Calculate Rolling 30-Day Moving Averages in Pandas

Question

Given a daily sales DataFrame with a DatetimeIndex and `revenue`, compute a 30-day rolling mean `rolling_mean_30d` and rolling standard deviation `rolling_std_30d`.

Accepted Answer

Optimal Solution:
import pandas as pd

# Ensure DatetimeIndex is sorted
df = df.sort_index()

# Rolling 30-day window calculation
df['rolling_mean_30d'] = df['revenue'].rolling(window=30, min_periods=7).mean()
df['rolling_std_30d'] = df['revenue'].rolling(window=30, min_periods=7).std()

Technical Explanation:
`.rolling(window=30, min_periods=7)` computes trailing statistics. `min_periods=7` allows generating outputs once at least 7 days of historical data exist instead of producing 29 NaNs.

date	revenue
2026-01-01	10000
2026-01-02	12000