Waktu perjalanan rata-rata

W20529 melakukan 291 perjalanan dalam himpunan data kita. Berapa lama rata-rata perjalanannya? Kita dapat menggunakan fungsi bawaan Python sum() dan len() untuk menghitungnya.

Berdasarkan latihan kode terakhir Anda, data telah dimuat sebagai onebike_durations. Setiap entri adalah jumlah detik sepeda berada di luar dok.

Latihan ini adalah bagian dari kursus

Bekerja dengan Tanggal dan Waktu di Python

Petunjuk latihan

Hitung total_elapsed_time untuk semua perjalanan dalam onebike_durations.
Hitung number_of_trips untuk onebike_durations.
Bagi total_elapsed_time dengan number_of_trips untuk mendapatkan durasi rata-rata perjalanan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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(____ / ____)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Bekerja dengan Tanggal dan Waktu di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: Tanggal dan waktu Exercise 2: Membuat datetime secara manual Exercise 3: Menghitung kejadian sebelum dan sesudah tengah hari Exercise 4: Mencetak dan mengurai datetime Exercise 5: Mengubah string menjadi datetime Exercise 6: Mengurai pasangan string menjadi datetime Exercise 7: Membuat ulang format ISO dengan strftime()Exercise 8: Unix timestamps Exercise 9: Bekerja dengan durasi Exercise 10: Mengubah pasangan datetime menjadi durasi Exercise 11: Waktu perjalanan rata-rata

Latihan Saat Ini

Exercise 12: Panjang-pendeknya alasan waktu itu rumit

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