Use SQLAlchemy ORMs to Access Plaid Data in Python

Ready to get started?

Download for a free trial:

Download Now

Learn more:

Plaid Python Connector

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



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

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

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

Connecting to Plaid Data

Connecting to Plaid 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.

You can connect to Plaid using the embedded OAuth connectivity. When you connect, the Plaid OAuth endpoint opens in your browser. Log in and grant permissions to complete the OAuth process. See the OAuth section in the online Help documentation for more information on other OAuth authentication flows.

Optionally set the Account Id property to return data related to a specific Account.

Follow the procedure below to install SQLAlchemy and start accessing Plaid 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 Plaid Data in Python

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

engine = create_engine("plaid:///?AccountId=123456789&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Plaid 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"
	AccountId = Column(String,primary_key=True)
	Name = Column(String)
	...

Query Plaid 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("plaid:///?AccountId=123456789&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Transactions).filter_by(Name="Apple Store"):
	print("AccountId: ", instance.AccountId)
	print("Name: ", instance.Name)
	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

Transactions_table = Transactions.metadata.tables["Transactions"]
for instance in session.execute(Transactions_table.select().where(Transactions_table.c.Name == "Apple Store")):
	print("AccountId: ", instance.AccountId)
	print("Name: ", instance.Name)
	print("---------")

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

Insert Plaid Data

To insert Plaid 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 Plaid.

new_rec = Transactions(AccountId="placeholder", Name="Apple Store")
session.add(new_rec)
session.commit()

Update Plaid Data

To update Plaid 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 Plaid.

updated_rec = session.query(Transactions).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first()
updated_rec.Name = "Apple Store"
session.commit()

Delete Plaid Data

To delete Plaid 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()
session.delete(deleted_rec)
session.commit()

Free Trial & More Information

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