The Python ecosystem is quietly finishing a long migration. According to reporting by Neowin, 94% of the most popular packages on the Python Package Index (PyPI) now offer native support for Windows on Arm, and the data science stack is leading the way. Anaconda now hosts 2,500 Arm packages, while NumPy and SciPy publish native wheels specifically to help data scientists move off emulation.
That last detail is the important part. For years, Arm users running Windows had to lean on emulation to run packages built for Intel and AMD chips. Native packages remove that middleman entirely, and the speed and battery-life payoff is exactly what people buying Arm laptops were promised.
Why native Windows on Arm packages matter
PyPI is the official repository where Python developers share and install packages. When you run pip install something, you’re pulling from this index. For most of Python’s history, packages were built primarily for x86-64 (Intel/AMD) and, to a lesser extent, Apple Silicon on macOS. Windows on Arm users were largely left out of that picture.
The long-standing workaround has been Microsoft’s x86 emulation layer, which lets Arm-based Windows machines run traditional x86 apps. It works, but it comes with trade-offs: higher memory use, slower execution, and occasional compatibility hiccups. For compute-heavy work like numerical computing, that overhead is especially noticeable.
Native packages solve this by being compiled specifically for the Arm architecture. They run directly on the processor without translation, which typically means faster performance, lower power draw, and less memory pressure — the exact reasons people chose Arm hardware in the first place.
Anaconda’s 2,500 Arm packages
Anaconda, the data science platform that packages thousands of Python libraries for researchers and analysts, now hosts 2,500 Arm packages. That represents a substantial slice of its distribution, and it targets one of the most common reasons people use Windows on Arm for real work: data science and analytics.
Anaconda’s role here is notable because it bundles not just individual libraries but entire toolchains that data scientists depend on. Having native Arm builds means users don’t have to assemble working environments from x86 emulation or risk breaking their setups with incompatible packages.
This matters for professionals who’ve been hesitant to adopt Arm hardware for work. If your analysis tools run natively, the laptop stops being a “second machine” and becomes a legitimate primary workstation.
NumPy and SciPy ship native wheels
Two of the most foundational libraries in the Python data stack — NumPy and SciPy — now publish native wheels for Windows on Arm. These are pre-compiled installers tailored to the Arm platform, so pip can grab a version built for your exact hardware instead of falling back to emulation or source builds.
NumPy provides the array-computing foundation for much of Python’s scientific ecosystem, and SciPy builds on it for optimization, integration, and statistics. Both are heavily used in fields from finance to physics to machine learning, so native performance gains ripple outward across countless downstream tools.
The fact that these specific libraries are being highlighted suggests Microsoft and the Python community are prioritizing the packages that matter most to Arm adopters, rather than waiting for the entire index to catch up.
What this means for data scientists
For data scientists specifically, this is a meaningful shift. The migration off emulation has been one of the biggest practical barriers to adopting Windows on Arm for technical work. Native wheels mean faster data processing, longer battery life during long analysis sessions, and environments that behave the way you’d expect without emulation quirks.
In practice, this reduces the friction of setting up a new Arm machine. Instead of troubleshooting which packages work under emulation, you can install from native builds and get results that match what you’d see on a traditional laptop — often faster.
What this means for you
Even if you’re not a data scientist, the broader picture is relevant. A Python ecosystem that runs natively on Windows on Arm makes those machines more useful for development, scripting, and automation. If you own or are considering an Arm-based Windows laptop, the software gap that once held it back is closing fast.
The 94% figure refers to the most popular packages, so the libraries you’re most likely to actually use are the ones covered. Less popular or newer packages may still lag, but the core toolchain is largely there.
How to get it
If you’re on Windows on Arm, the setup is straightforward. Make sure your system is up to date, then install packages normally through pip or your preferred package manager. Anaconda users should pull the latest Arm-native installer, and NumPy and SciPy will now resolve to native wheels automatically when available.
For anyone still relying on emulation, it may be worth re-checking your environments now that native builds are widely available — you might find performance improvements without changing your workflow.
Source: Neowin
Over to you: Are you already running your Python workloads natively on Windows on Arm, or still leaning on emulation?
