How to Visualize Sage 300 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 Sage 300, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Sage 300-connected Python applications and scripts for visualizing Sage 300 data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Sage 300 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 300 data in Python. When you issue complex SQL queries from Sage 300, the driver pushes supported SQL operations, like filters and aggregations, directly to Sage 300 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Sage 300 Data

Connecting to Sage 300 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.

Sage 300 requires some initial setup in order to communicate over the Sage 300 Web API.

• Set up the security groups for the Sage 300 user. Give the Sage 300 user access to the option under Security Groups (per each module required).
• Edit both web.config files in the /Online/Web and /Online/WebApi folders; change the key AllowWebApiAccessForAdmin to true. Restart the webAPI app-pool for the settings to take.
• Once the user access is configured, click https://server/Sage300WebApi/ to ensure access to the web API.

Authenticate to Sage 300 using Basic authentication.

Connect Using Basic Authentication

You must provide values for the following properties to successfully authenticate to Sage 300. Note that the provider reuses the session opened by Sage 300 using cookies. This means that your credentials are used only on the first request to open the session. After that, cookies returned from Sage 300 are used for authentication.

• Url: Set this to the url of the server hosting Sage 300. Construct a URL for the Sage 300 Web API as follows: {protocol}://{host-application-path}/v{version}/{tenant}/ For example, http://localhost/Sage300WebApi/v1.0/-/.
• User: Set this to the username of your account.
• Password: Set this to the password of your account.

Follow the procedure below to install the required modules and start accessing Sage 300 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 300 Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Sage 300 data.

engine = create_engine("sage300:///?User=SAMPLE&Password=password&URL=http://127.0.0.1/Sage300WebApi/v1/-/&Company=SAMINC")

Execute SQL to Sage 300

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

df = pandas.read_sql("SELECT InvoiceUniquifier, ApprovedLimit FROM OEInvoices WHERE AllowPartialShipments = 'Yes'", engine)

Visualize Sage 300 Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Sage 300 data. The show method displays the chart in a new window.

df.plot(kind="bar", x="InvoiceUniquifier", y="ApprovedLimit")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Sage 300 to start building Python apps and scripts with connectivity to Sage 300 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("sage300:///?User=SAMPLE&Password=password&URL=http://127.0.0.1/Sage300WebApi/v1/-/&Company=SAMINC")
df = pandas.read_sql("SELECT InvoiceUniquifier, ApprovedLimit FROM OEInvoices WHERE AllowPartialShipments = 'Yes'", engine)

df.plot(kind="bar", x="InvoiceUniquifier", y="ApprovedLimit")
plt.show()