Zaman içinde üyeler vs. gündelik kullanıcılar

Sürücüler ya her an bisiklet alabilmek için yıllık ödeme yapan "Members" (Üyeler) ya da bisiklet istasyonundaki kiosktan ödeme yapan "Casual" (Gündelik) olabilir.

Ekim'den Aralık'a üyeler ve gündelik kullanıcılar aynı oranda mı azalıyor, yoksa biri diğerinden daha hızlı mı düşüş gösteriyor?

Daha önce olduğu gibi, rides senin için yüklendi. Seri içindeki her değerin kaç kez göründüğünü döndüren Pandas yöntemi .value_counts()'u kullanacaksın. Bu durumda, "Member" veya "Casual" sayımları.

Bu egzersiz

Python'da Tarihler ve Saatlerle Çalışmak

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

monthly_rides'ı, başlangıç tarihine göre ay bazında rides'ın yeniden örneklenmiş bir sürümü olacak şekilde ayarla.
Kaç Üye ve Gündelik sürüş olduğunu bulmak için .value_counts() yöntemini kullan ve bunları ay başına toplam sürüş sayısına böl.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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

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

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python'da Tarihler ve Saatlerle Çalışmak

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Pandas’ta tarih ve saat verilerini okuma Exercise 2: Pandas'ta bir csv dosyası yükleme Exercise 3: timedelta sütunları oluşturma Exercise 4: Pandas'ta datetime verilerini özetleme Exercise 5: Kaç tane keyif turu var?Exercise 6: Dışarısı soğuyor, W20529 Exercise 7: Zaman içinde üyeler vs. gündelik kullanıcılar

Geçerli Egzersiz

Exercise 8: groupby() ve resample() birleştirme Exercise 9: Pandas’ta ek tarih-saat (datetime) yöntemleri Exercise 10: Pandas'ta saat dilimleri Exercise 11: Haftanın gününe göre ne kadar uzun?Exercise 12: Sürüşler arasında ne kadar zaman var?Exercise 13: Kapanış