Use Dash to Build to Web Apps on ADP 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 ADP, the pandas module, and the Dash framework, you can build ADP-connected web applications for ADP data. This article shows how to connect to ADP with the CData Connector and use pandas and Dash to build a simple web app for visualizing ADP data.

With built-in, optimized data processing, the CData Python Connector offers unmatched performance for interacting with live ADP data in Python. When you issue complex SQL queries from ADP, the driver pushes supported SQL operations, like filters and aggregations, directly to ADP and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to ADP Data

Connecting to ADP 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.

Connect to ADP by specifying the following properties:

• SSLClientCert: Set this to the certificate provided during registration.
• SSLClientCertPassword: Set this to the password of the certificate.
• UseUAT: The connector makes requests to the production environment by default. If using a developer account, set UseUAT = true.
• RowScanDepth: The maximum number of rows to scan for the custom fields columns available in the table. The default value will be set to 100. Setting a high value may decrease performance.

The connector uses OAuth to authenticate with ADP. OAuth requires the authenticating user to interact with ADP using the browser. For more information, refer to the OAuth section in the Help documentation.

After installing the CData ADP Connector, follow the procedure below to install the other required modules and start accessing ADP 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 ADP 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.adp as mod
import plotly.graph_objs as go

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

cnxn = mod.connect("OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;SSLClientCert='c:\cert.pfx';SSLClientCertPassword='admin@123'InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to ADP

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 AssociateOID, WorkerID FROM Workers WHERE AssociateOID = 'G3349PZGBADQY8H8'", 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-adpedataplot'

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 ADP data and configure the app layout.

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

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='ADP Workers 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 ADP data.

python adp-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for ADP to start building Python apps with connectivity to ADP 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.adp as mod
import plotly.graph_objs as go

cnxn = mod.connect("OAuthClientId=YourClientId;OAuthClientSecret=YourClientSecret;SSLClientCert='c:\cert.pfx';SSLClientCertPassword='admin@123'InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT AssociateOID, WorkerID FROM Workers WHERE AssociateOID = 'G3349PZGBADQY8H8'", cnxn)
app_name = 'dash-adpdataplot'

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.AssociateOID, y=df.WorkerID, name='AssociateOID')

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='ADP Workers Data', barmode='stack')
		})
], className="container")

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