Use Dash to Build to Web Apps on Factorial Data

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData API Driver for Python, the pandas module, and the Dash framework, you can build Factorial-connected web applications for Factorial data. This article shows how to connect to Factorial with the CData Connector and use pandas and Dash to build a simple web app for visualizing Factorial data.

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

Connecting to Factorial Data

Connecting to Factorial 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.

Authentication

Factorial uses OAuth 2.0 for authentication to connect to your HR data or to allow other users to connect to their data.

Using OAuth Authentication

To connect using OAuth, follow these steps:

1. Navigate to your Factorial admin panel and create a new OAuth application.
2. Copy the Client ID and Client Secret from your application configuration.
3. Configure the following connection properties:

After setting the following connection properties, you are ready to connect:

• AuthScheme: Set this to OAuth.
• OAuthClientId: Set this to your OAuth Client ID.
• OAuthClientSecret: Set this to your OAuth Client Secret.
• Scope: Set this to specify the data access permissions (default: "read write").

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

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

cnxn = mod.connect("Profile=C:\profiles\Factorial.apip;AuthScheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

Execute SQL to Factorial

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 ,  FROM Agreements WHERE ProcessId = '123'", 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-apiedataplot'

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 Factorial data and configure the app layout.

trace = go.Bar(x=df., y=df., 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='Factorial Agreements 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 Factorial data.

python api-dash.py

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps with connectivity to Factorial 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.api as mod
import plotly.graph_objs as go

cnxn = mod.connect("Profile=C:\profiles\Factorial.apip;AuthScheme=OAuth;OAuthClientId=your_client_id;OAuthClientSecret=your_client_secret;CallbackUrl=your_callback_url;")

df = pd.read_sql("SELECT ,  FROM Agreements WHERE ProcessId = '123'", cnxn)
app_name = 'dash-apidataplot'

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., y=df., 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='Factorial Agreements Data', barmode='stack')
		})
], className="container")

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