Get started with the MyGeotab API wrapper

Published on December 28, 2017 in Fleet Management by Jonathan Stolle |  4 minute read

Learn how to adapt the API for your business with this helpful MyGeotab API wrapper tutorial by Software Developer Jonathan Stolle.

Read this helpful MyGeotab API wrapper tutorial to learn how to adapt the API for your business.


On the MyGeotab SDK page, you might have seen many examples on how to perform useful operations on your database with the MyGeotab API. Examples include adding new groups to your database or fetching various data from the Geotab Data Feed for all of your devices.


At its core, the MyGeotab API allows the user to make http requests (using https) to communicate with your database. On the SDK page, there are many examples using .Net (usually with C#) or javascript (also here).


But what if you don’t use either of these? You can use http requests with a programming language of your choice, but there are a few other cases for which we have an API wrapper, one of these being for Python, which will be the focus of this post.

Getting Started

The examples are done using Python 2.7.9 (though most of the code should be compatible with Python 3.x) in Jupyter (formerly IPython). That being said, if you are experienced with Python and have your own favourite IDE, you can follow along with that instead. 


Refer to the MyGeotab SDK, SDK forum, or the mygeotab-python documentation to get more insight into using the api wrapper.

A First Code

Let’s start with a small code example involving retrieving data from your database. Start with installing mygeotab with the package manager, pip:


!pip install ––upgrade mygeotab


Import the libraries:


import mygeotab as mg


import getpass


Login to your database (you can specify the server in mg.API, but if you do not, you will be routed through to the server your database is on).  Note that once you have successfully logged in, the password will be replaced by a session_id:


# input() instead of raw_input() in Python 3


database = raw_input (“Database: ”)


username = raw_input (“Username: ”)


password = getpass.getpass(“Password: ”)


api = mg.API(database=database, username = username, password = password)




password = None


# After saving password, password is set to none, but session_id is set to authenticate calls


print(api.credentials.password == None)


print(api.credentials.session_id == None)






As a simple these you can get all devices and see how many devices are in the database and look at a specific property of a given device:


# Get all devices


devices =‘Get’, typeName = ‘Device’)




# Result is a list of objects


print(‘Number of properties in first device’,len(devices[0]))


print(‘Acceleration Warning Threshold’, devices[0][‘accelerationWarningThreshold’])




(‘Number of properties in first device’, 68)


(‘Acceleration Warning Threshold’, 16)

Handling Data

The pandas module is convenient for dealing with large amounts of data. Use pip to install it if you do not already have the module on your computer. The data can be viewed in tabular format and then the statistics of the data are shown.


In this example, engine speed status data is retrieved, partially displaced and the statistics of its data (in rpm) are shown below:


# Read in engine speed status data


params= {‘deviceSearch’,:{‘id’:‘bC6E’},‘diagnosticSearch’:{‘id’:‘DiagnosticEngineSpeedId’}, ‘fromDate’:‘2017-08-12T00:00:00.000Z’}

statusData = api.get(‘StatusData’, search = params, resultsLimit = 1250)






import pandas as pd


import numpy as np


#insert data to dataframe


timeseries_df = pd.DataFrame(statusData)





Click to view table larger.


# Clean up data; a further step could be to replace the device id with its name (in devices object)


timeseries_df[‘device’]=timeseries_df[‘device’].map(lambda s: s[‘id’])


timeseries_df[‘diagnostic’]=timeseries_df[‘diagnostic’].map(lambda s: s[‘id’])




# Summary statistics




count 1250.00000


mean 1552.34800


std 611.15978


min 0.00000


25% 987.50000


50% 1520.50000


75% 2052.00000


max 3690.00000


Name: data, dtype: float64

Plotting Results

Visualization is an important tool for understanding your data. Pandas has a convenient matplotlib interface (pylab), though there are many other good plotting libraries available in Python.


Below, I start with a naive plot, showing the data as is. Then I separate the data by day and look at the time in the local timezone to get a better visualization. Finally, I look at the data over a narrow time range to see exactly what it looks like for a few close together trips.


