Time series primer
In general, time series serve two purposes. First, they help us to learn about the underlying process that generated the data. On the other hand, we would like to be able to forecast future values of the same or related series using existing data. When we measure temperature, precipitation or wind, we would like to learn more about more complex things, such as weather or the climate of a region and how various factors interact. At the same time, we might be interested in weather forecasting.
In this chapter we will explore the time series capabilities of Pandas. Apart from its powerful core data structures – the series and the DataFrame – Pandas comes with helper functions for dealing with time related data. With its extensive built-in optimizations, Pandas is capable of handling large time series with millions of data points with ease.
We will gradually approach time series, starting with the basic building blocks of date and time objects.
