the source code, makefile, and a dictionary compressed with cmix from 465211 to 90065 bytes. cmix v6 was released Sept. 3, 2014. The decompressor size includes the dictionary compressed with cmix from 465211 to 90207 bytes. cmix v7 was released Feb. 4, 2015. cmix v8 was released Nov. 10, 2015. cmix v9 was released Apr. 8, 2016. cmix v10 was released June 17, 2016. cmix v11 was released July 3, 2016. It incorporates a modification originally developed by Eugene Shelwien in which PPMd is included as a model. cmix v12 was released Nov. 7, 2016. It includes a LSTM model. cmix v13 was released Apr. 24, 2017. cmix v14 was released Nov. 22, 2017. cmix v15 was released May 19, 2018. cmix v16 was released Oct 6, 2018. cmix v17 was released Mar. 24, 2019. cmix v18 was released Aug. 2, 2019. cmix v19 was released Aug. 29, 2021. It has improvements based on the startlit (article reordering) and cmix-hp Hutter prize entries. It has a separate decompressor. cmix v20 was released Nov. 5, 2023. cmix v21 was released Sept. 17, 2024. Compression Compressed size Decompresser Total size Time (ns/byte) Program Options enwik8 enwik9 size (zip) enwik9+prog Comp Decomp Mem Notes ------- ------- ---------- ----------- ----------- ----------- ------ ------ ----- ----- cmix v1 16,076,381 128,647,538 279,185 x 128,926,723 181924 179706 20785 66 cmix v2 15,863,623 126,323,656 310,068 x 126,633,724 580083 577626 28152 66 cmix v3 15,809,519 125,971,560 274,992 x 126,246,552 267978 266622 26681 66 cmix v4 15,784,946 125,621,620 278,375 x 125,899,995 284243 282390 28976 66 cmix v5 15,769,367 125,526,628 163,552 s 125,690,180 282056 282647 28865 66 cmix v6 15,738,922 124,172,611 161,908 s 124,334,519 280749 282137 30882 66 cmix v7 15,738,825 124,168,463 166,785 s 124,335,248 280416 280904 30600 66 cmix v8 15,709,216 123,930,173 164,882 s 124,095,055 344244 346641 30311 66 cmix v9 15,627,536 123,874,398 161,911 s 124,036,309 346436 345681 26929 66 cmix v10 15,587,868 123,257,156 164,263 s 123,421,419 355721 355850 29924 66 cmix v11 15,566,358 122,977,954 172,261 s 123,150,215 377529 374440 27745 66 cmix v12 15,440,186 121,718,424 175,953 s 121,894,377 571339 574522 27865 66 cmix v13 15,323,969 120,480,684 177,979 s 120,658,664 617346 615987 27803 66 cmix v14 15,210,458 119,017,492 203,717 s 119,221,209 631838 627802 28287 83 cmix v15 15,111,677 117,959,016 217,830 s 118,176,846 650055 651716 28365 83 cmix v16 14,955,482 116,912,035 226,121 s 117,138,156 613898 658679 27708 83 cmix v17 14,877,373 116,394,271 208,263 s 116,602,534 641189 645651 25258 83 cmix v18 14,838,332 115,714,367 208,961 s 115,923,328 602867 601569 25738 83 cmix v19 14,837,987 111,470,932 223,485 sd 111,694,417 605110 601825 25528 83 cmix v20 14,760,552 109,877,715 241,725 sd 110,119,440 621780 619024 31650 83 cmix v21 -t 14,623,723 107,963,380 281,387 sd 108,244,767 622949 638442 30950 83 .1103 fx2-cmix fx-cmix (discussion) (self extracting enwik9) is an open source Hutter prize submission by Kaitz, Dec. 4, 2023. It is an optimization of the previous submissions cmix-hp, fast-cmix, and starlit. I only tested the supplied Linux self extracting archive. The extraction time of 60 hours was at 74% CPU (one thread) due to SSD disk thrashing on the second day. User time was 152496 s and system time was 9425 s (45 hours total). fx2-cmix ( discussion ) is an update to fx-cmix and a Hutter Prize winner by Byron Knoll and Kaido Orav (Kaitz), submitted Aug. 11, 2024 and accepted Oct. 8, 2024. Changes from fx-cmix are described in the readme file. Compression Compressed size Decompresser Total size Time (ns/byte) Program Options enwik8 enwik9 size (zip) enwik9+prog Comp Decomp Mem Alg Notes ------- ------- ---------- ----------- ----------- ----------- ------ ------ ----- --- ----- fx-cmix 112,142,259 0 xd 112,142,259 216836 8869 CM 97 fx2-cmix 110,351,665 0 xd 110,351,665 272072 8811 CM 97 110,351,665 0 xd 110,351,665 289079 9522 CM 101 .1135 tensorflow-compress tensorflow-compress v1 is a free, open source experimental file compressor by Byron Knoll, July 20, 2020. It uses a LSTM neural network accelerated by a GPU if available. It uses a dictionary and preprocessor from NNCP by default, or from cmix. The test results for v1 use the default settings and were tested by the author on an Intel Xeon 2.30 GHz, 13 GB RAM with a Tesla P100 GPU. It uses 10138 MiB CPU RAM and 15525 MiB GPU RAM. It is run as a Colab notebook. v2 was released Sept. 7, 2020. It runs on a V100 GPU using 2669 MB CPU RAM and 15621 MB GPU RAM. The decompressor contains a cohab notebook, NNCP preprocessor source code and makefile, and a dictionary created by the NNCP preprocessor. v3 was released Nov. 29, 2020. It uses 3252 MiB of CPU RAM on a 2.00 GHz Xeon and 15621 MiB of GPU RAM on a Tesla V100. v4 was released Aug. 10, 2022. It uses 5696 MiB of CPU RAM and 39664 GPU RAM on an Intel Xeon 2.2 GHz, 83 GB RAM, A100 GPU. Program enwik8 enwik9 Prog Total Comp Deco Mem Note --------- ---------- ----------- -------- --------- ---- ---- ---- ---- tensorflow-compress v1 20,119,747 159,716,240 88,870sd 159,805,110 72260 82259 25663 86 tensorflow-compress v2 16,828,585 127,146,379 175,047sd 127,321,426 157196 142820 18290 87 tensorflow-compress v3 16,128,954 118,938,744 54,597sd 118,993,341 300104 300408 18873 87 tensorflow-compress v4 15,905,037 113,542,413 55,283sd 113,597,696 291394 290803 45360 94 .1137 cmix-hp cmix-hp (mirror) is a Hutter prize submission by Byron Knoll, June 10, 2021. It is a simple modification to startlit (May 31 2021 submission) to enlarge the PPMD model and map it to 21.4 GB virtual memory. to meet the Hutter prize requirement of using at most 10 GB RAM and 100 GB disk. It uses 94% CPU on the SSD swapping 45 GB. cmix-hp v2 was released Aug. 1, 2021. cmix-hp v3 was released Aug. 9, 2021. Compression Compressed size Decompresser Total size Time (ns/byte) Program Options enwik8 enwik9 size (zip) enwik9+prog Comp Decomp Mem Alg Notes ------- ------- ---------- ----------- ----------- ----------- ------ ------ ----- --- ----- cmix-hp v1 15,957,339 113,712,798 0 xd 113,712,798 189420 194280 6873 CM 89 cmix-hp v2 15,221,487 113,816,319 0 xd 113,816,319 198900 6720 CM 89 cmix-hp v3 113,788,598 0 xd 113,788,598 188460 188040 6693 CM 89 .1137 fast-cmix fast-cmix-hp (source code) (Linux self extracting archive) is a free, open source Hutter prize entry by Saurabh Kumar, Apr. 20, 2023. It is a speed optimization of cmix-hp and starlit. It is a self extracting archive (archive9) for 64 bit Linux, size 113,746,218 bytes, that creates enwik9. Compression was not tested. Extraction time was 190,507 s at 88% CPU on a Lenovo 82HT Core i7-1165G7, 2.80 GHz, 4 cores, 8 threads, 16 GB under Ubuntu in Windows 11/WSL. Extraction time was 121,971 s at 99% CPU on an AMD Ryzon 7-3700x, 3.6 GHZ, 8 cores, 16 threads, 64 GB. In both cases, it runs in 1 thread. .1149 starlit starlit is a Hutter prize submission by Artemiy Margaritov on May 10, 2021, updated May 31, 2021. It is a free, open source, Linux compressor that produces a self extracting archive for enwik9 as a special case. It satisfies the Hutter prize rules of using less than 10 GiB of memory (the figure shown is in 1000 KiB), and 20 GB of disk space and compressing and decompressing in less than 50,000/(geekbench 5 score) hours each. I tested on a Lenovo Intel Core i7-1165G7, 2.80 GHz, 16 GB (geekbench 5 = 1427 single thread, 4667 multithreaded) in an Ubuntu 20.04 shell window under Windows 10 with the screen/sleep saver and WiFi turned off for 2 days each to compress and decompress. starlit compresses by first reordering the articles in enwik9 to maximize mutual information between consecutive articles, then uses the dictionary preprocessor from phda9 and compresses using a reduced version of cmix to decrease memory usage from 32 GB to 10 GB and increase speed. The compressor is built from the supplied bash scripts by compiling with clang++-12 in Linux with different parts optimized for size or speed. Then the dictionary and article order list (both text files) are compressed with the newly created cmix and appended to the executable. The size is 124,984 bytes before appending and 401,505 bytes afterward. (A precompiled cmix is supplied optimized for an AMD Zen 2 with size 114,012 bytes before appending, which I did not use). The new executable then compresses enwik9 by extracting the compressed article order and dictionary and an additional 17 GB of temporary files to produce an executable file named archive9. To decompress, archive9 is run, which extracts the dictionary, article order list, and 17 GB of temporary files, and 2 days later, the output as a file named enwik9_uncompressed. No other files are required to d | 2026-01-13T08:49:02 |
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https://dev.to/rohit_gavali_0c2ad84fe4e0/lessons-from-running-the-same-debugging-prompt-through-different-ai-systems-1l37#comments | Lessons from running the same debugging prompt through different AI systems - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Rohit Gavali Posted on Dec 23, 2025 Lessons from running the same debugging prompt through different AI systems # webdev # programming # ai Last Tuesday, I spent three hours chasing a memory leak in a Next.js application that was crashing our staging environment every six hours. The pattern was clear—memory usage would climb steadily until the process died—but the cause was invisible. No obvious infinite loops, no massive data structures, nothing in the profiler that screamed "this is your problem." Out of frustration, I did something I'd never done before: I took the exact same debugging prompt—code snippet, error logs, system metrics, everything—and ran it through four different AI systems back-to-back. Claude, GPT-4, Gemini, and Grok. Same problem, same context, four completely different approaches. What I learned in those twenty minutes changed how I think about AI-assisted debugging entirely. The Prompt That Started Everything Here's what I fed each system: Next.js app, memory usage climbing from 150MB to 2GB over 6 hours then crashes. No obvious leaks in heap snapshots. Using React Server Components, streaming SSR, and edge runtime. Event listeners properly cleaned up. What am I missing? Enter fullscreen mode Exit fullscreen mode Simple, direct, frustrating. The kind of problem where you've already tried the obvious solutions and you're starting to question your career choices. Four Systems, Four Personalities Claude came back like a senior engineer doing a code review. It asked clarifying questions first. "Are you caching API responses? How are you handling streaming cleanup? Have you checked for dangling promises in your server components?" It didn't rush to conclusions. It wanted to understand the full system before offering theories. When it finally suggested causes, they were architectural—focusing on how Next.js handles server component lifecycle and where streaming responses might not be properly closed. It pointed me toward the after() hook and suggested auditing my middleware chain for response streams that might not be terminating. GPT-4 behaved like a textbook come to life. It gave me a structured, methodical breakdown: "Here are the seven most common causes of memory leaks in Next.js applications with streaming SSR." Each point had an explanation, example code, and specific things to check. Comprehensive, organized, slightly generic. It suggested checking my database connection pooling, verifying that fetch requests in server components weren't being cached indefinitely, and looking for event emitters that might not be garbage collected. Solid advice, but it felt like it was working from first principles rather than debugging instinct. Gemini went for breadth over depth. It immediately started pattern matching across similar issues it had "seen" before. "This sounds like the Next.js 14.2 streaming bug that was patched in 14.2.3. Also possibly related to Vercel's edge runtime memory management. Have you tried..." It threw out five different possibilities rapid-fire, each one plausible, none of them developed deeply. Useful if you want to brainstorm many angles quickly, less useful if you want to methodically work through a single theory. Grok surprised me by being the most opinionated. It basically said "This is almost certainly your middleware chain. Next.js middleware runs on every request in the edge runtime and if you're not properly cleaning up, memory accumulates. Check your logging middleware first." Bold, direct, and—as it turned out—partially right. My logging middleware was indeed holding references longer than it should have been, though that wasn't the whole story. The Pattern That Emerged After working through all four responses, something clicked. Each AI wasn't better or worse—each one was optimized for a different debugging strategy. Claude excels at architectural debugging. When your problem is systemic, when the bug emerges from how different parts of your system interact, Claude's tendency to ask questions and think holistically is invaluable. It's the AI you want when you need to step back and reconsider your entire approach. GPT-4 is your methodical checklist generator. When you need comprehensive coverage of all possibilities, when you want to make sure you haven't missed something obvious, GPT-4's structured, textbook approach prevents blind spots. It's the AI you want when you need discipline, not intuition. Gemini shines at pattern recognition across domains. When you're debugging something that might be a known issue, or when you want to quickly explore many possible causes, Gemini's breadth helps you cast a wider net. It's the AI you want when you're still in the hypothesis generation phase. Grok cuts through ambiguity with confident theories. When you're paralyzed by too many possibilities, when you need someone to just pick the most likely cause and run with it, Grok's directness can be clarifying. It's the AI you want when you need momentum over completeness. The Real Discovery Here's what those twenty minutes taught me: using a single AI for debugging is like using only a hammer because it's the best tool you own. The most effective debugging session I've had in months happened because I stopped treating AI as "an assistant" and started treating different AIs as different modes of thought. When I needed systematic analysis, I consulted GPT-4. When I needed architectural insight, I asked Claude. When I got stuck on a hunch, I bounced it off Grok. This isn't about playing them against each other. It's about understanding that different cognitive approaches reveal different aspects of the same problem. The memory leak wasn't just one thing—it was a confluence of middleware behavior, streaming lifecycle issues, and subtle edge runtime quirks. No single AI caught all of it because no single debugging approach would have either. The Practical Protocol After this experience, I developed a new debugging workflow that leverages these differences deliberately: Start with breadth using Gemini to generate hypotheses. Let it throw out five or six possible causes without committing to any single theory. This prevents premature narrowing of your investigation. Move to structure with GPT-4o to systematically work through each hypothesis. Use its love of comprehensive checklists to ensure you're testing each theory properly and not missing obvious checks. Go architectural with Claude when structural issues emerge. If the problem seems to stem from how components interact rather than a single buggy function, Claude's systems-thinking approach becomes invaluable. Get decisive with Grok when you're drowning in possibilities. Sometimes you just need someone to say "it's probably this, check here first" to break analysis paralysis. The key is treating this not as consensus-building but as perspective-gathering . You're not looking for three AIs to agree on the answer. You're collecting different lenses through which to view the same problem. What This Means for How We Debug The traditional debugging narrative is linear: identify the problem, form a hypothesis, test it, repeat until solved. But modern systems are too complex for purely linear thinking. You need multiple angles of attack simultaneously. Different AI systems naturally provide those angles. Using Crompt AI to access multiple models in one interface means you're not just getting different answers—you're developing different ways of thinking about the problem in real-time. This isn't about outsourcing debugging to AI. It's about expanding your cognitive toolkit by borrowing different reasoning styles as needed. The AIs aren't solving the problem for you. They're helping you think about it from angles your default mental model might miss. The Debugging Blind Spot Here's what's interesting: after running this experiment several more times with different bugs, I noticed a pattern in my own thinking. I was gravitating toward certain AIs based on my cognitive comfort zone, not based on what the problem actually needed. When debugging frontend issues, I defaulted to Claude because I naturally think architecturally about UI systems. When debugging backend performance, I reached for GPT-4 because I prefer methodical profiling. But some of my biggest breakthroughs came when I forced myself to consult the AI whose approach felt least natural to me. The memory leak? Grok's aggressive "it's probably your middleware" hunch was right, but I initially dismissed it because it felt too simple. Claude's architectural perspective helped me understand why the middleware was leaking. GPT-4's systematic approach ensured I tested the fix properly. Gemini pointed me to similar issues in the Next.js GitHub issues that confirmed my theory. The bug wasn't solved by one AI. It was solved by thinking through the problem from four different angles. The Synthesis Problem The hardest part of this approach isn't accessing different AIs—it's synthesizing their perspectives into actionable insight. Each system gives you a piece of the puzzle, but you're still responsible for seeing the complete picture. This is where tools like the Research Assistant become valuable. Not for the initial debugging, but for organizing and connecting the different theories you've collected. When you've got four different explanations of the same bug, you need a way to map their relationships and contradictions. The Data Extractor helps when you're comparing system metrics across different debugging sessions. The Document Summarizer becomes useful when you're trying to distill lessons from multiple debugging attempts into principles you can apply next time. But the synthesis itself? That's still on you. The AIs can't do that part—and they shouldn't. That synthesis is where the learning happens. The Meta-Lesson Running the same debugging prompt through different AI systems taught me something bigger than debugging strategy. It revealed how much our choice of thinking tool shapes what we're able to see. If you only use one AI, you'll only develop one mode of problem-solving. If you only use Claude, you'll become great at architectural thinking but potentially weak at systematic elimination. If you only use GPT-4, you'll be thorough but potentially miss bold hunches. If you only use Gemini, you'll be great at generating possibilities but struggle to go deep on any single theory. The real skill isn't learning to use AI for debugging. It's learning to think like different AIs do, using them to expand your own cognitive range rather than narrow it. The Practice Next time you hit a truly stubborn bug, try this: don't ask just one AI for help. Ask three or four, deliberately choosing systems with different approaches. Don't look for consensus—look for complementary insights. Notice which perspectives you naturally gravitate toward and which ones feel uncomfortable. The uncomfortable ones are probably expanding your thinking the most. Use platforms like Crompt that let you switch between models seamlessly, so you're not managing multiple interfaces while trying to debug. The tool should facilitate perspective-gathering, not add cognitive overhead. The goal isn't to crowdsource debugging. It's to develop the kind of multi-perspective thinking that the best senior engineers have naturally—the ability to look at the same problem from architectural, systematic, intuitive, and pattern-matching angles simultaneously. The AIs just make that kind of cognitive flexibility more accessible to the rest of us. That memory leak taught me more than how to debug Next.js. It taught me that the limitation isn't the AI's intelligence—it's our tendency to use AI as an extension of our existing thinking rather than a way to think differently. Want to experiment with multi-perspective debugging? Try Crompt AI free and see how different models approach the same problem differently—because sometimes the bug isn't in your code, it's in how you're thinking about it. Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Rohit Gavali Follow Joined Aug 11, 2025 More from Rohit Gavali Why AI Breaks Down in Long-Lived Systems (And What Devs Miss) # webdev # programming # ai What Happened When I Let AI Handle My Debugging Sessions # webdev # programming # ai How AI Explains Code Correctly but Misses Architectural Context # webdev # programming # ai 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV Forem — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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They’ve also helped smaller organisations and startups explode in growth — completely transforming them from root to branch. Partner feedback web.dev wouldn't be what it is today without Set’s direction. The major redesign, with the new design and component systems they shipped have set our team up for the future. Paul Kinlan Lead for Web & Chrome Developer Relations, Google Working with Set has revolutionised our approach to launching our digital brands. Their solutions have enabled us to deploy new websites faster than ever before, completely transforming our paid digital testing processes. Thanks to Set, we're empowered to test, iterate, and refine rapidly, all without needing external developers. They’re more than just a design and development team; they’re essential partners in our digital growth. Will Kunhardt Co-founder, The Lost Estate We started working with Set on a small design system for our resource centre. This allowed us to rapidly iterate and eventually expand to a whole-site redesign with their help. We've built up a really good, ongoing relationship with Set because of the success of this iterative approach, and continue to further improve OCEG with them as our design strategy partners. Scott Mitchell Founder & CEO, OCEG I have identified your superpower. Your ability to strike a balance between giving us what we wanted and not including elements that you believed wouldn’t add value to our brand was truly commendable. Akanksha Maulik Director, Capitalmind I worked with Set on a creative technology build that really set out to do something bold and ambitious from a design perspective. The Set team delivered a result I was thrilled with – outstanding quality to time and budget and a frictionless workflow. Unreservedly recommend Set and already looking forward to working together again. Tom Rhumbold Founder, A Human Future Set Studio have been fantastic. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Keerthi Posted on Sep 22, 2021 • Edited on Sep 24, 2021 Create react app vs Vite # webdev # javascript # vite # react I have always relied on the npm command create-react-app to create the starter files for any React.js project. It does what it says on the tin, and creates all my starter template files, setups a local dev server and dev environment. Over the years I have become a little impatient because it takes around 3-4 minutes to setup a basic barebones app. Recently I have come to know about a faster way to setup React apps, which also gives you all the useful features that create-react-app gives you too. It is using a tool called Vite . Vite is another build tool like Webpack (create-react-app uses Webpack under the hood, read more here ). In this post I will take you through the steps on how to install React.js app using Vite and point out some differences too. You can also see a video on the comparison of the two installation methods. In the Video below, You will discover that the installation time, plus time to run local server is astonishingly fast for Vite. So how do we start the ball rolling You can refer to the Vite docs , From there, you can choose from a few methods to start off your installation. We are going to use the template method. In their docs, the listed methods are: #npm 6.x npm init vite@latest my-vue-app --template vue #npm 7+, extra double-dash is needed: npm init vite@latest my-vue-app -- --template vue #yarn yarn create vite my-vue-app --template vue Enter fullscreen mode Exit fullscreen mode But these commands are for installing Vue.js, just as side note, Vite was originally developed for Vue.js but has been modified to use with other frameworks including React.js. For our case, all we need to do is replace the keyword after '--template', from vue to react. And dont forget to replace the app name to your choosing. So assuming that we are running npm version 6.x, we will run the following command: npm init vite@latest my-react-app --template react Enter fullscreen mode Exit fullscreen mode Then we will cd into our directory and install the remainder of the starter files and run the dev server: cd my-react-app npm install npm run dev Enter fullscreen mode Exit fullscreen mode If you goto the browser. You should see a React logo with a counter and a button, as below. Directory structure of the our newly created app The thing to note here is that, main.js is the root file that imports/loads App.js. There is also a new file called vite.config.js, this is circled in the above image. This file is used to turn on and set new features for your build process. I will come to this file in the next section below. One last thing about importing files... I have noticed that out the box this setup does not allow for absolute paths. With create-react-app, you can do import x from 'components/x' . With Vite, you have to do the relative pathing, like ```import x from '../../../' To fix this we need to change the vite.config.js file, which looks like this: ```javascript import { defineConfig } from 'vite' import reactRefresh from '@vitejs/plugin-react-refresh' // https://vitejs.dev/config/ export default defineConfig({ plugins: [reactRefresh()] }) Enter fullscreen mode Exit fullscreen mode we need to add an extra setting to resolve the path, this change will go after the "plugins" settings. It will end up looking like this after the change: import { defineConfig } from ' vite ' import reactRefresh from ' @vitejs/plugin-react-refresh ' import path from ' path ' // https://vitejs.dev/config/ export default defineConfig ({ plugins : [ reactRefresh ()], resolve : { alias : { ' @ ' : path . resolve ( __dirname , ' ./src ' ), }, }, }) Enter fullscreen mode Exit fullscreen mode and this will allow us to refer to paths as import x from '@/component/x' !IMPORTATNT to prefix with '@' in path. conclusion I did find Vite impressingly fast. It took me 55 secs to install and run on local server. I have not done much heavy development using Vite but it looks promising. It is too early for me to say if I will use it on any bigger projects in the future. There are other methods of installing React.js using Vite, these methods are maintained by other communities. Check out other community maintained templates here , you can also find one with Tailwind. Please leave comments on your experiences too. Note: Vite has templates to build apps in the following frameworks vanilla vanilla-ts vue vue-ts react react-ts preact preact-ts lit-element lit-element-ts svelte svelte-ts Enter fullscreen mode Exit fullscreen mode so to create a build in react typescript , just change the last bit to "react-ts" after the "--template" , so it becomes: npm init vite@latest my-react-app --template react-ts Enter fullscreen mode Exit fullscreen mode Top comments (20) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Collapse Expand R. Maulana Citra R. Maulana Citra R. Maulana Citra Follow I write about web dev stuff Location Serang, Indonesia Work Front End @Skyshi Digital Indonesia Joined Mar 3, 2021 • Sep 24 '21 Dropdown menu Copy link Hide Vite is cool, I love how things are fast on dev server. I also made boilerplate for daily projects with Tailwind, if you want to check it out, see it on my GitHub here Like comment: Like comment: 4 likes Like Comment button Reply Collapse Expand Keerthi Keerthi Keerthi Follow I am UI developer, technologist, UI designer. Keen cook. Location london Work ui developer Joined Aug 7, 2020 • Sep 24 '21 Dropdown menu Copy link Hide Thats awesome, you should contribute to the community here github.com/vitejs/awesome-vite#tem... . They have one for react and tailwind already, maybe you can add yours as well. Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand R. Maulana Citra R. Maulana Citra R. Maulana Citra Follow I write about web dev stuff Location Serang, Indonesia Work Front End @Skyshi Digital Indonesia Joined Mar 3, 2021 • Oct 5 '21 Dropdown menu Copy link Hide thank you bro, I have added mine too, and it was merged already! Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand James Thomson James Thomson James Thomson Follow Just another front-end web dev junkie Location Australia Work Senior Frontend Engineer at Complish Joined Feb 22, 2019 • Sep 22 '21 Dropdown menu Copy link Hide I've recently switched a Vue CLI project to Vite. It's impressive how fast things are - but makes complete sense when there's no build step needed when developing. One thing I've found less intuitive are images, especially dynamically referenced ones (e.g. in a loop). I've had to create a utility for this: export function getImageUrl (name) { return new URL(`../assets/${name}`, import.meta.url).href; } Enter fullscreen mode Exit fullscreen mode Is this also the case in React? Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Keerthi Keerthi Keerthi Follow I am UI developer, technologist, UI designer. Keen cook. Location london Work ui developer Joined Aug 7, 2020 • Sep 23 '21 Dropdown menu Copy link Hide Yes , Similar in react Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Herberth Obregón Herberth Obregón Herberth Obregón Follow 🧩 Web Components 💻 Typescript First 🐳 ☸️ K8s Location GT Education Science and Systems Engineer Work CIO/CTO at HireX Joined Jan 1, 2020 • Sep 23 '21 Dropdown menu Copy link Hide I moved to vitejs for lit-element (now only lit) and is amazing! 💯💯🚀 Web pack is very slow to spinup a dev server Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand Keerthi Keerthi Keerthi Follow I am UI developer, technologist, UI designer. Keen cook. Location london Work ui developer Joined Aug 7, 2020 • Sep 23 '21 Dropdown menu Copy link Hide Firts tme I am hearing of lit-elemnt, Intresting, what apps are you building with it? Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Herberth Obregón Herberth Obregón Herberth Obregón Follow 🧩 Web Components 💻 Typescript First 🐳 ☸️ K8s Location GT Education Science and Systems Engineer Work CIO/CTO at HireX Joined Jan 1, 2020 • Sep 25 '21 Dropdown menu Copy link Hide It is one of the main "frameworks" of modern development, vitejs.dev/guide/#scaffolding-your... Vite support the main popular frameworks vue, react, lit-element and svelte I choose Lit-element because is the closest thing to js vanilla with all the power of web components (the performance is amazing ⚡️). Eventually I consider that web components are going to be so robust that you won't need a framework. Lit-element is the framework for web components par excellence. Stencil I don't like like Lit I build all empleo.gt with Lit Which next will be migrated to hirex.app for worldwide version Like comment: Like comment: 4 likes Like Thread Thread Keerthi Keerthi Keerthi Follow I am UI developer, technologist, UI designer. Keen cook. Location london Work ui developer Joined Aug 7, 2020 • Sep 26 '21 Dropdown menu Copy link Hide Thanks, Nice to know that about Lit, will look at it. Also good luck with your app too Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Wagner Wagner Wagner Follow Joined Feb 25, 2021 • Sep 23 '21 Dropdown menu Copy link Hide Why don't you use package.json inside each directory and refers to files like "@components/MyCompoment"?! You don't need do setup anything else. Just a package.json in each folder with content: { "name": "components" } Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Ivan Jeremic Ivan Jeremic Ivan Jeremic Follow Web/Software Developer Joined Dec 9, 2018 • Sep 23 '21 Dropdown menu Copy link Hide This is so dirty I can't believe people do this. Like comment: Like comment: 16 likes Like Comment button Reply Collapse Expand dragos dragos dragos Follow Indie app builder focused on simple, practical products. Currently building Vet Record, a pet health tracker for everyday owners. Location Beograd Education Completed an online course by Carnegie Mellon University Joined Oct 15, 2019 • Sep 23 '21 Dropdown menu Copy link Hide Stiil too much bugs Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand Daniel Tkach Daniel Tkach Daniel Tkach Follow Joined Sep 4, 2020 • Oct 4 '21 Dropdown menu Copy link Hide On vite? I'm just researching if I should switch to vite. Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Renan "Firehawk" Lazarotto Renan "Firehawk" Lazarotto Renan "Firehawk" Lazarotto Follow Hiya! I'm a fullstack developer, with experience with PHP, JavaScript and Go. I'm also an Android enthusiast and I like pretty much everything related to tech. Location Brazil Education Barchelor Degree in IT Pronouns he/him Work FullStack developer @ Hammer Consult Joined Dec 16, 2019 • Sep 22 '21 Dropdown menu Copy link Hide I have switched from CRA to Vite just because CRA is so slow! Vite is blazing fast even on my aging machine. Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Keerthi Keerthi Keerthi Follow I am UI developer, technologist, UI designer. Keen cook. Location london Work ui developer Joined Aug 7, 2020 • Sep 22 '21 Dropdown menu Copy link Hide Thats good to hear. CRA has always been so slow. But I had to put up with it. Other option was configuring webpack, which was way worse in terms of time to setup. Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Rami Rami Rami Follow I am a self taught web developer and secondary school student ✌ Location مصر Education self-taught Work Captain Dev Joined Nov 14, 2019 • Sep 22 '21 Dropdown menu Copy link Hide Vite is really cool, I hope they support Angular in the near future. Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Wagner Wagner Wagner Follow Joined Feb 25, 2021 • Sep 23 '21 Dropdown menu Copy link Hide Angular is a waste of time! A poor framework, too much verbose. Like comment: Like comment: 12 likes Like Comment button Reply Collapse Expand Jerry Jerry Jerry Follow follow for dev, javascript/typescript react, aws and cloud tips and more. Location British Columbia Work Software Engineer Joined Aug 14, 2018 • Mar 4 '23 Dropdown menu Copy link Hide This is a great overview! If you want a deep dive understanding of Vite, I wrote about here - jerrychang.ca/writing/vite-how-it-... Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Audace Audace Audace Follow Programmer Joined Feb 23, 2024 • Feb 23 '24 Dropdown menu Copy link Hide I have the problem with vite + react. When I run the localhost, see in the terminal [vite] hmr update. And after that in the browser nothing display on the screen. Screen is blank. Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Audace Audace Audace Follow Programmer Joined Feb 23, 2024 • Feb 23 '24 Dropdown menu Copy link Hide I have the problem Like comment: Like comment: 1 like Like Comment button Reply View full discussion (20 comments) Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Keerthi Follow I am UI developer, technologist, UI designer. Keen cook. Location london Work ui developer Joined Aug 7, 2020 More from Keerthi Crash course in interactive 3d animation with React-three-fiber and React-spring # react # webdev # threejs A crash course in React.js and D3 # react # javascript # d3js # webdev Scroll animation in Javascript using IntersectionObserver # javascript # webdev # css # html 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://peps.python.org/numerical/ | Numerical Index | peps.python.org Following system colour scheme Selected dark colour scheme Selected light colour scheme Python Enhancement Proposals Python » PEP Index » Numerical Index Toggle light / dark / auto colour theme Numerical Index PEP Title Authors PA 1 PEP Purpose and Guidelines Barry Warsaw, Jeremy Hylton, David Goodger, Alyssa Coghlan PA 2 Procedure for Adding New Modules Brett Cannon, Martijn Faassen PW 3 Guidelines for Handling Bug Reports Jeremy Hylton PA 4 Deprecation of Standard Modules Brett Cannon, Martin von Löwis PS 5 Guidelines for Language Evolution Paul Prescod PS 6 Bug Fix Releases Aahz, Anthony Baxter PA 7 Style Guide for C Code Guido van Rossum, Barry Warsaw PA 8 Style Guide for Python Code Guido van Rossum, Barry Warsaw, Alyssa Coghlan PW 9 Sample Plaintext PEP Template Barry Warsaw PA 10 Voting Guidelines Barry Warsaw PA 11 CPython platform support Martin von Löwis, Brett Cannon PA 12 Sample reStructuredText PEP Template David Goodger, Barry Warsaw, Brett Cannon PA 13 Python Language Governance The Python core team and community IA 20 The Zen of Python Tim Peters PW 42 Feature Requests Jeremy Hylton SF 100 Python Unicode Integration Marc-André Lemburg 2.0 IA 101 Doing Python Releases 101 Barry Warsaw, Guido van Rossum IS 102 Doing Python Micro Releases Anthony Baxter, Barry Warsaw, Guido van Rossum IW 103 Collecting information about git Oleg Broytman IF 160 Python 1.6 Release Schedule Fred L. Drake, Jr. 1.6 IF 200 Python 2.0 Release Schedule Jeremy Hylton 2.0 SF 201 Lockstep Iteration Barry Warsaw 2.0 SF 202 List Comprehensions Barry Warsaw 2.0 SF 203 Augmented Assignments Thomas Wouters 2.0 SR 204 Range Literals Thomas Wouters 2.0 SF 205 Weak References Fred L. Drake, Jr. 2.1 IW 206 Python Advanced Library A.M. Kuchling SF 207 Rich Comparisons Guido van Rossum, David Ascher 2.1 SF 208 Reworking the Coercion Model Neil Schemenauer, Marc-André Lemburg 2.1 SW 209 Multi-dimensional Arrays Paul Barrett, Travis Oliphant 2.2 SR 210 Decoupling the Interpreter Loop David Ascher 2.1 SR 211 Adding A New Outer Product Operator Greg Wilson 2.1 SR 212 Loop Counter Iteration Peter Schneider-Kamp 2.1 SD 213 Attribute Access Handlers Paul Prescod 2.1 SF 214 Extended Print Statement Barry Warsaw 2.0 SS 215 String Interpolation Ka-Ping Yee 2.1 IW 216 Docstring Format Moshe Zadka SF 217 Display Hook for Interactive Use Moshe Zadka 2.1 SF 218 Adding a Built-In Set Object Type Greg Wilson, Raymond Hettinger 2.2 SD 219 Stackless Python Gordon McMillan 2.1 IR 220 Coroutines, Generators, Continuations Gordon McMillan SF 221 Import As Thomas Wouters 2.0 SD 222 Web Library Enhancements A.M. Kuchling 2.1 SF 223 Change the Meaning of x Escapes Tim Peters 2.0 SR 224 Attribute Docstrings Marc-André Lemburg 2.1 SR 225 Elementwise/Objectwise Operators Huaiyu Zhu, Gregory Lielens 2.1 IF 226 Python 2.1 Release Schedule Jeremy Hylton 2.1 SF 227 Statically Nested Scopes Jeremy Hylton 2.1 SW 228 Reworking Python’s Numeric Model Moshe Zadka, Guido van Rossum SF 229 Using Distutils to Build Python A.M. Kuchling 2.1 SF 230 Warning Framework Guido van Rossum 2.1 SR 231 __findattr__() Barry Warsaw 2.1 SF 232 Function Attributes Barry Warsaw 2.1 SD 233 Python Online Help Paul Prescod 2.1 SF 234 Iterators Ka-Ping Yee, Guido van Rossum 2.1 SF 235 Import on Case-Insensitive Platforms Tim Peters 2.1 SF 236 Back to the __future__ Tim Peters 2.1 SF 237 Unifying Long Integers and Integers Moshe Zadka, Guido van Rossum 2.2 SF 238 Changing the Division Operator Moshe Zadka, Guido van Rossum 2.2 SR 239 Adding a Rational Type to Python Christopher A. Craig, Moshe Zadka 2.2 SR 240 Adding a Rational Literal to Python Christopher A. Craig, Moshe Zadka 2.2 SS 241 Metadata for Python Software Packages A.M. Kuchling SW 242 Numeric Kinds Paul F. Dubois 2.2 SW 243 Module Repository Upload Mechanism Sean Reifschneider 2.1 SR 244 The directive statement Martin von Löwis 2.1 SR 245 Python Interface Syntax Michel Pelletier 2.2 SR 246 Object Adaptation Alex Martelli, Clark C. Evans 2.5 IF 247 API for Cryptographic Hash Functions A.M. Kuchling IF 248 Python Database API Specification v1.0 Greg Stein, Marc-André Lemburg IF 249 Python Database API Specification v2.0 Marc-André Lemburg SF 250 Using site-packages on Windows Paul Moore 2.2 IF 251 Python 2.2 Release Schedule Barry Warsaw, Guido van Rossum 2.2 SF 252 Making Types Look More Like Classes Guido van Rossum 2.2 SF 253 Subtyping Built-in Types Guido van Rossum 2.2 SR 254 Making Classes Look More Like Types Guido van Rossum 2.2 SF 255 Simple Generators Neil Schemenauer, Tim Peters, Magnus Lie Hetland 2.2 SR 256 Docstring Processing System Framework David Goodger IA 257 Docstring Conventions David Goodger, Guido van Rossum SR 258 Docutils Design Specification David Goodger SR 259 Omit printing newline after newline Guido van Rossum 2.2 SF 260 Simplify xrange() Guido van Rossum 2.2 SF 261 Support for “wide” Unicode characters Paul Prescod 2.2 SR 262 A Database of Installed Python Packages A.M. Kuchling SF 263 Defining Python Source Code Encodings Marc-André Lemburg, Martin von Löwis 2.3 SF 264 Future statements in simulated shells Michael Hudson 2.2 SR 265 Sorting Dictionaries by Value Grant Griffin 2.2 SW 266 Optimizing Global Variable/Attribute Access Skip Montanaro 2.3 SD 267 Optimized Access to Module Namespaces Jeremy Hylton 2.2 SR 268 Extended HTTP functionality and WebDAV Greg Stein 2.x SD 269 Pgen Module for Python Jonathan Riehl 2.2 SR 270 uniq method for list objects Jason Petrone 2.2 SR 271 Prefixing sys.path by command line option Frédéric B. Giacometti 2.2 IF 272 API for Block Encryption Algorithms v1.0 A.M. Kuchling SF 273 Import Modules from Zip Archives James C. Ahlstrom 2.3 SF 274 Dict Comprehensions Barry Warsaw 2.7, 3.0 SR 275 Switching on Multiple Values Marc-André Lemburg 2.6 SR 276 Simple Iterator for ints Jim Althoff 2.3 SF 277 Unicode file name support for Windows NT Neil Hodgson 2.3 SF 278 Universal Newline Support Jack Jansen 2.3 SF 279 The enumerate() built-in function Raymond Hettinger 2.3 SD 280 Optimizing access to globals Guido van Rossum 2.3 SR 281 Loop Counter Iteration with range and xrange Magnus Lie Hetland 2.3 SF 282 A Logging System Vinay Sajip, Trent Mick 2.3 IF 283 Python 2.3 Release Schedule Guido van Rossum 2.3 SR 284 Integer for-loops David Eppstein, Gregory Ewing 2.3 SF 285 Adding a bool type Guido van Rossum 2.3 SD 286 Enhanced Argument Tuples Martin von Löwis 2.3 IA 287 reStructuredText Docstring Format David Goodger SW 288 Generators Attributes and Exceptions Raymond Hettinger 2.5 SF 289 Generator Expressions Raymond Hettinger 2.4 IA 290 Code Migration and Modernization Raymond Hettinger IS 291 Backward Compatibility for the Python 2 Standard Library Neal Norwitz 2.3 SF 292 Simpler String Substitutions Barry Warsaw 2.4 SF 293 Codec Error Handling Callbacks Walter Dörwald 2.3 SR 294 Type Names in the types Module Oren Tirosh 2.5 SR 295 Interpretation of multiline string constants Stepan Koltsov 3.0 SW 296 Adding a bytes Object Type Scott Gilbert 2.3 SR 297 Support for System Upgrades Marc-André Lemburg 2.6 SW 298 The Locked Buffer Interface Thomas Heller 2.3 SR 299 Special __main__() function in modules Jeff Epler 2.3 SF 301 Package Index and Metadata for Distutils Richard Jones 2.3 SF 302 New Import Hooks Just van Rossum, Paul Moore 2.3 SR 303 Extend divmod() for Multiple Divisors Thomas Bellman 2.3 SW 304 Controlling Generation of Bytecode Files Skip Montanaro SF 305 CSV File API Kevin Altis, Dave Cole, Andrew McNamara, Skip Montanaro, Cliff Wells 2.3 IW 306 How to Change Python’s Grammar Michael Hudson, Jack Diederich, Alyssa Coghlan, Benjamin Peterson SF 307 Extensions to the pickle protocol Guido van Rossum, Tim Peters 2.3 SF 308 Conditional Expressions Guido van Rossum, Raymond Hettinger 2.5 SF 309 Partial Function Application Peter Harris 2.5 SR 310 Reliable Acquisition/Release Pairs Michael Hudson, Paul Moore 2.4 SF 311 Simplified Global Interpreter Lock Acquisition for Extensions Mark Hammond 2.3 SD 312 Simple Implicit Lambda Roman Suzi, Alex Martelli 2.4 SR 313 Adding Roman Numeral Literals to Python Mike Meyer 2.4 SS 314 Metadata for Python Software Packages 1.1 A.M. Kuchling, Richard Jones 2.5 SR 315 Enhanced While Loop Raymond Hettinger, W Isaac Carroll 2.5 SD 316 Programming by Contract for Python Terence Way SR 317 Eliminate Implicit Exception Instantiation Steven Taschuk 2.4 SF 318 Decorators for Functions and Methods Kevin D. Smith, Jim J. Jewett, Skip Montanaro, Anthony Baxter 2.4 SR 319 Python Synchronize/Asynchronize Block Michel Pelletier 2.4 IF 320 Python 2.4 Release Schedule Barry Warsaw, Raymond Hettinger, Anthony Baxter 2.4 SW 321 Date/Time Parsing and Formatting A.M. Kuchling 2.4 SF 322 Reverse Iteration Raymond Hettinger 2.4 SD 323 Copyable Iterators Alex Martelli 2.5 SF 324 subprocess - New process module Peter Astrand 2.4 SR 325 Resource-Release Support for Generators Samuele Pedroni 2.4 SR 326 A Case for Top and Bottom Values Josiah Carlson, Terry Reedy 2.4 SF 327 Decimal Data Type Facundo Batista 2.4 SF 328 Imports: Multi-Line and Absolute/Relative Aahz 2.4, 2.5, 2.6 SR 329 Treating Builtins as Constants in the Standard Library Raymond Hettinger 2.4 SR 330 Python Bytecode Verification Michel Pelletier 2.6 SF 331 Locale-Independent Float/String Conversions Christian R. Reis 2.4 SR 332 Byte vectors and String/Unicode Unification Skip Montanaro 2.5 IF 333 Python Web Server Gateway Interface v1.0 Phillip J. Eby SW 334 Simple Coroutines via SuspendIteration Clark C. Evans 3.0 SR 335 Overloadable Boolean Operators Gregory Ewing 3.3 SR 336 Make None Callable Andrew McClelland SD 337 Logging Usage in the Standard Library Michael P. Dubner 2.5 SF 338 Executing modules as scripts Alyssa Coghlan 2.5 IW 339 Design of the CPython Compiler Brett Cannon SR 340 Anonymous Block Statements Guido van Rossum SF 341 Unifying try-except and try-finally Georg Brandl 2.5 SF 342 Coroutines via Enhanced Generators Guido van Rossum, Phillip J. Eby 2.5 SF 343 The “with” Statement Guido van Rossum, Alyssa Coghlan 2.5 SS 344 Exception Chaining and Embedded Tracebacks Ka-Ping Yee 2.5 SS 345 Metadata for Python Software Packages 1.2 Richard Jones 2.7 SW 346 User Defined (“with”) Statements Alyssa Coghlan 2.5 PF 347 Migrating the Python CVS to Subversion Martin von Löwis SR 348 Exception Reorganization for Python 3.0 Brett Cannon SR 349 Allow str() to return unicode strings Neil Schemenauer 2.5 IR 350 Codetags Micah Elliott SR 351 The freeze protocol Barry Warsaw 2.5 SF 352 Required Superclass for Exceptions Brett Cannon, Guido van Rossum 2.5 SF 353 Using ssize_t as the index type Martin von Löwis 2.5 SS 354 Enumerations in Python Ben Finney 2.6 SR 355 Path - Object oriented filesystem paths Björn Lindqvist 2.5 IF 356 Python 2.5 Release Schedule Neal Norwitz, Guido van Rossum, Anthony Baxter 2.5 SF 357 Allowing Any Object to be Used for Slicing Travis Oliphant 2.5 SF 358 The “bytes” Object Neil Schemenauer, Guido van Rossum 2.6, 3.0 SW 359 The “make” Statement Steven Bethard 2.6 PF 360 Externally Maintained Packages Brett Cannon IF 361 Python 2.6 and 3.0 Release Schedule Neal Norwitz, Barry Warsaw 2.6, 3.0 SF 362 Function Signature Object Brett Cannon, Jiwon Seo, Yury Selivanov, Larry Hastings 3.3 SR 363 Syntax For Dynamic Attribute Access Ben North SW 364 Transitioning to the Py3K Standard Library Barry Warsaw 2.6 SR 365 Adding the pkg_resources module Phillip J. Eby SF 366 Main module explicit relative imports Alyssa Coghlan 2.6, 3.0 SS 367 New Super Calvin Spealman, Tim Delaney 2.6 SD 368 Standard image protocol and class Lino Mastrodomenico 2.6, 3.0 SW 369 Post import hooks Christian Heimes 2.6, 3.0 SF 370 Per user site-packages directory Christian Heimes 2.6, 3.0 SF 371 Addition of the multiprocessing package to the standard library Jesse Noller, Richard Oudkerk 2.6, 3.0 SF 372 Adding an ordered dictionary to collections Armin Ronacher, Raymond Hettinger 2.7, 3.1 IF 373 Python 2.7 Release Schedule Benjamin Peterson 2.7 PF 374 Choosing a distributed VCS for the Python project Brett Cannon, Stephen J. Turnbull, Alexandre Vassalotti, Barry Warsaw, Dirkjan Ochtman IF 375 Python 3.1 Release Schedule Benjamin Peterson 3.1 SF 376 Database of Installed Python Distributions Tarek Ziadé 2.7, 3.2 SR 377 Allow __enter__() methods to skip the statement body Alyssa Coghlan 2.7, 3.1 SF 378 Format Specifier for Thousands Separator Raymond Hettinger 2.7, 3.1 SW 379 Adding an Assignment Expression Jervis Whitley 2.7, 3.2 SF 380 Syntax for Delegating to a Subgenerator Gregory Ewing 3.3 SW 381 Mirroring infrastructure for PyPI Tarek Ziadé, Martin von Löwis SR 382 Namespace Packages Martin von Löwis 3.2 SF 383 Non-decodable Bytes in System Character Interfaces Martin von Löwis 3.1 SF 384 Defining a Stable ABI Martin von Löwis 3.2 PF 385 Migrating from Subversion to Mercurial Dirkjan Ochtman, Antoine Pitrou, Georg Brandl SS 386 Changing the version comparison module in Distutils Tarek Ziadé PA 387 Backwards Compatibility Policy Benjamin Peterson SF 389 argparse - New Command Line Parsing Module Steven Bethard 2.7, 3.2 SR 390 Static metadata for Distutils Tarek Ziadé 2.7, 3.2 SF 391 Dictionary-Based Configuration For Logging Vinay Sajip 2.7, 3.2 IF 392 Python 3.2 Release Schedule Georg Brandl 3.2 SF 393 Flexible String Representation Martin von Löwis 3.3 IA 394 The “python” Command on Unix-Like Systems Kerrick Staley, Alyssa Coghlan, Barry Warsaw, Petr Viktorin, Miro Hrončok, Carol Willing SW 395 Qualified Names for Modules Alyssa Coghlan 3.4 IW 396 Module Version Numbers Barry Warsaw SF 397 Python launcher for Windows Mark Hammond, Martin von Löwis 3.3 IF 398 Python 3.3 Release Schedule Georg Brandl 3.3 IF 399 Pure Python/C Accelerator Module Compatibility Requirements Brett Cannon 3.3 SD 400 Deprecate codecs.StreamReader and codecs.StreamWriter Victor Stinner 3.3 PR 401 BDFL Retirement Barry Warsaw, Brett Cannon SR 402 Simplified Package Layout and Partitioning Phillip J. Eby 3.3 SD 403 General purpose decorator clause (aka “@in” clause) Alyssa Coghlan 3.4 IF 404 Python 2.8 Un-release Schedule Barry Warsaw 2.8 SF 405 Python Virtual Environments Carl Meyer 3.3 SW 406 Improved Encapsulation of Import State Alyssa Coghlan, Greg Slodkowicz 3.4 PD 407 New release cycle and introducing long-term support versions Antoine Pitrou, Georg Brandl, Barry Warsaw SR 408 Standard library __preview__ package Alyssa Coghlan, Eli Bendersky 3.3 SF 409 Suppressing exception context Ethan Furman 3.3 SR 410 Use decimal.Decimal type for timestamps Victor Stinner 3.3 IS 411 Provisional packages in the Python standard library Alyssa Coghlan, Eli Bendersky 3.3 SF 412 Key-Sharing Dictionary Mark Shannon 3.3 PW 413 Faster evolution of the Python Standard Library Alyssa Coghlan SF 414 Explicit Unicode Literal for Python 3.3 Armin Ronacher, Alyssa Coghlan 3.3 SF 415 Implement context suppression with exception attributes Benjamin Peterson 3.3 SR 416 Add a frozendict builtin type Victor Stinner 3.3 SF 417 Including mock in the Standard Library Michael Foord 3.3 SF 418 Add monotonic time, performance counter, and process time functions Cameron Simpson, Jim J. Jewett, Stephen J. Turnbull, Victor Stinner 3.3 SD 419 Protecting cleanup statements from interruptions Paul Colomiets 3.3 SF 420 Implicit Namespace Packages Eric V. Smith 3.3 SF 421 Adding sys.implementation Eric Snow 3.3 SW 422 Simpler customisation of class creation Alyssa Coghlan, Daniel Urban 3.5 ID 423 Naming conventions and recipes related to packaging Benoit Bryon SF 424 A method for exposing a length hint Alex Gaynor 3.4 SF 425 Compatibility Tags for Built Distributions Daniel Holth 3.4 IW 426 Metadata for Python Software Packages 2.0 Alyssa Coghlan, Daniel Holth, Donald Stufft SF 427 The Wheel Binary Package Format 1.0 Daniel Holth SF 428 The pathlib module – object-oriented filesystem paths Antoine Pitrou 3.4 IF 429 Python 3.4 Release Schedule Larry Hastings 3.4 IF 430 Migrating to Python 3 as the default online documentation Alyssa Coghlan SS 431 Time zone support improvements Lennart Regebro SW 432 Restructuring the CPython startup sequence Alyssa Coghlan, Victor Stinner, Eric Snow SS 433 Easier suppression of file descriptor inheritance Victor Stinner 3.4 IA 434 IDLE Enhancement Exception for All Branches Todd Rovito, Terry Reedy SF 435 Adding an Enum type to the Python standard library Barry Warsaw, Eli Bendersky, Ethan Furman 3.4 SF 436 The Argument Clinic DSL Larry Hastings 3.4 SR 437 A DSL for specifying signatures, annotations and argument converters Stefan Krah 3.4 PS 438 Transitioning to release-file hosting on PyPI Holger Krekel, Carl Meyer SR 439 Inclusion of implicit pip bootstrap in Python installation Richard Jones 3.4 SF 440 Version Identification and Dependency Specification Alyssa Coghlan, Donald Stufft SF 441 Improving Python ZIP Application Support Daniel Holth, Paul Moore 3.5 SF 442 Safe object finalization Antoine Pitrou 3.4 SF 443 Single-dispatch generic functions Łukasz Langa 3.4 ID 444 Python Web3 Interface Chris McDonough, Armin Ronacher SF 445 Add new APIs to customize Python memory allocators Victor Stinner 3.4 SF 446 Make newly created file descriptors non-inheritable Victor Stinner 3.4 SD 447 Add __getdescriptor__ method to metaclass Ronald Oussoren SF 448 Additional Unpacking Generalizations Joshua Landau 3.5 PF 449 Removal of the PyPI Mirror Auto Discovery and Naming Scheme Donald Stufft SF 450 Adding A Statistics Module To The Standard Library Steven D’Aprano 3.4 SF 451 A ModuleSpec Type for the Import System Eric Snow 3.4 IF 452 API for Cryptographic Hash Functions v2.0 A.M. Kuchling, Christian Heimes SF 453 Explicit bootstrapping of pip in Python installations Donald Stufft, Alyssa Coghlan SF 454 Add a new tracemalloc module to trace Python memory allocations Victor Stinner 3.4 SR 455 Adding a key-transforming dictionary to collections Antoine Pitrou 3.5 SF 456 Secure and interchangeable hash algorithm Christian Heimes 3.4 IF 457 Notation For Positional-Only Parameters Larry Hastings SA 458 Secure PyPI downloads with signed repository metadata Trishank Karthik Kuppusamy, Vladimir Diaz, Marina Moore, Lukas Puehringer, Joshua Lock, Lois Anne DeLong, Justin Cappos SW 459 Standard Metadata Extensions for Python Software Packages Alyssa Coghlan SW 460 Add binary interpolation and formatting Antoine Pitrou 3.5 SF 461 Adding % formatting to bytes and bytearray Ethan Furman 3.5 PW 462 Core development workflow automation for CPython Alyssa Coghlan SR 463 Exception-catching expressions Chris Angelico 3.5 PF 464 Removal of the PyPI Mirror Authenticity API Donald Stufft SF 465 A dedicated infix operator for matrix multiplication Nathaniel J. Smith 3.5 SF 466 Network Security Enhancements for Python 2.7.x Alyssa Coghlan 2.7.9 S 467 Minor API improvements for binary sequences Alyssa Coghlan, Ethan Furman 3.15 SF 468 Preserving the order of **kwargs in a function. Eric Snow 3.6 SW 469 Migration of dict iteration code to Python 3 Alyssa Coghlan 3.5 PF 470 Removing External Hosting Support on PyPI Donald Stufft SF 471 os.scandir() function – a better and faster directory iterator Ben Hoyt 3.5 SR 472 Support for indexing with keyword arguments Stefano Borini, Joseph Martinot-Lagarde 3.6 SR 473 Adding structured data to built-in exceptions Sebastian Kreft PW 474 Creating forge.python.org Alyssa Coghlan SF 475 Retry system calls failing with EINTR Charles-François Natali, Victor Stinner 3.5 SF 476 Enabling certificate verification by default for stdlib http clients Alex Gaynor 2.7.9, 3.4.3, 3.5 SF 477 Backport ensurepip (PEP 453) to Python 2.7 Donald Stufft, Alyssa Coghlan IF 478 Python 3.5 Release Schedule Larry Hastings 3.5 SF 479 Change StopIteration handling inside generators Chris Angelico, Guido van Rossum 3.5 S 480 Surviving a Compromise of PyPI: End-to-end signing of packages Trishank Karthik Kuppusamy, Vladimir Diaz, Justin Cappos, Marina Moore PW 481 Migrate CPython to Git, Github, and Phabricator Donald Stufft IF 482 Literature Overview for Type Hints Łukasz Langa IF 483 The Theory of Type Hints Guido van Rossum, Ivan Levkivskyi SF 484 Type Hints Guido van Rossum, Jukka Lehtosalo, Łukasz Langa 3.5 SF 485 A Function for testing approximate equality Christopher Barker 3.5 SF 486 Make the Python Launcher aware of virtual environments Paul Moore 3.5 SF 487 Simpler customisation of class creation Martin Teichmann 3.6 SF 488 Elimination of PYO files Brett Cannon 3.5 SF 489 Multi-phase extension module initialization Petr Viktorin, Stefan Behnel, Alyssa Coghlan 3.5 SR 490 Chain exceptions at C level Victor Stinner 3.6 SD 491 The Wheel Binary Package Format 1.9 Daniel Holth SF 492 Coroutines with async and await syntax Yury Selivanov 3.5 SF 493 HTTPS verification migration tools for Python 2.7 Alyssa Coghlan, Robert Kuska, Marc-André Lemburg 2.7.12 IF 494 Python 3.6 Release Schedule Ned Deily 3.6 SF 495 Local Time Disambiguation Alexander Belopolsky, Tim Peters 3.6 IR 496 Environment Markers James Polley PR 497 A standard mechanism for backward compatibility Ed Schofield SF 498 Literal String Interpolation Eric V. Smith 3.6 SD 499 python -m foo should also bind ‘foo’ in sys.modules Cameron Simpson, Chris Angelico, Joseph Jevnik 3.10 SR 500 A protocol for delegating datetime methods to their tzinfo implementations Alexander Belopolsky, Tim Peters SW 501 General purpose template literal strings Alyssa Coghlan, Nick Humrich 3.12 IR 502 String Interpolation - Extended Discussion Mike G. Miller 3.6 SF 503 Simple Repository API Donald Stufft SW 504 Using the System RNG by default Alyssa Coghlan 3.6 SD 505 None-aware operators Mark E. Haase, Steve Dower 3.8 SF 506 Adding A Secrets Module To The Standard Library Steven D’Aprano 3.6 PR 507 Migrate CPython to Git and GitLab Barry Warsaw SF 508 Dependency specification for Python Software Packages Robert Collins SS 509 Add a private version to dict Victor Stinner 3.6 SR 510 Specialize functions with guards Victor Stinner 3.6 SR 511 API for code transformers Victor Stinner 3.6 PF 512 Migrating from hg.python.org to GitHub Brett Cannon IS 513 A Platform Tag for Portable Linux Built Distributions Robert T. McGibbon, Nathaniel J. Smith IA 514 Python registration in the Windows registry Steve Dower SF 515 Underscores in Numeric Literals Georg Brandl, Serhiy Storchaka 3.6 SR 516 Build system abstraction for pip/conda etc Robert Collins, Nathaniel J. Smith SF 517 A build-system independent format for source trees Nathaniel J. Smith, Thomas Kluyver SF 518 Specifying Minimum Build System Requirements for Python Projects Brett Cannon, Nathaniel J. Smith, Donald Stufft SF 519 Adding a file system path protocol Brett Cannon, Koos Zevenhoven 3.6 SF 520 Preserving Class Attribute Definition Order Eric Snow 3.6 SW 521 Managing global context via ‘with’ blocks in generators and coroutines Nathaniel J. Smith 3.6 SR 522 Allow BlockingIOError in security sensitive APIs Alyssa Coghlan, Nathaniel J. Smith 3.6 SF 523 Adding a frame evaluation API to CPython Brett Cannon, Dino Viehland 3.6 SF 524 Make os.urandom() blocking on Linux Victor Stinner 3.6 SF 525 Asynchronous Generators Yury Selivanov 3.6 SF 526 Syntax for Variable Annotations Ryan Gonzalez, Philip House, Ivan Levkivskyi, Lisa Roach, Guido van Rossum 3.6 SF 527 Removing Un(der)used file types/extensions on PyPI Donald Stufft SF 528 Change Windows console encoding to UTF-8 Steve Dower 3.6 SF 529 Change Windows filesystem encoding to UTF-8 Steve Dower 3.6 SF 530 Asynchronous Comprehensions Yury Selivanov 3.6 SW 531 Existence checking operators Alyssa Coghlan 3.7 SD 532 A circuit breaking protocol and binary operators Alyssa Coghlan, Mark E. Haase 3.8 SD 533 Deterministic cleanup for iterators Nathaniel J. Smith SD 534 Improved Errors for Missing Standard Library Modules Tomáš Orsava, Petr Viktorin, Alyssa Coghlan SD 535 Rich comparison chaining Alyssa Coghlan 3.8 SW 536 Final Grammar for Literal String Interpolation Philipp Angerer 3.7 IF 537 Python 3.7 Release Schedule Ned Deily 3.7 SF 538 Coercing the legacy C locale to a UTF-8 based locale Alyssa Coghlan 3.7 SF 539 A New C-API for Thread-Local Storage in CPython Erik M. Bray, Masayuki Yamamoto 3.7 SF 540 Add a new UTF-8 Mode Victor Stinner 3.7 PF 541 Package Index Name Retention Łukasz Langa SR 542 Dot Notation Assignment In Function Header Markus Meskanen SW 543 A Unified TLS API for Python Cory Benfield, Christian Heimes 3.7 SF 544 Protocols: Structural subtyping (static duck typing) Ivan Levkivskyi, Jukka Lehtosalo, Łukasz Langa 3.8 PA 545 Python Documentation Translations Julien Palard, Inada Naoki, Victor Stinner SR 546 Backport ssl.MemoryBIO and ssl.SSLObject to Python 2.7 Victor Stinner, Cory Benfield 2.7 SD 547 Running extension modules using the -m option Marcel Plch, Petr Viktorin 3.7 SR 548 More Flexible Loop Control R David Murray 3.7 SR 549 Instance Descriptors Larry Hastings 3.7 SW 550 Execution Context Yury Selivanov, Elvis Pranskevichus 3.7 IW 551 Security transparency in the Python runtime Steve Dower 3.7 SF 552 Deterministic pycs Benjamin Peterson 3.7 SF 553 Built-in breakpoint() Barry Warsaw 3.7 SS 554 Multiple Interpreters in the Stdlib Eric Snow 3.13 SW 555 Context-local variables (contextvars) Koos Zevenhoven 3.7 SD 556 Threaded garbage collection Antoine Pitrou 3.7 SF 557 Data Classes Eric V. Smith 3.7 SW 558 Defined semantics for locals() Alyssa Coghlan 3.13 SR 559 Built-in noop() Barry Warsaw 3.7 SF 560 Core support for typing module and generic types Ivan Levkivskyi 3.7 SF 561 Distributing and Packaging Type Information Emma Harper Smith 3.7 SF 562 Module __getattr__ and __dir__ Ivan Levkivskyi 3.7 SS 563 Postponed Evaluation of Annotations Łukasz Langa 3.7 SF 564 Add new time functions with nanosecond resolution Victor Stinner 3.7 SF 565 Show DeprecationWarning in __main__ Alyssa Coghlan 3.7 SF 566 Metadata for Python Software Packages 2.1 Dustin Ingram 3.x SF 567 Context Variables Yury Selivanov 3.7 SD 568 Generator-sensitivity for Context Variables Nathaniel J. Smith 3.8 IF 569 Python 3.8 Release Schedule Łukasz Langa 3.8 SF 570 Python Positional-Only Parameters Larry Hastings, Pablo Galindo Salgado, Mario Corchero, Eric N. Vander Weele 3.8 IS 571 The manylinux2010 Platform Tag Mark Williams, Geoffrey Thomas, Thomas Kluyver SF 572 Assignment Expressions Chris Angelico, Tim Peters, Guido van Rossum 3.8 SF 573 Module State Access from C Extension Methods Petr Viktorin, Alyssa Coghlan, Eric Snow, Marcel Plch 3.9 SF 574 Pickle protocol 5 with out-of-band data Antoine Pitrou 3.8 SW 575 Unifying function/method classes Jeroen Demeyer 3.8 SW 576 Rationalize Built-in function classes Mark Shannon 3.8 SW 577 Augmented Assignment Expressions Alyssa Coghlan 3.8 SF 578 Python Runtime Audit Hooks Steve Dower 3.8 IF 579 Refactoring C functions and methods Jeroen Demeyer SR 580 The C call protocol Jeroen Demeyer 3.8 PF 581 Using GitHub Issues for CPython Mariatta SR 582 Python local packages directory Kushal Das, Steve Dower, Donald Stufft, Alyssa Coghlan 3.12 IW 583 A Concurrency Memory Model for Python Jeffrey Yasskin SF 584 Add Union Operators To dict Steven D’Aprano, Brandt Bucher 3.9 SF 585 Type Hinting Generics In Standard Collections Łukasz Langa 3.9 SF 586 Literal Types Michael Lee, Ivan Levkivskyi, Jukka Lehtosalo 3.8 SF 587 Python Initialization Configuration Victor Stinner, Alyssa Coghlan 3.8 IF 588 GitHub Issues Migration Plan Mariatta SF 589 TypedDict: Type Hints for Dictionaries with a Fixed Set of Keys Jukka Lehtosalo 3.8 SF 590 Vectorcall: a fast calling protocol for CPython Mark Shannon, Jeroen Demeyer 3.8 SF 591 Adding a final qualifier to typing Michael J. Sullivan, Ivan Levkivskyi 3.8 SF 592 Adding “Yank” Support to the Simple API Donald Stufft SF 593 Flexible function and variable annotations Till Varoquaux, Konstantin Kashin 3.9 SF 594 Removing dead batteries from the standard library Christian Heimes, Brett Cannon 3.11 IW 595 Improving bugs.python.org Ezio Melotti, Berker Peksag IF 596 Python 3.9 Release Schedule Łukasz Langa 3.9 SF 597 Add optional EncodingWarning Inada Naoki 3.10 IW 598 Introducing incremental feature releases Alyssa Coghlan 3.9 IS 599 The manylinux2014 Platform Tag Dustin Ingram SF 600 Future ‘manylinux’ Platform Tags for Portable Linux Built Distributions Nathaniel J. Smith, Thomas Kluyver SR 601 Forbid return/break/continue breaking out of finally Damien George, Batuhan Taskaya 3.8 PA 602 Annual Release Cycle for Python Łukasz Langa 3.9 S 603 Adding a frozenmap type to collections Yury Selivanov SF 604 Allow writing union types as X | Y Philippe PRADOS, Maggie Moss 3.10 IR 605 A rolling feature release stream for CPython Steve Dower, Alyssa Coghlan 3.9 SR 606 Python Compatibility Version Victor Stinner 3.9 IF 607 Reducing CPython’s Feature Delivery Latency Łukasz Langa, Steve Dower, Alyssa Coghlan 3.9 SR 608 Coordinated Python release Miro Hrončok, Victor Stinner 3.9 PA 609 Python Packaging Authority (PyPA) Governance Dustin Ingram, Pradyun Gedam, Sumana Harihareswara SF 610 Recording the Direct URL Origin of installed distributions Stéphane Bidoul, Chris Jerdonek SW 611 The one million limit Mark Shannon SF 612 Parameter Specification Variables Mark Mendoza 3.10 SF 613 Explicit Type Aliases Shannon Zhu 3.10 SF 614 Relaxing Grammar Restrictions On Decorators Brandt Bucher 3.9 SF 615 Support for the IANA Time Zone Database in the Standard Library Paul Ganssle 3.9 SF 616 String methods to remove prefixes and suffixes Dennis Sweeney 3.9 SF 617 New PEG parser for CPython Guido van Rossum, Pablo Galindo Salgado, Lysandros Nikolaou 3.9 SF 618 Add Optional Length-Checking To zip Brandt Bucher 3.10 IA 619 Python 3.10 Release Schedule Pablo Galindo Salgado 3.10 SW 620 Hide implementation details from the C API Victor Stinner 3.12 SF 621 Storing project metadata in pyproject.toml Brett Cannon, Dustin Ingram, Paul Ganssle, Pradyun Gedam, Sébastien Eustace, Thomas Kluyver, Tzu-ping Chung SS 622 Structural Pattern Matching Brandt Bucher, Daniel F Moisset, Tobias Kohn, Ivan Levkivskyi, Guido van Rossum, Talin 3.10 SF 623 Remove wstr from Unicode Inada Naoki 3.10 SF 624 Remove Py_UNICODE encoder APIs Inada Naoki 3.11 SF 625 Filename of a Source Distribution Tzu-ping Chung, Paul Moore SF 626 Precise line numbers for debugging and other tools. Mark Shannon 3.10 SF 627 Recording installed projects Petr Viktorin SF 628 Add math.tau Alyssa Coghlan 3.6 SF 629 Versioning PyPI’s Simple API Donald Stufft IF 630 Isolating Extension Modules Petr Viktorin SS 631 Dependency specification in pyproject.toml based on PEP 508 Ofek Lev SF 632 Deprecate distutils module Steve Dower 3.10 SR 633 Dependency specification in pyproject.toml using an exploded TOML table Laurie Opperman, Arun Babu Neelicattu SF 634 Structural Pattern Matching: Specification Brandt Bucher, Guido van Rossum 3.10 IF 635 Structural Pattern Matching: Motivation and Rationale Tobias Kohn, Guido van Rossum 3.10 IF 636 Structural Pattern Matching: Tutorial Daniel F Moisset 3.10 SR 637 Support for indexing with keyword arguments Stefano Borini 3.10 S 638 Syntactic Macros Mark Shannon SF 639 Improving License Clarity with Better Package Metadata Philippe Ombredanne, C.A.M. Gerlach, Karolina Surma SR 640 Unused variable syntax Thomas Wouters 3.10 SR 641 Using an underscore in the version portion of Python 3.10 compatibility tags Brett Cannon, Steve Dower, Barry Warsaw 3.10 SR 642 Explicit Pattern Syntax for Structural Pattern Matching Alyssa Coghlan 3.10 SF 643 Metadata for Package Source Distributions Paul Moore SF 644 Require OpenSSL 1.1.1 or newer Christian Heimes 3.10 SW 