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Python Connector Libraries for Sage 300 Data Connectivity. Integrate Sage 300 with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

How to use SQLAlchemy ORM to access Sage 300 Data in Python

Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of Sage 300 data.

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems effectively. With the CData Python Connector for Sage 300 and the SQLAlchemy toolkit, you can build Sage 300-connected Python applications and scripts. This article shows how to use SQLAlchemy to connect to Sage 300 data to query Sage 300 data.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Sage 300 data in Python. When you issue complex SQL queries from Sage 300, the CData Connector pushes supported SQL operations, like filters and aggregations, directly to Sage 300 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Sage 300 Data

Connecting to Sage 300 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.

Sage 300 requires some initial setup in order to communicate over the Sage 300 Web API.

  • Set up the security groups for the Sage 300 user. Give the Sage 300 user access to the option under Security Groups (per each module required).
  • Edit both web.config files in the /Online/Web and /Online/WebApi folders; change the key AllowWebApiAccessForAdmin to true. Restart the webAPI app-pool for the settings to take.
  • Once the user access is configured, click https://server/Sage300WebApi/ to ensure access to the web API.

Authenticate to Sage 300 using Basic authentication.

Connect Using Basic Authentication

You must provide values for the following properties to successfully authenticate to Sage 300. Note that the provider reuses the session opened by Sage 300 using cookies. This means that your credentials are used only on the first request to open the session. After that, cookies returned from Sage 300 are used for authentication.

  • Url: Set this to the url of the server hosting Sage 300. Construct a URL for the Sage 300 Web API as follows: {protocol}://{host-application-path}/v{version}/{tenant}/ For example, http://localhost/Sage300WebApi/v1.0/-/.
  • User: Set this to the username of your account.
  • Password: Set this to the password of your account.

Follow the procedure below to install SQLAlchemy and start accessing Sage 300 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 Sage 300 Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Sage 300 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("sage300:///?User=SAMPLE&Password=password&URL=")

Declare a Mapping Class for Sage 300 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 OEInvoices 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 OEInvoices(base): __tablename__ = "OEInvoices" InvoiceUniquifier = Column(String,primary_key=True) ApprovedLimit = Column(String) ...

Query Sage 300 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("sage300:///?User=SAMPLE&Password=password&URL=") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(OEInvoices).filter_by(AllowPartialShipments="Yes"): print("InvoiceUniquifier: ", instance.InvoiceUniquifier) print("ApprovedLimit: ", instance.ApprovedLimit) 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

OEInvoices_table = OEInvoices.metadata.tables["OEInvoices"] for instance in session.execute( == "Yes")): print("InvoiceUniquifier: ", instance.InvoiceUniquifier) print("ApprovedLimit: ", instance.ApprovedLimit) print("---------")

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Sage 300 to start building Python apps and scripts with connectivity to Sage 300 data. Reach out to our Support Team if you have any questions.