Excelling at manipulating time-series data, pandas was created initially for use in finance, and from its inception, it has had facilities for managing complete date and time-series operations to handle complex financial scenarios. These capabilities have been progressively expanded and refined over all of its versions.

The representations of dates, times, and time intervals and periods provided by pandas, which are pandas's own, are above and beyond those provided in other Python frameworks such as SciPy and NumPy. The pandas implementations provide additional capabilities that are required to model time-series data, and to transform data across different frequencies, periods, and calendars for different organizations and financial markets.

Specific dates and times in pandas are represented using the pandas Timestamp class. Timestamp is based on NumPy's dtype datetime64 and has higher precision than Python's built-in datetime object. This increased precision...