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Use SQLAlchemy ORMs to Access DataRobot Data in Python

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

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

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

Connecting to DataRobot Data

Connecting to DataRobot 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 connect to DataRobot, the following connection properties are required: User, Password, and PredictionInstance. DataRobotKey may also be required depending on your type of DataRobot predictions instance. If using the Predictions API, DataFile is required. The CSV DataFile should include a header row as the first row of the datafile. APIKey is not required, but can be supplied. If not supplied, the driver will handle obtaining an APIKey.

User, DataRobotKey, and APIKey are the credentials for the DataRobot account.

ProjectID, DataFile, and ModelId are the parameters for the project, dataset, and model type.

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

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

engine = create_engine("datarobot///?PredictionInstance=myinstance.orm.datarobot.com&DataFile=PATH\TO\input_file.csv&DataRobotKey=123-abc-456-def&User=username&Password=password")

Declare a Mapping Class for DataRobot 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 Predictions 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 Predictions(base):
	__tablename__ = "Predictions"
	Id = Column(String,primary_key=True)
	Prediction1Value = Column(String)
	...

Query DataRobot 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("datarobot///?PredictionInstance=myinstance.orm.datarobot.com&DataFile=PATH\TO\input_file.csv&DataRobotKey=123-abc-456-def&User=username&Password=password")
factory = sessionmaker(bind=engine)
session = factory()
for instance in session.query(Predictions).filter_by(Id="1"):
	print("Id: ", instance.Id)
	print("Prediction1Value: ", instance.Prediction1Value)
	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

Predictions_table = Predictions.metadata.tables["Predictions"]
for instance in session.execute(Predictions_table.select().where(Predictions_table.c.Id == "1")):
	print("Id: ", instance.Id)
	print("Prediction1Value: ", instance.Prediction1Value)
	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 DataRobot Python Connector to start building Python apps and scripts with connectivity to DataRobot data. Reach out to our Support Team if you have any questions.