645 Allow writing optional types as x? Maggie Moss SF 646 Variadic Generics Mark Mendoza, Matthew Rahtz, Pradeep Kumar Srinivasan, Vincent Siles 3.11 SF 647 User-Defined Type Guards Eric Traut 3.10 SR 648 Extensible customizations of the interpreter at startup Mario Corchero 3.11 SF 649 Deferred Evaluation Of Annotations Using Descriptors Larry Hastings 3.14 SW 650 Specifying Installer Requirements for Python Projects Vikram Jayanthi, Dustin Ingram, Brett Cannon SR 651 Robust Stack Overflow Handling Mark Shannon SF 652 Maintaining the Stable ABI Petr Viktorin 3.10 S 653 Precise Semantics for Pattern Matching Mark Shannon SF 654 Exception Groups and except* Irit Katriel, Yury Selivanov, Guido van Rossum 3.11 SF 655 Marking individual TypedDict items as required or potentially-missing David Foster 3.11 SF 656 Platform Tag for Linux Distributions Using Musl Tzu-ping Chung SF 657 Include Fine Grained Error Locations in Tracebacks Pablo Galindo Salgado, Batuhan Taskaya, Ammar Askar 3.11 SA 658 Serve Distribution Metadata in the Simple Repository API Tzu-ping Chung IF 659 Specializing Adaptive Interpreter Mark Shannon SF 660 Editable installs for pyproject.toml based builds (wheel based) Daniel Holth, Stéphane Bidoul SD 661 Sentinel Values Tal Einat SR 662 Editable installs via virtual wheels Bernát Gábor IR 663 Standardizing Enum str(), repr(), and format() behaviors Ethan Furman 3.11 IA 664 Python 3.11 Release Schedule Pablo Galindo Salgado 3.11 SR 665 A file format to list Python dependencies for reproducibility of an application Brett Cannon, Pradyun Gedam, Tzu-ping Chung SR 666 Reject Foolish Indentation Laura Creighton 2.2 SF 667 Consistent views of namespaces Mark Shannon, Tian Gao 3.13 SA 668 Marking Python base environments as “externally managed” Geoffrey Thomas, Matthias Klose, Filipe Laíns, Donald Stufft, Tzu-ping Chung, Stefano Rivera, Elana Hashman, Pradyun Gedam SF 669 Low Impact Monitoring for CPython Mark Shannon 3.12 SF 670 Convert macros to functions in the Python C API Erlend Egeberg Aasland, Victor Stinner 3.11 S 671 Syntax for late-bound function argument defaults Chris Angelico 3.12 IA 672 Unicode-related Security Considerations for Python Petr Viktorin SF 673 Self Type Pradeep Kumar Srinivasan, James Hilton-Balfe 3.11 SD 674 Disallow using macros as l-values Victor Stinner 3.12 SF 675 Arbitrary Literal String Type Pradeep Kumar Srinivasan, Graham Bleaney 3.11 PA 676 PEP Infrastructure Process Adam Turner SR 677 Callable Type Syntax Steven Troxler, Pradeep Kumar Srinivasan 3.11 SF 678 Enriching Exceptions with Notes Zac Hatfield-Dodds 3.11 SR 679 New assert statement syntax with parentheses Pablo Galindo Salgado, Stan Ulbrych 3.15 SF 680 tomllib: Support for Parsing TOML in the Standard Library Taneli Hukkinen, Shantanu Jain 3.11 SF 681 Data Class Transforms Erik De Bonte, Eric Traut 3.11 SF 682 Format Specifier for Signed Zero John Belmonte 3.11 SF 683 Immortal Objects, Using a Fixed Refcount Eric Snow, Eddie Elizondo 3.12 SF 684 A Per-Interpreter GIL Eric Snow 3.12 SF 685 Comparison of extra names for optional distribution dependencies Brett Cannon SA 686 Make UTF-8 mode default Inada Naoki 3.15 SA 687 Isolating modules in the standard library Erlend Egeberg Aasland, Petr Viktorin 3.12 SF 688 Making the buffer protocol accessible in Python Jelle Zijlstra 3.12 SF 689 Unstable C API tier Petr Viktorin 3.12 SR 690 Lazy Imports Germán Méndez Bravo, Carl Meyer 3.12 SA 691 JSON-based Simple API for Python Package Indexes Donald Stufft, Pradyun Gedam, Cooper Lees, Dustin Ingram SF 692 Using TypedDict for more precise **kwargs typing Franek Magiera 3.12 IA 693 Python 3.12 Release Schedule Thomas Wouters 3.12 S 694 Upload 2.0 API for Python Package Indexes Barry Warsaw, Donald Stufft, Ee Durbin SF 695 Type Parameter Syntax Eric Traut 3.12 SF 696 Type Defaults for Type Parameters James Hilton-Balfe 3.13 SF 697 Limited C API for Extending Opaque Types Petr Viktorin 3.12 SF 698 Override Decorator for Static Typing Steven Troxler, Joshua Xu, Shannon Zhu 3.12 SA 699 Remove private dict version field added in PEP 509 Ken Jin 3.12 SF 700 Additional Fields for the Simple API for Package Indexes Paul Moore SA 701 Syntactic formalization of f-strings Pablo Galindo Salgado, Batuhan Taskaya, Lysandros Nikolaou, Marta Gómez Macías 3.12 SF 702 Marking deprecations using the type system Jelle Zijlstra 3.13 SA 703 Making the Global Interpreter Lock Optional in CPython Sam Gross 3.13 SW 704 Require virtual environments by default for package installers Pradyun Gedam SF 705 TypedDict: Read-only items Alice Purcell 3.13 SF 706 Filter for tarfile.extractall Petr Viktorin 3.12 SR 707 A simplified signature for __exit__ and __aexit__ Irit Katriel 3.12 SP 708 Extending the Repository API to Mitigate Dependency Confusion Attacks Donald Stufft SF 709 Inlined comprehensions Carl Meyer 3.12 S 710 Recording the provenance of installed packages Fridolín Pokorný S 711 PyBI: a standard format for distributing Python Binaries Nathaniel J. Smith SR 712 Adding a “converter” parameter to dataclasses.field Joshua Cannon 3.13 SR 713 Callable Modules Amethyst Reese 3.12 SA 714 Rename dist-info-metadata in the Simple API Donald Stufft SF 715 Disabling bdist_egg distribution uploads on PyPI William Woodruff S 718 Subscriptable functions James Hilton-Balfe 3.15 IA 719 Python 3.13 Release Schedule Thomas Wouters 3.13 I 720 Cross-compiling Python packages Filipe Laíns 3.12 SF 721 Using tarfile.data_filter for source distribution extraction Petr Viktorin 3.12 SR 722 Dependency specification for single-file scripts Paul Moore SF 723 Inline script metadata Ofek Lev SW 724 Stricter Type Guards Rich Chiodo, Eric Traut, Erik De Bonte 3.13 S 725 Specifying external dependencies in pyproject.toml Pradyun Gedam, Jaime Rodríguez-Guerra, Ralf Gommers SR 726 Module __setattr__ and __delattr__ Sergey B Kirpichev 3.13 SW 727 Documentation in Annotated Metadata Sebastián Ramírez 3.13 SA 728 TypedDict with Typed Extra Items Zixuan James Li 3.15 PA 729 Typing governance process Jelle Zijlstra, Shantanu Jain SF 730 Adding iOS as a supported platform Russell Keith-Magee 3.13 PA 731 C API Working Group Charter Guido van Rossum, Petr Viktorin, Victor Stinner, Steve Dower, Irit Katriel PA 732 The Python Documentation Editorial Board Joanna Jablonski IF 733 An Evaluation of Python’s Public C API Erlend Egeberg Aasland, Domenico Andreoli, Stefan Behnel, Carl Friedrich Bolz-Tereick, Simon Cross, Steve Dower, Tim Felgentreff, David Hewitt, Shantanu Jain, Wenzel Jakob, Irit Katriel, Marc-Andre Lemburg, Donghee Na, Karl Nelson, Ronald Oussoren, Antoine Pitrou, Neil Schemenauer, Mark Shannon, Stepan Sindelar, Gregory P. Smith, Eric Snow, Victor Stinner, Guido van Rossum, Petr Viktorin, Carol Willing, William Woodruff, David Woods, Jelle Zijlstra SF 734 Multiple Interpreters in the Stdlib Eric Snow 3.14 SF 735 Dependency Groups in pyproject.toml Stephen Rosen SR 736 Shorthand syntax for keyword arguments at invocation Joshua Bambrick, Chris Angelico 3.14 SF 737 C API to format a type fully qualified name Victor Stinner 3.13 SF 738 Adding Android as a supported platform Malcolm Smith 3.13 SA 739 build-details.json 1.0 — a static description file for Python build details Filipe Laíns 3.14 SF 740 Index support for digital attestations William Woodruff, Facundo Tuesca, Dustin Ingram SF 741 Python Configuration C API Victor Stinner 3.14 SF 742 Narrowing types with TypeIs Jelle Zijlstra 3.13 S 743 Add Py_OMIT_LEGACY_API to the Python C API Victor Stinner, Petr Viktorin 3.15 I 744 JIT Compilation Brandt Bucher, Savannah Ostrowski 3.13 IA 745 Python 3.14 Release Schedule Hugo van Kemenade 3.14 S 746 Type checking Annotated metadata Adrian Garcia Badaracco 3.15 S 747 Annotating Type Forms David Foster, Eric Traut 3.15 S 748 A Unified TLS API for Python Joop van de Pol, William Woodruff 3.14 SF 749 Implementing PEP 649 Jelle Zijlstra 3.14 SF 750 Template Strings Jim Baker, Guido van Rossum, Paul Everitt, Koudai Aono, Lysandros Nikolaou, Dave Peck 3.14 SF 751 A file format to record Python dependencies for installation reproducibility Brett Cannon S 752 Implicit namespaces for package repositories Ofek Lev, Jarek Potiuk SA 753 Uniform project URLs in core metadata William Woodruff, Facundo Tuesca SR 754 IEEE 754 Floating Point Special Values Gregory R. Warnes 2.3 P 755 Implicit namespace policy for PyPI Ofek Lev SW 756 Add PyUnicode_Export() and PyUnicode_Import() C functions Victor Stinner 3.14 SF 757 C API to import-export Python integers Sergey B Kirpichev, Victor Stinner 3.14 SF 758 Allow except and except* expressions without parentheses Pablo Galindo Salgado, Brett Cannon 3.14 SW 759 External Wheel Hosting Barry Warsaw, Emma Harper Smith SW 760 No More Bare Excepts Pablo Galindo Salgado, Brett Cannon 3.14 PA 761 Deprecating PGP signatures for CPython artifacts Seth Michael Larson 3.14 IF 762 REPL-acing the default REPL Pablo Galindo Salgado, Łukasz Langa, Lysandros Nikolaou, Emily Morehouse-Valcarcel 3.13 SW 763 Limiting deletions on PyPI William Woodruff, Alexis Challande S 764 Inline typed dictionaries Victorien Plot 3.15 SF 765 Disallow return/break/continue that exit a finally block Irit Katriel, Alyssa Coghlan 3.14 I 766 Explicit Priority Choices Among Multiple Indexes Michael Sarahan S 767 Annotating Read-Only Attributes Eneg 3.15 SF 768 Safe external debugger interface for CPython Pablo Galindo Salgado, Matt Wozniski, Ivona Stojanovic 3.14 SR 769 Add a ‘default’ keyword argument to ‘attrgetter’, ‘itemgetter’ and ‘getitem’ Facundo Batista 3.14 SA 770 Improving measurability of Python packages with Software Bill-of-Materials Seth Larson S 771 Default Extras for Python Software Packages Thomas Robitaille, Jonathan Dekhtiar P 772 Packaging Council governance process Barry Warsaw, Deb Nicholson, Pradyun Gedam SA 773 A Python Installation Manager for Windows Steve Dower SD 774 Removing the LLVM requirement for JIT builds Savannah Ostrowski 3.14 SW 775 Make zlib required to build CPython Gregory P. Smith, Stan Ulbrych, Petr Viktorin 3.14 I 776 Emscripten Support Hood Chatham 3.14 S 777 How to Re-invent the Wheel Emma Harper Smith SD 778 Supporting Symlinks in Wheels Emma Harper Smith SF 779 Criteria for supported status for free-threaded Python Thomas Wouters, Matt Page, Sam Gross 3.14 S 780 ABI features as environment markers Klaus Zimmermann, Ralf Gommers 3.14 S 781 Make TYPE_CHECKING a built-in constant Inada Naoki 3.15 SF 782 Add PyBytesWriter C API Victor Stinner 3.15 S 783 Emscripten Packaging Hood Chatham SF 784 Adding Zstandard to the standard library Emma Harper Smith 3.14 S 785 New methods for easier handling of ExceptionGroups Zac Hatfield-Dodds 3.14 SD 787 Safer subprocess usage using t-strings Nick Humrich, Alyssa Coghlan 3.15 S 788 Protecting the C API from Interpreter Finalization Peter Bierma 3.15 S 789 Preventing task-cancellation bugs by limiting yield in async generators Zac Hatfield-Dodds, Nathaniel J. Smith 3.14 IA 790 Python 3.15 Release Schedule Hugo van Kemenade 3.15 SF 791 math.integer — submodule for integer-specific mathematics functions Neil Girdhar, Sergey B Kirpichev, Tim Peters, Serhiy Storchaka 3.15 SF 792 Project status markers in the simple index William Woodruff, Facundo Tuesca SA 793 PyModExport: A new entry point for C extension modules Petr Viktorin 3.15 SA 794 Import Name Metadata Brett Cannon SA 798 Unpacking in Comprehensions Adam Hartz, Erik Demaine 3.15 SA 799 A dedicated profiling package for organizing Python profiling tools Pablo Galindo Salgado, László Kiss Kollár 3.15 S 800 Disjoint bases in the type system Jelle Zijlstra 3.15 IA 801 Reserved Barry Warsaw S 802 Display Syntax for the Empty Set Adam Turner 3.15 S 803 Stable ABI for Free-Threaded Builds Petr Viktorin 3.15 S 804 An external dependency registry and name mapping mechanism Pradyun Gedam, Ralf Gommers, Michał Górny, Jaime Rodríguez-Guerra, Michael Sarahan S 806 Mixed sync/async context managers with precise async marking Zac Hatfield-Dodds 3.15 S 807 Index support for Trusted Publishing William Woodruff S 808 Including static values in dynamic project metadata Henry Schreiner, Cristian Le S 809 Stable ABI for the Future Steve Dower 3.15 SA 810 Explicit lazy imports Pablo Galindo Salgado, Germán Méndez Bravo, Thomas Wouters, Dino Viehland, Brittany Reynoso, Noah Kim, Tim Stumbaugh 3.15 PA 811 Defining Python Security Response Team membership and responsibilities Seth Michael Larson S 814 Add frozendict built-in type Victor Stinner, Donghee Na 3.15 S 815 Deprecate RECORD.jws and RECORD.p7s Konstantin Schütze, William Woodruff I 816 WASI Support Brett Cannon S 819 JSON Package Metadata Emma Harper Smith S 820 PySlot: Unified slot system for the C API Petr Viktorin 3.15 S 822 Dedented Multiline String (d-string) Inada Naoki 3.15 PR 2026 Calendar versioning for Python Hugo van Kemenade 3.26 PF 3000 Python 3000 Guido van Rossum PW 3001 Procedure for reviewing and improving standard library modules Georg Brandl PF 3002 Procedure for Backwards-Incompatible Changes Steven Bethard PF 3003 Python Language Moratorium Brett Cannon, Jesse Noller, Guido van Rossum PF 3099 Things that will Not Change in Python 3000 Georg Brandl PF 3100 Miscellaneous Python 3.0 Plans Brett Cannon SF 3101 Advanced String Formatting Talin 3.0 SF 3102 Keyword-Only Arguments Talin 3.0 SR 3103 A Switch/Case Statement Guido van Rossum 3.0 SF 3104 Access to Names in Outer Scopes Ka-Ping Yee 3.0 SF 3105 Make print a function Georg Brandl 3.0 SF 3106 Revamping dict.keys(), .values() and .items() Guido van Rossum 3.0 SF 3107 Function Annotations Collin Winter, Tony Lownds 3.0 SF 3108 Standard Library Reorganization Brett Cannon 3.0 SF 3109 Raising Exceptions in Python 3000 Collin Winter 3.0 SF 3110 Catching Exceptions in Python 3000 Collin Winter 3.0 SF 3111 Simple input built-in in Python 3000 Andre Roberge 3.0 SF 3112 Bytes literals in Python 3000 Jason Orendorff 3.0 SF 3113 Removal of Tuple Parameter Unpacking Brett Cannon 3.0 SF 3114 Renaming iterator.next() to iterator.__next__() Ka-Ping Yee 3.0 SF 3115 Metaclasses in Python 3000 Talin 3.0 SF 3116 New I/O Daniel Stutzbach, Guido van Rossum, Mike Verdone 3.0 SR 3117 Postfix type declarations Georg Brandl 3.0 SF 3118 Revising the buffer protocol Travis Oliphant, Carl Banks 3.0 SF 3119 Introducing Abstract Base Classes Guido van Rossum, Talin 3.0 SF 3120 Using UTF-8 as the default source encoding Martin von Löwis 3.0 SF 3121 Extension Module Initialization and Finalization Martin von Löwis 3.0 SR 3122 Delineation of the main module Brett Cannon SF 3123 Making PyObject_HEAD conform to standard C Martin von Löwis 3.0 SD 3124 Overloading, Generic Functions, Interfaces, and Adaptation Phillip J. Eby SR 3125 Remove Backslash Continuation Jim J. Jewett SR 3126 Remove Implicit String Concatenation Jim J. Jewett, Raymond Hettinger SF 3127 Integer Literal Support and Syntax Patrick Maupin 3.0 SR 3128 BList: A Faster List-like Type Daniel Stutzbach 2.6, 3.0 SF 3129 Class Decorators Collin Winter 3.0 SR 3130 Access to Current Module/Class/Function Jim J. Jewett 3.0 SF 3131 Supporting Non-ASCII Identifiers Martin von Löwis 3.0 SF 3132 Extended Iterable Unpacking Georg Brandl 3.0 SR 3133 Introducing Roles Collin Winter 3.0 SF 3134 Exception Chaining and Embedded Tracebacks Ka-Ping Yee 3.0 SF 3135 New Super Calvin Spealman, Tim Delaney, Lie Ryan 3.0 SR 3136 Labeled break and continue Matt Chisholm 3.1 SF 3137 Immutable Bytes and Mutable Buffer Guido van Rossum 3.0 SF 3138 String representation in Python 3000 Atsuo Ishimoto 3.0 SR 3139 Cleaning out sys and the “interpreter” module Benjamin Peterson 3.0 SR 3140 str(container) should call str(item), not repr(item) Oleg Broytman, Jim J. Jewett SF 3141 A Type Hierarchy for Numbers Jeffrey Yasskin 3.0 SR 3142 Add a “while” clause to generator expressions Gerald Britton 3.0 SD 3143 Standard daemon process library Ben Finney 3.x SF 3144 IP Address Manipulation Library for the Python Standard Library Peter Moody 3.3 SW 3145 Asynchronous I/O For subprocess.Popen Eric Pruitt, Charles R. McCreary, Josiah Carlson 3.2 SW 3146 Merging Unladen Swallow into CPython Collin Winter, Jeffrey Yasskin, Reid Kleckner 3.3 SF 3147 PYC Repository Directories Barry Warsaw 3.2 SF 3148 futures - execute computations asynchronously Brian Quinlan 3.2 SF 3149 ABI version tagged .so files Barry Warsaw 3.2 SD 3150 Statement local namespaces (aka “given” clause) Alyssa Coghlan 3.4 SF 3151 Reworking the OS and IO exception hierarchy Antoine Pitrou 3.3 SR 3152 Cofunctions Gregory Ewing 3.3 SS 3153 Asynchronous IO support Laurens Van Houtven SF 3154 Pickle protocol version 4 Antoine Pitrou 3.4 SF 3155 Qualified name for classes and functions Antoine Pitrou 3.3 SF 3156 Asynchronous IO Support Rebooted: the “asyncio” Module Guido van Rossum 3.3 IF 3333 Python Web Server Gateway Interface v1.0.1 Phillip J. Eby IF 8000 Python Language Governance Proposal Overview Barry Warsaw PF 8001 Python Governance Voting Process Brett Cannon, Christian Heimes, Donald Stufft, Eric Snow, Gregory P. Smith, Łukasz Langa, Mariatta, Nathaniel J. Smith, Pablo Galindo Salgado, Raymond Hettinger, Tal Einat, Tim Peters, Zachary Ware IF 8002 Open Source Governance Survey Barry Warsaw, Łukasz Langa, Antoine Pitrou, Doug Hellmann, Carol Willing IR 8010 The Technical Leader Governance Model Barry Warsaw IR 8011 Python Governance Model Lead by Trio of Pythonistas Mariatta, Barry Warsaw IR 8012 The Community Governance Model Łukasz Langa IR 8013 The External Council Governance Model Steve Dower IR 8014 The Commons Governance Model Jack Jansen IR 8015 Organization of the Python community Victor Stinner IA 8016 The Steering Council Model Nathaniel J. Smith, Donald Stufft IF 8100 January 2019 Steering Council election Nathaniel J. Smith, Ee Durbin IF 8101 2020 Term Steering Council election Ewa Jodlowska, Ee Durbin IF 8102 2021 Term Steering Council election Ewa Jodlowska, Ee Durbin, Joe Carey IF 8103 2022 Term Steering Council election Ewa Jodlowska, Ee Durbin, Joe Carey IF 8104 2023 Term Steering Council election Ee Durbin IF 8105 2024 Term Steering Council election Ee Durbin IF 8106 2025 Term Steering Council election Ee Du | 2026-01-13T08:49:02 |
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https://docs.python.org/3/glossary.html#term-mutable | Glossary — Python 3.14.2 documentation Theme Auto Light Dark Previous topic Deprecations Next topic About this documentation This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » Glossary | Theme Auto Light Dark | Glossary ¶ >>> ¶ The default Python prompt of the interactive shell. Often seen for code examples which can be executed interactively in the interpreter. ... ¶ Can refer to: The default Python prompt of the interactive shell when entering the code for an indented code block, when within a pair of matching left and right delimiters (parentheses, square brackets, curly braces or triple quotes), or after specifying a decorator. The three dots form of the Ellipsis object. abstract base class ¶ Abstract base classes complement duck-typing by providing a way to define interfaces when other techniques like hasattr() would be clumsy or subtly wrong (for example with magic methods ). ABCs introduce virtual subclasses, which are classes that don’t inherit from a class but are still recognized by isinstance() and issubclass() ; see the abc module documentation. Python comes with many built-in ABCs for data structures (in the collections.abc module), numbers (in the numbers module), streams (in the io module), import finders and loaders (in the importlib.abc module). You can create your own ABCs with the abc module. annotate function ¶ A function that can be called to retrieve the annotations of an object. This function is accessible as the __annotate__ attribute of functions, classes, and modules. Annotate functions are a subset of evaluate functions . annotation ¶ A label associated with a variable, a class attribute or a function parameter or return value, used by convention as a type hint . Annotations of local variables cannot be accessed at runtime, but annotations of global variables, class attributes, and functions can be retrieved by calling annotationlib.get_annotations() on modules, classes, and functions, respectively. See variable annotation , function annotation , PEP 484 , PEP 526 , and PEP 649 , which describe this functionality. Also see Annotations Best Practices for best practices on working with annotations. argument ¶ A value passed to a function (or method ) when calling the function. There are two kinds of argument: keyword argument : an argument preceded by an identifier (e.g. name= ) in a function call or passed as a value in a dictionary preceded by ** . For example, 3 and 5 are both keyword arguments in the following calls to complex() : complex ( real = 3 , imag = 5 ) complex ( ** { 'real' : 3 , 'imag' : 5 }) positional argument : an argument that is not a keyword argument. Positional arguments can appear at the beginning of an argument list and/or be passed as elements of an iterable preceded by * . For example, 3 and 5 are both positional arguments in the following calls: complex ( 3 , 5 ) complex ( * ( 3 , 5 )) Arguments are assigned to the named local variables in a function body. See the Calls section for the rules governing this assignment. Syntactically, any expression can be used to represent an argument; the evaluated value is assigned to the local variable. See also the parameter glossary entry, the FAQ question on the difference between arguments and parameters , and PEP 362 . asynchronous context manager ¶ An object which controls the environment seen in an async with statement by defining __aenter__() and __aexit__() methods. Introduced by PEP 492 . asynchronous generator ¶ A function which returns an asynchronous generator iterator . It looks like a coroutine function defined with async def except that it contains yield expressions for producing a series of values usable in an async for loop. Usually refers to an asynchronous generator function, but may refer to an asynchronous generator iterator in some contexts. In cases where the intended meaning isn’t clear, using the full terms avoids ambiguity. An asynchronous generator function may contain await expressions as well as async for , and async with statements. asynchronous generator iterator ¶ An object created by an asynchronous generator function. This is an asynchronous iterator which when called using the __anext__() method returns an awaitable object which will execute the body of the asynchronous generator function until the next yield expression. Each yield temporarily suspends processing, remembering the execution state (including local variables and pending try-statements). When the asynchronous generator iterator effectively resumes with another awaitable returned by __anext__() , it picks up where it left off. See PEP 492 and PEP 525 . asynchronous iterable ¶ An object, that can be used in an async for statement. Must return an asynchronous iterator from its __aiter__() method. Introduced by PEP 492 . asynchronous iterator ¶ An object that implements the __aiter__() and __anext__() methods. __anext__() must return an awaitable object. async for resolves the awaitables returned by an asynchronous iterator’s __anext__() method until it raises a StopAsyncIteration exception. Introduced by PEP 492 . atomic operation ¶ An operation that appears to execute as a single, indivisible step: no other thread can observe it half-done, and its effects become visible all at once. Python does not guarantee that high-level statements are atomic (for example, x += 1 performs multiple bytecode operations and is not atomic). Atomicity is only guaranteed where explicitly documented. See also race condition and data race . attached thread state ¶ A thread state that is active for the current OS thread. When a thread state is attached, the OS thread has access to the full Python C API and can safely invoke the bytecode interpreter. Unless a function explicitly notes otherwise, attempting to call the C API without an attached thread state will result in a fatal error or undefined behavior. A thread state can be attached and detached explicitly by the user through the C API, or implicitly by the runtime, including during blocking C calls and by the bytecode interpreter in between calls. On most builds of Python, having an attached thread state implies that the caller holds the GIL for the current interpreter, so only one OS thread can have an attached thread state at a given moment. In free-threaded builds of Python, threads can concurrently hold an attached thread state, allowing for true parallelism of the bytecode interpreter. attribute ¶ A value associated with an object which is usually referenced by name using dotted expressions. For example, if an object o has an attribute a it would be referenced as o.a . It is possible to give an object an attribute whose name is not an identifier as defined by Names (identifiers and keywords) , for example using setattr() , if the object allows it. Such an attribute will not be accessible using a dotted expression, and would instead need to be retrieved with getattr() . awaitable ¶ An object that can be used in an await expression. Can be a coroutine or an object with an __await__() method. See also PEP 492 . BDFL ¶ Benevolent Dictator For Life, a.k.a. Guido van Rossum , Python’s creator. binary file ¶ A file object able to read and write bytes-like objects . Examples of binary files are files opened in binary mode ( 'rb' , 'wb' or 'rb+' ), sys.stdin.buffer , sys.stdout.buffer , and instances of io.BytesIO and gzip.GzipFile . See also text file for a file object able to read and write str objects. borrowed reference ¶ In Python’s C API, a borrowed reference is a reference to an object, where the code using the object does not own the reference. It becomes a dangling pointer if the object is destroyed. For example, a garbage collection can remove the last strong reference to the object and so destroy it. Calling Py_INCREF() on the borrowed reference is recommended to convert it to a strong reference in-place, except when the object cannot be destroyed before the last usage of the borrowed reference. The Py_NewRef() function can be used to create a new strong reference . bytes-like object ¶ An object that supports the Buffer Protocol and can export a C- contiguous buffer. This includes all bytes , bytearray , and array.array objects, as well as many common memoryview objects. Bytes-like objects can be used for various operations that work with binary data; these include compression, saving to a binary file, and sending over a socket. Some operations need the binary data to be mutable. The documentation often refers to these as “read-write bytes-like objects”. Example mutable buffer objects include bytearray and a memoryview of a bytearray . Other operations require the binary data to be stored in immutable objects (“read-only bytes-like objects”); examples of these include bytes and a memoryview of a bytes object. bytecode ¶ Python source code is compiled into bytecode, the internal representation of a Python program in the CPython interpreter. The bytecode is also cached in .pyc files so that executing the same file is faster the second time (recompilation from source to bytecode can be avoided). This “intermediate language” is said to run on a virtual machine that executes the machine code corresponding to each bytecode. Do note that bytecodes are not expected to work between different Python virtual machines, nor to be stable between Python releases. A list of bytecode instructions can be found in the documentation for the dis module . callable ¶ A callable is an object that can be called, possibly with a set of arguments (see argument ), with the following syntax: callable ( argument1 , argument2 , argumentN ) A function , and by extension a method , is a callable. An instance of a class that implements the __call__() method is also a callable. callback ¶ A subroutine function which is passed as an argument to be executed at some point in the future. class ¶ A template for creating user-defined objects. Class definitions normally contain method definitions which operate on instances of the class. class variable ¶ A variable defined in a class and intended to be modified only at class level (i.e., not in an instance of the class). closure variable ¶ A free variable referenced from a nested scope that is defined in an outer scope rather than being resolved at runtime from the globals or builtin namespaces. May be explicitly defined with the nonlocal keyword to allow write access, or implicitly defined if the variable is only being read. For example, in the inner function in the following code, both x and print are free variables , but only x is a closure variable : def outer (): x = 0 def inner (): nonlocal x x += 1 print ( x ) return inner Due to the codeobject.co_freevars attribute (which, despite its name, only includes the names of closure variables rather than listing all referenced free variables), the more general free variable term is sometimes used even when the intended meaning is to refer specifically to closure variables. complex number ¶ An extension of the familiar real number system in which all numbers are expressed as a sum of a real part and an imaginary part. Imaginary numbers are real multiples of the imaginary unit (the square root of -1 ), often written i in mathematics or j in engineering. Python has built-in support for complex numbers, which are written with this latter notation; the imaginary part is written with a j suffix, e.g., 3+1j . To get access to complex equivalents of the math module, use cmath . Use of complex numbers is a fairly advanced mathematical feature. If you’re not aware of a need for them, it’s almost certain you can safely ignore them. concurrency ¶ The ability of a computer program to perform multiple tasks at the same time. Python provides libraries for writing programs that make use of different forms of concurrency. asyncio is a library for dealing with asynchronous tasks and coroutines. threading provides access to operating system threads and multiprocessing to operating system processes. Multi-core processors can execute threads and processes on different CPU cores at the same time (see parallelism ). concurrent modification ¶ When multiple threads modify shared data at the same time. Concurrent modification without proper synchronization can cause race conditions , and might also trigger a data race , data corruption, or both. context ¶ This term has different meanings depending on where and how it is used. Some common meanings: The temporary state or environment established by a context manager via a with statement. The collection of keyvalue bindings associated with a particular contextvars.Context object and accessed via ContextVar objects. Also see context variable . A contextvars.Context object. Also see current context . context management protocol ¶ The __enter__() and __exit__() methods called by the with statement. See PEP 343 . context manager ¶ An object which implements the context management protocol and controls the environment seen in a with statement. See PEP 343 . context variable ¶ A variable whose value depends on which context is the current context . Values are accessed via contextvars.ContextVar objects. Context variables are primarily used to isolate state between concurrent asynchronous tasks. contiguous ¶ A buffer is considered contiguous exactly if it is either C-contiguous or Fortran contiguous . Zero-dimensional buffers are C and Fortran contiguous. In one-dimensional arrays, the items must be laid out in memory next to each other, in order of increasing indexes starting from zero. In multidimensional C-contiguous arrays, the last index varies the fastest when visiting items in order of memory address. However, in Fortran contiguous arrays, the first index varies the fastest. coroutine ¶ Coroutines are a more generalized form of subroutines. Subroutines are entered at one point and exited at another point. Coroutines can be entered, exited, and resumed at many different points. They can be implemented with the async def statement. See also PEP 492 . coroutine function ¶ A function which returns a coroutine object. A coroutine function may be defined with the async def statement, and may contain await , async for , and async with keywords. These were introduced by PEP 492 . CPython ¶ The canonical implementation of the Python programming language, as distributed on python.org . The term “CPython” is used when necessary to distinguish this implementation from others such as Jython or IronPython. current context ¶ The context ( contextvars.Context object) that is currently used by ContextVar objects to access (get or set) the values of context variables . Each thread has its own current context. Frameworks for executing asynchronous tasks (see asyncio ) associate each task with a context which becomes the current context whenever the task starts or resumes execution. cyclic isolate ¶ A subgroup of one or more objects that reference each other in a reference cycle, but are not referenced by objects outside the group. The goal of the cyclic garbage collector is to identify these groups and break the reference cycles so that the memory can be reclaimed. data race ¶ A situation where multiple threads access the same memory location concurrently, at least one of the accesses is a write, and the threads do not use any synchronization to control their access. Data races lead to non-deterministic behavior and can cause data corruption. Proper use of locks and other synchronization primitives prevents data races. Note that data races can only happen in native code, but that native code might be exposed in a Python API. See also race condition and thread-safe . deadlock ¶ A situation in which two or more tasks (threads, processes, or coroutines) wait indefinitely for each other to release resources or complete actions, preventing any from making progress. For example, if thread A holds lock 1 and waits for lock 2, while thread B holds lock 2 and waits for lock 1, both threads will wait indefinitely. In Python this often arises from acquiring multiple locks in conflicting orders or from circular join/await dependencies. Deadlocks can be avoided by always acquiring multiple locks in a consistent order. See also lock and reentrant . decorator ¶ A function returning another function, usually applied as a function transformation using the @wrapper syntax. Common examples for decorators are classmethod() and staticmethod() . The decorator syntax is merely syntactic sugar, the following two function definitions are semantically equivalent: def f ( arg ): ... f = staticmethod ( f ) @staticmethod def f ( arg ): ... The same concept exists for classes, but is less commonly used there. See the documentation for function definitions and class definitions for more about decorators. descriptor ¶ Any object which defines the methods __get__() , __set__() , or __delete__() . When a class attribute is a descriptor, its special binding behavior is triggered upon attribute lookup. Normally, using a.b to get, set or delete an attribute looks up the object named b in the class dictionary for a , but if b is a descriptor, the respective descriptor method gets called. Understanding descriptors is a key to a deep understanding of Python because they are the basis for many features including functions, methods, properties, class methods, static methods, and reference to super classes. For more information about descriptors’ methods, see Implementing Descriptors or the Descriptor How To Guide . dictionary ¶ An associative array, where arbitrary keys are mapped to values. The keys can be any object with __hash__() and __eq__() methods. Called a hash in Perl. dictionary comprehension ¶ A compact way to process all or part of the elements in an iterable and return a dictionary with the results. results = {n: n ** 2 for n in range(10)} generates a dictionary containing key n mapped to value n ** 2 . See Displays for lists, sets and dictionaries . dictionary view ¶ The objects returned from dict.keys() , dict.values() , and dict.items() are called dictionary views. They provide a dynamic view on the dictionary’s entries, which means that when the dictionary changes, the view reflects these changes. To force the dictionary view to become a full list use list(dictview) . See Dictionary view objects . docstring ¶ A string literal which appears as the first expression in a class, function or module. While ignored when the suite is executed, it is recognized by the compiler and put into the __doc__ attribute of the enclosing class, function or module. Since it is available via introspection, it is the canonical place for documentation of the object. duck-typing ¶ A programming style which does not look at an object’s type to determine if it has the right interface; instead, the method or attribute is simply called or used (“If it looks like a duck and quacks like a duck, it must be a duck.”) By emphasizing interfaces rather than specific types, well-designed code improves its flexibility by allowing polymorphic substitution. Duck-typing avoids tests using type() or isinstance() . (Note, however, that duck-typing can be complemented with abstract base classes .) Instead, it typically employs hasattr() tests or EAFP programming. dunder ¶ An informal short-hand for “double underscore”, used when talking about a special method . For example, __init__ is often pronounced “dunder init”. EAFP ¶ Easier to ask for forgiveness than permission. This common Python coding style assumes the existence of valid keys or attributes and catches exceptions if the assumption proves false. This clean and fast style is characterized by the presence of many try and except statements. The technique contrasts with the LBYL style common to many other languages such as C. evaluate function ¶ A function that can be called to evaluate a lazily evaluated attribute of an object, such as the value of type aliases created with the type statement. expression ¶ A piece of syntax which can be evaluated to some value. In other words, an expression is an accumulation of expression elements like literals, names, attribute access, operators or function calls which all return a value. In contrast to many other languages, not all language constructs are expressions. There are also statement s which cannot be used as expressions, such as while . Assignments are also statements, not expressions. extension module ¶ A module written in C or C++, using Python’s C API to interact with the core and with user code. f-string ¶ f-strings ¶ String literals prefixed with f or F are commonly called “f-strings” which is short for formatted string literals . See also PEP 498 . file object ¶ An object exposing a file-oriented API (with methods such as read() or write() ) to an underlying resource. Depending on the way it was created, a file object can mediate access to a real on-disk file or to another type of storage or communication device (for example standard input/output, in-memory buffers, sockets, pipes, etc.). File objects are also called file-like objects or streams . There are actually three categories of file objects: raw binary files , buffered binary files and text files . Their interfaces are defined in the io module. The canonical way to create a file object is by using the open() function. file-like object ¶ A synonym for file object . filesystem encoding and error handler ¶ Encoding and error handler used by Python to decode bytes from the operating system and encode Unicode to the operating system. The filesystem encoding must guarantee to successfully decode all bytes below 128. If the file system encoding fails to provide this guarantee, API functions can raise UnicodeError . The sys.getfilesystemencoding() and sys.getfilesystemencodeerrors() functions can be used to get the filesystem encoding and error handler. The filesystem encoding and error handler are configured at Python startup by the PyConfig_Read() function: see filesystem_encoding and filesystem_errors members of PyConfig . See also the locale encoding . finder ¶ An object that tries to find the loader for a module that is being imported. There are two types of finder: meta path finders for use with sys.meta_path , and path entry finders for use with sys.path_hooks . See Finders and loaders and importlib for much more detail. floor division ¶ Mathematical division that rounds down to nearest integer. The floor division operator is // . For example, the expression 11 // 4 evaluates to 2 in contrast to the 2.75 returned by float true division. Note that (-11) // 4 is -3 because that is -2.75 rounded downward . See PEP 238 . free threading ¶ A threading model where multiple threads can run Python bytecode simultaneously within the same interpreter. This is in contrast to the global interpreter lock which allows only one thread to execute Python bytecode at a time. See PEP 703 . free variable ¶ Formally, as defined in the language execution model , a free variable is any variable used in a namespace which is not a local variable in that namespace. See closure variable for an example. Pragmatically, due to the name of the codeobject.co_freevars attribute, the term is also sometimes used as a synonym for closure variable . function ¶ A series of statements which returns some value to a caller. It can also be passed zero or more arguments which may be used in the execution of the body. See also parameter , method , and the Function definitions section. function annotation ¶ An annotation of a function parameter or return value. Function annotations are usually used for type hints : for example, this function is expected to take two int arguments and is also expected to have an int return value: def sum_two_numbers ( a : int , b : int ) -> int : return a + b Function annotation syntax is explained in section Function definitions . See variable annotation and PEP 484 , which describe this functionality. Also see Annotations Best Practices for best practices on working with annotations. __future__ ¶ A future statement , from __future__ import <feature> , directs the compiler to compile the current module using syntax or semantics that will become standard in a future release of Python. The __future__ module documents the possible values of feature . By importing this module and evaluating its variables, you can see when a new feature was first added to the language and when it will (or did) become the default: >>> import __future__ >>> __future__ . division _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192) garbage collection ¶ The process of freeing memory when it is not used anymore. Python performs garbage collection via reference counting and a cyclic garbage collector that is able to detect and break reference cycles. The garbage collector can be controlled using the gc module. generator ¶ A function which returns a generator iterator . It looks like a normal function except that it contains yield expressions for producing a series of values usable in a for-loop or that can be retrieved one at a time with the next() function. Usually refers to a generator function, but may refer to a generator iterator in some contexts. In cases where the intended meaning isn’t clear, using the full terms avoids ambiguity. generator iterator ¶ An object created by a generator function. Each yield temporarily suspends processing, remembering the execution state (including local variables and pending try-statements). When the generator iterator resumes, it picks up where it left off (in contrast to functions which start fresh on every invocation). generator expression ¶ An expression that returns an iterator . It looks like a normal expression followed by a for clause defining a loop variable, range, and an optional if clause. The combined expression generates values for an enclosing function: >>> sum ( i * i for i in range ( 10 )) # sum of squares 0, 1, 4, ... 81 285 generic function ¶ A function composed of multiple functions implementing the same operation for different types. Which implementation should be used during a call is determined by the dispatch algorithm. See also the single dispatch glossary entry, the functools.singledispatch() decorator, and PEP 443 . generic type ¶ A type that can be parameterized; typically a container class such as list or dict . Used for type hints and annotations . For more details, see generic alias types , PEP 483 , PEP 484 , PEP 585 , and the typing module. GIL ¶ See global interpreter lock . global interpreter lock ¶ The mechanism used by the CPython interpreter to assure that only one thread executes Python bytecode at a time. This simplifies the CPython implementation by making the object model (including critical built-in types such as dict ) implicitly safe against concurrent access. Locking the entire interpreter makes it easier for the interpreter to be multi-threaded, at the expense of much of the parallelism afforded by multi-processor machines. However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O. As of Python 3.13, the GIL can be disabled using the --disable-gil build configuration. After building Python with this option, code must be run with -X gil=0 or after setting the PYTHON_GIL=0 environment variable. This feature enables improved performance for multi-threaded applications and makes it easier to use multi-core CPUs efficiently. For more details, see PEP 703 . In prior versions of Python’s C API, a function might declare that it requires the GIL to be held in order to use it. This refers to having an attached thread state . global state ¶ Data that is accessible throughout a program, such as module-level variables, class variables, or C static variables in extension modules . In multi-threaded programs, global state shared between threads typically requires synchronization to avoid race conditions and data races . hash-based pyc ¶ A bytecode cache file that uses the hash rather than the last-modified time of the corresponding source file to determine its validity. See Cached bytecode invalidation . hashable ¶ An object is hashable if it has a hash value which never changes during its lifetime (it needs a __hash__() method), and can be compared to other objects (it needs an __eq__() method). Hashable objects which compare equal must have the same hash value. Hashability makes an object usable as a dictionary key and a set member, because these data structures use the hash value internally. Most of Python’s immutable built-in objects are hashable; mutable containers (such as lists or dictionaries) are not; immutable containers (such as tuples and frozensets) are only hashable if their elements are hashable. Objects which are instances of user-defined classes are hashable by default. They all compare unequal (except with themselves), and their hash value is derived from their id() . IDLE ¶ An Integrated Development and Learning Environment for Python. IDLE — Python editor and shell is a basic editor and interpreter environment which ships with the standard distribution of Python. immortal ¶ Immortal objects are a CPython implementation detail introduced in PEP 683 . If an object is immortal, its reference count is never modified, and therefore it is never deallocated while the interpreter is running. For example, True and None are immortal in CPython. Immortal objects can be identified via sys._is_immortal() , or via PyUnstable_IsImmortal() in the C API. immutable ¶ An object with a fixed value. Immutable objects include numbers, strings and tuples. Such an object cannot be altered. A new object has to be created if a different value has to be stored. They play an important role in places where a constant hash value is needed, for example as a key in a dictionary. Immutable objects are inherently thread-safe because their state cannot be modified after creation, eliminating concerns about improperly synchronized concurrent modification . import path ¶ A list of locations (or path entries ) that are searched by the path based finder for modules to import. During import, this list of locations usually comes from sys.path , but for subpackages it may also come from the parent package’s __path__ attribute. importing ¶ The process by which Python code in one module is made available to Python code in another module. importer ¶ An object that both finds and loads a module; both a finder and loader object. interactive ¶ Python has an interactive interpreter which means you can enter statements and expressions at the interpreter prompt, immediately execute them and see their results. Just launch python with no arguments (possibly by selecting it from your computer’s main menu). It is a very powerful way to test out new ideas or inspect modules and packages (remember help(x) ). For more on interactive mode, see Interactive Mode . interpreted ¶ Python is an interpreted language, as opposed to a compiled one, though the distinction can be blurry because of the presence of the bytecode compiler. This means that source files can be run directly without explicitly creating an executable which is then run. Interpreted languages typically have a shorter development/debug cycle than compiled ones, though their programs generally also run more slowly. See also interactive . interpreter shutdown ¶ When asked to shut down, the Python interpreter enters a special phase where it gradually releases all allocated resources, such as modules and various critical internal structures. It also makes several calls to the garbage collector . This can trigger the execution of code in user-defined destructors or weakref callbacks. Code executed during the shutdown phase can encounter various exceptions as the resources it relies on may not function anymore (common examples are library modules or the warnings machinery). The main reason for interpreter shutdown is that the __main__ module or the script being run has finished executing. iterable ¶ An object capable of returning its members one at a time. Examples of iterables include all sequence types (such as list , str , and tuple ) and some non-sequence types like dict , file objects , and objects of any classes you define with an __iter__() method or with a __getitem__() method that implements sequence semantics. Iterables can be used in a for loop and in many other places where a sequence is needed ( zip() , map() , …). When an iterable object is passed as an argument to the built-in function iter() , it returns an iterator for the object. This iterator is good for one pass over the set of values. When using iterables, it is usually not necessary to call iter() or deal with iterator objects yourself. The for statement does that automatically for you, creating a temporary unnamed variable to hold the iterator for the duration of the loop. See also iterator , sequence , and generator . iterator ¶ An object representing a stream of data. Repeated calls to the iterator’s __next__() method (or passing it to the built-in function next() ) return successive items in the stream. When no more data are available a StopIteration exception is raised instead. At this point, the iterator object is exhausted and any further calls to its __next__() method just raise StopIteration again. Iterators are required to have an __iter__() method that returns the iterator object itself so every iterator is also iterable and may be used in most places where other iterables are accepted. One notable exception is code which attempts multiple iteration passes. A container object (such as a list ) produces a fresh new iterator each time you pass it to the iter() function or use it in a for loop. Attempting this with an iterator will just return the same exhausted iterator object used in the previous iteration pass, making it appear like an empty container. More information can be found in Iterator Types . CPython implementation detail: CPython does not consistently apply the requirement that an iterator define __iter__() . And also please note that free-threaded CPython does not guarantee thread-safe behavior of iterator operations. key function ¶ A key function or collation function is a callable that returns a value used for sorting or ordering. For example, locale.strxfrm() is used to produce a sort key that is aware of locale specific sort conventions. A number of tools in Python accept key functions to control how elements are ordered or grouped. They include min() , max() , sorted() , list.sort() , heapq.merge() , heapq.nsmallest() , heapq.nlargest() , and itertools.groupby() . There are several ways to create a key function. For example. the str.casefold() method can serve as a key function for case insensitive sorts. Alternatively, a key function can be built from a lambda expression such as lambda r: (r[0], r[2]) . Also, operator.attrgetter() , operator.itemgetter() , and operator.methodcaller() are three key function constructors. See the Sorting HOW TO for examples of how to create and use key functions. keyword argument ¶ See argument . lambda ¶ An anonymous inline function consisting of a single expression which is evaluated when the function is called. The syntax to create a lambda function is lambda [parameters]: expression LBYL ¶ Look before you leap. This coding style explicitly tests for pre-conditions before making calls or lookups. This style contrasts with the EAFP approach and is characterized by the presence of many if statements. In a multi-threaded environment, the LBYL approach can risk introducing a race condition between “the looking” and “the leaping”. For example, the code, if key in mapping: return mapping[key] can fail if another thread removes key from mapping after the test, but before the lookup. This issue can be solved with locks or by using the EAFP approach. See also thread-safe . lexical analyzer ¶ Formal name for the tokenizer ; see token . list ¶ A built-in Python sequence . Despite its name it is more akin to an array in other languages than to a linked list since access to elements is O (1). list comprehension ¶ A compact way to process all or part of the elements in a sequence and return a list with the results. result = ['{:#04x}'.format(x) for x in range(256) if x % 2 == 0] generates a list of strings containing even hex numbers (0x..) in the range from 0 to 255. The if clause is optional. If omitted, all elements in range(256) are processed. lock ¶ A synchronization primitive that allows only one thread at a time to access a shared resource. A thread must acquire a lock before accessing the protected resource and release it afterward. If a thread attempts to acquire a lock that is already held by another thread, it will block until the lock becomes available. Python’s threading module provides Lock (a basic lock) and RLock (a reentrant lock). Locks are used to prevent race conditions and ensure thread-safe access to shared data. Alternative design patterns to locks exist such as queues, producer/consumer patterns, and thread-local state. See also deadlock , and reentrant . loader ¶ An object that loads a module. It must define the exec_module() and create_module() methods to implement the Loader interface. A loader is typically returned by a finder . See also: Finders and loaders importlib.abc.Loader PEP 302 locale encoding ¶ On Unix, it is the encoding of the LC_CTYPE locale. It can be set with locale.setlocale(locale.LC_CTYPE, new_locale) . On Windows, it is the ANSI code page (ex: "cp1252" ). On Android and VxWorks, Python uses "utf-8" as the locale encoding. locale.getencoding() can be used to get the locale encoding. See also the filesystem encoding and error handler . magic method ¶ An informal synonym for special method . mapping ¶ A container object that supports arbitrary key lookups and implements the methods specified in the collections.abc.Mapping or collections.abc.MutableMapping abstract base classes . Examples include dict , collections.defaultdict , collections.OrderedDict and collections.Counter . meta path finder ¶ A finder returned by a search of sys.meta_path . Meta path finders are related to, but different from path entry finders . See importlib.abc.MetaPathFinder for the methods that meta path finders implement. metaclass ¶ The class of a class. Class definitions create a class name, a class dictionary, and a list of base classes. The metaclass is responsible for taking those three arguments and creating the class. Most object oriented programming languages provide a default implementation. What makes Python special is that it is possible to create custom metaclasses. Most users never need this tool, but when the need arises, metaclasses can provide powerful, elegant solutions. They have been used for logging attribute access, adding thread-safety, tracking object creation, implementing singletons, and many other tasks. More information can be found in Metaclasses . method ¶ A function which is defined inside a class body. If called as an attribute of an instance of that class, the method will get the instance object as its first argument (which is usually called self ). See function and nested scope . method resolution order ¶ Method Resolution Order is the order in which base classes are searched for a member during lookup. See The Python 2.3 Method Resolution Order for details of the algorithm used by the Python interpreter since the 2.3 release. module ¶ An object that serves as an organizational unit of Python code. Modules have a namespace containing arbitrary Python objects. Modules are loaded into Python by the process of importing . See also package . module spec ¶ A namespace containing the import-related information used to load a module. An instance of importlib.machinery.ModuleSpec . See also Module specs . MRO ¶ See method resolution order . mutable ¶ An object with state that is allowed to change during the course of the program. In multi-threaded programs, mutable objects that are shared between threads require careful synchronization to avoid race conditions . See also immutable , thread-safe , and concurrent modification . named tuple ¶ The term “named tuple” applies to any type or class that inherits from tuple and whose indexable elements are also accessible using named attributes. The type or class may have other features as well. Several built-in types are named tuples, including the values returned by time.localtime() and os.stat() . Another example is sys.float_info : >>> sys . float_info [ 1 ] # indexed access 1024 >>> sys . float_info . max_exp # named field access 1024 >>> isinstance ( sys . float_info , tuple ) # kind of tuple True Some named tuples are built-in types (such as the above examples). Alternatively, a named tuple can be created from a regular class definition that inherits from tuple and that defines named fields. Such a class can be written by hand, or it can be created by inheriting typing.NamedTuple , or with the factory function collections.namedtuple() . The latter techniques also add some extra methods that may not be found in hand-written or built-in named tuples. namespace ¶ The place where a variable is stored. Namespaces are implemented as dictionaries. There are the local, global and built-in namespaces as well as nested namespaces in objects (in methods). Namespaces support modularity by preventing naming conflicts. For instance, the functions builtins.open and os.open() are distinguished by their namespaces. Namespaces also aid readability and maintainability by making it clear which module implements a function. For instance, writing random.seed() or itertools.islice() makes it clear that those functions are implemented by the random and itertools modules, respectively. namespace package ¶ A package which serves only as a container for subpackages. Namespace packages may have no physical representation, and specifically are not like a regular package because they have no __init__.py file. Namespace packages allow several individually installable packages to have a common parent package. Otherwise, it is recommended to use a regular package . For more information, see PEP 420 and Namespace packages . See also module . native code ¶ Code that is compiled to machine instructions and runs directly on the processor, as opposed to code that is interpreted or runs in a virtual machine. In the context of Python, native code typically refers to C, C++, Rust or Fortran code in extension modules that can be called from Python. See also extension module . nested scope ¶ The ability to refer to a variable in an enclosing definition. For instance, a function defined inside another function can refer to variables in the outer function. Note that nested scopes by default work only for reference and not for assignment. Local variables both read and write in the innermost scope. Likewise, global variables read and write to the global namespace. The nonlocal allows writing to outer scopes. new-style class ¶ Old name for the flavor of classes now used for all class objects. In earlier Python versions, only new-style classes could use Python’s newer, versatile features like __slots__ , descriptors, properties, __getattribute__() , class methods, and static methods. non-deterministic ¶ Behavior where the outcome of a program can vary between executions with the same inputs. In multi-threaded programs, non-deterministic behavior often results from race conditions where the relative timing or interleaving of threads affects the result. Proper synchronization using locks and other synchronization primitives helps ensure deterministic behavior. object ¶ Any data with state (attributes or value) and defined behavior (methods). Also the ultimate base class of any new-style class . optimized scope ¶ A scope where target local variable names are reliably known to the compiler when the code is compiled, allowing optimization of read and write access to these names. The local namespaces for functions, generators, coroutines, comprehensions, and generator expressions are optimized in this fashion. Note: most interpreter optimizations are applied to all scopes, only those relying on a known set of local and nonlocal variable names are restricted to optimized scopes. optional module ¶ An extension module that is part of the standard library , but may be absent in some builds of CPython , usually due to missing third-party libraries or because the module is not available for a given platform. See Requirements for optional modules for a list of optional modules that require third-party libraries. package ¶ A Python module which can contain submodules or recursively, subpackages. Technically, a package is a Python module with a __path__ attribute. See also regular package and namespace package . parallelism ¶ Executing multiple operations at the same time (e.g. on multiple CPU cores). In Python builds with the global interpreter lock (GIL) , only one thread runs Python bytecode at a time, so taking advantage of multiple CPU cores typically involves multiple processes (e.g. multiprocessing ) or native extensions that release the GIL. In free-threaded Python, multiple Python threads can run Python code simultaneously on different cores. parameter ¶ A named entity in a function (or method) definition that specifies an argument (or in some cases, arguments) that the function can accept. There are five kinds of parameter: positional-or-keyword : specifies an argument that can be passed either positionally or as a keyword argument . This is the default kind of parameter, for example foo and bar in the following: def func ( foo , bar = None ): ... positional-only : specifies an argument that can be supplied only by position. Positional-only parameters can be defined by including a / character in the parameter list of the function definition after them, for example posonly1 and posonly2 in the following: def func ( posonly1 , posonly2 , / , positional_or_keyword ): ... keyword-only : specifies an argument that can be supplied only by keyword. Keyword-only parameters can be defined by including a single var-positional parameter or bare * in the parameter list of the function definition before them, for example kw_only1 and kw_only2 in the following: def func ( arg , * , kw_only1 , kw_only2 ): ... var-positional : specifies that an arbitrary sequence of positional arguments can be provided (in addition to any positional arguments already accepted by other parameters). Such a parameter can be defined by prepending the parameter name with * , for example args in the following: def func ( * args , ** kwargs ): ... var-keyword : specifies that arbitrarily many keyword arguments can be provided (in addition to any keyword arguments already accepted by other parameters). Such a parameter can be defined by prepending the parameter name with ** , for example kwargs in the example above. Parameters can specify both optional and required arguments, as well as default values for some optional arguments. See also the argument glossary entry, the FAQ question on the difference between arguments and parameters , the inspect.Parameter class, the Function definitions section, and PEP 362 . path entry ¶ A single location on the import path which the path based finder consults to find modules for importing. path entry finder ¶ A finder returned by a callable on sys.path_hooks (i.e. a path entry hook ) which knows how to locate modules given a path entry . See importlib.abc.PathEntryFinder for the methods that path entry finders implement. path entry hook ¶ A callable on the sys.path_hooks list which returns a path entry finder if it knows how to find modules on a specific path entry . path based finder ¶ One of the default meta path finders which searches an import path for modules. path-like object ¶ An object representing a file system path. A path-like object is either a str or bytes object representing a path, or an object implementing the os.PathLike protocol. An object that supports the os.PathLike protocol can be converted to a str or bytes file system path by calling the os.fspath() function; os.fsdecode() and os.fsencode() can be used to guarantee a str or bytes result instead, respectively. Introduced by PEP 519 . PEP ¶ Python Enhancement Proposal. A PEP is a design document providing information to the Python community, or describing a new feature for Python or its processes or environment. PEPs should provide a concise technical specification and a rationale for proposed features. PEPs are intended to be the primary mechanisms for proposing major new features, for collecting community input on an issue, and for documenting the design decisions that have gone into Python. The PEP author is responsible for building consensus within the community and documenting dissenting opinions. See PEP 1 . portion ¶ A set of files in a single directory (possibly stored in a zip file) that contribute to a namespace package, as defined in PEP 420 . positional argument ¶ See argument . provisional API ¶ A provisional API is one which has been deliberately excluded from the standard library’s backwards compatibility guarantees. While major changes to such interfaces are not expected, as long as they are marked provisional, backwards incompatible changes (up to and including removal of the interface) may occur if deemed necessary by core developers. Such changes will not be made | 2026-01-13T08:49:02 |
