How to Visualize SAS Data Sets 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 SAS Data Sets, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build SAS Data Sets-connected Python applications and scripts for visualizing SAS Data Sets data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to SAS Data Sets 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 Data Sets data in Python. When you issue complex SQL queries from SAS Data Sets, the driver 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 the required modules and start accessing SAS Data Sets 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 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.

engine = create_engine("sasdatasets:///?URI=C:/myfolder")

Execute SQL to SAS Data Sets

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

df = pandas.read_sql("SELECT name, borough FROM restaurants WHERE cuisine = 'American'", engine)

Visualize SAS Data Sets Data

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

df.plot(kind="bar", x="name", y="borough")
plt.show()

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.

Full Source Code

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

engine = create_engine("sasdatasets:///?URI=C:/myfolder")
df = pandas.read_sql("SELECT name, borough FROM restaurants WHERE cuisine = 'American'", engine)

df.plot(kind="bar", x="name", y="borough")
plt.show()