Mengakhiri waktu musim panas (daylight saving time)

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Latihan ini adalah bagian dari kursus

Bekerja dengan Tanggal dan Waktu di Python

SkillTag.level.intermediateSkillTag.label

4.8+

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: Offset UTC Exercise 2: Membuat datetime yang sadar zona waktu Exercise 3: Mengatur zona waktu Exercise 4: Pukul berapa sepeda berangkat dalam UTC?Exercise 5: Basis data zona waktu Exercise 6: Menetapkan perjalanan sepeda ke zona waktu yang benar Exercise 7: Sepeda berangkat pukul berapa? (Edisi Global)Exercise 8: Mulainya waktu hemat siang hari Exercise 9: Berapa jam yang berlalu saat pergantian daylight saving?Exercise 10: 29 Maret, sepanjang satu dekade Exercise 11: Mengakhiri waktu musim panas (daylight saving time)

Latihan Saat Ini

Exercise 12: Menemukan datetime yang ambigu Exercise 13: Membersihkan data daylight saving dengan 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