Menggabungkan groupby() dan resample()

Metode yang sangat kuat di Pandas adalah .groupby(). Jika .resample() mengelompokkan baris berdasarkan informasi waktu atau tanggal, .groupby() mengelompokkan baris berdasarkan nilai pada satu atau lebih kolom. Sebagai contoh, rides.groupby('Member type').size() akan memberi tahu kita berapa banyak perjalanan untuk setiap jenis anggota di seluruh DataFrame.

.resample() dapat dipanggil setelah .groupby(). Misalnya, berapa lama durasi median perjalanan per bulan, dan per jenis keanggotaan?

Latihan ini adalah bagian dari kursus

Bekerja dengan Tanggal dan Waktu di Python

Petunjuk latihan

Lengkapi pemanggilan .groupby() untuk mengelompokkan berdasarkan 'Member type', dan pemanggilan .resample() untuk melakukan resampling berdasarkan 'Start date', per bulan.
Cetak nilai median Duration untuk tiap grup.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Group rides by member type, and resample to the month
grouped = rides.groupby('____')\
  .resample('____', on = '____')

# Print the median duration for each group
print(grouped[____].____)

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: 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: Membaca data tanggal dan waktu di Pandas Exercise 2: Memuat file CSV di Pandas Exercise 3: Membuat kolom timedelta Exercise 4: Merangkum data datetime di Pandas Exercise 5: Berapa banyak joyride?Exercise 6: Di luar makin dingin, W20529 Exercise 7: Perbandingan member vs pengendara kasual dari waktu ke waktu Exercise 8: Menggabungkan groupby() dan resample()

Latihan Saat Ini

Exercise 9: Metode datetime tambahan di Pandas Exercise 10: Zona waktu di Pandas Exercise 11: Berapa lama per hari kerja?Exercise 12: Berapa lama jeda antarperjalanan?Exercise 13: Ringkasan