3D Scatter Plots in Matplotlib

Introduction

3D scatter plots are wonderful tools for exploring the relationship between dimensional data. In addition, they have been incredibly helpful in exploratory data analysis.

Each visualization created by Matplotlib comprises a Figure object and one or more Axes objects. The Axes objects are the data plots placed on the Figure object's canvas, which serves as the visualization's skeleton.

The ax.scatter3D() method of the matplotlib package is used to create a 3D scatter plot.

Matplotlib 3D Scatter Plot

To create a 3D scatter plot, we can use the matplotlib library's scatter3D() function, which accepts x, y, and z data sets.

Syntax

Parameters:

• x, y: float or array-like The data points.
• s: float or array-like, optional The marker size in points**2. The default is rcParams['lines.markersize'] ** 2.
• c: array-like or list of colors or color The marker colors
• Zdir : {'x', 'y', 'z', '-x', '-y', '-z'}, default is 'z' The axis direction for the zs.
• Depthshade: bool, default: True Whether to shade the scatter markers to give the appearance of depth.
• Data : indexable object, optional
• **kwargs : All other arguments

Code Example

Let's employ the ax.scatter3D() function to make a simple 3D scatter plot.

Output:

• Import the mplot3d toolkit and other libraries required for 3D plotting: NumPy and pandas, for data visualization: pyplot from matplotlib for plotting the data points.
• Define x,y, and z - data coordinates values for plotting.
• By using the scatter3D() method of the matplotlib library, plot the 3D scatter plot.
• We can display a plot on the output screen Using the show() method.

How to Customize 3D Scatter Plots in Matplotlib

Transform Your Career

Choose from our industry-leading programs designed for career success

NSDC Certified

Modern Software and AI Engineering Program

Master full-stack development with AI integration

12 MonthsDuration

AI-LedCurriculum

Career SupportSupport

+1000 more

Go to Program

NSDC Certified

Modern Data Science and ML with specialisation in AI

Advanced data science techniques with AI specialization

12 MonthsDuration

AI-LedCurriculum

Career SupportSupport

+1000 more

Go to Program

NSDC Certified

Advanced AIML with Specialisation in Agentic AI

Deep dive into AIML with focus on Agentic systems

12 MonthsDuration

AI-LedCurriculum

Career SupportSupport

+1000 more

Go to Program

NSDC Certified

DevOps, Cloud & AI Platform Engineering

Build and manage AI-powered cloud infrastructure

12 MonthsDuration

AI-LedCurriculum

Career SupportSupport

+1000 more

Go to Program

NSDC Certified

AI Engineering Advanced Certification by IIT-Roorkee

Premier AI engineering certification from IIT-Roorkee

3 MonthsDuration

AI-LedCurriculum

Career SupportSupport

Go to Program

Customize Color in 3D Scatter Plot

A 3D plot can benefit significantly from the addition of colour. This makes it possible for us to understand the third dimension better.

Colorbar

Color bars enhance the understanding of data plots by visually separating dimensions over a range of values.

Using the get_cmap() method, we can create a colormap and plot the colorbar.

Color by Value

By passing color as a parameter in the scatter3D() function, we can color the 3D scatter plots by values.

We must use the c parameter to do this. This function enables us to pass in a single colour or an array of numbers to colour based on value, if we wish to retain the same colour for all points.

Depthshade

Depthshade is a parameter that determines whether to shade the scatter markers to give the appearance of depth. Also, to turn off transparency in the 3D scatter plot, we can use the depthshade parameter.

By default, Matplotlib pyplot's 3D plots will have depthshade=True value. To turn the value off, set the parameter to False.

Background Color

By default, the background color of the pyplot is "White." However, the background color of the plot can be altered using the axis module's set_facecolor() method.

figure(facecolor=’color') method is used to change the outer background color of the pyplot.

For better understanding of these functions, let's take an example.

Output:

Customize Marker in a 3D Scatter Plot

Markers are points in a 3D scatter plot to represent the data points.

Marker size

The argument s in plt.scatter3D() corresponds to markersize and denotes the markersize**2.

The syntax to change the marker size is:

• s: specifies the marker size in points**2.
• marker: specifies the marker style.

Marker Color

To change the marker colour we simply have to pass color as an argument to the scatter3D(). We can also change the edge color of the marker by using edgecolor as an argument in the Scatter3D() method.

The syntax to change the color and edge color of the marker:

• color: specifies the color of the marker.
• edgecolor: specifies the edge color of the marker.

