How to Visualize Talkdesk Data in Python with pandas

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Python Connector for Talkdesk, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Talkdesk-connected Python applications and scripts for visualizing Talkdesk data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Talkdesk data, execute queries, and visualize the results.

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

Connecting to Talkdesk Data

Connecting to Talkdesk 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.

Talkdesk uses the OAuth 2.0 Client Credentials grant. There is no browser-based authorization step and no callback URL.

Set the following connection properties:

• AccountName: The name of your Talkdesk account.
• Region: The region where your Talkdesk instance is deployed. Supported values are US (default), EU, CA, AU, UK, and FedRamp.
• OAuthClientId: The Client Id assigned when you registered your custom OAuth application.
• OAuthClientSecret: The Client Secret assigned to your custom OAuth application.

Creating a Custom OAuth Application

1. Log in to your Talkdesk account and select OAuth Clients from the navigation menu.
2. Click Create OAuth Client and give the client a descriptive name.
3. Set Grant Type to Client Credentials.
4. Click Add scopes and select the scopes for the data you want to access.
5. Click Create and copy the Client Id and Client Secret.

When you connect, the driver automatically requests an access token from Talkdesk, caches it, and refreshes it when it expires. Make sure the scopes selected for the application match the views you plan to query, or the token request can fail.

Follow the procedure below to install the required modules and start accessing Talkdesk through Python objects.

Install Required Modules

Use the pip utility to install the pandas & Matplotlib modules and the SQLAlchemy toolkit:

pip install pandas
pip install matplotlib
pip install sqlalchemy

Be sure to import the module with the following:

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engine

Visualize Talkdesk Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Talkdesk data.

engine = create_engine("talkdesk:///?AccountName=myAccount&Region=US&OAuthClientId=myClientId&OAuthClientSecret=myClientSecret")

Execute SQL to Talkdesk

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

df = pandas.read_sql("SELECT Id, Name FROM Users WHERE Active = 'true'", engine)

Visualize Talkdesk Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Talkdesk data. The show method displays the chart in a new window.

df.plot(kind="bar", x="Id", y="Name")
plt.show()

Free Trial & More Information

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

Full Source Code

import pandas
import matplotlib.pyplot as plt
from sqlalchemy import create_engin

engine = create_engine("talkdesk:///?AccountName=myAccount&Region=US&OAuthClientId=myClientId&OAuthClientSecret=myClientSecret")
df = pandas.read_sql("SELECT Id, Name FROM Users WHERE Active = 'true'", engine)

df.plot(kind="bar", x="Id", y="Name")
plt.show()