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  3. Working with Dates and Times in Python
  • 1

    Dates and Calendars

    Free

    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.

Initializing

Exercise

Average trip time

W20529 took 291 trips in our data set. How long were the trips on average? We can use the built-in Python functions sum() and len() to make this calculation.

Based on your last coding exercise, the data has been loaded as onebike_durations. Each entry is a number of seconds that the bike was out of the dock.

Instructions

100 XP
  • Calculate total_elapsed_time across all trips in onebike_durations.
  • Calculate number_of_trips for onebike_durations.
  • Divide total_elapsed_time by number_of_trips to get the average trip length.