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Rumprun VS NumPy

Compare Rumprun VS NumPy and see what are their differences

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Rumprun logo Rumprun

The Rumprun unikernel and toolchain for various platforms - rumpkernel/rumprun

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Rumprun Landing page
    Landing page //
    2023-10-18
  • NumPy Landing page
    Landing page //
    2023-05-13

Rumprun features and specs

No features have been listed yet.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Rumprun videos

XPDS15 - Deploying Real-World Software Today as Unikernels on Xen with Rumprun

NumPy videos

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Category Popularity

0-100% (relative to Rumprun and NumPy)
Automated Testing
100 100%
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Data Science And Machine Learning
Testing
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Data Science Tools
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100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Rumprun and NumPy

Rumprun Reviews

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NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than Rumprun. While we know about 121 links to NumPy, we've tracked only 4 mentions of Rumprun. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Rumprun mentions (4)

  • A future without containers? ( thoughts )
    Wow, just now seeing this topic. I work for a cloud company hosted in AWS. We started out, Netflix/Spotify style microservices. We were all on ec2 images generate by packer (and later with AWS Image Factory). When Docker hit, we kicked the tires but never did anything with it beyond using it for running unit tests, and later, infrastructure tests. 5 years ago, during a hackathon, our little group began... Source: almost 3 years ago
  • Ask HN: Whatโ€™s the most secure OS for servers? Why?
    > Why not? Most people won't spend the time to learn OS/distro building. I donโ€™t know how good they are and have never used any, but thereโ€™s tooling for building the ultimate stripped down kernel, unikernels (https://en.wikipedia.org/wiki/Unikernel) A quick Google gives me https://nanovms.com/, https://github.com/solo-io/unik and https://github.com/rumpkernel/rumprun. - Source: Hacker News / over 3 years ago
  • The big idea around unikernels
    Great entrant in the space that is actually usable: https://www.unikraft.org Promising project that's inactive but was one of the first ones I found with reasonable ergonomics and no lock-in to a specific language that I didn't use: https://github.com/rumpkernel/rumprun Unfortunately it looks to be unmaintained as of now, but I expect the examples still work etc (https://github.com/rumpkernel/rumprun/issues/135). - Source: Hacker News / almost 4 years ago
  • Is Rump kernel dead?
    Then there is the rumprun unikernel (that runs on qemu and baremetal x86), the sources of which you can find here https://github.com/rumpkernel/rumprun (and some more projects in the github org: https://github.com/rumpkernel). These projects have not been actively maintained for many years. Source: about 4 years ago

NumPy mentions (121)

  • Top 5 GitHub Repositories for Data Science in 2026
    The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโ€ฆ. - Source: dev.to / 14 days ago
  • Your 2025 Roadmap to Becoming an AI Engineer for Free for Vue.js Developers
    AI starts with math and coding. You donโ€™t need a PhDโ€”just high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI, thanks to tools like TensorFlow and NumPy. If you know JavaScript from Vue.js, Pythonโ€™s syntax is straightforward. - Source: dev.to / about 2 months ago
  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 8 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / about 1 year ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. Itโ€™s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / about 1 year ago
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What are some alternatives?

When comparing Rumprun and NumPy, you can also consider the following products

OSv - OSv is an open source project to build the best OS for cloud workloads

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

unittest - Testing Frameworks

OpenCV - OpenCV is the world's biggest computer vision library

Criterion - A dead-simple, yet extensible, C test framework.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.