Learn more
 

Ready to get started?

Download a free trial of the Dynamics GP Connector to get started:

 Download Now

Learn more:

Dynamics GP Icon Dynamics GP Python Connector

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

In this article

Related articles

How to use SQLAlchemy ORM to access Dynamics GP Data in Python


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

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

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

Connecting to Dynamics GP Data

Connecting to Dynamics GP 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 set the User and Password connection properties.

To connect set the URL to the Web services endpoint; for example, http://{servername}:{port}/Dynamics/GPService. Additionally, set CompanyId; you can obtain this value in the company setup window: Click Tools -> Setup -> Company.

By default, data summaries are not returned to save performance. Set LookupIds to true to return details such as line items; however, note that entities must be retrieved one at a time.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Dynamics GP 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("dynamicsgp:///?CompanyId=mycompanyId&user=myuser&password=mypassword&URL= http://{servername}:{port}/Dynamics/GPService")

Declare a Mapping Class for Dynamics GP 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 SalesInvoice 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 SalesInvoice(base): __tablename__ = "SalesInvoice" CustomerName = Column(String,primary_key=True) TotalAmount = Column(String) ...

Query Dynamics GP 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("dynamicsgp:///?CompanyId=mycompanyId&user=myuser&password=mypassword&URL= http://{servername}:{port}/Dynamics/GPService") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(SalesInvoice).filter_by(CustomerName="Bob"): print("CustomerName: ", instance.CustomerName) print("TotalAmount: ", instance.TotalAmount) 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

SalesInvoice_table = SalesInvoice.metadata.tables["SalesInvoice"] for instance in session.execute(SalesInvoice_table.select().where(SalesInvoice_table.c.CustomerName == "Bob")): print("CustomerName: ", instance.CustomerName) print("TotalAmount: ", instance.TotalAmount) print("---------")

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

Insert Dynamics GP Data

To insert Dynamics GP 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 Dynamics GP.

new_rec = SalesInvoice(CustomerName="placeholder", CustomerName="Bob") session.add(new_rec) session.commit()

Update Dynamics GP Data

To update Dynamics GP 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 Dynamics GP.

updated_rec = session.query(SalesInvoice).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.CustomerName = "Bob" session.commit()

Delete Dynamics GP Data

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