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.)
Based on our record, NumPy should be more popular than Maxima. It has been mentiond 107 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.
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 / 25 days 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) 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 / about 2 months ago
Use wxmaxima, a free and open-source computer algebra system:. Source: 5 months ago
There are several options, here is one of them: https://maxima.sourceforge.io. Source: 12 months ago
You may use maxima cas (https://maxima.sourceforge.io/) to solve symbolic complex problems. Source: about 1 year ago
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / about 2 months ago
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / 2 months ago
Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 6 months ago
A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 7 months ago
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