Menemukan duplikat

Pembaruan baru pada pipeline data yang memasok ride_sharing telah menambahkan kolom ride_id, yang merepresentasikan pengenal unik untuk setiap perjalanan.

Namun, pembaruan tersebut bertepatan dengan waktu durasi perjalanan rata-rata yang jauh lebih pendek dan tahun lahir pengguna yang tidak wajar karena ditetapkan di masa depan. Yang paling penting, jumlah perjalanan yang dilakukan meningkat 20% dalam semalam, sehingga Anda menduga mungkin ada duplikat lengkap dan tidak lengkap di DataFrame ride_sharing.

Dalam latihan ini, Anda akan mengonfirmasi kecurigaan tersebut dengan menemukan duplikat-duplikat itu. Contoh ride_sharing tersedia di lingkungan Anda, begitu juga semua paket yang telah Anda gunakan sejauh ini.

Latihan ini adalah bagian dari kursus

Membersihkan Data di Python

Petunjuk latihan

Temukan baris duplikat ride_id dalam DataFrame ride_sharing sambil mengatur keep ke False.
Subset ride_sharing pada duplicates, urutkan berdasarkan ride_id, dan tetapkan hasilnya ke duplicated_rides.
Cetak kolom ride_id, duration, dan user_birth_year dari duplicated_rides sesuai urutan tersebut.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Find duplicates
duplicates = ____.____(____, ____)

# Sort your duplicated rides
duplicated_rides = ride_sharing[____].____('____')

# Print relevant columns of duplicated_rides
print(duplicated_rides[['____','____','____']])

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Membersihkan Data di Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.

Exercise 1: Kendala tipe data Exercise 2: Tipe data umum Exercise 3: Data numerik atau ... ?Exercise 4: Menjumlahkan string dan menggabungkan angka Exercise 5: Kendala rentang data Exercise 6: Kendala ukuran ban Exercise 7: Kembali ke masa depan Exercise 8: Kendala keunikan Exercise 9: Seberapa besar subset Anda?Exercise 10: Menemukan duplikat

Latihan Saat Ini

Exercise 11: Menangani duplikasi

Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.

Exercise 1: Membership constraints Exercise 2: Members only Exercise 3: Finding consistency Exercise 4: Categorical variables Exercise 5: Categories of errors Exercise 6: Inconsistent categories Exercise 7: Remapping categories Exercise 8: Cleaning text data Exercise 9: Removing titles and taking names Exercise 10: Keeping it descriptive

In this chapter, you'll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You'll also gain invaluable skills that will help you verify that values have been added correctly, and that missing values don't negatively impact your analyses.

Exercise 1: Uniformity Exercise 2: Ambiguous dates Exercise 3: Uniform currencies Exercise 4: Uniform dates Exercise 5: Cross field validation Exercise 6: Cross field or no cross field?Exercise 7: How's our data integrity?Exercise 8: Completeness Exercise 9: Is this missing at random?Exercise 10: Missing investors Exercise 11: Follow the money

Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this chapter, you'll learn how to link records by calculating the similarity between strings—you'll then use your new skills to join two restaurant review datasets into one clean master dataset.

Exercise 1: Comparing strings Exercise 2: Minimum edit distance Exercise 3: The cutoff point Exercise 4: Remapping categories II Exercise 5: Generating pairs Exercise 6: To link or not to link?Exercise 7: Pairs of restaurants Exercise 8: Similar restaurants Exercise 9: Linking DataFrames Exercise 10: Getting the right index Exercise 11: Linking them together!Exercise 12: Congratulations!