pandas pyodbc Warning: SQLAlchemy Connectable Support

When working with pandas and pyodbc to query SQL Server databases, you may encounter a warning message that can be both confusing and concerning. This article explains the cause of this warning and provides the best practices for resolving it.

The Warning Message

When using a pyodbc Connection object directly with pandas read_sql_query(), you might see:

UserWarning: pandas only support SQLAlchemy connectable(engine/connection) or
database string URI or sqlite3 DBAPI2 connection
other DBAPI2 objects are not tested, please consider using SQLAlchemy

This warning indicates that pandas prefers SQLAlchemy connections over raw DBAPI2 connections (like pyodbc) for all database systems except SQLite.

Why This Warning Appears

Pandas has gradually shifted toward standardizing on SQLAlchemy as its preferred database interface. While pyodbc connections still work for basic operations, pandas developers recommend using SQLAlchemy for:

• Better cross-database compatibility
• Connection pooling capabilities
• More robust SQL generation
• Future-proofing your code

WARNING

The warning doesn't mean pyodbc is deprecated! It simply indicates that pandas developers haven't tested or officially support all DBAPI2 connections except for SQLite.

Recommended Solution: Use SQLAlchemy Engine

The most future-proof approach is to create a SQLAlchemy Engine using your pyodbc connection string:

For Standard Authentication

import pandas as pd
import sqlalchemy as sa
from sqlalchemy.engine import URL

# Your existing connection string
connection_string = (
    "Driver={ODBC Driver 17 for SQL Server};"
    "Server=tcp:your_server;"
    "Database=your_database;"
    "TrustServerCertificate=no;"
    "Connection Timeout=600;"
    "UID=your_username;"
    "PWD=your_password;"
)

# Create SQLAlchemy connection URL
connection_url = URL.create(
    "mssql+pyodbc",
    query={"odbc_connect": connection_string}
)

# Create engine
engine = sa.create_engine(connection_url)

# Use with pandas
def readAnyTable(tablename, date):
    query = f"""
        SELECT *
        FROM [your_database].[dbo].[{tablename}]
        WHERE Asof >= '{date}'
    """
    
    return pd.read_sql_query(query, engine)

For Windows Authentication (Trusted Connection)

import pandas as pd
import sqlalchemy as sa
from sqlalchemy.engine import URL

connection_string = (
    "Driver={ODBC Driver 17 for SQL Server};"
    "Server=tcp:your_server;"
    "Database=your_database;"
    "TrustServerCertificate=no;"
    "Connection Timeout=600;"
    "Trusted_Connection=yes;"
)

connection_url = URL.create(
    "mssql+pyodbc",
    query={"odbc_connect": connection_string}
)

engine = sa.create_engine(connection_url)

Using urllib.parse (Alternative Approach)

import pandas as pd
import sqlalchemy as sa
import urllib.parse

server = 'your_server'
database = 'your_database'
username = 'your_username'
password = 'your_password'

params = urllib.parse.quote_plus(
    f"DRIVER={{ODBC Driver 17 for SQL Server}};"
    f"SERVER={server};"
    f"DATABASE={database};"
    f"UID={username};"
    f"PWD={password};"
)

engine = sa.create_engine(f"mssql+pyodbc:///?odbc_connect={params}")

Alternative: Suppressing the Warning

If you cannot use SQLAlchemy due to organizational constraints or other reasons, you can suppress the specific warning:

Targeted Warning Suppression

import pandas as pd
import pyodbc
from warnings import filterwarnings

# Suppress only the specific pandas SQL warning
filterwarnings("ignore", 
               category=UserWarning, 
               message='.*pandas only supports SQLAlchemy connectable.*')

# Your existing code
def readAnyTable(tablename, date):
    conn = pyodbc.connect(cnxn)
    
    query_result = pd.read_sql_query(
        f''' 
             SELECT *
             FROM [your_database].[dbo].[{tablename}]
            where Asof >= '{date}'
        ''', conn)
            
    conn.close()
    
    return query_result

DANGER

Avoid blanket warning suppression! Using warnings.filterwarnings('ignore') without specifying the exact warning can hide important messages that might indicate real problems in your code.

Context-Specific Suppression

import warnings
import pandas as pd
import pyodbc

def readAnyTable(tablename, date):
    conn = pyodbc.connect(cnxn)
    
    with warnings.catch_warnings():
        warnings.simplefilter("ignore", UserWarning)
        
        query_result = pd.read_sql_query(
            f''' 
                 SELECT *
                 FROM [your_database].[dbo].[{tablename}]
                where Asof >= '{date}'
            ''', conn)
            
    conn.close()
    
    return query_result

Best Practices

1. Prefer SQLAlchemy for new projects and when possible in existing code
2. Use parameterized queries to prevent SQL injection (especially important when converting to SQLAlchemy)
3. Implement connection pooling with SQLAlchemy for better performance
4. Test both approaches if migrating from direct pyodbc to SQLAlchemy

Parameterized Query Example

# With SQLAlchemy (recommended)
def readAnyTable(tablename, date):
    query = sa.text("""
        SELECT *
        FROM your_database.dbo.[:tablename]
        WHERE Asof >= :date
    """)
    
    return pd.read_sql_query(
        query, 
        engine, 
        params={"tablename": tablename, "date": date}
    )

Conclusion

While the warning about pyodbc connections in pandas can be alarming, it doesn't mean your code is broken. The pandas team is encouraging migration to SQLAlchemy for better maintainability and features. For production code, prefer the SQLAlchemy approach. For quick scripts or situations where SQLAlchemy isn't feasible, use targeted warning suppression rather than blanket ignore statements.

The SQLAlchemy approach future-proofs your code and provides additional benefits like connection pooling and more robust SQL generation capabilities.