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Use pandas to Visualize Sage 50 UK Data in Python

The CData Python Connector for Sage 50 UK enables you use pandas and other modules to analyze and visualize live Sage 50 UK 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 50 UK, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Sage 50 UK-connected Python applications and scripts for visualizing Sage 50 UK data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Sage 50 UK 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 50 UK data in Python. When you issue complex SQL queries from Sage 50 UK, the driver pushes supported SQL operations, like filters and aggregations, directly to Sage 50 UK and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Sage 50 UK Data

Connecting to Sage 50 UK 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.

Note: Only Sage 50 UK 2012 and above are supported.

The User and Password properties, under the Connection section, must be set to valid Sage 50 UK user credentials. These values will be the same used to log in to the Sage 50 UK software.

Additionally, the URL property, under the Connection section, will need to be set to the address of the company dataset desired. To obtain the address, do the following:

  1. If you have not already done so, open the Sage 50 UK software.
  2. Click Tools -> Internet Options.
  3. Select the SData Settings tab.
  4. Click the Details button next to Sage 50 Accounts. A window is displayed containing a list of company names along with the address to their corresponding datasets.
  5. Set the URL property to the value in the address field next to the company desired.

Follow the procedure below to install the required modules and start accessing Sage 50 UK 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 50 UK Data in Python

You can now connect with a connection string. Use the create_engine function to create an Engine for working with Sage 50 UK data.

engine = create_engine("sage50uk:///?URL=http://your-server:5493/sdata/accounts50/GCRM/your-address&User=Manager")

Execute SQL to Sage 50 UK

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, FinanceBalance FROM TradingAccounts WHERE TradingAccountUUID = 'c2ef66a5-a545-413b-9312-79a53caadbc4'", engine)

Visualize Sage 50 UK Data

With the query results stored in a DataFrame, use the plot function to build a chart to display the Sage 50 UK data. The show method displays the chart in a new window.

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

Free Trial & More Information

Download a free, 30-day trial of the Sage 50 UK Python Connector to start building Python apps and scripts with connectivity to Sage 50 UK 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("sage50uk:///?URL=http://your-server:5493/sdata/accounts50/GCRM/your-address&User=Manager")
df = pandas.read_sql("SELECT Name, FinanceBalance FROM TradingAccounts WHERE TradingAccountUUID = 'c2ef66a5-a545-413b-9312-79a53caadbc4'", engine)

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