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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

How many joyrides?

Suppose you have a theory that some people take long bike rides before putting their bike back in the same dock. Let's call these rides "joyrides".

You only have data on one bike, so while you can't draw any bigger conclusions, it's certainly worth a look.

Are there many joyrides? How long were they in our data set? Use the median instead of the mean, because we know there are some very long trips in our data set that might skew the answer, and the median is less sensitive to outliers.

Instructions

100 XP
  • Create a Pandas Series which is True when Start station and End station are the same, and assign the result to joyrides.
  • Calculate the median duration of all rides.
  • Calculate the median duration of joyrides.