Book Image

Mastering Python for Data Science

By : Samir Madhavan
Book Image

Mastering Python for Data Science

By: Samir Madhavan

Overview of this book

Table of Contents (19 chapters)
Mastering Python for Data Science
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
7
Estimating the Likelihood of Events
Index

A 3D plot of a surface


We'll now plot a 3D plot, where the Sin function is plotted against the sum of the square values of the two axes:

>>> from mpl_toolkits.mplot3d import Axes3D
>>> fig = plt.figure()
>>> ax = Axes3D(fig)
>>> X = np.arange(-4, 4, 0.25)
>>> Y = np.arange(-4, 4, 0.25)
>>> X, Y = np.meshgrid(X, Y)
>>> R = np.sqrt(X**2 + Y**2)
>>> Z = np.sin(R)
>>> ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='hot')

After the preceding code is executed we'll get the following output:

In the preceding code, we defined the x and y axes with values ranging from -4 to 4. We created a coordinate matrix with meshgrid(), then squared the values of x and y, and finally, summed them up. This was then fed to the plot_surface function. The rstride and cstride parameters in simple terms help in sizing the cell on the surface.

Let's adjust the view using view_int. The following is the view at 0 degree elevation and 0...