Use Dash to Build to Web Apps on SAS Data Sets Data

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 module, and the Dash framework, you can build SAS Data Sets-connected web applications for SAS Data Sets data. This article shows how to connect to SAS Data Sets with the CData Connector and use pandas and Dash to build a simple web app for visualizing SAS Data Sets data.

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.

After installing the CData SAS Data Sets Connector, follow the procedure below to install the other required modules and start accessing SAS Data Sets through Python objects.

Install Required Modules

Use the pip utility to install the required modules and frameworks:

pip install pandas
pip install dash
pip install dash-daq

Visualize SAS Data Sets Data in Python

Once the required modules and frameworks are installed, we are ready to build our web app. Code snippets follow, but the full source code is available at the end of the article.

First, be sure to import the modules (including the CData Connector) with the following:

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.sasdatasets as mod
import plotly.graph_objs as go

You can now connect with a connection string. Use the connect function for the CData SAS Data Sets Connector to create a connection for working with SAS Data Sets data.

cnxn = mod.connect("URI=C:/myfolder;")

Execute SQL to SAS Data Sets

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

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

Configure the Web App

With the query results stored in a DataFrame, we can begin configuring the web app, assigning a name, stylesheet, and title.

app_name = 'dash-sasdatasetsedataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'

Configure the Layout

The next step is to create a bar graph based on our SAS Data Sets data and configure the app layout.

trace = go.Bar(x=df.name, y=df.borough, name='name')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='SAS Data Sets restaurants Data', barmode='stack')
		})
], className="container")

Set the App to Run

With the connection, app, and layout configured, we are ready to run the app. The last lines of Python code follow.

if __name__ == '__main__':
    app.run_server(debug=True)

Now, use Python to run the web app and a browser to view the SAS Data Sets data.

python sasdatasets-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for SAS Data Sets to start building Python apps with connectivity to SAS Data Sets data. Reach out to our Support Team if you have any questions.

Full Source Code

import os
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
import cdata.sasdatasets as mod
import plotly.graph_objs as go

cnxn = mod.connect("URI=C:/myfolder;")

df = pd.read_sql("SELECT name, borough FROM restaurants WHERE cuisine = 'American'", cnxn)
app_name = 'dash-sasdatasetsdataplot'

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.title = 'CData + Dash'
trace = go.Bar(x=df.name, y=df.borough, name='name')

app.layout = html.Div(children=[html.H1("CData Extension + Dash", style={'textAlign': 'center'}),
	dcc.Graph(
		id='example-graph',
		figure={
			'data': [trace],
			'layout':
			go.Layout(title='SAS Data Sets restaurants Data', barmode='stack')
		})
], className="container")

if __name__ == '__main__':
    app.run_server(debug=True)