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Get the Report →How to Visualize Sage Intacct Data in Python with pandas
Use pandas and other modules to analyze and visualize live Sage Intacct 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 Intacct, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Sage Intacct-connected Python applications and scripts for visualizing Sage Intacct data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Sage Intacct 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 Intacct data in Python. When you issue complex SQL queries from Sage Intacct, the driver pushes supported SQL operations, like filters and aggregations, directly to Sage Intacct and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).
About Sage Intacct Data Integration
CData provides the easiest way to access and integrate live data from Sage Intact. Customers use CData connectivity to:
- Access Sage Intacct without worrying about API updates or changes.
- Access custom objects and fields in HubSpot with no extra configuration steps involved.
- Write data back to Sage Intacct using embedded Web Services credentials with Basic authentication.
- Use SQL stored procedures to perform functional operations like approving or declining vendors, inserting engagements, and creating or deleting custom objects or fields.
Users frequently integrate Sage Intact with analytics tools such as Tableau, Power BI, and Excel, and leverage our tools to replicate Workday data to databases or data warehouses.
To learn about how other customers are using CData's Sage Intacct solutions, check out our blog: Drivers in Focus: Accounting Connectivity.
Getting Started
Connecting to Sage Intacct Data
Connecting to Sage Intacct 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 connect using the Login method, the following connection properties are required: User, Password, CompanyId, SenderId and SenderPassword.
User, Password, and CompanyId are the credentials for the account you wish to connect to.
SenderId and SenderPassword are the Web Services credentials assigned to you by Sage Intacct.
Follow the procedure below to install the required modules and start accessing Sage Intacct 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 Intacct Data in Python
You can now connect with a connection string. Use the create_engine function to create an Engine for working with Sage Intacct data.
engine = create_engine("sageintacct:///?User=myusername&CompanyId=TestCompany&Password=mypassword&SenderId=Test&SenderPassword=abcde123")
Execute SQL to Sage Intacct
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, TotalDue FROM Customer WHERE CustomerId = '12345'", engine)
Visualize Sage Intacct Data
With the query results stored in a DataFrame, use the plot function to build a chart to display the Sage Intacct data. The show method displays the chart in a new window.
df.plot(kind="bar", x="Name", y="TotalDue") plt.show()

Free Trial & More Information
Download a free, 30-day trial of the CData Python Connector for Intacct to start building Python apps and scripts with connectivity to Sage Intacct 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("sageintacct:///?User=myusername&CompanyId=TestCompany&Password=mypassword&SenderId=Test&SenderPassword=abcde123") df = pandas.read_sql("SELECT Name, TotalDue FROM Customer WHERE CustomerId = '12345'", engine) df.plot(kind="bar", x="Name", y="TotalDue") plt.show()