Use Dash to Build to Web Apps on Zendesk Data

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Zendesk Python Connector

Python Connector Libraries for Zendesk Data Connectivity. Integrate Zendesk with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Zendesk enables you to create Python applications that use pandas and Dash to build Zendesk-connected web apps.

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

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

Connecting to Zendesk Data

Connecting to Zendesk 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 Zendesk

To connect, set the URL and provide authentication. The URL is your Zendesk Support URL: https://{subdomain}.zendesk.com.

Authenticating to Zendesk

You can authenticate using the Basic or OAuth methods.

Using Basic Authentication

To use Basic authentication, specify your email address and password or your email address and an API token. Set User to your email address and follow the steps below to provide the Password or ApiToken.

  • Enable password access in the Zendesk Support admin interface at Admin > Channels > API.
  • Manage API tokens in the Zendesk Support Admin interface at Admin > Channels > API. More than one token can be active at the same time. Deleting a token deactivates it permanently.

Using OAuth Authentication

See the Getting Started guide in the CData driver documentation for an authentication guide.

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

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

cnxn = mod.connect("URL=https://subdomain.zendesk.com;User=my@email.com;Password=test123;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")")

Execute SQL to Zendesk

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 Id, Subject FROM Tickets WHERE Industry = 'Floppy Disks'", 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-zendeskedataplot'

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 Zendesk data and configure the app layout.

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

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='Zendesk Tickets 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 Zendesk data.

python zendesk-dash.py

Free Trial & More Information

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

cnxn = mod.connect("URL=https://subdomain.zendesk.com;User=my@email.com;Password=test123;InitiateOAuth=GETANDREFRESH;OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

df = pd.read_sql("SELECT Id, Subject FROM Tickets WHERE Industry = 'Floppy Disks'", cnxn)
app_name = 'dash-zendeskdataplot'

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.Id, y=df.Subject, name='Id')

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='Zendesk Tickets Data', barmode='stack')
		})
], className="container")

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