The CData Python Connector for SAS xpt enables you to create ETL applications and pipelines for SAS xpt data in Python with petl.
The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for SAS xpt and the petl framework, you can build SAS xpt-connected applications and pipelines for extracting, transforming, and loading SAS xpt data. This article shows how to connect to SAS xpt with the CData Python Connector and use petl and pandas to extract, transform, and load 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 driver 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.).
After installing the CData SAS xpt Connector, follow the procedure below to install the other required modules and start accessing SAS xpt through Python objects.
Install Required Modules
Use the pip utility to install the required modules and frameworks:
pip install petl pip install pandas
Build an ETL App for SAS xpt.sasxpt as mod
You can now connect with a connection string. Use the connect function for the CData SAS xpt Connector to create a connection for working with SAS xpt data.
cnxn = mod.connect("URI=C:/folder;")
Create a SQL Statement to Query SAS xpt
Use SQL to create a statement for querying SAS xpt. In this article, we read data from the SampleTable_1 entity.
sql = "SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = '100'"
Extract, Transform, and Load the SAS xpt Data
With the query results stored in a DataFrame, we can use petl to extract, transform, and load the SAS xpt data. In this example, we extract SAS xpt data, sort the data by the Column1 column, and load the data into a CSV file.
table1 = etl.fromdb(cnxn,sql) table2 = etl.sort(table1,'Column1') etl.tocsv(table2,'sampletable_1_data.csv')
With the CData Python Connector for SAS xpt, you can work with SAS xpt data just like you would with any database, including direct access to data in ETL packages like petl.
Free Trial & More Information
Download a free, 30-day trial of the SAS xpt Python Connector to start building Python apps and scripts with connectivity to SAS xpt data. Reach out to our Support Team if you have any questions.
Full Source Code
import petl as etl import pandas as pd import cdata.sasxpt as mod cnxn = mod.connect("URI=C:/folder;") sql = "SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = '100'" table1 = etl.fromdb(cnxn,sql) table2 = etl.sort(table1,'Column1') etl.tocsv(table2,'sampletable_1_data.csv')