In this article, I’ll walk you through how to visualize 3D surface plots with Python using the Matplotlib library. Surface plots are three-dimensional data diagrams.
Rather than showing individual data points, surface plots show a functional relationship between a designated dependent variable (Y) and two independent variables (X and Z). The path is a path associated with the contour plots.
How Surface Plots are Created?
It is important to understand how surface plots are constructed. A two-dimensional grid of X and Z is constructed. The range of this grid is equal to the range of the data. Then, a Y value is calculated for each grid point. This Y value is a weighted average of all data values that are “close” to this grid point. (The average number of points is specified by the user.)
The three-dimensional surface is constructed using these average values. Therefore, the area graph does not show the variation at every point on the grid. These graphs are useful in regression analysis to visualize the relationship between a dependent variable and two independent variables.
While creating 3D surface plots always remember that multiple regression assumes that the surface is a perfectly flat surface. Therefore, the area plot allows you to visually determine whether multiple regression is appropriate.
Creating 3D Surface Plots with Python using Matplotlib
To create 3D surface plots with Python using matplotlib, we first need to create an instance of the Axes3D class. 3D axes can be added to a matplotlib figure by passing a projection = ‘3d’ keyword argument to the add_axes or add_subplot methods.
Let’s see how to create and visualize 3D Surface plots with Python by using the matplotlib library in Python:
This is how we can visualize 3D plots with Python programming language. I hope you liked this article, please free to ask your valuable questions in the comments section below.