Use pandas to Visualize Oracle Data in Python

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Oracle Python Connector

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

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

Connecting to Oracle Data

Connecting to Oracle 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.

To connect to Oracle, you'll first need to update your PATH variable and ensure it contains a folder location that includes the native DLLs. The native DLLs can be found in the lib folder inside the installation directory. Once you've done this, set the following to connect:

  • Port: The port used to connect to the server hosting the Oracle database.
  • User: The user Id provided for authentication with the Oracle database.
  • Password: The password provided for authentication with the Oracle database.
  • Service Name: The service name of the Oracle database.

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

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

engine = create_engine("oracleoci:///?User=myuser&Password=mypassword&Server=localhost&Port=1521")

Execute SQL to Oracle

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

df = pandas.read_sql("SELECT CompanyName, City FROM Customers WHERE Country = 'US'", engine)

Visualize Oracle Data

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

df.plot(kind="bar", x="CompanyName", y="City")

Free Trial & More Information

Download a free, 30-day trial of the Oracle Python Connector to start building Python apps and scripts with connectivity to Oracle 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("oracleoci:///?User=myuser&Password=mypassword&Server=localhost&Port=1521")
df = pandas.read_sql("SELECT CompanyName, City FROM Customers WHERE Country = 'US'", engine)

df.plot(kind="bar", x="CompanyName", y="City")