Example:

Output:

Customise 3D Scatter Plot Axis

Axis limits By default, the input values are used to set the range of values on the axes automatically.

Axis ticks We can modify the ticks for each axis of a 3D scatter plot with the following syntax.

set_zlabel() method

The set_zlabel() method is used to add a label to the z-axis of the plot.

Legend

Adding color alone does not make it possible to tell each of the data values apart. Instead, we can add a legend by passing in legend(True) in the scatter() function. By default, Matplotlib will configure the best location for placing the legend.

ax.legend() method is used to add a legend to the plot.

Label

ax.set_xlabel(), ax.set_ylabel(), and ax.set_zlabel() function is used to add labels to the plot. We pass X-axis, Y-axis, and Z-axis to respective functions.

Here is an example demonstrating the customization of the 3D Scatter plot axis.

The following output will be produced.

How to Rotate a 3D Scatter Plot

Change view angle

Sometimes, we need to visualize the 3D plot from a different perspective, and that's when rotating 3D plot comes into play.

The view_init() method changes the view angle of a 3D Scatter plot. To turn on the interactivity feature in the Jupyter notebook, use %matplotlib notebook.

The syntax to change the view angle is given below:

• elev: to specify the elevation angle in the z plane.
• azim: to specify the azimuth angle in the x,y plane.

Example:

Customise 3D Scatter Plot Title

The matplotlib.pyplot.title() method adds title to a 3D scatter plot.

Matplotlib 3D Scatter Text

Matplotlib's ax. text() method adds text to a 3D scatter plot at a fixed location. We can pass three locations (x, y, z ) where we want the text placed. With the annotate() function, we can specify both the point we want to label and the position for the label.

Matplotlib 3d Scatter with Line and Surface.

• To connect the 3D scatter points, we can use the ax.plot3D() method with x, y, and z data points.

Below is an example where we plot a 3D scatter graph with a line:

Output:

• A Surface Plot is a representation of a three-dimensional dataset. The ax.plot_surface() method is used to create 3D surface plots.

Matplotlib 3D Scatter Transparency

Matplotlib allows its users to customize the opacity of each of the data points. This is done using the alpha parameter. 0 represents full transparency, whereas 1 represents no transparency. By default, the alpha value is 1. It ranges between 0 to 1. We can decrease the transparency by reducing the value of alpha parameter.

Matplotlib 3D Scatter Grid

Sometimes it can be a little challenging to pinpoint the locations of data values with so much blank space around them. This is why using a grid in the plot may be helpful.

By default, all the plots will have gridlines when we plot a 3D scatter plot. Therefore, pass the value of grid() as 'False' to remove gridlines and pass the value 'True' to get it back.

Matplotlib 3D Scatter Size

There are two ways to change the size of the 3D scatter plot:

• Using "plt.figure()" method:

We can pass the figsize parameter to the plt.figure() method to change the size of the plot.

Here, w and h specify width and height, respectively.

• Using "set_size_inches()" method:

The set_size_inches() method sets the figure size in inches.

Syntax:

Axis Limit in 3D Scatter Plot

• To set the axes limits, we can use the xlim() and ylim() functions for the x and y axes, respectively.
• To set the limit of z-axis, we use the set_zlim() method

The minimum and maximum values of variables that will be plotted along the x, y, and z axes are determined by Matplotlib automatically. However, it is possible to set the limits explicitly using these functions.

Zoom Method in Matplotlib 3D Scatter Plot

The axis.zoom() function is used to zoom in or out on the axis.

Syntax:

• direction: This parameter is used to set the value to zoom in (direction > 0) or zoom out (direction <= 0).

Origin

To get the origin of the axes, we can use the np.zeros() method of the NumPy library.

Eg :

DataFrame

Using Pandas, we can create a data frame.

• After creating figures using the figure() method and defining projection in 3D, we have to create the pandas DataFrame. Then we use the np.random.rand() method.
• Using Pandas, we can produce a list of the axes of the data frame.
• Finally,we use the ax.scatter3D() method to get the required scatter plot.

Syntax and Parameters:

This is the general syntax for plotting data for a 3D data frame. Here,x,y, and z represent the data frame coordinates.

Now,let's go through an example,

Output:

Animation

Python, together with Matplotlib, allows for robust data visualization and exploration. In addition, the animation module works like magic for dynamic plotting.

Syntax:

Delve Deeper: Our Data Science Course is Your Next Step. Enroll Now and Transform Your Understanding into Practical Expertise.