Hexbin Mapbox in Python

How to make a map with Hexagonal Binning of data in Python with Plotly.

New to Plotly?

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.

Simple Count Hexbin¶

This page details the use of a figure factory. For more examples with Choropleth maps, see this page.

In order to use mapbox styles that require a mapbox token, set the token with plotly.express. You can also use styles that do not require a mapbox token. See more information on this page.

In [1]:

import plotly.figure_factory as ff
import plotly.express as px

px.set_mapbox_access_token(open(".mapbox_token").read())
df = px.data.carshare()

fig = ff.create_hexbin_mapbox(
    data_frame=df, lat="centroid_lat", lon="centroid_lon",
    nx_hexagon=10, opacity=0.9, labels={"color": "Point Count"},
)
fig.update_layout(margin=dict(b=0, t=0, l=0, r=0))
fig.show()

Count Hexbin with Minimum Count and Opacity¶

In [2]:

import plotly.figure_factory as ff
import plotly.express as px

px.set_mapbox_access_token(open(".mapbox_token").read())
df = px.data.carshare()

fig = ff.create_hexbin_mapbox(
    data_frame=df, lat="centroid_lat", lon="centroid_lon",
    nx_hexagon=10, opacity=0.5, labels={"color": "Point Count"},
    min_count=1,
)
fig.show()

Display the Underlying Data¶

In [3]:

import plotly.figure_factory as ff
import plotly.express as px

px.set_mapbox_access_token(open(".mapbox_token").read())
df = px.data.carshare()

fig = ff.create_hexbin_mapbox(
    data_frame=df, lat="centroid_lat", lon="centroid_lon",
    nx_hexagon=10, opacity=0.5, labels={"color": "Point Count"},
    min_count=1, color_continuous_scale="Viridis",
    show_original_data=True,
    original_data_marker=dict(size=4, opacity=0.6, color="deeppink")
)
fig.show()

Compute the Mean Value per Hexbin¶

In [4]:

import plotly.figure_factory as ff
import plotly.express as px
import numpy as np

px.set_mapbox_access_token(open(".mapbox_token").read())
df = px.data.carshare()

fig = ff.create_hexbin_mapbox(
    data_frame=df, lat="centroid_lat", lon="centroid_lon",
    nx_hexagon=10, opacity=0.9, labels={"color": "Average Peak Hour"},
    color="peak_hour", agg_func=np.mean, color_continuous_scale="Icefire", range_color=[0,23]
)
fig.show()

Compute the Sum Value per Hexbin¶

In [5]:

import plotly.figure_factory as ff
import plotly.express as px
import numpy as np

px.set_mapbox_access_token(open(".mapbox_token").read())
df = px.data.carshare()

fig = ff.create_hexbin_mapbox(
    data_frame=df, lat="centroid_lat", lon="centroid_lon",
    nx_hexagon=10, opacity=0.9, labels={"color": "Summed Car.Hours"},
    color="car_hours", agg_func=np.sum, color_continuous_scale="Magma"
)
fig.show()

Hexbin with Animation¶

In [6]:

import plotly.figure_factory as ff
import plotly.express as px
import numpy as np

px.set_mapbox_access_token(open(".mapbox_token").read())
np.random.seed(0)

N = 500
n_frames = 12
lat = np.concatenate([
    np.random.randn(N) * 0.5 + np.cos(i / n_frames * 2 * np.pi) + 10
    for i in range(n_frames)
])
lon = np.concatenate([
    np.random.randn(N) * 0.5 + np.sin(i / n_frames * 2 * np.pi)
    for i in range(n_frames)
])
frame = np.concatenate([
    np.ones(N, int) * i for i in range(n_frames)
])

fig = ff.create_hexbin_mapbox(
    lat=lat, lon=lon, nx_hexagon=15, animation_frame=frame,
    color_continuous_scale="Cividis", labels={"color": "Point Count", "frame": "Period"},
    opacity=0.5, min_count=1,
    show_original_data=True, original_data_marker=dict(opacity=0.6, size=4, color="deeppink")
)
fig.update_layout(margin=dict(b=0, t=0, l=0, r=0))
fig.layout.sliders[0].pad.t=20
fig.layout.updatemenus[0].pad.t=40
fig.show()

Reference¶

For more info on Plotly maps, see: https://plotly.com/python/maps.
For more info on using colorscales with Plotly see: https://plotly.com/python/heatmap-and-contour-colorscales/
For more info on ff.create_annotated_heatmap(), see the full function reference

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