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Maxima VS NumPy

Compare Maxima VS NumPy and see what are their differences

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Maxima logo Maxima

Maxima is a fairly complete computer algebra system written in Lisp with an emphasis on symbolic computation.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Maxima Landing page
    Landing page //
    2022-06-15
  • NumPy Landing page
    Landing page //
    2023-05-13

Maxima features and specs

  • Open-source
    Maxima is freely available and open-source, allowing users to access, modify, and distribute the software without any cost.
  • Symbolic Computation
    Maxima specializes in symbolic computation, providing robust tools for algebraic manipulations, differentiation, integration, and equation solving.
  • Customizability
    As an open-source software, Maxima is highly customizable, and users can modify the source code or write their own functions to extend its capabilities.
  • Documentation
    Maxima comes with extensive documentation, including a comprehensive manual and numerous tutorials, which can aid both beginners and advanced users.
  • Integration with Other Tools
    Maxima can be integrated with other software tools and languages such as Python (via SymPy), providing flexibility and additional functionality for complex computations.

Possible disadvantages of Maxima

  • User Interface
    The default user interface of Maxima is less polished compared to commercial alternatives, which may affect the user experience, especially for beginners.
  • Performance
    While suitable for many tasks, Maxima may not perform as efficiently as some specialized commercial software for very large or complex computations.
  • Learning Curve
    Due to its depth and the nature of symbolic computation, there can be a steep learning curve for new users who are not already familiar with similar tools or mathematical concepts.
  • Community Support
    As an open-source project, support primarily comes from the user community, which may not always be as responsive or comprehensive as professional, paid support services.
  • Updates and Maintenance
    Maxima updates and maintenance depend on community contributions, which can sometimes lead to less frequent updates or delayed fixes for bugs compared to commercial 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.

Maxima videos

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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 Maxima and NumPy)

Maxima

NumPy

Technical Computing
100 100%
Technical Computing
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Numerical Computation
100 100%
Numerical Computation
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 Maxima and NumPy

Maxima Reviews

  1. Guest
    Stable (slow but steady growth)

    I've been using Maxima since my undergraduate (over 10 years), now with Ubuntu20.04 lts, I become a newbie of SageMath. For a small project (both symbolical and numerical), in particular, student lab activities, Maxima has been a powerful tool for analyzing and visualizing data. (The Android version is also fantastic, but the poor keyboard.)

    Mathematica is always enemy/friend. (My coworkers are all Mathematica speakers.)

    Competitors: Wolfram Mathematica
    Pros:    Easy for cli user|Lithghter
    Cons:    Good for advanced users of computers (cli is sometimes hard for newbie)|Hard to find official references, tutors, etc
7 Best MATLAB alternatives for Linux
Another alternative to MATLAB is Maxima which is a computer algebra system (CAS) for manipulation of symbolic and numerical expressions including differentiation, integration, Laplace transformation, linear algebraic equations, tensors, etc.
Source: cyanogenmods.org
Matlab Alternatives
Another alternative of Matlab is Maxima which was inspired by the legendary Algebra system Macsyma. It is a system used for manipulating numerical expressions such as Taylor series, Laplace transformations, Vectors, Tensors, and Matrices. Very accurate results are provided by using exact floating numbers, fractional values, and integers. The Source Forge file manager...
Source: www.educba.com
10 Best MATLAB Alternatives [For Beginners and Professionals]
Maxima is extracted from Macsyma, a computer algebra system developed in the late 1960s by MIT. Maxima is frequently updated to fix bugs for a more optimized coding experience
Source: thetechtop10.com
4 open source alternatives to MATLAB
Check out Maxima, it is a system for the manipulation of symbolic and numerical expressions, including differentiation, integration, Taylor series, Laplace transforms, ordinary differential equations, systems of linear equations, polynomials, sets, lists, vectors, matrices and tensors.
Source: opensource.com
3 Open Source Alternatives to MATLAB
Maxima, another frequently updated alternative to MATLAB. It's based on Macsyma, a "legendary computer algebra system" developed at MIT in the 1960s, can be compiled on Linux, Mac OS X, and Windows, and is available under GPLv2.
Source: www.openhealthnews.com

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 should be more popular than Maxima. It has been mentiond 122 times since March 2021. 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.

Maxima mentions (27)

  • Integral Calculator
    I think the really neat piece of software behind this is maxima (https://maxima.sourceforge.io/), a rather influential computer algebra system of ancient lineage still in use today in more place than you might think. - Source: Hacker News / over 2 years ago
  • Rye: Homoiconic dynamic programming language with some new ideas
    In the maxima computer algebra system[1] which was ancestrally based on lisp it has a single quote operator[2] which delays evaluation of something and a "double quote" (which acually two single quotes rather than an actual double quote) operator[3] which asks maxima to evaluate some expression immediately rather than leaving it in symbolic form.[4] [1] https://maxima.sourceforge.io/ [2]... - Source: Hacker News / over 2 years ago
  • True or False
    Use wxmaxima, a free and open-source computer algebra system:. Source: over 2 years ago
  • C++ library for solving EQUATIONS
    There are several options, here is one of them: https://maxima.sourceforge.io. Source: over 3 years ago
  • Do you know computer algebra software capable of managing systems of multiple equations with multiple unknowns and multiple variables?
    You may use maxima cas (https://maxima.sourceforge.io/) to solve symbolic complex problems. Source: over 3 years ago
View more

NumPy mentions (122)

View more

What are some alternatives?

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

MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming

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 processing—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.

GNU Octave - GNU Octave is a programming language for scientific computing.

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

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