Creating parallel coordinates plots 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 parallel coordinates plot, also known as a parallel coordinates chart or parallel coordinates diagram, is a visualization technique used to display multivariate data. It is particularly useful when dealing with datasets that have numerous variables or dimensions.

Features of the parallel coordinates plot

Some of the key features of a parallel coordinates plot include:

• Easy creation: Plotly Express provides a high-level interface that simplifies the creation of Parallel Coordinates Plots. With just a few lines of code, you can create a basic plot and customize it further as needed.

• Interactive visualization: The generated plot is interactive by default. Users can hover over the lines to display tooltips with detailed information about the data points, including the values of each variable. This feature enables the exploration and analysis of the data.

• Color mapping: Using the parameter, you can assign colors to the lines in the plot based on a specific variable. This allows you to visually represent categorical or continuous variables, making identifying patterns or groupings within the data easier.

• Variable reordering: The order of the variables along the vertical axes can be rearranged. This feature helps organize the plot to emphasize certain patterns or relationships among the variables.

• Line smoothing: Parallel coordinates plots can become visually cluttered when dealing with large datasets or when variables have many unique values. Plotly Express provides the line_group parameter that allows you to group lines based on a specific variable and display them as smoothed curves, reducing the visual clutter.

• Dynamic axis scaling: The plot automatically scales the vertical axes based on the range of values for each variable. This ensures that all lines are visible and properly proportioned within the plot.

• Plot customization: Plotly Express offers various customization options to modify the appearance of the plot. You can change line colors, line widths, axis labels, plot titles, legend position, background color, and more using the update_* methods provided by Plotly Express.

• Layout customization: You can customize the overall layout of the figure using the update_layout method. This includes modifying the plot margins, adding annotations, setting the plot size, and adjusting other layout-related parameters.

• Exporting options: The resulting Parallel Coordinates Plot can be easily exported to different file formats, including HTML, PNG, SVG, and PDF, using the write_* methods provided by Plotly Express. This allows you to save and share the visualization with others.

Syntax

The parallel_coordinates function syntax typically follows this structure.

Parameters

The followings are the parameters of parallel_coordinates function:

• data_frame: The pandas DataFrame object containing the data.

• dimensions: A list of dimensions (variable names) that you want to include in the plot.

• color (optional): A variable used for color-coding the lines in the plot. It can represent a categorical or continuous variable.

• labels (optional): A dictionary specifying custom labels for the dimensions. It allows you to provide more descriptive names for the variables in the plot.

• line_group (optional): A variable used to group lines based on a specific category. The lines are then displayed as smoothed curves, reducing visual clutter.

• line_group_labels (optional): A dictionary specifying custom labels for the line groups defined by line_group.

• color_continuous_scale (optional): A string or color scale to represent continuous color mapping. It can be a predefined color scale or a custom-defined one.

• range_color (optional): A tuple specifying the minimum and maximum values for the color mapping. It is useful when mapping a continuous variable to colors.

• title (optional): The title of the plot.

• template (optional): The name of the template to be used for the plot. Plotly provides various templates with different styles and themes.

• labels (optional): A dictionary specifying custom axis labels for the dimensions.

• width (optional): The width of the lines in the plot.

• opacity (optional): The opacity of the lines in the plot (0.0 to 1.0).

• render_mode (optional): The rendering mode for the lines. It can be 'svg' (default) for vector graphics or 'webgl' for WebGL rendering.

Return type

The px.parallel_coordinates() 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 parallel coordinates plot using a sample dataset called iris provided by Plotly Express. Used attributes (sepal_width, sepal_length, petal_width, and petal_length) are defined as follows:

• sepal_width: This feature represents the width of the sepal, which is the outermost whorl of a flower. It is measured in centimeters.

• sepal_length: This feature represents the length of the sepal. It is also measured in centimeters.

• petal_width: This feature represents the width of the petal, which is the inner whorl of a flower. It is measured in centimeters.

• petal_length: This feature represents the length of the petal. It is also measured in centimeters.

Code explanation

The code above is explained in detail below:

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

• Line 6: Loads the iris dataset provided by Plotly Express into a pandas DataFrame called df. The px.data.iris() function retrieves the dataset.

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

• Line 12: We pass in the df DataFrame as the data source in parallel_coordinates function. The dimensions parameter specifies the columns (sepal_width, sepal_length, petal_width, petal_length) to include as dimensions in the plot. The color_continuous_scale parameter is used to set the color scale of the lines. In this example, we set it to a single color scale defined as [(0, 'blue')], where 0 is the minimum value in the color scale and 'blue' is the color assigned to it.

• Line 15: Display the plot using the fig.show() method, which shows the interactive plot.

Conclusion

The parallel coordinates plot in Plotly Express offers a powerful and intuitive way to visualize multivariate data. By representing each attribute as a separate axis and connecting the data points with lines, this plot provides a clear overview of the relationships and patterns among different variables. The flexibility to customize dimensions, color-coding, and other visual aspects makes it a versatile tool for data exploration and analysis. Whether used for identifying trends, comparing data points, or detecting outliers, the parallel coordinates plot in Plotly Express is valuable in gaining insights and communicating complex information effectively.