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Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →About 94% of the 1,000 most-downloaded PyPI projects had a latest release with either a native Windows on Arm wheel or a platform-independent wheel in Microsoft’s September 2026 scan. That is a major improvement for Arm64 Windows developers, but it does not mean 94% of Python applications will run natively without checking their complete dependency trees.
The result, reported on 14 October 2026 by the Microsoft Windows Developer Blog, measures published wheel availability for the newest release of each project.
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What the 94% statistic actually measures
Microsoft scanned the 1,000 most-downloaded PyPI projects in September 2026. Approximately 94% of those projects published, in their latest release, at least one of two qualifying wheel types:
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win_arm64wheel containing compiled Arm64 binaries. - A platform-independent wheel, commonly tagged in the
py3-none-anystyle, containing Python code that does not depend on a processor architecture.
Because both categories count, the figure is broader than “94% have native Arm64 binaries.” It is also a package-level measurement rather than an application compatibility guarantee. As Chris Anderson, a Distinguished Engineer for Windows Platform and Developer, put it: “This measures published wheel availability, not end-to-end application compatibility.”
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The denominator matters too: these are the 1,000 most-downloaded projects, not every package on PyPI and not every Python application in use.
Native wheels, universal wheels and dependency trees
Native Arm64 wheels
A win_arm64 wheel is compiled for Windows on Arm. Installing one avoids translating that package’s native code through x64 emulation and is generally the preferred path for performance-sensitive extensions.
Platform-independent wheels
A py3-none-any-style wheel is architecture-independent Python code. It can run on Arm64 Windows, but it may import another package that contains native code. The top-level wheel therefore cannot prove that the application is fully native.
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Why the full graph must be tested
Resolve and test every pinned dependency, including transitive dependencies. A web framework, notebook, or data package may be universal while one lower-level library still requires an Arm64 build or an x64 fallback. The practical unit of compatibility is the complete lockfile or requirements set, not the name of the package at the top of the install command.
Python 3.15 changes the default on Arm64 Windows
Python 3.15 now selects the native Arm64 interpreter by default on Windows on Arm devices. CPython treats Windows on Arm as a Tier 2 platform, with continuous testing and release-quality commitments.
On an Arm64 Windows computer, install Python 3.15 through the current Python Install Manager. Start the interpreter with python and check its startup banner; it should report 64-bit (ARM64). Confirming the interpreter architecture before creating a virtual environment prevents accidental use of an x64 installation.
Which parts of the Python ecosystem are ready
Microsoft’s announcement identifies broad coverage across common development workloads. Availability still depends on the particular release and on any native packages pulled in beneath it.
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| Workload | Examples identified as available | Wheel or support detail |
|---|---|---|
| Scientific computing | NumPy, SciPy, pandas, Polars, Matplotlib, scikit-learn, Pillow, h5py, statsmodels and XGBoost | NumPy and SciPy publish native Windows on Arm wheels; the other listed projects have shipped Arm64 wheels |
| Packaging and Rust-based extensions | pip, venv, virtualenv, setuptools, build, uv, pydantic-core, Ruff, orjson and tokenizers | Native or platform-independent packages are available |
| Web and cloud | Django, Flask, FastAPI, Requests, HTTPX and several cloud SDKs | Platform-independent wheels are available, subject to dependencies |
| Notebooks and testing | JupyterLab, Notebook, ipykernel, pyzmq, psutil, VS Code’s Jupyter experience, pytest, Black, mypy and coverage | Listed as available for Windows on Arm workflows |
For data-science users, Anaconda’s main channel contains more than 2,500 Windows on Arm packages, according to Microsoft. That expands the number of prebuilt options beyond PyPI alone.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where native coverage is still incomplete
Microsoft says broader native wheel coverage is still needed for PyArrow, gRPC, OpenCV and database clients. These gaps can affect otherwise portable applications because data, networking and database stacks often include compiled components.
PyTorch and NVIDIA CUDA components are publicly available for validation. Anaconda provides CUDA Toolkit packages and plans additional builds, but support should be checked for the exact framework, Python version and hardware combination you intend to deploy.
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When a native wheel is unavailable, x64 packages remain usable through Windows emulation. Emulation is a practical compatibility fallback while maintainers add Arm64 builds, although a native package is preferable for workloads that spend substantial time in compiled code.
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- Use a native interpreter. Install Python 3.15 with the Python Install Manager and verify the banner says
64-bit (ARM64). - Create an isolated environment. Build a fresh virtual environment with
venvorvirtualenvso an older x64 installation cannot satisfy dependencies unnoticed. - Install the pinned graph. Resolve the exact requirements or lockfile used by the application, rather than testing only its top-level package.
- Inspect compiled dependencies. Pay particular attention to packages that perform numerical processing, image or video work, database access, networking, or GPU acceleration. Check whether each release offers a
win_arm64wheel, a platform-independent wheel, or only an x64 build. - Run the real test suite. Exercise imports, native extensions, database connections, notebook kernels, command-line entry points and production-like workloads on an Arm64 machine.
- Test continuously on Arm. GitHub-hosted
windows-11-armandwindows-11-vs2026-armrunners let maintainers compile packages and run tests natively before publishing. - Keep an emulation fallback. If one dependency has no Arm64 release, document the x64 package and verify that Windows emulation is acceptable for the affected workload.
What this means for Windows on Arm developers
The 94% result shows that the common Python surface is approaching a native-by-default experience: the interpreter chooses Arm64, major scientific and web libraries are available, and native CI runners are now part of the build pipeline. It does not eliminate compatibility work. Teams still need to validate every pinned dependency, especially where compiled extensions or GPU libraries are involved.
For a new project, start with Python 3.15 on an Arm64 runner and make the architecture part of continuous integration. For an existing project, audit the lockfile first; the remaining problem is usually a small number of transitive native dependencies rather than the headline package itself.
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