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

How to use SQLAlchemy ORM to access Square Data in Python

Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Square data.

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

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

Connecting to Square Data

Connecting to Square 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.

Square uses the OAuth authentication standard. To authenticate using OAuth, you will need to register an app with Square to obtain the OAuthClientId, OAuthClientSecret, and CallbackURL. See the "Getting Started" chapter of the help documentation for a guide to using OAuth.

Additionally, you must specify the LocationId. You can retrieve the Ids for your Locations by querying the Locations table. Alternatively, you can set the LocationId in the search criteria of your query.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Square 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("square:///?OAuthClientId=MyAppId&OAuthClientSecret=MyAppSecret&CallbackURL=http://localhost:33333&LocationId=MyDefaultLocation&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Declare a Mapping Class for Square 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 Refunds 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 Refunds(base): __tablename__ = "Refunds" Reason = Column(String,primary_key=True) RefundedMoneyAmount = Column(String) ...

Query Square 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("square:///?OAuthClientId=MyAppId&OAuthClientSecret=MyAppSecret&CallbackURL=http://localhost:33333&LocationId=MyDefaultLocation&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(Refunds).filter_by(Type="FULL"): print("Reason: ", instance.Reason) print("RefundedMoneyAmount: ", instance.RefundedMoneyAmount) 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

Refunds_table = Refunds.metadata.tables["Refunds"] for instance in session.execute( == "FULL")): print("Reason: ", instance.Reason) print("RefundedMoneyAmount: ", instance.RefundedMoneyAmount) print("---------")

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

Insert Square Data

To insert Square 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 Square.

new_rec = Refunds(Reason="placeholder", Type="FULL") session.add(new_rec) session.commit()

Update Square Data

To update Square 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 Square.

updated_rec = session.query(Refunds).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Type = "FULL" session.commit()

Delete Square Data

To delete Square 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(Refunds).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 Square to start building Python apps and scripts with connectivity to Square data. Reach out to our Support Team if you have any questions.