1. Congratulations!
Congratulations, you've now grappled with the nuts and bolts of iterators in Python Land and survived. You've even applied your newfound skills to an example from the world of big data: loading and working on a file chunk by chunk!
2. What’s next?
What's up next, I hear you ask? Well, that's a great question. We've got some of the most important Pythonista tools in store: comprehensions and generators. These terms may not mean much to you yet, but soon you won't be able to forget them. In fact, you may even want more because they are that useful. List comprehensions, for example, allow us to create lists from other lists or from columns of DataFrames, among many other objects. Why would we want to do this? Check this out, for example. Let's say that we have a DataFrame with a column of time-stamped data and we want to extract the year from it. We could extract the column from the DataFrame, loop over its entries using, for example, a for loop, and within the for loop, extract the desired information. In that case, we would have had to initialize an empty list in which to store the retrieved data. A list comprehension, however, allows us to perform this entire operation, in a single line of code and it's waaaaaaay more efficient than the equivalent for loop code. This is one of a number of Pythonista Data Science chops that you'll learn in the rest of this course.
3. Let's practice!
Moreover, the entire final chapter will be dedicated to using the skills you're developing to extract meaningful information from a real-world dataset, the World Bank World Development Indicators dataset which, according to the World Bank, 'presents the most current and accurate global development data available, and includes national, regional and global estimates.' See you in the next Chapter!