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Use pandas to Visualize Sage 200 Data in Python

The CData Python Connector for Sage 200 enables you use pandas and other modules to analyze and visualize live Sage 200 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 200, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Sage 200-connected Python applications and scripts for visualizing Sage 200 data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Sage 200 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 200 data in Python. When you issue complex SQL queries from Sage 200, the driver pushes supported SQL operations, like filters and aggregations, directly to Sage 200 and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Sage 200 Data

Connecting to Sage 200 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.

  • Schema: Determines which Sage 200 edition you are connecting to. Specify either StandardUK or ProfessionalUK.
  • Subscription Key: Provides access to the APIs that are used to establish a connection. You will first need to log into the Sage 200 API website and subscribe to the API edition that matches your account. You can do so here: Afterwards, the subscription key may be found in your profile after logging into Sage 200.

Follow the procedure below to install the required modules and start accessing Sage 200 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 200 Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Sage 200 data.

engine = create_engine("sage200:///?SubscriptionKey=12345&Schema=StandardUK&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Sage 200

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

df = pandas.read_sql("SELECT Id, Code FROM Banks WHERE Code = '12345'", engine)

Visualize Sage 200 Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Sage 200 data. The show method displays the chart in a new window.

df.plot(kind="bar", x="Id", y="Code")

Free Trial & More Information

Download a free, 30-day trial of the Sage 200 Python Connector to start building Python apps and scripts with connectivity to Sage 200 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("sage200:///?SubscriptionKey=12345&Schema=StandardUK&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Id, Code FROM Banks WHERE Code = '12345'", engine)

df.plot(kind="bar", x="Id", y="Code")