3D Scatter Plotting in Python using Matplotlib

A 3D Scatter Plot is a mathematical diagram, the most basic version of three-dimensional plotting used to display the properties of data as three variables of a dataset using the cartesian coordinates.To create a 3D Scatter plot, Matplotlib’s mplot3d toolkit is used to enable three dimensional plotting.Generally 3D scatter plot is created by using ax.scatter3D() the function of the matplotlib library which accepts a data sets of X, Y and Z to create the plot while the rest of the attributes of the function are the same as that of two dimensional scatter plot.
Example 1: Let’s create a basic 3D scatter plot using the ax.scatter3D() function.

Python3

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


# Creating dataset
z = np.random.randint(100, size =(50))
x = np.random.randint(80, size =(50))
y = np.random.randint(60, size =(50))

# Creating figure
fig = plt.figure(figsize = (10, 7))
ax = plt.axes(projection ="3d")

# Creating plot
ax.scatter3D(x, y, z, color = "green")
plt.title("simple 3D scatter plot")

# show plot
plt.show()

Output :

Example 2 : For better understanding Let’s take another example.

Python3

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


# Creating dataset
z = 4 * np.tan(np.random.randint(10, size =(500))) + np.random.randint(100, size =(500))
x = 4 * np.cos(z) + np.random.normal(size = 500)
y = 4 * np.sin(z) + 4 * np.random.normal(size = 500)

# Creating figure
fig = plt.figure(figsize = (16, 9))
ax = plt.axes(projection ="3d")

# Add x, y gridlines
ax.grid(b = True, color ='grey',
        linestyle ='-.', linewidth = 0.3,
        alpha = 0.2)


# Creating color map
my_cmap = plt.get_cmap('hsv')

# Creating plot
sctt = ax.scatter3D(x, y, z,
                    alpha = 0.8,
                    c = (x + y + z),
                    cmap = my_cmap,
                    marker ='^')

plt.title("simple 3D scatter plot")
ax.set_xlabel('X-axis', fontweight ='bold')
ax.set_ylabel('Y-axis', fontweight ='bold')
ax.set_zlabel('Z-axis', fontweight ='bold')
fig.colorbar(sctt, ax = ax, shrink = 0.5, aspect = 5)

# show plot
plt.show()

Output :