Ready to get started?

Learn more about Sage US Connectivity Solutions

Learn More

Use pandas to Visualize Sage US Data in Python

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

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

Connecting to Sage US Data

Connecting to Sage US 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 Application Id and Company Name connection string options are required to connect to Sage as a data source. You can obtain an Application Id by contacting Sage directly to request access to the Sage 50 SDK.

Sage must be installed on the machine. The Sage.Peachtree.API.dll and Sage.Peachtree.API.Resolver.dll assemblies are required. These assemblies are installed with Sage in C:\Program Files\Sage\Peachtree\API\. Additionally, the Sage SDK requires .NET Framework 4.0 and is only compatible with 32-bit applications. To use the Sage SDK in Visual Studio, set the Platform Target property to "x86" in Project -> Properties -> Build.

You must authorize the application to access company data: To authorize your application to access Sage, restart the Sage application, open the company you want to access, and connect with your application. You will then be prompted to set access permissions for the application in the resulting dialog.

While the compiled executable will require authorization only once, during development you may need to follow this process to reauthorize a new build. To avoid restarting the Sage application when developing with Visual Studio, click Build -> Configuration Manager and uncheck "Build" for your project.

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

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

engine = create_engine("sage50us:///?ApplicationId=8dfafu4V4ODmh1fM0xx&CompanyName=Bellwether Garden Supply - Premium")

Execute SQL to Sage US

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, LastInvoiceAmount FROM Customer WHERE Name = 'ALDRED'", engine)

Visualize Sage US Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the Sage US Python Connector to start building Python apps and scripts with connectivity to Sage US 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("sage50us:///?ApplicationId=8dfafu4V4ODmh1fM0xx&CompanyName=Bellwether Garden Supply - Premium")
df = pandas.read_sql("SELECT Name, LastInvoiceAmount FROM Customer WHERE Name = 'ALDRED'", engine)

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