Doğru indeksi almak

Burada, users_1 ve users_2 adlı iki DataFrame arasındaki olası eşleşmeleri içeren matches adlı bir DataFrame var. Her bir DataFrame'in satır indeksleri sırasıyla uid_1 ve uid_2 içinde tutuluyor.

             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
     ...            ...         ...        ...              ...            ...
     ...            ...         ...        ...              ...            ...

uid_1 indeks sütununun tüm değerlerini nasıl çıkarırsın?

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Kursa Ücretsiz Başlayın

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: Dizgileri karşılaştırma Exercise 2: Asgari düzenleme mesafesi Exercise 3: Eşik değeri Exercise 4: Kategorileri yeniden eşleme II Exercise 5: Eşleşmeler oluşturma Exercise 6: Bağlamak mı, bağlamamak mı?Exercise 7: Restoran çiftleri Exercise 8: Benzer restoranlar Exercise 9: DataFrame'leri ilişkilendirme Exercise 10: Doğru indeksi almak

Geçerli Egzersiz

Exercise 11: Hepsini bir araya bağlayalım!Exercise 12: Tebrikler!