# numpy - **Canonical URI:** https://www.vioscale.ai/software/numpy - **Category:** Analytics & BI Software - **Vendor:** Numpy - **Homepage:** https://numpy.org - **Also known as:** numpy - **Profile claimed by vendor:** no - **Last updated:** 2026-08-05T12:46:42.198Z ## Vioscale score **48.8 / 100**, confidence 33% (low). Composite of weighted, independently-sourced signals (no user reviews, no vendor payment). | Signal | Score | Weight | Contribution | Evidence present | |---|--:|--:|--:|:--:| | capabilities | 28.9 | 0.187 | 5.4 | ✓ | | github_stars | 85.1 | 0.029 | 2.5 | ✓ | | integrations | 37.4 | 0.105 | 3.9 | ✓ | | github_activity | 63.1 | 0.105 | 6.6 | ✓ | | release_cadence | 89.4 | 0.058 | 5.2 | ✓ | | security_posture | 0 | 0.07 | 0 | - | | package_downloads | 0 | 0.152 | 0 | - | | stackoverflow_activity | 0 | 0.07 | 0 | - | ## Pricing _As of 2026-08-05, [verify at source](https://numpy.org). Independently observed._ Open source · Free tier > Open source (BSD license) ## About NumPy is a fundamental numerical computing library providing N-dimensional arrays and comprehensive mathematical functions. It brings the speed of compiled C and Fortran code to Python with an easy-to-use Python interface. _Independently observed._ ## Platform & deployment - **Deployment:** Self-hosted ## Integrations (19) _Independently observed._ - QuTiP - PyQuil - Qiskit - PennyLane - Pandas - statsmodels - Xarray - Seaborn - SciPy - PyWavelets - python-control - HyperSpy - Scikit-image - OpenCV - Mahotas - NetworkX - graph-tool - igraph - PyGSP ## Capabilities _The capabilities that matter for Analytics & BI Software. "-" = undocumented, not absent._ | Capability | Supported | |---|:--:| | **Core** | | | Self-serve dashboards | - | | **Modelling** | | | SQL editor | - | | Semantic / metrics layer | - | | Data modelling | - | | **Core** | | | Rich visualisations | - | | **Delivery** | | | Scheduled reports & alerts | - | | Embedded analytics | - | | **Deployment** | | | Hosting | Self-hosted only | | **Data** | | | Warehouse-native (no extract) | - | | **Extensibility** | | | Public API | ✓ | | **Ops** | | | Git-based version control | - | ## FAQ ### What is numpy and what does it do? Numpy is a Python library for scientific computing that provides N-dimensional array objects and mathematical functions. It includes capabilities for linear algebra operations, Fourier transforms, and random number generation. _Source: [https://numpy.org](https://numpy.org)._ ### Is numpy free to use? Yes, numpy is completely free and open source under a liberal BSD license. No payment, subscription, or commercial licensing is required. _Source: [https://numpy.org](https://numpy.org)._ ### How is numpy deployed? Numpy is self-hosted, meaning you install and run it on your own systems as a Python package. Once installed, you can import it directly into your Python applications without external dependencies. _Source: [https://numpy.org](https://numpy.org)._ ### What other libraries integrate with numpy? Numpy integrates with a broad ecosystem of scientific Python libraries including Pandas, SciPy, Scikit-image, Xarray, NetworkX, OpenCV, Seaborn, statsmodels, and PyWavelets. This enables you to build comprehensive data analysis and scientific computing workflows. _Source: [https://numpy.org](https://numpy.org)._ ### Is numpy actively maintained? Numpy is maintained by a vibrant community of developers. As a fundamental library in Python's scientific computing ecosystem, it receives ongoing development and support. _Source: [https://numpy.org](https://numpy.org)._ ### What license does numpy use? Numpy is distributed under a liberal BSD license, which permits flexible use, modification, and distribution of the code with minimal legal restrictions. _Source: [https://numpy.org](https://numpy.org)._ ### Who should use numpy? Numpy is designed for Python developers and scientists working with numerical data and array-based computing. It serves anyone needing mathematical functions and efficient operations on N-dimensional arrays. _Source: [https://numpy.org](https://numpy.org)._ ## Facts Every value below carries its source and our confidence. Facts are re-crawled on a freshness schedule. ### integrations | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | integrations.count | 19 | [link](https://numpy.org) | 2026-08-05 | 60% (medium) | ### language | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | language.primary | Python | [link](https://github.com/numpy/numpy) | 2026-07-31 | 90% (high) | ### pricing | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | pricing.free_tier | yes | [link](https://numpy.org) | 2026-08-05 | 60% (medium) | | pricing.price_level | free | [link](https://numpy.org) | 2026-08-05 | 60% (medium) | | pricing.model | open_source | [link](https://numpy.org) | 2026-08-05 | 60% (medium) | | pricing.transparent | yes | [link](https://numpy.org) | 2026-08-05 | 60% (medium) | ### activity | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | activity.commits_last_30d | 100 | [link](https://github.com/numpy/numpy/pulse) | 2026-07-31 | 65% (medium) | ### adoption | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | adoption.github_stars | 32,464 | [link](https://github.com/numpy/numpy) | 2026-07-31 | 90% (high) | ### release | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | release.cadence_days | 19 | [link](https://github.com/numpy/numpy/releases) | 2026-07-31 | 70% (medium) | ### content | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | content.faq | `[{"answer":"Numpy is a Python library for scientific computing that provides N-dimensional array objects and mathematical functions. It includes capabilities for linear algebra operations, Fourier transforms, and random number generation.","source":"https://numpy.org","question":"What is numpy and what does it do?","confidence":0.95},{"answer":"Yes, numpy is completely free and open source under a liberal BSD license. No payment, subscription, or commercial licensing is required.","source":"https://numpy.org","question":"Is numpy free to use?","confidence":0.95},{"answer":"Numpy is self-hosted, meaning you install and run it on your own systems as a Python package. Once installed, you can import it directly into your Python applications without external dependencies.","source":"https://numpy.org","question":"How is numpy deployed?","confidence":0.85},{"answer":"Numpy integrates with a broad ecosystem of scientific Python libraries including Pandas, SciPy, Scikit-image, Xarray, NetworkX, OpenCV, Seaborn, statsmodels, and PyWavelets. This enables you to build comprehensive data analysis and scientific computing workflows.","source":"https://numpy.org","question":"What other libraries integrate with numpy?","confidence":0.9},{"answer":"Numpy is maintained by a vibrant community of developers. As a fundamental library in Python's scientific computing ecosystem, it receives ongoing development and support.","source":"https://numpy.org","question":"Is numpy actively maintained?","confidence":0.9},{"answer":"Numpy is distributed under a liberal BSD license, which permits flexible use, modification, and distribution of the code with minimal legal restrictions.","source":"https://numpy.org","question":"What license does numpy use?","confidence":0.95},{"answer":"Numpy is designed for Python developers and scientists working with numerical data and array-based computing. It serves anyone needing mathematical functions and efficient operations on N-dimensional arrays.","source":"https://numpy.org","question":"Who should use numpy?","confidence":0.85}]` | [link](https://numpy.org) | 2026-07-31 | 91% (high) | ### reliability | Attribute | Value | Source | Retrieved | Confidence | |---|---|---|---|---| | reliability.status_page | yes | [link](https://numpy.org/status) | 2026-08-05 | 60% (medium) | --- *Source: Vioscale (https://www.vioscale.ai/software/numpy). Independent, evidence-based software intelligence. Cite the canonical URI.*