The fundamental task of data analysis is to generalize some trends and shared properties over a dataset of multiple—probably many—data points. Imagine how that would look in a standard Python distribution: you'll have a list of, say, Person objects, each with its own values. To run some aggregate statistics, we would have to loop over each object, pull its properties, and calculate the statistics. If we need to get a few measurements, the code will quickly grow large and unmaintainable.
Instead, many computations in data analysis can be vectorized. Here, vectorization is a fancy term for saying the same exact loops will be run in C, rather than Python, which speeds things up by a few orders of magnitude. At the same time, it means that we won't need to explicitly write those loops, making code cleaner and more readable...
