Data Science and Machine Learning (Theory and Projects) A to Z - Matplotlib for Data Visualization: Matplotlib 3D Introd

The video tutorial introduces 3D plotting in Matplotlib, covering various plot types like parametric curves, scatter plots, and surface plots. It explains how to set up 3D plots by importing necessary libraries and setting projections. The tutorial demonstrates generating data for a parametric 3D curve and customizing the plot with labels and legends. It emphasizes the consistency of the interface between 2D and 3D plotting, making it easy to switch between them. The video concludes with a preview of upcoming topics on 3D scatter plots.

What types of 3D plots are mentioned as being supported by Matplotlib?

• Line graphs and histograms

• Bar plots and pie charts

• Parametric curves, scatter plots, surface plots, and contour plots

• Heatmaps and box plots

Which module must be imported to work with 3D plots in Matplotlib?

• numpy

• matplotlib.pyplot

• pandas

• mpl_toolkits.mplot3d

What is the purpose of setting the projection to '3D' in a plot?

• To adjust the plot size

• To enable 3D rendering of the plot

• To change the color scheme

• To add a legend to the plot

How is the parameter 'R' defined in the context of generating a parametric curve?

• As the square of Z plus one

• As the sine of Theta

• As the tangent of Z

• As the cosine of Theta

What is the correct way to set the X label in a 3D plot?

• ax.Xlabel('X')

• ax.set_xlabel('X')

• plt.xlabel('X')

• ax.label('X')

What is a key advantage of the 3D plotting interface in Matplotlib?

• It requires no additional imports

• It supports only scatter plots

• It is identical to the 2D plotting interface

• It automatically generates data

What is the purpose of setting the figure size in a plot?

• To change the plot's color

• To adjust the plot's dimensions

• To enable 3D projection

• To add a title to the plot