pandas has extensive built-in capabilities to represent dates, time, and various intervals of time. Many of the calculations required to work with time-series data require both a richer and more accurate representation of the concepts of time than are provided in Python or NumPy.

To address this, pandas provides its own representations of dates, time, time intervals, and periods. The pandas implementations provide additional capabilities that are required to model time-series data. These include capabilities such as being able to transform data across different frequencies to change the frequency of sampled data and to apply different calendars to take into account things such as business days and holidays in financial calculations.

We will examine several of the common constructs in both Python and pandas to represent dates, time, and combinations of both, as well as intervals of time. There are many details to each of these, so here, we will focus...