Pasangan restoran

Pada pelajaran sebelumnya, Anda telah membersihkan himpunan data restaurants agar siap digunakan untuk membangun mesin rekomendasi restoran. Anda memiliki DataFrame baru bernama restaurants_new berisi restoran baru untuk melatih model Anda, yang diambil dari sumber data baru.

Anda sudah membersihkan kolom cuisine_type dan city menggunakan teknik yang dipelajari sepanjang kursus. Namun, Anda menemukan duplikasi dengan salah ketik pada nama restoran yang memerlukan record linkage alih-alih melakukan join dengan restaurants.

Dalam latihan ini, Anda akan melakukan langkah pertama dalam record linkage dan menghasilkan pasangan baris yang mungkin antara restaurants dan restaurants_new. Kedua DataFrame, pandas, dan recordlinkage sudah tersedia di lingkungan Anda.

Latihan ini adalah bagian dari kursus

Membersihkan Data di Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create an indexer and object and find possible pairs
indexer = ____

# Block pairing on cuisine_type
indexer.____(____)

# Generate pairs
pairs = indexer.____(____, ____)

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 Exercise 5: Membuat pasangan Exercise 6: Mengaitkan atau tidak?Exercise 7: Pasangan restoran

Latihan Saat Ini

Exercise 8: Restoran serupa Exercise 9: Menghubungkan DataFrame Exercise 10: Mengambil indeks yang benar Exercise 11: Menghubungkannya bersama!Exercise 12: Selamat!