Note: PyScript is under active developement and something in this tutorial may change in the future. Please check documentation on pyscript.net for current updates
This workshop consists of 3 chapters. In each chapter, by walking through hands-on exercises, we can use PyScirpt to create data visualisations and deploy Sci-Kit learn models, which are some of the most common tasks in data sciences.
In this chapter, we will go through the basics of using PyScript and running Python in browsers. First, we will go through how to start using PyScript: to call in the pyscript.js script, prepare the python environment to run the Python code, and write a "hello world" program in Python.
With PyScript, fully customisable interactive visualisation can be created easily. In this chapter, we will do several exercises, first, we are going to generate a visualisation with Python. Then we start adding an interactive user interface and make the visual changes depending on the user input.
In this chapter, we will use a very simple machine learning example to try deploying a trained model with PyScript to the front end. Through the exercise, we will create a user interface that can accept input, pass it to the trained model that we loaded in via PyScript and provide an output back to the web page.
We expect no prior knowledge of PyScript, however, we expect participants are familiar with using Python and some data science libraries like Pandas, SciKit-learn and visualisation libraries like Matplotlib.
This workshop is for Pythonistas or Data Scientists who need to deploy data science projects or present data science findings. They may be interested in learning PyScript and seeing if it can help them with their work.
Contributors:
- Andrés Felipe Méndez @andresfelipemendez