Azure Python Connector

Connecting to Azure Table with Python

CData Python Connectors leverage the Database API (DB-API) interface to make it easy to work with Azure Table 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 Azure Table
• Query Azure Table to retrieve or update data
• Connect your Azure Table data with Python data tools.

Connecting to Azure Table in Python

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

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

#Create cursor and iterate over results
cur = conn.cursor()
cur.execute("SELECT * FROM Table")
 
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 Azure Table Data with pandas

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

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

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

df.plot()
plt.show()

More Than Read-Only: Full Update/CRUD Support

Azure Table 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 Azure Table Connector as easily as interacting with a database table.