How to Visualize HCL Domino Data in Python with pandas via CData Connect AI

The rich ecosystem of Python modules lets you get to work quickly and integrate your systems more effectively. With the CData Connect AI Python SDK, the pandas and Matplotlib modules, you can build HCL Domino-connected Python applications and scripts for visualizing HCL Domino data. This article shows how to connect to Connect AI and use the pandas and Matplotlib built-in functions to query HCL Domino data and visualize the results.

The Connect AI Python SDK (cdata-connect-ai) is a DB-API 2.0 (PEP 249) compliant client, so pandas can read query results directly from the SDK connection object. There is no driver to install per source and no SQLAlchemy engine to configure: connect with a Personal Access Token and pass the connection straight to pandas.read_sql.

Connect to HCL Domino in Connect AI

CData Connect AI uses a straightforward, point-and-click interface to connect to data sources.

1. Log into Connect AI, click Sources, and then click Add Connection

2. Select "HCL Domino" from the Add Connection panel

3. Enter the necessary authentication properties to connect to HCL Domino.

   Connecting to Domino

   To connect to Domino data, set the following properties:

   • URL: The host name or IP of the server hosting the Domino database. Include the port of the server hosting the Domino database. For example: http://sampleserver:1234/
   • DatabaseScope: The name of a scope in the Domino Web UI. The driver exposes forms and views for the schema governed by the specified scope. In the Domino Admin UI, select the Scopes menu in the sidebar. Set this property to the name of an existing scope.

   Authenticating with Domino

   Domino supports authenticating via login credentials or an Entra ID (formerly Azure AD) OAuth application:

   Login Credentials

   To authenticate with login credentials, set the following properties:

   • AuthScheme: Set this to "OAuthPassword"
   • User: The username of the authenticating Domino user
   • Password: The password associated with the authenticating Domino user

   The driver uses the login credentials to automatically perform an OAuth token exchange.

   EntraID (formerly AzureAD)

   This authentication method uses Entra ID (formerly Azure AD) as an IdP to obtain a JWT token. You need to create a custom OAuth application in Entra ID (formerly Azure AD) and configure it as an IdP. To do so, follow the instructions in the Help documentation. Then set the following properties:

   • AuthScheme: Set this to "EntraID (formerly AzureAD)"
   • InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
   • OAuthClientId: The Client ID obtained when setting up the custom OAuth application.
   • OAuthClientSecret: The Client secret obtained when setting up the custom OAuth application.
   • CallbackURL: The redirect URI defined when you registered your app. For example: https://localhost:33333
   • AzureTenant: The Microsoft Online tenant being used to access data. Supply either a value in the form companyname.microsoft.com or the tenant ID.

     The tenant ID is the same as the directory ID shown in the Azure Portal's Entra ID (formerly Azure AD) > Properties page.

   
4. Click Save & Test
5. Navigate to the Permissions tab and update the user-based permissions.

Generate a Personal Access Token (PAT)

The Python SDK authenticates to Connect AI with your account email and a Personal Access Token (PAT). It is best practice to create a separate PAT for each application to maintain granularity of access.

1. Click the Gear icon () at the top right of the Connect AI app to open the Settings page.
2. On the Settings page, go to the Access Tokens section and click Create PAT.
3. Give the PAT a name and click Create.
4. The PAT is only visible at creation, so copy it and store it securely.

Install Required Modules

Install the SDK (with the pandas extra) and Matplotlib using the pip utility:

pip install "cdata-connect-ai[full]"
pip install matplotlib

Visualize HCL Domino Data in Python

Import the modules, then connect to Connect AI with your account email and PAT. Identifiers are three-part: <Connection>.<Schema>.<Table>, where the connection name defaults to the source name (for example, Domino1).

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

Query HCL Domino with pandas

Use the read_sql function from pandas to execute a SQL statement and store the result set in a DataFrame. Pass the SDK connection directly, no engine required.

df = pandas.read_sql(
    "SELECT Name, Address "
    "FROM [Domino1].[Domino].[ByName] "
    "WHERE City = 'Miami'",
    conn,
)

Note: pandas may print a UserWarning stating that it only officially supports SQLAlchemy connectables. This is expected when passing a DB-API connection directly—the query runs correctly and the warning is safe to ignore.

Visualize HCL Domino Data

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

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

conn.close()

More Information and Free Trial

Now you can read live HCL Domino data into pandas through the CData Connect AI Python SDK. For more information on connecting to HCL Domino (and hundreds of other data sources), visit the Connect AI page. Sign up for a free trial and start working with live HCL Domino data in Python.

Full Source Code

import pandas
import matplotlib.pyplot as plt
import cdata_connect_ai

conn = cdata_connect_ai.connect(
    username="[email protected]",
    password="<your_pat>",
)

df = pandas.read_sql(
    "SELECT Name, Address "
    "FROM [Domino1].[Domino].[ByName] "
    "WHERE City = 'Miami'",
    conn,
)

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

conn.close()