Turning raw data
into business
decisions

import pandas as pd
import sqlalchemy as sa

# Connect to Azure SQL
engine = sa.create_engine(
    f"mssql+pyodbc://{config}"
)

# Pull half-hourly readings
df = pd.read_sql("""
    SELECT site, utility,
           SUM(value) AS total_kwh
    FROM   readings
    WHERE  date >= DATEADD(d,-30,GETDATE())
    GROUP  BY site, utility
""", engine)

# Flag anomalies
anomalies = df[
    df.total_kwh > df.total_kwh.mean()
    + 2 * df.total_kwh.std()
]

print(f"⚠ {len(anomalies)} sites flagged")
# → Ready for PowerBI export ✓