Learn more
 

Ready to get started?

Download a free trial of the SASxpt Connector to get started:

 Download Now

Learn more:

SAS XPORT files Icon SASxpt Python Connector

Python Connector Libraries for SAS xpt (XPORT) file connectivity. Integrate SASxpt with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

In this article

Related articles

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


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

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

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

Connecting to SAS xpt Data

Connecting to SAS xpt 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.

Connecting to Local SASXpt Files

You can connect to local SASXpt file by setting the URI to a folder containing SASXpt files.

Connecting to S3 data source

You can connect to Amazon S3 source to read SASXpt files. Set the following properties to connect:

  • URI: Set this to the folder within your bucket that you would like to connect to.
  • AWSAccessKey: Set this to your AWS account access key.
  • AWSSecretKey: Set this to your AWS account secret key.
  • TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).

Connecting to Azure Data Lake Storage Gen2

You can connect to ADLS Gen2 to read SASXpt files. Set the following properties to connect:

  • URI: Set this to the name of the file system and the name of the folder which contacts your SASXpt files.
  • AzureAccount: Set this to the name of the Azure Data Lake storage account.
  • AzureAccessKey: Set this to our Azure DataLakeStore Gen 2 storage account access key.
  • TemporaryLocalFolder: Set this to the path, or URI, to the folder that is used to temporarily download SASXpt file(s).

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

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

Declare a Mapping Class for SAS xpt 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 SampleTable_1 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 SampleTable_1(base): __tablename__ = "SampleTable_1" Id = Column(String,primary_key=True) Column1 = Column(String) ...

Query SAS xpt 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("sasxpt:///?URI=C:/folder") factory = sessionmaker(bind=engine) session = factory() for instance in session.query(SampleTable_1).filter_by(Column2="100"): print("Id: ", instance.Id) print("Column1: ", instance.Column1) 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

SampleTable_1_table = SampleTable_1.metadata.tables["SampleTable_1"] for instance in session.execute(SampleTable_1_table.select().where(SampleTable_1_table.c.Column2 == "100")): print("Id: ", instance.Id) print("Column1: ", instance.Column1) 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 SASxpt to start building Python apps and scripts with connectivity to SAS xpt data. Reach out to our Support Team if you have any questions.