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Which is more popular?
Based on our record, NumPy
should be more popular than Maxima.
It has been mentioned 122 times since March 2021.
social mentions
27 vs 122
Technical Computing popularity
100% vs 0%
alternatives listed
89 vs 189
Base details
Website, pricing, platforms and company facts side by side.
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
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.
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
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
An editorial look at what each product does well and who it suits.
MaximaNumPy
No analysis of Maxima yet.
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.
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...
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,...
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,...
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...
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...
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...
Recommendations tracked on public social media and blogs since March 2021.
Maxima27 mentionsNumPy122 mentions
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
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)...
- Source: Hacker News
/
over 2 years ago
True or False
Use wxmaxima, a free and open-source computer algebra system:.
Source:
almost 3 years ago
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...
- Source: dev.to
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about 1 year ago
AI starts with math and coding. You don’t need a PhD—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,...
- Source: dev.to
/
about 1 year ago
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.
MATLAB
Wolfram Mathematica
GNU Octave
Scilab
Sage Math
Julia
Mathcad
Pandas
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
Figure Eight
