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

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

Connecting to Keap Data

Connecting to Keap 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.

Start by setting the Profile connection property to the location of the Keap Profile on disk (e.g. C:\profiles\Keap.apip). Next, set the ProfileSettings connection property to the connection string for Keap (see below).

Keap API Profile Settings

Log into your Keap web app, click the user bubble in the bottom left, choose Settings > API, then generate a new Personal Access Token or Service Account Key.

After installing the CData Keap Connector, follow the procedure below to install the other required modules and start accessing Keap 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 Keap 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 Keap Connector to create a connection for working with Keap data.

cnxn = mod.connect("Profile=C:\profiles\Keap.apip;ProfileSettings='APIKey=your_api_key';")

Execute SQL to Keap

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, Email FROM Account WHERE Email = '[email protected]'", 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 Keap data and configure the app layout.

trace = go.Bar(x=df.Name, y=df.Email, 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='Keap Account 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 Keap 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 Keap 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\Keap.apip;ProfileSettings='APIKey=your_api_key';")

df = pd.read_sql("SELECT Name, Email FROM Account WHERE Email = '[email protected]'", 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.Name, y=df.Email, 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='Keap Account Data', barmode='stack')
		})
], className="container")

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