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How to create 3d scatter plot using Matplotlib?

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by sneha@openweaver.com dot icon Updated: Jul 12, 2023

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A scatter plot is a type of graph. It displays the relationship between two numerical variables. It consists of a horizontal x-axis representing one variable and a vertical y-axis. It represents another variable. Each data point is represented as a dot on the graph. It is its position determined by the values of the variables it represents.  

 

They help in analyzing data because they help examine the relationship between variables. They can reveal patterns, trends, and correlations. It might need to be clarified when looking at the data in a tabular form. They are used in statistics, data analysis, finance, and social sciences. Scatter plots can analyze relationships between various types of data. It includes numeric variables, categorical variables, and combinations of both.  

 

They provide a visual representation that helps identify patterns, trends, and correlations. It enables deeper insights into the data. Scatter plots offer various analyses that can gain insights from the data. It offers Descriptive Statistics, Correlation, and Regression Analysis to gain insights.  

 

These analysis techniques provide valuable insights into the data relationships depicted. They aid in understanding the data and drawing conclusions. It helps in making predictions and guiding further investigation or decision-making processes. To create a 3D scatter plot in Python, you can use Matplotlib. It provides a versatile set of functions for data visualization. Here's a step-by-step guide on creating a 3D scatter plot,  

  • Creating the data frame, plotting the data, and creating the axes. Make sure you have the necessary libraries installed. You can install Matplotlib using pip by running the command in the command prompt.  
  • Import the matplotlib.pyplot for plotting and NumPy for data generation. Add the following lines at the beginning of your Python script.  
  • Generate the data points that will be plotted on the 3D scatter plot. You can create the data using NumPy's random functions. Here's an example of generating random data for x, y, and z coordinates. 
  • Create a figure object and add a 3D subplot using the projection='3d' parameter. This will create a 3D coordinate system. 
  • Set the scatter () function to plot the data points on the 3D scatter plot. Select the x, y, and z coordinates as input parameters. You can customize the marker's appearance, such as size or color.  
  • You can customize plot aspects, including axis labels, titles, and viewing angles. Use the appropriate functions provided by Matplotlib to set these properties.  
  • Finally, use the plt.show() function to display the 3D scatter plot on your screen.  

 

When interpreting the results of a 3D scatter plot, there are several tips to consider. It helps identify patterns and make informed decisions. You can Visualize the Shape, Evaluate the Distribution, and Consider the Axis Values. You can Identify Outliers, Analyze Clusters or Groupings, and Consider the Visual Perspective. It can also Examine Variable Relationships, Cross-reference with Other Data, or Analysis.  


Here is an example of how to create a 3d scatter plot using Matplotlib.



Fig1: Preview of Output when the code is run in IDE.

Code


In this solution we're creating 3d scatter plot using Matplotlib.

Scatter plotting 3D Numpy array using matplotlib

PythondotLines of Code : 55dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
from matplotlib import pyplot as plt
from matplotlib.colors import BoundaryNorm
import numpy as np

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

num_layers = 75
num_pnt = 150
z, x, y = np.meshgrid(np.arange(1, num_layers + 1), np.arange(num_pnt), np.arange(num_pnt), indexing='ij')

# create some random test data, suppose all values outside a cone are zero
array = np.random.rand(num_layers, num_pnt, num_pnt) ** 2
array[(x - num_pnt / 2) ** 2 + (y - num_pnt / 2) ** 2 > (num_layers - z) ** 2] = 0

array[array == 0] = np.nan  # replace zeros by NaN to make them invisible

bounds = [0, 0.0001, 0.001, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 1.00]
norm = BoundaryNorm(bounds, len(bounds) - 1)
cmap = plt.get_cmap('turbo', len(bounds) - 1)
scat = ax.scatter(x, y, z, c=array, marker='o', cmap=cmap, norm=norm)
cbar = plt.colorbar(scat, ax=ax, ticks=bounds, format='%.4f')
plt.show()

from matplotlib import pyplot as plt
from matplotlib.colors import BoundaryNorm
from matplotlib.ticker import FuncFormatter
import numpy as np

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

num_layers = 75
num_pnt = 150
z, x, y = np.meshgrid(np.arange(1, num_layers + 1), np.arange(num_pnt), np.arange(num_pnt), indexing='ij')

# create some random test data, suppose all values outside a cone are zero
array = np.random.rand(num_layers, num_pnt, num_pnt) ** 2
array[np.abs((x - num_pnt / 2) ** 2 + (y - num_pnt / 2) ** 2 - (num_layers - z) ** 2) > 5] = 0

# make the arrays 1D, so they are easier to filter
array = array.ravel()
filter = array != 0
x = x.ravel()[filter]
y = y.ravel()[filter]
z = z.ravel()[filter]
array = array[filter]

bounds = [0, 0.0001, 0.001, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 1.00]
norm = BoundaryNorm(bounds, len(bounds) - 1)
cmap = plt.get_cmap('turbo', len(bounds) - 1)
scat = ax.scatter(x, y, z, c=array, marker='o', cmap=cmap, norm=norm)
cbar = plt.colorbar(scat, ax=ax, ticks=bounds, format=FuncFormatter(lambda x, pos: f'{x * 100:3g} %'))
plt.show()

Instructions

Follow the steps carefully to get the output easily.

  1. Install Jupyter Notebook on your computer.
  2. Open terminal and install the required libraries with following commands.
  3. Install Numpy - pip install numpy
  4. Install matplotlib - pip install matplotlib
  5. Copy the snippet using the 'copy' button and paste it into that file.
  6. Run the file using run button.


