Use pandas to Visualize Oracle Sales Data in Python

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Oracle Sales Python Connector

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

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

Connecting to Oracle Sales Data

Connecting to Oracle Sales 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 Sales uses Basic authentication over SSL; after setting the following connection properties, you are ready to connect:

  • Username: Set this to the user name that you use to log into your Oracle Cloud service.
  • Password: Set this to your password.
  • HostURL: Set this to the Web address (URL) of your Oracle Cloud service.

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

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

engine = create_engine("oraclesalescloud:///?HostURL= Username=abc123& Password=abcdef")

Execute SQL to Oracle Sales

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

df = pandas.read_sql("SELECT OptyId, Name FROM Opportunities WHERE CreatedBy = 'Jack'", engine)

Visualize Oracle Sales Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Oracle Sales Python Connector to start building Python apps and scripts with connectivity to Oracle Sales 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("oraclesalescloud:///?HostURL= Username=abc123& Password=abcdef")
df = pandas.read_sql("SELECT OptyId, Name FROM Opportunities WHERE CreatedBy = 'Jack'", engine)

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