How do you handle missing values in a Pandas DataFrame? Describe different strategies and when you would use each.

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Handling missing data is a critical skill in data engineering. The approach depends on why data is missing and how it will be used downstream. Detection comes first. Pandas represents missing values as NaN for numeric data and None or NaN for other types.