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At the core of time-series data is time. Time-series data is a sequence of observations or data points captured in successive order and at regular time intervals. In the context of a pandas DataFrame, time-series data has an ordered index of type DatetimeIndex, as you have seen in earlier chapters. The DatetimeIndex offers an easy and efficient slicing, indexing, and time-based grouping of data.
Being familiar with manipulating date and time in time-series data is an essential component of time series analysis and modeling. In this chapter, you will find recipes for common scenarios when working with date and time in time-series data.
Python has several built-in modules for working with date and time, such as the datetime, time, calendar, and zoneinfo modules. Additionally, there are other popular libraries in Python that further extend the capability to work with and manipulate date and time, such as dateutil, pytz, and arrow...
