Data Analytics using Python Visualizations - Creating 3D Plots Including 3D Contours

This video tutorial covers the creation of 3D plots using Matplotlib, including 3D axes, Helix plots, scatter plots, and contour plots. It explains the use of projection 3D for creating 3D axes and demonstrates practical examples like drawing a Helix and generating scatter and contour plots. The tutorial also discusses customizing plots with color parameters and provides insights into the mathematical formulas used for plotting.

What is the primary purpose of using 3D plots?

• To visualize three-dimensional data

• To create animations

• To enhance color contrast

• To represent two-dimensional data

Which function is used to create a 3D axis in Matplotlib?

• axis3D()

• projection='3D'

• projection='2D'

• plot3D()

What mathematical function is used to define the X-coordinate in a Helix?

• Exponential

• Sine

• Tangent

• Cosine

How is random data generated for 3D scatter plots in the tutorial?

• Using a linear function

• Using a normal random function from Numpy

• Using a sine wave

• Using a constant value

What effect does setting the color parameter to Z have in a 3D scatter plot?

• It changes the shape of the plot

• It makes points darker along the Z-axis

• It changes the size of the points

• It makes points lighter along the X-axis

What is required to create a 3D contour plot?

• A mesh grid and 3D data

• Only X and Y data

• A single set of data

• A 2D plot function

How does a 3D contour plot differ from a 2D contour plot?

• It requires a different data format

• It uses the contour3D function

• It uses a different plotting library

• It does not require a mesh grid