🔐 Merge Excel Lists with SQL Databases (The Safe Way)

The Problem

You have a list of 500 account numbers in Excel. You need to pull data from your SQL database for those accounts.

Bad approach:

# ⚠️ DANGEROUS - SQL Injection risk!
query = f"SELECT * FROM accounts WHERE account_id IN ({','.join(account_list)})"

Professional approach: Parameterized queries.

The Secure Solution

import pandas as pd
import pyodbc

# Load your Excel list
accounts = pd.read_excel('accounts.xlsx')['Account_ID'].tolist()

# Create safe parameterized query
placeholders = ','.join(['?'] * len(accounts))
query = f"""
    SELECT Account_ID, Name, Balance
    FROM Accounts
    WHERE Account_ID IN ({placeholders})
"""

# Execute safely with parameter binding
conn = pyodbc.connect('Driver={SQL Server};Server=MyServer;Database=MyDB;...')
results = pd.read_sql_query(query, conn, params=accounts)

# Merge back with original Excel data
final = pd.merge(
    pd.read_excel('accounts.xlsx'),
    results,
    on='Account_ID',
    how='left'
)

final.to_excel('accounts_with_data.xlsx', index=False)

Why Parameterized Queries?

🔒 Security

Prevents SQL injection attacks
Safe even with untrusted input

⚡ Performance

Database can cache execution plan
Faster for repeated queries

🎯 Reliability

Handles special characters automatically
No quote escaping needed

Handle Large Lists (10,000+ items)

def query_in_batches(conn, table, column, values, batch_size=1000):
    """Query large lists in batches"""
    results = []
    
    for i in range(0, len(values), batch_size):
        batch = values[i:i+batch_size]
        placeholders = ','.join(['?'] * len(batch))
        query = f"SELECT * FROM {table} WHERE {column} IN ({placeholders})"
        
        batch_results = pd.read_sql_query(query, conn, params=batch)
        results.append(batch_results)
    
    return pd.concat(results, ignore_index=True)

# Use it:
large_results = query_in_batches(conn, 'Accounts', 'Account_ID', account_list)

The Bottom Line

✅ Secure - No SQL injection risks
✅ Fast - Handles thousands of IDs
✅ Reliable - Automatic type handling
✅ Professional - Production-ready code

Stop building SQL strings. Use parameters. 🔒

Part of the “From Excel Hell to Python Heaven” series.