lego-mindstorms VS PythonDataScienceHandbook

I use Python for programming the 51515 and in my experience, the best place to share code for projects is GitHub. You can take a look at my LEGO MINDSTORMS repo right here. A quick search in the site also shows other repositories. Since it is a relatively new product, its user base isn't as large as others (e.g., EV3 one) is, but I expect it to grow fast in the short future.

You can find the code for it here and a little video showing the functionality here.

You can see pictures of my boxes here

I thought the same. However, there is actually a way, although it is a bit more hidden and it isn't defined in the official documentation. In short, you can access that functionality not through MSHub, but through hub. Take a look at the example mshub_vs_hub, where I explain it in more detail. (If that link doesn't work, try this one then).

Python Data Science Handbook

Python Data Science Handbook — learn to use Python libraries such as NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools to effectively store, manipulate, and gain insight from data

I don't know what tools you will be using but if you will be using Python you can start with Python Data Science Handbook by Jake VanderPlas and Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting DataData Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data which gives a very good outlook on the data science and big data frame work. PS: Jake's book is also available as jupyter notebooks so you can read and run the code at the same time.

Python Data Science Handbook by Jake VanderPlas (https://jakevdp.github.io/PythonDataScienceHandbook/)

Otherwise you can work through courses on Datacamp, Coursera, Udemy, etc, or check out this book for a more general non-spatial perspective.

7. Data Science Handbook Are you looking for a comprehensive guide to data science with Python? Look no further than the Data Science Handbook by Jake VanderPlas. This repository contains the entire book, which introduces essential tools and techniques used in data science, including IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn. It’s a fantastic resource for anyone looking to deepen their understanding of data science concepts and best practices.