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

How to use SQLAlchemy ORM to access SAS Data Sets Data in Python

Create Python applications and scripts that use SQLAlchemy Object-Relational Mappings of SAS Data Sets data.

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

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

Connecting to SAS Data Sets Data

Connecting to SAS Data Sets 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.

Set the following connection properties to connect to your SAS DataSet files:

Connecting to Local Files

  • Set the Connection Type to "Local." Local files support SELECT, INSERT, and DELETE commands.
  • Set the URI to a folder containing SAS files, e.g. C:\PATH\TO\FOLDER\.

Connecting to Cloud-Hosted SAS DataSet Files

While the driver is capable of pulling data from SAS DataSet files hosted on a variety of cloud data stores, INSERT, UPDATE, and DELETE are not supported outside of local files in this driver.

Set the Connection Type to the service hosting your SAS DataSet files. A unique prefix at the beginning of the URI connection property is used to identify the cloud data store and the remainder of the path is a relative path to the desired folder (one table per file) or single file (a single table). For more information, refer to the Getting Started section of the Help documentation.

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

You can now connect with a connection string. Use the create_engine function to create an Engine for working with SAS Data Sets 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("sasdatasets:///?URI=C:/myfolder")

Declare a Mapping Class for SAS Data Sets 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 restaurants 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 restaurants(base): __tablename__ = "restaurants" name = Column(String,primary_key=True) borough = Column(String) ...

Query SAS Data Sets 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("sasdatasets:///?URI=C:/myfolder") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(restaurants).filter_by(cuisine="American"): print("name: ", print("borough: ", instance.borough) 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

restaurants_table = restaurants.metadata.tables["restaurants"] for instance in session.execute( == "American")): print("name: ", print("borough: ", instance.borough) print("---------")

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

Insert SAS Data Sets Data

To insert SAS Data Sets 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 SAS Data Sets.

new_rec = restaurants(name="placeholder", cuisine="American") session.add(new_rec) session.commit()

Update SAS Data Sets Data

To update SAS Data Sets 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 SAS Data Sets.

updated_rec = session.query(restaurants).filter_by(SOME_ID_COLUMN="SOME_ID_VALUE").first() updated_rec.cuisine = "American" session.commit()

Delete SAS Data Sets Data

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