Use SQLAlchemy ORMs to Access NetSuite Data in Python

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NetSuite Python Connector

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

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

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

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

Connecting to NetSuite Data

Connecting to NetSuite 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 User and Password properties, under the Authentication section, must be set to valid NetSuite user credentials. In addition, the AccountId must be set to the ID of a company account that can be used by the specified User. The RoleId can be optionally specified to log in the user with limited permissions.

See the "Getting Started" chapter of the help documentation for more information on connecting to NetSuite.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with NetSuite 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("netsuite:///?Account Id=XABC123456&Password=password&User=user&Role Id=3&Version=2013_1")

Declare a Mapping Class for NetSuite 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 SalesOrder 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 SalesOrder(base): __tablename__ = "SalesOrder" CustomerName = Column(String,primary_key=True) SalesOrderTotal = Column(String) ...

Query NetSuite 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("netsuite:///?Account Id=XABC123456&Password=password&User=user&Role Id=3&Version=2013_1") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(SalesOrder).filter_by(Class_Name="Furniture : Office"): print("CustomerName: ", instance.CustomerName) print("SalesOrderTotal: ", instance.SalesOrderTotal) 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

SalesOrder_table = SalesOrder.metadata.tables["SalesOrder"] for instance in session.execute( == "Furniture : Office")): print("CustomerName: ", instance.CustomerName) print("SalesOrderTotal: ", instance.SalesOrderTotal) print("---------")

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

Insert NetSuite Data

To insert NetSuite 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 NetSuite.

new_rec = SalesOrder(CustomerName="placeholder", Class_Name="Furniture : Office") session.add(new_rec) session.commit()

Update NetSuite Data

To update NetSuite 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 NetSuite.

updated_rec = session.query(SalesOrder).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.Class_Name = "Furniture : Office" session.commit()

Delete NetSuite Data

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