5.4 - Handling Missing Data
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Practice Questions
Test your understanding with targeted questions
What method can be used to detect missing values in a DataFrame?
💡 Hint: Look for a method that identifies null entries.
What function drops rows with any missing values?
💡 Hint: Think of a function that removes unwanted entries.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does the command df.isnull().sum() do?
💡 Hint: Focus on what it means to check for 'null'.
True or False: Forward fill is used to fill missing values based on the next value in a column.
💡 Hint: Think about the direction in which the values are filled.
1 more question available
Challenge Problems
Push your limits with advanced challenges
Given a dataset with 20% missing values in a key feature, evaluate the best approach to handle this. Discuss options of dropping, filling with mean, or a combination.
💡 Hint: Consider both the impact of losing data against the data's significance.
Evaluate a dataset where all rows for users under 18 are missing their income data. Discuss implications of dropping these rows versus filling values and potential biases introduced.
💡 Hint: Think about how data loss may disproportionately affect certain groups.
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