Use pandas to Visualize OData Services in Python

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

The CData Python Connector for OData enables you use pandas and other modules to analyze and visualize live OData services 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 OData, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build OData-connected Python applications and scripts for visualizing OData services. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to OData services, execute queries, and visualize the results.

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

Connecting to OData Services

Connecting to OData services 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 User and Password properties, under the Authentication section, must be set to valid OData user credentials. In addition, you will need to specify a URL to a valid OData server organization root or OData services file.

Follow the procedure below to install the required modules and start accessing OData 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 OData Services in Python

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

engine = create_engine("odata:///?URL= Version=4.0&Data Format=ATOM")

Execute SQL to OData

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

df = pandas.read_sql("SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'", engine)

Visualize OData Services

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

df.plot(kind="bar", x="OrderName", y="Freight")

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Download a free, 30-day trial of the OData Python Connector to start building Python apps and scripts with connectivity to OData services. 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("odata:///?URL= Version=4.0&Data Format=ATOM")
df = pandas.read_sql("SELECT OrderName, Freight FROM Orders WHERE ShipCity = 'New York'", engine)

df.plot(kind="bar", x="OrderName", y="Freight")