Join Charles Kelly for an in-depth discussion in this video How to use the exercise files, part of NumPy Data Science Essential Training.
- [Instructor] If you have access to the exercise files for this course, you can download them to your desktop as I've done here. The exercise files for this course are Jupyter interactive notebooks. I'll explain how you can download and install software for writing to and reading from Jupyter Notebooks in the video titled "Installing Software." I'll explain how to use Jupyter Notebooks in the chapter titled "Jupyter Notebooks." The exercise files are contained in Starting and Finish folders.
For example, if you go to a chapter, and go to a video title, you'll see folders "Finish" and "Starting." The notebooks in the Starting folder contain import statements and small amounts of sample code. The notebooks in the Finish folder contain code that we develop within each video. I suggest that you open both the Starting and Finish code before you begin watching each video. You are welcome to type what you see me type into the cells within the notebook from the Starting folder, however, my preferred workflow, when learning new information from a notebook, is to open a new notebook, such as the ones in the Starting folder, and copy and paste information from the source notebook, such as those in the Finish folder, into my new notebook.
After I cut and paste code, I often change it a small amount and watch the change in the result, and compare it with the result from the original code. In my opinion, this is one of the many benefits of working with interactive notebooks as a learning tool. I say this from my perspective as a teacher and a lifelong learner. If you don't have access to the exercise files, that's okay, you can still follow along by watching how I use the interactive notebooks. Let's begin.
- Using Jupyter Notebook
- Creating NumPy arrays from Python structures
- Slicing arrays
- Using Boolean masking and broadcasting techniques
- Plotting in Jupyter notebooks
- Joining and splitting arrays
- Rearranging array elements
- Creating universal functions
- Finding patterns
- Building magic squares and magic cubes with NumPy and Python