Execute queries with mssql-python

The mssql-python driver provides cursor methods for SQL query execution, parameterized queries, batch operations, and prepared statements.

Basic query execution

Use a cursor's execute() method to run SQL statements:

import mssql_python

conn = mssql_python.connect(connection_string)
cursor = conn.cursor()

cursor.execute("SELECT Name, ListPrice FROM Production.Product WHERE Color = 'Black'")
rows = cursor.fetchall()

for row in rows:
    print(row.Name, row.ListPrice)

cursor.close()
conn.close()

Parameterized queries

Always use parameterized queries to prevent SQL injection. The driver's default paramstyle is pyformat (named placeholders), but it also supports qmark (positional placeholders). Use qmark for ODBC {CALL} escape sequences.

Pyformat style (default)

Use named placeholders with %(name)s syntax and pass a dictionary:

cursor.execute(
    "SELECT Name, ListPrice FROM Production.Product WHERE Color = %(color)s AND ListPrice > %(price)s",
    {"color": "Black", "price": 10.00}
)

Qmark style

Use positional placeholders with ? and pass a tuple or list:

cursor.execute(
    "SELECT Name, ListPrice FROM Production.Product WHERE ProductSubcategoryID = ? AND ListPrice > ?",
    (1, 10.00)
)

The driver automatically detects the parameter style based on your SQL query and parameter types.

INSERT, UPDATE, DELETE operations

For data modification statements, use parameterized queries and commit the transaction:

cursor.execute("CREATE TABLE #ExecDemo (Name NVARCHAR(50), CategoryID INT, Price DECIMAL(10,2))")
cursor.execute(
    "INSERT INTO #ExecDemo (Name, CategoryID, Price) VALUES (%(name)s, %(category)s, %(price)s)",
    {"name": "New Product", "category": 1, "price": 19.99}
)
conn.commit()

print(f"Rows affected: {cursor.rowcount}")

Batch execution with executemany()

Use executemany() to efficiently insert multiple rows. The driver uses column-wise parameter binding for high performance:

products = [
    {"name": "Product A", "category": 1, "price": 10.00},
    {"name": "Product B", "category": 1, "price": 15.00},
    {"name": "Product C", "category": 2, "price": 20.00},
]

cursor.execute("CREATE TABLE #BatchDemo (Name NVARCHAR(50), CategoryID INT, Price DECIMAL(10,2))")
cursor.executemany(
    "INSERT INTO #BatchDemo (Name, CategoryID, Price) VALUES (%(name)s, %(category)s, %(price)s)",
    products
)
conn.commit()

print(f"Rows inserted: {cursor.rowcount}")

With qmark style:

products = [
    ("Product A", 1, 10.00),
    ("Product B", 1, 15.00),
    ("Product C", 2, 20.00),
]

cursor.execute("CREATE TABLE #QmarkDemo (Name NVARCHAR(50), CategoryID INT, Price DECIMAL(10,2))")
cursor.executemany(
    "INSERT INTO #QmarkDemo (Name, CategoryID, Price) VALUES (?, ?, ?)",
    products
)
conn.commit()

Multi-statement batch execution

Use batch_execute() on the connection to execute multiple different statements in a single call:

results, cursor = conn.batch_execute(
    [
        "CREATE TABLE #BatchExec (Name NVARCHAR(50), CategoryID INT)",
        "INSERT INTO #BatchExec (Name, CategoryID) VALUES (%(name)s, %(cat)s)",
        "SELECT COUNT(*) FROM #BatchExec"
    ],
    [
        None,                                 # No params for CREATE
        {"name": "New Item", "cat": 1},       # Params for INSERT
        None                                  # No params for SELECT
    ]
)

print(f"CREATE result: {results[0]}")
print(f"INSERT affected: {results[1]} rows")
print(f"Row count: {results[2][0][0]}")

Prepared statements

The driver prepares queries by default (use_prepare=True). When you execute the same SQL string multiple times on the same cursor, the driver automatically reuses the prepared statement on subsequent calls:

# First execution prepares the statement
cursor.execute(
    "SELECT Name, ListPrice FROM Production.Product WHERE ProductSubcategoryID = %(subcategory_id)s",
    {"subcategory_id": 1},
)
rows1 = cursor.fetchall()

# Same SQL string on same cursor → driver reuses the prepared plan
cursor.execute(
    "SELECT Name, ListPrice FROM Production.Product WHERE ProductSubcategoryID = %(subcategory_id)s",
    {"subcategory_id": 2},
)
rows2 = cursor.fetchall()

To skip preparation and use direct execution instead:

cursor.execute(
    "SELECT Name, ListPrice FROM Production.Product WHERE ProductSubcategoryID = 1",
    use_prepare=False  # Uses SQLExecDirectW instead of SQLPrepareW
)

Connection-level execute

For simple one-off queries, use execute() directly on the connection:

# Creates cursor, executes, and returns cursor
cursor = conn.execute("SELECT TOP 10 Name, ListPrice FROM Production.Product")
rows = cursor.fetchall()
cursor.close()

Stored procedures

Call stored procedures by using EXECUTE or the ODBC {CALL} escape syntax. For information about output parameters, multiple result sets, and transaction patterns, see Stored procedures.

cursor.execute(
    "EXECUTE dbo.uspGetManagerEmployees @BusinessEntityID = %(business_entity_id)s",
    {"business_entity_id": 16}
)
rows = cursor.fetchall()

Set input sizes

Use setinputsizes() to declare parameter types explicitly, which can improve performance for batch operations:

cursor.setinputsizes([
    (mssql_python.SQL_WVARCHAR, 50, 0),   # NVARCHAR(50)
    (mssql_python.SQL_INTEGER, 0, 0),     # INT
])

cursor.executemany(
    "SELECT ProductID, Name FROM Production.Product WHERE Name LIKE ? AND ProductSubcategoryID = ?",
    [("Road%", 2), ("Mountain%", 1)]
)

Error handling

Wrap database operations in try-except blocks:

try:
    cursor.execute("CREATE TABLE #ErrDemo (Name NVARCHAR(50) NOT NULL)")
    cursor.execute("INSERT INTO #ErrDemo (Name) VALUES (%(name)s)", {"name": None})
    conn.commit()
except mssql_python.IntegrityError as e:
    print(f"Constraint violation: {e}")
    conn.rollback()
except mssql_python.ProgrammingError as e:
    print(f"SQL error: {e}")
    conn.rollback()

Best practices

1. Always use parameterized queries to prevent SQL injection.
2. Use bulk copy for bulk inserts instead of multiple execute() calls.
3. Commit transactions explicitly when you disable autocommit.
4. Close cursors and connections when done to release resources.
5. Use context managers for automatic resource cleanup:

with mssql_python.connect(connection_string) as conn:
    with conn.cursor() as cursor:
        cursor.execute("SELECT TOP 5 Name, ListPrice FROM Production.Product")
        rows = cursor.fetchall()
# Connection and cursor automatically closed

Related content

• Retrieve data
• Data type mappings
• Transaction management
• Cursor management