How to draw lines, great circles, and contours on maps in Python.
Plotly Studio: Transform any dataset into an interactive data application in minutes with AI. Sign up for early access now.
In [1]:
import plotly.express as px
df = px.data.gapminder().query("year == 2007")
fig = px.line_geo(df, locations="iso_alpha",
color="continent", # "continent" is one of the columns of gapminder
projection="orthographic")
fig.show()
Lines on Maps from GeoPandas¶
Given a GeoPandas geo-data frame with linestring or multilinestring features, one can extra point data and use px.line_geo().
In [2]:
import plotly.express as px
import geopandas as gpd
import shapely.geometry
import numpy as np
import wget
# download a zipped shapefile
wget.download("https://plotly.github.io/datasets/ne_50m_rivers_lake_centerlines.zip")
# open a zipped shapefile with the zip:// pseudo-protocol
geo_df = gpd.read_file("zip://ne_50m_rivers_lake_centerlines.zip")
lats = []
lons = []
names = []
for feature, name in zip(geo_df.geometry, geo_df.name):
if isinstance(feature, shapely.geometry.linestring.LineString):
linestrings = [feature]
elif isinstance(feature, shapely.geometry.multilinestring.MultiLineString):
linestrings = feature.geoms
else:
continue
for linestring in linestrings:
x, y = linestring.xy
lats = np.append(lats, y)
lons = np.append(lons, x)
names = np.append(names, [name]*len(y))
lats = np.append(lats, None)
lons = np.append(lons, None)
names = np.append(names, None)
fig = px.line_geo(lat=lats, lon=lons, hover_name=names)
fig.show()
Lines on Maps with plotly.graph_objects¶ US Flight Paths Map¶
In [3]:
import plotly.graph_objects as go
import pandas as pd
df_airports = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df_airports.head()
df_flight_paths = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')
df_flight_paths.head()
fig = go.Figure()
fig.add_trace(go.Scattergeo(
locationmode = 'USA-states',
lon = df_airports['long'],
lat = df_airports['lat'],
hoverinfo = 'text',
text = df_airports['airport'],
mode = 'markers',
marker = dict(
size = 2,
color = 'rgb(255, 0, 0)',
line = dict(
width = 3,
color = 'rgba(68, 68, 68, 0)'
)
)))
flight_paths = []
for i in range(len(df_flight_paths)):
fig.add_trace(
go.Scattergeo(
locationmode = 'USA-states',
lon = [df_flight_paths['start_lon'][i], df_flight_paths['end_lon'][i]],
lat = [df_flight_paths['start_lat'][i], df_flight_paths['end_lat'][i]],
mode = 'lines',
line = dict(width = 1,color = 'red'),
opacity = float(df_flight_paths['cnt'][i]) / float(df_flight_paths['cnt'].max()),
)
)
fig.update_layout(
title_text = 'Feb. 2011 American Airline flight paths<br>(Hover for airport names)',
showlegend = False,
geo = dict(
scope = 'north america',
projection_type = 'azimuthal equal area',
showland = True,
landcolor = 'rgb(243, 243, 243)',
countrycolor = 'rgb(204, 204, 204)',
),
)
fig.show()
Performance improvement: put many lines in the same trace¶
For very large amounts (>1000) of lines, performance may become critical. If you can relinquish setting individual line styles (e.g. opacity), you can put multiple paths into one trace. This makes the map render faster and reduces the script execution time and memory consumption.
Use None between path coordinates to create a break in the otherwise connected paths.
In [4]:
import plotly.graph_objects as go
import pandas as pd
df_airports = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')
df_airports.head()
df_flight_paths = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_aa_flight_paths.csv')
df_flight_paths.head()
fig = go.Figure()
fig.add_trace(go.Scattergeo(
locationmode = 'USA-states',
lon = df_airports['long'],
lat = df_airports['lat'],
hoverinfo = 'text',
text = df_airports['airport'],
mode = 'markers',
marker = dict(
size = 2,
color = 'rgb(255, 0, 0)',
line = dict(
width = 3,
color = 'rgba(68, 68, 68, 0)'
)
)))
lons = []
lats = []
import numpy as np
lons = np.empty(3 * len(df_flight_paths))
lons[::3] = df_flight_paths['start_lon']
lons[1::3] = df_flight_paths['end_lon']
lons[2::3] = None
lats = np.empty(3 * len(df_flight_paths))
lats[::3] = df_flight_paths['start_lat']
lats[1::3] = df_flight_paths['end_lat']
lats[2::3] = None
fig.add_trace(
go.Scattergeo(
locationmode = 'USA-states',
lon = lons,
lat = lats,
mode = 'lines',
line = dict(width = 1,color = 'red'),
opacity = 0.5
)
)
fig.update_layout(
title_text = 'Feb. 2011 American Airline flight paths<br>(Hover for airport names)',
showlegend = False,
geo = go.layout.Geo(
scope = 'north america',
projection_type = 'azimuthal equal area',
showland = True,
landcolor = 'rgb(243, 243, 243)',
countrycolor = 'rgb(204, 204, 204)',
),
height=700,
)
fig.show()
London to NYC Great Circle¶
In [5]:
import plotly.graph_objects as go
fig = go.Figure(data=go.Scattergeo(
lat = [40.7127, 51.5072],
lon = [-74.0059, 0.1275],
mode = 'lines',
line = dict(width = 2, color = 'blue'),
))
fig.update_layout(
title_text = 'London to NYC Great Circle',
showlegend = False,
geo = dict(
resolution = 50,
showland = True,
showlakes = True,
landcolor = 'rgb(204, 204, 204)',
countrycolor = 'rgb(204, 204, 204)',
lakecolor = 'rgb(255, 255, 255)',
projection_type = "equirectangular",
coastlinewidth = 2,
lataxis = dict(
range = [20, 60],
showgrid = True,
dtick = 10
),
lonaxis = dict(
range = [-100, 20],
showgrid = True,
dtick = 20
),
)
)
fig.show()
In [6]:
import plotly.graph_objects as go
import pandas as pd
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/globe_contours.csv')
df.head()
scl = ['rgb(213,62,79)', 'rgb(244,109,67)', 'rgb(253,174,97)', \
'rgb(254,224,139)', 'rgb(255,255,191)', 'rgb(230,245,152)', \
'rgb(171,221,164)', 'rgb(102,194,165)', 'rgb(50,136,189)'
]
n_colors = len(scl)
fig = go.Figure()
for i, (lat, lon) in enumerate(zip(df.columns[::2], df.columns[1::2])):
fig.add_trace(go.Scattergeo(
lon = df[lon],
lat = df[lat],
mode = 'lines',
line = dict(width = 2, color = scl[i % n_colors]
)))
fig.update_layout(
title_text = 'Contour lines over globe<br>(Click and drag to rotate)',
showlegend = False,
geo = dict(
showland = True,
showcountries = True,
showocean = True,
countrywidth = 0.5,
landcolor = 'rgb(230, 145, 56)',
lakecolor = 'rgb(0, 255, 255)',
oceancolor = 'rgb(0, 255, 255)',
projection = dict(
type = 'orthographic',
rotation = dict(
lon = -100,
lat = 40,
roll = 0
)
),
lonaxis = dict(
showgrid = True,
gridcolor = 'rgb(102, 102, 102)',
gridwidth = 0.5
),
lataxis = dict(
showgrid = True,
gridcolor = 'rgb(102, 102, 102)',
gridwidth = 0.5
)
)
)
fig.show()
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(debug=True, use_reloader=False) # Turn off reloader if inside Jupyter
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4