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https://tatanotes.com/blog/bik-rejects-overlays | TataNotes – Web Accessibility Personal Blog BIK Rejects Overlays for EN/WCAG Compliance – TataNotes Skip to main content TataNotes… …all about a11y Blog About me News BIK Rejects Overlays for EN/WCAG Compliance A clarification is issued by a German accessibility testing organization regarding use of accessibility overlays. Reading time: around 1 min Publication date January 5, 2026 Accessibility overlays are tools aimed to improve accessibility on the webpages. Unfortunately, not only do they fail to improve web accessibility, but they can even be harmful, and create additional barriers for users who rely on assistive technologies. By using overlays, owners introduce additional risks — compromising the accessibility of their websites can lead to legal consequences and reputation damages. German BIK testing methodology has been updated to explicitly reject the use of overlays for EN/WCAG compliance. This means that a website using an accessibility overlay will not be evaluated using the BIK methodology (machine-translated excerpt): The Committee on Accessible Information Technology pursuant to § 5 BITV 2.0 points out that providers using overlay tools on their websites “risk that their offerings for beneficial assistive technologies are no longer accessible (deterioration of accessibility)”. Such potential interactions cannot ensure compliance for a page with overlay tool, even if it remains in the default state. BIK decides on the following determination: BIK verifiers cannot currently make a reliable statement of compliance in accordance with EN/WCAG for websites that use an overlay tool. Accordingly, BIK test results from websites that use an overlay tool cannot be published. It is not possible to use a BIK test mark on websites that use an overlay tool. — 4.1.1. No message of conformity for a website with overlay tool I hope that this rule will encourage business owners to pay more attention to the usability of their services and mindfully apply the best accessibility practices instead of using one-size-fits-none tools, and I hope that other methodologies will follow BIK’s example. Tatyana Bayramova © 2024–present Important: All rights reserved | 2026-01-13T08:49:02 |
https://dev.to/loiconlyone/jai-galere-pendant-3-semaines-pour-monter-un-cluster-kubernetes-et-voila-ce-que-jai-appris-30l6#larchitecture-spoiler-%C3%A7a-marche-maintenant | J'ai galéré pendant 3 semaines pour monter un cluster Kubernetes (et voilà ce que j'ai appris) - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. 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Maker Forem Follow A community for makers, hobbyists, and professionals to discuss Arduino, Raspberry Pi, 3D printing, and much more. HMPL.js Forem Follow For developers using HMPL.js to build fast, lightweight web apps. A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse BeardDemon Posted on Jan 10 J'ai galéré pendant 3 semaines pour monter un cluster Kubernetes (et voilà ce que j'ai appris) # kubernetes # devops # learning Le contexte Bon, soyons honnêtes. Au début, j'avais un gros bordel de scripts bash éparpillés partout. Genre 5-6 fichiers avec des noms comme install-docker.sh , setup-k8s-FINAL-v3.sh (oui, le v3...). À chaque fois que je devais recréer mon infra, c'était 45 minutes de galère + 10 minutes à me demander pourquoi ça marchait pas. J'avais besoin de quelque chose de plus propre pour mon projet SAE e-commerce. Ce que je voulais vraiment Pas un truc de démo avec minikube. Non. Je voulais: 3 VMs qui tournent vraiment (1 master + 2 workers) Tout automatisé - je tape une commande et ça se déploie ArgoCD pour faire du GitOps (parce que push to deploy c'est quand même cool) Des logs centralisés (Loki + Grafana) Et surtout : pouvoir tout péter et tout recréer en 10 minutes L'architecture (spoiler: ça marche maintenant) ┌─────────────────────────────────────────┐ │ Mon PC (Debian) │ │ ┌──────────┐ ┌──────────┐ ┌─────────┐ │ │ Master │ │ Worker 1 │ │ Worker 2│ │ │ .56.10 │ │ .56.11 │ │ .56.12 │ │ └──────────┘ └──────────┘ └─────────┘ └─────────────────────────────────────────┘ Enter fullscreen mode Exit fullscreen mode Chaque VM a 4Go de RAM et 4 CPUs. Oui, ça bouffe des ressources. Non, ça passe pas sur un laptop pourri. Comment c'est organisé J'ai tout mis dans un repo bien rangé (pour une fois): ansible-provisioning/ ├── Vagrantfile # Les 3 VMs ├── playbook.yml # Le chef d'orchestre ├── manifests/ # Mes applis K8s │ ├── apiclients/ │ ├── apicatalogue/ │ ├── databases/ │ └── ... (toutes mes APIs) └── roles/ # Les briques Ansible ├── docker/ ├── kubernetes/ ├── k8s-master/ └── argocd/ Enter fullscreen mode Exit fullscreen mode Chaque rôle fait UN truc. C'est ça qui a changé ma vie. Shell scripts → Ansible : pourquoi j'ai migré Avant (la galère) J'avais un script prepare-system.sh qui ressemblait à ça: #!/bin/bash swapoff -a sed -i '/swap/d' /etc/fstab modprobe br_netfilter # ... 50 lignes de commandes # Aucune gestion d'erreur # Si ça plante au milieu, bonne chance Enter fullscreen mode Exit fullscreen mode Le pire ? Si je relançais le script après un fail, tout pétait. Genre le sed essayait de supprimer une ligne qui existait plus. Classique. Après (je respire enfin) Maintenant j'ai un rôle Ansible system-prepare : - name : Virer le swap shell : swapoff -a ignore_errors : yes - name : Enlever le swap du fstab lineinfile : path : /etc/fstab regexp : ' .*swap.*' state : absent - name : Charger br_netfilter modprobe : name : br_netfilter state : present Enter fullscreen mode Exit fullscreen mode La différence ? Je peux relancer 10 fois, ça fait pas de conneries C'est lisible par un humain Si ça plante, je sais exactement où Le Vagrantfile (ou comment lancer 3 VMs d'un coup) Vagrant . configure ( "2" ) do | config | config . vm . box = "debian/bullseye64" # Config libvirt (KVM/QEMU) config . vm . provider "libvirt" do | libvirt | libvirt . memory = 4096 libvirt . cpus = 4 libvirt . management_network_address = "192.168.56.0/24" end # NFS pour partager les manifests config . vm . synced_folder "." , "/vagrant" , type: "nfs" , nfs_version: 4 # Le master config . vm . define "vm-master" do | vm | vm . vm . network "private_network" , ip: "192.168.56.10" vm . vm . hostname = "master" end # Les 2 workers ( 1 .. 2 ). each do | i | config . vm . define "vm-slave- #{ i } " do | vm | vm . vm . network "private_network" , ip: "192.168.56.1 #{ i } " vm . vm . hostname = "slave- #{ i } " end end # Ansible se lance automatiquement config . vm . provision "ansible" do | ansible | ansible . playbook = "playbook.yml" ansible . groups = { "master" => [ "vm-master" ], "workers" => [ "vm-slave-1" , "vm-slave-2" ] } end end Enter fullscreen mode Exit fullscreen mode Un vagrant up et boom, tout se monte tout seul. Le playbook : l'ordre c'est important --- # 1. Tous les nœuds en même temps - name : Setup de base hosts : k8s_cluster roles : - system-prepare # Swap off, modules kernel - docker # Docker + containerd - kubernetes # kubelet, kubeadm, kubectl # 2. Le master d'abord - name : Init master hosts : master roles : - k8s-master # kubeadm init + Flannel # 3. Les workers ensuite, un par un - name : Join workers hosts : workers serial : 1 # IMPORTANT: un à la fois roles : - k8s-worker # 4. Les trucs bonus sur le master - name : Dashboard + ArgoCD + Monitoring hosts : master roles : - k8s-dashboard - argocd - logging - metrics-server Enter fullscreen mode Exit fullscreen mode Le serial: 1 c'est crucial. J'avais essayé sans, les deux workers essayaient de join en même temps et ça partait en cacahuète. Les rôles en détail Rôle: k8s-master (le chef d'orchestre) C'est lui qui initialise le cluster. Voici les parties importantes: - name : Init cluster k8s command : kubeadm init --apiserver-advertise-address=192.168.56.10 --pod-network-cidr=10.244.0.0/16 when : not k8s_initialise.stat.exists - name : Copier config kubectl copy : src : /etc/kubernetes/admin.conf dest : /home/vagrant/.kube/config owner : vagrant group : vagrant - name : Installer Flannel (réseau pod) shell : | kubectl apply -f https://github.com/flannel-io/flannel/releases/latest/download/kube-flannel.yml environment : KUBECONFIG : /home/vagrant/.kube/config - name : Générer commande join pour les workers copy : content : " kubeadm join 192.168.56.10:6443 --token {{ k8s_token.stdout }} --discovery-token-ca-cert-hash sha256:{{ k8s_ca_hash.stdout }}" dest : /vagrant/join.sh mode : ' 0755' - name : Créer fichier .master-ready copy : content : " Master initialized" dest : /vagrant/.master-ready Enter fullscreen mode Exit fullscreen mode Le fichier .master-ready c'est un flag pour dire aux workers "go, vous pouvez join maintenant". Rôle: k8s-worker (le suiveur patient) - name : Attendre que le fichier .master-ready existe wait_for : path : /vagrant/.master-ready timeout : 600 - name : Joindre le cluster shell : bash /vagrant/join.sh args : creates : /etc/kubernetes/kubelet.conf register : join_result failed_when : - join_result.rc != 0 - " 'already exists in the cluster' not in join_result.stderr" - name : Attendre que le node soit Ready shell : | for i in {1..60}; do STATUS=$(kubectl get node $(hostname) -o jsonpath='{.status.conditions[?(@.type=="Ready")].status}') if [ "$STATUS" = "True" ]; then exit 0 fi sleep 5 done exit 1 Enter fullscreen mode Exit fullscreen mode Le worker attend gentiment que le master soit prêt avant de faire quoi que ce soit. Les galères que j'ai rencontrées Galère #1: NFS qui marche pas Au début, le partage NFS entre l'hôte et les VMs plantait. Symptôme: mount.nfs: Connection timed out Enter fullscreen mode Exit fullscreen mode Solution: # Sur l'hôte sudo apt install nfs-kernel-server sudo systemctl start nfs-server sudo ufw allow from 192.168.56.0/24 Enter fullscreen mode Exit fullscreen mode Le firewall bloquait les connexions NFS. Classique. Galère #2: Kubeadm qui timeout Le kubeadm init prenait 10 minutes et finissait par timeout. Cause: Pas assez de RAM sur les VMs (j'avais mis 2Go). Solution: Passer à 4Go par VM. Ça bouffe mais c'est nécessaire. Galère #3: Les workers qui join pas Les workers restaient en NotReady même après le join. Cause: Flannel (le CNI) était pas encore installé sur le master. Solution: Attendre que Flannel soit complètement déployé avant de faire join les workers: - name : Attendre Flannel command : kubectl wait --for=condition=ready pod -l app=flannel -n kube-flannel --timeout=300s environment : KUBECONFIG : /etc/kubernetes/admin.conf Enter fullscreen mode Exit fullscreen mode Galère #4: Ansible qui relance tout à chaque fois Au début, chaque vagrant provision refaisait TOUT depuis zéro. Solution: Ajouter des conditions when partout: - name : Init cluster k8s command : kubeadm init ... when : not k8s_initialise.stat.exists # ← Ça sauve des vies Enter fullscreen mode Exit fullscreen mode L'idempotence c'est vraiment la base avec Ansible. Les commandes utiles au quotidien # Lancer tout cd ansible-provisioning && vagrant up # Vérifier l'état du cluster vagrant ssh vm-master -c 'kubectl get nodes' # Voir les pods vagrant ssh vm-master -c 'kubectl get pods -A' # Refaire le provisioning (sans détruire les VMs) vagrant provision # Tout péter et recommencer vagrant destroy -f && vagrant up # SSH sur le master vagrant ssh vm-master # Logs d'un pod vagrant ssh vm-master -c 'kubectl logs -n apps apicatalogue-xyz' Enter fullscreen mode Exit fullscreen mode ArgoCD et les applications Une fois le cluster monté, ArgoCD déploie automatiquement mes apps. Voici comment je déclare l'API Catalogue: apiVersion : argoproj.io/v1alpha1 kind : Application metadata : name : catalogue-manager-application namespace : argocd spec : destination : namespace : apps server : https://kubernetes.default.svc source : path : ansible-provisioning/manifests/apicatalogue repoURL : https://github.com/uha-sae53/Vagrant.git targetRevision : main project : default syncPolicy : automated : prune : true selfHeal : true Enter fullscreen mode Exit fullscreen mode ArgoCD surveille mon repo GitHub. Dès que je change un manifest, ça se déploie automatiquement. Metrics Server et HPA J'ai aussi ajouté le Metrics Server pour l'auto-scaling: - name : Installer Metrics Server shell : | kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/latest/download/components.yaml environment : KUBECONFIG : /etc/kubernetes/admin.conf - name : Patcher pour ignorer TLS (dev seulement) shell : | kubectl patch deployment metrics-server -n kube-system --type='json' \ -p='[{"op": "add", "path": "/spec/template/spec/containers/0/args/-", "value": "--kubelet-insecure-tls"}]' Enter fullscreen mode Exit fullscreen mode Avec ça, mes pods peuvent scaler automatiquement en fonction de la charge CPU/RAM. Le résultat final Après tout ça, voici ce que je peux faire: # Démarrer tout de zéro vagrant up # ⏱️ 8 minutes plus tard... # Vérifier que tout tourne vagrant ssh vm-master -c 'kubectl get pods -A' # Résultat: # NAMESPACE NAME READY STATUS # apps apicatalogue-xyz 1/1 Running # apps apiclients-abc 1/1 Running # apps apicommandes-def 1/1 Running # apps api-panier-ghi 1/1 Running # apps frontend-jkl 1/1 Running # argocd argocd-server-xxx 1/1 Running # logging grafana-yyy 1/1 Running # logging loki-0 1/1 Running # kube-system metrics-server-zzz 1/1 Running Enter fullscreen mode Exit fullscreen mode Tout fonctionne, tout est automatisé. Conclusion Ce que j'ai appris: Ansible > scripts shell (vraiment, vraiment) L'idempotence c'est pas un luxe Tester chaque rôle séparément avant de tout brancher Les workers doivent attendre le master (le serial: 1 sauve des vies) 4Go de RAM minimum par VM pour K8s Le code complet est sur GitHub: https://github.com/uha-sae53/Vagrant Des questions ? Ping moi sur Twitter ou ouvre une issue sur le repo. Et si vous galérez avec Kubernetes, vous êtes pas seuls. J'ai passé 3 semaines là-dessus, c'est normal que ce soit compliqué au début. Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse BeardDemon Follow Nananère je suis très sérieux... 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https://docs.suprsend.com/docs/java-sdk | Integrate Java SDK - SuprSend, Notification infrastructure for Product teams Skip to main content SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Community Trust Center Platform Status Postman Collection Developer Resources Overview Updates and Versioning Versioning and Support Policy SDK Changelog Authentication API Keys and Secrets Service Token Best Practices for Key & Token Management MCP Overview BETA Quickstart Tool List Building with LLMs Security Security SDKs and APIs SDKs SDK Overview SuprSend Backend SDK Python SDK Node.js SDK Java SDK Integrate Java SDK Manage Users Objects Send and Track Events Trigger Workflow from API Tenants Lists Broadcast Go SDK SuprSend Client SDK Management API REST API Postman Collection Features Validate Trigger Payload Type Safety Testing Testing the Template Test Mode Monitoring and Logging Logs Data Out Contact Us Get Started SuprSend, Notification infrastructure for Product teams home page Search... ⌘ K Ask AI Contact Us Get Started Get Started Search... Navigation Java SDK Integrate Java SDK Documentation API Reference Management API CLI Reference Developer Resources Changelog Documentation API Reference Management API CLI Reference Developer Resources Changelog Java SDK Integrate Java SDK OpenAI Open in ChatGPT Install & Initialize SuprSend Java SDK using your workspace credentials for sending notifications. OpenAI Open in ChatGPT Installation For SDK installation, you’ll have to add the SuprSend jar file. You can include the jar using following two ways: Option 1. As a Maven dependency for maven projects from downloaded jar suprsend-java-sdk is present as a maven dependency on maven central. Add following code to your pom.xml to include the sdk xml Copy Ask AI < dependencies > < dependency > < groupId > com.suprsend </ groupId > < artifactId > suprsend-java-sdk </ artifactId > < version > 0.5.0 </ version > </ dependency > </ dependencies > Option 2. As a jar file for non maven projects Click here to download the latest version of java SDK from releases section and add it as an External Jar in your build path. suprsend-java-sdk is available as a JAR with name- suprsend-java-sdk-0.5.0-jar-with-dependencies.jar JDK version 8 and above is supported Please check your Java development kit version. If it is lower than supported version, upgrade it to the latest version Initialization For initializing SDK, you need WORKSPACE KEY and WORKSPACE SECRET . Request Copy Ask AI import suprsend.Suprsend; Suprsend suprsend = new Suprsend ( "WORKSPACE KEY" , "WORKSPACE SECRET" ); Replace WORKSPACE KEY and WORKSPACE SECRET with your workspace values. You will get both the tokens from Developers -> API Keys section. Constructor to test SDK in debug mode Constructor allows you to view HTTP calls to Suprsend in your console. The final parameter is a boolean parameter which denotes whether value for “debug” is true or false. Default value for the same is false. Request Copy Ask AI import suprsend.Suprsend; Suprsend suprsend = new Suprsend ( "WORKSPACE KEY" , "WORKSPACE SECRET" , true ); Was this page helpful? Yes No Suggest edits Raise issue Previous Manage Users Manage user profiles and communication channels programmatically with the Java SDK. Next ⌘ I x github linkedin youtube Powered by On this page Installation Initialization Constructor to test SDK in debug mode | 2026-01-13T08:49:02 |
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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account Future Close Privacy Policy Last Updated: September 01, 2023 This Privacy Policy is designed to help you understand how DEV Community Inc. (" DEV ," " we ," or " us ") collects, use, and discloses your personal information. What's With the Defined Terms? You'll notice that some words appear in quotes in this Privacy Policy. They're called "defined terms," and we use them so that we don't have to repeat the same language again and again. They mean the same thing in every instance, to help us make sure that this Privacy Policy is consistent. We've included the defined terms throughout because we want it to be easy for you to read them in context. 1. WHAT DOES THIS PRIVACY POLICY APPLY TO? 2. PERSONAL INFORMATION WE COLLECT 3. HOW WE USE YOUR INFORMATION 4. HOW WE DISCLOSE YOUR INFORMATION 5. 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If you buy stuff on our shop site https://shop.dev.to/ (as either a User or Forem Operator), or otherwise if you pay us in connection with your use of the Forem service, we may collect personal information and details associated with your purchases, including payment information. Any payments made via our Services are processed by third-party payment processors, such as Stripe, Shopify, and PayPal. We do not directly collect or store any payment card information entered through our Services, but may receive information associated with your payment card information (e.g., your billing details). Your Communications with Us (Users and Forem Operators) . We may collect personal information, such as email address, phone number, or mailing address when you request information about our Services, register for our newsletter or loyalty program, request customer or technical support, apply for a job, or otherwise communicate with us. Surveys . We may contact you to participate in surveys. If you decide to participate, you may be asked to provide certain information, which may include personal information (for example, your home address). Sweepstakes or Contests . We may collect personal information you provide for any sweepstakes or contests that we offer. In some jurisdictions, we are required to publicly share information of sweepstakes and contest winners. Conferences, Trade Shows, and Other Events . We may collect personal information from individuals when we attend conferences, trade shows, and other events. Business Development and Strategic Partnerships . We may collect personal information from individuals and third parties to assess and pursue potential business opportunities. Job Applications . We may post job openings and opportunities on our Services. If you reply to one of these postings by submitting your application, CV and/or cover letter to us, we will collect and use your information to assess your qualifications. B. Information Collected Automatically We may collect personal information automatically when you use our Services: Automatic Data Collection . We may collect certain information automatically when you use our Services, such as your Internet protocol (IP) address, user settings, MAC address, cookie identifiers, mobile carrier, mobile advertising and other unique identifiers, browser or device information, location information (including approximate location derived from IP address), and Internet service provider. We may also automatically collect information regarding your use of our Services, such as pages that you visit before, during and after using our Services, information about the links you click, the types of content you interact with, the frequency and duration of your activities, and other information about how you use our Services. In addition, we may collect information that other people provide about you when they use our Services, including information about you when they tag you in their posts. Cookies, Pixel Tags/Web Beacons, and Other Technologies . We, as well as third parties that provide content, advertising, or other functionality on our Services, may use cookies, pixel tags, local storage, and other technologies (" Technologies ") to automatically collect information through your use of our Services. Cookies . Cookies are small text files placed in device browsers that store preferences and facilitate and enhance your experience. Pixel Tags/Web Beacons . A pixel tag (also known as a web beacon) is a piece of code embedded in our Services that collects information about engagement on our Services. The use of a pixel tag allows us to record, for example, that a user has visited a particular web page or clicked on a particular advertisement. We may also include web beacons in e-mails to understand whether messages have been opened, acted on, or forwarded. Our uses of these Technologies fall into the following general categories: Operationally Necessary . This includes Technologies that allow you access to our Services, applications, and tools that are required to identify irregular website behavior, prevent fraudulent activity and improve security or that allow you to make use of our functionality. Performance-Related . We may use Technologies to assess the performance of our Services, including as part of our analytic practices to help us understand how individuals use our Services ( see Analytics below ). Functionality-Related . We may use Technologies that allow us to offer you enhanced functionality when accessing or using our Services. This may include identifying you when you sign into our Services or keeping track of your specified preferences, interests, or past items viewed. Analytics . We may use Technologies and other third-party tools to process analytics information on our Services. Some of our analytics partners include Google Analytics. For more information,please visit Google Analytics' Privacy Policy . To learn more about how to opt-out of Google Analytics' use of your information, please click here . Social Media Platforms . Our Services may contain social media buttons such as Twitter, Facebook, GitHub, Instagram, and Twitch (that might include widgets such as the "share this" button or other interactive mini programs). These features may collect your IP address, which page you are visiting on our Services, and may set a cookie to enable the feature to function properly. Your interactions with these platforms are governed by the privacy policy of the company providing it. See the "Your Privacy Choices and Rights" section below to understand your choices regarding these Technologies. C. Information Collected from Other Sources We may obtain information about you from other sources, including through third-party services and organizations. For example, if you access our Services through a third-party application, such as an app store, a third-party login service (e.g., through Twitter, Apple, or GitHub), or a social networking site, we may collect whatever information about you from that third-party application that you have made available via your privacy settings. 3. HOW WE USE YOUR INFORMATION We use your information for a variety of business purposes, including to provide our Services, for administrative purposes, and to market our products and Services, as described below. A. Provide Our Services We use your information to fulfill our contract with you and provide you with our Services, such as: Managing your information and accounts; Providing access to certain areas, functionalities, and features of our Services; Answering requests for customer or technical support; Communicating with you about your account, activities on our Services, and policy changes; Processing your financial information and other payment methods for products or Services purchased; Processing applications if you apply for a job we post on our Services; and Allowing you to register for events. B. Administrative Purposes We use your information for various administrative purposes, such as: Pursuing our legitimate interests such as direct marketing, research and development (including marketing research), network and information security, and fraud prevention; Detecting security incidents, protecting against malicious, deceptive, fraudulent or illegal activity, and prosecuting those responsible for that activity; Measuring interest and engagement in our Services, including for usage-based billing purposes; Short-term, transient use, such as contextual customization of ads; Improving, optimizing, upgrading, or enhancing our Services; Developing new products and Services; Ensuring internal quality control and safety; Authenticating and verifying individual identities, including requests to exercise your rights under this policy; Debugging to identify and repair errors with our Services; Auditing relating to interactions, transactions and other compliance activities; Enforcing our agreements and policies; and Complying with our legal obligations. C. Marketing and Advertising our Products and Services We may use your personal information to tailor and provide you with content and advertisements for our Services, such as via email. If you have any questions about our marketing practices, you may contact us at any time as set forth in the "Contact Us" section below. D. Other Purposes We also use your information for other purposes as requested by you or as permitted by applicable law. Consent . We may use personal information for other purposes that are clearly disclosed to you at the time you provide personal information or with your consent. Automated Decision Making. We may engage in automated decision making, including profiling, such as to suggest topics or other Users for you to follow. DEV's processing of your personal information will not result in a decision based solely on automated processing that significantly affects you unless such a decision is necessary as part of a contract we have with you, we have your consent, or we are permitted by law to engage in such automated decision making. If you have questions about our automated decision making, you may contact us as set forth in the "Contact Us" section below. De-identified and Aggregated Information . We may use personal information and other information about you to create de-identified and/or aggregated information, such as de-identified demographic information, information about the device from which you access our Services, or other analyses we create. For example, we may collect system-wide information to ensure availability of the platform, or measure aggregate data trends to analyze and optimize our Services. Share Content with Friends or Colleagues. Our Services may offer various tools and functionalities. For example, we may allow you to provide information about your friends through our referral services. Our referral services may allow you to forward or share certain content with a friend or colleague, such as an email inviting your friend to use our Services. Please only share with us contact information of people with whom you have a relationship (e.g., relative, friend neighbor, or co-worker). 4. HOW WE DISCLOSE YOUR INFORMATION We disclose your information to third parties for a variety of business purposes, including to provide our Services, to protect us or others, or in the event of a major business transaction such as a merger, sale, or asset transfer, as described below. A. Disclosures to Provide our Services The categories of third parties with whom we may share your information are described below. Service Providers . We may share your personal information with our third-party service providers who use that information to help us provide our Services. This includes service providers that provide us with IT support, hosting, payment processing, customer service, and related services. For example, our Shop site is run by Shopify, who handle your shipping details on our behalf. Business Partners . We may share your personal information with business partners to provide you with a product or service you have requested. We may also share your personal information to business partners with whom we jointly offer products or services. Other Users . As described above in the "Personal Information We Collect" section of this Privacy Policy, our Service allows Users to share their profiles, and any posts, chats, etc. with other Users and with the general public, including to those who do not use our Services. APIs/SDKs . We may use third-party Application Program Interfaces ("APIs") and Software Development Kits ("SDKs") as part of the functionality of our Services. For more information about our use of APIs and SDKs, please contact us as set forth in the "Contact Us" section below. B . Disclosures to Protect Us or Others We may access, preserve, and disclose any information we store associated with you to external parties if we, in good faith, believe doing so is required or appropriate to: comply with law enforcement or national security requests and legal process, such as a court order or subpoena; protect your, our, or others' rights, property, or safety; enforce our policies or contracts; collect amounts owed to us; or assist with an investigation or prosecution of suspected or actual illegal activity. C. Disclosure in the Event of Merger, Sale, or Other Asset Transfers If we are involved in a merger, acquisition, financing due diligence, reorganization, bankruptcy, receivership, purchase or sale of assets, or transition of service to another provider, your information may be sold or transferred as part of such a transaction, as permitted by law and/or contract. 5. YOUR PRIVACY CHOICES AND RIGHTS Your Privacy Choices . The privacy choices you may have about your personal information are determined by applicable law and are described below. Email Communications . If you receive an unwanted email from us, you can use the unsubscribe link found at the bottom of the email to opt out of receiving future emails. Note that you will continue to receive transaction-related emails regarding products or Services you have requested. We may also send you certain non-promotional communications regarding us and our Services, and you will not be able to opt out of those communications (e.g., communications regarding our Services or updates to our Terms or this Privacy Policy). Mobile Devices . We may send you push notifications through our mobile application. You may opt out from receiving these push notifications by changing the settings on your mobile device. "Do Not Track." Do Not Track (" DNT ") is a privacy preference that users can set in certain web browsers. Please note that we do not respond to or honor DNT signals or similar mechanisms transmitted by web browsers. Cookies and Interest-Based Advertising . You may stop or restrict the placement of Technologies on your device or remove them by adjusting your preferences as your browser or device permits. However, if you adjust your preferences, our Services may not work properly. Please note that cookie-based opt-outs are not effective on mobile applications. Please note you must separately opt out in each browser and on each device. Your Privacy Rights . In accordance with applicable law, you may have the right to: Access Personal Information about you, including: (i) confirming whether we are processing your personal information; (ii) obtaining access to or a copy of your personal information; Request Correction of your personal information where it is inaccurate, incomplete or outdated. In some cases, we may provide self-service tools that enable you to update your personal information; Request Deletion, Anonymization or Blocking of your personal information when processing is based on your consent or when processing is unnecessary, excessive or noncompliant; Request Restriction of or Object to our processing of your personal information when processing is noncompliant; Withdraw Your Consent to our processing of your personal information. If you refrain from providing personal information or withdraw your consent to processing, some features of our Service may not be available; Request Data Portability and Receive an Electronic Copy of Personal Information that You Have Provided to Us; Be Informed about third parties with which your personal information has been shared; and Request the Review of Decisions Taken Exclusively Based on Automated Processing if such decisions could affect your data subject rights. If you would like to exercise any of these rights, please contact us as set forth in "Contact Us" below. We will process such requests in accordance with applicable laws. 6. INTERNATIONAL DATA TRANSFERS All information processed by us may be transferred, processed, and stored anywhere in the world, including, but not limited to, the United States or other countries, which may have data protection laws that are different from the laws where you live. We always strive to safeguard your information consistent with the requirements of applicable laws. 7. RETENTION OF PERSONAL INFORMATION We store the personal information we collect as described in this Privacy Policy for as long as you use our Services or as necessary: to fulfill the purpose or purposes for which it was collected, to provide our Services, to resolve disputes, to establish legal defenses, to conduct audits, to pursue legitimate business purposes, to enforce our agreements, and to comply with applicable laws. 8. SUPPLEMENTAL DISCLOSURES FOR CALIFORNIA RESIDENTS Refer-a-Friend and Similar Incentive Programs . As described above in the How We Use Your Personal Information section ("Share Content with Friends or Colleagues" subsection), we may offer referral programs or other incentivized data collection programs. For example, we may offer incentives to you such as discounts or promotional items or credit in connection with these programs, wherein you provide your personal information in exchange for a reward, or provide personal information regarding your friends or colleagues (such as their email address) and receive rewards when they sign up to use our Services. (The referred party may also receive rewards for signing up via your referral.) These programs are entirely voluntary and allow us to grow our business and provide additional benefits to you. The value of your data to us depends on how you ultimately use our Services, whereas the value of the referred party's data to us depends on whether the referred party ultimately becomes a User or Forem Operator and uses our Services. Said value will be reflected in the incentive offered in connection with each program. Accessibility . This Privacy Policy uses industry-standard technologies and was developed in line with the World Wide Web Consortium's Web Content Accessibility Guidelines, version 2.1* . * If you wish to print this policy, please do so from your web browser or by saving the page as a PDF. California Shine the Light . The California "Shine the Light" law permits users who are California residents to request and obtain from us once a year, free of charge, a list of the third parties to whom we have disclosed their personal information (if any) for their direct marketing purposes in the prior calendar year, as well as the type of personal information disclosed to those parties. Right for Minors to Remove Posted Content . Where required by law, California residents under the age of 18 may request to have their posted content or information removed from the publicly-viewable portions of the Services by contacting us directly as set forth in the "Contact Us" section below or by logging into their account and removing the content or information using our self-service tools. 9. SUPPLEMENTAL NOTICE FOR NEVADA RESIDENTS If you are a resident of Nevada, you have the right to opt-out of the sale of certain Personal Information to third parties who intend to license or sell that Personal Information. You can exercise this right by contacting us as set forth in the "Contact Us\" section below with the subject line "Nevada Do Not Sell Request" and providing us with your name and the email address associated with your account. Please note that we do not currently sell your Personal Information as sales are defined in Nevada Revised Statutes Chapter 603A. If you have any questions, please contact us as set forth below. 10. CHILDREN'S INFORMATION The Services are not directed to children under 13 (or other age as required by local law), and we do not knowingly collect personal information from children. If you are a parent or guardian and believe your child has uploaded personal information to our site without your consent, you may contact us as described in the "Contact Us" section below. If we become aware that a child has provided us with personal information in violation of applicable law, we will delete any personal information we have collected, unless we have a legal obligation to keep it, and terminate the child's account if applicable. 11. OTHER PROVISIONS Third-Party Websites or Applications . The Services may contain links to other websites or applications, and other websites or applications may reference or link to our Services. These third-party services are not controlled by us. We encourage our users to read the privacy policies of each website and application with which they interact. We do not endorse, screen or approve, and are not responsible for, the privacy practices or content of such other websites or applications. Providing personal information to third-party websites or applications is at your own risk. Changes to Our Privacy Policy . We may revise this Privacy Policy from time to time in our sole discretion. If there are any material changes to this Privacy Policy, we will notify you as required by applicable law. You understand and agree that you will be deemed to have accepted the updated Privacy Policy if you continue to use our Services after the new Privacy Policy takes effect. 12. CONTACT US If you have any questions about our privacy practices or this Privacy Policy, or to exercise your rights as detailed in this Privacy Policy, please contact us at: support@dev.to . 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV Future — News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Home About Contact Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . Future © 2025 - 2026. Stay on the cutting edge, and shape tomorrow Log in Create account | 2026-01-13T08:49:02 |
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https://future.forem.com/privacy#4-how-we-disclose-your-information | Privacy Policy - Future Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account Future Close Privacy Policy Last Updated: September 01, 2023 This Privacy Policy is designed to help you understand how DEV Community Inc. (" DEV ," " we ," or " us ") collects, use, and discloses your personal information. What's With the Defined Terms? You'll notice that some words appear in quotes in this Privacy Policy. They're called "defined terms," and we use them so that we don't have to repeat the same language again and again. They mean the same thing in every instance, to help us make sure that this Privacy Policy is consistent. We've included the defined terms throughout because we want it to be easy for you to read them in context. 1. WHAT DOES THIS PRIVACY POLICY APPLY TO? 2. PERSONAL INFORMATION WE COLLECT 3. HOW WE USE YOUR INFORMATION 4. HOW WE DISCLOSE YOUR INFORMATION 5. YOUR PRIVACY CHOICES AND RIGHTS 6. INTERNATIONAL DATA TRANSFERS 7. RETENTION OF PERSONAL INFORMATION 8. SUPPLEMENTAL DISCLOSURES FOR CALIFORNIA RESIDENTS 9. SUPPLEMENTAL NOTICE FOR NEVADA RESIDENTS 10. CHILDREN'S INFORMATION 11. OTHER PROVISIONS 12. CONTACT US 1. WHAT DOES THIS PRIVACY POLICY APPLY TO? This Privacy Policy applies to personal information processed by us, including on our websites, mobile applications, and other online or offline offerings — basically anything we do. To make this Privacy Policy easier to read, our websites, mobile applications, and other offerings are all collectively called the " Services. " Beyond this Privacy Policy, your use of the Services is subject to our DEV Community Terms and our Forem Terms. The Services include both our own community forum at https://www.dev.to (the " DEV Community ") and the open source tool we provide called " Forem ," available at https://www.forem.com which allows our customers to create and operate their own online forums. We collect personal information from two categories of people: (1) our customers, who use Forem and our hosting services to run and host their own forums (we'll call them " Forem Operators "), and (2) the people who interact with DEV-hosted forums, including forums provided by Forem Operators utilizing Forem and separately our own DEV Community (we'll call them " Users "). An Important Note for Users Since we provide hosting services for Forem Operators, technically we also process your information on their behalf. That processing is governed by the contracts that we have in place with each Forem Operator, not this Privacy Policy. In other words, when you share your data on a DEV-hosted forum operated by a Forem Operator, we at DEV are basically just the "pipes" — we process the data on behalf of the Forem Operator, but don't do anything with it ourselves beyond what we're required to do under our contract (and by law). So, if you post your information on a DEV-powered forum provided by a Forem Operator, that Forem Operator's privacy policy applies, and any questions or requests relating to your data on that service should be directed to that Forem Operator, not us. Likewise, if you use our mobile application, you may also interact with forums that use DEV's open-source tools but do all their hosting and data collection themselves. For those forums, we at DEV have no access to your data, so be sure to read the privacy policy of any third-party hosted forum before posting. 2. PERSONAL INFORMATION WE COLLECT The categories of personal information we collect depend on whether you're a User or Forem Operator, how you interact with us, our Services, and the requirements of applicable law. Breaking it down, we collect three types of information: (1) information that you provide to us directly, (2) information we obtain automatically when you use our Services, and (3) information we get about you from other sources (such as third-party services and organizations). More details are below. A. Information You Provide to Us Directly We may collect the following personal information that you provide to us. Account Creation (for Forem Operators): We'll require your name and email address to get started, as well as some details about the Forem you want to run, such as: whether you're running the Forem on your own behalf or as part of an organization, and details about the community you want to support (how big is it, what topics does it cover, where do members currently communicate, how/if the community earns money, whether the community is open, invite-only or paid, any existing social media accounts, etc.) You'll need to tell us a bit about your personal coding background, and you'll have the option to provide your DEV username as well, if you are a member of the DEV.to community. Account Creation (for Users) : We collect name and email address from users that create an account on DEV Community. For other forums created by Forem Operators using Forem, the Forem Operator determines what information is required for User account creation for their respective forums. Interactive Features (for Users) . Like any other social network, both we and other Users of our Services may collect personal information that you submit or make available through our interactive features (e.g., messaging and chat features, commenting functionalities, forums, blogs, posts, and other social media pages). While we do have private messages that are only between you and the person you're messaging (as well as us and the Forem Operator, as applicable), any information you provide using the public sharing features of the Services, such as the information you post to your public profile or the topics you follow is public, including to recruiters and prospective employers, and is not subject to any of the privacy protections we mention in this Privacy Policy except where legally required. Please exercise caution before revealing any information that may identify you in the real world to others. Purchases . If you buy stuff on our shop site https://shop.dev.to/ (as either a User or Forem Operator), or otherwise if you pay us in connection with your use of the Forem service, we may collect personal information and details associated with your purchases, including payment information. Any payments made via our Services are processed by third-party payment processors, such as Stripe, Shopify, and PayPal. We do not directly collect or store any payment card information entered through our Services, but may receive information associated with your payment card information (e.g., your billing details). Your Communications with Us (Users and Forem Operators) . We may collect personal information, such as email address, phone number, or mailing address when you request information about our Services, register for our newsletter or loyalty program, request customer or technical support, apply for a job, or otherwise communicate with us. Surveys . We may contact you to participate in surveys. If you decide to participate, you may be asked to provide certain information, which may include personal information (for example, your home address). Sweepstakes or Contests . We may collect personal information you provide for any sweepstakes or contests that we offer. In some jurisdictions, we are required to publicly share information of sweepstakes and contest winners. Conferences, Trade Shows, and Other Events . We may collect personal information from individuals when we attend conferences, trade shows, and other events. Business Development and Strategic Partnerships . We may collect personal information from individuals and third parties to assess and pursue potential business opportunities. Job Applications . We may post job openings and opportunities on our Services. If you reply to one of these postings by submitting your application, CV and/or cover letter to us, we will collect and use your information to assess your qualifications. B. Information Collected Automatically We may collect personal information automatically when you use our Services: Automatic Data Collection . We may collect certain information automatically when you use our Services, such as your Internet protocol (IP) address, user settings, MAC address, cookie identifiers, mobile carrier, mobile advertising and other unique identifiers, browser or device information, location information (including approximate location derived from IP address), and Internet service provider. We may also automatically collect information regarding your use of our Services, such as pages that you visit before, during and after using our Services, information about the links you click, the types of content you interact with, the frequency and duration of your activities, and other information about how you use our Services. In addition, we may collect information that other people provide about you when they use our Services, including information about you when they tag you in their posts. Cookies, Pixel Tags/Web Beacons, and Other Technologies . We, as well as third parties that provide content, advertising, or other functionality on our Services, may use cookies, pixel tags, local storage, and other technologies (" Technologies ") to automatically collect information through your use of our Services. Cookies . Cookies are small text files placed in device browsers that store preferences and facilitate and enhance your experience. Pixel Tags/Web Beacons . A pixel tag (also known as a web beacon) is a piece of code embedded in our Services that collects information about engagement on our Services. The use of a pixel tag allows us to record, for example, that a user has visited a particular web page or clicked on a particular advertisement. We may also include web beacons in e-mails to understand whether messages have been opened, acted on, or forwarded. Our uses of these Technologies fall into the following general categories: Operationally Necessary . This includes Technologies that allow you access to our Services, applications, and tools that are required to identify irregular website behavior, prevent fraudulent activity and improve security or that allow you to make use of our functionality. Performance-Related . We may use Technologies to assess the performance of our Services, including as part of our analytic practices to help us understand how individuals use our Services ( see Analytics below ). Functionality-Related . We may use Technologies that allow us to offer you enhanced functionality when accessing or using our Services. This may include identifying you when you sign into our Services or keeping track of your specified preferences, interests, or past items viewed. Analytics . 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https://docs.python.org/ja/3/ | 3.14.2 Documentation テーマ 自動 ライト ダーク ダウンロード これらのドキュメントのダウンロード バージョンごとのドキュメント Python 3.15 (in development) Python 3.14 (stable) Python 3.13 (stable) Python 3.12 (security-fixes) Python 3.11 (security-fixes) Python 3.10 (security-fixes) Python 3.9 (EOL) Python 3.8 (EOL) Python 3.7 (EOL) Python 3.6 (EOL) Python 3.5 (EOL) Python 3.4 (EOL) Python 3.3 (EOL) Python 3.2 (EOL) Python 3.1 (EOL) Python 3.0 (EOL) Python 2.7 (EOL) Python 2.6 (EOL) 全てのバージョン その他のリソース PEP Index Beginner's Guide Book List Audio/Visual Talks Python Developer’s Guide ナビゲーション 索引 モジュール | Python » 3.14.2 Documentation » | テーマ 自動 ライト ダーク | Python 3.14.2 ドキュメント ようこそ! Python 3.14.2 公式ドキュメントへ。 ドキュメント: What's new in Python 3.14? または Python 2.0 以降の全ての "What's new" ドキュメント チュートリアル ここから始めましょう: Python構文と機能のツアー ライブラリ リファレンス 標準ライブラリとビルトイン 言語リファレンス 構文と言語要素 Pythonのセットアップと使い方 Pythonのインストール方法、configure方法、使い方 Python HOWTO トピックのより深いマニュアル Python モジュールのインストール サードパーティモジュールと PyPI.org Python モジュールの配布 他の人が使えるようにモジュールを公開する 拡張と埋め込み C/C++ プログラマ向け Python の C API C API リファレンス FAQ よくある質問(解答つき!) 非推奨 非推奨となった機能 索引、用語集、検索: 全モジュール索引 全てのモジュールとライブラリ 総合索引 全ての関数、クラス、用語 用語集 用語の説明 検索 このドキュメントから検索する 全体の目次 章/節一覧 プロジェクト情報: 問題の報告 ドキュメントへの貢献 ドキュメントのダウンロード Python の歴史とライセンス Copyright このドキュメントについて ダウンロード これらのドキュメントのダウンロード バージョンごとのドキュメント Python 3.15 (in development) Python 3.14 (stable) Python 3.13 (stable) Python 3.12 (security-fixes) Python 3.11 (security-fixes) Python 3.10 (security-fixes) Python 3.9 (EOL) Python 3.8 (EOL) Python 3.7 (EOL) Python 3.6 (EOL) Python 3.5 (EOL) Python 3.4 (EOL) Python 3.3 (EOL) Python 3.2 (EOL) Python 3.1 (EOL) Python 3.0 (EOL) Python 2.7 (EOL) Python 2.6 (EOL) 全てのバージョン その他のリソース PEP Index Beginner's Guide Book List Audio/Visual Talks Python Developer’s Guide « ナビゲーション 索引 モジュール | Python » 3.14.2 Documentation » | テーマ 自動 ライト ダーク | © Copyright 2001 Python Software Foundation. このページはPython Software Foundation License Version 2でライセンスされています。 ドキュメント内のサンプル、レシピ等のコードは、Zero Clause BSDライセンスの下で追加的にライセンスされています。 詳しくは 歴史とライセンス を参照してください。 Pythonソフトウェア財団は非営利法人です。 寄付をするには 最終更新日: 1月 13, 2026 (06:47 UTC) バグを報告する Sphinx 8.2.3を使って構築されています。 | 2026-01-13T08:49:02 |
https://peps.python.org/pep-0010/ | PEP 10 – Voting Guidelines | peps.python.org Following system colour scheme Selected dark colour scheme Selected light colour scheme Python Enhancement Proposals Python » PEP Index » PEP 10 Toggle light / dark / auto colour theme PEP 10 – Voting Guidelines Author : Barry Warsaw <barry at python.org> Status : Active Type : Process Created : 07-Mar-2002 Post-History : 07-Mar-2002 Table of Contents Abstract Rationale Voting Scores References Copyright Abstract This PEP outlines the python-dev voting guidelines. These guidelines serve to provide feedback or gauge the “wind direction” on a particular proposal, idea, or feature. They don’t have a binding force. Rationale When a new idea, feature, patch, etc. is floated in the Python community, either through a PEP or on the mailing lists (most likely on python-dev [1] ), it is sometimes helpful to gauge the community’s general sentiment. Sometimes people just want to register their opinion of an idea. Sometimes the BDFL wants to take a straw poll. Whatever the reason, these guidelines have been adopted so as to provide a common language for developers. While opinions are (sometimes) useful, but they are never binding. Opinions that are accompanied by rationales are always valued higher than bare scores (this is especially true with -1 votes). Voting Scores The scoring guidelines are loosely derived from the Apache voting procedure [2] , with of course our own spin on things. There are 4 possible vote scores: +1 I like it +0 I don’t care, but go ahead -0 I don’t care, so why bother? -1 I hate it You may occasionally see wild flashes of enthusiasm (either for or against) with vote scores like +2, +1000, or -1000. These aren’t really valued much beyond the above scores, but it’s nice to see people get excited about such geeky stuff. References [ 1 ] Python Developer’s Guide, ( http://www.python.org/dev/ ) [ 2 ] Apache Project Guidelines and Voting Rules ( http://httpd.apache.org/dev/guidelines.html ) Copyright This document has been placed in the public domain. Source: https://github.com/python/peps/blob/main/peps/pep-0010.rst Last modified: 2025-02-01 08:55:40 GMT Contents Abstract Rationale Voting Scores References Copyright Page Source (GitHub) | 2026-01-13T08:49:02 |
https://peps.python.org/pep-0602/ | PEP 602 – Annual Release Cycle for Python | peps.python.org Following system colour scheme Selected dark colour scheme Selected light colour scheme Python Enhancement Proposals Python » PEP Index » PEP 602 Toggle light / dark / auto colour theme PEP 602 – Annual Release Cycle for Python Author : Łukasz Langa <lukasz at python.org> PEP-Delegate : Brett Cannon <brett at python.org> Discussions-To : Discourse thread Status : Active Type : Process Created : 04-Jun-2019 Python-Version : 3.9 Post-History : 09-Oct-2023 Resolution : Python-Dev thread Table of Contents Abstract Implementation Seventeen months to develop a feature version 2 years of full support, 3 more years of security fixes Annual release cadence Example Dependent Policies Deprecations The term of the Steering Council The term of the Release Manager Rationale and Goals Non-goals Non-risks Risks Python redistribution The testing matrix Other policies may depend on the release cadence Rejected Ideas Keep the current 18 month release cadence Double the release cadence to achieve 9 months between feature versions Keep “4 betas over 4 months and a final month for the release candidate” Slow down releases but don’t freeze feature development with Beta 1 Long-Term Support Releases Copyright Abstract This document describes a change in the release calendar for Python starting with Python 3.9. This change accelerates the release cadence such that feature versions are released predictably every twelve months, in October every year. Implementation Seventeen months to develop a feature version This PEP proposes that Python 3.X.0 will be developed for around 17 months: The first five months overlap with Python 3.(X-1).0’s beta and release candidate stages and are thus unversioned. The next seven months are spent on versioned alpha releases where both new features are incrementally added and bug fixes are included. The following three months are spent on four versioned beta releases where no new features can be added but bug fixes are still included. The final two months are spent on two release candidates (or more, if necessary) and conclude with the release of the final release of Python 3.X.0. 2 years of full support, 3 more years of security fixes After the release of Python 3.X.0, the 3.X series is maintained for five years: During the first twenty four months (2 years) it receives bugfix updates and full releases (sources and installers for Windows and macOS) are made approximately every other month. For the next thirty six months (3 years) it receives security updates and source-only releases are made on an as-needed basis (no fixed cadence). The final source-only release is made five years after 3.X.0. Note: 2 years of full support start with Python 3.13 . Python versions 3.9 - 3.12 operate on a calendar with 1½ year of full support, followed by 3½ more years of security fixes. Annual release cadence Feature development of Python 3.(X+1).0 starts as soon as Python 3.X.0 Beta 1 is released. This creates a twelve-month delta between Python feature versions. Example 3.9 development begins: Tuesday, 2019-06-04 3.9.0 alpha 1: Monday, 2019-10-14 3.9.0 alpha 2: Monday, 2019-11-18 3.9.0 alpha 3: Monday, 2019-12-16 3.9.0 alpha 4: Monday, 2020-01-13 3.9.0 alpha 5: Monday, 2020-02-17 3.9.0 alpha 6: Monday, 2020-03-16 3.9.0 alpha 7: Monday, 2020-04-13 3.9.0 beta 1: Monday, 2020-05-18 (No new features beyond this point.) 3.9.0 beta 2: Monday, 2020-06-08 3.9.0 beta 3: Monday, 2020-06-29 3.9.0 beta 4: Monday, 2020-07-20 3.9.0 candidate 1: Monday, 2020-08-10 3.9.0 candidate 2: Monday, 2020-09-14 3.9.0 final: Monday, 2020-10-05 Figure 1. Consequences of the annual release cycle on the calendar. In comparison, if this PEP is rejected and Python keeps the current release schedule: 3.9 development begins: Tuesday, 2019-06-04 3.9.0 alpha 1: Monday, 2020-08-03 (10 months later) 3.9.0 alpha 2: Monday, 2020-09-07 3.9.0 alpha 3: Monday, 2020-10-05 3.9.0 alpha 4: Monday, 2020-11-02 3.9.0 beta 1: Monday, 2020-11-30 (6 months later) 3.9.0 beta 2: Monday, 2021-01-04 3.9.0 beta 3: Monday, 2021-02-01 3.9.0 beta 4: Monday, 2021-03-01 3.9.0 candidate 1: Monday, 2021-03-29 3.9.0 candidate 2: Monday, 2021-04-05 (if necessary) 3.9.0 final: Monday, 2021-04-19 (6 months later) Dependent Policies Deprecations The current policy around breaking changes assumes at least two releases before a deprecated feature is removed from Python or a __future__ behavior is enabled by default. This is documented in PEP 387 . This PEP proposes to keep this policy of at least two releases before making a breaking change. The term of the Steering Council The current wording of PEP 13 states that “a new council is elected after each feature release”. This PEP proposes to keep this policy as it will lead to a consistent election schedule. The term of the Release Manager The current undocumented convention is for a single Release Manager to handle two feature releases of Python. This PEP proposes to keep this policy, allowing for the term to be extended to more releases with approval from the Steering Council and the Cabal of Release Managers. In particular, since this PEP is authored by the active Release Manager and its effect would shorten the term of the Release Manager, the author is open to managing the release of a third feature release to compensate for the disruption. Rationale and Goals This change provides the following advantages: makes releases smaller: since doubling the cadence doesn’t double our available development resources, consecutive releases are going to be smaller in terms of features; puts features and bug fixes in hands of users sooner; creates a more gradual upgrade path for users, by decreasing the surface of change in any single release; creates a predictable calendar for releases where the final release is always in October (so after the annual core sprint), and the beta phase starts in late May (so after PyCon US sprints), which is especially important for core developers who need to plan to include Python involvement in their calendar; decreases the urge to rush features shortly before “Beta 1” due to the risk of them “slipping for 18 months”; allows for synchronizing the schedule of Python release management with external distributors like Fedora who’ve been historically very helpful in finding regressions early not only in core Python but also in third-party libraries, helping moving the community forward to support the latest version of Python from Day 1; increases the explicit alpha release phase, which provides meaningful snapshots of progress on new features; significantly cuts the implicit “alpha 0” release phase which provides limited use for new development anyway (it overlaps with the beta of the currently developed , still unreleased, version). Non-goals Adopting an annual release calendar allows for natural switching to calendar versioning, for example by calling Python 3.9 “Python 3.20” since it’s released in October ‘20 and so on (“Python 3.23” would be the one released in October ‘23). While the ease of switching to calendar versioning can be treated as an advantage of an annual release cycle, this PEP does not advocate for or against a change in how Python is versioned. Should the annual release cycle be adopted, the versioning question will be dealt with in a separate PEP. Non-risks This change does not shorten the currently documented support calendar for a Python release, both in terms of bugfix releases and security fixes. This change does not accelerate the velocity of development. Python is not going to become incompatible faster or accrue new features faster. It’s just that features are going to be released more gradually as they are developed. Consequently, while this change introduces the ability for users to upgrade much faster, it does not require them to do so. Say, if they upgrade every second release, their experience with Python is going to be similar to the current situation. Risks Python redistribution This requires changes to how integrators, like Linux distributions, release Python within their systems. The testing matrix This eventually increases the testing matrix for library and application maintainers that want to support all actively supported Python versions by one or two: Figure 2. Testing matrix in the 18-month cadence vs. the 12-month The “extended bugfix support at the discretion of the Release Manager” stage of the current release cycle is not codified. If fact, PEP 101 currently states that after the release of Python 3.(X+1).0 only one last bugfix release is made for Python 3.X.0. However, in practice at least the last four versions of Python 3 overlapped with stable releases of the next version for around six months. Figure 2 is including this information to demonstrate that overlap between stable version releases with the 12-month release cadence will be nothing new. Other policies may depend on the release cadence Although identified dependent policies were addressed in a previous section, it is entirely possible there are some other areas which implicitly rely on the timing of Python releases. Rejected Ideas Keep the current 18 month release cadence This is undesirable both for core developers and end users. From the perspective of the core developer: it makes contribution scheduling harder due to irregular release dates every year; it creates a surge of rushed commits before (and even after!) Beta 1 due to the stress involved with “missing a release”; ironically, after Beta 1 it creates a false sense of having “plenty of time” before the next release, time that passes quickly regardless; it causes certain elements of the workflow to be executed so rarely that they are not explicitly documented, let alone automated. More importantly, from the perspective of the user: it creates releases with many new features, some being explicitly incompatible and some being accidentally incompatible, which makes the upgrade cost relatively high every time; it sits on features and incompatible bug fixes for over a year before becoming available to the user; and more specifically it causes every “point zero” release to be extra risky for users. While we provide and recommend testing with alphas and betas, “point zero” is the first release of a given Python version for many users. The bigger a release is feature-wise, the more potential problems are hiding in “point zero releases”. Double the release cadence to achieve 9 months between feature versions This was originally proposed in PEP 596 and rejected as both too irregular and too short. This would not give any of the benefits of a regular release calendar but it would shorten all development phases, especially the beta + RC phases. This was considered dangerous. Keep “4 betas over 4 months and a final month for the release candidate” While this would make the release calendar a bit cleaner, it would make it very hard for external distributors like Fedora to release the newest version of Python as soon as possible. We are adjusting Python’s calendar here in the hope that this will enable Fedora to integrate the newest version of Python with the newest version of Fedora as both are being developed which makes both projects better. Slow down releases but don’t freeze feature development with Beta 1 This is described in PEP 598 . This proposal includes non-standard concepts like the “incremental feature release” which makes it hard to understand. The presented advantages are unclear while the unfamiliarity of the scheme poses a real risk of user and integrator confusion. Long-Term Support Releases Each version of Python is effectively long-term support: it’s supported for five years, with the first eighteen months allowing regular bug fixes and security updates. For the remaining time security updates are accepted and promptly released. No extended support in the vein of Python 2.7 is planned going forward. Copyright This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive. Source: https://github.com/python/peps/blob/main/peps/pep-0602.rst Last modified: 2024-05-28 05:47:01 GMT Contents Abstract Implementation Seventeen months to develop a feature version 2 years of full support, 3 more years of security fixes Annual release cadence Example Dependent Policies Deprecations The term of the Steering Council The term of the Release Manager Rationale and Goals Non-goals Non-risks Risks Python redistribution The testing matrix Other policies may depend on the release cadence Rejected Ideas Keep the current 18 month release cadence Double the release cadence to achieve 9 months between feature versions Keep “4 betas over 4 months and a final month for the release candidate” Slow down releases but don’t freeze feature development with Beta 1 Long-Term Support Releases Copyright Page Source (GitHub) | 2026-01-13T08:49:02 |
