Monitor your internet with python

Joseph · 9 mins read · Discuss on Reddit
python data-visualization

Monitor your internet with python
I live in Germany and Germany is known for its diversity. The people are diverse, the culture is diverse and the quality of internet connections are equally as diverse.

Having spoken to my internet provider who insisted I had no problems with my internet connection I decided to write a program to monitor my upload and download speed.

Getting started

We will be using two cool packages in python namely speedtest and matplotlib. speedtest will provide us with the information about our internet connection and matplotlib will allow us to generate graphs based on this information.

Whether you’re in a virtual environment or not using one you will have to install speedtest and matplotlib.

pip install matplotlib
pip install speedtest-cli
We will also be using a few built in python packages datetime and csv. CSV (or comma-separated values) are a quick way of storing data. We will be using this to store the information about our internet speeds and then using matplotlib to make this information visual.

Gathering data

Create a python file called

and let’s put the following code on the inside

import speedtest
s = speedtest.Speedtest()
while True:
    print(, s.upload())
and we should be greeted by the following output before hitting ctrl + C to get out of this infinite loop.

75020309.22943133 24381170.373616524
105192450.00822952 40653433.153288215
So by creating a new instance of speedtest as s and testing the upload and download speed we are given the upload and download speed in bits per second. To convert this to megabits per second (Mb/s) we can do the following to include the time of the test too:

import speedtest
import datetime
import time
s = speedtest.Speedtest()

while True:
    time_now ="%H:%M:%S")
    downspeed = round((round( / 1048576), 2)
    upspeed = round((round(s.upload()) / 1048576), 2)
    print(f"time: {time_now}, downspeed: {downspeed} Mb/s, upspeed: {upspeed} Mb/s")
    # 60 seconds sleep

Note: Added a delay due to feedback received by an awesome Reddit user.

which yields to:

time: 12:44:15, downspeed: 95.04 Mb/s, upspeed: 32.85 Mb/s
time: 12:44:35, downspeed: 99.46 Mb/s, upspeed: 38.76 Mb/s
time: 12:44:56, downspeed: 100.59 Mb/s, upspeed: 38.94 Mb/s
Now we will move on to recording this in a CSV file. CSVs are large text files which values separated by commas. Here is an example of one of mine:

We start with a header row then have the information split up. We can think about this like an excel sheet:

a table with the same information as the code block above

In order to record into a csv file in python we need to import the CSV package and ‘open’ a CSV file (if one doesn’t exist it will create one).

import speedtest
import datetime
import csv
import time

s = speedtest.Speedtest()

with open('test.csv', mode='w') as speedcsv:
    csv_writer = csv.DictWriter(speedcsv, fieldnames=['time', 'downspeed', 'upspeed'])
    while True:
        time_now ="%H:%M:%S")
        downspeed = round((round( / 1048576), 2)
        upspeed = round((round(s.upload()) / 1048576), 2)
            'time': time_now,
            'downspeed': downspeed,
            "upspeed": upspeed
        # 60 seconds sleep
So while you let this code run for 4-5 minutes we can discuss what is going on. Line 7 with open essentiallly creates a csv file with the name test.csv with the headers name, downspeed and upspeed and writes them into the csv. Then the loop begins and every time a test is performed by speedtest it writes a new row into the csv with the time, download speed and upload speed we specified before. So let’s go and look at that now.

Cool now we are gathering information and storing it in a csv. You could do all sorts of clever stuff with the filename like name it today’s date using datetime but for now I will keep it as test.

You could also set up a check to see if a certain amount of time has passed and to stop the application after that but I will leave that up to your own creativity.

Visualising the data

Let’s make another python file to generate the graph of our internet connection. This is where we will use matplotlib.

import matplotlib.pyplot as plt
import csv
import matplotlib.ticker as ticker
times = []
download = []
upload = []

The easiest way to generate a graph is by using an array. In order to populate our arrays we will have to iterate through our new csv file

with open('test.csv', 'r') as csvfile:
    plots = csv.reader(csvfile, delimiter=',')
    for row in plots:

print(times, "\n", download, "\n", upload)
['12:51:16', '12:51:37', '12:51:57', '12:52:17', '12:52:38']
    [99.29, 100.67, 99.7, 92.89, 99.4]
    [38.66, 38.79, 38.79, 31.99, 38.96]
So now we are parsing our data! The next(csvfile) essentially skips the row of headers (that were for our benefit only, not python’s). Now we come on to using matplotlib which I am by no standards an expert on. Their documentation is extensive.

plt.figure(30, 30)
plt.plot(times, download, label='download', color='r')
plt.plot(times, upload, label='upload', color='b')
plt.ylabel('speed in Mb/s')
plt.title("internet speed")
plt.savefig('test_graph.jpg', bbox_inches='tight')
This code tells matplotlib or plt object to create a figure with the lines labelled ‘download’ and ‘upload’ to be plotted against their respective arrays. So for every item in the download and time array, a mark will be made on the graph against it. Finally the x and y-axis get labels and the graph gets saved.

A graph generated by python - showing a varying upload and download speed

Potential improvements

This is a short form of a project I recently finished which contains automated uploads to a cloud platform, schedules itself daily does some annotations of the lowest download speed. I initially made it to complain to my internet provider however I know that it will be like showing a graph to a brick wall.

showing a graph to a brick wall
Here is part II of the post where we send a tweet to the ISP telling them about our low internet speed.

If you have any questions about this tutorial please don’t hesitate to reach out to me.


About Author

Developer fluent in Rails, MERN and Python. Now learning Go and looking for a position in Berlin.

Please share your Feedback:

Did you enjoy reading or think it can be improved? Don’t forget to leave your thoughts in the comments section below! If you liked this article, please share it with your friends, and read a few more!