Hello Python 3.15, goodbye 3.10, and now I can use 3.11

Python 3.15 has been released, adding PEP 810 explicit lazy imports, PEP 798 unpacking in comprehensions, and a new Tachyon sampling profiler in the standard library as profiling.sampling, while the existing cProfile tracing profiler is renamed profiling.tracing. Miguel Grinberg's benchmarks against the 3.15.0rc3 release found performance mostly unchanged from Python 3.14, and the release marks the end of support for Python 3.10.

Python 3.15 is here https://www.python.org/downloads/release/python-3150/ Here's the essential What's new in Python 3.15 https://docs.python.org/3.15/whatsnew/3.15.html guide, this year curated by 3.14 and 3.15 release manager Hugo van Kemenade. As someone who builds open source software that works in every supported Python version, I get to say goodbye to Python 3.10 and start embracing features from Python 3.11. First, some highlights from Python 3.15. I'm most excited about Explicit lazy imports https://docs.python.org/3.15/whatsnew/3.15.html pep-810-explicit-lazy-imports from PEP 810 https://peps.python.org/pep-0810/ . These let you import modules like so: python lazy import json lazy from pathlib import Path The module will then be imported the first time those symbols are used in the code that follows. My LLM https://llm.datasette.io/ CLI tool implements plugins. I had to recommend manual lazy imports https://llm.datasette.io/en/stable/plugins/advanced-model-plugins.html tip-lazily-load-expensive-dependencies in the plugin author documentation, because simply installing plugins that used heavy dependencies such as sentence transformers could add multiple seconds to every invocation of the llm command. Lazy imports directly address this startup cost. The new sublist for sublist in lists unpacking syntax is neat. Here's an illustration lifted from the documentation https://docs.python.org/3.15/whatsnew/3.15.html pep-798-unpacking-in-comprehensions : lists = 1, 2 , 3, 4 , 5 L for L in lists 1, 2, 3, 4, 5 equivalent to x for L in lists for x in L sets = {1, 2}, {2, 3}, {3, 4} { s for s in sets} {1, 2, 3, 4} equivalent to {x for s in sets for x in s} dicts = {'a': 1}, {'b': 2}, {'a': 3} { d for d in dicts} {'a': 3, 'b': 2} equivalent to {k: v for d in dicts for k,v in d.items } PEP 798: Unpacking in Comprehensions https://peps.python.org/pep-0798/ by Adam Hartz and Erik Demaine proposed this as an extension of the unpacking syntax from PEP 448 https://peps.python.org/pep-0448/ , which landed in Python 3.5 https://docs.python.org/3/whatsnew/3.5.html pep-448-additional-unpacking-generalizations back in 2015. Python's existing tracing profiler has been renamed from cProfile to profiling.tracing https://docs.python.org/3.15/library/profiling.tracing.html module-profiling.tracing , and has been joined by an exciting new sampling profiler called Tachyon, aka profiling.sampling https://docs.python.org/3.15/whatsnew/3.15.html tachyon-high-frequency-statistical-sampling-profiler . Sampling profilers work by periodically capturing stack traces from running processes, then using those to calculate which functions have the most time spent inside of them. They're very low overhead and can provide a deep insight into running systems, without needing to instrument those systems first. I've previously used tools https://simonwillison.net/tags/profiling/ like py-spy https://github.com/benfred/py-spy for this. Now this capability is baked into the standard library. If you have the PID of an existing Python 3.15 process you can start gathering samples by running this I had to use sudo on macOS : sudo uv run --python 3.15 python -m profiling.sampling attach 1234 Interact with the main program a bit, then hit Ctrl+C to stop gathering and see a report, which looks something like this: It can also produce flame graphs like this https://simonw.github.io/research/python-3-15-web-edit-profiling/artifacts/flamegraph.html and heat maps like this https://simonw.github.io/research/python-3-15-web-edit-profiling/artifacts/heatmap/index.html , and can even output a format that can be viewed in the Firefox profiler UI. Miguel Grinberg investigated How fast is Python 3.15? https://blog.miguelgrinberg.com/post/how-fast-is-python-3-15 the other day against the 3.15.0rc3 release and found performance to be mostly the same as 3.14, with the exception of the experimental JIT. The JIT gave a 1.2x boost over the standard 3.15 interpreter on a Fibonacci test , the first time Miguel observed a meaningful JIT performance increase. Miguel's explorations of 3.14 https://blog.miguelgrinberg.com/post/python-3-14-is-here-how-fast-is-it last year found significant boosts against Python 3.13 across many more categories. I used the following sequence of commands to get uv to use Python 3.15 on my Mac. First I had to upgrade uv itself to ensure it knew about the new release: uv self update Then I installed the stable 3.15 release: uv python install 3.15 And told uv that I wanted that to be the default for every uv run command, unless the project itself pinned a different version: uv python pin --global 3.15 Now the following reports 3.15 as the version: uv run python --version I already have a bunch of tools installed using uv tool install . I reinstalled them to use Python 3.15 like this: uv tool upgrade datasette --python 3.15 Then I can confirm the version like this: datasette --get /-/versions.json | jq The release of 3.15 means that Python 3.10 released in 2021 is officially no longer supported https://devguide.python.org/versions/ . My policy for my main open source projects is to keep them working in every supported Python version. I can now drop 3.10 and start using features that were introduced in 3.11. Here's the What's new in Python 3.11 https://docs.python.org/3/whatsnew/3.11.html document. I finally get to rely on tomllib https://docs.python.org/3/library/tomllib.html module-tomllib being available Datasette makes extensive use of asyncio . 3.11 adds TaskGroup https://docs.python.org/3/library/asyncio-task.html asyncio.TaskGroup and timeout https://docs.python.org/3/library/asyncio-task.html asyncio.timeout , and several more https://docs.python.org/3/whatsnew/3.11.html asyncio new features. datetime.UTC is a new in 3.11 alias for datetime.timezone.utc . Python's SQLite support got some significant upgrades in 3.11, including: step , value , inverse , and finalize methods. BLOB binary values stored in a SQLite row. set authorizer None to remove a SQLite authorizer. Datasette had a workaround for this Plus a whole bunch of improvements to Python's typing system https://docs.python.org/3/whatsnew/3.11.html new-feat-related-type-hints-311 . I plan to land Python 3.11 improvements to a bunch of my projects over the next few weeks. Here's my first Datasette PR https://github.com/simonw/datasette/pull/2998 removing that set authorizer None workaround. I've also updated my click-app https://github.com/simonw/click-app , datasette-plugin https://github.com/simonw/datasette-plugin , python-lib https://github.com/simonw/python-lib , and llm-plugin https://github.com/simonw/llm-plugin cookiecutter templates to test against Python 3.11 through Python 3.15. Tags: open-source https://simonwillison.net/tags/open-source , profiling https://simonwillison.net/tags/profiling , python https://simonwillison.net/tags/python , sqlite https://simonwillison.net/tags/sqlite , uv https://simonwillison.net/tags/uv