Lines on Mapbox in Python

How to draw a line on Map in Python with Plotly.


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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.

Mapbox Access Token and Base Map Configuration

To plot on Mapbox maps with Plotly you may need a Mapbox account and a public Mapbox Access Token. See our Mapbox Map Layers documentation for more information.

To draw a line on your map, you either can use px.line_mapbox() in Plotly Express, or Scattermapbox traces. Below we show you how to draw a line on Mapbox using Plotly Express.

Lines on Mapbox maps using Plotly Express

In [1]:
import pandas as pd

us_cities = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv")
us_cities = us_cities.query("State in ['New York', 'Ohio']")

import plotly.express as px

fig = px.line_mapbox(us_cities, lat="lat", lon="lon", color="State", zoom=3, height=300)

fig.update_layout(mapbox_style="open-street-map", mapbox_zoom=4, mapbox_center_lat = 41,
    margin={"r":0,"t":0,"l":0,"b":0})

fig.show()

Lines on Mapbox maps from GeoPandas

Given a GeoPandas geo-data frame with linestring or multilinestring features, one can extra point data and use px.line_mapbox().

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_mapbox(lat=lats, lon=lons, hover_name=names,
                     mapbox_style="open-street-map", zoom=1)
fig.show()

Lines on Mapbox maps using Scattermapbox traces

This example uses go.Scattermapbox and sets the mode attribute to a combination of markers and line.

In [3]:
import plotly.graph_objects as go

fig = go.Figure(go.Scattermapbox(
    mode = "markers+lines",
    lon = [10, 20, 30],
    lat = [10, 20,30],
    marker = {'size': 10}))

fig.add_trace(go.Scattermapbox(
    mode = "markers+lines",
    lon = [-50, -60,40],
    lat = [30, 10, -20],
    marker = {'size': 10}))

fig.update_layout(
    margin ={'l':0,'t':0,'b':0,'r':0},
    mapbox = {
        'center': {'lon': 10, 'lat': 10},
        'style': "open-street-map",
        'center': {'lon': -20, 'lat': -20},
        'zoom': 1})

fig.show()

Reference

See function reference for px.(line_mapbox) or https://plotly.com/python/reference/scattermapbox/ for more information about mapbox and their 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