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

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

Connecting to Oracle Eloqua Reporting Data

Connecting to Oracle Eloqua Reporting 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.

Oracle Eloqua Reporting supports the following authentication methods:

• Basic authentication (User and Password)
• OAuth 2.0 code grant flow
• OAuth 2.0 password grant flow

Basic Authentication (User and Password)

To perform authentication with a user and password, specify these properties:

• AuthScheme: Basic.
• Company: The company name associated with your Oracle Eloqua Reporting account.
• User: Your login account name.
• Password: Your login password.

OAuth Authentication (Code Grant Flow)

To authenticate with the OAuth code grant flow, you must set AuthScheme to OAuth and create a custom OAuth application. For information about how to create a custom OAuth application, see the Help documentation.

Then set the following properties:

• InitiateOAuth: GETANDREFRESH. Used to automatically get and refresh the OAuthAccessToken.
• OAuthClientId: The client Id assigned when you registered your application.
• OAuthClientSecret: The client secret that was assigned when you registered your application.
• CallbackURL: The redirect URI that was defined when you registered your application.

When you connect, the driver opens Oracle Eloqua Reporting's OAuth endpoint in your default browser. Log in and grant permissions to the application. When the access token expires, the driver refreshes it automatically.

OAuth Authentication (Password Grant Flow)

With the OAuth password grant flow, you can use your OAuth application's credentials alongside your user credentials to authenticate without the need to grant permission manually via a browser prompt. You must create an OAuth app (see the Help documentation) to use this authentication method.

Set the following properties:

• AuthScheme: OAuthPassword
• Company: The company's unique identifier.
• User: Your login account name.
• Password: Your login password.
• OAuthClientId: The client Id assigned when you registered your custom OAuth application.
• OAuthClientSecret: The client secret assigned when you registered your custom OAuth application.

Follow the procedure below to install the required modules and start accessing Oracle Eloqua Reporting 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 Oracle Eloqua Reporting Data in Python

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

engine = create_engine("oracleeloquareporting:///?AuthScheme=Basic&User=user&Password=password&Company=MyCompany")

Execute SQL to Oracle Eloqua Reporting

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 WHERE = ''", engine)

Visualize Oracle Eloqua Reporting Data

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

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

👁 Oracle Eloqua Reporting data in a Python plot (Salesforce is shown).

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Oracle Eloqua Reporting to start building Python apps and scripts with connectivity to Oracle Eloqua Reporting 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("oracleeloquareporting:///?AuthScheme=Basic&User=user&Password=password&Company=MyCompany")
df = pandas.read_sql("SELECT , FROM WHERE = ''", engine)

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