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Use pandas to Visualize Acumatica Data in Python

The CData Python Connector for Acumatica enables you use pandas and other modules to analyze and visualize live Acumatica 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 Acumatica, the pandas & Matplotlib modules, and the SQLAlchemy toolkit, you can build Acumatica-connected Python applications and scripts for visualizing Acumatica data. This article shows how to use the pandas, SQLAlchemy, and Matplotlib built-in functions to connect to Acumatica data, execute queries, and visualize the results.

With built-in optimized data processing, the CData Python Connector offers unmatched performance for interacting with live Acumatica data in Python. When you issue complex SQL queries from Acumatica, the driver pushes supported SQL operations, like filters and aggregations, directly to Acumatica and utilizes the embedded SQL engine to process unsupported operations client-side (often SQL functions and JOIN operations).

Connecting to Acumatica Data

Connecting to Acumatica 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.

Set the following connection properties to connect to Acumatica:

  • User: Set this to your username.
  • Password: Set this to your password.
  • Company: Set this to your company.
  • Url: Set this to your Acumatica URL, in the format http://{Acumatica ERP instance URL}/entity/{Endpoint name}/{Endpoint version}/.
    For example: https://acumatica.com/entity/Default/17.200.001/

See the Getting Started guide in the CData driver documentation for more information.

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

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

engine = create_engine("acumatica:///?Url = https://try.acumatica.com/ISV/entity/Default/17.200.001/&User=user&Password=password&Company=CompanyName")

Execute SQL to Acumatica

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, location_displayname FROM Events WHERE Id = '1'", engine)

Visualize Acumatica Data

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

df.plot(kind="bar", x="Id", y="location_displayname")
plt.show()

Free Trial & More Information

Download a free, 30-day trial of the Acumatica Python Connector to start building Python apps and scripts with connectivity to Acumatica 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("acumatica:///?Url = https://try.acumatica.com/ISV/entity/Default/17.200.001/&User=user&Password=password&Company=CompanyName")
df = pandas.read_sql("SELECT Id, location_displayname FROM Events WHERE Id = '1'", engine)

df.plot(kind="bar", x="Id", y="location_displayname")
plt.show()