How to Visualize SAS xpt Data in Python with pandas

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for SASxpt, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build SAS xpt-connected Python applications and scripts for visualizing SAS xpt data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to SAS xpt data, execute queries, and visualize the results.

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.

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 the required modules and start accessing SAS xpt through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize 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.

engine = create_engine("sasxpt:///?URI=C:/folder")

Execute SQL to SAS xpt

Use the read_sql function from pandas to execute any SQL statement and store the resultset in a DataFrame.

df = pandas.read_sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = '100'", engine)

Visualize SAS xpt Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the SAS xpt data. The show method displays the chart in a new window.

df.plot(kind="bar", x="Id", y="Column1")
plt.show()

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.

Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("sasxpt:///?URI=C:/folder")
df = pandas.read_sql("SELECT Id, Column1 FROM SampleTable_1 WHERE Column2 = '100'", engine)

df.plot(kind="bar", x="Id", y="Column1")
plt.show()