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Python Connector Libraries for TaxJar Data Connectivity. Integrate TaxJar with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

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Use SQLAlchemy ORMs to Access TaxJar Data in Python


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

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

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

Connecting to TaxJar Data

Connecting to TaxJar 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.

To authenticate to the TaxJar API, you will need to first obtain the API Key from the TaxJar UI.

NOTE: the API is available only for Professional and Premium TaxJar plans.

If you already have a Professional or Premium plan you can find the API Key by logging in the TaxJar UI and navigating to Account -> TaxJar API. After obtaining the API Key, you can set it in the APIKey connection property.

Additional Notes

  • By default, the CData connector will retrieve data of the last 3 months in cases where the entity support date range filtering. You can set StartDate to specify the minimum creation date of the data retrieved.
  • If the API Key has been created for a sandbox API account please set UseSandbox to true, but not all endpoints will work as expected. For more information, refer to the TaxJar developer documentation.

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

Install Required Modules

Use the pip utility to install the SQLAlchemy toolkit and SQLAlchemy ORM package:

pip install sqlalchemy pip install sqlalchemy.orm

Be sure to import the appropriate modules:

from sqlalchemy import create_engine, String, Column from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker

Model TaxJar Data in Python

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

NOTE: Users should URL encode the any connection string properties that include special characters. For more information, refer to the SQL Alchemy documentation.

engine = create_engine("taxjar:///?APIKey=3bb04218ef8t80efdf1739abf7257144")

Declare a Mapping Class for TaxJar 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 Orders 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 Orders(base): __tablename__ = "Orders" TransactionID = Column(String,primary_key=True) UserID = Column(String) ...

Query TaxJar 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("taxjar:///?APIKey=3bb04218ef8t80efdf1739abf7257144") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Orders).filter_by(TransactionID="123"): print("TransactionID: ", instance.TransactionID) print("UserID: ", instance.UserID) print("---------")

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

Using the execute Method

Orders_table = Orders.metadata.tables["Orders"] for instance in session.execute(Orders_table.select().where(Orders_table.c.TransactionID == "123")): print("TransactionID: ", instance.TransactionID) print("UserID: ", instance.UserID) print("---------")

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

Insert TaxJar Data

To insert TaxJar 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 TaxJar.

new_rec = Orders(TransactionID="placeholder", TransactionID="123") session.add(new_rec) session.commit()

Update TaxJar Data

To update TaxJar 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 TaxJar.

updated_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.TransactionID = "123" session.commit()

Delete TaxJar Data

To delete TaxJar 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 records (rows).

deleted_rec = session.query(Orders).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() session.delete(deleted_rec) session.commit()

Free Trial & More Information

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