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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account Future Close Privacy Policy Last Updated: September 01, 2023 This Privacy Policy is designed to help you understand how DEV Community Inc. (" DEV ," " we ," or " us ") collects, use, and discloses your personal information. What's With the Defined Terms? You'll notice that some words appear in quotes in this Privacy Policy. They're called "defined terms," and we use them so that we don't have to repeat the same language again and again. They mean the same thing in every instance, to help us make sure that this Privacy Policy is consistent. We've included the defined terms throughout because we want it to be easy for you to read them in context. 1. WHAT DOES THIS PRIVACY POLICY APPLY TO? 2. PERSONAL INFORMATION WE COLLECT 3. HOW WE USE YOUR INFORMATION 4. HOW WE DISCLOSE YOUR INFORMATION 5. 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https://peps.python.org/pep-0676/ | PEP 676 – PEP Infrastructure Process | peps.python.org Following system colour scheme Selected dark colour scheme Selected light colour scheme Python Enhancement Proposals Python » PEP Index » PEP 676 Toggle light / dark / auto colour theme PEP 676 – PEP Infrastructure Process Author : Adam Turner <adam at python.org> Sponsor : Mariatta <mariatta at python.org> PEP-Delegate : Barry Warsaw <barry at python.org> Discussions-To : Discourse thread Status : Active Type : Process Created : 01-Nov-2021 Post-History : 23-Sep-2021, 30-Nov-2021 Resolution : Discourse message Table of Contents Abstract Motivation Rationale Simplifying and Centralising Infrastructure Quality-of-Life Improvements and Resolving Issues Specification Backwards Compatibility Security Implications How to Teach This Reference Implementation Rejected Ideas Acknowledgements Footnotes Copyright Abstract This PEP addresses the infrastructure around rendering PEP files from reStructuredText files to HTML webpages. We aim to specify a self-contained and maintainable solution for PEP readers, authors, and editors. Motivation As of November 2021, Python Enhancement Proposals (PEPs) are rendered in a multi-system, multi-stage process. A continuous integration (CI) task runs a docutils script to render all PEP files individually. The CI task then uploads a tar archive to a server, where it is retrieved and rendered into the python.org website periodically. This places a constraint on the python.org website to handle raw HTML uploads and handle PEP rendering, and makes the appropriate place to raise issues unclear in some cases [1] . This PEP provides a specification for self-contained rendering of PEPs. This would: reduce the amount of distributed configuration for supporting PEPs enable quality-of-life improvements for those who read, write, and review PEPs solve a number of outstanding issues, and lay the path for improvements save volunteer maintainers’ time We propose that PEPs are accessed through peps.python.org at the top-level (for example peps.python.org/pep-0008 ), and that all custom tooling to support rendering PEPs is hosted in the python/peps repository. Rationale Simplifying and Centralising Infrastructure As of November 2021, to locally render a PEP file, a PEP author or editor needs to create a full local instance of the python.org website and run a number of disparate scripts, following documentation that lives outside of the python/peps repository. By contrast, the proposed implementation provides a single Makefile and a Python script to render all PEP files, with options to target a web-server or the local filesystem. Using a single repository to host all tooling will clarify where to raise issues, reducing volunteer time spent in triage. Simplified and centralised tooling may also reduce the barrier to entry to further improvements, as the scope of the PEP rendering infrastructure is well defined. Quality-of-Life Improvements and Resolving Issues There are several requests for additional features in reading PEPs, such as: syntax highlighting [2] use of .. code-block:: directives [2] support for SVG images [3] typographic quotation marks [4] additional footer information [5] intersphinx functionality [6] dark mode theme [7] These are “easy wins” from this proposal, and would serve to improve the quality-of-life for consumers of PEPs (including reviewers and writers). For example, the current (as of November 2021) system runs periodically on a schedule. This means that updates to PEPs cannot be circulated immediately, reducing productivity. The reference implementation renders and publishes all PEPs on every commit to the repository, solving the issue by design. The reference implementation fixes several issues [8] . For example: list styles are currently not respected by python.org ’s stylesheets support for updating images in PEPs is challenging in python.org Third-party providers such as Read the Docs or Netlify can enhance this experience with features such as automatic rendering of pull requests. Specification The proposed specification for rendering the PEP files to HTML is as per the reference implementation . The rendered PEPs MUST be available at peps.python.org . These SHOULD be hosted as static files, and MAY be behind a content delivery network (CDN). A service to render previews of pull requests SHOULD be provided. This service MAY be integrated with the hosting and deployment solution. The following redirect rules MUST be created for the python.org domain: /peps/ -> https://peps.python.org/ /dev/peps/ -> https://peps.python.org/ /peps/(.*)\.html -> https://peps.python.org/$1 /dev/peps/(.*) -> https://peps.python.org/$1 The following nginx configuration would achieve this: location ~ ^/dev/peps/?(.*)$ { return 308 https://peps.python.org/ $1/ ; } location ~ ^/peps/(.*)\.html$ { return 308 https://peps.python.org/ $1/ ; } location ^/(dev/)?peps(/.*)? $ { return 308 https://peps.python.org/ ; } Redirects MUST be implemented to preserve URL fragments for backward compatibility purposes. Backwards Compatibility Due to server-side redirects to the new canonical URLs, links in previously published materials referring to the old URL schemes will be guaranteed to work. All PEPs will continue to render correctly, and a custom stylesheet in the reference implementation improves presentation for some elements (most notably code blocks and block quotes). Therefore, this PEP presents no backwards compatibility issues. Security Implications The main python.org website will no longer process raw HTML uploads, closing a potential threat vector. PEP rendering and deployment processes will use modern, well-maintained code and secure automated platforms, further reducing the potential attack surface. Therefore, we see no negative security impact. How to Teach This The new canonical URLs will be publicised in the documentation. However, this is mainly a backend infrastructure change, and there should be minimal end-user impact. PEP 1 and PEP 12 will be updated as needed. Reference Implementation The proposed implementation has been merged into the python/peps repository in a series of pull requests [9] . It uses the Sphinx documentation system with a custom theme (supporting light and dark colour schemes) and extensions. This already automatically renders all PEPs on every commit, and publishes them to python.github.io/peps . The high level documentation for the system covers how to render PEPs locally and the implementation of the system . Rejected Ideas It would likely be possible to amend the current (as of November 2021) rendering process to include a subset of the quality-of-life improvements and issue mitigations mentioned above. However, we do not believe that this would solve the distributed tooling issue. It would be possible to use the output from the proposed rendering system and import it into python.org . We would argue that this would be the worst of both worlds, as a great deal of complexity is added whilst none is removed. Acknowledgements Hugo van Kemenade Pablo Galindo Salgado Éric Araujo Mariatta C.A.M. Gerlach Footnotes [ 1 ] For example, pythondotorg#1024 , pythondotorg#1038 , pythondotorg#1387 , pythondotorg#1388 , pythondotorg#1393 , pythondotorg#1564 , pythondotorg#1913 , [2] ( 1 , 2 ) Requested: pythondotorg#1063 , pythondotorg#1206 , pythondotorg#1638 , peps#159 , comment in peps#1571 , peps#1577 , [ 3 ] Requested: peps#701 [ 4 ] Requested: peps#165 [ 5 ] Requested: pythondotorg#1564 [ 6 ] Requested: comment in peps#2 [ 7 ] Requested: in python-dev [ 8 ] As of November 2021, see peps#1387 , pythondotorg#824 , pythondotorg#1556 , [ 9 ] Implementation PRs: peps#1930 , peps#1931 , peps#1932 , peps#1933 , peps#1934 Copyright This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive. Source: https://github.com/python/peps/blob/main/peps/pep-0676.rst Last modified: 2025-08-08 15:00:59 GMT Contents Abstract Motivation Rationale Simplifying and Centralising Infrastructure Quality-of-Life Improvements and Resolving Issues Specification Backwards Compatibility Security Implications How to Teach This Reference Implementation Rejected Ideas Acknowledgements Footnotes Copyright Page Source (GitHub) | 2026-01-13T08:49:02 |
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https://caniuse.com/?search=Temporal | "Temporal" | Can I use... Support tables for HTML5, CSS3, etc Home News Compare browsers About January 10, 2026 - New feature: CSS Grid Lanes Can I use Search ? Settings 50 results found. Temporal A modern API for working with date and time, meant to supersede the original `Date` API. javascript built-in: temporal api javascript built-in: temporal api: duration javascript built-in: temporal api: duration: abs javascript built-in: temporal api: duration: add javascript built-in: temporal api: duration: compare javascript built-in: temporal api: duration: days javascript built-in: temporal api: duration: `duration()` constructor javascript built-in: temporal api: duration: from javascript built-in: temporal api: duration: sign javascript built-in: temporal api: duration: with javascript built-in: temporal api: instant javascript built-in: temporal api: instant: add javascript built-in: temporal api: instant: compare javascript built-in: temporal api: instant: from javascript built-in: temporal api: instant: fromepochmilliseconds javascript built-in: temporal api: instant: fromepochnanoseconds javascript built-in: temporal api: instant: `instant()` constructor javascript built-in: temporal api: instant: round javascript built-in: temporal api: instant: since javascript built-in: temporal api: instant: until javascript built-in: temporal api: now javascript built-in: temporal api: now: instant javascript built-in: temporal api: now: plaindateiso javascript built-in: temporal api: now: plaindatetimeiso javascript built-in: temporal api: now: plaintimeiso javascript built-in: temporal api: now: timezoneid javascript built-in: temporal api: now: zoneddatetimeiso javascript built-in: temporal api: plaindate javascript built-in: temporal api: plaindatetime javascript built-in: temporal api: plaindatetime: compare javascript built-in: temporal api: plaindatetime: from javascript built-in: temporal api: plaindatetime: `plaindatetime()` constructor javascript built-in: temporal api: plaindate: compare javascript built-in: temporal api: plaindate: from javascript built-in: temporal api: plaindate: `plaindate()` constructor javascript built-in: temporal api: plainmonthday javascript built-in: temporal api: plainmonthday: from javascript built-in: temporal api: plainmonthday: `plainmonthday()` constructor javascript built-in: temporal api: plaintime javascript built-in: temporal api: plaintime: compare javascript built-in: temporal api: plaintime: from javascript built-in: temporal api: plainyearmonth javascript built-in: temporal api: plainyearmonth: compare javascript built-in: temporal api: plainyearmonth: from javascript built-in: temporal api: plainyearmonth: `plainyearmonth()` constructor javascript built-in: temporal api: zoneddatetime javascript built-in: temporal api: zoneddatetime: compare javascript built-in: temporal api: zoneddatetime: from javascript built-in: temporal api: zoneddatetime: `zoneddatetime()` constructor Can I use... Browser support tables for modern web technologies Created & maintained by @Fyrd , design by @Lensco . Support data contributions by the GitHub community . Usage share statistics by StatCounter GlobalStats for December, 2025 Location detection provided by ipinfo.io . Browser testing done via Support via Patreon Become a caniuse Patron to support the site and disable ads for only $1/month! or Log in Site links Home Feature index Browser usage table Feature suggestion list Caniuse data on GitHub Legend Green ✅ = Supported Red ❌ = Not supported Greenish yellow ◐ = Partial support Gray ﹖ = Support unknown | 2026-01-13T08:49:02 |
https://www.youtube.com/watch?v=5X-WEQflCL0&list=PLNG_1j3cPCaZZ7etkzWA7JfdmKWT0pMsa&index=9 | Learning in the Browser - YouTube 정보 보도자료 저작권 문의하기 크리에이터 광고 개발자 약관 개인정보처리방침 정책 및 안전 YouTube 작동의 원리 새로운 기능 테스트하기 © 2026 Google LLC, Sundar Pichai, 1600 Amphitheatre Parkway, Mountain View CA 94043, USA, 0807-882-594 (무료), yt-support-solutions-kr@google.com, 호스팅: Google LLC, 사업자정보 , 불법촬영물 신고 크리에이터들이 유튜브 상에 게시, 태그 또는 추천한 상품들은 판매자들의 약관에 따라 판매됩니다. 유튜브는 이러한 제품들을 판매하지 않으며, 그에 대한 책임을 지지 않습니다. var ytInitialData = {"responseContext":{"serviceTrackingParams":[{"service":"CSI","params":[{"key":"c","value":"WEB"},{"key":"cver","value":"2.20260109.01.00"},{"key":"yt_li","value":"0"},{"key":"GetWatchNext_rid","value":"0xdc22466ec17d035b"}]},{"service":"GFEEDBACK","params":[{"key":"logged_in","value":"0"},{"key":"visitor_data","value":"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%3D%3D"}]},{"service":"GUIDED_HELP","params":[{"key":"logged_in","value":"0"}]},{"service":"ECATCHER","params":[{"key":"client.version","value":"2.20260109"},{"key":"client.name","value":"WEB"}]}],"mainAppWebResponseContext":{"loggedOut":true,"trackingParam":"kx_fmPxhoPZRvDRjYx1IYjEZk5sCYCevI50mAyi48to0gjHRgkussh7BwOcCE59TDtslLKPQ-SS"},"webResponseContextExtensionData":{"webResponseContextPreloadData":{"preloadMessageNames":["twoColumnWatchNextResults","results","videoPrimaryInfoRenderer","videoViewCountRenderer","menuRenderer","menuServiceItemRenderer","segmentedLikeDislikeButtonViewModel","likeButtonViewModel","toggleButtonViewModel","buttonViewModel","modalWithTitleAndButtonRenderer","buttonRenderer","dislikeButtonViewModel","unifiedSharePanelRenderer","menuFlexibleItemRenderer","videoSecondaryInfoRenderer","videoOwnerRenderer","subscribeButtonRenderer","subscriptionNotificationToggleButtonRenderer","menuPopupRenderer","confirmDialogRenderer","metadataRowContainerRenderer","compositeVideoPrimaryInfoRenderer","itemSectionRenderer","messageRenderer","secondaryResults","lockupViewModel","thumbnailViewModel","thumbnailOverlayBadgeViewModel","thumbnailBadgeViewModel","thumbnailHoverOverlayToggleActionsViewModel","lockupMetadataViewModel","decoratedAvatarViewModel","avatarViewModel","contentMetadataViewModel","sheetViewModel","listViewModel","listItemViewModel","badgeViewModel","continuationItemRenderer","autoplay","playerOverlayRenderer","menuNavigationItemRenderer","watchNextEndScreenRenderer","endScreenVideoRenderer","thumbnailOverlayTimeStatusRenderer","thumbnailOverlayNowPlayingRenderer","playerOverlayAutoplayRenderer","playerOverlayVideoDetailsRenderer","autoplaySwitchButtonRenderer","quickActionsViewModel","decoratedPlayerBarRenderer","multiMarkersPlayerBarRenderer","chapterRenderer","notificationActionRenderer","speedmasterEduViewModel","engagementPanelSectionListRenderer","adsEngagementPanelContentRenderer","engagementPanelTitleHeaderRenderer","chipBarViewModel","chipViewModel","sectionListRenderer","macroMarkersListRenderer","macroMarkersInfoItemRenderer","macroMarkersListItemRenderer","toggleButtonRenderer","structuredDescriptionContentRenderer","videoDescriptionHeaderRenderer","factoidRenderer","viewCountFactoidRenderer","expandableVideoDescriptionBodyRenderer","horizontalCardListRenderer","richListHeaderRenderer","videoDescriptionTranscriptSectionRenderer","videoDescriptionInfocardsSectionRenderer","desktopTopbarRenderer","topbarLogoRenderer","fusionSearchboxRenderer","topbarMenuButtonRenderer","multiPageMenuRenderer","hotkeyDialogRenderer","hotkeyDialogSectionRenderer","hotkeyDialogSectionOptionRenderer","voiceSearchDialogRenderer","cinematicContainerRenderer"]},"ytConfigData":{"visitorData":"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%3D%3D","rootVisualElementType":3832},"webPrefetchData":{"navigationEndpoints":[{"clickTrackingParams":"CAAQg2ciEwjkmvD_kIiSAxVkSA8CHVniKtMyDHJlbGF0ZWQtYXV0b0i9kZS_kMLlv-UBmgEFCAMQ-B3KAQRipkXm","commandMetadata":{"webCommandMetadata":{"url":"/watch?v=ZcwA0xt8FlQ\u0026pp=QAFIAQ%3D%3D","webPageType":"WEB_PAGE_TYPE_WATCH","rootVe":3832}},"watchEndpoint":{"videoId":"ZcwA0xt8FlQ","params":"EAEYAdoBBAgBKgA%3D","playerParams":"QAFIAQ%3D%3D","watchEndpointSupportedPrefetchConfig":{"prefetchHintConfig":{"prefetchPriority":0,"countdownUiRelativeSecondsPrefetchCondition":-3}}}},{"clickTrackingParams":"CAAQg2ciEwjkmvD_kIiSAxVkSA8CHVniKtMyDHJlbGF0ZWQtYXV0b0i9kZS_kMLlv-UBmgEFCAMQ-B3KAQRipkXm","commandMetadata":{"webCommandMetadata":{"url":"/watch?v=ZcwA0xt8FlQ\u0026pp=QAFIAQ%3D%3D","webPageType":"WEB_PAGE_TYPE_WATCH","rootVe":3832}},"watchEndpoint":{"videoId":"ZcwA0xt8FlQ","params":"EAEYAdoBBAgBKgA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https://dev.to/t/performance | Performance - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Performance Follow Hide Tag for content related to software performance. Create Post submission guidelines Articles should be obviously related to software performance in some way. Possible topics include, but are not limited to: Performance Testing Performance Analysis Optimising for performance Scalability Resilience But most of all, be kind and humble. 💜 Older #performance posts 1 2 3 4 5 6 7 8 9 … 75 … 246 Posts Left menu 👋 Sign in for the ability to sort posts by relevant , latest , or top . Right menu SLMs, LLMs and a Devious Logic Puzzle Test Ben Santora Ben Santora Ben Santora Follow Jan 12 SLMs, LLMs and a Devious Logic Puzzle Test # llm # performance # testing 5 reactions Comments Add Comment 5 min read Observing Behavioral Anomalies in Web Applications Beyond Signature Scanners 0x7b 0x7b 0x7b Follow Jan 12 Observing Behavioral Anomalies in Web Applications Beyond Signature Scanners # monitoring # performance # security # testing Comments Add Comment 1 min read The Buy Button Is the Slowest Part of Most E Commerce Sites ar abid ar abid ar abid Follow Jan 13 The Buy Button Is the Slowest Part of Most E Commerce Sites # webdev # performance # frontend # ecommerce Comments Add Comment 3 min read I got tired of waiting for Gradle, so I built a runtime that runs Kotlin like Python. Srikar Sunchu Srikar Sunchu Srikar Sunchu Follow Jan 13 I got tired of waiting for Gradle, so I built a runtime that runs Kotlin like Python. # kotlin # performance # productivity # tooling 10 reactions Comments 1 comment 2 min read Moving from Nextjs to Qwik Jaime Jaime Jaime Follow Jan 12 Moving from Nextjs to Qwik # nextjs # qwik # javascript # performance Comments Add Comment 5 min read Stop Random Pod Scheduling: Master Kubernetes Affinity & Anti-Affinity with NGINX (Practical Guide for DevOps & SRE) Srinivasaraju Tangella Srinivasaraju Tangella Srinivasaraju Tangella Follow Jan 13 Stop Random Pod Scheduling: Master Kubernetes Affinity & Anti-Affinity with NGINX (Practical Guide for DevOps & SRE) # devops # kubernetes # performance # tutorial Comments Add Comment 4 min read Why Next.js Is Better Than Plain React for Modern Web Development Farhad Rahimi Klie Farhad Rahimi Klie Farhad Rahimi Klie Follow Jan 13 Why Next.js Is Better Than Plain React for Modern Web Development # nextjs # react # webdev # performance Comments Add Comment 3 min read Your WordPress Site Is Bleeding Memory — Here's How to Stop It Prakhar Prakhar Prakhar Follow Jan 12 Your WordPress Site Is Bleeding Memory — Here's How to Stop It # wordpress # webdev # performance # wordpressplugin Comments Add Comment 4 min read Hogo: Ignite Your Node.js Performance with Atomic Request Coalescing Mahmud Rahman Mahmud Rahman Mahmud Rahman Follow Jan 12 Hogo: Ignite Your Node.js Performance with Atomic Request Coalescing # node # performance # backend # javascript Comments Add Comment 2 min read How to Build SEO-Friendly Ecommerce Product Pages ar abid ar abid ar abid Follow Jan 12 How to Build SEO-Friendly Ecommerce Product Pages # frontend # performance # tutorial # webdev Comments Add Comment 3 min read Why Your Python Code Takes Hours Instead of Seconds (A 3-Line Fix) Samuel Ochaba Samuel Ochaba Samuel Ochaba Follow Jan 12 Why Your Python Code Takes Hours Instead of Seconds (A 3-Line Fix) # python # performance # beginners # programming Comments Add Comment 2 min read Why Should We Optimize JSON for LLMs Del Rosario Del Rosario Del Rosario Follow Jan 12 Why Should We Optimize JSON for LLMs # json # llm # webdev # performance Comments Add Comment 5 min read Speculative Decoding: Cómo Acelerar LLMs 2.4x Sin Cambiar el Modelo Abdessamad Ammi Abdessamad Ammi Abdessamad Ammi Follow Jan 12 Speculative Decoding: Cómo Acelerar LLMs 2.4x Sin Cambiar el Modelo # llm # performance # mlop Comments Add Comment 4 min read O(1) Country Selection on a 3D Globe with GPU Picking and Hemisphere Detection Emmanuel Emmanuel Emmanuel Follow Jan 11 O(1) Country Selection on a 3D Globe with GPU Picking and Hemisphere Detection # threejs # angular # performance # algorithms Comments Add Comment 11 min read I Added a Cache and the System Got Slower: The Hidden Cost of Caching yusuf yonturk yusuf yonturk yusuf yonturk Follow Jan 11 I Added a Cache and the System Got Slower: The Hidden Cost of Caching # programming # performance # backend # redis 1 reaction Comments Add Comment 3 min read Small Language Models Are Eating the World (And Why That's Great) SATINATH MONDAL SATINATH MONDAL SATINATH MONDAL Follow Jan 11 Small Language Models Are Eating the World (And Why That's Great) # ai # edge # performance # mobile Comments Add Comment 13 min read Choosing the Right LLM for the Umbraco CMS Developer MCP: An Quick Cost and Performance Analysis Phil Whittaker Phil Whittaker Phil Whittaker Follow Jan 11 Choosing the Right LLM for the Umbraco CMS Developer MCP: An Quick Cost and Performance Analysis # llm # mcp # performance Comments Add Comment 6 min read [Learning Documents] [Golang] Go 1.19 Sort is Faster Evan Lin Evan Lin Evan Lin Follow Jan 11 [Learning Documents] [Golang] Go 1.19 Sort is Faster # algorithms # go # performance Comments Add Comment 1 min read Book Review: Ecosystem Competition Strategy Evan Lin Evan Lin Evan Lin Follow Jan 11 Book Review: Ecosystem Competition Strategy # discuss # react # performance Comments Add Comment 10 min read High-performance GPUs or TPUs vs CPUs Neweraofcoding Neweraofcoding Neweraofcoding Follow Jan 11 High-performance GPUs or TPUs vs CPUs # architecture # machinelearning # performance # ai Comments Add Comment 2 min read Angular State Management: Signals vs Simple Properties - Which Should I Use? Mohamed Fri Mohamed Fri Mohamed Fri Follow Jan 11 Angular State Management: Signals vs Simple Properties - Which Should I Use? # discuss # performance # typescript # angular Comments Add Comment 1 min read Dynamic Arrays: Low-Level Implementation & Amortized Analysis ali ehab algmass ali ehab algmass ali ehab algmass Follow Jan 11 Dynamic Arrays: Low-Level Implementation & Amortized Analysis # algorithms # computerscience # performance Comments Add Comment 4 min read Unsafe Rust: When and Why Aviral Srivastava Aviral Srivastava Aviral Srivastava Follow Jan 11 Unsafe Rust: When and Why # learning # performance # rust Comments Add Comment 8 min read Why Fast Page Loads Don’t Always Mean Fast User Experience ar abid ar abid ar abid Follow Jan 11 Why Fast Page Loads Don’t Always Mean Fast User Experience # performance # webdev # javascript # serverless 1 reaction Comments Add Comment 2 min read HTTP Caching Explained (The Way I Learned It in Production) Nishar Arif Nishar Arif Nishar Arif Follow Jan 11 HTTP Caching Explained (The Way I Learned It in Production) # beginners # webdev # tutorial # performance 1 reaction Comments Add Comment 4 min read loading... trending guides/resources I built an app in every frontend framework TOON vs JSON: A Modern Data Format Showdown I Replaced Redis with PostgreSQL (And It's Faster) Choosing the Right Chunking Strategy: A Comprehensive Guide to RAG Optimization Choosing Between Vue.js and Next.js: A Practical Guide for Developers Are We Losing Our Manners in Software Development? libmalloc, jemalloc, tcmalloc, mimalloc - Exploring Different Memory Allocators Why Your Vue App Is Reactive Too Much (and How to Fix It) Understanding Hetzner SSD VPS Performance and Best Practices JSON vs MessagePack vs Protobuf in Go — My Real Benchmarks and What They Mean in Production Why Edge Computing Forced Me to Write Better Code (And Why That's the Future) Anthropic Bought Bun: Here's What It Really Means for Us How to track Claude Code usage + analytics Best LLM inference providers. Groq vs. Cerebras: Which Is the Fastest AI Inference Provider? Why it's time to ditch UUIDv4 and switch to UUIDv7! I tested the top 3 AI coding models on real engineering problems. The results surprised me. GPUI Component: Because Desktop Apps Shouldn't Make You Cry 🧹 How to Clear Cache in Windows Using PowerShell (Complete Guide) Image optimization and compression techniques for ultra-fast Laravel/PHP image uploads and display C# Performance Optimization: Using Span<T> and stackalloc to Eliminate Allocations 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . 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https://peps.python.org/pep-0013/ | PEP 13 – Python Language Governance | peps.python.org Following system colour scheme Selected dark colour scheme Selected light colour scheme Python Enhancement Proposals Python » PEP Index » PEP 13 Toggle light / dark / auto colour theme PEP 13 – Python Language Governance Author : The Python core team and community Status : Active Type : Process Topic : Governance Created : 16-Dec-2018 Table of Contents Abstract Current steering council Specification The steering council Composition Mandate Powers Electing the council Term Vacancies Conflicts of interest Ejecting core team members Vote of no confidence The core team Role Prerogatives Membership Changing this document History Creation of this document History of council elections History of amendments Acknowledgements Copyright Abstract This PEP defines the formal governance process for Python, and records how this has changed over time. Currently, governance is based around a steering council. The council has broad authority, which they seek to exercise as rarely as possible. Current steering council The 2025 term steering council consists of: Barry Warsaw Donghee Na Emily Morehouse Gregory P. Smith Pablo Galindo Salgado Per the results of the vote tracked in PEP 8106 . The core team consists of those listed in the private https://github.com/python/voters/ repository which is publicly shared via https://devguide.python.org/developers/ . Specification The steering council Composition The steering council is a 5-person committee. Mandate The steering council shall work to: Maintain the quality and stability of the Python language and CPython interpreter, Make contributing as accessible, inclusive, and sustainable as possible, Formalize and maintain the relationship between the core team and the PSF, Establish appropriate decision-making processes for PEPs, Seek consensus among contributors and the core team before acting in a formal capacity, Act as a “court of final appeal” for decisions where all other methods have failed. Powers The council has broad authority to make decisions about the project. For example, they can: Accept or reject PEPs Enforce or update the project’s code of conduct Work with the PSF to manage any project assets Delegate parts of their authority to other subcommittees or processes However, they cannot modify this PEP, or affect the membership of the core team, except via the mechanisms specified in this PEP. The council should look for ways to use these powers as little as possible. Instead of voting, it’s better to seek consensus. Instead of ruling on individual PEPs, it’s better to define a standard process for PEP decision making (for example, by accepting one of the other 801x series of PEPs). It’s better to establish a Code of Conduct committee than to rule on individual cases. And so on. To use its powers, the council votes. Every council member must either vote or explicitly abstain. Members with conflicts of interest on a particular vote must abstain. Passing requires a strict majority of non-abstaining council members. Whenever possible, the council’s deliberations and votes shall be held in public. Electing the council A council election consists of two phases: Phase 1: Candidates advertise their interest in serving. Candidates must be nominated by a core team member. Self-nominations are allowed. Phase 2: Each core team member can assign zero to five stars to each candidate. Voting is performed anonymously. The outcome of the vote is determined using the STAR voting system , modified to use the Multi-winner Bloc STAR approach. If a tie that is not automatically resolved by the election software occurs, it may be resolved by mutual agreement among the candidates, or else the winner will be chosen at random. Each phase lasts one to two weeks, at the outgoing council’s discretion. For the initial election, both phases will last two weeks. The election process is managed by a returns officer nominated by the outgoing steering council. For the initial election, the returns officer will be nominated by the PSF Executive Director. The council should ideally reflect the diversity of Python contributors and users, and core team members are encouraged to vote accordingly. Term A new council is elected after each feature release. Each council’s term runs from when their election results are finalized until the next council’s term starts. There are no term limits. Vacancies Council members may resign their position at any time. Whenever there is a vacancy during the regular council term, the council may vote to appoint a replacement to serve out the rest of the term. If a council member drops out of touch and cannot be contacted for a month or longer, then the rest of the council may vote to replace them. Conflicts of interest While we trust council members to act in the best interests of Python rather than themselves or their employers, the mere appearance of any one company dominating Python development could itself be harmful and erode trust. In order to avoid any appearance of conflict of interest, at most 2 members of the council can work for any single employer. In a council election, if 3 of the top 5 vote-getters work for the same employer, then whichever of them ranked lowest is disqualified and the 6th-ranking candidate moves up into 5th place; this is repeated until a valid council is formed. During a council term, if changing circumstances cause this rule to be broken (for instance, due to a council member changing employment), then one or more council members must resign to remedy the issue, and the resulting vacancies can then be filled as normal. Ejecting core team members In exceptional circumstances, it may be necessary to remove someone from the core team against their will. (For example: egregious and ongoing code of conduct violations.) This can be accomplished by a steering council vote, but unlike other steering council votes, this requires at least a two-thirds majority. With 5 members voting, this means that a 3:2 vote is insufficient; 4:1 in favor is the minimum required for such a vote to succeed. In addition, this is the one power of the steering council which cannot be delegated, and this power cannot be used while a vote of no confidence is in process. If the ejected core team member is also on the steering council, then they are removed from the steering council as well. Vote of no confidence In exceptional circumstances, the core team may remove a sitting council member, or the entire council, via a vote of no confidence. A no-confidence vote is triggered when a core team member calls for one publicly on an appropriate project communication channel, and another core team member seconds the proposal within one week. The vote lasts for two weeks. Core team members vote for or against. If at least two thirds of voters express a lack of confidence, then the vote succeeds. There are two forms of no-confidence votes: those targeting a single member, and those targeting the council as a whole. The initial call for a no-confidence vote must specify which type is intended. If a single-member vote succeeds, then that member is removed from the council and the resulting vacancy can be handled in the usual way. If a whole-council vote succeeds, the council is dissolved and a new council election is triggered immediately. The core team Role The core team is the group of trusted volunteers who manage Python. They assume many roles required to achieve the project’s goals, especially those that require a high level of trust. They make the decisions that shape the future of the project. Core team members are expected to act as role models for the community and custodians of the project, on behalf of the community and all those who rely on Python. They will intervene, where necessary, in online discussions or at official Python events on the rare occasions that a situation arises that requires intervention. They have authority over the Python Project infrastructure, including the Python Project website itself, the Python GitHub organization and repositories, the bug tracker, the mailing lists, IRC channels, etc. Prerogatives Core team members may participate in formal votes, typically to nominate new team members and to elect the steering council. Membership Python core team members demonstrate: a good grasp of the philosophy of the Python Project a solid track record of being constructive and helpful significant contributions to the project’s goals, in any form willingness to dedicate some time to improving Python As the project matures, contributions go beyond code. Here’s an incomplete list of areas where contributions may be considered for joining the core team, in no particular order: Working on community management and outreach Providing support on the mailing lists and on IRC Triaging tickets Writing patches (code, docs, or tests) Reviewing patches (code, docs, or tests) Participating in design decisions Providing expertise in a particular domain (security, i18n, etc.) Managing the continuous integration infrastructure Managing the servers (website, tracker, documentation, etc.) Maintaining related projects (alternative interpreters, core infrastructure like packaging, etc.) Creating visual designs Core team membership acknowledges sustained and valuable efforts that align well with the philosophy and the goals of the Python project. It is granted by receiving at least two-thirds positive votes in a core team vote that is open for one week and is not vetoed by the steering council. Note The devguide has a suggested template to use for such votes. Core team members are always looking for promising contributors, teaching them how the project is managed, and submitting their names to the core team’s vote when they’re ready. There’s no time limit on core team membership. However, in order to provide the general public with a reasonable idea of how many people maintain Python, core team members who have stopped contributing are encouraged to declare themselves as “inactive”. Those who haven’t made any non-trivial contribution in two years may be asked to move themselves to this category, and moved there if they don’t respond. To record and honor their contributions, inactive team members will continue to be listed alongside active core team members; and, if they later resume contributing, they can switch back to active status at will. While someone is in inactive status, though, they lose their active privileges like voting or nominating for the steering council, and commit access. The initial active core team members will consist of everyone currently listed in the “Python core” team on GitHub (access granted for core members only), and the initial inactive members will consist of everyone else who has been a committer in the past. Changing this document Changes to this document require at least a two-thirds majority of votes cast in a core team vote which should be open for two weeks. No vote is required to update note blocks and the “Current steering council” and “History of council elections” sections with current information. History Creation of this document The Python project was started by Guido van Rossum, who served as its Benevolent Dictator for Life (BDFL) from inception until July 2018, when he stepped down . After discussion, a number of proposals were put forward for a new governance model, and the core devs voted to choose between them. The overall process is described in PEP 8000 and PEP 8001 , a review of other projects was performed in PEP 8002 , and the proposals themselves were written up as the 801x series of PEPs. Eventually the proposal in PEP 8016 was selected as the new governance model, and was used to create the initial version of this PEP. The 8000-series PEPs are preserved for historical reference (and in particular, PEP 8016 contains additional rationale and links to contemporary discussions), but this PEP is now the official reference, and will evolve following the rules described herein. History of council elections January 2019: PEP 8100 December 2019: PEP 8101 December 2020: PEP 8102 December 2021: PEP 8103 December 2022: PEP 8104 December 2023: PEP 8105 December 2024: PEP 8106 December 2025: PEP 8107 History of amendments 2019-04-17: Added the vote length for core devs and changes to this document. 2024-12-10 : Adopted Multi-winner Bloc STAR voting for council elections. 2024-12-10 : Added a one-week deadline for seconding a vote of no confidence. 2025-11-12 : Clarified that the software used for elections may resolve ties automatically if possible. Acknowledgements This PEP began as PEP 8016 , which was written by Nathaniel J. Smith and Donald Stufft, based on a Django governance document written by Aymeric Augustin, and incorporated feedback and assistance from numerous others. Copyright This document has been placed in the public domain. Source: https://github.com/python/peps/blob/main/peps/pep-0013.rst Last modified: 2025-11-12 11:59:17 GMT Contents Abstract Current steering council Specification The steering council Composition Mandate Powers Electing the council Term Vacancies Conflicts of interest Ejecting core team members Vote of no confidence The core team Role Prerogatives Membership Changing this document History Creation of this document History of council elections History of amendments Acknowledgements Copyright Page Source (GitHub) | 2026-01-13T08:49:02 |
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Right menu Stop Generating "AI Slop": The Developer's Guide to Google Stitch Seif Almotaz Bellah Seif Almotaz Bellah Seif Almotaz Bellah Follow Dec 22 '25 Stop Generating "AI Slop": The Developer's Guide to Google Stitch # ui # vibecoding # webdev # ai Comments Add Comment 5 min read Cars Don’t Fail Suddenly-Software Taught Me That VechtronAI VechtronAI VechtronAI Follow Dec 22 '25 Cars Don’t Fail Suddenly-Software Taught Me That # ai # systemdesign # iot # automotive 3 reactions Comments Add Comment 3 min read Securing IoT Devices Without Agents Using Network-Based Machine Learning Asma Eman Asma Eman Asma Eman Follow Dec 27 '25 Securing IoT Devices Without Agents Using Network-Based Machine Learning # iot # security # webdev # ai Comments Add Comment 11 min read How to Design an AI Practice Loop That Actually Improves Skill Allen Bailey Allen Bailey Allen Bailey Follow Dec 23 '25 How to Design an AI Practice Loop That Actually Improves Skill # ai # learning # productivity Comments Add Comment 3 min read The new Supabase power for Kiro Yuri Yuri Yuri Follow for Supabase Dec 23 '25 The new Supabase power for Kiro # aws # backend # ai # programming 5 reactions Comments Add Comment 3 min read Building Context-Aware Agents with LangGraph Richard Abishai Richard Abishai Richard Abishai Follow Dec 22 '25 Building Context-Aware Agents with LangGraph # ai # langgraph # agents # automation Comments Add Comment 3 min read Z-Image-Turbo-Anime: Complete Guide to Lightning-Fast Anime AI Image Generation Garyvov Garyvov Garyvov Follow Jan 5 Z-Image-Turbo-Anime: Complete Guide to Lightning-Fast Anime AI Image Generation # ai # deeplearning # performance Comments Add Comment 11 min read A Practical Guide to Building AI Agents with Java and Spring AI - Part 6 - Multi-Modal Multi-Model Yuriy Bezsonov Yuriy Bezsonov Yuriy Bezsonov Follow Dec 22 '25 A Practical Guide to Building AI Agents with Java and Spring AI - Part 6 - Multi-Modal Multi-Model # java # springboot # tutorial # ai 1 reaction Comments Add Comment 9 min read When long chats drift: hidden errors in AI-assisted coding workflows Himanshu Jetani Himanshu Jetani Himanshu Jetani Follow Dec 26 '25 When long chats drift: hidden errors in AI-assisted coding workflows # productivity # ai # programming # promptengineering Comments Add Comment 3 min read Why Most Dashboards Lie (And It’s Not a BI Problem) Caio H R Santana Caio H R Santana Caio H R Santana Follow Dec 22 '25 Why Most Dashboards Lie (And It’s Not a BI Problem) # datascience # ai # architecture # productthinking Comments Add Comment 1 min read Top AI in Manufacturing Trends for 2026 Edwin Lisowski Edwin Lisowski Edwin Lisowski Follow Dec 22 '25 Top AI in Manufacturing Trends for 2026 # manufacturing # ai Comments Add Comment 2 min read Modern Alternatives: Flask-SocketIO vs. FastAPI and Quart Lalit Mishra Lalit Mishra Lalit Mishra Follow Dec 21 '25 Modern Alternatives: Flask-SocketIO vs. FastAPI and Quart # webdev # programming # ai # python Comments 1 comment 5 min read AntiGravity Agent terminated due to error. Solution found Mayuresh Smita Suresh Mayuresh Smita Suresh Mayuresh Smita Suresh Follow Jan 5 AntiGravity Agent terminated due to error. Solution found # antigravity # programming # ai # webdev 7 reactions Comments Add Comment 1 min read I Built a Framework to Decode Communication Dynamics. Only One Model Could Actually Execute It Matvey Zaharov Matvey Zaharov Matvey Zaharov Follow Dec 26 '25 I Built a Framework to Decode Communication Dynamics. Only One Model Could Actually Execute It # discuss # ai # datascience # learning 1 reaction Comments Add Comment 15 min read I Replaced My Side Project Backend with AI — Here’s What Broke First Tanisha Tanisha Tanisha Follow Dec 24 '25 I Replaced My Side Project Backend with AI — Here’s What Broke First # discuss # ai # backend 7 reactions Comments 1 comment 2 min read Quiz game Sydney Chidyaka Sydney Chidyaka Sydney Chidyaka Follow Dec 23 '25 Quiz game # codepen # programming # ai # javascript Comments Add Comment 1 min read I Built an MCP Server to Check Domain Availability from AI Assistants dorukardahan dorukardahan dorukardahan Follow Dec 26 '25 I Built an MCP Server to Check Domain Availability from AI Assistants # ai # typescript # opensource # webdev Comments Add Comment 2 min read The GPT Client Is Becoming the Runtime for Human–AI Collaboration yuer yuer yuer Follow Dec 23 '25 The GPT Client Is Becoming the Runtime for Human–AI Collaboration # ai # architecture # llm Comments Add Comment 3 min read Your December 2025 AI Coffee Break: 5 Major Developments You Need to Know Ethan Zhang Ethan Zhang Ethan Zhang Follow Dec 22 '25 Your December 2025 AI Coffee Break: 5 Major Developments You Need to Know # news # ai # machinelearning # technology Comments Add Comment 5 min read Vibe Coding: Does Coding Feel Different to You Now? AI Miracle AI Miracle AI Miracle Follow Dec 22 '25 Vibe Coding: Does Coding Feel Different to You Now? # vibecoding # programming # ai Comments Add Comment 1 min read The Future of AI in Software Development: 2026 and Beyond Mahdi BEN RHOUMA Mahdi BEN RHOUMA Mahdi BEN RHOUMA Follow Dec 23 '25 The Future of AI in Software Development: 2026 and Beyond # webdev # programming # ai # javascript Comments Add Comment 1 min read The Silent Security Crisis: Why Your AI Systems Need Rejection Logging (And Most Don't Have It) John R. Black III John R. Black III John R. Black III Follow Dec 23 '25 The Silent Security Crisis: Why Your AI Systems Need Rejection Logging (And Most Don't Have It) # cybersecurity # ai # systemdesign # zerotrust Comments Add Comment 4 min read YouTube launches AI-powered Playables Builder beta to let creators design andshare their own games Saiki Sarkar Saiki Sarkar Saiki Sarkar Follow Dec 23 '25 YouTube launches AI-powered Playables Builder beta to let creators design andshare their own games # news # ai # gamedev Comments Add Comment 2 min read Code Rigor vs AI Chaos: Should We Reinvent PHP Standards for PrestaShop Merchants? 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account Future Close Privacy Policy Last Updated: September 01, 2023 This Privacy Policy is designed to help you understand how DEV Community Inc. (" DEV ," " we ," or " us ") collects, use, and discloses your personal information. What's With the Defined Terms? You'll notice that some words appear in quotes in this Privacy Policy. They're called "defined terms," and we use them so that we don't have to repeat the same language again and again. They mean the same thing in every instance, to help us make sure that this Privacy Policy is consistent. We've included the defined terms throughout because we want it to be easy for you to read them in context. 1. WHAT DOES THIS PRIVACY POLICY APPLY TO? 2. PERSONAL INFORMATION WE COLLECT 3. HOW WE USE YOUR INFORMATION 4. HOW WE DISCLOSE YOUR INFORMATION 5. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Keerthi Posted on Sep 22, 2021 • Edited on Sep 24, 2021 Create react app vs Vite # webdev # javascript # vite # react I have always relied on the npm command create-react-app to create the starter files for any React.js project. It does what it says on the tin, and creates all my starter template files, setups a local dev server and dev environment. Over the years I have become a little impatient because it takes around 3-4 minutes to setup a basic barebones app. Recently I have come to know about a faster way to setup React apps, which also gives you all the useful features that create-react-app gives you too. It is using a tool called Vite . Vite is another build tool like Webpack (create-react-app uses Webpack under the hood, read more here ). In this post I will take you through the steps on how to install React.js app using Vite and point out some differences too. You can also see a video on the comparison of the two installation methods. In the Video below, You will discover that the installation time, plus time to run local server is astonishingly fast for Vite. So how do we start the ball rolling You can refer to the Vite docs , From there, you can choose from a few methods to start off your installation. We are going to use the template method. In their docs, the listed methods are: #npm 6.x npm init vite@latest my-vue-app --template vue #npm 7+, extra double-dash is needed: npm init vite@latest my-vue-app -- --template vue #yarn yarn create vite my-vue-app --template vue Enter fullscreen mode Exit fullscreen mode But these commands are for installing Vue.js, just as side note, Vite was originally developed for Vue.js but has been modified to use with other frameworks including React.js. For our case, all we need to do is replace the keyword after '--template', from vue to react. And dont forget to replace the app name to your choosing. So assuming that we are running npm version 6.x, we will run the following command: npm init vite@latest my-react-app --template react Enter fullscreen mode Exit fullscreen mode Then we will cd into our directory and install the remainder of the starter files and run the dev server: cd my-react-app npm install npm run dev Enter fullscreen mode Exit fullscreen mode If you goto the browser. You should see a React logo with a counter and a button, as below. Directory structure of the our newly created app The thing to note here is that, main.js is the root file that imports/loads App.js. There is also a new file called vite.config.js, this is circled in the above image. This file is used to turn on and set new features for your build process. I will come to this file in the next section below. One last thing about importing files... I have noticed that out the box this setup does not allow for absolute paths. With create-react-app, you can do import x from 'components/x' . With Vite, you have to do the relative pathing, like ```import x from '../../../' To fix this we need to change the vite.config.js file, which looks like this: ```javascript import { defineConfig } from 'vite' import reactRefresh from '@vitejs/plugin-react-refresh' // https://vitejs.dev/config/ export default defineConfig({ plugins: [reactRefresh()] }) Enter fullscreen mode Exit fullscreen mode we need to add an extra setting to resolve the path, this change will go after the "plugins" settings. It will end up looking like this after the change: import { defineConfig } from ' vite ' import reactRefresh from ' @vitejs/plugin-react-refresh ' import path from ' path ' // https://vitejs.dev/config/ export default defineConfig ({ plugins : [ reactRefresh ()], resolve : { alias : { ' @ ' : path . resolve ( __dirname , ' ./src ' ), }, }, }) Enter fullscreen mode Exit fullscreen mode and this will allow us to refer to paths as import x from '@/component/x' !IMPORTATNT to prefix with '@' in path. conclusion I did find Vite impressingly fast. It took me 55 secs to install and run on local server. I have not done much heavy development using Vite but it looks promising. It is too early for me to say if I will use it on any bigger projects in the future. There are other methods of installing React.js using Vite, these methods are maintained by other communities. Check out other community maintained templates here , you can also find one with Tailwind. Please leave comments on your experiences too. Note: Vite has templates to build apps in the following frameworks vanilla vanilla-ts vue vue-ts react react-ts preact preact-ts lit-element lit-element-ts svelte svelte-ts Enter fullscreen mode Exit fullscreen mode so to create a build in react typescript , just change the last bit to "react-ts" after the "--template" , so it becomes: npm init vite@latest my-react-app --template react-ts Enter fullscreen mode Exit fullscreen mode Top comments (20) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Collapse Expand R. Maulana Citra R. Maulana Citra R. Maulana Citra Follow I write about web dev stuff Location Serang, Indonesia Work Front End @Skyshi Digital Indonesia Joined Mar 3, 2021 • Sep 24 '21 Dropdown menu Copy link Hide Vite is cool, I love how things are fast on dev server. I also made boilerplate for daily projects with Tailwind, if you want to check it out, see it on my GitHub here Like comment: Like comment: 4 likes Like Comment button Reply Collapse Expand Keerthi Keerthi Keerthi Follow I am UI developer, technologist, UI designer. Keen cook. Location london Work ui developer Joined Aug 7, 2020 • Sep 24 '21 Dropdown menu Copy link Hide Thats awesome, you should contribute to the community here github.com/vitejs/awesome-vite#tem... . They have one for react and tailwind already, maybe you can add yours as well. Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand R. Maulana Citra R. Maulana Citra R. Maulana Citra Follow I write about web dev stuff Location Serang, Indonesia Work Front End @Skyshi Digital Indonesia Joined Mar 3, 2021 • Oct 5 '21 Dropdown menu Copy link Hide thank you bro, I have added mine too, and it was merged already! Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand James Thomson James Thomson James Thomson Follow Just another front-end web dev junkie Location Australia Work Senior Frontend Engineer at Complish Joined Feb 22, 2019 • Sep 22 '21 Dropdown menu Copy link Hide I've recently switched a Vue CLI project to Vite. It's impressive how fast things are - but makes complete sense when there's no build step needed when developing. One thing I've found less intuitive are images, especially dynamically referenced ones (e.g. in a loop). I've had to create a utility for this: export function getImageUrl (name) { return new URL(`../assets/${name}`, import.meta.url).href; } Enter fullscreen mode Exit fullscreen mode Is this also the case in React? Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Keerthi Keerthi Keerthi Follow I am UI developer, technologist, UI designer. Keen cook. Location london Work ui developer Joined Aug 7, 2020 • Sep 23 '21 Dropdown menu Copy link Hide Yes , Similar in react Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Herberth Obregón Herberth Obregón Herberth Obregón Follow 🧩 Web Components 💻 Typescript First 🐳 ☸️ K8s Location GT Education Science and Systems Engineer Work CIO/CTO at HireX Joined Jan 1, 2020 • Sep 23 '21 Dropdown menu Copy link Hide I moved to vitejs for lit-element (now only lit) and is amazing! 💯💯🚀 Web pack is very slow to spinup a dev server Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand Keerthi Keerthi Keerthi Follow I am UI developer, technologist, UI designer. Keen cook. Location london Work ui developer Joined Aug 7, 2020 • Sep 23 '21 Dropdown menu Copy link Hide Firts tme I am hearing of lit-elemnt, Intresting, what apps are you building with it? Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Herberth Obregón Herberth Obregón Herberth Obregón Follow 🧩 Web Components 💻 Typescript First 🐳 ☸️ K8s Location GT Education Science and Systems Engineer Work CIO/CTO at HireX Joined Jan 1, 2020 • Sep 25 '21 Dropdown menu Copy link Hide It is one of the main "frameworks" of modern development, vitejs.dev/guide/#scaffolding-your... Vite support the main popular frameworks vue, react, lit-element and svelte I choose Lit-element because is the closest thing to js vanilla with all the power of web components (the performance is amazing ⚡️). Eventually I consider that web components are going to be so robust that you won't need a framework. Lit-element is the framework for web components par excellence. Stencil I don't like like Lit I build all empleo.gt with Lit Which next will be migrated to hirex.app for worldwide version Like comment: Like comment: 4 likes Like Thread Thread Keerthi Keerthi Keerthi Follow I am UI developer, technologist, UI designer. Keen cook. Location london Work ui developer Joined Aug 7, 2020 • Sep 26 '21 Dropdown menu Copy link Hide Thanks, Nice to know that about Lit, will look at it. Also good luck with your app too Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Wagner Wagner Wagner Follow Joined Feb 25, 2021 • Sep 23 '21 Dropdown menu Copy link Hide Why don't you use package.json inside each directory and refers to files like "@components/MyCompoment"?! You don't need do setup anything else. Just a package.json in each folder with content: { "name": "components" } Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Ivan Jeremic Ivan Jeremic Ivan Jeremic Follow Web/Software Developer Joined Dec 9, 2018 • Sep 23 '21 Dropdown menu Copy link Hide This is so dirty I can't believe people do this. Like comment: Like comment: 16 likes Like Comment button Reply Collapse Expand dragos dragos dragos Follow Indie app builder focused on simple, practical products. Currently building Vet Record, a pet health tracker for everyday owners. Location Beograd Education Completed an online course by Carnegie Mellon University Joined Oct 15, 2019 • Sep 23 '21 Dropdown menu Copy link Hide Stiil too much bugs Like comment: Like comment: 3 likes Like Comment button Reply Collapse Expand Daniel Tkach Daniel Tkach Daniel Tkach Follow Joined Sep 4, 2020 • Oct 4 '21 Dropdown menu Copy link Hide On vite? I'm just researching if I should switch to vite. Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Renan "Firehawk" Lazarotto Renan "Firehawk" Lazarotto Renan "Firehawk" Lazarotto Follow Hiya! I'm a fullstack developer, with experience with PHP, JavaScript and Go. I'm also an Android enthusiast and I like pretty much everything related to tech. Location Brazil Education Barchelor Degree in IT Pronouns he/him Work FullStack developer @ Hammer Consult Joined Dec 16, 2019 • Sep 22 '21 Dropdown menu Copy link Hide I have switched from CRA to Vite just because CRA is so slow! Vite is blazing fast even on my aging machine. Like comment: Like comment: 2 likes Like Comment button Reply Collapse Expand Keerthi Keerthi Keerthi Follow I am UI developer, technologist, UI designer. Keen cook. Location london Work ui developer Joined Aug 7, 2020 • Sep 22 '21 Dropdown menu Copy link Hide Thats good to hear. CRA has always been so slow. But I had to put up with it. Other option was configuring webpack, which was way worse in terms of time to setup. Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Rami Rami Rami Follow I am a self taught web developer and secondary school student ✌ Location مصر Education self-taught Work Captain Dev Joined Nov 14, 2019 • Sep 22 '21 Dropdown menu Copy link Hide Vite is really cool, I hope they support Angular in the near future. Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Wagner Wagner Wagner Follow Joined Feb 25, 2021 • Sep 23 '21 Dropdown menu Copy link Hide Angular is a waste of time! A poor framework, too much verbose. Like comment: Like comment: 12 likes Like Comment button Reply Collapse Expand Jerry Jerry Jerry Follow follow for dev, javascript/typescript react, aws and cloud tips and more. Location British Columbia Work Software Engineer Joined Aug 14, 2018 • Mar 4 '23 Dropdown menu Copy link Hide This is a great overview! If you want a deep dive understanding of Vite, I wrote about here - jerrychang.ca/writing/vite-how-it-... Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Audace Audace Audace Follow Programmer Joined Feb 23, 2024 • Feb 23 '24 Dropdown menu Copy link Hide I have the problem with vite + react. When I run the localhost, see in the terminal [vite] hmr update. And after that in the browser nothing display on the screen. Screen is blank. Like comment: Like comment: 1 like Like Comment button Reply Collapse Expand Audace Audace Audace Follow Programmer Joined Feb 23, 2024 • Feb 23 '24 Dropdown menu Copy link Hide I have the problem Like comment: Like comment: 1 like Like Comment button Reply View full discussion (20 comments) Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Keerthi Follow I am UI developer, technologist, UI designer. Keen cook. Location london Work ui developer Joined Aug 7, 2020 More from Keerthi Crash course in interactive 3d animation with React-three-fiber and React-spring # react # webdev # threejs A crash course in React.js and D3 # react # javascript # d3js # webdev Scroll animation in Javascript using IntersectionObserver # javascript # webdev # css # html 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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Skip to content ▼ Close Python PSF Docs PyPI Jobs Community ▲ The Python Network Donate ≡ Menu Search This Site GO A A Smaller Larger Reset Socialize LinkedIn Mastodon Chat on IRC Twitter About Applications Quotes Getting Started Help Python Brochure Downloads All releases Source code Windows macOS Android Other Platforms License Alternative Implementations Documentation Docs Audio/Visual Talks Beginner's Guide FAQ Non-English Docs PEP Index Python Books Python Essays Community Diversity Mailing Lists IRC Forums PSF Annual Impact Report Python Conferences Special Interest Groups Python Logo Python Wiki Code of Conduct Community Awards Get Involved Shared Stories Success Stories Arts Business Education Engineering Government Scientific Software Development News Python News PSF Newsletter PSF News PyCon US News News from the Community Events Python Events User Group Events Python Events Archive User Group Events Archive Submit an Event Python >>> Special Interest Groups Python Special Interest Groups About There are a number of Special Interest Groups (SIGs) for focused collaborative efforts to develop, improve, or maintain specific Python resources. Each SIG has a charter, a coordinator, a mailing list, and a directory on the Python website. SIG membership is informal, defined by subscription to the SIG's mailing list. Anyone can join a SIG, and participate in the development discussions via the SIG's mailing list. Below is the list of currently active Python SIGs, with links to their resources. The link in the first column directs you to the SIG's home page: a page with more information about the SIG. The links in the "Info" column direct you to the SIG's archives, and to the SIG's Mailman page, which you can use to subscribe or unsubscribe yourself and to change your subscription options. The SIG mailing lists are managed by GNU Mailman , a web-based interface for mailing lists written in Python. Retired There is also a list of retired SIGs ; these SIGs existed in the past but are no longer active. Their archives and home pages are retained. A retired SIG can be revived, using the same criteria as for creating a new SIG. 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Skip to content ▼ Close Python PSF Docs PyPI Jobs Community ▲ The Python Network Donate ≡ Menu Search This Site GO A A Smaller Larger Reset Socialize LinkedIn Mastodon Chat on IRC Twitter About Applications Quotes Getting Started Help Python Brochure Downloads All releases Source code Windows macOS Android Other Platforms License Alternative Implementations Documentation Docs Audio/Visual Talks Beginner's Guide FAQ Non-English Docs PEP Index Python Books Python Essays Community Diversity Mailing Lists IRC Forums PSF Annual Impact Report Python Conferences Special Interest Groups Python Logo Python Wiki Code of Conduct Community Awards Get Involved Shared Stories Success Stories Arts Business Education Engineering Government Scientific Software Development News Python News PSF Newsletter PSF News PyCon US News News from the Community Events Python Events User Group Events Python Events Archive User Group Events Archive Submit an Event Python >>> Downloads >>> Other Platforms Download Python for other platforms Python has been ported to a number of specialized and/or older platforms, listed below in alphabetical order. Note that these ports often lag well behind the latest Python release. Python for AIX AIX binary packages for Python are available from IBM AIX Toolbox in RPM format. They can be installed using dnf package manager. Visit the Get Started page for more details. `_ is a port to the `Amiga Research OS `_. Tim Ocock maintains `AmigaPython `_. Older versions of Python for the Amiga can be found at `Aminet `_. --> `_. For all Python-related stuff on BeOS, `search here `_. --> Python for HP-UX You can purchase ActivePython (commercial and community versions, including scientific computing modules, not open source) Python for IBM i (formerly AS/400, iSeries) Both Python 2 and Python 3 are available from IBM in RPM form. They can be installed with the yum package manager or with the IBM i Access Client Solutions product. To get started with RPM-based open source packages for IBM i, visit http://ibm.biz/ibmi-rpms . These RPM packages require a version of IBM i in active (not extended) support. Python for iOS and iPadOS Briefcase (from the BeeWare project) and Buildozer (from the Kivy project) are two tools that can be used to deploy Python code as an iOS app. Python-Apple-support is a project that provides pre-compiled Python frameworks that can be embedded into an Xcode project. PythonKit can be used to provide Swift integration with Python. Pythonista is a complete development environment for writing Python scripts including third-party libraries and system integration on your iPad or iPhone. Pyto also provides a complete development environment for running Python 3 including many third-party libraries and system integration on an iPad or iPhone. Alternate Python packages for Linux You can purchase ActivePython (commercial and community versions, including scientific computing modules, not open source) `_. --> `_, built on the DJGPP platform, is also available. --> `_. --> `_. --> `__ once completed a port of Python to the Sony PlayStation 2. Contact him for more info. --> `__. It has most modules running and can even use the PSP's built in wifi, albeit awkwardly. --> `_. --> `_ --> Python for RISC OS Python is available for RISC OS, and can be obtained using the PackMan package manager. `__ can be found at archive.org. This page also includes some pre-ported external libraries as well as RISC OS specific extensions and documentation, written by Dietmar Schwertberger. --> `_. There are downloads available at ` `_. --> Python for Solaris You can purchase ActivePython (commercial and community versions, including scientific computing modules, not open source), or build from source if you have a C compiler. UNIX Packages has a variety of Python versions for a variety of Solaris versions. These use the standard Sun pkgadd. Python for UEFI Environment Standard CPython version 3.6.8 port for the Unified Extensible Firmware Interface (UEFI) shell environment is available through the Tianocore open source project. This provides the standard Python scripting capabilities on UEFI environment, helping the UEFI based firmware and platform developer community to use it for platform, firmware validation, debug and the like. Python for UEFI source code and build instructions are available here . Currently build support is enabled using VS2019 and GCC5 tool chains for x86 and x64 bit platforms. `_ is available. --> `__. --> `_ page. --> Python for z/OS `_ has a ported version of Python 2.4.1. --> Rocket Software provides a port of Python for z/OS . Python for z/OS is available from IBM for no license charge. It is available in PAX format from Early Programs Web Tool or SMP/E format from Shopz . Optional no-cost Subscription and Support (S&S) is available in the Shopz ordering process. Please visit the IBM Open Enterprise SDK for Python product page for more information. The PSF The Python Software Foundation is the organization behind Python. 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Oct. 26, 2009 Download Gzipped source tarball Download bzip2 compressed source tarball Python 2.6.3 - Oct. 2, 2009 Download Gzipped source tarball Download bzip2 compressed source tarball Python 3.1.1 - Aug. 17, 2009 Download Gzipped source tarball Download bzip2 compressed source tarball Python 3.1.0 - June 26, 2009 Download Gzipped source tarball Download bzip2 compressed source tarball Python 2.6.2 - April 14, 2009 Download Gzipped source tarball Download bzip2 compressed source tarball Python 3.0.1 - Feb. 13, 2009 Download Gzipped source tarball Download bzip2 compressed source tarball Python 2.5.4 - Dec. 23, 2008 Download Gzipped source tarball Download bzip2 compressed source tarball Python 2.4.6 - Dec. 19, 2008 Download Gzipped source tarball Download bzip2 compressed source tarball Python 2.5.3 - Dec. 19, 2008 Download Gzipped source tarball Download bzip2 compressed source tarball Python 2.6.1 - Dec. 4, 2008 Download Gzipped source tarball Download bzip2 compressed source tarball Python 3.0.0 - 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Nov. 21, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 3.5.0rc4 - Sept. 9, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 3.5.0rc3 - Sept. 8, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 3.5.0rc2 - Aug. 25, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 3.5.0rc1 - Aug. 11, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 3.5.0b4 - July 26, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 3.5.0b3 - July 5, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 3.5.0b2 - June 1, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 3.5.0b1 - May 24, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 2.7.10rc1 - May 11, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 3.5.0a4 - April 20, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 3.5.0a3 - March 30, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 3.5.0a2 - March 9, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 3.5.0a1 - Feb. 8, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 3.4.3rc1 - Feb. 8, 2015 Download Gzipped source tarball Download XZ compressed source tarball Python 2.7.9rc1 - Nov. 26, 2014 Download Gzipped source tarball Download XZ compressed source tarball Python 3.3.6rc1 - Oct. 4, 2014 Download Gzipped source tarball Download XZ compressed source tarball Python 3.2.6rc1 - Oct. 4, 2014 Download Gzipped source tarball Download XZ compressed source tarball Python 3.4.2rc1 - Sept. 22, 2014 Download Gzipped source tarball Download XZ compressed source tarball Python 2.7.7rc1 - May 17, 2014 Download Gzipped source tarball Download XZ compressed source tarball Python 3.4.1rc1 - 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https://future.forem.com/privacy#3-how-we-use-your-information | Privacy Policy - Future Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account Future Close Privacy Policy Last Updated: September 01, 2023 This Privacy Policy is designed to help you understand how DEV Community Inc. (" DEV ," " we ," or " us ") collects, use, and discloses your personal information. What's With the Defined Terms? You'll notice that some words appear in quotes in this Privacy Policy. They're called "defined terms," and we use them so that we don't have to repeat the same language again and again. They mean the same thing in every instance, to help us make sure that this Privacy Policy is consistent. We've included the defined terms throughout because we want it to be easy for you to read them in context. 