A3D Scatter Plot is a mathematical diagram that visualizes data points in three dimensions, allowing us to observe relationships between three variables of a dataset.Matplotlib provides a built-in toolkit calledmplot3d, which enables three-dimensional plotting. To create a 3D Scatter Plot, we use theax.scatter3D() function from Matplotlib's mplot3d module. This function requires three sets of values—X, Y, and Z coordinates—to define the position of each point in the 3D space. Example:

The following example demonstrates how to create a simple 3D scatter plot using ax.scatter3D().

importnumpyasnpimportmatplotlib.pyplotaspltfrommpl_toolkits.mplot3dimportAxes3Dnp.random.seed(42)x=np.random.rand(50)y=np.random.rand(50)z=np.random.rand(50)# Create a figure and 3D axisfig=plt.figure(figsize=(8,6))ax=fig.add_subplot(111,projection='3d')# Create scatter plotax.scatter3D(x,y,z,color='red',marker='o')# Labelsax.set_xlabel('X Axis')ax.set_ylabel('Y Axis')ax.set_zlabel('Z Axis')ax.set_title('Basic 3D Scatter Plot')plt.show()

Output:

Explanation: In this example, we generate three sets of random data and use scatter3D() to visualize them in a 3D space. The points are marked in red with circular markers.

Installation and setup

Before proceeding, ensure you have Matplotlib installed. If not, install it using:

pip install matplotlib

Now, let's explore various examples to understand how 3D scatter plots work.

Example 1: 3D Scatter Plot with Color Mapping

To enhance visualization, we can use color mappingbased on the Z-values of the data points.

x=np.random.rand(100)y=np.random.rand(100)z=np.random.rand(100)colors=z# Color mapped to z-values# Create figure and 3D axisfig=plt.figure(figsize=(8,6))ax=fig.add_subplot(111,projection='3d')# Scatter plot with color mappingsc=ax.scatter3D(x,y,z,c=colors,cmap='viridis',marker='^')plt.colorbar(sc,ax=ax,label='Z Value')# Labelsax.set_xlabel('X Axis')ax.set_ylabel('Y Axis')ax.set_zlabel('Z Axis')ax.set_title('3D Scatter Plot with Color Mapping')plt.show()

Output:

Explanation:In this example, colors of the points are assigned based on the Z-values using the viridis colormap, making it easier to interpret variations in the dataset.

Example 2: 3D Scatter Plot with Different Markers and Sizes

To improve visualization, we can use different markers and vary the size of the points based on another dataset.

x=np.random.rand(100)y=np.random.rand(100)z=np.random.rand(100)sizes=100*np.random.rand(100)# Size of markerscolors=np.random.rand(100)# Color variation# Create figure and 3D axisfig=plt.figure(figsize=(8,6))ax=fig.add_subplot(111,projection='3d')# Scatter plot with varying marker size and colorssc=ax.scatter3D(x,y,z,s=sizes,c=colors,cmap='coolwarm',alpha=0.7,marker='D')plt.colorbar(sc,ax=ax,label='Color Mapping')# Labelsax.set_xlabel('X Axis')ax.set_ylabel('Y Axis')ax.set_zlabel('Z Axis')ax.set_title('3D Scatter Plot with Different Markers and Sizes')plt.show()

Output:

Explanation: Here, we adjust marker sizes randomly to improve visualization and use the coolwarm colormap to enhance the color distribution.

Example 3: Customization and additional features

This example demonstrates how to create a 3D surface plot using matplotlib and numpy while incorporating customization options to enhance visualization. The code plots a 3D function and applies various modifications, such as adjusting the viewing angle, enabling the grid and changing the background color.

importmatplotlib.pyplotaspltimportnumpyasnpfrommpl_toolkits.mplot3dimportAxes3Dfig=plt.figure()ax=fig.add_subplot(111,projection='3d')x=np.linspace(-5,5,100)y=np.linspace(-5,5,100)X,Y=np.meshgrid(x,y)Z=np.sin(np.sqrt(X**2+Y**2))# Plot surfaceax.plot_surface(X,Y,Z,cmap='viridis')# Customizationax.view_init(elev=30,azim=60)# Adjust view angleax.grid(True)# Add gridax.set_facecolor('lightgray')# Set background colorplt.show()

Output:

Explanation: This code creates a 3D surface plot using Matplotlib. It generates X, Y coordinates using meshgrid. The surface is plotted with aviridis colormap. Customizations include adjusting theviewing angle, enabling the grid and changing thebackground color for better visualization.

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