# ‘%matplotlib inline’ is a magic command for jupyter


import pylab


%matplotlib inline


# Make initial plot


timeseries_df.plot(title=‘Engine Speed (rpm) vs DateTime’,x=‘dateTime’,y=‘data’,figsize=(12,8),



<matplotlib.axes._subplots.AxesSubplot at 0x7f7886fd3ad0>




# Adjust plot time data to local time zone (from utc)


import datetime


from dateutil import tz




timeseries_df[‘dateTime’]=timeseries_df[‘dateTime’].map(lambda s: s.astimezone(tzlocal))


# To see data more clearly, separate by day


timeseries_df[‘date’]=timeseries_df[‘dateTime’].map(lambda s:


timeseries_df[‘time’]=timeseries_df[‘dateTime’].apply(lambda s: s.time)


# See data from multiple days


searchCondition = timeseries_df[‘date’].map(lambda s: (s >,8,11)) and s <,8,16))


pivot_df = pd.pivot_table(timeseries_df[searchCondition],


values=[‘data’], index=[‘time’], columns=[‘date’])




fig.set_ylabel(‘Engine Speed (rpm)’,{‘size’:20})


fig.set_xlabel(‘Time of day’,{‘size’:20})


<matplotlib.text.Text at 0x7f7886e09910>




# Zoom in on one part by changing axis limits


searchCondition = timeseries_df[‘date’].map(lambda s: (s >,8,12)) and s <,8,14))

pivot_df = pd.pivot_table(timeseries_df[searchCondition],


values=[‘data’], index=[‘time’], columns=[‘date’])




fig.set_ylabel(‘Engine Speed (rpm)’,{‘size’:20})


fig.set_xlabel(‘Time of day’,{‘size’:20})


fig.set_xlim(datetime.time(hour=11,minute=40),datetime.time(hour=12, minute = 0))

(42000.0, 43200.0)




Creating Your Own API Wrapper

Geotab has always embraced the open platform approach to allow users flexibility to make the best use of their own data. Many Geotab customers create their own SDK Add-Ins, which can be used by other customers (e.g., the Geotab Marketplace of business-specific solutions). Developing your own API wrapper (say for Java, C++, or another language) or contributing to an existing one is one way you can contribute to the Geotab ecosystem.


Here is an example of how to write one method (a credential-less api call) in Python, though similar functionality can be implemented in other languages. I return the version of MyGeotab on the server on which the database resides in the examples above:


import requests


def MakeCall (serverName, command, payload):


if not (‘https://’ in serverName):

serverName = ‘https://’ + serverName


return requests.get(serverName + ‘/apiv1/’ + command, data=payload).json()

MakeCall(api.credentials.server, ‘GetVersion’, {})

{u’jsonrpc’: u’2.0′, u’result’: u’5.7.1708.338′}

Allows Follow DEV Best Practices

When developing or using api wrappers, be sure to follow the best practices that have been established. For tips, see this collection of blog posts:

If you are just starting out, you might want to check out this Intro to the Geotab Software Development Kit and the DEV channel.


If you have more inquiries about mygeotab api wrappers, check out the SDK forum!

If you liked this post, let us know!


Geotab's blog posts are intended to provide information and encourage discussion on topics of interest to the telematics community at large. Geotab is not providing technical, professional or legal advice through these blog posts. While every effort has been made to ensure the information in this blog post is timely and accurate, errors and omissions may occur, and the information presented here may become out-of-date with the passage of time.

Get industry tips and insights

Sign up for monthly news and tips from our award-winning fleet management blog. You can unsubscribe at any time.

Republish this article for free

Other posts you might like

Two women in a business meeting sitting in front of charts and a laptop

Empowering Success: Unleashing the Domino Effect of Connected Women

Insights from Women in Fleet Leadership at Geotab Connect

February 16, 2024

Construction worker looking over at something

Routes to riches – Geotab Routing and Optimization drives operational efficiency and cost management

Geotab's Routing and Optimization software blends economic intelligence with operational strategy, reshaping fleet management for improved cost and resource efficiency.

February 15, 2024

Person standing behind a container with signal illustration

How asset tracking drives risk management and lower insurance costs

Learn how asset trackers improve risk management and reduce insurance costs in transportation and logistics, featuring expert insights from Marsh, a leader in insurance and risk advice.

February 15, 2024

multiple vehicles on the road

What is ADAS?

ADAS are in-vehicle technologies designed to enhance vehicle safety and assist the driver in better controlling the vehicle.

January 12, 2024