SciPy and NumPy

Chapter 5. Conclusion

5.1 Summary

This book is meant to help you as the reader to become familiar with SciPy and NumPy and to walk away with tools that you can use for your own research. The online documentation for SciPy and NumPy is comprehensive, and it takes time to sort out what you want from the packages. We all want to learn new tools and use them with as little time and effort possible. Hopefully, this book was able to do that for you.

We have covered how to utilize NumPy arrays for array indexing, math operations, and loading and saving data. With SciPy, we went over tools that are important for scientific research, such as optimization, interpolation, integration, clustering, statistics, and more. The bulk of the material we discussed was on SciPy since there are so many modules in it.

As a bonus, we learned about two powerful scikit packages. Scikit-image is a powerful package that extends beyond the imaging capabilities of SciPy. With scikit-learn, we demonstrated how to employ machine learning to solve problems that would have been otherwise tough to solve.

5.2 What’s Next?

You are now familiar with SciPy, NumPy, and two scikit packages. The functions and tools we covered should allow you to comfortably approach your research investigations with more confidence. Moreover, using these resources, you probably see new ways of solving problems that you were not aware of before. If you’re looking for more (e.g., indefinite integrals), then you should look for other packages. ...