1. WHAT DOES THIS PRIVACY POLICY APPLY TO? 2. PERSONAL INFORMATION WE COLLECT 3. HOW WE USE YOUR INFORMATION 4. HOW WE DISCLOSE YOUR INFORMATION 5. YOUR PRIVACY CHOICES AND RIGHTS 6. INTERNATIONAL DATA TRANSFERS 7. RETENTION OF PERSONAL INFORMATION 8. SUPPLEMENTAL DISCLOSURES FOR CALIFORNIA RESIDENTS 9. SUPPLEMENTAL NOTICE FOR NEVADA RESIDENTS 10. CHILDREN'S INFORMATION 11. OTHER PROVISIONS 12. CONTACT US 1. WHAT DOES THIS PRIVACY POLICY APPLY TO? This Privacy Policy applies to personal information processed by us, including on our websites, mobile applications, and other online or offline offerings — basically anything we do. To make this Privacy Policy easier to read, our websites, mobile applications, and other offerings are all collectively called the " Services. " Beyond this Privacy Policy, your use of the Services is subject to our DEV Community Terms and our Forem Terms. The Services include both our own community forum at https://www.dev.to (the " DEV Community ") and the open source tool we provide called " Forem ," available at https://www.forem.com which allows our customers to create and operate their own online forums. We collect personal information from two categories of people: (1) our customers, who use Forem and our hosting services to run and host their own forums (we'll call them " Forem Operators "), and (2) the people who interact with DEV-hosted forums, including forums provided by Forem Operators utilizing Forem and separately our own DEV Community (we'll call them " Users "). An Important Note for Users Since we provide hosting services for Forem Operators, technically we also process your information on their behalf. That processing is governed by the contracts that we have in place with each Forem Operator, not this Privacy Policy. In other words, when you share your data on a DEV-hosted forum operated by a Forem Operator, we at DEV are basically just the "pipes" — we process the data on behalf of the Forem Operator, but don't do anything with it ourselves beyond what we're required to do under our contract (and by law). So, if you post your information on a DEV-powered forum provided by a Forem Operator, that Forem Operator's privacy policy applies, and any questions or requests relating to your data on that service should be directed to that Forem Operator, not us. Likewise, if you use our mobile application, you may also interact with forums that use DEV's open-source tools but do all their hosting and data collection themselves. For those forums, we at DEV have no access to your data, so be sure to read the privacy policy of any third-party hosted forum before posting. 2. PERSONAL INFORMATION WE COLLECT The categories of personal information we collect depend on whether you're a User or Forem Operator, how you interact with us, our Services, and the requirements of applicable law. Breaking it down, we collect three types of information: (1) information that you provide to us directly, (2) information we obtain automatically when you use our Services, and (3) information we get about you from other sources (such as third-party services and organizations). More details are below. A. Information You Provide to Us Directly We may collect the following personal information that you provide to us. Account Creation (for Forem Operators): We'll require your name and email address to get started, as well as some details about the Forem you want to run, such as: whether you're running the Forem on your own behalf or as part of an organization, and details about the community you want to support (how big is it, what topics does it cover, where do members currently communicate, how/if the community earns money, whether the community is open, invite-only or paid, any existing social media accounts, etc.) You'll need to tell us a bit about your personal coding background, and you'll have the option to provide your DEV username as well, if you are a member of the DEV.to community. Account Creation (for Users) : We collect name and email address from users that create an account on DEV Community. For other forums created by Forem Operators using Forem, the Forem Operator determines what information is required for User account creation for their respective forums. Interactive Features (for Users) . Like any other social network, both we and other Users of our Services may collect personal information that you submit or make available through our interactive features (e.g., messaging and chat features, commenting functionalities, forums, blogs, posts, and other social media pages). While we do have private messages that are only between you and the person you're messaging (as well as us and the Forem Operator, as applicable), any information you provide using the public sharing features of the Services, such as the information you post to your public profile or the topics you follow is public, including to recruiters and prospective employers, and is not subject to any of the privacy protections we mention in this Privacy Policy except where legally required. Please exercise caution before revealing any information that may identify you in the real world to others. Purchases . If you buy stuff on our shop site https://shop.dev.to/ (as either a User or Forem Operator), or otherwise if you pay us in connection with your use of the Forem service, we may collect personal information and details associated with your purchases, including payment information. Any payments made via our Services are processed by third-party payment processors, such as Stripe, Shopify, and PayPal. We do not directly collect or store any payment card information entered through our Services, but may receive information associated with your payment card information (e.g., your billing details). Your Communications with Us (Users and Forem Operators) . We may collect personal information, such as email address, phone number, or mailing address when you request information about our Services, register for our newsletter or loyalty program, request customer or technical support, apply for a job, or otherwise communicate with us. Surveys . We may contact you to participate in surveys. If you decide to participate, you may be asked to provide certain information, which may include personal information (for example, your home address). Sweepstakes or Contests . We may collect personal information you provide for any sweepstakes or contests that we offer. In some jurisdictions, we are required to publicly share information of sweepstakes and contest winners. Conferences, Trade Shows, and Other Events . We may collect personal information from individuals when we attend conferences, trade shows, and other events. Business Development and Strategic Partnerships . We may collect personal information from individuals and third parties to assess and pursue potential business opportunities. Job Applications . We may post job openings and opportunities on our Services. If you reply to one of these postings by submitting your application, CV and/or cover letter to us, we will collect and use your information to assess your qualifications. B. Information Collected Automatically We may collect personal information automatically when you use our Services: Automatic Data Collection . We may collect certain information automatically when you use our Services, such as your Internet protocol (IP) address, user settings, MAC address, cookie identifiers, mobile carrier, mobile advertising and other unique identifiers, browser or device information, location information (including approximate location derived from IP address), and Internet service provider. We may also automatically collect information regarding your use of our Services, such as pages that you visit before, during and after using our Services, information about the links you click, the types of content you interact with, the frequency and duration of your activities, and other information about how you use our Services. In addition, we may collect information that other people provide about you when they use our Services, including information about you when they tag you in their posts. Cookies, Pixel Tags/Web Beacons, and Other Technologies . We, as well as third parties that provide content, advertising, or other functionality on our Services, may use cookies, pixel tags, local storage, and other technologies (" Technologies ") to automatically collect information through your use of our Services. Cookies . Cookies are small text files placed in device browsers that store preferences and facilitate and enhance your experience. Pixel Tags/Web Beacons . A pixel tag (also known as a web beacon) is a piece of code embedded in our Services that collects information about engagement on our Services. The use of a pixel tag allows us to record, for example, that a user has visited a particular web page or clicked on a particular advertisement. We may also include web beacons in e-mails to understand whether messages have been opened, acted on, or forwarded. Our uses of these Technologies fall into the following general categories: Operationally Necessary . This includes Technologies that allow you access to our Services, applications, and tools that are required to identify irregular website behavior, prevent fraudulent activity and improve security or that allow you to make use of our functionality. Performance-Related . We may use Technologies to assess the performance of our Services, including as part of our analytic practices to help us understand how individuals use our Services ( see Analytics below ). Functionality-Related . We may use Technologies that allow us to offer you enhanced functionality when accessing or using our Services. This may include identifying you when you sign into our Services or keeping track of your specified preferences, interests, or past items viewed. Analytics . We may use Technologies and other third-party tools to process analytics information on our Services. Some of our analytics partners include Google Analytics. For more information,please visit Google Analytics' Privacy Policy . To learn more about how to opt-out of Google Analytics' use of your information, please click here . Social Media Platforms . Our Services may contain social media buttons such as Twitter, Facebook, GitHub, Instagram, and Twitch (that might include widgets such as the "share this" button or other interactive mini programs). These features may collect your IP address, which page you are visiting on our Services, and may set a cookie to enable the feature to function properly. Your interactions with these platforms are governed by the privacy policy of the company providing it. See the "Your Privacy Choices and Rights" section below to understand your choices regarding these Technologies. C. Information Collected from Other Sources We may obtain information about you from other sources, including through third-party services and organizations. For example, if you access our Services through a third-party application, such as an app store, a third-party login service (e.g., through Twitter, Apple, or GitHub), or a social networking site, we may collect whatever information about you from that third-party application that you have made available via your privacy settings. 3. HOW WE USE YOUR INFORMATION We use your information for a variety of business purposes, including to provide our Services, for administrative purposes, and to market our products and Services, as described below. A. Provide Our Services We use your information to fulfill our contract with you and provide you with our Services, such as: Managing your information and accounts; Providing access to certain areas, functionalities, and features of our Services; Answering requests for customer or technical support; Communicating with you about your account, activities on our Services, and policy changes; Processing your financial information and other payment methods for products or Services purchased; Processing applications if you apply for a job we post on our Services; and Allowing you to register for events. B. Administrative Purposes We use your information for various administrative purposes, such as: Pursuing our legitimate interests such as direct marketing, research and development (including marketing research), network and information security, and fraud prevention; Detecting security incidents, protecting against malicious, deceptive, fraudulent or illegal activity, and prosecuting those responsible for that activity; Measuring interest and engagement in our Services, including for usage-based billing purposes; Short-term, transient use, such as contextual customization of ads; Improving, optimizing, upgrading, or enhancing our Services; Developing new products and Services; Ensuring internal quality control and safety; Authenticating and verifying individual identities, including requests to exercise your rights under this policy; Debugging to identify and repair errors with our Services; Auditing relating to interactions, transactions and other compliance activities; Enforcing our agreements and policies; and Complying with our legal obligations. C. Marketing and Advertising our Products and Services We may use your personal information to tailor and provide you with content and advertisements for our Services, such as via email. If you have any questions about our marketing practices, you may contact us at any time as set forth in the "Contact Us" section below. D. Other Purposes We also use your information for other purposes as requested by you or as permitted by applicable law. Consent . We may use personal information for other purposes that are clearly disclosed to you at the time you provide personal information or with your consent. Automated Decision Making. We may engage in automated decision making, including profiling, such as to suggest topics or other Users for you to follow. DEV's processing of your personal information will not result in a decision based solely on automated processing that significantly affects you unless such a decision is necessary as part of a contract we have with you, we have your consent, or we are permitted by law to engage in such automated decision making. If you have questions about our automated decision making, you may contact us as set forth in the "Contact Us" section below. De-identified and Aggregated Information . We may use personal information and other information about you to create de-identified and/or aggregated information, such as de-identified demographic information, information about the device from which you access our Services, or other analyses we create. For example, we may collect system-wide information to ensure availability of the platform, or measure aggregate data trends to analyze and optimize our Services. Share Content with Friends or Colleagues. Our Services may offer various tools and functionalities. For example, we may allow you to provide information about your friends through our referral services. Our referral services may allow you to forward or share certain content with a friend or colleague, such as an email inviting your friend to use our Services. Please only share with us contact information of people with whom you have a relationship (e.g., relative, friend neighbor, or co-worker). 4. HOW WE DISCLOSE YOUR INFORMATION We disclose your information to third parties for a variety of business purposes, including to provide our Services, to protect us or others, or in the event of a major business transaction such as a merger, sale, or asset transfer, as described below. A. Disclosures to Provide our Services The categories of third parties with whom we may share your information are described below. Service Providers . We may share your personal information with our third-party service providers who use that information to help us provide our Services. This includes service providers that provide us with IT support, hosting, payment processing, customer service, and related services. For example, our Shop site is run by Shopify, who handle your shipping details on our behalf. Business Partners . We may share your personal information with business partners to provide you with a product or service you have requested. We may also share your personal information to business partners with whom we jointly offer products or services. Other Users . As described above in the "Personal Information We Collect" section of this Privacy Policy, our Service allows Users to share their profiles, and any posts, chats, etc. with other Users and with the general public, including to those who do not use our Services. APIs/SDKs . We may use third-party Application Program Interfaces ("APIs") and Software Development Kits ("SDKs") as part of the functionality of our Services. For more information about our use of APIs and SDKs, please contact us as set forth in the "Contact Us" section below. B . Disclosures to Protect Us or Others We may access, preserve, and disclose any information we store associated with you to external parties if we, in good faith, believe doing so is required or appropriate to: comply with law enforcement or national security requests and legal process, such as a court order or subpoena; protect your, our, or others' rights, property, or safety; enforce our policies or contracts; collect amounts owed to us; or assist with an investigation or prosecution of suspected or actual illegal activity. C. Disclosure in the Event of Merger, Sale, or Other Asset Transfers If we are involved in a merger, acquisition, financing due diligence, reorganization, bankruptcy, receivership, purchase or sale of assets, or transition of service to another provider, your information may be sold or transferred as part of such a transaction, as permitted by law and/or contract. 5. YOUR PRIVACY CHOICES AND RIGHTS Your Privacy Choices . The privacy choices you may have about your personal information are determined by applicable law and are described below. Email Communications . If you receive an unwanted email from us, you can use the unsubscribe link found at the bottom of the email to opt out of receiving future emails. Note that you will continue to receive transaction-related emails regarding products or Services you have requested. We may also send you certain non-promotional communications regarding us and our Services, and you will not be able to opt out of those communications (e.g., communications regarding our Services or updates to our Terms or this Privacy Policy). Mobile Devices . We may send you push notifications through our mobile application. You may opt out from receiving these push notifications by changing the settings on your mobile device. "Do Not Track." Do Not Track (" DNT ") is a privacy preference that users can set in certain web browsers. Please note that we do not respond to or honor DNT signals or similar mechanisms transmitted by web browsers. Cookies and Interest-Based Advertising . You may stop or restrict the placement of Technologies on your device or remove them by adjusting your preferences as your browser or device permits. However, if you adjust your preferences, our Services may not work properly. Please note that cookie-based opt-outs are not effective on mobile applications. Please note you must separately opt out in each browser and on each device. Your Privacy Rights . In accordance with applicable law, you may have the right to: Access Personal Information about you, including: (i) confirming whether we are processing your personal information; (ii) obtaining access to or a copy of your personal information; Request Correction of your personal information where it is inaccurate, incomplete or outdated. In some cases, we may provide self-service tools that enable you to update your personal information; Request Deletion, Anonymization or Blocking of your personal information when processing is based on your consent or when processing is unnecessary, excessive or noncompliant; Request Restriction of or Object to our processing of your personal information when processing is noncompliant; Withdraw Your Consent to our processing of your personal information. If you refrain from providing personal information or withdraw your consent to processing, some features of our Service may not be available; Request Data Portability and Receive an Electronic Copy of Personal Information that You Have Provided to Us; Be Informed about third parties with which your personal information has been shared; and Request the Review of Decisions Taken Exclusively Based on Automated Processing if such decisions could affect your data subject rights. If you would like to exercise any of these rights, please contact us as set forth in "Contact Us" below. We will process such requests in accordance with applicable laws. 6. INTERNATIONAL DATA TRANSFERS All information processed by us may be transferred, processed, and stored anywhere in the world, including, but not limited to, the United States or other countries, which may have data protection laws that are different from the laws where you live. We always strive to safeguard your information consistent with the requirements of applicable laws. 7. RETENTION OF PERSONAL INFORMATION We store the personal information we collect as described in this Privacy Policy for as long as you use our Services or as necessary: to fulfill the purpose or purposes for which it was collected, to provide our Services, to resolve disputes, to establish legal defenses, to conduct audits, to pursue legitimate business purposes, to enforce our agreements, and to comply with applicable laws. 8. SUPPLEMENTAL DISCLOSURES FOR CALIFORNIA RESIDENTS Refer-a-Friend and Similar Incentive Programs . As described above in the How We Use Your Personal Information section ("Share Content with Friends or Colleagues" subsection), we may offer referral programs or other incentivized data collection programs. For example, we may offer incentives to you such as discounts or promotional items or credit in connection with these programs, wherein you provide your personal information in exchange for a reward, or provide personal information regarding your friends or colleagues (such as their email address) and receive rewards when they sign up to use our Services. (The referred party may also receive rewards for signing up via your referral.) These programs are entirely voluntary and allow us to grow our business and provide additional benefits to you. The value of your data to us depends on how you ultimately use our Services, whereas the value of the referred party's data to us depends on whether the referred party ultimately becomes a User or Forem Operator and uses our Services. Said value will be reflected in the incentive offered in connection with each program. Accessibility . This Privacy Policy uses industry-standard technologies and was developed in line with the World Wide Web Consortium's Web Content Accessibility Guidelines, version 2.1* . * If you wish to print this policy, please do so from your web browser or by saving the page as a PDF. California Shine the Light . The California "Shine the Light" law permits users who are California residents to request and obtain from us once a year, free of charge, a list of the third parties to whom we have disclosed their personal information (if any) for their direct marketing purposes in the prior calendar year, as well as the type of personal information disclosed to those parties. Right for Minors to Remove Posted Content . Where required by law, California residents under the age of 18 may request to have their posted content or information removed from the publicly-viewable portions of the Services by contacting us directly as set forth in the "Contact Us" section below or by logging into their account and removing the content or information using our self-service tools. 9. SUPPLEMENTAL NOTICE FOR NEVADA RESIDENTS If you are a resident of Nevada, you have the right to opt-out of the sale of certain Personal Information to third parties who intend to license or sell that Personal Information. You can exercise this right by contacting us as set forth in the "Contact Us\" section below with the subject line "Nevada Do Not Sell Request" and providing us with your name and the email address associated with your account. Please note that we do not currently sell your Personal Information as sales are defined in Nevada Revised Statutes Chapter 603A. If you have any questions, please contact us as set forth below. 10. CHILDREN'S INFORMATION The Services are not directed to children under 13 (or other age as required by local law), and we do not knowingly collect personal information from children. If you are a parent or guardian and believe your child has uploaded personal information to our site without your consent, you may contact us as described in the "Contact Us" section below. If we become aware that a child has provided us with personal information in violation of applicable law, we will delete any personal information we have collected, unless we have a legal obligation to keep it, and terminate the child's account if applicable. 11. OTHER PROVISIONS Third-Party Websites or Applications . The Services may contain links to other websites or applications, and other websites or applications may reference or link to our Services. These third-party services are not controlled by us. We encourage our users to read the privacy policies of each website and application with which they interact. We do not endorse, screen or approve, and are not responsible for, the privacy practices or content of such other websites or applications. Providing personal information to third-party websites or applications is at your own risk. Changes to Our Privacy Policy . We may revise this Privacy Policy from time to time in our sole discretion. If there are any material changes to this Privacy Policy, we will notify you as required by applicable law. You understand and agree that you will be deemed to have accepted the updated Privacy Policy if you continue to use our Services after the new Privacy Policy takes effect. 12. CONTACT US If you have any questions about our privacy practices or this Privacy Policy, or to exercise your rights as detailed in this Privacy Policy, please contact us at: support@dev.to . 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV Future — News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Home About Contact Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . Future © 2025 - 2026. Stay on the cutting edge, and shape tomorrow Log in Create account | 2026-01-13T08:49:02 |
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https://future.forem.com/privacy#6-international-data-transfers | Privacy Policy - Future Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. DUMB DEV Community Follow Memes and software development shitposting Design Community Follow Web design, graphic design and everything in-between Security Forem Follow Your central hub for all things security. From ethical hacking and CTFs to GRC and career development, for beginners and pros alike Golf Forem Follow A community of golfers and golfing enthusiasts Crypto Forem Follow A collaborative community for all things Crypto—from Bitcoin to protocol development and DeFi to NFTs and market analysis. Parenting Follow A place for parents to the share the joys, challenges, and wisdom that come from raising kids. We're here for them and for each other. Forem Core Follow Discussing the core forem open source software project — features, bugs, performance, self-hosting. Maker Forem Follow A community for makers, hobbyists, and professionals to discuss Arduino, Raspberry Pi, 3D printing, and much more. HMPL.js Forem Follow For developers using HMPL.js to build fast, lightweight web apps. A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account Future Close Privacy Policy Last Updated: September 01, 2023 This Privacy Policy is designed to help you understand how DEV Community Inc. (" DEV ," " we ," or " us ") collects, use, and discloses your personal information. What's With the Defined Terms? You'll notice that some words appear in quotes in this Privacy Policy. They're called "defined terms," and we use them so that we don't have to repeat the same language again and again. They mean the same thing in every instance, to help us make sure that this Privacy Policy is consistent. We've included the defined terms throughout because we want it to be easy for you to read them in context. 1. WHAT DOES THIS PRIVACY POLICY APPLY TO? 2. PERSONAL INFORMATION WE COLLECT 3. HOW WE USE YOUR INFORMATION 4. HOW WE DISCLOSE YOUR INFORMATION 5. YOUR PRIVACY CHOICES AND RIGHTS 6. INTERNATIONAL DATA TRANSFERS 7. RETENTION OF PERSONAL INFORMATION 8. SUPPLEMENTAL DISCLOSURES FOR CALIFORNIA RESIDENTS 9. SUPPLEMENTAL NOTICE FOR NEVADA RESIDENTS 10. CHILDREN'S INFORMATION 11. OTHER PROVISIONS 12. CONTACT US 1. WHAT DOES THIS PRIVACY POLICY APPLY TO? This Privacy Policy applies to personal information processed by us, including on our websites, mobile applications, and other online or offline offerings — basically anything we do. To make this Privacy Policy easier to read, our websites, mobile applications, and other offerings are all collectively called the " Services. " Beyond this Privacy Policy, your use of the Services is subject to our DEV Community Terms and our Forem Terms. The Services include both our own community forum at https://www.dev.to (the " DEV Community ") and the open source tool we provide called " Forem ," available at https://www.forem.com which allows our customers to create and operate their own online forums. We collect personal information from two categories of people: (1) our customers, who use Forem and our hosting services to run and host their own forums (we'll call them " Forem Operators "), and (2) the people who interact with DEV-hosted forums, including forums provided by Forem Operators utilizing Forem and separately our own DEV Community (we'll call them " Users "). An Important Note for Users Since we provide hosting services for Forem Operators, technically we also process your information on their behalf. That processing is governed by the contracts that we have in place with each Forem Operator, not this Privacy Policy. In other words, when you share your data on a DEV-hosted forum operated by a Forem Operator, we at DEV are basically just the "pipes" — we process the data on behalf of the Forem Operator, but don't do anything with it ourselves beyond what we're required to do under our contract (and by law). So, if you post your information on a DEV-powered forum provided by a Forem Operator, that Forem Operator's privacy policy applies, and any questions or requests relating to your data on that service should be directed to that Forem Operator, not us. Likewise, if you use our mobile application, you may also interact with forums that use DEV's open-source tools but do all their hosting and data collection themselves. For those forums, we at DEV have no access to your data, so be sure to read the privacy policy of any third-party hosted forum before posting. 2. PERSONAL INFORMATION WE COLLECT The categories of personal information we collect depend on whether you're a User or Forem Operator, how you interact with us, our Services, and the requirements of applicable law. Breaking it down, we collect three types of information: (1) information that you provide to us directly, (2) information we obtain automatically when you use our Services, and (3) information we get about you from other sources (such as third-party services and organizations). More details are below. A. Information You Provide to Us Directly We may collect the following personal information that you provide to us. Account Creation (for Forem Operators): We'll require your name and email address to get started, as well as some details about the Forem you want to run, such as: whether you're running the Forem on your own behalf or as part of an organization, and details about the community you want to support (how big is it, what topics does it cover, where do members currently communicate, how/if the community earns money, whether the community is open, invite-only or paid, any existing social media accounts, etc.) You'll need to tell us a bit about your personal coding background, and you'll have the option to provide your DEV username as well, if you are a member of the DEV.to community. Account Creation (for Users) : We collect name and email address from users that create an account on DEV Community. For other forums created by Forem Operators using Forem, the Forem Operator determines what information is required for User account creation for their respective forums. Interactive Features (for Users) . Like any other social network, both we and other Users of our Services may collect personal information that you submit or make available through our interactive features (e.g., messaging and chat features, commenting functionalities, forums, blogs, posts, and other social media pages). While we do have private messages that are only between you and the person you're messaging (as well as us and the Forem Operator, as applicable), any information you provide using the public sharing features of the Services, such as the information you post to your public profile or the topics you follow is public, including to recruiters and prospective employers, and is not subject to any of the privacy protections we mention in this Privacy Policy except where legally required. Please exercise caution before revealing any information that may identify you in the real world to others. Purchases . If you buy stuff on our shop site https://shop.dev.to/ (as either a User or Forem Operator), or otherwise if you pay us in connection with your use of the Forem service, we may collect personal information and details associated with your purchases, including payment information. Any payments made via our Services are processed by third-party payment processors, such as Stripe, Shopify, and PayPal. We do not directly collect or store any payment card information entered through our Services, but may receive information associated with your payment card information (e.g., your billing details). Your Communications with Us (Users and Forem Operators) . We may collect personal information, such as email address, phone number, or mailing address when you request information about our Services, register for our newsletter or loyalty program, request customer or technical support, apply for a job, or otherwise communicate with us. Surveys . We may contact you to participate in surveys. If you decide to participate, you may be asked to provide certain information, which may include personal information (for example, your home address). Sweepstakes or Contests . We may collect personal information you provide for any sweepstakes or contests that we offer. In some jurisdictions, we are required to publicly share information of sweepstakes and contest winners. Conferences, Trade Shows, and Other Events . We may collect personal information from individuals when we attend conferences, trade shows, and other events. Business Development and Strategic Partnerships . We may collect personal information from individuals and third parties to assess and pursue potential business opportunities. Job Applications . We may post job openings and opportunities on our Services. If you reply to one of these postings by submitting your application, CV and/or cover letter to us, we will collect and use your information to assess your qualifications. B. Information Collected Automatically We may collect personal information automatically when you use our Services: Automatic Data Collection . We may collect certain information automatically when you use our Services, such as your Internet protocol (IP) address, user settings, MAC address, cookie identifiers, mobile carrier, mobile advertising and other unique identifiers, browser or device information, location information (including approximate location derived from IP address), and Internet service provider. We may also automatically collect information regarding your use of our Services, such as pages that you visit before, during and after using our Services, information about the links you click, the types of content you interact with, the frequency and duration of your activities, and other information about how you use our Services. In addition, we may collect information that other people provide about you when they use our Services, including information about you when they tag you in their posts. Cookies, Pixel Tags/Web Beacons, and Other Technologies . We, as well as third parties that provide content, advertising, or other functionality on our Services, may use cookies, pixel tags, local storage, and other technologies (" Technologies ") to automatically collect information through your use of our Services. Cookies . Cookies are small text files placed in device browsers that store preferences and facilitate and enhance your experience. Pixel Tags/Web Beacons . A pixel tag (also known as a web beacon) is a piece of code embedded in our Services that collects information about engagement on our Services. The use of a pixel tag allows us to record, for example, that a user has visited a particular web page or clicked on a particular advertisement. We may also include web beacons in e-mails to understand whether messages have been opened, acted on, or forwarded. Our uses of these Technologies fall into the following general categories: Operationally Necessary . This includes Technologies that allow you access to our Services, applications, and tools that are required to identify irregular website behavior, prevent fraudulent activity and improve security or that allow you to make use of our functionality. Performance-Related . We may use Technologies to assess the performance of our Services, including as part of our analytic practices to help us understand how individuals use our Services ( see Analytics below ). Functionality-Related . We may use Technologies that allow us to offer you enhanced functionality when accessing or using our Services. This may include identifying you when you sign into our Services or keeping track of your specified preferences, interests, or past items viewed. Analytics . We may use Technologies and other third-party tools to process analytics information on our Services. Some of our analytics partners include Google Analytics. For more information,please visit Google Analytics' Privacy Policy . To learn more about how to opt-out of Google Analytics' use of your information, please click here . Social Media Platforms . Our Services may contain social media buttons such as Twitter, Facebook, GitHub, Instagram, and Twitch (that might include widgets such as the "share this" button or other interactive mini programs). These features may collect your IP address, which page you are visiting on our Services, and may set a cookie to enable the feature to function properly. Your interactions with these platforms are governed by the privacy policy of the company providing it. See the "Your Privacy Choices and Rights" section below to understand your choices regarding these Technologies. C. Information Collected from Other Sources We may obtain information about you from other sources, including through third-party services and organizations. For example, if you access our Services through a third-party application, such as an app store, a third-party login service (e.g., through Twitter, Apple, or GitHub), or a social networking site, we may collect whatever information about you from that third-party application that you have made available via your privacy settings. 3. HOW WE USE YOUR INFORMATION We use your information for a variety of business purposes, including to provide our Services, for administrative purposes, and to market our products and Services, as described below. A. Provide Our Services We use your information to fulfill our contract with you and provide you with our Services, such as: Managing your information and accounts; Providing access to certain areas, functionalities, and features of our Services; Answering requests for customer or technical support; Communicating with you about your account, activities on our Services, and policy changes; Processing your financial information and other payment methods for products or Services purchased; Processing applications if you apply for a job we post on our Services; and Allowing you to register for events. B. Administrative Purposes We use your information for various administrative purposes, such as: Pursuing our legitimate interests such as direct marketing, research and development (including marketing research), network and information security, and fraud prevention; Detecting security incidents, protecting against malicious, deceptive, fraudulent or illegal activity, and prosecuting those responsible for that activity; Measuring interest and engagement in our Services, including for usage-based billing purposes; Short-term, transient use, such as contextual customization of ads; Improving, optimizing, upgrading, or enhancing our Services; Developing new products and Services; Ensuring internal quality control and safety; Authenticating and verifying individual identities, including requests to exercise your rights under this policy; Debugging to identify and repair errors with our Services; Auditing relating to interactions, transactions and other compliance activities; Enforcing our agreements and policies; and Complying with our legal obligations. C. Marketing and Advertising our Products and Services We may use your personal information to tailor and provide you with content and advertisements for our Services, such as via email. If you have any questions about our marketing practices, you may contact us at any time as set forth in the "Contact Us" section below. D. Other Purposes We also use your information for other purposes as requested by you or as permitted by applicable law. Consent . We may use personal information for other purposes that are clearly disclosed to you at the time you provide personal information or with your consent. Automated Decision Making. We may engage in automated decision making, including profiling, such as to suggest topics or other Users for you to follow. DEV's processing of your personal information will not result in a decision based solely on automated processing that significantly affects you unless such a decision is necessary as part of a contract we have with you, we have your consent, or we are permitted by law to engage in such automated decision making. If you have questions about our automated decision making, you may contact us as set forth in the "Contact Us" section below. De-identified and Aggregated Information . We may use personal information and other information about you to create de-identified and/or aggregated information, such as de-identified demographic information, information about the device from which you access our Services, or other analyses we create. For example, we may collect system-wide information to ensure availability of the platform, or measure aggregate data trends to analyze and optimize our Services. Share Content with Friends or Colleagues. Our Services may offer various tools and functionalities. For example, we may allow you to provide information about your friends through our referral services. Our referral services may allow you to forward or share certain content with a friend or colleague, such as an email inviting your friend to use our Services. Please only share with us contact information of people with whom you have a relationship (e.g., relative, friend neighbor, or co-worker). 4. HOW WE DISCLOSE YOUR INFORMATION We disclose your information to third parties for a variety of business purposes, including to provide our Services, to protect us or others, or in the event of a major business transaction such as a merger, sale, or asset transfer, as described below. A. Disclosures to Provide our Services The categories of third parties with whom we may share your information are described below. Service Providers . We may share your personal information with our third-party service providers who use that information to help us provide our Services. This includes service providers that provide us with IT support, hosting, payment processing, customer service, and related services. For example, our Shop site is run by Shopify, who handle your shipping details on our behalf. Business Partners . We may share your personal information with business partners to provide you with a product or service you have requested. We may also share your personal information to business partners with whom we jointly offer products or services. Other Users . As described above in the "Personal Information We Collect" section of this Privacy Policy, our Service allows Users to share their profiles, and any posts, chats, etc. with other Users and with the general public, including to those who do not use our Services. APIs/SDKs . We may use third-party Application Program Interfaces ("APIs") and Software Development Kits ("SDKs") as part of the functionality of our Services. For more information about our use of APIs and SDKs, please contact us as set forth in the "Contact Us" section below. B . Disclosures to Protect Us or Others We may access, preserve, and disclose any information we store associated with you to external parties if we, in good faith, believe doing so is required or appropriate to: comply with law enforcement or national security requests and legal process, such as a court order or subpoena; protect your, our, or others' rights, property, or safety; enforce our policies or contracts; collect amounts owed to us; or assist with an investigation or prosecution of suspected or actual illegal activity. C. 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We may also send you certain non-promotional communications regarding us and our Services, and you will not be able to opt out of those communications (e.g., communications regarding our Services or updates to our Terms or this Privacy Policy). Mobile Devices . We may send you push notifications through our mobile application. You may opt out from receiving these push notifications by changing the settings on your mobile device. "Do Not Track." Do Not Track (" DNT ") is a privacy preference that users can set in certain web browsers. Please note that we do not respond to or honor DNT signals or similar mechanisms transmitted by web browsers. Cookies and Interest-Based Advertising . You may stop or restrict the placement of Technologies on your device or remove them by adjusting your preferences as your browser or device permits. However, if you adjust your preferences, our Services may not work properly. Please note that cookie-based opt-outs are not effective on mobile applications. Please note you must separately opt out in each browser and on each device. Your Privacy Rights . In accordance with applicable law, you may have the right to: Access Personal Information about you, including: (i) confirming whether we are processing your personal information; (ii) obtaining access to or a copy of your personal information; Request Correction of your personal information where it is inaccurate, incomplete or outdated. In some cases, we may provide self-service tools that enable you to update your personal information; Request Deletion, Anonymization or Blocking of your personal information when processing is based on your consent or when processing is unnecessary, excessive or noncompliant; Request Restriction of or Object to our processing of your personal information when processing is noncompliant; Withdraw Your Consent to our processing of your personal information. If you refrain from providing personal information or withdraw your consent to processing, some features of our Service may not be available; Request Data Portability and Receive an Electronic Copy of Personal Information that You Have Provided to Us; Be Informed about third parties with which your personal information has been shared; and Request the Review of Decisions Taken Exclusively Based on Automated Processing if such decisions could affect your data subject rights. If you would like to exercise any of these rights, please contact us as set forth in "Contact Us" below. We will process such requests in accordance with applicable laws. 6. INTERNATIONAL DATA TRANSFERS All information processed by us may be transferred, processed, and stored anywhere in the world, including, but not limited to, the United States or other countries, which may have data protection laws that are different from the laws where you live. We always strive to safeguard your information consistent with the requirements of applicable laws. 7. RETENTION OF PERSONAL INFORMATION We store the personal information we collect as described in this Privacy Policy for as long as you use our Services or as necessary: to fulfill the purpose or purposes for which it was collected, to provide our Services, to resolve disputes, to establish legal defenses, to conduct audits, to pursue legitimate business purposes, to enforce our agreements, and to comply with applicable laws. 8. SUPPLEMENTAL DISCLOSURES FOR CALIFORNIA RESIDENTS Refer-a-Friend and Similar Incentive Programs . As described above in the How We Use Your Personal Information section ("Share Content with Friends or Colleagues" subsection), we may offer referral programs or other incentivized data collection programs. For example, we may offer incentives to you such as discounts or promotional items or credit in connection with these programs, wherein you provide your personal information in exchange for a reward, or provide personal information regarding your friends or colleagues (such as their email address) and receive rewards when they sign up to use our Services. (The referred party may also receive rewards for signing up via your referral.) These programs are entirely voluntary and allow us to grow our business and provide additional benefits to you. The value of your data to us depends on how you ultimately use our Services, whereas the value of the referred party's data to us depends on whether the referred party ultimately becomes a User or Forem Operator and uses our Services. Said value will be reflected in the incentive offered in connection with each program. Accessibility . This Privacy Policy uses industry-standard technologies and was developed in line with the World Wide Web Consortium's Web Content Accessibility Guidelines, version 2.1* . * If you wish to print this policy, please do so from your web browser or by saving the page as a PDF. California Shine the Light . The California "Shine the Light" law permits users who are California residents to request and obtain from us once a year, free of charge, a list of the third parties to whom we have disclosed their personal information (if any) for their direct marketing purposes in the prior calendar year, as well as the type of personal information disclosed to those parties. Right for Minors to Remove Posted Content . Where required by law, California residents under the age of 18 may request to have their posted content or information removed from the publicly-viewable portions of the Services by contacting us directly as set forth in the "Contact Us" section below or by logging into their account and removing the content or information using our self-service tools. 9. SUPPLEMENTAL NOTICE FOR NEVADA RESIDENTS If you are a resident of Nevada, you have the right to opt-out of the sale of certain Personal Information to third parties who intend to license or sell that Personal Information. You can exercise this right by contacting us as set forth in the "Contact Us\" section below with the subject line "Nevada Do Not Sell Request" and providing us with your name and the email address associated with your account. Please note that we do not currently sell your Personal Information as sales are defined in Nevada Revised Statutes Chapter 603A. If you have any questions, please contact us as set forth below. 10. CHILDREN'S INFORMATION The Services are not directed to children under 13 (or other age as required by local law), and we do not knowingly collect personal information from children. If you are a parent or guardian and believe your child has uploaded personal information to our site without your consent, you may contact us as described in the "Contact Us" section below. If we become aware that a child has provided us with personal information in violation of applicable law, we will delete any personal information we have collected, unless we have a legal obligation to keep it, and terminate the child's account if applicable. 11. OTHER PROVISIONS Third-Party Websites or Applications . The Services may contain links to other websites or applications, and other websites or applications may reference or link to our Services. These third-party services are not controlled by us. We encourage our users to read the privacy policies of each website and application with which they interact. We do not endorse, screen or approve, and are not responsible for, the privacy practices or content of such other websites or applications. Providing personal information to third-party websites or applications is at your own risk. Changes to Our Privacy Policy . We may revise this Privacy Policy from time to time in our sole discretion. If there are any material changes to this Privacy Policy, we will notify you as required by applicable law. You understand and agree that you will be deemed to have accepted the updated Privacy Policy if you continue to use our Services after the new Privacy Policy takes effect. 12. CONTACT US If you have any questions about our privacy practices or this Privacy Policy, or to exercise your rights as detailed in this Privacy Policy, please contact us at: support@dev.to . 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV Future — News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Home About Contact Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . Future © 2025 - 2026. Stay on the cutting edge, and shape tomorrow Log in Create account | 2026-01-13T08:49:02 |
https://www.youtube.com/watch?v=8pDqJVdNa44 | How A Small Team of Developers Created React at Facebook | React.js: The Documentary - YouTube 정보 보도자료 저작권 문의하기 크리에이터 광고 개발자 약관 개인정보처리방침 정책 및 안전 YouTube 작동의 원리 새로운 기능 테스트하기 © 2026 Google LLC, Sundar Pichai, 1600 Amphitheatre Parkway, Mountain View CA 94043, USA, 0807-882-594 (무료), yt-support-solutions-kr@google.com, 호스팅: Google LLC, 사업자정보 , 불법촬영물 신고 크리에이터들이 유튜브 상에 게시, 태그 또는 추천한 상품들은 판매자들의 약관에 따라 판매됩니다. 유튜브는 이러한 제품들을 판매하지 않으며, 그에 대한 책임을 지지 않습니다. var ytInitialData = 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https://docs.python.org/3/faq/programming.html | Programming FAQ — Python 3.14.2 documentation Theme Auto Light Dark Table of Contents Programming FAQ General Questions Core Language Numbers and strings Performance Sequences (Tuples/Lists) Objects Modules Previous topic General Python FAQ Next topic Design and History FAQ This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » Python Frequently Asked Questions » Programming FAQ | Theme Auto Light Dark | Programming FAQ ¶ Contents Programming FAQ General Questions Is there a source code level debugger with breakpoints, single-stepping, etc.? Are there tools to help find bugs or perform static analysis? How can I create a stand-alone binary from a Python script? Are there coding standards or a style guide for Python programs? Core Language Why am I getting an UnboundLocalError when the variable has a value? What are the rules for local and global variables in Python? Why do lambdas defined in a loop with different values all return the same result? How do I share global variables across modules? What are the “best practices” for using import in a module? Why are default values shared between objects? How can I pass optional or keyword parameters from one function to another? What is the difference between arguments and parameters? Why did changing list ‘y’ also change list ‘x’? How do I write a function with output parameters (call by reference)? How do you make a higher order function in Python? How do I copy an object in Python? How can I find the methods or attributes of an object? How can my code discover the name of an object? What’s up with the comma operator’s precedence? Is there an equivalent of C’s “?:” ternary operator? Is it possible to write obfuscated one-liners in Python? What does the slash(/) in the parameter list of a function mean? Numbers and strings How do I specify hexadecimal and octal integers? Why does -22 // 10 return -3? How do I get int literal attribute instead of SyntaxError? How do I convert a string to a number? How do I convert a number to a string? How do I modify a string in place? How do I use strings to call functions/methods? Is there an equivalent to Perl’s chomp() for removing trailing newlines from strings? Is there a scanf() or sscanf() equivalent? What does UnicodeDecodeError or UnicodeEncodeError error mean? Can I end a raw string with an odd number of backslashes? Performance My program is too slow. How do I speed it up? What is the most efficient way to concatenate many strings together? Sequences (Tuples/Lists) How do I convert between tuples and lists? What’s a negative index? How do I iterate over a sequence in reverse order? How do you remove duplicates from a list? How do you remove multiple items from a list How do you make an array in Python? How do I create a multidimensional list? How do I apply a method or function to a sequence of objects? Why does a_tuple[i] += [‘item’] raise an exception when the addition works? I want to do a complicated sort: can you do a Schwartzian Transform in Python? How can I sort one list by values from another list? Objects What is a class? What is a method? What is self? How do I check if an object is an instance of a given class or of a subclass of it? What is delegation? How do I call a method defined in a base class from a derived class that extends it? How can I organize my code to make it easier to change the base class? How do I create static class data and static class methods? How can I overload constructors (or methods) in Python? I try to use __spam and I get an error about _SomeClassName__spam. My class defines __del__ but it is not called when I delete the object. How do I get a list of all instances of a given class? Why does the result of id() appear to be not unique? When can I rely on identity tests with the is operator? How can a subclass control what data is stored in an immutable instance? How do I cache method calls? Modules How do I create a .pyc file? How do I find the current module name? How can I have modules that mutually import each other? __import__(‘x.y.z’) returns <module ‘x’>; how do I get z? When I edit an imported module and reimport it, the changes don’t show up. Why does this happen? General Questions ¶ Is there a source code level debugger with breakpoints, single-stepping, etc.? ¶ Yes. Several debuggers for Python are described below, and the built-in function breakpoint() allows you to drop into any of them. The pdb module is a simple but adequate console-mode debugger for Python. It is part of the standard Python library, and is documented in the Library Reference Manual . You can also write your own debugger by using the code for pdb as an example. The IDLE interactive development environment, which is part of the standard Python distribution (normally available as Tools/scripts/idle3 ), includes a graphical debugger. PythonWin is a Python IDE that includes a GUI debugger based on pdb. The PythonWin debugger colors breakpoints and has quite a few cool features such as debugging non-PythonWin programs. PythonWin is available as part of pywin32 project and as a part of the ActivePython distribution. Eric is an IDE built on PyQt and the Scintilla editing component. trepan3k is a gdb-like debugger. Visual Studio Code is an IDE with debugging tools that integrates with version-control software. There are a number of commercial Python IDEs that include graphical debuggers. They include: Wing IDE Komodo IDE PyCharm Are there tools to help find bugs or perform static analysis? ¶ Yes. Pylint and Pyflakes do basic checking that will help you catch bugs sooner. Static type checkers such as Mypy , Pyre , and Pytype can check type hints in Python source code. How can I create a stand-alone binary from a Python script? ¶ You don’t need the ability to compile Python to C code if all you want is a stand-alone program that users can download and run without having to install the Python distribution first. There are a number of tools that determine the set of modules required by a program and bind these modules together with a Python binary to produce a single executable. One is to use the freeze tool, which is included in the Python source tree as Tools/freeze . It converts Python byte code to C arrays; with a C compiler you can embed all your modules into a new program, which is then linked with the standard Python modules. It works by scanning your source recursively for import statements (in both forms) and looking for the modules in the standard Python path as well as in the source directory (for built-in modules). It then turns the bytecode for modules written in Python into C code (array initializers that can be turned into code objects using the marshal module) and creates a custom-made config file that only contains those built-in modules which are actually used in the program. It then compiles the generated C code and links it with the rest of the Python interpreter to form a self-contained binary which acts exactly like your script. The following packages can help with the creation of console and GUI executables: Nuitka (Cross-platform) PyInstaller (Cross-platform) PyOxidizer (Cross-platform) cx_Freeze (Cross-platform) py2app (macOS only) py2exe (Windows only) Are there coding standards or a style guide for Python programs? ¶ Yes. The coding style required for standard library modules is documented as PEP 8 . Core Language ¶ Why am I getting an UnboundLocalError when the variable has a value? ¶ It can be a surprise to get the UnboundLocalError in previously working code when it is modified by adding an assignment statement somewhere in the body of a function. This code: >>> x = 10 >>> def bar (): ... print ( x ) ... >>> bar () 10 works, but this code: >>> x = 10 >>> def foo (): ... print ( x ) ... x += 1 results in an UnboundLocalError : >>> foo () Traceback (most recent call last): ... UnboundLocalError : local variable 'x' referenced before assignment This is because when you make an assignment to a variable in a scope, that variable becomes local to that scope and shadows any similarly named variable in the outer scope. Since the last statement in foo assigns a new value to x , the compiler recognizes it as a local variable. Consequently when the earlier print(x) attempts to print the uninitialized local variable and an error results. In the example above you can access the outer scope variable by declaring it global: >>> x = 10 >>> def foobar (): ... global x ... print ( x ) ... x += 1 ... >>> foobar () 10 This explicit declaration is required in order to remind you that (unlike the superficially analogous situation with class and instance variables) you are actually modifying the value of the variable in the outer scope: >>> print ( x ) 11 You can do a similar thing in a nested scope using the nonlocal keyword: >>> def foo (): ... x = 10 ... def bar (): ... nonlocal x ... print ( x ) ... x += 1 ... bar () ... print ( x ) ... >>> foo () 10 11 What are the rules for local and global variables in Python? ¶ In Python, variables that are only referenced inside a function are implicitly global. If a variable is assigned a value anywhere within the function’s body, it’s assumed to be a local unless explicitly declared as global. Though a bit surprising at first, a moment’s consideration explains this. On one hand, requiring global for assigned variables provides a bar against unintended side-effects. On the other hand, if global was required for all global references, you’d be using global all the time. You’d have to declare as global every reference to a built-in function or to a component of an imported module. This clutter would defeat the usefulness of the global declaration for identifying side-effects. Why do lambdas defined in a loop with different values all return the same result? ¶ Assume you use a for loop to define a few different lambdas (or even plain functions), e.g.: >>> squares = [] >>> for x in range ( 5 ): ... squares . append ( lambda : x ** 2 ) This gives you a list that contains 5 lambdas that calculate x**2 . You might expect that, when called, they would return, respectively, 0 , 1 , 4 , 9 , and 16 . However, when you actually try you will see that they all return 16 : >>> squares [ 2 ]() 16 >>> squares [ 4 ]() 16 This happens because x is not local to the lambdas, but is defined in the outer scope, and it is accessed when the lambda is called — not when it is defined. At the end of the loop, the value of x is 4 , so all the functions now return 4**2 , i.e. 16 . You can also verify this by changing the value of x and see how the results of the lambdas change: >>> x = 8 >>> squares [ 2 ]() 64 In order to avoid this, you need to save the values in variables local to the lambdas, so that they don’t rely on the value of the global x : >>> squares = [] >>> for x in range ( 5 ): ... squares . append ( lambda n = x : n ** 2 ) Here, n=x creates a new variable n local to the lambda and computed when the lambda is defined so that it has the same value that x had at that point in the loop. This means that the value of n will be 0 in the first lambda, 1 in the second, 2 in the third, and so on. Therefore each lambda will now return the correct result: >>> squares [ 2 ]() 4 >>> squares [ 4 ]() 16 Note that this behaviour is not peculiar to lambdas, but applies to regular functions too. How do I share global variables across modules? ¶ The canonical way to share information across modules within a single program is to create a special module (often called config or cfg). Just import the config module in all modules of your application; the module then becomes available as a global name. Because there is only one instance of each module, any changes made to the module object get reflected everywhere. For