Density Heatmap in Python

How to make a density heatmap in Python with Plotly.

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Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Density map with `plotly.express`¶

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.

With px.density_map, each row of the DataFrame is represented as a point smoothed with a given radius of influence.

In [1]:

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()

Density map with `plotly.graph_objects`¶

If Plotly Express does not provide a good starting point, it is also possible to use the more generic go.Densitymap class from plotly.graph_objects.

In [2]:

import pandas as pd
quakes = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')

import plotly.graph_objects as go
fig = go.Figure(go.Densitymap(lat=quakes.Latitude, lon=quakes.Longitude, z=quakes.Magnitude,
                                 radius=10))
fig.update_layout(map_style="open-street-map", map_center_lon=180)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

Mapbox Maps¶

Mapbox traces are deprecated and may be removed in a future version of Plotly.py.

The earlier examples using px.density_map and go.Densitymap use Maplibre for rendering. These traces were introduced in Plotly.py 5.24. These trace types are now the recommended way to make tile-based density heatmaps. There are also traces that use Mapbox: density_mapbox and go.Densitymapbox.

To use these trace types, in some cases you may need a Mapbox account and a public Mapbox Access Token. See our Mapbox Map Layers documentation for more information.

Here's one of the earlier examples rewritten to use px.density_mapbox.

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_mapbox(df, lat='Latitude', lon='Longitude', z='Magnitude', radius=10,
                        center=dict(lat=0, lon=180), zoom=0,
                        mapbox_style="open-street-map")
fig.show()

Stamen Terrain base map with Mapbox (Stadia Maps token needed): density heatmap with `plotly.express`¶

Some base maps require a token. To use "stamen" base maps, you'll need a Stadia Maps token, which you can provide to the mapbox_accesstoken parameter on fig.update_layout. Here, we have the token saved in a file called .mapbox_token, load it in to the variable token, and then pass it to mapbox_accesstoken.

import plotly.express as px
import pandas as pd

token = open(".mapbox_token").read() # you will need your own token

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')

fig = px.density_mapbox(df, lat='Latitude', lon='Longitude', z='Magnitude', radius=10,
                        center=dict(lat=0, lon=180), zoom=0,
                        map_style="stamen-terrain")
fig.update_layout(mapbox_accesstoken=token)
fig.show()

Reference¶

See function reference for px.(density_map) or https://plotly.com/python/reference/densitymap/ for available attribute options.

For Mapbox-based maps, see function reference for px.(density_mapbox) or https://plotly.com/python/reference/densitymapbox/.

What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter

Density map with plotly.express¶

Density map with plotly.graph_objects¶