Durata media del viaggio

W20529 ha effettuato 291 viaggi nel nostro insieme di dati. Quanto sono durati in media i viaggi? Possiamo usare le funzioni integrate di Python sum() e len() per fare questo calcolo.

In base al tuo ultimo esercizio di codifica, i dati sono stati caricati come onebike_durations. Ogni voce rappresenta un numero di secondi in cui la bici è stata fuori dalla stazione di aggancio.

Questo esercizio fa parte del corso

Lavorare con date e orari in Python

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Istruzioni dell'esercizio

Calcola total_elapsed_time su tutti i viaggi in onebike_durations.
Calcola number_of_trips per onebike_durations.
Dividi total_elapsed_time per number_of_trips per ottenere la durata media del viaggio.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# What was the total duration of all trips?
total_elapsed_time = ____(____)

# What was the total number of trips?
number_of_trips = ____(____)
  
# Divide the total duration by the number of trips
print(____ / ____)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Lavorare con date e orari in Python

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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.

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: Date e orari Exercise 2: Creare oggetti datetime a mano Exercise 3: Contare gli eventi prima e dopo mezzogiorno Exercise 4: Stampare e analizzare oggetti datetime Exercise 5: Convertire stringhe in datetime Exercise 6: Analizzare coppie di stringhe come datetime Exercise 7: Ricreare il formato ISO con strftime()Exercise 8: Timestamp Unix Exercise 9: Lavorare con le durate Exercise 10: Trasformare coppie di datetime in durate Exercise 11: Durata media del viaggio

Esercizio in corso

Exercise 12: Il lungo e il corto del perché il tempo è difficile

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 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