Due to its roots in finance, pandas excels in manipulating time-series data. Its abilities have been continuously refined over all of its versions to progressively increase its capabilities for time-series manipulation. These capabilities are the core of pandas and do not require additional libraries, unlike R, which requires the inclusion of Zoo to provide this functionality.

The core of the time-series functionality in pandas revolves around the use of specialized indexes that represent measurements of data at one or more timestamps. These indexes in pandas are referred to as DatetimeIndex objects. These are incredibly powerful objects, and their being core to pandas provides the ability to automatically align data based on dates and time, making working with sequences of data collected and time-stamped as easy as with any other type of indexes.

We will now examine how to create time-series data and DatetimeIndex objects both using explicit timestamp objects...