Apakah ini missing at random?

Anda telah melihat dalam video latihan bahwa ada berbagai jenis missingness saat mengamati data yang hilang. Sebagai pengingat, jenis missingness dapat dijelaskan sebagai berikut:

Missing Completely at Random: Tidak ada hubungan sistematis antara nilai hilang pada suatu kolom dengan nilai pada kolom lain maupun nilainya sendiri.
Missing at Random: Ada hubungan sistematis antara nilai hilang pada suatu kolom dengan nilai lain yang teramati.
Missing not at Random: Ada hubungan sistematis antara nilai hilang pada suatu kolom dengan nilai yang tidak teramati.

Anda memiliki DataFrame yang berisi skor kepuasan pelanggan untuk suatu layanan. Jenis missingness apakah berikut ini?

Kolom satisfaction_score pelanggan dengan nilai hilang untuk pelanggan yang sangat tidak puas.

Latihan ini adalah bagian dari kursus

Membersihkan Data di Python

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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: Keseragaman Exercise 2: Tanggal ambigu Exercise 3: Penyamaan mata uang Exercise 4: Tanggal seragam Exercise 5: Validasi lintas bidang Exercise 6: Lintas kolom atau bukan lintas kolom?Exercise 7: Bagaimana integritas data kita?Exercise 8: Kelengkapan Exercise 9: Apakah ini missing at random?

Latihan Saat Ini

Exercise 10: Investor yang hilang Exercise 11: Ikuti aliran dana

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!