Use pandas to Visualize Oracle Eloqua Data in Python

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Eloqua Python Connector

Python Connector Libraries for Oracle Eloqua Data Connectivity. Integrate Oracle Eloqua with popular Python tools like Pandas, SQLAlchemy, Dash & petl.

The CData Python Connector for Oracle Eloqua enables you use pandas and other modules to analyze and visualize live Oracle Eloqua data in Python.

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, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Oracle Eloqua-connected Python applications and scripts for visualizing Oracle Eloqua data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Oracle Eloqua 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 data in Python. When you issue complex SQL queries from Oracle Eloqua, the driver pushes supported SQL operations, like filters and aggregations, directly to Oracle Eloqua and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Oracle Eloqua Data

Connecting to Oracle Eloqua 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.

There are two authentication methods available for connecting to Oracle Eloqua: Login and OAuth. The Login method requires you to have the Company, User, and Password of the user.

If you do not have access to the username and password or do not wish to require them, you can use OAuth authentication. OAuth is better suited for allowing other users to access their own data. Using login credentials is better suited for accessing your own data.

Follow the procedure below to install the required modules and start accessing Oracle Eloqua 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 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 data.

engine = create_engine("oracleeloqua:///?User=user&Password=password&Company=CData")

Execute SQL to Oracle Eloqua

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

df = pandas.read_sql("SELECT Name, ActualCost FROM Campaign WHERE ShipCity = 'New York'", engine)

Visualize Oracle Eloqua Data

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

df.plot(kind="bar", x="Name", y="ActualCost")

Free Trial & More Information

Download a free, 30-day trial of the Oracle Eloqua Python Connector to start building Python apps and scripts with connectivity to Oracle Eloqua 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("oracleeloqua:///?User=user&Password=password&Company=CData")
df = pandas.read_sql("SELECT Name, ActualCost FROM Campaign WHERE ShipCity = 'New York'", engine)

df.plot(kind="bar", x="Name", y="ActualCost")