To begin to understand time-series data we need to first examine how pandas represents dates, time, and intervals of time. pandas provides extensive built-in facilities to represent these concepts as the representations of these concepts are not implemented by Python or NumPy robustly enough to handle the many concepts needed to process time-series data.
Some of the additional capabilities include being able to transform data across different frequencies and to apply different calendars to take into account things such as business days and holidays in financial calculations.
