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Use SQLAlchemy ORMs to Access Data in Python

The CData Python Connector for enables you to create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for and the SQLAlchemy toolkit, you can build -connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to data to query, update, delete, and insert data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live data in Python. When you issue complex SQL queries from , the CData Connector pushes supported SQL operations, like filters and aggregations, directly to and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Data

Connecting to data looks just like connecting to any relational data source. Create a connection string using the required connection properties. For this article, you will pass the connection string as a parameter to the create_engine function.

The primary method for performing basic authentication is to provide your login credentials, as follows:

  • User: Set this to your username.
  • Password: Set this to your password.

Optionally, if you are making use of a sandbox environment, set the following:

  • UseSandbox: Set this to true if you are authenticating with a sandbox account.

Authenticating Using Account Number and License Key

Alternatively, you can authenticate using your account number and license key. Connect to data using the following:

  • AccountId: Set this to your Account Id. The Account Id is listed in the upper right hand corner of the admin console.
  • LicenseKey: Set this to your Avalara Avatax license key. You can generate a license key by logging into Avalara Avatax as an account adminstrator and navigating to Settings -> Reset License Key.

Follow the procedure below to install SQLAlchemy and start accessing through Python objects.

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit:

pip install sqlalchemy

Be sure to import the module with the following:

import sqlalchemy

Model Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with data.

engine = create_engine("avalaraavatax///?User=MyUser&Password=MyPassword")

Declare a Mapping Class for Data

After establishing the connection, declare a mapping class for the table you wish to model in the ORM (in this article, we will model the Transactions table). Use the sqlalchemy.ext.declarative.declarative_base function and create a new class with some or all of the fields (columns) defined.

base = declarative_base()
class Transactions(base):
	__tablename__ = "Transactions"
	Id = Column(String,primary_key=True)
	TotalTax = Column(String)

Query Data

With the mapping class prepared, you can use a session object to query the data source. After binding the Engine to the session, provide the mapping class to the session query method.

Using the query Method

engine = create_engine("avalaraavatax///?User=MyUser&Password=MyPassword")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Transactions).filter_by(Code="051349"):
	print("Id: ", instance.Id)
	print("TotalTax: ", instance.TotalTax)

Alternatively, you can use the execute method with the appropriate table object. The code below works with an active session.

Using the execute Method

Transactions_table = Transactions.metadata.tables["Transactions"]
for instance in session.execute( == "051349")):
	print("Id: ", instance.Id)
	print("TotalTax: ", instance.TotalTax)

For examples of more complex querying, including JOINs, aggregations, limits, and more, refer to the Help documentation for the extension.

Insert Data

To insert data, define an instance of the mapped class and add it to the active session. Call the commit function on the session to push all added instances to .

new_rec = Transactions(Id="placeholder", Code="051349")

Update Data

To update data, fetch the desired record(s) with a filter query. Then, modify the values of the fields and call the commit function on the session to push the modified record to .

updated_rec = session.query(Transactions).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Code = "051349"

Delete Data

To delete data, fetch the desired record(s) with a filter query. Then delete the record with the active session and call the commit function on the session to perform the delete operation on the provided recoreds (rows).

deleted_rec = session.query(Transactions).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()

Free Trial & More Information

Download a free, 30-day trial of the Python Connector to start building Python apps and scripts with connectivity to data. Reach out to our Support Team if you have any questions.