🚀Simple interpolation in Scipy instead of complex optimization
SciPy is a set of math algorithms and helper functions built on an extension of the NumPy Python library. It adds many high-level commands and classes for manipulating and visualizing data, allowing a DS specialist to get away with a regular Python code development environment without complex math systems like MATLAB, IDL, Octave, R-Lab, and SciLab.
For example, interpolation of experimental data in complex scientific or business research. Having obtained the interpolation function from Scipy, you can use it in further calculations. This is useful when additional collection and experimentation is expensive or time-consuming, such as semiconductor development, chemical process optimization, production planning, etc.
Interpolation will help to conduct simulations with datasets where data points are collected at a large interval. For example, you can create an interpolation function using linear, quadratic, or cubic splines and run the interpolation function to evaluate the results of an experiment or simulation on a dense mesh.
This method does not guarantee the best results in all situations, but it is suitable for most real-life situations. Despite the fact that interpolation requires smoothness (continuity) of the function, this assumption can be applied to most real ones, which are not too jumpy, but rather smooth for interpolation methods.
Scipy interpolation routines work in both 2D and 1D cases. For example, you can get a smooth interpolated 2D surface from sparse data using Scipy interpolation, creating a 4900-point matrix from 400 actual data points.
In Scipy, the scipy.interpolate package is responsible for interpolation, which contains spline functions and classes, one-dimensional and multidimensional interpolation classes, Lagrange and Taylor polynomial interpolators, and wrappers for the FITPACK and DFITPACK functions.
https://towardsdatascience.com/optimizing-complex-simulations-use-scipy-interpolation-dc782c27dcd2
https://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html