The correct option is lateness_df['delay_minutes'].mean().

This calls the Pandas Series method mean on the delay_minutes column and it computes the arithmetic average of the numeric values while skipping missing values by default. Using the column reference ensures you operate on the Series rather than the whole DataFrame and Pandas returns the scalar average you need.

lateness_df['delay_minutes'].median() is incorrect because median returns the middle value and not the arithmetic mean. Use mean when the question asks for the average and use median only when you need the central value that is robust to outliers.

Avg(lateness_df['delay_minutes']) is incorrect because Avg is not a built in Pandas or Python function. The proper approach on a Series is to call the .mean() method or to pass a NumPy array to NumPy functions.

np.mean(lateness_df) is incorrect as written because calling NumPy mean on the entire DataFrame can produce unexpected results or errors if non numeric columns are present. To use NumPy you would need to call np.mean(lateness_df['delay_minutes']) but the option shown does not target the specific column so it is not the right choice.

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