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Density heatmap in Python

How to make a density heatmap in Python with Plotly.

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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()

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 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()

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()

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

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