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

Connecting to Salesforce in Python

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

import cdata.salesforce as mod
conn = mod.connect("User=user@domain.com; Password=password;")
 
#Create cursor and iterate over results
cur = conn.cursor()
cur.execute("SELECT * FROM Leads")
 
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 Salesforce Data with pandas

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

engine = create_engine("salesforce///Password=password&User=user")
 
df = pandas.read_sql("SELECT * FROM Leads", engine)
 
df.plot()
plt.show()

More Than Read-Only: Full Update/CRUD Support

Salesforce 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 Salesforce Connector as easily as interacting with a database table.

CData Python Connectors in Action!

Watch the video overview for a first hand-look at the powerful data integration capabilities included in the CData Python Connectors.

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