Pandas was developed in the context of financial modeling, so as you might expect, it contains a fairly 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:
- Time stamps reference particular moments in time (e.g., July 4th, 2015 at 7:00am).
- Time intervals and periods reference a length of time between a particular beginning and end point; for example, the year 2015. 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 reference an exact length of time (e.g., a duration of 22.56 seconds).
In this section, we will introduce how to work with each of these types of date/time data in Pandas.
This short section 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.
After listing some resources that go into more depth, 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 timespans.
While the time series tools provided by Pandas tend to be the most useful for data science applications, it is helpful to see their relationship to other packages used in Python.
Native Python dates and times:
Python's basic objects for working with dates and times reside in the built-in
Along with the third-party
dateutil module, you can use it to quickly perform a host of useful functionalities on dates and times.
For example, you can manually build a date using the