example: config.py: x = 0 # Default value of the 'x' configuration setting mod.py: import config config . x = 1 main.py: import config import mod print ( config . x ) Note that using a module is also the basis for implementing the singleton design pattern, for the same reason. What are the “best practices” for using import in a module? ¶ In general, don’t use from modulename import * . Doing so clutters the importer’s namespace, and makes it much harder for linters to detect undefined names. Import modules at the top of a file. Doing so makes it clear what other modules your code requires and avoids questions of whether the module name is in scope. Using one import per line makes it easy to add and delete module imports, but using multiple imports per line uses less screen space. It’s good practice if you import modules in the following order: standard library modules – e.g. sys , os , argparse , re third-party library modules (anything installed in Python’s site-packages directory) – e.g. dateutil , requests , PIL.Image locally developed modules It is sometimes necessary to move imports to a function or class to avoid problems with circular imports. Gordon McMillan says: Circular imports are fine where both modules use the “import <module>” form of import. They fail when the 2nd module wants to grab a name out of the first (“from module import name”) and the import is at the top level. That’s because names in the 1st are not yet available, because the first module is busy importing the 2nd. In this case, if the second module is only used in one function, then the import can easily be moved into that function. By the time the import is called, the first module will have finished initializing, and the second module can do its import. It may also be necessary to move imports out of the top level of code if some of the modules are platform-specific. In that case, it may not even be possible to import all of the modules at the top of the file. In this case, importing the correct modules in the corresponding platform-specific code is a good option. Only move imports into a local scope, such as inside a function definition, if it’s necessary to solve a problem such as avoiding a circular import or are trying to reduce the initialization time of a module. This technique is especially helpful if many of the imports are unnecessary depending on how the program executes. You may also want to move imports into a function if the modules are only ever used in that function. Note that loading a module the first time may be expensive because of the one time initialization of the module, but loading a module multiple times is virtually free, costing only a couple of dictionary lookups. Even if the module name has gone out of scope, the module is probably available in sys.modules . Why are default values shared between objects? ¶ This type of bug commonly bites neophyte programmers. Consider this function: def foo ( mydict = {}): # Danger: shared reference to one dict for all calls ... compute something ... mydict [ key ] = value return mydict The first time you call this function, mydict contains a single item. The second time, mydict contains two items because when foo() begins executing, mydict starts out with an item already in it. It is often expected that a function call creates new objects for default values. This is not what happens. Default values are created exactly once, when the function is defined. If that object is changed, like the dictionary in this example, subsequent calls to the function will refer to this changed object. By definition, immutable objects such as numbers, strings, tuples, and None , are safe from change. Changes to mutable objects such as dictionaries, lists, and class instances can lead to confusion. Because of this feature, it is good programming practice to not use mutable objects as default values. Instead, use None as the default value and inside the function, check if the parameter is None and create a new list/dictionary/whatever if it is. For example, don’t write: def foo ( mydict = {}): ... but: def foo ( mydict = None ): if mydict is None : mydict = {} # create a new dict for local namespace This feature can be useful. When you have a function that’s time-consuming to compute, a common technique is to cache the parameters and the resulting value of each call to the function, and return the cached value if the same value is requested again. This is called “memoizing”, and can be implemented like this: # Callers can only provide two parameters and optionally pass _cache by keyword def expensive ( arg1 , arg2 , * , _cache = {}): if ( arg1 , arg2 ) in _cache : return _cache [( arg1 , arg2 )] # Calculate the value result = ... expensive computation ... _cache [( arg1 , arg2 )] = result # Store result in the cache return result You could use a global variable containing a dictionary instead of the default value; it’s a matter of taste. How can I pass optional or keyword parameters from one function to another? ¶ Collect the arguments using the * and ** specifiers in the function’s parameter list; this gives you the positional arguments as a tuple and the keyword arguments as a dictionary. You can then pass these arguments when calling another function by using * and ** : def f ( x , * args , ** kwargs ): ... kwargs [ 'width' ] = '14.3c' ... g ( x , * args , ** kwargs ) What is the difference between arguments and parameters? ¶ Parameters are defined by the names that appear in a function definition, whereas arguments are the values actually passed to a function when calling it. Parameters define what kind of arguments a function can accept. For example, given the function definition: def func ( foo , bar = None , ** kwargs ): pass foo , bar and kwargs are parameters of func . However, when calling func , for example: func ( 42 , bar = 314 , extra = somevar ) the values 42 , 314 , and somevar are arguments. Why did changing list ‘y’ also change list ‘x’? ¶ If you wrote code like: >>> x = [] >>> y = x >>> y . append ( 10 ) >>> y [10] >>> x [10] you might be wondering why appending an element to y changed x too. There are two factors that produce this result: Variables are simply names that refer to objects. Doing y = x doesn’t create a copy of the list – it creates a new variable y that refers to the same object x refers to. This means that there is only one object (the list), and both x and y refer to it. Lists are mutable , which means that you can change their content. After the call to append() , the content of the mutable object has changed from [] to [10] . Since both the variables refer to the same object, using either name accesses the modified value [10] . If we instead assign an immutable object to x : >>> x = 5 # ints are immutable >>> y = x >>> x = x + 1 # 5 can't be mutated, we are creating a new object here >>> x 6 >>> y 5 we can see that in this case x and y are not equal anymore. This is because integers are immutable , and when we do x = x + 1 we are not mutating the int 5 by incrementing its value; instead, we are creating a new object (the int 6 ) and assigning it to x (that is, changing which object x refers to). After this assignment we have two objects (the ints 6 and 5 ) and two variables that refer to them ( x now refers to 6 but y still refers to 5 ). Some operations (for example y.append(10) and y.sort() ) mutate the object, whereas superficially similar operations (for example y = y + [10] and sorted(y) ) create a new object. In general in Python (and in all cases in the standard library) a method that mutates an object will return None to help avoid getting the two types of operations confused. So if you mistakenly write y.sort() thinking it will give you a sorted copy of y , you’ll instead end up with None , which will likely cause your program to generate an easily diagnosed error. However, there is one class of operations where the same operation sometimes has different behaviors with different types: the augmented assignment operators. For example, += mutates lists but not tuples or ints ( a_list += [1, 2, 3] is equivalent to a_list.extend([1, 2, 3]) and mutates a_list , whereas some_tuple += (1, 2, 3) and some_int += 1 create new objects). In other words: If we have a mutable object ( list , dict , set , etc.), we can use some specific operations to mutate it and all the variables that refer to it will see the change. If we have an immutable object ( str , int , tuple , etc.), all the variables that refer to it will always see the same value, but operations that transform that value into a new value always return a new object. If you want to know if two variables refer to the same object or not, you can use the is operator, or the built-in function id() . How do I write a function with output parameters (call by reference)? ¶ Remember that arguments are passed by assignment in Python. Since assignment just creates references to objects, there’s no alias between an argument name in the caller and callee, and so no call-by-reference per se. You can achieve the desired effect in a number of ways. By returning a tuple of the results: >>> def func1 ( a , b ): ... a = 'new-value' # a and b are local names ... b = b + 1 # assigned to new objects ... return a , b # return new values ... >>> x , y = 'old-value' , 99 >>> func1 ( x , y ) ('new-value', 100) This is almost always the clearest solution. By using global variables. This isn’t thread-safe, and is not recommended. By passing a mutable (changeable in-place) object: >>> def func2 ( a ): ... a [ 0 ] = 'new-value' # 'a' references a mutable list ... a [ 1 ] = a [ 1 ] + 1 # changes a shared object ... >>> args = [ 'old-value' , 99 ] >>> func2 ( args ) >>> args ['new-value', 100] By passing in a dictionary that gets mutated: >>> def func3 ( args ): ... args [ 'a' ] = 'new-value' # args is a mutable dictionary ... args [ 'b' ] = args [ 'b' ] + 1 # change it in-place ... >>> args = { 'a' : 'old-value' , 'b' : 99 } >>> func3 ( args ) >>> args {'a': 'new-value', 'b': 100} Or bundle up values in a class instance: >>> class Namespace : ... def __init__ ( self , / , ** args ): ... for key , value in args . items (): ... setattr ( self , key , value ) ... >>> def func4 ( args ): ... args . a = 'new-value' # args is a mutable Namespace ... args . b = args . b + 1 # change object in-place ... >>> args = Namespace ( a = 'old-value' , b = 99 ) >>> func4 ( args ) >>> vars ( args ) {'a': 'new-value', 'b': 100} There’s almost never a good reason to get this complicated. Your best choice is to return a tuple containing the multiple results. How do you make a higher order function in Python? ¶ You have two choices: you can use nested scopes or you can use callable objects. For example, suppose you wanted to define linear(a,b) which returns a function f(x) that computes the value a*x+b . Using nested scopes: def linear ( a , b ): def result ( x ): return a * x + b return result Or using a callable object: class linear : def __init__ ( self , a , b ): self . a , self . b = a , b def __call__ ( self , x ): return self . a * x + self . b In both cases, taxes = linear ( 0.3 , 2 ) gives a callable object where taxes(10e6) == 0.3 * 10e6 + 2 . The callable object approach has the disadvantage that it is a bit slower and results in slightly longer code. However, note that a collection of callables can share their signature via inheritance: class exponential ( linear ): # __init__ inherited def __call__ ( self , x ): return self . a * ( x ** self . b ) Object can encapsulate state for several methods: class counter : value = 0 def set ( self , x ): self . value = x def up ( self ): self . value = self . value + 1 def down ( self ): self . value = self . value - 1 count = counter () inc , dec , reset = count . up , count . down , count . set Here inc() , dec() and reset() act like functions which share the same counting variable. How do I copy an object in Python? ¶ In general, try copy.copy() or copy.deepcopy() for the general case. Not all objects can be copied, but most can. Some objects can be copied more easily. Dictionaries have a copy() method: newdict = olddict . copy () Sequences can be copied by slicing: new_l = l [:] How can I find the methods or attributes of an object? ¶ For an instance x of a user-defined class, dir(x) returns an alphabetized list of the names containing the instance attributes and methods and attributes defined by its class. How can my code discover the name of an object? ¶ Generally speaking, it can’t, because objects don’t really have names. Essentially, assignment always binds a name to a value; the same is true of def and class statements, but in that case the value is a callable. Consider the following code: >>> class A : ... pass ... >>> B = A >>> a = B () >>> b = a >>> print ( b ) <__main__.A object at 0x16D07CC> >>> print ( a ) <__main__.A object at 0x16D07CC> Arguably the class has a name: even though it is bound to two names and invoked through the name B the created instance is still reported as an instance of class A . However, it is impossible to say whether the instance’s name is a or b , since both names are bound to the same value. Generally speaking it should not be necessary for your code to “know the names” of particular values. Unless you are deliberately writing introspective programs, this is usually an indication that a change of approach might be beneficial. In comp.lang.python, Fredrik Lundh once gave an excellent analogy in answer to this question: The same way as you get the name of that cat you found on your porch: the cat (object) itself cannot tell you its name, and it doesn’t really care – so the only way to find out what it’s called is to ask all your neighbours (namespaces) if it’s their cat (object)… ….and don’t be surprised if you’ll find that it’s known by many names, or no name at all! What’s up with the comma operator’s precedence? ¶ Comma is not an operator in Python. Consider this session: >>> "a" in "b" , "a" (False, 'a') Since the comma is not an operator, but a separator between expressions the above is evaluated as if you had entered: ( "a" in "b" ), "a" not: "a" in ( "b" , "a" ) The same is true of the various assignment operators ( = , += etc). They are not truly operators but syntactic delimiters in assignment statements. Is there an equivalent of C’s “?:” ternary operator? ¶ Yes, there is. The syntax is as follows: [ on_true ] if [ expression ] else [ on_false ] x , y = 50 , 25 small = x if x < y else y Before this syntax was introduced in Python 2.5, a common idiom was to use logical operators: [ expression ] and [ on_true ] or [ on_false ] However, this idiom is unsafe, as it can give wrong results when on_true has a false boolean value. Therefore, it is always better to use the ... if ... else ... form. Is it possible to write obfuscated one-liners in Python? ¶ Yes. Usually this is done by nesting lambda within lambda . See the following three examples, slightly adapted from Ulf Bartelt: from functools import reduce # Primes < 1000 print ( list ( filter ( None , map ( lambda y : y * reduce ( lambda x , y : x * y != 0 , map ( lambda x , y = y : y % x , range ( 2 , int ( pow ( y , 0.5 ) + 1 ))), 1 ), range ( 2 , 1000 ))))) # First 10 Fibonacci numbers print ( list ( map ( lambda x , f = lambda x , f :( f ( x - 1 , f ) + f ( x - 2 , f )) if x > 1 else 1 : f ( x , f ), range ( 10 )))) # Mandelbrot set print (( lambda Ru , Ro , Iu , Io , IM , Sx , Sy : reduce ( lambda x , y : x + ' \n ' + y , map ( lambda y , Iu = Iu , Io = Io , Ru = Ru , Ro = Ro , Sy = Sy , L = lambda yc , Iu = Iu , Io = Io , Ru = Ru , Ro = Ro , i = IM , Sx = Sx , Sy = Sy : reduce ( lambda x , y : x + y , map ( lambda x , xc = Ru , yc = yc , Ru = Ru , Ro = Ro , i = i , Sx = Sx , F = lambda xc , yc , x , y , k , f = lambda xc , yc , x , y , k , f :( k <= 0 ) or ( x * x + y * y >= 4.0 ) or 1 + f ( xc , yc , x * x - y * y + xc , 2.0 * x * y + yc , k - 1 , f ): f ( xc , yc , x , y , k , f ): chr ( 64 + F ( Ru + x * ( Ro - Ru ) / Sx , yc , 0 , 0 , i )), range ( Sx ))): L ( Iu + y * ( Io - Iu ) / Sy ), range ( Sy ))))( - 2.1 , 0.7 , - 1.2 , 1.2 , 30 , 80 , 24 )) # \___ ___/ \___ ___/ | | |__ lines on screen # V V | |______ columns on screen # | | |__________ maximum of "iterations" # | |_________________ range on y axis # |____________________________ range on x axis Don’t try this at home, kids! What does the slash(/) in the parameter list of a function mean? ¶ A slash in the argument list of a function denotes that the parameters prior to it are positional-only. Positional-only parameters are the ones without an externally usable name. Upon calling a function that accepts positional-only parameters, arguments are mapped to parameters based solely on their position. For example, divmod() is a function that accepts positional-only parameters. Its documentation looks like this: >>> help ( divmod ) Help on built-in function divmod in module builtins: divmod(x, y, /) Return the tuple (x//y, x%y). Invariant: div*y + mod == x. The slash at the end of the parameter list means that both parameters are positional-only. Thus, calling divmod() with keyword arguments would lead to an error: >>> divmod ( x = 3 , y = 4 ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : divmod() takes no keyword arguments Numbers and strings ¶ How do I specify hexadecimal and octal integers? ¶ To specify an octal digit, precede the octal value with a zero, and then a lower or uppercase “o”. For example, to set the variable “a” to the octal value “10” (8 in decimal), type: >>> a = 0o10 >>> a 8 Hexadecimal is just as easy. Simply precede the hexadecimal number with a zero, and then a lower or uppercase “x”. Hexadecimal digits can be specified in lower or uppercase. For example, in the Python interpreter: >>> a = 0xa5 >>> a 165 >>> b = 0XB2 >>> b 178 Why does -22 // 10 return -3? ¶ It’s primarily driven by the desire that i % j have the same sign as j . If you want that, and also want: i == ( i // j ) * j + ( i % j ) then integer division has to return the floor. C also requires that identity to hold, and then compilers that truncate i // j need to make i % j have the same sign as i . There are few real use cases for i % j when j is negative. When j is positive, there are many, and in virtually all of them it’s more useful for i % j to be >= 0 . If the clock says 10 now, what did it say 200 hours ago? -190 % 12 == 2 is useful; -190 % 12 == -10 is a bug waiting to bite. How do I get int literal attribute instead of SyntaxError? ¶ Trying to lookup an int literal attribute in the normal manner gives a SyntaxError because the period is seen as a decimal point: >>> 1. __class__ File "<stdin>" , line 1 1.__class__ ^ SyntaxError : invalid decimal literal The solution is to separate the literal from the period with either a space or parentheses. >>> 1 . __class__ <class 'int'> >>> ( 1 ) . __class__ <class 'int'> How do I convert a string to a number? ¶ For integers, use the built-in int() type constructor, e.g. int('144') == 144 . Similarly, float() converts to a floating-point number, e.g. float('144') == 144.0 . By default, these interpret the number as decimal, so that int('0144') == 144 holds true, and int('0x144') raises ValueError . int(string, base) takes the base to convert from as a second optional argument, so int( '0x144', 16) == 324 . If the base is specified as 0, the number is interpreted using Python’s rules: a leading ‘0o’ indicates octal, and ‘0x’ indicates a hex number. Do not use the built-in function eval() if all you need is to convert strings to numbers. eval() will be significantly slower and it presents a security risk: someone could pass you a Python expression that might have unwanted side effects. For example, someone could pass __import__('os').system("rm -rf $HOME") which would erase your home directory. eval() also has the effect of interpreting numbers as Python expressions, so that e.g. eval('09') gives a syntax error because Python does not allow leading ‘0’ in a decimal number (except ‘0’). How do I convert a number to a string? ¶ To convert, e.g., the number 144 to the string '144' , use the built-in type constructor str() . If you want a hexadecimal or octal representation, use the built-in functions hex() or oct() . For fancy formatting, see the f-strings and Format String Syntax sections, e.g. "{:04d}".format(144) yields '0144' and "{:.3f}".format(1.0/3.0) yields '0.333' . How do I modify a string in place? ¶ You can’t, because strings are immutable. In most situations, you should simply construct a new string from the various parts you want to assemble it from. However, if you need an object with the ability to modify in-place unicode data, try using an io.StringIO object or the array module: >>> import io >>> s = "Hello, world" >>> sio = io . StringIO ( s ) >>> sio . getvalue () 'Hello, world' >>> sio . seek ( 7 ) 7 >>> sio . write ( "there!" ) 6 >>> sio . getvalue () 'Hello, there!' >>> import array >>> a = array . array ( 'w' , s ) >>> print ( a ) array('w', 'Hello, world') >>> a [ 0 ] = 'y' >>> print ( a ) array('w', 'yello, world') >>> a . tounicode () 'yello, world' How do I use strings to call functions/methods? ¶ There are various techniques. The best is to use a dictionary that maps strings to functions. The primary advantage of this technique is that the strings do not need to match the names of the functions. This is also the primary technique used to emulate a case construct: def a (): pass def b (): pass dispatch = { 'go' : a , 'stop' : b } # Note lack of parens for funcs dispatch [ get_input ()]() # Note trailing parens to call function Use the built-in function getattr() : import foo getattr ( foo , 'bar' )() Note that getattr() works on any object, including classes, class instances, modules, and so on. This is used in several places in the standard library, like this: class Foo : def do_foo ( self ): ... def do_bar ( self ): ... f = getattr ( foo_instance , 'do_' + opname ) f () Use locals() to resolve the function name: def myFunc (): print ( "hello" ) fname = "myFunc" f = locals ()[ fname ] f () Is there an equivalent to Perl’s chomp() for removing trailing newlines from strings? ¶ You can use S.rstrip("\r\n") to remove all occurrences of any line terminator from the end of the string S without removing other trailing whitespace. If the string S represents more than one line, with several empty lines at the end, the line terminators for all the blank lines will be removed: >>> lines = ( "line 1 \r\n " ... " \r\n " ... " \r\n " ) >>> lines . rstrip ( " \n\r " ) 'line 1 ' Since this is typically only desired when reading text one line at a time, using S.rstrip() this way works well. Is there a scanf() or sscanf() equivalent? ¶ Not as such. For simple input parsing, the easiest approach is usually to split the line into whitespace-delimited words using the split() method of string objects and then convert decimal strings to numeric values using int() or float() . split() supports an optional “sep” parameter which is useful if the line uses something other than whitespace as a separator. For more complicated input parsing, regular expressions are more powerful than C’s sscanf and better suited for the task. What does UnicodeDecodeError or UnicodeEncodeError error mean? ¶ See the Unicode HOWTO . Can I end a raw string with an odd number of backslashes? ¶ A raw string ending with an odd number of backslashes will escape the string’s quote: >>> r 'C:\this\will\not\work \' File "<stdin>" , line 1 r 'C:\this\will\not\work \' ^ SyntaxError : unterminated string literal (detected at line 1) There are several workarounds for this. One is to use regular strings and double the backslashes: >>> 'C: \\ this \\ will \\ work \\ ' 'C:\\this\\will\\work\\' Another is to concatenate a regular string containing an escaped backslash to the raw string: >>> r 'C:\this\will\work' ' \\ ' 'C:\\this\\will\\work\\' It is also possible to use os.path.join() to append a backslash on Windows: >>> os . path . join ( r 'C:\this\will\work' , '' ) 'C:\\this\\will\\work\\' Note that while a backslash will “escape” a quote for the purposes of determining where the raw string ends, no escaping occurs when interpreting the value of the raw string. That is, the backslash remains present in the value of the raw string: >>> r 'backslash \' preserved' "backslash\\'preserved" Also see the specification in the language reference . Performance ¶ My program is too slow. How do I speed it up? ¶ That’s a tough one, in general. First, here are a list of things to remember before diving further: Performance characteristics vary across Python implementations. This FAQ focuses on CPython . Behaviour can vary across operating systems, especially when talking about I/O or multi-threading. You should always find the hot spots in your program before attempting to optimize any code (see the profile module). Writing benchmark scripts will allow you to iterate quickly when searching for improvements (see the timeit module). It is highly recommended to have good code coverage (through unit testing or any other technique) before potentially introducing regressions hidden in sophisticated optimizations. That being said, there are many tricks to speed up Python code. Here are some general principles which go a long way towards reaching acceptable performance levels: Making your algorithms faster (or changing to faster ones) can yield much larger benefits than trying to sprinkle micro-optimization tricks all over your code. Use the right data structures. Study documentation for the Built-in Types and the collections module. When the standard library provides a primitive for doing something, it is likely (although not guaranteed) to be faster than any alternative you may come up with. This is doubly true for primitives written in C, such as builtins and some extension types. For example, be sure to use either the list.sort() built-in method or the related sorted() function to do sorting (and see the Sorting Techniques for examples of moderately advanced usage). Abstractions tend to create indirections and force the interpreter to work more. If the levels of indirection outweigh the amount of useful work done, your program will be slower. You should avoid excessive abstraction, especially under the form of tiny functions or methods (which are also often detrimental to readability). If you have reached the limit of what pure Python can allow, there are tools to take you further away. For example, Cython can compile a slightly modified version of Python code into a C extension, and can be used on many different platforms. Cython can take advantage of compilation (and optional type annotations) to make your code significantly faster than when interpreted. If you are confident in your C programming skills, you can also write a C extension module yourself. See also The wiki page devoted to performance tips . What is the most efficient way to concatenate many strings together? ¶ str and bytes objects are immutable, therefore concatenating many strings together is inefficient as each concatenation creates a new object. In the general case, the total runtime cost is quadratic in the total string length. To accumulate many str objects, the recommended idiom is to place them into a list and call str.join() at the end: chunks = [] for s in my_strings : chunks . append ( s ) result = '' . join ( chunks ) (another reasonably efficient idiom is to use io.StringIO ) To accumulate many bytes objects, the recommended idiom is to extend a bytearray object using in-place concatenation (the += operator): result = bytearray () for b in my_bytes_objects : result += b Sequences (Tuples/Lists) ¶ How do I convert between tuples and lists? ¶ The type constructor tuple(seq) converts any sequence (actually, any iterable) into a tuple with the same items in the same order. For example, tuple([1, 2, 3]) yields (1, 2, 3) and tuple('abc') yields ('a', 'b', 'c') . If the argument is a tuple, it does not make a copy but returns the same object, so it is cheap to call tuple() when you aren’t sure that an object is already a tuple. The type constructor list(seq) converts any sequence or iterable into a list with the same items in the same order. For example, list((1, 2, 3)) yields [1, 2, 3] and list('abc') yields ['a', 'b', 'c'] . If the argument is a list, it makes a copy just like seq[:] would. What’s a negative index? ¶ Python sequences are indexed with positive numbers and negative numbers. For positive numbers 0 is the first index 1 is the second index and so forth. For negative indices -1 is the last index and -2 is the penultimate (next to last) index and so forth. Think of seq[-n] as the same as seq[len(seq)-n] . Using negative indices can be very convenient. For example S[:-1] is all of the string except for its last character, which is useful for removing the trailing newline from a string. How do I iterate over a sequence in reverse order? ¶ Use the reversed() built-in function: for x in reversed ( sequence ): ... # do something with x ... This won’t touch your original sequence, but build a new copy with reversed order to iterate over. How do you remove duplicates from a list? ¶ See the Python Cookbook for a long discussion of many ways to do this: https://code.activestate.com/recipes/52560/ If you don’t mind reordering the list, sort it and then scan from the end of the list, deleting duplicates as you go: if mylist : mylist . sort () last = mylist [ - 1 ] for i in range ( len ( mylist ) - 2 , - 1 , - 1 ): if last == mylist [ i ]: del mylist [ i ] else : last = mylist [ i ] If all elements of the list may be used as set keys (i.e. they are all hashable ) this is often faster mylist = list ( set ( mylist )) This converts the list into a set, thereby removing duplicates, and then back into a list. How do you remove multiple items from a list ¶ As with removing duplicates, explicitly iterating in reverse with a delete condition is one possibility. However, it is easier and faster to use slice replacement with an implicit or explicit forward iteration. Here are three variations.: mylist [:] = filter ( keep_function , mylist ) mylist [:] = ( x for x in mylist if keep_condition ) mylist [:] = [ x for x in mylist if keep_condition ] The list comprehension may be fastest. How do you make an array in Python? ¶ Use a list: [ "this" , 1 , "is" , "an" , "array" ] Lists are equivalent to C or Pascal arrays in their time complexity; the primary difference is that a Python list can contain objects of many different types. The array module also provides methods for creating arrays of fixed types with compact representations, but they are slower to index than lists. Also note that NumPy and other third party packages define array-like structures with various characteristics as well. To get Lisp-style linked lists, you can emulate cons cells using tuples: lisp_list = ( "like" , ( "this" , ( "example" , None ) ) ) If mutability is desired, you could use lists instead of tuples. Here the analogue of a Lisp car is lisp_list[0] and the analogue of cdr is lisp_list[1] . Only do this if you’re sure you really need to, because it’s usually a lot slower than using Python lists. How do I create a multidimensional list? ¶ You probably tried to make a multidimensional array like this: >>> A = [[ None ] * 2 ] * 3 This looks correct if you print it: >>> A [[None, None], [None, None], [None, None]] But when you assign a value, it shows up in multiple places: >>> A [ 0 ][ 0 ] = 5 >>> A [[5, None], [5, None], [5, None]] The reason is that replicating a list with * doesn’t create copies, it only creates references to the existing objects. The *3 creates a list containing 3 references to the same list of length two. Changes to one row will show in all rows, which is almost certainly not what you want. The suggested approach is to create a list of the desired length first and then fill in each element with a newly created list: A = [ None ] * 3 for i in range ( 3 ): A [ i ] = [ None ] * 2 This generates a list containing 3 different lists of length two. You can also use a list comprehension: w , h = 2 , 3 A = [[ None ] * w for i in range ( h )] Or, you can use an extension that provides a matrix datatype; NumPy is the best known. How do I apply a method or function to a sequence of objects? ¶ To call a method or function and accumulate the return values is a list, a list comprehension is an elegant solution: result = [ obj . method () for obj in mylist ] result = [ function ( obj ) for obj in mylist ] To just run the method or function without saving the return values, a plain for loop will suffice: for obj in mylist : obj . method () for obj in mylist : function ( obj ) Why does a_tuple[i] += [‘item’] raise an exception when the addition works? ¶ This is because of a combination of the fact that augmented assignment operators are assignment operators, and the difference between mutable and immutable objects in Python. This discussion applies in general when augmented assignment operators are applied to elements of a tuple that point to mutable objects, but we’ll use a list and += as our exemplar. If you wrote: >>> a_tuple = ( 1 , 2 ) >>> a_tuple [ 0 ] += 1 Traceback (most recent call last): ... TypeError : 'tuple' object does not support item assignment The reason for the exception should be immediately clear: 1 is added to the object a_tuple[0] points to ( 1 ), producing the result object, 2 , but when we attempt to assign the result of the computation, 2 , to element 0 of the tuple, we get an error because we can’t change what an element of a tuple points to. Under the covers, what this augmented assignment statement is doing is approximately this: >>> result = a_tuple [ 0 ] + 1 >>> a_tuple [ 0 ] = result Traceback (most recent call last): ... TypeError : 'tuple' object does not support item assignment It is the assignment part of the operation that produces the error, since a tuple is immutable. When you write something like: >>> a_tuple = ([ 'foo' ], 'bar' ) >>> a_tuple [ 0 ] += [ 'item' ] Traceback (most recent call last): ... TypeError : 'tuple' object does not support item assignment The exception is a bit more surprising, and even more surprising is the fact that even though there was an error, the append worked: >>> a_tuple [ 0 ] ['foo', 'item'] To see why this happens, you need to know that (a) if an object implements an __iadd__() magic method, it gets called when the += augmented assignment is executed, and its return value is what gets used in the assignment statement; and (b) for lists, __iadd__() is equivalent to calling extend() on the list and returning the list. That’s why we say that for lists, += is a “shorthand” for list.extend() : >>> a_list = [] >>> a_list += [ 1 ] >>> a_list [1] This is equivalent to: >>> result = a_list . __iadd__ ([ 1 ]) >>> a_list = result The object pointed to by a_list has been mutated, and the pointer to the mutated object is assigned back to a_list . The end result of the assignment is a no-op, since it is a pointer to the same object that a_list was previously pointing to, but the assignment still happens. Thus, in our tuple example what is happening is equivalent to: >>> result = a_tuple [ 0 ] . __iadd__ ([ 'item' ]) >>> a_tuple [ 0 ] = result Traceback (most recent call last): ... TypeError : 'tuple' object does not support item assignment The __iadd__() succeeds, and thus the list is extended, but even though result points to the same object that a_tuple[0] already points to, that final assignment still results in an error, because tuples are immutable. I want to do a complicated sort: can you do a Schwartzian Transform in Python? ¶ The technique, attributed to Randal Schwartz of the Perl community, sorts the elements of a list by a metric which maps each element to its “sort value”. In Python, use the key | 2026-01-13T08:49:02 |
https://dev.to/evanlin/golang-trying-out-go-proposal-45713-multi-module-workspaces-1k86 | Golang: Trying out Go Proposal 45713 'Multi-Module Workspaces' - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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Report Abuse Evan Lin Posted on Jan 11 • Originally published at evanlin.com on Jan 11 Golang: Trying out Go Proposal 45713 'Multi-Module Workspaces' # go # tooling # tutorial title: [Learning Notes][Golang: Gotip] Quick Taste of Go Proposal 45713 'Multi-Module Workspaces' published: false date: 2021-08-19 00:00:00 UTC tags: canonical_url: http://www.evanlin.com/go-workspaces/ ---  - Proposal: [45713 Workspace Mode](https://go.googlesource.com/proposal/+/master/design/45713-workspace.md) - PR: [45713](https://github.com/golang/go/issues/45713) - Demo Video: [YouTube](https://www.youtube.com/watch?v=wQglU5aB5NQ) # Summary Vendoring and Dependency Management are problems Golang has been trying to solve. Through `go mod`, you can manage the first layer of packages. With `go mod vendor`, you can download the related dependent packages in the local `vendor/`. If you want to modify the upper layer of dependency, you can modify it directly. But what if you want to modify the files of the dependency's upper layer and its even upper layer? In the past, you could modify them one by one through `go mod edit`, but it would be quite complex once there are many files. Is there any way to quickly make some confirmations locally, so that it's easier to submit a PR to upstream? Here, we introduce a feature that is undergoing final review (if approved, it's expected to be a go 1.18 feature: Multi-Module Workspaces). The proposal author is very thoughtful and also released a Demo Video with a big dog. I must say, because there's a cute big dog, I obediently finished reading the proposal. <iframe width="560" height="315" src="https://www.youtube.com/embed/wQglU5aB5NQ" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe> # How to Install Enter fullscreen mode Exit fullscreen mode # Get the latest version of golang source code go install golang.org/dl/gotip@latest Download gotip based on a certain CL gotip download dev.cmdgo Look at the modified related commands, pay attention to initwork, editwork ... gotip help mod The commands are: download download modules to local cache edit edit go.mod from tools or scripts editwork edit go.work from tools or scripts graph print module requirement graph init initialize new module in current directory initwork initialize workspace file tidy add missing and remove unused modules vendor make vendored copy of dependencies verify verify dependencies have expected content why explain why packages or modules are needed Enter fullscreen mode Exit fullscreen mode ## Related Articles: - [45713 Workspace Mode](https://go.googlesource.com/proposal/+/master/design/45713-workspace.md) - [Demo Video YouTube](https://www.youtube.com/watch?v=wQglU5aB5NQ) Enter fullscreen mode Exit fullscreen mode Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Evan Lin Follow Attitude is Everything. @golangtw Co-Organizer / LINE Taiwan Technology Evangelist. Golang GDE. Location Taipei Work Technology Evangelist at LINE Corp. Joined Jun 16, 2020 More from Evan Lin [TIL] Golang community discussion about PTT BBS # community # backend # discuss # go Go 1.16: Retracting Versions in Go Modules # go # learning # tooling [Learning Notes] Golang: A Simple Introduction to New Features in Golang 1.16 # go # learning # tutorial 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV Forem — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . Forem © 2016 - 2026. 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https://peps.python.org/pep-0011/ | PEP 11 – CPython platform support | peps.python.org Following system colour scheme Selected dark colour scheme Selected light colour scheme Python Enhancement Proposals Python » PEP Index » PEP 11 Toggle light / dark / auto colour theme PEP 11 – CPython platform support Author : Martin von Löwis <martin at v.loewis.de>, Brett Cannon <brett at python.org> Status : Active Type : Process Created : 07-Jul-2002 Post-History : 18-Aug-2007 , 14-May-2014 , 20-Feb-2015 , 10-Mar-2022 Table of Contents Abstract Rationale Support tiers Tier 1 Tier 2 Tier 3 All other platforms Notes Microsoft Windows Legacy C Locale Unsupporting platforms No-longer-supported platforms Discussions Copyright Abstract This PEP documents how an operating system (platform) becomes supported in CPython, what platforms are currently supported, and documents past support. Rationale Over time, the CPython source code has collected various pieces of platform-specific code, which, at some point in time, was considered necessary to use CPython on a specific platform. Without access to this platform, it is not possible to determine whether this code is still needed. As a result, this code may either break during CPython’s evolution, or it may become unnecessary as the platforms evolve as well. Allowing these fragments to grow poses the risk of unmaintainability: without having experts for a large number of platforms, it is not possible to determine whether a certain change to the CPython source code will work on all supported platforms. To reduce this risk, this PEP specifies what is required for a platform to be considered supported by CPython as well as providing a procedure to remove code for platforms with few or no CPython users. This PEP also lists what platforms are supported by the CPython interpreter. This lets people know what platforms are directly supported by the CPython development team. Support tiers Platform support is broken down into tiers . Each tier comes with different requirements which lead to different promises being made about support. To be promoted to a tier, steering council support is required and is expected to be driven by team consensus. Demotion to a lower tier occurs when the requirements of the current tier are no longer met for a platform for an extended period of time based on the judgment of the release manager or steering council. For platforms which no longer meet the requirements of any tier by b1 of a new feature release, an announcement will be made to warn the community of the pending removal of support for the platform (e.g. in the b1 announcement). If the platform is not brought into line for at least one of the tiers by the first release candidate, it will be listed as unsupported in this PEP. Tier 1 STATUS CI failures block releases. Changes which would break the main branch are not allowed to be merged; any breakage should be fixed or reverted immediately. All core developers are responsible to keep main , and thus these platforms, working. Failures on these platforms block a release . Target Triple Notes aarch64-apple-darwin clang aarch64-unknown-linux-gnu glibc, gcc i686-pc-windows-msvc x86_64-pc-windows-msvc x86_64-unknown-linux-gnu glibc, gcc Tier 2 STATUS Must have a reliable buildbot. At least two core developers are signed up to support the platform. Changes which break any of these platforms are to be fixed or reverted within 24 hours . Failures on these platforms block a release . Target Triple Notes Contacts aarch64-unknown-linux-gnu glibc, clang Victor Stinner, Gregory P. Smith wasm32-unknown-wasip1 WASI SDK, Wasmtime Brett Cannon, Michael Droettboom x86_64-apple-darwin macOS, clang Sam Gross, Barry Warsaw, Ronald Oussoren x86_64-unknown-linux-gnu glibc, clang Victor Stinner, Gregory P. Smith Tier 3 STATUS Must have a reliable buildbot. At least one core developer is signed up to support the platform. No response SLA to failures. Failures on these platforms do not block a release. Target Triple Notes Contacts aarch64-linux-android Russell Keith-Magee, Petr Viktorin aarch64-pc-windows-msvc Steve Dower arm64-apple-ios iOS on device Russell Keith-Magee, Ned Deily arm64-apple-ios-simulator iOS on M1 macOS simulator Russell Keith-Magee, Ned Deily armv7l-unknown-linux-gnueabihf 32-bit Raspberry Pi OS, gcc Gregory P. Smith aarch64-unknown-linux-gnu 64-bit Raspberry Pi OS, gcc Savannah Ostrowski powerpc64le-unknown-linux-gnu glibc, clang glibc, gcc Victor Stinner Victor Stinner s390x-unknown-linux-gnu glibc, gcc Victor Stinner wasm32-unknown-emscripten emcc Russell Keith-Magee x86_64-linux-android Russell Keith-Magee, Petr Viktorin x86_64-unknown-freebsd BSD libc, clang Victor Stinner All other platforms Support for a platform may be partial within the code base, such as from active development around platform support or accidentally. Code changes to platforms not listed in the above tiers may be rejected or removed from the code base without a deprecation process if they cause a maintenance burden or obstruct general improvements. Platforms not listed here may be supported by the wider Python community in some way. If your desired platform is not listed above, please perform a search online to see if someone is already providing support in some form. Notes Microsoft Windows Windows versions prior to Windows 10 follow Microsoft’s Fixed Lifecycle Policy , with a mainstream support phase for 5 years after release, where the product is generally commercially available, and an additional 5 year extended support phase, where paid support is still available and certain bug fixes are released. Extended Security Updates (ESU) is a paid program available to high-volume enterprise customers as a “last resort” option to receive certain security updates after extended support ends. ESU is considered a distinct phase that follows the expiration of extended support. Windows 10 and later follow Microsoft’s Modern Lifecycle Policy , which varies per-product, per-version, per-edition and per-channel. Generally, feature updates (1709, 22H2) occur every 6-12 months and are supported for 18-36 months; Server and IoT editions, and LTSC channel releases are supported for 5-10 years, and the latest feature release of a major version (Windows 10, Windows 11) generally receives new updates for at least 10 years following release. Microsoft’s Windows Lifecycle FAQ has more specific and up-to-date guidance. CPython’s Windows support currently follows Microsoft’s lifecycles. A new feature release X.Y.0 will support all Windows versions whose extended support phase has not yet expired. Subsequent bug fix releases will support the same Windows versions as the original feature release, even if no longer supported by Microsoft. New versions of Windows released while CPython is in maintenance mode may be supported at the discretion of the core team and release manager. As of 2024, our current interpretation of Microsoft’s lifecycles is that Windows for IoT and embedded systems is out of scope for new CPython releases, as the intent of those is to avoid feature updates. Windows Server will usually be the oldest version still receiving free security fixes, and that will determine the earliest supported client release with equivalent API version (which will usually be past its end-of-life). Each feature release is built by a specific version of Microsoft Visual Studio. That version should have mainstream support when the release is made. Developers of extension modules will generally need to use the same Visual Studio release; they are concerned both with the availability of the versions they need to use, and with keeping the zoo of versions small. The CPython source tree will keep unmaintained build files for older Visual Studio releases, for which patches will be accepted. Such build files will be removed from the source tree 3 years after the extended support for the compiler has ended (but continue to remain available in revision control). Legacy C Locale Starting with CPython 3.7.0, *nix platforms are expected to provide at least one of C.UTF-8 (full locale), C.utf8 (full locale) or UTF-8 ( LC_CTYPE -only locale) as an alternative to the legacy C locale. Any Unicode-related integration problems that occur only in the legacy C locale and cannot be reproduced in an appropriately configured non-ASCII locale will be closed as “won’t fix”. Unsupporting platforms If a platform drops out of tiered support, a note must be posted in this PEP that the platform is no longer actively supported. This note must include: The name of the system, The first release number that does not support this platform anymore, and The first release where the historical support code is actively removed. In some cases, it is not possible to identify the specific list of systems for which some code is used (e.g. when autoconf tests for absence of some feature which is considered present on all supported systems). In this case, the name will give the precise condition (usually a preprocessor symbol) that will become unsupported. At the same time, the CPython build must be changed to produce a warning if somebody tries to install CPython on this platform. On platforms using autoconf, configure should also be made emit a warning about the unsupported platform. This gives potential users of the platform a chance to step forward and offer maintenance. We do not treat a platform that loses Tier 3 support any worse than a platform that was never supported. No-longer-supported platforms Name: MS-DOS, MS-Windows 3.x Unsupported in: Python 2.0 Code removed in: Python 2.1 Name: SunOS 4 Unsupported in: Python 2.3 Code removed in: Python 2.4 Name: DYNIX Unsupported in: Python 2.3 Code removed in: Python 2.4 Name: dgux Unsupported in: Python 2.3 Code removed in: Python 2.4 Name: Minix Unsupported in: Python 2.3 Code removed in: Python 2.4 Name: Irix 4 and –with-sgi-dl Unsupported in: Python 2.3 Code removed in: Python 2.4 Name: Linux 1 Unsupported in: Python 2.3 Code removed in: Python 2.4 Name: Systems defining __d6_pthread_create (configure.in) Unsupported in: Python 2.3 Code removed in: Python 2.4 Name: Systems defining PY_PTHREAD_D4, PY_PTHREAD_D6, or PY_PTHREAD_D7 in thread_pthread.h Unsupported in: Python 2.3 Code removed in: Python 2.4 Name: Systems using –with-dl-dld Unsupported in: Python 2.3 Code removed in: Python 2.4 Name: Systems using –without-universal-newlines, Unsupported in: Python 2.3 Code removed in: Python 2.4 Name: MacOS 9 Unsupported in: Python 2.4 Code removed in: Python 2.4 Name: Systems using –with-wctype-functions Unsupported in: Python 2.6 Code removed in: Python 2.6 Name: Win9x, WinME, NT4 Unsupported in: Python 2.6 (warning in 2.5 installer) Code removed in: Python 2.6 Name: AtheOS Unsupported in: Python 2.6 (with “AtheOS” changed to “Syllable”) Build broken in: Python 2.7 (edit configure to re-enable) Code removed in: Python 3.0 Details: http://www.syllable.org/discussion.php?id=2320 Name: BeOS Unsupported in: Python 2.6 (warning in configure) Build broken in: Python 2.7 (edit configure to re-enable) Code removed in: Python 3.0 Name: Systems using Mach C Threads Unsupported in: Python 3.2 Code removed in: Python 3.3 Name: SunOS lightweight processes (LWP) Unsupported in: Python 3.2 Code removed in: Python 3.3 Name: Systems using –with-pth (GNU pth threads) Unsupported in: Python 3.2 Code removed in: Python 3.3 Name: Systems using Irix threads Unsupported in: Python 3.2 Code removed in: Python 3.3 Name: OSF* systems (issue 8606) Unsupported in: Python 3.2 Code removed in: Python 3.3 Name: OS/2 (issue 16135) Unsupported in: Python 3.3 Code removed in: Python 3.4 Name: VMS (issue 16136) Unsupported in: Python 3.3 Code removed in: Python 3.4 Name: Windows 2000 Unsupported in: Python 3.3 Code removed in: Python 3.4 Name: Windows systems where COMSPEC points to command.com Unsupported in: Python 3.3 Code removed in: Python 3.4 Name: RISC OS Unsupported in: Python 3.0 (some code actually removed) Code removed in: Python 3.4 Name: IRIX Unsupported in: Python 3.7 Code removed in: Python 3.7 Name: Systems without multithreading support Unsupported in: Python 3.7 Code removed in: Python 3.7 Discussions April 2022: Consider adding a Tier 3 to tiered platform support (Victor Stinner) March 2022: Proposed tiered platform support (Brett Cannon) February 2015: Update to PEP 11 to clarify garnering platform support (Brett Cannon) May 2014: Where is our official policy of what platforms we do support? (Brett Cannon) August 2007: PEP 11 update - Call for port maintainers to step forward (Skip Montanaro) Copyright This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive. Source: https://github.com/python/peps/blob/main/peps/pep-0011.rst Last modified: 2025-10-09 17:15:34 GMT Contents Abstract Rationale Support tiers Tier 1 Tier 2 Tier 3 All other platforms Notes Microsoft Windows Legacy C Locale Unsupporting platforms No-longer-supported platforms Discussions Copyright Page Source (GitHub) | 2026-01-13T08:49:02 |
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https://dev.to/walteralleyz/why-portugal-can-be-your-next-career-step-4h38 | Why Portugal Can Be Your Next Career Step - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Walter Alleyz Posted on Jan 11 Why Portugal Can Be Your Next Career Step # career # international # workplace # braziliandevs Moving abroad can be a challenging career decision. Beyond that, there are many factors to consider: culture, people, convenience, quality of life, and what a new place can offer. Every detail can make a significant difference when deciding to take such a step. In this article, we will discuss the benefits of moving to Portugal in terms of career growth and professional potential. When You Hit a Career Plateau It is a common experience for almost every IT professional to face the question of how to continue evolving in their career. Many of us have already been through this phase and managed to move forward by learning new tools, studying and implementing new technologies, expanding our network, building connections, and attending workshops, among other strategies. However, there comes a point when our career hits a plateau. This often happens when you can no longer identify what to study next, how to increase your potential, or what steps to take to improve your salary. If this sounds familiar, it may be time to look abroad and seek opportunities to grow beyond the boundaries of technology itself. Why Moving to Portugal Is More Than a Lifestyle Choice The official language of Portugal is Portuguese, but you can hear many different languages spoken across the country. This is because Portugal is a popular tourist destination in Europe. You can easily find people who speak English. In addition, Portuguese people are known for their strong English skills, so you are unlikely to face communication barriers—especially if your goal is to improve your own communication skills. Beyond improving communication skills, you will be exposed to a wide range of cultures, including Portuguese culture itself—and this is an important factor to consider. Understanding different cultures is essential, as it helps you navigate diverse environments, collaborate more effectively with people from different backgrounds, and build stronger, more meaningful connections over time. Portugal also offers a wide variety of culinary options, with natural and healthy food easily found in any grocery store. People are warm and welcoming, and you will find yourself busy exploring natural parks and beautiful architecture throughout the country. Optimizing Your Social Profile to Attract International Companies “If you’re not visible, you won’t be found.” Preparing your social profile is the best choice when your goal is to move abroad. If you are using LinkedIn, the first step is to create a clear and meaningful title for your profession. For example, if you are a Business Analyst specializing in AI and BI, make that explicit. If you are a Java Developer with Cloud Computing experience, a title like Cloud Java Developer might be more effective. Choosing the right title makes it easier for headhunters to find you. The About section on LinkedIn is one of the most important parts of your profile. A strong About section should be concise, clear, and focused on your skills, achievements, and career goals. Start with a hook that grabs attention, then describe your experience, key accomplishments, and what you are passionate about. Take a look at a good example of an About section: “I am a Software Engineer specializing in Java and Python, with 5 years of experience building scalable web applications and cloud-based solutions. I have led teams of up to 4 developers and implemented projects that improved system performance by 30%. Currently, I am seeking opportunities abroad where I can leverage my technical expertise and leadership skills to drive impactful projects. Feel free to connect to discuss potential collaborations.” Organize experiences in reverse chronological order, starting with the most recent. Use bullet points to make it easy to read. A well-written About and Experience section significantly increases your chances of being discovered by headhunters and potential employers. Remember to write everything in English, since you are targeting the international market. Most importantly, focus on building meaningful connections: engage with people, show genuine interest in topics that matter, and make your objectives clear. Final Thoughts Feeling stuck in your career is a common experience for many IT professionals. It often occurs when you’re unsure of the next step, when acquiring more technical knowledge alone isn’t enough, and when it becomes necessary to seek new experiences beyond the tech bubble. In this article, I aimed to explain why Portugal is a great option for starting your international career, the benefits of living abroad, and the real impact it can have - both in terms of career growth and personal development. Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Walter Alleyz Follow Location Brasil Education Software Engineering Work Developer Joined Oct 5, 2021 Trending on DEV Community Hot I Didn’t “Become” a Senior Developer. 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https://peps.python.org/pep-0729/ | PEP 729 – Typing governance process | peps.python.org Following system colour scheme Selected dark colour scheme Selected light colour scheme Python Enhancement Proposals Python » PEP Index » PEP 729 Toggle light / dark / auto colour theme PEP 729 – Typing governance process Author : Jelle Zijlstra <jelle.zijlstra at gmail.com>, Shantanu Jain <hauntsaninja at gmail.com> Discussions-To : Discourse thread Status : Active Type : Process Topic : Governance , Typing Created : 19-Sep-2023 Post-History : 04-Oct-2023 , 20-Sep-2023 Resolution : 20-Nov-2023 Table of Contents Abstract Motivation PEPs are the only specification It’s hard to evolve the specification The type system is underspecified The Steering Council is not well-placed to solve the above problems Endorsements Specification Mandate Operations and process Relationship with the Steering Council Relationship with type checkers Projects Conformance test suite Specification for the type system User-facing reference for the type system Amendments Rejected ideas Writing the specification from scratch Alternate governance mechanisms Do nothing Contact Copyright Abstract This PEP proposes a new way to govern the Python type system: a council that is responsible for maintaining and developing the Python type system. The council will maintain a specification and conformance test suite and will initially be appointed by the Python Steering Council. Motivation The Python type system was created by PEP 484 , almost ten years ago. The type system is now widely used, and typing has become an important tool for writing good, maintainable Python code. Many changes have been made to the type system to cover more use cases and improve usability. Several type checkers have been created, each with their own strengths. The type annotation syntax has driven several major innovations in the Python ecosystem, such as the popular dataclasses module, runtime type checking and validation by packages such as Pydantic , and static compilation by tools such as mypyc . However, as the type system has grown, several interrelated problems with the current way to manage the type system have become apparent. PEPs are the only specification The Python type system was initially created by a PEP ( PEP 484 ), and changes to the type system are still made by PEPs. The specification for the Python type system, to the extent there is one, consists of this series of PEPs. But Standards Track PEPs aren’t meant to be living documents or specifications; they are change proposals. An example may illustrate the problem here. Around the same time as the introduction of the typing module by PEP 484 , PEP 3156 introduced the asyncio module, another major new feature that has been instrumental to the success of Python 3. Both modules have evolved greatly since their initial creation and inspired changes to the core language. However, asyncio and typing are different in an essential aspect: a user who uses asyncio interacts only with the standard library itself, while a user of typing has to also think about an external tool, the type checker. The Python language reference covers the symbols in the typing module, but does not (and should not) go into detail on how the full type system should be interpreted by type checkers. That material currently exists only in the PEPs. This problem is shared by the packaging ecosystem, which attempts to solve it by maintaining a separate set of PyPA specifications . It’s hard to evolve the specification Because the PEPs are the only specification we have, anything that could be seen as a change to the specification would theoretically require a new PEP. But that is often too heavy a process for a small change. Sometimes changes are made directly to old PEPs instead, but that goes against the idea that accepted and implemented PEPs become historical documents that should no longer be changed. Some concrete examples include: PEP 484 explicitly says that typing.NoReturn cannot be used in argument annotations. Nevertheless, type checkers have long accepted such usage. A 2023 discussion noted that PEP 561 ’s description of partial stubs is unclear, and major type checkers did not implement it exactly as specified. The widely used third-party typing_extensions package provides backports of new type system features. Type checkers are expected to treat symbols in this module the same as symbols in typing , but this is not explicitly specified in any of the PEPs. The type system is underspecified While the PEPs provide a specification, they are often not sufficiently precise (sometimes intentionally so). This is especially true as the combinatorial complexity of the type system has grown. It ends up falling to individual type checkers to decide how to navigate underspecified areas. In cases where type checkers informally coordinate, this results in de facto standards that aren’t clearly recorded anywhere, making the type system less accessible to newcomers. For example: How @overload matching works How ParamSpec should work with methods The concept of recursive aliases Semantics of variable initialization Reachability semantics of annotations on __exit__ Symbol visibility Use of NoReturn for exhaustiveness checking The Steering Council is not well-placed to solve the above problems The SC has the entire language in its remit, and is not well-placed to make decisions that are purely about the type system – if only because they don’t have the time to deal with type system arcana alongside their other responsibilities. This is similar in spirit to the reasons why the Steering Council sometimes uses PEP delegation. Endorsements This PEP was endorsed by maintainers of all major type checkers, including Rebecca Chen (pytype) , Eric Traut (Pyright) , and privately by maintainers of mypy and Pyre. Specification We propose the creation of a new group, the Typing Council. This group will be responsible for developing and maintaining the Python type system, and for solving the above problems. The “operations and process” section describes how this group would operate and be governed. The more exciting “projects” section describes solutions to the above problems that the Typing Council could shepherd. Mandate The Typing Council’s mandate is to ensure that the Python type system is: Useful : The type system should serve common use cases. As identified by PEP 484 , the primary use case is static analysis, but there are others, such as runtime type checking, static compilation, IDE support, and documentation. The Typing Council should consider all of these use cases when making decisions, and be open to supporting additional use cases as they come up. Usable : The type system should be easy to use for Python developers. It should be ergonomic to write well-typed Python code that is accepted by type checkers. There should be good documentation for the type system. Stable : As the type system matures, users should be able to rely on their typed code continuing to work and be able to trust their mental model for the type system. Changes should be made with care and in a way that minimizes disruption. Nevertheless, the type system should be able to evolve, and it does not make sense to use the same compatibility guidelines for type checker behavior as for Python itself. Of course, the existence and runtime behavior of objects in the typing module does follow Python’s standard compatibility policy in PEP 387 . Operations and process The council would have three to five members, comprised of prominent community members, such as Python core developers and maintainers of major type checkers. The members should include people affiliated with a variety of projects related to type checking, which may include type checkers, CPython, typeshed, or other projects. The initial members of the council are: Eric Traut (Pyright; author of PEP 647 , PEP 681 , and PEP 695 ) Guido van Rossum (core developer; author of PEP 484 and PEP 526 ) Jelle Zijlstra (core developer; typeshed; pyanalyze; author of PEP 688 and PEP 702 ) Rebecca Chen (pytype) Shantanu Jain (core developer; typeshed; mypy) Current membership of the council is recorded in the python/typing-council repository. There is no term limit for council members. Council members may resign their position at any time. There is an expectation that each member serves at most five consecutive years before resigning. If there is a vacancy and there are three or more remaining members, it is up to the Council to decide whether to appoint a new member. To determine replacements, nominations will be collected from the typing community. Self-nominations are allowed. The existing Typing Council will then decide the replacement member(s) from the nominees. The expectation is that this would be done by fiat, but the Typing Council can choose a replacement by any means they see fit, including a vote. The Typing Council remains accountable to the Steering Council. At any point, for any reason, the Steering Council could (publicly or privately) make a specific change or request a non-specific change to the composition of the Typing Council. We acknowledge that this is a not particularly democratic structure and puts a lot of faith in the Typing Council. However, the Python community has a long history of success with not particularly democratic structures! We believe self-governance, cycling of membership, and accountability to the Steering Council will be sufficient to ensure that the Typing Council is meeting the needs of the community. The council would operate primarily through reviews of GitHub PRs. Regular meetings are likely not necessary, but the council may set up video calls, a private chat, or whatever other mechanism they decide upon internally. The council should aim for transparency, posting all decisions publicly on discuss.python.org , with a rationale if possible. Before making a decision, the council should give all interested community members a chance to weigh in. There should be at least a week between the start of a discussion and the council’s decision. Members of the council will be eligible to sponsor PEPs. If this PEP is accepted, PEP 1 should be amended to note this fact. Relationship with the Steering Council Just like today, the Python Steering Council would remain responsible for the overall direction of the