Creare colonne timedelta

All'inizio del corso hai scritto un ciclo per sottrarre oggetti datetime e determinare per quanto tempo la nostra bici di esempio era fuori dai dock. Ora farai lo stesso con Pandas.

rides è già stato caricato per te.

Questo esercizio fa parte del corso

Lavorare con date e orari in Python

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Istruzioni dell'esercizio

Sottrai la colonna Start date dalla colonna End date per ottenere una Series di timedelta; assegna il risultato a ride_durations.
Converti ride_durations in secondi e assegna il risultato alla colonna 'Duration' di rides.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Subtract the start date from the end date
ride_durations = ____ - ____

# Convert the results to seconds
____[____] = ride_durations.____

print(rides['Duration'].head())

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: 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: Lettura di dati di data e ora in Pandas Exercise 2: Caricare un file CSV in Pandas Exercise 3: Creare colonne timedelta

Esercizio in corso

Exercise 4: Riepilogare i dati datetime in Pandas Exercise 5: Quante joyride?Exercise 6: Fa freddo là fuori, W20529 Exercise 7: Confronto nel tempo: abbonati vs occasionali Exercise 8: Combinare groupby() e resample()Exercise 9: Metodi datetime aggiuntivi in Pandas Exercise 10: Fusi orari in Pandas Exercise 11: Quanto dura per giorno della settimana?Exercise 12: Quanto tempo passa tra una corsa e l’altra?Exercise 13: Riepilogo