Members vs casual riders over time

Riders can either be "Members", meaning they pay yearly for the ability to take a bike at any time, or "Casual", meaning they pay at the kiosk attached to the bike dock.

Do members and casual riders drop off at the same rate over October to December, or does one drop off faster than the other?

As before, rides has been loaded for you. You're going to use the Pandas method .value_counts(), which returns the number of instances of each value in a Series. In this case, the counts of "Member" or "Casual".

This exercise is part of the course

Working with Dates and Times in Python

Exercise instructions

Set monthly_rides to be a resampled version of rides, by month, based on start date.
Use the method .value_counts() to find out how many Member and Casual rides there were, and divide them by the total number of rides per month.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Resample rides to be monthly on the basis of Start date
monthly_rides = rides.____(____)['Member type']

# Take the ratio of the .value_counts() over the total number of rides
print(monthly_rides.____() / monthly_rides.size())

Edit and Run Code

This exercise is part of the course

Working with Dates and Times in Python

IntermediateSkill Level

4.8+

Start Course for 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.

Exercise 1: Dates in Python Exercise 2: Which day of the week?Exercise 3: How many hurricanes come early?Exercise 4: Math with dates Exercise 5: Subtracting dates Exercise 6: Counting events per calendar month Exercise 7: Putting a list of dates in order Exercise 8: Turning dates into strings Exercise 9: Printing dates in a friendly format Exercise 10: Representing dates in different ways

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.

Exercise 1: Dates and times Exercise 2: Creating datetimes by hand Exercise 3: Counting events before and after noon Exercise 4: Printing and parsing datetimes Exercise 5: Turning strings into datetimes Exercise 6: Parsing pairs of strings as datetimes Exercise 7: Recreating ISO format with strftime()Exercise 8: Unix timestamps Exercise 9: Working with durations Exercise 10: Turning pairs of datetimes into durations Exercise 11: Average trip time Exercise 12: The long and the short of why time is hard

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.

Exercise 1: UTC offsets Exercise 2: Creating timezone aware datetimes Exercise 3: Setting timezones Exercise 4: What time did the bike leave in UTC?Exercise 5: Time zone database Exercise 6: Putting the bike trips into the right time zone Exercise 7: What time did the bike leave? (Global edition)Exercise 8: Starting daylight saving time Exercise 9: How many hours elapsed around daylight saving?Exercise 10: March 29, throughout a decade Exercise 11: Ending daylight saving time Exercise 12: Finding ambiguous datetimes Exercise 13: Cleaning daylight saving data with fold

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.

Exercise 1: Reading date and time data in Pandas Exercise 2: Loading a csv file in Pandas Exercise 3: Making timedelta columns Exercise 4: Summarizing datetime data in Pandas Exercise 5: How many joyrides?Exercise 6: It's getting cold outside, W20529 Exercise 7: Members vs casual riders over time

Current Exercise

Exercise 8: Combining groupby() and resample()Exercise 9: Additional datetime methods in Pandas Exercise 10: Timezones in Pandas Exercise 11: How long per weekday?Exercise 12: How long between rides?Exercise 13: Wrap-up