hexbin-mapbox.md

jupyter

jupytext kernelspec language_info plotly notebook_metadata_filter text_representation all extension format_name format_version jupytext_version .md markdown 1.2 1.4.2 display_name language name Python 3 python python3 codemirror_mode file_extension mimetype name nbconvert_exporter pygments_lexer version name version ipython 3 .py text/x-python python python ipython3 3.7.7 description display_as language layout name order page_type permalink thumbnail How to make a map with Hexagonal Binning of data in Python with Plotly. maps python base Hexbin Mapbox 14 u-guide python/hexbin-mapbox/ thumbnail/hexbin_mapbox.jpg

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.

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

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

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

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

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

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