Python language and would continue to decide on typing-related PEPs. The Typing Council would provide written opinions and recommendations to the Steering Council on typing-related PEPs. However, smaller changes to the type system could be made by the Typing Council directly. The Steering Council could also choose to delegate decisions on some PEPs to the Typing Council (exactly as any other PEP delegation). Some examples of how past and recent issues could have been handled under this model: A PEP like PEP 695 (type parameter syntax), which changes the language syntax, would need to be decided upon by the Steering Council; the Typing Council would merely provide opinion or endorsement. Similarly, PEPs like PEP 702 (deprecations) would be decided upon by the Steering Council, because it concerns runtime behaviour beyond pure typing. Other examples that would need to be decided by the SC include PEP 718 (subscriptable functions) and PEP 727 (documentation metadata). A PEP like PEP 698 ( @override ), which affects only users of type checkers and does not change the overall language, would also by default be decided upon by the Steering Council. However, such PEPs could be delegated to the Typing Council for a decision (like any other PEP delegation). Other examples of PEPs that could potentially be delegated include PEP 647 (type guards), PEP 655 (individual required TypedDict items), PEP 673 ( Self ), and PEP 675 ( Literal ). Adding a smaller feature, such as typing.Never as an alias for typing.NoReturn , would be done by means of a PR to the spec and conformance test suite. The Typing Council would then decide whether or not to merge the PR. They may ask for the feature to be specified and discussed in a PEP if they feel that is warranted. If there is confusion about the interpretation of some part of the spec, like happened recently with partial stubs in PEP 561 , somebody would make a PR to the typing specification to clarify the spec, and then the Typing Council would decide on the spec change. The runtime typing module will continue to be maintained by the CPython core developer team. However, any changes to the runtime module that affect type checker behavior should be made in conjunction with a change to the specification (see below) and should be approved by the Typing Council. For example, in Python 3.11 the core developers added the new function typing.assert_type() . If the Typing Council had been in place, this change would require a matching change to the specification and approval by the Typing Council. On the other hand, Python 3.11 also added the typing.get_overloads() introspection helper. As this function does not affect type checker behavior, it would not require approval by the Typing Council. However, as support for runtime type checkers is within the remit of the Council, they should monitor such changes and provide feedback when appropriate. Relationship with type checkers The Typing Council has no direct authority over type checkers; it cannot force them to implement particular features or make behavior changes. Type checkers are incentivized to follow the specification set out by the Council because it allows them to take advantage of shared resources, such as libraries that expose typing information that follows the specification, the stub files in typeshed, the typing standard library module, and user documentation that covers the standard type system. Type checkers are free to extend the type system or deviate from the specification, but they should document such differences clearly. The fact that type checkers need to implement any decisions made by the Typing Council acts as a useful brake on the Council, ensuring that its decisions are conservative and well-considered. Individual type checkers remain free to innovate as they see fit, and successful innovations can be incorporated into the standard type system. Projects Here are some efforts a Typing Council would be responsible for. Conformance test suite A conformance test suite would provide machine checkable documentation for how type checkers should check Python code, accompanied by the results of major type checker implementations on the test suite. A rough sketch for what this could look like was created by Shantanu . This would contain prescriptive tests from behavior prescribed by previous PEPs and descriptive tests that let us document behavior of existing implementations in areas that are not prescribed by any standard. These descriptions would be useful to inform efforts below and to identify areas of focus for standardization. Specification for the type system A specification would initially be created by stitching together the specification sections from the existing PEPs, and then gradually improved to clarify points of confusion and cover more areas. A draft of such a stitched-together spec was created by Jelle . The specification has a few audiences: For type checkers, it provides a description of how an idealized type checker should behave. Individual type checkers have different goals and technical constraints and they are free to deviate from the spec if they do not have the resources to fully implement it or if they believe a different behavior better serves their users. However, they should document such deviations from the spec. For projects such as typeshed, or libraries that want to be compatible with multiple type checkers, it provides a set of rules that they can follow to make their code understood by type checkers. For people who want to propose changes to the type system, it provides a foundation for any new proposals. Notably, the specification is not aimed at application developers who use typing. Such users typically do not need to worry about compatibility across type checkers. They are better served by a more informal user-facing reference, which is discussed in the next section. There are different opinions within the community about how formal such a specification should be. While this document recommends an incremental approach that builds off existing specification, it does not aim to prescribe a final state. The Typing Council would provide a mechanism to allow the specification to evolve to meet the level of formality that the community desires, for instance, by incorporating parts of Kevin Millikin’s document on “Python Static Types” as a means to achieve a better formalisation of the spec. Proposed changes to the specification, including PEPs, should generally be accompanied by the following: Buy-in from type checker maintainers to confirm that the change can be implemented and maintained within their type checkers. For changes to existing features, a survey of the behavior of existing type checkers. If existing type checkers behave roughly similarly, that is evidence that their shared behavior should be made part of the specification. Changes to the conformance test suite that demonstrate the specified behavior. User-facing reference for the type system Documentation is important for the success of the Python type system, so the Typing Council should ensure that there is good documentation for the type system. As mentioned previously, PEPs are point in time change proposals aimed at multiple audiences that are hard to clarify. This makes them ill-suited as user documentation. The specification discussed in the previous section would be a living document, but it would likely be too technical to serve as documentation for normal usage. Therefore, a separate user-facing reference for the type system would be useful. Such an effort could expand the documentation on typing.python.org and reuse material from the documentation sections of individual type checkers and the CPython documentation. Amendments This PEP serves as a charter for the Typing Council. Changes to its operation can be made either through a new PEP or through a change to this PEP. In either case, the change would be decided upon by the Steering Council after discussion in the community. Rejected ideas Writing the specification from scratch This PEP proposes creating the typing specification by starting from the existing PEPs, then clarifying and improving the specification as necessary. Some members of the community prefer to start from scratch, writing a new, more formal specification covering the entire type system. This could provide a more solid basis for the specification. However, this would be a much larger undertaking. The existing formalization effort by Kevin Millikin is a good start, but so far covers only a subset of PEP 484. Covering the rest of the type system would likely require several times more effort when we consider that major type system features such as typing.Protocol , typing.Literal , and typing.TypedDict were introduced only after PEP 484. It is not clear that there is even energy in the community for such a huge undertaking. Even if someone steps up to do all the work of putting together a specification, lots of effort would be required from community members and type checker maintainers to consider whether the specification accurately reflects current behavior, and if not, whether the specification or the type checkers should change. Starting with the existing PEPs creates a lower-quality specification, but it means that the Typing Council can immediately start making a difference anywhere in the type system by improving and clarifying the specification. A formalization effort can still proceed by gradually replacing sections of the specification. Alternate governance mechanisms An earlier draft of this PEP suggested that the Steering Council appoint members of the Typing Council each year. The current Steering Council suggested that it would be better to have the Typing Council self-organise and avoid the need for the Steering Council to continuously supervise the Typing Council. Alternate governance mechanisms are possible, including more democratic ones, but these typically raise several thorny questions, require much heavier amounts of process and are potentially more divisive. For example, see the PEP 8000 series, or recent discussions about alternative governance in other Python subcommunities. Ultimately, the Typing Council exists under the authority of the Steering Council, and so can rely on it to bootstrap governance and serve as an accountability mechanism. Do nothing We are hopeful substantial progress will be made on projects that improve the type system regardless of whether this PEP is accepted. We anticipate projects like specification or the potential for PEP delegation would benefit more from a Typing Council, and projects like end user documentation would benefit less. Certainly the bottleneck is likely to be contributor effort, not governance. However, currently the tools available to the community to resolve potential contention are either establishment of approximate consensus or the exercise of power by individual projects or contributors. While very valuable, the former is a slow process that can often end in inaction. The latter can result in a less consistent ecosystem. Finally, easily legible governance structures make the community more accessible and equitable. Contact To ask the Typing Council for a decision, community members may open an issue in the python/typing-council repository. Copyright This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive. Source: https://github.com/python/peps/blob/main/peps/pep-0729.rst Last modified: 2025-03-05 16:28:34 GMT Contents Abstract Motivation PEPs are the only specification It’s hard to evolve the specification The type system is underspecified The Steering Council is not well-placed to solve the above problems Endorsements Specification Mandate Operations and process Relationship with the Steering Council Relationship with type checkers Projects Conformance test suite Specification for the type system User-facing reference for the type system Amendments Rejected ideas Writing the specification from scratch Alternate governance mechanisms Do nothing Contact Copyright Page Source (GitHub) | 2026-01-13T08:49:03 |
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The Services include both our own community forum at https://www.dev.to (the " DEV Community ") and the open source tool we provide called " Forem ," available at https://www.forem.com which allows our customers to create and operate their own online forums. We collect personal information from two categories of people: (1) our customers, who use Forem and our hosting services to run and host their own forums (we'll call them " Forem Operators "), and (2) the people who interact with DEV-hosted forums, including forums provided by Forem Operators utilizing Forem and separately our own DEV Community (we'll call them " Users "). An Important Note for Users Since we provide hosting services for Forem Operators, technically we also process your information on their behalf. That processing is governed by the contracts that we have in place with each Forem Operator, not this Privacy Policy. In other words, when you share your data on a DEV-hosted forum operated by a Forem Operator, we at DEV are basically just the "pipes" — we process the data on behalf of the Forem Operator, but don't do anything with it ourselves beyond what we're required to do under our contract (and by law). So, if you post your information on a DEV-powered forum provided by a Forem Operator, that Forem Operator's privacy policy applies, and any questions or requests relating to your data on that service should be directed to that Forem Operator, not us. Likewise, if you use our mobile application, you may also interact with forums that use DEV's open-source tools but do all their hosting and data collection themselves. For those forums, we at DEV have no access to your data, so be sure to read the privacy policy of any third-party hosted forum before posting. 2. PERSONAL INFORMATION WE COLLECT The categories of personal information we collect depend on whether you're a User or Forem Operator, how you interact with us, our Services, and the requirements of applicable law. Breaking it down, we collect three types of information: (1) information that you provide to us directly, (2) information we obtain automatically when you use our Services, and (3) information we get about you from other sources (such as third-party services and organizations). More details are below. A. Information You Provide to Us Directly We may collect the following personal information that you provide to us. Account Creation (for Forem Operators): We'll require your name and email address to get started, as well as some details about the Forem you want to run, such as: whether you're running the Forem on your own behalf or as part of an organization, and details about the community you want to support (how big is it, what topics does it cover, where do members currently communicate, how/if the community earns money, whether the community is open, invite-only or paid, any existing social media accounts, etc.) You'll need to tell us a bit about your personal coding background, and you'll have the option to provide your DEV username as well, if you are a member of the DEV.to community. Account Creation (for Users) : We collect name and email address from users that create an account on DEV Community. For other forums created by Forem Operators using Forem, the Forem Operator determines what information is required for User account creation for their respective forums. Interactive Features (for Users) . Like any other social network, both we and other Users of our Services may collect personal information that you submit or make available through our interactive features (e.g., messaging and chat features, commenting functionalities, forums, blogs, posts, and other social media pages). While we do have private messages that are only between you and the person you're messaging (as well as us and the Forem Operator, as applicable), any information you provide using the public sharing features of the Services, such as the information you post to your public profile or the topics you follow is public, including to recruiters and prospective employers, and is not subject to any of the privacy protections we mention in this Privacy Policy except where legally required. Please exercise caution before revealing any information that may identify you in the real world to others. Purchases . If you buy stuff on our shop site https://shop.dev.to/ (as either a User or Forem Operator), or otherwise if you pay us in connection with your use of the Forem service, we may collect personal information and details associated with your purchases, including payment information. Any payments made via our Services are processed by third-party payment processors, such as Stripe, Shopify, and PayPal. We do not directly collect or store any payment card information entered through our Services, but may receive information associated with your payment card information (e.g., your billing details). Your Communications with Us (Users and Forem Operators) . We may collect personal information, such as email address, phone number, or mailing address when you request information about our Services, register for our newsletter or loyalty program, request customer or technical support, apply for a job, or otherwise communicate with us. Surveys . We may contact you to participate in surveys. If you decide to participate, you may be asked to provide certain information, which may include personal information (for example, your home address). Sweepstakes or Contests . We may collect personal information you provide for any sweepstakes or contests that we offer. In some jurisdictions, we are required to publicly share information of sweepstakes and contest winners. Conferences, Trade Shows, and Other Events . We may collect personal information from individuals when we attend conferences, trade shows, and other events. Business Development and Strategic Partnerships . We may collect personal information from individuals and third parties to assess and pursue potential business opportunities. Job Applications . We may post job openings and opportunities on our Services. If you reply to one of these postings by submitting your application, CV and/or cover letter to us, we will collect and use your information to assess your qualifications. B. Information Collected Automatically We may collect personal information automatically when you use our Services: Automatic Data Collection . We may collect certain information automatically when you use our Services, such as your Internet protocol (IP) address, user settings, MAC address, cookie identifiers, mobile carrier, mobile advertising and other unique identifiers, browser or device information, location information (including approximate location derived from IP address), and Internet service provider. We may also automatically collect information regarding your use of our Services, such as pages that you visit before, during and after using our Services, information about the links you click, the types of content you interact with, the frequency and duration of your activities, and other information about how you use our Services. In addition, we may collect information that other people provide about you when they use our Services, including information about you when they tag you in their posts. Cookies, Pixel Tags/Web Beacons, and Other Technologies . We, as well as third parties that provide content, advertising, or other functionality on our Services, may use cookies, pixel tags, local storage, and other technologies (" Technologies ") to automatically collect information through your use of our Services. Cookies . Cookies are small text files placed in device browsers that store preferences and facilitate and enhance your experience. Pixel Tags/Web Beacons . A pixel tag (also known as a web beacon) is a piece of code embedded in our Services that collects information about engagement on our Services. The use of a pixel tag allows us to record, for example, that a user has visited a particular web page or clicked on a particular advertisement. We may also include web beacons in e-mails to understand whether messages have been opened, acted on, or forwarded. Our uses of these Technologies fall into the following general categories: Operationally Necessary . This includes Technologies that allow you access to our Services, applications, and tools that are required to identify irregular website behavior, prevent fraudulent activity and improve security or that allow you to make use of our functionality. Performance-Related . We may use Technologies to assess the performance of our Services, including as part of our analytic practices to help us understand how individuals use our Services ( see Analytics below ). Functionality-Related . We may use Technologies that allow us to offer you enhanced functionality when accessing or using our Services. This may include identifying you when you sign into our Services or keeping track of your specified preferences, interests, or past items viewed. Analytics . We may use Technologies and other third-party tools to process analytics information on our Services. Some of our analytics partners include Google Analytics. For more information,please visit Google Analytics' Privacy Policy . To learn more about how to opt-out of Google Analytics' use of your information, please click here . Social Media Platforms . Our Services may contain social media buttons such as Twitter, Facebook, GitHub, Instagram, and Twitch (that might include widgets such as the "share this" button or other interactive mini programs). These features may collect your IP address, which page you are visiting on our Services, and may set a cookie to enable the feature to function properly. Your interactions with these platforms are governed by the privacy policy of the company providing it. See the "Your Privacy Choices and Rights" section below to understand your choices regarding these Technologies. C. Information Collected from Other Sources We may obtain information about you from other sources, including through third-party services and organizations. For example, if you access our Services through a third-party application, such as an app store, a third-party login service (e.g., through Twitter, Apple, or GitHub), or a social networking site, we may collect whatever information about you from that third-party application that you have made available via your privacy settings. 3. HOW WE USE YOUR INFORMATION We use your information for a variety of business purposes, including to provide our Services, for administrative purposes, and to market our products and Services, as described below. A. Provide Our Services We use your information to fulfill our contract with you and provide you with our Services, such as: Managing your information and accounts; Providing access to certain areas, functionalities, and features of our Services; Answering requests for customer or technical support; Communicating with you about your account, activities on our Services, and policy changes; Processing your financial information and other payment methods for products or Services purchased; Processing applications if you apply for a job we post on our Services; and Allowing you to register for events. B. Administrative Purposes We use your information for various administrative purposes, such as: Pursuing our legitimate interests such as direct marketing, research and development (including marketing research), network and information security, and fraud prevention; Detecting security incidents, protecting against malicious, deceptive, fraudulent or illegal activity, and prosecuting those responsible for that activity; Measuring interest and engagement in our Services, including for usage-based billing purposes; Short-term, transient use, such as contextual customization of ads; Improving, optimizing, upgrading, or enhancing our Services; Developing new products and Services; Ensuring internal quality control and safety; Authenticating and verifying individual identities, including requests to exercise your rights under this policy; Debugging to identify and repair errors with our Services; Auditing relating to interactions, transactions and other compliance activities; Enforcing our agreements and policies; and Complying with our legal obligations. C. Marketing and Advertising our Products and Services We may use your personal information to tailor and provide you with content and advertisements for our Services, such as via email. If you have any questions about our marketing practices, you may contact us at any time as set forth in the "Contact Us" section below. D. Other Purposes We also use your information for other purposes as requested by you or as permitted by applicable law. Consent . We may use personal information for other purposes that are clearly disclosed to you at the time you provide personal information or with your consent. Automated Decision Making. We may engage in automated decision making, including profiling, such as to suggest topics or other Users for you to follow. DEV's processing of your personal information will not result in a decision based solely on automated processing that significantly affects you unless such a decision is necessary as part of a contract we have with you, we have your consent, or we are permitted by law to engage in such automated decision making. If you have questions about our automated decision making, you may contact us as set forth in the "Contact Us" section below. De-identified and Aggregated Information . We may use personal information and other information about you to create de-identified and/or aggregated information, such as de-identified demographic information, information about the device from which you access our Services, or other analyses we create. For example, we may collect system-wide information to ensure availability of the platform, or measure aggregate data trends to analyze and optimize our Services. Share Content with Friends or Colleagues. Our Services may offer various tools and functionalities. For example, we may allow you to provide information about your friends through our referral services. Our referral services may allow you to forward or share certain content with a friend or colleague, such as an email inviting your friend to use our Services. Please only share with us contact information of people with whom you have a relationship (e.g., relative, friend neighbor, or co-worker). 4. HOW WE DISCLOSE YOUR INFORMATION We disclose your information to third parties for a variety of business purposes, including to provide our Services, to protect us or others, or in the event of a major business transaction such as a merger, sale, or asset transfer, as described below. A. Disclosures to Provide our Services The categories of third parties with whom we may share your information are described below. Service Providers . We may share your personal information with our third-party service providers who use that information to help us provide our Services. This includes service providers that provide us with IT support, hosting, payment processing, customer service, and related services. For example, our Shop site is run by Shopify, who handle your shipping details on our behalf. Business Partners . We may share your personal information with business partners to provide you with a product or service you have requested. We may also share your personal information to business partners with whom we jointly offer products or services. Other Users . As described above in the "Personal Information We Collect" section of this Privacy Policy, our Service allows Users to share their profiles, and any posts, chats, etc. with other Users and with the general public, including to those who do not use our Services. APIs/SDKs . We may use third-party Application Program Interfaces ("APIs") and Software Development Kits ("SDKs") as part of the functionality of our Services. For more information about our use of APIs and SDKs, please contact us as set forth in the "Contact Us" section below. B . Disclosures to Protect Us or Others We may access, preserve, and disclose any information we store associated with you to external parties if we, in good faith, believe doing so is required or appropriate to: comply with law enforcement or national security requests and legal process, such as a court order or subpoena; protect your, our, or others' rights, property, or safety; enforce our policies or contracts; collect amounts owed to us; or assist with an investigation or prosecution of suspected or actual illegal activity. C. Disclosure in the Event of Merger, Sale, or Other Asset Transfers If we are involved in a merger, acquisition, financing due diligence, reorganization, bankruptcy, receivership, purchase or sale of assets, or transition of service to another provider, your information may be sold or transferred as part of such a transaction, as permitted by law and/or contract. 5. YOUR PRIVACY CHOICES AND RIGHTS Your Privacy Choices . The privacy choices you may have about your personal information are determined by applicable law and are described below. Email Communications . If you receive an unwanted email from us, you can use the unsubscribe link found at the bottom of the email to opt out of receiving future emails. Note that you will continue to receive transaction-related emails regarding products or Services you have requested. We may also send you certain non-promotional communications regarding us and our Services, and you will not be able to opt out of those communications (e.g., communications regarding our Services or updates to our Terms or this Privacy Policy). Mobile Devices . We may send you push notifications through our mobile application. You may opt out from receiving these push notifications by changing the settings on your mobile device. "Do Not Track." Do Not Track (" DNT ") is a privacy preference that users can set in certain web browsers. Please note that we do not respond to or honor DNT signals or similar mechanisms transmitted by web browsers. Cookies and Interest-Based Advertising . You may stop or restrict the placement of Technologies on your device or remove them by adjusting your preferences as your browser or device permits. However, if you adjust your preferences, our Services may not work properly. Please note that cookie-based opt-outs are not effective on mobile applications. Please note you must separately opt out in each browser and on each device. Your Privacy Rights . In accordance with applicable law, you may have the right to: Access Personal Information about you, including: (i) confirming whether we are processing your personal information; (ii) obtaining access to or a copy of your personal information; Request Correction of your personal information where it is inaccurate, incomplete or outdated. In some cases, we may provide self-service tools that enable you to update your personal information; Request Deletion, Anonymization or Blocking of your personal information when processing is based on your consent or when processing is unnecessary, excessive or noncompliant; Request Restriction of or Object to our processing of your personal information when processing is noncompliant; Withdraw Your Consent to our processing of your personal information. If you refrain from providing personal information or withdraw your consent to processing, some features of our Service may not be available; Request Data Portability and Receive an Electronic Copy of Personal Information that You Have Provided to Us; Be Informed about third parties with which your personal information has been shared; and Request the Review of Decisions Taken Exclusively Based on Automated Processing if such decisions could affect your data subject rights. If you would like to exercise any of these rights, please contact us as set forth in "Contact Us" below. We will process such requests in accordance with applicable laws. 6. INTERNATIONAL DATA TRANSFERS All information processed by us may be transferred, processed, and stored anywhere in the world, including, but not limited to, the United States or other countries, which may have data protection laws that are different from the laws where you live. We always strive to safeguard your information consistent with the requirements of applicable laws. 7. RETENTION OF PERSONAL INFORMATION We store the personal information we collect as described in this Privacy Policy for as long as you use our Services or as necessary: to fulfill the purpose or purposes for which it was collected, to provide our Services, to resolve disputes, to establish legal defenses, to conduct audits, to pursue legitimate business purposes, to enforce our agreements, and to comply with applicable laws. 8. SUPPLEMENTAL DISCLOSURES FOR CALIFORNIA RESIDENTS Refer-a-Friend and Similar Incentive Programs . As described above in the How We Use Your Personal Information section ("Share Content with Friends or Colleagues" subsection), we may offer referral programs or other incentivized data collection programs. For example, we may offer incentives to you such as discounts or promotional items or credit in connection with these programs, wherein you provide your personal information in exchange for a reward, or provide personal information regarding your friends or colleagues (such as their email address) and receive rewards when they sign up to use our Services. (The referred party may also receive rewards for signing up via your referral.) These programs are entirely voluntary and allow us to grow our business and provide additional benefits to you. The value of your data to us depends on how you ultimately use our Services, whereas the value of the referred party's data to us depends on whether the referred party ultimately becomes a User or Forem Operator and uses our Services. Said value will be reflected in the incentive offered in connection with each program. Accessibility . This Privacy Policy uses industry-standard technologies and was developed in line with the World Wide Web Consortium's Web Content Accessibility Guidelines, version 2.1* . * If you wish to print this policy, please do so from your web browser or by saving the page as a PDF. California Shine the Light . The California "Shine the Light" law permits users who are California residents to request and obtain from us once a year, free of charge, a list of the third parties to whom we have disclosed their personal information (if any) for their direct marketing purposes in the prior calendar year, as well as the type of personal information disclosed to those parties. Right for Minors to Remove Posted Content . Where required by law, California residents under the age of 18 may request to have their posted content or information removed from the publicly-viewable portions of the Services by contacting us directly as set forth in the "Contact Us" section below or by logging into their account and removing the content or information using our self-service tools. 9. SUPPLEMENTAL NOTICE FOR NEVADA RESIDENTS If you are a resident of Nevada, you have the right to opt-out of the sale of certain Personal Information to third parties who intend to license or sell that Personal Information. You can exercise this right by contacting us as set forth in the "Contact Us\" section below with the subject line "Nevada Do Not Sell Request" and providing us with your name and the email address associated with your account. Please note that we do not currently sell your Personal Information as sales are defined in Nevada Revised Statutes Chapter 603A. If you have any questions, please contact us as set forth below. 10. CHILDREN'S INFORMATION The Services are not directed to children under 13 (or other age as required by local law), and we do not knowingly collect personal information from children. If you are a parent or guardian and believe your child has uploaded personal information to our site without your consent, you may contact us as described in the "Contact Us" section below. If we become aware that a child has provided us with personal information in violation of applicable law, we will delete any personal information we have collected, unless we have a legal obligation to keep it, and terminate the child's account if applicable. 11. OTHER PROVISIONS Third-Party Websites or Applications . The Services may contain links to other websites or applications, and other websites or applications may reference or link to our Services. These third-party services are not controlled by us. We encourage our users to read the privacy policies of each website and application with which they interact. We do not endorse, screen or approve, and are not responsible for, the privacy practices or content of such other websites or applications. Providing personal information to third-party websites or applications is at your own risk. Changes to Our Privacy Policy . We may revise this Privacy Policy from time to time in our sole discretion. If there are any material changes to this Privacy Policy, we will notify you as required by applicable law. You understand and agree that you will be deemed to have accepted the updated Privacy Policy if you continue to use our Services after the new Privacy Policy takes effect. 12. CONTACT US If you have any questions about our privacy practices or this Privacy Policy, or to exercise your rights as detailed in this Privacy Policy, please contact us at: support@dev.to . 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV Future — News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Home About Contact Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . Future © 2025 - 2026. Stay on the cutting edge, and shape tomorrow Log in Create account | 2026-01-13T08:49:03 |
https://gg.forem.com/terms | Web Site Terms and Conditions of Use - Gamers Forem Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. DUMB DEV Community Follow Memes and software development shitposting Design Community Follow Web design, graphic design and everything in-between Security Forem Follow Your central hub for all things security. From ethical hacking and CTFs to GRC and career development, for beginners and pros alike Golf Forem Follow A community of golfers and golfing enthusiasts Crypto Forem Follow A collaborative community for all things Crypto—from Bitcoin to protocol development and DeFi to NFTs and market analysis. Parenting Follow A place for parents to the share the joys, challenges, and wisdom that come from raising kids. We're here for them and for each other. Forem Core Follow Discussing the core forem open source software project — features, bugs, performance, self-hosting. Maker Forem Follow A community for makers, hobbyists, and professionals to discuss Arduino, Raspberry Pi, 3D printing, and much more. HMPL.js Forem Follow For developers using HMPL.js to build fast, lightweight web apps. A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account Gamers Forem Close Web Site Terms and Conditions of Use 1. Terms By accessing this web site, you are agreeing to be bound by these web site Terms and Conditions of Use, our Privacy Policy , all applicable laws and regulations, and agree that you are responsible for compliance with any applicable local laws. If you do not agree with any of these terms, you are prohibited from using or accessing this site. The materials contained in this web site are protected by applicable copyright and trade mark law. 2. Use License Permission is granted to temporarily download one copy of the materials (information or software) on DEV Community's web site for personal, non-commercial transitory viewing only. This is the grant of a license, not a transfer of title, and under this license you may not: modify or copy the materials; use the materials for any commercial purpose, or for any public display (commercial or non-commercial); attempt to decompile or reverse engineer any software contained on DEV Community's web site; remove any copyright or other proprietary notations from the materials; or transfer the materials to another person or "mirror" the materials on any other server. This license shall automatically terminate if you violate any of these restrictions and may be terminated by DEV Community at any time. Upon terminating your viewing of these materials or upon the termination of this license, you must destroy any downloaded materials in your possession whether in electronic or printed format. 3. Disclaimer The materials on DEV Community's web site are provided "as is". DEV Community makes no warranties, expressed or implied, and hereby disclaims and negates all other warranties, including without limitation, implied warranties or conditions of merchantability, fitness for a particular purpose, or non-infringement of intellectual property or other violation of rights. Further, DEV Community does not warrant or make any representations concerning the accuracy, likely results, or reliability of the use of the materials on its Internet web site or otherwise relating to such materials or on any sites linked to this site. 4. Limitations In no event shall DEV Community or its suppliers be liable for any damages (including, without limitation, damages for loss of data or profit, or due to business interruption,) arising out of the use or inability to use the materials on DEV Community's Internet site, even if DEV Community or an authorized representative has been notified orally or in writing of the possibility of such damage. Because some jurisdictions do not allow limitations on implied warranties, or limitations of liability for consequential or incidental damages, these limitations may not apply to you. 5. Revisions and Errata The materials appearing on DEV Community's web site could include technical, typographical, or photographic errors. DEV Community does not warrant that any of the materials on its web site are accurate, complete, or current. DEV Community may make changes to the materials contained on its web site at any time without notice. DEV Community does not, however, make any commitment to update the materials. 6. Links DEV Community has not reviewed all of the sites linked to its Internet web site and is not responsible for the contents of any such linked site. The inclusion of any link does not imply endorsement by DEV Community of the site. Use of any such linked web site is at the user's own risk. 7. Copyright / Takedown Users agree and certify that they have rights to share all content that they post on DEV Community — including, but not limited to, information posted in articles, discussions, and comments. This rule applies to prose, code snippets, collections of links, etc. Regardless of citation, users may not post copy and pasted content that does not belong to them. DEV Community does not tolerate plagiarism of any kind, including mosaic or patchwork plagiarism. Users assume all risk for the content they post, including someone else's reliance on its accuracy, claims relating to intellectual property, or other legal rights. If you believe that a user has plagiarized content, misrepresented their identity, misappropriated work, or otherwise run afoul of DMCA regulations, please email support@dev.to. DEV Community may remove any content users post for any reason. 8. Site Terms of Use Modifications DEV Community may revise these terms of use for its web site at any time without notice. By using this web site you are agreeing to be bound by the then current version of these Terms and Conditions of Use. 9. DEV Community Trademarks and Logos Policy All uses of the DEV Community logo, DEV Community badges, brand slogans, iconography, and the like, may only be used with express permission from DEV Community. DEV Community reserves all rights, even if certain assets are included in DEV Community open source projects. Please contact support@dev.to with any questions or to request permission. 10. Reserved Names DEV Community has the right to maintain a list of reserved names which will not be made publicly available. These reserved names may be set aside for purposes of proactive trademark protection, avoiding user confusion, security measures, or any other reason (or no reason). Additionally, DEV Community reserves the right to change any already-claimed name at its sole discretion. In such cases, DEV Community will make reasonable effort to find a suitable alternative and assist with any transition-related concerns. 11. Content Policy The following policy applies to comments, articles, and all other works shared on the DEV Community platform: Users must make a good-faith effort to share content that is on-topic, of high-quality, and is not designed primarily for the purposes of promotion or creating backlinks. Posts must contain substantial content — they may not merely reference an external link that contains the full post. If a post contains affiliate links, that fact must be clearly disclosed. For instance, with language such as: “This post includes affiliate links; I may receive compensation if you purchase products or services from the different links provided in this article.” DEV Community reserves the right to remove any content that it deems to be in violation of this policy at its sole discretion. Additionally, DEV Community reserves the right to restrict any user’s ability to participate on the platform at its sole discretion. 12. Fees, Payment, Renewal Fees for Paid Services .Fees for Paid Services. Some of our Services may be offered for a fee (collectively, “Paid Services”). This section applies to any purchases of Paid Services. By using a Paid Service, you agree to pay the specified fees. Depending on the Paid Service, there may be different kinds of fees, for instance some that are one-time, recurring, and/or based on an advertising campaign budget that you set. For recurring fees (AKA Subscriptions), your subscription begins on your purchase date, and we’ll bill or charge you in the automatically-renewing interval (such as monthly, annually) you select, on a pre-pay basis until you cancel, which you can do at any time by contacting plusplus@dev.to . Payment. You must provide accurate and up-to-date payment information. By providing your payment information, you authorize us to store it until you request deletion. If your payment fails, we suspect fraud, or Paid Services are otherwise not paid for or paid for on time (for example, if you contact your bank or credit card company to decline or reverse the charge of fees for Paid Services), we may immediately cancel or revoke your access to Paid Services without notice to you. You authorize us to charge any updated payment information provided by your bank or payment service provider (e.g., new expiration date) or other payment methods provided if we can’t charge your primary payment method. Automatic Renewal. By enrolling in a subscription, you authorize us to automatically charge the then-applicable fees for each subsequent subscription period until the subscription is canceled. If you received a discount, used a coupon code, or subscribed during a free trial or promotion, your subscription will automatically renew for the full price of the subscription at the end of the discount period. This means that unless you cancel a subscription, it’ll automatically renew and we’ll charge your payment method(s). The date for the automatic renewal is based on the date of the original purchase and cannot be changed. You can view your renewal date(s), cancel, or manage subscriptions by contacting plusplus@dev.to . Fees and Changes. We may change our fees at any time in accordance with these Terms and requirements under applicable law. This means that we may change our fees going forward or remove or update features or functionality that were previously included in the fees. If you don’t agree with the changes, you must cancel your Paid Service. Refunds. There are no refunds and all payments are final. European Users: You have the right to withdraw from the transaction within fourteen (14) days from the date of the purchase without giving any reason as long as your purchase was not of downloadable content or of a customized nature, and (i) the service has not been fully performed, or (ii) subject to other limitations as permitted by law. If you cancel this contract, we will reimburse you all payments we have received from you, without undue delay and no later than within fourteen days from the day on which we received the notification of your cancellation of this contract. For this repayment, we will use the same means of payment that you used for the original transaction, unless expressly agreed otherwise with you; you will not be charged for this repayment. 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https://docs.python.org/3/library/stdtypes.html#old-string-formatting | Built-in Types — Python 3.14.2 documentation Theme Auto Light Dark Table of Contents Built-in Types Truth Value Testing Boolean Operations — and , or , not Comparisons Numeric Types — int , float , complex Bitwise Operations on Integer Types Additional Methods on Integer Types Additional Methods on Float Additional Methods on Complex Hashing of numeric types Boolean Type - bool Iterator Types Generator Types Sequence Types — list , tuple , range Common Sequence Operations Immutable Sequence Types Mutable Sequence Types Lists Tuples Ranges Text and Binary Sequence Type Methods Summary Text Sequence Type — str String Methods Formatted String Literals (f-strings) Debug specifier Conversion specifier Format specifier Template String Literals (t-strings) printf -style String Formatting Binary Sequence Types — bytes , bytearray , memoryview Bytes Objects Bytearray Objects Bytes and Bytearray Operations printf -style Bytes Formatting Memory Views Set Types — set , frozenset Mapping Types — dict Dictionary view objects Context Manager Types Type Annotation Types — Generic Alias , Union Generic Alias Type Standard Generic Classes Special Attributes of GenericAlias objects Union Type Other Built-in Types Modules Classes and Class Instances Functions Methods Code Objects Type Objects The Null Object The Ellipsis Object The NotImplemented Object Internal Objects Special Attributes Integer string conversion length limitation Affected APIs Configuring the limit Recommended configuration Previous topic Built-in Constants Next topic Built-in Exceptions This page Report a bug Show source Navigation index modules | next | previous | Python » 3.14.2 Documentation » The Python Standard Library » Built-in Types | Theme Auto Light Dark | Built-in Types ¶ The following sections describe the standard types that are built into the interpreter. The principal built-in types are numerics, sequences, mappings, classes, instances and exceptions. Some collection classes are mutable. The methods that add, subtract, or rearrange their members in place, and don’t return a specific item, never return the collection instance itself but None . Some operations are supported by several object types; in particular, practically all objects can be compared for equality, tested for truth value, and converted to a string (with the repr() function or the slightly different str() function). The latter function is implicitly used when an object is written by the print() function. Truth Value Testing ¶ Any object can be tested for truth value, for use in an if or while condition or as operand of the Boolean operations below. By default, an object is considered true unless its class defines either a __bool__() method that returns False or a __len__() method that returns zero, when called with the object. [ 1 ] If one of the methods raises an exception when called, the exception is propagated and the object does not have a truth value (for example, NotImplemented ). Here are most of the built-in objects considered false: constants defined to be false: None and False zero of any numeric type: 0 , 0.0 , 0j , Decimal(0) , Fraction(0, 1) empty sequences and collections: '' , () , [] , {} , set() , range(0) Operations and built-in functions that have a Boolean result always return 0 or False for false and 1 or True for true, unless otherwise stated. (Important exception: the Boolean operations or and and always return one of their operands.) Boolean Operations — and , or , not ¶ These are the Boolean operations, ordered by ascending priority: Operation Result Notes x or y if x is true, then x , else y (1) x and y if x is false, then x , else y (2) not x if x is false, then True , else False (3) Notes: This is a short-circuit operator, so it only evaluates the second argument if the first one is false. This is a short-circuit operator, so it only evaluates the second argument if the first one is true. not has a lower priority than non-Boolean operators, so not a == b is interpreted as not (a == b) , and a == not b is a syntax error. Comparisons ¶ There are eight comparison operations in Python. They all have the same priority (which is higher than that of the Boolean operations). Comparisons can be chained arbitrarily; for example, x < y <= z is equivalent to x < y and y <= z , except that y is evaluated only once (but in both cases z is not evaluated at all when x < y is found to be false). This table summarizes the comparison operations: Operation Meaning < strictly less than <= less than or equal > strictly greater than >= greater than or equal == equal != not equal is object identity is not negated object identity Unless stated otherwise, objects of different types never compare equal. The == operator is always defined but for some object types (for example, class objects) is equivalent to is . The < , <= , > and >= operators are only defined where they make sense; for example, they raise a TypeError exception when one of the arguments is a complex number. Non-identical instances of a class normally compare as non-equal unless the class defines the __eq__() method. Instances of a class cannot be ordered with respect to other instances of the same class, or other types of object, unless the class defines enough of the methods __lt__() , __le__() , __gt__() , and __ge__() (in general, __lt__() and __eq__() are sufficient, if you want the conventional meanings of the comparison operators). The behavior of the is and is not operators cannot be customized; also they can be applied to any two objects and never raise an exception. Two more operations with the same syntactic priority, in and not in , are supported by types that are iterable or implement the __contains__() method. Numeric Types — int , float , complex ¶ There are three distinct numeric types: integers , floating-point numbers , and complex numbers . In addition, Booleans are a subtype of integers. Integers have unlimited precision. Floating-point numbers are usually implemented using double in C; information about the precision and internal representation of floating-point numbers for the machine on which your program is running is available in sys.float_info . Complex numbers have a real and imaginary part, which are each a floating-point number. To extract these parts from a complex number z , use z.real and z.imag . (The standard library includes the additional numeric types fractions.Fraction , for rationals, and decimal.Decimal , for floating-point numbers with user-definable precision.) Numbers are created by numeric literals or as the result of built-in functions and operators. Unadorned integer literals (including hex, octal and binary numbers) yield integers. Numeric literals containing a decimal point or an exponent sign yield floating-point numbers. Appending 'j' or 'J' to a numeric literal yields an imaginary number (a complex number with a zero real part) which you can add to an integer or float to get a complex number with real and imaginary parts. The constructors int() , float() , and complex() can be used to produce numbers of a specific type. Python fully supports mixed arithmetic: when a binary arithmetic operator has operands of different numeric types, the operand with the “narrower” type is widened to that of the other, where integer is narrower than floating point. Arithmetic with complex and real operands is defined by the usual mathematical formula, for example: x + complex ( u , v ) = complex ( x + u , v ) x * complex ( u , v ) = complex ( x * u , x * v ) A comparison between numbers of different types behaves as though the exact values of those numbers were being compared. [ 2 ] All numeric types (except complex) support the following operations (for priorities of the operations, see Operator precedence ): Operation Result Notes Full documentation x + y sum of x and y x - y difference of x and y x * y product of x and y x / y quotient of x and y x // y floored quotient of x and y (1)(2) x % y remainder of x / y (2) -x x negated +x x unchanged abs(x) absolute value or magnitude of x abs() int(x) x converted to integer (3)(6) int() float(x) x converted to floating point (4)(6) float() complex(re, im) a complex number with real part re , imaginary part im . im defaults to zero. (6) complex() c.conjugate() conjugate of the complex number c divmod(x, y) the pair (x // y, x % y) (2) divmod() pow(x, y) x to the power y (5) pow() x ** y x to the power y (5) Notes: Also referred to as integer division. For operands of type int , the result has type int . For operands of type float , the result has type float . In general, the result is a whole integer, though the result’s type is not necessarily int . The result is always rounded towards minus infinity: 1//2 is 0 , (-1)//2 is -1 , 1//(-2) is -1 , and (-1)//(-2) is 0 . Not for complex numbers. Instead convert to floats using abs() if appropriate. Conversion from float to int truncates, discarding the fractional part. See functions math.floor() and math.ceil() for alternative conversions. float also accepts the strings “nan” and “inf” with an optional prefix “+” or “-” for Not a Number (NaN) and positive or negative infinity. Python defines pow(0, 0) and 0 ** 0 to be 1 , as is common for programming languages. The numeric literals accepted include the digits 0 to 9 or any Unicode equivalent (code points with the Nd property). See the Unicode Standard for a complete list of code points with the Nd property. All numbers.Real types ( int and float ) also include the following operations: Operation Result math.trunc(x) x truncated to Integral round(x[, n]) x rounded to n digits, rounding half to even. If n is omitted, it defaults to 0. math.floor(x) the greatest Integral <= x math.ceil(x) the least Integral >= x For additional numeric operations see the math and cmath modules. Bitwise Operations on Integer Types ¶ Bitwise operations only make sense for integers. The result of bitwise operations is calculated as though carried out in two’s complement with an infinite number of sign bits. The priorities of the binary bitwise operations are all lower than the numeric operations and higher than the comparisons; the unary operation ~ has the same priority as the other unary numeric operations ( + and - ). This table lists the bitwise operations sorted in ascending priority: Operation Result Notes x | y bitwise or of x and y (4) x ^ y bitwise exclusive or of x and y (4) x & y bitwise and of x and y (4) x << n x shifted left by n bits (1)(2) x >> n x shifted right by n bits (1)(3) ~x the bits of x inverted Notes: Negative shift counts are illegal and cause a ValueError to be raised. A left shift by n bits is equivalent to multiplication by pow(2, n) . A right shift by n bits is equivalent to floor division by pow(2, n) . Performing these calculations with at least one extra sign extension bit in a finite two’s complement representation (a working bit-width of 1 + max(x.bit_length(), y.bit_length()) or more) is sufficient to get the same result as if there were an infinite number of sign bits. Additional Methods on Integer Types ¶ The int type implements the numbers.Integral abstract base class . In addition, it provides a few more methods: int. bit_length ( ) ¶ Return the number of bits necessary to represent an integer in binary, excluding the sign and leading zeros: >>> n = - 37 >>> bin ( n ) '-0b100101' >>> n . bit_length () 6 More precisely, if x is nonzero, then x.bit_length() is the unique positive integer k such that 2**(k-1) <= abs(x) < 2**k . Equivalently, when abs(x) is small enough to have a correctly rounded logarithm, then k = 1 + int(log(abs(x), 2)) . If x is zero, then x.bit_length() returns 0 . Equivalent to: def bit_length ( self ): s = bin ( self ) # binary representation: bin(-37) --> '-0b100101' s = s . lstrip ( '-0b' ) # remove leading zeros and minus sign return len ( s ) # len('100101') --> 6 Added in version 3.1. int. bit_count ( ) ¶ Return the number of ones in the binary representation of the absolute value of the integer. This is also known as the population count. Example: >>> n = 19 >>> bin ( n ) '0b10011' >>> n . bit_count () 3 >>> ( - n ) . bit_count () 3 Equivalent to: def bit_count ( self ): return bin ( self ) . count ( "1" ) Added in version 3.10. int. to_bytes ( length = 1 , byteorder = 'big' , * , signed = False ) ¶ Return an array of bytes representing an integer. >>> ( 1024 ) . to_bytes ( 2 , byteorder = 'big' ) b'\x04\x00' >>> ( 1024 ) . to_bytes ( 10 , byteorder = 'big' ) b'\x00\x00\x00\x00\x00\x00\x00\x00\x04\x00' >>> ( - 1024 ) . to_bytes ( 10 , byteorder = 'big' , signed = True ) b'\xff\xff\xff\xff\xff\xff\xff\xff\xfc\x00' >>> x = 1000 >>> x . to_bytes (( x . bit_length () + 7 ) // 8 , byteorder = 'little' ) b'\xe8\x03' The integer is represented using length bytes, and defaults to 1. An OverflowError is raised if the integer is not representable with the given number of bytes. The byteorder argument determines the byte order used to represent the integer, and defaults to "big" . If byteorder is "big" , the most significant byte is at the beginning of the byte array. If byteorder is "little" , the most significant byte is at the end of the byte array. The signed argument determines whether two’s complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised. The default value for signed is False . The default values can be used to conveniently turn an integer into a single byte object: >>> ( 65 ) . to_bytes () b'A' However, when using the default arguments, don’t try to convert a value greater than 255 or you’ll get an OverflowError . Equivalent to: def to_bytes ( n , length = 1 , byteorder = 'big' , signed = False ): if byteorder == 'little' : order = range ( length ) elif byteorder == 'big' : order = reversed ( range ( length )) else : raise ValueError ( "byteorder must be either 'little' or 'big'" ) return bytes (( n >> i * 8 ) & 0xff for i in order ) Added in version 3.2. Changed in version 3.11: Added default argument values for length and byteorder . classmethod int. from_bytes ( bytes , byteorder = 'big' , * , signed = False ) ¶ Return the integer represented by the given array of bytes. >>> int . from_bytes ( b ' \x00\x10 ' , byteorder = 'big' ) 16 >>> int . from_bytes ( b ' \x00\x10 ' , byteorder = 'little' ) 4096 >>> int . from_bytes ( b ' \xfc\x00 ' , byteorder = 'big' , signed = True ) -1024 >>> int . from_bytes ( b ' \xfc\x00 ' , byteorder = 'big' , signed = False ) 64512 >>> int . from_bytes ([ 255 , 0 , 0 ], byteorder = 'big' ) 16711680 The argument bytes must either be a bytes-like object or an iterable producing bytes. The byteorder argument determines the byte order used to represent the integer, and defaults to "big" . If byteorder is "big" , the most significant byte is at the beginning of the byte array. If byteorder is "little" , the most significant byte is at the end of the byte array. To request the native byte order of the host system, use sys.byteorder as the byte order value. The signed argument indicates whether two’s complement is used to represent the integer. Equivalent to: def from_bytes ( bytes , byteorder = 'big' , signed = False ): if byteorder == 'little' : little_ordered = list ( bytes ) elif byteorder == 'big' : little_ordered = list ( reversed ( bytes )) else : raise ValueError ( "byteorder must be either 'little' or 'big'" ) n = sum ( b << i * 8 for i , b in enumerate ( little_ordered )) if signed and little_ordered and ( little_ordered [ - 1 ] & 0x80 ): n -= 1 << 8 * len ( little_ordered ) return n Added in version 3.2. Changed in version 3.11: Added default argument value for byteorder . int. as_integer_ratio ( ) ¶ Return a pair of integers whose ratio is equal to the original integer and has a positive denominator. The integer ratio of integers (whole numbers) is always the integer as the numerator and 1 as the denominator. Added in version 3.8. int. is_integer ( ) ¶ Returns True . Exists for duck type compatibility with float.is_integer() . Added in version 3.12. Additional Methods on Float ¶ The float type implements the numbers.Real abstract base class . float also has the following additional methods. classmethod float. from_number ( x ) ¶ Class method to return a floating-point number constructed from a number x . If the argument is an integer or a floating-point number, a floating-point number with the same value (within Python’s floating-point precision) is returned. If the argument is outside the range of a Python float, an OverflowError will be raised. For a general Python object x , float.from_number(x) delegates to x.__float__() . If __float__() is not defined then it falls back to __index__() . Added in version 3.14. float. as_integer_ratio ( ) ¶ Return a pair of integers whose ratio is exactly equal to the original float. The ratio is in lowest terms and has a positive denominator. Raises OverflowError on infinities and a ValueError on NaNs. float. is_integer ( ) ¶ Return True if the float instance is finite with integral value, and False otherwise: >>> ( - 2.0 ) . is_integer () True >>> ( 3.2 ) . is_integer () False Two methods support conversion to and from hexadecimal strings. Since Python’s floats are stored internally as binary numbers, converting a float to or from a decimal string usually involves a small rounding error. In contrast, hexadecimal strings allow exact representation and specification of floating-point numbers. This can be useful when debugging, and in numerical work. float. hex ( ) ¶ Return a representation of a floating-point number as a hexadecimal string. For finite floating-point numbers, this representation will always include a leading 0x and a trailing p and exponent. classmethod float. fromhex ( s ) ¶ Class method to return the float represented by a hexadecimal string s . The string s may have leading and trailing whitespace. Note that float.hex() is an instance method, while float.fromhex() is a class method. A hexadecimal string takes the form: [ sign ] [ '0x' ] integer [ '.' fraction ] [ 'p' exponent ] where the optional sign may by either + or - , integer and fraction are strings of hexadecimal digits, and exponent is a decimal integer with an optional leading sign. Case is not significant, and there must be at least one hexadecimal digit in either the integer or the fraction. This syntax is similar to the syntax specified in section 6.4.4.2 of the C99 standard, and also to the syntax used in Java 1.5 onwards. In particular, the output of float.hex() is usable as a hexadecimal floating-point literal in C or Java code, and hexadecimal strings produced by C’s %a format character or Java’s Double.toHexString are accepted by float.fromhex() . Note that the exponent is written in decimal rather than hexadecimal, and that it gives the power of 2 by which to multiply the coefficient. For example, the hexadecimal string 0x3.a7p10 represents the floating-point number (3 + 10./16 + 7./16**2) * 2.0**10 , or 3740.0 : >>> float . fromhex ( '0x3.a7p10' ) 3740.0 Applying the reverse conversion to 3740.0 gives a different hexadecimal string representing the same number: >>> float . hex ( 3740.0 ) '0x1.d380000000000p+11' Additional Methods on Complex ¶ The complex type implements the numbers.Complex abstract base class . complex also has the following additional methods. classmethod complex. from_number ( x ) ¶ Class method to convert a number to a complex number. For a general Python object x , complex.from_number(x) delegates to x.