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Get the Report →How to Visualize SAS xpt Data in Python with pandas
Use pandas and other modules to analyze and visualize live SAS xpt data in Python.
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()