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

Connecting to Oracle Service Cloud Data

Connecting to Oracle Service Cloud 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.

Using Basic Authentication

You must set the following to authenticate to Oracle Service Cloud:

• Url: The Url of the account to connect to.
• User: The username of the authenticating account.
• Password: The password of the authenticating account.

Follow the procedure below to install the required modules and start accessing Oracle Service Cloud 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 Service Cloud Data in Python

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

engine = create_engine("oracleservicecloud:///?Url=https://abc.rightnowdemo.com&User=user&Password=password")

Execute SQL to Oracle Service Cloud

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, LookupName FROM Accounts WHERE DisplayOrder = '12'", engine)

Visualize Oracle Service Cloud Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Oracle Service Cloud to start building Python apps and scripts with connectivity to Oracle Service Cloud 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("oracleservicecloud:///?Url=https://abc.rightnowdemo.com&User=user&Password=password")
df = pandas.read_sql("SELECT Id, LookupName FROM Accounts WHERE DisplayOrder = '12'", engine)

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