Menghapus gelar dan mengambil nama

Saat mengumpulkan metadata responden survei dalam DataFrame airlines, nama lengkap responden disimpan di kolom full_name. Namun setelah ditinjau lebih lanjut, Anda menemukan bahwa banyak nama diawali dengan gelar kehormatan seperti "Dr.", "Mr.", "Ms." dan "Miss".

Tujuan akhir Anda adalah membuat dua kolom baru bernama first_name dan last_name, yang masing-masing berisi nama depan dan nama belakang responden. Namun sebelum itu, Anda perlu menghapus gelar kehormatan tersebut.

DataFrame airlines tersedia di lingkungan Anda, bersama pandas sebagai pd.

Latihan ini adalah bagian dari kursus

Membersihkan Data di Python

Petunjuk latihan

Hapus "Dr.", "Mr.", "Miss" dan "Ms." dari full_name dengan menggantinya menggunakan string kosong "" secara berurutan.
Jalankan pernyataan assert menggunakan .str.contains() untuk menguji apakah full_name masih mengandung salah satu gelar tersebut.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Replace "Dr." with empty string ""
airlines['full_name'] = airlines['full_name'].____.____("____","")

# Replace "Mr." with empty string ""
airlines['full_name'] = ____

# Replace "Miss" with empty string ""
____

# Replace "Ms." with empty string ""
____

# Assert that full_name has no honorifics
assert airlines['full_name'].str.contains('Ms.|Mr.|Miss|Dr.').any() == False

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 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

Latihan Saat Ini

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!