Lesson 2: Python
If you’re not familiar with Python—or even if you are—it’s a good idea to start with this (rapid) introduction to Python. It will also give you a feel for Jupyter Notebook. To gain the most value from these example Notebooks, you should modify and run the cells yourself (and add your own cells).
You can view the Notebook in html here but we also strongly recommend working with our Notebooks locally by performing a git clone on
https://github.com/makeabilitylab/signals.git and running Jupyter Notebook on your system (see installation notes).
Additionally, for quick interactive access to editable versions, you can use: .
In the next lesson, you will learn about and use the NumPy scientific computing library for Python. NumPy provides a suite of methods to help process and analyze time-series data.
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