__complex__() . If __complex__() is not defined then it falls back to __float__() . If __float__() is not defined then it falls back to __index__() . Added in version 3.14. Hashing of numeric types ¶ For numbers x and y , possibly of different types, it’s a requirement that hash(x) == hash(y) whenever x == y (see the __hash__() method documentation for more details). For ease of implementation and efficiency across a variety of numeric types (including int , float , decimal.Decimal and fractions.Fraction ) Python’s hash for numeric types is based on a single mathematical function that’s defined for any rational number, and hence applies to all instances of int and fractions.Fraction , and all finite instances of float and decimal.Decimal . Essentially, this function is given by reduction modulo P for a fixed prime P . The value of P is made available to Python as the modulus attribute of sys.hash_info . CPython implementation detail: Currently, the prime used is P = 2**31 - 1 on machines with 32-bit C longs and P = 2**61 - 1 on machines with 64-bit C longs. Here are the rules in detail: If x = m / n is a nonnegative rational number and n is not divisible by P , define hash(x) as m * invmod(n, P) % P , where invmod(n, P) gives the inverse of n modulo P . If x = m / n is a nonnegative rational number and n is divisible by P (but m is not) then n has no inverse modulo P and the rule above doesn’t apply; in this case define hash(x) to be the constant value sys.hash_info.inf . If x = m / n is a negative rational number define hash(x) as -hash(-x) . If the resulting hash is -1 , replace it with -2 . The particular values sys.hash_info.inf and -sys.hash_info.inf are used as hash values for positive infinity or negative infinity (respectively). For a complex number z , the hash values of the real and imaginary parts are combined by computing hash(z.real) + sys.hash_info.imag * hash(z.imag) , reduced modulo 2**sys.hash_info.width so that it lies in range(-2**(sys.hash_info.width - 1), 2**(sys.hash_info.width - 1)) . Again, if the result is -1 , it’s replaced with -2 . To clarify the above rules, here’s some example Python code, equivalent to the built-in hash, for computing the hash of a rational number, float , or complex : import sys , math def hash_fraction ( m , n ): """Compute the hash of a rational number m / n. Assumes m and n are integers, with n positive. Equivalent to hash(fractions.Fraction(m, n)). """ P = sys . hash_info . modulus # Remove common factors of P. (Unnecessary if m and n already coprime.) while m % P == n % P == 0 : m , n = m // P , n // P if n % P == 0 : hash_value = sys . hash_info . inf else : # Fermat's Little Theorem: pow(n, P-1, P) is 1, so # pow(n, P-2, P) gives the inverse of n modulo P. hash_value = ( abs ( m ) % P ) * pow ( n , P - 2 , P ) % P if m < 0 : hash_value = - hash_value if hash_value == - 1 : hash_value = - 2 return hash_value def hash_float ( x ): """Compute the hash of a float x.""" if math . isnan ( x ): return object . __hash__ ( x ) elif math . isinf ( x ): return sys . hash_info . inf if x > 0 else - sys . hash_info . inf else : return hash_fraction ( * x . as_integer_ratio ()) def hash_complex ( z ): """Compute the hash of a complex number z.""" hash_value = hash_float ( z . real ) + sys . hash_info . imag * hash_float ( z . imag ) # do a signed reduction modulo 2**sys.hash_info.width M = 2 ** ( sys . hash_info . width - 1 ) hash_value = ( hash_value & ( M - 1 )) - ( hash_value & M ) if hash_value == - 1 : hash_value = - 2 return hash_value Boolean Type - bool ¶ Booleans represent truth values. The bool type has exactly two constant instances: True and False . The built-in function bool() converts any value to a boolean, if the value can be interpreted as a truth value (see section Truth Value Testing above). For logical operations, use the boolean operators and , or and not . When applying the bitwise operators & , | , ^ to two booleans, they return a bool equivalent to the logical operations “and”, “or”, “xor”. However, the logical operators and , or and != should be preferred over & , | and ^ . Deprecated since version 3.12: The use of the bitwise inversion operator ~ is deprecated and will raise an error in Python 3.16. bool is a subclass of int (see Numeric Types — int, float, complex ). In many numeric contexts, False and True behave like the integers 0 and 1, respectively. However, relying on this is discouraged; explicitly convert using int() instead. Iterator Types ¶ Python supports a concept of iteration over containers. This is implemented using two distinct methods; these are used to allow user-defined classes to support iteration. Sequences, described below in more detail, always support the iteration methods. One method needs to be defined for container objects to provide iterable support: container. __iter__ ( ) ¶ Return an iterator object. The object is required to support the iterator protocol described below. If a container supports different types of iteration, additional methods can be provided to specifically request iterators for those iteration types. (An example of an object supporting multiple forms of iteration would be a tree structure which supports both breadth-first and depth-first traversal.) This method corresponds to the tp_iter slot of the type structure for Python objects in the Python/C API. The iterator objects themselves are required to support the following two methods, which together form the iterator protocol : iterator. __iter__ ( ) ¶ Return the iterator object itself. This is required to allow both containers and iterators to be used with the for and in statements. This method corresponds to the tp_iter slot of the type structure for Python objects in the Python/C API. iterator. __next__ ( ) ¶ Return the next item from the iterator . If there are no further items, raise the StopIteration exception. This method corresponds to the tp_iternext slot of the type structure for Python objects in the Python/C API. Python defines several iterator objects to support iteration over general and specific sequence types, dictionaries, and other more specialized forms. The specific types are not important beyond their implementation of the iterator protocol. Once an iterator’s __next__() method raises StopIteration , it must continue to do so on subsequent calls. Implementations that do not obey this property are deemed broken. Generator Types ¶ Python’s generator s provide a convenient way to implement the iterator protocol. If a container object’s __iter__() method is implemented as a generator, it will automatically return an iterator object (technically, a generator object) supplying the __iter__() and __next__() methods. More information about generators can be found in the documentation for the yield expression . Sequence Types — list , tuple , range ¶ There are three basic sequence types: lists, tuples, and range objects. Additional sequence types tailored for processing of binary data and text strings are described in dedicated sections. Common Sequence Operations ¶ The operations in the following table are supported by most sequence types, both mutable and immutable. The collections.abc.Sequence ABC is provided to make it easier to correctly implement these operations on custom sequence types. This table lists the sequence operations sorted in ascending priority. In the table, s and t are sequences of the same type, n , i , j and k are integers and x is an arbitrary object that meets any type and value restrictions imposed by s . The in and not in operations have the same priorities as the comparison operations. The + (concatenation) and * (repetition) operations have the same priority as the corresponding numeric operations. [ 3 ] Operation Result Notes x in s True if an item of s is equal to x , else False (1) x not in s False if an item of s is equal to x , else True (1) s + t the concatenation of s and t (6)(7) s * n or n * s equivalent to adding s to itself n times (2)(7) s[i] i th item of s , origin 0 (3)(8) s[i:j] slice of s from i to j (3)(4) s[i:j:k] slice of s from i to j with step k (3)(5) len(s) length of s min(s) smallest item of s max(s) largest item of s Sequences of the same type also support comparisons. In particular, tuples and lists are compared lexicographically by comparing corresponding elements. This means that to compare equal, every element must compare equal and the two sequences must be of the same type and have the same length. (For full details see Comparisons in the language reference.) Forward and reversed iterators over mutable sequences access values using an index. That index will continue to march forward (or backward) even if the underlying sequence is mutated. The iterator terminates only when an IndexError or a StopIteration is encountered (or when the index drops below zero). Notes: While the in and not in operations are used only for simple containment testing in the general case, some specialised sequences (such as str , bytes and bytearray ) also use them for subsequence testing: >>> "gg" in "eggs" True Values of n less than 0 are treated as 0 (which yields an empty sequence of the same type as s ). Note that items in the sequence s are not copied; they are referenced multiple times. This often haunts new Python programmers; consider: >>> lists = [[]] * 3 >>> lists [[], [], []] >>> lists [ 0 ] . append ( 3 ) >>> lists [[3], [3], [3]] What has happened is that [[]] is a one-element list containing an empty list, so all three elements of [[]] * 3 are references to this single empty list. Modifying any of the elements of lists modifies this single list. You can create a list of different lists this way: >>> lists = [[] for i in range ( 3 )] >>> lists [ 0 ] . append ( 3 ) >>> lists [ 1 ] . append ( 5 ) >>> lists [ 2 ] . append ( 7 ) >>> lists [[3], [5], [7]] Further explanation is available in the FAQ entry How do I create a multidimensional list? . If i or j is negative, the index is relative to the end of sequence s : len(s) + i or len(s) + j is substituted. But note that -0 is still 0 . The slice of s from i to j is defined as the sequence of items with index k such that i <= k < j . If i is omitted or None , use 0 . If j is omitted or None , use len(s) . If i or j is less than -len(s) , use 0 . If i or j is greater than len(s) , use len(s) . If i is greater than or equal to j , the slice is empty. The slice of s from i to j with step k is defined as the sequence of items with index x = i + n*k such that 0 <= n < (j-i)/k . In other words, the indices are i , i+k , i+2*k , i+3*k and so on, stopping when j is reached (but never including j ). When k is positive, i and j are reduced to len(s) if they are greater. When k is negative, i and j are reduced to len(s) - 1 if they are greater. If i or j are omitted or None , they become “end” values (which end depends on the sign of k ). Note, k cannot be zero. If k is None , it is treated like 1 . Concatenating immutable sequences always results in a new object. This means that building up a sequence by repeated concatenation will have a quadratic runtime cost in the total sequence length. To get a linear runtime cost, you must switch to one of the alternatives below: if concatenating str objects, you can build a list and use str.join() at the end or else write to an io.StringIO instance and retrieve its value when complete if concatenating bytes objects, you can similarly use bytes.join() or io.BytesIO , or you can do in-place concatenation with a bytearray object. bytearray objects are mutable and have an efficient overallocation mechanism if concatenating tuple objects, extend a list instead for other types, investigate the relevant class documentation Some sequence types (such as range ) only support item sequences that follow specific patterns, and hence don’t support sequence concatenation or repetition. An IndexError is raised if i is outside the sequence range. Sequence Methods Sequence types also support the following methods: sequence. count ( value , / ) ¶ Return the total number of occurrences of value in sequence . sequence. index ( value[, start[, stop] ) ¶ Return the index of the first occurrence of value in sequence . Raises ValueError if value is not found in sequence . The start or stop arguments allow for efficient searching of subsections of the sequence, beginning at start and ending at stop . This is roughly equivalent to start + sequence[start:stop].index(value) , only without copying any data. Caution Not all sequence types support passing the start and stop arguments. Immutable Sequence Types ¶ The only operation that immutable sequence types generally implement that is not also implemented by mutable sequence types is support for the hash() built-in. This support allows immutable sequences, such as tuple instances, to be used as dict keys and stored in set and frozenset instances. Attempting to hash an immutable sequence that contains unhashable values will result in TypeError . Mutable Sequence Types ¶ The operations in the following table are defined on mutable sequence types. The collections.abc.MutableSequence ABC is provided to make it easier to correctly implement these operations on custom sequence types. In the table s is an instance of a mutable sequence type, t is any iterable object and x is an arbitrary object that meets any type and value restrictions imposed by s (for example, bytearray only accepts integers that meet the value restriction 0 <= x <= 255 ). Operation Result Notes s[i] = x item i of s is replaced by x del s[i] removes item i of s s[i:j] = t slice of s from i to j is replaced by the contents of the iterable t del s[i:j] removes the elements of s[i:j] from the list (same as s[i:j] = [] ) s[i:j:k] = t the elements of s[i:j:k] are replaced by those of t (1) del s[i:j:k] removes the elements of s[i:j:k] from the list s += t extends s with the contents of t (for the most part the same as s[len(s):len(s)] = t ) s *= n updates s with its contents repeated n times (2) Notes: If k is not equal to 1 , t must have the same length as the slice it is replacing. The value n is an integer, or an object implementing __index__() . Zero and negative values of n clear the sequence. Items in the sequence are not copied; they are referenced multiple times, as explained for s * n under Common Sequence Operations . Mutable Sequence Methods Mutable sequence types also support the following methods: sequence. append ( value , / ) ¶ Append value to the end of the sequence This is equivalent to writing seq[len(seq):len(seq)] = [value] . sequence. clear ( ) ¶ Added in version 3.3. Remove all items from sequence . This is equivalent to writing del sequence[:] . sequence. copy ( ) ¶ Added in version 3.3. Create a shallow copy of sequence . This is equivalent to writing sequence[:] . Hint The copy() method is not part of the MutableSequence ABC , but most concrete mutable sequence types provide it. sequence. extend ( iterable , / ) ¶ Extend sequence with the contents of iterable . For the most part, this is the same as writing seq[len(seq):len(seq)] = iterable . sequence. insert ( index , value , / ) ¶ Insert value into sequence at the given index . This is equivalent to writing sequence[index:index] = [value] . sequence. pop ( index = -1 , / ) ¶ Retrieve the item at index and also removes it from sequence . By default, the last item in sequence is removed and returned. sequence. remove ( value , / ) ¶ Remove the first item from sequence where sequence[i] == value . Raises ValueError if value is not found in sequence . sequence. reverse ( ) ¶ Reverse the items of sequence in place. This method maintains economy of space when reversing a large sequence. To remind users that it operates by side-effect, it returns None . Lists ¶ Lists are mutable sequences, typically used to store collections of homogeneous items (where the precise degree of similarity will vary by application). class list ( iterable = () , / ) ¶ Lists may be constructed in several ways: Using a pair of square brackets to denote the empty list: [] Using square brackets, separating items with commas: [a] , [a, b, c] Using a list comprehension: [x for x in iterable] Using the type constructor: list() or list(iterable) The constructor builds a list whose items are the same and in the same order as iterable ’s items. iterable may be either a sequence, a container that supports iteration, or an iterator object. If iterable is already a list, a copy is made and returned, similar to iterable[:] . For example, list('abc') returns ['a', 'b', 'c'] and list( (1, 2, 3) ) returns [1, 2, 3] . If no argument is given, the constructor creates a new empty list, [] . Many other operations also produce lists, including the sorted() built-in. Lists implement all of the common and mutable sequence operations. Lists also provide the following additional method: sort ( * , key = None , reverse = False ) ¶ This method sorts the list in place, using only < comparisons between items. Exceptions are not suppressed - if any comparison operations fail, the entire sort operation will fail (and the list will likely be left in a partially modified state). sort() accepts two arguments that can only be passed by keyword ( keyword-only arguments ): key specifies a function of one argument that is used to extract a comparison key from each list element (for example, key=str.lower ). The key corresponding to each item in the list is calculated once and then used for the entire sorting process. The default value of None means that list items are sorted directly without calculating a separate key value. The functools.cmp_to_key() utility is available to convert a 2.x style cmp function to a key function. reverse is a boolean value. If set to True , then the list elements are sorted as if each comparison were reversed. This method modifies the sequence in place for economy of space when sorting a large sequence. To remind users that it operates by side effect, it does not return the sorted sequence (use sorted() to explicitly request a new sorted list instance). The sort() method is guaranteed to be stable. A sort is stable if it guarantees not to change the relative order of elements that compare equal — this is helpful for sorting in multiple passes (for example, sort by department, then by salary grade). For sorting examples and a brief sorting tutorial, see Sorting Techniques . CPython implementation detail: While a list is being sorted, the effect of attempting to mutate, or even inspect, the list is undefined. The C implementation of Python makes the list appear empty for the duration, and raises ValueError if it can detect that the list has been mutated during a sort. Thread safety Reading a single element from a list is atomic : lst [ i ] # list.__getitem__ The following methods traverse the list and use atomic reads of each item to perform their function. That means that they may return results affected by concurrent modifications: item in lst lst . index ( item ) lst . count ( item ) All of the above methods/operations are also lock-free. They do not block concurrent modifications. Other operations that hold a lock will not block these from observing intermediate states. All other operations from here on block using the per-object lock. Writing a single item via lst[i] = x is safe to call from multiple threads and will not corrupt the list. The following operations return new objects and appear atomic to other threads: lst1 + lst2 # concatenates two lists into a new list x * lst # repeats lst x times into a new list lst . copy () # returns a shallow copy of the list Methods that only operate on a single elements with no shifting required are atomic : lst . append ( x ) # append to the end of the list, no shifting required lst . pop () # pop element from the end of the list, no shifting required The clear() method is also atomic . Other threads cannot observe elements being removed. The sort() method is not atomic . Other threads cannot observe intermediate states during sorting, but the list appears empty for the duration of the sort. The following operations may allow lock-free operations to observe intermediate states since they modify multiple elements in place: lst . insert ( idx , item ) # shifts elements lst . pop ( idx ) # idx not at the end of the list, shifts elements lst *= x # copies elements in place The remove() method may allow concurrent modifications since element comparison may execute arbitrary Python code (via __eq__() ). extend() is safe to call from multiple threads. However, its guarantees depend on the iterable passed to it. If it is a list , a tuple , a set , a frozenset , a dict or a dictionary view object (but not their subclasses), the extend operation is safe from concurrent modifications to the iterable. Otherwise, an iterator is created which can be concurrently modified by another thread. The same applies to inplace concatenation of a list with other iterables when using lst += iterable . Similarly, assigning to a list slice with lst[i:j] = iterable is safe to call from multiple threads, but iterable is only locked when it is also a list (but not its subclasses). Operations that involve multiple accesses, as well as iteration, are never atomic. For example: # NOT atomic: read-modify-write lst [ i ] = lst [ i ] + 1 # NOT atomic: check-then-act if lst : item = lst . pop () # NOT thread-safe: iteration while modifying for item in lst : process ( item ) # another thread may modify lst Consider external synchronization when sharing list instances across threads. See Python support for free threading for more information. Tuples ¶ Tuples are immutable sequences, typically used to store collections of heterogeneous data (such as the 2-tuples produced by the enumerate() built-in). Tuples are also used for cases where an immutable sequence of homogeneous data is needed (such as allowing storage in a set or dict instance). class tuple ( iterable = () , / ) ¶ Tuples may be constructed in a number of ways: Using a pair of parentheses to denote the empty tuple: () Using a trailing comma for a singleton tuple: a, or (a,) Separating items with commas: a, b, c or (a, b, c) Using the tuple() built-in: tuple() or tuple(iterable) The constructor builds a tuple whose items are the same and in the same order as iterable ’s items. iterable may be either a sequence, a container that supports iteration, or an iterator object. If iterable is already a tuple, it is returned unchanged. For example, tuple('abc') returns ('a', 'b', 'c') and tuple( [1, 2, 3] ) returns (1, 2, 3) . If no argument is given, the constructor creates a new empty tuple, () . Note that it is actually the comma which makes a tuple, not the parentheses. The parentheses are optional, except in the empty tuple case, or when they are needed to avoid syntactic ambiguity. For example, f(a, b, c) is a function call with three arguments, while f((a, b, c)) is a function call with a 3-tuple as the sole argument. Tuples implement all of the common sequence operations. For heterogeneous collections of data where access by name is clearer than access by index, collections.namedtuple() may be a more appropriate choice than a simple tuple object. Ranges ¶ The range type represents an immutable sequence of numbers and is commonly used for looping a specific number of times in for loops. class range ( stop , / ) ¶ class range ( start , stop , step = 1 , / ) The arguments to the range constructor must be integers (either built-in int or any object that implements the __index__() special method). If the step argument is omitted, it defaults to 1 . If the start argument is omitted, it defaults to 0 . If step is zero, ValueError is raised. For a positive step , the contents of a range r are determined by the formula r[i] = start + step*i where i >= 0 and r[i] < stop . For a negative step , the contents of the range are still determined by the formula r[i] = start + step*i , but the constraints are i >= 0 and r[i] > stop . A range object will be empty if r[0] does not meet the value constraint. Ranges do support negative indices, but these are interpreted as indexing from the end of the sequence determined by the positive indices. Ranges containing absolute values larger than sys.maxsize are permitted but some features (such as len() ) may raise OverflowError . Range examples: >>> list ( range ( 10 )) [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] >>> list ( range ( 1 , 11 )) [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] >>> list ( range ( 0 , 30 , 5 )) [0, 5, 10, 15, 20, 25] >>> list ( range ( 0 , 10 , 3 )) [0, 3, 6, 9] >>> list ( range ( 0 , - 10 , - 1 )) [0, -1, -2, -3, -4, -5, -6, -7, -8, -9] >>> list ( range ( 0 )) [] >>> list ( range ( 1 , 0 )) [] Ranges implement all of the common sequence operations except concatenation and repetition (due to the fact that range objects can only represent sequences that follow a strict pattern and repetition and concatenation will usually violate that pattern). start ¶ The value of the start parameter (or 0 if the parameter was not supplied) stop ¶ The value of the stop parameter step ¶ The value of the step parameter (or 1 if the parameter was not supplied) The advantage of the range type over a regular list or tuple is that a range object will always take the same (small) amount of memory, no matter the size of the range it represents (as it only stores the start , stop and step values, calculating individual items and subranges as needed). Range objects implement the collections.abc.Sequence ABC, and provide features such as containment tests, element index lookup, slicing and support for negative indices (see Sequence Types — list, tuple, range ): >>> r = range ( 0 , 20 , 2 ) >>> r range(0, 20, 2) >>> 11 in r False >>> 10 in r True >>> r . index ( 10 ) 5 >>> r [ 5 ] 10 >>> r [: 5 ] range(0, 10, 2) >>> r [ - 1 ] 18 Testing range objects for equality with == and != compares them as sequences. That is, two range objects are considered equal if they represent the same sequence of values. (Note that two range objects that compare equal might have different start , stop and step attributes, for example range(0) == range(2, 1, 3) or range(0, 3, 2) == range(0, 4, 2) .) Changed in version 3.2: Implement the Sequence ABC. Support slicing and negative indices. Test int objects for membership in constant time instead of iterating through all items. Changed in version 3.3: Define ‘==’ and ‘!=’ to compare range objects based on the sequence of values they define (instead of comparing based on object identity). Added the start , stop and step attributes. See also The linspace recipe shows how to implement a lazy version of range suitable for floating-point applications. Text and Binary Sequence Type Methods Summary ¶ The following table summarizes the text and binary sequence types methods by category. Category str methods bytes and bytearray methods Formatting str.format() str.format_map() f-strings printf-style String Formatting printf-style Bytes Formatting Searching and Replacing str.find() str.rfind() bytes.find() bytes.rfind() str.index() str.rindex() bytes.index() bytes.rindex() str.startswith() bytes.startswith() str.endswith() bytes.endswith() str.count() bytes.count() str.replace() bytes.replace() Splitting and Joining str.split() str.rsplit() bytes.split() bytes.rsplit() str.splitlines() bytes.splitlines() str.partition() bytes.partition() str.rpartition() bytes.rpartition() str.join() bytes.join() String Classification str.isalpha() bytes.isalpha() str.isdecimal() str.isdigit() bytes.isdigit() str.isnumeric() str.isalnum() bytes.isalnum() str.isidentifier() str.islower() bytes.islower() str.isupper() bytes.isupper() str.istitle() bytes.istitle() str.isspace() bytes.isspace() str.isprintable() Case Manipulation str.lower() bytes.lower() str.upper() bytes.upper() str.casefold() str.capitalize() bytes.capitalize() str.title() bytes.title() str.swapcase() bytes.swapcase() Padding and Stripping str.ljust() str.rjust() bytes.ljust() bytes.rjust() str.center() bytes.center() str.expandtabs() bytes.expandtabs() str.strip() bytes.strip() str.lstrip() str.rstrip() bytes.lstrip() bytes.rstrip() Translation and Encoding str.translate() bytes.translate() str.maketrans() bytes.maketrans() str.encode() bytes.decode() Text Sequence Type — str ¶ Textual data in Python is handled with str objects, or strings . Strings are immutable sequences of Unicode code points. String literals are written in a variety of ways: Single quotes: 'allows embedded "double" quotes' Double quotes: "allows embedded 'single' quotes" Triple quoted: '''Three single quotes''' , """Three double quotes""" Triple quoted strings may span multiple lines - all associated whitespace will be included in the string literal. String literals that are part of a single expression and have only whitespace between them will be implicitly converted to a single string literal. That is, ("spam " "eggs") == "spam eggs" . See String and Bytes literals for more about the various forms of string literal | 2026-01-13T08:49:03 |
https://dev.to/bemals_dvanitha_5b14b68f9/protecting-your-website-with-cloudflare-security-performance-and-reliability-part-1-24jk | Protecting Your Website with Cloudflare: Security, Performance, and Reliability [Part 1] - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Bemals Dvanitha Posted on Jan 9 Protecting Your Website with Cloudflare: Security, Performance, and Reliability [Part 1] # devops # security # webdev # cloudflare Application availability and security are just as important in today's web infrastructure as application logic. Distributed denial-of-service attacks, abusive traffic, automated crawlers, and common web exploits can impair performance or completely stop services, even with well-configured servers and properly encrypted connections. Operating as a security and performance layer between users and origin servers, Cloudflare is located at the network edge. It protects against large-scale DDoS attacks, malicious bots, and abusive request patterns by stopping traffic before it reaches your infrastructure. It also reduces latency through intelligent caching and worldwide content delivery. Cloudflare provides a wide range of controls for contemporary threats in addition to standard CDN functionality. These include rate limiting to safeguard APIs and login endpoints, Turnstile for seamless human verification, AI-driven bot and crawler management, adaptive DDoS mitigation, and integrated defenses against frequent attacks like SQL injection, cross-site scripting, and credential abuse. This post will discuss how a website can be strengthened against actual threats using Cloudflare. We'll concentrate on useful setups and essential features, such as DDoS defense, crawler control, Turnstile, rate limiting, caching techniques, and common attack prevention, demonstrating how they cooperate to lower attack surface while preserving user experience and performance. Connecting Your Domain to Cloudflare via Nameserver Update You must assign DNS authority by changing your domain's nameservers at the domain provider (such as GoDaddy or Namecheap) in order to put Cloudflare in front of your website. At this point, no DNS records have been changed; this is the only necessary adjustment. The following steps avoid needless changes during onboarding and adhere to a safe, production-ready workflow. 1. Add Your Domain in Cloudflare From the Cloudflare dashboard, navigate to Domains → Onboard a domain and enter your existing domain name. When prompted to import DNS configuration, select: Manually enter DNS records (Advanced) Even if you do not plan to add records immediately, this option gives you full control and avoids assumptions made by automated scans. During onboarding, Cloudflare presents initial controls for AI crawlers and training bots. You can: Block AI training bots globally Allow them selectively Or leave them unblocked This setting can be changed later and does not affect nameserver activation, but Cloudflare applies it once traffic starts passing through its network. Continue with the setup. 2. Select a Cloudflare Plan To continue, select the Free plan. This plan already consists of: DDoS defense at the network layer DNS for Global Anycast Caching and CDN Basic bot detection and WAF SSL for all Later on, you can upgrade without having to switch nameservers once more. 3. Obtain Cloudflare Nameservers Cloudflare will now assign two authoritative nameservers for your domain These values are unique per domain. At this point, Cloudflare will show the domain status as Pending until nameserver delegation is completed. 4. Replace Nameservers at Your Domain Provider (GoDaddy Example) For GoDaddy, the process is: Open your domain settings Go to DNS / Nameservers Choose Custom nameservers Remove all existing nameserver entries Paste the two Cloudflare nameservers exactly as provided Save the changes 5. Verify Nameserver Propagation Nameserver changes require global propagation. To confirm progress, use: whatsmydns.net 6. Confirm Domain Activation in Cloudflare Once propagation finishes: Cloudflare will mark the domain as Active DNS authority is fully delegated Cloudflare now sits in front of your infrastructure Creating DNS Records and Routing Traffic Through Cloudflare Once your domain is Active in Cloudflare, Cloudflare is now the authoritative DNS provider. The next step is to create DNS records that point traffic to your application server. In this setup, the origin server is an Amazon Web Services EC2 instance or any other vpc running NGINX. Origin Server Setup (NGINX + SSL) The EC2 instance runs NGINX as the web server and reverse proxy. SSL termination on the origin is handled using Certbot with Let’s Encrypt. To avoid repeating implementation details, the full server-side setup—including: NGINX installation Reverse proxy configuration SSL certificate issuance Automatic renewal —is covered in detail in the following article: 👉 Boost Your Website’s Security: NGINX and SSL Setup with Certbot Made Easy 🔗 full_guide_for_nginx_certbot_setup This Cloudflare guide intentionally focuses on edge-level protection, while the linked article covers origin-level security. Create DNS Records in Cloudflare Navigate to: Domain → DNS → Records This is where Cloudflare resolves hostnames to your origin server and determines whether traffic is proxied through its edge. Example: Creating an A Record for an API or Application Add a new record with the following values: Type: A Name: api (or @ for root domain) IPv4 address: <EC2_PUBLIC_IP> Proxy status: Proxied (orange cloud enabled) TTL: Auto Enter fullscreen mode Exit fullscreen mode Key points: The Proxied status ensures traffic passes through Cloudflare Cloudflare now hides the origin IP and applies security controls Requests no longer reach the EC2 instance directly Once saved, Cloudflare immediately begins routing traffic. Verify DNS Propagation To confirm the DNS record is resolving globally, use: whatsmydns.net Confirm Application Reachability After DNS propagation completes, validate application access via HTTPS: check-ssl Architecture Overview and Design Rationale By combining: Cloudflare at the edge (DNS, DDoS, bot control, rate limiting) NGINX on EC2 as the origin End-to-end HTTPS via Certbot You get: Reduced attack surface Hidden origin IP Built-in DDoS mitigation Secure, encrypted traffic from client to server This separation keeps responsibilities clear and the system easier to maintain. What’s Next As this guide has grown to cover multiple layers of infrastructure—domain configuration, Cloudflare onboarding, DNS routing, and origin server setup—it makes sense to split the remaining topics into a follow-up article. In Part 2, we’ll focus entirely on Cloudflare’s edge-level security and traffic controls, including: AI Crawl Control and bot behavior management Rate limiting for APIs and sensitive endpoints Caching strategies to reduce origin load and improve latency Turnstile for user-friendly request validation Additional protections for common abuse and automated attacks Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close Add reaction Like Unicorn Exploding Head Raised Hands Fire Jump to Comments Save Boost More... Copy link Copy link Copied to Clipboard Share to X Share to LinkedIn Share to Facebook Share to Mastodon Share Post via... Report Abuse Rob Fox Posted on Jan 12 Shift-Left Reliability # sre # devops # cicd # platformengineering We've become exceptionally good at incident response. Modern teams restore service quickly, run thoughtful postmortems, and hold themselves accountable through corrective actions. And yet… A team ships a change that passes every test, gets all the required approvals, and still brings down checkout for 47 minutes. The postmortem conclusion? "We should have known our latency SLO was already at 94% before deploying." Many postmortems point to the same root cause: changes we introduced ourselves. Not hardware failures. Not random outages. Just software behaving exactly as we told it to. We continue to treat reliability as something to evaluate once those changes are already live. This isn't a failure of tooling or process. It's a question of when we decide whether a system is ready. The paradox We've invested heavily in observing and responding to failure - better alerting, faster incident response, thorough postmortems. Teams care deeply about reliability and spend significant time optimizing how they respond to incidents. But when in a service's lifecycle are they supposed to define reliability? Where's the innovation that happens before deployment? Where reliability decisions actually happen today I've seen multiple teams running identical technology stacks with completely different SLOs, metrics, and alerts. Nobody told them what to implement, what's best-practice or how to tune their alerts. They want to be good reliability citizens, but getting from the theory in the handbook to putting that theory into practice is not straightforward. Services regularly move into production with SLOs being created months later - or never. Dashboards are missing, insufficient, or inconsistent. "Looks fine to me" during PR reviews. Tribal knowledge. Varying levels of understanding across teams. Reliability is fundamentally bespoke and ungoverned. That's the core issue. The missing layer GitHub gave us version control for code. Terraform gave us version control for infrastructure. Security has transformed with shift-left - finding flaws as code is written, not after deployment. We're still missing version control for reliability. We need a specification that defines requirements, validates them against reality, and generates the artifacts: dashboards, SLOs, alerts, escalation policies. If the specification is validated and the artifacts created, the same tool can check in real-time whether a service is in breach - and block high-risk deployments in CI/CD. What shift-left reliability actually means Shift-left reliability doesn't mean more alerts and dashboards, more postmortems or more people in the room. It means: Spec - Define reliability requirements as code before production deployment Validate - Test those requirements against reality Enforce - Gate deployments through CI/CD Engineers don't write PromQL or Grafana JSON - they declare intent, and reliability becomes deterministic. Outcomes are predictable, consistent, transparent, and follow best practice. An executable reliability contract Keep it simple. A team creates a service.yaml file with their reliability intent: name: payment-api tier: critical type: api team: payments dependencies: - postgresql - redis Enter fullscreen mode Exit fullscreen mode Here is a complete service.yaml example . Tooling validates metrics, SLOs, and error budgets then generates these artifacts automatically. This is the approach I am exploring with an open-source project called NthLayer. NthLayer runs in any CI/CD pipeline - GitHub Actions, ArgoCD, Jenkins, Tekton, GitLab CI. The goal isn't to be an inflexible blocker; it's visible risk and explicit decisions. Overrides are fine when they're intentional, logged, and owned. When a deployment is attempted, the specification is evaluated against reality: $ nthlayer check-deploy - service payment-api ERROR: Deployment blocked - availability SLO at 99.2% (target: 99.95%) - error budget exhausted: -47 minutes remaining - 3 P1 incidents in last 7 days Exit code: 2 (BLOCKED) Enter fullscreen mode Exit fullscreen mode Why now? SLOs have had 8+ years to mature and move from the Google SRE Handbook into mainstream practice. GitOps has normalized declarative configuration. Platform Engineering has matured as a discipline. The concepts are ready but the tooling has lagged behind. This is a deliberate shift in approach. Reliability is no longer up for debate during incidents. Services have defined owners with deterministic standards. We can stop reinventing the reliability wheel every time a new service is onboarded. If requirements change, update the service.yaml , run NthLayer and every service benefits from adopting the new standard. What this does not replace NthLayer doesn't replace service catalogs, developer portals, observability platforms, or incident management. It doesn't predict failures or eliminate human judgment. It's upstream of all these systems. The goal: a reliability specification, automated deployment gates and to reduce cognitive load to implement best practices. Open questions I don't have all the answers but two questions I keep returning to are: Contract Drift: What happens when the spec says 99.95% but reality has been 99.5% for months? Is the contract wrong, or is the service broken? Emergency Overrides: How should they work? Who approves? How do you prevent them from becoming the default? The timing problem Where do reliability decisions actually happen in your organization? What would it look like to decide readiness before deployment? What reliability rules do you wish you could enforce automatically? The timing problem isn't going away. The only question is whether you address it before deployment - or learn about it in the postmortem. NthLayer is open source and looking for early adopters. If you're tired of reliability being an afterthought: pip install nthlayer nthlayer init nthlayer check-deploy --service your-service Enter fullscreen mode Exit fullscreen mode → github.com/rsionnach/nthlayer Star the repo, open an issue, or tell me I'm wrong. I want to hear how reliability decisions happen in your organization. Rob Fox is a Senior Site Reliability Engineer focused on platform and reliability tooling. He's exploring how reliability engineering can move earlier in the software delivery lifecycle. Find him on GitHub . Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Rob Fox Follow Sr Site Reliability Engineer. Building NthLayer, an open-source tool for shift-left reliability. Opinions are my own. github.com/rsionnach Location Dublin, Ireland Joined Jan 6, 2026 Trending on DEV Community Hot The First Week at a Startup Taught Me More Than I Expected # startup # beginners # career # learning How I Built an AI Terraform Review Agent on Serverless AWS # aws # terraform # serverless # devops How to Crack Any Software Developer Interview in 2026 (Updated for AI & Modern Hiring) # softwareengineering # programming # career # interview 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV DEV Community — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. 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https://dev.to/evanlin/golang-trying-out-go-proposal-45713-multi-module-workspaces-1k86#comments | Golang: Trying out Go Proposal 45713 'Multi-Module Workspaces' - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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Report Abuse Evan Lin Posted on Jan 11 • Originally published at evanlin.com on Jan 11 Golang: Trying out Go Proposal 45713 'Multi-Module Workspaces' # go # tooling # tutorial title: [Learning Notes][Golang: Gotip] Quick Taste of Go Proposal 45713 'Multi-Module Workspaces' published: false date: 2021-08-19 00:00:00 UTC tags: canonical_url: http://www.evanlin.com/go-workspaces/ ---  - Proposal: [45713 Workspace Mode](https://go.googlesource.com/proposal/+/master/design/45713-workspace.md) - PR: [45713](https://github.com/golang/go/issues/45713) - Demo Video: [YouTube](https://www.youtube.com/watch?v=wQglU5aB5NQ) # Summary Vendoring and Dependency Management are problems Golang has been trying to solve. Through `go mod`, you can manage the first layer of packages. With `go mod vendor`, you can download the related dependent packages in the local `vendor/`. If you want to modify the upper layer of dependency, you can modify it directly. But what if you want to modify the files of the dependency's upper layer and its even upper layer? In the past, you could modify them one by one through `go mod edit`, but it would be quite complex once there are many files. Is there any way to quickly make some confirmations locally, so that it's easier to submit a PR to upstream? Here, we introduce a feature that is undergoing final review (if approved, it's expected to be a go 1.18 feature: Multi-Module Workspaces). The proposal author is very thoughtful and also released a Demo Video with a big dog. I must say, because there's a cute big dog, I obediently finished reading the proposal. <iframe width="560" height="315" src="https://www.youtube.com/embed/wQglU5aB5NQ" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe> # How to Install Enter fullscreen mode Exit fullscreen mode # Get the latest version of golang source code go install golang.org/dl/gotip@latest Download gotip based on a certain CL gotip download dev.cmdgo Look at the modified related commands, pay attention to initwork, editwork ... gotip help mod The commands are: download download modules to local cache edit edit go.mod from tools or scripts editwork edit go.work from tools or scripts graph print module requirement graph init initialize new module in current directory initwork initialize workspace file tidy add missing and remove unused modules vendor make vendored copy of dependencies verify verify dependencies have expected content why explain why packages or modules are needed Enter fullscreen mode Exit fullscreen mode ## Related Articles: - [45713 Workspace Mode](https://go.googlesource.com/proposal/+/master/design/45713-workspace.md) - [Demo Video YouTube](https://www.youtube.com/watch?v=wQglU5aB5NQ) Enter fullscreen mode Exit fullscreen mode Top comments (0) Subscribe Personal Trusted User Create template Templates let you quickly answer FAQs or store snippets for re-use. Submit Preview Dismiss Code of Conduct • Report abuse Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink . Hide child comments as well Confirm For further actions, you may consider blocking this person and/or reporting abuse Evan Lin Follow Attitude is Everything. @golangtw Co-Organizer / LINE Taiwan Technology Evangelist. Golang GDE. Location Taipei Work Technology Evangelist at LINE Corp. Joined Jun 16, 2020 More from Evan Lin [TIL] Golang community discussion about PTT BBS # community # backend # discuss # go Go 1.16: Retracting Versions in Go Modules # go # learning # tooling [Learning Notes] Golang: A Simple Introduction to New Features in Golang 1.16 # go # learning # tutorial 💎 DEV Diamond Sponsors Thank you to our Diamond Sponsors for supporting the DEV Community Google AI is the official AI Model and Platform Partner of DEV Neon is the official database partner of DEV Algolia is the official search partner of DEV Forem — A space to discuss and keep up software development and manage your software career Home DEV++ Podcasts Videos DEV Education Tracks DEV Challenges DEV Help Advertise on DEV DEV Showcase About Contact Free Postgres Database Software comparisons Forem Shop Code of Conduct Privacy Policy Terms of Use Built on Forem — the open source software that powers DEV and other inclusive communities. Made with love and Ruby on Rails . Forem © 2016 - 2026. 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https://docs.python.org/pl/3/ | 3.14.2 Documentation Motyw auto jasny ciemny Pobieranie Pobierz te dokumenty Dokumentacja według wersji Python 3.15 (in development) Python 3.14 (stable) Python 3.13 (stable) Python 3.12 (security-fixes) Python 3.11 (security-fixes) Python 3.10 (security-fixes) Python 3.9 (EOL) Python 3.8 (EOL) Python 3.7 (EOL) Python 3.6 (EOL) Python 3.5 (EOL) Python 3.4 (EOL) Python 3.3 (EOL) Python 3.2 (EOL) Python 3.1 (EOL) Python 3.0 (EOL) Python 2.7 (EOL) Python 2.6 (EOL) Wszystkie wersje Inne zasoby PEP Index Beginner's Guide Book List Audio/Visual Talks Python Developer’s Guide Nawigacja indeks moduły | Python » 3.14.2 Documentation » | Motyw auto jasny ciemny | Python 3.14.2 - dokumentacja Witamy! To jest oficjalna dokumentacja Pythona 3.14.2. Sekcje dokumentacji: Co nowego w Pythonie 3.14? Lub wszystkie dokumenty „Co nowego” od 2.0 Tutorial Zacznij tutaj: wycieczka po składni i funkcjach Pythona Dokumentacja biblioteki Biblioteka standardowa i funkcje wbudowane Dokumentacja języka Składnia i elementy języka Konfiguracja i użytkowanie Pythona Jak zainstalować, skonfigurować i używać Pythona Pythonowe „Jak to zrobić?” Szczegółowe podręczniki tematyczne Instalacja modułów Pythona Moduły zewnętrzne i PyPI.org Dystrybucja modułów Pythona Publikowanie modułów do użytku przez inne osoby Rozszerzanie i embedowanie Dla programistów C/C++ API C Pythona Dokumentacja API C Często zadawane pytania Często zadawane pytania (z odpowiedziami!) Deprecjacje Wycofywane funkcjonalności Indeksy, glosariusz i wyszukiwanie: Globalny spis modułów Wszystkie moduły i biblioteki Indeks ogólny Wszystkie funkcje, klasy i pojęcia Glosariusz Wyjaśnienia terminów Strona wyszukiwania Wyszukaj w dokumentacji Pełny spis treści Spis wszystkich sekcji i podsekcji Informacje o projekcie: Zgłaszanie błędów Contributing to docs Pobierz dokumentację Historia i licencja Pythona Prawa autorskie O dokumentacji Pobieranie Pobierz te dokumenty Dokumentacja według wersji Python 3.15 (in development) Python 3.14 (stable) Python 3.13 (stable) Python 3.12 (security-fixes) Python 3.11 (security-fixes) Python 3.10 (security-fixes) Python 3.9 (EOL) Python 3.8 (EOL) Python 3.7 (EOL) Python 3.6 (EOL) Python 3.5 (EOL) Python 3.4 (EOL) Python 3.3 (EOL) Python 3.2 (EOL) Python 3.1 (EOL) Python 3.0 (EOL) Python 2.7 (EOL) Python 2.6 (EOL) Wszystkie wersje Inne zasoby PEP Index Beginner's Guide Book List Audio/Visual Talks Python Developer’s Guide « Nawigacja indeks moduły | Python » 3.14.2 Documentation » | Motyw auto jasny ciemny | © Prawa autorskie 2001 Python Software Foundation. Ta strona jest objęta licencją Python Software Foundation w wersji 2. Przykłady, przepisy i inny kod w dokumentacji są dodatkowo objęte licencją Zero Clause BSD. Zobacz Historię i licencję aby uzyskać więcej informacji. Python Software Foundation jest organizacją non-profit. Prosimy o wsparcie. Ostatnia aktualizacja sty 13, 2026 (07:00 UTC). Znalazłeś(-aś) błąd ? Stworzone za pomocą Sphinx 8.2.3. | 2026-01-13T08:49:03 |
https://dev.to/help/writing-editing-scheduling#Creating-a-Series | Writing, Editing and Scheduling - DEV Help - DEV Community Forem Feed Follow new Subforems to improve your feed DEV Community Follow A space to discuss and keep up software development and manage your software career Future Follow News and discussion of science and technology such as AI, VR, cryptocurrency, quantum computing, and more. Open Forem Follow A general discussion space for the Forem community. If it doesn't have a home elsewhere, it belongs here Gamers Forem Follow An inclusive community for gaming enthusiasts Music Forem Follow From composing and gigging to gear, hot music takes, and everything in between. Vibe Coding Forem Follow Discussing AI software development, and showing off what we're building. Popcorn Movies and TV Follow Movie and TV enthusiasm, criticism and everything in-between. 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A space to share projects, ask questions, and discuss server-driven templating Dropdown menu Dropdown menu Skip to content Navigation menu Search Powered by Algolia Search Log in Create account DEV Community Close DEV Help The latest help documentation, tips and tricks from the DEV Community. Help > Writing, Editing and Scheduling Writing, Editing and Scheduling In this article The Editor Drafting and publishing a post: Scheduling a post: Creating a Series Cross-posting Content Helpful Resources DEV Editor guide Markdown Cheatsheet Best Practices for Writing on DEV Guidelines for Avoiding Plagiarism on DEV Guidelines for AI-assisted Articles on DEV Common Questions Q: How do I set a canonical URL on my post? Q: How do I set a cover image for my post? Q: Do I own the articles that I publish? Q: Can I cross-post something I've already written on my own blog or Medium? Q: Can I use profanity in my posts? Q: Why has my post been removed? Q: Will you put ads on my posts' pages? Explore the ins and outs of writing, editing, scheduling, and managing articles. The Editor The DEV editor is your primary tool for writing and sharing posts. With a Markdown -based syntax and flexible options for embedding content, the editor is one of the main ways DEV members express themselves. Drafting, scheduling, and publishing posts are all options; importing via RSS is also a feature that we provide. Learn how to use the DEV editor to create and format your articles effectively: Drafting and publishing a post: Click on " Write a Post " in the top right corner of the site. Follow the prompts to fill out the necessary inputs. Give your post a title, write the body content, add appropriate tags, and fill out any other optional fields. If you're not ready to share your article, just click "Save draft" in the bottom left. You can access your drafts from your user dashboard and return to editing your post whenever you wish. Once you're ready to share your post, click the "Publish" button in the bottom left. Note: if you are using the Basic Markdown editor you interface is more minimalistic, and you'll need to change published: false to published: true in the Front Matter of the post, then save to publish your post. Congratulations, your post should be published! You should see the article listed on your public profile. Note that you can access analytics for each post you've shared from your user dashboard by clicking on the ... beside the article title. Scheduling a post: To schedule a post, you may open a draft or start writing a new post. Once you've got your post set up, click on the hexagon icon in the bottom left-hand corner near the Publish button. See "Schedule Publication" and use the inputs to select a date and time for the post to go live. Note: this feature is set to your local time zone. Creating a Series DEV provides authors with the ability to link articles together in a series. A series has a title and an associated page to hold all the entries (e.g. Sloan's Inbox ). Most often this is done for articles that are thematically related or recurring weekly posts. We have a handy guide here that explains step-by-step how to create a series on DEV. Note: If you've written the first entry in a series and are wondering why the series title is not easily visible, it's because we don't actually display information about a post being part of a series until there is more than one entry in the series. Once you write your second entry in the series, the Table of Contents and title for the series should appear. Cross-posting Content DEV offers a variety of features for those who want to cross-post content from elsewhere on the web. We encourage folks to share articles from their personal and company blogs! Notably, we offer folks the ability to import content via RSS and set canonical links on any posts that are shared. Using the RSS Feed on DEV Community Configure RSS Feed: Navigate to extensions within the settings. Under "Publishing to DEV Community 👩💻👨💻 from RSS," enter your blog's RSS feed URL. You will see the option to "Mark the RSS source as canonical URL" or "Replace links with DEV Community links." Check the info below (Specifying a Canonical URL) to help you decide which option to select. Click "submit feed settings." Edit Post Drafts Before Publishing Go to your user dashboard. Click edit beside the post you want to post. Save each draft after making changes. Publish Post when ready. How to Specify a Canonical URL Members reposting content often worry about original posts becoming less discoverable in search engines and their website losing visibility as the newer publishing platform (e.g., DEV) might surpass the original blog. Fortunately, DEV allows authors to address these concerns. By inputting a canonical URL, contributors can ensure search engines understand the original source. This prevents any penalties for reposting, and search engine crawlers boost the ranking of the original article. Option 1 (RSS Import): Check the "Mark the RSS source as canonical URL by default" box upon import. Option 2 (Individual Posts): Identify your editor version in /settings/customization. Rich + Markdown Editor: Click the gear icon next to "Save draft" and enter the original post's URL in the "Canonical URL" field. Basic Markdown Editor: Add canonical_url: X to the post's front matter, specifying the original post's URL. Following these steps ensures proper attribution and maintains the visibility of your content. Helpful Resources Below you'll find various resources we recommend for better understanding DEV's writing policies and tools. DEV Editor guide A quick guide that provides you with technical tips for using the DEV Editor and our brand of Markdown. You can also find it by clicking the "?" page in the editor . Markdown Cheatsheet A handy cheatsheet for commonly-used Markdown formatting syntax. Best Practices for Writing on DEV A helpful series that offers both technical tips and general guidance for making the best-fit article for DEV. 🙌 Guidelines for Avoiding Plagiarism on DEV This resource offers guidance for how to avoid plagiarism. We take a strong stance against plagiarism on DEV; please don't hesitate to report any plagiarism to us. Guidelines for AI-assisted Articles on DEV These guidelines detail our requirements for properly labelling AI-assisted content on DEV. Please don't hesitate to report any content that is written with AI-assistance if it isn't following these guidelines. Common Questions Q: How do I set a canonical URL on my post? In the post editor, click the hexagon icon in the bottom left-hand corner beside "save draft" and you'll see an input box to designate a Canonical URL. Note: if you are using the Basic Markdown editor you must add a line for it inside the triple dashes (aka Front Matter), like so: --- title: published: false tags: canonical_url: <https://mycoolsite.com/my-post> --- Q: How do I set a cover image for my post? If using the Rich + Markdown editor, then click the "Add a cover image" button above the title of the post. If using the Basic Markdown editor, include cover_image: [url] in the front matter of your post. Note: you may change your editor type from your settings . Q: Do I own the articles that I publish? Yes, you own the rights to the content you create and post on dev.to and you have the full authority to post, edit, and remove your content as you see fit. Q: Can I cross-post something I've already written on my own blog or Medium? Absolutely, as long as you have the rights you need to do so! And if it's of high quality, we'll feature it. Q: Can I use profanity in my posts? We don't disallow profanity in general, but we do have an internal policy of not promoting posts that have profanity in the title, so you might want to keep that in mind. If your profanity is targeted at individuals or hateful, then it would cross the lines of what's acceptable via our Code of Conduct and we may take necessary action to remove you content. Q: Why has my post been removed? Your post is subject to removal at the discretion of the moderators if they believe it does not meet the requirements of our Code of Conduct . If you think we may have made a mistake, please email us at support@dev.to . Q: Will you put ads on my posts' pages? It's possible. We do allow organizations to purchase advertisements with DEV. 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https://www.highlight.io/docs/general/company/open-source/contributing/architecture | Application Architecture Star us on GitHub Star Docs Sign in Sign up General Docs Welcome to highlight.io Get Started Roadmap Company Values Compliance & Security Open Source Contributing Overview GraphQL Backend Frontend (app.highlight.io) Landing Site (highlight.io) Documentation End to End SDK Example Apps Adding an SDK Application Architecture GitHub Code Spaces Code Style Good First Issues Self-hosting Self-hosted [Dev] Self-hosted [Hobby] Self-hosted [Enterprise] Telemetry Our Competitors Product Philosophy Product Features Session Replay Overview Canvas & Iframe Dev-tool Window Recording Tracking Users & Recording Events Filtering Sessions GraphQL Live Mode Performance Impact Player Session Caching Rage Clicks Request Proxying Session Search Extracting the Session URL Session Search Deep Linking Shadow Dom + Web Components Error Monitoring Overview Enhancing Errors with GitHub Error Search Filtering Errors Grouping Errors Managing Errors Manually Reporting Errors Sourcemaps General Features Overview Alerts Comments Digests Environments Search Segments Services Webhooks Logging Overview Log Alerts Log Search Tracing Overview Trace Search Dashboards Overview Dashboard Management Metrics Tutorials Service Latency Web Vitals & Page Speed User Engagement User Analytics Graphing Drilldown Event Search Dashboard Variables SQL Editor Metrics (beta) Overview Frequently Asked Questions. Integrations Integrations Overview Amplitude Integration ClickUp Integration Discord Integration Electron Support Front Integration GitHub Integration Grafana Integration Overview Setup Dashboards Alerts Height Integration Intercom Integration Jira Integration LaunchDarkly Integration Linear Integration Mixpanel Integration Nuxt Integration Pendo Integration Segment Integration Slack Integration Vercel Integration WordPress Plugin Highlight.io Changelog Overview Changelog 12 (02/17) Changelog 13 (02/24) Changelog 14 (03/03) Changelog 15 (03/11) Changelog 16 (03/19) Changelog 17 (04/07) Changelog 18 (04/26) Changelog 19 (05/22) Changelog 20 (06/06) Changelog 21 (06/21) Changelog 22 (08/07) Changelog 23 (08/22) Changelog 24 (09/11) Changelog 25 (10/03) Changelog 26 (11/08) Changelog 27 (12/22) Changelog 28 (3/6) Changelog 29 (4/2) Menu Highlight Docs Welcome to highlight.io Get Started Roadmap Company Values Compliance & Security Open Source Contributing Overview GraphQL Backend Frontend (app.highlight.io) Landing Site (highlight.io) Documentation End to End SDK Example Apps Adding an SDK Application Architecture GitHub Code Spaces Code Style Good First Issues Self-hosting Self-hosted [Dev] Self-hosted [Hobby] Self-hosted [Enterprise] Telemetry Our Competitors Product Philosophy Product Features Session Replay Overview Canvas & Iframe Dev-tool Window Recording Tracking Users & Recording Events Filtering Sessions GraphQL Live Mode Performance Impact Player Session Caching Rage Clicks Request Proxying Session Search Extracting the Session URL Session Search Deep Linking Shadow Dom + Web Components Error Monitoring Overview Enhancing Errors with GitHub Error Search Filtering Errors Grouping Errors Managing Errors Manually Reporting Errors Sourcemaps General Features Overview Alerts Comments Digests Environments Search Segments Services Webhooks Logging Overview Log Alerts Log Search Tracing Overview Trace Search Dashboards Overview Dashboard Management Metrics Tutorials Service Latency Web Vitals & Page Speed User Engagement User Analytics Graphing Drilldown Event Search Dashboard Variables SQL Editor Metrics (beta) Overview Frequently Asked Questions. Integrations Integrations Overview Amplitude Integration ClickUp Integration Discord Integration Electron Support Front Integration GitHub Integration Grafana Integration Overview Setup Dashboards Alerts Height Integration Intercom Integration Jira Integration LaunchDarkly Integration Linear Integration Mixpanel Integration Nuxt Integration Pendo Integration Segment Integration Slack Integration Vercel Integration WordPress Plugin Highlight.io Changelog Overview Changelog 12 (02/17) Changelog 13 (02/24) Changelog 14 (03/03) Changelog 15 (03/11) Changelog 16 (03/19) Changelog 17 (04/07) Changelog 18 (04/26) Changelog 19 (05/22) Changelog 20 (06/06) Changelog 21 (06/21) Changelog 22 (08/07) Changelog 23 (08/22) Changelog 24 (09/11) Changelog 25 (10/03) Changelog 26 (11/08) Changelog 27 (12/22) Changelog 28 (3/6) Changelog 29 (4/2) Getting Started Getting Started with Highlight Fullstack Mapping Browser React.js Next.js Remix Vue.js Angular Gatsby.js SvelteKit Electron highlight.run SDK Overview Canvas & WebGL Console Messages Content-Security-Policy Identifying Users iframe Recording Monkey Patches Browser OpenTelemetry Persistent Asset Storage Privacy Proxying Highlight React.js Error Boundary Recording Network Requests and Responses Recording WebSocket Events Salesforce Lightning Web Components (LWC) Data Export Sourcemap Configuration Tracking Events Troubleshooting Upgrading Highlight Versioning Sessions & Errors Other React Native (beta) Server Go Overview chi Echo Fiber Gin GORM gqlgen Logrus Manual Tracing gorilla mux JS Overview Apollo AWS Lambda Cloudflare Workers Express.js Firebase Hono Nest.js Next.js Node.js Pino tRPC Winston Python Overview AWS Lambda Azure Functions Django FastAPI Flask Google Cloud Functions Loguru Other Frameworks Python AI / LLM Libraries Python Libraries Ruby Overview Other Frameworks Ruby on Rails Rust Overview actix-web No Framework Hosting Providers Overview Metrics in AWS Logging in AWS Logging in Azure Fly.io NATS Log Shipper Logging in GCP Heroku Log Drain Render Log Stream Logging in Trigger.dev Vercel Log Drain Elixir Overview Elixir App Java: All Frameworks PHP: All Frameworks C# .NET ASP C# .NET 4 ASP Docker / Docker Compose File Fluent Forward curl OpenTelemetry Protocol (OTLP) Syslog RFC5424 Systemd / Journald Native OpenTelemetry Overview Error Monitoring Logging Tracing Browser Instrumentation Metrics Fullstack Frameworks Overview Next.js Fullstack Overview Next.js Page Router Guide Next.js App Router Guide Edge Runtime Advanced Config Remix Walkthrough Self Host & Local Dev Overview Development deployment guide. Integrations Microsoft Teams self-hosted Hobby deployment guide. Traefik SSL Proxying. Docs Home SDK Client SDK API Reference Cloudflare Worker SDK API Reference Go SDK API Reference Hono SDK API Reference Java SDK API Reference Next.JS SDK API Reference Node.JS SDK API Reference Python SDK API Reference Ruby SDK API Reference Rust SDK API Reference Docs / Highlight Docs / Company / Open Source / Contributing / Application Architecture Application Architecture Here's the high level structure of the code that you'll want to start tinkering with. SDKs sdk/ Firstload Client highlight-node / other SDKs Public Graph backend/public-graph/graph/schema.resolvers.go SDK data ingest GraphQL endpoint, hosted locally at http://localhost:8082/public Private Graph backend/private-graph/graph/schema.resolvers.go GraphQL endpoint for frontend, hosted locally at http://localhost:8082/private Workers backend/worker.go Public graph worker processPublicWorkerMessage Async worker Start General Architecture Diagram Code Structure Diagram Kafka Diagram InfluxDB Diagram OpenTelemetry Diagram Adding an SDK GitHub Code Spaces Community / Support Suggest Edits? Follow us! [object Object] | 2026-01-13T08:49:03 |
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