Plotly.Express.Density

In this article, we will discover how we can use the density_heatmap() function from Plotly Express module to create the density heatmap plots.

Let’s dive in.

Function Syntax

The density_heatmap() function has a syntax as shown in the following:

The following is a list of the most useful parameters that you need to know when creating the density heatmaps using the density_heatmap() function:

data_frame – Specifies the data frame containing the column names used in the plot.
x – Sets the values used to position the marks along the x axis in the cartesian plane.
y – Sets the values used to position the marks along the y axis in the cartesian plane.
z – Positions the the marks along the z axis.
facet_row – Sets the values used to assign the marks to facetted subplots in the vertical direction.
facet_col – Sets the values used to assign the marks to facetted subplots along the horizontal direction.
orientation – Defines the orientation for the plot.
histfunc – Defines the aggregate function used in the plot.
title – Sets the title for the figure.
width/height – Defines the width and height of the resulting figure in pixels.

Practical Example

The following code illustrates how to create a density heatmap using the density_heatmap() function:

import plotly.express as px
df = px.data.iris()
fig = px.density_heatmap(df, x='sepal_length', y='sepal_width')
fig.show()

The previous code returns the density heatmap as shown in the following:

Setting the Number of Bins

We can specify the number of bins that we wish to display using the nbinsx and nbinsy parameters as shown in the following:

import plotly.express as px
df = px.data.iris()
fig = px.density_heatmap(df, x='sepal_length', y='sepal_width', nbinsx=30, nbinsy=30)
fig.show()

The resulting figure is as follows:

Adding Marginal Plots

You can add the marginal plots to a density heatmap using the marginal_x and marginal_y parameters as shown in the following:

import plotly.express as px
df = px.data.iris()
fig = px.density_heatmap(df, x='sepal_length', y='sepal_width', nbinsx=30, nbinsy=30, marginal_x='histogram', marginal_y='histogram')
fig.show()

The previous code adds the marginal histograms on both x and y axis of the density heatmap.

The resulting figure is as follows:

Specifying a Color Scale

We can also specify a desired colorscale for the heatmap using the color_continous_scale parameter as shown in the following:

fig = px.density_heatmap(df, x='sepal_length', y='sepal_width', nbinsx=30, nbinsy=30, marginal_x='histogram', marginal_y='histogram', color_continuous_scale=px.colors.sequential.Inferno)
fig.show()

Output Figure:

Creating Facetted Density Heatmap

You can also create the facetted density subplots using the facet_row and facet_col parameters as illustrated in the following code:

import plotly.express as px
df = px.data.tips()
fig = px.density_heatmap(df, x="total_bill", y="tip", facet_row="sex", facet_col="smoker")
fig.show()

Output Figure:

And that’s it.

Conclusion

This article explores how you can create the various types of density heatmaps using Plotly Express. Check the document for more.

Plotly.Express.Density_Heatmap