Merge Excel Lists with SQL Databases (The Safe Way)

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

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)

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)