SQL vs Python: When to Use Which for Data Analysis (2026)

-- SQL (Window Functions)
SELECT 
    order_date,
    user_id,
    amount,
    SUM(amount) OVER (
        PARTITION BY user_id 
        ORDER BY order_date 
        ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
    ) AS rolling_7d_revenue,
    DENSE_RANK() OVER (
        PARTITION BY order_date 
        ORDER BY amount DESC
    ) AS daily_spender_rank
FROM orders;

# Python (Pandas)
import pandas as pd

# Sort for rolling calculation
df = df.sort_values(by=['user_id', 'order_date'])

# 7-day rolling sum per user
df['rolling_7d_revenue'] = (
    df.groupby('user_id')['amount']
      .rolling(window=7, min_periods=1)
      .sum()
      .reset_index(level=0, drop=True)
)

# Daily spender rank
df['daily_spender_rank'] = (
    df.groupby('order_date')['amount']
      .rank(method='dense', ascending=False)
      .astype(int)
)