Technical requirements
You can find all the code for this book on GitHub at the following link: https://github.com/PacktPublishing/Machine-Learning-for-Streaming-Data-with-Python. If you are not yet familiar with Git and GitHub, the easiest way to download the notebooks and code samples is the following:
- Go to the link of the repository.
- Go to the green Code button.
- Select Download ZIP:
Figure 1.1 – GitHub interface example
When you download the ZIP file, you unzip it in your local environment, and you will be able to access the code through your preferred Python editor.
Setting up a Python environment
To follow along with this book, you can download the code in the repository and execute it using your preferred Python editor.
If you are not yet familiar with Python environments, I would advise you to check out Anaconda (https://www.anaconda.com/products/individual), which comes with the Jupyter Notebook and JupyterLab, which are both great for executing notebooks. It also comes with Spyder and VSCode for editing scripts and programs.
If you have difficulty installing Python or the associated programs on your machine, you can check out Google Colab (https://colab.research.google.com/) or Kaggle Notebooks (https://www.kaggle.com/code), which both allow you to run Python code in online notebooks for free, without any setup to do.
Note
The code in the book will generally use Colab and Kaggle Notebooks with Python version 3.7.13 and you can set up your own environment to mimic this.
