Three-dimensional Plotting in Python using Matplotlib

Matplotlib was introduced keeping in mind, only two-dimensional plotting. But at the time when the release of 1.0 occurred, the 3d utilities were developed upon the 2d and thus, we have 3d implementation of data available today! The 3d plots are enabled by importing the mplot3d toolkit. In this article, we will deal with the 3d plots using matplotlib.
Example:

Python3

import numpy as np
import matplotlib.pyplot as plt


fig = plt.figure()
ax = plt.axes(projection ='3d')

Output:

With the above syntax three -dimensional axes are enabled and data can be plotted in 3 dimensions. 3 dimension graph gives a dynamic approach and makes data more interactive. Like 2-D graphs, we can use different ways to represent 3-D graph. We can make a scatter plot, contour plot, surface plot, etc. Let’s have a look at different 3-D plots.

Plotting 3-D Lines and Points

Graph with lines and point are the simplest 3 dimensional graph. ax.plot3d and ax.scatter are the function to plot line and point graph respectively.
Example 1: 3 dimensional line graph

Python3

# importing mplot3d toolkits, numpy and matplotlib
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()

# syntax for 3-D projection
ax = plt.axes(projection ='3d')

# defining all 3 axis
z = np.linspace(0, 1, 100)
x = z * np.sin(25 * z)
y = z * np.cos(25 * z)

# plotting
ax.plot3D(x, y, z, 'green')
ax.set_title('3D line plot geeks for geeks')
plt.show()

Output:

Example 2: 3 dimensional scattered graph

Python3

# importing mplot3d toolkits
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()

# syntax for 3-D projection
ax = plt.axes(projection ='3d')

# defining axes
z = np.linspace(0, 1, 100)
x = z * np.sin(25 * z)
y = z * np.cos(25 * z)
c = x + y
ax.scatter(x, y, z, c = c)

# syntax for plotting
ax.set_title('3d Scatter plot geeks for geeks')
plt.show()

Output:

Plotting Surface graphs and Wireframes

Surface graph and Wireframes graph work on gridded data. They take grid value and plot it on three-dimensional surface.
Example 1: Surface graph

Python3

# importing libraries
from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt

# defining surface and axes
x = np.outer(np.linspace(-2, 2, 10), np.ones(10))
y = x.copy().T
z = np.cos(x ** 2 + y ** 3)

fig = plt.figure()

# syntax for 3-D plotting
ax = plt.axes(projection ='3d')

# syntax for plotting
ax.plot_surface(x, y, z, cmap ='viridis', edgecolor ='green')
ax.set_title('Surface plot geeks for geeks')
plt.show()

Output:

Example 2: Wireframes

Python3

from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt


# function for z axis
def f(x, y):
    return np.sin(np.sqrt(x ** 2 + y ** 2))

# x and y axis
x = np.linspace(-1, 5, 10)
y = np.linspace(-1, 5, 10)

X, Y = np.meshgrid(x, y)
Z = f(X, Y)

fig = plt.figure()
ax = plt.axes(projection ='3d')
ax.plot_wireframe(X, Y, Z, color ='green')
ax.set_title('wireframe geeks for geeks');

Output:

Plotting Contour Graphs

Contour graph takes all the input data in two-dimensional regular grids, and the Z data is evaluated at every point.We use ax.contour3D function to plot a contour graph.
Example:

Python3

from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt


# function for z axis
def f(x, y):
    return np.sin(np.sqrt(x ** 2 + y ** 3))

# x and y axis
x = np.linspace(-1, 5, 10)
y = np.linspace(-1, 5, 10)
X, Y = np.meshgrid(x, y)
Z = f(X, Y)

fig = plt.figure()
ax = plt.axes(projection ='3d')

# ax.contour3D is used plot a contour graph
ax.contour3D(X, Y, Z)

Output:

Plotting Surface Triangulations

The above graph is sometimes overly restricted and inconvenient. So by this method, we use a set of random draws. The function ax.plot_trisurf is used to draw this graph. It is not that clear but more flexible.
Example:

Python3

from mpl_toolkits import mplot3d
import numpy as np
import matplotlib.pyplot as plt


# angle and radius
theta = 2 * np.pi * np.random.random(100)
r = 6 * np.random.random(100)

# all three axes
x = np.ravel(r * np.sin(theta))
y = np.ravel(r * np.cos(theta))
z = f(x, y)

ax = plt.axes(projection ='3d')
ax.scatter(x, y, z, c = z, cmap ='viridis', linewidth = 0.25);

ax = plt.axes(projection ='3d')
ax.plot_trisurf(x, y, z, cmap ='viridis', edgecolor ='green');

Output:

Plotting Möbius strip

Möbius strip also called the twisted cylinder, is a one-sided surface without boundaries. To create the Möbius strip think about its parameterization, it’s a two-dimensional strip, and we need two intrinsic dimensions. Its angle range from 0 to 2 pie around the loop and width ranges from -1 to 1.
Example:

Python3

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Define the parameters of the Möbius strip
R = 2

# Define the Möbius strip surface
u = np.linspace(0, 2*np.pi, 100)
v = np.linspace(-1, 1, 100)
u, v = np.meshgrid(u, v)
x = (R + v*np.cos(u/2)) * np.cos(u)
y = (R + v*np.cos(u/2)) * np.sin(u)
z = v * np.sin(u/2)

# Create the plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# Plot the Möbius strip surface
ax.plot_surface(x, y, z, alpha=0.5)

# Set plot properties
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_title('Möbius Strip')

# Set the limits of the plot
ax.set_xlim([-3, 3])
ax.set_ylim([-3, 3])
ax.set_zlim([-3, 3])

# Show the plot
plt.show()

Output: