How to work with Jupyter notebook

In this article, you will learn how to use Jupyter Notebook to analyse data persisted in the Quix platform

Why this is important

Although Quix is a realtime platform, to build realtime in-memory models and data processing pipelines, we need to understand data first. To do that, Quix offers a Data catalogue that makes data discovery and analysis so much easier.

Preparation

You’ll need some data stored in the Quix platform. You can use our Quick Start guide to do this using only the Quix portal.

You will also need Python 3 environment set up in your local environment.

Install required libraries

python3 -m pip install jupyter
python3 -m pip install requests
python3 -m pip install pandas

Run Jupyter notebook server

jupyter-notebook

Create a new notebook file

Run jupyter with the following command:

jupyter notebook

Then create a new Python3 notebook

new file

Connecting Jupyter notebook to Data Catalogue

The Quix web application has a python code generator to help you connect your Jupyter notebook with Quix.

To generate python code, follow those steps:
  1. Go to the platform

  2. Select workspace

  3. Go to the Data catalogue

  4. Select data to visualize

  5. Select parameters, events, aggregation and time range

  6. Press Connect button

  7. Select Python language

connect python

Copy Python code to your Jupyter notebook and execute.

jupyter results
If you want to use this generated code for a long time, replace the temporary token with PAT token. See authenticate your requests how to do that.

Too much data

If you find that the query results in more data than can be handled by Jupyter Notebooks try using the aggregation feature to reduce the amount of data returned.

For more info on aggregation check out these docs.

We’ve created a short video on it too