In summary, as datasets become larger and larger, it becomes important to consider how to manage memory while processing data, and how to store data to make it easy to work with and retrieve. Sufficiently large datasets should be processed entry by entry and not as a whole in order to preserve memory. For datasets that will need to be accessed frequently, it can be helpful to store and retrieve the data through a local database instance. This concludes the third and final section of Practical Data Wrangling! Congratulations!

In this book, I've made an effort to cover a wide range of approaches to data wrangling in order to give you the flexibility to tackle both standard and non-standard data wrangling challenges practically, efficiently, effectively, and with confidence. You should now have a broad and powerful set of tools that you can use to manipulate data and get the results that you need. 

Of course, not everything could fit in this book. As you approach various datasets in your work, you will certainly come across challenges that will go beyond the demonstrations used in the chapters of this book, and you will continue to learn new tools and approaches to meet those challenges. As you continue to learn, my hope is that this book will have provided you with a strong starting place, and I hope that you've come away with a new sense of your ability to tackle data wrangling challenges. :)