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Axiom VS NumPy

Compare Axiom VS NumPy and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Axiom logo Axiom

Axiom is a general purpose Computer Algebra system.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Axiom Landing page
    Landing page //
    2021-09-21
  • NumPy Landing page
    Landing page //
    2023-05-13

Axiom features and specs

  • Mathematical Rigor
    Axiom is designed with a focus on mathematical correctness and formalism, making it suitable for research and educational use in complex math-related fields.
  • Rich Documentation
    The system includes extensive documentation and materials that help users understand its approach and capabilities, which is beneficial for learning and application development.
  • Open Source
    Axiom is open source, allowing users to contribute to its development, customize the software according to their needs, and use it without licensing fees.
  • Wide Range of Functions
    It supports a wide variety of mathematical computations, encompassing symbolic manipulation, algebraic functions, and numerical calculations.

Possible disadvantages of Axiom

  • Complexity
    Due to its focus on mathematical rigor, Axiom can be complex to learn and use, especially for users without a strong background in mathematics.
  • Limited Community Support
    Compared to other more popular CAS (Computer Algebra Systems), Axiom has a smaller user and developer community, which may result in slower updates and fewer resources for troubleshooting.
  • Performance
    Some users report that Axiom can be slower than other CAS systems when handling certain computations, which could be a concern for performance-intensive applications.
  • User Interface
    The user interface is less modern and intuitive compared to newer systems, which might not meet the expectations of users looking for more user-friendly software.

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.

Axiom videos

+ Add

Storm Axiom Video Review

More videos:

  • Review - Axiom by Storm | Full and uncut review | get yours at bowlerx.com
  • Review - Axiom Verge Review

NumPy videos

+ Add

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 Axiom and NumPy)

Axiom

NumPy

Productivity
100 100%
Productivity
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Music
100 100%
Music
0% 0
Data Science Tools
0 0%
Data Science Tools
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 Axiom and NumPy

Axiom 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.
Source: www.analyticsvidhya.com
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 Axiom. While we know about 122 links to NumPy, we've tracked only 1 mention of Axiom. 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.

Axiom mentions (1)

  • Axiom Computer Algebra system (in development since 1971)
    The target result is proven algorithms, something missing in current CAS work. (Note that this is project goal F on http://axiom-developer.org) The effort involves building a parallel architecture to the current Axiom. - Source: Hacker News / over 2 years ago

NumPy mentions (122)

View more

What are some alternatives?

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

Mubert - Craft high-quality content using next-gen royalty-free music powered by AI.

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

Wolfram Mathematica - Mathematica has characterized the cutting edge in specialized processingโ€”and gave the chief calculation environment to a large number of pioneers, instructors, understudies, and others around the globe.

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

Scale - Get human tasks done with just one line of code.

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

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