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# Fill in the complete data table including years where moving average can't be computed full_df = pd.DataFrame({ "Year": list(range(1992, 2001)), "Students": [1332, 1397, 1457, 1592, 1662, 1805, 1910, 2027, 2050] }) # Add 5-Year Total and Average with NaN where not computable full_df['5-Year Total'] = full_df['Students'].rolling(window=5).sum() full_df['5-Year Average'] = full_df['Students'].rolling(window=5).mean() full_df['Center Year'] = full_df['Year'].rolling(window=5).apply(lambda x: int(x.iloc[2])) # Replacing NaN with '-' for display purposes display_df = full_df.copy() display_df[['5-Year Total', '5-Year Average', 'Center Year']] = display_df[['5-Year Total', '5-Year Average', 'Center Year']].fillna('-') # Convert to string for cleaner formatting display_df = display_df.astype(str) display_df
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