Quality means doing it right when no one is looking.
Our main goal with this book was to promote elegant uses of the NumPy and SciPy libraries. While teaching you how to do effective scientific analysis with SciPy, we hope to have inspired in you the feeling that quality code is something worth striving for.
Where to Next?
Now that you know enough SciPy to analyze whatever data gets thrown your way, how do you move forward? We said when we started that we couldn’t hope to cover all there is to know about the library and all its offshoots. Before we part ways, we want to point you to the many resources available to help you.
We mentioned in the preface that SciPy is a community. A great way to continue learning is to subscribe to the main mailing lists for NumPy, SciPy, pandas, Matplotlib, scikit-image, and other libraries you might be interested in, and read them regularly.
And when you do get stuck in your own work, don’t be afraid to seek help there! We are a friendly bunch! The main requirement when seeking help is to show that you’ve tried a bit of problem solving yourself, and to provide others with a minimal script and enough sample data to demonstrate your problem and how you’ve tried to fix it.
- No: “I need to generate a big array of random Gaussians. Can someone help?”
- No: “I have this huge library at https://github.com/ron_obvious. If you look in the statistics library, there’s a part that really needs random Gaussians. Can someone ...