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About 94% of the 1,000 most-downloaded PyPI projects published 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 Python developers using Arm-based Windows PCs, but it is not a guarantee that 94% of complete Python applications will run natively: the result measures published wheel availability, not end-to-end application compatibility.
Microsoft reported the figure on October 14, 2026, alongside the release of Python 3.15’s native-by-default Windows on Arm experience. Python 3.15 now selects the native Arm64 interpreter by default on Windows on Arm devices, while Windows on Arm remains a CPython Tier 2 platform with continuous testing and release-quality commitments.
What the 94% figure actually measures
The denominator is specific: the 1,000 PyPI projects with the most downloads in September 2026. Microsoft checked the latest release of each project and counted it when that release offered either a native Windows Arm64 wheel or a platform-independent wheel. It did not test every version, operating system configuration, or application assembled from those packages.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsNative Arm64 wheels and universal Python wheels are different
A wheel tagged for win_arm64 contains binaries compiled for Windows on Arm. It can run without translating those native components. A wheel in the py3-none-any family contains architecture-independent Python code and can work on Arm because it has no machine-specific binary, but it does not prove that every dependency in an application is also architecture-independent. Both wheel types count toward the 94% result.
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A top-level wheel does not prove dependency compatibility
A package may publish a platform-independent wheel while depending on another package that includes a native extension. Microsoft therefore advises testing the complete, pinned dependency graph rather than checking only the package named in an application’s requirements file. The relevant question is whether every installed dependency has an Arm64 build, is architecture-independent, or can be handled through emulation.
Why Python 3.15 changes the Windows on Arm experience
On an Arm64 Windows device, the current Python Install Manager can install Python 3.15 with the native Arm64 interpreter selected by default. At startup, verify that the interpreter reports 64-bit (ARM64); that confirms the process itself is native rather than an x64 installation running under emulation.
Native interpreter selection removes a common source of confusion, but it does not convert x64-only extensions into Arm binaries. Projects that still distribute only x64 wheels can continue to run through Windows x64 emulation, provided their dependencies and workloads behave correctly under that compatibility layer.
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Which parts of the Python stack are available
Microsoft’s October 2026 overview lists broad coverage across scientific computing, web development, packaging and testing. Availability below refers to published native Arm64 or platform-independent packages; individual release versions and transitive dependencies still need checking.
| Area | Packages and tools identified as available | What to verify |
|---|---|---|
| Scientific computing | NumPy, SciPy, pandas, Polars, Matplotlib, scikit-learn, Pillow, h5py, statsmodels and XGBoost | Native wheel tags for the exact release and any compiled dependencies |
| Packaging and Rust extensions | pip, venv, virtualenv, setuptools, build, uv, pydantic-core, Ruff, orjson and tokenizers | Whether your lock file selects an Arm64 wheel instead of an x64 artifact |
| Web and cloud | Django, Flask, FastAPI, Requests, HTTPX and several cloud SDKs | SDK-specific native extensions and service-client dependencies |
| Notebooks and testing | JupyterLab, Jupyter Notebook, ipykernel, pyzmq, psutil, VS Code’s Jupyter experience, pytest, Black, mypy and coverage | Kernel, messaging and test-environment packages in the full environment |
Anaconda’s main channel, as reported by Microsoft, includes more than 2,500 Windows on Arm packages. That gives data-science users another substantial source of Arm-ready binaries, although channel coverage should be checked against the packages and versions in a particular environment.
Where native coverage is still incomplete
Microsoft says broader native wheel coverage is still needed for PyArrow, gRPC, OpenCV and database clients. These components often sit low in a dependency tree, so an application can appear compatible until it installs a connector, image-processing module, analytics engine or RPC library.
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How to check whether an application is Arm-compatible
- Start with a native interpreter. On the Arm64 Windows machine, install Python 3.15 through the current Python Install Manager and confirm the startup banner says
64-bit (ARM64). - Freeze the environment. Use the application’s pinned requirements or lock file. Do not audit an unconstrained “latest” installation and assume the result will remain stable.
- Inspect every dependency. Check direct and transitive packages for a
win_arm64wheel or a platform-independent wheel. Pay particular attention to packages that ship C, C++, Fortran or Rust extensions. - Exercise real application paths. Import every major module, run representative tests, and cover database, networking, notebook, image, scientific or GPU features that the production workload uses. Installation success alone does not establish runtime compatibility.
- Build and test natively in CI. GitHub-hosted
windows-11-armandwindows-11-vs2026-armrunners let maintainers compile packages and execute tests on Arm hardware before publishing a release. - Keep an emulation fallback. If one dependency remains x64-only, use the Windows x64 compatibility layer while waiting for a native build, and record that exception so a future dependency update can remove it.
Do Windows on Arm developers still need x64 emulation?
Often, yes. Native support is now broad enough for common web, data-science, notebook and testing workflows, but the remaining gaps are concentrated in important native-extension categories. A project can therefore have a native Python 3.15 interpreter and mostly native dependencies while still requiring emulation for one package.
Use native wheels whenever they exist: they avoid translation overhead and make CI results match the target architecture. Use x64 emulation as a compatibility bridge, not as evidence that the dependency graph is Arm-native. When a package publishes a new Arm64 wheel, update the pin, rerun the full test suite on an Arm runner and remove the exception only after the application’s real workload passes.
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What maintainers should measure before claiming Arm support
Package maintainers and application teams should compare five separate properties:
- Whether the project publishes a native Arm64 wheel.
- Whether transitive dependencies contain native extensions without Arm64 builds.
- Whether native CI runners are available for compilation and testing.
- Whether the project’s package-manager channels carry the required versions.
- Whether x64 emulation provides a workable fallback for any remaining gap.
That distinction is why the headline is encouraging but deliberately narrower than “94% of Python applications run natively.” As Chris Anderson, Distinguished Engineer for Windows Platform and Developer, wrote, “This measures published wheel availability, not end-to-end application compatibility.”
What the result means for Python users
For a new Windows on Arm project, the default path is now substantially better: install the native Python 3.15 interpreter, choose Arm-ready or architecture-independent packages, and test the complete locked environment on native CI. For an existing project, expect most mainstream components to be available but plan for targeted investigation around data formats, computer vision, RPC, database drivers and GPU tooling.
The practical takeaway is not to count packages at the top level. Count the binaries your application actually installs, then validate the workload on Arm hardware. The 94% scan shows that the ecosystem has moved close to mainstream usability, while the remaining dependency-level gaps still determine whether a particular application is genuinely native.
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