Over 11 examples of Heatmaps including changing color, size, log axes, and more in Python.

var data = [ { z: [[1, 20, 30], [20, 1, 60], [30, 60, 1]], type: 'heatmap' } ]; Plotly.newPlot('myDiv', data); In this example we also show how to ignore hovertext when we have missing values in the data by setting the hoverongaps to False.

fig <- plot_ly(z = volcano, colors = "Greys", type = "heatmap") fig

# Load the plotly package library(plotly) # Data: mtcars: data <- as.matrix(mtcars) # basic heatmap p <- plot_ly(x=colnames(data), y=rownames(data), z = data, type = "heatmap") %>% layout(margin = list(l=120)) p # save the widget # ...

The following example uses this prompt structure with the delivery_location and factory_location columns from the built-in Plotly Studio dataset, with a count aggregation: Products by Factory and Delivery Location Chart: - Type: heatmap - X: Delivery location - Y: Factory location - Values: Count of products Data: - Aggregation: count by Factory location and Delivery location

import plotly.express as px import numpy as np np.random.seed(1) z = np.random.randn(20, 20) fig = px.imshow(z, text_auto=".2f", color_continuous_scale='Greys', aspect="auto") fig.show() px.imshow() can produce more elaborate annotated heatmaps. The example below renders a periodic table: the cell labels come from a custom text array, while customdata combined with a hovertemplate displays the element name and atomic mass on hover.

] harvest = np.array( [ [0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0], [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0], [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0], [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0], [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0], [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1], [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3] ] ) trace = go.Heatmap( x = vegetables, y = farmers, z = harvest, type = 'heatmap', colorscale = 'Viridis' ) data = [trace] fig = go.Figure(data = data) iplot(fig)

August 17, 2023 - Plotly Express is a high-level interface for Plotly that makes it easier to create complex visualizations with less code. It's particularly useful for creating heatmaps. Here's an example of how to create a heatmap with Plotly Express:

January 5, 2026 - Lastly, we used the update_xaxes() function to bring the X axis title to the top of the heatmap, by parsing “top” to the side = argument. Do you need more explanations on how to make plotly heatmaps in Python?

July 24, 2024 - This example creates a heatmap representing hypothetical activity levels during different times of the day across three days of the week.

Then, by default, the resulting heatmap will have N partitions along the y axis and M partitions along the x axis. In other words, the i-th row/ j-th column cell in z is mapped to the i-th partition of the y axis (starting from the bottom of the plot) and the j-th partition of the x-axis (starting from the left of the plot).

February 2, 2024 - We can use the imshow() function of plotly.express to create a heatmap of the given data. The imshow() function excepts only 2D data as input. For example, let’s create a 2D matrix and pass it inside the imshow() function.

Smith line and scatter plots · API Reference · All Namespaces · Summary: This example shows how to create heatmap charts in F#. Let's first create some data for the purpose of creating example charts: open Plotly.NET let matrix = [ [ 1.; 1.5; 0.7; 2.7 ]; [ 2.; 0.5; 1.2; 1.4 ]; [ 0.1; 2.6; 2.4; 3.0 ] ] let rownames = [ "p3"; "p2"; "p1" ] let colnames = [ "Tp0"; "Tp30"; "Tp60"; "Tp160" ] A heatmap chart can be created using the Chart.Heatmap functions.

Use with `tick0`. Must be a positive number, or special strings available to "log" and "date" axes. If the axis `type` is "log", then ticks are set every 10^(n"dtick) where n is the tick number. For example, to set a tick mark at 1, 10, 100, 1000, ...

After adding data, go to the 'Traces' section under the 'Structure' menu on the left-hand side. Choose the 'Type' of trace, then choose 'Heatmap' under 'Simple' chart type.

data_list[i].append(data[all_endpoints[i]][versions[j]]) layout = go.Layout( autosize=True, height=800, plot_bgcolor='rgba(249,249,249,1)', showlegend=False, title='Heatmap of hits per endpoint per version', xaxis=go.XAxis(title='Versions', type='category'), yaxis=dict(type='category', autorange='reversed'), margin=go.Margin( l=200 ) ) trace = go.Heatmap( z=data_list, x=versions, y=all_endpoints, colorscale=[[0, 'rgb(255, 255, 255)'], [0.01, 'rgb(240,240,240)'],[1, 'rgb(1, 1, 1)']], colorbar=dict( titleside='top', tickmode='array', tickvals=[1, 0], ticktext=['100%', '0%'], # ticks='outside' ) ) return plotly.offline.plot(go.Figure(data=[trace], layout=layout), output_type='div', show_link=False)

October 23, 2022 - import plotly.express as px # Sample data data = [[5, 23, 15, 47], [25, 5, 25, 12], [31, 35, 25, 41], [12, 18, 5, 7]] fig = px.imshow(data) fig.show() ... Note that you can make use of the zmin and zmax arguments to define the range of the heatmap.

January 22, 2025 - Plotly Graph Objects heatmap with added interactivity. This plot shows measurement locations (wells) and variations within our Acoustic Compressional Slowness.

August 16, 2023 - Plotly Express is a high-level interface for Plotly that makes it easier to create complex visualizations with less code. It's particularly useful for creating heatmaps. Here's an example of how to create a heatmap with Plotly Express: