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.
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.
Base Map Configuration¶
Plotly figures made with Plotly Express px.scatter_geo, px.line_geo or px.choropleth functions or containing go.Choropleth or go.Scattergeo graph objects have a go.layout.Geo object which can be used to control the appearance of the base map onto which data is plotted.
Geographical Scatter Plot with px.scatter_geo¶
Here we show 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.
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.
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = px.scatter_geo(df, locations="iso_alpha",
size="pop", # size of markers, "pop" is one of the columns of gapminder
)
fig.show()
Customize geographical scatter plot¶
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = px.scatter_geo(df, 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()
import plotly.express as px
import geopandas as gpd
geo_df = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
px.set_mapbox_access_token(open(".mapbox_token").read())
fig = px.scatter_geo(geo_df,
lat=geo_df.geometry.y,
lon=geo_df.geometry.x,
hover_name="name")
fig.show()
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¶
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¶
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 function reference for px.(scatter_geo) or https://plotly.com/python/reference/scattergeo/ and https://plotly.com/python/reference/layout/geo/ for more information and chart attribute options!
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
