Use pandas to Visualize Sage Intacct Data in Python

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Intacct Python Connector

Python Connector Libraries for Intacct Data Connectivity. Integrate Intacct with popular Python tools like Pandas, SQLAlchemy, Dash & petl.



The CData Python Connector for Sage Intacct enables you 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 Sage 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).

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 Sage Intacct Python Connector 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()