Tetap deskriptif

Untuk lebih memahami pengalaman pelancong di Bandara San Francisco, tim penjaminan mutu mengirimkan kuesioner kualitatif kepada semua pelancong yang memberikan nilai terburuk untuk semua kategori yang mungkin. Tujuan dari kuesioner ini adalah mengidentifikasi pola umum dalam apa yang dikatakan pelancong tentang bandara tersebut.

Tanggapan mereka disimpan dalam kolom survey_response. Setelah ditelaah, Anda menyadari beberapa jawaban menggunakan jumlah karakter paling sedikit tanpa banyak substansi. Dalam latihan ini, Anda akan mengisolasi tanggapan dengan jumlah karakter lebih dari 40, dan memastikan DataFrame baru Anda berisi tanggapan dengan 40 karakter atau lebih menggunakan pernyataan assert.

DataFrame airlines sudah tersedia di lingkungan Anda, dan pandas telah diimpor sebagai pd.

Latihan ini adalah bagian dari kursus

Membersihkan Data di Python

Petunjuk latihan

Dengan menggunakan DataFrame airlines, simpan panjang setiap entri pada kolom survey_response ke resp_length dengan .str.len().
Isolasi baris airlines dengan resp_length lebih dari 40.
Lakukan assert bahwa panjang survey_response terkecil di airlines_survey sekarang lebih besar dari 40.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Store length of each row in survey_response column
resp_length = ____

# Find rows in airlines where resp_length > 40
airlines_survey = airlines[____ > ____]

# Assert minimum survey_response length is > 40
assert ____.str.len().____ > _____

# Print new survey_response column
print(airlines_survey['survey_response'])

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 Exercise 10: Tetap deskriptif

Latihan Saat Ini

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!