SQL Server Driver

Connecting to SQL Server with Python

CData Python Connectors leverage the Database API (DB-API) interface to make it easy to work with SQL Server from a wide range of standard Python data tools. Connecting to and working with your data in Python follows a basic pattern, regardless of data source:

• Configure the connection properties to SQL Server
• Query SQL Server to retrieve or update data
• Connect your SQL Server data with Python data tools.

Connecting to SQL Server in Python

To connect to your data from Python, import the extension and create a connection:

import cdata.sql as mod
conn = mod.connect("[email protected]; Password=password;")

#Create cursor and iterate over results
cur = conn.cursor()
cur.execute("SELECT * FROM SQLTable")
 
rs = cur.fetchall()
 
for row in rs:
print(row)

Once you import the extension, you can work with all of your enterprise data using the python modules and toolkits that you already know and love, quickly building apps that help you drive business.

Visualize SQL Server Data with pandas

The data-centric interfaces of the SQL Server Python Connector make it easy to integrate with popular tools like pandas and SQLAlchemy to visualize data in real-time.

engine = create_engine("sql///Password=password&User=user")

df = pandas.read_sql("SELECT * FROM SQLTable", engine)

df.plot()
plt.show()

More Than Read-Only: Full Update/CRUD Support

SQL Server Connector goes beyond read-only functionality to deliver full support for Create, Read Update, and Delete operations (CRUD). Your end-users can interact with the data presented by the SQL Server Connector as easily as interacting with a database table.