How to Visualize Gong 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 API Driver for Python, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Gong-connected Python applications and scripts for visualizing Gong data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Gong data, execute queries, and visualize the results.

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

Connecting to Gong Data

Connecting to Gong 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.

To authenticate to Gong, you can use API Key authentication with your Gong API Key and API Secret.

Authentication

To authenticate to Gong, you must provide your Gong API Key and API Secret, along with your tenant Domain. These credentials are combined and Base64-encoded to form the Basic authentication header used for all API requests.

Using API Key Authentication

To authenticate using an API Key, you need to obtain your API Key and API Secret from your Gong account settings.

You can then connect by setting the AuthScheme to APIKey and providing your credentials:

• AuthScheme: Set this to APIKey.
• APIKey: Set this to your Gong API Key.
• APISecret: Set this to your Gong API Secret.
• Domain: Set this to your Gong tenant domain (e.g., us-36533.api.gong.io).

Example connection string

Profile=C:\profiles\Gong.apip;AuthScheme=APIKey;ProfileSettings='APIKey=your_api_key;APISecret=your_api_secret;Domain=your-tenant.api.gong.io';

Follow the procedure below to install the required modules and start accessing Gong 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 Gong Data in Python

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

engine = create_engine("api:///?Profile=C:\profiles\Gong.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_key&APISecret=your_api_secret&Domain=your-tenant.api.gong.io'")

Execute SQL to Gong

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

df = pandas.read_sql("SELECT ,  FROM AnsweredScorecards WHERE  = ''", engine)

Visualize Gong Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData API Driver for Python to start building Python apps and scripts with connectivity to Gong 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("api:///?Profile=C:\profiles\Gong.apip&AuthScheme=APIKey&ProfileSettings='APIKey=your_api_key&APISecret=your_api_secret&Domain=your-tenant.api.gong.io'")
df = pandas.read_sql("SELECT ,  FROM AnsweredScorecards WHERE  = ''", engine)

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