How to Visualize SAS xpt Data in Python with pandas via CData Connect AI

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

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so pandas can read query results directly from the SDK connection object. There is no driver to install per source and no SQLAlchemy engine to configure: connect with a Personal Access Token and pass the connection straight to pandas.read_sql.

Connect to SAS xpt in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "SAS xpt" from the Add Connection panel

3. Enter the necessary authentication properties to connect to SAS xpt.

   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).
   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK (with the pandas extra) and Matplotlib using the pip utility:

pip install "cdata-connect-ai[full]"
pip install matplotlib

Visualize SAS xpt Data in Python

Import the modules, then connect to Connect AI with your account email and PAT. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, SASXpt1).

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Query SAS xpt with pandas

Use the read_sql function from pandas to execute a SQL statement and store the result set in a DataFrame. Pass the SDK connection directly, no engine required.

df = pandas.read_sql(
    "SELECT Id, Column1 "
    "FROM [SASXpt1].[SASXpt].[SampleTable_1] "
    "WHERE Column2 = '100'",
    conn,
)

Note: pandas may print a UserWarning stating that it only officially supports SQLAlchemy connectables. This is expected when passing a DB-API connection directly—the query runs correctly and the warning is safe to ignore.

Visualize SAS xpt Data

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

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

conn.close()

More Information and Free Trial

Now you can read live SAS xpt data into pandas through the CData Connect AI Python SDK. For more information on connecting to SAS xpt (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live SAS xpt data in Python.

Full Source Code

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

df = pandas.read_sql(
    "SELECT Id, Column1 "
    "FROM [SASXpt1].[SASXpt].[SampleTable_1] "
    "WHERE Column2 = '100'",
    conn,
)

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

conn.close()