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

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

Connecting to PingOne Data

Connecting to PingOne 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 connect to PingOne, configure these properties:

• : The region where the data for your PingOne organization is being hosted.
• : The type of authentication to use when connecting to PingOne.
• Either (required when using the default PingOne domain) or , configured as described below.

Configuring WorkerAppEnvironmentId

is the ID of the PingOne environment in which your Worker application resides. This parameter is used only when the environment is using the default PingOne domain (auth.pingone). It is configured after you have created the custom OAuth application you will use to authenticate to PingOne, as described in Creating a Custom OAuth Application in the Help documentation.

First, find the value for this property:

1. From the home page of your PingOne organization, move to the navigation sidebar and click Environments.
2. Find the environment in which you have created your custom OAuth/Worker application (usually Administrators), and click Manage Environment. The environment's home page displays.
3. In the environment's home page navigation sidebar, click Applications.
4. Find your OAuth or Worker application details in the list.
5. Copy the value in the Environment ID field. It should look similar to:
   

   WorkerAppEnvironmentId='11e96fc7-aa4d-4a60-8196-9acf91424eca'

Now set to the value of the Environment ID field.

Configuring AuthorizationServerURL

is the base URL of the PingOne authorization server for the environment where your application is located. This property is only used when you have set up a custom domain for the environment, as described in the PingOne platform API documentation. See Custom Domains.

Authenticating to PingOne with OAuth

PingOne supports both OAuth and OAuthClient authentication. In addition to performing the configuration steps described above, there are two more steps to complete to support OAuth or OAuthCliet authentication:

• Create and configure a custom OAuth application, as described in Creating a Custom OAuth Application in the Help documentation.
• To ensure that the driver can access the entities in Data Model, confirm that you have configured the correct roles for the admin user/worker application you will be using, as described in Administrator Roles in the Help documentation.
• Set the appropriate properties for the authscheme and authflow of your choice, as described in the following subsections.

OAuth (Authorization Code grant)

Set to OAuth.

Desktop Applications

Get and Refresh the OAuth Access Token

After setting the following, you are ready to connect:

• : GETANDREFRESH. To avoid the need to repeat the OAuth exchange and manually setting the each time you connect, use .
• : The Client ID you obtained when you created your custom OAuth application.
• : The Client Secret you obtained when you created your custom OAuth application.
• : The redirect URI you defined when you registered your custom OAuth application. For example: https://localhost:3333

When you connect, the driver opens PingOne's OAuth endpoint in your default browser. Log in and grant permissions to the application. The driver then completes the OAuth process:

1. The driver obtains an access token from PingOne and uses it to request data.
2. The OAuth values are saved in the location specified in , to be persisted across connections.

The driver refreshes the access token automatically when it expires.

For other OAuth methods, including Web Applications, Headless Machines, or Client Credentials Grant, refer to the Help documentation.

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

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

engine = create_engine("pingone:///?AuthScheme=OAuth&WorkerAppEnvironmentId=eebc33a8-xxxx-4f3a-yyyy-d3e5262fd49e&Region=NA&OAuthClientId=client_id&OAuthClientSecret=client_secret&InitiateOAuth=GETANDREFRESH")

Execute SQL to PingOne

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, Username FROM [CData].[Administrators].Users WHERE EmployeeType = 'Contractor'", engine)

Visualize PingOne Data

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

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

👁 PingOne data in a Python plot (Salesforce is shown).

Free Trial & More Information

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

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Full Source Code

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

engine = create_engine("pingone:///?AuthScheme=OAuth&WorkerAppEnvironmentId=eebc33a8-xxxx-4f3a-yyyy-d3e5262fd49e&Region=NA&OAuthClientId=client_id&OAuthClientSecret=client_secret&InitiateOAuth=GETANDREFRESH")
df = pandas.read_sql("SELECT Id, Username FROM [CData].[Administrators].Users WHERE EmployeeType = 'Contractor'", engine)

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