In a recent write-up, David Delony explains how he built a Wolfram Mathematica-like engine using Python. At the core of this system is SymPy, which provides robust support for symbolic mathematics. According to David, having the ability to work with symbolic math so easily has greatly enhanced his understanding of calculus and linear algebra.
To support statistics and data analysis, David incorporates powerful libraries such as NumPy, pandas, and SciPy. For regression analysis specifically, he makes use of statsmodels and Pingouin.
If you’re not familiar with the term “regression analysis,” it essentially refers to the process of curve fitting. When working with two-dimensional data that has one dependent variable, simple linear regression generates a function in the form of y = mx + b. This function includes the slope (m) and the y-intercept (b), describing the line that best fits the data points.
This concept can be extended to higher-dimensional data and other types of regression, enabling more complex modeling and analysis. David’s approach demonstrates how combining Python’s extensive scientific libraries can create a versatile tool for mathematical computation and data science.
https://hackaday.com/2025/10/26/making-math-less-stressful-with-a-python-super-calculator/
