How to Visualize Suadeo 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 Suadeo, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Suadeo-connected Python applications and scripts for visualizing Suadeo data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Suadeo data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Suadeo data in Python. When you issue complex SQL queries from Suadeo, the driver pushes supported SQL operations, like filters and aggregations, directly to Suadeo and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Suadeo Data

Connecting to Suadeo 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.

The driver uses the OAuth 2.0 Resource Owner Password Credentials (ROPC) grant to authenticate to Suadeo. Authentication occurs directly using your credentials; there is no browser-based authorization flow or refresh token.

Set the following connection properties:

• URL: The base URL of your Suadeo instance.
• User: Your Suadeo username.
• Password: Your Suadeo password.
• AuthenticationName: The name identifier for the authentication configuration in your Suadeo instance. Different authentication names can be configured for different environments or use cases.

When you connect, the driver sends your credentials to the Suadeo OAuth token endpoint, receives an access token, and uses it for all subsequent requests. A new access token is obtained automatically when needed during the session.

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

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

engine = create_engine("suadeo:///?URL=https://mysuadeoinstance&User=username&Password=password&AuthenticationName=your_auth_name")

Execute SQL to Suadeo

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, Name FROM Customers WHERE Status = 'Active'", engine)

Visualize Suadeo Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Suadeo to start building Python apps and scripts with connectivity to Suadeo 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("suadeo:///?URL=https://mysuadeoinstance&User=username&Password=password&AuthenticationName=your_auth_name")
df = pandas.read_sql("SELECT Id, Name FROM Customers WHERE Status = 'Active'", engine)

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