Use pandas to Visualize Dynamics GP Data in Python

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Dynamics GP Python Connector

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

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

Connecting to Dynamics GP Data

Connecting to Dynamics GP 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 authenticate set the User and Password connection properties.

To connect set the URL to the Web services endpoint; for example, http://{servername}:{port}/Dynamics/GPService. Additionally, set CompanyId; you can obtain this value in the company setup window: Click Tools -> Setup -> Company.

By default, data summaries are not returned to save performance. Set LookupIds to true to return details such as line items; however, note that entities must be retrieved one at a time.

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

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

engine = create_engine("dynamicsgp:///?CompanyId=mycompanyId&user=myuser&password=mypassword&URL= http://{servername}:{port}/Dynamics/GPService")

Execute SQL to Dynamics GP

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

df = pandas.read_sql("SELECT CustomerName, TotalAmount FROM SalesInvoice WHERE CustomerName = 'Bob'", engine)

Visualize Dynamics GP Data

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

df.plot(kind="bar", x="CustomerName", y="TotalAmount")

Free Trial & More Information

Download a free, 30-day trial of the Dynamics GP Python Connector to start building Python apps and scripts with connectivity to Dynamics GP 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("dynamicsgp:///?CompanyId=mycompanyId&user=myuser&password=mypassword&URL= http://{servername}:{port}/Dynamics/GPService")
df = pandas.read_sql("SELECT CustomerName, TotalAmount FROM SalesInvoice WHERE CustomerName = 'Bob'", engine)

df.plot(kind="bar", x="CustomerName", y="TotalAmount")