15. Window Functions

s = pd.Series([1, 2, 3, 4, 5])

s.rolling(window=3).mean()      # moving average (min 3 obs)
s.rolling(3, min_periods=1).sum()   # allow partial windows
s.expanding().mean()            # cumulative running mean
s.ewm(span=3).mean()            # exponentially weighted mean

# on a DataFrame with dates
df['ma7'] = df['sales'].rolling(7).mean()   # 7-day moving average

💡 Tip: rolling(7).mean() smooths noisy daily data. ewm reacts faster to recent changes than a simple moving average.

⭐ Interview Question: Rolling vs expanding? Rolling uses the last N observations (fixed window); expanding uses all observations up to the current point (growing window).