Interactive Course

Working with Dates and Times in Python

Learn how to work with dates and times in Python.

4 hours
14 Videos
48 Exercises
3,219 Participants
4,100 XP

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Course Description

You'll probably never have a time machine, but how about a machine for analyzing time? As soon as time enters any analysis, things can get weird. It's easy to get tripped up on day and month boundaries, time zones, daylight saving time, and all sorts of other things that can confuse the unprepared. If you're going to do any kind of analysis involving time, you’ll want to use Python to sort it out. Working with data sets on hurricanes and bike trips, we’ll cover counting events, figuring out how much time has elapsed between events and plotting data over time. You'll work in both standard Python and in Pandas, and we'll touch on the dateutil library, the only timezone library endorsed by the official Python documentation. After this course, you'll confidently handle date and time data in any format like a champion.

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.

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.

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.

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.

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Dates and Calendars
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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.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now
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.
View Chapter Details Play Chapter Now

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Max Shron

Data Scientist and Author

Max Shron is a Data Scientist and author. Prior to his role as Director of Data at Warby Parker, he founded the consultancy Polynumeral, whose clients ranged from NGOs to the Fortune 500. He was the first Data Scientist at OkCupid, where he helped create many popular blog posts. He wrote Thinking with Data (O'Reilly 2015), a book learning from design and rhetoric to help data scientists solve the right problems.

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Working with Dates and Times in Python

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Course Description

Dates and Calendars

Dates in Python

Which day of the week?

How many hurricanes come early?

Math with dates

Subtracting dates

Counting events per calendar month

Putting a list of dates in order

Turning dates into strings

Printing dates in a friendly format

Representing dates in different ways

Time Zones and Daylight Saving

UTC offsets

Creating timezone aware datetimes

Setting timezones

What time did the bike leave in UTC?

Time zone database

Putting the bike trips into the right time zone

What time did the bike leave? (Global edition)

Starting daylight saving time

How many hours elapsed around daylight saving?

March 29, throughout a decade

Ending daylight saving time

Finding ambiguous datetimes

Cleaning daylight saving data with fold

Combining Dates and Times

Dates and times

Creating datetimes by hand

Counting events before and after noon

Printing and parsing datetimes

Turning strings into datetimes

Parsing pairs of strings as datetimes

Recreating ISO format with strftime()

Unix timestamps

Working with durations

Turning pairs of datetimes into durations

Average trip time

The long and the short of why time is hard

Easy and Powerful: Dates and Times in Pandas

Reading date and time data in Pandas

Loading a csv file in Pandas

Making timedelta columns

Summarizing datetime data in Pandas

How many joyrides?

It's getting cold outside, W20529

Members vs casual riders over time

Combining groupby() and resample()

Additional datetime methods in Pandas

Timezones in Pandas

How long per weekday?

How long between rides?

Wrap-up