Pemetaan ulang kategori II

Pada latihan sebelumnya, Anda menentukan bahwa titik batas jarak untuk memetakan ulang salah ketik pada jenis masakan 'american', 'asian', dan 'italian' yang tersimpan di kolom cuisine_type adalah 80.

Dalam latihan ini, Anda akan menggabungkan semuanya dengan mencari kecocokan dengan skor kemiripan sama dengan atau lebih tinggi dari 80 menggunakan fungsi extract() dari fuzywuzzy.process, untuk setiap jenis masakan yang benar, lalu menggantikan kecocokan tersebut dengan nilai yang benar. Ingat, saat membandingkan sebuah string dengan sebuah array string menggunakan process.extract(), keluarannya adalah daftar tuple dengan format:

(closest match, similarity score, index of match)

DataFrame restaurants sudah tersedia di lingkungan Anda, dan Anda juga memiliki daftar categories yang berisi jenis masakan yang benar ('italian', 'asian', dan 'american').

Latihan ini adalah bagian dari kursus

Membersihkan Data di Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Inspect the unique values of the cuisine_type column
print(____)

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: 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: Membandingkan string Exercise 2: Jarak edit minimum Exercise 3: Titik batas (cutoff)Exercise 4: Pemetaan ulang kategori II

Latihan Saat Ini

Exercise 5: Membuat pasangan Exercise 6: Mengaitkan atau tidak?Exercise 7: Pasangan restoran Exercise 8: Restoran serupa Exercise 9: Menghubungkan DataFrame Exercise 10: Mengambil indeks yang benar Exercise 11: Menghubungkannya bersama!Exercise 12: Selamat!