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Tesla Supercharging Stations in Python

How to plot car-travel routes between USA and Canada Telsa Supercharging Stations in Python.

New to Plotly?¶

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You can set up Plotly to work in online or offline mode, or in jupyter notebooks.
We also have a quick-reference cheatsheet (new!) to help you get started!

Version Check¶

Run pip install plotly --upgrade to update your Plotly version

In [1]:
import plotly

Mapbox Access Token¶

To plot on Mapbox maps with Plotly you'll need a Mapbox account and a Public Mapbox Access Token which you can add to your Plotly settings. If you're using a Plotly On-Premise server, please see additional instructions here:

Google Maps API¶

In order to use the Google Maps - Directions API, you need to create an account with Google and get your API key here.

In [2]:
import plotly.plotly as py
from plotly.graph_objs import *

import numpy as np
import requests
import copy
import googlemaps

# add your google maps api key here
my_google_maps_api_key = 'YOUR_API_KEY'

Parse Tesla Locations¶

Perform a GET request to retrieve the HTML of the Google Maps Page with all Tesla locations, then parse through to collect all the USA, Canada ones and store them in a dictionary. The dictionary is indexed by address and each address has a parameter for postal_code, country, latitude and longitude. Be Patient, it takes a while.

In [3]:
r = requests.get(',-50,42,-142,d?search=supercharger,&name=Canada')
r_copy = copy.deepcopy(r.text)
In [4]:
supercharger_locations = {}
params_for_locations = ['latitude":"', 'longitude":"']
location_param = 'location_id":"'

while True:
    # add address line to the dictionary
    index = r_copy.find(location_param)
    if index == -1:
    index += len(location_param)

    index_end = index
    while r_copy[index_end] != '"':
        index_end += 1
    address_line_1 = r_copy[index:index_end]
    address_line_1 = str(address_line_1)
    supercharger_locations[address_line_1] = {}

    for param in params_for_locations:
        index = r_copy.find(param)
        if index == -1:
        index += len(param)

        index_end = index
        while r_copy[index_end] != '"':
            index_end += 1
        supercharger_locations[address_line_1][param[0:-3]] = r_copy[index:index_end]

    r_copy = r_copy[index_end:len(r.text)]  # slice off the traversed code

all_keys = supercharger_locations.keys()

Table of Locations¶

Create a table with a sample of the supercharger_locations data.

In [5]:
import plotly.plotly as py
import plotly.figure_factory as ff

data_matrix = [['Location ID', 'Latitude', 'Longitude']]
first_ten_keys = supercharger_locations.keys()[0:10]

for key in first_ten_keys:
    row = [key,

table = ff.create_table(data_matrix)
py.iplot(table, filename='supercharger-locations-sample')

Plot the Route¶

The server_key should be replaced with your own Google Maps Directions API key.

Be careful! Make sure you are picking a start and end point that can be driven between, eg. both in the United States of America. Otherwise, the Google Maps API cannot comupute directions and will return an empty list.

In [6]:
def plot_route_between_tesla_stations(address_start, address_end, zoom=3, endpt_size=6):
    start = (supercharger_locations[address_start]['latitude'], supercharger_locations[address_start]['longitude'])
    end = (supercharger_locations[address_end]['latitude'], supercharger_locations[address_end]['longitude'])
    #start = address_start
    #end = address_end
    directions = gmaps.directions(start, end)
    steps = []
    steps.append(start)  # add starting coordinate to trip
    for index in range(len(directions[0]['legs'][0]['steps'])):
        start_coords = directions[0]['legs'][0]['steps'][index]['start_location']
        steps.append((start_coords['lat'], start_coords['lng']))

        if index == len(directions[0]['legs'][0]['steps']) - 1:
            end_coords = directions[0]['legs'][0]['steps'][index]['end_location']
            steps.append((end_coords['lat'], end_coords['lng']))

    steps.append(end)  # add ending coordinate to trip

    mapbox_access_token = "ADD_YOUR_TOKEN_HERE"

    data = Data([
            lat=[item_x[0] for item_x in steps],
            lon=[item_y[1] for item_y in steps],
                size=[endpt_size] + [4 for j in range(len(steps) - 2)] + [endpt_size]
    layout = Layout(
                lat=np.mean([float(step[0]) for step in steps]),
                lon=np.mean([float(step[1]) for step in steps]),

    fig = dict(data=data, layout=layout)
    return fig

server_key = my_google_maps_api_key
gmaps = googlemaps.Client(key=server_key)
address_start = supercharger_locations.keys()[0]
address_end = supercharger_locations.keys()[501] 

fig = plot_route_between_tesla_stations(address_start, address_end, zoom=10.2, endpt_size=20)
py.iplot(fig, filename='tesla-driving-directions-between-superchargers')


See to visualize Tesla supercharging stations with MYSQL and for more information on how to plot scatterplots on maps.

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