Вивчайте Challenge: Flag Duplicate Entries | Handling Missing and Duplicate Data

In many real-world data cleaning scenarios, you may want to flag duplicate entries rather than remove them right away. Flagging gives you the flexibility to review duplicates, analyze their patterns, and make informed decisions about which ones to keep or discard. For instance, in customer databases, you may want to investigate why duplicates occur before deletion, or in transactional data, you might need to audit the records before any removal. By marking duplicates, you can also generate reports, track data quality issues, and collaborate with others on resolution strategies without losing potentially valuable information.


              123456789
            
import pandas as pd

data = {
    "name": ["Alice", "Bob", "Alice", "Charlie", "Bob"],
    "age": [25, 30, 25, 35, 30]
}

df = pd.DataFrame(data)
print(df)

Завдання

Swipe to start coding

Write a function that adds a new column called is_duplicate to the DataFrame. Each row in this column should be True if the row is a duplicate of a previous row (based on all columns), and False otherwise. The function must return the modified DataFrame.

Рішення

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 2. Розділ 6

single

Запитати АІ

Запитайте про що завгодно або спробуйте одне із запропонованих запитань, щоб почати наш чат

Awesome!

Completion rate improved to 5.56

Свайпніть щоб показати меню


              123456789
            
import pandas as pd

data = {
    "name": ["Alice", "Bob", "Alice", "Charlie", "Bob"],
    "age": [25, 30, 25, 35, 30]
}

df = pd.DataFrame(data)
print(df)