Scientific computing & reproducibility · NumPy developers / NumFOCUS
NumPy
Foundational open-source Python library for fast, array-based numerical computing.
NumPy provides the n-dimensional array object that almost the entire Python scientific stack is built on top of, including pandas, SciPy and scikit-learn. It adds vectorized, broadcasting arithmetic over arrays implemented in C, which is orders of magnitude faster than looping in pure Python, plus linear algebra, Fourier transform and random-number routines. It has no UI and no hosted service; it's installed as a library into a Python environment, typically pinned to an exact version (via conda or pip) as part of making a numerical analysis reproducible. Fiscally sponsored by NumFOCUS, a nonprofit that supports open-source scientific computing projects, it has no commercial tier or paid support offering of its own.
At a glance
| Vendor | NumPy developers / NumFOCUS |
|---|---|
| Pricing model | Open source + paid options |
| Free tier | Yes |
| Deployment | Self-hosted |
| Open source | Yes (BSD-3-Clause) |
| Best for | Any Python-based numerical or scientific computing workflow that needs fast array operations. |
Pricing
Free and open source; fiscally sponsored by NumFOCUS with no paid tier.
Pricing has not been verified yet — see the vendor's site.
Features
- N-dimensional array object with broadcasting
- Vectorized arithmetic implemented in C
- Linear algebra and Fourier transform routines
- Random number generation
- C API for integration with lower-level languages
- Foundation for pandas, SciPy and scikit-learn
Integrations
Profile last reviewed September 21, 2026