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2 cards from this section. Quick, good the night before a test.
What does NaN stand for in a dataset?
Think about what missing data implies.
What is one method to handle missing data?
Consider ways to eliminate incompleteness.
What does NaN stand for?
Consider what 'missing' means.
What is one method to handle missing data?
Think about how completeness affects data quality.
Given a dataset with missing values in multiple columns, provide a Python code to impute using both mean and median.
Remember to import the necessary libraries first.
Discuss a scenario where imputing data could introduce bias and provide a suggestion to mitigate this.
Think about distribution shapes and imputation differences.
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