I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.


I found this code snippet by searching for "Create 3d scatter plot using Matplotlib" in kandi. You can try any such use case!

Dependent Libraries


matplotlibby matplotlib

Python doticonstar image 17559 doticonVersion:v3.7.1doticon
no licences License: No License (null)

matplotlib: plotting with Python

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            matplotlibby matplotlib

            Python doticon star image 17559 doticonVersion:v3.7.1doticonno licences License: No License

            matplotlib: plotting with Python
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                      numpyby numpy

                      Python doticonstar image 23755 doticonVersion:v1.25.0rc1doticon
                      License: Permissive (BSD-3-Clause)

                      The fundamental package for scientific computing with Python.

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                                numpyby numpy

                                Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

                                The fundamental package for scientific computing with Python.
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                                          You can also search for any dependent libraries on kandi like " numpy / matplotlib"

                                          Environment Tested


                                          I tested this solution in the following versions. Be mindful of changes when working with other versions.

                                          1. The solution is created in Python3.9.6.
                                          2. The solution is tested on numpy 1.21.5 version.


                                          Using this solution, we are able to create a 3d scatter plot using Matplotlib.


                                          This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us to create a 3d scatter plot using Matplotlib.

                                          Support


                                          1. For any support on kandi solution kits, please use the chat
                                          2. For further learning resources, visit the Open Weaver Community learning page.

                                          FAQ:  

                                          1. What plotting utilities are available for creating 3d scatter plots in Python?  

                                          In Python, several plotting utilities are available for creating 3D scatter plots. Here are some used libraries and their corresponding plotting utilities:  

                                          • Matplotlib  
                                          • Plotly  
                                          • Seaborn  
                                          • Mayavi  
                                          • Plotnine  

                                           

                                          2. How does Matplotlib's two-dimensional display help to create a 3d scatter plot?  

                                          Matplotlib provides utilities. It enables the creation of 3D scatter plots through its mplot3d toolkit. This toolkit extends the functionality to support three-dimensional plots, including 3D scatter plots.  

                                           

                                          To create a 3D scatter plot using Matplotlib, the Axes3D class from the mpl toolkits. mplot3d module is utilized. This class is inherited from the Axes class, which handles creating 2D plots. The Axes3D class adds extra functionality to handle the third dimension required.  

                                           

                                          By creating an instance of the Axes3D class, you can access methods specific to 3D plotting. One of the key methods is scatter (), which helps create a scatter plot in the 3D space.  

                                           

                                          3. What is the difference between Scatter Plots and Surface Plots?  

                                          Scatter plots and surface plots are both visualizations used in data analysis. But they represent data in different ways and serve different purposes. Here are the key differences between scatter plots and surface plots:  

                                          Scatter Plots:  

                                          • Data Representation: It displays individual data points as markers in a 2D or 3D space. Each point represents the values of two or three variables.  
                                          • Variable Relationships: Scatter plots visualize the relationship between two or three continuous variables. They help identify data patterns, trends, clusters, or correlations.  
                                          • Data Distribution: It provides insights into the distribution and spread of data. They can reveal anomalies and help assess the variability of data points.  
                                          • Marker Properties: It allows customization of marker properties. It can be color, size, shape, and transparency. These properties can represent extra categorical or numerical variables.  

                                          Surface Plots:  

                                          • Data Representation: It represents data as a continuous surface or a mesh grid. The surface is constructed by connecting data points. It creates a smooth representation of the underlying function or dataset.  
                                          • Variable Relationships: It visualizes the relationship between two continuous and dependent variables. It represents a third dimension. They visually represent how the Z variable changes on X and Y.  
                                          • Interpolation: It uses interpolation techniques to estimate the values between data points. It creates a smooth surface representation. This interpolation helps visualize the continuous nature of the underlying function or dataset.  
                                          • 3D Visualization: They are displayed in a 3D space. It examines the surface's shape, contours, and variations from different angles.  

                                           

                                          4. How can I use the NumPy library to create a 3d scatter plot in Python?  

                                          To create a 3D scatter plot using the NumPy library, you should use NumPy. It helps in data manipulation and Matplotlib for visualization. Here's a step-by-step guide:  

                                          • Ensure that you have NumPy and Matplotlib installed. You can install them using pip with the following command:  

                                          pip install NumPy matplotlib  

                                          • Import the required modules, including NumPy and Matplotlib's 3D toolkit (mplot3d)  
                                          • Create NumPy arrays representing your data points in three dimensions (X, Y, Z). You can use NumPy functions like random or linespace. It helps generate data or import data from an external source.  
                                          • Set up the figure and create a 3D subplot using the projection='3d' parameter. 
                                          • Use the scatter () function of the Axes3D object to create the 3D scatter plot. Provide the X, Y, and Z arrays as input.  
                                          • You can set labels, add titles, view angle adjusting, and market property changing.  
                                          • Use plt.show() to display the 3D scatter plot.  

                                           

                                          5. Can many subplots be included in one 3d scatter plot using Python?  

                                          Actually, including many subplots within a single 3D scatter plot is possible. Subplots create separate plots arranged in a grid or other configurations. It helps in the independent visualization and analysis of different datasets or variables. But subplots are designed for 2D plots. It does not extend to the 3D plotting capabilities of Matplotlib.  

                                           

                                          If you need to visualize many 3D scatter plots side by side, you can create separate subplots. Each of them will be displaying a 3D scatter plot. This way, you can have many 3D scatter plots within a single figure arranged in a grid or any desired layout. Each subplot can have its own data points, markers, labels, and customization. 

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