Working with Date and Time in Python
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, which offers efficient slicing, indexing, and time-based grouping of data.
Being familiar with manipulating the date and time in time-series data is an essential component of time-series analysis and modeling. This chapter provides 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 datetime, time, calendar, and zoneinfo. Additionally, there are other popular libraries in Python that further extend its capability to work with and manipulate the date and time, such as dateutil, pytz, and arrow, to name a few.
In this chapter, you will...
