Use pandas to Visualize Dynamics NAV Data in Python

Ready to get started?

Download a free trial of the Dynamics NAV Connector to get started:

 Download Now

Learn more:

Dynamics NAV Icon Dynamics NAV Python Connector

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

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

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

Connecting to Dynamics NAV Data

Connecting to Dynamics NAV 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.

Before you can connect, OData Services will need to be enabled on the server. Once OData Services are enabled, you will be able to query any Services that are published on the server.

The User and Password properties, under the Authentication section, must be set to valid Dynamics NAV user credentials. In addition, you will need to specify a URL to a valid Dynamics NAV server organization root and a ServerInstance. If there is not a Service Default Company for the server, you will need to set the Company as well.

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

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

engine = create_engine("dynamicsnav:///?http://myserver:7048&User=myserver\Administrator&Password=admin&ServerInstance=DYNAMICSNAV71")

Execute SQL to Dynamics NAV

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

df = pandas.read_sql("SELECT Name, Prices_Including_VAT FROM Customer WHERE Name = 'Bob'", engine)

Visualize Dynamics NAV Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Dynamics NAV Python Connector to start building Python apps and scripts with connectivity to Dynamics NAV 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("dynamicsnav:///?http://myserver:7048&User=myserver\Administrator&Password=admin&ServerInstance=DYNAMICSNAV71")
df = pandas.read_sql("SELECT Name, Prices_Including_VAT FROM Customer WHERE Name = 'Bob'", engine)

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