Representation of dates, time, and intervals
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.
The datetime, day, and time objects
The datetime object is part of the datetime library and not a part of pandas. This class can be utilized to construct objects representing several common patterns such as a fixed point in time using a date and time, or simply a day without a time component, or a time without a date component.
The datetime objects do not have the accuracy needed...
