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Scatter Plots on Maps in Python

How to make scatter plots on maps in Python. Scatter plots on maps highlight geographic areas and can be colored by value.

Geographical Scatter Plot with px.scatter_geo

For data available as a tidy pandas DataFrame, use the Plotly Express function px.scatter_geo for a geographical scatter plot. The size argument is used to set the size of markers from a given column of the DataFrame.

In [1]:
import plotly.express as px
gapminder = px.data.gapminder().query("year == 2007")
fig = px.scatter_geo(gapminder, locations="iso_alpha",
                     size="pop", # size of markers, "pop" is one of the columns of gapminder
                     )
fig.show()

Customize geographical scatter plot

In [2]:
import plotly.express as px
gapminder = px.data.gapminder().query("year == 2007")
fig = px.scatter_geo(gapminder, locations="iso_alpha",
                     color="continent", # which column to use to set the color of markers
                     hover_name="country", # column added to hover information
                     size="pop", # size of markers
                     projection="natural earth")
fig.show()

U.S. Airports Map

Here we show how to use go.Scattergeo from plotly.graph_objects.

Simple U.S. Airports Map

In [3]:
import plotly.graph_objects as go

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df['text'] = df['airport'] + '' + df['city'] + ', ' + df['state'] + '' + 'Arrivals: ' + df['cnt'].astype(str)

fig = go.Figure(data=go.Scattergeo(
        lon = df['long'],
        lat = df['lat'],
        text = df['text'],
        mode = 'markers',
        marker_color = df['cnt'],
        ))

fig.update_layout(
        title = 'Most trafficked US airports<br>(Hover for airport names)',
        geo_scope='usa',
    )
fig.show()

Styled U.S. Airports Map

In [4]:
import plotly.graph_objects as go

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df['text'] = df['airport'] + '' + df['city'] + ', ' + df['state'] + '' + 'Arrivals: ' + df['cnt'].astype(str)


fig = go.Figure(data=go.Scattergeo(
        locationmode = 'USA-states',
        lon = df['long'],
        lat = df['lat'],
        text = df['text'],
        mode = 'markers',
        marker = dict(
            size = 8,
            opacity = 0.8,
            reversescale = True,
            autocolorscale = False,
            symbol = 'square',
            line = dict(
                width=1,
                color='rgba(102, 102, 102)'
            ),
            colorscale = 'Blues',
            cmin = 0,
            color = df['cnt'],
            cmax = df['cnt'].max(),
            colorbar_title="Incoming flights<br>February 2011"
        )))

fig.update_layout(
        title = 'Most trafficked US airports<br>(Hover for airport names)',
        geo = dict(
            scope='usa',
            projection_type='albers usa',
            showland = True,
            landcolor = "rgb(250, 250, 250)",
            subunitcolor = "rgb(217, 217, 217)",
            countrycolor = "rgb(217, 217, 217)",
            countrywidth = 0.5,
            subunitwidth = 0.5
        ),
    )
fig.show()

North American Precipitation Map

In [5]:
import plotly.graph_objects as go

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2015_06_30_precipitation.csv')

scl = [0,"rgb(150,0,90)"],[0.125,"rgb(0, 0, 200)"],[0.25,"rgb(0, 25, 255)"],\
[0.375,"rgb(0, 152, 255)"],[0.5,"rgb(44, 255, 150)"],[0.625,"rgb(151, 255, 0)"],\
[0.75,"rgb(255, 234, 0)"],[0.875,"rgb(255, 111, 0)"],[1,"rgb(255, 0, 0)"]

fig = go.Figure(data=go.Scattergeo(
    lat = df['Lat'],
    lon = df['Lon'],
    text = df['Globvalue'].astype(str) + ' inches',
    marker = dict(
        color = df['Globvalue'],
        colorscale = scl,
        reversescale = True,
        opacity = 0.7,
        size = 2,
        colorbar = dict(
            titleside = "right",
            outlinecolor = "rgba(68, 68, 68, 0)",
            ticks = "outside",
            showticksuffix = "last",
            dtick = 0.1
        )
    )
))

fig.update_layout(
    geo = dict(
        scope = 'north america',
        showland = True,
        landcolor = "rgb(212, 212, 212)",
        subunitcolor = "rgb(255, 255, 255)",
        countrycolor = "rgb(255, 255, 255)",
        showlakes = True,
        lakecolor = "rgb(255, 255, 255)",
        showsubunits = True,
        showcountries = True,
        resolution = 50,
        projection = dict(
            type = 'conic conformal',
            rotation_lon = -100
        ),
        lonaxis = dict(
            showgrid = True,
            gridwidth = 0.5,
            range= [ -140.0, -55.0 ],
            dtick = 5
        ),
        lataxis = dict (
            showgrid = True,
            gridwidth = 0.5,
            range= [ 20.0, 60.0 ],
            dtick = 5
        )
    ),
    title='US Precipitation 06-30-2015<br>Source: <a href="http://water.weather.gov/precip/">NOAA</a>',
)
fig.show()

Reference

See https://plot.ly/python/reference/#scattergeo and https://plot.ly/python/reference/#layout-geo for more information and chart attribute options!