Creating a density contour 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 density contour plot, also known as a contour density plot, is a graphical representation that displays the distribution of data points in a two-dimensional space. It is useful for visualizing the density or intensity of data points across different regions of the plot.

Features of the density contour plot

Some of the key features of a density contour plot include:

• Contour levels: Plotly Express automatically determines the contour levels based on the data distribution. It creates contour lines or filled contours to represent different density levels. The number of contour levels can be adjusted using the ncontours parameter.

• Color mapping: The color mapping in the density contour plot represents the intensity or density of data points. Plotly Express provides various color scales to choose from, including built-in color palettes like Viridis, Jet, Hot, and more. We can control the color scale using the color_continuous_scale parameter.

• Smoothing: Plotly Express uses a kernel density estimation algorithm to estimate the density at each point in the plot. We can adjust the smoothing of the density estimation using the contour_smoothing parameter. Higher values result in smoother contours, while lower values capture more local variations.

• Hover information: By default, the density contour plot displays hover information that shows the x and y coordinates and the estimated density value at a specific point. We can further customize the hover text and formatting using the hover_data and hover_name parameters.

• Axis labels and title: Plotly Express automatically generates axis labels based on the column names specified in the x and y parameters. We can customize the axis labels using the labels parameter. Additionally, we can set a title for the plot using the title parameter.

• Subplots and faceting: Plotly Express allows we to create subplots or facets based on categorical variables. We can use the facet_row and facet_col parameters to create separate density contour plots for different groups or categories within our data.

• Interactive features: The density contour plot created with Plotly Express is interactive by default. We can pan, zoom, and hover over the plot to explore specific data points and their associated values. Plotly Express also provides additional interactive features like range sliders and buttons to toggle different aspects of the plot.

Syntax

The density_contour function syntax typically follows this structure:

Parameters

The following are some of the parameters of the density_contour function:

• data_frame: Specifies the DataFrame or data source containing the data points.

• x: Specifies the column name or key representing the x-axis values of the data points.

• y: Specifies the column name or key representing the y-axis values of the data points.

• hover_data: Specifies additional data to display when hovering over the plot. We can provide a list of column names or keys from the DataFrame to show corresponding values when interacting with the plot.

• labels: Sets custom labels for the x and y axes.

• title: Sets the title of the plot.

• facet_row and facet_col: Allow faceting the plot based on categorical variables. We can specify one or more columns to create separate density contour plots for different groups or categories.

Return type

The px.density_contour() 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 density contour plot using a sample dataset called iris provided by Plotly Express. The used attributes (sepal_width and sepal_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.

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 violin plot and pandas as pd for handling data in a DataFrame.

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

• 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 in the console. It helps to quickly inspect the data and verify its structure.

• Line 12: We create the density contour plot using px.density_contour(). It specifies the DataFrame (data_frame=df) as the 'iris' dataset and the x-axis and y-axis columns as 'sepal_width' and 'sepal_length' respectively.

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

Conclusion

The density contour plot of Plotly Express is a powerful visualization tool that effectively represents data density and distribution in a two-dimensional space. By creating contours based on density levels, the plot provides insights into the concentration and intensity of data points across different regions. Thanks to its high-level interface and numerous customization options, Plotly Express makes density contour plots easy. Whether exploring patterns in scientific data or analyzing spatial distributions, density contour plots offer a clear and visually appealing way to understand the underlying patterns and relationships within a dataset.