Course
Working with Dates and Times in Python
IntermediateSkill Level
Updated 11/2025PythonProgramming4 hr14 videos48 Exercises4,100 XP77,574Statement of Accomplishment
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Prerequisites
Data Manipulation with pandas1
Dates and Calendars
Hurricanes (also known as cyclones or typhoons) hit the U.S. state of Florida several times per year. To start off this course, you'll learn how to work with date objects in Python, starting with the dates of every hurricane to hit Florida since 1950. You'll learn how Python handles dates, common date operations, and the right way to format dates to avoid confusion.
2
Combining Dates and Times
Bike sharing programs have swept through cities around the world -- and luckily for us, every trip gets recorded! Working with all of the comings and goings of one bike in Washington, D.C., you'll practice working with dates and times together. You'll parse dates and times from text, analyze peak trip times, calculate ride durations, and more.
3
Time Zones and Daylight Saving
In this chapter, you'll learn to confidently tackle the time-related topic that causes people the most trouble: time zones and daylight saving. Continuing with our bike data, you'll learn how to compare clocks around the world, how to gracefully handle "spring forward" and "fall back," and how to get up-to-date timezone data from the dateutil library.
4
Easy and Powerful: Dates and Times in Pandas
To conclude this course, you'll apply everything you've learned about working with dates and times in standard Python to working with dates and times in Pandas. With additional information about each bike ride, such as what station it started and stopped at and whether or not the rider had a yearly membership, you'll be able to dig much more deeply into the bike trip data. In this chapter, you'll cover powerful Pandas operations, such as grouping and plotting results by time.
Working with Dates and Times in Python
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FAQs
What Python libraries are used in this course?
You will use the datetime module in standard Python, pandas for time-aware DataFrames, and the dateutil library for timezone handling.
What datasets does this course use?
You will work with data on hurricanes hitting Florida and bike trip records, using both to practice counting events and calculating elapsed time.
Is this course suitable for beginners?
This is an intermediate course. You should know pandas and basic Python before starting, as the exercises build on data manipulation fundamentals.
Does this course cover time zones and daylight saving time?
Yes. You will learn how to handle time zones and daylight saving time using the dateutil library, which is endorsed by the official Python documentation.
What will I be able to do after completing this course?
You will be able to parse dates in any format, calculate time differences, handle time zones, and plot data over time using both standard Python and pandas.
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