groupby() en resample() combineren

Een heel krachtige methode in Pandas is .groupby(). Waar .resample() rijen groepeert op basis van tijd- of datuminformatie, groepeert .groupby() rijen op basis van de waarden in één of meer kolommen. Bijvoorbeeld, rides.groupby('Member type').size() vertelt ons hoeveel ritten er per lidtype waren in onze hele DataFrame.

.resample() kan worden aangeroepen na .groupby(). Bijvoorbeeld: hoe lang was de mediaanrit per maand, en per lidmaatschapstype?

Deze oefening maakt deel uit van de cursus

Werken met datums en tijden in Python

Cursus bekijken

Oefeninstructies

Maak de .groupby()-aanroep af om te groeperen op 'Member type', en de .resample()-aanroep om te resamplen op basis van 'Start date', per maand.
Print de mediaan van Duration voor elke groep.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Group rides by member type, and resample to the month
grouped = rides.groupby('____')\
  .resample('____', on = '____')

# Print the median duration for each group
print(grouped[____].____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Werken met datums en tijden in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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: Datum- en tijdgegevens in Pandas inlezen Exercise 2: Een csv-bestand laden in Pandas Exercise 3: Timedelta-kolommen maken Exercise 4: Datum-tijdgegevens samenvatten in Pandas Exercise 5: Hoeveel joyrides?Exercise 6: Het wordt koud buiten, W20529 Exercise 7: Leden vs. losse rijders in de tijd Exercise 8: groupby() en resample() combineren

Huidige oefening

Exercise 9: Aanvullende datetime-methoden in Pandas Exercise 10: Tijdzones in Pandas Exercise 11: Hoe lang per weekdag?Exercise 12: Hoeveel tijd tussen ritten?Exercise 13: Afronding