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

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.

import pandas as pd df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv') import plotly.express as px fig = px.density_map(df, lat='Latitude', lon='Longitude', z='Magnitude', radius=10, center=dict(lat=0, lon=180), zoom=0, map_style="open-street-map") fig.show()

Python > Figure Reference > heatmap Traces · A plotly.graph_objects.Heatmap trace is a graph object in the figure's data list with any of the named arguments or attributes listed below.

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).

] 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)

July 24, 2024 - The .Heatmap() function in Plotly is utilized to generate heatmap visualizations, which are graphical representations of data where the individual values contained in a matrix are represented as colors.

February 2, 2024 - This article teaches you to create a heatmap using the imshow() and Heatmap() function of Plotly in Python.

January 5, 2026 - How to make heatmaps in Python (examples) - Install and import plotly - Basic plotly heatmap - Customizing axes and labels on heatmap

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.

Plotly Graph Objects is a Python library that provides a flexible and powerful way to create interactive data visualizations. It is part of the larger Plotly ecosystem, which includes Plotly Express and Plotly.py.

September 5, 2020 - import plotly.figure_factory as ff import numpy as np feature_x = np.arange(0, 10, 2) feature_y = np.arange(0, 10, 3) # Creating 2-D grid of features [X, Y] = np.meshgrid(feature_x, feature_y) Z = np.cos(X / 2) + np.sin(Y / 4) fig = ff.create_annotated_heatmap(Z, colorscale='rainbow') fig.show()

January 22, 2025 - We can use the following code to create our heatmap with Plotly Graph Objects.

October 8, 2022 - import pandas as pd import plotly.express as px # Data with latitude/longitude and values df = pd.read_csv('https://raw.githubusercontent.com/R-CoderDotCom/data/main/sample_datasets/population_galicia.csv') fig = px.density_mapbox(df, lat = 'latitude', lon = 'longitude', z = 'tot_pob') radius = 8, center = dict(lat = 42.83, lon = -8.35), zoom = 6, mapbox_style = 'stamen-watercolor') fig.show() You can override the default color palette used for the spatial heatmap with the color_continuous_scale argument of the function.

August 17, 2023 - Plotly heatmap is a type of heatmap that can be generated using the Plotly library in Python. It offers a range of features that make it a powerful tool for creating complex heatmap visualizations.

September 5, 2020 - import plotly.graph_objects as go feature_x = np.arange(0, 50, 2) feature_y = np.arange(0, 50, 3) # Creating 2-D grid of features [X, Y] = np.meshgrid(feature_x, feature_y) Z = np.cos(X / 2) + np.sin(Y / 4) fig = go.Figure(data = go.Heatmap(x = feature_x, y = feature_y, z = Z,)) fig.show()

March 5, 2025 - While it is based on plotly.js which itself is a native Javascript binding, it has been expanded to support Python, R and other popular scripting languages. By employing a few lines of JSON in your Python script, you can easily invoke interactive visualizations including but not limited to line charts, histograms, radar plots, heatmaps and more.

The px.imshow() function can be used to display heatmaps (as well as full-color images, as its name suggests). It accepts both array-like objects like lists of lists and numpy or xarray arrays, as well as supported DataFrame objects.