Mengambil indeks yang benar

Berikut adalah sebuah DataFrame bernama matches yang berisi kemungkinan pasangan antara dua DataFrame, users_1 dan users_2. Indeks baris masing-masing DataFrame disimpan di uid_1 dan uid_2.

             first_name  address_1  address_2  marriage_status  date_of_birth
uid_1 uid_2                                                                  
0     3              1          1          1                1              0
     ...            ...         ...        ...              ...            ...
     ...            ...         ...        ...              ...            ...
1     3              1          1          1                1              0
     ...            ...         ...        ...              ...            ...
     ...            ...         ...        ...              ...            ...

Bagaimana cara mengekstrak semua nilai dari kolom indeks uid_1?

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Membersihkan Data di Python

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Latihan ini adalah bagian dari kursus

Membersihkan Data di Python

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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 Exercise 8: Restoran serupa Exercise 9: Menghubungkan DataFrame Exercise 10: Mengambil indeks yang benar

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Exercise 11: Menghubungkannya bersama!Exercise 12: Selamat!