Python Data Science Handbook, 2nd Edition

Chapter 23. Working with Time Series

Pandas was originally developed in the context of financial modeling, so as you might expect, it contains an extensive set of tools for working with dates, times, and time-indexed data. Date and time data comes in a few flavors, which we will discuss here:

Timestamps

Particular moments in time (e.g., July 4, 2021 at 7:00 a.m.).

Time intervals and periods

A length of time between a particular beginning and end point; for example, the month of June 2021. Periods usually reference a special case of time intervals in which each interval is of uniform length and does not overlap (e.g., 24-hour-long periods comprising days).

Time deltas or durations

An exact length of time (e.g., a duration of 22.56 seconds).

This chapter will introduce how to work with each of these types of date/time data in Pandas. This is by no means a complete guide to the time series tools available in Python or Pandas, but instead is intended as a broad overview of how you as a user should approach working with time series. We will start with a brief discussion of tools for dealing with dates and times in Python, before moving more specifically to a discussion of the tools provided by Pandas. Finally, we will review some short examples of working with time series data in Pandas.

Dates and Times in Python

The Python world has a number of available representations of dates, times, deltas, and time spans. While the time series tools provided by Pandas tend to be the ...