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

df['date'] = pd.to_datetime(df['date'])
df = df.set_index('date').sort_index()

# Rolling 30-day window calculation
df['rolling_mean_30d'] = df['revenue'].rolling(window=30, min_periods=7).mean().round(2)
df['rolling_std_30d'] = df['revenue'].rolling(window=30, min_periods=7).std().round(2)

result_df = df.reset_index()
result_df['date'] = result_df['date'].dt.strftime('%Y-%m-%d')

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
2026-01-03	11000
2026-01-04	13000
2026-01-05	9500
2026-01-06	10500
2026-01-07	11500
2026-01-08	12500
2026-01-09	10800
2026-01-10	11200

date	revenue	rolling_mean_30d	rolling_std_30d
2026-01-01	10000	NULL	NULL
2026-01-02	12000	NULL	NULL
2026-01-03	11000	NULL	NULL
2026-01-04	13000	NULL	NULL
2026-01-05	9500	NULL	NULL
2026-01-06	10500	NULL	NULL
2026-01-07	11500	11071.43	1205.15
2026-01-08	12500	11250	1224.74
2026-01-09	10800	11200	1155.42
2026-01-10	11200	11200	1089.34