Menemukan konsistensi

Dalam latihan ini dan sepanjang bab ini, Anda akan bekerja dengan DataFrame airlines yang berisi respons survei tentang Bandara San Francisco dari pelanggan maskapai.

DataFrame ini memuat metadata penerbangan seperti maskapai, tujuan, waktu tunggu, serta jawaban atas pertanyaan kunci mengenai kebersihan, keamanan, dan kepuasan. Sebuah DataFrame lain bernama categories telah dibuat, berisi semua nilai yang benar dan mungkin untuk kolom-kolom survei.

Dalam latihan ini, Anda akan menggunakan kedua DataFrame tersebut untuk menemukan jawaban survei dengan nilai yang tidak konsisten, lalu menghapusnya, secara efektif melakukan outer dan inner join pada kedua DataFrame seperti yang ditunjukkan dalam latihan video. Paket pandas telah diimpor sebagai pd, dan DataFrame airlines serta categories sudah tersedia di environment Anda.

Latihan ini adalah bagian dari kursus

Membersihkan Data di Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Print categories DataFrame
print(____)

# Print unique values of survey columns in airlines
print('Cleanliness: ', airlines['cleanliness'].____, "\n")
print('Safety: ', ____, "\n")
print('Satisfaction: ', ____, "\n")

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: Data type constraints Exercise 2: Common data types Exercise 3: Numeric data or ... ?Exercise 4: Summing strings and concatenating numbers Exercise 5: Data range constraints Exercise 6: Tire size constraints Exercise 7: Back to the future Exercise 8: Uniqueness constraints Exercise 9: How big is your subset?Exercise 10: Finding duplicates Exercise 11: Treating duplicates

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: Kendala keanggotaan Exercise 2: Khusus anggota Exercise 3: Menemukan konsistensi

Latihan Saat Ini

Exercise 4: Variabel kategori Exercise 5: Kategori kesalahan Exercise 6: Kategori tidak konsisten Exercise 7: Memetakan ulang kategori Exercise 8: Membersihkan data teks Exercise 9: Menghapus gelar dan mengambil nama Exercise 10: Tetap deskriptif

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!