Use pandas to Visualize Reckon Data in Python

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Reckon Python Connector

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

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

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

Connecting to Reckon Data

Connecting to Reckon 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.

When you are connecting to a local Reckon instance, you do not need to set any connection properties.

Requests to Reckon are made through the Remote Connector. The Remote Connector runs on the same machine as Reckon and accepts connections through a lightweight, embedded Web server. The server supports SSL/TLS, enabling users to connect securely from remote machines.

The first time you connect to your company file, you will need to authorize the Remote Connector with Reckon. See the "Getting Started" chapter of the help documentation for a guide.

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

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

engine = create_engine("reckon:///?User=RCUser&Password=RCUserPassword&URL=http://remotehost:8166")

Execute SQL to Reckon

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, CustomerBalance FROM Customers WHERE Type = 'Commercial'", engine)

Visualize Reckon Data

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

df.plot(kind="bar", x="Name", y="CustomerBalance")

Free Trial & More Information

Download a free, 30-day trial of the Reckon Python Connector to start building Python apps and scripts with connectivity to Reckon 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("reckon:///?User=RCUser&Password=RCUserPassword&URL=http://remotehost:8166")
df = pandas.read_sql("SELECT Name, CustomerBalance FROM Customers WHERE Type = 'Commercial'", engine)

df.plot(kind="bar", x="Name", y="CustomerBalance")