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

Connecting to Oracle HCM Cloud Data

Connecting to Oracle HCM 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 HCM Cloud:

• Url: The Url of your account.
• User: The user of your account.
• Password: The password of your account.

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

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

Execute SQL to Oracle HCM 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 SiteId, SiteName FROM RecruitingCESites WHERE Language = 'English'", engine)

Visualize Oracle HCM Cloud Data

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

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

👁 Oracle HCM Cloud 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 HCM Cloud to start building Python apps and scripts with connectivity to Oracle HCM Cloud 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("oraclehcm:///?Url=https://abc.oraclecloud.com&User=user&Password=password")
df = pandas.read_sql("SELECT SiteId, SiteName FROM RecruitingCESites WHERE Language = 'English'", engine)

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