How to Visualize Reckon Accounts Hosted Data in Python with pandas

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

Connecting to Reckon Accounts Hosted Data

Connecting to Reckon Accounts Hosted 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.

The connector makes requests to Reckon Accounts Hosted through OAuth. Specify the following connection properties:

• SubscriptionKey: Required. You get this value when you created your developer account.
• CountryVersion: Defaults to 2021.R2.AU.
• CompanyFile: Required. The path to the company file.
• User: Required. The username of the company file.
• Password: Required. The password of the company file.
• InitiateOAuth: Set this to GETANDREFRESH to let the driver handle access tokens.
• CallbackURL: The redirectURI of your Custom OAuth App.
• OAuthClientId: The client id of your Custom OAuth App.
• OAuthClientSecret: The client secret of your Custom OAuth App.

CData provides an embedded OAuth application that simplifies OAuth desktop authentication. See the Help documentation for information on other OAuth authentication methods (web, headless, etc.), creating custom OAuth applications, and reasons for doing so.

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

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

engine = create_engine("reckonaccountshosted:///?SubscriptionKey=my_subscription_key&CountryVersion=2021.R2.AU&CompanyFile=Q:/CompanyName.QBW&User=my_user&Password=my_password&CallbackURL=http://localhost:33333&OAuthClientId=my_oauth_client_id&OAuthClientSecret=my_oauth_client_secret&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")

Execute SQL to Reckon Accounts Hosted

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, Balance FROM Accounts WHERE IsActive = 'true'", engine)

Visualize Reckon Accounts Hosted Data

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

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

Free Trial & More Information

Download a free, 30-day trial of the CData Python Connector for Reckon Accounts Hosted to start building Python apps and scripts with connectivity to Reckon Accounts Hosted 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("reckonaccountshosted:///?SubscriptionKey=my_subscription_key&CountryVersion=2021.R2.AU&CompanyFile=Q:/CompanyName.QBW&User=my_user&Password=my_password&CallbackURL=http://localhost:33333&OAuthClientId=my_oauth_client_id&OAuthClientSecret=my_oauth_client_secret&InitiateOAuth=GETANDREFRESH&OAuthSettingsLocation=/PATH/TO/OAuthSettings.txt")
df = pandas.read_sql("SELECT Name, Balance FROM Accounts WHERE IsActive = 'true'", engine)

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