How to create a 3D line plot with Plotly Express in Python

Plotly Express is a Python library that allows us to create line plots quickly and easily, with customizable parameters and an interactive interface.

A 3D line plot displays lines in a three-dimensional coordinate system, allowing us to visualize relationships or trends between three variables. To create a 3D line plot using Plotly Express, we typically need a dataset with three numerical variables: one for the x-axis, one for the y-axis, and one for the z-axis. Each combination of these variables represents a point in 3D space, and the lines are drawn to connect these points.

Features of the Plotly Express' 3D line plot

Some of the key features of a 3d line plot produced using the Plotly Express include:

• Interactive rotation and zooming: Plotly Express provides interactive controls to rotate and zoom into the 3D plot, allowing us to view it from different angles and examine details of interest.

• Hover information: When we move the cursor over a line segment in the 3D plot, Plotly Express displays information associated with that particular point, such as the coordinates or any other additional data we provide. This feature enables to explore the specific values at different locations in the plot.

• Customizable line properties: We can customize various aspects of the lines in the 3D plot, including color, thickness, and opacity. Plotly Express offers intuitive methods to change these properties based on different conditions or variables in the dataset.

• Marker customization: In addition to the lines, we can add markers to each point in the 3D plot. Plotly Express allows to customize the appearance of the markers, such as shape, size, and color. This can help highlight specific points or add additional information to the visualization.

• Multiple line plots: Plotly Express supports overlaying multiple line plots in the same 3D space. We can plot multiple lines with different colors or styles, making comparing and analyzing multiple datasets or variables easier.

• Data animation: Plotly Express allows the creation of animations with the 3D line plot. We can animate the lines based on a variable in the dataset, such as time, to visualize how the lines change throughout the animation.

• Plot customization: Plotly Express provides a wide range of options to customize the overall appearance of the plot. We can modify the axes labels, titles, tick marks, grid lines, background color, and overall layout.

• Export and sharing: Once we have created a 3D line plot, we can export it to various file formats, including static images (e.g., PNG, SVG) or interactive HTML files. This makes it easy to save and share visualizations with others.

Syntax

The line_3d function syntax typically follows this structure:

Parameters

When creating a 3D line plot using Plotly Express, we can pass several parameters to the px.line_3d() function to customize the plot. Here are some commonly used parameters:

• data_frame: This is the DataFrame or data source containing the data to be plotted.

• x, y, z: These are Strings specifying the column names or keys in the data_frame corresponding to the x, y, and z coordinates, respectively.

• color: This specifies the column name or key in the data_frame that determines the color of the lines. We can also pass a numeric array to assign a specific color to each line segment.

• line_group: This specifies the column name or key in the data_frame that determines the grouping of lines. Lines with the same line_group value will be connected together.

• line_dash: This specifies the dash style of the lines. It can be a string representing the dash pattern (e.g., 'dash', 'dot', 'dashdot') or a numeric array to create a custom dash pattern.

• line_width: This specifies the width of the lines.

• hover_name: This specifies the column name or key in the data_frame that provides the hover information for each line segment.

• labels: This is a dictionary specifying custom labels for the x, y, and z axes. For example, labels={'x': 'X-axis', 'y': 'Y-axis', 'z': 'Z-axis'}.

• title: This specifies the title of the plot.

• template: This specifies the template theme to use for the plot. Plotly Express provides several built-in templates like 'plotly', 'plotly_white', 'plotly_dark', etc.

Return type

The px.line_3d() function returns a Plotly figure object that can be displayed with fig.show(). The figure object contains all the information required to produce the 3D line plot, including the data, layout, and style.

Implementation

In the following playground, we create a 3D line plot using a sample dataset called "election" provided by Plotly Express. The used attributes (Joly, Coderre, and total) are defined as follows:

• Joly: This column represents the number of votes received by the candidate named Joly in each district. It contains the vote count for the specific candidate throughout the election.

• Coderre: This column represents the votes received by the candidate Coderre in each district. It contains the vote count for the specific candidate throughout the election.

• total: This column represents the total number of votes cast in each district, including votes for all candidates. It provides an overall count of the total votes in each district.

Code explanation

The code above is explained in detail below:

• Lines 2–3: We import the required libraries for the code: plotly.express as px for creating the box plot, and pandas as pd for handling data in a DataFrame.

• Line 6: We load the Plotly election dataset using the px.data.election() function and assigns it to the variable df. The df variable will now hold the dataset, allowing us to access and analyze its contents.

• Line 9: We print the first five rows of the loaded dataset. The head() function retrieves the top rows of the DataFrame and print() displays the result to the console. It helps to quickly inspect the data and verify its structure.

• Line 12: We create a 3D line plot using the px.line_3d() function from Plotly Express. It takes the df dataset as input and specifies the columns to be used as the plot's x, y, and z coordinates. In this case, the total column is used for the x-axis, Coderre column for the y-axis, and Joly column for the z-axis.

• Line 15: We update the attributes of the plot traces, which are the individual lines in the 3D line plot. Here, we set the line_color to red and the line _width to 2 units. This customization is optional and can be adjusted according to the preferences.

• Line 18: We display the plot using the fig.show() method, which shows the interactive plot.

Conclusion

The 3D line plot functionality of Plotly Express offers a powerful and user-friendly tool for visualizing complex data in three dimensions. It enables the creation of interactive and dynamic plots that allow for exploring and analyzing trends and relationships between variables. With the ability to customize the plot appearance and the option to rotate and zoom in on the plot, users can easily gain insights and understand the data better. This feature is particularly beneficial for visualizing spatial data, time series, or any data with three continuous variables. Whether it's for scientific research, market analysis, or data presentation, the 3D line plot of Plotly Express proves to be a valuable and visually appealing tool for data visualization.